UNASSIGNED: Accumulating evidence shows that human health and disease are closely related to the microbes in the human body.
UNASSIGNED: In this manuscript, a new computational model based on graph attention networks and sparse autoencoders, called GCANCAE, was proposed for inferring possible microbe-disease associations. In GCANCAE, we first constructed a heterogeneous network by combining known microbe-disease relationships, disease similarity, and microbial similarity. Then, we adopted the improved GCN and the CSAE to extract neighbor relations in the adjacency matrix and novel feature representations in heterogeneous networks. After that, in order to estimate the likelihood of a potential microbe associated with a disease, we integrated these two types of representations to create unique eigenmatrices for diseases and microbes, respectively, and obtained predicted scores for potential microbe-disease associations by calculating the inner product of these two types of eigenmatrices.
UNASSIGNED: Based on the baseline databases such as the HMDAD and the Disbiome, intensive experiments were conducted to evaluate the prediction ability of GCANCAE, and the experimental results demonstrated that GCANCAE achieved better performance than state-of-the-art competitive methods under the frameworks of both 2-fold and 5-fold CV. Furthermore, case studies of three categories of common diseases, such as asthma, irritable bowel syndrome (IBS), and type 2 diabetes (T2D), confirmed the efficiency of GCANCAE.

Dopamine modulates working memory in the prefrontal cortex (PFC) and is crucial for obsessive-compulsive disorder (OCD). However, the mechanism is unclear. Here we establish a biophysical model of the effect of dopamine (DA) in PFC to explain the mechanism of how high dopamine concentrations induce persistent neuronal activities with the network plunging into a deep, stable attractor state. The state develops a defect in working memory and tends to obsession and compulsion. Weakening the reuptake of dopamine acts on synaptic plasticity according to Hebbian learning rules and reward learning, which in turn affects the strength of neuronal synaptic connections, resulting in the tendency of compulsion and learned obsession. In addition, we elucidate the potential mechanisms of dopamine antagonists in OCD, indicating that dopaminergic drugs might be available for treatment, even if the abnormality is a consequence of glutamate hypermetabolism rather than dopamine. The theory highlights the significance of early intervention and behavioural therapies for obsessive-compulsive disorder. It potentially offers new approaches to dopaminergic pharmacotherapy and psychotherapy for OCD patients.

There is evidence that the subthalamic nucleus (STN) and globus pallidus pars externa (GPe) involve in the development of Parkinson\'s disease, a neurodegenerative disorder characterized by motor and non-motor symptoms and loss of dopaminergic neurons in which the error index (EI) in firing patterns is widely used to address the related issues. Whether and how this interaction mechanism of STN and GPe affects EI in Parkinson\'s disease is uncertain. To account for this, we propose a kind of basal ganglia-thalamic network model associated with Parkinson\'s disease coupled with neurons, and investigate the effect of synaptic conductance of STN and GPe on EI in this network, as well as their internal relationship under EI as an index. The results show a relationship like a piecewise function between the error index and the slope of the state transition function of synaptic conductance from STN to GPe ( g snge ) and from GPe to STN ( g gesn ). And there is an approximate negative correlation between EI and g gesn . Increasing g snge and decreasing g gesn can improve the fidelity of thalamus information transmission and alleviate Parkinson\'s disease effectively. These obtained results can give some theoretical evidence that the abnormal synaptic releases of STN and GPe may be the symptoms of the development of Parkinson\'s disease, and further enrich the understanding of the pathogenesis and treatment mechanism of Parkinson\'s disease.

Cellular electrophysiology is the foundation of many fields, from basic science in neurology, cardiology, oncology to safety critical applications for drug safety testing, clinical phenotyping, etc. Patch-clamp voltage clamp is the gold standard technique for studying cellular electrophysiology. Yet, the quality of these experiments is not always transparent, which may lead to erroneous conclusions for studies and applications. Here, we have developed a new computational approach that allows us to explain and predict the experimental artefacts in voltage-clamp experiments. The computational model captures the experimental procedure and its inadequacies, including: voltage offset, series resistance, membrane capacitance and (imperfect) amplifier compensations, such as series resistance compensation and supercharging. The computational model was validated through a series of electrical model cell experiments. Using this computational approach, the artefacts in voltage-clamp experiments of cardiac fast sodium current, one of the most challenging currents to voltage clamp, were able to be resolved and explained through coupling the observed current and the simulated membrane voltage, including some typically observed shifts and delays in the recorded currents. We further demonstrated that the typical way of averaging data for current-voltage relationships would lead to biases in the peak current and shifts in the peak voltage, and such biases can be in the same order of magnitude as those differences reported for disease-causing mutations. Therefore, the presented new computational pipeline will provide a new standard of assessing the voltage-clamp experiments and interpreting the experimental data, which may be able to rectify and provide a better understanding of ion channel mutations and other related applications.

Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly application programming interface, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNet\'s focus on higher-order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.

Behavioral responses to many odorants are not fixed but are flexible, varying based on organismal needs. How such variations arise and the role of various neuromodulators in achieving flexible neural-to-behavioral mapping is not fully understood. In this study, we examined how serotonin modulates the neural and behavioral responses to odorants in locusts (Schistocerca americana). Our results indicated that serotonin can increase or decrease appetitive behavior in an odor-specific manner. On the other hand, in the antennal lobe, serotonergic modulation enhanced odor-evoked response strength but left the temporal features or the combinatorial response profiles unperturbed. This result suggests that serotonin allows for sensitive and robust recognition of odorants. Nevertheless, the uniform neural response amplification appeared to be at odds with the observed stimulus-specific behavioral modulation. We show that a simple linear model with neural ensembles segregated based on behavioral relevance is sufficient to explain the serotonin-mediated flexible mapping between neural and behavioral responses.

Artificial intelligence methods and techniques creatively support the processes of developing and improving methods for selecting shredders for the processing of polymer materials. This allows to optimize the fulfillment of selection criteria, which may include not only indicators related to shredding efficiency and recyclate quality but also energy consumption. The aim of this paper is to select methods of analysis based on artificial intelligence (AI) with independent rule extraction, i.e., data-based methods (machine learning-ML). This study took into account real data sets (feature matrix 1982 rows × 40 columns) describing the shredding process, including energy consumption used to optimize the parameters for the energy efficiency of the shredder. Each of the 1982 records in a .csv file (feature vector) has 40 numbers divided by commas. The data were divided into a learning set (70% of the data), a testing set (20% of the data), and a validation set (10% of the data). Cross-validation showed that the best model was LbfgsLogisticRegressionOva (0.9333). This promotes the development of the basis for an intelligent shredding methodology with a high level of innovation in the processing and recycling of polymer materials within the Industry 4.0 paradigm.

OBJECTIVE: The electroretinogram (ERG) is the summed response from all levels of the retinal processing of light, and exhibits several profound nonlinearities in the underlying processing pathways. Accurate computational models of the ERG are important, both for understanding the multifold processes of light transduction to ecologically useful signals by the retina, and for their diagnostic capabilities for the identification and characterization of retinal disease mechanisms. There are, however, very few computational models of the ERG waveform, and none that account for the full extent of its features over time.
METHODS: This study takes the neuroanalytic approach to modeling the ERG waveform, defined as a computational model based on the main features of the transmitter kinetics of the retinal neurons.
RESULTS: The present neuroanalytic model of the human rod ERG is elaborated from the same general principles as that of Hood and Birch (Vis Neurosci 8(2):107-126, 1992), but incorporates the more recent understanding of the early nonlinear stages of ERG generation by Robson and Frishman (Prog Retinal Eye Res 39:1-22, 2014). As a result, it provides a substantially better match than previous models of rod responses in six different waveform features of the ERG flash intensity series on which the Hood and Birch model was based.
CONCLUSIONS: The neuroanalytic approach extends previous models of the component waves of the ERG, and can be structured to provide an accurate characterization of the full timecourse of the ERG waveform. The approach thus holds promise for advancing the theoretical understanding of the retinal kinetics of the light response.

Introduction: Clinics increasingly require readily deployable tubular substitutes to restore the functionality of structures like ureters and blood vessels. Despite extensive exploration of various materials, both synthetic and biological, the optimal solution remains elusive. Drawing on abundant literature experiences, there is a pressing demand for a substitute that not only emulates native tissue by providing requisite signals and growth factors but also exhibits appropriate mechanical resilience and behaviour. Methods: This study aims to assess the potential of porcine ureters by characterizing their biomechanical properties in their native configuration through ring and membrane flexion tests. In order to assess the tissue morphology before and after mechanical tests and the eventual alteration of tissue microstructure that would be inserted in material constitutive description, histological staining was performed on samples. Corresponding computational analyses were performed to mimic the experimental campaign to identify the constitutive material parameters. Results: The absence of any damages to muscle and collagen fibres, which only compacted after mechanical tests, was demonstrated. The experimental tests (ring and membrane flexion tests) showed non-linearity for material and geometry and the viscoelastic behaviour of the native porcine ureter. Computational models were descriptive of the mechanical behaviour ureteral tissue, and the material model feasible. Discussion: This analysis will be useful for future comparison with decellularized tissue for the evaluation of the aggression of cell removal and its effect on microstructure. The computational model could lay the basis for a reliable tool for the prediction of solicitation in the case of tubular substitutions in subsequent simulations.

Theories of category learning have typically focused on how the underlying category structure affects the category representations acquired by learners. However, there is limited research as to how other factors affect what representations are learned and utilized and how representations might change across the time course of learning. We used a novel \"5/5\" categorization task developed from the well-studied 5/4 task with the addition of one more stimulus to clarify an ambiguity in the 5/4 prototypes. We used multiple methods including computational modeling to identify whether participants categorized on the basis of exemplar or prototype representations. We found that, overall, for the stimuli we used (schematic robot-like stimuli), learning was best characterized by the use of prototypes. Most importantly, we found that relative use of prototype and exemplar strategies changed across learning, with use of exemplar representations decreasing and prototype representations increasing across blocks.