Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential gene expression provides leads to environmentally regulated soybean seed protein content

Soybean is an important global source of plant-based protein. A persistent trend has been observed over the past two decades that soybeans grown in western Canada have lower seed protein content than soybeans grown in eastern Canada. In this study, 10 soybean genotypes ranging in average seed protein content were grown in an eastern location (control) and three western locations (experimental) in Canada. Seed protein and oil contents were measured for all lines in each location. RNA-sequencing and differential gene expression analysis were used to identify differentially expressed genes that may account for relatively low protein content in western-grown soybeans. Differentially expressed genes were enriched for ontologies and pathways that included amino acid biosynthesis, circadian rhythm, starch metabolism, and lipid biosynthesis. Gene ontology, pathway mapping, and quantitative trait locus (QTL) mapping collectively provide a close inspection of mechanisms influencing nitrogen assimilation and amino acid biosynthesis between soybeans grown in the East and West. It was found that western-grown soybeans had persistent upregulation of asparaginase (an asparagine hydrolase) and persistent downregulation of asparagine synthetase across 30 individual differential expression datasets. This specific difference in asparagine metabolism between growing environments is almost certainly related to the observed differences in seed protein content because of the positive correlation between seed protein content at maturity and free asparagine in the developing seed. These results provided pointed information on seed protein-related genes influenced by environment. This information is valuable for breeding programs and genetic engineering of geographically optimized soybeans.
Electrochemical and optical studies on photoactive BiVO4-TiO2/poly 3,4-ethylenedioxythiophene assemblies in gel electrolyte: Role of inorganic/organic interfaces in surface functionalization

Inorganic/organic interface assemblies were created from poly 3,4-ethylenedioxythio­phene (PEDOT) interfaced with amorphous BiVO4 and with BiVO4-TiO2. Electrochemical cells-based thermoplastic gel electrolytes containing KI/I2 were used to study the photoelectrochemical behavior of the Inorganic/organic interface electrodes. Optical studies show that doping BiVO4 with TiO2 narrowed the optical band gap to allow more absorption in the visible region and increases solar energy conversion. Evidence for both direct and indirect band gaps was observed. Refractive index data indicates that BiVO4 and BiVO4/TiO2 obey the anomalous dispassion multiple-oscillator model. Chronoampero­metry of these assemblies shows the phenomena of dark current, which correlates to the presence of random electron/hole generation in the depletion layer. PEDOT enhances the photoactivity of BiVO4 only. Electrochemical impedance spectroscopy studies indicated that both kinetic and diffusional control at high and low frequencies, respectively. Furthermore, studies show that as frequency increases, the conductivity increases due to dispersion and charge carrier hopping. All photoactivity outcomes were reproducible.
Critical thinking and the conditions of democracy

It is often argued, rightly, that critical thinking is a necessary condition for democracy. This essay looks at the other side of that relationship, how certain democratic conditions are necessary for critical thinking to flourish. In turn, this dynamic, interactive account of democratic culture, institutions, and dispositions has implications for how we think about critical thinking itself.
The ameliorative effects of choline on ethanol-induced cell death in the neural tube of susceptible BXD strains of mice

Introduction Fetal alcohol spectrum disorders (FASD) are the leading preventable cause of intellectual disability, providing the impetus for evaluating various potential treatments to ameliorate ethanol’s teratogenic effects, particularly in the nervous system. One treatment is the dietary supplement choline which has been shown to mitigate at least some of ethanol’s teratogenic effects. The present study was designed to investigate the effects of genetics on choline’s efficacy in ameliorating cell death in the developing neural tube. Previously, we examined BXD recombinant inbred mice, and their parental C57BL/6 J (B6) and DBA/2 J strains, and identified strains that were sensitive to ethanol’s teratogenic actions. Thus, we used these strains to identify response to choline treatment. Materials and methods Timed pregnant mice from 4 strains (B6, BXD51, BXD73, BXD2) were given either ethanol or isocaloric maltose-dextrin (5.8 g/kg in two administrations separated by 2 h) with choline at one of 3 doses: 0, 100 or 250 mg/kg. Subjects were exposed via intragastric gavage on embryonic day 9 and embryos were collected 7 h after the initial ethanol administrations. Cell death was analyzed using TUNEL staining in the developing forebrain and brainstem. Results Choline ameliorated the ethanol-induced cell death across all 4 strains without causing enhanced cell death in control mice. Choline was effective in both the developing telencephalon and in the brainstem. Both doses diminished cell death, with some differences across strains and brain regions, although the 100 mg/kg dose was most consistent in mitigating ethanol-related cell death. Comparisons across strains showed that there was an effect of strain, particularly in the forebrain at the higher dose. Discussion These results show that choline is effective in ameliorating ethanol-induced cell death at this early stage of nervous system development. However, there were some strain differences in its efficacy, especially at the high dose, providing further evidence of the importance of genetics in influencing the ability of choline to protect against prenatal alcohol exposure.
Development of Patient-Reported Outcome Scale for Patients with Diabetic Foot and Its Reliability and Validity Test

Objective To construct a self-reported outcome scale for diabetic foot patients, and to test its reliability and validity. Methods Through literature reading and interviews with 30 patients, a pool of scale items was formed. The items were classified and sorted out according to the expected scale structure framework. After two rounds of expert consultation and a small range of test dressing, the initial scale was formed. Through the investigation of 85 patients with diabetic foot, item differentiation analysis, correlation analysis and exploratory factor analysis were used to screen the items. Cronbach’s α coefficient, retest reliability and content and structure validity analysis were used to determine the feasibility and validity of the scale. Results The final scale included 4 first-level items and 22 second-level items. The critical ratio method showed that the scores of each item in the high group and the low group were significantly different (P < 0.05). Correlation analysis showed that the correlation coefficient between each item and the total score was 0.431 to 0.829; The content validity index of the scale was 0.91, the exploratory factor analysis identified three common factors, and the cumulative variance contribution rate was 75.381%. The confirmatory factor analysis showed that the model fit well. The Cronbach’s α coefficient of the scale was 0.934 and the retest reliability coefficient was 0.926. Conclusion The self-reported outcome scale for diabetic foot patients has good reliability and validity, and can be used to investigate the health status of diabetic foot patients and evaluate the therapeutic effect.
Deep Learning Techniques for the Classification of Brain Tumor: A Comprehensive Survey

Researchers have given immense consideration to unsupervised approaches because of their tendency for automatic feature generation and excellent performance with a reduced error margin. Deep learning (DL) models are emerging as vital methods for image analysis in medical fields, such as classification, segmentation, and reconstruction. Deep learning relies on learning hierarchical features and data representation, making it superior to its antecedent. Deep learning models efficiently discover descriptive information about the optimal representation of various brain tumors when applied for brain tumor classification from MRI. Despite various efforts, there remains a gap in the current literature for inclusive representation of recently developed deep learning-based classification methods. The current study attempts to fill this gap by briefly reviewing the current state of the art on brain tumor segmentation and classification methods while focusing on deep learning methods. The proposed survey dedicates itself to review the current state of the art on automated classification techniques for brain tumor MRI to produce an inclusive picture of the most recent and worthy of adoption models proposed in this area. Despite various attempts to conduct surveys on brain tumor segmentation and classification techniques, no such study could be found in the current literature that has dedicated its focus to the most effective approach toward classification. This research begins by identifying major brain tumor segmentation and classification classes while presenting its focused area and reviewing the most recent state-of-the-art classification approach, the deep learning-based classification method. The powerful learning ability of deep learning mechanisms has been reviewed for their performance, and a comparison between them is presented to encourage its applications. Future recommendations and directions are also drawn up to establish a pursuable course for welcoming widespread adoption of potential applications in the area.
Establishment and Validation of a Predictive Model for Post-Treatment Anxiety Based on Patient Attributes and Pre-Treatment Anxiety Scores

Objective In this study, we aim to establish and evaluate a predictive model for post-treatment anxiety state based on basic patient attributes and pre-treatment SAS scores, with the expectation that this model will guide clinical precision intervention. Methods Data were collected from 606 patients with breast cancer who underwent surgery at our hospital between January 1, 2015 and December 30, 2018 and 144 newly diagnosed patients with breast cancer who were admitted between June 1, 2019 and December 30, 2019, for a total of 750 patients with breast cancer. The relationship between SAS_A scores and prognosis was verified by analyzing patient baseline characteristics, follow-up data, pre-treatment self-rating anxiety scale (SAS) scores, and SAS_A scores in follow-up period after the end of treatment. A risk prediction model was developed in view of the SAS_A scores, which was then screened, validated, and simplified by scoring, with a nomogram plotted. Results The SAS_A score can be utilized to differentiate prognosis. In K-M analysis, the high SAS_A score group had a significantly poorer progression-free survival rate than the low score group, p-value < 0.0001. Through model feature selection and clinical analysis, all variables were finally incorporated to establish a predictive model with a ROC AUC of 0.721 (0.637–0.805) for the validation set and external data, and an AUC of 0.810 (0.719–0.902) for external data, demonstrating good predictive performance. Calibration curves and probability distribution maps were constructed. DCA and CIC analyses demonstrated that model intervention could boost clinical benefits more effectively than intervention for all patients. Conclusion Using a predictive model to guide clinical management for anxiety in breast cancer patients is feasible, but additional research is required.
Recent advances in steroidal glycoalkaloid biosynthesis in the genus Solanum

Steroidal glycoalkaloids (SGAs) are specialized metabolites found in members of Solanum species, and are also known as toxic substances in Solanum food crops such as tomato (Solanum lycopersicum), potato (Solanum tuberosum), and eggplant (Solanum melongena). SGA biosynthesis can be divided into two main parts: formation of steroidal aglycones, which are derived from cholesterol, and glycosylation at the C-3 hydroxy group. This review focuses on recent studies that shed light on the complete process of the aglycone formation in SGA biosynthesis and structural diversification of SGAs by duplicated dioxygenases, as well as the development of non-toxic potatoes through genome editing using these findings.
Impact of Air Refreshing and Cloud Ice Uptake Limitations on Vertical Profiles and Wet Depositions of Nitrate, Ammonium, and Sulfate

The impacts of cloud mixing and uptake on wet scavenging are not adequately resolved in global models which can lead to an overestimation of the removal of water‐soluble gases and aerosols from the atmosphere. To address this issue, we develop and implement novel parameterizations to consider the impacts of these processes. Our analysis of vertical profiles of nitric acid, inorganic nitrate, ammonium, and sulfate concentrations during the Atmospheric Tomography Mission periods indicates that air refreshing limitation has a significant impact above 800 hPa, while cloud ice uptake limitation plays an important role above 500 hPa. Incorporating these two processes resulted in a reduction of wet depositions of these species across source regions and a slight increase in their downwind regions. Wet depositions of nitrate, ammonium, and sulfate were reduced in source regions by 22.7%, 8.4%, and 8.3%, respectively and increased in downwind regions by 10.1%, 7.0%, and 4.3%, respectively.
Giardia duodenalis (Styles, 1902) in Cattle: Isolation of Calves with Diarrhoea and Manure Treatment in the Lagoon Presented as Risk Factors in Latvian Herds

Giardia duodenalis is a waterborne zoonotic protozoan that causes gastrointestinal tract inflammation in humans, cattle, and other animals. The aim of the present study was to estimate the prevalence and potential risk factors for Giardia infection in cattle in Latvia. During 2020–2021, a total of 973 individual faecal samples from cattle aged from 1 day to 12 years old, from 32 cattle herds, were tested for Giardia cyst presence with immunofluorescence staining followed by Giardia assemblage differentiation targeting beta-giardin gene. Using a questionnaire, information was collected to estimate the potential risk factors for G. duodenalis infection in cattle herds. Giardia was found in 8.4% of the examined cattle with a mean intensity of 5756 cysts per gram of faeces. The highest prevalence was observed in the 0 to 3-month-old calves (16.4%). At least one Giardia shedding animal was found in 27 herds with an overall prevalence of 84.4%. Significantly higher prevalence was found for cattle infected with G. duodenalis assemblage E compared to that infected with assemblage A: 88.7% and 11.3%, respectively. Protective factors such as age and rodent control and change of shoes were found to be significant for Giardia infection, while isolating calves for diarrhoea and water bodies (ponds/lakes) in pasture were potential risk factors in Latvian cattle.
Optimized Extraction of Polyphenols from Unconventional Edible Plants: LC-MS/MS Profiling of Polyphenols, Biological Functions, Molecular Docking, and Pharmacokinetics Study

Plant bioactive phenolic metabolites have recently attracted the attention of researchers due to their numerous health advantages. Therefore, this study aimed to investigate with advanced techniques the bioactive metabolites and antioxidant and antidiabetic capacity of four unconventional edible plant leaves: lemongrass (Cymbopogon citratus (DC.) Stapf), chicory (Cichorium intybus L.), moringa (Moringa oleifera Lam.), and ryegrass (Lolium perenne L.). The extraction process was optimized using different solvents. These plants’ phenolic composition, identification, and characterization have been determined herein using LCESI-QTOF-MS/MS. This research identified 85 phenolic compounds, including 24 phenolic acids, 31 flavonoids, 7 stilbenes and lignans, and 17 other metabolites. Moreover, the study determined that moringa has the highest total phenolic content (TPC; 18.5 ± 1.01 mg GAE/g), whereas ryegrass has the lowest (3.54 ± 0.08 mg GAE/g) among the selected plants. It seems that, compared to other plants, moringa was found to have the highest antioxidant potential and antidiabetic potential. In addition, twenty-two phenolic compounds were quantified in these chosen edible plants. Rosmarinic acid, chlorogenic acid, chicoric acid, ferulic acid, protocatechuic acid, and caffeic acid were the most abundant phenolic acids. In silico molecular docking was also conducted to investigate the structure–function relationship of phenolic compounds to inhibit the alpha-glucosidase. Finally, the simulated pharmacokinetic characteristics of the most common substances were also predicted. In short, this investigation opens the way for further study into these plants’ pharmaceutical and dietary potential.
Seeking abiotic surrogates for temperate reef invertebrate assemblages using an Autonomous Underwater Vehicle (AUV)

Seabed habitat mapping has become a popular precursor to Marine Protected Area (MPA) design. Data acquired through habitat mapping is used as a surrogate measure for biodiversity, therefore simplifying the planning process for more effective reserves. The problem with this method is that it assumes areas with similar habitat variable measures will support the same species. Here, with the use of data acquired with an Autonomous Underwater Vehicle (AUV), I report that habitat variables in temperate rocky reefs off the east coast of Australia can be successful predictors of sessile invertebrate species richness and relative abundance. Temperate rocky reefs at approximately 30 metres depth within Batemans Marine Park were surveyed for this study. The best model for predicting species richness and relative abundance was composed of bathymetric variance and aspect. Together these variables explained 31% of the variation in sessile invertebrate species richness and 50% of the variation in relative abundance. Assemblages within rocky reefs differed at all scales investigated (<1 metre to 25 kilometres), but most significantly at the smallest scale. The conservation implications of these findings suggest that several rocky reefs must be protected as assemblages varied significantly among them. This study provides future researchers with information on patterns of distribution required to understand the processes that drive change in sessile invertebrate assemblages. Understanding the spatial scale at which assemblages vary is the first step towards predictive models that will greatly benefit MPA design in the future.
Genome analysis of a plasmid-bearing myxobacterim Myxococcus sp. strain MxC21 with salt-tolerant property

Myxobacteria are widely distributed in various habitats of soil and oceanic sediment. However, it is unclear whether soil-dwelling myxobacteria tolerate a saline environment. In this study, a salt-tolerant myxobacterium Myxococcus sp. strain MxC21 was isolated from forest soil with NaCl tolerance >2% concentration. Under 1% salt-contained condition, strain MxC21 could kill and consume bacteria prey and exhibited complex social behaviors such as S-motility, biofilm, and fruiting body formation but adopted an asocial living pattern with the presence of 1.5% NaCl. To investigate the genomic basis of stress tolerance, the complete genome of MxC21 was sequenced and analyzed. Strain MxC21 consists of a circular chromosome with a total length of 9.13 Mbp and a circular plasmid of 64.3 kb. Comparative genomic analysis revealed that the genomes of strain MxC21 and M. xanthus DK1622 share high genome synteny, while no endogenous plasmid was found in DK1622. Further analysis showed that approximately 21% of its coding genes from the genome of strain MxC21 are predominantly associated with signal transduction, transcriptional regulation, and protein folding involved in diverse niche adaptation such as salt tolerance, which enables social behavior such as gliding motility, sporulation, and predation. Meantime, a high number of genes are also found to be involved in defense against oxidative stress and production of antimicrobial compounds. All of these functional genes may be responsible for the potential salt-toleration. Otherwise, strain MxC21 is the second reported myxobacteria containing indigenous plasmid, while only a small proportion of genes was specific to the circular plasmid of strain MxC21, and most of them were annotated as hypothetical proteins, which may have a direct relationship with the habitat adaptation of strain MxC21 under saline environment. This study provides an inspiration of the adaptive evolution of salt-tolerant myxobacterium and facilitates a potential application in the improvement of saline soil in future.
A Graph Correlated Anomaly Detection with Fuzzy Model for Distributed Wireless Sensor Networks

Wireless sensor networks have limited power for processing data, storage, and communication. Due to power shortages and anonymous attacks, sensor nodes may produce faulty or anomaly data which affects the accuracy of the entire system. Effective anomaly detection is essential to make an accurate prediction of the result. Moreover, clustering-based anomaly detection reduces energy consumption by avoiding individual sensory data reporting to the base station. The proposed methodology consists of two phases: Correlated graph clustering, and anomaly detection using a Fuzzy model. In the first phase, the spatial correlation of the sensor readings is used to generate a graph, partitioned into clusters. The intra-cluster and inter-cluster temporal correlations are analyzed to refine the optimized cluster structure. Finally, a fuzzy Mamdani model is used to classify the clusters as either normal or anomalous based on their membership values. The proposed approach leverages both spatial and temporal correlation between sensor measurements to form optimized clusters that are more effective for anomaly detection. The Experiments performed on a real-world dataset of WSNs indicate the efficacy of the proposed methodology, which shows significant improvement over traditional anomaly detection methods the electronic file of your paper will be formatted further for final publication.
Daily Quantity and Kinematic Characteristics of Leg Movement in a Child With SMA (2 Copies SMN2)

Purpose: This case report describes daily leg movement quantity and kinematic characteristics of a child with spinal muscular atrophy (SMA) with 2 copies of SMN2, who was symptomatic at the time of treatment with disease-modifying therapies. Key Points: Compared with infants with typical development, this child had differing values for leg movement quantity, duration, average acceleration, and peak acceleration measured across full days in the natural environment by wearable sensors. In addition, movement quantity and clinician-rated outcomes increased with age. Conclusions: Wearable sensors recorded movement quantity and kinematic characteristics in a treated infant with SMA (2 copies SMN2). These movement parameters were consistently different compared anecdotally with published data from infants with typical development, demonstrating their potential to add unique and complementary information to the assessment of motor function in SMA. Recommendations: Larger longitudinal studies are needed to determine the utility of wearable sensors as an assessment tool and an early predictor of motor outcomes in children with SMA.