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Gully erosion can lead to the destruction of farmland and the reduction in crop yield. Gully mapping from remote sensing images is critical for quickly obtaining the distribution of gullies at regional scales and arranging corresponding prevention and control measures. The narrow and irregular shapes and similar colors to the surrounding farmland make mapping erosion gullies in sloping farmland from remote sensing images challenging. To implement gully erosion mapping, we developed a small training samples-oriented lightweight deep leaning model, called asymmetric non-local LinkNet (ASNL-LinkNet). The ASNL-LinkNet integrates global context information through an asymmetric non-local operation and conducts multilayer feature fusion to improve the robustness of the extracted features. Experiment results show that the proposed ASNL-LinkNet achieves the best performance when compared with other deep learning methods. The quantitative evaluation results in the three test areas show that the F1-score of erosion gully recognition varies from 0.62 to 0.72. This study provides theoretical reference and practical guidance for monitoring erosion gullies on slope farmland in the black soil region of Northeast China.

Sarcopenia, characterized by the loss of skeletal muscle mass and function, is a significant complication in patients with cirrhosis. This condition not only exacerbates the overall morbidity and mortality associated with liver disease but also complicates patient management, increasing the risk of hospitalization, infections, and hepatic encephalopathy. Despite its clinical significance, sarcopenia in cirrhotic patients remains underdiagnosed and undertreated. This review aims to summarize current knowledge on the pathophysiology of sarcopenia in cirrhosis, including mechanisms such as altered metabolism, hormonal imbalances, and inflammation. Additionally, we explore diagnostic challenges and discuss emerging therapeutic strategies, including nutritional support, exercise, and pharmacological interventions. By highlighting the gaps in existing research and proposing directions for future studies, this review seeks to improve the management and outcomes of cirrhotic patients affected by sarcopenia.

Abstract Background Sarcopenia, an age-related disorder characterized by loss of skeletal muscle mass and function, is recently recognized as a complication in elderly patients with type 2 diabetes mellitus (T2DM). Skeletal muscles play a crucial role in glycemic metabolism, utilizing around 80% of blood glucose. Accordingly, we aimed to explore the relationship between glucose metabolism and muscle mass in T2DM. Methods We employed the AWGS 2019 criteria for diagnosing low muscle mass and 1999 World Health Organization (WHO) diabetes diagnostic standards. This study included data of 191 individuals aged 60 and above with T2DM of Shanghai Pudong Hospital from November 2021 to November 2022. Fasting C-peptide (FPCP), fasting plasma glucose (FPG), 2-hour postprandial plasma glucose (PPG) and postprandial 2-hour C-peptide (PPCP), glycated hemoglobin A1c (HbA1c), glycated albumin (GA), serum lipids spectrum, renal and hepatic function, hemoglobin, and hormone were measured. Based on the findings of univariate analysis, logistic regression and receiver operating characteristic (ROC) curves were established. Results Participants with low muscle mass had significantly lower alanine and aspartate aminotransferase, and both FPCP and PPCP levels (P

Measuring causal brain network is a significant topic for exploring complex brain functions. While various data-driven algorithms have been proposed, they still have some drawbacks such as ignoring time non-separability, cumbersome parameter settings, and poor robustness. To solve these deficiencies, we developed a novel framework: “time-shift permutation cross-mapping, TPCM,” integrating steps of: (1) delayed improved phase-space reconstruction (DIPSR), (2) rank transformation of embedding vectors’ distances, (3) cross-mapping with a fitting estimation, and (4) causality quantification using multi-delays. Based on the synthetic models and comparison with baseline methods, numerical validation results demonstrate that TPCM significantly improves the robustness for data length with or without noise interference, and achieves the best quantification accuracy in detecting time delay and coupling strength, with the highest determination coefficient ( ${R}^{{2}}={0}.{96}\text {)}$ of fitting verse coupling parameters. The developed TPCM was finally applied to ictal electrocorticogram (ECoG) analysis of patients with drug-resistant epilepsy (DRE). A total of 17 patients with DRE were included into the retrospective study. For 8 patients undergoing successful surgeries, the causal coupling strength ( $0.58~\pm ~0.20$ ) within epileptogenic zone network is significantly higher than those suffering failed surgeries ( $0.38~\pm ~0.16$ ) with ${P}\lt {0}. {001}$ through Mann-Whitney-U-test. Therefore, the epileptic brain network measured by TPCM is a credible biomarker for predicting surgical outcomes. These findings additionally confirm TPCM’s superior performance and promising potential to advance precision medicine for neurological disorders.

Fungi are rich sources of secondary metabolites of agrochemical, pharmaceutical, and food importance, such as mycotoxins, antibiotics, and antitumor agents. Secondary metabolites play vital roles in fungal pathogenesis, growth and development, oxidative status modulation, and adaptation/resistance to various environmental stresses. LaeA contains an S-adenosylmethionine binding site and displays methyltransferase activity. The members of velvet proteins include VeA, VelB, VelC, VelD and VosA for each member with a velvet domain. LaeA and velvet proteins can form multimeric complexes such as VosA-VelB and VelB-VeA-LaeA. They belong to global regulators and are mainly impacted by light. One of their most important functions is to regulate gene expressions that are responsible for secondary metabolite biosynthesis. The aim of this mini-review is to represent the newest cognition of the biosynthetic regulation of mycotoxins and other fungal secondary metabolites by LaeA and velvet proteins. In most cases, LaeA and velvet proteins positively regulate production of fungal secondary metabolites. The regulated fungal species mainly belong to the toxigenic fungi from the genera of Alternaria, Aspergillus, Botrytis, Fusarium, Magnaporthe, Monascus, and Penicillium for the production of mycotoxins. We can control secondary metabolite production to inhibit the production of harmful mycotoxins while promoting the production of useful metabolites by global regulation of LaeA and velvet proteins in fungi. Furthermore, the regulation by LaeA and velvet proteins should be a practical strategy in activating silent biosynthetic gene clusters (BGCs) in fungi to obtain previously undiscovered metabolites.

Chemical study of the nematicidal biocontrol fungus Pochonia chlamydosporia PC-170 led to discovery of six resorcylic acid lactones (RALs), including three nematicidal glycosylated RALs, monocillin VI glycoside (1), colletogloeolactone A (2) and monocillin II glycoside (3), and three antibacterial non-glycosylated RALs, monocillin VI (4), monocillin IV (5) and monocillin II (6). The planar structure of the new compound monocillin VI glycoside (1) was elucidated using HRESIMS and NMR data, and its monosaccharide configuration was further determined through sugar hydrolysis experiment and GC–MS analysis method. Furthermore, their two biosynthetic-related PKS genes, pchE and pchI, were identified through the gene knockout experiment. The glycosylated RALs 1–3 exhibited nematicidal activity against Meloidogyne incognita, with LC50 values of 94, 152 and 64 μg/mL, respectively, and thus had great potential in the development of new nematicidal natural products to control M. incognita in the future.

Obstructive Sleep Apnea (OSA), a sleep disorder with high prevalence, is normally accompanied by affective, autonomic, and cognitive abnormalities, and is deemed to be linked to functional brain alterations. To investigate alterations in brain functional connectivity properties in patients with OSA, a comparative analysis of global and local topological properties of brain networks was conducted between patients with OSA and healthy controls (HCs), utilizing functional near-infrared spectroscopy (fNIRS) imaging. A total of 148 patients with OSA and 150 healthy individuals were involved. Firstly, quantitative alterations in blood oxygen concentration, changes in functional connectivity, and variations in graph theory-based network topological characteristics were assessed. Then, with Mann–Whitney statistics, this study compared whether there are significant differences in the above characteristics between patients with OSA and HCs. Lastly, the study further examined the correlation between the altered characteristics and the apnea hypopnea index (AHI) using linear regression. Results revealed a higher mean and standard deviation of hemoglobin concentration in the superior temporal gyrus among patients with OSA compared to HCs. Resting-state functional connectivity (RSFC) exhibited a slight increase between the superior temporal gyrus and other specific areas in patients with OSA. Notably, neither patients with OSA nor HCs demonstrated significant small-world network properties. Patients with OSA displayed an elevated clustering coefficient (p < 0.05) and local efficiency (p < 0.05). Additionally, patients with OSA exhibited a tendency towards increased nodal betweenness centrality (p < 0.05) and degree centrality (p < 0.05) in the right supramarginal gyrus, as well as a trend towards higher betweenness centrality (p < 0.05) in the right precentral gyrus. The results of multiple linear regressions indicate that the influence of the AHI on RSFC between the right precentral gyrus and right superior temporal gyrus (p < 0.05), as well as between the right precentral gyrus and right supramarginal gyrus (p < 0.05), are statistically significant. These findings suggest that OSA may compromise functional brain connectivity and network topological properties in affected individuals, serving as a potential neurological mechanism underlying the observed abnormalities in brain function associated with OSA.