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Impaired tumor cell antigen presentation contributes significantly to immune evasion. This study identifies Berbamine hydrochloride (Ber), a compound derived from traditional Chinese medicine, as an effective inhibitor of autophagy that enhances antigen presentation in tumor cells. Ber increases MHC-I-mediated antigen presentation in melanoma cells, improving recognition and elimination by CD8+ T cells. Mutation of Atg4b, which blocks autophagy, also raises MHC-I levels on the cell surface, and further treatment with Ber under these conditions does not increase MHC-I, indicating Ber's role in blocking autophagy to enhance MHC-I expression. Additionally, Ber treatment leads to the accumulation of autophagosomes, with elevated levels of LC3-II and p62, suggesting a disrupted autophagic flux. Fluorescence staining and co-localization analyses reveal that Ber likely inhibits lysosomal acidification without hindering autophagosome–lysosome fusion. Importantly, Ber treatment suppresses melanoma growth in mice and enhances CD8+ T cell infiltration, supporting its therapeutic potential. Our findings demonstrate that Ber disturbs late-stage autophagic flux through abnormal lysosomal acidification, enhancing MHC-I-mediated antigen presentation and curtailing tumor immune escape. [ABSTRACT FROM AUTHOR]

For the particular engineering-geological conditions and natural environment in permafrost regions, the highway has a high damage ratio during the operation. In warm and ice-rich permafrost regions, highway pavement and embankment have poor serviceability, which threatens driving the vehicle and safe operation. Traditional field surveys on highway damage cannot entirely, rapidly and precisely obtain the distress information along the whole road. In this paper, the Qinghai-Tibet Highway (QTH) pavement distress was investigated in 550-km-long permafrost regions based on the field surveys in 2014 and 2019 and the unmanned aerial vehicle (UAV) imagery. The type and damage ratio of distress were extracted by the remote sensing image classification method. The pavement roughness and embankment height were acquired by the spatial analysis of geographical information system. This paper provides a novel insight and efficient method for the distress investigation and exploration of distress formation in permafrost region, which can be applied in other similar engineering projects in the cold and inclement permafrost region. Furthermore, this paper also presents valuable and first-hand field data for evaluating highway serviceability and prevention of road damage during operation and maintenance stages. [ABSTRACT FROM AUTHOR]

Permafrost is a significant part of the cryosphere, which has gained increasing attention from scientists, policy-makers, and the general public due to global warming, environmental degradation, water shortages, and intense human activities. Although many permafrost research review articles have been published, these studies were predominantly limited to either one subject or one field, while systematic studies about permafrost based on bibliometric analysis methods remain limited. We aim to fill this gap by conducting a bibliometric analysis of 13,697 articles in the field of permafrost research from 1942 to 2021, collected from the Web of Science core collection database. The results indicate that permafrost research is a typically multi-author, multi-country, and multi-institution cooperative field, involved in many research fields. The cumulative number of publications has presented an exponential increase over the past 80 years, with an average annual growth rate of 10.40%. Since 2000, China has seen a rapid growth in the number of publications per year, surpassing the USA in 2016 and leading in the years since then. In addition, the authors from China have great contributions in publications, and there is good room for permafrost development in the future according to the authors' M-index ranking. After the analysis of authors' keywords, we found that, compared to the conventional methods, machine learning and interferometric synthetic aperture radar (InSAR) are new technological approaches introduced in recent years, and the Qinghai–Tibet Plateau has become a popular study area. The results presented here can help related researchers, scholars, and students in the field to better understand the past developments, current status, and future trends of permafrost research. Furthermore, this paper presents and expands the general process of the bibliometric method used in permafrost studies, which can provide researchers with new inspirations and improve discipline research approach. [ABSTRACT FROM AUTHOR]

A cycle of a matroid is a disjoint union of circuits. A matroid is supereulerian if it contains a spanning cycle. To answer an open problem of Bauer in 1985, Catlin proved in [J. Graph Theory 12 (1988) 29–44] that for sufficiently large n $n$, every 2‐edge‐connected simple graph G $G$ with n=∣V(G)∣ $n=| V(G)| $ and minimum degree δ(G)≥n5 $\delta (G)\ge \frac{n}{5}$ is supereulerian. In [Eur. J. Combinatorics, 33 (2012), 1765–1776], it is shown that for any connected simple regular matroid M $M$, if every cocircuit D $D$ of M $M$ satisfies ∣D∣≥maxr(M)−55,6 $| D| \ge \max \left\{\frac{r(M)-5}{5},6\right\}$, then M $M$ is supereulerian. We prove the following. (i) Let M $M$ be a connected simple regular matroid. If every cocircuit D $D$ of M $M$ satisfies ∣D∣≥maxr(M)+110,9 $| D| \ge \max \left\{\frac{r(M)+1}{10},9\right\}$, then M $M$ is supereulerian. (ii) For any real number c $c$ with 0

Vehicle detection in aerial images has a wide range of applications, and the majority of vehicle detection methods use the bounding-box approach for localization. But the bounding-box approach usually yields low precision and recall rates, especially under the dense vehicle density situations where the close spatial proximity of the vehicles confuses the bounding-box detectors. This letter proposes a Fully Convolutional Lightweight Pyramid Network (FCLPN) to detect vehicles in visible-spectrum aerial images. Unlike the bounding-box approach, FCLPN performs pixel-level localization and classification. FCLPN trained on the DLR-3K dataset is directly tested on DLR-3K, VEDAI, COWC, and VAID datasets to validate its generalization strength. Experimental results show that FCLPN performs better than state-of-the-art methods in aerial vehicle detection in terms of Precision, F1 score, and mAP. [ABSTRACT FROM AUTHOR]

The environmental damage caused by mining activities is often quantified by land use change, which is a good way to reveal the extent the environment being impacted on a spatial and temporal scale. In order to understand the impact of mining activities in the high cool-altitude areas on environment, a high-altitude open pit mine in Tianshan Mountain, Xinjiang Prefecture, was taken as the research object. Integrating the spectral characteristics and texture information of ground objects, the supervised classification method and object-oriented classification method were used to classify the Landsat 7/8 Satellite RS images which was obtained in the different years including 2000, 2005, 2010, 2015 and 2019, totally divided four periods. The accuracy of the classification results was verified, and the spatial distribution characteristics and temporal variation characteristics of land use were analyzed by GIS statistical analysis method, for the purpose to reveal the impact of mining activities on the special environment ranges. The results show that the classification accuracy of the supervised classification using SVM algorithm is as high as 95%. The intensity and direction of land use transfer in mining area are quite different before and after mining. The bare land and grassland are basically stable before mining, and the glaciers, snow and water are transferred to each other. After mining for a long time, the intensity of mutual transfer among various types increasing, the direction is diverse, and changes over a long time. By 2019, the land use in the mining area has basically returned to the level of 2000, the initial state, and the transfer of land use tends to a new dynamic balance. [ABSTRACT FROM AUTHOR]

The identification of intrinsically disordered proteins and their functional roles is largely dependent on the performance of computational predictors, necessitating a high standard of accuracy in these tools. In this context, we introduce a novel series of computational predictors, termed PDFll (Predictors of Disorder and Function of proteins from the Language of Life), which are designed to offer precise predictions of protein disorder and associated functional roles based on protein sequences. PDFll is developed through a two-step process. Initially, it leverages large-scale protein language models (pLMs), trained on an extensive dataset comprising billions of protein sequences. Subsequently, the embeddings derived from pLMs are integrated into streamlined, yet sophisticated, deep-learning models to generate predictions. These predictions notably surpass the performance of existing state-of-the-art predictors, particularly those that forecast disorder and function without utilizing evolutionary information.

The field of understanding the association between genes and diseases is rapidly expanding, making it challenging for researchers to keep up with the influx of new publications and genetic datasets. Fortunately, there are now several regularly updated databases available that focus on cataloging gene-disease relationships. The development of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 system has revolutionized the field of gene editing, providing a highly efficient, accurate, and reliable method for exploring gene-disease associations. However, currently, there is no resource specifically dedicated to collecting and integrating the latest experimentally supported gene-disease association data derived from genome-wide CRISPR screening. To address this gap, we have developed the CRISPR-Based Gene-Disease Associations (CBGDA) database, which includes over 200 manually curated gene-disease association data derived from genome-wide CRISPR screening studies. Through CBGDA, users can explore gene-disease association data derived from genome-wide CRISPR screening, gaining insights into the expression patterns of genes in different diseases, associated chemical data, and variant information. This provides a novel perspective on understanding the associations between genes and diseases. What is more, CBGDA integrates data from several other databases and resources, enhancing its comprehensiveness and utility. In summary, CBGDA offers a fresh perspective and comprehensive insights into the research on gene-disease associations. It fills the gap by providing a dedicated resource for accessing up-to-date, experimentally supported gene-disease association data derived from genome-wide CRISPR screening. Database URL: http://cbgda.zhounan.org/main., (© The Author(s) 2024. Published by Oxford University Press.)