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This document discusses the application of machine learning in the field of seismology. It highlights the potential of machine learning techniques in various areas such as earthquake detection, ground motion simulation, earthquake forecasting, and seismic inversion. The document provides a summary of 15 articles that cover topics such as earthquake detection using neural networks, predicting geotechnical parameters through artificial neural networks, automatic classification of seismic events using convolutional neural networks, and the use of machine learning for earthquake precursors detection. The articles emphasize the integration of machine learning into seismological research and provide examples of its application in the field. [Extracted from the article]

Pepper (Capsicum annuum L.) is one of the most important economic crops in the world. By controlling the transport and distribution of photosynthetic products between cells and organs, sugar transporters are widely involved in growth and development, environmental adaptation, and microbial interactions. The present study was carried out at the genome-wide level to systematically characterize sugar transporters. As a result, 50 MST, 3 SUT, and 29 SWEET genes were identified and classified. The expression pattern of sugar transporters in pepper was analyzed by transcriptomic data. The expression properties of sugar transporters were further explored in pepper varieties with significant differences in weight, shape, and pungency. It was shown that the pepper sugar transporter genes had obvious spatiotemporal specific expression characteristics and exhibited variety-specific expression preferences. We focus on analyzing candidate genes that may be involved in fruit development and expansion. We further explore the response of pepper sugar transporters to adversity stress using a structural equation model. Finally, we found that the MST, SUT, and SWEET families are collectively involved in balancing pepper resistance to abiotic stress by coordinating the expression strengths of different family members. Our study may contribute to the functional study of pepper sugar transporter genes and create the prospect of utilizing sugar transporter gene resources to improve pepper variety. [ABSTRACT FROM AUTHOR]

Diffuse large B‐cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease that requires personalized clinical treatment. Assigning patients to different risk categories and cytogenetic abnormality and genetic mutation groups has been widely applied for prognostic stratification of DLBCL. Increasing evidence has demonstrated that dysregulated metabolic processes contribute to the initiation and progression of DLBCL. Metabolic competition within the tumor microenvironment is also known to influence immune cell metabolism. However, metabolism‐ and immune‐related stratification has not been established. Here, 1660 genes involved in 84 metabolic pathways were selected and tested to establish metabolic clusters (MECs) of DLBCL. MECs established based on independent lymphoma datasets distinguished different survival outcomes. The CIBERSORT algorithm and EcoTyper were applied to quantify the relative abundance of immune cell types and identify variation in cell states for 13 lineages comprising the tumor micro environment among different MECs, respectively. Functional characterization showed that MECs were an indicator of the immune microenvironment and correlated with distinctive mutational characteristics and oncogenic signaling pathways. The novel immune‐related MECs exhibited promising clinical prognostic value and potential for informing DLBCL treatment decisions. [ABSTRACT FROM AUTHOR]

Summary: Follicular lymphoma (FL), the most common indolent lymphoma, is a clinically and genetically heterogeneous disease. However, the prognostic value of driver gene mutations and copy number alterations has not been systematically assessed. Here, we analysed the clinical‐biological features of 415 FL patients to identify variables associated with disease progression within 24 months of first‐line therapy (POD24). Patients with B symptoms, elevated lactate dehydrogenase and β2‐microglobulin levels, unfavourable baseline haemoglobin levels, advanced stage, and high‐risk FL International Prognostic Index (FLIPI) scores had an increased risk of POD24, with FLIPI being the most important factor in logistic regression. HIST1H1D, identified as a driver mutation, was correlated with POD24. Gains of 6p22.2 (HIST1H1D) and 18q21.33 (BCL2) and loss of 1p36.13 (NBPF1) predicted POD24 independent of FLIPI. Gene expression profiling of FL samples showed that the POD24 cohort was significantly enriched in the inflammatory response (mediated by interferon and tumour necrosis factor), cell cycle regulation (transcription, replication and proliferation) sets and PI3K‐AKT–mTOR signalling. This result was further validated with transcriptome‐wide information provided by RNA‐seq at single‐cell resolution. Our study, performed on a large cohort of FL patients, highlights the importance of distinctive genetic alterations and gene expression relevant to disease diagnosis and early progression. [ABSTRACT FROM AUTHOR]