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Many mRNA-based vaccines have been investigated for their specific potential to activate dendritic cells (DCs), the highly-specialized antigen-presenting cells of the immune system that play a key role in inducing effective CD4+ and CD8+ T-cell responses. In this paper we report a new vaccine/gene delivery platform that demonstrates the benefits of using a self-amplifying (“replicon”) mRNA that is protected in a viral-protein capsid. Purified capsid protein from the plant virus Cowpea Chlorotic Mottle Virus (CCMV) is used to in vitro assemble monodisperse virus-like particles (VLPs) containing reporter proteins (e.g., Luciferase or eYFP) or the tandem-repeat model antigen SIINFEKL in RNA gene form, coupled to the RNA-dependent RNA polymerase from the Nodamura insect virus. Incubation of immature DCs with these VLPs results in increased activation of maturation markers – CD80, CD86 and MHC-II – and enhanced RNA replication levels, relative to incubation with unpackaged replicon mRNA. Higher RNA uptake/replication and enhanced DC activation were detected in a dose-dependent manner when the CCMV-VLPs were pre-incubated with anti-CCMV antibodies. In all experiments the expression of maturation markers correlates with the RNA levels of the DCs. Overall, these studies demonstrate that: VLP protection enhances mRNA uptake by DCs; coupling replicons to the gene of interest increases RNA and protein levels in the cell; and the presence of anti-VLP antibodies enhances mRNA levels and activation of DCs in vitro. Finally, preliminary in vivo experiments involving mouse vaccinations with SIINFEKL-replicon VLPs indicate a small but significant increase in antigen-specific T cells that are doubly positive for IFN and TFN induction. [ABSTRACT FROM AUTHOR]

Sudangrass, Sorghum sudanense (Piper) Stapf, is a vigorous forage crop that has also been used for biogas, paper, and electricity production. Due to the large biomass yields achieved by sudangrass and the large area of potential growth in Argentina seven sudangrass accessions from a collection of S. sudanense were analyzed to evaluate their potential as feedstocks for lignocellulosic bioethanol production, and to assess whether there is an association between the response to biotic and abiotic stresses and the composition of the biomass. The biomass composition was analyzed for major cell wall polymers, monosaccharides, and elemental composition. On average, 68% of stem lignocellulosic biomass was comprised of matrix polysaccharides and crystalline cellulose, representing a potential source of sugars for bioethanol production. Xylose was the predominant matrix polysaccharide monosaccharide comprising, on average, 45% of the total sugars, followed by arabinose, glucose, galactose, galacturonic acid, mannose, glucuronic acid, and fucose. Rhamnose was not detected in any of the biomasses analyzed. Silica was the most abundant element in sudangrass stem, followed by chloride, calcium, phosphorus and sulfur. We performed saccharification analyses after pretreatments. Alkaline pretreatment was more effective than water pretreatment. Sodium hydroxide pretreatment exposed different levels of recalcitrance among sudangrass accessions, whereas the water pretreatment did not. Phenological traits were also evaluated, showing significant variability among accessions. The comparison of major cell wall polymers and monosaccharide composition between tolerant and susceptible accessions to abiotic and biotic stresses suggests an association between the composition of the biomass and the response to stress. [ABSTRACT FROM AUTHOR]

When listening to music, humans can easily identify and move to the beat. Numerous experimental studies have identified brain regions that may be involved with beat perception and representation. Several theoretical and algorithmic approaches have been proposed to account for this ability. Related to, but different from the issue of how we perceive a beat, is the question of how we learn to generate and hold a beat. In this paper, we introduce a neuronal framework for a beat generator that is capable of learning isochronous rhythms over a range of frequencies that are relevant to music and speech. Our approach combines ideas from error-correction and entrainment models to investigate the dynamics of how a biophysically-based neuronal network model synchronizes its period and phase to match that of an external stimulus. The model makes novel use of on-going faster gamma rhythms to form a set of discrete clocks that provide estimates, but not exact information, of how well the beat generator spike times match those of a stimulus sequence. The beat generator is endowed with plasticity allowing it to quickly learn and thereby adjust its spike times to achieve synchronization. Our model makes generalizable predictions about the existence of asymmetries in the synchronization process, as well as specific predictions about resynchronization times after changes in stimulus tempo or phase. Analysis of the model demonstrates that accurate rhythmic time keeping can be achieved over a range of frequencies relevant to music, in a manner that is robust to changes in parameters and to the presence of noise. [ABSTRACT FROM AUTHOR]

Across the mammalian nervous system, neurotrophins control synaptic plasticity, neuromodulation, and neuronal growth. The neurotrophin Brain Derived Neurotrophic Factor (BDNF) is known to promote structural and functional synaptic plasticity in the hippocampus, the cerebral cortex, and many other brain areas. In recent years, a wealth of data has been accumulated revealing the paramount importance of BDNF for neuronal function. BDNF signaling gives rise to multiple complex signaling pathways that mediate neuronal survival and differentiation during development, and formation of new memories. These different roles of BDNF for neuronal function have essential consequences if BDNF signaling in the brain is reduced. Thus, BDNF knock-out mice or mice that are deficient in BDNF receptor signaling via TrkB and p75 receptors show deficits in neuronal development, synaptic plasticity, and memory formation. Accordingly, BDNF signaling dysfunctions are associated with many neurological and neurodegenerative conditions including Alzheimer's and Huntington's disease. However, despite the widespread implications of BDNF-dependent signaling in synaptic plasticity in healthy and pathological conditions, the interplay of the involved different biochemical pathways at the synaptic level remained mostly unknown. In this paper, we investigated the role of BDNF/TrkB signaling in spike-timing dependent plasticity (STDP) in rodent hippocampus CA1 pyramidal cells, by implementing the first subcellular model of BDNF regulated, spike timing-dependent long-term potentiation (t-LTP). The model is based on previously published experimental findings on STDP and accounts for the observed magnitude, time course, stimulation pattern and BDNF-dependence of t-LTP. It allows interpreting the main experimental findings concerning specific biomolecular processes, and it can be expanded to take into account more detailed biochemical reactions. The results point out a few predictions on how to enhance LTP induction in such a way to rescue or improve cognitive functions under pathological conditions. [ABSTRACT FROM AUTHOR]

In this paper we set the previously reported pressure-dependent, ordinary differential equation outflow model by Smith and Gardiner for the human eye, into a new three-dimensional (3D) porous media outflow model of the eye, and calibrate model parameters using data reported in the literature. Assuming normal outflow through anterior pathways, we test the ability of 3D flow model to predict the pressure elevation with a silicone oil tamponade. Then assuming outflow across the retinal pigment epithelium is normal, we test the ability of the 3D model to predict the pressure elevation in Schwartz-Matsuo syndrome. For the first time we find the flow model can successfully model both conditions, which helps to build confidence in the validity and accuracy of the 3D pressure-dependent outflow model proposed here. We employ this flow model to estimate the translaminar pressure gradient within the optic nerve head of a normal eye in both the upright and supine postures, and during the day and at night. Based on a ratio of estimated and measured pressure gradients, we define a factor of safety against acute interruption of axonal transport at the laminar cribrosa. Using a completely independent method, based on the behaviour of dynein molecular motors, we compute the factor of safety against stalling the dynein molecule motors, and so compromising retrograde axonal transport. We show these two independent methods for estimating factors of safety agree reasonably well and appear to be consistent. Taken together, the new 3D pressure-dependent outflow model proves itself to capable of providing a useful modeling platform for analyzing eye behaviour in a variety of physiological and clinically useful contexts, including IOP elevation in Schwartz-Matsuo syndrome and with silicone oil tamponade, and potentially for risk assessment for optic glaucomatous neuropathy. [ABSTRACT FROM AUTHOR]

The balance of sleep and wake is plastic and changes to meet environmental demands. Mechanisms that allow an animal to suppress sleep and maintain waking in potentially adverse situations could serve adaptive functions in evolution. The fruit fly, Drosophila melanogaster, is well poised as a system in which to explore these questions. The environment changes sleep and wake in flies, e.g., starvation induces waking in Drosophila as it does in many animals. Further, the sophisticated neurobiological toolkit available to Drosophila researchers gives the fly a great advantage as a system to investigate the precise neurobiological mechanisms underlying these adaptive changes. In a paper in this issue of PLOS Biology, Yurgel and colleagues elegantly exploit the advantages of the Drosophila model to map starvation-induced wakefulness to a single pair of peptidergic neurons and their partners. [ABSTRACT FROM AUTHOR]

Blocking cytokine interleukin 10 (IL-10) at the time of immunisation enhances vaccine induced T cell responses and improves control of tumour cell growth in vivo. However, the effect of an IL-10 blockade on the biological function of macrophages has not been explored. In the current paper, a macrophage precursor cell line, U937 cells, was selected to investigate the differential expression of proteins and relevant cell signalling pathway changes, when stimulated with lipopolysaccharide (LPS) in the presence of antibodies to IL-10 or IL-10 receptor. We used a quantitative proteomic strategy to investigate variations in protein profiles of U937 cells following the treatments with LPS, LPS plus human anti-IL10 antibody and anti-IL10R antibody in 24hrs, respectively. The LPS treatment significantly activated actin-related cell matrix formation and immune response pathways. The addition of anti-IL10 and anti-IL10R antibody further promoted the immune response and potentially effect macrophage survival through PI3K/AKT signalling; however, the latter appeared to also upregulated oncogene XRCC5 and Cajal body associated processes. [ABSTRACT FROM AUTHOR]

It is evident through biology research that, biological neural network could be implemented through two means: by congenital heredity, or by posteriority learning. However, traditionally, artificial neural network, especially the Deep learning Neural Networks (DNNs) are implemented only through exhaustive training and learning. Fixed structure is built, and then parameters are trained through huge amount of data. In this way, there are a lot of redundancies in the implemented artificial neural network. This redundancy not only requires more effort to train the network, but also costs more computing resources when used. In this paper, we proposed a bionic way to implement artificial neural network through construction rather than training and learning. The hierarchy of the neural network is designed according to analysis of the required functionality, and then module design is carried out to form each hierarchy. We choose the Drosophila’s visual neural network as a test case to verify our method’s validation. The results show that the bionic artificial neural network built through our method could work as a bionic compound eye, which can achieve the detection of the object and their movement, and the results are better on some properties, compared with the Drosophila’s biological compound eyes. [ABSTRACT FROM AUTHOR]

The current understanding of mammalian kidney development is largely based on mouse models. Recent landmark studies revealed pervasive differences in renal embryogenesis between mouse and human. The scarcity of detailed gene expression data in humans therefore hampers a thorough understanding of human kidney development and the possible developmental origin of kidney diseases. In this paper, we present a single-cell transcriptomics study of the human fetal kidney. We identified 22 cell types and a host of marker genes. Comparison of samples from different developmental ages revealed continuous gene expression changes in podocytes. To demonstrate the usefulness of our data set, we explored the heterogeneity of the nephrogenic niche, localized podocyte precursors, and confirmed disease-associated marker genes. With close to 18,000 renal cells from five different developmental ages, this study provides a rich resource for the elucidation of human kidney development, easily accessible through an interactive web application. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this paper was to describe the time trends in the prevalence of multimorbidity and polypharmacy in Flanders (Belgium) between 2000 and 2015, while controlling for age and sex. Methods: Data were available from Intego, a Flemish-Belgian general practice-based morbidity registration network. The practice population between 2000 and 2015 was used as the denominator, representing a mean of 159,946 people per year. Age and gender-standardised prevalence rates were used for the trends of multimorbidity and polypharmacy in the total population and for subgroups. Joinpoint regression analyses were used to analyse the time trends and breaks in trends, for the entire population as well as for specific age and sex groups. Results: Overall, in 2015, 22.7% of the population had multimorbidity, while the overall prevalence of polypharmacy was 20%. Throughout the study period the standardised prevalence rate of multimorbidity rose for both sexes and in all age groups. The largest relative increase in multimorbidity was observed in the younger age groups (up to the age of 50 years). The prevalence of polypharmacy showed a significant increase between 2000 and 2015 for all age groups except the youngest (0–25 years). Conclusion: For all adult age groups multimorbidity and polypharmacy are frequent, dynamic over time and increasing. This asks for both epidemiological and interventional studies to improve the management of the resulting complex care. [ABSTRACT FROM AUTHOR]

The aim of the paper was to assess indicators of muscle and intestinal damage in triathletes. The study involved 15 triathletes whose objective for the season was to start in the XTERRA POLAND 2017 event (1,500-m swimming, 36-km cycling, and 10-km mountain running). Before the 14-week preparatory period, the competitors’ body composition was measured, aerobic capacity was tested (graded treadmill test) and blood samples were collected to determine markers showing the level of muscle and intestinal damage. Subsequent tests for body composition were carried out before and after the competition. Blood samples for biochemical indicators were collected the day before the competition, after the completed race, and 24 and 48 hours later. A significant decrease in body mass was observed after completing the race (–3.1±1.5%). The mean maximal oxygen uptake level among the studied athletes equalled 4.9±0.4 L·min–1, 58.8±4.5 mL·kg–1·min–1. The significant increase in concentrations of cortisol, c-reactive protein and myoglobin after the competition, significantly correlated with the significant increase in zonulin concentration (post 1h: r = 0.88, p = 0.007, r = 0,79, p = 0.001, r = 0.78, p = 0.001, and post 12h: r = 0.75, p = 0.01, r = 0.71, p = 0.011, r = 0.83, p = 0.02). No significant changes in the concentration of tumour necrosis factor alpha among the examined competitors were noted at following stages of the study. The results of our research showed that in order to monitor overload in the training of triathletes, useful markers reflecting the degree of muscle and intestinal damage include cortisol, testosterone, testosterone to cortisol ratio, c-reactive protein, myoglobin and zonulin. Changes in muscle cell damage markers strongly correlated with changes in zonulin concentration at particular stages of the study. Thus, one can expect that the concentrations of markers depicting the level of muscle cell damage after an intense and long-lasting effort will significantly influence the level of the intestinal barrier. [ABSTRACT FROM AUTHOR]

Plasma cell segmentation is the first stage of a computer assisted automated diagnostic tool for multiple myeloma (MM). Owing to large variability in biological cell types, a method for one cell type cannot be applied directly on the other cell types. In this paper, we present PCSeg Tool for plasma cell segmentation from microscopic medical images. These images were captured from bone marrow aspirate slides of patients with MM. PCSeg has a robust pipeline consisting of a pre-processing step, the proposed modified multiphase level set method followed by post-processing steps including the watershed and circular Hough transform to segment clusters of cells of interest and to remove unwanted cells. Our modified level set method utilizes prior information about the probability densities of regions of interest (ROIs) in the color spaces and provides a solution to the minimal-partition problem to segment ROIs in one of the level sets of a two-phase level set formulation. PCSeg tool is tested on a number of microscopic images and provides good segmentation results on single cells as well as efficient segmentation of plasma cell clusters. [ABSTRACT FROM AUTHOR]

In a growing plant root, the inner vascular system is sealed off by an epithelium, the endodermis. The space between all of the cells in the endodermal layer is filled with an impermeable mass called the Casparian strip, which closes the spaces between cells in the endodermal layer. The role of the Casparian strip has been proposed to prevent backflow of water and nutrients into the soil, but as mutant plants lacking the Casparian strip only have weak phenotypes, the view that it serves an essential function in plants has been challenged. In an accompanying paper, it is shown that loss of the Casparian strip impacts the ability of the plant to take up ammonium and allocate it to the shoots. [ABSTRACT FROM AUTHOR]

Speech recognition (SR) has been improved significantly by artificial neural networks (ANNs), but ANNs have the drawbacks of biologically implausibility and excessive power consumption because of the nonlocal transfer of real-valued errors and weights. While spiking neural networks (SNNs) have the potential to solve these drawbacks of ANNs due to their efficient spike communication and their natural way to utilize kinds of synaptic plasticity rules found in brain for weight modification. However, existing SNN models for SR either had bad performance, or were trained in biologically implausible ways. In this paper, we present a biologically inspired convolutional SNN model for SR. The network adopts the time-to-first-spike coding scheme for fast and efficient information processing. A biological learning rule, spike-timing-dependent plasticity (STDP), is used to adjust the synaptic weights of convolutional neurons to form receptive fields in an unsupervised way. In the convolutional structure, the strategy of local weight sharing is introduced and could lead to better feature extraction of speech signals than global weight sharing. We first evaluated the SNN model with a linear support vector machine (SVM) on the TIDIGITS dataset and it got the performance of 97.5%, comparable to the best results of ANNs. Deep analysis on network outputs showed that, not only are the output data more linearly separable, but they also have fewer dimensions and become sparse. To further confirm the validity of our model, we trained it on a more difficult recognition task based on the TIMIT dataset, and it got a high performance of 93.8%. Moreover, a linear spike-based classifier—tempotron—can also achieve high accuracies very close to that of SVM on both the two tasks. These demonstrate that an STDP-based convolutional SNN model equipped with local weight sharing and temporal coding is capable of solving the SR task accurately and efficiently. [ABSTRACT FROM AUTHOR]

Surface electromyography is often used to assess muscle activity and muscle function. A wavelet approach provides information about the intensity of muscle activity and motor unit recruitment strategies at every time point of the gait cycle. The aim was to review papers that employed wavelet analyses to investigate electromyograms of lower extremity muscles during walking and running. Eleven databases were searched up until June 1st 2017. The composition was based on the PICO model and the PRISMA checklist. First author, year, subject characteristics, intervention, outcome measures & variables, results and wavelet specification were extracted. Eighteen studies included the use of wavelets to investigate electromyograms of lower extremity muscles. Three main topics were discussed: 1.) The capability of the method to correctly assign participants to a specific group (recognition rate) varied between 68.4%-100%. 2.) Patients with ankle osteoarthritis or total knee arthroplasty presented a delayed muscle activation in the early stance phase but a prolonged activation in mid stance. 3.) Atrophic muscles did not contain type II muscle fiber components but more energy in their lower frequencies. The simultaneous information of time, frequency and intensity is of high clinical relevance because it offers valuable information about pre-and reflex activation behavior on different walking and running speeds as well as spectral changes towards high or low frequencies at every time point of the gait cycle. [ABSTRACT FROM AUTHOR]

B cells develop high affinity receptors during the course of affinity maturation, a cyclic process of mutation and selection. At the end of affinity maturation, a number of cells sharing the same ancestor (i.e. in the same “clonal family”) are released from the germinal center; their amino acid frequency profile reflects the allowed and disallowed substitutions at each position. These clonal-family-specific frequency profiles, called “substitution profiles”, are useful for studying the course of affinity maturation as well as for antibody engineering purposes. However, most often only a single sequence is recovered from each clonal family in a sequencing experiment, making it impossible to construct a clonal-family-specific substitution profile. Given the public release of many high-quality large B cell receptor datasets, one may ask whether it is possible to use such data in a prediction model for clonal-family-specific substitution profiles. In this paper, we present the method “Substitution Profiles Using Related Families” (SPURF), a penalized tensor regression framework that integrates information from a rich assemblage of datasets to predict the clonal-family-specific substitution profile for any single input sequence. Using this framework, we show that substitution profiles from similar clonal families can be leveraged together with simulated substitution profiles and germline gene sequence information to improve prediction. We fit this model on a large public dataset and validate the robustness of our approach on two external datasets. Furthermore, we provide a command-line tool in an open-source software package () implementing these ideas and providing easy prediction using our pre-fit models. [ABSTRACT FROM AUTHOR]

In the human retina, rod and cone cells detect incoming light with a molecule called rhodopsin. After rhodopsin molecules are activated (by photon impact), these molecules activate the rest of the signalling process for a brief period of time until they are deactivated by a multistage process. First, active rhodopsin is phosphorylated multiple times. Following this, they are further inhibited by the binding of molecules called arrestins. Finally, they decay into opsins. The time required for each of these stages becomes progressively longer, and each stage further reduces the activity of rhodopsin. However, while this deactivation process itself is well researched, the roles of the above stages in signal (and image) processing are poorly understood. In this paper, we will show that the activity of rhodopsin molecules during the deactivation process can be described as the fractional integration of an incoming signal. Furthermore, we show how this affects an image; specifically, the effect of fractional integration in video and signal processing and how it reduces noise and the improves adaptability under different lighting conditions. Our experimental results provide a better understanding of vertebrate and human vision, and why the rods and cones of the retina differ from the light detectors in cameras. [ABSTRACT FROM AUTHOR]

Blue whales are sighted every year around the Azores islands, which apparently provide an important seasonal foraging area. In this paper we aim to characterize habitat preferences and analyze the temporal distribution of blue whales around São Miguel Island. To do so, we applied Generalized Additive Models to an opportunistic cetacean occurrence dataset and remotely sensed environmental data on bathymetry, sea surface temperature, chlorophyll concentration and altimetry. We provide a brief description of the oceanography of the area, emphasizing its high spatio-temporal variability. In order to capture this dynamism, we used environmental data with two different spatial resolutions (low and high) and three different temporal resolutions (daily, weekly and monthly), thus accounting for both long-term oceanographic events such as the spring bloom, and shorter-term features such as eddies or fronts. Our results show that blue whales have a well-defined ecological niche around the Azores. They usually cross the archipelago from March to June and habitat suitability is highest in dynamic areas (with high Eddy Kinetic Energy) characterized by convergence or aggregation zones where productivity is enhanced. Multi-scale studies are useful to understand the ecological niche and habitat requirements of highly mobile species that can easily react to short-term changes in the environment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into a continuous time-delaying system. Then we analyze the exponential stability and asymptotic stability of the equilibrium points for this model. By constructing a suitable Lyapunov function, using the Lyapunov stability theorem and some inequality techniques, some sufficient criteria for ensuring the stability of equilibrium points are obtained. Finally, numerical examples are given to demonstrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

Computational models of normal liver function and xenobiotic induced liver damage are increasingly being used to interpret in vitro and in vivo data and as an approach to the de novo prediction of the liver’s response to xenobiotics. The microdosimetry (dose at the level of individual cells) of xenobiotics vary spatially within the liver because of both compound-independent and compound-dependent factors. In this paper, we build model liver lobules to investigate the interplay between vascular structure, blood flow and cellular transport that lead to regional variations in microdosimetry. We then compared simulation results obtained using this complex spatial model with a simpler linear pipe model of a sinusoid and a very simple single box model. We found that variations in diffusive transport, transporter-mediated transport and metabolism, coupled with complex liver sinusoid architecture and blood flow distribution, led to three essential patterns of xenobiotic exposure within the virtual liver lobule: (1) lobular-wise uniform, (2) radially varying and (3) both radially and azimuthally varying. We propose to use these essential patterns of exposure as a reference for selection of model representations when a computational study involves modeling detailed hepatic responses to xenobiotics. [ABSTRACT FROM AUTHOR]

The last decade has seen the emergence of new views about the mechanisms underlying specificity (or, conversely, generalization) of visual skill learning. Here, we trained participants at orientation discrimination paradigm at a peripheral position to induce position and orientation specificity and to test its underlying mechanisms. Specifically, we aimed to test whether the within-quadrant spatial gradient of generalization is determined by cortical magnification, which would show that retinotopic plasticity contributes to learning and specificity. Additionally, we aimed to test whether late parts of the learning relate differently to specificity compared to early parts. This is relevant in the context of double training papers, which suggest that rule-based mechanisms of specificity in fast, early learning also would apply to late, slower learning. Our data showed partial but significant position and orientation specificity within quadrants. Interestingly, specificity was greatest for those participants who had continued to show threshold decreases during the last five sessions of training (late, asymptotic learning). Performance gains during early learning were less related to specificity. A trend for skill to spread over larger distances towards periphery than towards central vision suggested contributions to transfer of early visual areas showing cortical magnification of central vision. Control experiments however did not support this hypothesis. In summary, our study demonstrates significant specificity after extensive perceptual learning, and indicates that asymptotic learning recruits specific mechanisms that promote specificity, and that may not be recruited yet in early parts of the learning. The contributions of different mechanisms to early and late learning suggests that following these different learning periods, generalization relies on different principles and is subjected to different limits. [ABSTRACT FROM AUTHOR]

It is well accepted that influenza A virus predisposes individuals to often more severe superinfections with Streptococcus pneumonia. However, the mechanisms that lead to this synergy are not clearly understood. Recent data suggests that competent Th17 immunity is crucial to clearance and protection from invasive pneumococcal disease of the lung. We demonstrate that early influenza infection significantly reduced levels of pneumococcus driven IL-12p70, IL-23 and IL-27 in human monocytes with significant impairment of IL-17A and IFN-γ in HKSP-treated allogeneic mixed lymphocyte cultures. We also provide evidence to suggest that the hemagglutinin component of the virus is at least partially responsible for this downward pressure on IL-17 responses but surprisingly this suppression occurs despite robust IL-23 levels in hemagglutinin-treated monocyte cultures. This study demonstrates that influenza can directly affect the immunological pathways that promote appropriate responses to Streptococcus pneumonia in human immune cells. Importance: Influenza virus is highly contagious and poses substantial public health problems due to its strong association with morbidity and mortality. Approximately 250,000–500,000 deaths are caused by seasonal influenza virus annually, and this figure increases during periods of pandemic infections. Most of these deaths are due to secondary bacterial pneumonia. Influenza-bacterial superinfection can result in hospitalisation and/or death of both patients with pre-existing lung disease or previously healthy individuals. The importance of our research is in determining that influenza and its component haemagglutinin has a direct effect on the classic pneumococcus induced pathways to IL-17A in our human ex vivo model. Our understanding of the mechanism which leaves people exposed to influenza infection during superinfection remain unresolved. This paper demonstrates that early infection of monocytes inhibits an arm of immunity crucial to bacterial clearance. Understanding this mechanism may provide alternative interventions in the case of superinfection with antimicrobial resistant strains of bacteria. [ABSTRACT FROM AUTHOR]

Demosponges possess a leucon-type canal system which is characterized by a highly complex network of canal segments and choanocyte chambers. As sponges are sessile filter feeders, their aquiferous system plays an essential role in various fundamental physiological processes. Due to the morphological and architectural complexity of the canal system and the strong interdependence between flow conditions and anatomy, our understanding of fluid dynamics throughout leuconoid systems is patchy. This paper provides comprehensive morphometric data on the general architecture of the canal system, flow measurements and detailed cellular anatomical information to help fill in the gaps. We focus on the functional cellular anatomy of the aquiferous system and discuss all relevant cell types in the context of hydrodynamic and evolutionary constraints. Our analysis is based on the canal system of the tropical demosponge Tethya wilhelma, which we studied using scanning electron microscopy. We found a hitherto undescribed cell type, the reticuloapopylocyte, which is involved in flow regulation in the choanocyte chambers. It has a highly fenestrated, grid-like morphology and covers the apopylar opening. The minute opening of the reticuloapopylocyte occurs in an opened, intermediate and closed state. These states permit a gradual regulation of the total apopylar opening area. In this paper the three states are included in a theoretical study into flow conditions which aims to draw a link between functional cellular anatomy, the hydrodynamic situation and the regular body contractions seen in T. wilhelma. This provides a basis for new hypotheses regarding the function of bypass elements and the role of hydrostatic pressure in body contractions. Our study provides insights into the local and global flow conditions in the sponge canal system and thus enhances current understanding of related physiological processes. [ABSTRACT FROM AUTHOR]

Scorpion venom heat-resistant peptide (SVHRP) is a component purified from Buthus martensii Karsch scorpion venom. Although scorpions and their venom have been used in Traditional Chinese Medicine (TCM) to treat chronic neurological disorders, the underlying mechanisms of these treatments remain unknown. We applied SVHRP in vitro and in vivo to understand its effects on the neurogenesis and maturation of adult immature neurons and explore associated molecular mechanisms. SVHRP administration increased the number of 5-bromo-2’-dexoxyuridine (BrdU)-positive cells, BrdU- positive/neuron-specific nuclear protein (NeuN)-positive neurons, and polysialylated-neural cell adhesion molecule (PSA-NCAM)-positive immature neurons in the subventricular zone (SVZ) and subgranular zone (SGZ) of hippocampus. Furthermore immature neurons incubated with SVHRP-pretreated astrocyte-conditioned medium exhibited significantly increased neurite length compared with those incubated with normal astrocyte-conditioned medium. This neurotrophic effect was further confirmed in vivo by detecting an increased average single area and whole area of immature neurons in the SGZ, SVZ and olfactory bulb (OB) in the adult mouse brain. In contrast to normal astrocyte-conditioned medium, higher concentrations of brain-derived neurotrophic factor (BDNF) but not nerve growth factor (NGF) or glial cell line-derived neurotrophic factor (GDNF) was detected in the conditioned medium of SVHRP-pretreated astrocytes, and blocking BDNF using anti-BDNF antibodies eliminated these SVHRP-dependent neurotrophic effects. In SVHRP treated mouse brain, more glial fibrillary acidic protein (GFAP)-positive cells were detected. Furthermore, immunohistochemistry revealed increased numbers of GFAP/BDNF double-positive cells, which agrees with the observed changes in the culture system. This paper describes novel effects of scorpion venom-originated peptide on the stem cells and suggests the potential therapeutic values of SVHRP. [ABSTRACT FROM AUTHOR]

This paper presents a method for predicting the total nitrogen content in sandalwood using digital image processing. The goal of this study is to provide a real-time, efficient, and highly automated nutritional diagnosis system for producers by analyzing images obtained in forests. Using images acquired from field servers, which were installed in six forest farms of different cities located in northern Hainan Province, we propose a new segmentation algorithm and define a new indicator named “growth status" (GS), which includes two varieties: GSMER (the ratio of sandalwood pixels to the minimum enclosing rectangle pixels) and GSMCC (the ratio of sandalwood pixels to minimum circumscribed circle pixels). We used the error-in-variable model by considering the errors that exist in independent variables. After comparison and analysis, the obtained results show that (1) The b and L channels in the Lab color system have complementary advantages. By combining this system with the Otsu method, median filtering and a morphological operation, sandalwood can be separated from the background. (2) The fitting degree of the models improves after adding the GS indicator and shows that GSMCC performs better than GSMER. (3) After using the error-in-variable model to estimate the parameters, the accuracy and precision of the model improved compared to the results obtained using the least squares method. The optimal model for predicting the total nitrogen content is . This study demonstrates the use of Internet of Things technology in forestry and provides guidance for the nutritional diagnosis of the important sandalwood tree species. [ABSTRACT FROM AUTHOR]

Background: Non-union affects up to 10% of fractures and is associated with substantial morbidity. There is currently no single effective therapy for the treatment or prevention of non-union. Potential treatments are currently selected for clinical trials based on results from limited animal studies, with no attempt to compare results between therapies to determine which have the greatest potential to treat non-union. Aim: The aim of this systematic review was to define the range of therapies under investigation at the preclinical stage for the prevention or treatment of fracture non-union. Additionally, through meta-analysis, it aimed to identify the most promising therapies for progression to clinical investigation. Methods: MEDLINE and Embase were searched from 1St January 2004 to 10th April 2017 for controlled trials evaluating an intervention to prevent or treat fracture non-union. Data regarding the model used, study intervention and outcome measures were extracted, and risk of bias assessed. Results: Of 5,171 records identified, 197 papers describing 204 therapies were included. Of these, the majority were only evaluated once (179/204, 88%), with chitosan tested most commonly (6/204, 3%). Substantial variation existed in model design, length of survival and duration of treatment, with results poorly reported. These factors, as well as a lack of consistently used objective outcome measures, precluded meta-analysis. Conclusion: This review highlights the variability and poor methodological reporting of current non-union research. The authors call for a consensus on the standardisation of animal models investigating non-union, and suggest journals apply stringent criteria when considering animal work for publication. [ABSTRACT FROM AUTHOR]

Pain and inflammation are complex clinical conditions that are present in a wide variety of disorders. Most drugs used to treat pain and inflammation have potential side effects, which makes it necessary to search for new sources of bioactive molecules. In this paper, we describe the ability of LASSBio-1586, an N-acylhydrazone derivative, to attenuate nociceptive behavior and the inflammatory response in mice. Antinociceptive activity was evaluated through acetic acid-induced writhing and formalin-induced nociception tests. In these experimental models, LASSBio-1586 significantly (p<0.05) reduced nociceptive behavior. Several methods of acute and chronic inflammation induced by different chemical (carrageenan, histamine, croton oil, arachidonic acid) and physical (cotton pellet) agents were used to evaluate the anti-inflammatory effect of LASSBio-1586. LASSBio-1586 exhibited potent anti-inflammatory activity in all tests (p<0.05). Study of the mechanism of action demonstrated the possible involvement of the nitrergic, serotonergic and histamine signaling pathways. In addition, a molecular docking study was performed, indicating that LASSBio-1586 is able to block the COX-2 enzyme, reducing arachidonic acid metabolism and consequently decreasing the production of prostaglandins, which are important inflammatory mediators. In summary, LASSBio-1586 exhibited relevant antinociceptive and anti-inflammatory potential and acted on several targets, making it a candidate for a new multi-target oral anti-inflammatory drug. [ABSTRACT FROM AUTHOR]

Deep learning techniques, e.g., Convolutional Neural Networks (CNNs), have been explosively applied to the research in the fields of information retrieval and natural language processing. However, few research efforts have addressed semantic indexing with deep learning. The use of semantic indexing in the biomedical literature has been limited for several reasons. For instance, MEDLINE citations contain a large number of semantic labels from automatically annotated MeSH terms, and for a great deal of the literature, only the information of the title and the abstract is readily available. In this paper, we propose a Boltzmann Convolutional neural network framework (B-CNN) for biomedicine semantic indexing. In our hybrid learning framework, the CNN can adaptively deal with features of documents that have sequence relationships, and can capture context information accordingly; the Deep Boltzmann Machine (DBM) merges global (the entity in each document) and local information through its training with undirected connections. Additionally, we have designed a hierarchical coarse to fine style indexing structure for learning and classifying documents, and a novel feature extension approach with word sequence embedding and Wikipedia categorization. Comparative experiments were conducted for semantic indexing of biomedical abstract documents; these experiments verified the encouraged performance of our B-CNN model. [ABSTRACT FROM AUTHOR]

This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. It is based on a Neural Network that considers input contexts of different length in order to ease the use of a fallback mechanism together with the precomputation of softmax normalization constants for these inputs. The proposed approach is empirically validated, showing their capability to emulate lower order N-grams with a single Neural Network. A machine translation task shows that the proposed model constitutes a good solution to the normalization cost of the output softmax layer of Neural Networks, for some practical cases, without a significant impact in performance while improving the system speed. [ABSTRACT FROM AUTHOR]

Countries, research institutions, and scholars are interested in identifying and promoting high-impact and transformative scientific research. This paper presents a novel set of text- and citation-based metrics that can be used to identify high-impact and transformative works. The 11 metrics can be grouped into seven types: Radical-Generative, Radical-Destructive, Risky, Multidisciplinary, Wide Impact, Growing Impact, and Impact (overall). The metrics are exemplified, validated, and compared using a set of 10,778,696 MEDLINE articles matched to the Science Citation Index ExpandedTM. Articles are grouped into six 5-year periods (spanning 1983–2012) using publication year and into 6,159 fields constructed using comparable MeSH terms, with which each article is tagged. The analysis is conducted at the level of a field-period pair, of which 15,051 have articles and are used in this study. A factor analysis shows that transformativeness and impact are positively related (ρ = .402), but represent distinct phenomena. Looking at the subcomponents of transformativeness, there is no evidence that transformative work is adopted slowly or that the generation of important new concepts coincides with the obsolescence of existing concepts. We also find that the generation of important new concepts and highly cited work is more risky. Finally, supporting the validity of our metrics, we show that work that draws on a wider range of research fields is used more widely. [ABSTRACT FROM AUTHOR]

Many collective phenomena in Nature emerge from the -partial- synchronisation of the units comprising a system. In the case of the brain, this self-organised process allows groups of neurons to fire in highly intricate partially synchronised patterns and eventually lead to high level cognitive outputs and control over the human body. However, when the synchronisation patterns are altered and hypersynchronisation occurs, undesirable effects can occur. This is particularly striking and well documented in the case of epileptic seizures and tremors in neurodegenerative diseases such as Parkinson’s disease. In this paper, we propose an innovative, minimally invasive, control method that can effectively desynchronise misfiring brain regions and thus mitigate and even eliminate the symptoms of the diseases. The control strategy, grounded in the Hamiltonian control theory, is applied to ensembles of neurons modelled via the Kuramoto or the Stuart-Landau models and allows for heterogeneous coupling among the interacting unities. The theory has been complemented with dedicated numerical simulations performed using the small-world Newman-Watts network and the random Erdős-Rényi network. Finally the method has been compared with the gold-standard Proportional-Differential Feedback control technique. Our method is shown to achieve equivalent levels of desynchronisation using lesser control strength and/or fewer controllers, being thus minimally invasive. [ABSTRACT FROM AUTHOR]

FAcilitates Chromatin Transcription (FACT) is a complex of SSRP1 and SPT16 that is involved in chromatin remodeling during transcription, replication, and DNA repair. FACT has been mostly studied in cell-free or single cell model systems because general FACT knockout (KO) is embryonically lethal (E3.5). FACT levels are limited to the early stages of development and stem cell niches of adult tissues. FACT is upregulated in poorly differentiated aggressive tumors. Importantly, FACT inhibition (RNAi) is lethal for tumors but not normal cells, making FACT a lucrative target for anticancer therapy. To develop a better understanding of FACT function in the context of the mammalian organism under normal physiological conditions and in disease, we aimed to generate a conditional FACT KO mouse model. Because SPT16 stability is dependent on the SSRP1-SPT16 association and the presence of SSRP1 mRNA, we targeted the Ssrp1 gene using a CreERT2- LoxP approach to generate the FACT KO model. Here, we highlight the limitations of the CreERT2-LoxP (Rosa26) system that we encountered during the generation of this model. In vitro studies showed an inefficient excision rate of ectopically expressed CreERT2 (retroviral CreERT2) in fibroblasts with homozygous floxed Ssrp1. In vitro and in vivo studies showed that the excision efficiency could only be increased with germline expression of two alleles of Rosa26CreERT2. The expression of one germline Rosa26CreERT2 allele led to the incomplete excision of Ssrp1. The limited efficiency of the CreERT2-LoxP system may be sufficient for studies involving the deletion of genes that interfere with cell growth or viability due to the positive selection of the phenotype. However, it may not be sufficient for studies that involve the deletion of genes supporting growth, or those crucial for development. Although CreERT2-LoxP is broadly used, it has limitations that have not been widely discussed. This paper aims to encourage such discussions. [ABSTRACT FROM AUTHOR]

Plasmodium falciparum infections presenting either as symptomatic or asymptomatic may contain sexual stage parasites (gametocytes) that are crucial to malaria transmission. In this study, the prevalence of microscopic and submicroscopic asexual and gametocyte parasite stages were assessed in asymptomatic children from two communities in southern Ghana. Eighty children aged twelve years and below, none of whom exhibited signs of clinical malaria living in Obom and Cape Coast were sampled twice, one during the rainy (July 2015) and subsequently during the dry (January 2016) season. Venous blood was used to prepare thick and thin blood smears, spot a rapid malaria diagnostic test (PfHRP2 RDT) as well as prepare filter paper blood spots. Blood cell pellets were preserved in Trizol for RNA extraction. Polymerase chain reaction (PCR) and semi-quantitative real time reverse transcriptase PCR (qRT-PCR) were used to determine submicroscopic parasite prevalence. In both sites 87% (95% CI: 78–96) of the asymptomatic individuals surveyed were parasites positive during the 6 month study period. The prevalence of asexual and gametocyte stage parasites in the rainy season were both significantly higher in Obom than in Cape Coast (P < 0.001). Submicroscopic gametocyte prevalence was highest in the rainy season in Obom but in the dry season in Cape Coast. Parasite prevalence determined by PCR was similar to that determined by qRT-PCR in Obom but significantly lower than that determined by qRT-PCR in Cape Coast. Communities with varying parasite prevalence exhibit seasonal variations in the prevalence of gametocyte carriers. Submicroscopic asymptomatic parasite and gametocyte carriage is very high in southern Ghana, even during the dry season in communities with low microscopic parasite prevalence and likely to be missed during national surveillance exercises. [ABSTRACT FROM AUTHOR]

Triticum vulgare has been extensively used in traditional medicine thanks to its properties of accelerating tissue repair. The specific extract of Triticum vulgare manufactured by Farmaceutici Damor (TVE-DAMOR) is already present in some pharmaceutical formulations used in the treatment of decubitus ulcers, skin lesions and burns. It has been recently suggested that this Triticum vulgare extract may possess potential anti-inflammatory properties. In the light of these premises the aim of the present paper was to verify the anti-inflammatory role of TVE, using the LPS-stimulated microglia model of inflammation. In particular the effect of different concentrations of TVE on the release of several mediators of inflammation such as nitric oxide, IL-6, PGE2 and TNF alpha was evaluated. More important, the anti-inflammatory effect of TVE was confirmed also in primary rat microglia cultures. The results of the present study show that TVE exerts anti-inflammatory properties since it reduces the release of all the evaluated markers of inflammation, such as NO, IL6, TNF alpha and PGE2 in LPS-activated BV2 microglial cells. Intriguingly, TVE reduced microglia activation and NO release also in primary microglia. Indeed, to verify the pathway of modulation of the inflammatory markers reported above, we found that TVE restores the cytoplasmic expression of p65 protein, kwown as specific marker associated with activation of inflammatory response. The evidence for an inhibitory activity on inflammation of this specific extract of Triticum vulgare may open the way to the possibility of a therapeutical use of the Triticum vulgare extract as an anti-inflammatory compound in certain pathological states such as burns, decubitus ulcers, folliculitis and inflammation of peripheral nerve. [ABSTRACT FROM AUTHOR]

The dendritic spines of pyramidal neurons are the targets of most excitatory synapses in the cerebral cortex. They have a wide variety of morphologies, and their morphology appears to be critical from the functional point of view. To further characterize dendritic spine geometry, we used in this paper over 7,000 individually 3D reconstructed dendritic spines from human cortical pyramidal neurons to group dendritic spines using model-based clustering. This approach uncovered six separate groups of human dendritic spines. To better understand the differences between these groups, the discriminative characteristics of each group were identified as a set of rules. Model-based clustering was also useful for simulating accurate 3D virtual representations of spines that matched the morphological definitions of each cluster. This mathematical approach could provide a useful tool for theoretical predictions on the functional features of human pyramidal neurons based on the morphology of dendritic spines. [ABSTRACT FROM AUTHOR]

Seizures occur in a recurrent manner with intermittent states of interictal and ictal discharges (IIDs and IDs). The transitions to and from IDs are determined by a set of processes, including synaptic interaction and ionic dynamics. Although mathematical models of separate types of epileptic discharges have been developed, modeling the transitions between states remains a challenge. A simple generic mathematical model of seizure dynamics (Epileptor) has recently been proposed by Jirsa et al. (2014); however, it is formulated in terms of abstract variables. In this paper, a minimal population-type model of IIDs and IDs is proposed that is as simple to use as the Epileptor, but the suggested model attributes physical meaning to the variables. The model is expressed in ordinary differential equations for extracellular potassium and intracellular sodium concentrations, membrane potential, and short-term synaptic depression variables. A quadratic integrate-and-fire model driven by the population input current is used to reproduce spike trains in a representative neuron. In simulations, potassium accumulation governs the transition from the silent state to the state of an ID. Each ID is composed of clustered IID-like events. The sodium accumulates during discharge and activates the sodium-potassium pump, which terminates the ID by restoring the potassium gradient and thus polarizing the neuronal membranes. The whole-cell and cell-attached recordings of a 4-AP-based in vitro model of epilepsy confirmed the primary model assumptions and predictions. The mathematical analysis revealed that the IID-like events are large-amplitude stochastic oscillations, which in the case of ID generation are controlled by slow oscillations of ionic concentrations. The IDs originate in the conditions of elevated potassium concentrations in a bath solution via a saddle-node-on-invariant-circle-like bifurcation for a non-smooth dynamical system. By providing a minimal biophysical description of ionic dynamics and network interactions, the model may serve as a hierarchical base from a simple to more complex modeling of seizures. [ABSTRACT FROM AUTHOR]

Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500 × 500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (∼6 million of samples in 24 hours) with far fewer samples (∼2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, two kinds of titanium surfaces with novel micro/nano hierarchical structures, namely Etched (E) surface and Sandblast and etched (SE) surface, were successfully fabricated by NH4OH and H2O2 mixture. And their cellular responses of MG63 were investigated compared with Sandblast and acid-etching (SLA) surface. Scanning electron microscope (SEM), Surface profiler, X-ray photoelectron spectroscopy (XPS), and Contact angle instrument were employed to assess the surface morphologies, roughness, chemistry and wettability respectively. Hierarchical structures with micro holes of 10–30 μm in diameter and nano pits of tens of nanometers in diameter formed on both E and SE surfaces. The size of micro holes is very close to osteoblast cell, which makes them wonderful beds for osteoblast. Moreover, these two kinds of surfaces possess similar roughness and superior hydrophilicity to SLA. Reactive oxygen species were detected on E and SE surface, and thus considerable antimicrobial performance and well fixation can be speculated on them. The cell experiments also demonstrated a boost in cell attachment, and that proliferation and osteogenic differentiation were achieved on them, especially on SE surface. The results indicate that the treatment of pure titanium with H2O2/NH4OH is an effective technique to improve the initial stability of implants and enhance the osseointegration, which may be a promising surface treatment to titanium implant. [ABSTRACT FROM AUTHOR]

Background: Diffusion tensor imaging (DTI) has been suggested as a potential biomarker of disease progression, neurodegeneration and de/remyelination in MS. However, the pathological substrates that underpin alterations in brain diffusivity are not yet fully delineated. We propose that in highly cohesive fiber tracts: 1) a relative increase in parallel (axial) diffusivity (AD) may serve as a measure of increased extra-cellular space (ESC) within the core of chronic MS lesions and, as a result, may provide an estimate of the degree of tissue destruction, and 2) the contribution of the increased extra-cellular water to perpendicular (radial) diffusivity (RD) can be eliminated to provide a more accurate assessment of membranal (myelin) loss. Objective: The purpose of this study was to isolate the contribution of extra-cellular water and demyelination to observed DTI indices in the core of chronic MS lesions, using the OR as an anatomically cohesive tract. Method: Pre- and post-gadolinium (Gd) enhanced T1, T2 and DTI images were acquired from 75 consecutive RRMS patients. In addition, 25 age and gender matched normal controls were imaged using an identical MRI protocol (excluding Gd). The optic radiation (OR) was identified in individual patients using probabilistic tractography. The T2 lesions were segmented and intersected with the OR. Average eigenvalues were calculated within the core of OR lesions mask. The proportion of extra-cellular space (ECS) within the lesional core was calculated based on relative increase of AD, which was then used to normalise the perpendicular eigenvalues to eliminate the effect of the expanded ECS. In addition, modelling was implemented to simulate potential effect of various factors on lesional anisotropy. Results: Of 75 patients, 41 (55%) demonstrated sizable T2 lesion volume within the ORs. All lesional eigenvalues were significantly higher compared to NAWM and controls. There was a strong correlation between AD and RD within the core of OR lesions, which was, however, not seen in OR NAWM of MS patients or normal controls. In addition, lesional anisotropy (FA) was predominantly driven by the perpendicular diffusivity, while in NAWM and in OR of normal controls all eigenvectors contributed to variation in FA. Estimated volume of ECS component constituted significant proportion of OR lesional volume and correlated significantly with lesional T1 hypointensity. While perpendicular diffusivity dropped significantly following normalisation, it still remained higher compared with diffusivity in OR NAWM. The “residual” perpendicular diffusivity also showed a substantial reduction of inter-subject variability. Both observed and modelled diffusion data suggested anisotropic nature of water diffusion in ESC. In addition, the simulation procedure offered a possible explanation for the discrepancy in relationship between eigenvalues and anisotropy in lesional tissue and NAWM. Conclusion: This paper presents a potential technique for more reliably quantifying the effects of neurodegeneration (tissue loss) versus demyelination in OR MS lesions. This may provide a simple and effective way for applying single tract diffusion analysis in MS clinical trials, with particular relevance to pro-remyelinating and neuroprotective therapeutics. [ABSTRACT FROM AUTHOR]

Hepatitis B virus (HBV) infection is a liver disorder that can result in cirrhosis, liver failure and hepatocellular carcinoma. HBV infection remains a major global health problem, as it affects more 350 million people chronically and kills roughly 600,000 people annually. Drugs currently used against HBV include IFN-α that decreases viremia, inflammation and the growth of liver fibrosis, and adefovir that decreases the viral load. Each of these drugs can have severe side-effects. In the present paper, we consider the treatment of chronic HBV by a combination of IFN-α and adefovir, and raise the following question: What should be the optimal ratio between IFN-α and adefovir in order to achieve the best ‘efficacy’ under constraints on the total amount of the drugs; here the efficacy is measured by the reduction of the levels of inflammation and of fibrosis? We develop a mathematical model of HBV pathogenesis by a system of partial differential equations (PDEs) and use the model to simulate a ‘synergy map’ which addresses the above question. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale Drop-seq dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at . [ABSTRACT FROM AUTHOR]

This paper proposes a shared synapse architecture for autoencoders (AEs), and implements an AE with the proposed architecture as a digital circuit on a field-programmable gate array (FPGA). In the proposed architecture, the values of the synapse weights are shared between the synapses of an input and a hidden layer, and between the synapses of a hidden and an output layer. This architecture utilizes less of the limited resources of an FPGA than an architecture which does not share the synapse weights, and reduces the amount of synapse modules used by half. For the proposed circuit to be implemented into various types of AEs, it utilizes three kinds of parameters; one to change the number of layers’ units, one to change the bit width of an internal value, and a learning rate. By altering a network configuration using these parameters, the proposed architecture can be used to construct a stacked AE. The proposed circuits are logically synthesized, and the number of their resources is determined. Our experimental results show that single and stacked AE circuits utilizing the proposed shared synapse architecture operate as regular AEs and as regular stacked AEs. The scalability of the proposed circuit and the relationship between the bit widths and the learning results are also determined. The clock cycles of the proposed circuits are formulated, and this formula is used to estimate the theoretical performance of the circuit when the circuit is used to construct arbitrary networks. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) released by virtually every cell of all organisms are involved in processes of intercellular communication through the delivery of their functional mRNAs, proteins and bioactive lipids. We previously demonstrated that mouse embryonic stem cell-released EVs (mESEVs) are able to transfer their content to different target retinal cells, inducing morphological and biochemical changes in them. The main objective of this paper is to characterize EVs derived from human embryonic stem cells (hESEVs) and investigate the effects that they have on cultured retinal glial, progenitor Müller cells, which are known to give rise to retinal neurons under specific conditions. This would allow us to establish if hESEVs have a pro-regenerative potential not yet described that could be used in the future for treatment of human retinal degenerative diseases. Initially, we showed that hESEVs are heterogeneous in size, contain mRNAs and proteins involved in the induction and maintenance of stem cell pluripotency and can be internalized by cultured Müller cells. After a single exposure to hESEVs these cells display changes in their gene expression profile, and with multiple exposures they de-differentiate and trans-differentiate into retinal neuronal precursors. hESEVs were then fractionated into microvesicles (MVs) and exosomes (EXOs), which were characterized by size, specific surface proteins and biochemical/molecular components. We demonstrate that despite the similar internalization of non-fractionated hESEVs, MVs and EXOs by Müller progenitor cells, in vitro, only the release of MVs’ cargo into the cells’ cytoplasm induces specific changes in their levels of pluripotency mRNAs and early retinal proteins. EXOs do not produce any detectable effect. Thus, we conclude that MVs and MVs-containing hESEVs are promising agents that possibly could promote the regeneration of diseased or damaged retinas in vivo through inducing glial Müller cells to become replacement neurons. [ABSTRACT FROM AUTHOR]

Due to its strong proliferation-reducing effects on keratinocytes, and also anti-inflammatory properties, the isoflavone genistein has already been proposed as a possible antipsoriatic compound. As there is still no detailed information on this topic, we examined the effects of genistein by using an in vitro model of both, normal and “psoriasis-like” keratinocytes at this stage of our work exhaustively testing the selected flavonoid in a mono-treated experimental design. Gene expression studies revealed transcriptional changes that confirms known disease-associated pathways and highlights many psoriasis-related genes. Our results suggested that aberrant expression of genes contributing to the progress of psoriasis could be improved by the action of genistein. Genistein prevented “cytokine mix” as well as TNF-α-induced NF-κB nuclear translocation, with no effect on the PI3K signaling cascade, indicating the luck of turning this pathway into NF-κB activation. It could have attenuated TNF-α and LPS-induced inflammatory responses by suppressing ROS activation. Regardless of the type of keratinocyte stimulation used, reduction of cytokine IL-8, IL-20 and CCL2 production (both at RNA and protein level) following genistein treatment was visible. Because investigations of other groups supported our commentary on potential administration of genistein as a potential weapon in the armamentarium against psoriasis, it is believed that this paper should serve to encourage researchers to conduct further studies on this subject. [ABSTRACT FROM AUTHOR]

Oncolytic virus (OV) is a replication competent virus that selectively invades cancer cells; as these cells die under the viral burden, the released virus particles proceed to infect other cancer cells. Oncolytic viruses are designed to also be able to stimulate the anticancer immune response. Thus, one may represent an OV by two parameters: its replication potential and its immunogenicity. In this paper we consider a combination therapy with OV and a checkpoint inhibitor, anti-PD-1. We evaluate the efficacy of the combination therapy in terms of the tumor volume at some later time, for example, 6 months from initial treatment. Since T cells kill not only virus-free cancer cells but also virus-infected cancer cells, the following question arises: Does increasing the amount of the checkpoint inhibitor always improve the efficacy? We address this question, by a mathematical model consisting of a system of partial differential equations. We use the model to construct, by simulations, an efficacy map in terms of the doses of the checkpoint inhibitor and the OV injection. We show that there are regions in the map where an increase in the checkpoint inhibitor actually decreases the efficacy of the treatment. We also construct efficacy maps with checkpoint inhibitor vs. the replication potential of the virus that show the same antagonism, namely, an increase in the checkpoint inhibitor may actually decrease the efficacy. These results have implications for clinical trials. [ABSTRACT FROM AUTHOR]

Food can trigger a diverse array of symptoms in food allergic individuals from isolated local symptoms affecting skin or gut to multi-system severe reactions (systemic anaphylaxis). Although we know that gastrointestinal and systemic manifestations of food allergy are mediated by tissue mast cells (MCs), it is not clear why allergen exposure by the oral route can result in such distinct clinical manifestations. Our aim was to assess the contribution of mast cell subsets to different manifestations of food allergy. We used two common models of IgE-mediated food allergy, one resulting in systemic anaphylaxis and the other resulting in acute gastrointestinal symptoms, to study the immune basis of allergic reactions. We used responders and non-responders in each model system, as well as naïve controls to identify the association of mast cell activation with clinical reactivity rather than sensitization. Systemic anaphylaxis was uniquely associated with activation of connective tissue mast cells (identified by release of mouse mast cell protease (MMCP) -7 into the serum) and release of histamine, while activation of mucosal mast cells (identified by release of MMCP-1 in the serum) did not correlate with symptoms. Gastrointestinal manifestations of food allergy were associated with an increase of MMCP-1-expressing mast cells in the intestine, and evidence of both mucosal and connective tissue mast cell activation. The data presented in this paper demonstrates that mast cell heterogeneity is an important contributor to manifestations of food allergy, and identifies the connective tissue mast cell subset as key in the development of severe systemic anaphylaxis. [ABSTRACT FROM AUTHOR]

In this paper, a new four-variable dynamical system is proposed to set chaotic circuit composed of memristor and Josephson junction, and the dependence of chaotic behaviors on nonlinearity is investigated. A magnetic flux-controlled memristor is used to couple with the RCL-shunted junction circuit, and the dynamical behaviors can be modulated by changing the coupling intensity between the memristor and the RCL-shunted junction. Bifurcation diagram and Lyapunov exponent are calculated to confirm the emergence of chaos in the improved dynamical system. The outputs and dynamical behaviors can be controlled by the initial setting and external stimulus as well. As a result, chaos can be suppressed and spiking occurs in the sampled outputs under negative feedback, while applying positive feedback type via memristor can be effective to trigger chaos. Furthermore, it is found that the number of multi-attractors in the Jerk circuit can be modulated when memristor coupling is applied on the circuit. These results indicate that memristor coupling can be effective to control chaotic circuits and it is also useful to reproduce dynamical behaviors for neuronal activities. [ABSTRACT FROM AUTHOR]

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results. [ABSTRACT FROM AUTHOR]

One goal of modern day neuroscience is the establishment of molecular maps that assign unique features to individual neuron types. Such maps provide important starting points for neuron classification, for functional analysis, and for developmental studies aimed at defining the molecular mechanisms of neuron identity acquisition and neuron identity diversification. In this resource paper, we describe a nervous system-wide map of the potential expression sites of 244 members of the largest gene family in the C. elegans genome, rhodopsin-like (class A) G-protein-coupled receptor (GPCR) chemoreceptors, using classic gfp reporter gene technology. We cover representatives of all sequence families of chemoreceptor GPCRs, some of which were previously entirely uncharacterized. Most reporters are expressed in a very restricted number of cells, often just in single cells. We assign GPCR reporter expression to all but two of the 37 sensory neuron classes of the sex-shared, core nervous system. Some sensory neurons express a very small number of receptors, while others, particularly nociceptive neurons, coexpress several dozen GPCR reporter genes. GPCR reporters are also expressed in a wide range of inter- and motorneurons, as well as non-neuronal cells, suggesting that GPCRs may constitute receptors not just for environmental signals, but also for internal cues. We observe only one notable, frequent association of coexpression patterns, namely in one nociceptive amphid (ASH) and two nociceptive phasmid sensory neurons (PHA, PHB). We identified GPCRs with sexually dimorphic expression and several GPCR reporters that are expressed in a left/right asymmetric manner. We identified a substantial degree of GPCR expression plasticity; particularly in the context of the environmentally-induced dauer diapause stage when one third of all tested GPCRs alter the cellular specificity of their expression within and outside the nervous system. Intriguingly, in a number of cases, the dauer-specific alterations of GPCR reporter expression in specific neuron classes are maintained during postdauer life and in some case new patterns are induced post-dauer, demonstrating that GPCR gene expression may serve as traits of life history. Taken together, our resource provides an entry point for functional studies and also offers a host of molecular markers for studying molecular patterning and plasticity of the nervous system. [ABSTRACT FROM AUTHOR]