MeStanG—Resource for High-Throughput Sequencing Standard Data Sets Generation for Bioinformatic Methods Evaluation and Validation

Metagenomics analysis has enabled the measurement of the microbiome diversity in environmental samples without prior targeted enrichment. Functional and phylogenetic studies based on microbial diversity retrieved using HTS platforms have advanced from detecting known organisms and discovering unknown species to applications in disease diagnostics. Robust validation processes are essential for test reliability, requiring standard samples and databases deriving from real samples and in silico generated artificial controls. We propose a MeStanG as a resource for generating HTS Nanopore data sets to evaluate present and emerging bioinformatics pipelines. MeStanG allows samples to be designed with user-defined organism abundances expressed as number of reads, reference sequences, and predetermined or custom errors by sequencing profiles. The simulator pipeline was evaluated by analyzing its output mock metagenomic samples containing known read abundances using read mapping, genome assembly, and taxonomic classification on three scenarios: a bacterial community composed of nine different organisms, samples resembling pathogen-infected wheat plants, and a viral pathogen serial dilution sampling. The evaluation was able to report consistently the same organisms, and their read abundances as provided in the mock metagenomic sample design. Based on this performance and its novel capacity of generating exact number of reads, MeStanG can be used by scientists to develop mock metagenomic samples (artificial HTS data sets) to assess the diagnostic performance metrics of bioinformatic pipelines, allowing the user to choose predetermined or customized models for research and training

Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety

This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (especially their variant LSTM) in time series data modeling. This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs. Secondly, the similarities and differences between CNNs and fully connected neural networks in processing image data are analyzed. Furthermore, the advantages and disadvantages of RNNs and traditional statistical modeling methods in processing time series data are discussed. Finally, the application directions of CNNs in food safety detection and RNNs in food safety risk prediction are compared. This paper also discusses combining these deep learning models with technologies such as the Internet of Things (IoT), blockchain, and federated learning to improve the accuracy and efficiency of food safety detection and risk warning. Finally, this paper mentions the limitations of RNNs and CNNs in the field of food safety, as well as the challenges in the interpretability of the model, and suggests the use of interpretable artificial intelligence (XAI) technology to improve the transparency of the model

Quaternary Segmentation Characteristics of the Hunhe Fault, Northeast China

The northern segment of the Tanlu fault zone, which encompasses the Dunhua–Mishan and Yilan–Yitong fault zones, plays a critical role in the tectonic framework of Northeast China. This study focuses on the Hunhe fault, part of the Liaoning segment of the Dunhua–Mishan fault zone, which exhibits concealed characteristics and an NE–NEE orientation. We employ remote sensing and field investigations to accurately delineate the Hunhe fault’s location, scale, and tectonic activity. The findings indicate that the Hunhe fault displays significant spatial variability in tectonic activity. Some segments show evidence of late Quaternary activity, contradicting prior research that classified the Hunhe fault as an active fault during the MIS (Marine Isotope Stages) 20-103MIS 20-103- MIS6-19MIS6-19 period and assessed its seismic potential differently. Recent field investigations suggest considerable spatial variability in tectonic activity, indicating segmental characteristics. In this study, the Hunhe fault is divided into segments based on five aspects: the fault structure and movement characteristics of the fault; transverse faults and obstruction structures; geological and geomorphological characteristics; seismic features; and fault activity. The detailed segments are as follows: the Shenyang segment, the Fushun segment, the Zhangdang-Nan Zamu segment, and the Nan Zamu to Ying Emeng East section. These findings aim to enhance the understanding of the seismic hazard potential associated with the Hunhe fault, highlighting the need for ongoing research to address its complexities and implications for regional seismic risk assessment

Association Between Urinary Metal Levels and Chronic Kidney Dysfunction in Rural China: A Study on Sex-Specific Differences

Background: While current epidemiological studies have documented associations between environmental metals and renal dysfunction, the majority have concentrated on plasma metal levels. The relationship between urinary metal exposure and chronic kidney disease (CKD) remains contentious, particularly within specific demographic groups. Methods: This cross-sectional study included 2919 rural Chinese adults recruited between 2018 and 2019. Urine metals were measured by ICP-MS. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify metals significantly associated with CKD. Then, we used binary logistic regression, along with restricted cubic spline (RCS) models, to assess the individual exposure effects of specific metals on CKD. Quantile g-computation, weighted quantile sum regression, and Bayesian kernel machine regression (BKMR) models were applied to evaluate combined effects of metal exposures on CKD. Gender-stratified analyses were also conducted to explore these associations. Results: LASSO identified seven metals (V, Cu, Rb, Sr, Ba, W, Pb) with significant impacts on CKD. In single-metal models, Cu and W exhibited a positive correlation with CKD, whereas V, Rb, Sr, Ba, and Pb showed significant negative correlations (all p < 0.05). RCS analysis revealed nonlinear associations between V, Cu, Ba, Pb, and CKD (all p-nonlinear < 0.05). In the multi-metal model, quantile-based g-computation demonstrated a collective negative association with CKD risk for the seven mixed urinary metal exposures (OR (95% CI) = −0.430 (−0.656, −0.204); p < 0.001), with V, Rb, Sr, Ba, and Pb contributing to this effect. The WQS model analysis further confirmed this joint negative association (OR (95% CI): −0.885 (−1.083, −0.899); p < 0.001), with V as the main contributor. BKMR model analysis indicated an overall negative impact of the metal mixture on CKD risk. Interactions may exist between V and Cu, as well as Cu and Sr and Pb. The female subgroup in the BKMR model demonstrated consistency with the overall association. Conclusions: Our study findings demonstrate a negative association between the urinary metal mixture and CKD risk, particularly notable in females. Joint exposure to multiple urinary metals may involve synergistic or antagonistic interactions influencing renal function. Further research is needed to validate these observations and elucidate underlying mechanisms

Wear Resistance of B4C-TiB2 Ceramic Composite

The effects of microstructure and mechanical properties on the wear resistance of B4C-TiB2 ceramic composite were studied. The composite was hot pressed from a B4C-TiO2 precursor at a temperature range of 1800 and 1850 °C. Both the relative density and amount of TiB2 secondary phase of the B4C-TiB2 composite increased with the amount of TiO2 sintering additive in B4C-TiO2 precursor. The hardness of the composite increased with a secondary phase portion up to 29.8 vol.% TiB2. However, the positive effect of TiB2 secondary phase on the fracture toughness of B4C-TiB2 composite was measured in the complete experimental range, with the highest average attained value of 7.51 MPa·m1/2. The wear resistance of B4C-TiB2 composite increased with both the hardness and fracture toughness. The best wear resistance was achieved with the composite with a higher hardness value of 29.74 GPa. This sample consisted of 29.8 vol.% TiB2 secondary phase and reached a fracture toughness value of 6.91 MPa·m1/2. The fracture-induced mechanical wear of B4C-TiB2 composite was the main wear mechanism during the pin-on-disc wear test. Transgranular fracture with pullout of the surface and micro-crack formation in the direction perpendicular to the wear direction was observed on the worn surfaces

Motivation to Clinical Trial Participation: Health Information Distrust and Healthcare Access as Explanatory Variables and Gender as Moderator

Background: There is significant underrepresentation in clinical trials across diverse populations. Less is known about how health system-related factors, such as relationships and trust, mediate the motivation for clinical trial participation. We aimed to investigate whether health system-related factors explain the association between sociodemographic factors and motivation for participation. Additionally, we explored whether the mediating effects differ by gender. Methods: We used the Health Information National Trends Survey 2020 cycle-4 data. Motivation for clinical trial participation, assessed through eight items, was the outcome variable (range 1–4). Predictors included age, race, ethnicity, education, general health, and depression. The health system-related explanatory variables were health information distrust, having a regular provider, and the frequency of healthcare visits. Gender was the moderator. A structural equation model (SEM) was used for the overall and gender-stratified analyses. Results: Among the 3865 participants (mean [SE] age of 48.4 [0.53] years, 51.4% women, and 24.3% non-White), older age (β = −0.170; p < 0.001) and non-White race (β = −0.078; p < 0.01) were negatively associated, and higher education (β = 0.117; p < 0.001) was positively associated with motivation. Higher distrust (β = −0.094; p < 0.01) decreased motivation, whereas having a regular provider increased motivation (β = 0.087; p < 0.01). The gender-stratified SEM revealed that women, but not men, with higher distrust showed lower motivation (β = −0.121; p < 0.01), and men, but not women, with a regular healthcare provider showed higher motivation (β = 0.116; p < 0.01). Conclusions: Our study found that women with higher distrust showed lower motivation, while men with a regular healthcare provider demonstrated higher motivation. These gender differences highlight the need for tailored recruitment approaches that account for their distinct relationships with the health system

Integrated Solution Combining Low-Frequency Forced Oscillation Technique and Continuous Equivital Sensor Monitoring for Assessment of Non-Invasive Ambulatory Respiratory Mechanics

Early assessment of respiratory mechanics is crucial for early-stage diagnosing and managing lung diseases, leading to greater patient outcomes. Traditional methods like spirometry are limited in continuous monitoring and patient compliance as they require forced maneuvers with significant patient cooperation, which may not be available in fragile individuals. The Forced Oscillation Technique (FOT) is a non-invasive measurement method, only based on the tidal breathing at rest from the patient for a limited time period. The proposed solution integrates low-frequency FOT with continuous monitoring using Equivital (EQV) sensors to enhance respiratory mechanics information with heart rate variability. Data were collected over a two-hour period from six healthy volunteers, measuring respiratory impedance every 7 min and continuously recording physiological parameters. The best-fitting fractional-order models for impedance data were identified using genetic algorithms. This study also explores the correlation between impedance model parameters and EQV data, discussing the potential of AI tools for forecasting respiratory properties. Our findings indicate that combined monitoring techniques and AI analysis provides additional complementary information, subsequently aiding the improved evaluation of respiratory function and tissue mechanics. The proposed protocol allows for ambulatory assessment and can be easily performed in normal breathing conditions

Correction: Soliman et al. Evaluating Antimicrobial Activity and Wound Healing Effect of Rod-Shaped Nanoparticles. Polymers 2022, 14, 2637

In the original publication, there was a mistake in Figure 7 as published [...

Synergistic Chemical Modification and Physical Adsorption for the Efficient Curing of Soluble Phosphorus/Fluorine in Phosphogypsum

Phosphogypsum (PG), a by-product of phosphoric acid production, contains high levels of fluorine and phosphorus impurities, which negatively impact the strength and setting time of PG-based cement materials and pose environmental risks. This study explores a dual approach combining physical adsorption using zeolite powder and chemical modification with quicklime (CaO) to immobilize these impurities. The composition of 90 wt.% PG, 5 wt.% zeolite powder, and 5 wt.% quicklime reduces the soluble phosphorus to below the detection limits and significantly lowers the free water content in the PG. Through SEM, XRD, and FT-IR analyses, it was found that zeolite powder adsorbs fluorine and phosphorus through encapsulation, while quicklime chemically reacts to form insoluble calcium phosphate and calcium fluoride. This transformation decreases the solubility, mitigating potential environmental contamination. The combination of physical adsorption and chemical conversion provides a sustainable strategy to reduce environmental hazards and enhance PG’s suitability for cement-based materials. The findings from this research offer a promising pathway for the sustainable utilization of PG, providing a mechanism for its safe incorporation into building materials, while addressing both environmental and material performance concerns

Supervised vs. Self-Managed Exercise Therapy for Improving Shoulder Function After Traumatic Dislocation and Sprain: A Systematic Review and Meta-Analysis

Trauma-induced shoulder dislocations and sprains rank among the most common upper extremity injuries, with contact sports accounting for the majority of cases. These injuries often lead to substantial impairments in joint function and long recovery times, requiring targeted therapeutic interventions to restore mobility and prevent recurrent injuries. Given the pivotal role of exercise therapy in restoring shoulder function, this study systematically reviews the literature on the comparative effectiveness of supervised versus self-managed exercise therapy following acute shoulder trauma. PubMed, Cochrane CENTRAL, Embase, Web of Science, and Science Direct were searched up to 13 December 2024. Conservative and post-surgical treatment modes were analyzed separately. Five studies with a total 689 participants were included (conservative: n = 538 across two studies; post-surgical: n = 151 across three studies). Both treatment modes showed similar pooled effects (standardized mean difference, SMDconservative: −0.35, 95% CI [−1.39, 0.69]; SMDpost-surgical: −0.23, 95% CI [−1.21, 0.75]), with a marginal improvement in shoulder function favoring supervised therapy. Four studies had some risk of bias, and one had serious risk; GRADE certainty was low. Supervised exercise therapy may offer slightly greater functional improvements over self-managed training, but evidence is limited by heterogeneity and low certainty. Further high-quality trials with standardized protocols and improved adherence tracking are needed to establish more definitive conclusions and guide clinical decision-making