Miracle-dz/UniFAR-FTUs huggingface dataset:
Reservoir hosts experiencing food stress alter transmission dynamics for a zoonotic pathogen

Food limitation is a universal stressor for wildlife populations and is increasingly exacerbated by human activities. Anthropogenic environmental change can significantly alter the availability and quality of food resources for reservoir hosts and impact host–pathogen interactions in the wild. The state of the host's nutritional reserves at the time of infection is a key factor influencing infection outcomes by altering host resistance. Combining experimental and model-based approaches, we investigate how an environmental stressor affects host resistance to West Nile virus (WNV). Using American robins (Turdus migratorius), a species considered a superspreader of WNV, we tested the effect of acute food deprivation immediately prior to infection on host viraemia. Here, we show that robins food deprived for 48 h prior to infection, developed higher virus titres and were infectious longer than robins fed normally. To gain an understanding about the epidemiological significance of food-stressed hosts, we developed an agent-based model that simulates transmission dynamics of WNV between an avian host and the mosquito vector. When simulating a nutritionally stressed host population, the mosquito infection rate rose significantly, reaching levels that represent an epidemiological risk. An understanding of the infection disease dynamics in wild populations is critical to predict and mitigate zoonotic disease outbreaks.
Investigation of sag behaviour for aluminium conductor steel reinforced considering tensile stress distribution

Sag calculation plays an important role in overhead line design. Since the tensile stress of aluminium conductor steel reinforced (ACSR) is required for the sag calculation, an analysis on sag behaviour when considering the tensile stress distribution can be very useful to improve the accuracy of sag results. First, this paper analyses the ACSR tensile stress distribution arising from the temperature maldistribution through proposing a new calculation formula. A finite-element analysis (FEA) model of ACSR is conducted for the solution of the new formula. By using the results, the error and limitations of the existing sag calculation methods for ACSR are discussed. As the critical point of sag calculation, knee-point temperature is solved iteratively involving the tensile stress maldistribution phenomenon in aluminium wires. Based on this iterative solution, an improved analytical method for the ACSR sag calculation considering the creep effect is presented and also compared with the hybrid sag method. The results show that these two methods are basically coincident without the consideration of creep effect, while there are non-negligible differences between them as the creep strain is involved. Compared with the existing analytical methods, the improved sag calculation method proposed in this paper can be applied in more extensive situations.
Responses of Flowering Plant and Butterfly Communities to Experimental Herbicide and Seeding Treatments for Native Grassland Restoration

Globally, grasslands are among the most degraded habitats, and within that category tallgrass prairies are especially threatened. To restore native species in tallgrass prairie, it is often necessary to concurrently remove exotic plant species while restoring the disturbance processes that many prairie taxa depend upon. Here, we coupled one herbicide application and seed mixture addition with tri-annual prescribed fire and annual cattle grazing to explore the consequences for floral resource (i.e., nectar-producing) plant communities and butterfly communities up to four years later. Each site was divided into three equally sized patches which were placed into either control, “spray-only”, or “spray-and-seed” treatment groups. We quantified both floral resource plant and butterfly communities in the year prior to herbicide application and the four years following that treatment. In the four years post-treatment, we found that floral resource abundance, richness, and diversity increased over time but did not significantly vary among treatments. No response to treatment was observed in butterflies, but butterfly abundance decreased while richness was stable and diversity increased over time, which may indicate that the changes to floral resources at the patch scale supported more speciose and diverse butterfly communities at the site scale. Butterfly abundance decreasing over time could be an effect of baseline management or unrelated regional factors. Regardless of whether either treatment benefited butterflies, we find support for a one-time herbicide application changing the floral community in desirable ways and at least not harming butterflies.
Pandemic management by a spatio–temporal mathematical model

Abstract Many researchers have tried to predict the impact of the COVID-19 outbreak on morbidity, in order to help policy-makers find optimal isolation policies. However, despite the development and use of many models and sophisticated tools, these forecasting attempts have largely failed. We present a model that considers the severity of the disease and the heterogeneity of contacts between the population in complex space–time dynamics. Using mathematical and computational methods, the applied tool was developed to analyze and manage the COVID-19 pandemic (from an epidemiological point of view), with a particular focus on population heterogeneity in terms of age, susceptibility, and symptom severity. We show improved strategies to prevent an epidemic outbreak. We evaluated the model in three countries, obtaining an average mean square error of 0.067 over a full month of the basic reproduction number (R0). The goal of this study is to create a theoretical framework for crisis management that integrates accumulated epidemiological considerations. An applied result is an open-source program for predicting the outcome of an isolation strategy for future researchers and developers who can use and extend our model.
Automatic Test Case Generation Method Based on Improved Whale Optimization Algorithm

∗ In view of the slow convergence speed and parameter control of the existing heuristic algorithm in the automatic test case generation, this paper proposed to apply the whale optimization algo-rithm(Abbreviation: WOA) to the automatic test case generation, and used chaos strategy to improve WOA, and In order to solve the problem that WOA initialization is not uniform and easy to fall into local optimal solution, uses chaos initialization was used instead of random algorithm to initialize the population and solved the problem of uneven distribution of particles, consequently when the optimal value fell into the local optimal solution, the chaos disturbance operation was carried out on the optimal value. Based on this, an automatic test case generation method based on improved whale algorithm was proposed. This method aimed at one path at a time and used the improved whale optimization algorithm to optimize the population and find the optimal value.
A Measurement Framework for Explicit and Implicit Urban Traffic Sensing

Urban traffic sensing has been investigated extensively by different real-time sensing approaches due to important applications such as navigation and emergency services. Basically, the existing traffic sensing approaches can be classified into two categories by sensing natures, i.e., explicit and implicit sensing. In this article, we design a measurement framework called EXIMIUS for a large-scale data-driven study to investigate the strengths and weaknesses of two sensing approaches by using two particular systems for traffic sensing as concrete examples. In our investigation, we utilize TB-level data from two systems: (i) GPS data from five thousand vehicles, (ii) signaling data from three million cellphone users, from the Chinese city Hefei. Our study adopts a widely used concept called crowdedness level to rigorously explore the impacts of contexts on traffic conditions including population density, region functions, road categories, rush hours, holidays, weather, and so on, based on various context data. We quantify the strengths and weaknesses of these two sensing approaches in different scenarios and then we explore the possibility of unifying two sensing approaches for better performance by using a truth discovery-based data fusion scheme. Our results provide a few valuable insights for urban sensing based on explicit and implicit data from transportation and telecommunication domains.
Management of Traffic Signals using Deep Reinforcement Learning in Bidirectional Recurrent Neural Network in ITS

The traffic flow management is primarily done through traffic signals, whose inefficient control causes numerous problems, such as long waiting time and huge waste of energy. To improve traffic flow efficiency, obtaining real-time traffic information as an input and dynamically adjusting the traffic signal duration accordingly is essential. Among the existing methods, Deep Reinforcement Learning (DRL) has shown to be the most effective solution. In this paper, a dynamic mechanism to control the traffic signal of a large scale road network is proposed using policy gradient method. A single agent is trained with spatio–temporal data of the multiple intersections of the network to alleviate congestion. The proposed system is implemented in two different types of deep bidirectional Recurrent Neural Network (RNN) - Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The simulation experiments demonstrate that the proposed system could reduce traffic congestion in terms of different simulation metrics during high density traffic flows.
Design and operation of smart hybrid microgrid

Abstract Renewable energy resources based hybrid microgrid has emerged as a significant component of the power system. Nevertheless, the control and management of power, voltage and frequency under transition between grid-connected mode (GCM) and islanded mode (IM) is a major concern. This paper proposes a self-sustained and flexible novel cooperative control strategy (CCS) for smart hybrid microgrid (SHMG) under GCM and IM mode operations. In islanded operation of SHMG, conventional droop offers ease of control but its execution with seamless transition between operating modes poses poor voltage regulation and frequency deviation. For this purpose, an intelligent cooperative controller is proposed. It consists of a modified P/f-Q/V droop control (MDC) algorithm along with soft computing based VF-controller unit (VFCU). The proposed control scheme is implemented to regulate voltage and frequency. It also enhances the power quality with unbalanced non-linear load and source intermittencies. The absence of a communication link reduces cost and also eliminates the risk of single point breakdown. The SHMG has been simulated in MATLAB and implemented in real-time. The obtained experimental results demonstrate the competency and high robustness of the proposed scheme.
Integration of health care in the Basque Country during COVID-19: the importance of an integrated care management approach in times of emergency

Abstract The main objective of this study is to analyse the process of integration of health care implemented in the public health system (Osakidetza) of the Autonomous Community of the Basque Country (CAPV), and assess whether the steps taken to date have helped or hindered the work of health personnel in times of COVID-19. Based on a case study, an assessment is made of the way in which certain tools of the integration process have been applied, if they have worked well and if they have led to better management of the pandemic. For the purpose of this study, a qualitative methodology is chosen consisting of a case study and in-depth interviews with health personnel at the front line of the integration process and the fight against COVID-19. This study makes two fundamental contributions. First, it analyses the health integration process in recent years in the public health system of the Basque Country. Second, it gathers the perceptions of different agents related to the Basque Health System of the way in which the tools of the integration process implemented in recent years have worked during the pandemic, detailing the positive and negative perceptions in this regard. Our conclusions offer a series of strategic recommendations linked to comprehensive patient care and the use of tools related to teleconsulting: the unified medical record, electronic prescription, and non-face-to-face care channels.
Polymer Electrolytes Containing Solvate Ionic Liquids: A New Approach To Achieve High Ionic Conductivity, Thermal Stability, and a Wide Potential Window

We describe here the electrochemical properties and battery performance of polymer electrolytes composed of ABA-triblock copolymers and Li-glyme solvate ionic liquids (SILs), which consist of the [Li(glyme)]+ complex cation and bis(trifluoromethanesulfoly)amide ([TFSA]−) anion, to simultaneously achieve high ionic conductivity, thermal stability, and a wide potential window. Three different block copolymers, consisting of a SIL-incompatible A segment (polystyrene, PSt) and SIL-compatible B segments (poly(methyl methacrylate) (PMMA), poly(ethylene oxide) (PEO), and poly(butyl acrylate) (PBA)) were synthesized. The SILs were solidified with the copolymers through physical cross-linking by the self-assembly of the PSt segment. The thermal and electrochemical properties of the polymer electrolytes were significantly affected by the stability of the [Li(glyme)]+ complex in the block copolymer B segments, and the preservation of the SILs contributed to their thermal stabilities and oxidation stabilities greater.
Segregation of neural crest specific lineage trajectories from a heterogeneous neural plate border territory only emerges at neurulation

The epiblast of vertebrate embryos is comprised of neural and non-neural ectoderm, with the border territory at their intersection harbouring neural crest and cranial placode progenitors. Here we profile avian epiblast cells as a function of time using single-cell RNA-seq to define transcriptional changes in the emerging ‘neural plate border’. The results reveal gradual establishment of heterogeneous neural plate border signatures, including novel genes that we validate by fluorescent in situ hybridisation. Developmental trajectory analysis shows that segregation of neural plate border lineages only commences at early neurulation, rather than at gastrulation as previously predicted. We find that cells expressing the prospective neural crest marker Pax7 contribute to multiple lineages, and a subset of premigratory neural crest cells shares a transcriptional signature with their border precursors. Together, our results suggest that cells at the neural plate border remain heterogeneous until early neurulation, at which time progenitors become progressively allocated toward defined lineages.
The degree of context un/familiarity impacts the emotional feeling and preaware cardiac-brain activity: a study with emotionally salient naturalistic paradigm using DENS Dataset

Emotion experiments with naturalistic paradigms are emerging and giving new insights into dynamic brain activity. Context familiarity is considered as an important dimensions of emotion processing by appraisal theorists. However, how the context un/familiarity of the naturalistic stimuli influences the central and autonomic activity is not probed yet [check it]. Hence, we tried to address this issue in this work by breaking it down into three questions. 1) What is the relation between context un/familiarity with the neural correlates of self-assessment affective dimensions viz. valence and arousal; 2) the influence of context un/familiarity in cardiac-brain mutual interaction during emotion processing; 3. ) brain network reorganization to accommodate the degree of context familiarity. We found that the less-context familiarity is primarily attributed to negative emotion feeling mediated by lack of predictability of sensory experience. Whereas, with high-context familiarity, both positive and negative emotions are felt. For less-context familiarity, the arousal activity is negatively correlated with EEG power. In addition, the cardiac activity for both high and less context familiarity is modulated before the reported self-awareness of emotional feeling. The correlation of cortical regions with cardiac activity and connectivity patterns reveals that ECG is modulated by salient feature during pre-awareness and correlates with AIC and conceptual hub in high-familiarity. Whereas, for the low familiarity, the cardiac activity is correlated with the exteroceptive sensory regions. In addition, we found that OFC and dmPFC have high connectivity with less-context familiarity, whereas AIC has high connectivity with high-context familiarity. To the best of our knowledge, the context familiarity and its influence on cardiac and brain activity have never been reported with a naturalistic paradigm. Hence, this study significantly contributes to understanding automatic processing of emotions by analyzing the effect of context un/familiarity on affective feelings, the dynamics of cardiac-brain mutual interaction, and the brain’s effective connectivity during pre-awareness.
Charting brain growth and aging at high spatial precision

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and use normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1,985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision making.
Distinct Progressions of Neuronal Activity Changes Underlie the Formation and Consolidation of a Gustatory Associative Memory

Acquiring new memories is a multistage process. Numerous studies have convincingly demonstrated that initially acquired memories are labile and are stabilized only by later consolidation processes. These multiple phases of memory formation are known to involve modification of both cellular excitability and synaptic connectivity, which in turn change neuronal activity at both the single neuron and ensemble levels. However, the specific mapping between the known phases of memory and the changes in neuronal activity at different organizational levels—the single-neuron, population representations, and ensemble-state dynamics—remains unknown. Here we address this issue in the context of conditioned taste aversion learning by continuously tracking gustatory cortex neuronal taste responses in alert male and female rats during the 24 h following a taste–malaise pairing. We found that the progression of activity changes depends on the neuronal organizational level: whereas the population response changed continuously, the population mean response amplitude and the number of taste-responsive neurons only increased during the acquisition and consolidation phases. In addition, the known quickening of the ensemble-state dynamics associated with the faster rejection of harmful foods appeared only after consolidation. Overall, these results demonstrate how complex dynamics in the different representational levels of cortical activity underlie the formation and stabilization of memory within the cortex. SIGNIFICANCE STATEMENT Memory formation is a multiphased process; early acquired memories are labile and consolidate to their stable forms over hours and days. The progression of memory is assumed to be supported by changes in neuronal activity, but the mapping between memory phases and neuronal activity changes remains elusive. Here we tracked cortical neuronal activity over 24 h as rats acquired and consolidated a taste–malaise association memory, and found specific differences between the progression at the single-neuron and populations levels. These results demonstrate how balanced changes on the single-neuron level lead to changes in the network-level representation and dynamics required for the stabilization of memories.
KnowMore: An Automated Knowledge Discovery Tool for the FAIR SPARC Datasets

This manuscript provides the methods and outcomes of KnowMore, the Grand Prize winning automated knowledge discovery tool developed by our team during the 2021 NIH SPARC FAIR Data Codeathon. The NIH SPARC program generates rich datasets from neuromodulation researches, curated according to the Findable, Accessible, Interoperable, and Reusable (FAIR) SPARC data standards. These datasets are publicly available through the SPARC Data Portal at sparc.science. Currently, the process of simultaneously comparing and analyzing multiple SPARC datasets is tedious because it requires investigating each dataset of interest individually and downloading all of them to conduct cross-analyses. It is crucial to enhance this process to enable rapid discoveries across SPARC datasets. To fill this need, we created KnowMore, a tool integrated into the SPARC Portal that only requires the user to select their datasets of interest to launch an automated discovery process. KnowMore uses several SPARC resources (Pennsieve, o2S2PARC, SciCrunch, protocols.io, Biolucida), data science methods, and Machine Learning algorithms in the back end to generate various visualizations in the front end intended to help the user identify potential similarities, differences, and relations across the datasets. These visualizations can lead to a new discovery, new hypothesis, or simply guide the user to the next logical step in their discovery process. The outcome of this project is a SPARC portal-ready code architecture that helps researchers to use SPARC datasets more efficiently and fully leverages their FAIR characteristics. The tool has been built and documented such that more data analysis methods and visualization items could be easily added. The potential for automated discoveries from SPARC datasets is huge given the unique SPARC data ecosystem promoting FAIR data practices, and KnowMore has only demonstrated a small highlight of what could be achieved to speed up discoveries from SPARC datasets.