Miracle-dz/UniFAR-FTUs huggingface dataset:
A Python-based educational software tool for visualizing bioinformatics alignment algorithms

Bioinformatics education can be defined as the teaching and learning of how to use software tools, along with mathematical and statistical analysis, to solve biological problems. Although many resources are available, most students still struggle to understand even the simplest sequence alignment algorithms. Applying visualizations to these topics benefits both lecturers and students. Unfortunately, educational software for visualizing step-by-step processes in the user experience of sequence alignment algorithms is rare. In this article, an educational visualization tool for biological sequence alignment is presented, and the source code is released in order to encourage the collaborative power of open-source software, with the expectation of further contributions from the community in the future. Two different modules are integrated to enable a student to investigate the characteristics of alignment algorithms.
Interrogating the Diversity of Vaginal, Endometrial, and Fecal Microbiomes in Healthy and Metritis Dairy Cattle

Simple Summary Research on the microorganisms in the reproductive tract of cows has become increasingly popular. Reproductive pathogens, including bacteria, caused uterine disease and decrease fertility. Using sequencing techniques endometrial microbiomes in healthy animals and those with metritis were compared. Our study has identified uterine microbiome profiles that are positively and negatively associated with uterine health. Since it is important to know which bacteria live in healthy or diseased animals, this information will enable the development of treatment options for cows that not only reduce antibiotic use but improve fertility. An improved understanding of changes to the bacteria communities will help to identify animals that can successfully become pregnant again after calving. Abstract The bovine genital tract harbors a dynamic microbiome. Genital tract microbial communities in healthy animals have been characterized using next-generation sequencing methods showing that microbe compositions differ between the vagina and uterus, more so during the postpartum period. Pre-calving fecal and vaginal, and endometrial swabs at the different postpartum intervals were collected from dairy cows. Microbiomes in these samples were determined based on bacterial 16S amplicon sequencing and compared between healthy (H; n = 10) control animals and cows that developed metritis (M; n = 10) within 21 days postpartum (DPP). Compared to healthy animals the pre-calving fecal and vaginal microbiomes of metritis animals were more abundant in sequences from the phylum Fusobacteria and the bacterial genera such as Escherichia-Shigella and Histophilus. In addition, compared to healthy animals, metritis cows harboured low microbial species diversity in the endometrium, as well as decreasing Proteobacteria and increasing Fusobacteria, Firmicutes, Actinobacteria, and Bacteroidetes abundances. The greatest taxonomic compositional deviations in endometrial microbial communities between the metritis and health cows were detected between 7 and 10 DPP. There was high taxonomic similarity detected between postpartum endometrial microbiomes and the prepartum vaginal and fecal microbiomes suggesting that colonization through bacteria ascending from the rectum and vagina to the uterine cavity might play a major role in establishing the endometrial microbiome postpartum. A deeper understanding of the establishment and dynamics of postpartum endometrial microbial communities in cows will thus provide crucial basic knowledge to guide the development of genital microbiome manipulation strategies for preventing uterine disease and improving fertility in dairy cows.
Fast algorithm for robust subspace recovery

This paper presents a fast algorithm for robust subspace recovery. The datasets considered include points drawn around a low-dimensional subspace of a higher dimensional ambient space, and a possibly large portion of points that do not lie nearby this subspace. The proposed algorithm, which we refer to as Fast Median Subspace (FMS), is designed to robustly determine the underlying subspace of such datasets, while having lower computational complexity than existing methods. Numerical experiments on synthetic and real data demonstrate its competitive speed and accuracy.
Storminess over the North Atlantic and northwestern Europe—A review

This review assesses storm studies over the North Atlantic and northwestern Europe regarding the occurrence of potential long‐term trends. Based on a systematic review of available articles, trends are classified according to different geographical regions, datasets, and time periods. Articles that used measurement and proxy data, reanalyses, regional and global climate model data on past and future trends are evaluated for changes in storm climate. The most important result is that trends in storm activity depend critically on the time period analysed. An increase in storm numbers is evident for the reanalyses period for the most recent decades, whereas most long‐term studies show merely decadal variability for the last 100–150 years.
A Hybrid Residual Attention Convolutional Neural Network for Compressed Sensing Magnetic Resonance Image Reconstruction

We propose a dual-domain deep learning technique for accelerating compressed sensing magnetic resonance image reconstruction. An advanced convolutional neural network with residual connectivity and an attention mechanism was developed for frequency and image domains. First, the sensor domain subnetwork estimates the unmeasured frequencies of k-space to reduce aliasing artifacts. Second, the image domain subnetwork performs a pixel-wise operation to remove blur and noisy artifacts. The skip connections efficiently concatenate the feature maps to alleviate the vanishing gradient problem. An attention gate in each decoder layer enhances network generalizability and speeds up image reconstruction by eliminating irrelevant activations. The proposed technique reconstructs real-valued clinical images from sparsely sampled k-spaces that are identical to the reference images. The performance of this novel approach was compared with state-of-the-art direct mapping, single-domain, and multi-domain methods. With acceleration factors (AFs) of 4 and 5, our method improved the mean peak signal-to-noise ratio (PSNR) to 8.67 and 9.23, respectively, compared with the single-domain Unet model; similarly, our approach increased the average PSNR to 3.72 and 4.61, respectively, compared with the multi-domain W-net. Remarkably, using an AF of 6, it enhanced the PSNR by 9.87 ± 1.55 and 6.60 ± 0.38 compared with Unet and W-net, respectively.
Blockchain-Based Market Procurement of Reactive Power

A flexible and adaptable market procurement of reactive power has the potential to improve network efficiency, voltage stability, and operational costs. In Europe, such a dynamic procurement must be in accordance with the European Union directive EU 2019/944 on non-frequency ancillary services. In accordance with this situation, a blockchain-based framework of market procurement of reactive power is proposed and presented in this paper. The devised framework is in alignment with the European Union’s directive to establish a non-discriminatory, transparent, and free market. Moreover, the blockchain-based framework is applicable at all network levels and allows for the variety of different distributed resources to participate. A two-layer blockchain topology is proposed to overcome scalability and transaction time issues. The first main-layer blockchain acts as the agent for trust and guarantees the immutability of data as well as the remuneration of participating market players. The second-layer blockchain facilitates direct access to tendering processes or auctions, fast transactions, and low transaction fees for involved stakeholders. Additionally, a decentralized oracle network is proposed to integrate external data into the second-layer blockchain. Data required for verifying the physical reactive power transactions are delivered by smart meters. The entire procurement process is automated by deploying smart contracts at the various blockchain layers. Thus, in principle, the involved stakeholders are the system operators and market players. For the purpose of validation, the holistic market procurement process is demonstrated and analyzed in a hardware-in-the-loop environment involving reactive power procurement at the distribution network level.
Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach

Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.
Improving Bit-Error-Rate Performance Using Modulation Coding Techniques for Spin-Torque Transfer Magnetic Random Access Memory

In non-volatile random-access memory (RAM) technologies, the spin-torque transfer magnetic random-access memory (STT-MRAM) is a promising candidate. STT-MRAM has attracted attention owing to its advantages, such as a high density, high endurance, and high-speed writing/reading. Moreover, STT-MRAM is utilized to replace dynamic random-access memory (DRAM) in Internet of Things (IoT) and artificial intelligence (AI) applications. However, because the magnetic tunnel junction is used to write data, STT-MRAM must face process variation and thermal fluctuation problems. This causes errors in writing and reading processes. These errors are independent of each other. Therefore, to solve them and increase the reliability of STT-MRAM, the data are encoded before storage in STT-MRAM devices. In this study, we propose a method for designing codewords using modulation coding techniques. Our codewords increase the minimum Hamming distance and inheritance of the sparse code characteristic to exploit the asymmetric probability of the write errors. The simulation results show that our codewords can improve the bit error rate performance of STT-MRAM compared with previous works.
Current disruption in the Earth's magnetosphere: Observations and models

Observations and models of current disruption in the Earth's magnetosphere are briefly reviewed. At the approach of current disruption onset, the cross‐tail current sheet shows a rapid growth in the current density, a large upsurge in the duskward ion bulk speed to nearly the ion thermal speed, an increase in the plasma pressure and its isotropy, a rise in the plasma beta, and a decrease in the current sheet thickness to a length scale comparable to the thermal ion gyroradius. During current disruption, there are (1) large changes in the local magnetic and electric fields, (2) significant magnetic and electric fluctuations over a broad frequency range, (3) magnetic field‐aligned counterstreaming electron beams, (4) ion energization perpendicular to the magnetic field, and (5) reduction in the cross‐tail current by an amount similar to that built up during the growth phase. Observations further indicate that regions of local reversal of the north‐south magnetic field component are not necessarily sites of intense particle energization. Remote sensing of disruption activities shows that at least some current disruptions are not caused by a disturbance propagating earthward from the tail beyond 10 RE downstream. The timescale involved is comparable to or shorter than the ion gyroperiod. Current disruption thus has spatial and temporal scales outside the MHD regime. Several models for current disruption are briefly discussed. Two roles are considered for the cross‐field current instability proposed for current disruption. It can provide anomalous resistivity for magnetic reconnection as advocated by the traditional viewpoint or act singly to instigate global changes of the magnetosphere during the initial substorrn expansion phase. The latter role is elaborated by showing that the instability may modify significantly the local current density and any such process will alter the force equilibrium in the current sheet and give rise to an efficient plasma and energy transport on a global scale. Furthermore, such a process can generate field‐aligned current with intensity comparable to those associated with an auroral breakup arc at substorrn expansion onset. This scenario leads to a new emphasis that in addition to magnetic reconnection, rapid conversion of magnetic energy into particle energy in magnetotail systems may take place without a magnetic X line or separatrix playing the key role in energy conversion.
A Bibliometric Analysis of Consumer Neuroscience towards Sustainable Consumption

Consumer neuroscience is a new paradigm for studying consumer behavior, focusing on neuroscientific tools to explore the underlying neural processes and behavioral implications of consumption. Based on the bibliometric analysis tools, this paper provides a review of progress in research on consumer neuroscience during 2000–2021. In this paper, we identify research hotspots and frontiers in the field through a statistical analysis of bibliometric indicators, including the number of publications, countries, institutions, and keywords. Aiming at facilitating carbon neutrality via sustainable consumption, this paper discusses the prospects of applying neuroscience to sustainable consumption. The results show 364 publications in the field during 2000–2021, showing a rapid upward trend, indicating that consumer neuroscience research is gaining ground. The majority of these consumer neuroscience studies chose to use electroencephalogram tools, accounting for 63.8% of the total publications; the cutting-edge research mainly involved event-related potential (ERP) studies of various marketing stimuli interventions, functional magnetic resonance imaging (fMRI)-based studies of consumer decision-making and emotion-specific brain regions, and machine-learning-based studies of consumer decision-making optimization models.
Calculation of undulator radiation spectral and angular distributions

The validity of an approximation used in the derivation of the standard analytic formula for the radiation produced by an ideal undulator is examined. Efficient algorithms for calculating the spectral and angular distributions including the effect of electron beam emittance are described. Results from a computer program that allows a number of different calculations to be performed with a range of approximations are presented.
Impact of Ultrasonication on the Self-Assembly Behavior and Gel Properties of Bovine Bone Collagen I

This study deliberated the effect of ultrasonic treatment on collagen self-assembly behavior and collagen fibril gel properties. Bovine bone collagen I which had undergone ultrasonic treatment with different power (0–400 W) and duration (0–60 min) was analyzed. SDS-PAGE and spectroscopic analysis revealed that ultrasonic treatment decreased collagen molecular order degree and the number of hydrogen bonds, stretching collagen telopeptide regions while maintaining the integrity of the collagen triple-helical structure. Ultrasonic treatment (p ≤ 200 W, t ≤ 15 min) dispersed the collagen aggregates more evenly, and accelerated collagen self-assembly rate with a decreased but more homogeneous fibril diameter (82.78 ± 16.47–115.52 ± 19.51 nm) and D-periodicity lengths (62.1 ± 2.9–66.5 ± 1.8 nm) than that of the untreated collagen (119.15 ± 27.89 nm; 66.5 ± 1.8 nm). Meanwhile, ultrasonic treatment (p ≤ 200 W, t ≤ 15 min) decreased the viscoelasticity index and gel strength, enhancing thermal stability and promoting specific surface area and porosity of collagen fibril gels than that of the untreated collagen fibril gel. These results testified that collagen self-assembly behavior and collagen fibril gel properties can be regulated by ultrasonic treatment through multi-hierarchical structural alteration. This study provided a new approach for controlling in vitro collagen fibrillogenesis process so as to manufacture novel desirable collagen-based biomaterials with propitious performances for further valorization.
Estimations optimales du noyau de la chaleur sur les groupes de type Heisenberg

Abstract We study the asymptotic property for the heat kernel on Heisenberg type groups. Also, we give sharp estimates for derivatives of the heat kernel. In particular, we obtain sharp upper and lower bounds for the heat kernel on Heisenberg type groups, and an estimate of type for all h > 0 and g, g′.
A Two-Level Iterative Reconstruction Method for Compressed Sensing MRI

This paper proposed a novel two-level iterative reconstruction method for compressed sensing magnetic resonance imaging (CS-MRI). For the model of this method, we incorporated the phase correction matrix and region of support (ROS) to guarantee more accurate reconstruction. For the algorithm of the method, we proposed an iterative strategy to reduce memory and computation time, and a two-level strategy to take into account both low and high frequency k-space data separately. Simulation results on normal brain image and angiogram of cerebral demonstrated that the median square error of this proposed method was much less than the traditional method. The error reduction ratios are 11.94% for brain image and 4.53% for angiogram of cerebral, respectively.
Fundamental parameters of “normal” B stars in the solar neighborhood

Abstract Understanding phenomena of activity in stars, like pulsations or magnetism, benefits from systematic comparisons of some key physical parameters of active with those of “normal” stars. Here we concentrate on a careful derivation of fundamental parameters of a well selected sample of 27 “normal” B stars in nearby OB associations and in the field. A quantitative spectral analysis methodology based on hybrid non-LTE techniques is applied to high-resolution and high-S/N spectra. Results derived from the pure spectroscopic analysis are compared to other data/indicators of stellar parameters in order to prove the reliability of the method. Very good agreement is obtained among all of them. Besides the fundamental parameters, the chemical composition of the stars is also determined at high precision, turning out to be highly homogeneous. A comparative study of the present results with those of well known active massive stars will help to improve our understanding of the driving mechanisms of activity.
Galectin-1: A Traditionally Immunosuppressive Protein Displays Context-Dependent Capacities

Galectin–Carbohydrate interactions are indispensable to pathogen recognition and immune response. Galectin-1, a ubiquitously expressed 14-kDa protein with an evolutionarily conserved β-galactoside binding site, translates glycoconjugate recognition into function. That galectin-1 is demonstrated to induce T cell apoptosis has led to substantial attention to the immunosuppressive properties of this protein, such as inducing naive immune cells to suppressive phenotypes, promoting recruitment of immunosuppressing cells as well as impairing functions of cytotoxic leukocytes. However, only in recent years have studies shown that galectin-1 appears to perform a pro-inflammatory role in certain diseases. In this review, we describe the anti-inflammatory function of galectin-1 and its possible mechanisms and summarize the existing therapies and preclinical efficacy relating to these agents. In the meantime, we also discuss the potential causal factors by which galectin-1 promotes the progression of inflammation.
IAU Symposium 317 Summary

Abstract The assembly of the halo yields fundamental information on the formation and evolution of galaxies: this was quite exhaustively discussed at this very important symposium. I present a brief personal summary of the meeting, outlining those points that I found more exciting and suggestive. I also remarked a few areas that were possibly not enough expanded. I found this research field extremely interesting and I think there are great expectations for new developments in the next few years, thanks to the new large spectroscopic surveys and the ESA GAIA satellite.
An Improved Agro Deep Learning Model for Detection of Panama Wilts Disease in Banana Leaves

Recently, Panama wilt disease that attacks banana leaves has caused enormous economic losses to farmers. Early detection of this disease and necessary preventive measures can avoid economic damage. This paper proposes an improved method to predict Panama wilt disease based on symptoms using an agro deep learning algorithm. The proposed deep learning model for detecting Panama wilts disease is essential because it can help accurately identify infected plants in a timely manner. It can be instrumental in large-scale agricultural operations where Panama wilts disease could spread quickly and cause significant crop loss. Additionally, deep learning models can be used to monitor the effectiveness of treatments and help farmers make informed decisions about how to manage the disease best. This method is designed to predict the severity of the disease and its consequences based on the arrangement of color and shape changes in banana leaves. The present proposed method is compared with its previous methods, and it achieved 91.56% accuracy, 91.61% precision, 88.56% recall and 81.56% F1-score.
Spin foam models: the dynamics of quantum geometry

In this paper, we give an overview of the main techniques developed in the context of background independent gravity in order to tackle the problem of the dynamics. We briefly introduce loop quantum gravity, highlighting some of its achievements and then present spin foam models which allows the construction of dynamical transition amplitudes between loop quantum gravity states. We then present three types of applications of these sets of ideas. First, we describe how the coupling to matter can be achieved in this context and how this has allowed us to show that in 2+1 the effective geometry created by quantum gravity is non-commutative. Then we present how we can extract information of the graviton propagator from spin foam amplitudes and finally we emphasize some recent application to quantum cosmology.