Miracle-dz/UniFAR-FTUs huggingface dataset:
A Manganese-independent Aldolase Enables Staphylococcus aureus To Resist Host-imposed Metal Starvation

Staphylococcus aureus and other pathogens consume glucose during infection. Glucose utilization increases the demand for transition metals, such as manganese, a nutrient that the host limits as a defense mechanism against invading pathogens. ABSTRACT The preferred carbon source of Staphylococcus aureus and many other pathogens is glucose, and its consumption is critical during infection. However, glucose utilization increases the cellular demand for manganese, a nutrient sequestered by the host as a defense against invading pathogens. Therefore, bacteria must balance glucose metabolism with the increasing demand that metal-dependent processes, such as glycolysis, impose upon the cell. A critical regulator that enables S. aureus to resist nutritional immunity is the ArlRS two-component system. This work revealed that ArlRS regulates the expression of FdaB, a metal-independent fructose 1,6-bisphosphate aldolase. Further investigation revealed that when S. aureus is metal-starved by the host, FdaB functionally replaces the metal-dependent isozyme FbaA, thereby allowing S. aureus to resist host-imposed metal starvation in culture. Although metal-dependent aldolases are canonically zinc-dependent, this work uncovered that FbaA requires manganese for activity and that FdaB protects S. aureus from manganese starvation. Both FbaA and FdaB contribute to the ability of S. aureus to cause invasive disease in wild-type mice. However, the virulence defect of a strain lacking FdaB was reversed in calprotectin-deficient mice, which have defects in manganese sequestration, indicating that this isozyme contributes to the ability of this pathogen to overcome manganese limitation during infection. Cumulatively, these observations suggest that the expression of the metal-independent aldolase FdaB allows S. aureus to alleviate the increased demand for manganese that glucose consumption imposes, and highlights the cofactor flexibility of even established metalloenzyme families. IMPORTANCE Staphylococcus aureus and other pathogens consume glucose during infection. Glucose utilization increases the demand for transition metals, such as manganese, a nutrient that the host limits as a defense mechanism against invading pathogens. Therefore, pathogenic bacteria must balance glucose and manganese requirements during infection. The two-component system ArlRS is an important regulator that allows S. aureus to adapt to both glucose and manganese starvation. Among the genes regulated by ArlRS is the metal-independent fructose 1,6-bisphosphate aldolase fdaB, which functionally substitutes for the metal-dependent isoenzyme FbaA and enables S. aureus to survive host-imposed manganese starvation. Unexpectedly, and differing from most characterized metal-dependent aldolases, FbaA requires manganese for activity. Cumulatively, these findings reveal a new mechanism for overcoming nutritional immunity as well as the cofactor plasticity of even well-characterized metalloenzyme families.
Latest Advances in Laboratory Detection of Mycoplasma genitalium

Mycoplasma genitalium is an important sexually transmitted pathogen affecting both men and women. Its extremely slow growth in vitro and very demanding culture requirements necessitate the use of molecular-based diagnostic tests for its detection in clinical specimens. ABSTRACT Mycoplasma genitalium is an important sexually transmitted pathogen affecting both men and women. Its extremely slow growth in vitro and very demanding culture requirements necessitate the use of molecular-based diagnostic tests for its detection in clinical specimens. The recent availability of U.S. Food and Drug Administration (FDA)-cleared commercial molecular-based assays has enabled diagnostic testing to become more widely available in the United States and no longer limited to specialized reference laboratories. Advances in the knowledge of the epidemiology and clinical significance of M. genitalium as a human pathogen made possible by the availability of molecular-based testing have led to updated guidelines for diagnostic testing and treatment that have been published in various countries. This review summarizes the importance of M. genitalium as an agent of human disease, explains the necessity of obtaining a microbiological diagnosis, describes currently available diagnostic methods, and discusses how the emergence of antimicrobial resistance has complicated treatment alternatives and influenced the development of diagnostic tests for resistance detection, with an emphasis on developments over the past few years.
DEAD Box Protein DhR1 Is a Global Regulator Involved in the Bacterial Fitness and Virulence of Riemerella anatipestifer

R. anatipestifer infection has been a continued and serious problem in many duck farms, but little is known about the mechanism underlying the pathogenesis of R. anatipestifer and how R. anatipestifer adapts to the external environment and thereby persists in duck farms. The results of this study demonstrate that the DEAD box protein DhR1 is required for the tolerance of R. anatipestifer to cold, pH, and other stresses, and it is also necessary for biofilm formation, iron utilization, and virulence in ducklings, demonstrating multiple functions of DhR1. ABSTRACT DEAD box proteins perform diverse cellular functions in bacteria. Our group previously reported that the transposon Tn4531 insertion in Riean_0395 (designated dhR1), which encodes a putative DEAD box helicase, attenuated the virulence of R. anatipestifer strain YZb1. Here, we show that, compared to the wild-type (WT) R. anatipestifer strain Yb2, the growth or survival of the ΔdhR1 mutant in tryptic soy broth (TSB) was significantly decreased in response to cold, pH, osmotic stress, ethanol, Triton X-100, and oxidative stress, and the dhR1 deletion significantly reduced biofilm formation and the adhesion capacity to Vero cells, whereas the growth of ΔdhR1 was less impaired in iron-limited TSB. Moreover, the virulence of ΔdhR1 in ducklings was attenuated by about 80-fold, compared to the WT. In addition, a transcriptome analysis showed that the dhR1 deletion in the strain Yb2 affected the expression of 58 upregulated genes and 98 downregulated genes that are responsible for various functions. Overall, our work reveals that the deletion of DhR1 results in a broad effect on the bacterial fitness, biofilm formation, iron utilization, and virulence of R. anatipestifer, which makes it a global regulator. IMPORTANCE R. anatipestifer infection has been a continued and serious problem in many duck farms, but little is known about the mechanism underlying the pathogenesis of R. anatipestifer and how R. anatipestifer adapts to the external environment and thereby persists in duck farms. The results of this study demonstrate that the DEAD box protein DhR1 is required for the tolerance of R. anatipestifer to cold, pH, and other stresses, and it is also necessary for biofilm formation, iron utilization, and virulence in ducklings, demonstrating multiple functions of DhR1.
Parent–Child Attachment and Dynamic Emotion Regulation: A Systematic Review

Although there is evidence parent–child attachment security is associated with trait-like emotion indices, trait perspectives do not fully capture children's responses to context, an important emotion regulation component. This paper evaluates whether attachment is associated with two dynamic emotion indicators: emotion reactivity and emotion recovery. We review conceptual and empirical connections, describe the dynamic emotion perspective, discuss hypotheses, and review evidence. Our review (15 studies) shows that secure attachment was more consistently related to recovery than reactivity, avoidant attachment was related to low emotion reactivity and recovery, ambivalent attachment was associated with greater emotion reactivity, and disorganized attachment was related to high reactivity and recovery difficulties. We close by comparing trait-like and dynamic emotion conclusions then propose future research directions.
The influence of the field orientation on physical demands in soccer small-sided games

The aim of this study was to compare the physical demands of SSG with either greater length (SSGlength) or width (SSGwidth) with official soccer matches. Sixteen Brazilian U-20 national-level soccer players participated in two sessions with SSGlength (4v4, 40 m length and 26 m width) and two sessions with SSGwidth (4v4, 26 m length and 40 m width). External and internal load variables were monitored and compared to official games (12.8 ± 8.9 games per player). Data were standardized to the time and compared between the experimental conditions by a one-way analysis of variance (ANOVA) with repeated measures (level of significance set at p < 0.05). The effect size was calculated by the partial ETA squared (ɳp2). Results showed that the total distance covered during SSGlength was significantly higher compared to official matches (8.1%; p = 0.001) and SSGwidth (5.2%; p = 0.049). Highest values were registered in the matches when compared to SSGlength and SSGwidth for high-speed distance, sprint distance, maximal speed and number of sprints. The number of accelerations was significantly lower during official matches than SSGwidth (36.5%; p = 0.002) and SSGlength (45.0%; p < 0.001). The number of high-speed actions was higher in official games when compared to SSGwidth (47.1%; p < 0.001). No differences were found between matches and SSGlength (p = 0.059). We concluded that the physical demands of the 4v4 SSGs adopted in this study were different from the physical demands of official match play. The use of a field with a greater width rather than length does not appear to make the physical demands of the SSG more similar to the official games.
Cooperation Leads Change: Subordinate Moqi, Leader-member Exchange, and Change-oriented Behaviors among Chinese Workers

This paper developed and tested a theoretical model linking subordinates’ moqi with their supervisor with change-oriented behaviors—specifically, creativity and taking charge—via leader–member exchange (LMX). Using a sample of 189 matched subordinate–supervisor dyads from 22 firms in China, a hierarchical modeling analysis showed that, as hypothesized, subordinate moqi with the supervisor positively affected employees’ creativity and taking charge. Additionally, LMX mediated the relationship between subordinate moqi with the supervisor and employees’ creativity and taking charge. This paper contributes to the current understanding of leadership from a followership perspective and its influence on change-oriented behaviors.
DIGITOUR: Automatic Digital Tours for Real-Estate Properties

A virtual or digital tour is a form of virtual reality technology which allows a user to experience a specific location remotely. Currently, these virtual tours are created by following a 2-step strategy. First, a photographer clicks a 360° equirectangular image; then, a team of annotators manually links these images for the “walkthrough” user experience. The major challenge in the mass adoption of virtual tours is the time and cost involved in manual annotation/linking of images. Therefore, this paper presents an end-to-end pipeline to automate the generation of 3D virtual tours using equirectangular images for real-estate properties. We propose a novel HSV-based coloring scheme for paper tags that need to be placed at different locations before clicking the equirectangular images using 360° cameras. These tags have two characteristics: i) they are numbered to help the photographer for placement of tags in sequence and; ii) bi-colored, which allows better learning of tag detection (using YOLOv5 architecture) in an image and digit recognition (using custom MobileNet architecture) tasks. Finally, we link/connect all the equirectangular images based on detected tags. We show the efficiency of the proposed pipeline on a real-world equirectangular image dataset collected from the Housing.com database.
Neural Networks at a Fraction with Pruned Quaternions

Contemporary state-of-the-art neural networks have increasingly large numbers of parameters, which prevents their deployment on devices with limited computational power. Pruning is one technique to remove unnecessary weights and reduce resource requirements for training and inference. In addition, for ML tasks where the input data is multi-dimensional, using higher-dimensional data embeddings such as complex numbers or quaternions has been shown to reduce the parameter count while maintaining accuracy. In this work, we conduct pruning on real and quaternion-valued implementations of different architectures on classification tasks. We find that for some architectures, at very high sparsity levels, quaternion models provide higher accuracies than their real counterparts. For example, at the task of image classification on CIFAR-10 using Conv-4, at of the number of parameters as the original model, the pruned quaternion version outperforms the pruned real by more than . Experiments on various network architectures and datasets show that for deployment in extremely resource-constrained environments, a sparse quaternion network might be a better candidate than a real sparse model of similar architecture.
Improving performance in collaborative games through personality-based matchmaking

Automatic player matchmaking in video games is usually focused on balancing players by taking into account their past performance. The objectives of video games in which the winning condition is clear and specifically defined by their design make this approach widely used and effective. However, this approach is not necessarily optimal when the main objective is not only winning but also obtaining other rewards like creative achievements, enjoyment, socialization or world construction. This paper describes a formal approach for automatic matchmaking that takes into account the players’ personality traits. This form of matchmaking is oriented towards game genres in which competition is not necessarily the general dynamic. An implementation of the system, evaluation in a virtual escape room and results are provided. Preliminary results suggest that the approach is potentially effective.
Technology Leadership in Malaysian Schools: The Way Forward to Education 4.0 - ICT Utilization and Digital Transformation

Education 4.0 is the answer to the global needs for the advanced integration of humans and technology. Leading school’s technology utilization can be the way forward to support Education 4.0 realization. This study aims to investigate the effects and roles of principals’ technology leadership towards teachers’ ICT utilization and students’ academic performance in secondary schools in Selangor, Malaysia. This empirical study uses a set of questionnaires to gather information from respondents who are in the teaching profession. A total of 310 questionnaires were completed and analyzed. The findings have shown significant positive impacts between the effects of the technology leadership roles of principals on teachers’ effective ICT utilization and students’ academic performance. The integration of ICT and technological tools in schools has a great challenge towards the new era of the Education 4.0 system. This suggests that principals who embrace technology will effectively lead their schools to acquire educational resources to enhance student engagement and learning.
Mathematical War Theories in Business: Lanchester's Equations Law, to the Commercial World

This paper deals with the connection of the most well-known mathematical theory of war, Lanchester’s equations Law, to the commercial world. In order to evaluate a range of military conflicts throughout WWII, mathematical models in military operations research and combat modeling were frequently deployed. One of these, Lanchester’s models describe the outcome of warfare between two similar forces without backups, for time [0, t).
Bayesian exponentially tilted empirical likelihood

While empirical likelihood has been shown to exhibit many of the properties of conventional parametric likelihoods, a formal probabilistic interpretation has so far been lacking. We show that a likelihood function very closely related to empirical likelihood naturally arises from a nonparametric Bayesian procedure which places a type of noninformative prior on the space of distributions. This prior gives preference to distributions having a small support and, among those sharing the same support, it favours entropy-maximising distributions. The resulting nonparametric Bayesian procedure admits a computationally convenient representation as an empirical-likelihood-type likelihood where the probability weights are obtained via exponential tilting. The proposed methodology provides an attractive alternative to the Bayesian bootstrap as a nonparametric limit of a Bayesian procedure for moment condition models. Copyright 2005, Oxford University Press.
Thermal Modeling of Electric Motors Used in Very Small Size Soccer Robots

This paper presents a study of the thermophysical properties of electric motors and of the structure of the robots used by ITAndroids' Very Small Size (VSS) team. To perform the modeling of the robot structure, the General Lumped Capacitance Analysis was used, assuming that the motor's internal temperature can be considered approximately uniform. This hypothesis was validated by means of Biot's Number, and the heat source term was evaluated in order to define which energy dissipation sources would be considered in the modeling. The obtained model was evaluated through numerical simulations to analyze the motors' temperature profile for different current values. We compared the obtained results with similar experiments found in the literature, concluding that the model could predict the system's behavior in a satisfactory manner. Therefore, this work contributes to the research area by providing a model able to predict the temperature of the electric motor under different workloads, thus, allowing the team to anticipate its overheating and prevent its premature burnout.
An Efficient Voxel-Based Deep Learning Approach for Ligand Binding Site Detection

Identifying potential ligand binding sites on proteins is an essential step in structure-based drug design. The computational detection of binding cavities can be improved by combining previous knowledge on protein-ligand binding and deep learning algorithms. In this paper, we present VoxelProt, an efficient deep learning approach for ligand binding site detection. VoxelProt uses the proteins 3-dimensional structure to create a surface point cloud. This point cloud is combined with octree-based sparse voxels and a feature vector containing chemical and geometric information to enable prediction of binding sites via binary classification. In this work, the model systems used to test the performance of VoxelProt are proteins bound to Adenosine Di-Phosphate (ADP), a byproduct of ATP hydrolysis, the primary energy-generating process in living cells. A 5-fold cross-validation was performed on a small size dataset. Training, testing, and validation are performed on voxel data based on protein surface concavity with good prediction accuracy. We test the performance of VoxelProt against multiple metrics, including accuracy, precision, sensitivity, F-score, and AUC-ROC. In addition, we explored modifications of VoxelProt from three perspectives to find the appropriate architecture. Statistical tests are employed to compare the performance of our original VoxelProt architecture to these 3 modifications. We find that balancing input datasets to keep similar ratios of positive to negative instances does not significantly affect performance. However, the addition of atom type features improves performance, and adding more fully connected layers to the model reduces prediction performance. VoxelProt exhibits good predictive ability while requiring less memory due to sparse voxelization, showing promise as an efficient approach to identifying ligand binding sites in more general and complex problems such as in the presence of multiple ligands or during protein dynamics.
TLU-Net: Transfer Learning Framework using U-Net Convolutional Neural Networks for CT-based Lungs and COVID-19 Segmentation

Medical imaging has been a crucial component of COVID-19 clinical procedures. However, CT scans for lung and COVID-19 infection diagnosis are time-consuming and require specific knowledge. In addition, diverse imaging modalities, such as regions of interest (organs or lesions) and picture quality, make the automated segmentation of these infectious fragments difficult. In this paper, we proposed TLU-Net based on an optimized design of U-Net architectures to facilitate successful transfer learning for the segmentation of medical images from a source domain, such as lungs, to a target domain, such as COVID-19 infection. We have investigated the effective transfer learning methodologies for U-Net topologies, hyperparameters, freezing, and multiple models/datasets in the lungs and COVID-19 domains. Due to our improved and lightweight U-Net design, experiments with TLU-Net employing three lung and COVID-19 infection domain datasets increased segmentation accuracy and reduced training time.
Learned Reconstruction Methods With Convergence Guarantees: A survey of concepts and applications

In recent years, deep learning has achieved remarkable empirical success for image reconstruction. This has catalyzed an ongoing quest for the precise characterization of the correctness and reliability of data-driven methods in critical use cases, for instance, in medical imaging. Notwithstanding the excellent performance and efficacy of deep learning-based methods, concerns have been raised regarding the approaches’ stability, or lack thereof, with serious practical implications. Significant advances have been made in recent years to unravel the inner workings of data-driven image recovery methods, challenging their widely perceived black-box nature. In this article, we specify relevant notions of convergence for data-driven image reconstruction, which forms the basis of a survey of learned methods with mathematically rigorous reconstruction guarantees. An example that is highlighted is the role of input-convex neural networks (ICNNs), offering the possibility to combine the power of deep learning with classical convex regularization theory for devising methods that are provably convergent. This survey article is aimed at both methodological researchers seeking to advance the frontiers of our understanding of data-driven image reconstruction methods as well as practitioners by providing an accessible description of useful convergence concepts and by placing some of the existing empirical practices on a solid mathematical foundation.
Probability density function and moments of the field in a slab of one‐dimensional random medium

The problem of a plane wave normally incident on a slab of one‐dimensional random medium is studied. The refractive index variations of the random medium are taken to be a stationary Gaussian‐Markov process. By employing an invariant imbedding technique and by using the Markov property of the refractive index variations, two cascaded diffusion equations are obtained for the probability density function of the reflection coefficient and the field in the slab. These equations are then solved approximately for small refractive index fluctuations and an expression is obtained for mean intensity in the slab interior.