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Background Brucellosis, as a serious zoonotic infectious disease, has been recognized as a re-emerging disease in the developing countries worldwide. In china, the incidence of brucellosis is increasing each year, seriously threatening the health of humans as well as animal populations. Despite a quite number of diagnostic methods currently being used for brucellosis, innovative technologies are still needed for its rapid and accurate diagnosis, especially in area where traditional diagnostic is unavailable. Methodology/Principal findings In this study, a total of 22 B cell linear epitopes were predicted from five Brucella outer membrane proteins (OMPs) using an immunoinformatic approach. These epitopes were then chemically synthesized, and with the method of indirect ELISA (iELISA), each of them displayed a certain degree of capability in identifying human brucellosis positive sera. Subsequently, a fusion protein consisting of the 22 predicted epitopes was prokaryotically expressed and used as diagnostic antigen in a newly established brucellosis testing method, nano-ZnO modified paper-based ELISA (nano-p-ELISA). According to the verifying test using a collection of sera collected from brucellosis and non-brucellosis patients, the sensitivity and specificity of multiepitope based nano-p-ELISA were 92.38% and 98.35% respectively. The positive predictive value was 98.26% and the negative predictive value was 91.67%. The multiepitope based fusion protein also displayed significantly higher specificity than Brucella lipopolysaccharide (LPS) antigen. Conclusions B cell epitopes are important candidates for serologically testing brucellosis. Multiepitope fusion protein based nano-p-ELISA displayed significantly sensitivity and specificity compared to Brucella LPS antigen. The strategy applied in this study will be helpful to develop rapid and accurate diagnostic method for brucellosis in human as well as animal populations., Author summary Brucellosis is one of the most important zoonosis in the world and has caused tremendous economic losses in agriculture and animal husbandry in many countries. Developing rapid, sensitive and specific diagnostic methods is very important for early detection and treatment of brucellosis patients. In this study, a novel diagnostic technique, nano-ZnO modified paper ELISA, was established. The antigen used in this technique was a fusion protein containing multiple B cell epitopes, which were predicted from Brucella major outer membrane proteins such as Bp26, Omp31, Omp16, Omp2b and Omp25. Comparing to traditional LPS antigen, this multiepitope based antigen displayed considerably higher sensitivity and higher specificity in laboratory. With the strategy described in this paper, more efficient epitopes and protein antigen can be identified in the future. Currently, LPS antigen is only prepared from live Brucella, while protein antigen can be produced in large quantities in prokaryotic expression system. In addition to nano-p-ELISA, this protein antigen can also be used for development other methods such as fluorescent polarization assay (FPA) and immunochromatographic assay (ICA) to meet the varied demand for brucellosis testing.

The breakthrough potentials of research papers can be explained by their boundary-spanning qualities. Here, for the first time, we apply the structural variation analysis (SVA) model and its affiliated metrics to investigate the extent to which such qualities characterize a group of Nobel Prize winning papers. We find that these papers share remarkable boundary-spanning traits, marked by exceptional abilities to connect disparate and topically-diverse clusters of research papers. Further, their publications exert structural variations on the scale that significantly alters the betweenness centrality distributions of existing intellectual space. Overall, SVA not only provides a set of leading indicators for describing future Nobel Prize winning papers, but also broadens our understanding of the similar prize-winning properties that may have been overlooked among other regular publications., 27 pages, 8 figures, 9 tables. Submitted to Frontiers in Research Metrics and Analytics

Rocky desertification is a bottleneck that reduces ecological and environmental security in karst areas. Paper mulberry, a unique deciduous tree, shows good performance in rocky desertification areas. Its resistance mechanisms are therefore of high interest. In this study, a lysine acetylation proteomics analysis of paper mulberry seedling leaves was conducted in combination with the purification of acetylated protein by high-precision nano LC-MS/MS. We identified a total of 7130 acetylation sites in 3179 proteins. Analysis of the modified sites showed a predominance of nine motifs. Six positively charged residues: lysine (K), arginine (R), and histidine (H), serine (S), threonine (T), and tyrosine (Y) occurred most frequently at the +1 position, phenylalanine (F) was both detected both upstream and downstream of the acetylated lysines; and the sequence logos showed a strong preference for lysine and arginine around acetylated lysines. Functional annotation revealed that the identified enzymes were mainly involved in translation, transcription, ribosomal structure and biological processes, showing that lysine acetylation can regulate various aspects of primary carbon and nitrogen metabolism and secondary metabolism. Acetylated proteins were enriched in the chloroplast, cytoplasm, and nucleus, and many stress response-related proteins were also discovered to be acetylated, including PAL, HSP70, and ERF. HSP70, an important protein involved in plant abiotic and disease stress responses, was identified in paper mulberry, although it is rarely found in woody plants. This may be further examined in research in other plants and could explain the good adaptation of paper mulberry to the karst environment. However, these hypotheses require further verification. Our data can provide a new starting point for the further analysis of the acetylation function in paper mulberry and other plants.

In most low-resource settings, microscopy still is the standard method for diagnosis of cutaneous leishmaniasis, despite its limited sensitivity. In Ethiopia, the more sensitive molecular methods are not yet routinely used. This study compared five PCR methods with microscopy on two sample types collected from patients with a suspected lesion to advise on optimal diagnosis of Leishmania aethiopica. Between May and July 2018, skin scrapings (SS) and blood exudate from the lesion spotted on filter paper (dry blood spot, DBS) were collected for PCR from 111 patients of four zones in Southern Ethiopia. DNA and RNA were simultaneously extracted from both sample types. DNA was evaluated by a conventional PCR targeting ITS-1 and three probe-based real-time PCRs: one targeting the SSU 18S rRNA and two targeting the kDNA minicircle sequence (the ‘Mary kDNA PCR’ and a newly designed ‘LC kDNA PCR’ for improved L. aethiopica detection). RNAs were tested with a SYBR Green-based RT-PCR targeting spliced leader (SL) RNA. Giemsa-stained SS smears were examined by microscopy. Of the 111 SS, 100 were positive with at least two methods. Sensitivity of microscopy, ITS PCR, SSU PCR, Mary kDNA PCR, LC kDNA PCR and SL RNA PCR were respectively 52%, 22%, 64%, 99%, 100% and 94%. Microscopy-based parasite load correlated well with real-time PCR Ct-values. Despite suboptimal sample storage for RNA detection, the SL RNA PCR resulted in congruent results with low Ct-values. DBS collected from the same lesion showed lower PCR positivity rates compared to SS. The kDNA PCRs showed excellent performance for diagnosis of L. aethiopica on SS. Lower-cost SL RNA detection can be a complementary high-throughput tool. DBS can be used for PCR in case microscopy is negative, the SS sample can be sent to the referral health facility where kDNA PCR method is available., Author summary Cutaneous leishmaniasis is a neglected tropical disease and causing a public health problem in Ethiopia. Microscopy is still the standard method for detection of the parasite in Ethiopia, and also in many other low resource settings. A more sensitive method is needed for timely diagnosis and treatment. In this study, we compared five molecular methods on samples collected from patients with a skin lesion suspected of cutaneous leishmaniasis to advice on optimal diagnosis of L. aethiopica. We collected two sample types from the same lesion (skin scrapings and lesion fluid on filter paper) and isolated both DNA and RNA of them. Majority (90.1%) of the samples from skin scrapings were positive in two or more methods and the molecular methods had a higher sensitivity than the conventional methods. Interestingly, we evaluated for the first time a new molecular method designed to improve L. aethiopica detection. Also, we showed that RNA detection performed well for samples that were collected under difficult field conditions. Samples collected on filter paper showed less positive results than skin scraped samples, but could still be the method of choice for easy sampling and transport in resource-limited settings as it performed better than microscopy.

Determining the distribution of the Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis, YFP) in the Yangtze River has to date relied on traditional visual and counting methods, but such field surveys are time-consuming and expensive. Analyses using environmental DNA (eDNA) to investigate the presence and range of endangered aquatic species have proven to be more economical and effective detection methods, and are a non-invasive approach to sampling. A challenge of relying on eDNA for YFP monitoring is that the Yangtze River is characterized by high turbidity and a strong current. Here, we used an eDNA-based approach to estimate the presence of YFP at 18 sites in the Yangtze River in August 2017 and at an additional 11 sites in January 2018. At each sampling site, we filtered six 1 L water samples with 5 µm pore size filter paper and quantified the amount of YFP eDNA in each water sample using quantitative real-time polymerase chain reaction (qPCR). In addition, YFP eDNA was successfully detected in locations where we visually observed YFP, as well as in locations where YFP were not observed directly. We found that our eDNA-based method had higher detection rates than traditional field survey methods. Although YFP was visually observed in the Yangtze River in winter, water samples collected during the summer contained significantly higher YFP eDNA than winter water samples. Our results demonstrate the potential effectiveness of eDNA detection methods in determining the distribution of YFP in the Yangtze River.

The pre-aerobic process of coking wastewater treatment has strong capacity of decarbonization and detoxification, which contribute to the subsequent dinitrogen of non-carbon source/heterotrophic denitrification. The COD removal rate can reach > 90% in the first aerobic bioreactor of the novel O/H/O coking wastewater treatment system during long-term operation. The physico-chemical characteristics of influent and effluent coking wastewater in the first aerobic bioreactor were analyzed to examine how they correlated with bacterial communities. The diversity of the activated sludge microbial community was investigated using a culture-independent molecular approach. The microbial community functional profiling and detailed pathways were predicted from the 16S rRNA gene-sequencing data by the PICRUSt software and the KEGG database. High-throughput MiSeq sequencing results revealed a distinct microbial composition in the activated sludge of the first aerobic bioreactor of the O/H/O system. Proteobacteria, Bacteroidetes, and Chlorobi were the decarbonization and detoxification dominant phyla with the relative abundance of 84.07 ± 5.45, 10.89 ± 6.31, and 2.96 ± 1.12%, respectively. Thiobacillus, Rhodoplanes, Lysobacter, and Leucobacter were the potential major genera involved in the crucial functional pathways related to the degradation of phenols, cyanide, benzoate, and naphthalene. These results indicated that the comprehensive understanding of the structure and function diversity of the microbial community in the bioreactor will be conducive to the optimal coking wastewater treatment.

Samples of bleached kraft pine cellulosic pulp, either treated with an enzyme preparation (a Thermomyces lanuginosus xylanase, an Aspergillus sp. cellulase, and a multienzyme preparation NS-22086 containing both these activities) or untreated, were refined in a laboratory PFI mill. The treatment with cellulases contained in the last two preparations significantly improved the pulp’s susceptibility to refining (the target freeness value of 30°SR was achieved in a significantly shorter time), increased water retention value (WRV) and fines contents while the weighted average fiber length was significantly reduced. These changes of pulp parameters caused deterioration of paper strength properties. The treatment with the xylanase, which partially hydrolyzed xylan, small amounts of which are associated with cellulose fibers, only slightly loosened the structure of fibers. These subtle changes positively affected the susceptibility of the pulp to refining (refining energy was significantly reduced) and improved the static strength properties of paper. Thus, the treatment of kraft pulps with xylanases may lead to substantial savings of refining energy without negative effects on paper characteristics.

Miscanthus sinensis Andersson is a pioneer plant species that grows naturally at mining sites. Miscanthus sinensis can detoxify aluminium (Al) by producing phytosiderophores, such as chlorogenic acid, citric acid, and malic acid, and localizing Al in cell walls. Root-endophytic Chaetomium cupreum, which produces microbial siderophores, enhances Al tolerance in M. sinensis. However, we could not determine whether the siderophores produced by C. cupreum actually enhance Al tolerance in M. sinensis, because the microbial siderophores have not yet been identified in previous research. The purpose of this study was to clarify how C. cupreum chemically increases Al tolerance in M. sinensis under acidic mining site conditions, especially considering siderophores. Using instrumental analyses, the siderophore produced by C. cupreum was identified as oosporein. Comparison of the stability constant between Al and phytosiderophores and oosporein indicated that oosporein could detoxify Al similarly to chlorogenic acid, which shows higher stability constant than citric acid and malic acid. Inoculation test of C. cupreum onto M. sinensis in acidic mine soil showed that C. cupreum promoted seedling growth, and enhanced Al tolerance via inducing chlorogenic-acid production and producing oosporein. These results suggested that C. cupreum could chemically enhance Al tolerance and might promote growth via reducing excessive Al in cell walls, the main site of Al accumulation. In addition, the chemical enhancement of Al tolerance by C. cupreum might be important for M. sinensis to adapt to acidic mining sites.

The arthropod melanization immune response is activated by extracellular protease cascades predominantly comprised of CLIP-domain serine proteases (CLIP-SPs) and serine protease homologs (CLIP-SPHs). In the malaria vector, Anopheles gambiae, the CLIP-SPHs SPCLIP1, CLIPA8, and CLIPA28 form the core of a hierarchical cascade downstream of mosquito complement that is required for microbial melanization. However, our understanding of the regulatory relationship of the CLIP-SPH cascade with the catalytic CLIP-SPs driving melanization is incomplete. Here, we report on the development of a novel screen to identify melanization pathway components based on the quantitation of melanotic mosquito excreta, eliminating the need for microdissections or hemolymph enzymatic assays. Using this screen, we identified CLIPC9 and subsequent functional analyses established that this protease is essential for the melanization of both Escherichia coli and the rodent malaria parasite Plasmodium berghei. Mechanistically, septic infection with E. coli promotes CLIPC9 cleavage and both full-length and cleaved CLIPC9 localize to this bacterium in a CLIPA8-dependent manner. The steady state level of CLIPC9 in the hemolymph is regulated by thioester-containing protein 1 (TEP1), suggesting it functions downstream of mosquito complement. In support, CLIPC9 cleavage is inhibited following SPCLIP1, CLIPA8, and CLIPA28 knockdown positioning it downstream of the CLIP-SPH cascade. Moreover, like CLIPA8 and CLIPA28, CLIPC9 processing is negatively regulated by serine protease inhibitor 2 (SRPN2). This report demonstrates how our novel excretion-based approach can be utilized to dissect the complex protease networks regulating mosquito melanization. Collectively, our findings establish that CLIPC9 is required for microbial melanization in An. gambiae and shed light on how the CLIP-SPH cascade regulates this potent immune response., Author summary Mosquito vector competence for Plasmodium, antifungal defense, and lifespan are all influenced by the melanization response. Despite its importance, our understanding of the proteins comprising the An. gambiae melanization cascade is incomplete. To streamline the discovery of melanization pathway components in this disease vector, we developed a screening method that is able to identify proteins with far fewer mosquitoes than other approaches. This technique facilitated our discovery that the serine protease CLIPC9 is required for the melanization of E. coli bacteria and malaria parasites. CLIPC9 activation and localization to bacteria is regulated by members of the CLIPA subfamily, highlighting that these serine protease homologs broadly regulate the melanization immune response. Traditionally viewed as ‘co-factors’ for the prophenoloxidase activating CLIPBs, this work demonstrates that CLIPAs can also control the activation of the catalytic proteases driving melanization. This work identifies a new player in the melanization hierarchy and provides the field with an efficient approach for dissecting the complex protease cascades controlling this important immune response.

Efficient conversion of cellulosic sugars in cellulosic hydrolysates is important for economically viable production of biofuels from lignocellulosic biomass, but the goal remains a critical challenge. The present study reports a new approach for simultaneous fermentation of cellobiose and xylose by using the co-culture consisting of recombinant Saccharomyces cerevisiae specialist strains. The co-culture system can provide competitive advantage of modularity compared to the single culture system and can be tuned to deal with fluctuations in feedstock composition to achieve robust and cost-effective biofuel production. This study characterized fermentation kinetics of the recombinant cellobiose-consuming S. cerevisiae strain EJ2, xylose-consuming S. cerevisiae strain SR8, and their co-culture. The motivation for kinetic modeling was to provide guidance and prediction of using the co-culture system for simultaneous fermentation of mixed sugars with adjustable biomass of each specialist strain under different substrate concentrations. The kinetic model for the co-culture system was developed based on the pure culture models and incorporated the effects of product inhibition, initial substrate concentration and inoculum size. The model simulations were validated by results from independent fermentation experiments under different substrate conditions, and good agreement was found between model predictions and experimental data from batch fermentation of cellobiose, xylose and their mixtures. Additionally, with the guidance of model prediction, simultaneous co-fermentation of 60 g/L cellobiose and 20 g/L xylose was achieved with the initial cell densities of 0.45 g dry cell weight /L for EJ2 and 0.9 g dry cell weight /L SR8. The results demonstrated that the kinetic modeling could be used to guide the design and optimization of yeast co-culture conditions for achieving simultaneous fermentation of cellobiose and xylose with improved ethanol productivity, which is critically important for robust and efficient renewable biofuel production from lignocellulosic biomass.

Sugarcane bagasse is a vast lignocellulosic byproduct generated in the industry with ~50% humidity (1 kg dry matter associated with 1 kg water). Although the presence of water brings deleterious consequences for combustion, storage and sugar extraction, the location of water in fresh bagasse remains unknown. In this work, we use synchrotron X-ray microtomography for non-invasive 3D imaging of fresh bagasse particles, which allows the visualization of intraparticle water. The sclerified fiber cells in the sheaths surrounding xylem vessels are often found full of water. We suggest this can be juice preserved from the native stalks as many sclerified fibers seem to keep their structural integrity despite the mechanical action during sugarcane crushing. The microtomograms of fresh bagasse also shows mineral particles adhered to biomass surfaces, with adhesion presumably favored by the presence of water. In summary, this work unveils the location of water in fresh bagasse, solving an old mystery of sugarcane technology.

Endo-s-1,4-glucanase AkEG21 belonging to glycosyl hydrolase family 45 (GHF45) is the most abundant cellulase in the digestive fluid of sea hare (Aplysia kurodai). The specific activity of this 21-kDa enzyme is considerably lower than those of other endo s-1,4-glucanases in the digestive fluid of A. kurodai, therefore its role in whole cellulose hydrolysis by sea hare is still uncertain. Although AkEG21 has a catalytic domain without a cellulose binding domain, it demonstrated stable binding to cellulose fibers, similar to that of fungal cellobiohydrolase (CBH) 1 and CBH 2, which is strongly inhibited by cellohexaose, suggesting the involvement of the catalytic site in cellulose binding. Cellulose-bound AkEG21 hydrolyzed cellulose to cellobiose, cellotriose and cellotetraose, but could not digest an external substrate, azo-carboxymethyl cellulose. Cellulose hydrolysis was considerably stimulated by the synergistic action of cellulose-bound AkEG21 and AkEG45, another s-1,4-endoglucanase present in the digestive fluid of sea hare; however no synergy in carboxymethylcellulose hydrolysis was observed. When AkEG21 was removed from the digestive fluid by immunoprecipitation, the cellulose hydrolyzing activity of the fluid was significantly reduced, indicating a critical role of AkEG21 in cellulose hydrolysis by A. kurodai. These findings suggest that AkEG21 is a processive endoglucanase functionally equivalent to the CBH, which provides a CBH-independent mechanism for the mollusk to digest seaweed cellulose to glucose.

This study examined the effect of debagging time on color and flavor / taste compounds in the non-red apple cultivar 'Golden Delicious' and red cultivar 'Qinguan' at mid and late stages of fruit development. Debagging briefly improved the red color in both cultivars, the peel of 'Golden Delicious' presenting pale-pink hue. However, rapid anthocyanin accumulation occurred in apple peel at a specific time (after 179 days after flowering (DAF) in 'Qinguan') and was unaltered by debagging time in the red cultivar 'Qinguan'. Furthermore, untimely debagging had a detrimental effect on the content of anthocyanin. All sugars increased and organic acids decreased in apple pulp at mid to late stages of development. Bagging treatment reduced the content of most sugars and organic acids, as well as, the overall total. However, glucose and citric acid contents were higher in bagged fruit than non-bagged fruit; the maximum occurred in T7 treatment that was no-debagging at DAF 159 / 196 ('Golden delicious' / 'Qinguan'), i.e., 24.35 and 0.07 mg g-1 FW in 'Golden delicious', and 38.86 and 0.06 mg g-1 FW in 'Qinguan', respectively. In a word, bagging treatment can alter the pattern of peel color development in apple fruit; however, it remains difficult to alter the timing of rapid anthocyanin accumulation as it is regulated solely by development. Moreover, bagging treatment reduced the total accumulation of sugars and organic acids, and even the over total in pulp, but increased the glucose and citric acid contents in apple pulp.

Response to volatile environmental chemosensory cues is essential for insect survival. The odorant receptor (OR) family is an important class of receptors that detects volatile molecules; guiding insects towards food, mates, and oviposition sites. ORs are odorant-gated ion channels, consisting of a variable odorant specificity subunit and a conserved odorant receptor co-receptor (Orco) subunit, in an unknown stoichiometry. The Orco subunit possesses an allosteric site to which modulators can bind and noncompetitively inhibit odorant activation of ORs. In this study, we characterized several halogen-substituted versions of a phenylthiophenecarboxamide Orco antagonist structure. Orco antagonist activity was assessed on ORs from Drosophila melanogaster flies and Culex quinquefasciatus mosquitoes, expressed in Xenopus laevis oocytes and assayed by two-electrode voltage clamp electrophysiology. One compound, OX1w, was also shown to inhibit odorant activation of a panel of Anopheles gambiae mosquito ORs activated by diverse odorants. Next, we asked whether Orco antagonist OX1w could affect insect olfactory behavior. A Drosophila melanogaster larval chemotaxis assay was utilized to address this question. Larvae were robustly attracted to highly diluted ethyl acetate in a closed experimental chamber. Attraction to ethyl acetate was Orco dependent and also required the odorant specificity subunit Or42b. The addition of the airborne Orco antagonist OX1w to the experimental chamber abolished larval chemotaxis towards ethyl acetate. The Orco antagonist was not a general inhibitor of sensory behavior, as behavioral repulsion from a light source was unaffected. This is the first demonstration that an airborne Orco antagonist can alter olfactory behavior in an insect. These results suggest a new approach to insect control and emphasize the need to develop more potent Orco antagonists.

The symport of lactose and H+ is an important physiological process in E. coli, for it is closely related to cellular energy supply. In this paper, the symport of H+ and lactose by E. coli LacY protein is computationally simulated using a newly proposed cotransport model that takes the "leakage" phenomenon (uncoupled sugar translocation) into account and also satisfies the static head equilibrium condition. Then, we study the equilibrium properties of this cotransport process including equilibrium solution and the time required to reach equilibrium state, when varying the parameters of the initial state of cotransport system. It can be found that in the presence of leakage, H+ and lactose will reach their equilibrium state separately, but the intensity of "leakage" has almost no effect on the equilibrium solution, while the stronger leakage is, the shorter the time required for H+ and lactose to reach equilibrium. For E. coli cells with different periplasm and cytoplasm volumes, when cotransport is performed at constant initial particle concentration, the time for cytoplasm pH to be stabilized increases monotonically with the periplasm to cytoplasm volume ratio. For a certain E. coli cell, as it continues to lose water and contract, the time for cytoplasm pH to be stabilized by cotransport also increases monotonically when the cell survives. The above phenomena and other findings in this paper may help us to not only further validate or improve the model, but also deepen our understanding of the cotransport process of E. coli LacY protein., 10 figures

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention-based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://github.com/EESI/sequence_attention (a python package) and https://github.com/EESI/seq2att (a command line tool)., Author summary Microbiomes are communities of microscopic organisms found everywhere, including on and in the human body. For example, the gut microbiome plays an important role in digestion, and changes in composition are associated with changes in health or disease, e.g., inflammatory bowel disease (IBD). Today, microbiome composition is often obtained from high-throughput sequencing, which generates many short DNA reads from multiple organisms in a sample. In this paper, we present a novel deep learning framework, Read2Pheno, to predict phenotype from all the reads in a set of biological samples. An attention mechanism allows visualization of specific subregions (sets of bases) which are important in classifying the reads according to phenotype or taxon labels. We evaluate the framework on sequencing data for 16S rRNA genes, genetic markers used to identify microbial taxonomy. We show that Read2Pheno performs comparably as conventional methods on three distinct data sets from the American Gut Project, IBD patients and controls, and a comprehensive taxonomic database. Moreover, Read2Pheno results can be readily interpreted—e.g., to identify regions of the 16S rRNA gene to target for PCR diagnostics—without additional pre/post-processing steps that can introduce complexity and error.

Replication of DNA is an essential process in all domains of life. A protein often involved without exception in replication is the sliding clamp. The sliding clamp encircles the DNA and helps replicative polymerase stay attached to the replication machinery increasing the processivity of the polymerase. In eukaryotes and archaea the sliding clamp is called the Proliferating Cell Nuclear Antigen (PCNA) and consists of two domains. This PCNA forms a trimer encircling the DNA as a hexamer. In bacteria, the structure of the sliding clamp is highly conserved, but the protein itself, called beta clamp, contains three domains, which dimerize to form a hexamer. The bulk of literature touts a conservation of the structure of the sliding clamp, but fails to recognize conservation of protein sequence among sliding clamps. In this paper we have used PSI blast to the second interation in NCBI to show a statistically significant sequence homology betweenPyrococcus furiosusPCNA andKallipyga gabonensisbeta clamp. The last two domains of beta clamp align with the two domains of PCNA. This homology data demonstrates that PCNA and beta clamp arose from a common ancestor. In this paper, we have further used beta clamp and PCNA sequences from diverse bacteria, archaea and eukarya to build maximum likelihood phylogenetic tree. Most, but not all, species in different domains of life harbor one sliding clamp from vertical inheritance. Some of these species that have two or more sliding clamps have acquired them from gene duplication or horizontal gene transfer events.

In recent years, Sporosarcina pasteurii (S. pasteurii) has become one of the most popular bacteria in microbially induced calcium carbonate precipitation (MICP). Various applications have been developed based on the efficient urease that can induce the precipitation of calcium carbonate. However, the metabolic mechanism related to biomineralization of S. pasteurii has not been clearly elucidated. The process of bacterial culture and biomineralization consumes a large amount of urea or ammonium salts, which are usually used as agricultural fertilizers, not to mention probable environmental pollutions caused by the excessive use of these raw materials. Therefore, it is urgent to reveal the mechanism of nitrogen utilization and metabolism of S. pasteurii. In this paper, we compared the growth and gene expression of S. pasteurii under three different culture conditions through transcriptome analyses. GO and KEGG analyses revealed that both ammonium and urea were direct nitrogen sources of S. pasteurii, and the bacteria could not grow normally in the absence of ammonium or urea. To the best of our knowledge, this paper is the first one to reveal the nitrogen utilization mechanism of S. pasteurii through transcriptome methods. Furthermore, the presence of ammonium might promote the synthesis of intracellular ATP and enhance the motility of the bacteria. There should be an ATP synthesis mechanism associated with urea hydrolysis catalyzed by urease in S. pasteurii.

With the rapid development of the mobile internet, people are becoming more dependent on the internet to express their comments on products or stores; meanwhile, text sentiment classification of these comments has become a research hotspot. In existing methods, it is fairly popular to apply a deep learning method to the text classification task. Aiming at solving information loss, weak context and other problems, this paper makes an improvement based on the transformer model to reduce the difficulty of model training and training time cost and achieve higher overall model recall and accuracy in text sentiment classification. The transformer model replaces the traditional convolutional neural network (CNN) and the recurrent neural network (RNN) and is fully based on the attention mechanism; therefore, the transformer model effectively improves the training speed and reduces training difficulty. This paper selects e-commerce reviews as research objects and applies deep learning theory. First, the text is preprocessed by word vectorization. Then the IN standardized method and the GELUs activation function are applied based on the original model to analyze the emotional tendencies of online users towards stores or products. The experimental results show that our method improves by 9.71%, 6.05%, 5.58% and 5.12% in terms of recall and approaches the peak level of the F1 value in the test model by comparing BiLSTM, Naive Bayesian Model, the serial BiLSTM_CNN model and BiLSTM with an attention mechanism model. Therefore, this finding proves that our method can be used to improve the text sentiment classification accuracy and effectively apply the method to text classification.

Biological systems are made up of components that change their actions (and interactions) over time and coordinate with other components nearby. Together with a large state space, the complexity of this behaviour can make it difficult to create concise mathematical models that can be easily extended or modified. This paper introduces the Beacon Calculus, a process algebra designed to simplify the task of modelling interacting biological components. Its breadth is demonstrated by creating models of DNA replication dynamics, the gene expression dynamics in response to DNA methylation damage, and a multisite phosphorylation switch. The flexibility of these models is shown by adapting the DNA replication model to further include two topics of interest from the literature: cooperative origin firing and replication fork barriers. The Beacon Calculus is supported with the open-source simulator bcs (https://github.com/MBoemo/bcs.git) to allow users to develop and simulate their own models., Author summary Simulating a model of a biological system can suggest ideas for future experiments and help ensure that conclusions about a mechanism are consistent with data. The Beacon Calculus is a new language that makes modelling simple by allowing users to simulate a biological system in only a few lines of code. This simplicity is critical as it allows users the freedom to come up with new ideas and rapidly test them. Models written in the Beacon Calculus are also easy to modify and extend, allowing users to add new features to the model or incorporate it into a larger biological system. We demonstrate the breadth of applications in this paper by applying the Beacon Calculus to DNA replication and DNA damage repair, both of which have implications for genome stability and cancer. We also apply it to multisite phosphorylation, which is important for cellular signalling. To enable users to create their own models, we created the open-source Beacon Calculus simulator bcs (https://github.com/MBoemo/bcs.git) which is easy to install and is well-supported by documentation and examples.

Background & aimsIn our previous study, a Seesaw model was proposed for the fluctuation of crucial anti- (IL-10) and pro-inflammatory (Il-6 & IL-17A) cytokines through vitamin D3. In this paper, however, it is intended to extend the mentioned model by assessing the expression mRNA levels of IL-27 and TGF-β1 as well as the changes of plasma levels of IL-27, TGF-β1, IL-17A, IL-10, and IL-6 after treatment by vitamin D3.MethodVenous blood samples were drawn from Healthy Participants (HP, n = 25) and First-Degree Relative Participants (FDRP, n = 25) as control groups and Multiple Sclerosis Participants (MSP, n = 25) before and after eight weeks of supplementation with 50000 IU vitamin D3. The mRNA expression and plasma concentrations were gauged by using Real-Time PCR and ELISA assay, respectively.ResultsThe mRNA surfaces of IL-27, as well as TGF-β1, were up-regulated. However, the plasma levels of TGF-β1, IL-17A, and IL-6 were significantly different among the three groups. In addition, the plasma levels of IL-27, TGF-β1, IL-10, IL-17A, and IL-6 significantly changed following the administration of vitamin D3.ConclusionThe findings of this paper illustrate that anti-inflammatory cytokines could have a key role in immunomodulatory functions due to their anti-inflammatory functions. To conclude, this might contribute to preventing the pathophysiological process of MS. Also, the proposed model could be used as a preventive way on disposed people to multiple sclerosis, particularly in first degree relatives of these patients.

Multiscale geometric analysis (MGA) is not only characterized by multi-resolution, time-frequency localization, multidirectionality and anisotropy, but also outdoes the limitations of wavelet transform in representing high-dimensional singular data such as edges and contours. Therefore, researchers have been exploring new MGA-based image compression standards rather than the JPEG2000 standard. However, due to the difference in terms of the data structure, redundancy and decorrelation between wavelet and MGA, as well as the complexity of the coding scheme, so far, no definitive researches have been reported on the MGA-based image coding schemes. In addressing this problem, this paper proposes an image data compression approach using the hidden Markov model (HMM)/pulse-coupled neural network (PCNN) model in the contourlet domain. First, a sparse decomposition of an image was performed using a contourlet transform to obtain the coefficients that show the multiscale and multidirectional characteristics. An HMM was then adopted to establish links between coefficients in neighboring subbands of different levels and directions. An Expectation-Maximization (EM) algorithm was also adopted in training the HMM in order to estimate the state probability matrix, which maintains the same structure of the contourlet decomposition coefficients. In addition, each state probability can be classified by the PCNN based on the state probability distribution. Experimental results show that the HMM/PCNN -contourlet model proposed in this paper leads to better compression performance and offer a more flexible encoding scheme.

Peer-to-Peer (P2P) lending provides convenient and efficient financing channels for small and medium-sized enterprises and individuals, and therefore it has developed rapidly since entering the market. However, due to the imperfection of the credit system and the influence of cyberspace restrictions, P2P network lending faces frequent borrower credit risk crises during the transaction process, with a high proportion of borrowers default. This paper first analyzes the basic development of China’s P2P online lending and the credit risks of borrowers in the industry. Then according to the characteristics of P2P network lending and previous studies, a credit risk assessment indicators system for borrowers in P2P lending is formulated with 29 indicators. Finally, on the basis of the credit risk assessment indicators system constructed in this paper, BP neural network is built based on the BP algorithm, which is trained by the LM algorithm (Levenberg-Marquardt), Scaled Conjugate Gradient, and Bayesian Regularization respectively, to complete the credit risk assessment model. By comparing the results of three mentioned training methodologies, the BP neural network trained by the LM algorithm is finally adopted to construct the credit risk assessment model of borrowers in P2P lending, in which the input layer node is 9, the hidden layer node is 11 and output layer node is 1. The model can provide practical guidance for China and other countries’ P2P lending platforms, and therefore to establish and improve an accurate and effective borrower credit risk management system.

Positive-strand (+)RNA viruses take advantage of the host cells by subverting a long list of host protein factors and transport vesicles and cellular organelles to build membranous viral replication organelles (VROs) that support robust RNA replication. How RNA viruses accomplish major recruitment tasks of a large number of cellular proteins are intensively studied. In case of tomato bushy stunt virus (TBSV), a single viral replication protein, named p33, carries out most of the recruitment duties. Yet, it is currently unknown how the viral p33 replication protein, which is membrane associated, is capable of the rapid and efficient recruitment of numerous cytosolic host proteins to facilitate the formation of large VROs. In this paper, we show that, TBSV p33 molecules do not recruit each cytosolic host factor one-by-one into VROs, but p33 targets a cytosolic protein interaction hub, namely Rpn11, which interacts with numerous other cytosolic proteins. The highly conserved Rpn11, called POH1 in humans, is the metalloprotease subunit of the proteasome, which couples deubiquitination and degradation of proteasome substrates. However, TBSV takes advantage of a noncanonical function of Rpn11 by exploiting Rpn11’s interaction with highly abundant cytosolic proteins and the actin network. We provide supporting evidence that the co-opted Rpn11 in coordination with the subverted actin network is used for delivering cytosolic proteins, such as glycolytic and fermentation enzymes, which are readily subverted into VROs to produce ATP locally in support of VRO formation, viral replicase complex assembly and viral RNA replication. Using several approaches, including knockdown of Rpn11 level, sequestering Rpn11 from the cytosol into the nucleus in plants or temperature-sensitive mutation in Rpn11 in yeast, we show the inhibition of recruitment of glycolytic and fermentation enzymes into VROs. The Rpn11-assisted recruitment of the cytosolic enzymes by p33, however, also requires the combined and coordinated role of the subverted actin network. Accordingly, stabilization of the actin filaments by expression of the Legionella VipA effector in yeast and plant, or via a mutation of ACT1 in yeast resulted in more efficient and rapid recruitment of Rpn11 and the selected glycolytic and fermentation enzymes into VROs. On the contrary, destruction of the actin filaments via expression of the Legionella RavK effector led to poor recruitment of Rpn11 and glycolytic and fermentation enzymes. Finally, we confirmed the key roles of Rpn11 and the actin filaments in situ ATP production within TBSV VROs via using a FRET-based ATP-biosensor. The novel emerging theme is that TBSV targets Rpn11 cytosolic protein interaction hub driven by the p33 replication protein and aided by the subverted actin filaments to deliver several co-opted cytosolic pro-viral factors for robust replication within VROs., Author summary (+)RNA viruses have to co-opt numerous host proteins to support their replication in infected cells. These viruses induce the biogenesis of viral replication organelles (VROs), the sites of replication, in cells. However, what the mechanism of subversion for most of the cytosolic host proteins is not yet dissected. In this paper, the authors used a plant (+)RNA virus, tomato bushy stunt virus (TBSV), to study the role of a cellular proteasomal protein, called Rpn11 (POH1), as a protein interaction hub. They show that knockdown of Rpn11 or retargeting Rpn11 into the nucleus and destruction of actin filaments diminishes TBSV replication in yeast and plant cells. This effect is due to diminished recruitment of pro-viral metabolic enzymes into VROs. Overall, data presented support a novel viral recruitment strategy for cytosolic host factors. Via the small viral replication protein, TBSV targets the cytosolic proteasomal Rpn11 protein interaction hub protein and the co-opted and stabilized actin filaments. The combined and coordinated subversion of Rpn11 and the actin network allows tombusviruses to gain access to abundant cytosolic proteins, such as the glycolytic and fermentation enzymes, which are then efficiently delivered to perform pro-viral functions within the VROs.

This paper uses mathematical modeling to study the mechanisms of surround suppression in the primate visual cortex. We present a large-scale neural circuit model consisting of three interconnected components: LGN and two input layers (Layer 4Ca and Layer 6) of the primary visual cortex V1, covering several hundred hypercolumns. Anatomical structures are incorporated and physiological parameters from realistic modeling work are used. The remaining parameters are chosen to produce model outputs that emulate experimentally observed size-tuning curves. Our two main results are: (i) we discovered the character of the long-range connections in Layer 6 responsible for surround effects in the input layers; and (ii) we showed that a net-inhibitory feedback, i.e., feedback that excites I-cells more than E-cells, from Layer 6 to Layer 4 is conducive to producing surround properties consistent with experimental data. These results are obtained through parameter selection and model analysis. The effects of nonlinear recurrent excitation and inhibition are also discussed. A feature that distinguishes our model from previous modeling work on surround suppression is that we have tried to reproduce realistic lengthscales that are crucial for quantitative comparison with data. Due to its size and the large number of unknown parameters, the model is computationally challenging. We demonstrate a strategy that involves first locating baseline values for relevant parameters using a linear model, followed by the introduction of nonlinearities where needed. We find such a methodology effective, and propose it as a possibility in the modeling of complex biological systems., Author summary The visual cortex is a part of the cortex that processes visual signal. The visual signal from the retina is relayed through the lateral geniculate nucleus (LGN), and enters the primary visual cortex directly. The primary visual cortex consists of many layers. A phenomenon called surround suppression, which means a reduction of neuronal activities in response to stimuli of increasing size, is well-observed in all layers of the primate primary visual cortex. In this paper, we built a large scale mathematical model consisting of the LGN input and tens of thousands of neuron groups in Layer 4 and Layer 6 of the primary visual cortex to study the mechanism of surround suppression. This model is constrained by both anatomical structures and realistic physiological parameters. Remaining unknown parameters are reverse-engineered from experimental data. We showed how the character of the surround suppression depends on the long-range connections in the feedback layer, as well as the feedback current from Layer 6 to Layer 4.

As antimicrobial resistance increases, it is crucial to develop new treatment strategies to counter the emerging threat. In this paper, we consider combination therapies involving conventional antibiotics and debridement, coupled with a novel anti-adhesion therapy, and their use in the treatment of antimicrobial resistant burn wound infections. Our models predict that anti-adhesion–antibiotic–debridement combination therapies can eliminate a bacterial infection in cases where each treatment in isolation would fail. Antibiotics are assumed to have a bactericidal mode of action, killing bacteria, while debridement involves physically cleaning a wound (e.g. with a cloth); removing free bacteria. Anti-adhesion therapy can take a number of forms. Here we consider adhesion inhibitors consisting of polystyrene microbeads chemically coupled to a protein known as multivalent adhesion molecule 7, an adhesin which mediates the initial stages of attachment of many bacterial species to host cells. Adhesion inhibitors competitively inhibit bacteria from binding to host cells, thus rendering them susceptible to removal through debridement. An ordinary differential equation model is developed and the antibiotic-related parameters are fitted against new in vitro data gathered for the present study. The model is used to predict treatment outcomes and to suggest optimal treatment strategies. Our model predicts that anti-adhesion and antibiotic therapies will combine synergistically, producing a combined effect which is often greater than the sum of their individual effects, and that anti-adhesion–antibiotic–debridement combination therapy will be more effective than any of the treatment strategies used in isolation. Further, the use of inhibitors significantly reduces the minimum dose of antibiotics required to eliminate an infection, reducing the chances that bacteria will develop increased resistance. Lastly, we use our model to suggest treatment regimens capable of eliminating bacterial infections within clinically relevant timescales., Author summary Since the development of the first antibiotics, bacteria have utilised and developed resistance mechanisms, helping them to avoid being eliminated and to survive within a host. Traditionally, the solution to this problem has been to treat with multiple antibiotics, switching to a new type when the one currently in use proves ineffective. However, the development of antibiotics has slowed significantly in the past two decades, while multi-drug resistant strains, otherwise known as ‘super bugs’, are on the rise. In answer to this challenge, alternative approaches, such as anti-adhesion therapy, are being developed as a complement or alternative to traditional antimicrobials. In this paper we formulate and analyse a mathematical model of a combination therapy, applied in the context of an infected burn wound, bringing together antibiotics, anti-adhesion therapy and debridement (the physical cleaning of a wound). We use our models to make sense of how these treatments interact to combat a bacterial infection, to predict treatment outcomes for a range of strategies and to suggest optimal treatment regimens. It is hoped that this study will guide future experimental and clinical research, helping biomedical researchers to identify the most promising approaches to treatment.

Complex eukaryotic promoters normally contain multiple cis-regulatory sequences for different transcription factors (TFs). The binding patterns of the TFs to these sites, as well as the way the TFs interact with each other and with the RNA polymerase (RNAp), lead to combinatorial problems rarely understood in detail, especially under varying epigenetic conditions. The aim of this paper is to build a model describing how the main regulatory cluster of the olfactory receptor Or59b drives transcription of this gene in Drosophila. The cluster-driven expression of this gene is represented as the equilibrium probability of RNAp being bound to the promoter region, using a statistical thermodynamic approach. The RNAp equilibrium probability is computed in terms of the occupancy probabilities of the single TFs of the cluster to the corresponding binding sites, and of the interaction rules among TFs and RNAp, using experimental data of Or59b expression to tune the model parameters. The model reproduces correctly the changes in RNAp binding probability induced by various mutation of specific sites and epigenetic modifications. Some of its predictions have also been validated in novel experiments., Author summary The paper proposes and validates experimentally a model for the fine-graded regulation of a gene, called Or59b, coding for an olfactory receptor in Drosophila. The model is based on statistical thermodynamical theory, theory that so far has been mostly used for prokaryotes. In order to apply it to our more complex eukaryotic system, we have performed a large number of “perturbative” in vivo experiments (mutations, knockdown, knockout, epigenetic conditions) meant to unravel the regulatory rules by which the Or59b main regulatory cluster drives gene expression in as much detail as possible. We make use of the knowledge of the Or59b cis-regulatory module acquired in this way to set up the model and to identify its parameters. The model predictions are then tested experimentally in new epigenetic conditions. These new experiments validate the model behavior and confirm its predictive power.

Gram-negative bacteria, as well as some Gram-positive bacteria, possess hair-like appendages known as fimbriae, which play an important role in adhesion of the bacteria to surfaces or to other bacteria. Unlike the sex pili or flagellum, the fimbriae are quite numerous, with of order 1000 fimbriae appendages per bacterial cell. In this paper, a recently developed hybrid model for bacterial biofilms is used to examine the role of fimbriae tension force on the mechanics of bacterial biofilms. Each bacterial cell is represented in this model by a spherocylindrical particle, which interact with each other through collision, adhesion, lubrication force, and fimbrial force. The bacterial cells absorb water and nutrients and produce extracellular polymeric substance (EPS). The flow of water and EPS, and nutrient diffusion within these substances, is computed using a continuum model that accounts for important effects such as osmotic pressure gradient, drag force on the bacterial cells, and viscous shear. The fimbrial force is modeled using an outer spherocylinder capsule around each cell, which can transmit tensile forces to neighboring cells with which the fimbriae capsule collides. We find that the biofilm structure during the growth process is dominated by a balance between outward drag force on the cells due to the EPS flow away from the bacterial colony and the inward tensile fimbrial force acting on chains of cells connected by adhesive fimbriae appendages. The fimbrial force also introduces a large rotational motion of the cells and disrupts cell alignment caused by viscous torque imposed by the EPS flow. The current paper characterizes the competing effects of EPS drag and fimbrial force using a series of computations with different values of the ratio of EPS to bacterial cell production rate and different numbers of fimbriae per cell.

An MSK model of care for hip and knee patients integrated with an electronic referral solution (eReferral) has been deployed within four subregions across Ontario. Referrals are sent from primary care offices to a central intake (CI), where the referral forms are reviewed and forwarded, if appropriate, to a rapid access clinic (RAC) where patients are assessed by an advanced practice clinician (APC). The pragmatic design of eReferral allows for a seamless flow of electronic orthopedic referrals from primary care to CI. It also enables CI to process and transcribe faxed referrals into the eReferral system for a smooth flow of data electronically to the RACs. In general, wait time is the time interval between receiving the patient’s referral at CI or the surgeon’s office until receiving the orthopedic surgeon's first consultation. Wait time is further broken down into wait 1 a and wait 1 b. Wait 1 a is the time between the receipt of the referral at CI until the date of the first initial assessment at the RAC. This study aimed at: a) assessing the processing time of orthopedic referrals at central intakes (CI) to be forwarded to the RAC, b) assessing the wait time (wait 1 a) of orthopedic referrals processed through the eReferral system to receive an initial assessment at the RACs. c) comparing the ability of the RACs to meet the target wait time for assessment (four weeks) by the method of referral (eReferrals vs. fax). d) evaluating patients’ satisfaction with the length of time they waited to receive care at the RACs with eReferral. We used Ocean eReferral database to access MSK hip and knee referral data processed through the system. Patients whose referrals were initiated electronically through the system and opted to receive email notification of their referral status had the opportunity to take an online satisfaction survey embedded in the booked appointment notification message. There were 1,723 patients initially referred electronically for hip, and knee pain consults, while 13,780 referrals started as paper-based and transcribed into the system to be forwarded later electronically by CI to a RAC. Higher mean processing time at CI by 21.76 days for paper-based referral was detected as opposed to referrals received electronically (p

We describe Orbit Image Analysis, an open-source whole slide image analysis tool. The tool consists of a generic tile-processing engine which allows the execution of various image analysis algorithms provided by either Orbit itself or from other open-source platforms using a tile-based map-reduce execution framework. Orbit Image Analysis is capable of sophisticated whole slide imaging analyses due to several key features. First, Orbit has machine-learning capabilities. This deep learning segmentation can be integrated with complex object detection for analysis of intricate tissues. In addition, Orbit can run locally as standalone or connect to the open-source image server OMERO. Another important characteristic is its scale-out functionality, using the Apache Spark framework for distributed computing. In this paper, we describe the use of Orbit in three different real-world applications: quantification of idiopathic lung fibrosis, nerve fibre density quantification, and glomeruli detection in the kidney., Author summary Whole slide images (WSI) are digital scans of samples, e.g. tissue sections. It is very convenient to view samples in this digital form, and with the increasing computation power it can also be used for quantification. These images are often too large to be analysed with standard tools. To overcome this issue, we created on open-source tool called Orbit Image Analysis which divides the images into smaller parts and allows the analysis of it with either embedded algorithms or the integration of existing tools. It also provides mechanisms to process huge amounts of images in distributed computing environments, such as clusters or cloud infrastructures. In this paper we describe the Orbit system and demonstrate its application based on three real-word use-cases.

Nowadays, location based service (LBS) is one of the most popular mobile apps and following with humongous of location data been produced. The publishing of location data can provide benefit for promoting the quality of service, optimizing the commercial environment as well as harmonizing the infrastructure construction. However, as location data may contain some sensitive or confidential information, the publishing may reveal privacy and bring hazards. So the published data had to be disposed to protect the privacy. In order to cope with this problem, a number of algorithms based on the strategy of k-anonymity were proposed, but this is not enough for the privacy protection, as the correlation between the sensitive region and the background knowledge can be used to infer the real location. Thus, consider about this condition, in this paper a ε-sensitive correlation privacy protection scheme is proposed, and provides correlation indistinguishable to the location data. In this scheme, entropy is first used to determine the location centroid of each cell to build up the voronoi diagram. Then the coordinate of the untreated location data that is located in the cell is transferred into the centroid vicinity. Accordingly, the sensitive correlation is destroyed by the coordinate of each published data. The process of transferring the location data is determined by metrics of ε-sensitive correlation privacy, and is rigorous in mathematical justification. At last, security analysis is proposed in this paper to verify the privacy ability of our proposed algorithm based on voronoi diagram and entropy, and then we utilize the comparative experiment to further affirm the advantage of this algorithm in the location data privacy protection as well as the availability of published data.

A biophysical model that captures molecular homeostatic control of ions at the perisynaptic cradle (PsC) is of fundamental importance for understanding the interplay between astroglial and neuronal compartments. In this paper, we develop a multi-compartmental mathematical model which proposes a novel mechanism whereby the flow of cations in thin processes is restricted due to negatively charged membrane lipids which result in the formation of deep potential wells near the dipole heads. These wells restrict the flow of cations to “hopping” between adjacent wells as they transverse the process, and this surface retention of cations will be shown to give rise to the formation of potassium (K+) and sodium (Na+) microdomains at the PsC. We further propose that a K+ microdomain formed at the PsC, provides the driving force for the return of K+ to the extracellular space for uptake by the neurone, thereby preventing K+ undershoot. A slow decay of Na+ was also observed in our simulation after a period of glutamate stimulation which is in strong agreement with experimental observations. The pathological implications of microdomain formation during neuronal excitation are also discussed., Author summary During periods of neuronal activity, ionic homeostasis in the surrounding extracellular space (ECS) is disturbed. To provide a healthy environment for continued neuronal function, excess ions such as potassium must be buffered away from the ECS; a vital supportive role provided by astrocyte cells. It has long been thought that astrocytes not only removed ions from the ECS but also transport them to other areas of the brain where their concentrations are lower. However, while our computational model simulations agree that astrocytes do remove these ions from the ECS they also show that these ions are mainly stored locally at the PsC to be returned to the ECS, thus restoring ionic homeostasis. Furthermore, we detail in this paper that this happens because of a previously overlooked biophysical phenomenon that is only dominant in thin astrocyte processes. The flow of these cations within thin processes is primarily by surface conduction where they experience the attraction of fixed negative charge at the membrane inner surface. This negative charge constrains cation movement along the surface and so their flow rate is restricted. Consequently, ions such as potassium that are released during neuronal excitation enter the PsC and are stored locally due to the low conductance pathway between the PsC and the astrocyte soma. Our simulations also show that this local build-up of K+ is returned to the ECS after the neuronal activity dies off which could potentially explain why K+ undershoot has not been observed; this result agrees with experimental observations. Moreover, the same mechanism can also explain the transient behaviour of Na+ ions whereby in thin processes a slow decay time constant is experimentally observed. These findings have important implications for the role of astrocytes in regulating neuronal excitability under physiological and pathological conditions, and therefore highlight the significance of the work presented in this paper.

Kaempferol (kae) and its glycosides are widely distributed in nature and show multiple bioactivities, yet few reports have compared them. In this paper, we report the antitumor, antioxidant and anti-inflammatory activity differences of kae, kae-7-O-glucoside (kae-7-O-glu), kae-3-O-rhamnoside (kae-3-O-rha) and kae-3-O-rutinoside (kae-3-O-rut). Kae showed the highest antiproliferation effect on the human hepatoma cell line HepG2, mouse colon cancer cell line CT26 and mouse melanoma cell line B16F1. Kae also significantly inhibited AKT phosphorylation and cleaved caspase-9, caspase-7, caspase-3 and PARP in HepG2 cells. A kae-induced increase in DPPH and ABTS radical scavenging activity, inhibition of concanavalin A (Con A)-induced activation of T cell proliferation and NO or ROS production in LPS-induced RAW 264.7 macrophage cells were also seen. Kae glycosides were used to produce kae via environment-friendly enzymatic hydrolysis. Kae-7-O-glu and kae-3-O-rut were hydrolyzed to kae by β-glucosidase and/or α-L-rhamnosidase. This paper demonstrates the application of enzymatic catalysis to obtain highly biologically active kae. This work provides a novel and efficient preparation of high-value flavone-related products.

In this paper, we will investigate the effect of diffusion, which is ubiquitous in nature, on the immune system using a reaction-diffusion model in order to understand the dynamical behavior of complex patterns and control the dynamics of different patterns. Through control theory and linear stability analysis of local equilibrium, we obtain the optimal condition under which the system loses stability and a Turing pattern occurs. By combining mathematical analysis and numerical simulation, we show the possible patterns and how these patterns evolve. In addition, we establish a bridge between the complex patterns and the biological mechanism using the results from a previous study in Nature Cell Biology. The results in this paper can help us better understand the biological significance of the immune system.

BackgroundBody size, nutrition and socioeconomic position (SEP) in early life have been associated with a wide range of long-term health effects. Epigenetics is one possible mechanism through which these early life exposures can impact later life health. We conducted a systematic review examining the observational evidence for the impact of body size, nutrition and SEP in early life on the epigenome in humans.MethodsThis systematic review is registered with the PROSPERO database (registration number: CRD42016050193). Three datasets were simultaneously searched using Ovid and the resulting studies were evaluated by at least two independent reviewers. Studies measuring epigenetic markers either at the same time as, or after, the early life exposure and have a measure of body size, nutrition or SEP in early life (up to 12 years), written in English and from a community-dwelling participants were included.ResultsWe identified 90 eligible studies. Seventeen of these papers examined more than one early life exposure of interest. Fifty six papers examined body size, 37 nutrition and 17 SEP. All of the included papers examined DNA methylation (DNAm) as the epigenetic marker. Overall there was no strong evidence for a consistent association between these early life variables in DNAm which may be due to the heterogeneous study designs, data collection methods and statistical analyses.ConclusionsDespite these inconclusive results, the hypothesis that the early life environment can impact DNAm, potentially persisting into adult life, was supported by some studies and warrants further investigation. We provide recommendations for future studies.

Established cell lines are widely used in research, however an appealing question is the comparability of the cells between various laboratories, their characteristics and stability in time. Problematic is also the cell line misidentification, genetic and phenotypic shift or Mycoplasma contamination which are often forgotten in research papers. The monocyte/macrophage-like cell line RAW 264.7 has been one of the most commonly used myeloid cell line for more than 40 years. Despite its phenotypic and functional stability is often discussed in literature or at various scientific discussion panels, their stability during the consecutive passages has not been confirmed in any solid study. So far, only a few functional features of these cells have been studied, for example their ability to differentiate into osteoclasts. Therefore, in the present paper we have investigated the phenotype and functional stability of the RAW 264.7 cell line from passage no. 5 till passage no. 50. We found out that the phenotype (expression of particular macrophage-characteristic genes and surface markers) and functional characteristics (phagocytosis and NO production) of RAW 264.7 cell line remains stable through passages: from passage no. 10 up to passage no. 30. Overall, our results indicated that the RAW 264.7 cell line should not be used after the passage no. 30 otherwise it may influence the data reliability.

Several studies have suggested that free d-Asp has a crucial role in N-methyl d-Asp receptor-mediated neurotransmission playing very important functions in physiological and pathological processes. This paper describes the development of an analytical procedure for the direct and simultaneous determination of free d-Asp, l-Asp and N-methyl d-Asp in specimens of different mouse brain tissues using chiral LC-MS/MS in Multiple Reaction Monitoring scan mode. After comparing three procedures and different buffers and extraction solvents, a simple preparation procedure was selected the analytes of extraction. The method was validated by analyzing l-Asp, d-Asp and N-methyl d-Asp recovery at different spiked concentrations (50, 100 and 200 pg/μl) yielding satisfactory recoveries (75-110%), and good repeatability. Limits of detection (LOD) resulted to be 0.52 pg/μl for d-Asp, 0.46 pg/μl for l-Asp and 0.54 pg/μl for NMDA, respectively. Limits of quantification (LOQ) were 1.57 pg/μl for d-Asp, 1.41 pg/μl for l-Asp and 1.64 pg/μl for NMDA, respectively. Different concentration levels were used for constructing the calibration curves which showed good linearity. The validated method was then successfully applied to the simultaneous detection of d-Asp, l-Asp and NMDA in mouse brain tissues. The concurrent, sensitive, fast, and reproducible measurement of these metabolites in brain tissues will be useful to correlate the amount of free d-Asp with relevant neurological processes, making the LC-MS/MS MRM method well suited, not only for research work but also for clinical analyses. Several studies have suggested that free D-Asp has a crucial role in N-methyl D-Asp receptor-mediated neurotransmission playing very important functions in physiological and pathological processes. This paper describes the development of an analytical procedure for the direct and simultaneous determination of free D-Asp, L-Asp and N-methyl D-Asp in specimens of different mouse brain tissues using chiral LC-MS/MS in Multiple Reaction Monitoring scan mode. After comparing three procedures and different buffers and extraction solvents, a simple preparation procedure was selected the analytes of extraction. The method was validated by analyzing L-Asp, D-Asp and N-methyl D-Asp recovery at different spiked concentrations (50, 100 and 200 pg/μl) yielding satisfactory recoveries (75-110%), and good repeatability. Limits of detection (LOD) resulted to be 0.52 pg/μl for D-Asp, 0.46 pg/μl for L-Asp and 0.54 pg/μl for NMDA, respectively. Limits of quantification (LOQ) were 1.57 pg/μl for D-Asp, 1.41 pg/μl for L-Asp and 1.64 pg/μl for NMDA, respectively. Different concentration levels were used for constructing the calibration curves which showed good linearity. The validated method was then successfully applied to the simultaneous detection of D-Asp, L-Asp and NMDA in mouse brain tissues. The concurrent, sensitive, fast, and reproducible measurement of these metabolites in brain tissues will be useful to correlate the amount of free D-Asp with relevant neurological processes, making the LC-MS/MS MRM method well suited, not only for research work but also for clinical analyses.

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process.

This paper aims to optimize a desirable fare structure for the public transit service along a bus-subway corridor with the consideration of those factors related to equity in trip, including travel distance and comfort level. The travel distance factor is represented by the distance-based fare strategy, which is an existing differential strategy. The comfort level one is considered in the area-based fare strategy which is a new differential strategy defined in this paper. Both factors are referred to by the combined fare strategy which is composed of distance-based and area-based fare strategies. The flat fare strategy is applied to determine a reference level of social welfare and obtain the general passenger flow along transit lines, which is used to divide areas or zones along the corridor. This problem is formulated as a bi-level program, of which the upper level maximizes the social welfare and the lower level capturing traveler choice behavior is a variable-demand stochastic user equilibrium assignment model. A genetic algorithm is applied to solve the bi-level program while the method of successive averages is adopted to solve the lower-level model. A series of numerical experiments are carried out to illustrate the performance of the models and solution methods. Numerical results indicate that all three differential fare strategies play a better role in enhancing the social welfare than the flat fare strategy and that the fare structure under the combined fare strategy generates the highest social welfare and the largest resulting passenger demand, which implies that the more equity factors a differential fare strategy involves the more desirable fare structure the strategy has.

In clinical neuropsychology the cognitive abilities of neurological patients are commonly estimated using well-established paper-based tests. Typically, scores on some tests remain relatively well preserved, whilst others exhibit a significant and disproportionate decline. Scores on those tests that measure preserved cognitive functions (so-called ‘hold’ tests) may be used to estimate premorbid abilities, including scores in non-hold tests that would have been expected prior to the onset of cognitive impairment. Many hold tests entail word reading, with each word being graded as correctly or incorrectly pronounced. Inevitably, such tests are likely to contain words that provide little or no diagnostic power (i.e., can be eliminated without negatively affecting prediction accuracy). In this paper, a genetic algorithm is developed and demonstrated, using n=92 neurologically healthy participants, to identify optimal word subsets from the National Adult Reading Test that minimize the mean error in predicting the most widely used clinical measure of IQ and cognitive ability, the Wechsler Adult Intelligence Scale Fourth Edition IQ. In addition to requiring only 17-20 of the original 50 words (suggesting that this test could be revised to be up to 66% shorter) and minimizing mean prediction error, the algorithm increases the proportion of the variance in the predicted variable explained in comparison to using all words (from r^2 = 0.46 to r^2 = 0.61). In a clinical setting this would improve estimates of premorbid cognitive function and, if an abbreviated revision to this test were to be adopted, reduce the arduousness of the test for patients. The proposed method is evaluated with jackknifing and leave one out cross validation. The general approach may be used to optimize the relationship between any two psychological tests by finding the question subset in one test that minimizes the prediction error in a second test by training the genetic algorithm using data collected from participants upon whom both tests have been administered. This approach may also be used to develop new predictive tests, since it provides a method to identify an optimal subset of a set of candidate questions (for which empirical data have been collected) that maximizes prediction accuracy and the proportion of variance in the predicted variable that can be explained.

A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemistry programs. The data for all of these assays are presented and analyzed to show how outstanding leads for many indications can be selected. These results reveal the immense potential for translating the dispersed expertise in biological assays involving human pathogens into drug discovery starting points, by providing open access to new families of molecules, and emphasize how a small additional investment made to help acquire and distribute compounds, and sharing the data, can catalyze drug discovery for dozens of different indications. Another lesson is that when multiple screens from different groups are run on the same library, results can be integrated quickly to select the most valuable starting points for subsequent medicinal chemistry efforts., Author Summary Malaria leads to the loss of over 440,000 lives annually; accelerating research to discover new candidate drugs is a priority. Medicines for Malaria Venture (MMV) has distilled over 25,000 compounds that kill malaria parasites in vitro into a group of 400 representative compounds, called the "Malaria Box". These Malaria Box sets were distributed free-of-charge to research laboratories in 30 different countries that work on a wide variety of pathogens. Fifty-five groups compiled >290 assay results for this paper describing the many activities of the Malaria Box compounds. The collective results suggest a potential mechanism of action for over 130 compounds against malaria and illuminate the most promising compounds for further malaria drug development research. Excitingly some of these compounds also showed outstanding activity against other disease agents including fungi, bacteria, other single-cellular parasites, worms, and even human cancer cells. The results have ignited over 30 drug development programs for a variety of diseases. This open access effort was so successful that MMV has begun to distribute another set of compounds with initial activity against a wider range of infectious agents that are of public health concern, called the Pathogen Box, available now to scientific labs all over the world (www.PathogenBox.org).

Lung cancer, primarily non-small-cell lung cancer (NSCLC), is the leading cause of cancer deaths in the United States and worldwide. While early detection significantly improves five-year survival, there are no reliable diagnostic tools for early detection. Several exosomal microRNAs (miRs) are overexpressed in NSCLC, and have been suggested as potential biomarkers for early detection. The present paper develops a mathematical model for early stage of NSCLC with emphasis on the role of the three highest overexpressed miRs, namely miR-21, miR-205 and miR-155. Simulations of the model provide quantitative relationships between the tumor volume and the total mass of each of the above miRs in the tumor. Because of the positive correlation between these miRs in the tumor tissue and in the blood, the results of the paper may be viewed as a first step toward establishing a combination of miRs 21, 205, 155 and possibly other miRs as serum biomarkers for early detection of NSCLC.

A prerequisite for reliable detection of low-density Plasmodium infections in malaria pre-elimination settings is the availability of ultra-sensitive and high-throughput molecular tools. We developed a SYBR Green real-time PCR restriction fragment length polymorphism assay (cytb-qPCR) targeting the cytochrome b gene of the four major human Plasmodium species (P. falciparum, P. vivax, P. malariae, and P. ovale) for parasite detection and species determination with DNA extracted from dried blood spots collected on filter paper. The performance of cytb-qPCR was first compared against four reference PCR methods using serially diluted Plasmodium samples. The detection limit of the cytb-qPCR was 1 parasite/mu l (p/mu l) for P. falciparum and P. ovale, and 2 p/mu l for P. vivax and P. malariae, while the reference PCRs had detection limits of 0.5-10 p/mu l. The ability of the PCR methods to detect low-density Plasmodium infections was then assessed using 2977 filter paper samples collected during a cross-sectional survey in Zanzibar, a malaria pre-elimination setting in sub-Saharan Africa. Field samples were defined as 'final positive' if positive in at least two of the five PCR methods. Cytb-qPCR preformed equal to or better than the reference PCRs with a sensitivity of 100% (65/65; 95% CI 94.5-100%) and a specificity of 99.9%(2910/2912; 95% CI 99.7-100%) when compared against 'final positive' samples. The results indicate that the cytb-qPCR may represent an opportunity for improved molecular surveillance of low-density Plasmodium infections in malaria pre-elimination settings.

Spinocerebellar ataxia type 3 (SCA3), also known as Machado-Joseph disease, is an autosomal dominant neurodegenerative disorder, characterized by progressive ataxia, spasticity, and ocular movement abnormalities. The SCA3 gene, ATXN3, was identified in 1994 and contains a CAG repeat tract that increases from 12–41 to 62–84 in the disease state to produce ataxin-3 protein containing a polyglutamine (polyQ) expansion [1]. SCA3 is the most common inherited ataxia, yet a thorough understanding of the molecular basis of this disease has remained elusive. Initial studies found that ataxin-3 interacts with two proteins, HHR23A and HHR23B, that are both homologs of the DNA repair protein Rad23 [2]. Further work revealed ataxin-3 as a bona fide deubiquitinating enzyme [3,4], thereby providing a crucial insight into ataxin-3’s normal function. More recently, ataxin-3 was shown to interact with the transcription factor FOXO4 as a transcriptional coactivator in the oxidative stress response [5]. Thus, while some very basic functions of ataxin-3 have been established, these advances have yielded nonintersecting lines of investigation and have not provided an underlying mechanism for SCA3 disease pathogenesis. In this issue of PLOS Genetics, two complementary papers establish a key function of ataxin-3 through its interaction with the DNA end-processing enzyme polynucleotide kinase 3′-phosphatase (PNKP) and reveal how this interplay contributes to SCA3 pathogenesis [6,7]. The work by Chatterjee et al. uncovers the interaction between ataxin-3 and PKNP in a screen for PNKP-associated proteins. PNKP is a 3′ DNA phosphatase involved in repair of single- and double-strand breaks through the single-strand break repair and nonhomologous end joining pathways. Interestingly, mutations in PNKP have been linked to disorders with neurological symptoms including neurodegeneration, progressive polyneuropathy, cerebellar atrophy, microcephaly, mild epilepsy, seizures, developmental delay, and intellectual disability [8,9]. PNKP is an enzyme with key roles in DNA damage repair, particularly maintaining genomic stability of neural cells, and in providing resistance of cancer cells to genotoxic therapeutic agents. To this end, PNKP-targeting drugs are being developed with the goal of potentially increasing the sensitivity of human tumors to gamma radiation [10]. Chatterjee et al. show that ataxin-3 is a robust interactor with PNKP and promotes PNKP’s phosphatase and DNA repair activities (Fig. 1). Importantly, expression of mutant ataxin-3 leads to decreased phosphatase activity in vitro and in the brains of SCA3 transgenic mice. Figure 1 Interaction of ataxin-3 and PNKP in normal and disease states. The accompanying work from Gao et al. shows that PNKP also interacts with polyQ-expanded ataxin-3. Interestingly, the PNKP–ATXN3 interaction predominantly occurs outside of the nucleus in SCA3 patient brains, and the authors document colocalization of PNKP with polyQ-containing aggregates in SCA3 patient brains. Contrary to the nuclear inclusions observed by other groups, these aggregates appear outside of the nucleus and suggest that polyQ-ataxin-3 aggregates may sequester PNKP in the cytoplasm, resulting in a loss of normal nuclear PNKP function. Further work will need to delineate this possibility and further explore this unexpected phenomenon. Gao et al. also demonstrate a dramatic increase in DNA damage in mutant ataxin-3 cells, SCA3 mouse brain, and brain sections from SCA3 patients, corroborating results observed in the Chatterjee et al. paper. Importantly, the authors link this increase in DNA damage to activation of the ataxia-telangiectasia-mutated (ATM) signaling pathway. Previous work had shown mutant ataxin-3 results in p53 activation and apoptotic cell death [11]. The current work corroborates this earlier finding and documents that this effect can be rescued by PNKP overexpression, implicating loss of PNKP activity in the path of p53-mediated apoptosis. In a separate tack, Gao et al. implicate the c-Abl/PKCδ arm of the ATM signaling pathway by illustrating PKCδ phosphorylation and nuclear translocation. While these two papers clearly connect PNKP to SCA3 pathogenesis, remaining work will need to clarify this relationship. In many experiments, expression of polyQ-ataxin-3 had the same effect as depletion of either ATXN3 or PNKP. It seems clear that mutant ataxin-3 is responsible for the loss of PKNP function, but can the proposed sequestration of PNKP in polyQ-ataxin-3 aggregates fully account for failure of DNA damage repair (Fig. 1)? Studies of other polyQ diseases suggest that interactions in the soluble phase are an important aspect of the pathogenic cascade; hence, it is also possible that mutant ataxin-3 deleteriously interacts with PNKP independent of aggregate sequestration (Fig. 1). Although Chatterjee et al. found that polyQ-ataxin-3 does not interact with the phosphatase domain of PNKP in vitro, could mutant ataxin-3 nonetheless interact with PNKP in its native complex to inactivate its phosphatase activity? Interaction of polyQ disease–associated proteins in their normal native complexes has emerged as a likely scenario for altering the functions of presumed interactors in SCA1 and spinobulbar muscular atrophy [12,13], two related polyQ disorders. The identification of an interaction between PNKP and ataxin-3 is intriguing, as there were previous hints from the SCA3 field that DNA damage might be involved in this disease. One of the first groups to examine ataxin-3 function discovered a putative interaction with the human homologs of Rad23, involved in nucleotide excision repair of ultraviolet (UV)-damaged DNA [2]. The interaction of ataxin-3 and FOXO4 highlighted a possible connection to the oxidative stress response. While Gao et al. claim that oxidative stress is not the driving force behind an increase in DNA damage in SCA3, future work should address the specific link between polyQ-ataxin-3 and the oxidative stress response. Furthermore, one crucial piece of missing information is whether the deubiquitination activity of ataxin-3 somehow modulates PKNP function. Despite these remaining questions, these two studies provide an exciting link between polyQ-mediated cerebellar degeneration and neuronal DNA damage. It has long been recognized that mutations in other DNA repair proteins, such as aprataxin and TDP1, result in ataxia and selective cerebellar neurodegeneration [14–16], highlighting the importance of DNA damage repair in the cerebellum. A link between ataxin-3 and PNKP thus reinforces the importance of DNA repair in maintaining healthy cerebellar neurons and suggests targets for therapy development for SCA3 cerebellar degeneration, with possible application to other cerebellar degenerative diseases.

Frailty is a common condition in older age and is a public health concern which requires integrated care and involves different stakeholders. This meta-synthesis focuses on experiences,\ud understanding, and attitudes towards screening, care, intervention and prevention for frailty across frail and healthy older persons, caregivers, health and social care practitioners.\ud Studies published since 2001 were identified through search of electronic databases; 81 eligible papers were identified and read in full, and 45 papers were finally included and synthesized. The synthesis was conducted with a meta-ethnographic approach. We identified four key themes: Uncertainty about malleability of frailty; Strategies to prevent or to respond to frailty; Capacity to care and person and family-centred service provision; Power\ud and choice. A bottom-up approach which emphasises and works in synchrony with frail older people's and their families' values, goals, resources and optimisation strategies is necessary.\ud A greater employment of psychological skills, enhancing communication abilities and tools to overcome disempowering attitudes should inform care organisation, resulting in\ud more efficient and satisfactory use of services. Public health communication about prevention and management of frailty should be founded on a paradigm of resilience, balanced\ud acceptance, and coping. Addressing stakeholders' views about the preventability of frailty was seen as a salient need.

Genomic signal processing (GSP) refers to the use of signal processing for the analysis of genomic data. GSP methods require the transformation or mapping of the genomic data to a numeric representation. To date, several DNA numeric representations (DNR) have been proposed; however, it is not clear what the properties of each DNR are and how the selection of one will affect the results when using a signal processing technique to analyze them. In this paper, we present an experimental study of the characteristics of nine of the most frequently-used DNR. The objective of this paper is to evaluate the behavior of each representation when used to measure the similarity of a given pair of DNA sequences.

Homeostatic and adaptive control mechanisms are essential for keeping organisms structurally and functionally stable. Integral feedback is a control theoretic concept which has long been known to keep a controlled variable A robustly (i.e. perturbation-independent) at a given set-point A(set) by feeding the integrated error back into the process that generates A. The classical concept of homeostasis as robust regulation within narrow limits is often considered as unsatisfactory and even incompatible with many biological systems which show sustained oscillations, such as circadian rhythms and oscillatory calcium signaling. Nevertheless, there are many similarities between the biological processes which participate in oscillatory mechanisms and classical homeostatic (non-oscillatory) mechanisms. We have investigated whether biological oscillators can show robust homeostatic and adaptive behaviors, and this paper is an attempt to extend the homeostatic concept to include oscillatory conditions. Based on our previously published kinetic conditions on how to generate biochemical models with robust homeostasis we found two properties, which appear to be of general interest concerning oscillatory and homeostatic controlled biological systems. The first one is the ability of these oscillators ("oscillatory homeostats") to keep the average level of a controlled variable at a defined set-point by involving compensatory changes in frequency and/or amplitude. The second property is the ability to keep the period/frequency of the oscillator tuned within a certain well-defined range. In this paper we highlight mechanisms that lead to these two properties. The biological applications of these findings are discussed using three examples, the homeostatic aspects during oscillatory calcium and p53 signaling, and the involvement of circadian rhythms in homeostatic regulation.

An integrative cell migration model incorporating focal adhesion (FA) dynamics, cytoskeleton and nucleus remodeling, actin motor activity, and lamellipodia protrusion is developed for predicting cell spreading and migration behaviors. This work is motivated by two experimental works: (1) cell migration on 2-D substrates under various fibronectin concentrations and (2) cell spreading on 2-D micropatterned geometries. These works suggest (1) cell migration speed takes a maximum at a particular ligand density (∼1140 molecules/µm2) and (2) that strong traction forces at the corners of the patterns may exist due to combined effects exerted by actin stress fibers (SFs). The integrative model of this paper successfully reproduced these experimental results and indicates the mechanism of cell migration and spreading. In this paper, the mechanical structure of the cell is modeled as having two elastic membranes: an outer cell membrane and an inner nuclear membrane. The two elastic membranes are connected by SFs, which are extended from focal adhesions on the cortical surface to the nuclear membrane. In addition, the model also includes ventral SFs bridging two focal adhesions on the cell surface. The cell deforms and gains traction as transmembrane integrins distributed over the outer cell membrane bond to ligands on the ECM surface, activate SFs, and form focal adhesions. The relationship between the cell migration speed and fibronectin concentration agrees with existing experimental data for Chinese hamster ovary (CHO) cell migrations on fibronectin coated surfaces. In addition, the integrated model is validated by showing persistent high stress concentrations at sharp geometrically patterned edges. This model will be used as a predictive model to assist in design and data processing of upcoming microfluidic cell migration assays., Author Summary Cell migration is a complex, multifaceted process, triggered by chemotaxis and haptotatic responses from the extracellular matrix (ECM). It is triggered by a thin lamellipodium protrusion at the leading edge, followed by the assembly of a number of focal adhesions between the lamellipodium base and the ECM. Afterwards, actin stress fibers extend from nascent focal adhesions, some of which connect to the nucleus. In this work, we have developed a dynamic model of cell migration incorporating these four mechanisms of cell biology, such as remodeling of cell and nuclear membranes, focal adhesion dynamics, actin motor activity, and lamellipodia protrusion at the leading edge. We successfully compared our model with existing experimental works of cell migration on (1) substrates with various fibronectin coating concentrations, and (2) cell spreading on three patterned surfaces. Finally, our model demonstrates how actin stress fibers anchored at the trailing edge play a key role, leading to an increase in cell migration speed. Thereby, the model will not only provide new insights on better building such an experiment, but also further experiments will allow us to better validate the model.

Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells.

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.