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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

Background A recent paper by Tomasetti and Vogelstein (Science 2015 347 78–81) suggested that the variation in natural cancer risk was largely explained by the total number of stem-cell divisions, and that most cancers arose by chance. They proposed an extra-risk score as way of distinguishing the effects of the stochastic, replicative component of cancer risk from other causative factors, specifically those due to the external environment and inherited mutations. Objectives We tested the hypothesis raised by Tomasetti and Vogelstein by assessing the degree of correlation of stem cell divisions and their extra-risk score with radiation- and tobacco-associated cancer risk. Methods We fitted a variety of linear and log-linear models to data on stem cell divisions per year and cumulative stem cell divisions over lifetime and natural cancer risk, some taken from the paper of Tomasetti and Vogelstein, augmented using current US lifetime cancer risk data, and also radiation- and tobacco-associated cancer risk. Results The data assembled by Tomasetti and Vogelstein, as augmented here, are inconsistent with the power-of-age relationship commonly observed for cancer incidence and the predictions of a multistage carcinogenesis model, if one makes the strong assumption of homogeneity of numbers of driver mutations across cancer sites. Analysis of the extra-risk score and various other measures (number of stem cell divisions per year, cumulative number of stem cell divisions over life) considered by Tomasetti and Vogelstein suggests that these are poorly predictive of currently available estimates of radiation- or smoking-associated cancer risk–for only one out of 37 measures or logarithmic transformations thereof is there a statistically significant correlation (p

Activities of transcription factors (TFs) are temporally modulated to regulate dynamic cellular processes, including development, homeostasis, and disease. Recent developments of bioinformatic tools have enabled the analysis of TF activities using transcriptome data. However, because these methods typically use exon-based target expression levels, the estimated TF activities have limited temporal accuracy. To address this, we proposed a TF activity measure based on intron-level information in time-series RNA-seq data, and implemented it to decode the temporal control of TF activities during dynamic processes. We showed that TF activities inferred from intronic reads can better recapitulate instantaneous TF activities compared to the exon-based measure. By analyzing public and our own time-series transcriptome data, we found that intron-based TF activities improve the characterization of temporal phasing of cycling TFs during circadian rhythm, and facilitate the discovery of two temporally opposing TF modules during T cell activation. Collectively, we anticipate that the proposed approach would be broadly applicable for decoding global transcriptional architecture during dynamic processes., Author summary Many health-related cellular processes, such as immune response and disease progression, involve dynamic changes of gene expression state, which are orchestrated by transcription factors. Dissecting the activities of transcription factors is thus important for understanding cellular processes and for interfering with dysregulated processes. Our ability to analyze transcription factor activities has been facilitated by genome-wide gene expression data from high-throughput assays such as RNA sequencing. Existing methods typically estimate transcription factor activities based on the expression levels of matured mRNAs of target genes. However, because the levels of matured mRNAs are affected by transcriptional and post-transcriptional regulatory activities, the estimated transcription factor activities may not faithfully recapitulate the regulatory activities of transcription factors. In this paper, we proposed and validated an alternative approach for analyzing transcription factor activities using the expression levels of unmatured mRNAs of target genes, allowing us to decode how transcription factor activities are temporally controlled during key biological processes. Our results provide insights into the temporal phasing of key circadian regulator activities in mouse liver, and uncover two temporally opposing modules of transcription factors that dictate the immune responses in T cells. Therefore, this approach can help understand the regulatory principles of dynamic cellular processes.

Recent developments in dental implant have heightened the urgent need to natural tissue adhesives estrogenic materials with ability of promoting the proliferation and osteoblastic differentiation in human dental pulp-derived stem cells, to provide better integration of tissue for dentistry. Up to now, far little attention has been paid to adhesives extract of the root of Ferula sp. which contains biomaterial compounds with estrogenic activities. Prior to undertaking the investigation, analysis of the extract of the root of F. ovina revealed a novel terpenoid, and we identified it as Fenoferin. So far, this paper has focused on Fenoferin compared to Ferutinin and root extract to determine if Fenoferin caused changes in craniofacial cartilage, bone (ceratohyal) and tooth mineralization. Following the purpose of study, we used zebrafish as a well-developed model system for studying bone development, so the developing zebrafish larvae were exposed to various concentration of compounds at 2dpf, and the histological analyses were performed at 6dpf. The result of the current study highlights the importance of F. ovina in studies related to dental regenerative medicine.

In this essay, we show that 3 distinct approaches to immunological exhaustion coexist and that they only partially overlap, generating potential misunderstandings. Exploring cases ranging from viral infections to cancer, we propose that it is crucial, for experimental and therapeutic purposes, to clarify these approaches and their interconnections so as to make the concept of exhaustion genuinely operational., Author summary In this essay, we have written a critical review on immunological exhaustion. We believe that this widely used concept often remains in fact imprecise because there exist 3 different approaches to exhaustion, namely, in terms of dysfunction, cause, and marker, and those are not sufficiently well distinguished and articulated in most scientific papers. We also propose to talk about “exhaustion” for and only for the phenomena in which all 3 approaches are aligned. This can be called the “convergence strategy”: T cells would be described as “exhausted” if and only if they are simultaneously dysfunctional, express given markers such as programmed cell death protein 1 (PD-1), and induced by a specific cause such as chronicity. This strategy could be perfectly reasonable, and we see much value in adopting it, because it would force everyone to be more specific and rigorous when talking about exhaustion. Clarifying the characteristics of cells defined as “exhausted,” and using a convergent strategy to define T-cell exhaustion could have major experimental and clinical consequences, including for viral infections such as the Coronavirus Disease 2019 (COVID-19).

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy’s performance in spike extraction and scalability are state-of-the-art., Author summary Roughly 290 million electrical action potentials occur every second in the human brain, facilitating the propagation of signals among cells in the nervous system and driving most of our daily operations. New methods in brain imaging are emerging that have the speed and resolution to capture events in the brain at the pace at which neurons typically communicate. These methods measure voltage in neurons by using light, and therefore can access very detailed brain signaling patterns. However, the adoption of these methods by a larger community, and not a restricted set of experts, is limited by the lack of computational tools, thereby greatly hindering progress in this field. In this paper, we present VolPy, a software framework that greatly facilitates the preprocessing of this new type of imaging datasets. This pipeline incorporates efficient and optimized algorithms that can identify neurons and extract their activity with great accuracy. The presented software will make this new imaging modality accessible to a wide audience.

We propose a developmental model inspired by the cortico-basal system (CX-BG) for vocal learning in babies and for solving the correspondence mismatch problem they face when they hear unfamiliar voices, with different tones and pitches. This model is based on the neural architecture INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy minimization is used for rapidly exploring, selecting and learning the optimal choices of actions to perform (eg sound production) in order to reproduce and control as accurately as possible the spike trains representing desired perceptions (eg sound categories). We detail in this paper the CX-BG system responsible for linking causally the sound and motor primitives at the order of a few milliseconds. Two experiments performed with a small and a large audio database show the capabilities of exploration, generalization and robustness to noise of our neural architecture in retrieving audio primitives during vocal learning and during acoustic matching with unheared voices (different genders and tones)., Author summary We designed a developmental architecture inspired by the cortico-basal system for early vocal learning. Our neural system explores, evaluates and strengthens the motor primitives that match the best the sound repertoire created also dynamically. After a babbling process in which the network tests and aligns pronounced sound and motor vocal tracks, it is used for listening to novel voices, solving the correspondence problem.

The partial success of tumor immunotherapy induced by checkpoint blockade, which is not antigen-specific, suggests that the immune system of some patients contain antigen receptors able to specifically identify tumor cells. Here we focused on T-cell receptor (TCR) repertoires associated with spontaneous breast cancer. We studied the alpha and beta chain CDR3 domains of TCR repertoires of CD4 T cells using deep sequencing of cell populations in mice and applied the results to published TCR sequence data obtained from human patients. We screened peripheral blood T cells obtained monthly from individual mice spontaneously developing breast tumors by 5 months. We then looked at identical TCR sequences in published human studies; we used TCGA data from tumors and healthy tissues of 1,256 breast cancer resections and from 4 focused studies including sequences from tumors, lymph nodes, blood and healthy tissues, and from single cell dataset of 3 breast cancer subjects. We now report that mice spontaneously developing breast cancer manifest shared, Public CDR3 regions in both their alpha and beta and that a significant number of women with early breast cancer manifest identical CDR3 sequences. These findings suggest that the development of breast cancer is associated, across species, with biomarker, exclusive TCR repertoires., Author summary More than 1,100 cancer treatments are currently in clinical testing. Of these, 295 are immune-oncology medicines and vaccines. For Checkpoint Immunotherapy alone, 52,539 patients were enrolled in 2017. Of these treatments, 108 are for breast cancer, the leading cancer diagnosed in women. Although checkpoint immunotherapy has yet to be proven beneficial in most types of breast cancer, current observations of which specific T cell go into the tumor, and how these cells are connected to the response to treatment, have lead to a new understating of the role of the immune system. Yet, and in spite of this intense research into the role of T cells in tumors, very little is known about how clones of T cells behave during tumor formation. The paper we present here studies this clonal behavior over time. We study it during mammary tumor development in mice. We see that a set of T cell clones, which comes from a diversity of different nucleotide sequences, stands out in its behavior compared with other T cell clones. Importantly, we show that these mouse T cell clones have a strong connection to human breast cancer. The specific identification of a subset of T cell clones, together with the behavior of these clones over time, is critical to our understanding of the effect of modifications to the immune system, such as the modifications made by immunotherapy treatments, and to our understanding of how to better control the behavior of the immune system.

Funder: National Institute for Health Research; funder-id: http://dx.doi.org/10.13039/501100000272, Background: Understanding the T cell response to SARS-CoV-2 is critical to vaccine development, epidemiological surveillance and disease control strategies. This systematic review critically evaluates and synthesises the relevant peer-reviewed and pre-print literature published from 01/01/2020-26/06/2020. Methods: For this systematic review, keyword-structured literature searches were carried out in MEDLINE, Embase and COVID-19 Primer. Papers were independently screened by two researchers, with arbitration of disagreements by a third researcher. Data were independently extracted into a pre-designed Excel template and studies critically appraised using a modified version of the MetaQAT tool, with resolution of disagreements by consensus. Findings were narratively synthesised. Results: 61 articles were included. 55 (90%) studies used observational designs, 50 (82%) involved hospitalised patients with higher acuity illness, and the majority had important limitations. Symptomatic adult COVID-19 cases consistently show peripheral T cell lymphopenia, which positively correlates with increased disease severity, duration of RNA positivity, and non-survival; while asymptomatic and paediatric cases display preserved counts. People with severe or critical disease generally develop more robust, virus-specific T cell responses. T cell memory and effector function has been demonstrated against multiple viral epitopes, and, cross-reactive T cell responses have been demonstrated in unexposed and uninfected adults, but the significance for protection and susceptibility, respectively, remains unclear. Conclusion: A complex pattern of T cell response to SARS-CoV-2 infection has been demonstrated, but inferences regarding population level immunity are hampered by significant methodological limitations and heterogeneity between studies, as well as a striking lack of research in asymptomatic or pauci-symptomatic individuals. In contrast to antibody responses, population-level surveillance of the T cell response is unlikely to be feasible in the near term. Focused evaluation in specific sub-groups, including vaccine recipients, should be prioritised.

Searching for new high temperature superconductors has long been a key research issue. Fe-based superconductors attract researchers’ attention due to their high transition temperature, strong irreversibility field, and excellent crystallographic symmetry. By using doping methods and dopant levels, different types of new Fe-based superconductors are synthesized. The transition temperature is a key indicator to measure whether new superconductors are high temperature superconductors. However, the condition for measuring transition temperature are strict, and the measurement process is dangerous. There is a strong relationship between the lattice parameters and the transition temperature of Fe-based superconductors. To avoid the difficulties in measuring transition temperature, in this paper, we adopt a machine learning method to build a model based on the lattice parameters to predict the transition temperature of Fe-based superconductors. The model results are in accordance with available transition temperatures, showing 91.181% accuracy. Therefore, we can use the proposed model to predict unknown transition temperatures of Fe-based superconductors.

Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works.

Refereed/Peer-reviewed Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number, placement, and insertion depths of electrodes on speech recognition abilities. However, these do not account for all inter-subject variability and to what extent these factors affect speech recognition abilities has not been studied. In this paper, an information theoretic method and machine learning technique are unified in a model to investigate the extent to which key factors limit cochlear implant electrode discrimination. The framework uses a neural network classifier to predict which electrode is stimulated for a given simulated activation pattern of the auditory nerve, and mutual information is then estimated between the actual stimulated electrode and predicted ones. We also investigate how and to what extent the choices of parameters affect the performance of the model. The advantages of this framework include i) electrode discrimination ability is quantified using information theory, ii) it provides a flexible framework that may be used to investigate the key factors that limit the performance of cochlear implant users, and iii) it provides insights for future modeling studies of other types of neural prostheses.

Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell death is preceded by slight morphological changes in cell shape and texture. In this paper, we trained a neural network to classify cells undergoing cell death. We found that the network was able to highly predict cell death after one hour of exposure to camptothecin. Moreover, this prediction largely outperforms human ability. Finally, we provide a simple python tool that can broadly be used to detect cell death.

All approved coronavirus disease 2019 (COVID-19) vaccines in current use are safe, effective, and reduce the risk of severe illness. Although data on the immunological presentation of patients with COVID-19 is limited, increasing experimental evidence supports the significant contribution of B and T cells towards the resolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Despite the availability of several COVID-19 vaccines with high efficacy, more effective vaccines are still needed to protect against the new variants of SARS-CoV-2. Employing a comprehensive immunoinformatic prediction algorithm and leveraging the genetic closeness with SARS-CoV, we have predicted potential immune epitopes in the structural proteins of SARS-CoV-2. The S and N proteins of SARS-CoV-2 and SARS-CoVs are main targets of antibody detection and have motivated us to design four multi-epitope vaccines which were based on our predicted B- and T-cell epitopes of SARS-CoV-2 structural proteins. The cardinal epitopes selected for the vaccine constructs are predicted to possess antigenic, non-allergenic, and cytokine-inducing properties. Additionally, some of the predicted epitopes have been experimentally validated in published papers. Furthermore, we used the C-ImmSim server to predict effective immune responses induced by the epitope-based vaccines. Taken together, the immune epitopes predicted in this study provide a platform for future experimental validations which may facilitate the development of effective vaccine candidates and epitope-based serological diagnostic assays.

Much of the current research on supervised modelling is focused on maximizing outcome prediction accuracy. However, in engineering disciplines, an arguably more important goal is that of feature extraction, the identification of relevant features associated with the various outcomes. For instance, in microbial communities, the identification of keystone species can often lead to improved prediction of future behavioral shifts. This paper proposes a novel feature extractor based on Deep Learning, which is largely agnostic to underlying assumptions regarding the training data. Starting from a collection of microbial species abundance counts, the Deep Learning model first trains itself to classify the selected distinct habitats. It then identifies indicator species associated with the habitats. The results are then compared and contrasted with those obtained by traditional statistical techniques. The indicator species are similar when compared at top taxonomic levels such as Domain and Phylum, despite visible differences in lower levels such as Class and Order. More importantly, when our estimated indicators are used to predict final habitat labels using simpler models (such as Support Vector Machines and traditional Artificial Neural Networks), the prediction accuracy is improved. Overall, this study serves as a preliminary step that bridges modern, black-box Machine Learning models with traditional, domain expertise-rich techniques.

In this paper, an extensive simulation program is conducted to find out the optimal ANN model to predict the shear strength of fiber-reinforced polymer (FRP) concrete beams containing both flexural and shear reinforcements. For acquiring this purpose, an experimental database containing 125 samples is collected from the literature and used to find the best architecture of ANN. In this database, the input variables consist of 9 inputs, such as the ratio of the beam width, the effective depth, the shear span to the effective depth, the compressive strength of concrete, the longitudinal FRP reinforcement ratio, the modulus of elasticity of longitudinal FRP reinforcement, the FRP shear reinforcement ratio, the tensile strength of FRP shear reinforcement, the modulus of elasticity of FRP shear reinforcement. Thereafter, the selection of the appropriate architecture of ANN model is performed and evaluated by common statistical measurements. The results show that the optimal ANN model is a highly efficient predictor of the shear strength of FRP concrete beams with a maximum R2 value of 0.9634 on the training part and an R2 of 0.9577 on the testing part, using the best architecture. In addition, a sensitivity analysis using the optimal ANN model over 500 Monte Carlo simulations is performed to interpret the influence of reinforcement type on the stability and accuracy of ANN model in predicting shear strength. The results of this investigation could facilitate and enhance the use of ANN model in different real-world problems in the field of civil engineering.

A conventional model of public opinion analysis is no longer suitable when the internet is the primary arena of information dissemination. Thus, a more practical approach is urgently needed to deal with this dynamic and complicated phenomenon of propagating public opinion. This paper proposes that the outbreak of internet public opinion and its negative impacts, such as the occurrence of major security incidents, are a result of coupling and the complex interaction of many factors. The Functional Resonance Analysis Method model is composed of those factors and considers the stages of network information dissemination, the unique propagation rule, and textual sentiment resonance on the internet. Moreover, it is the first public opinion governance method that simultaneously highlights the complex system, functional identification, and functional resonance. It suggests a more effective method to shorten the dissipation time of negative public opinion and is a considerable improvement over previous models for risk-prediction. Based on resonance theory and deep learning, this study establishes public opinion resonance functions, which made it possible to analyze public opinion triggers and build a simulation model to explore the patterns of public opinion development through long-term data capture. The simulation results of the Functional Resonance Analysis Method suggest that the resonance in the model is consistent with the evolution of public opinion in real situations and that the components of the resonance of public opinion can be separated into eleven subjective factors and three objective factors. In addition, managing the subjective factors can significantly accelerate the dissipation of negative opinions.

Background Eosinophilic meningitis (EM) is a rare clinical syndrome caused by both infectious and noninfectious diseases. In tropical pacific countries, Angiostrongylus cantonensis is the most common cause. However, the EM definition varies in the literature, and its relation to parasitic meningitis (PM) remains unclear. Methodology/Principal findings Adult and adolescent patients of 13 years old or above with suspected central nervous system (CNS) infections with abnormal CSF findings were prospectively enrolled at a tertiary referral hospital in Hanoi, Vietnam from June 2012 to May 2014. Patients with EM or suspected PM (EM/PM) were defined by the presence of either ≥10% eosinophils or an absolute eosinophil cell counts of ≥10/mm3 in the CSF or blood eosinophilia (>16% of WBCs) without CSF eosinophils. In total 679 patients were enrolled: 7 (1.03%) had ≥10% CSF eosinophilia, 20 (2.95%) had ≥10/mm3 CSF eosinophilia, and 7 (1.03%) had >16% blood eosinophilia. The patients with ≥10% CSF eosinophilia were significantly younger (p = 0.017), had a lower body temperature (p = 0.036) than patients with ≥10/mm3 CSF eosinophilia among whom bacterial pathogens were detected in 72.2% (13/18) of those who were tested by culture and/or PCR. In contrast, the characteristics of the patients with >16% blood eosinophilia resembled those of patients with ≥10% CSF eosinophilia. We further conducted serological tests and real-time PCR to identify A. cantonensis. Serology or real-time PCR was positive in 3 (42.8%) patients with ≥10% CSF eosinophilia and 6 (85.7%) patients with >16% blood eosinophilia without CSF eosinophils but none of patients with ≥10/mm3 CSF eosinophilia. Conclusions The etiology of PM in northern Vietnam is A. cantonensis. The eosinophil percentage is a more reliable predictor of parasitic EM than absolute eosinophil count in the CSF. Patients with PM may present with a high percentage of eosinophils in the peripheral blood but not in the CSF., Author summary Eosinophilic meningitis (EM) is a rare meningitis accompanied by eosinophils in the CSF and caused by multiple etiologies. Angiostrongylus cantonensis, which is a rat lungworm parasite, is the most common cause in tropical Asia. Previous papers have defined EM as CSF eosinophils ≥10% or CSF eosinophils ≥10/mm3. However, the relationship of EM to parasitic meningitis (PM) remains unclear. This prospective study enrolled 679 patients with suspected CNS infection who were admitted to a tertiary referral hospital in Hanoi, Vietnam from June 2012 to May 2014. The characteristics of patients with ≥10% CSF eosinophilia resembled those of patients with >16% blood eosinophilia without CSF eosinophils, whereas those of patients with ≥10/mm3 CSF eosinophilia were comparable with those of patients with typical bacterial meningitis. Serology or real-time PCR for A. cantonensis was positive in 3 out of 7 patients with ≥10% CSF eosinophilia and 6 out of 7 patients with > 16% blood eosinophilia without CSF eosinophils but none of patients with ≥10/mm3 CSF eosinophilia. The percentage, in contrast to the absolute eosinophil count in CSF, is reliable for predicting parasitic EM. Patients with PM may present with eosinophilia in the peripheral blood but not in the CSF.

Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows., Author summary Combining data from many different studies is an attractive way of capturing new aspects of the biology being studied. Biological variance attributable to cell type, cellular niche, origin, disease status or environmental stimuli is the basis of most small-n transcriptome studies. In aggregation, these promise to capture emergent dimensions of a biology that is not possible to view from any individual study. However biological signal is easily swamped by technical artifact, especially when data is generated on platforms with profoundly different data structures. This is the case when comparing microarray data to RNAseq, or RNAseq to single cell profiling. Consequently, transcriptome atlases are generally comprised from a small number of donors/conditions surveyed using one technology platform. In this paper we present a simple and scalable data integration method that is platform agnostic. We provide a proof-of-principle by constructing an atlas of blood cells that combines many data sets measured on different platforms, and that in combination, recapitulates the known blood hierarchy. The atlas provides a reference to compare external samples to, allowing users to benchmark new derivation or isolation methods. It also provides a reference point for new data types, such as the classification of single cells. The approach allows for FAIR data reuse and robust identification of molecular signatures across multiple studies and experimental conditions.

The brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable computations might be achieved despite biological complexity. Crucially, our analysis is not limited to analyzing the stability of fixed geometric objects in state space (e.g points, lines, planes), but rather the stability of state trajectories which may be complex and time-varying., Author summary Stability is essential for any complex system including, and perhaps especially, the brain. The brain’s neural networks are highly dynamic and noisy. Activity fluctuates from moment to moment and can be highly variable. Yet it is critical that these networks reach a consistent state (or sequence of states) for their computations to make sense. Failures in stability have consequences ranging from mild (e.g incorrect decisions) to severe (disease states). In this paper we use tools from control theory and dynamical systems theory to find mechanisms which produce stability in recurrent neural networks (RNNs). We show that a kind of “unlearning” (inhibitory Hebbian and excitatory anti-Hebbian plasticity), balance of excitation and inhibition, and sparse anatomical connectivity all lead to stability. Crucially, we focus on the stability of neural trajectories. This is different from traditional studies of stability of fixed points or planes. We do not assess what trajectories our networks will follow but, rather, when these trajectories will all converge towards each other to achieve stability.

Despite the importance of dendritic targeting in neural circuit assembly, the mechanisms by which it is controlled still remain incompletely understood. We previously showed that in the developing Drosophila antennal lobe, the Wnt5 protein forms a gradient that directs the ~45˚ rotation of a cluster of projection neuron (PN) dendrites, including the adjacent DA1 and VA1d dendrites. We report here that the Van Gogh (Vang) transmembrane planar cell polarity (PCP) protein is required for the rotation of the DA1/VA1d dendritic pair. Cell type-specific rescue and mosaic analyses showed that Vang functions in the olfactory receptor neurons (ORNs), suggesting a codependence of ORN axonal and PN dendritic targeting. Loss of Vang suppressed the repulsion of the VA1d dendrites by Wnt5, indicating that Wnt5 signals through Vang to direct the rotation of the DA1 and VA1d glomeruli. We observed that the Derailed (Drl)/Ryk atypical receptor tyrosine kinase is also required for the rotation of the DA1/VA1d dendritic pair. Antibody staining showed that Drl/Ryk is much more highly expressed by the DA1 dendrites than the adjacent VA1d dendrites. Mosaic and epistatic analyses showed that Drl/Ryk specifically functions in the DA1 dendrites in which it antagonizes the Wnt5-Vang repulsion and mediates the migration of the DA1 glomerulus towards Wnt5. Thus, the nascent DA1 and VA1d glomeruli appear to exhibit Drl/Ryk-dependent biphasic responses to Wnt5. Our work shows that the final patterning of the fly olfactory map is the result of an interplay between ORN axons and PN dendrites, wherein converging pre- and postsynaptic processes contribute key Wnt5 signaling components, allowing Wnt5 to orient the rotation of nascent synapses through a PCP mechanism., Author summary During brain development, the processes of nerve cells, axons and dendrites, grow over long distances to find and connect with each other to form synapses in precise locations. Understanding the mechanisms that control the growth of these neurites is important for understanding normal brain functions like neuronal plasticity and neural diseases like autism. Although much progress has been made by studying the development of axons and dendrites separately, the mechanisms that guide neuronal processes to their final locations are still incompletely understood. In particular, careful observation of converging pre- and postsynaptic processes suggests that their targeting may be coordinated. Whether the final targeting of axons and dendrites are functionally linked and what molecular mechanisms may be involved are unknown. In this paper we show that, in the developing Drosophila olfactory circuit, coalescing axons and dendrites respond to the extracellular Wnt5 signal in a codependent manner. We demonstrate that the converging axons and dendrites contribute different signaling components to the Wnt5 pathway, the Vang Gogh and Derailed transmembrane receptors respectively, which allow Wnt5 to coordinately guide the targeting of the neurites. Our work thus reveals a novel mechanism of neural circuit patterning and the molecular mechanism that controls it.

Electrical stimulation of neural systems is a key tool for understanding neural dynamics and ultimately for developing clinical treatments. Many applications of electrical stimulation affect large populations of neurons. However, computational models of large networks of spiking neurons are inherently hard to simulate and analyze. We evaluate a reduced mean-field model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons which can be used to efficiently study the effects of electrical stimulation on large neural populations. The rich dynamical properties of this basic cortical model are described in detail and validated using large network simulations. Bifurcation diagrams reflecting the network’s state reveal asynchronous up- and down-states, bistable regimes, and oscillatory regions corresponding to fast excitation-inhibition and slow excitation-adaptation feedback loops. The biophysical parameters of the AdEx neuron can be coupled to an electric field with realistic field strengths which then can be propagated up to the population description. We show how on the edge of bifurcation, direct electrical inputs cause network state transitions, such as turning on and off oscillations of the population rate. Oscillatory input can frequency-entrain and phase-lock endogenous oscillations. Relatively weak electric field strengths on the order of 1 V/m are able to produce these effects, indicating that field effects are strongly amplified in the network. The effects of time-varying external stimulation are well-predicted by the mean-field model, further underpinning the utility of low-dimensional neural mass models., Author summary Weak electrical inputs to the brain in vivo using transcranial electrical stimulation or in isolated cortex in vitro can affect the dynamics of the underlying neural populations. However, it is poorly understood what the exact mechanisms are that modulate the activity of neural populations as a whole and why the responses are so diverse in stimulation experiments. Despite this, electrical stimulation techniques are being developed for the treatment of neurological diseases in humans. To better understand these interactions, it is often necessary to simulate and analyze very large networks of neurons, which can be computationally demanding. In this theoretical paper, we present a reduced model of coupled neural populations that represents a piece of cortical tissue. This efficient model retains the dynamical properties of the large network of neurons it is based on while being several orders of magnitude faster to simulate. Due to the biophysical properties of the neuron model, an electric field can be coupled to the population. We show that weak electric fields often used in stimulation experiments can lead to entrainment of neural oscillations on the population level, and argue that the responses critically depend on the dynamical state of the neural system.

Over the past two decades, substantial amount of work has been conducted to characterize different odorant receptors, neuroanatomy and odorant response properties of the early olfactory system of Drosophila melanogaster. Yet many odorant receptors remain only partially characterized, and the odorant transduction process and the axon hillock spiking mechanism of the olfactory sensory neurons (OSNs) have yet to be fully determined. Identity and concentration, two key characteristics of the space of odorants, are encoded by the odorant transduction process. Detailed molecular models of the odorant transduction process are, however, scarce for fruit flies. To address these challenges we advance a comprehensive model of fruit fly OSNs as a cascade consisting of an odorant transduction process (OTP) and a biophysical spike generator (BSG). We model odorant identity and concentration using an odorant-receptor binding rate tensor, modulated by the odorant concentration profile, and an odorant-receptor dissociation rate tensor, and quantitatively describe the mechanics of the molecular ligand binding/dissociation of the OTP. We model the BSG as a Connor-Stevens point neuron. The resulting spatio-temporal encoding model of the Drosophila antenna provides a theoretical foundation for understanding the neural code of both odorant identity and odorant concentration and advances the state-of-the-art in a number of ways. First, it quantifies on the molecular level the spatio-temporal level of complexity of the transformation taking place in the antennae. The concentration-dependent spatio-temporal code at the output of the antenna circuits determines the level of complexity of olfactory processing in the downstream neuropils, such as odorant recognition and olfactory associative learning. Second, the model is biologically validated using multiple electrophysiological recordings. Third, the model demonstrates that the currently available data for odorant-receptor responses only enable the estimation of the affinity of the odorant-receptor pairs. The odorant-dissociation rate is only available for a few odorant-receptor pairs. Finally, our model calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies., Author summary Identity and concentration, intrinsically embedded in the odorant space, are two key characteristics of olfactory coding that define the level of complexity of neural processing throughout the olfactory system in the fruit fly. In this paper we advance a theoretical foundation for understanding these two characteristics by quantifying mathematically the odorant space and devising a biophysical model of the olfactory sensory neurons (OSNs). To validate our modeling approach, we propose and apply an algorithm to estimate the affinity value and the dissociation rate, the two characteristics that define odorant identity, of multiple odorant-receptor pairs. We then evaluate our model with a multitude of odorant waveforms and demonstrate that the model output reproduces the temporal responses of OSNs obtained from in vivo electrophysiology recordings. Furthermore, we evaluate the model at the OSN population level and quantify on the molecular level the spatio-temporal level of complexity of the transformation taking place between the odorant space and the OSNs. The resulting concentration-dependent spatio-temporal code determines the level of complexity of the input space driving olfactory processing in the downstream neuropils. Lastly, our model demonstrates that the currently available data for OSN responses only enables estimation of affinity value. This calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.

Antigen recognition by T-cells is guided by the T-cell receptor (TCR) heterodimer formed by α and β chains. A huge diversity of TCR sequences should be maintained by the immune system in order to be able to mount an effective response towards foreign pathogens, so, due to cooperative binding of α and β chains to the pathogen, any constraints on chain pairing can have a profound effect on immune repertoire structure, diversity and antigen specificity. By integrating available structural data and paired chain sequencing results we were able to show that there are almost no constraints on pairing in TCRαβ complexes, allowing naive T-cell repertoire to reach the highest possible diversity. Additional analysis reveals that the specific choice of contacting amino acids can still have a profound effect on complex conformation. Moreover, antigen-driven selection can distort the uniform landscape of chain pairing, while small, yet significant, differences in the pairing can be attributed to various specialized T-cell subsets such as MAIT and iNKT T-cells, as well as other TCR sets specific to certain antigens., Author summary In the present paper we study chain pairing preferences in the T-cell receptor (TCR) heterodimer complex. The TCR molecule is formed by α and β chains and binding of both of these chains to an antigen presented by the major histocompatibility complex (MHC) molecule is required in order to trigger an immune response against foreign pathogens and neoantigens. We show that chain pairing in the TCR complex is nearly random ensuring a highly diverse set of TCRs required for recognition of a vast set of antigens. Our results also show that chain pairing preferences can nevertheless influence TCR complex geometry and biases in TCR chain pairing can be used to identify antigen-driven selection or selection towards specialized subsets of T-cells such as mucosal-associated and natural killer invariant T-cells.

Connectomes are spatially embedded networks whose architecture has been shaped by physical constraints and communication needs throughout evolution. Using a decentralized navigation protocol, we investigate the relationship between the structure of the connectomes of different species and their spatial layout. As a navigation strategy, we use greedy routing where nearest neighbors, in terms of geometric distance, are visited. We measure the fraction of successful greedy paths and their length as compared to shortest paths in the topology of connectomes. In Euclidean space, we find a striking difference between the navigability properties of mammalian and non-mammalian species, which implies the inability of Euclidean distances to fully explain the structural organization of their connectomes. In contrast, we find that hyperbolic space, the effective geometry of complex networks, provides almost perfectly navigable maps of connectomes for all species, meaning that hyperbolic distances are exceptionally congruent with the structure of connectomes. Hyperbolic maps therefore offer a quantitative meaningful representation of connectomes that suggests a new cartography of the brain based on the combination of its connectivity with its effective geometry rather than on its anatomy only. Hyperbolic maps also provide a universal framework to study decentralized communication processes in connectomes of different species and at different scales on an equal footing., Author summary Recent advances in network science include the discovery that complex networks have a hidden geometry and that this geometry is hyperbolic. Studying complex networks through the lens of their effective hyperbolic geometry has led to valuable insights on the organization of a variety of complex systems ranging from the Internet to the metabolism of E. coli and humans. In this paper, we show that this methodology can also be used to infer high-quality maps of connectomes, where brain regions are given coordinates in hyperbolic space such that the closer they are the more likely that they are connected. Additionally, we find that, even if Euclidean space is typically assumed as the natural geometry of the brain, distances in hyperbolic space offer a more accurate interpretation of the structure of connectomes, which suggests a new perspective for the mapping of the organization of the brain’s neuroanatomical regions.

This paper proposes an approach to accurately estimate the impedance value of a high impedance fault (HIF) and the distance from its fault location for a distribution system. Based on the three-phase voltage and current waveforms which are monitored through a single measurement in the network, several features are extracted using discrete wavelet transform (DWT). The extracted features are then fed into the optimized artificial neural network (ANN) to estimate the HIF impedance and its distance. The particle swarm optimization (PSO) technique is employed to optimize the parameters of the ANN to enhance the performance of fault impedance and distance estimations. Based on the simulation results, the proposed method records encouraging results compared to other methods of similar complexity for both HIF impedance values and estimated distances.

Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of dimensionality. Artificial Neural Networks (ANNs) have achieved promising performance in EEG-based Brain-Computer Interface (BCI) applications, but they involve computationally intensive training algorithms and hyperparameter optimization methods. Thus, an awareness of the quality-cost trade-off, although usually overlooked, is highly beneficial. In this paper, we apply a hyperparameter optimization procedure based on Genetic Algorithms to Convolutional Neural Networks (CNNs), Feed-Forward Neural Networks (FFNNs), and Recurrent Neural Networks (RNNs), all of them purposely shallow. We compare their relative quality and energy-time cost, but we also analyze the variability in the structural complexity of networks of the same type with similar accuracies. The experimental results show that the optimization procedure improves accuracy in all models, and that CNN models with only one hidden convolutional layer can equal or slightly outperform a 6-layer Deep Belief Network. FFNN and RNN were not able to reach the same quality, although the cost was significantly lower. The results also highlight the fact that size within the same type of network is not necessarily correlated with accuracy, as smaller models can and do match, or even surpass, bigger ones in performance. In this regard, overfitting is likely a contributing factor since deep learning approaches struggle with limited training examples.

For the complicated operation process, many risk factors, and long cycle of urban logistics, it is difficult to manage the security of urban logistics and it enhances the risk. Therefore, to study a set of effective management mode for the safe operation of urban logistics and improve the risk prediction mechanism, is the primary research item of urban logistics security management. This paper summarizes the risk factors to public security in the process of urban logistics, including pick up, warehouse storage, transport, and the end distribution. Generalized regression neural network (GRNN) is combined with particle swarm optimization (PSO) to predict accidents, and the Apriori algorithm is used to analyze the combination of high-frequency risk factors. The results show that the method of combining GRNN with PSO is effective in accident prediction and has a powerful generalization ability. It can prevent the occurrence of unnecessary urban logistics public accidents, improve the ability of relevant departments to deal with emergency incidents, and minimize the impact of urban logistics accidents on social and public security.

Cell homeostasis requires the correct levels of reactive oxygen species (ROS) to be maintained as these regulate the proliferation and differentiation of cells, and control the immune response and inflammation. High levels of ROS can cause oxidative stress, leading to protein, lipid and DNA damage, or even cell death. Under physiological conditions, the rate of autophagy remains stable; however, it can be accelerated by a number of exogenous stimuli such as oxidative stress, starvation or hypoxia, leading to cell death. The present paper examines the effect of Conidiobolus coronatus infection on the immune response, oxidative stress processes and autophagy in the greater wax moth, Galleria mellonella. Fungal infection was found to result in the disorganization of the cytoskeleton of the larval immune cells and the enhancement of oxidative defense processes. Lipid peroxidation and autophagy were also induced in the hemocytes. Our findings show that G. mellonella is an ideal model for exploring immune mechanisms.

The ability of muscles to produce force depends, among others, on their anatomical features and it is altered by ageing-associated weakening. However, a clear characterisation of these features, highly relevant for older individuals, is still lacking. This study hence aimed at characterising muscle volume, length, and physiological cross-sectional area (PCSA) and their variability, between body sides and between individuals, in a group of post-menopausal women. Lower-limb magnetic resonance images were acquired from eleven participants (69 (7) y. o., 66.9 (7.7) kg, 159 (3) cm). Twenty-three muscles were manually segmented from the images and muscle volume, length and PCSA were calculated from this dataset. Personalised maximal isometric force was then calculated using the latter information. The percentage difference between the muscles of the two lower limbs was up to 89% and 22% for volume and length, respectively, and up to 84% for PCSA, with no recognisable pattern associated with limb dominance. Between-subject coefficients of variation reached 36% and 13% for muscle volume and length, respectively. Generally, muscle parameters were similar to previous literature, but volumes were smaller than those from in-vivo young adults and slightly higher than ex-vivo ones. Maximal isometric force was found to be on average smaller than those obtained from estimates based on linear scaling of ex-vivo-based literature values. In conclusion, this study quantified for the first time anatomical asymmetry of lower-limb muscles in older women, suggesting that symmetry should not be assumed in this population. Furthermore, we showed that a scaling approach, widely used in musculoskeletal modelling, leads to an overestimation of the maximal isometric force for most muscles. This heavily questions the validity of this approach for older populations. As a solution, the unique dataset of muscle segmentation made available with this paper could support the development of alternative population-based scaling approaches, together with that of automatic tools for muscle segmentation.

Most feature extraction algorithms for music audio signals use Fourier transforms to obtain coefficients that describe specific aspects of music information within the sound spectrum, such as the timbral texture, tonal texture and rhythmic activity. In this paper, we introduce a new method for extracting features related to the rhythmic activity of music signals using the topological properties of a graph constructed from an audio signal. We map the local standard deviation of a music signal to a visibility graph and calculate the modularity (Q), the number of communities (Nc), the average degree (〈k〉), and the density (Δ) of this graph. By applying this procedure to each signal in a database of various musical genres, we detected the existence of a hierarchy of rhythmic self-similarities between musical styles given by these four network properties. UsingQ,Nc, 〈k〉 and Δ as input attributes in a classification experiment based on supervised artificial neural networks, we obtained an accuracy higher than or equal to the beat histogram in 70% of the musical genre pairs, using only four features from the networks. Finally, when performing the attribute selection test withQ,Nc, 〈k〉 and Δ, along with the main signal processing field descriptors, we found that the four network properties were among the top-ranking positions given by this test.

BackgroundPsychological aspects of labor and birth have received little attention within maternity care service planning or clinical practice. The aim of this paper is to propose a model demonstrating how neurohormonal processes, in particular oxytocinergic mechanisms, not only control the physiological aspects of labor and birth, but also contribute to the subjective psychological experiences of birth. In addition, sensory information from the uterus as well as the external environment might influence these neurohormonal processes thereby influencing the progress of labor and the experience of birth.MethodologyIn this new model of childbirth, we integrated the findings from two previous systematic reviews, one on maternal plasma levels of oxytocin during physiological childbirth and one meta-synthesis of women´s subjective experiences of physiological childbirth.FindingsThe neurobiological processes induced by the release of endogenous oxytocin during birth influence maternal behaviour and feelings in connection with birth in order to facilitate birth. The psychological experiences during birth may promote an optimal transition to motherhood. The spontaneous altered state of consciousness, that some women experience, may well be a hallmark of physiological childbirth in humans. The data also highlights the crucial role of one-to-one support during labor and birth. The physiological importance of social support to reduce labor stress and pain necessitates a reconsideration of many aspects of modern maternity care.ConclusionBy listening to women's experiences and by observing women during childbirth, factors that contribute to an optimized process of labor, such as the mothers' wellbeing and feelings of safety, may be identified. These observations support the integrative role of endogenous oxytocin in coordinating the neuroendocrine, psychological and physiological aspects of labor and birth, including oxytocin mediated. decrease of pain, fear and stress, support the need for midwifery one-to-one support in labour as well as the need for maternity care that optimizes the function of these neuroendocrine processes even when birth interventions are used. Women and their partners would benefit from understanding the crucial role that endogenous oxytocin plays in the psychological and neuroendocrinological process of labor.