Miracle-dz/UniFAR-FTUs huggingface dataset:
5-ALA Improves the Low Temperature Tolerance of Common Bean Seedlings through a Combination of Hormone Transduction Pathways and Chlorophyll Metabolism

Low-temperature stress is a key factor limiting the yield and quality of the common bean. 5-aminolevulinic acid (5-ALA), an antioxidant in plants, has been shown to modulate plant cold stress responses. However, the molecular mechanisms of 5-ALA-induced physiological and chemical changes in common bean seedlings under cold stress remains unknown. This study explored the physiological and transcriptome changes of common bean seedlings in response to cold stress after 5-ALA pretreatment. Physiological results showed that exogenous 5-ALA promotes the growth of common bean plants under cold stress, increases the activity of antioxidant enzymes (superoxide dismutase: 23.8%; peroxidase: 10.71%; catalase: 9.09%) and proline content (24.24%), decreases the relative conductivity (23.83%), malondialdehyde (33.65%), and active oxygen content, and alleviates the damage caused by cold to common bean seedlings. Transcriptome analysis revealed that 214 differentially expressed genes (DEGs) participate in response to cold stress. The DEGs are mainly concentrated in indole alkaloid biosynthesis, carotenoid biosynthesis, porphyrin, and chlorophyll metabolism. It is evident that exogenous 5-ALA alters the expression of genes associated with porphyrin and chlorophyll metabolism, as well as the plant hormone signal transduction pathway, which helps to maintain the energy supply and metabolic homeostasis under low-temperature stress. The results reveal the effect that applying exogenous 5-ALA has on the cold tolerance of the common bean and the molecular mechanism of its response to cold tolerance, which provides a theoretical basis for exploring and improving plant tolerance to low temperatures.
Risk factors and drug discovery for cognitive impairment in type 2 diabetes mellitus using artificial intelligence interpretation and graph neural networks

Background Among the 382 million diabetic patients worldwide, approximately 30% experience neuropathy, and one-fifth of these patients eventually develop diabetes cognitive impairment (CI). However, the mechanism underlying diabetes CI remains unknown, and early diagnostic methods or effective treatments are currently not available. Objective This study aimed to explore the risk factors for CI in patients with type 2 diabetes mellitus (T2DM), screen potential therapeutic drugs for T2DM-CI, and provide evidence for preventing and treating T2DM-CI. Methods This study focused on the T2DM population admitted to the First Affiliated Hospital of Hunan College of Traditional Chinese Medicine and the First Affiliated Hospital of Hunan University of Chinese Medicine. Sociodemographic data and clinical objective indicators of T2DM patients admitted from January 2018 to December 2022 were collected. Based on the Montreal Cognitive Assessment (MoCA) Scale scores, 719 patients were categorized into two groups, the T2DM-CI group with CI and the T2DM-N group with normal cognition. The survey content included demographic characteristics, laboratory serological indicators, complications, and medication information. Six machine learning algorithms were used to analyze the risk factors of T2DM-CI, and the Shapley method was used to enhance model interpretability. Furthermore, we developed a graph neural network (GNN) model to identify potential drugs associated with T2DM-CI. Results Our results showed that the T2DM-CI risk prediction model based on Catboost exhibited superior performance with an area under the receiver operating characteristic curve (AUC) of 0.95 (specificity of 93.17% and sensitivity of 78.58%). Diabetes duration, age, education level, aspartate aminotransferase (AST), drinking, and intestinal flora were identified as risk factors for T2DM-CI. The top 10 potential drugs related to T2DM-CI, including Metformin, Liraglutide, and Lixisenatide, were selected by the GNN model. Some herbs, such as licorice and cuscutae semen, were also included. Finally, we discovered the mechanism of herbal medicine interventions in gut microbiota. Conclusion The method based on Interpreting AI and GNN can identify the risk factors and potential drugs associated with T2DM-CI.
The influence of electronic word of mouth through Tiktok on purchasing intention of skincare products in Indonesia

The ever-expanding internet has brought social media become a medium by which Electronic Word of Mouth (eWOM) is disseminated, particularly among skincare users. This study aims to assess the impact of eWOM through TikTok on skincare product purchase intention by developing a conceptual model of hypotheses that encompasses a multiplicity of factors that might be associated. The researcher investigates what factors impact eWOM usefulness & eWOM credibility and why consumers may adopt it when making a purchase. To examine our research model, a quantitative approach is employed using a sample of 373 Indonesian respondents via a web-based questionnaire. Structural equation modeling (SEM) analysis with the program SmartPLS was employed to test ten proposed hypotheses. The results indicate that source expertise is the most significant factor that may impact eWOM credibility in addition to perceived persuasiveness, perceived informativeness, and source trustworthiness. Additionally, source trustworthiness is the main predictor of eWOM credibility in addition to perceived persuasiveness and source expertise with regard to TikTok. It was found that eWOM usefulness and credibility together increase the possibility of adopting an eWOM message, and eWOM adoption mediates the influence of credibility and usefulness on customer purchase intention toward skincare products on TikTok. Only a few studies have examined TikTok as a medium of eWOM distribution and its influence on skincare product purchase intention, therefore this study provides vital insights into eWOM literature by identifying the new platform. This study helps brands learn what to consider when designing marketing strategies to enhance purchase intentions.
A Novel Variant of Avian Reovirus Is Pathogenic to Vaccinated Chickens

Avian reovirus (ARV) infections, characterized by severe arthritis, tenosynovitis, pericarditis, and poor weight gain, have become increasingly serious in recent years. The economic impact is significant as it causes growth inhibition and immunosuppression. Some commercial poultry in China have been widely vaccinated with available ARV vaccines; however, infections continue to occur even after vaccination. This study aimed to isolate a novel variant, ARV-SD19/11103, from the joint tissues of infected broiler chickens vaccinated with ARV vaccines in Shandong Province. Genetic evolution analysis of the major protective antigen σC gene in ARVs showed that ARV-SD19/11103 was located in the genotype cluster I but not in the same sub-cluster as the S1133 vaccine strain. The amino acid sequence similarity between SD19/11103 and vaccine strains S1133, 1733, and 2408 was <80%. After analyzing the amino acid sequences of the σC protein, 33 amino acid differences were found between the new variant isolate and the vaccine strains. This novel variant showed obvious pathogenicity in specific pathogen-free chicken embryos and chicks and could cause serious disease in chickens vaccinated with commercially available ARV vaccines. Cross-neutralization experiments further demonstrated a significant antigenic difference between the novel variant and genotype cluster I ARV strains. The novel variant strain isolated in this study provides an important theoretical basis for understanding the prevalence and genetic evolutionary characteristics of ARV variant strains in our country. This study identified the causes of ARVs circulating and emphasizes the needs for developing new vaccines against novel ARV variants.
Deformation of evaporites near the Waste Isolation Pilot Plant (WIPP) site

Layered evaporite units of Ochoan age in the Delaware Basin are 1000 m thick. They are divided into three stratigraphic units (listed in order of increasing age): the Rustler Formation, the Salado Formation, the Castile Formation. These units, especially the Castile, are deformed along portions of the margin of the Delaware Basin and in some areas internal to the basin. Hypotheses of origin of deformation considered are: gravity foundering; gravity sliding; gypsum dehydration; dissolution; and depositional variations. Gravity foundering and sliding are considered the most probable causes of deformation. However, no hypothesis adequately answers why the deformation has a limited areal distribution. A possible explanation would be areal variations in rock strength caused by variations of intergranular water content. Age and timing of deformation are also crucial. Standard stratigraphic arguments based on superposition may not apply to such a highly incompetent material as halite. Gravity foundering could have happened at any time since deposition including the present; gravity sliding would probably have occurred since basin tilting began in the Cenozoic. Deformation could be ongoing. However, the strain rates are such (10/sup -16/ s/sup -1/) that deformation would progress slowly relative to the facility's time frame of 2.5 x 10/sup 5/more » y. Deformation of Salado units would be minimal (<10 m) or nonexistent, but within this time frame, upper anhydrite units of the Castile could fracture and provide the volume for a brine reservoir. Such Volumes would be small (<1%) and would require 10/sup 4/ to 10/sup 6/ y to develop. At these strain rates, fractures that connect the fractured anhydrites of the Castile with the middle Salado could not develop. Deformation should not directly jeopardize the facility over the next 2.5 x 10/sup 5/ y. « less
SiO2 Modification of Silicon Carbide Membrane via an Interfacial In Situ Sol–Gel Process for Improved Filtration Performance

Silicon carbide (SiC) membrane has emerged as a promising class of inorganic ceramic membranes with many advantageous attributes and has been used for a variety of industrial microfiltration (MF) processes. The state-of-the-art industrial manufacturing of SiC membranes based on the particle sintering method can only achieve an average pore size that ranges from 40 nm to a few micrometers, which is still unsatisfactory for ultrafiltration (UF) applications. Thus, the pore size control of SiC membranes remains a focus of continuing study. Herein, we provide an in situ sol–gel modification strategy to tailor the pore size of SiC membranes by a superficial deposition of SiO2 onto the membrane surface and membrane pore channels. Our in situ sol–gel modification method is simple and effective. Furthermore, the physical characteristics and the filtration performance of the membrane can easily be controlled by the in situ reaction time. With an optimal in situ reaction time of 30 min, the average pore size of the membrane can be reduced from macropores (400 nm) to mesopores (below 20 nm), and the retention ability for 20 nm fluorescent PS microspheres can be improved from 5% to 93%; the resultant SiC/SiO2 composite membranes are imparted with water permeance of 77 L·m−2·h−1·bar−1, improved anti-protein-fouling properties, excellent performance, and anti-acid stabilities. Therefore, modified SiC/SiO2 membranes based on the in situ sol–gel process have great potential as UF membranes for a variety of industrial processes.
Influence of electrostatic collection on scavenging of submicron-sized aerosols by cloud droplets and raindrops

Abstract We explicitly calculate the efficiency with which submicron-sized aerosol particles (APs) with a diameter smaller than 0.2 collide with cloud droplets and raindrops in air due to individual and combined, simultaneous action of all ice nucleation and all collection mechanisms, including electrostatic forces caused by electric charges on the cloud drops and APs. Brownian diffusion is a predominant collection mechanism up to the critical diameter of APs, which depends on the value of the coefficient of electrostatic collection For larger APs, electroscavenging (ES) is the dominant collection mechanism. When ice nucleation is added to all collection processes, at some moments a larger value of results in a lower mass of APs in rainwater, but only for and Ice nucleation lowers the relative AP precipitation mass (RAPM) compared with a numerical experiment when all collection processes are included for almost 7% at the end of integration for while for all other values of RAPM increases. affects the mass more than the number of APs in cloud water when ice nucleation and all collection processes act simultaneously. The greatest scavenging in the air occurs for the largest ES significantly affects the mass of APs in precipitation at the end of the integration period, while its influence on the number of APs is not that important. The influence of APs charge distribution and an image charge on RAPM and RAPN is significant. For the RAPN the influence of APs distribution compared to an image charge is more significant. The addition of image force to Coulomb force and APs bipolar charge distribution contributes to RAPM for 3.9% and RAPN for 1.6%. Copyright © 2023 American Association for Aerosol Research
Tigger: A Database Proxy That Bounces With User-Bypass

Developers often deploy database-specific network proxies whereby applications connect transparently to the proxy instead of directly connecting to the database management system (DBMS). This indirection improves system performance through connection pooling, load balancing, and other DBMS-specific optimizations. Instead of simply forwarding packets, these proxies implement DBMS protocol logic (i.e., at the application layer) to achieve this behavior. Consequently, existing proxies are user-space applications that process requests as they arrive on network sockets and forward them to the appropriate destinations. This approach incurs inefficiencies as the kernel repeatedly copies buffers between user-space and kernel-space, and the associated system calls add CPU overhead. This paper presents user-bypass, a technique to eliminate these overheads by leveraging modern operating system features that support custom code execution. User-bypass pushes application logic into kernel-space via Linux's eBPF infrastructure. To demonstrate its benefits, we implemented Tigger, a PostgreSQL-compatible DBMS proxy using user-bypass to eliminate the overheads of traditional proxy design. We compare Tigger's performance against other state-of-the-art proxies widely used in real-world deployments. Our experiments show that Tigger outperforms other proxies --- in one scenario achieving both the lowest transaction latencies (up to 29% reduction) and lowest CPU utilization (up to 42% reduction). The results show that user-bypass implementations like Tigger are well-suited to DBMS proxies' unique requirements.
Quality Control of Semi-Solid Die Casting by Filling Pressure Based on Machine Learning Method

In the actual semi-solid die-casting production, the existence of several uncertain factors can impose an effect on the final product quality, which poses a challenge to semi-solid production. However, data analysis such as machine learning (ML) can help producers eliminate this problem. In order to quickly identify defective castings, a new model of predicting quality by real-time injection pressure data will be generated in terms of ML in this research. Quality assessment will be based on non-filling defect, density and tensile properties. The result of cross-validation shows that the classifier can achieve a confidence level of 0.95 for the quality classification. In addition, this research will find key intervals by the importance given by the model and analyze the effects of process on filling pressure. According to the result of feature screening, the surface quality problems are related to speed-pressure conversion and feeding displacement of plunger, the flowing state of slurry in filling affects the formation of defects and tensile properties. This work will make semi-solid die casting more automatically and efficiently, and thus provides support for semi-solid sustainable development.
Supramolecular Assemblies of Melamine-2-Thiobarbiturate and Melamine-Barbiturate-2-Thiobarbiturate: Experimental and Theoretical Studies

In this work, we considered the formation of supramolecular assemblies of melamine-thiobarbiturate and melamine-barbiturate-thiobarbiturate. It is known that thiobarbituric acid can form many tautomers, as well as different motifs due to the change of C2=O to C2=S hydrogen bonds. We formed the crystal. The resulting crystals were studied with scanning electron microscopy (SEM), optical fluorescence microscopy, single crystal and powder (PXRD) X-ray diffraction analyses, and solid state nuclear magnetic resonance (ss NMR). These systems were theoretically studied using density functional theory (DFT) and classical molecular dynamics (MD) simulations. Interestingly, just as in the case of melamine barbiturate, during the crystallization process, hydrogen from the C5 moiety of thiobarbituric acid migrates to the melamine molecule. In addition, the resulting melamine thiobarbiturate crystals exhibit fluorescence behavior in the red region (~565–605 nm), while the melamine barbiturate crystals are fluorescent in the green region (512–542 nm).
Species-Specific Responses to Human Trampling Indicate Alpine Plant Size Is More Sensitive than Reproduction to Disturbance

Human disturbance, such as trampling, is an integral component of global change, yet we lack a comprehensive understanding of its effects on alpine ecosystems. Many alpine systems are seeing a rapid increase in recreation and in understudied regions, such as the Coast Mountains of British Columbia, yet disturbance impacts on alpine plants remain unclear. We surveyed disturbed (trail-side) and undisturbed (off-trail) transects along elevational gradients of popular hiking trails in the T’ak’t’ak’múy’in tl’a In’inyáxa7n region (Garibaldi Provincial Park), Canada, focusing on dominant shrubs (Phyllodoce empetriformis, Cassiope mertensiana, Vaccinium ovalifolium) and graminoids (Carex spp). We used a hierarchical Bayesian framework to test for disturbance by elevation effects on total plant percent cover, maximum plant height and diameter (growth proxies), and buds, flowers, and fruits (reproduction proxies). We found that trampling reduces plant cover and impacts all species, but that effects vary by species and trait, and disturbance effects only vary with elevation for one species’ trait. Growth traits are more sensitive to trampling than reproductive traits, which may lead to differential impacts on population persistence and species-level fitness outcomes. Our study highlights that disturbance responses are species-specific, and this knowledge can help land managers minimize disturbance impacts on sensitive vegetation types.
NIMBUS-5 sounder data processing system. Part 2: Results

The Nimbus-5 spacecraft carries infrared and microwave radiometers for sensing the temperature distribution of the atmosphere. Methods developed for obtaining temperature profiles from the combined set of infrared and microwave radiation measurements are described. Algorithms used to determine (a) vertical temperature and water vapor profiles, (b) cloud height, fractional coverage, and liquid water content, (c) surface temperature, and (d) total outgoing longwave radiation flux are described. Various meteorological results obtained from the application of the Nimbus-5 sounding data processing system during 1973 and 1974 are presented.
Do you hear the people sing? Comparison of synchronized URL and narrative themes in 2020 and 2023 French protests

Introduction France has seen two key protests within the term of President Emmanuel Macron: one in 2020 against Islamophobia, and another in 2023 against the pension reform. During these protests, there is much chatter on online social media platforms like Twitter. Methods In this study, we aim to analyze the differences between the online chatter of the 2 years through a network-centric view, and in particular the synchrony of users. This study begins by identifying groups of accounts that work together through two methods: temporal synchronicity and narrative similarity. We also apply a bot detection algorithm to identify bots within these networks and analyze the extent of inorganic synchronization within the discourse of these events. Results Overall, our findings suggest that the synchrony of users in 2020 on Twitter is much higher than that of 2023, and there are more bot activity in 2020 compared to 2023.
Modulation of miR-146b Expression during Aging and the Impact of Physical Activity on Its Expression and Chondrogenic Progenitors

The finding of molecules associated with aging is important for the prevention of chronic degenerative diseases and for longevity strategies. MicroRNAs (miRNAs) are post-transcriptional regulators involved in many biological processes and miR-146b-5p has been shown to be involved in different degenerative diseases. However, miR-146b-5p modulation has not been evaluated in mesenchymal stem cells (MSCs) commitment or during aging. Therefore, the modulation of miR-146b-5p in the commitment and differentiation of mesenchymal cells as well as during maturation and aging in zebrafish model were analyzed. In addition, circulating miR-146b-5p was evaluated in human subjects at different age ranges. Thus, the role of physical activity in the modulation of miR-146b-5p was also investigated. To achieve these aims, RT (real-time)-PCR, Western blot, cell transfections, and three-dimensional (3D) culture techniques were applied. Our findings show that miR-146b-5p expression drives MSCs to adipogenic differentiation and increases during zebrafish maturation and aging. In addition, miR-146b-5p expression is higher in females compared to males and it is associated with the aging in humans. Interestingly, we also observed that the physical activity of walking downregulates circulating miR-146b-5p levels in human females and increases the number of chondroprogenitors. In conclusion, miR-146b-5p can be considered an age-related marker and can represent a useful marker for identifying strategies, such as physical activity, aimed at counteracting the degenerative processes of aging.
Comparing the Performance of Machine Learning Algorithms in the Automatic Classification of Psychotherapeutic Interactions in Avatar Therapy

(1) Background: Avatar Therapy (AT) is currently being studied to help patients suffering from treatment-resistant schizophrenia. Facilitating annotations of immersive verbatims in AT by using classification algorithms could be an interesting avenue to reduce the time and cost of conducting such analysis and adding objective quantitative data in the classification of the different interactions taking place during the therapy. The aim of this study is to compare the performance of machine learning algorithms in the automatic annotation of immersive session verbatims of AT. (2) Methods: Five machine learning algorithms were implemented over a dataset as per the Scikit-Learn library: Support vector classifier, Linear support vector classifier, Multinomial Naïve Bayes, Decision Tree, and Multi-layer perceptron classifier. The dataset consisted of the 27 different types of interactions taking place in AT for the Avatar and the patient for 35 patients who underwent eight immersive sessions as part of their treatment in AT. (3) Results: The Linear SVC performed best over the dataset as compared with the other algorithms with the highest accuracy score, recall score, and F1-Score. The regular SVC performed best for precision. (4) Conclusions: This study presented an objective method for classifying textual interactions based on immersive session verbatims and gave a first comparison of multiple machine learning algorithms on AT.