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Using three different lightning observation systems, we have observed the first stepping processes of three lightning discharges with unprecedented details. We found that each of the first stepping processes contains a clear upward fast negative breakdown (FNB) corresponding to the preliminary breakdown (PB) pulse peak. Prior to the FNB from the lightning initiation, most sources are located in a fan‐shape area, and the FNB grows from this area forming a channel with a length from 45 to 120 m. During the period of about 100 microseconds immediately preceding the PB pulse, multiple 1–12 MHz radio bursts are located within or near the fan shape area. These radio bursts may indicate stem/space leaders. Eventually the FNB decayed and scattered in another fan shape area at the upper end of the channel. Based on these findings, we have proposed a complete picture of the first stepping process. Plain Language Summary Understanding the first stepping process of a lightning is one of the most important keys in solving the long‐standing lightning initiation mystery. In this study, by taking advantage of combining three different types of state‐of‐the‐art lightning observation systems, we have observed the first stepping processes of three close lightning discharges. We found that each of the first stepping processes contains a distinct fast upward movement that matches the preliminary breakdown pulse peak and forms a channel. Before this movement, multiple sources are mapped spreading out in a fan shape area. The fast movement apparently starts from this fan‐shape area and ends with a new fan‐shape area. We also found that a few of the sources exhibited more power and are likely to indicate the stem/space leaders which are required to form a step. Based on these new findings, we have proposed a complete picture of the first stepping process of a lightning discharge. Key Points We observed the first stepping processes of three close lightning discharges in Japan with unprecedented details We found that each of the first stepping processes contains a clear fast negative breakdown (FNB) forming a channel The FNB channel starts from a fan‐shaped discharge area and ends in another fan‐shaped area

Single-cell RNA-sequencing data has revolutionized our ability to understand of the patterns of cell–cell and ligand–receptor connectivity that influence the function of tissues and organs. However, the quantification and visualization of these patterns in a way that informs tissue biology are major computational and epistemological challenges. Here, we present Connectome , a software package for R which facilitates rapid calculation and interactive exploration of cell–cell signaling network topologies contained in single-cell RNA-sequencing data. Connectome can be used with any reference set of known ligand–receptor mechanisms. It has built-in functionality to facilitate differential and comparative connectomics, in which signaling networks are compared between tissue systems. Connectome focuses on computational and graphical tools designed to analyze and explore cell–cell connectivity patterns across disparate single-cell datasets and reveal biologic insight. We present approaches to quantify focused network topologies and discuss some of the biologic theory leading to their design.

We have developed and deployed a 3D Interferometer-type Lightning Mapping Array (InLMA) for observing winter lightning in Japan. InLMA consists of three broadband interferometers installed at three stations with a distance from 3 to 5 km. At each interferometer station, three discone antennas were installed, forming a right triangle with a separation of 75 m along their two orthogonal baselines. The output of each InLMA antenna is passed through a 400 MHz low-pass filter and then recorded at 1 GS/s with 16-bit accuracy. A new method has been proposed for finding 3D solutions of a lightning mapping system that consists of multiple interferometers. Using the InLMA, we have succeeded in mapping a positive cloud-to-ground (CG) lightning flash in winter, particularly its preliminary breakdown (PB) process. A study on individual PB pulse processes allows us to infer that each PB pulse process contains many small-scale discharges scattering in a height range of about 150 m. These small-scale discharges in a series of PB pulses appear to be continuous in space, though discontinuous in time. We have also examined the positive return stroke in the CG flash and found a 3D average return stroke speed of 7.5 × 107 m/s.

Adoptive cell therapy (ACT) using tumor infiltrating lymphocytes (TIL) is being studied in multiple tumor types. However, little is known about clonal cell expansion in vitro and persistence of the ACT product in vivo. We performed single-cell RNA and T-Cell Receptor (TCR) sequencing on serial blood and tumor samples from a patient undergoing ACT, who did not respond. We found that clonal expansion varied during preparation of the ACT product, and only one expanded clone was preserved in the ACT product. The TCR of the preserved clone which persisted and remained activated for five months was previously reported as specific for cytomegalovirus and had upregulation of granzyme family genes and genes associated with effector functions ( HLA-DQB1, LAT, HLA-DQA1, and KLRD1). Clones that contracted during TIL preparation had features of exhaustion and apoptosis . At disease progression, all previously detected clonotypes were detected. New clonotypes appearing in blood or tumor at disease progression were enriched for genes associated with cytotoxicity or stemness ( FGFBP2, GNLY, GZMH, GZMK, IL7R, SELL and KLF2 ), and these might be harnessed for alternative cellular therapy or cytokine therapy. In-depth single-cell analyses of serial samples from additional ACT-treated patients is warranted, and viral- versus tumor-specificity should be carefully analyzed.

High-dimensional biological data, including single-cell omics, are prevalent in modern biology for characterizing a wide range of biological processes and phenomena. Although our capacity to generate such data has expanded at an unprecedented pace, significant challenges remain in extracting the informative features and the underlying biological signals. In particular, these datasets not only exhibit high dimensionality but also suffer from issues including inherent noise, low signal-to-noise ratio, and intrinsic heterogeneity. While machine learning models hold great promise for analyzing such data, their complexity and sensitivity to data characteristics often impede biological interpretability. To address these interconnected challenges, this thesis develops three rigorous frameworks that prioritize interpretable, data-driven modeling across diverse biological scenarios. In chapter 1, we introduce Locally Sparse Interpretable Network (LSPIN), a novel neural network model that directly addresses the challenge of heterogeneity in supervised learning tasks. As a dual-network model, LSPIN identifies the most predictive features for each sample via a gating network while predicts the outcome via its prediction network. We show that LSPIN achieves state-of-the-art performance on various real-world datasets while maintaining interpretability. This architecture proves particularly valuable for survival analysis and marker gene identification, where understanding feature importance at the sample level provides crucial biological insights.In chapter 2, we present Biwhitened Principal Component Analysis (BiPCA), a mathematically grounded model for processing high-dimensional omics count data. BiPCA is specifically designed to handle the complex noise structure in the data. It reveals the underlying rank of the data through a rigorous noise standardization procedure termed biwhitening, then optimally denoises the data to recover the underlying signals. In addition, BiPCA is highly adaptive—capable of accommodating a wide range of distributions from different modalities—and can further assess how well it fits the data. We demonstrate its application on more than 100 datasets and highlight the benefits of its accurate rank estimation to data analysis. We also show its superior performance on single-cell downstream tasks such as enhancing marker gene expressions, preserving cell neighborhood, and mitigating batch effect.In chapter 3, we address the growing need for multi-modal analysis by developing mmDUFS (Multi-modal Differentiable Unsupervised Feature Selection). mmDUFS bridges the gap between single-modality approaches by identifying both shared biological processes and modality-specific signals through novel graph operators. In addition, mmDUFS identifies the informative biological features associated with these processes to provide further interpretability. The method significantly outperforms existing approaches in synthetic benchmarks and revealed novel biological insights in single-cell multi-omics applications.Together, these three frameworks—LSPIN, BiPCA, and mmDUFS—illustrate how interpretable, data-driven modeling can tackle the core challenges of high-dimensional biological data. LSPIN employs sample-specific gating to identify the most predictive features, BiPCA accurately selects the signals and optimally denoises data to reveal the underlying structures, and mmDUFS extends the capabilities to multi-modal data, capturing both shared and modality-specific signals. By prioritizing versatile and rigorous methodology, each framework advances our ability to extract meaningful biological information from complex datasets, thereby opening new avenues for discovery in modern, data-intensive biomedical research.

    Purpose Peanut pod maturity and splitting are two important traits that can significantly affect yields and quality. However, the investigation of the natural variability and genetic underpinnings of these two characteristics in peanuts remains limited. The aim of this study is to explore the genetic loci and related genes associated with the pod shattering trait in peanuts, laying the foundation for investigating the molecular mechanisms underlying pod shattering trait formation. Method In this research, a comprehensive genome-wide association study (GWAS) was carried out to analyze peanut pod maturity and analyzing phenotypic data, splitting percentage utilizing U.S. peanut mini core collection. Result A total of 19 distinct single nucleotide polymorphisms (SNPs) were detected from this study, with 6 by pod maturity and 13 SNPs by splitting traits, respectively. SNPs exhibit high genetic stability and representativeness. SNP markers are used for linkage analysis and gene mapping in peanut populations. Based on these loci, a total of 95 genes were identified. we found that the majority of the genes associated with PS degree encoded PPR superfamily proteins and transport-related proteins based on the most significant loci, such as F0IT9C , WWV0ES , BX8F04 , AEE9K3 , PFB0AA , and M24FW3 . The main functions of the PPR protein include participating in RNA splicing, RNA stability, RNA cleavage, RNA translation, RNA editing, and RNA responding to non-biological stress in plants. For PM, we identified MYB transcription factor, and the main functions of MYB transcription factors include hormone response, environmental response, and regulation of plant phenylpropanoid secondary metabolic pathways.

Mashup is a new type of application that integrates multiple Web APIs. For mashup application development, the quality of the selected APIs is particularly important. However, with the rapid development of Internet technology, the number of Web APIs is increasing rapidly. It is unrealistic for mashup developers to manually select appropriate APIs from a large number of services. For existing methods, there is a problem of data sparsity, because one mashup is related to a few APIs, and another problem of over-reliance on semantic information. To solve these problems in current service discovery approaches, we propose a service discovery approach based on a knowledge map (SDKG). We embed service-related information into the knowledge graph, alleviating the impact of data sparsity and mining deep relationships between services, which improves the accuracy of service discovery. Experimental results show that our approach has obvious advantages in accuracy compared with the existing mainstream service discovery approaches.

HCV p7 protein is a cation-selective ion channel, playing an essential role during the life cycle of HCV viruses. To understand the cation-selective mechanism, we constructed a hexameric model in lipid bilayers of HCV p7 protein for HCB JFH-1 strain, genotype 2a. In this structural model, His9 and Val6 were key factors for the HCV cation-selective ion channel. The histidine residues at position 9 in the hexameric model formed a first gate for HCV p7 channel, acting as a selectivity filter for cations. The valines mentioned above formed a second gate for HCV p7 channel, serving as a hydrophobic filter for the dehydrated cations. The binding pocket for the channel blockers, e.g., amantadine and rimantadine, was composed of residues 20–26 in H2 helix and 52–60 in H3 helix in i + 2 monomer. However, the molecular volumes for both amantadine and rimantadine were too small for the binding pocket of HCV p7 channel. Thus, designing a compound similar with rimantadine and having much larger volume would be an effective strategy for discovering inhibitors against HCV p7 channel. To achieve this point, we used rimantadine as a structural template to search ChEMBL database for the candidates employing favorable binding affinities to HCV p7 channel. As a result, six candidates were identified to have potential to be novel inhibitors against HCV p7 channel.

Tissue homeostasis is controlled by cellular circuits governing cell growth, organization, and differentation. In this study we identify previously undescribed cell-to-cell communication that mediates information flow from mechanosensitive pleural mesothelial cells to alveolar-resident stem-like tuft cells in the lung. We find mesothelial cells to express a combination of mechanotransduction genes and lineage-restricted ligands which makes them uniquely capable of responding to tissue tension and producing paracrine cues acting on parenchymal populations. In parallel, we describe a large population of stem-like alveolar tuft cells that express the endodermal stem cell markers Sox9 and Lgr5 and a receptor profile making them uniquely sensitive to cues produced by pleural Mesothelium. We hypothesized that crosstalk from mesothelial cells to alveolar tuft cells might be central to the regulation of post-penumonectomy lung regeneration. Following pneumonectomy, we find that mesothelial cells display radically altered phenotype and ligand expression, in a pattern that closely tracks with parenchymal epithelial proliferation and alveolar tissue growth. During an initial pro-inflammatory stage of tissue regeneration, Mesothelium promotes epithelial proliferation via WNT ligand secretion, orchestrates an increase in microvascular permeability, and encourages immune extravasation via chemokine secretion. This stage is followed first by a tissue remodeling period, characterized by angiogenesis and BMP pathway sensitization, and then a stable return to homeostasis. Coupled with key changes in parenchymal structure and matrix production, the cumulative effect is a now larger organ including newly-grown, fully-functional tissue parenchyma. This study paints Mesothelial cells as a key orchestrating cell type that defines the boundary of the lung and exerts critical influence over the tissue-level signaling state regulating resident stem cell populations. The cellular circuits unearthed here suggest that human lung regeneration might be inducible through well-engineered approaches targeting the induction of tissue regeneration and safe return to homeostasis.Competing Interest StatementJCS received lecture honoraria from Boehringer Ingelheim and Kinevant. LEN is a founder and shareholder in Humacyte, Inc, which is a regenerative medicine company. Humacyte produces engineered blood vessels from allogeneic smooth muscle cells for vascular surgery. LEN spouse has equity in Humacyte, and LEN serves on the Humacyte Board of Directors. LEN is an inventor on patents that are licensed to Humacyte and that produce royalties for LEN. Humacyte did not influence the conduct, description or interpretation of the findings in this report. NK reports personal fees from Biogen Idec, Boehringer Ingelheim, Third Rock, Pliant, Numedii, Indalo, Theravance for consulting and non-financial support from Miragen, all outside the submitted work. In addition, NK has patents on new therapies in Pulmonary Fibrosis with royalties paid by biotech, and a patent on blood biomarkers in pulmonary fibrosis.Footnotes* https://figshare.com/projects/Obata2023/191505

Detecting changes in gene coordination patterns between biological conditions and identifying the cell populations in which these changes occur are key challenges in single-cell analysis. Existing approaches often compare gene co-expression between predefined cell clusters or rely on aligning cells across conditions. These strategies can be suboptimal when changes occur within small subpopulations or when batch effects obscure the underlying biological signal. To address these challenges, we introduce DiCoLo, a framework that identifies genes exhibiting differential co-localization, defined as changes in coordinated expression within localized cell neighborhoods -subsets of highly similar cells in the transcriptomic space. Importantly, DiCoLo does not rely on cell clustering or cross-condition alignment. For each condition, DiCoLo constructs a gene graph using Optimal Transport distances that reflect gene co-localization patterns across the cell manifold. Then, it identifies differential gene programs by detecting changes in connectivity patterns between the gene graphs. We show that DiCoLo robustly identifies differential gene co-localization even under weak signals or complex batch effects, outperforming existing methods across multiple benchmark datasets. When applied to mouse hair follicle development data, DiCoLo reveals coordinated gene programs and emerging cell populations driven by perturbations in morphogen signaling that underlie dermal condensate differentiation. Overall, these results establish DiCoLo as a powerful framework for uncovering localized differential transcriptional coordinated patterns in single-cell data.