Social media uses amongst adolescents: motives, minority stress and eudaimonic well-being / Uso de redes sociales en adolescentes: motivación, estrés de minorías y bienestar eudaimónico

Introduction: The scientific evidence regarding the effects of online social media use on the well-being of adolescents is mixed. In gen-eral, passive uses (receiving, viewing content without interacting) and more screen time are related to lower well-being when compared with active uses (direct interactions and interpersonal exchanges). Objectives:This study ex-amines the types and motives for social media usage amongst adolescents, differentiating them by gender identity and sexual orientation, as well as its effects on eudaimonic well-being and minority stress. Method: A cross-sectional study was conducted with 1259 adolescents, aged 14 to 19 (M= 16.19; SD= 1.08), analysing the Scale of Motives for Using Social Net-working Sites, eudaimonic well-being, the Sexual Minority Adolescent Stress Inventory, screen time and profile type. Results:The results found that longer use time is related to finding partners, social connection and friendships; that gay and bisexual (GB) adolescents perceive more distal stressors online;and that females have higher levels of well-being. Discus-sion: The public profiles of GB males increase self-expression, although minority stress can be related to discrimination, rejection or exclusion. Dif-ferentiated socialization may contribute to a higher level of well-being in females, with both active and passive uses positively effecting eudaimonic well-being in adolescents.(AU)

Introduction: The scientific evidence regarding the effects of online social media use on the well-being of adolescents is mixed. In general, passive uses (receiving, viewing content without interacting) and more screen time are related to lower well-being when compared with active uses (direct interactions and interpersonal exchanges). Objectives: This study examines the types and motives for social media usage amongst adolescents, differentiating them by gender identity and sexual orientation, as well as its effects on eudaimonic well-being and minority stress. Method: A cross-sectional study was conducted with 1259 adolescents, aged 14 to 19 (M = 16.19; SD = 1.08), analysing the Scale of Motives for Using Social Networking Sites, eudaimonic well-being, the Sexual Minority Adolescent Stress Inventory, screen time and profile type. Results: The results found that longer use time is related to finding partners, social connection and friendships; that gay and bisexual (GB) adolescents perceive more distal stressors online; and that females have higher levels of well-being. Discussion: The public profiles of GB males increase self-expression, although minority stress can be related to discrimination, rejection or exclusion. Differentiated socialization may contribute to a higher level of well-being in females, with both active and passive uses positively effecting eudaimonic well-being in adolescents.(AU)

Psychedelics in developmental stuttering to modulate brain functioning: a new therapeutic perspective?

Developmental stuttering (DS) is a neurodevelopmental speech-motor disorder characterized by symptoms such as blocks, repetitions, and prolongations. Persistent DS often has a significant negative impact on quality of life, and interventions for it have limited efficacy. Herein, we briefly review existing research on the neurophysiological underpinnings of DS -specifically, brain metabolic and default mode/social-cognitive networks (DMN/SCN) anomalies- arguing that psychedelic compounds might be considered and investigated (e.g., in randomized clinical trials) for treatment of DS. The neural background of DS is likely to be heterogeneous, and some contribution from genetically determinants of metabolic deficiencies in the basal ganglia and speech-motor cortical regions are thought to play a role in appearance of DS symptoms, which possibly results in a cascade of events contributing to impairments in speech-motor execution. In persistent DS, the difficulties of speech are often linked to a series of associated aspects such as social anxiety and social avoidance. In this context, the SCN and DMN (also influencing a series of fronto-parietal, somato-motor, and attentional networks) may have a role in worsening dysfluencies. Interestingly, brain metabolism and SCN/DMN connectivity can be modified by psychedelics, which have been shown to improve clinical evidence of some psychiatric conditions (e.g., depression, post-traumatic stress disorder, etc.) associated with psychological constructs such as rumination and social anxiety, which also tend to be present in persistent DS. To date, while there have been no controlled trials on the effects of psychedelics in DS, anecdotal evidence suggests that these agents may have beneficial effects on stuttering and its associated characteristics. We suggest that psychedelics warrant investigation in DS.

Resilience of the slow component in timescale-separated synchronized oscillators.

Physiological networks are usually made of a large number of biological oscillators evolving on a multitude of different timescales. Phase oscillators are particularly useful in the modelling of the synchronization dynamics of such systems. If the coupling is strong enough compared to the heterogeneity of the internal parameters, synchronized states might emerge where phase oscillators start to behave coherently. Here, we focus on the case where synchronized oscillators are divided into a fast and a slow component so that the two subsets evolve on separated timescales. We assess the resilience of the slow component by, first, reducing the dynamics of the fast one using Mori-Zwanzig formalism. Second, we evaluate the variance of the phase deviations when the oscillators in the two components are subject to noise with possibly distinct correlation times. From the general expression for the variance, we consider specific network structures and show how the noise transmission between the fast and slow components is affected. Interestingly, we find that oscillators that are among the most robust when there is only a single timescale, might become the most vulnerable when the system undergoes a timescale separation. We also find that layered networks seem to be insensitive to such timescale separations.

Reverse social contagion as a mechanism for regulating mass behaviors in highly integrated social systems.

Mass behavior is the rapid adoption of similar conduct by all group members, with potentially catastrophic outcomes such as mass panic. Yet, these negative consequences are rare in integrated social systems such as social insect colonies, thanks to mechanisms of social regulation. Here, we test the hypothesis that behavioral deactivation between active individuals is a powerful social regulator that reduces energetic spending in groups. Borrowing from scaling theories for human settlements and using behavioral data on harvester ants, we derive ties between the hypermetric scaling of the interaction network and the hypometric scaling of activity levels, both relative to the colony size. We use elements of economics theory and metabolic measurements collected with the behavioral data to link activity and metabolic scalings with group size. Our results support the idea that metabolic scaling across social systems is the product of different balances between their social regulation mechanisms.

Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation.

Background: Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods: Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results: The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Conclusions: Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

Packing in multimodal crystals and particle assemblies as models for dynamic packing during spontaneous and enforced particle movement: Settling, viscosity and elasticity.

Colloid particles (CP, 10-8-10-6 m = 10-1000 nm) are used as models for atom scale processes, such as crystallization since the process is experimentally observable. Packing of atoms in crystals resemble mono-, bi-, and trimodal packing of noncharged hard spheres (particles). When the size of one particle exceeds the two others an excluded volume consisting of small particles is created around large particles. This is also the case when colloid particles are dispersed in water. The formation of an excluded volume does not require attraction forces, but it is enforced by the presence of dissolved primary (cations) and secondary (protons of surface hydroxyls) potential determining ions. The outcome is an interfacial solid-liquid charge. This excluded volume, denoted Stern layer is characterized by the surface potential and charge density. Charge neutrality is identified by point of zero charge (pHpzc and pcpzc). Outside Stern layer another excluded volume is formed of loosely bound counterions which interact with Stern layer. The extent of this diffuse layer is given by inverse Debye length and effective ζ-potential. The overall balance between attractive and repulsive energies is provided by Derjaguin-Landau-Veerwey-Overbeek (DLVO) model. Charge neutrality is identified at isoelectric point (pHiep and pciep). The dependence of viscosity and yield stress on shear rate may be modeled by von Smoluchowski's volumetric collision frequency multiplied by some total interaction energy given by DLVO model. Equilibrium and dynamic models for settling and enforced particle movement (viscosity) are presented. Both compressive yield stress (sedimentation) and cohesive energy (viscoelasticity) are characterized by power law exponents of volume fraction. The transition of disperse suspensions (sols) to spanning clusters (gels) is identified by oscillatory rheology. The slope of linear plots of logarithmic storage (G´) and loss (G") moduli against logarithm of frequency or logarithm of volume fraction provide power law exponents from the slopes. These exponents relate to percolation and fractal dimensions characterizing the particle network. Moreover, it identifies the structure formation process either as diffusion limited cluster-cluster (DLCCA) or as reaction limited cluster-cluster (RLCCA) aggregation.

[Bayesian network prediction study on the impact of occupational health psychological factors on insomnia among thermal power generation workers].

Objective: To explore the risk factors of insomnia among employees in the thermal power generation industry and the network relationships between their interactions, and to provide scientific basis for personalized interventions for high-risk groups with insomnia. Methods: In November 2022, 860 employees of a typical thermal power generation enterprise were selected as the research subjects by cluster sampling. On-site occupational health field surveys and questionnaire surveys were used to collect basic information, occupational characteristics, anxiety, depression, stress, occupational stress, and insomnia. The interaction between insomnia and occupational health psychological factors was evaluated by using structural equation model analysis and Bayesian network construction. Results: The detection rates of anxiety, depression and stress were 34.0% (292/860), 32.1% (276/860) and 18.0% (155/860), respectively. The total score of occupational stress was (445.3±49.9) points, and 160 workers (18.6%) were suspected of insomnia, and 578 workers (67.2%) had insomnia. Structural equation model analysis showed that occupational stress had a significant effect on the occurrence of insomnia in thermal power generation workers (standardized load coefficient was 0.644), and occupational health psychology had a low effect on insomnia (standardized load coefficient was 0.065). However, the Bayesian network model further analysis found that anxiety and stress were the two parent nodes of insomnia, with direct causal relationships, the arc strength was-8.607 and -15.665, respectively. The model prediction results showed that the probability of insomnia occurring was predicted to be 0 in the cases of no stress and anxiety, low stress without anxiety, and no stress with low anxiety. When high stress with low anxiety and low stress with high anxiety occurred, the predicted probability of insomnia occurring were 0.38 and 0.47, respectively. When both high stress and high anxiety occurred simultaneously, the predicted probability of insomnia occurring was 0.51. Conclusion: Bayesian network risk assessment can intuitively reveal and predict the insomnia risk of thermal power generation workers and the network interaction relationship between the risks. Anxiety and stress are the direct causal risks of insomnia, and stress is the main risk of individual insomnia of thermal power generation workers. The occurrence of insomnia can be reduced based on scientific intervention of stress conditions.

The Global Intellectual Disability Nursing Research Collaboratory: Forming an international transformational nursing network.

AIM: This paper explains how we created the Global Intellectual Disability Nurse Research Collaboratory (GIDNRC), a transformative network. The GIDNRC aims to make improvements in the understanding, research, policy, clinical care, and support provided to people with an intellectual disability. BACKGROUND: In 2022, the World Health Organization (WHO) called upon healthcare leaders internationally to take actions to promote more equal healthcare for disabled persons. This paper promotes the GIDNRC as a way for professionals to work together to make more equal healthcare throughout the world for people with intellectual disabilities. SOURCES OF EVIDENCE: We created this paper by reviewing peer-reviewed literature and research, international policies, and nursing networking initiatives. DISCUSSION: This paper explores current policy, research, and practice issues that formed the basis of beginning the GIDNRC, including how the COVID-19 pandemic changed care. CONCLUSION: Nurses are over 50% of the world's health workforce. Therefore, they have the potential to make a large impact in making care for people with intellectual disability much more equal than currently exists throughout the world. However, barriers exist. Forming the GIDNRC, as well as using the World Wide Web, offers an opportunity to address barriers to this goal. IMPLICATIONS FOR NURSING PRACTICE: Nurses can address the needs of people with intellectual disability in their daily nursing practice. The GIDNRC aims to strengthen these clinical skills, understand how care may vary throughout the world, and share knowledge, good practices, and new ways to approach care for people with an intellectual disability worldwide. IMPLICATIONS FOR NURSING POLICY: International nursing policy should actively focus on the needs of people with intellectual disabilities and the role nurses play in addressing these health needs. The GIDNRC may provide an important way to achieve developments in this policy.

Association of physical function with connectivity in the sensorimotor and dorsal attention networks: why examining specific components of physical function matters.

Declining physical function with aging is associated with structural and functional brain network organization. Gaining a greater understanding of network associations may be useful for targeting interventions that are designed to slow or prevent such decline. Our previous work demonstrated that the Short Physical Performance Battery (eSPPB) score and body mass index (BMI) exhibited a statistical interaction in their associations with connectivity in the sensorimotor cortex (SMN) and the dorsal attention network (DAN). The current study examined if components of the eSPPB have unique associations with these brain networks. Functional magnetic resonance imaging was performed on 192 participants in the BNET study, a longitudinal and observational trial of community-dwelling adults aged 70 or older. Functional brain networks were generated for resting state and during a motor imagery task. Regression analyses were performed between eSPPB component scores (gait speed, complex gait speed, static balance, and lower extremity strength) and BMI with SMN and DAN connectivity. Gait speed, complex gait speed, and lower extremity strength significantly interacted with BMI in their association with SMN at rest. Gait speed and complex gait speed were interacted with BMI in the DAN at rest while complex gait speed, static balance, and lower extremity strength interacted with BMI in the DAN during motor imagery. Results demonstrate that different components of physical function, such as balance or gait speed and BMI, are associated with unique aspects of brain network organization. Gaining a greater mechanistic understanding of the associations between low physical function, body mass, and brain physiology may lead to the development of treatments that not only target specific physical function limitations but also specific brain networks.

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks.

BACKGROUND: Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples. METHODS: We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA. RESULTS: Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples. CONCLUSIONS: In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

Emergent scale-free networks.

Many complex systems-from the Internet to social, biological, and communication networks-are thought to exhibit scale-free structure. However, prevailing explanations require that networks grow over time, an assumption that fails in some real-world settings. Here, we explain how scale-free structure can emerge without growth through network self-organization. Beginning with an arbitrary network, we allow connections to detach from random nodes and then reconnect under a mixture of preferential and random attachment. While the numbers of nodes and edges remain fixed, the degree distribution evolves toward a power-law with an exponent γ = 1 + 1 p that depends only on the proportion p of preferential (rather than random) attachment. Applying our model to several real networks, we infer p directly from data and predict the relationship between network size and degree heterogeneity. Together, these results establish how scale-free structure can arise in networks of constant size and density, with broad implications for the structure and function of complex systems.

Translational symmetry in convolutions with localized kernels causes an implicit bias toward high frequency adversarial examples.

Adversarial attacks are still a significant challenge for neural networks. Recent efforts have shown that adversarial perturbations typically contain high-frequency features, but the root cause of this phenomenon remains unknown. Inspired by theoretical work on linear convolutional models, we hypothesize that translational symmetry in convolutional operations together with localized kernels implicitly bias the learning of high-frequency features, and that this is one of the main causes of high frequency adversarial examples. To test this hypothesis, we analyzed the impact of different choices of linear and non-linear architectures on the implicit bias of the learned features and adversarial perturbations, in spatial and frequency domains. We find that, independently of the training dataset, convolutional operations have higher frequency adversarial attacks compared to other architectural parameterizations, and that this phenomenon is exacerbated with stronger locality of the kernel (kernel size) end depth of the model. The explanation for the kernel size dependence involves the Fourier Uncertainty Principle: a spatially-limited filter (local kernel in the space domain) cannot also be frequency-limited (local in the frequency domain). Using larger convolution kernel sizes or avoiding convolutions (e.g., by using Vision Transformers or MLP-style architectures) significantly reduces this high-frequency bias. Looking forward, our work strongly suggests that understanding and controlling the implicit bias of architectures will be essential for achieving adversarial robustness.

Composing recurrent spiking neural networks using locally-recurrent motifs and risk-mitigating architectural optimization.

In neural circuits, recurrent connectivity plays a crucial role in network function and stability. However, existing recurrent spiking neural networks (RSNNs) are often constructed by random connections without optimization. While RSNNs can produce rich dynamics that are critical for memory formation and learning, systemic architectural optimization of RSNNs is still an open challenge. We aim to enable systematic design of large RSNNs via a new scalable RSNN architecture and automated architectural optimization. We compose RSNNs based on a layer architecture called Sparsely-Connected Recurrent Motif Layer (SC-ML) that consists of multiple small recurrent motifs wired together by sparse lateral connections. The small size of the motifs and sparse inter-motif connectivity leads to an RSNN architecture scalable to large network sizes. We further propose a method called Hybrid Risk-Mitigating Architectural Search (HRMAS) to systematically optimize the topology of the proposed recurrent motifs and SC-ML layer architecture. HRMAS is an alternating two-step optimization process by which we mitigate the risk of network instability and performance degradation caused by architectural change by introducing a novel biologically-inspired "self-repairing" mechanism through intrinsic plasticity. The intrinsic plasticity is introduced to the second step of each HRMAS iteration and acts as unsupervised fast self-adaptation to structural and synaptic weight modifications introduced by the first step during the RSNN architectural "evolution." We demonstrate that the proposed automatic architecture optimization leads to significant performance gains over existing manually designed RSNNs: we achieve 96.44% on TI46-Alpha, 94.66% on N-TIDIGITS, 90.28% on DVS-Gesture, and 98.72% on N-MNIST. To the best of the authors' knowledge, this is the first work to perform systematic architecture optimization on RSNNs.

NoC simulation steered by NEST: McAERsim and a Noxim patch.

Introduction: Great knowledge was gained about the computational substrate of the brain, but the way in which components and entities interact to perform information processing still remains a secret. Complex and large-scale network models have been developed to unveil processes at the ensemble level taking place over a large range of timescales. They challenge any kind of simulation platform, so that efficient implementations need to be developed that gain from focusing on a set of relevant models. With increasing network sizes imposed by these models, low latency inter-node communication becomes a critical aspect. This situation is even accentuated, if slow processes like learning should be covered, that require faster than real-time simulation. Methods: Therefore, this article presents two simulation frameworks, in which network-on-chip simulators are interfaced with the neuroscientific development environment NEST. This combination yields network traffic that is directly defined by the relevant neural network models and used to steer the network-on-chip simulations. As one of the outcomes, instructive statistics on network latencies are obtained. Since time stamps of different granularity are used by the simulators, a conversion is required that can be exploited to emulate an intended acceleration factor. Results: By application of the frameworks to scaled versions of the cortical microcircuit model-selected because of its unique properties as well as challenging demands-performance curves, latency, and traffic distributions could be determined. Discussion: The distinct characteristic of the second framework is its tree-based source-address driven multicast support, which, in connection with the torus topology, always led to the best results. Although currently biased by some inherent assumptions of the network-on-chip simulators, the results suit well to those of previous work dealing with node internals and suggesting accelerated simulations to be in reach.

Forecasting deep learning-based risk assessment of vector-borne diseases using hybrid methodology.

BACKGROUND: Dengue fever is rapidly becoming Malaysia's most pressing health concern, as the reported cases have nearly doubled over the past decade. Without efficacious antiviral medications, vector control remains the primary strategy for battling dengue, while the recently introduced tetravalent immunization is being evaluated. The most significant and dangerous risk increasing recently is vector-borne illnesses. These illnesses induce significant human sickness and are transmitted by blood-feeding arthropods such as fleas, parasites, and mosquitos. A thorough grasp of various factors is necessary to improve prediction accuracy and typically generate inaccurate and unstable predictions, as well as machine learning (ML) models, weather-driven mechanisms, and numerical time series. OBJECTIVE: In this research, we propose a novel method for forecasting vector-borne disease risk using Radial Basis Function Networks (RBFNs) and the Darts Game Optimizer (DGO) algorithm. METHODS: The proposed approach entails training the RBFNs with historical disease data and enhancing their parameters with the DGO algorithm. To prepare the RBFNs, we used a massive dataset of vector-borne disease incidences, climate variables, and geographical data. The DGO algorithm proficiently searches the RBFN parameter space, fine-tuning the model's architecture to increase forecast accuracy. RESULTS: RBFN-DGO provides a potential method for predicting vector-borne disease risk. This study advances predictive demonstrating in public health by shedding light on effectively controlling vector-borne diseases to protect human populations. We conducted extensive testing to evaluate the performance of the proposed method to standard optimization methods and alternative forecasting methods. CONCLUSION: According to the findings, the RBFN-DGO model beats others in terms of accuracy and robustness in predicting the likelihood of vector-borne illness occurrences.

An application of the Bayesian network model based on the EN-ESL-GA algorithm: Exploring the predictors of heart disease in middle-aged and elderly people in China.

BACKGROUND: The morbidity and mortality of heart disease are increasing in middle-aged and elderly people in China. It is necessary to explore relationships and interactive associations between heart disease and its risk factors in order to prevent heart disease. OBJECTIVE: To establish a Bayesian network model of heart disease and its influencing factors in middle-aged and elderly people in China, and explore the applicability of the elite-based structure learner using genetic algorithm based on ensemble learning (EN-ESL-GA) algorithm in etiology analysis and disease prediction. METHODS: Based on the 2013 national tracking survey data from China Health and Retirement Longitudinal Study (CHARLS) database, EN-ESL-GA algorithm was used to learn the Bayesian network structure. Then we input the data and the learned network structure into the Netica software for parameter learning and inference analysis. RESULTS: The Bayesian network model based on the EN-ESL-GAalgorithm can effectively excavate the complex network relationships and interactive associations between heart disease and its risk factors in middle-aged and elderly people in China. CONCLUSIONS: The Bayesian network model based on the EN-ESL-GA algorithm has good applicability and application prospect in the prediction of diseases prevalence risk.

Environmental, socioeconomic, and health factors associated with gut microbiome species and strains in isolated Honduras villages.

Despite a growing interest in the gut microbiome of non-industrialized countries, data linking deeply sequenced microbiomes from such settings to diverse host phenotypes and situational factors remain uncommon. Using metagenomic data from a community-based cohort of 1,871 people from 19 isolated villages in the Mesoamerican highlands of western Honduras, we report associations between bacterial species and human phenotypes and factors. Among them, socioeconomic factors account for 51.44% of the total associations. Meta-analysis of species-level profiles across several datasets identified several species associated with body mass index, consistent with previous findings. Furthermore, the inclusion of strain-phylogenetic information modifies the overall relationship between the gut microbiome and the phenotypes, especially for some factors like household wealth (e.g., wealthier individuals harbor different strains of Eubacterium rectale). Our analysis suggests a role that gut microbiome surveillance can play in understanding broad features of individual and public health.

Emergent perceptual biases from state-space geometry in trained spiking recurrent neural networks.

A stimulus held in working memory is perceived as contracted toward the average stimulus. This contraction bias has been extensively studied in psychophysics, but little is known about its origin from neural activity. By training recurrent networks of spiking neurons to discriminate temporal intervals, we explored the causes of this bias and how behavior relates to population firing activity. We found that the trained networks exhibited animal-like behavior. Various geometric features of neural trajectories in state space encoded warped representations of the durations of the first interval modulated by sensory history. Formulating a normative model, we showed that these representations conveyed a Bayesian estimate of the interval durations, thus relating activity and behavior. Importantly, our findings demonstrate that Bayesian computations already occur during the sensory phase of the first stimulus and persist throughout its maintenance in working memory, until the time of stimulus comparison.

Artificial intelligence derived categorizations significantly improve HOMA IR/ß indicators: Combating diabetes through cross-interacting drugs.

Improvements in the homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of beta-cell function (HOMA-ß) significantly reduce the risk of disabling diabetic pathies. Nanoparticle (AuNP-AgNP)-metformin are concentration dependent cross-interacting drugs as they may have a synergistic as well as antagonistic effect(s) on HOMA indicators when administered concurrently. We have employed a blend of machine learning: Artificial Neural Network (ANN), and evolutionary optimization: multiobjective Genetic Algorithms (GA) to discover the optimum regime of the nanoparticle-metformin combination. We demonstrated how to successfully employ a tested and validated ANN to classify the exposed drug regimen into categories of interest based on gradient information. This study also prescribed standard categories of interest for the exposure of multiple diabetic drug regimen. The application of categorization greatly reduces the time and effort involved in reaching the optimum combination of multiple drug regimen based on the category of interest. Exposure of optimum AuNP, AgNP and Metformin to Diabetic rats significantly improved HOMA ß functionality (∼63 %), Insulin resistance (HOMA IR) of Diabetic animals was also reduced significantly (∼54 %). The methods explained in the study are versatile and are not limited to only diabetic drugs.

Learning sequence attractors in recurrent networks with hidden neurons.

The brain is targeted for processing temporal sequence information. It remains largely unclear how the brain learns to store and retrieve sequence memories. Here, we study how recurrent networks of binary neurons learn sequence attractors to store predefined pattern sequences and retrieve them robustly. We show that to store arbitrary pattern sequences, it is necessary for the network to include hidden neurons even though their role in displaying sequence memories is indirect. We develop a local learning algorithm to learn sequence attractors in the networks with hidden neurons. The algorithm is proven to converge and lead to sequence attractors. We demonstrate that the network model can store and retrieve sequences robustly on synthetic and real-world datasets. We hope that this study provides new insights in understanding sequence memory and temporal information processing in the brain.