Browse Articles

A regional analysis of healthcare utilization trends during consecutive disasters in Puerto Rico using private claims data

Molecular Evolution of Target Organosulfur Enables High-Performance Aqueous Zinc Batteries

Seven-year overall survival of trastuzumabe versus alternative systemic therapies in a Brazilian breast cancer cohort

Creation of Segmented Platelets with Diverse Crystalline Cores Using Double Crystalline Triblock Copolymers

Comparing the impact of landscape on the gut microbiome of Apis mellifera in Atlantic Forest and Caatinga Biomes

Intermolecular Misfolding Captured in Parallelly Organized Titin

Seasonal and cyclical variations in short-term postoperative outcomes of colorectal cancer: a time series analysis

Impact of Secondary Sphere Hydrogen Bonds on O₂ Reactivity within a Nonheme Iron Complex

Retraction Note: Tobacco-smoking-related prevalence of methanogens in the oral fluid microbiota

Layout optimization of multi-level cold chain storage facilities in agricultural producing areas considering type and capacity constraints

The effective circulation of fresh agricultural products is conducive to increasing farmers’ income and improving the living standards of urban residents. Cold chain storage facilities in agricultural producing areas play an important role in ensuring the quality of agricultural products, extending the freshness period of goods, and improving logistics efficiency. Different types of fresh produce have different requirements for refrigeration and often require transshipment due to quantity constraints. In addition, there are economies of scale in the construction and operation of cold chain storage facilities. Based on the above considerations, with the aim of minimizing the total daily cost, an optimization model for the layout of multi-level cold chain storage facilities is established to determine the number, location, type and capacity of cold chain storage facilities at the same time. Genetic algorithm is chosen to solve the model according to the characteristics of the model. Taking J County of China as an example, the model is proved to have strong operability and applicability. It is of guiding significance and reference value to optimize the layout of cold chain storage facilities in rural areas.

Comparative determination of factors affecting attitude level towards healthy nutrition

Hyperspectral technology and machine learning models to estimate the fruit quality parameters of mango and strawberry crops

Using chemical laboratory procedures to estimate the fruit quality parameters (biochemical parameters) of mango "Succarri" and strawberry "Florida" as indicators of ripening degrees in a large area presents challenges such as low throughput, labor intensity, time consumption, and the need for multiple samples. So, using spectral reflectance-based proximal remote sensing to quickly and accurately measure biochemical parameters in different fruits is important to find the best time to harvest, make food ripen faster, and the processing of food easier. This has significant economic and ecological advantages. The objective of this study was to evaluate the biochemical parameters of mango and strawberry fruits at various ripening stages. This was done by utilizing a combination of established and newly developed spectral reflectance indices (SRIs) in conjunction with machine learning (ML) models, including artificial neural networks (ANN), random forests (RF), and decision trees (DT). For mango fruit, the parameters estimated were chlorophyll content, total soluble solids (TSS), and firmness, whereas for strawberry fruit, the parameters were L*, b*, TSS, and firmness. These results revealed significant differences in SRI values across various ripening stages, indicating variances in the fruit’s biochemical parameters. The newly developed SRIs showed superior efficacy in evaluating these parameters. The integration of SRIs with diverse ML models proved to be a successful strategy for precisely estimating biochemical parameters. For mango’s biochemical parameter prediction, the ANN models demonstrated R2 values ranging from 0.92 to 1.00 and from 0.93 to 0.98 for training and testing, respectively. On the other hand, the RF models exhibited R2 values ranging from 0.98 to 1.00 and from 0.93 to 0.99 during training and testing, respectively. The DT models showed high performance, with R2 values ranging from 0.95 to 1.00 and from 0.88 to 0.99 for the training and testing phases. For strawberry’s biochemical parameter prediction, the ANN models achieved R2 values between 0.75 and 0.91 and between 0.58 and 0.91 during training and testing phases, respectively. On the other hand, RF models showed R2 values between 0.85 and 0.91 during training and between 0.74 and 0.86 during testing. The DT models demonstrated excellent results, with R2 values ranging from 0.75 to 0.91 for the training set and 0.74 to 0.81 for the testing set. It can be concluded that combining SRIs with ML models, such as ANN, RF, and DT, can accurately predict the biochemical properties of mango and strawberry fruits.

Drug-resistant epilepsy associated with peripheral complement decreases and sex-specific cytokine imbalances: a pilot study

Bioactivity assessment of peptides derived from salted jellyfish (Rhopilema hispidum) byproducts

The identification of multifunctional peptides derived from marine byproducts represents a significant challenge in the field. In Thailand, the fisheries industry exports salted jellyfish, which results in low-value byproducts primarily employed for animal feed. Previous studies have indicated the bioactivities of jellyfish protein hydrolysates from Lobonema smitthii; however, the multifunctional properties of Rhopilema hispidum remain largely unexplored. This research aimed to characterize synthetic bioactive peptides sourced from the byproducts of salted jellyfish (R. hispidum), with a specific emphasis on their antioxidant, angiotensin-I-converting enzyme (ACE) inhibitory, and anti-inflammatory activities. The hydrolysate obtained from the umbrella portion, subjected to pepsin treatment at a 3:20 enzyme-to-substrate ratio for 48 h at 37 °C, demonstrated the highest levels of antioxidant activity (DPPH =  1.85 ± 0.05 mM TE/mg protein, ABTS =  7.28 ± 0.03 mM TE/mg protein, FRAP =  3.04 ± 0.12 mM FeSO4/mg protein). Following purification, 18 novel peptides exhibiting high antioxidant scores (FRS+CHEL >  0.48) were identified and synthesized. Notably, the peptide MVVACVLPEA exhibited significant antioxidant (DPPH =  56.07 mM TE/mg protein), ACE inhibitory (91.69%), and anti-inflammatory activities (NO release =  34.59 µ M) without cytotoxic effects, although it is important to note that two other peptides did demonstrate cytotoxicity. This investigation reports a total of 16 synthesized peptides that possess triple functional activities—antioxidant, ACE inhibitory, and anti-inflammatory—without cytotoxicity, thus highlighting their potential applications in health-related fields.

Effect and mechanism of coal desulfurization using a surfactant-assisted NaClO-NaOH system

Detection of hepatitis B virus mRNA from single cell RNA sequencing data without prior knowledge

The ability to detect microbial reads from sequencing data has significantly advanced microbiome and infectious disease research. Recently, INVADEseq introduced a technique to extract microbial reads from single-cell RNA sequencing (scRNA-seq) data following 16S rRNA amplification. We hypothesized that this approach could be leveraged to detect viruses in eukaryotic cells without such amplification or prior knowledge, provided they produce viral mRNAs containing poly-A tails. To test this, we aimed to detect Hepatitis B Virus (HBV) reads from liver samples of patients with chronic HBV infection, both with and without HBsAg loss. We successfully detected HBV reads in the liver of viraemic patients, predominantly in hepatocytes and, to a lesser extent, in Kupffer cells. Functionally cured HBV patients with HBsAg loss had undetectable HBV mRNA in the liver. This study demonstrates the ability to extract and identify viral reads from scRNA-seq data without prior knowledge and without specific amplification. This approach can be used for screening scRNA-seq data for the presence of viral reads at single-cell resolution, potentially enhancing our understanding of the cellular distribution of viruses and virus-host interactions.

The protective effect of sodium-glucose cotransporter-2 inhibitor on left ventricular global longitudinal strain in patients with type 2 diabetes mellitus according to disease duration

Individual differences in associative/semantic priming: Spreading of activation in semantic memory and epistemically unwarranted beliefs

Starting from the enhanced spreading of activation through semantic memory (one of the explanatory mechanisms attempting to explain some manifestations observed in schizophrenia) and the psychosis continuum (a dimensional approach to psychotic disorders, where ‘normality’ and ‘psychopathology’ are not qualitatively different in nature but placed on varying levels of the same continuum), the main aim of the present research was to explore whether there are individual differences in associative/semantic priming in people with different levels of epistemically unwarranted beliefs (EUB). Participants varying in paranormal, pseudoscientific and conspiracy endorsement completed a primed lexical decision task containing related prime-target words (e.g., bulb-light) and unrelated prime-target words (e.g., sock-light). Bayesian linear mixed-effects models over response times (RTs) revealed a main direct priming effect (faster RTs in related pairs than in unrelated ones), a main facilitatory effect for some EUB scores (i.e., the higher the value for EUB score, the faster RTs), and an interactive effect between the experimental manipulation and some EUB scores (the higher the EUB score, the smaller the direct priming effect). These results are consistent with predictions made from the enhanced spreading of activation explanatory mechanism, but other alternative accounts are also discussed.

Corporate internal control, capacity utilization and total factor productivity

Based on Internal Control (IC) theory and Principal-agent theory, this study explores the impacts of IC on capacity utilization and total factor productivity, and the internal mechanism among them. The results show that effective IC improves total factor productivity and capacity utilization. Sufficient capacity utilization has a mediating effect for the impact of IC and total factor productivity. Heterogeneity discussion shows that with higher environmental uncertainty, effective IC has a more significant marginal effect on total factor productivity and capacity utilization, and sufficient capacity utilization has a greater mediating effect between IC and total factor productivity. Finally, it is suggested that regulators guide enterprises to strengthen IC construction, to improve capacity utilization and total factor productivity. Enterprises facilitate the mechanism that effective IC improves capacity utilization, and increases total factor productivity. This study enriches the literature on IC enabling corporate operation, and has practical significance for shaping competitive advantages.

Mendelian randomization analysis to identify potential drug targets for osteoarthritis

Background Osteoarthritis (OA) is a prevalent chronic joint disease for which there is a lack of effective treatments. In this study, we used Mendelian randomization analysis to identify circulating proteins that are causally associated with OA-related traits, providing important insights into potential drug targets for OA. Method Causal associations between 1553 circulating proteins and five OA-related traits were assessed in large-scale two-sample MR analyses using Wald ratio or inverse variance weighting, and the results were corrected for Bonferroni. In addition, sensitivity analyses were performed to validate the reliability of the MR results, including reverse MR analysis and Steiger filtering to ensure the causal direction between circulating proteins and OA; Bayesian co-localization and phenotypic scanning were used to eliminate confounding effects and horizontal pleiotropy. External validation was performed to exclude incidental findings using novel plasma protein quantitative trait loci. Finally, the online analysis tool Enrichr was utilized to screen drugs and molecular docking was performed to predict binding modes and energies between proteins and drugs to identify the most stable and likely binding modes and drugs. Result Four proteins were ultimately found to be reliably and causally associated with three OA-related features: DNAJB12 and USP8 were associated with knee OA, IL12B with spinal OA, and RGMB with thumb OA. The ORs for the above proteins were 1.51 (95% CI, 1.26–1.81), 1.72 (95% CI, 1.42–2.08), 0.87 (95% CI, 0.81–0.92), and 0.59 (95% CI, 0.47–0.75), respectively. Drug-predicting small molecules (doxazosin, XEN 103, and montelukast) that simultaneously target three proteins, DNAJB12, USP8, and IL12B, docked well. Conclusion Based on our comprehensive analysis, we can draw the conclusion that there is a causal relationship between the genetic levels of DNAJB12, USP8, IL12B, and RGMB and the risk of respective OA.They may be potential options for OA screening and prevention in clinical practice. They can also serve as candidate molecules for future mechanism exploration and drug target selection.