Miracle-dz/UniFAR-FTUs huggingface dataset:
Melatonin and Abiotic Stress Tolerance in Crop Plants

Increasing food demand by the growing human population and declining crop productivity due to climate change affect global food security. To meet the challenges, developing improved crops that can tolerate abiotic stresses is a priority. Melatonin in plants, also known as phytomelatonin, is an active component of the various cellular mechanisms that alleviates oxidative damage in plants, hence supporting the plant to survive abiotic stress conditions. Exogenous melatonin strengthens this defence mechanism by enhancing the detoxification of reactive by-products, promoting physiological activities, and upregulating stress-responsive genes to alleviate damage during abiotic stress. In addition to its well-known antioxidant activity, melatonin protects against abiotic stress by regulating plant hormones, activating ER stress-responsive genes, and increasing protein homoeostasis, heat shock transcription factors and heat shock proteins. Under abiotic stress, melatonin enhances the unfolded protein response, endoplasmic reticulum-associated protein degradation, and autophagy, which ultimately protect cells from programmed cell death and promotes cell repair resulting in increased plant survival.
Fast Bayesian Lasso for High-Dimensional Regression

The lasso (Tibshirani, 1996) is an essential tool in modern high-dimensional regression and variable selection. The Bayesian lasso of Park and Casella (2008) interprets the lasso objective function as a posterior under a Laplace prior and proposes a three-step Gibbs sampler to sample from this posterior. The Bayesian lasso yields a natural approach to quantifying the uncertainty of lasso estimates. Furthermore, the Gibbs sampler for the Bayesian lasso has been shown to be geometrically ergodic (Khare and Hobert, 2013). The geometric rate constant of this Markov chain, however, tends to 1 if the number of regression coefficients grows faster than the sample size (Rajaratnam and Sparks, 2015). Thus, convergence of the Bayesian lasso Gibbs sampler can still be quite slow in modern high-dimensional settings despite the apparent theoretical safeguard of geometric ergodicity. In order to address this challenge, we propose a new method to draw from the same posterior via a tractable two-step blocked Gibbs sampler, which we call the fast Bayesian lasso. We provide a theoretical underpinning to the new method by proving rigorously that the fast Bayesian lasso is geometrically ergodic. We then demonstrate numerically that this blocked sampler exhibits vastly superior convergence behavior in high-dimensional regimes.
Image Fields in the Rectangular Vacuum Vessels of the ISIS Synchrotron

ISIS is the pulsed spallation neutron source based at Rutherford Appleton Laboratory in the UK. Operation is based on a 50 Hz, 800 MeV proton synchrotron, accelerating up to 3 × 10 protons per pulse, which provides beam to two target stations. Space charge effects contribute significantly to beam loss. Fields from the intense beam interact strongly with their environment. At ISIS the vacuum vessel is rectangular and profiled to follow the shape of the design beam envelope. Past studies have suggested that closed orbit induced image fields may contribute to beam loss under certain conditions. Image fields for parallel plate and rectangular geometries are reviewed, in particular their expansion as power series is determined. A simulation tool has been developed for particle tracking with space charge. The code contains both Fast Fourier Transform and Finite Element Analysis based field solvers, which have been used here to test the range of validity for the power series expansions for centred and off-centred beams. These expansions are then used to determine driving terms for the transverse beam motion. Of particular interest for ISIS is the resonant behaviour of beams with a harmonic closed orbit, which can be compared with the output of beam tracking simulations.
‘Clarifying missing at random and related definitions, and implications when coupled with exchangeability’

We clarify the key concept of missingness at random in incomplete data analysis. We first distinguish between data being missing at random and the missingness mechanism being a missing-at-random one, which we call missing always at random and which is more restrictive. We further discuss how, in general, neither of these conditions is a statement about conditional independence. We then consider the implication of the more restrictive missing-always-at-random assumption when coupled with full unit-exchangeability for the matrix of the variables of interest and the missingness indicators: the conditional distribution of the missingness indicators for any variable that can have a missing value can depend only on variables that are always fully observed. We discuss implications of this for modelling missingness mechanisms.
Association of Circadian Clock Gene Expression with Pediatric/Adolescent Asthma and Its Comorbidities

The pathology of asthma is characterized by marked day–night variation, which is likely controlled by circadian clock activity. This study aimed to clarify the association of core circadian clock gene expression with clinical features of asthma. For this purpose, we accessed the National Center for Biotechnology Information database and analyzed transcriptomes of peripheral blood mononuclear cells and clinical characteristics of 134 pediatric/adolescent patients with asthma. Based on the expression patterns of seven core circadian clock genes (CLOCK, BMAL1, PER1-3, CRY1-2), we identified three circadian clusters (CCs) with distinct comorbidities and transcriptomic expressions. In the three CC subtypes, allergic rhinitis, and atopic dermatitis, both asthma comorbidities occurred in different proportions: CC1 had a high proportion of allergic rhinitis and atopic dermatitis; CC2 had a high proportion of atopic dermatitis but a low proportion of allergic rhinitis; and CC3 had a high proportion of allergic rhinitis but a low proportion of atopic dermatitis. This might be associated with the low activity of the FcεRI signaling pathway in CC2 and the cytokine–cytokine receptor interaction pathways in CC3. This is the first report to consider circadian clock gene expression in subcategories of patients with asthma and to explore their contribution to pathophysiology and comorbidity.
A Qualitative Approach to Explore Perceptions, Opinions and Beliefs of Communities who Experienced Health Disparities towards Chronic Health Conditions

The prevalence of chronic medical conditions is associated with biological, behavioral, and social factors. In Puerto Rico (PR), events such as budget cuts to essential services in recent years have contributed to deepening health disparities. This study aimed to explore community perceptions, opinions, and beliefs about chronic health conditions in the southern region of Puerto Rico. Framed by a Community-Based Participatory Research (CBPR) approach, this qualitative study developed eight focus groups (n = 59) with adults (age of 21 or older) from southern Puerto Rico, in person and remotely, during 2020 and 2021. Eight open-ended questions were used for discussions, which were recorded, transcribed, and analyzed via computer analysis. Content analysis revealed four main dimensions: knowledge, vulnerabilities, barriers, and identified resources. Relevant topics included: concerns about mental health—depression, anxiety, substance use, and suicide; individual vulnerabilities—risk behaviors, and unhealthy habits; economic factors—health access and commercialization of health. Resource identification was also explored, and participants discussed the importance of alliances between public and private sectors. These topics were addressed across all focus groups, with various recommendations. The results highlight the importance of prioritizing identified community needs, evaluating available resources, and promoting tailored-made interventions to reduce risk factors for chronic health conditions.
Risk-Averse Optimal Combining Forecasts for Renewable Energy Trading Under CVaR Assessment of Forecast Errors

Renewable energy producer is often exposed to huge financial losses in some imbalance hours (meaning that the contracted energy in day-ahead market is not equal to the actual output in real time) caused by extremely large forecast error. To address this challenge, this paper integrates the forecast end-user's risk profile into the development of risk-averse combining forecast approach for renewable energy trading. First, the conditional value-at-risk (CVaR) is applied to evaluate the extreme prediction error of combined forecasts. Then, convex optimization models are formulated with the objective of minimizing the mean square error plus the CVaR of large error. Solving our proposed models determines the optimal weights for individual models participating in the combined forecasts. Finally, the value of risk-averse combined forecasts is verified through examining the financial performance of using risk-averse forecasts as inputs of the bidding strategy in renewable energy trading. Case studies on real-world datasets present that our proposed method not only reduces the mean error but also lowers the extreme error. More importantly, it decreases the imbalance energy and cost in renewable energy trading, thus being less exposed to the risk of large financial losses under extreme prediction errors.
Mast Cells as a Target—A Comprehensive Review of Recent Therapeutic Approaches

Mast cells (MCs) are the immune cells distributed throughout nearly all tissues, mainly in the skin, near blood vessels and lymph vessels, nerves, lungs, and the intestines. Although MCs are essential to the healthy immune response, their overactivity and pathological states can lead to numerous health hazards. The side effect of mast cell activity is usually caused by degranulation. It can be triggered by immunological factors, such as immunoglobulins, lymphocytes, or antigen–antibody complexes, and non-immune factors, such as radiation and pathogens. An intensive reaction of mast cells can even lead to anaphylaxis, one of the most life-threatening allergic reactions. What is more, mast cells play a role in the tumor microenvironment by modulating various events of tumor biology, such as cell proliferation and survival, angiogenesis, invasiveness, and metastasis. The mechanisms of the mast cell actions are still poorly understood, making it difficult to develop therapies for their pathological condition. This review focuses on the possible therapies targeting mast cell degranulation, anaphylaxis, and MC-derived tumors.
Request delay and survivability optimization for software defined‐wide area networking (SD‐WAN) using multi‐agent deep reinforcement learning

Data exchange between headquarters and local branches represents a major challenge issue for business success. For this issue, traditional solutions applied to wide area networks (WAN) remain unrealistic and require a good knowledge of the systems. Recently, software‐defined wide area networking (SD‐WAN) plays a pivotal role and constitutes, in general, a reliable solution for wide area networking. Compared to the classical WAN, SD‐WAN decouples the control plane from gateway devices. Moreover, SD‐WAN are based on centralized WAN management with dynamic reconfiguration, allowing it to control the entire service requirements. Nevertheless, traffic management is the most usual metric in SD‐WAN. It gives a clear indication on propagation latency, request delay and survivability; that is, network connectivity. In this article, to guarantee an efficient quality of service and deal with the increased delay problem, we investigate the problem of joint optimization of average request delay and survivability in the proposed SD‐WAN model. The optimization approaches are formulated so as to minimize the average request delay and maximize the network connectivity. Subsequently, we adopt a multi‐agent deep Q‐Network algorithm to solve it, where the reward function is reformulated based on the optimization objective. Simulation results show that our strategy improves the system's performance significantly in terms of request delay and survivability, compared to traditional baseline algorithms.
Mesenchymal Stem Cell Exosomes as Immunomodulatory Therapy for Corneal Scarring

Corneal scarring is a leading cause of worldwide blindness. Human mesenchymal stem cells (MSC) have been reported to promote corneal wound healing through secreted exosomes. This study investigated the wound healing and immunomodulatory effects of MSC-derived exosomes (MSC-exo) in corneal injury through an established rat model of corneal scarring. After induction of corneal scarring by irregular phototherapeutic keratectomy (irrPTK), MSC exosome preparations (MSC-exo) or PBS vehicle as controls were applied to the injured rat corneas for five days. The animals were assessed for corneal clarity using a validated slit-lamp haze grading score. Stromal haze intensity was quantified using in-vivo confocal microscopy imaging. Corneal vascularization, fibrosis, variations in macrophage phenotypes, and inflammatory cytokines were evaluated using immunohistochemistry techniques and enzyme-linked immunosorbent assays (ELISA) of the excised corneas. Compared to the PBS control group, MSC-exo treatment group had faster epithelial wound closure (0.041), lower corneal haze score (p = 0.002), and reduced haze intensity (p = 0.004) throughout the follow-up period. Attenuation of corneal vascularisation based on CD31 and LYVE-1 staining and reduced fibrosis as measured by fibronectin and collagen 3A1 staining was also observed in the MSC-exo group. MSC-exo treated corneas also displayed a regenerative immune phenotype characterized by a higher infiltration of CD163+, CD206+ M2 macrophages over CD80+, CD86+ M1 macrophages (p = 0.023), reduced levels of pro-inflammatory IL-1β, IL-8, and TNF-α, and increased levels of anti-inflammatory IL-10. In conclusion, topical MSC-exo could alleviate corneal insults by promoting wound closure and reducing scar development, possibly through anti-angiogenesis and immunomodulation towards a regenerative and anti-inflammatory phenotype.
Challenging traditions: Systematic review of practice, instruction, and motor skill acquisition in soccer

Williams and Hodges (2005) stated five myths that soccer coaches held when organizing their athletes’ practice. However, their arguments were based, majorly, in laboratory studies which limit their validity. Here, we performed a systematic review to verify the validity of their claims and map the current scenario of how coaches can manipulate factors of practice (augmented information, focus of attention, demonstration, and practice schedule) to support learning and performance in soccer. The results, in terms of each category or the myths presented, are, in most cases, not unanimous and much more must be performed to support, or criticize, coaches’ practice. We provide an encompassing view that provides general ideas for practice and future research.
The Role of Physical Therapies in Wound Healing and Assisted Scarring

Wound healing (WH) is a complex multistep process in which a failure could lead to a chronic wound (CW). CW is a major health problem and includes leg venous ulcers, diabetic foot ulcers, and pressure ulcers. CW is difficult to treat and affects vulnerable and pluripathological patients. On the other hand, excessive scarring leads to keloids and hypertrophic scars causing disfiguration and sometimes itchiness and pain. Treatment of WH includes the cleaning and careful handling of injured tissue, early treatment and prevention of infection, and promotion of healing. Treatment of underlying conditions and the use of special dressings promote healing. The patient at risk and risk areas should avoid injury as much as possible. This review aims to summarize the role of physical therapies as complementary treatments in WH and scarring. The article proposes a translational view, opening the opportunity to develop these therapies in an optimal way in clinical management, as many of them are emerging. The role of laser, photobiomodulation, photodynamic therapy, electrical stimulation, ultrasound therapy, and others are highlighted in a practical and comprehensive approach.
Vision Transformer With Enhanced Self-Attention for Few-Shot Ship Target Recognition in Complex Environments

Ship target recognition is essential for maritime transportation, commercial trade, maritime security, and monitoring illegal activity. The majority of previous ship target recognition models are based on fully supervised learning, which necessitates vast amounts of labeled training data. However, optical lenses capturing ship images in natural scenarios have numerous complex environments, making feature extraction difficult due to hardware, weather, light, waves, and other factors. Moreover, image annotation is also both expensive and time-consuming. How to effectively learn from unbalanced, limited, and sparsely annotated sample data is a significant challenge. In this article, we propose a new vision transformer with enhanced self-attention (SAVT) that can locate ship targets in complex environments with just a few shots. SAVT consists of a novel enhanced self-attention (ESA) module and a novel measurement self-adjusting computation (MAC) module to differentiate unique ship classes in complex environments with few labeled samples. The ESA module focuses on the features of the target region at each layer of the backbone network to ensure that the contour detailed features are extracted effectively from ship images with complex environments. The model is then enhanced in terms of speed and stability using the MAC module. The CIB-ships and public MAR-ships datasets are used to evaluate the proposed method. In this article, the results demonstrate that SAVT achieves good performance on few-shot labeled data under both one-shot and five-shot settings, while also providing valuable guidance for maritime ship target recognition with limited labeled samples.
Evaluation of cutting off entropy functional measure on trajectories of Markov diffusion process and information path functional

Impulse cuts off entropy functional measure (EF) on Markov diffusion process trajectories-solutions of Ito controllable stochastic equation. Process additive functional defines EF reducing the functional on trajectories to a regular integral functional. Compared to Shannon entropy measure of random state, cutting process on separated states decreases quantity information concealed in the states correlation holding hidden process information. Multidimensional process cutoffs generate finite information measure integrated by information path functional (IPF) whose information approaches EF measure at infinite dimensions restricting maximal information of the Markov diffusion process. Delta impulse and discrete impulse deliver equivalent information at each cutoff. Finite restriction limits impulses discrete actions cutting the regular integral on EF increments between the cutoffs. Finite impulse step-up action transfers EF increment to following impulse whose step-down action cuts off information and step-up action starts imaginary impulse carrying entropy increment to next real cut. Step-down cut generates maximal information while the step-up action delivers minimal information from impulse cut to next impulse step-down action. Virtual impulse transfers conjugated entropy increments during microprocess ending with adjoining increment within actual step-down action at cutoff. Extracting maximum of minimal impulse information and transferring minimal entropy between impulses implement maxmin-minimax principle of converting process entropy to information. Macroprocess integrates imaginary entropy of microprocess and cutoff information of real impulses in IPF information physical process. EF cut measures Feller kernel information. Estimation extracting information confirms nonadditivity of EF measured process fractions.
Radial vibration and equivalent circuit of thin elastic circular rings

The radial vibration of isotropic thin elastic circular rings is studied and the equivalent circuit is obtained. Based on the equivalent circuit, the resonance frequency equation is derived and the resonance frequency is obtained. By means of the numerical methods, the root of the resonance frequency equation is solved, and the relation between the roots and the Poisson's ratio is fitted. The relationship between the vibrational mode and the geometrical dimensions of the ring is analyzed. The conclusions are beneficial to the design and calculation of the radial vibration of the thin circular ring used in ultrasonic technologies.
Influence of the Application Time of Silane for the Bonding Performance between Feldspar or Lithium Disilicate Ceramics and Luting Resin Composites

A correct silanization time is essential for successful surface functionalization and sufficient bonding to dental ceramics. The shear bond strength (SBS) of lithium disilicate (LDS) and feldspar (FSC) ceramics and luting resin composite was investigated with respect to different silanization times, taking into account the physical properties of the individual surfaces. The SBS test was performed with a universal testing machine, and the fracture surfaces were evaluated by stereomicroscopy. The surface roughness of the prepared specimens was analyzed after etching. Changes in surface properties due to surface functionalization were evaluated by surface free energy (SFE) via contact angle measurement. Fourier transform infrared spectroscopy (FTIR) was used to determine the chemical binding. The roughness and SBS of the control group (no silane, etched) were higher for FSC than for LDS. Regarding the SFE, the dispersive fraction increased and the polar fraction decreased after silanization. FTIR confirmed the presence of silane on the surfaces. The SBS of LDS showed a significant increase from 5 to 15 s, depending on the silane and luting resin composite. For FSC, cohesive failure was observed for all samples. For LDS specimens, a silane application time of 15 to 60 s is recommended. Based on clinical conditions, no difference between the silanization times was observed for FSC specimens, indicating that etching alone produces sufficient bonding.
The Age of Ransomware: A Survey on the Evolution, Taxonomy, and Research Directions

The proliferation of ransomware has become a significant threat to cybersecurity in recent years, causing significant financial, reputational, and operational damage to individuals and organizations. This paper aims to provide a comprehensive overview of the evolution of ransomware, its taxonomy, and its state-of-the-art research contributions. We begin by tracing the origins of ransomware and its evolution over time, highlighting the key milestones and major trends. Next, we propose a taxonomy of ransomware that categorizes different types of ransomware based on their characteristics and behavior. Subsequently, we review the existing research over several years in regard to detection, prevention, mitigation, and prediction techniques. Our extensive analysis, based on more than 150 references, has revealed that significant research, specifically 72.8%, has focused on detecting ransomware. However, a lack of emphasis has been placed on predicting ransomware. Additionally, of the studies focused on ransomware detection, a significant portion, 70%, have utilized Machine Learning methods. This study uncovers a range of shortcomings in research pertaining to real-time protection and identifying zero-day ransomware, and two issues specific to Machine Learning models. Adversarial machine learning exploitation and concept drift have been identified as under-researched areas in the field. This survey is a constructive roadmap for researchers interested in ransomware research matters.