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Topological crystalline insulators (TCIs) are a class of materials with metallic surface states on high-symmetry crystal surfaces. TCIs discovered so far have cubic structures, which, compared to the layered structure of first-generation topological insulators such as and , offer the potential for branched structures or strong coupling with other materials for large proximity effects. In the present work we implement low-energy theory and the Green’s function technique on the tight-binding Hamiltonian to study the major electronic properties and electronic thermal conductivity (ETC) of pristine TCI SnTe (001). For the first time, we calculate the ETC of this material and explore the effects of strain and electric fields to tune its topological phase. The xx component dominates in the pristine case (5.311 at room temperature) aligning well with related experimental results on similar materials. We assess the impact of uniaxial and biaxial strains, observing an overall ETC increase (up to 159% for the xx component under uniaxial strain and 215% for the xy component (Anomalous Righi-Leduc effect) under biaxial strain). Applying an electric field further enhances ETC in all components (as high as 14.367 for xx component at 190 K). These findings highlight strain and electric field perturbations as effective methods to control the thermal properties of SnTe (001), offering insights into its future applications in thermoelectrics and tunable electronics.

Since the beginning of the COVID-19 pandemic, new and non-invasive digital technologies such as artificial intelligence (AI) had been introduced for mortality prediction of COVID-19 patients. The prognostic performances of the machine learning (ML)-based models for predicting clinical outcomes of COVID-19 patients had been mainly evaluated using demographics, risk factors, clinical manifestations, and laboratory results. There is a lack of information about the prognostic role of imaging manifestations in combination with demographics, clinical manifestations, and laboratory predictors. The purpose of the present study is to develop an efficient ML prognostic model based on a more comprehensive dataset including chest CT severity score (CT-SS). Fifty-five primary features in six main classes were retrospectively reviewed for 6854 suspected cases. The independence test of Chi-square was used to determine the most important features in the mortality prediction of COVID-19 patients. The most relevant predictors were used to train and test ML algorithms. The predictive models were developed using eight ML algorithms including the J48 decision tree (J48), support vector machine (SVM), multi-layer perceptron (MLP), k-nearest neighbourhood (k-NN), Naïve Bayes (NB), logistic regression (LR), random forest (RF), and eXtreme gradient boosting (XGBoost). The performances of the predictive models were evaluated using accuracy, precision, sensitivity, specificity, and area under the ROC curve (AUC) metrics. After applying the exclusion criteria, a total of 815 positive RT-PCR patients were the final sample size, where 54.85% of the patients were male and the mean age of the study population was 57.22 ± 16.76 years. The RF algorithm with an accuracy of 97.2%, the sensitivity of 100%, a precision of 94.8%, specificity of 94.5%, F1-score of 97.3%, and AUC of 99.9% had the best performance. Other ML algorithms with AUC ranging from 81.2 to 93.9% had also good prediction performances in predicting COVID-19 mortality. Results showed that timely and accurate risk stratification of COVID-19 patients could be performed using ML-based predictive models fed by routine data. The proposed algorithm with the more comprehensive dataset including CT-SS could efficiently predict the mortality of COVID-19 patients. This could lead to promptly targeting high-risk patients on admission, the optimal use of hospital resources, and an increased probability of survival of patients.

High ambient temperature is a major problem in commercial broiler production in the humid tropics because high producing broiler birds consume more feed, have higher metabolic activity, and thus higher body heat production. To evaluate the effects of two previously isolated potential probiotic strains ( Lactobacillus pentosus ITA23 and Lactobacillus acidophilus ITA44) on broilers growing under heat stress condition, a total of 192 chicks were randomly allocated into four treatment groups of 48 chickens each as follows: C L , birds fed with basal diet raised in 24 °C; P L , birds fed with basal diet plus 0.1 % probiotic mixture raised in 24 °C; C H , birds fed with basal diet raised in 35 °C; and P H , birds fed with basal diet plus 0.1 % probiotic mixture raised in 35 °C. The effects of probiotic mixture on the performance, expression of nutrient absorption genes of the small intestine, volatile fatty acids (VFA) and microbial population of cecal contents, antioxidant capacity of liver, and fatty acid composition of breast muscle were investigated. Results showed that probiotic positively affected the final body weight under both temperature conditions (P L and P H groups) compared to their respective control groups (C L and C H ). Probiotic supplementation numerically improved the average daily gain (ADG) under lower temperature, but significantly improved ADG under the higher temperature ( P  < 0.05) by sustaining high feed intake. Under the lower temperature environment, supplementation of the two Lactobacillus strains significantly increased the expression of the four sugar transporter genes tested (GLUT2, GLUT5, SGLT1, and SGLT4) indicating probiotic enhances the absorption of this nutrient. Similar but less pronounced effect was also observed under higher temperature (35 °C) condition. In addition, the probiotic mixture improved bacterial population of the cecal contents, by increasing beneficial bacteria and decreasing Escherichia coli population, which could be because of higher production of VFA in the cecum, especially at heat stress condition. The two Lactobacillus strains also improved the fatty acid profile of meat, including at heat stress. Generally, the two Lactobacillus strains can be considered as good potential probiotics for chickens due to their good probiotic properties and remarkable efficacy on broiler chickens.

The Amygdalus spinosissima (Rosaceae) plant has been used in the Iranian folk medicine as a remedy for the burn wound. Hence, in this study, we aimed to determine the possible medicinal potential of the plant focusing on the root part. The bioactive phenolic and flavonoid compounds present in the root extract of the Amygdalus spinosissima plant as well as its antioxidant and anti-inflammatory properties were determined. Moreover, the effects of root extract on learning and memory in mice were evaluated. The results revealed that the root methanolic extract contained phenolic and flavonoid compounds including apigenin, quercetin, rutin, kaempferol, gallic acid, syringic acid, ferulic acid, and ellagic acid. The extract possessed antioxidant, acetylcholinesterase (AChE), and butyrylcholinesterase (BChE) inhibitory activities in vitro. These biological activities were attributed to the presence of phenolics and flavonoids. The A. spinosissima root extract improved learning and memory function in scopolamine-induced memory dysfunction in mice as determined using the Morris water maze task. The extract modulated the AChE, BChE, and inflammatory genes and enhanced the expression of the antioxidant enzymes in the brain. Consequently, A. spinosissima root extract could be considered as a promising source of potent bioactive compounds in the retarding the development of neurodegenerative diseases such as Alzheimer's disease.

IntroductionOsteoporosis is a significant health concern characterized by weak and porous bones, particularly affecting menopausal women aged 50 and above, leading to increased risk of hip fractures and associated morbidity and mortality.Materials and MethodsWe conducted a study to assess the efficacy of single-strain versus mixed-strain probiotic supplementation on bone health using an ovariectomy (OVX) rat model of induced bone loss. The probiotics evaluated were Lactobacillus helveticus (L. helveticus), Bifidobacterium longum (B. longum), and a combination of both. Rats were divided into five groups: SHAM (Control negative), OVX (Control positive), OVX +L. helveticus, OVX + B. longum, and OVX + mixed L. helveticus and B. longum. Daily oral administration of probiotics at 10^8-10^9 CFU/mL began two weeks post-surgery and continued for 16 weeks.ResultsBoth single-strain and mixed-strain probiotic supplementation upregulated expression of osteoblastic genes (BMP- 2, RUNX-2, OSX), increased serum osteocalcin (OC) levels, and improved bone formation parameters. Serum C-terminal telopeptide (CTX) levels and bone resorption parameters were reduced. However, the single-strain supplementation demonstrated superior efficacy compared to the mixed-strain approach.ConclusionSupplementation with B. longum and L. helveticus significantly reduces bone resorption and improves bone health in OVX rats, with single-strain supplementation showing greater efficacy compared to a mixed-strain combination. These findings highlight the potential of probiotics as a therapeutic intervention for osteoporosis, warranting further investigation in human studies.

Plant root symbiosis with Arbuscular mycorrhizal (AM) fungi improves uptake of water and mineral nutrients, improving plant development under stressful conditions. Unraveling the unified transcriptomic signature of a successful colonization provides a better understanding of symbiosis. We developed a framework for finding the transcriptomic signature of Arbuscular mycorrhiza colonization and its regulating transcription factors in roots of . Expression profiles of roots in response to AM species were collected from four separate studies and were combined by direct merging meta-analysis. Batch effect, the major concern in expression meta-analysis, was reduced by three normalization steps: Robust Multi-array Average algorithm, Z-standardization, and quartiling normalization. Then, expression profile of 33685 genes in 18 root samples of as numerical features, as well as study ID and Arbuscular mycorrhiza type as categorical features, were mined by seven models: RELIEF, UNCERTAINTY, GINI INDEX, Chi Squared, RULE, INFO GAIN, and INFO GAIN RATIO. In total, 73 genes selected by machine learning models were up-regulated in response to AM (Z-value difference > 0.5). Feature weighting models also documented that this signature is independent from study (batch) effect. The AM inoculation signature obtained was able to differentiate efficiently between AM inoculated and non-inoculated samples. The AP2 domain class transcription factor, GRAS family transcription factors, and cyclin-dependent kinase were among the highly expressed meta-genes identified in the signature. We found high correspondence between the AM colonization signature obtained in this study and independent RNA-seq experiments on AM colonization, validating the repeatability of the colonization signature. Promoter analysis of upregulated genes in the transcriptomic signature led to the key regulators of AM colonization, including the essential transcription factors for endosymbiosis establishment and development such as factors. The approach developed in this study offers three distinct novel features: (I) it improves direct merging meta-analysis by integrating supervised machine learning models and normalization steps to reduce study-specific batch effects; (II) seven attribute weighting models assessed the suitability of each gene for the transcriptomic signature which contributes to robustness of the signature (III) the approach is justifiable, easy to apply, and useful in practice. Our integrative framework of meta-analysis, promoter analysis, and machine learning provides a foundation to reveal the transcriptomic signature and regulatory circuits governing Arbuscular mycorrhizal symbiosis and is transferable to the other biological settings.

Electric vehicles (EVs) offer significant potential to reduce greenhouse gas emissions from the transportation sector. This study focuses on understanding and modelling the transition from internal combustion engine vehicles (ICEs) to EVs, addressing the dynamics that drive this shift. Using a nonlinear model of opinion dynamics, we investigate the influence of effective price ratios between EVs and ICEs, EV model availability, and public charging stations on adoption rates. Historical data from Norway, a mature EV market, is utilized to validate the model and analyze the impact of policy measures, such as subsidies, on accelerating adoption. According to our model, affordability alone does not drive the transition. Factors like EV model availability and consumer trust in battery technology play crucial roles, as evidenced by the surge in hybrid vehicle adoption during the transition phase. This reflects hesitancy toward fully committing to EVs even when the technology is sufficiently mature. The model emphasizes the interplay of consumer opinion and market behaviour, highlighting the importance of policies that promote EV model availability and enhance battery reliability/increase trust in new technologies, alongside financial incentives. While the focus of this study was EV adoption, the modelling approach is relevant for the adoption of other low-carbon consumer technologies such as heat pumps. This research provides critical insights into the complexity of EV adoption and the multifaceted strategies needed to support the shift toward sustainable transportation systems. While different modelling approaches are necessary to model technology adoption, nonlinear models are particularly well-suited to capture the feedbacks and emergent dynamics that characterize EV adoption.

A 6-week experiment was performed to compare different carbon sources, i.e. sucrose, glycerol and rice bran, to a nitrogen ratio of 15:1 in a biofloc-based African catfish Clarias gariepinus culture system. Catfish survival, growth, whole-body proximate composition, body indices, liver histopathology and glycogen content were measured. Each treatment was triplicated in glass aquaria with each replicate containing 50 fish (500 fish/m 3 ) with an initial weight ± SD of 5.06 ± 0.05 g. Glycerol significantly increased total biofloc production, and both the sucrose and glycerol treatments generally had lower nitrogenous levels, compared to the control. These levels spiked at week 2 in the rice bran treatment, leading to significantly lower survival compared to all other treatments. At both weeks 3 and 6, liver histopathology of fish in the rice bran treatment revealed substantial vacuolation and less glycogen while the highest was in fish from the glycerol treatment. Fish growth was unaffected among the treatments, but survival was highest in the glycerol treatment. Rice bran appears unsuitable for C. gariepinus , likely due to being a slower-releasing carbon source. Instead, glycerol is recommended based on significantly higher biofloc production and subsequently improved water quality and survival of C. gariepinus during their nursery culture.

The engineering of thermostable enzymes is receiving increased attention. The paper, detergent, and biofuel industries, in particular, seek to use environmentally friendly enzymes instead of toxic chlorine chemicals. Enzymes typically function at temperatures below 60°C and denature if exposed to higher temperatures. In contrast, a small portion of enzymes can withstand higher temperatures as a result of various structural adaptations. Understanding the protein attributes that are involved in this adaptation is the first step toward engineering thermostable enzymes. We employed various supervised and unsupervised machine learning algorithms as well as attribute weighting approaches to find amino acid composition attributes that contribute to enzyme thermostability. Specifically, we compared two groups of enzymes: mesostable and thermostable enzymes. Furthermore, a combination of attribute weighting with supervised and unsupervised clustering algorithms was used for prediction and modelling of protein thermostability from amino acid composition properties. Mining a large number of protein sequences (2090) through a variety of machine learning algorithms, which were based on the analysis of more than 800 amino acid attributes, increased the accuracy of this study. Moreover, these models were successful in predicting thermostability from the primary structure of proteins. The results showed that expectation maximization clustering in combination with uncertainly and correlation attribute weighting algorithms can effectively (100%) classify thermostable and mesostable proteins. Seventy per cent of the weighting methods selected Gln content and frequency of hydrophilic residues as the most important protein attributes. On the dipeptide level, the frequency of Asn-Glu was the key factor in distinguishing mesostable from thermostable enzymes. This study demonstrates the feasibility of predicting thermostability irrespective of sequence similarity and will serve as a basis for engineering thermostable enzymes in the laboratory.

The present study was conducted to investigate the effects of altering the ratio of n-6 to n-3 fatty acids in the diet on meat quality, fatty acid composition of muscle, and expression of lipogenic genes in the muscle of Boer goats. A total of twenty-one Boer goats (5 months old; 31.66±1.07 kg body weight) were randomly assigned to three dietary treatments with n-6:n-3 fatty acid ratios of 2.27:1 (LR), 5.01:1 (MR) and 10.38:1 (HR), fed at 3.7% of body weight. After 100 days of feeding, all goats were slaughtered and the longissimus dorsi muscle was sampled for analysis of fatty acids and gene expression. The dietary treatments did not affect (P>0.05) the carcass traits, and meat quality of growing goats. The concentrations of cis-9,trans-11 conjugated linoleic acid, trans vaccenic acid, polyunsaturated fatty acids, and unsaturated/saturated fatty acid ratios linearly increased (P<0.01) with decreasing dietary n-6:n-3 fatty acid ratios, especially for LR in the longissimus dorsi muscle of goats. In contrast, the mRNA expression level of the PPARα and PPARγ was down-regulated and stearoyl-CoA desaturase up-regulated in the longissimus dorsi of growing goats with increasing dietary n-6:n-3 fatty acid ratios (P<0.01). In conclusion, the results obtained indicate that the optimal n-6:n-3 fatty acid ratio of 2.27:1 exerted beneficial effects on meat fatty acid profiles, leading towards an enrichment in n-3 polyunsaturated fatty acids and conjugated linoleic acid in goat intramuscular fat.