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Characteristics influencing COVID-19 testing and vaccination among Spanish-speaking Latine persons in North Carolina

Background Latine populations in the United States continue to be disproportionately affected by COVID-19 with high rates of infection and mortality. Our community-based participatory research partnership examined factors associated with COVID-19 testing and vaccination within a particularly hidden, underserved, and vulnerable population: Spanish-speaking Latines. Methods In 2023, native Spanish-speaking Latine interviewers conducted phone-based structured individual assessments with 180 Spanish-speaking, predominantly immigrant Latines across North Carolina. We used univariate and multivariable logistic regression analyses to examine associations between participant characteristics and COVID-19 testing and vaccination. Results Participant mean age was 41.7 (SD = 13), and 77.2% of the sample reported being cisgender women. Most participants reported immigrating from Latin American countries (89.9%), being uninsured (66.1%), and lacking US immigration documentation (51.1%). While most reported ever being COVID-19 tested (83.3%) and ever being vaccinated against COVID-19 (84.4%), only 24% were up to date with vaccination. Nearly half of the sample reported one or more barriers to COVID-19 testing, and over one-quarter reported one or more barriers to COVID-19 vaccination. Higher educational attainment was significantly associated with ever being tested (P = .031). Fewer concerns about the vaccine, including fewer worries about side effects and having more confidence in vaccine effectiveness and safety, was associated with ever being vaccinated (P < .001). Conclusions Spanish-speaking Latines face barriers to getting tested and vaccinated against COVID-19. Although ever testing and ever vaccination rates were high, being up to date with recommended vaccinations was low. Educational attainment and concerns about the vaccine were associated with COVID-19 testing and vaccination, respectively. Our findings suggest the need for culturally congruent strategies to address the challenges facing Spanish-speaking Latines in the United States.

A study on the determination of the factors affecting the happiness levels of older individuals during the COVID-19 pandemic in Turkish society

This study aims to determine the factors affecting the happiness levels of older individuals in Türkiye during the COVID-19 pandemic. The microdata set from the 2020 Life Satisfaction Survey conducted by the Turkish Statistical Institute was utilized, involving 1,863 individuals aged 60 and above. The relationship between happiness levels and various factors was investigated using the chi-square independence test, and the factors affecting happiness were further analyzed through generalized ordered logistic regression. According to the generalized ordered logistic regression model, participants in the 60–64 age group are 10.1% less likely to report happiness compared to those aged 65 and older. Men are 4.3% less likely than women to report happiness. Furthermore, individuals with no formal education and those with primary school education have a 14.4% and 9.4% higher likelihood of happiness, respectively, compared to university graduates. The literature on happiness demonstrates the relationship between different factors and happiness. This study determined that such factors as gender, age, educational status, source of happiness, health satisfaction, hope scale, and homeownership have an impact on the happiness levels of older individuals. The amount of societal support provided to older individuals can be an indicator of their level of happiness.

Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors

This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). ET achieved the highest performance among ML models, with an R-squared value of 0.7231 and a RMSE of 0.1512. Among DL models, ANN demonstrated the best performance, achieving an R-squared value of 0.7248 and a RMSE of 0.1516. The results show that DL models, especially ANN, did slightly better than the best ML models. This means that they are better at modeling non-linear dependencies in multivariate data. Preprocessing techniques, including feature scaling and parameter tuning, improved model performance by enhancing data consistency and optimizing hyperparameters. When compared to previous benchmarks, the performance of both ANN and ET demonstrates significant predictive accuracy gains in WT power output forecasting. This study’s novelty lies in directly comparing a diverse range of ML and DL algorithms while highlighting the potential of advanced computational approaches for renewable energy optimization.

Author Correction: Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations

Correction: Correction: Association of dietary sodium intake with impaired fasting glucose in adult cancer survivors: A population-based cross-sectional study

Ion bridging enables high-voltage polyether electrolytes for quasi-solid-state batteries

Anti-oxidation enhancement, inflammation alleviation, and microbial composition optimization of using tussah (Antheraea pernyi) silk fibroin peptides for hyperglycaemia remission

Objective This study aimed to evaluate the positive effects on anti-oxidation, anti-inflammation, and microbial composition optimization of diabetic mice using tussah (Antheraea pernyi) silk fibroin peptides (TSFP), providing the theoretical foundation for making the use of silk resources of A. pernyi and incorporating as a supplement into the hypoglycemic foods. Method The animal model of diabetes was established successfully. Alloxan-induced diabetic mice were orally administered using TSFP, and the hypoglycaemic effects in vivo were systematically investigated. Results The results indicated that TSFP could significantly reduce the fasting blood glucose (FBG) levels and suppress the mRNA expression of glycometabolism genes of diabetic mice. In addition, the TSFP could ameliorate the lipid dysbolism and contribute to a higher anti-oxidation capacity. Moreover, TSFP could alleviate pathological damages and hinder inflammatory processes of diabetic mice. Besides, the supplementation of TSFP presented a greater ability to shape and optimize the gut microbial composition by enriching the profitable bacteria and inhibiting the pathogenic microorganisms. Correlation analysis also revealed that the abundances of functional bacteria in the TSFP-treated groups exhibited better correlations with serum parameters, which would be of positive significance for blood glucose regulation and inflammation remission. Conclusions These results collectively corroborated the feasibility and superiority of using TSFP for hyperglycaemia remission via anti-oxidation enhancement, inflammation alleviation, and microbial composition optimization, contributing to a safely feasible and biologically efficient strategy for improving anti-diabetic effects.

Direct observation of chiral edge current at zero magnetic field in a magnetic topological insulator

Correction: Time series models for prediction of leptospirosis in different climate zones in Sri Lanka

Meta AI creates speech-to-speech translator that works in dozens of languages

Controllable synthesis of nonlayered high-κ Mn3O4 single-crystal thin films for 2D electronics

Vis-NIRS as an auxiliary tool in the classification of bovine carcasses

This work aimed to evaluate the use of Visible and Near-infrared Spectroscopy (Vis-NIRS) as a tool in the classification of bovine carcasses. A total of 133 animals (77 females, 29 males surgically castrated and 27 males immunologically castrated) were used. Vis-NIRS spectra were collected in a chilling room 24 h postmortem directly on the hanging carcasses over the longissimus thoracis between the surface of the 5th and 6th ribs. The data were evaluated by principal component analysis (PCA) and the partial least squares regression (PLSR) method. For the prediction of sex, the best model was the Standard Normal Variate (SNV) because it presented a relatively high coefficient of determination for prediction, presenting a percentage of correctness of 75.51% and an error of 24.49%. Regarding age, none of the models were able to differentiate the samples through Vis-NIRS. The findings confirm that Vis-NIRS prediction models are a valuable tool for differentiating carcasses based on sex. To further enhance the precision of these predictions, we recommend using Vis-NIRS equipment with the full infrared wavelength range to collect and predict sex and age in intact beef samples.

Step-necking growth of silicon nanowire channels for high performance field effect transistors

Investigation of biometabolites and novel antimicrobial peptides derived from promising source Cordyceps militaris and effect of non-small cell lung cancer genes computationally

Mushrooms are considered one of the safe and effective medications because they have great economic importance due to countless biological properties. Cordyceps militaris contains bioactive compounds with antioxidant, antimicrobial and anti-cancerous properties. This study was projected to analyze the potentials of biometabolites and to extract antimicrobial peptides and protein from the C. militaris. An in-vitro analysis of biometabolites and antimicrobial peptides was performed to investigate their pharmacological potentials followed by quantification and characterization of extracted protein. Computational analysis on non-small cell lung cancer genes (NSCLC) was performed on quantified compounds to interpret the biometabolites from C. militaris that could be potential drug candidate molecules with high specificity and potency. A total of 34 compounds representing 100% of total detected constituents identified were identified using GCMS analysis and 20 compounds using LC-MS which showed strong biological activities. FT-IR spectroscopy manifest powerful instant peaks to have different bioactive components including carboxylic acid, phenols, amines and alkanes present in methanolic extract of C. militaris. In C. militaris, higher protein concentration was observed in 70% concentration of protein extract (500 μg/ml ± 0.025). The best antioxidant activity (% Radical scavenging activity) of methanolic extracts was 80a ± 0.03, antidiabetic activity was 37 ± 0.057 and anti-inflammatory activity was 40 ± 0.021 at 12 mg/ml. Antibacterial activity for different concentrations of Cordyceps protein and methanolic extracts was significantly (p < 0.05). Indolizine, 2-(4-methylphenyl) has most binding affinity (micromolar) and optimized properties to be selected as the lead inhibitor. It interacts favorably with the active site of RET gene of NSCLC and is neuroprotective and hepatoprotective.

Unravelling genomic drivers of speciation in Musa through genome assemblies of wild banana ancestors

Abstract Hybridization between wild Musa species and subspecies from Southeast Asia is at the origin of cultivated bananas. The genomes of these cultivars are complex mosaics involving nine genetic groups, including two previously unknown contributors. This study provides continuous genome assemblies for six wild genetic groups, one of which represents one of the unknown ancestor, identified as M. acuminata ssp. halabanensis. The second unknown ancestor partially present in a seventh assembly appears related to M. a. ssp. zebrina. These assemblies provide key resources for banana genetics and for improving cultivar assemblies, including that of the emblematic triploid Cavendish. Comparative and phylogenetic analyses reveal an ongoing speciation process within Musa, characterised by large chromosome rearrangements and centromere differentiation through the integration of different types of repeated sequences, including rDNA tandem repeats. This speciation process may have been favoured by reproductive isolation related to the particular context of climate and land connectivity fluctuations in the Southeast Asian region.

Correction: Utilization of Artificial Intelligence for the automated recognition of fine arts

Random resistive memory-based deep extreme point learning machine for unified visual processing

Class-specific school closures for seasonal influenza: Optimizing timing and duration to prevent disease spread and minimize educational losses

School closures are a safe and important strategy for preventing infectious diseases in schools. However, the effects of school closures have not been fully demonstrated, and prolonged school closures have a negative impact on students and communities. This study evaluated class-specific school closure strategies to prevent the spread of seasonal influenza and determine the optimal timing and duration. We constructed a new model to describe the incidence of influenza in each class based on a stochastic susceptible-exposed-infected-removed model. We collected data on the number of infected absentees and class-specific school closures due to influenza from four high schools and the number of infected cases from the community in a Japanese city over three seasons (2016–2017, 2017–2018, and 2018–2019). The parameters included in the model were estimated using epidemic data. We evaluated the effects of class-specific school closures by measuring the reduced cumulative incidence of class closures per day. The greatest reduction in the cumulative absences per day was observed in the four-day class closure. When class-specific school closures lasted for four days, the reduction in the cumulative number of infections per class closure day was greater when the closure was timed earlier. The highest reduction in the number of class closures per person-day occurred when the threshold was around 5.0%. Large variations in the reduction of cumulative incidence were noted owing to stochastic factors. Reactive, class-specific school closures for seasonal influenza were most efficient when the percentage of newly infected students exceeded around 5.0%, with a closure duration of four days. The optimal strategy of class-specific school closure provides good long-term performance but may be affected by random variations.

Deep soil contributions to global nitrogen budgets

Comparison of actionable alterations in cancers with kinase fusion, mutation, and copy number alteration

Kinase-related gene fusion and point mutations play pivotal roles as drivers in cancer, necessitating optimized, targeted therapy against these alterations. The efficacy of molecularly targeted therapeutics varies depending on the specific alteration, with great success reported for such therapeutics in the treatment of cancer with kinase fusion proteins. However, the involvement of actionable alterations in solid tumors, especially regarding kinase fusions, remains unclear. Therefore, in this study, we aimed to compare the number of actionable alterations in patients with tyrosine or serine/threonine kinase domain fusions, mutations, and copy number alterations (CNAs). We analyzed 613 patients with 40 solid cancer types who visited our division between June 2020 and April 2024. Furthermore, to detect alterations involving multiple-fusion calling, we performed comprehensive genomic sequencing using FoundationOne® companion diagnostic (F1CDx) and FoundationOne® Liquid companion diagnostic (F1LCDx). Patient characteristics and genomic profiles were analyzed to assess the frequency and distribution of actionable alterations across different cancer types. Notably, 44 of the 613 patients had fusions involving kinases, transcriptional regulators, or tumor suppressors. F1CDx and F1LCDx detected 13 cases with kinase-domain fusions. We identified 117 patients with kinase-domain mutations and 58 with kinase-domain CNAs. The number of actionable alterations in patients with kinase-domain fusion, mutation, or CNA (median [interquartile range; IQR]) was 2 (1–3), 5 (3–7), and 6 (4–8), respectively. Patients with kinase fusion had significantly fewer actionable alterations than those with kinase-domain mutations and CNAs. However, those with fusion involving tumor suppressors tended to have more actionable alterations (median [IQR]; 4 [2–9]). Cancers with kinase fusions exhibited fewer actionable alterations than those with kinase mutations and CNAs. These findings underscore the importance of detecting kinase alterations and indicate the pivotal role of kinase fusions as strong drivers of cancer development, highlighting their potential as prime targets for molecular therapeutics.