Miracle-dz/UniFAR-FTUs huggingface dataset:
Reactive Task Performance Under Varying Loads in Division I Collegiate Soccer Athletes

This study was conducted to identify whether team-wide or positional differences exist in simple or choice reactivity of collegiate soccer athletes when completed under various loads. Much research exists surrounding the assessment of reaction time in the general population, but given variations in training, little insight exists surrounding how unique and elite populations may differ based upon performance demands and task translatability to training. Reactive performance was assessed using the Dynavision D2 in 24 female soccer players (19.73 ± 1.05 years old) from a team within a power five conference of the National Collegiate Athletic Association. Evaluated loads included two conditions of simple reactivity (no additional load and with a concurrent lower body motor task) and three conditions of choice reactivity (no additional load, with a concurrent lower body motor task, and prolonged durations). Paired t-tests and ANOVAs were used to identify differences in task performance based upon load and positional group. No significant load-based or positional differences existed in measured simple reaction times. Performances in choice reaction tasks across the team were found to be slower when completed across extended durations (p < 0.0001) and faster when completed concurrent with an added balance task (p = 0.0108), as compared to performance under normal conditions. By assessment of positional differences, goalkeepers tended to be slower than other positions in reactivity during choice tasks, despite no differences existing in simple task performance. Given the unique population utilized herein, measured reactivity in different tasks suggests a strong relation to the training demands of soccer, as well as those of goalkeepers as compared to field positions. Findings suggest that sport and positional demands may be substantial contributors to population- and individual-based reactivity performance.
Conditioned Taste Aversion as a Tool for Mitigating Human-Wildlife Conflicts

Modern wildlife management has dual mandates to reduce human-wildlife conflict (HWC) for burgeoning populations of people while supporting conservation of biodiversity and the ecosystem functions it affords. These opposing goals can sometimes be achieved with non-lethal intervention tools that promote coexistence between people and wildlife. One such tool is conditioned taste aversion (CTA), the application of an evolutionary relevant learning paradigm in which an animal associates a transitory illness to the taste, odor or other characteristic of a particular food item, resulting in a long-term change in its perception of palatability. Despite extensive support for the power of CTA in laboratory studies, field studies have exhibited mixed results, which erodes manager confidence in using this tool. Here we review the literature on CTA in the context of wildlife conservation and management and discuss how success could be increased with more use of learning theory related to CTA, particularly selective association, stimulus salience, stimulus generalization, and extinction of behavior. We apply learning theory to the chronological stages of CTA application in the field and illustrate them by synthesizing and reviewing past applications of CTA in HWC situations. Specifically, we discuss (1) when CTA is suitable, (2) how aversion can be most effectively (and safely) established, (3) how generalization of aversion from treated to untreated food can be stimulated and (4) how extinction of aversion can be avoided. For each question, we offer specific implementation suggestions and methods for achieving them, which we summarize in a decision-support table that might be used by managers to guide their use of CTA across a range of contexts. Additionally, we highlight promising ideas that may further improve the effectiveness of CTA field applications in the future. With this review, we aspire to demonstrate the diverse past applications of CTA as a non-lethal tool in wildlife management and conservation and facilitate greater application and efficacy in the future.
Emergence of Cooperative Impression With Self-Estimation, Thinking Time, and Concordance of Risk Sensitivity in Playing Hanabi

The authors evaluate the extent to which a user’s impression of an AI agent can be improved by giving the agent the ability of self-estimation, thinking time, and coordination of risk tendency. The authors modified the algorithm of an AI agent in the cooperative game Hanabi to have all of these traits, and investigated the change in the user’s impression by playing with the user. The authors used a self-estimation task to evaluate the effect that the ability to read the intention of a user had on an impression. The authors also show thinking time of an agent influences impression for an agent. The authors also investigated the relationship between the concordance of the risk-taking tendencies of players and agents, the player’s impression of agents, and the game experience. The results of the self-estimation task experiment showed that the more accurate the estimation of the agent’s self, the more likely it is that the partner will perceive humanity, affinity, intelligence, and communication skills in the agent. The authors also found that an agent that changes the length of thinking time according to the priority of action gives the impression that it is smarter than an agent with a normal thinking time when the player notices the difference in thinking time or an agent that randomly changes the thinking time. The result of the experiment regarding concordance of the risk-taking tendency shows that influence player’s impression toward agents. These results suggest that game agent designers can improve the player’s disposition toward an agent and the game experience by adjusting the agent’s self-estimation level, thinking time, and risk-taking tendency according to the player’s personality and inner state during the game.
An Understanding of Implicit Followership Toward New Employees' Self-Efficacy: The Mediating Role of Perceived Supervisor Support

Under turbulent, boundaryless, and Internet age, the characteristics of career sustainability development have shifted from the perspective of development within the organization to the career development track of self-efficacy. New employees usually face the difficult stage of adapting to the new environment and establishing interpersonal relationships with new colleagues. When new employees enter an organization, they usually have different implicit followership cognitions. Previous studies have focused on the treatment of new employees by the organization and the leader, however, the implicit followership cognitive state of new employees has not been studied specifically. This research integrates employees' positive and negative implicit followership, perceived supervisor support, workplace friendship, and perceived self-efficacy into a research framework. This study used a questionnaire survey by an online professional survey website. A total of 394 valid questionnaires were collected. Structural equation model (SEM) analysis was carried out and according to the results, new employees' positive and negative implicit followership significantly affects perceived supervisor support. Furthermore, perceived supervisor support had a significant impact on perceived self-efficacy. Moreover, perceived supervisor support was found in a mediating role between the relationship of implicit followership theories and perceived self-efficacy. Finally, workplace friendship was found to be a significant moderator in the relationship between perceived supervisor support and perceived self-efficacy. Based on the research results, business managers are suggested to pay more attention to new employees' self-cognition of their job roles and enhance the self-efficacy of new employees in the entry stage.
The Effect of Cognitive Load on the Retrieval of Long-Term Memory: An fMRI Study

One of the less well-understood aspects of memory function is the mechanism by which the brain responds to an increasing load of memory, either during encoding or retrieval. Identifying the brain structures which manage this increasing cognitive demand would enhance our knowledge of human memory. Despite numerous studies about the effect of cognitive loads on working memory processes, whether these can be applied to long-term memory processes is unclear. We asked 32 healthy young volunteers to memorize all possible details of 24 images over a 12-day period ending 2 days before the fMRI scan. The images were of 12 categories relevant to daily events, with each category including a high and a low load image. Behavioral assessments on a separate group of participants (#22) provided the average loads of the images. The participants had to retrieve these previously memorized images during the fMRI scan in 15 s, with their eyes closed. We observed seven brain structures showing the highest activation with increasing load of the retrieved images, viz. parahippocampus, cerebellum, superior lateral occipital, fusiform and lingual gyri, precuneus, and posterior cingulate gyrus. Some structures showed reduced activation when retrieving higher load images, such as the anterior cingulate, insula, and supramarginal and postcentral gyri. The findings of this study revealed that the mechanism by which a difficult-to-retrieve memory is handled is mainly by elevating the activation of the responsible brain areas and not by getting other brain regions involved, which is a help to better understand the LTM retrieval process in the human brain.
The Adaptive Evolution and Gigantism Mechanisms of the Hadal “Supergiant” Amphipod Alicella gigantea

Hadal trenches are commonly referred to as the deepest areas in the ocean and are characterized by extreme environmental conditions such as high hydrostatic pressures and very limited food supplies. Amphipods are considered the dominant scavengers in the hadal food web. Alicella gigantea is the largest hadal amphipod and, as such, has attracted a lot of attention. However, the adaptive evolution and gigantism mechanisms of the hadal “supergiant” remain unknown. In this study, the whole-body transcriptome analysis was conducted regarding the two hadal amphipods, one being the largest sized species A. gigantea from the New Britain Trench and another the small-sized species Bathycallisoma schellenbergi from the Marceau Trench. The size and weight measurement of the two hadal amphipods revealed that the growth of A. gigantea was comparatively much faster than that of B. schellenbergi. Phylogenetic analyses showed that A. gigantea and B. schellenbergi were clustered into a Lysianassoidea clade, and were distinct from the Gammaroidea consisting of shallow-water Gammarus species. Codon substitution analyses revealed that “response to starvation,” “glycerolipid metabolism,” and “meiosis” pathways were enriched among the positively selected genes (PSGs) of the two hadal amphipods, suggesting that hadal amphipods are subjected to intense food shortage and the pathways are the main adaptation strategies to survive in the hadal environment. To elucidate the mechanisms underlying the gigantism of A. gigantea, small-sized amphipods were used as the background for evolutionary analysis, we found the seven PSGs that were ultimately related to growth and proliferation. In addition, the evolutionary rate of the gene ontology (GO) term “growth regulation” was significantly higher in A. gigantea than in small-sized amphipods. By combining, those points might be the possible gigantism mechanisms of the hadal “supergiant” A. gigantea.
Estimating the Societal Benefits of Satellite Instruments: Application to a Break-even Analysis of the GeoXO Hyperspectral IR Sounder

This paper describes the process, and lessons learned in a preliminary benefits study of a proposed infrared hyperspectral sounder (HSS) for NOAA’s next generation geostationary satellite program (GeoXO). The valuation of government-owned satellite systems providing a public good with a complex array of instruments is a nascent field of study. Many different sensor configurations are possible, but there are restrictions due to physical and budget constraints. Accounting for economic considerations during the design and planning phase for satellite constellations helps to ensure that the most cost effective instruments are selected. To assess whether the HSS instrument should be included on GeoXO, we applied a value of information approach and found the benefits associated with this instrument are likely to substantially outweigh the costs. Value of information studies often focus on data and information that has a direct use case. Estimating benefits for the HSS is especially challenging because data are not used directly by decision makers. Instead these data along with information from other Earth observing (EO) satellites play a key role in producing the inputs necessary for modern numerical hydrometeorological modeling. We describe strategies to assess the marginal (i.e., incremental) contribution of an instrument that is part of a complex information production process. We make several recommendations that, if implemented, would improve the quality of future studies of this kind. This includes (1) a systems approach to observing system planning, (2) improving the design of observing system experiments (OSSE and OSE), and (3) better tracking of the decisions and needs of end-users, especially those external to the agency.
Modeling and Simulations of Polymers: A Roadmap

Molecular modeling and simulations are invaluable tools for the polymer science and engineering community. These computational approaches enable predictions and provide explanations of experimentally observed macromolecular structure, dynamics, thermodynamics, and microscopic and macroscopic material properties. With recent advances in computing power, polymer simulations can synergistically inform, guide, and complement in vitro macromolecular materials design and discovery efforts. To ensure that this growing power of simulations is harnessed correctly, and meaningful results are achieved, care must be taken to ensure the validity and reproducibility of these simulations. With these considerations in mind, in this Perspective we discuss our philosophy for carefully developing or selecting appropriate models, performing, and analyzing polymer simulations. We highlight best practices, key challenges, and important advances in model development/selection, computational method choices, advanced sampling met.
Machine Learning-Based Electroencephalographic Phenotypes of Schizophrenia and Major Depressive Disorder

Background: Psychiatric diagnosis is formulated by symptomatic classification; disease-specific neurophysiological phenotyping could help with its fundamental treatment. Here, we investigated brain phenotyping in patients with schizophrenia (SZ) and major depressive disorder (MDD) by using electroencephalography (EEG) and conducted machine-learning-based classification of the two diseases by using EEG components. Materials and Methods: We enrolled healthy controls (HCs) (n = 30) and patients with SZ (n = 34) and MDD (n = 33). An auditory P300 (AP300) task was performed, and the N1 and P3 components were extracted. Two-group classification was conducted using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Positive and negative symptoms and depression and/or anxiety symptoms were evaluated. Results: Considering both the results of statistical comparisons and machine learning-based classifications, patients and HCs showed significant differences in AP300, with SZ and MDD showing lower N1 and P3 than HCs. In the sum of amplitudes and cortical sources, the findings for LDA with classification accuracy (SZ vs. HCs: 71.31%, MDD vs. HCs: 74.55%), sensitivity (SZ vs. HCs: 77.67%, MDD vs. HCs: 79.00%), and specificity (SZ vs. HCs: 64.00%, MDD vs. HCs: 69.67%) supported these results. The SVM classifier showed reasonable scores between SZ and HCs and/or MDD and HCs. The comparison between SZ and MDD showed low classification accuracy (59.71%), sensitivity (65.08%), and specificity (54.83%). Conclusions: Patients with SZ and MDD showed deficiencies in N1 and P3 components in the sum of amplitudes and cortical sources, indicating attentional dysfunction in both early and late sensory/cognitive gating input. The LDA and SVM classifiers in the AP300 are useful to distinguish patients with SZ and HCs and/or MDD and HCs.
Children and Adults in a Household Cohort Study Have Robust Longitudinal Immune Responses Following SARS-CoV-2 Infection or Exposure

Children have reduced severity of COVID-19 compared to adults and typically have mild or asymptomatic disease. The immunological mechanisms underlying these age-related differences in clinical outcomes remain unexplained. Here, we quantify 23 immune cell populations in 141 samples from children and adults with mild COVID-19 and their PCR-negative close household contacts at acute and convalescent time points. Children with COVID-19 displayed marked reductions in myeloid cells during infection, most prominent in children under the age of five. Recovery from infection in both children and adults was characterised by the generation of CD8 TCM and CD4 TCM up to 9 weeks post infection. SARS-CoV-2-exposed close contacts also had immunological changes over time despite no evidence of confirmed SARS-CoV-2 infection on PCR testing. This included an increase in low-density neutrophils during convalescence in both exposed children and adults, as well as increases in CD8 TCM and CD4 TCM in exposed adults. In comparison to children with other common respiratory viral infections, those with COVID-19 had a greater change in innate and T cell-mediated immune responses over time. These findings provide new mechanistic insights into the immune response during and after recovery from COVID-19 in both children and adults.
Acute Effects of Different Exercise Intensities on Executive Function and Oculomotor Performance in Middle-Aged and Older Adults: Moderate-Intensity Continuous Exercise vs. High-Intensity Interval Exercise

A wealth of evidence has shown that a single bout of aerobic exercise can facilitate executive function. However, none of current studies on this topic have addressed whether the magnitude of the acute-exercise benefit on executive function and oculomotor performance is influenced by different aerobic exercise modes. The present study was thus aimed toward an investigation of the acute effects of high-intensity interval exercise (HIIE) vs. moderate-intensity continuous exercise (MICE) on executive-related oculomotor performance in healthy late middle-aged and older adults. Using a within-subject design, twenty-two participants completed a single bout of 30 min of HIIE, MICE, or a non-exercise-intervention (REST) session in a counterbalanced order. The behavioral [e.g., reaction times (RTs), coefficient of variation (CV) of the RT], and oculomotor (e.g., saccade amplitude, saccade latency, and saccadic peak velocity) indices were measured when participants performed antisaccade and prosaccade tasks prior to and after an intervention mode. The results showed that a 30-min single-bout of HIIE and MICE interventions shortened the RTs in the antisaccade task, with the null effect on the CV of the RT in the late middle-aged and older adults. In terms of oculomotor metrics, although the two exercise modes could not modify the performance in terms of saccade amplitudes and saccade latencies, the participants’ saccadic peak velocities while performing the oculomotor paradigm were significantly altered only following an acute HIIE intervention. The present findings suggested that a 30-min single-bout of HIIE and MICE interventions modulated post-exercise antisaccade control on behavioral performance (e.g., RTs). Nevertheless, the HIIE relative MICE mode appears to be a more effective aerobic exercise in terms of oculomotor control (e.g., saccadic peak velocities) in late middle-aged and older adults.
Low-Power Failure Detection for Environmental Monitoring Based on IoT

Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due to environmental and human factors, and battery depletion in particular is a major contributor to node failure. The existing failure detection algorithms seldom consider the problem of node battery consumption. In order to rectify this, we propose a low-power failure detector (LP-FD) that can provide an acceptable failure detection service and can save on the battery consumption of nodes. From simulation experiments, results show that the LP-FD can provide better detection speed, accuracy, overhead and battery consumption than other failure detection algorithms.
Peripheral Blood-Based Gene Expression Studies in Schizophrenia: A Systematic Review

Schizophrenia is a disorder that is characterized by delusions, hallucinations, disorganized speech or behavior, and socio-occupational impairment. The duration of observation and variability in symptoms can make the accurate diagnosis difficult. Identification of biomarkers for schizophrenia (SCZ) can help in early diagnosis, ascertaining the diagnosis, and development of effective treatment strategies. Here we review peripheral blood-based gene expression studies for identification of gene expression biomarkers for SCZ. A literature search was carried out in PubMed and Web of Science databases for blood-based gene expression studies in SCZ. A list of differentially expressed genes (DEGs) was compiled and analyzed for overlap with genetic markers, differences based on drug status of the participants, functional enrichment, and for effect of antipsychotics. This literature survey identified 61 gene expression studies. Seventeen out of these studies were based on expression microarrays. A comparative analysis of the DEGs (n = 227) from microarray studies revealed differences between drug-naive and drug-treated SCZ participants. We found that of the 227 DEGs, 11 genes (ACOT7, AGO2, DISC1, LDB1, RUNX3, SIGIRR, SLC18A1, NRG1, CHRNB2, PRKAB2, and ZNF74) also showed genetic and epigenetic changes associated with SCZ. Functional enrichment analysis of the DEGs revealed dysregulation of proline and 4-hydroxyproline metabolism. Also, arginine and proline metabolism was the most functionally enriched pathway for SCZ in our analysis. Follow-up studies identified effect of antipsychotic treatment on peripheral blood gene expression. Of the 27 genes compiled from the follow-up studies AKT1, DISC1, HP, and EIF2D had no effect on their expression status as a result of antipsychotic treatment. Despite the differences in the nature of the study, ethnicity of the population, and the gene expression analysis method used, we identified several coherent observations. An overlap, though limited, of genetic, epigenetic and gene expression changes supports interplay of genetic and environmental factors in SCZ. The studies validate the use of blood as a surrogate tissue for biomarker analysis. We conclude that well-designed cohort studies across diverse populations, use of high-throughput sequencing technology, and use of artificial intelligence (AI) based computational analysis will significantly improve our understanding and diagnostic capabilities for this complex disorder.