Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-driven modelling captures dynamics of the circadian clock of Neurospora crassa

Eukaryotic circadian clocks are based on self-sustaining, cell-autonomous oscillatory feedback loops that can synchronize with the environment via recurrent stimuli (zeitgebers) such as light. The components of biological clocks and their network interactions are becoming increasingly known, calling for a quantitative understanding of their role for clock function. However, the development of data-driven mathematical clock models has remained limited by the lack of sufficiently accurate data. Here we present a comprehensive model of the circadian clock of Neurospora crassa that describe free-running oscillations in constant darkness and entrainment in light-dark cycles. To parameterize the model, we measured high-resolution time courses of luciferase reporters of morning and evening specific clock genes in WT and a mutant strain. Fitting the model to such comprehensive data allowed estimating parameters governing circadian phase, period length and amplitude, and the response of genes to light cues. Our model suggests that functional maturation of the core clock protein Frequency (FRQ) causes a delay in negative feedback that is critical for generating circadian rhythms.
Microbiological and basic agrochemical properties of Eutric Cambisols in western and southwestern Serbia

One of the most common soil types in area of western and south-western Serbia is the Eutric Cambisol. In order to determine biogenity of this soil type, the presence of total microflora, fungi, actinomycetes, ammonifiers, Azotobacter sp. and oligonitrophils, as well as dehydrogenase activity were examined. Samples were taken from soils used in two different ways (plough fields and meadows). Standard microbiological methods of inoculation a certain decimal dilution on appropriate nutrient media were used. No correlation between the number of microorganisms and type of the soil usage was found. In localities that were under plough fields and meadows, the presence of Azotobacter sp. was confirmed, as an indicator of soil fertility. The correlation of dehydrogenase activity with the total number of microorganisms was not observed. Agrochemical analysis showed acidic to weakly acidic soil reaction, medium to high percentage of organic matter, low content of easily available phosphorus and high content of easily available potassium. There was no correlation between chemical properties of the soil and the number of microorganisms in examined soil type.
Young Adults’ Self-Regulatory Responses to Positive Career Goal Discrepancies: Testing Cross-Lagged Relationships

Researchers have assessed young people’s outcomes when they do not meet their career goals, but little is known about the consequences when they do better than expected (positive discrepancies). We (a) tested the cross-lagged relationships between positive career goal discrepancies and the career-related outcomes of upward goal revision, career exploration, and career coasting, and (b) assessed the indirect relationships between positive career goal discrepancy and outcomes through self-efficacy and outcome expectations. Using a sample of 314 young adults (76% female, Mage 19.13 years), we found that the standard causation model was the most parsimonious. Positive discrepancies predicted more upward goal revision and exploration and less coasting after a 6-month time lag, both directly and indirectly through outcome expectations. The findings highlight the importance of positive career goal appraisals in career goal-setting, exploration, management and clarify the roles of agency (self-efficacy and outcome expectations) as explanatory mechanisms in these relationships.
Effort–reward imbalance and job burnout in preschool teachers: A moderated mediation model

To explore the impact mechanism of effort–reward imbalance on job burnout, we surveyed 2,251 preschool teachers using measures of effort–reward imbalance, job burnout, future time perspective, and positive psychological capital. The results show that effort–reward imbalance, positive psychological capital, and future time perspective were significantly related to preschool teachers' job burnout. Future time perspective mediated the link between effort–reward imbalance and job burnout. Further, the direct effect of effort–reward imbalance on job burnout and the mediating role of future time perspective were both moderated by positive psychological capital, such that they were more significant in preschool teachers with low (vs. high) positive psychological capital. These findings offer guidance for the prevention of job burnout in preschool teachers.
Dietary Carotenoids in Head and Neck Cancer—Molecular and Clinical Implications

Head and neck cancer (HNC) is one of the most common cancers in the world according to GLOBCAN. In 2018, it was reported that HNC accounts for approximately 3% of all human cancers (51,540 new cases) and is the cause of nearly 1.5% of all cancer deaths (10,030 deaths). Despite great advances in treatment, HNC is indicated as a leading cause of death worldwide. In addition to having a positive impact on general health, a diet rich in carotenoids can regulate stages in the course of carcinogenesis; indeed, strong epidemiological associations exist between dietary carotenoids and HNS, and it is presumed that diets with carotenoids can even reduce cancer risk. They have also been proposed as potential chemotherapeutic agents and substances used in chemoprevention of HNC. The present review discusses the links between dietary carotenoids and HNC. It examines the prospective anticancer effect of dietary carotenoids against intracellular cell signalling and mechanisms, oxidative stress regulation, as well as their impact on apoptosis, cell cycle progression, cell proliferation, angiogenesis, metastasis, and chemoprevention; it also provides an overview of the limited preclinical and clinical research published in this arena. Recent epidemiological, key opinion-forming systematic reviews, cross-sectional, longitudinal, prospective, and interventional studies based on in vitro and animal models of HNC also indicate that high carotenoid content obtained from daily supplementation has positive effects on the initiation, promotion, and progression of HNC. This article presents these results according to their increasing clinical credibility.
Characteristics of music performance anxiety among undergraduate music students: A systematic review

Music performance anxiety (MPA) is described as a persistent apprehension that compromises performative skills both in solo and group performances. The aim of this systematic review was to discuss an overview of research articles addressing the characteristics of MPA as experienced by undergraduate music students. The search was undertaken in the databases Web of Science, PubMed, Scopus and Eric; of 1526 articles identified by the search keywords, 43 were selected for review through the PRISMA protocol. The study types included cross-sectional, experimental, longitudinal, descriptive, questionnaire survey, qualitative study, exploratory and retrospective follow-up designs. This review evidences the diversity of the contexts and topics addressed by current research, namely issues of prevalence, predictors of anxiety/risk factors, relationship between flow experience (FE) and MPA, and coping with MPA, adopting psychological, cognitive, physiological and behavioral perspectives. Notwithstanding, the review has also identified a lack of qualitative and longitudinal studies, studies on institutional teaching contexts, and of larger and more representative participant samples, which could promote a better understanding of the etiology of MPA among students attending higher education.
Anti-Blackness and Racial Disproportionality in Gifted Education

Black–White disparities in gifted enrollment persist across U.S. school systems. In this study, we examined whether these disparities depend on county-level rates of anti-Black bias. We drew data from the Civil Rights Data Collection, the Education Opportunity Project, and the Race Implicit Association Database. Based on a series of heteroskedastic fractional probit regression models, we found that county-level rates of anti-Black bias predict Black–White disparities in gifted and talented enrollment, with gaps being largest in counties with elevated rates of anti-Black bias and virtually non-existent in counties with low levels of anti-Black bias. These findings persist after accounting for achievement and other observable differences across counties, unobserved variation in state-level gifted and talented policies and are consistent for county-level estimates of both explicit and implicit measures of anti-Black bias.
Joint Scheduling and Resource Allocation for Hierarchical Federated Edge Learning

The concept of hierarchical federated edge learning (H-FEEL) has been recently proposed as an enhancement of federated learning model. Such a system generally consists of three entities, i.e., the server, helpers, and clients, in which each helper collects the trained gradients from clients nearby, aggregates them, and sends the result to the server for global model update. Due to limited communication resources, only a portion of helpers can be scheduled to upload their aggregated gradients in each round of the model training. And that necessitates a well-designed scheme for the joint helper scheduling and communication resources allocation. In this paper, we develop a training algorithm for the H-FEEL system which involves local gradient computing, weighted gradient uploading, and machine learning model updating phases. By characterizing these phases mathematically and analyzing one-round convergence bound of the training algorithm, we formulate an optimization problem to achieve the scheduling and resource allocation scheme. The problem simultaneously captures the uncertainty of the wireless channel and the importance of the weighted gradient. To solve the problem, we first transform it into an equivalent problem and then decompose the transformed problem into two subproblems: bit and sub-channel allocation and helper scheduling, which are mixed integer nonlinear programming and continuous nonlinear problems, respectively. For the first subproblem, we obtain an optimal solution of exponential complexity and a suboptimal solution that has polynomial complexity. For the second subproblem, we obtain a closed-form optimal solution in a special case and a suboptimal solution in the general case. The efficacy of our scheme is amply demonstrated via simulations and the analytical framework is shown to provide valuable design insights for the practical implementation of the H-FEEL system.
LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers

In this study, a framework for Android malware detection based on permissions is presented. This framework uses multiple linear regression methods. Application permissions, which are one of the most critical building blocks in the security of the Android operating system, are extracted through static analysis, and security analyzes of applications are carried out with machine learning techniques. Based on the multiple linear regression techniques, two classifiers are proposed for permission-based Android malware detection. These classifiers are compared on four different datasets with basic machine learning techniques such as support vector machine, k-nearest neighbor, Naive Bayes, and decision trees. In addition, using the bagging method, which is one of the ensemble learning, different classifiers are created, and the classification performance is increased. As a result, remarkable performances are obtained with classification algorithms based on linear regression models without the need for very complex classification algorithms.
What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients?

Resting-state functional MRI has been increasingly implemented in imaging protocols for the study of functional connectivity in glioma patients as a sequence able to capture the activity of brain networks and to investigate their properties without requiring the patients’ cooperation. The present review aims at describing the most recent results obtained through the analysis of resting-state fMRI data in different contexts of interest for brain gliomas: the identification and localization of functional networks, the characterization of altered functional connectivity, and the evaluation of functional plasticity in relation to the resection of the glioma. An analysis of the literature showed that significant and promising results could be achieved through this technique in all the aspects under investigation. Nevertheless, there is room for improvement, especially in terms of stability and generalizability of the outcomes. Further research should be conducted on homogeneous samples of glioma patients and at fixed time points to reduce the considerable variability in the results obtained across and within studies. Future works should also aim at establishing robust metrics for the assessment of the disruption of functional connectivity and its recovery at the single-subject level.
Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance

The efficiency of visible and near-infrared (VIS/NIR) sensors and predictive modeling for detecting and classifying South American Leaf Blight (SALB) (Pseudocercospora ulei) in rubber trees (Hevea brasiliensis) has been poorly explored. Furthermore, the performance of VIS/NIR analysis combined with machine learning (ML) algorithms for predicting photosynthetic alterations caused by SALB is unknown. Therefore, this study aimed to detect and classify the SALB levels, as well as to predict, for the first time, disease-induced photosynthetic changes in rubber trees. Leaf hyperspectral reflectance combined with five ML techniques (random forest (RF), boosted regression tree (BRT), bagged classification and regression trees (BCART), artificial neural network (ANN), and support vector machine (SVM)) were used. The RF, ANN, and BCART models achieved the best performance for classifying the SALB levels on the training dataset (accuracies of 98.0 to 99.8%), with 10-fold cross-validation repeated five times, and test dataset (accuracies of 97.1 to 100%). The ANN and RF models were better at predicting leaf gas exchange-related traits such as net CO2 assimilation rate (A) and extrinsic water use efficiency (WUEe) in the training (R2 ranged from 0.97 to 0.99) and testing (R2 ranged from 0.96 to 0.99) phases. In comparison, lower performances (R2 ranged from 0.24 to 0.52) were evidenced for the photochemical traits. This research provides a basis for future designs of a remote monitoring system based on early detection and accurate diagnosis of biotic stress caused by SALB, which is fundamental for more effective rubber crop protection.
Design of Integrated Micro-Fluxgate Magnetic Sensors: Advantages and Challenges of Numerical Analyses

Miniaturization and on-chip integration are major lines of research in many branches of science and technology developments, undoubtedly in sensor technology. Fluxgate magnetometers are very sensitive, and accurate magnetic sensors able to detect weak fields both AC and DC, which in recent years saw a great effort in minimizing their dimensions, weight, and power consumption. The physics behind the fluxgate principle is rather complex and makes simulations difficult and only partially used in the literature. The limited physical access to micro sensors for measurements and the need to optimize the entire integrated system, including the sensor geometry and the excitation and readout circuits, make numerical analyses particularly useful in the design of miniaturized sensors. After a thorough review of the miniaturized solutions proposed so far, the present paper examines in detail the possibility of adopting a model based approach for designing miniaturized fluxgate sensors. The model of the fluxgate effect of two different technologies proposed in the literature has been implemented to benchmark simulation results with real data. In addition to the advantages for an optimized design, the implementation and computational challenges of the numerical analyses are precisely outlined.
Representation of the autoconversion from cloud to rain using a weighted ensemble approach: a case study using WRF v4.1.3

Abstract. Cloud and precipitation processes remain among the largest sources of uncertainties in weather and climate modelling, and considerable attention has been paid to improving the representation of the cloud and precipitation processes in numerical models in the last several decades. In this study, we develop a weighted ensemble (named EN) scheme by employing several widely used autoconversion (ATC) schemes to represent the ATC from cloud water to rainwater. One unique feature of the EN approach is that the ATC rate is a weighted mean value based on the calculations from several ATC schemes within a microphysics scheme with a negligible increase in computation cost. The EN scheme is compared with the several commonly used ATC schemes by performing real case simulations. In terms of accumulated rainfall and extreme hourly rainfall rate, the EN scheme provides better simulations than by using the single Berry–Reinhardt scheme, which was originally used in the Thompson scheme. It is worth emphasizing, in the present study, that we only pay attention to the ATC process from cloud water into rainwater with the purpose of improving the modelling of the extreme rainfall events over southern China. Actually, any (source and sink) term in a cloud microphysics scheme can be treated with the same approach. The ensemble method proposed herein appears to have important implications for developing cloud microphysics schemes in numerical models, especially for the models with variable grid resolution, which would be expected to improve the representation of cloud microphysical processes in the weather and climate models.
Financial Conditions and Macroeconomic Downside Risks in the Euro Area

Motivated by empirically characterizing the relationship between financial conditions and downside macroeconomic risks in the euro area, I develop a regime-switching skew-normal model with time-varying probabilities of transitions. Using Bayesian methods, the model estimates show that a strong cyclical pattern emerges from the conditional skewness (a measure of the asymmetry of the predictive distribution), which has a tendency to rapidly decline to negative territory prior and during recessions. However, the inclusion of financial-specific information in time-varying probabilities does not help to anticipate such skewness nor more generally to provide advance warnings of tail risks.
A Wearable Circularly Polarized Antenna Backed by AMC Reflector for WBAN Communications

A new flexible circularly polarized (CP) wearable antenna is proposed for Wireless Body-Area Network systems (WBAN) operating at 5.8 GHz. The circular polarization is enabled by combining a microstrip-line monopole feed and an inverted L-shaped conformal metal strip extended from a coplanar waveguide. The proposed antenna shows satisfactory performance in terms of gain and specific absorption rate (SAR) at a separation of 12 mm from a human body model. To minimize body-antenna separation, a flexible 2×2 artificial magnetic conductor (AMC) was used as a reflector to achieve good performances in terms of gain and SAR. The total footprint of the proposed antenna is only 34.4 × 34.4 mm2 (0.384 λ0 2) board of semi-flexible Rogers RT-Duroid 5880 substrate. The final design provides a peak gain of 7.6 dBi and an efficiency of 94.7% when worn on the body. Furthermore, evaluation results indicate that the maximum SAR level decreased by up to 20.42% in comparison with the CP antenna without the AMC. Full-wave EM simulation and experimental results are performed, both in free space and proximity to the human body under different bending scenarios. Overall, the proposed antenna performance has been shown to be robust to structural deformation along the x-axis in comparison to previously reported designs. These features demonstrate that the proposed antenna is a strong candidate for off-body wearable applications.
Effect of Mixed Oxide-Based TiO2 on the Physicochemical Properties of Chitosan Films

The physicochemical, mechanical, and structural properties of chitosan-based films (CS) alone or CS-films with mixed oxide nanoparticles (TiO2-ZnO-MgO, TZM; CSTZM) at different concentrations (125, 250, and 500 μg mL−1) were investigated. The addition of nano-TZM promoted a color change (from colorless to white) in the film-forming solution, which increased its turbidity and it decreased viscosity. CSTZM were semitransparent (transmittance, T% decreased up to 49%) compared to CS-based films (T% = 95.5). CSTZM (particularly at a concentration of 500 μg mL−1) exhibited an improvement in the moisture content (decreased from 12.6 to 9.67%), water solubility (decreased from 14.94 to 10.22%), degree of swelling (increased from 19.79 to 36.28%), water vapor barrier (decreased from 6.62 x 10−16 to 4.33 x 10−16 g m−1 h−1 Pa−1), thermal stability (the endotherm peak increased from 99.5 to 157.7 °C), and mechanical properties (tensile strength and elongation at break increased from 4.15 to 4.98 kPa and 6.96 to 56.18%, respectively, while the modulus of elasticity decreased from 144 kPa to 4.11 kPa), without toxicity effects on Artemia salina (93.33% survival). X-ray diffraction and Fourier transform infrared studies demonstrated an interaction between CS-based films and nano-TZM. Overall, this film exhibited great potential for diverse industrial applications.
Preconditioning by voluntary wheel running attenuates later neuropathic pain via Nrf2 antioxidant signaling in rats

Animal and human studies have shown that exercise prior to nerve injury prevents later chronic pain, but the mechanisms of such preconditioning remain elusive. Given that exercise acutely increases formation of free radicals, triggering antioxidant compensation, we hypothesized that voluntary running preconditioning would attenuate neuropathic pain by supporting redox homeostasis after sciatic nerve injury in male and female rats. We show that 6 weeks of voluntary wheel running suppresses neuropathic pain development induced by chronic constriction injury (CCI) across both sexes. This protection was associated with reduced nitrotyrosine immunoreactivity—a marker for peroxynitrite—at the sciatic nerve injury site. Our data suggest that prior voluntary wheel running does not reduce production of peroxynitrite precursors, as expression levels of inducible nitric oxide synthase and NADPH oxidase 2 were unchanged. Instead, voluntary wheel running increased superoxide scavenging by elevating expression of superoxide dismutases 1 and 2. Prevention of neuropathic pain was further associated with activation of the master transcriptional regulator of the antioxidant response, nuclear factor E2-related factor 2 (Nrf2). Six weeks of prior voluntary wheel running increased Nrf2 nuclear translocation at the sciatic nerve injury site; in contrast, 3 weeks of prior wheel running, which failed to prevent neuropathic pain, had no effect on Nrf2 nuclear translocation. The protective effects of prior voluntary wheel running were mediated by Nrf2, as suppression was abolished across both sexes when Nrf2 activation was blocked during the running phase. This study provides insight into the mechanisms by which physical activity may prevent neuropathic pain.