A geographically weighted neural network model for digital soil mapping of heavy metal copper in coastal cities.

Assessing the spatial distribution of heavy metals in soil is essential for mitigating risks to human health and ensuring the sustainable use of soil resources. This study proposed a geographically weighted neural network (GWNN) model, leveraging deep learning and geographically weighted regression (GWR). The model was designed based on the GWR concept to address the spatial autocorrelation of copper (Cu) in soil and incorporated convolutional neural network (CNN) to capture the nonlinear relationships between Cu and environmental covariates.

Monitoring Soil Copper in Urban Land Using Visibale and Near-Infrared Spectroscopy with Spatially Nearby Samples.

Soil heavy metal contamination in urban land can affect biodiversity, ecosystem functions, and the health of city residents. Visible and near-infrared (Vis-NIR) spectroscopy is fast, inexpensive, non-destructive, and environmentally friendly compared to traditional methods of monitoring soil Cu, a common heavy metal found in urban soils. However, there has been limited research on using spatially nearby samples to build the Cu estimation model.

Human Expansion-Induced Biodiversity Crisis over Asia from 2000 to 2020.

Asia stands out as a priority for urgent biodiversity conservation due to its large protected areas (PAs) and threatened species. Since the 21st century, both the highlands and lowlands of Asia have been experiencing the dramatic human expansion. However, the threat degree of human expansion to biodiversity is poorly understood.

Competition between p53 and YY1 determines PHGDH expression and malignancy in bladder cancer.

Purpose: Serine metabolism is frequently dysregulated in many types of cancers and the tumor suppressor p53 is recently emerging as a key regulator of serine metabolism. However, the detailed mechanism remains unknown. Here, we investigate the role and underlying mechanisms of how p53 regulates the serine synthesis pathway (SSP) in bladder cancer (BLCA).

AF9 targets acetyl-modified STAT6 to diminish purine metabolism and accelerate cell apoptosis during metastasis.

Cell migration and invasion are two important steps for tumour metastasis, and involved the behaviors including metabolism remodeling and anti-apoptosis. However, it's still elusive that cancer cells how to antagonize apoptosis during tumour metastasis. In this study, we observed that super elongation complex (SEC) subunit AF9 depletion exacerbated cell migration and invasion but reduced the apoptosis during invasive migration.

Elevated nuclear PIGL disrupts the cMyc/BRD4 axis and improves PD-1 blockade therapy by dampening tumor immune evasion.

To improve the efficacy of lenvatinib in combination with programmed death-1 (PD-1) blockade therapy for hepatocellular carcinoma (HCC), we screened the suppressive metabolic enzymes that sensitize HCC to lenvatinib and PD-1 blockade, thus impeding HCC progression. After analysis of the CRISPR‒Cas9 screen, phosphatidylinositol-glycan biosynthesis class L (PIGL) ranked first in the positive selection list. PIGL depletion had no effect on tumor cell growth in vitro but reprogrammed the tumor microenvironment (TME) in vivo to support tumor cell survival.

Increased SPRY1 expression activates NF-κB signaling and promotes pancreatic cancer progression by recruiting neutrophils and macrophages through CXCL12-CXCR4 axis.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with a high mortality rate, in which about 90% of patients harbor somatic oncogenic point mutations in KRAS. SPRY family genes have been recognized as crucial negative regulators of Ras/Raf/ERK signaling. Here, we investigate the expression and role of SPRY proteins in PDAC.

Human expansion into Asian highlands in the 21st Century and its effects.

Most intensive human activities occur in lowlands. However, sporadic reports indicate that human activities are expanding in some Asian highlands. Here we investigate the expansions of human activities in highlands and their effects over Asia from 2000 to 2020 by combining earth observation data and socioeconomic data.

Spatiotemporal dynamics of forest ecosystem carbon budget in Guizhou: customisation and application of the CBM-CFS3 model for China.

Background: Countries seeking to mitigate climate change through forests require suitable modelling approaches to predict carbon (C) budget dynamics in forests and their responses to disturbance and management. The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) is a feasible and comprehensive tool for simulating forest C stock dynamics across broad levels, but discrepancies remain to be addressed in China. Taking Guizhou as the case study, we customised the CBM-CFS3 model according to China's context, including the modification of aboveground biomass C stock algorithm, addition of C budget accounting for bamboo forests, economic forests, and shrub forests, improvement of non-forest land belowground slow dead organic matter (DOM) pool initialisation, and other model settings.

The MFN1 and MFN2 mitofusins promote clustering between mitochondria and peroxisomes.

Mitochondria and peroxisomes are two types of functionally close-related organelles, and both play essential roles in lipid and ROS metabolism. However, how they physically interact with each other is not well understood. In this study, we apply the proximity labeling method with peroxisomal proteins and report that mitochondrial protein mitofusins (MFNs) are in proximity to peroxisomes.

Leukemia inhibitory factor receptor homodimerization mediated by acetylation of extracellular lysine promotes prostate cancer progression through the PDPK1/AKT/GCN5 axis.

Background: Prostate cancer (PCa), an inert tumour, has a long progression period, but valid biomarkers and methods for effectively and sensitively monitoring PCa progression are lacking, prompting us to identify new predictors for diagnosis and prognosis. Posttranslational modifications characterizing receptor activation are considered potentially strong indicators of disease progression.

Methods: The posttranscriptional regulation of leukaemia inhibitory factor receptor (LIFR) and its novel downstream signalling activity in PCa were studied using liquid mass spectrometry, genetically engineered mouse (GEM) models, organoid assays, lentivirus packaging, infection and stable cell line construction.

Recent Advances in Design and Preparation of Polymer-Based Thermal Management Material.

The boosting of consumer electronics and 5G technology cause the continuous increment of the power density of electronic devices and lead to inevitable overheating problems, which reduces the operation efficiency and shortens the service life of electronic devices. Therefore, it is the primary task and a prerequisite to explore innovative material for meeting the requirement of high heat dissipation performance. In comparison with traditional thermal management material (e.

Cytosolic GDH1 degradation restricts protein synthesis to sustain tumor cell survival following amino acid deprivation.

The mTORC1 pathway plays key roles in regulating various biological processes, including sensing amino acid deprivation and driving expression of ribosomal protein (RP)-coding genes. In this study, we observed that depletion of glutamate dehydrogenase 1 (GDH1), an enzyme that converts glutamate to α-ketoglutarate (αKG), confers resistance to amino acid deprivation on kidney renal clear cell carcinoma (KIRC) cells. Mechanistically, under conditions of adequate nutrition, GDH1 maintains RP gene expression in a manner dependent on its enzymatic activity.

Digital mapping of zinc in urban topsoil using multisource geospatial data and random forest.

This study aimed to map the spatial patterns of Zn in urban topsoil by using multisource geospatial data and machine learning method. Geological map, digital elevation models, and Landsat images were used to extract data related to geology, relief, and land use types and a vegetation index. Urban functional types were derived from the fusion of Systeme Probatoire d'Observation de la Terre 5 images, points of interest, and real-time Tencent user data.

Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches.

Due to rapid urbanization in China, lead (Pb) continues to accumulate in urban topsoil, resulting in soil degradation and increased public exposure. Mapping Pb concentrations in urban topsoil is therefore vital for the evaluation and control of this exposure risk. This study developed spatial models to map Pb concentrations in urban topsoil using proximal and remote sensing data.

Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China.

In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality parameters, especially using spectral reflectance of water, must satisfy certain preconditions (e.g.

Capturing RNA-protein interaction via CRUIS.

No RNA is completely naked from birth to death. RNAs function with and are regulated by a range of proteins that bind to them. Therefore, the development of innovative methods for studying RNA-protein interactions is very important.

Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI.

Accurate assessment of soil salinization is considered as one of the most important steps in combating global climate change, especially in arid and semi-arid regions. Multi-spectral remote sensing (RS) data including Landsat series provides the potential for frequent surveys for soil salinization at various scales and resolutions. Additionally, the recently launched Sentinel-2 satellite constellation has temporal revisiting frequency of 5 days, which has been proven to be an ideal approach to assess soil salinity.

Quantitative Phosphoproteomics Reveals System-Wide Phosphorylation Network Altered by Spry in Mouse Mammary Stromal Fibroblasts.

Understanding the fundamental role of the stroma in normal development and cancer progression has been an emerging focus in recent years. The receptor tyrosine kinase (RTK) signaling pathway has been reported playing critical roles in regulating the normal and cancer microenvironment, but the underlying mechanism is still not very clear. By applying the quantitative phosphoproteomic analysis of Sprouty proteins (SPRYs), generic modulators of RTK signaling and deleted mouse mammary fibroblasts, we quantified a total of 11,215 unique phosphorylation sites.

Spatiotemporal evolution of urban agglomerations in four major bay areas of US, China and Japan from 1987 to 2017: Evidence from remote sensing images.

As major urban agglomerations with strong urbanization, global bay areas are seldom detected and compared in detail regarding the spatiotemporal evolution of their urban expansion. In this work, a framework was applied for detecting and comparing the spatiotemporal evolution of urban agglomerations in four major bay areas: the San Francisco Bay Area and the New York Bay Area in the US, the Tokyo Bay Area in Japan, and the Guangdong-Hong Kong-Macau (GHM) Bay Area in China. Landsat images from 1987, 1997, 2007 and 2017 were employed to derive the four urban bay areas using the object-oriented support vector machine (O-SVM) classification method, and a multi-scale spatial analysis method was applied to detect the landscape characteristics and types of growth in the urban expansions.

Geo-detection of factors controlling spatial patterns of heavy metals in urban topsoil using multi-source data.

Heavy metal contamination has become a serious and widespread problem in urban environment. Understanding its controlling factors is vital for the identification, prevention, and remediation of pollution sources. This study aimed to identify the factors controlling heavy metal accumulation in urban topsoil using the geodetector method and multiple data sources.

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils.

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features.