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Objectives: Regulatory T (Treg) cells regulate immunity in autoimmune diseases and cancers. However, immunotherapies that target tumor‐infiltrating Treg cells often induce unwanted immune responses and tissue inflammation. Our research focussed on exploring the expression pattern of CD177 in tumor‐infiltrating Treg cells with the aim of identifying a potential target that can enhance immunotherapy effectiveness. Methods: Single‐cell RNA sequencing (scRNA‐seq) data and survival data were obtained from public databases. Twenty‐one colorectal cancer patient samples, including fresh tumor tissues, peritumoral tissues and peripheral blood mononuclear cells (PBMCs), were analysed using flow cytometry. The transendothelial activity of CD177+ Treg cells was substantiated using in vitro experiments. Results: ScRNA‐seq and flow cytometry results indicated that CD177 was exclusively expressed in intratumoral Treg cells. CD177+ Treg cells exhibited greater activation status and expressed elevated Treg cell canonical markers and immune checkpoint molecules than CD177− Treg cells. We further discovered that both intratumoral CD177+ Treg cells and CD177‐overexpressing induced Treg (iTreg) cells had lower levels of PD‐1 than their CD177− counterparts. Moreover, CD177 overexpression significantly enhanced the transendothelial migration of Treg cells in vitro. Conclusions: These results demonstrated that Treg cells with higher CD177 levels exhibited an enhanced activation status and transendothelial migration capacity. Our findings suggest that CD177 may serve as an immunotherapeutic target and that overexpression of CD177 may improve the efficacy of chimeric antigen receptor T (CAR‐T) cell therapy. [ABSTRACT FROM AUTHOR]

Accurately predicting landslide deformation based on monitoring data is key to successful early warning of landslide disasters. Landslide displacement–time curves offer an intuitive reflection of the landslide motion process and deformation predictions often reference the Saito curve for correlational analysis with cumulative deformation curves. Many scholars have applied machine learning techniques to individual landslide deformation predictions with considerable success. However, most landslide monitoring data lack a full lifecycle, making it challenging to predict unexperienced evolutionary stages. Cross-learning between similar landslide datasets provides a potential solution to issues of data scarcity and accurate prediction. First, this paper proposes a landslide classification and displacement machine learning method, along with predictive performance evaluation metrics. Further, it details a study of 13 landslides with evident deformation signs in the middle Qinling–Bashan Mountains area, conducting refined landslide classification. Based on a data-driven approach, this study conducts an analysis of the importance of characteristics influencing landslide deformation and establishes predictive models for similar-type landslide deformation, mixed-type landslide deformation, and individual landslide deformation using machine learning algorithms. The models trained on the dataset are used to predict the deformation of the West of Yinpo Yard landslide at different periods, with the predictive performance evaluated using two indices. The results indicate that the models trained on similar-type landslide data and those based on individual landslide data yielded comparable predictive performances, substantially addressing challenges such as insufficient early-stage monitoring data and low prediction accuracy. [ABSTRACT FROM AUTHOR]

Objectives: Extracorporeal membrane oxygenation (ECMO) plays an important role in providing temporary life support for patients with severe cardiac or pulmonary failure, but requires strict anticoagulation and monitoring. This network meta‐analysis systematically explored the most effective anticoagulation and monitoring strategies for patients receiving ECMO. Methods: MEDLINE, Embase, Web of Science, and the Cochrane Central Register of Controlled Trials were searched up to January 31, 2023, for studies comparing unfractionated heparin (UFH), argatroban (Arg), bivalirudin (Biv), and/or nafamostat mesylate (NM) in patients receiving ECMO. The primary outcomes included device‐related thrombosis, patient‐related thrombosis, and major bleeding events. The secondary outcomes included ECMO survival, ECMO duration, and in‐hospital mortality. Results: A total of 2522 patients from 23 trials were included in the study. Biv was associated with a decreased risk of device‐related thrombosis (odd ratio [OR] 0.51, 95% confidence interval [CI]: 0.33–0.84) compared with UFH, whereas NM (OR 2.2, 95% CI: 0.24–65.0) and Arg (OR 0.92, 95% CI: 0.43–2.0) did not reduce the risk of device‐related thrombosis compared with UFH. Biv was superior to Arg in decreasing the risk of device‐related thrombosis (OR 0.14, 95% CI: 0.03–0.51). Biv reduced the risk of patient‐related thrombosis compared with UFH (OR 0.44, 95% CI: 0.18–0.85); NM (OR 0.65, 95% CI: 0.14–3.3) and Arg (OR 3.1, 95% CI: 0.94–12.0) did not decrease risk of patient‐related thrombosis compared with UFH. No significant difference was observed in the risk of major bleeding between three alternatives and UFH: Biv (OR 0.54, 95% CI: 0.23–1.3), Arg (OR 1.3, 95% CI: 0.34–5.8), and NM (OR 0.60, 95% CI: 0.13–2.6). NM showed a reduced risk of in‐hospital mortality compared with UFH (OR 0.27, 95% CI: 0.091–0.77), whereas Arg (OR 0.43, 95% CI: 0.15–1.2) and Biv (OR 0.75, 95% CI: 0.52–1.1) did not decrease risk of in‐hospital mortality. Conclusions: Compared with UFH and Arg, Biv reduces the risk of thrombosis and appears to be a better choice for patients requiring ECMO. NM was associated with a reduced risk of in‐hospital mortality. [ABSTRACT FROM AUTHOR]

Existing immune checkpoint inhibitors focus on activating T cells and show limited effectiveness in gastric cancer (GC). SIGLEC10 is identified as a novel tumor-associated macrophage-related immune checkpoint in other cancer types. However, its immunosuppressive role and clinical significance in GC remain unclear. In this study, we find a dominant expression of SIGLEC10 on CD68+ macrophages in GC. SIGLEC10 can suppress the proliferation and function of tumor-infiltrating CD8+ T cells in vitro via the Akt/P38/Erk signaling pathway. Furthermore, in ex vivo and in vivo models, SIGLEC10 blockade promotes CD8+ T cell effector function. Finally, SIGLEC10+ macrophages are positively correlated with the adverse prognosis of GC. Our study highlights that SIGLEC10 directly suppresses T cell function and serves as a promising target for immunotherapy and suggests SIGLEC10+ macrophages as a novel potential predictor of the clinical prognosis of GC. [ABSTRACT FROM AUTHOR]

A phenotype may be associated with multiple genes that interact with each other in the form of a gene module or network. How to identify these relationships is one important aspect of comparative transcriptomics. However, it is still a challenge to align gene modules associated with different phenotypes. Although several studies attempted to address this issue in different aspects, a general framework is still needed. In this study, we introduce Module Alignment of TranscripTomE (MATTE), a novel approach to analyze transcriptomics data and identify differences in a modular manner. MATTE assumes that gene interactions modulate a phenotype and models phenotype differences as gene location changes. Specifically, we first represented genes by a relative differential expression to reduce the influence of noise in omics data. Meanwhile, clustering and aligning are combined to depict gene differences in a modular way robustly. The results show that MATTE outperformed state-of-the-art methods in identifying differentially expressed genes under noise in gene expression. In particular, MATTE could also deal with single-cell ribonucleic acid-seq data to extract the best cell-type marker genes compared to other methods. Additionally, we demonstrate how MATTE supports the discovery of biologically significant genes and modules, and facilitates downstream analyses to gain insight into breast cancer. The source code of MATTE and case analysis are available at https://github.com/zjupgx/MATTE. [ABSTRACT FROM AUTHOR]

With rapid economic development, extensive human activity has changed landscape patterns (LPs) dramatically, which has further influenced hydrological processes. However, the effects of LPs changes on hydrological processes, especially for the streamflow–sediment relationship in the subtropical monsoon climate zone, have not been reported. In our study, 10 watersheds with different sizes in the subtropical monsoon climate zone of southeastern China were chosen as the study area, and the effect of the 14 most commonly used landscape metrics (LMs) on 4 typical hydrological indices (water yields (WY), the runoff coefficient (RC), the soil erosion modulus (SEM), and the suspended sediment concentration (SSC)) were analyzed based on land use maps and hydrological data from 1990 to 2019. The results reveal that the LP characteristics within the study area have changed significantly. The number of patches and landscape shape indices were significantly positively correlated with watershed size (p < 0.01). For most watersheds, the largest patch index was negatively correlated with WY, RC, and SEM, and the perimeter area fractal dimension was positively correlated with WY, RC, SEM, and SSC. The effects of several LMs on the hydrological indices had scale effects. WY/RC and the interspersion and juxtaposition index were negatively correlated in most larger watersheds but were positively correlated in most smaller watersheds. Similar results were found for Shannon's diversity/evenness index and SEM. In general, an increase in a small patch of landscape and in landscape diversity would increase WY, the fragmentation of LPs would result in more soil erosion, and LPs would affect the relationship between streamflow and sediment yield. As a result, a proper decrease in landscape fragmentation and physical connectivity in the subtropical monsoon climate zone of southeastern China would benefit soil erosion prevention. These results enhance the knowledge about the relationship between LPs and hydrological processes in the subtropical monsoon climate zone of southeastern China and benefit local water and soil conservation efforts. [ABSTRACT FROM AUTHOR]

Herein, we report the function of copper sites in Cu-SSZ-13, Cu-ZSM-5 and Cu-Beta catalysts with the same Si/Al ratio (14) and Cu/Al ratio (0.4) on selective catalytic reduction of NO with NH3 (NH3-SCR) and reveal the relationship between active sites (Cu sites, acid sites) and catalytic activity. The results show that the amount of isolated Cu2+ ions in the catalysts directly determines the formation of strong Lewis acid sites and reaction intermediate NO3− ions, thus affecting the low-temperature SCR performance, while the amount of highly stable Cu+ ions and Brønsted acid sites is related to the high-temperature SCR performance of the catalysts. Consequently, it contains enough isolated Cu2+ ions, highly stable Cu+ ions and Brønsted acid sites, which endows Cu-SSZ-13 with excellent NH3-SCR activity. [ABSTRACT FROM AUTHOR]

Baseflow is an essential component of runoff, which is the dominant water resource for the dry season. To better manage water resources, it is vital to investigate the links between the multiple influencing factors and the baseflow for better prediction in light of global changes. Previous studies have seldom separated these influencing factors in the analysis, making it difficult to determine their effect on the baseflow. In this study, based on the analysis datasets generated by the Soil and Water Assessment Tool (SWAT) model, the control single variables, correlation analysis, and multiple linear regression (MRL) methods were firstly combined to analyze the influences of the chosen factors (land use, topography, and soil type) on the baseflow. The findings revealed that the ability of precipitation to replenish the baseflow was better in areas with a higher slope. The ability of precipitation to recharge the baseflow for different land uses was ranked as "forest land > grass land > agricultural land > urban land"; land use factors should be added to the baseflow prediction equation. The hydrological group is the main property of soil affecting the baseflow recharge. A regression model established using publicly acquired remote sensing data had a good performance (R2 = 0.84) on baseflow prediction on an annual scale. As a result of this information, relevant government officials and environmentalists may better manage water supplies in drought years. In addition, this regression model frame has the potential to be used for a baseflow inquiry inside an ungauged zone for a better ecological assessment. [ABSTRACT FROM AUTHOR]

Motivation Identifying genes that play a causal role in cancer evolution remains one of the biggest challenges in cancer biology. With the accumulation of high-throughput multi-omics data over decades, it becomes a great challenge to effectively integrate these data into the identification of cancer driver genes. Results Here, we propose MODIG, a graph attention network (GAT)-based framework to identify cancer driver genes by combining multi-omics pan-cancer data (mutations, copy number variants, gene expression and methylation levels) with multi-dimensional gene networks. First, we established diverse types of gene relationship maps based on protein–protein interactions, gene sequence similarity, KEGG pathway co-occurrence, gene co-expression patterns and gene ontology. Then, we constructed a multi-dimensional gene network consisting of approximately 20 000 genes as nodes and five types of gene associations as multiplex edges. We applied a GAT to model within-dimension interactions to generate a gene representation for each dimension based on this graph. Moreover, we introduced a joint learning module to fuse multiple dimension-specific representations to generate general gene representations. Finally, we used the obtained gene representation to perform a semi-supervised driver gene identification task. The experiment results show that MODIG outperforms the baseline models in terms of area under precision-recall curves and area under the receiver operating characteristic curves. Availability and implementation The MODIG program is available at https://github.com/zjupgx/modig. The code and data underlying this article are also available on Zenodo, at https://doi.org/10.5281/zenodo.7057241. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]