Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Basis of Potato Tuber Defects and Identification of Heat-Tolerant Clones

Heat stress during the potato growing season reduces tuber marketable yield and quality. Tuber quality deterioration includes external (heat sprouts, chained tubers, knobs) and internal (vascular discoloration, hollow heart, internal heat necrosis) tuber defects, as well as a reduction in their specific gravity and increases in reducing sugars that result in suboptimal (darker) processed products (french fries and chips). Successfully cultivating potatoes under heat-stress conditions requires planting heat-tolerant varieties that can produce high yields of marketable tubers, few external and internal tuber defects, high specific gravity, and low reducing sugars (in the case of processing potatoes). Heat tolerance is a complex trait, and understanding its genetic basis will aid in developing heat-tolerant potato varieties. A panel of 217 diverse potato clones was evaluated for yield and quality attributes in Dalhart (2019 and 2020) and Springlake (2020 and 2021), Texas, and genotyped with the Infinium 22 K V3 Potato Array. A genome-wide association study was performed to identify genomic regions associated with heat-tolerance traits using the GWASpoly package. Quantitative trait loci were identified on chromosomes 1, 3, 4, 6, 8, and 11 for external defects and on chromosomes 1, 2, 3, 10, and 11 for internal defects. Yield-related quantitative trait loci were detected on chromosomes 1, 6, and 10 pertaining to the average tuber weight and tuber number per plant. Genomic-estimated breeding values were calculated using the StageWise package. Clones with low genomic-estimated breeding values for tuber defects were identified as donors of good traits to improve heat tolerance. The identified genomic regions associated with heat-tolerance attributes and the genomic-estimated breeding values will be helpful to develop new potato cultivars with enhanced heat tolerance in potatoes.
Simulation of Speed Control on a PMSM Using a PI Controller

In order to manage the speed of a permanent magnet synchronous motor drive system, a practical approach for a PI controller has been developed and put into practice in this study. The adopted approach, which also preserves the proportional integral controller's straightforward structure and features, significantly enhanced the performance of the prior proportional integral controller. The final PI controller delivers quick and precise response, strong noise rejection, and minimal sensitivity to permanent magnet synchronous motor parameter fluctuations. The findings demonstrate that the suggested controller may provide best performance in terms of accuracy, parametric variation, and load torque disturbance when entering a proportional value of 0,013 and an integral value of 16,61. The proposed method has been exhaustively tested under various circumstances. The proposed solution not only possesses attributes like simplicity and ease of implementation for real-time, but also stability and effectiveness.
Association between triglyceride glucose index (TyG) and psychotic symptoms in patients with first-episode drug-naïve major depressive disorder

Objective Major depressive disorder (MDD) sufferers frequently have psychotic symptoms, yet the underlying triggers remain elusive. Prior research suggests a link between insulin resistance (IR) and increased occurrence of psychotic symptoms. Hence, this study sought to investigate the potential association between psychotic symptoms in Chinese patients experiencing their first-episode drug-naïve (FEDN) MDD and the triglyceride glucose (TyG) index, an alternative measure of insulin resistance (IR). Methods Between September 2016 and December 2018, 1,718 FEDN MDD patients with an average age of 34.9 ± 12.4 years were recruited for this cross-sectional study at the First Hospital of Shanxi Medical University in China. The study collected clinical and demographic data and included assessments of anxiety, depression, and psychotic symptoms using the 14-item Hamilton Anxiety Rating Scale (HAMA), the 17-item Hamilton Depression Rating Scale (HAMD-17), and the positive subscales of the Positive and Negative Syndrome Scale (PANSS), respectively. Measurements of metabolic parameters, fasting blood glucose (FBG), and thyroid hormones were also gathered. To assess the correlation between the TyG index and the likelihood of psychotic symptoms, the study used multivariable binary logistic regression analysis. Additionally, two-segmented linear regression models were employed to investigate possible threshold effects in case non-linearity relationships were identified. Results Among the patients, 9.95% (171 out of 1,718) exhibited psychotic symptoms. Multivariable logistic regression analysis showed a positive correlation between the TyG index and the likelihood of psychotic symptoms (OR = 2.12, 95% CI: 1.21-3.74, P = 0.01) after adjusting for confounding variables. Moreover, smoothed plots revealed a nonlinear relationship with the TyG index, revealing an inflection point at 8.42. Interestingly, no significant link was observed to the left of the inflection point (OR = 0.50, 95% CI: 0.04-6.64, P = 0.60), whereas beyond this point, a positive correlation emerged between the TyG index and psychotic symptoms (OR = 2.42, 95% CI: 1.31-4.48, P = 0.01). Particularly, a considerable 142% rise in the probability of experiencing psychotic symptoms was found with each incremental elevation in the TyG index. Conclusions Understanding the non-linear link between the TyG index and the risk of psychotic symptoms in Chinese patients with FEDN MDD highlights the potential for targeted therapeutic approaches. By acknowledging the threshold effect observed, there is an opportunity to mitigate risk factors associated with IR-related psychiatric comorbidities through tailored interventions. These preliminary results stress the need for further longitudinal research to solidify these insights and contribute to more effective therapeutic strategies.
Inhibition effect of rosemary and black cumin essential oils on some micro-organisms

The present study was conducted to investigate the antimicrobial activity of rosemary and black cumin essential oils against different isolates of microorganisms including two bacteria strains: gram-positive bacteria ( Staphylococcus aureus) and gram-negative bacteria ( Escherichia coli) as well as two fungus strains ( Aspergillus niger and Aspergillus flavus ) and one yeast strain ( Saccharomyces cerevisiae ). Four concentrations of rosemary and black cumin essential oils (5, 10, 15, or 20 µl /ml) were examined against all tested strains of microorganisms by the agar diffusion method. The results showed that all various concentrations of rosemary and black cumin essential oils possess a significant inhibitory effect on the microorganisms strains used in this study. Rosemary is significantly the most effective essential oil against all tested strains of microorganisms compared to black cumin essential oil. The higher concentration of the essential oil has the greatest effect on growth inhibition compared to other concentrations. Depending on the findings of this study, it can be said that the essential oils of rosemary and black cumin have a greater and wider range of antimicrobial activity against several food-borne bacteria, and the crude extract can be used to find biological active items that may act as a starting point for the creation of new antimicrobial compounds. So, it can be recommended to use these essential oils as a source of potential of the active components in food preservatives
Die neogene Entwicklung des zentralen Tien Schan, Kasachstan. Erste Ergebnisse von Apatit-Spaltspurdatierungen und morphotektonischer Analyse von Satellitendaten

Einführung Der Tien Schan ist ein etwa E–W erstrecktes, rund 2500 km langes und bis 250 km breites Gebirge in Zentralasien. Einzelne Gipfel sind über 7000 m hoch. Obwohl durch die Kollision Indiens mit Asien entstanden, ist der Tien Schan ein Intraplatten-Orogen, dessen Hebung lange nach dem Beginn der Kollision vor 50Ma und weit nördlich der Sutur einsetzte (Sobel & Dumitru 1997). Von Tibet ist der Tien Schan durch das kaum deformierte Tarim-Becken getrennt (Abb. 1). Hohe und schroffe Topographie, starke Seismizität (Molnar & Ghose 2000) und GPS-Daten zeigen, dass das Orogen auch heute sehr aktiv ist (Abdrakhmatov et al. 1996, Reigber et al. 2001). Der Tien Schan nimmt gegenwärtig etwa 40% der Gesamtkonvergenz Indiens mit Asien auf. Die Struktur des Tien Schan wird dominiert von E–Wstreichenden, nach N und S gerichteten Überschiebungen (Avouac et al. 1993, Yin et al. 1998), die sich meist deutlich in der Morphologie äußern. Auffallend ist die großräumige Gliederung des Orogens durch NW–SE-streichende dextrale Blattverschiebungen, die auch in das nördliche Vorland reichen (Tapponnier
Brain Tumor Detection Using Convolutional Neural Networks (CNNs)

Brain tumors are a serious medical condition that requires early detection for successful treatment. However, accurate diagnosis can be difficult and time-consuming, and current methods such as MRI scans can be expensive and may require highly trained specialists to interpret the results. A model of a brain tumor detection system using Convolutional Neural Networks (CNNs) has been proposed to address these challenges. To use this model, a dataset of medical images of the brain is collected, the dataset is then preprocessed, and the relevant feature is extracted from the images using CNNs. The developed CNN model is designed and trained to accurately detect the presence and location of brain tumors in the images. Optimization of the CNN model's performance is done by experimenting with different architectures, hyperparameters, and optimization techniques, and its performance is evaluated using metrics such as accuracy, sensitivity, specificity, and F1 score. The model training was carried out on MRI images containing tumors and without tumors. The developed CNN-based model achieved impressive accuracy in detecting brain tumors, demonstrating high precision and recall rates. This brain tumor detection system has the potential to significantly improve the accuracy and efficiency of brain tumor diagnosis, leading to better treatment outcomes and reducing the burden on healthcare systems.
Sex-driven variability in TSPO-expressing microglia in MS patients and healthy individuals

Background Males with multiple sclerosis (MS) have a higher risk for disability progression than females, but the reasons for this are unclear. Objective We hypothesized that potential differences in TSPO-expressing microglia between female and male MS patients could contribute to sex differences in clinical disease progression. Methods The study cohort consisted of 102 MS patients (mean (SD) age 45.3 (9.7) years, median (IQR) disease duration 12.1 (7.0–17.2) years, 72% females, 74% relapsing–remitting MS) and 76 age- and sex-matched healthy controls. TSPO-expressing microglia were measured using the TSPO-binding radioligand [11C](R)-PK11195 and brain positron emission tomography (PET). TSPO-binding was quantified as distribution volume ratio (DVR) in normal-appearing white matter (NAWM), thalamus, whole brain and cortical gray matter (cGM). Results Male MS patients had higher DVRs compared to female patients in the whole brain [1.22 (0.04) vs. 1.20 (0.02), p = 0.002], NAWM [1.24 (0.06) vs. 1.21 (0.05), p = 0.006], thalamus [1.37 (0.08) vs. 1.32 (0.02), p = 0.008] and cGM [1.25 (0.04) vs. 1.23 (0.04), p = 0.028]. Similarly, healthy men had higher DVRs compared to healthy women except for cGM. Of the studied subgroups, secondary progressive male MS patients had the highest DVRs in all regions, while female controls had the lowest DVRs. Conclusion We observed higher TSPO-binding in males compared to females among people with MS and in healthy individuals. This sex-driven inherent variability in TSPO-expressing microglia may predispose male MS patients to greater likelihood of disease progression.
The Relationship between the Parameters That Characterize a Built Living Space and the Health Status of Its Inhabitants

Housing is an essential component of the living environment, and it has a substantial effect on physical and mental well-being. Multiple housing factors, including inadequate ventilation, overcrowding, construction materials, and exposure to allergens and pollutants, have been linked to a variety of diseases, such as respiratory ailments and dermatologic, rheumatologic, and cardiovascular disorders. The present narrative review shows the current state of knowledge in the field by centralizing and evaluating scientific publications with a focus on this linkage, detailing the implications for health status and the benefits of using natural materials in construction, implementing green building concepts, and applying technological transfer, where various decision factors can contribute to improving quality of life. Therefore, it is achievable to enhance the indoor air quality (IAQ) by promoting ventilation and air filtration, decreasing mold and moisture, and employing low-emitting materials in building construction and development. Overall, promoting healthy housing environments through an enhanced IAQ and using sustainable building practices can have a substantial positive effect on public health. To reduce the risk of housing-related diseases, future research should concentrate on identifying the most effective interventions to improve the living environment–health condition axis.
Faculty Mentorship and Research Productivity, Salary, and Job Satisfaction

Studies have shown that mentorship is critical to the career and professional development of workers, including postsecondary faculty. Evidence from the literature on faculty-to-faculty mentorship has generally come from the medical field and/or focused only on the academic institution where the study was conducted. This study extends the literature by examining data reported by faculty across multiple institutions and fields in the Early Career Doctorates Survey (ECDS). Guided by a theoretical framework adapted from Higgins and Kram (2001), multiple linear regression models are applied to investigate which factors are associated with faculty attainment of mentorship, and how mentorship of faculty is associated with faculty productivity, salary, and job satisfaction. In contrast to previous literature, results indicate that women and men have similar likelihood of reporting having a formal/informal mentor, and that Black/African American faculty are more likely to report having a formal/informal mentor compared to White faculty. Furthermore, receiving mentorship does not appear to be associated with increased productivity or job satisfaction, but is associated with a 10% higher salary among faculty who reported having a mentor. These results, however, are limited to observable outcomes in the ECDS, and the benefits to mentoring may extend beyond those, including well-being, sense of belonging, and other variables not measured in the dataset. Overall, research findings contribute to existing efforts and ongoing conversations on faculty mentorship by offering additional evidence from a nationally representative sample, providing a benchmark for academic institutions to evaluate their professional development programs for faculty.
A Life-Stage Domain Aware Network for Bearing Health Prognosis Under Unseen Temporal Distribution Shift

Transfer learning-based methods for the remaining useful life (RUL) prediction of bearings require accessing target domains at model training stages, which limit the practical value as many real-world cases are with totally unseen domains. In addition, the life-stage domain shift within time-series data is rarely considered in tackling the unseen domain problems. To this end, this study is motivated to develop a novel network model possessing the awareness of the life-stage domain shift information to overcome challenges induced by unseen working conditions and intradomain distribution shifts. The development of the resulting model, the life-stage domain aware network, is composed of two parts. In the first part, an unsupervised learning scheme is proposed for handling the life-stage division via sensing intradomain distribution shifts. In the second part, a domain aware network tailored for life-stages is developed to build a shared domain-invariant latent space through the subdomain alignment at each stage. A preliminary theoretical analysis is conducted to show that invariant features can be learned under the proposed learning framework. The superiority of the RUL prediction model developed by the proposed method is validated through comparisons with the state-of-the-art methods on different bearing datasets.
IMPROVING TEACHER PROFESSIONAL COMPETENCE THROUGH THE TRAINING OF INFORMATION TECHNOLOGY-BASED LEARNING MEDIA IN SOUTHEAST SULAWESI

Abstract: This article aims to improve the competence of MIPA (Mathematics and Natural Sciences) teachers using Information Technology (IT)-based learning media. A participatory action research (PAR) was conducted on twenty-five teachers from 3T regions (Frontier, Outermost, and Least Developed) of Konawe Kepulauan Regency. The findings revealed that: 1) The initial comprehension of MIPA teachers was in the low category, with an average score of 43.84. 2) The competence level of MIPA teachers after training was high, with 81.12. 3) Training using IT-based learning media effectively improved teacher competence, particularly the professional competence of teachers in the external domain. The main implementation constraints encountered by teachers were a lack of knowledge about software application installation procedures and a lack of optimality in utilizing the various features possessed by each application. Therefore, IT training is needed to improve teachers' professional competence in optimizing the use of various applications in the 3T area. Abstrak: Artikel ini bertujuan untuk meningkatkan kompetensi guru MIPA (Matematika dan Ilmu Pengetahuan Alam) dengan menggunakan media pembelajaran berbasis Teknologi Informasi (TI). Penelitian tindakan partisipatif (PAR) dilakukan terhadap dua puluh lima guru daerah 3T (Terdepan, Terluar, dan Tertinggal) Kabupaten Konawe Kepulauan. Hasil penelitian menunjukkan bahwa: 1) Pemahaman awal guru MIPA berada pada kategori rendah dengan rata-rata skor 43,84. 2) Tingkat kompetensi guru MIPA setelah pelatihan termasuk tinggi yaitu 81,12. 3) Pelatihan penggunaan media pembelajaran berbasis IT efektif meningkatkan kompetensi guru, khususnya kompetensi profesional guru pada ranah eksternal. Kendala utama implementasi yang ditemui guru adalah kurangnya pengetahuan tentang tata cara instalasi aplikasi perangkat lunak dan kurang optimalnya penggunaan berbagai fitur yang dimiliki setiap aplikasi. Oleh karena itu, pelatihan IT diperlukan untuk meningkatkan kompetensi profesional guru dalam mengoptimalkan penggunaan berbagai aplikasi di bidang 3T.
The role of gut microorganisms and metabolites in intracerebral hemorrhagic stroke: a comprehensive review

Intracerebral hemorrhagic stroke, characterized by acute hemorrhage in the brain, has a significant clinical prevalence and poses a substantial threat to individuals’ well-being and productivity. Recent research has elucidated the role of gut microorganisms and their metabolites in influencing brain function through the microbiota-gut-brain axis (MGBA). This article provides a comprehensive review of the current literature on the common metabolites, short-chain fatty acids (SCFAs) and trimethylamine-N-oxide (TMAO), produced by gut microbiota. These metabolites have demonstrated the potential to traverse the blood–brain barrier (BBB) and directly impact brain tissue. Additionally, these compounds have the potential to modulate the parasympathetic nervous system, thereby facilitating the release of pertinent substances, impeding the buildup of inflammatory agents within the brain, and manifesting anti-inflammatory properties. Furthermore, this scholarly analysis delves into the existing dearth of investigations concerning the influence of gut microorganisms and their metabolites on cerebral functions, while also highlighting prospective avenues for future research.
Screening Local Sicilian Tomato Ecotypes to Evaluate the Response of Tomato Brown Rugose Fruit Virus Infection

Tomato is one of the most important crops worldwide, with a production of ≈190 million tons, but it is constantly threatened by several viral diseases. Tomato brown rugose fruit virus (ToBRFV), identified in 2014 on tomato plants and subsequently reported in many countries, represents one of the major threats to tomato crops, due to production losses, different transmission modes and its rapid spread. This work aimed to evaluate 37 local Sicilian tomato ecotypes against ToBRFV infection. After a preliminary screening by molecular analyses for tomato mosaic virus (ToMV) and pepino mosaic virus (PepMV), and ToBRFV detection, tomato plants were grown in a greenhouse for their morphological characterization and for evaluating resistance and tolerance to ToBRFV. Resistance and tolerance levels were estimated by mechanical inoculation with ToB SIC01/19 ToBRFV isolate in ten plants per ecotype and evaluating virus accumulation by RT-qPCR and visual observation of symptoms. All ecotypes were infected with ToBRFV, showing several symptoms with different disease severity. No tomato ecotype showed a high level of resistance, but two ecotypes, Pop27 and Pop35, showed very moderate symptoms and therefore a high tolerance. These Sicilian tomato ecotypes could be used in genetic breeding programs as parental ones to obtain cultivars tolerant to ToBRFV.
Research on supermarket pricing strategy based on deep learning and optimization model

To carry out reliable market demand analysis and improve the profit of supermarkets, this paper studied the automatic pricing and replenishment decision-making problem of vegetable products in fresh supermarkets. Firstly, the time series-LSTM prediction model is established. Secondly, the parameters of the Adam optimization algorithm and LSTM model are adjusted by grid search, Through the trained model, the daily sales volume, wholesale price, and sales price of each category in the next week are predicted. Finally, the pricing strategy of the total daily replenishment of each vegetable category in the next week is calculated.
Research on Partner Selection of PPP Project in Wastewater Treatment Based on Grey Nearness Incidence

The PPP financing model of wastewater treatment project can help local governments to solve the problem of capital shortage and bring advanced management experience. It is the key factor of the PPP project for appropriate social partners selection. The evaluation index system of partner selection was established. It covered four fields: corporate reputation, financial ability, operational ability and management ability. Some experts apply the Delphi method to score the qualitative indicators. The most appropriate partner was decided according to the comprehensive evaluate results of Grey nearness incidence. The results of the example application show that the decision model and the index system can easily and effectively determine the ordering of partners, and help government departments to make better decisions.