Miracle-dz/UniFAR-FTUs huggingface dataset:
An Exploratory Study on the Acoustic Musical Properties to Decrease Self-Perceived Anxiety

Musical listening is broadly used as an inexpensive and safe method to reduce self-perceived anxiety. This strategy is based on the emotivist assumption claiming that emotions are not only recognised in music but induced by it. Yet, the acoustic properties of musical work capable of reducing anxiety are still under-researched. To fill this gap, we explore whether the acoustic parameters relevant in music emotion recognition are also suitable to identify music with relaxing properties. As an anxiety indicator, the positive statements from the six-item Spielberger State-Trait Anxiety Inventory, a self-reported score from 3 to 12, are taken. A user-study with 50 participants assessing the relaxing potential of four musical pieces was conducted; subsequently, the acoustic parameters were evaluated. Our study shows that when using classical Western music to reduce self-perceived anxiety, tonal music should be considered. In addition, it also indicates that harmonicity is a suitable indicator of relaxing music, while the role of scoring and dynamics in reducing non-pathological listener distress should be further investigated.
Preventing Traffic Accidents Through Machine Learning Predictive Models

Road Traffic Accidents (RTA) are a serious issue of societies resulting in huge losses at the economic and social levels and responsible for millions of deaths and injuries every year in the world. For instance, in Ethiopia, the number of deaths due to traffic accidents is increasing from one year to another. Addis Ababa is one of the popular and known cities that encounter a high number of RTAs due to the increasing number of vehicles and population. The main objective of this paper is to apply machine learning algorithms to predict the accident severity and identify the major causes of accidents in crowded cities (application of Addis Ababa city). The required data are collected from Addis Ababa city police departments and 12316 records of the accident are used for data analysis. We applied seven machine learning classification algorithms (Logistic Regression, Naive Bayes, Decision Tree, Support Vector Machine, K Nearest Neighbor, Random Forest, and AdaBoost) for predicting accident severity and compared the performance to choose the best model. We applied random undersampling and SMOTE oversampling techniques to handle the class imbalance nature of the dependent features and Principal Component Analysis (PCA) for dimension reduction. The experimental result shows that Random Forest achieved a 93.76% F1 score with SMOTE over-sampled data set and about 18% feature size reduction. Moreover, light condition, driving experience, age band of the driver, type of road lane, and types of junctions are identified as major determinant factors of the accident. According to this study, these are major factors to RTA and need to be considered in the design of infrastructure, regulations and policies to reduce accidents.
SLiMAn: an integrative web server for exploring short linear motif-mediated interactions in interactomes

Cells express thousands of macromolecules, and their functioning relies on multiple networks of intermolecular interactions. These interactions can be experimentally determine at different spatial and temporal resolutions. But, physical interfaces are not often delineated directly especially in high-throughput experiments. However, numerous three-dimensional structures of complexes have been already solved and sequence conservation allows comparative modeling of additional complexes. A large fraction of protein-protein interactions involves domain and so-called SLiMs (for Short Linear Motifs). Often, SLiMs lie in disordered regions or loops. Their small size and loosely folded nature prevent straightforward detection. SLiMAn (Short Linear Motif Analysis), a new web server is provided to help thorough analysis of interactomics data. Starting from a list of putative interactants such as the output of an interactomics study, SLiMs (from ELM) and SLiM-recognition domains (from Pfam) are extracted and potential pairing are displayed. Additionally, filters are available to dig into the predicted results such as the motif E-value, IUpred2 scoring functions for disorder or BioGRID interaction matches. When structural templates are available, a given SLiM and its recognition domain can be modeled using SCWRL. We illustrate, here, the use of SLiMAn on three distinct examples including one real-case study. We oversee wide-range applications for SLiMAn in the context of massive analysis of protein-protein interactions at proteome-wide scale. This new web server is made freely available at http://sliman.cbs. cnrs.fr.
The Utility of Maternal Point of Care Ultrasound on Labor and Delivery Wards

Point-of-care ultrasonography (POCUS) refers to limited bedside ultrasound used to evaluate patients for conditions specific to the scope of their practice. Given the benefits of its application, interest in its use is increasing. We aimed to review the literature and assess the potential feasibility of using POCUS of the heart and lungs in the field of obstetrics. We aim to describe its relevance and value as an adjunctive tool for critically ill obstetric patients on labor and delivery wards.
Assessment of 2-Pentadecyl-2-oxazoline Role on Lipopolysaccharide-Induced Inflammation on Early Stage Development of Zebrafish (Danio rerio)

Lipopolysaccharide (LPS), or bacterial endotoxin, is an important virulence factor in several human and animal pathologies. Oxazoline of Palmitoylethanolamide (PEAOXA) has shown strong anti-inflammatory activity in several animal models. LPS was applied for 24 h to zebrafish embryos to induce inflammation, and then the anti-inflammatory action of PEAOXA was evaluated for the first time in the zebrafish model (Danio rerio). Different concentrations of PEAOXA were tested for toxicity on zebrafish embryonic development; only the highest concentration of 30 mg/L showed toxic effects. Quantitative RT-PCR was applied to detect Tumor necrosis factor-α, Interleukin 1β, 6, and 8, and members of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB). Exposure to LPS induced an increase in pro-inflammatory cytokines (tumor necrosis factor and interleukin 1, 6, and 8) in both gene and protein expression, as well as an increase of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) and the nuclear factor kappa light polypeptide enhancer in B-cells inhibitor (IκBα) gene expression. Furthermore, acute LPS exposure also induced an increase in tryptase release, related to mast cell activity, and in the production of apoptosis-related proteins (caspase 3, bax, and bcl-2). Treatment with PEAOXA 10 mg/L significantly counteracts LPS-induced inflammation in terms of cytokine expression and decreases tryptase release and the apoptosis pathway.
Histopathological aspects of resistance in wheat to Puccinia triticina, induced by Pseudomonas protegens CHA0 and β-aminobutyric acid

After perception of specific biotic or abiotic stimuli, such as root colonization by rhizobacteria or selected chemicals, plants can enhance their basal resistance against pathogens. Due to its likely sustainability, this induced resistance will be valuable for disease management in agriculture. This study examined resistance against wheat leaf rust (Puccinia triticina) induced by Pseudomonas protegens CHA0 (CHA0) and β-aminobutyric acid (BABA). Seed dressing with CHA0 reduced the number of sporulating pustules on leaves, and expression of resistance was visible as necrotic or chlorotic leaf flecks. Beneficial effect of CHA0 on wheat seedlings growth was observed in when they were challenged or not with leaf rust. BABA was tested at 10, 15 or 20 mM, and a dose-dependent reduction of leaf rust infections was observed with greatest protection at 20 mM. However, BABA treatment repressed plant growth at 20 mM. Balancing the BABA impact on plant growth and its protective capacity, 15 mM of the compound was selected as suitable to protect wheat seedlings against leaf rust, with the least impact on vegetative host growth. Histological aspects of the pathogen infection process was studied to understand mechanisms of behind the observed resistance. The pre-entry process was not affected by the two resistance inducers, but both treatments reduced fungus penetration and haustorium formation. Timing and amplitude of the resistance reactions were different after bacterial or chemical induction, leading to different levels of resistance. During fungal colonization of host tissues, high deposition of callose and accumulation of H2O2 in both CHA0- and BABA-treated plants indicated important contributions to resistance.
Knowledge-based deformable models for medical image analysis

The treatment of musculoskeletal disorders (MSD) is of paramount importance, as MSDs are chronic pathologies accounting for the largest fraction of temporary and permanent joint disabilities in industrialized societies. Accurate modeling of musculoskeletal structures is crucial for diagnosis and pre-operative planning and usually relies on Magnetic Resonance Imaging (MRI) for its ability to simultaneously image soft and bony structures composing the articulations. However, MRI images present, in addition to ubiquitous image artifacts, highly heterogeneous bone tissue intensities and articular structures with fuzzy boundaries. Furthermore, clinical MRI images are often acquired with limited field of view (FOV) or low image resolution, seriously compromising its use in automated image processing. In this thesis, we focus on the design of efficient image segmentation methods to create subject-specific musculoskeletal models from MRI data. We propose a novel prior knowledge construction for use in discrete deformable models to yield an effective modeling of musculoskeletal structures.
Sleep Apnea Detection Based on Multi-Scale Residual Network

Aiming at the fact that traditional convolutional neural networks cannot effectively extract signal features in complex application scenarios, a sleep apnea (SA) detection method based on multi-scale residual networks is proposed. First, we analyze the physiological mechanism of SA, which uses the RR interval signals and R peak signals derived from the ECG signals as input. Then, a multi-scale residual network is used to extract the characteristics of the original signals in order to obtain sensitive characteristics from various angles. Because the residual structure is used in the model, the problem of model degradation can be avoided. Finally, a fully connected layer is introduced for SA detection. In order to overcome the impact of class imbalance, a focal loss function is introduced to replace the traditional cross-entropy loss function, which makes the model pay more attention to learning difficult samples in the training phase. Experimental results from the Apnea-ECG dataset show that the accuracy, sensitivity and specificity of the proposed multi-scale residual network are 86.0%, 84.1% and 87.1%, respectively. These results indicate that the proposed method not only achieves greater recognition accuracy than other methods, but it also effectively resolves the problem of low sensitivity caused by class imbalance.
Analysis of Factors Influencing Stock Market Volatility Based on GARCH-MIDAS Model

This paper further extends the existing GARCH-MIDAS model to deal with the effect of microstructure noise in mixed frequency data. This paper has two highlights. First, according to the estimation of the long-term volatility components of the GARCH-MIDAS model, rAVGRV is adopted to substitute for the RV estimator. rAVGRV uses the rich data sources in tick-by-tick data and significantly corrects the impact of the microstructure noise on volatility estimation. Second, besides introducing macroeconomic variables (i.e., macroeconomic consistency index (MCI), deposits in financial institutions (DFI), industrial value-added (IVA), and M2), Chinese Economic Policy Uncertainty (CEPU) index and Infectious Disease Equity Market Volatility Tracker (EMV) are introduced in the long-run volatility component of the GARCH-MIDAS model. As indicated by the results of this paper, the rAVGRV-based GARCH-MIDAS is slightly better than the RV model-based GARCH-MIDAS. In addition to the common macroeconomic variables significantly impacting stock market volatility, CEPU also substantially impacts stock market volatility. Nevertheless, the effect of EMV on the stock market is insignificant.
Determinants of Pharmaceutical Review, Success and Duration

This paper estimates three duration models, one for each of the three clinical phases of drug development using publicly available data. Specifically, we estimate three discrete time mixed proportional hazard models, with some parameter constraints across models. We present the estimated relationship between particular drug characteristics and the probability of succeeding (and failing) each of the clinical review phases, as well as the expected duration of review. The characteristics we control for include, among others, primary indication, route of administration, and original material as well as fixed effects for vaccines and reformulations. We are also able to identify spill-over effects from sponsorship or licensing by large pharmaceutical companies, and the effect of "competition" in the form of same-disease drugs already in the review process. Additionally, we distinguish drugs being developed in the U.S., versus those who are likely developed abroad. Calendar time fixed-effects (both annual and quarterly) are also estimated which permit us to identify whether average durations are changing independently of the changing profile of the drugs under review, in other words, whether they are changing as a result of policy for example.On balance we find that primary disease indications are usually unrelated to a drug's eventual success or failure in review, but that different original materials and different routes of administration are significant. Competition in each of the phases is a determinant factor in both success and failure. Spillover effects seem to have a small impact, and so does being sponsored by a large pharmaceutical company, and being licensed by one. A major finding of our paper is that drugs developed only in the U.S. versus in the U.S. and in other countries simultaneously, move through the review process faster, but also have a lower probability of succeeding. This model can be used to simulate the path of drugs through the pipeline, and can be used in a variety of situations, including antitrust review, intellectual property, insurance, market valuation and social policy analysis.
Woody-plant encroachment: Precipitation, herbivory and grass-competition interact to affect shrub recruitment.

Woody-plant encroachment is a global phenomenon that has been affecting the southwestern US since the late 1800s. Drought, overgrazing, herbivory, and competition between grasses and shrub seedlings have been hypothesized as the main drivers of shrub establishment. However, there is limited knowledge about the interactions among these drivers. Using a rainfall manipulation system and various herbivore exclosures, we tested hypotheses about how precipitation (PPT), competition between grasses and shrub seedlings, and predation affect the germination and first-year survival of Mesquite (Prosopis glandulosa), a shrub that has encroached in Southern Great Plains and Chihuahuan Desert grasslands. We found that Mesquite germination and survival (i) increased with increasing PPT, then saturated at about the mean growing season PPT level, (ii) that competition between grasses and shrub seedlings had no effect on either germination or survival, and (iii) that herbivory by small mammals decreased seedling establishment and survival, while ant granivory showed no effect. In addition to its direct positive effect on survival, PPT had an indirect negative effect via increasing small mammal activity. Current models predict a decrease in PPT in the southwestern US with increased frequency of extreme events. The non-linear nature of PPT effects on Mesquite recruitment suggests asymmetric responses, wherein drought has a relatively greater negative effect than the positive effect of wet years. Indirect effects of PPT, through its effects on small mammal abundance, highlight the importance of accounting for interactions between biotic and abiotic drivers of shrub encroachment. This study provides quantitative basis for developing tools that can inform effective shrub management strategies in grasslands and savannas.
Fatores chave para o alinhamento estratégico vertical: Survey com executivos brasileiros

Alignment is a central concept in the field of strategy studies. However, prior research has tended to emphasize the subject in the context of the formulation versus the implementation of strategy. This study contributes to filling this gap by identifying and proposing a framework with key factors to promote strategic vertical alignment. These factors were submitted to 125 Brazilian executives in a quantitative study to evaluate their implementation. The results were evaluated by applying descriptive statistics. The results demonstrated an emphasis on short-term planning, a need to extend the participation of middle management in the formulation process, and gaps in management's capacity to implement strategy. Factors that can enhance people's involvement with the strategy are also discussed.
Selection of superior genotypes of Indian jujube (Ziziphus mauritiana Lamk.) as revealed by fruit‐related traits

Abstract The nutritional and medicinal benefits of Ziziphus mauritiana Lamk. have led to its attention. Here, morphological and pomological diversity of this species was investigated. Most of the characters recorded showed considerable differences among the genotypes studied. The range of ripening data was from mid‐February to mid‐March. Fruit weight ranged between 15.68 and 33.62 g with an average of 24.17. Strong diversity was observed among the genotypes in terms of fruit skin ground color, ranging from light green to orange. There were significant correlations between some characters especially between the traits related to fruit size. Principal component analysis (PCA) classified the traits into 12 main components, justifying 75.07% of the total variance. The studied genotypes were grouped into two main clusters, indicating strong diversity among them. The present information might be used to choose the genotypes with the desired traits. Twenty‐one genotypes were promising because of high values of fruit weight, fruit taste, fruit skin color, and fruit quality, and thus, they can be recommended for direct cultivation and also to be used in breeding programs. The genotypes with superior traits can be further used for improvement through selection and hybridization to get desired traits.
Optimal Capacity Configuration of VSM-Controlled Grid-Connected Inverters in a Multi-Inverter System Based on Hybrid-Mode Control Under Weak Grids

With the increasing penetration of renewable energy generation, the power grid shows weak grid characteristics, which seriously affect the stability of grid-connected inverters. For grid-connected inverters, there are two basic control modes: current source mode (CSM) and voltage source mode (VSM), also known as grid-following control and grid-forming control. Existing studies show that the CSM-controlled grid-connected inverters can operate stably under the strong grid, while the system stability margin decreases or even becomes unstable under the weak grid. The VSM-controlled grid-connected inverters are just the opposite. Some literature proposes a hybrid-mode control for the multi-inverter system under weak grids: the system contains both CSM-controlled grid-connected inverters and VSM-controlled grid-connected inverters, thus effectively improving the overall stability of the system. However, the existing literature does not quantify the capacity of the VSM-controlled grid-connected inverters in this system. Therefore, a stability analysis based on multi-performance-index constraints, such as stability margin and control bandwidth, is conducted for this hybrid-mode controlled system, and the optimal capacity configuration of VSM-controlled grid-connected inverters is obtained in this paper. Finally, the experimental results prove the correctness of the analysis.
Evolution and Stress Responses of CLO Genes and Potential Function of the GhCLO06 Gene in Salt Resistance of Cotton

The caleosin (CLO) protein family displays calcium-binding properties and plays an important role in the abiotic stress response. Here, a total of 107 CLO genes were identified in 15 plant species, while no CLO genes were detected in two green algal species. Evolutionary analysis revealed that the CLO gene family may have evolved mainly in terrestrial plants and that biological functional differentiation between species and functional expansion within species have occurred. Of these, 56 CLO genes were identified in four cotton species. Collinearity analysis showed that CLO gene family expansion mainly occurred through segmental duplication and whole-genome duplication in cotton. Sequence alignment and phylogenetic analysis showed that the CLO proteins of the four cotton species were mainly divided into two types: H-caleosins (class I) and L-caleosins (class II). Cis-acting element analysis and quantitative RT–PCR (qRT–PCR) suggested that GhCLOs might be regulated by abscisic acid (ABA) and methyl jasmonate (MeJA). Moreover, transcriptome data and qRT–PCR results revealed that GhCLO genes responded to salt and drought stresses. Under salt stress, gene-silenced plants (TRV: GhCLO06) showed obvious yellowing and wilting, higher malondialdehyde (MDA) content accumulation, and significantly lower activities of superoxide dismutase (SOD) and peroxidase (POD), indicating that GhCLO06 plays a positive regulatory role in cotton salt tolerance. In gene-silenced plants (TRV: GhCLO06), ABA-related genes (GhABF2, GhABI5, and GhNAC4) were significantly upregulated after salt stress, suggesting that the regulation of salt tolerance may be related to the ABA signaling pathway. This research provides an important reference for further understanding and analyzing the molecular regulatory mechanism of CLOs for salt tolerance.
Assessment and Improvement of the Seismic Safety of the “Bastione Farnesiano”, in the Central Archeological Area of Rome: A Calculation Method Between Need to Preserve and Uncertainties

ABSTRACT This article presents a study aiming at assessing the seismic safety and developing the rehabilitation design of a masonry retaining wall, known as Bastione Farnesiano, and placed around the Palatinum hill, in the central archeological area of Rome, in Italy. It is a singular artifact of its kind and hardly identifiable with known stereotypes or constructive models. The phase of survey, together with both the material and degradation analyses, showed the impossibility to define with certainty some features, even geometrical, of the monument, necessary to reach a judgment about its safety. Therefore, it was necessary to formulate the risk assessment problem by taking into due consideration all uncertainties and evaluating them in probabilistic terms. A simple mechanical model, considering different and alternative collapse modes, was developed and, after characterizing the uncertain parameters in probabilistic terms, Monte Carlo simulations were carried out. Based on the obtained results: (a) the value of the current risk index has been determined and (b) a sensitivity analysis has been performed in order to identify the parameters that mostly affect the monument safety. This sensitivity analysis has provided useful information that has allowed to orient the seismic amelioration design strategy by acting on one of the parameters that have greater impact on the risk reduction.