Miracle-dz/UniFAR-FTUs huggingface dataset:
Phylogenetic and Drug-Resistance Analysis of HIV-1 Sequences From an Extensive Paediatric HIV-1 Outbreak in Larkana, Pakistan

Introduction In April 2019, an HIV-1 outbreak among children occurred in Larkana, Pakistan, affecting more than a thousand children. It was assumed that the outbreak originated from a single source, namely a doctor at a private health facility. In this study, we performed subtype distribution, phylogenetic and drug-resistance analysis of HIV-1 sequences from 2019 outbreak in Larkana, Pakistan. Methods A total of 401 blood samples were collected between April–June 2019, from children infected with HIV-1 aged 0–15 years recruited into a case-control study to investigate the risk factors for HIV-1 transmission. Partial HIV-1 pol sequences were generated from 344 blood plasma samples to determine HIV-1 subtype and drug resistance mutations (DRM). Maximum-likelihood phylogenetics based on outbreak and reference sequences was used to identify transmission clusters and assess the relationship between outbreak and key population sequences between and within the determined clusters. Bayesian analysis was employed to identify the time to the most recent common recent ancestor (tMRCA) of the main Pakistani clusters. Results The HIV-1 circulating recombinant form (CRF) 02_AG and subtype A1 were most common among the outbreak sequences. Of the treatment-naïve participants, the two most common mutations were RT: E138A (8%) and RT: K219Q (8%). Four supported clusters within the outbreak were identified, and the median tMRCAs of the Larkana outbreak sequences were estimated to 2016 for both the CRF02_AG and the subtype A1 clusters. Furthermore, outbreak sequences exhibited no phylogenetic mixing with sequences from other high-risk groups of Pakistan. Conclusion The presence of multiple clusters indicated a multi-source outbreak, rather than a single source outbreak from a single health practitioner as previously suggested. The multiple introductions were likely a consequence of ongoing transmission within the high-risk groups of Larkana, and it is possible that the so-called Larkana strain was introduced into the general population through poor infection prevention control practices in healthcare settings. The study highlights the need to scale up HIV-1 prevention programmes among key population groups and improving infection prevention control in Pakistan.
Neuropsychiatric Symptoms in Mild Cognitive Impairment and Dementia Due to AD: Relation With Disease Stage and Cognitive Deficits

Background: The interaction between neuropsychiatric symptoms, mild cognitive impairment (MCI), and dementia is complex and remains to be elucidated. An additive or multiplicative effect of neuropsychiatric symptoms such as apathy or depression on cognitive decline has been suggested. Unraveling these interactions may allow the development of better prevention and treatment strategies. In the absence of available treatments for neurodegeneration, a timely and adequate identification of neuropsychiatric symptom changes in cognitive decline is highly relevant and can help identify treatment targets. Methods: An existing memory clinic-based research database of 476 individuals with MCI and 978 individuals with dementia due to Alzheimer's disease (AD) was reanalyzed. Neuropsychiatric symptoms were assessed in a prospective fashion using a battery of neuropsychiatric assessment scales: Middelheim Frontality Score, Behavioral Pathology in Alzheimer's Disease Rating Scale (Behave-AD), Cohen-Mansfield Agitation Inventory, Cornell Scale for Depression in Dementia (CSDD), and Geriatric Depression Scale (30 items). We subtyped subjects suffering from dementia as mild, moderate, or severe according to their Mini-Mental State Examination (MMSE) score and compared neuropsychiatric scores across these groups. A group of 126 subjects suffering from AD with a significant cerebrovascular component was examined separately as well. We compared the prevalence, nature, and severity of neuropsychiatric symptoms between subgroups of patients with MCI and dementia due to AD in a cross-sectional analysis. Results: Affective and sleep-related symptoms are common in MCI and remain constant in prevalence and severity across dementia groups. Depressive symptoms as assessed by the CSDD further increase in severe dementia. Most other neuropsychiatric symptoms (such as agitation and activity disturbances) progress in parallel with severity of cognitive decline. There are no significant differences in neuropsychiatric symptoms when comparing “pure” AD to AD with a significant vascular component. Conclusion: Neuropsychiatric symptoms such as frontal lobe symptoms, psychosis, agitation, aggression, and activity disturbances increase as dementia progresses. Affective symptoms such as anxiety and depressive symptoms, however, are more frequent in MCI than mild dementia but otherwise remain stable throughout the cognitive spectrum, except for an increase in CSDD score in severe dementia. There is no difference in neuropsychiatric symptoms when comparing mixed dementia (defined here as AD + significant cerebrovascular disease) to pure AD.
Childhood Trauma and Aggression in Persons Convicted for Homicide: An Exploratory Study Examines the Role of Plasma Oxytocin

Evidence has demonstrated the association between childhood trauma and criminality in adulthood, however, less is known about how best to explain the route from childhood trauma to adulthood aggression. Results from both human and animal studies have generated the hypothesis that dysfunction of the oxytocinergic system may correlate with pathological aggression. The current study represents a first exploratory examination to investigate the trajectory from childhood trauma to aggression, specifically, plasma oxytocin's role in this association. We assessed the childhood trauma experiences in a total of 108 participants, including 33 persons convicted for homicide and 75 non-offending healthy participants, using the Childhood Trauma Questionnaire, with in-depth clarification interviews for cross-validation. All participants were checked for aggression using the Modified Overt Aggression Scale and their plasma oxytocin levels were obtained. Results indicated that persons convicted for homicide had higher childhood trauma scores and lower plasma oxytocin levels than healthy controls. The plasma oxytocin levels were inversely correlated with childhood trauma in all participants. Further mediation models were constructed to explore these associations, in the best-fit model, the relationship between childhood trauma and aggression is mediated by plasma oxytocin levels in persons convicted for homicide. In conclusion, the association between childhood trauma and aggression of persons convicted for homicide is mediated by their plasma oxytocin levels. With leading to further theoretical consideration in the causality on how best to explain the interaction between childhood trauma and aggression, the current study may assist in developing further research and preventive strategies for aggression, particularly the importance of early identification of childhood trauma.
Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO

Pancreatic cancer is a highly malignant and metastatic tumor of the digestive system. Even after surgical removal of the tumor, most patients are still at risk of metastasis. Therefore, screening for metastatic biomarkers can identify precise therapeutic intervention targets. In this study, we analyzed 96 pancreatic cancer samples from The Cancer Genome Atlas (TCGA) without metastasis or with metastasis after R0 resection. We also retrieved data from metastatic pancreatic cancer cell lines from Gene Expression Omnibus (GEO), as well as collected sequencing data from our own cell lines, BxPC-3 and BxPC-3-M8. Finally, we analyzed the expression of metastasis-related genes in different datasets by the Limma and edgeR packages in R software, and enrichment analysis of differential gene expression was used to gain insight into the mechanism of pancreatic cancer metastasis. Our analysis identified six genes as risk factors for predicting metastatic status by LASSO regression, including zinc finger BED-Type Containing 2 (ZBED2), S100 calcium-binding protein A2 (S100A2), Jagged canonical Notch ligand 1 (JAG1), laminin subunit gamma 2 (LAMC2), transglutaminase 2 (TGM2), and the transcription factor hepatic leukemia factor (HLF). We used these six EMT-related genes to construct a risk-scoring model. The receiver operating characteristic (ROC) curve showed that the risk score could better predict the risk of metastasis. Univariate and multivariate Cox regression analyses revealed that the risk score was also an important predictor of pancreatic cancer. In conclusion, 6-mRNA expression is a potentially valuable method for predicting pancreatic cancer metastasis, assessing clinical outcomes, and facilitating future personalized treatment for patients with ductal adenocarcinoma of the pancreas (PDAC).
Poro-Viscoelastic Effects During Biomechanical Testing of Human Brain Tissue

Brain tissue is one of the softest tissues in the human body and the quantification of its mechanical properties has challenged scientists over the past decades. Associated experimental results in the literature have been contradictory as characterizing the mechanical response of brain tissue not only requires well-designed experimental setups that can record the ultrasoft response, but also appropriate approaches to analyze the corresponding data. Due to the extreme complexity of brain tissue behavior, nonlinear continuum mechanics has proven an expedient tool to analyze testing data and predict the mechanical response using a combination of hyper-, visco-, or poro-elastic models. Such models can not only allow for personalized predictions through finite element simulations, but also help to comprehensively understand the physical mechanisms underlying the tissue response. Here, we use a nonlinear poro-viscoelastic computational model to evaluate the effect of different intrinsic material properties (permeability, shear moduli, nonlinearity, viscosity) on the tissue response during different quasi-static biomechanical measurements, i.e., large-strain compression and tension as well as indentation experiments. We show that not only the permeability but also the properties of the viscoelastic solid largely control the fluid flow within and out of the sample. This reveals the close coupling between viscous and porous effects in brain tissue behavior. Strikingly, our simulations can explain why indentation experiments yield that white matter tissue in the human brain is stiffer than gray matter, while large-strain compression experiments show the opposite trend. These observations can be attributed to different experimental loading and boundary conditions as well as assumptions made during data analysis. The present study provides an important step to better understand experimental data previously published in the literature and can help to improve experimental setups and data analysis for biomechanical testing of brain tissue in the future.
On the Convergence of Stochastic Compositional Gradient Descent Ascent Method

The compositional minimax problem covers plenty of machine learning models such as the distributionally robust compositional optimization problem. However, it is yet another understudied problem to optimize the compositional minimax problem. In this paper, we develop a novel efficient stochastic compositional gradient descent ascent method for optimizing the compositional minimax problem. Moreover, we establish the theoretical convergence rate of our proposed method. To the best of our knowledge, this is the first work achieving such a convergence rate for the compositional minimax problem. Finally, we conduct extensive experiments to demonstrate the effectiveness of our proposed method.
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System

Recently, deep learning models have been widely explored in recommender systems. Though having achieved remarkable success, the design of task-aware recommendation models usually requires manual feature engineering and architecture engineering from domain experts. To relieve those efforts, we explore the potential of neural architecture search (NAS) and introduce AMEIR for Automatic behavior Modeling, interaction Exploration and multi-layer perceptron (MLP) Investigation in the Recommender system. Specifically, AMEIR divides the complete recommendation models into three stages of behavior modeling, interaction exploration, MLP aggregation, and introduces a novel search space containing three tailored subspaces that cover most of the existing methods and thus allow for searching better models. To find the ideal architecture efficiently and effectively, AMEIR realizes the one-shot random search in recommendation progressively on the three stages and assembles the search results as the final outcome. The experiment over various scenarios reveals that AMEIR outperforms competitive baselines of elaborate manual design and leading algorithmic complex NAS methods with lower model complexity and comparable time cost, indicating efficacy, efficiency, and robustness of the proposed method.
Temporal Induced Self-Play for Stochastic Bayesian Games

One practical requirement in solving dynamic games is to ensure that the players play well from any decision point onward. To satisfy this requirement, existing efforts focus on equilibrium refinement, but the scalability and applicability of existing techniques are limited. In this paper, we propose Temporal-Induced Self-Play (TISP), a novel reinforcement learning-based framework to find strategies with decent performances from any decision point onward. TISP uses belief-space representation, backward induction, policy learning, and non-parametric approximation. Building upon TISP, we design a policy-gradient-based algorithm TISP-PG. We prove that TISP-based algorithms can find approximate Perfect Bayesian Equilibrium in zero-sum one-sided stochastic Bayesian games with finite horizon. We test TISP-based algorithms in various games, including finitely repeated security games and a grid-world game. The results show that TISP-PG is more scalable than existing mathematical programming-based methods and significantly outperforms other learning-based methods.
Skeptical Reasoning with Preferred Semantics in Abstract Argumentation without Computing Preferred Extensions

We address the problem of deciding skeptical acceptance wrt. preferred semantics of an argument in abstract argumentation frameworks, i.e., the problem of deciding whether an argument is contained in all maximally admissible sets, a.k. a. preferred extensions. State-of-the-art algorithms solve this problem with iterative calls to an external SAT-solver to determine preferred extensions. We provide a new characterisation of skeptical acceptance wrt. preferred semantics that does not involve the notion of a preferred extension. We then develop a new algorithm that also relies on iterative calls to an external SAT-solver but avoids the costly part of maximising admissible sets. We present the results of an experimental evaluation that shows that this new approach significantly outperforms the state of the art. We also apply similar ideas to develop a new algorithm for computing the ideal extension.
Reinforcement Learning for Route Optimization with Robustness Guarantees

Application of deep learning to NP-hard combinatorial optimization problems is an emerging research trend, and a number of interesting approaches have been published over the last few years. In this work we address robust optimization, which is a more complex variant where a max-min problem is to be solved. We obtain robust solutions by solving the inner minimization problem exactly and apply Reinforcement Learning to learn a heuristic for the outer problem. The minimization term in the inner objective represents an obstacle to existing RL-based approaches, as its value depends on the full solution in a non-linear manner and cannot be evaluated for partial solutions constructed by the agent over the course of each episode. We overcome this obstacle by defining the reward in terms of the one-step advantage over a baseline policy whose role can be played by any fast heuristic for the given problem. The agent is trained to maximize the total advantage, which, as we show, is equivalent to the original objective. We validate our approach by solving min-max versions of standard benchmarks for the Capacitated Vehicle Routing and the Traveling Salesperson Problem, where our agents obtain near-optimal solutions and improve upon the baselines.
Advances and future directions of biochar characterization methods and applications

ABSTRACT Biochar is a carbon-rich by-product of the thermal conversion of organic feedstocks and is primarily used as a soil amendment. Identification and quantification of biochar properties are important to ensure optimal outcomes for agricultural or environmental applications. Advanced spectroscopic techniques have recently been adopted in biochar characterization. However, biochar characterization approaches rely entirely on the user's choice and accessibility to the new technology. The selection of proper methods is vital to accurately and consistently assess biochar properties. This review critically evaluates current biochar characterization methods of proximate, ultimate, physicochemical, surface and structural analyses, and important biochar properties for various applications.
Fine-Grained Air Quality Inference via Multi-Channel Attention Model

In this paper, we study the problem of fine-grained air quality inference that predicts the air quality level of any location from air quality readings of nearby monitoring stations. We point out the importance of explicitly modeling both static and dynamic spatial correlations, and consequently propose a novel multi-channel attention model (MCAM) that models static and dynamic spatial correlations as separate channels. The static channel combines the beauty of attention mechanisms and graph-based spatial modeling via an adapted bilateral filtering technique, which considers not only locations' Euclidean distances but also their similarity of geo-context features. The dynamic channel learns stations' time-dependent spatial influence on a target location at each time step via long short-term memory (LSTM) networks and attention mechanisms. In addition, we introduce two novel ideas, atmospheric dispersion theories and the hysteretic nature of air pollutant dispersion, to better model the dynamic spatial correlation. We also devise a multi-channel graph convolutional fusion network to effectively fuse the graph outputs, along with other features, from both channels. Our extensive experiments on real-world benchmark datasets demonstrate that MCAM significantly outperforms the state-of-the-art solutions.
Layer-Assisted Neural Topic Modeling over Document Networks

Neural topic modeling provides a flexible, efficient, and powerful way to extract topic representations from text documents. Unfortunately, most existing models cannot handle the text data with network links, such as web pages with hyperlinks and scientific papers with citations. To resolve this kind of data, we develop a novel neural topic model , namely Layer-Assisted Neural Topic Model (LANTM), which can be interpreted from the perspective of variational auto-encoders. Our major motivation is to enhance the topic representation encoding by not only using text contents, but also the assisted network links. Specifically, LANTM encodes the texts and network links to the topic representations by an augmented network with graph convolutional modules, and decodes them by maximizing the likelihood of the generative process. The neural variational inference is adopted for efficient inference. Experimental results validate that LANTM significantly outperforms the existing models on topic quality, text classification and link prediction.
A Road Network Enhanced Gate Recurrent Unit Model for Gather Prediction in Smart Cities

Gather prediction is an indispensable part of smart city projects. The city government can respond in advance based on gather predictions and greatly reduce the loss and risks caused by vicious gatherings. Compared with other trajectory prediction tasks (i.e., the recommendation of point of interest), gather prediction pay more attention to real-time trajectory data and requests stronger spatial-temporal dependence. At the same time, gather prediction is more focused on scenes with multiple types of trajectories. And the existing methods majorly rely on the trajectory data and ignore the great influence of geographical environment (i.e., road network structure). Therefore, this paper transforms the gather prediction into the trajectory prediction task with strong real-time condition in a certain city and conducts the gathering situations by predicting users’ aggregated movements in next minutes or hours. A novel Spatiotemporal Gate Recurrent Unit (STGRU) model is proposed, where spatiotemporal gates and road network gate are introduced to capture the spatiotemporal relationships between trajectories. Compared with existing methods, we improve the performance of the model by adding road network structure and external knowledges, as well as time and distance gates to reduce model parameters. The proposed STGRU is evaluated on three real-world trajectory datasets, and the experimental results demonstrate the effectiveness of the proposed model.
Multi-Agent Reinforcement Learning for Automated Peer-to-Peer Energy Trading in Double-Side Auction Market

With increasing prosumers employed with distributed energy resources (DER), advanced energy management has become increasingly important. To this end, integrating demand-side DER into electricity market is a trend for future smart grids. The double-side auction (DA) market is viewed as a promising peer-to-peer (P2P) energy trading mechanism that enables interactions among prosumers in a distributed manner. To achieve the maximum profit in a dynamic electricity market, prosumers act as price makers to simultaneously optimize their operations and trading strategies. However, the traditional DA market is difficult to be explicitly modelled due to its complex clearing algorithm and the stochastic bidding behaviors of the participants. For this reason, in this paper we model this task as a multi-agent reinforcement learning (MARL) problem and propose an algorithm called DA-MADDPG that is modified based on MADDPG by abstracting the other agents’ observations and actions through the DA market public information for each agent’s critic. The experiments show that 1) prosumers obtain more economic benefits in P2P energy trading w.r. t. the conventional electricity market independently trading with the utility company; and 2) DA-MADDPG performs better than the traditional Zero Intelligence (ZI) strategy and the other MARL algorithms, e.g., IQL, IDDPG, IPPO and MADDPG.
A Neural Network Auction For Group Decision Making Over a Continuous Space

We propose a system for conducting an auction over locations in a continuous space. It enables participants to express their preferences over possible choices of location in the space, selecting the location that maximizes the total utility of all agents. We prevent agents from tricking the system into selecting a location that improves their individual utility at the expense of others by using a pricing rule that gives agents no incentive to misreport their true preferences. The system queries participants for their utility in many random locations, then trains a neural network to approximate the preference function of each participant. The parameters of these neural network models are transmitted and processed by the auction mechanism, which composes these into differentiable models that are optimized through gradient ascent to compute the final chosen location and charged prices.