Is diet related to osteoarthritis? A univariable and multivariable Mendelian randomization study that investigates 45 dietary habits and osteoarthritis

BackgroundDiet is a safe intervention for many chronic diseases as a modifiable lifestyle. However, the potential causal effect of many dietary intake habits on the risk of osteoarthritis has not been fully understood. The purpose of this study was to reveal the potential causal relationship of 45 genetically predicted dietary intakes with osteoarthritis and its subtypes.MethodsData on 45 dietary intakes were obtained from the UK Biobank study of approximately 500,000 participants, and data on six osteoarthritis-related phenotypes were obtained from the Genetics of Osteoarthritis Consortium study of 826,690 participants. We performed univariable Mendelian randomization (MR), multivariable MR and linkage disequilibrium score regression (LDSC) analyses.ResultsIn univariate analyses, 59 potential associations between diet and osteoarthritis were found. After false discovery rate (FDR) correction and sensitivity analyses, 23 reliable causal evidence were identified. In multivariate analyses, controlling separately for the effects of body mass index, total body bone mineral density, and smoking status, eight robust causal relationships remained: Muesli intake was negatively associated with knee osteoarthritis, spine osteoarthritis and total knee replacement. Dried fruit intake had a negative association with osteoarthritis of knee and total knee replacement. Eating cheese may reduce the risk of osteoarthritis in the knee and spine. And alcohol usually taken with meals was associated with a reduced risk of total knee replacement. LDSC analyses showed significant genetic correlations between all exposures and their corresponding outcomes, respectively, in these eight causal relationships.ConclusionEvidence of dietary effects on osteoarthritis is provided in our study, which has important implications for the prevention, management, and intervention of osteoarthritis in common sites through rational dietary modification

Towards Open-Scenario Semi-supervised Medical Image Classification

Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution e.g., classes and domains. However, in practical scenarios, unlabeled data would be from unseen classes or unseen domains, and it is still challenging to exploit them by existing SSL methods. Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification. We first design a novel scoring mechanism, called dual-path outliers estimation, to identify samples from unseen classes. Meanwhile, to extract unseen-domain samples, we then apply an effective variational autoencoder (VAE) pre-training. After that, we conduct domain adaptation to fully exploit the value of the detected unseen-domain samples to boost semi-supervised training. We evaluated our proposed framework on dermatology and ophthalmology tasks. Extensive experiments demonstrate our model can achieve superior classification performance in various medical SSL scenarios

Cumulative Response of Ecosystem Carbon and Nitrogen Stocks to Chronic CO2 Exposure in a Subtropical Oak Woodland

·Rising atmospheric carbon dioxide (CO2) could alter the carbon (C) and nitrogen (N) content of ecosystems, yet the magnitude of these effects are not well known. We examined C and N budgets of a subtropical woodland after 11 yr of exposure to elevated CO2. ·We used open-top chambers to manipulate CO2 during regrowth after fire, and measured C, N and tracer 15N in ecosystem components throughout the experiment. ·Elevated CO2 increased plant C and tended to increase plant N but did not significantly increase whole-system C or N. Elevated CO2 increased soil microbial activity and labile soil C, but more slowly cycling soil C pools tended to decline. Recovery of a long-term 15N tracer indicated that CO2 exposure increased N losses and altered N distribution, with no effect on N inputs. · Increased plant C accrual was accompanied by higher soil microbial activity and increased C losses from soil, yielding no statistically detectable effect of elevated CO2 on net ecosystem C uptake. These findings challenge the treatment of terrestrial ecosystems responses to elevated CO2 in current biogeochemical models, where the effect of elevated CO2 on ecosystem C balance is described as enhanced photosynthesis and plant growth with decomposition as a first-order response

Monitoring Landscape Dynamics in Central U.S. Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data

Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals have been used for monitoring. Recently, the harmonized Landsat-8 and Sentinel-2 (HLS) data increased the temporal frequency of the data. Here we investigated if the increased data frequency provided adequate observations to characterize highly dynamic grassland processes. We evaluated HLS data available for 2016 to (1) determine if data from Sentinel-2 contributed to an improvement in characterizing landscape processes over Landsat-8 data alone, and (2) quantify how observation frequency impacted results. Specifically, we investigated into estimating annual vegetation phenology, detecting burn scars from fire, and modeling within-season wetland hydroperiod and growth of aquatic vegetation. We observed increased sensitivity to the start of the growing season (SOST) with the HLS data. Our estimates of the grassland SOST compared well with ground estimates collected at a phenological camera site. We used the Continuous Change Detection and Classification (CCDC) algorithm to assess if the HLS data improved our detection of burn scars following grassland fires and found that detection was considerably influenced by the seasonal timing of the fires. The grassland burned in early spring recovered too quickly to be detected as change events by CCDC; instead, the spectral characteristics following these fires were incorporated as part of the ongoing time-series models. In contrast, the spectral effects from late-season fires were detected both by Landsat-8 data and HLS data. For wetland-rich areas, we used a modified version of the CCDC algorithm to track within-season dynamics of water and aquatic vegetation. The addition of Sentinel-2 data provided the potential to build full time series models to better distinguish different wetland types, suggesting that the temporal density of data was sufficient for within-season characterization of wetland dynamics. Although the different data frequency, in both the spatial and temporal dimensions, could cause inconsistent model estimation or sensitivity sometimes; overall, the temporal frequency of the HLS data improved our ability to track within-season grassland dynamics and improved results for areas prone to cloud contamination. The results suggest a greater frequency of observations, such as from harmonizing data across all comparable Landsat and Sentinel sensors, is still needed. For our study areas, at least a 3-day revisit interval during the early growing season (weeks 14–17) is required to provide a \u3e50% probability of obtaining weekly clear observations

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer's accuracy of 93% and a user's accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of >= 95% for cultivated croplands and >= 76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season

Next-Generation Composite Coating System: Nanocoating

Nanocoating combines the protective properties of conventional coating system with effects on nanoscale such as high hardness, UV scattering, and uniform dispersing. Their facile and low-cost synthesis, together with superior protective properties and multi-functionalities, makes nanocoating attractive candidates for next-generation coating systems. In this review, a brief introduction regarding mainstream nanocoating and its related challenges including the zinc oxide-, titanium dioxide-, silica dioxide-, graphene-, carbon nanotube-based nanocoating system is presented. Finally, a perspective of the nanocoating is demonstrated

Association of antioxidants use with the risk of dementia among community-dwelling adults in the United Kingdom biobank

BackgroundData regarding the association between antioxidant supplementation and incident dementia are limited.MethodsWe included 494,632 adults (54.5% females) aged 40–71 years at baseline from the United Kingdom Biobank in the final analysis. Incident dementia was ascertained using hospital inpatient and death records up to January 2021.ResultsOver a median follow-up of 11.9 years, 7,128 new cases of all-cause dementia, 2,772 cases of Alzheimer’s disease, and 1,397 cases of vascular dementia were recorded. The hazard ratio (95% CI) for incident dementia associated with zinc supplementation was 0.84 (0.74–0.96), and the association remained significant after adjusting for all confounders (0.84 (0.74–0.96)). In the full model, zinc supplementation was associated with a reduced risk of Alzheimer’s disease [HR (95% CI): 0.71 (0.57–0.88)]. There was no significant association between zinc supplementation and the risk of vascular dementia. No significant associations with incident dementia were observed for other antioxidant supplementation. The association between zinc supplementation and incident dementia was significant among individuals with [HR (95% CI): 0.34 (0.15–0.77)] and without cataract [0.87 (0.77–0.99)] but it was stronger among those with cataract (p value for interaction = 0.0271).ConclusionOur findings suggest that zinc supplementation may help reduce the risk of all-cause dementia and Alzheimer’s disease in middle-aged or older adults, especially among those with cataracts

Exploring the potential mechanisms of Tongmai Jiangtang capsules in treating diabetic nephropathy through multi-dimensional data

BackgroundDiabetic nephropathy (DN) is a prevalent and debilitating disease that represents the leading cause of chronic kidney disease which imposes public health challenges Tongmai Jiangtang capsule (TMJT) is commonly used for the treatment of DN, albeit its underlying mechanisms of action are still elusive.MethodsThis study retrieved databases to identify the components and collect the targets of TMJT and DN. Target networks were constructed to screen the core components and targets. Samples from the GEO database were utilized to perform analyses of targets and immune cells and obtain significantly differentially expressed core genes (SDECGs). We also selected a machine learning model to screen the feature genes and construct a nomogram. Furthermore, molecular docking, another GEO dataset, and Mendelian randomization (MR) were utilized for preliminary validation. We subsequently clustered the samples based on SDECG expression and consensus clustering and performed analyses between the clusters. Finally, we scored the SDECG score and analyzed the differences between clusters.ResultsThis study identified 13 SDECGs between DN and normal groups which positively regulated immune cells. We also identified five feature genes (CD40LG, EP300, IL1B, GAPDH, and EGF) which were used to construct a nomogram. MR analysis indicated a causal link between elevated IL1B levels and an increased risk of DN. Clustering analysis divided DN samples into four groups, among which, C1 and CI were mainly highly expressed and most immune cells were up-regulated. C2 and CII were the opposite. Finally, we found significant differences in SDECG scores between C1 and C2, CI and CII, respectively.ConclusionTMJT may alleviate DN via core components (e.g. Denudatin B, hancinol, hirudinoidine A) targeting SDECGs (e.g. SRC, EGF, GAPDH), with the involvement of feature genes and modulation of immune and inflammation-related pathways. These findings have potential implications for clinical practice and future investigations