Latent class analysis-derived classification improves the cancer-specific death stratification of lymphomas: A large retrospective cohort study.

Lymphomas have diverse etiologies, treatment approaches, and prognoses. Accurate survival estimation is challenging for lymphoma patients due to their heightened susceptibility to non-lymphoma-related mortality. To overcome this challenge, we propose a novel lymphoma classification system that utilizes latent class analysis (LCA) and incorporates demographic and clinicopathological factors as indicators.

Differing associations of PM exposure with systolic and diastolic blood pressures across exposure durations in a predominantly non-Hispanic Black cohort.

Environmental health research has suggested that fine particulate matter (PM) exposure can lead to high blood pressures, but it is unclear whether the impacts remain the same for systolic and diastolic blood pressures (SBP and DBP). This study aimed to examine whether the effects of PM exposure on SBP and DBP differ using data from a predominantly non-Hispanic Black cohort collected between 2013 and 2019 in the US. PM exposure was assessed based on a satellite-derived model across exposure durations from 1 to 36 months.

Contextual Deprivation, Race and Ethnicity, and Income in Air Pollution and Cardiovascular Disease.

Importance: Socioeconomically disadvantaged subpopulations are more vulnerable to fine particulate matter (PM2.5) exposure. However, as prior studies focused on individual-level socioeconomic characteristics, how contextual deprivation modifies the association of PM2.

Low-cost optimization of industrial textile landfill sludge re-dewatering using ferrous sulfate and blast furnace slag.

This study was based on an industrial sludge landfill with a scale of 1 million cubic meters, which had been filled for more than 10 years. It focused on the secondary dewatering of industrial textile landfill sludge (LS) with a total organic carbon (TOC) content greater than 50% and a volatile suspended solids to suspended solids (VSS/SS) ratio of 0.59.

The S100 family is a prognostic biomarker and correlated with immune cell infiltration in pan-cancer.

Background: The S100 protein family is a group of small molecular EF-hand calcium-binding proteins that play critical roles in various biological processes, including promotion of growth, metastasis and immune evasion of tumor. However, the potential roles of S100 protein family expression in tumor microenvironment (TME) cell infiltration in pan-cancer remain elusive.

Methods: Herein, we conducted a comprehensive assessment of the expression patterns of the S100 protein family in pan-cancer, meticulously examining their correlation with characteristics of TME cell infiltration.

Cabrol procedure and its modifications: a systematic review and meta-analysis.

Background: The Cabrol procedure has undergone various modifications and developments since its invention. However, there is a notable gap in the literature regarding meta-analyses assessing it.

Methods: A systematic review and meta-analysis was conducted to evaluate the effectiveness and long-term outcomes of the Cabrol procedure and its modifications.

Dynamics of N6-methyladenosine modification during Alzheimer's disease development.

N6-methyladenosine (m6A) modification is a common RNA modification in the central nervous system and has been linked to various neurological disorders, including Alzheimer's disease (AD). However, the dynamic of mRNA m6A modification and m6A enzymes during the development of AD are not well understood. Therefore, this study examined the expression profiles of m6A and its enzymes in the development of AD.

Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China.

Ambient PM pollution is recognized as a leading environmental risk factor, causing significant mortality and morbidity in China. However, the specific contributions of individual PM constituents remain unclear, primarily due to the lack of a comprehensive ground monitoring network for constituents. This issue is particularly critical for carbonaceous species such as organic carbon (OC) and elemental carbon (EC), which are known for their significant health impacts, and understanding the OC/EC ratio is crucial for identifying pollution sources.

Self-Supervised 3D Behavior Representation Learning Based on Homotopic Hyperbolic Embedding.

Behavior sequences are generated by a series of spatio-temporal interactions and have a high-dimensional nonlinear manifold structure. Therefore, it is difficult to learn 3D behavior representations without relying on supervised signals. To this end, self-supervised learning methods can be used to explore the rich information contained in the data itself.

Duration-sensitive association between air pollution exposure and changes in cardiometabolic biomarkers: Evidence from a predominantly African American cohort.

Background: Ambient fine particulate matter (PM) exposure has been related to cardiometabolic diseases, but the underlying biological pathways remain unclear at the population level.

Objective: To investigate the effect of PM exposure on changes in multiple cardiometabolic biomarkers across different exposure durations.

Method: Data from a prospective cohort study were analyzed.

Per- and polyfluoroalkyl substances and sleep health in U.S. adults, NHANES 2005-2014.

Background: Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals that induce oxidative inflammatory responses and disrupt the endocrine and central nervous systems, all of which can influence sleep.

Objective: To investigate the association between PFAS exposure and sleep health measures in U.S.

Learning from pseudo-lesion: a self-supervised framework for COVID-19 diagnosis.

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019, and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training.

Influence of Low-Temperature Cap Layer Thickness on Luminescence Characteristics of Green InGaN/GaN Quantum Wells.

GaN cap layer with different thicknesses was grown on each InGaN well layer during MOCVD growth for InGaN/GaN multiple quantum well (MQW) samples to study the influence of the cap layer on the photoluminescence (PL) characteristics of MQWs. Through the temperature-dependent (TD) PL spectra, it was found that when the cap layer was too thick, the localized states of the quantum wells were relatively non-uniform. The thicker the well layer, the worse the uniformity of the localized states.

Breast Tumor Classification in Ultrasound Images by Fusion of Deep Convolutional Neural Network and Shallow LBP Feature.

Breast cancer is one of the most dangerous and common cancers in women which leads to a major research topic in medical science. To assist physicians in pre-screening for breast cancer to reduce unnecessary biopsies, breast ultrasound and computer-aided diagnosis (CAD) have been used to distinguish between benign and malignant tumors. In this study, we proposed a CAD system for tumor diagnosis using a multi-channel fusion method and feature extraction structure based on multi-feature fusion on breast ultrasound (BUS) images.

Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM.

In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VMD) and multi-scale permutation entropy (MPE), was proposed. Firstly, to solve the problem of insufficient decomposition and mode mixing caused by the improper selection of mode component and penalty factor in VMD algorithm, the whale optimization algorithm (WOA) was used to optimize the penalty factor and mode component number in the VMD algorithm, and the optimal parameter combination (, ) was obtained. Secondly, the optimal parameter combination (, ) was used for the VMD of the rolling bearing vibration signal to obtain several intrinsic mode functions (IMFs).

Surface mechanism and optimization of catalytic ozonation with CoFe oxides as catalyst for degradation of sodium p-toluenesulfonate in water.

In this study, the removals of sodium p-toluenesulfonate (NaTSA) by catalytic ozonation with two different cobalt-iron compounds, CoFe oxides prepared by co-precipitation/calcination (CPO) and CoFe oxides prepared by direct calcination (DCO), as the catalysts, had a difference of about 12%. It was found that the CPO surface contained active type c water, which was generally adsorbed on the oxygen vacancy. The test of oxygen temperature-programmed desorption (O-TPD) showed that the surface of CPO was rich in oxygen vacancy.

Preparation of Crystalline LaFeO Nanoparticles at Low Calcination Temperature: Precursor and Synthesis Parameter Effects.

Substantial effort has been devoted to fabricating nanocrystalline lanthanum ferrite (LaFeO), and calcination is the crucial process of crystallization in both high-temperature strategies and wet chemical methods. Lowering the calcination temperature gives the ability to resist the growth and agglomeration of nanoparticles, therefore contributing to preserve their unique nanostructures and properties. In this work, we prepared crystalline LaFeO nanoparticles with a calcination process at 500 °C, lower than the calcination temperature required in most wet chemistry methods.

Predicting spatiotemporally-resolved mean air temperature over Sweden from satellite data using an ensemble model.

Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reducing exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estimate daily mean Ta from satellite-based land surface temperature (Ts) over Sweden during 2001-2019 at a high spatial resolution of 1 × 1 km. The ensemble model incorporated four base models, including a generalized additive model (GAM), a generalized additive mixed model (GAMM), and two machine learning models (random forest [RF] and extreme gradient boosting [XGBoost]), and allowed the weights for each model to vary over space, with the best-performing model for each grid cell assigned the highest weight.

Magnetic cotton textile wastes pyrolyzed by ferric cerium oxide for degradation of p-nitrophenol by catalytic ozonation.

In this paper, magnetic cotton textile wastes pyrolyzed by ferric cerium oxide (FeCe oxide/PC) were synthesized for degradation of p-nitrophenol by catalytic ozonation, and the optimal Fe-Ce ratio was 10:1. Compared to FeCe oxide, the FeCe oxide/PC not only greatly improved the degradation efficiency of PNP, but also reduced the dosage of catalyst. Through the BET test, the FeCe oxide/PC has a high specific surface area to absorb part of the pollutants.

Study on mechanical and ablative properties of EPDM/OMMT thermal insulating nanocomposites.

In order to enhance the elongation at break, the ablation resistant properties as well as the tensile strength of the thermal insulating materials, organo-montmorillonite (OMMT) was introduced into the short aramid fibers reinforced Ethylene-Propylene-Diene Monomer (EPDM) based nanocomposites. The effects of OMMT content on the mechanical and ablative properties of the nanocomposites were investigated systematically. X-ray diffraction (XRD) and transmission electron microscopy (TEM) confirm that EPDM-matrix has been intercalated into OMMT interlayers after a mixing process on a two-roll mill.