Miracle-dz/UniFAR-FTUs huggingface dataset:
Mice exhibit stochastic and efficient action switching during probabilistic decision making

In probabilistic and nonstationary environments, individuals must use internal and external cues to flexibly make decisions that lead to desirable outcomes. To gain insight into the process by which animals choose between actions, we trained mice in a task with time-varying reward probabilities. In our implementation of such a “two-armed bandit” task, thirsty mice use information about recent action and action-outcome histories to choose between two ports that deliver water probabilistically. Here, we comprehensively modeled choice behavior in this task, including the trial-to-trial changes in port selection – i.e. action switching behavior. We find that mouse behavior is, at times, deterministic and, at others, apparently stochastic. The behavior deviates from that of a theoretically optimal agent performing Bayesian inference in a Hidden Markov Model (HMM). We formulate a set of models based on logistic regression, reinforcement learning, and ‘sticky’ Bayesian inference that we demonstrate are mathematically equivalent and that accurately describe mouse behavior. The switching behavior of mice in the task is captured in each model by a stochastic action policy, a history-dependent representation of action value, and a tendency to repeat actions despite incoming evidence. The models parsimoniously capture behavior across different environmental conditionals by varying the ‘stickiness’ parameter, and, like the mice, they achieve nearly maximal reward rates. These results indicate that mouse behavior reaches near-maximal performance with reduced action switching and can be described by a set of equivalent models with a small number of relatively fixed parameters. Significance To obtain rewards in changing and uncertain environments, animals must adapt their behavior. We found that mouse choice and trial-to-trial switching behavior in a dynamic and probabilistic two-choice task could be modeled by equivalent theoretical, algorithmic, and descriptive models. These models capture components of evidence accumulation, choice history bias, and stochasticity in mouse behavior. Furthermore, they reveal that mice adapt their behavior in different environmental contexts by modulating their level of ‘stickiness’ to their previous choice. Despite deviating from the behavior of a theoretically ideal observer, the empirical models achieve comparable levels of near-maximal reward. These results make predictions to guide interrogation of the neural mechanisms underlying flexible decision-making strategies.
Predicting individual shelter dog behaviour after adoption using longitudinal behavioural assessment: a hierarchical Bayesian approach

Predicting the behaviour of shelter dogs after adoption is an important, but difficult, endeavour. Differences between shelter and post-adoption environments, between- and within-individual heterogeneity in behaviour, uncertainty in behavioural predictions and measurement error all hinder the accurate assessment of future behaviour. This study integrates 1) a longitudinal behavioural assessment with 2) a novel joint hierarchical Bayesian mixture model that accounts for individual variation, missing data and measurement error to predict behaviour post-adoption. We analysed shelter observations (> 28,000 records) and post-adoption reports (from telephone surveys) on the behaviour of 241 dogs across eight contexts. Dog behaviour at the shelter correlated positively with behaviour post-adoption within contexts (r = 0.38; 95% highest density interval: [0.20, 0.55]), and behavioural repeatability was approximately 20% higher post-adoption than at the shelter for behaviour within contexts. Although measurement error was higher post-adoption than at the shelter, we found few differences in individual-level, latent probabilities of different behaviours post-adoption versus at the shelter. This good predictive ability was aided by accurate representation of uncertainty in individual-level predictions. We conclude that longitudinal assessment paired with a sufficient inferential framework to model latent behavioural profiles with uncertainty enables reasonably accurate estimation of post-adoption behaviour.
Changes of Voice Production in Artificial Acoustic Environments

The physical production of speech level dynamic range is directly affected by the physiological features of the speaker such as vocal tract size and lung capacity; however, the regulation of these production systems is affected by the perception of the communication environment and auditory feedback. The current study examined the effects of room acoustics in an artificial setting on voice production in terms of sound pressure level and the relationship with the perceived vocal comfort and vocal control. Three independent room acoustic parameters were considered: gain (alteration of the sidetone or playback of one’s own voice), reverberation time, and background noise. An increase in the sidetone led to a decrease in vocal sound pressure levels, thus increasing vocal comfort and vocal control. This effect was consistent in the different reverberation times considered. Mid-range reverberation times (T30 ≈ 1.3 s) led to a decrease in vocal sound pressure level along with an increase in vocal comfort and vocal control, however, the effect of the reverberation time was smaller than the effect of the gain. The presence of noise amplified the aforementioned effects for the variables analyzed.
Vaccine-Induced Protection in Two Murine Models of Invasive Pulmonary Aspergillosis

Life-threatening, invasive fungal infections (IFIs) cause over 1.5 million deaths worldwide and are a major public health concern with high mortality rates even with medical treatment. Infections with the opportunistic fungal pathogen, Aspergillus fumigatus are among the most common. Despite the growing clinical need, there are no licensed vaccines for IFIs. Here we evaluated the immunogenicity and protective efficacy of an A. fumigatus recombinant protein vaccine candidate, AF.KEX1, in experimental murine models of drug-induced immunosuppression. Immunization of healthy mice with AF.KEX1 and adjuvant induced a robust immune response. Following AF.KEX1 or sham immunization, mice were immunosuppressed by treatment with either cortisone acetate or hydrocortisone and the calcineurin inhibitor, tacrolimus. To test vaccine efficacy, immunosuppressed mice were intranasally challenged with A. fumigatus conidia (Af293) and weight and body temperature were monitored for 10 days. At study termination, organism burden in the lungs was evaluated by quantitative PCR and Gomori’s methanamine silver staining. In both models of immunosuppression, AF.KEX1 vaccinated mice experienced decreased rates of mortality and significantly lower lung organism burden compared to non-vaccinated controls. The lung fungal burden was inversely correlated with the peak anti-AF.KEX1 IgG titer achieved following vaccination. These studies provide the basis for further evaluation of a novel vaccine strategy to protect individuals at risk of invasive aspergillosis due to immunosuppressive treatments.
Working in Inclusive or Non-Inclusive Contexts: Relations Between Collaborative Variables and Special Education Teachers’ Burnout

Over the last two decades, the nature of teachers' work has undergone significant structural changes due to political, social, organizational, technological, and philosophical factors. With the development of inclusive policies, the roles and functions of special education teachers (SETs) have evolved over the years. Inclusive work in general classrooms is often seen as a source of stress for SETs as they may have less autonomy at work, and more complex collaboration than their peers working in special classes. This stress can lead to a burnout syndrome, which is characterized by emotional exhaustion, depersonalization and a lack of personal accomplishment. Less is known about the effects of teaching contexts on the SETs’ health. Therefore, this study aimed to compare the perceived health of a sample of SETs in Switzerland (N = 306) working in inclusive contexts (n = 79) with those working in non-inclusive contexts (n = 227). The sample filled out a self-reported questionnaire measuring personal variables, burnout at work with the Maslach Burnout Inventory, and collaborative variables with the School Quality Survey. Research results show three main findings. 1. Differences in perceived health at work are not significant between the two subgroups. 2. The two subsamples are not exhausted or depersonalized, but present a reduced personal achievement. 3. The collaboration-related variables exert different effects on the three burnout dimensions.
Double-Stranded RNA Targeting Dicer-Like Genes Compromises the Pathogenicity of Plasmopara viticola on Grapevine

Downy mildew caused by Plasmopara viticola is one of the most devastating diseases of grapevine, attacking all green parts of the plant. The damage is severe when the infection at flowering stage is left uncontrolled. P. viticola management consumes a significant amount of classical pesticides applied in vineyards, requiring efficient and environmentally safe disease management options. Spray-induced gene silencing (SIGS), through the application of exogenous double-stranded RNA (dsRNA), has shown promising results for the management of diseases in crops. Here, we developed and tested the potential of dsRNA targeting P. viticola Dicer-like (DCL) genes for SIGS-based crop protection strategy. The exogenous application of PvDCL1/2 dsRNA, a chimera of PvDCL1 and PvDCL2, highly affected the virulence of P. viticola. The reduced expression level of PvDCL1 and PvDCL2 transcripts in infected leaves, treated with PvDCL1/2 dsRNA, was an indication of an active RNA interference mechanism inside the pathogen to compromise its virulence. Besides the protective property, the PvDCL1/2 dsRNA also exhibited a curative role by reducing the disease progress rate of already established infection. Our data provide a promising future for PvDCL1/2 dsRNA as a new generation of RNA-based resistant plants or RNA-based agrochemical for the management of downy mildew disease in grapevine.
Steroid Hormone Biosynthesis Metabolism Is Associated With Fatigue Related to Androgen Deprivation Therapy for Prostate Cancer

Background Androgen deprivation therapy (ADT) is a cornerstone treatment for prostate cancer. Despite the clinical benefits, ADT is associated with multiple adverse effects including fatigue. The goal of the study was to examine metabolomic changes to better understand cancer-related fatigue specific to ADT treatment. Methods A total of 160 plasma samples collected from participants with (+ADT, n = 58) or without neoadjuvant ADT (−ADT, n = 102) prior to radiation therapy for treatment of non-metastatic localized prostate cancer were included in the study. Fatigue and sleep-related impairment were measured using the Patient Reported Outcomes Measurement Information System. Plasma metabolites were identified and measured using untargeted ultrahigh-performance liquid chromatography/mass spectrometry metabolomics analyses. Partial least square discriminant analysis was used to identify discriminant metabolite features, and the diagnostic performance of selected classifiers was quantified using AUROC curve analysis. Pathway enrichment analysis was performed using metabolite sets enrichment analyses. Findings Steroid hormone biosynthesis pathways, including androstenedione metabolism as well as androgen and estrogen metabolism, were overrepresented by metabolites that significantly discriminated samples in the +ADT from the −ADT group. Additional overrepresented metabolic pathways included amino acid metabolism, glutathione metabolism, and carnitine synthesis. Of the metabolites that were significantly different between the groups, steroid hormone biosynthesis metabolites were most significantly correlated with fatigue severity. Sleep-related impairment was strongly correlated with fatigue severity and inversely correlated with ADT-induced reduction in androsterone sulfate. Conclusions Patients with non-metastatic prostate cancer receiving neoadjuvant ADT prior to radiation therapy reported relatively more severe fatigue. Increased fatigue in this population may be attributable to sleep-related impairment associated with alterations in steroid hormone biosynthesis. Findings in this study provide a basis for further research of changes in sleep patterns and their role in this specific subcategory of cancer-related fatigue caused by the treatment.
Acute Radiation Syndrome and the Microbiome: Impact and Review

Study of the human microbiota has been a centuries-long endeavor, but since the inception of the National Institutes of Health (NIH) Human Microbiome Project in 2007, research has greatly expanded, including the space involving radiation injury. As acute radiation syndrome (ARS) is multisystemic, the microbiome niches across all areas of the body may be affected. This review highlights advances in radiation research examining the effect of irradiation on the microbiome and its potential use as a target for medical countermeasures or biodosimetry approaches, or as a medical countermeasure itself. The authors also address animal model considerations for designing studies, and the potential to use the microbiome as a biomarker to assess radiation exposure and predict outcome. Recent research has shown that the microbiome holds enormous potential for mitigation of radiation injury, in the context of both radiotherapy and radiological/nuclear public health emergencies. Gaps still exist, but the field is moving forward with much promise.
Investigating the Influence of Structure and Heterogeneity in Waste Rock Piles on Mass Loading Rates—A Reactive Transport Modeling Study

Placement methods and material availability during waste rock pile (WRP) construction may create significant heterogeneities in physical and geochemical parameters (such as grain size, permeability, mineralogy, and reactivity) and influence the internal pile structure. Due to the enormous scale of WRPs, it is difficult to capture the influence of heterogeneities on mine drainage composition and evolution. Although laboratory- or field-scale experimental studies have provided much insight, it is often challenging to translate these results to full scale WRPs. This study uses a numerical modeling approach to investigate the influence of physical and chemical heterogeneities, structure, and scale on the release of acid rock drainage (ARD) through 2D reactive transport simulations. Specifically, the sensitivity of drainage quality to parameters including grain size distribution, sulfide mineral weathering rates, abundance and distribution of primary minerals, and pile structure as a function of construction methods are investigated. The geochemical model includes sulfide oxidation, pH buffering by calcite dissolution, and ferrihydrite and gypsum as secondary phases. Simulation results indicate that the implications of heterogeneity and construction method are scale-dependent; when grain size distribution trends observed in a pile's core are applied to the entirety of a pile, results between push- and end-dumping methods vary substantially—however, predicted drainage for different construction methods become more similar when features such as traffic surfaces, structural variation, and multiple benches are also considered. For all scales and construction methods investigated, simulated results demonstrate that pile heterogeneity and structure decrease peak mass loading rates 2 to 3-fold, but cause prolonged ARD release compared to the homogeneous case. These findings have implications for the economics of planning water treatment facilities for life of mine and closure operations.
Oncogenic Landscape of Somatic Mutations Perturbing Pan-Cancer lncRNA-ceRNA Regulation

Competing endogenous RNAs (ceRNA) are transcripts that communicate with and co-regulate each other by competing for the binding of shared microRNAs (miRNAs). Long non-coding RNAs (lncRNAs) as a type of ceRNA constitute a competitive regulatory network determined by miRNA response elements (MREs). Mutations in lncRNA MREs destabilize their original regulatory pathways. Study of the effects of lncRNA somatic mutations on ceRNA mechanisms can clarify tumor mechanisms and contribute to the development of precision medicine. Here, we used somatic mutation profiles collected from TCGA to characterize the role of lncRNA somatic mutations in the ceRNA regulatory network in 33 cancers. The 31,560 mutation sites identified by TargetScan and miRanda affected the balance of 70,811 ceRNA regulatory pathways. Putative mutations were categorized as high or low based on mutation frequencies. Multivariate multiple regression revealed a significant effect of 162 high-frequency mutations in six cancer types on the expression levels of target mRNAs (ceMs) through the ceRNA mechanism. Low-frequency mutations in multiple cancers perturbing 1624 ceM have been verified by Student’s t-test, indicating a significant mechanism of changes in the expression level of oncogenic genes. Oncogenic signaling pathway studies involving ceMs indicated functional heterogeneity of multiple cancers. Furthermore, we identified that lncRNA, perturbing ceMs associated with patient survival, have potential as biomarkers. Our collective findings revealed individual differences in somatic mutations perturbing ceM expression and impacting tumor heterogeneity.
How Turbulent is the Magnetically Closed Corona?

We argue that the magnetically closed corona evolves primarily quasi-statically, punctuated by many localized bursts of activity associated with magnetic reconnection at a myriad of small current sheets. The sheets form by various processes that do not involve a traditional turbulent cascade whereby energy flows losslessly through a continuum of spatial scales starting from the large scale of the photospheric driving. If such an inertial range is a defining characteristic of turbulence, then the magnetically closed corona is not a turbulent system. It nonetheless has a complex structure that bears no direct relationship to the pattern of driving.
REVIEW OF A SIMPLE POWERFUL POLARIZER FOR TESTING OF EDIBLE OIL QUALITY

In this paper, we have shown that a simple pair of polarizer can be used to measure natural optical activity in total complex oil (vegetable oil and fats). A similar concept with quantum physics has been discussed to describe briefly the probability in finding a particle within certain region with the change of polarization due to the interaction light with saturated fatty acids. The light source was fluorescence lamp and the experimental condition was kept stable, i.e. constant temperature and constant humidity. The angle of polarization was measured repeatedly until 50 times. Our relative uncertainty measurement has been obtained up to 5% and this is obviously clear to be able to differentiate various different quality of oil. More important result is that ordinary light polarization can be used as powerful test to show various quality level of oils better than recent standard methods. A good prospect has also been obtained for halal level testing. It is found that average value of polarization in lard (pig oil) is relatively higher than the others. Keywords: polarization, oil quality, saturated fatty acid
A New Structure of 2-State Number-Conserving Cellular Automata

SUMMARY Two-state number-conserving cellular automaton (NCCA) is a cellular automaton of which cell states are 0 or 1, and the total sum of all the states of cells is kept for any time step. It is a kind of particle-based modeling of physical systems. We introduce a new structure of its value-1 patterns, which we call a “bundle pair” and a “bundle quad”. By employing this structure, we show a relation between the neighborhood size n and n (cid:0) 2 NCCAs.
An Approach to Development of Interactive Adaptive Software Tool to Support Data Analysis Activity

In the recent decades, databases have been used in any field of human activity to keep valuable data about ongoing processes. Large amounts of data stored in enterprise databases are useless without having a specialized software tool for data discovery or querying. Most business users that make data-driven decisions usually do not have special training and experience in database querying using special formal languages. Existing solutions are based on “query wizards” and database query forms that require knowledge of a database schema and inconvenient for users without special training. Proposed approach is based on the content-based filtering of already executed queries by usage frequency and similarity criteria in order to suggest relevant queries that may be re-used. It is a baseline of the interactive adaptive system for data analysis, which design and development is outlined in this study. Software prototype was demonstrated and its usage was discussed. Conclusions were made and future research was formulated.
Face Recognition in the Presence of Non-Gaussian Noise

A method for detecting a face and recognizing a person or a group of persons at pictures or videos that can be corrupted by non-gaussian noise using different architectures of convolutional neural networks is proposed. The technical details of building a deep learning-based face recognition system are discussed. The test results confirm the prospects of using the developed method for solving tasks of face detection and recognition.
Performance Analysis of Wireless Local Area Network for a High-/Low-Priority Traffic Ratio at Different Numbers of Access Categories

The performance of Wireless Local Area Network (WLAN) is highly dependent on the processes that are implemented in the Medium Access Control (MAC) sublayer regulated by the IEEE 802.11 standard. In turn, various parameters affect the performance of the MAC sublayer, the most important of which is the number of stations in the network and the offered load. With the massive growth of multimedia traffic, research of the network performance depending on traffic types is relevant. In this paper, we present the impact of a high-/low-priority traffic ratio on WLAN performance with different numbers of access categories. The simulation results show different impact of high-/low-priority traffic ratio on the performance of the MAC sublayer of wireless LANs depending on different network-sizes and on network conditions. Performance of the large network with two access categories and with the prevalent high-priority traffic is significantly higher than in the case of using four categories on the MAC sublayer. This allows us to conclude that the performance improvement of the large network with the prevalent high-priority traffic can be achieved by an adaptive adjustment of the access categories number on the MAC sublayer.
Laser Assisted Synthesis, Structural and Magnetic Characterization of Gadolinium Germano-Silicide Nanoparticles in Liquid

In this paper the ternary Gd5(Si1−xGex 4 nanoparticles (NPs) were synthesized by laser assisted techniques in liquid and their crystal structure, morphology and magnetic properties were investigated. To prepare compound gadolinium germano–silicide NPs we used laser irradiation processes in two different ways. First, the compound NPs were synthesized by four step process which involved the sequential ablation of silicon, gadolinium and germanium targets followed by additional laser irradiation of the formed colloid. Second, laser irradiation of the relevant target consisted of Gd5(Si1−xGex 4 alloy produced by thermal melting of the stoichiometric mixture of the virgin components (powders) in an argon atmosphere in a water-cooled graphite oven was used. The last two-step approach was shown to be more effective the production of Gd–Si–Ge alloys for practical applications.