Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 Burden in Long-Term Care Facilities in the Omicron Era: Public Health Action Not Yet Redundant

Since the beginning of the pandemic, public health authorities have provided support to long-term care facilities (LTCFs) for the implementation of risk mitigation measures. Nevertheless, the necessity of these measures has been doubted, especially after vaccines and antiviral treatment became available. Here, we present the burden of COVID-19 infection in LTCFs during the first 9 months of 2022 across Greece. We tested the possible association of LTCF characteristics and public health response with the occurrence of clusters (two or more linked cases in LTCFs) with facilities recording one case as reference. After excluding LTCFs with sporadic cases, we tested the effect of the abovementioned variables on attack rate (cases/total number of persons in the LTCF). The disease burden in LTCFs was high and substantially varied among facilities, with hospitalization and case fatality rates ranging from 2 to 80% (median 14%, IQR 27%) and from 1 to 50% (median 5%, IQR 7%), respectively. The probability of transmission inside the facility increased when notification of public health authorities was delayed (p-Value < 0.001) after adjusting for vaccination status and phase of the pandemic. Results showed that active support from public health authorities is still important in reducing the burden in LTCFs.
Identifying Changes of Functional Brain Networks using Graph Theory

Analytic tools for addressing spontaneous brain activity, as acquired with fMRI during the “restingstate,” have grown dramatically over the past decade. Along with each new technique, novel hypotheses about the functional organization of the brain are also available to researchers. We review six prominent categories of resting-state fMRI data analysis: seed-based functional connectivity, independent component analysis, clustering, pattern classification, graph theory, and two “local” methods. In surveying these methods, we address their underlying assumptions, methodologies, and novel applications.
Effect of a Fourth Dose of mRNA Vaccine and of Immunosuppression in Preventing SARS-CoV-2 Breakthrough Infections in Heart Transplant Patients

Patients with heart transplantation (HT) have an increased risk of COVID-19 disease and the efficacy of vaccines on antibody induction is lower, even after three or four doses. The aim of our study was to assess the efficacy of four doses on infections and their interplay with immunosuppression. We included in this retrospective study all adult HT patients (12/21–11/22) without prior infection receiving a third or fourth dose of mRNA vaccine. The endpoints were infections and the combined incidence of ICU hospitalizations/death after the last dose (6-month survival rate). Among 268 patients, 62 had an infection, and 27.3% received four doses. Following multivariate analysis, three vs. four doses, mycophenolate (MMF) therapy, and HT < 5 years were associated with an increased risk of infection. MMF ≥ 2000 mg/day independently predicted infection, together with the other variables, and was associated with ICU hospitalization/death. Patients on MMF had lower levels of anti-RBD antibodies, and a positive antibody response after the third dose was associated with a lower probability of infection. In HT patients, a fourth dose of vaccine against SARS-CoV-2 reduces the risk of infection at six months. Mycophenolate, particularly at high doses, reduces the clinical effectiveness of the fourth dose and the antibody response to the vaccine.
Student Perceptions of Online Engagement

This paper reports on research that extends knowledge about higher education students’ perceptions of online engagement. In particular, the study aimed to identify what students thought engagement was and how they experienced it. Understanding students’ views about online engagement will provide a more comprehensive understanding of the topic and should assist instructional designers to support academic staff to develop online courses that are more likely to engage their students. Using a mixed-methods approach, the study found that students felt most engaged with learning when doing practical, hands-on activities. Additional findings from the qualitative and quantitative data are highlighted, with some differences between the students’ perceptions in the different types of data, particularly concerning social engagement. This suggests that further research is warranted. The paper offers several practical implications for student learning.
Short-Term Wind Power Prediction Based on Multi-Parameters Similarity Wind Process Matching and Weighed-Voting-Based Deep Learning Model Selection

Sufficiently accurate short-term wind power prediction is important for the grid dispatch of the power system. To improve the accuracy by selecting suitable model for each piece of wind processes, this paper presents a short-term wind power prediction method based on multi-parameters similarity wind process matching and weighed-voting-based deep learning model selection. First, a novel multi-parameters similarity wind process matching method is presented to match each forecast target sample with groups of highly similar historical wind processes, in which each 96h-time-scale sample is divided into multiple wind processes by a tumbling time window, and a combinational similarity matching algorithm that consider four similarity indexes is proposed to judge the similarity among wind processes. Second, a weighed-voting-based deep learning model selection method, in which the matched highly similar historical wind processes are introduced to vote the optimal candidate deep learning model, is proposed to select the optimal model from LSTM, BLSTM, CNN, CNN-LSTM, CNN-BLSTM, and SDAE for each forecast target wind process. Case studies are presented to verify the effectiveness and superiority of the proposed method. Based on this new method, the 24h-day-ahead and 96h-short-term prediction RMSE can be decreased by 0.69% to 1.7% and 1.15% to 2.2% respectively compared to single deep learning model, which demonstrates the effectiveness of the proposed method.
Effects of Gamification in a Teacher Education Program, 2010 to 2020

The use of game elements as a means to stimulate students’ motivation has increased in a number of disciplines across the world after 2010, but apart from a few exceptional cases, it has not been possible to show a significant effect on academic achievement. In this study, the effects of game elements on academic achievement have been investigated among 458 students in a teacher education program, distributed across 10 course instances over 11 years (2010–2020). Statistical analyses indicated that a game element consisting of a number of hand-in assignments contributed significantly to the total points of the final written exam. Although a comparison with the students’ results in the two previous modules showed that two thirds of the positive effect could be explained by selection, the remaining one third of the positive effect could still be explained in terms of the game elements significantly improving the achievements on the final written exam.
High-Tc Josephson Junction Arrays for THz Applications

High-Tc superconductors (HTS) like yttrium barium copper oxide (YBa2Cu3O7-x) pave the way towards readily available and precise voltage standards at liquid nitrogen temperatures. They can be driven by terahertz radiation, thus achieving higher output voltages with a significantly reduced junction count. The reduced effort in cooling also simplifies the overall application. The use of a gallium focused ion beam (Ga-FIB) represents a promising method to tailor the geometry of Josephson junctions after conventional patterning of an array to tune the junction parameters individually. In this work, we present a Josephson junction array made from the HTS YBa2Cu3O7-x based on grain-boundary Josephson junctions. The junction parameters are investigated and afterwards the influence of the Ga-FIB on the junctions is examined. This serves as an important step to show the suitability of this process for HTS voltage standards. Furthermore, the high frequency properties of the array and the associated Shapiro steps are characterized in the terahertz range. This technique represents an important step towards versatile HTS voltage standards.
Multi-Thickness Target Plate Impact Experimental Approach to Failure Waves in Soda-lime Glass and Its Numerical Simulation

Abstract A multi-thickness target plate impact experimental technique is proposed in this paper and adopted in the research on the so-called failure wave phenomenon in soda-lime glasses, in which, four sub-targets in different thicknesses embedded in the target ring are impacted simultaneously and the longitudinal stress temporal curves at the backing surface of each of the sub-targets are measured by four manganin piezo-resistive stress sensors. Hence, the failure wave trajectory under a certain dynamic loading can be obtained by only one test, which can reduce considerably the experimental expense as well as the experimental period, and, more importantly, the measurement uncertainty resulted from different loading conditions in repetitious impact experiments is avoided. It is found that the propagating velocity of failure wave is approximate to a constant and increases with the magnitude of the impact loading, and there always exists an initial delay time for the initiation of failure wave behind the precursory shock wave, which decreases with the magnitude of impact loads. Moreover, a numerical simulation for the failure wave propagation is carried out by using the LS-DYNA applied software, together with a statistical isotropic elastic microcrack model to describe the dynamic damage evolvement of soda-lime glasses. It is demonstrated that both the critical damage value distributions and the free surface particle velocity temporal curves can be used to determine the failure wave trajectory, and the numerical results are consistent substantially with the experimental data.
Bactericidal and plant defense elicitation activities of Eucalyptus oil decrease the severity of infections by Xylella fastidiosa on almond plants

The activity of Eucalyptus essential oil against eleven strains pertaining to six species of plant pathogenic bacteria was studied using growth inhibition and contact assays. All strains were susceptible to the formulation EGL2, and Xylella fastidiosa subspecies and Xanthomonas fragariae were the most sensitive. The bactericidal effect was strong causing 4.5 to 6.0 log reductions in survival in 30 min at concentrations in the range of 0.75 to 15.0 μl/ml depending on the bacteria tested. Transmission electron microscopy of the formulation EGL2 against the three X. fastidiosa subspecies studied allowed the observation of a strong lytic effect on bacterial cells. In addition, the preventive spray application of EGL2 to potted pear plants subsequently inoculated with Erwinia amylovora significantly decreased the severity of infections. Almond plants treated by endotherapy or soil drenching, and then inoculated with X. fastidiosa showed a significant decrease in disease severity as well as in the levels of the pathogen, depending on the strategy used (endotherapy/soil drenching, preventive/curative). The treatment by endotherapy in almond plants induced the expression of several genes involved in plant defense. It was concluded that the reduction of infections by the Eucalyptus oil treatments was due to the combination of its bactericidal and plant defense induction activities.
Mechanism of frequency lock-in in vortex-induced vibrations at low Reynolds numbers

In this study, a CFD-based linear dynamics model combined with the direct Computational Fluid Dynamics/Computational Structural Dynamics (CFD/CSD) simulation method is utilized to study the physical mechanisms underlying frequency lock-in in vortex-induced vibrations (VIVs). An identification method is employed to construct the reduced-order models (ROMs) of unsteady aerodynamics for the incompressible flow past a vibrating cylinder at low Reynolds numbers ( $Re$ ). Reduced-order-model-based fluid–structure interaction models for VIV are also constructed by coupling ROMs and structural motion equations. The effects of the natural frequency of the cylinder, mass ratio and structural damping coefficient on the dynamics of the coupled system at $Re=60$ are investigated. The results show that the frequency lock-in phenomenon at low Reynolds numbers can be divided into two patterns according to different induced mechanisms. The two patterns are ‘resonance-induced lock-in’ and ‘flutter-induced lock-in’. When the natural frequency of the cylinder is in the vicinity of the eigenfrequency of the uncoupled wake mode (WM), only the WM is unstable. The dynamics of the coupled system is dominated by resonance. Meanwhile, for relatively high natural frequencies (i.e. greater than the eigenfrequency of the uncoupled WM), the structure mode becomes unstable, and the coupling between the two unstable modes eventually leads to flutter. Flutter is the root cause of frequency lock-in and the higher vibration amplitude of the cylinder than that of the resonance region. This result provides evidence for the finding of De Langre (J. Fluids Struct., vol. 22, 2006, pp. 783–791) that frequency lock-in is caused by coupled-mode flutter. The linear model exactly predicts the onset reduced velocity of frequency lock-in compared with that of direct numerical simulations. In addition, the transition frequency predicted by the linear model is in close coincidence with the amplitude of the lift coefficient of a fixed cylinder for high mass ratios. Therefore, it confirms that linear models can capture a significant part of the inherent physics of the frequency lock-in phenomenon.
Model reduction for non-minimal state-space systems

This paper is concerned with discrete-time model reduction via order-reduction of a discrete-time left coprime factor representation of a given model or controller. The proposed model reduction scheme is suitable for both, stable and unstable models, and for non-minimal state-space systems. Balanced singular perturbation approximation is considered rather than balanced truncation. Numerical issues are also considered.
Verification of Codes and Calculations

Background discussion, definitions, and descriptions are given for some terms related to confidence building in computational fluid dynamics. The two principal distinctions made are between verification vs validation and between verification of codes vs verification of individual calculations. Also discussed are numerical errors vs conceptual modeling errors; iterative convergence vs grid convergence (or residual accuracy vs discretization accuracy); confirmation, calibration, tuning, and certification; error taxonomies; and customer illusions vs customer care. Emphasis is given to rigorous code verification via systematic grid convergence using the method of manufactured solutions, and a simple method for uniform reporting of grid convergence studies using the Grid Convergence Index (GCI). Also discussed are surrogate single-grid error indicators.
Establishing a human bone marrow single cell reference atlas to study ageing and diseases

Introduction Ageing in the human bone marrow is associated with immune function decline that results in the elderly being vulnerable to illnesses. A comprehensive healthy bone marrow consensus atlas can serve as a reference to study the immunological changes associated with ageing, and to identify and study abnormal cell states. Methods We collected publicly available single cell transcriptomic data of 145 healthy samples encompassing a wide spectrum of ages ranging from 2 to 84 years old to construct our human bone marrow atlas. The final atlas has 673,750 cells and 54 annotated cell types. Results We first characterised the changes in cell population sizes with respect to age and the corresponding changes in gene expression and pathways. Overall, we found significant age-associated changes in the lymphoid lineage cells. The naïve CD8+ T cell population showed significant shrinkage with ageing while the effector/memory CD4+ T cells increased in proportion. We also found an age-correlated decline in the common lymphoid progenitor population, in line with the commonly observed myeloid skew in haematopoiesis among the elderly. We then employed our cell type-specific ageing gene signatures to develop a machine learning model that predicts the biological age of bone marrow samples, which we then applied to healthy individuals and those with blood diseases. Finally, we demonstrated how to identify abnormal cell states by mapping disease samples onto the atlas. We accurately identified abnormal plasma cells and erythroblasts in multiple myeloma samples, and abnormal cells in acute myeloid leukaemia samples. Discussion The bone marrow is the site of haematopoiesis, a highly important bodily process. We believe that our healthy bone marrow atlas is a valuable reference for studying bone marrow processes and bone marrow-related diseases. It can be mined for novel discoveries, as well as serve as a reference scaffold for mapping samples to identify and investigate abnormal cells.
A neural network for phenotyping Fusarium‐damaged kernels (FDKs) in wheat and its impact on genomic selection accuracy

Fusarium head blight (FHB) remains one of the most destructive diseases in wheat. Primarily caused by the mycotoxigenic fungi Fusarium graminearum, FHB results in both widespread yield loss and deoxynivalenol (DON) contamination of wheat grain. Phenotyping for Fusarium‐damaged kernels (FDKs) is the most efficient estimate of resistance to DON accumulation outside of performing costly and time‐consuming laboratory assays. However, manual phenotyping for FDKs can be tedious and highly subjective to observers. This study developed and tested an open‐access, easy‐to‐use, and effective method for phenotyping FDKs using a neural network capable of analyzing cell phone camera images. Quantitative genetic analysis of FDK data generated by our trained neural network found that the trait had a broad sense heritability of 0.48, and its phenotypic and genetic correlations with DON were 0.41 and 0.58, respectively. To determine if our neural network‐derived FDK data could be useful in a modern breeding scenario, we included it in a multi‐trait genomic selection (GS) model and evaluated the model's ability to predict DON. We found that including FDK data generated by our trained neural network on the test set during GS model training more than doubled GS accuracy, but the highest accuracy was obtained using conventional FDK data. Although further training is needed to improve the capabilities of our neural network, initial testing shows encouraging results and demonstrates the possibility of providing an automated and objective phenotyping method for FDKs that could be widely deployed to support FHB resistance breeding efforts.
Planimetric instability of channels with variable width

We study the steady three-dimensional flow field and bed topography in a channel with sinusoidally varying width, under the assumptions of small-amplitude width variations and sufficiently wide channel to neglect nonlinear effects and sidewall effects. The aim of the work is to investigate the role of width variations in producing channel bifurcation in braided rivers. We infer incipient bifurcation in cases where the growth of a central bar leads to planimetric instability of the channel, i.e. when the given infinitesimal width perturbation is enhanced. Results of the three-dimensional model suggest that the equilibrium bottom profile mainly consists of a purely longitudinal component, uniformly distributed over the cross-section, which induces deposition at the wide section and scour at the constriction, and of a transverse component in the form of a central bar (wide sections) and scour (constrictions), with longitudinal wavelength equal to that of width variations. A comparison between the results of the three-dimensional model and those obtained by means of a two-dimensional depth-averaged approach shows that the transverse component is mainly related to three-dimensional effects. Theoretical findings display a satisfactory agreement with results of flume experiments. Transverse variations are responsible for the planimetric instability of the channel; we find that in the range of values of Shields stress typical of braided rivers, the incipient bifurcation is enhanced as the width ratio of the channel increases.
Prevalence of household food insecurity in the marginal area of Birjand, Iran

Introduction The right to food is a human right. Food insecurity refers to the inability to prepare proper and healthy food. Food insecurity is associated with adverse outcomes among households. To determine the prevalence of food insecurity, its level, and the factors affecting it among households covered by health centers in marginalized areas of Birjand city. Methods This descriptive-analytical cross-sectional study was performed on 396 households aged 20-60 years covered by health centers in the marginal areas of Birjand in 2019. We used a 9-item Household Food Insecurity Access Scale (HFIAS) questionnaire. Chi-square, Fisher, and logistic regression analyses were performed using SPSS 22 software at a significance level of less than 0.05. Results The mean age of the subjects was 34.04 ± 9.41 years. In total, 48% of the subjects had a level of food insecurity, of which 7.6% had severe, 12.6% moderate, and 27.5% mild insecurity. The prevalence of food insecurity in families with low members and higher levels of education is significantly lower (p < 001). Backward Logistic regression showed that illiteracy (OR: 8.26) and primary education (OR: 2.65), rental housing status (OR: 1.69), lower-income levels being covered by support organizations (OR: 3.13), employed members between 3-4 and more than five (OR: 2.73 and 2.93, respectively), and low weight (OR: 2.55) were associated with food insecurity. The prevalence of food insecurity in this study was high. Conclusion The high prevalence of insecurity in studied households is an alarm for the authorities, which can be due to poverty and the low level of nutritional literacy of households' heads and mothers. Helping to improve the quality of life of families and food insecurity of households by improving physical and economic access, increasing literacy and nutritional culture, and reducing the burden of non-communicable diseases related to nutrition is a suggested solution.
Mechanism of Hair Curling via Laccase-Assisted Tyrosine Grafting Using BSA as a Model Protein

ABSTRACT Commercial hair perming uses strong reducing agents and harms hair fiber’s quality even human health. In this study, tyrosine is adopted as a cross-linking agent between thiols and/or amines as the shape-changing of hair involves the breakage of disulfide bonds and the rearrangement of new bonds between keratin molecules. To investigate the mechanism of keratin cross-linking, bovine serum albumin (BSA) is used as a model protein. Disulfide bonds in BSA are successfully reduced by L-cysteine to provide free thiols. Four new cross-linked peptides are formed inter- or intra-BSA monomers, indicating that tyrosine can be adopted as a cross-linking agent not only between amines but also between thiols. Moreover, curling of Asian hair is conducted using tyrosine as a perming agent by the laccase-assisted reaction. The optimized operational conditions are hair with L-cysteine pre-treatment (50.0 mM) followed by laccase-assisted grafting with 3.0 mM tyrosine. The reshaped hair performed a better perming performance than commercial perming product before washing, although a lower perming efficiency after washing. The curling process could be accomplished without strength loss of hair fibers and with a blow-drier easily. Hence, this new methodology may lead to the development of a gentle and user-friendly approach in the hair care industry.