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Abstract Background Evidence suggests that hepatocyte mitochondrial dysfunction leads to abnormal lipid metabolism, redox imbalance, and programmed cell death, driving the onset and progression of non-alcoholic steatohepatitis (NASH). Identifying hub mitochondrial genes linked to NASH may unveil potential therapeutic targets. Methods Mitochondrial hub genes implicated in NASH were identified via analysis using 134 algorithms. Results The Random Forest algorithm (RF), the most effective among the 134 algorithms, identified three genes: Aldo–keto reductase family 1 member B10 (AKR1B10), thymidylate synthase (TYMS), and triggering receptor expressed in myeloid cell 2 (TREM2). They were upregulated and positively associated with genes promoting inflammation, genes involved in lipid synthesis, fibrosis, and nonalcoholic steatohepatitis activity scores in patients with NASH. Moreover, using these three genes, patients with NASH were accurately categorized into cluster 1, exhibiting heightened disease severity, and cluster 2, distinguished by milder disease activity. Conclusion These three genes are pivotal mitochondrial genes implicated in NASH progression.

Rapid deforestation has been well-documented in Colombia after the 2016 peace agreement with FARC. While many analysis using remote sensing identify land cover change, structural connectivity variables are less studied for understanding landscape transformation. In this work, we used data from the Landsat archive from 2011 to 2021, the Continuous Change Detection and Classification algorithm (CCDC), and Morphological Spatial Pattern Analysis (MSPA) to analyze deforestation, land cover change, and landscape connectivity in northwestern Colombia’s Amazon. We examined the spatial patterns in three specific subsets in the Colombian arc of deforestation, with a special focus on the surroundings of the National Natural Park Serranía de Chiribiquete. Our results confirm changes in structural connectivity linked to pasture expansion from the conversion forest to pasture during the analyzed period showing changes in the borders of Serranía de Chiribiquete National Park along rivers and roads, where cattle is transported. Before 2016, the average annual deforested area in the three study areas was 27.93 km²; after 2016, this number increased to 73.36 km². The outcomes of our study contribute to an improved understanding of pasture dynamics. They can aid decision-making in areas that play a key role in ecological networks necessary for long-term conservation efforts. Our quantitative assessments have revealed a temporal shrinking of the core area, indicative of a decline in forest cover. Concurrently, we observed an expansion of the edge and background areas, which is consistent with the proliferation of pastures. This study presents methodology and data in support of policy-making for sustainable land use and ecological connectivity to mitigate further environmental degradation in the area.

Abstract Background Ferroptosis, is characterized by lipid peroxidation of fatty acids in the presence of iron ions, which leads to cell apoptosis. This leads to the disruption of metabolic pathways, ultimately resulting in liver dysfunction. Although ferroptosis is linked to nonalcoholic steatohepatitis (NASH), understanding the key ferroptosis-related genes (FRGs) involved in NASH remains incomplete. NASH may be targeted therapeutically by identifying the genes responsible for ferroptosis. Methods To identify ferroptosis-related genes and develop a ferroptosis-related signature (FeRS), 113 machine-learning algorithm combinations were used. Results The FeRS constructed using the Generalized Linear Model Boosting algorithm and Gradient Boosting Machine algorithms exhibited the best prediction performance for NASH. Eight FRGs, with ZFP36 identified by the algorithms as the most crucial, were incorporated into in FeRS. ZFP36 is significantly enriched in various immune cell types and exhibits significant positive correlations with most immune signatures. Conclusion ZFP36 is a key FRG involved in NASH pathogenesis.

The time function method is one of the most commonly used methods for predicting surface dynamic displacements in coal mine areas. In which, the accuracy and reliability of the predicted displacements, to a large extent, depends on the selected mathematical functions for describing the “S”-typed dynamic subsidence at a single surface point (referred to as time functions). Nearly all of the existing studies primarily improve or introduce “S”-shaped growth functions with a single object to minimizing the fitting residuals between the in-situ monitored and the model-fitted subsidence. Such a strategy, however, would result in “overfitting” (or over-parameterization), thereby increasing the complexity of the constructed time function model and the difficulty of model parameter inversion. To this end, the optimal selection of time functions was analyzed in this paper using two indicators of fitting residual and model complexity, rather than the former one in existing studies. More specifically, time-series subsidence observations at 103 field points in seven coal mining areas with different geological mining conditions were selected to be observation samples for ensuring the applicability of the optimal time function. Then, 12 common “S-shaped” growth models were chosen to candidates, and the theoretical analysis and Akaike information criterion (AIC) were further used to analyze the optimal selection of time function from the chosen 12 “S”-shaped models. The results show that: ① Among the 12 selected models, the mean mis-fitting error of the five four-parameter models is about 3.51 cm, which is obviously smaller than that of the two-parameter Knothe model (14.10 cm), but just slightly smaller than the six three-parameter models (4.78 cm); ② In the view of making a trade-off between fitting residuals and model complexity (assessing by the AIC), the AICs of the six three-parameter models are smaller than those of the four-parameter and two-parameter models.This indicates that the three-parameters models are preferrable to describe the temporal evolution of subsidence at a single point, and the four-parameter and two-parameters models may be over-fitted and under-fitted, respectively; ③ Among the six selected models, the optimal selection of time function is related to the lithology of the overburden rock strata; that is, Hossfeld model, which has not been introduced into the time function method, is preferrable under soft and medium-hard overburden strata, whereas Weibull model is preferrable under hard overburden strata.

Field ridges are commonly viewed as the stable semi-natural habitats for maintaining plant diversity in the agricultural landscape. The high plant diversity could further support higher animal diversity. But following the adoption of well-facilitated farmland construction measures in China, many field ridges have been disproportionately neglected or destroyed. Empirical studies delineating the relationships between plant and animal diversity in these field ridges in the paddy landscape remain scant, especially in China, which has the most rice production. A two-year field ridge evaluation was conducted in the Chengdu Plain area, covering 30 paddy landscapes. This investigation scrutinizes the shape attributes of field ridges, their plant diversity, and the associated animal α-diversity and community compositions, including spiders, carabids, birds, frogs, and rice planthoppers. In the results of Pearson’s correlation analysis, a significant inconsistent correlation was observed between plant diversity and animal diversity. The analysis of community structure heterogeneity also revealed no correspondence for species composition between plant and animal communities (i.e., spiders, carabids, and birds), while the non-metric multidimensional scale analysis indicated a substantial difference in the species composition of spiders or plants even within the same field ridge between 2020 and 2021. We argue that the implementation of intensive management practices in paddy landscapes, such as machine ploughing and harvesting and herbicide spraying with drones, leads to a scarcity of stable animal and plant communities in field ridges. Therefore, besides retaining these field ridges in paddy landscapes, maintaining the long-term stable ridges by refraining from herbicide spraying or artificial weeding, as well as avoiding winter wheat cultivating in field ridges, will contribute to protecting biodiversity of field ridges as semi-natural habitats.

Summary: To date, genome-wide association studies (GWAS) have revealed over 200 genetic risk loci associated with prostate cancer; yet, true disease-causing variants remain elusive. Identification of causal variants and their targets from association signals is complicated by high linkage disequilibrium and limited availability of functional genomics data for specific tissue/cell types. Here, we integrated statistical fine-mapping and functional annotation from prostate-specific epigenomic profiles, 3D genome features, and quantitative trait loci data to distinguish causal variants from associations and identify target genes. Our fine-mapping analysis yielded 3,395 likely causal variants, and multiscale functional annotation linked them to 487 target genes. We prioritized rs10486567 as a genome-wide top-ranked SNP and predicted HOTTIP as its target. Deletion of the rs10486567-associated enhancer in prostate cancer cells decreased their capacity for invasive migration. HOTTIP overexpression in enhancer-KO cell lines rescued defective invasive migration. Furthermore, we found that rs10486567 regulates HOTTIP through allele-specific long-range chromatin interaction.