bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks
Authors: Yosofvand Mohammad , Edmiston Sharon N. , Smithy James W. , Peng Xiyu , Kostrzewa Caroline E. , Lin Bridget , Ehrich Fiona , Reiner Allison , Miedema Jayson , Moy Andrea P. , Orlow Irene , Postow Michael A. , Panageas Katherine , Seshan Venkatraman E. , Callahan Margaret K. , Thomas Nancy E. , Shen Ronglai 
Published: August 2024

AbstractThe multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.
Identifying unmeasured heterogeneity in microbiome data via quantile thresholding (QuanT)
Authors: Lu Jiuyao , Satten Glen A. , Meyer Katie A. , Launer Lenore J. , Ling Wodan , Zhao Ni 
Published: August 2024

AbstractMicrobiome data, like other high-throughput data, suffer from technical heterogeneity stemming from differential experimental designs and processing. In addition to measured artifacts such as batch effects, there is heterogeneity due to unknown or unmeasured factors, which lead to spurious conclusions if unaccounted for. With the advent of large-scale multi-center microbiome studies and the increasing availability of public datasets, this issue becomes more pronounced. Current approaches for addressing unmeasured heterogeneity in high-throughput data were developed for microarray and/or RNA sequencing data. They cannot accommodate the unique characteristics of microbiome data such as sparsity and over-dispersion. Here, we introduce Quantile Thresholding (QuanT), a novel non-parametric approach for identifying unmeasured heterogeneity tailored to microbiome data. QuanT applies quantile regression across multiple quantile levels to threshold the microbiome abundance data and uncovers latent heterogeneity using thresholded binary residual matrices. We validated QuanT using both synthetic and real microbiome datasets, demonstrating its superiority in capturing and mitigating heterogeneity and improving the accuracy of downstream analyses, such as prediction analysis, differential abundance tests, and community-level diversity evaluations.
A florigen-expressing subpopulation of companion cells expresses other small proteins and reveals a nitrogen-sensitiveFTrepressor
Authors: Takagi Hiroshi , Ito Shogo , Shim Jae Sung , Kubota Akane , Hempton Andrew K. , Lee Nayoung , Suzuki Takamasa , Yang Chansie , Nolan Christine T. , Bubb Kerry L. , Alexandre Cristina M. , Kurihara Daisuke , Sato Yoshikatsu , Tada Yasuomi , Kiba Takatoshi , Pruneda-Paz Jose L. , Queitsch Christine , Cuperus Josh T. , Imaizumi Takato 
Published: August 2024

AbstractThe precise onset of flowering is crucial to ensure successful plant reproduction. The geneFLOWERING LOCUS T(FT) encodes florigen, a mobile signal produced in leaves that initiates flowering at the shoot apical meristem. In response to seasonal changes,FTis induced in phloem companion cells located in distal leaf regions. Thus far, a detailed molecular characterization of theFT-expressing cells has been lacking. Here, we used bulk nuclei RNA-seq and single nuclei RNA (snRNA)-seq to investigate gene expression inFT-expressing cells and other phloem companion cells. Our bulk nuclei RNA-seq demonstrated thatFT-expressing cells in cotyledons and in true leaves differed transcriptionally. Within the true leaves, our snRNA-seq analysis revealed that companion cells with highFTexpression form a unique cluster in which many genes involved in ATP biosynthesis are highly upregulated. The cluster also expresses other genes encoding small proteins, including the flowering and stem growth inducer FPF1-LIKE PROTEIN 1 (FLP1) and the anti-florigen BROTHER OF FT AND TFL1 (BFT). In addition, we found that the promoters ofFTand the genes co-expressed withFTin the cluster were enriched for the consensus binding motifs of NITRATE-INDUCIBLE GARP-TYPE TRANSCRIPTIONAL REPRESSOR 1 (NIGT1). Overexpression of the paralogousNIGT1.2andNIGT1.4repressedFTexpression and significantly delayed flowering under nitrogen-rich conditions, consistent with NIGT1s acting as nitrogen-dependentFTrepressors. Taken together, our results demonstrate that majorFT-expressing cells show a distinct expression profile that suggests that these cells may produce multiple systemic signals to regulate plant growth and development.
Balancing Selectivity and Generality in Object Recognition through Structured Interconnectivity
Authors: Zhang Yiyuan , Liu Jirui , Liu Jia 
Published: August 2024

AbstractBalancing selectivity and generality in object recognition is a significant challenge, as it requires the ability to discern fine details that set objects apart while simultaneously embracing the common threads that classify them into one single category. Here we investigated how the brain addresses this challenge by examining the relationship between the interconnectivity of neural networks, the dimensionality of neural space, and the balance of selectivity and generality using neurophysiological data and computational modeling. We found that higher interconnectivity in the TEa of macaques’ IT cortex was associated with lower dimensionality and increased generality, while lower interconnectivity in the TEO correlated with higher dimensionality and enhanced selectivity. To establish the causal link, we developed a brain-inspired computational model formed through Hebbian and anti-Hebbian rules, with wiring length constraints derived from biological brains. The resulting structured interconnectivity created an optimal dimensionality of the neural space, allowing for efficient energy distribution across the representational manifold embedded in the neural space to balance selectivity and generality. Interestingly, this structured interconnectivity placed the network in a critical state that balances adaptability and stability, and fostered a cognitive module with cognitive impenetrability. In summary, our study underscores the importance of structured interconnectivity in achieving a balance between selectivity and generality, providing a unifying view of balancing two extreme demands in object recognition.
Homoploid Hybrid Speciation in a Marine Pelagic FishMegalaspis cordyla(Carangidae)
Authors: Muto Nozomu , Su Yong-Chao , Hata Harutaka , Quan Nguyen Van , Vilasri Veera , Ghaffar Mazlan Abd. , Babaran Ricardo P. 
Published: August 2024

AbstractHomoploid hybrid speciation (HHS) is an enigmatic evolutionary process where new species arise through hybridization of divergent lineages without changes in chromosome number. Although increasingly documented in various taxa and ecosystems, convincing cases of HHS in marine fishes have been lacking. This study presents evidence of HHS in Torpedo scadMegalaspis cordylabased on comprehensive genomic, morphological, and ecological analyses. A Principal Component Analysis using thousands of SNPs identified three sympatric clusters in the western Pacific. Genome-wide differentiation between the clusters and the admixed nature of a cluster between the others were evident from population genomic analyses, species tree estimation, mitochondrial DNA divergence, and tests of introgression. Multiple statistical methods for hybrid detection also supported the admixed ancestry of this cluster. Moreover, model-based demographic inference favored a hybrid speciation scenario over introgression. Examination of occurrence data and ecologically relevant morphological characters suggested ecological differences between the clusters, potentially contributing to reproductive isolation and niche partitioning in sympatry. The clusters are morphologically distinguishable and thus can be taxonomically recognized as separate species. The hybrid cluster is restricted to the coasts of Taiwan and Japan, where all three clusters coexist. The parental clusters are additionally found in lower latitudes such as the coasts of the Philippines, Vietnam, Thailand, and Malaysia, where they display non-overlapping distributions. Given the geographical distributions, estimated times of the species formation, and patterns of historical demographic changes, we propose that the Pleistocene glacial cycles were the primary driver of HHS in this system. Based on this argument, we develop an ecogeographic model of HHS in marine coastal ecosystems, including a novel hypothesis to explain the initial stages of HHS.
Molecular and physiological characterization of tillering and shade tolerance of dwarf mutants of perennial ryegrass
Authors: Kumar Rahul , Yer Huseyin , Li Wei , Jiang Xiangning , Gai Ying , Duan Hui , Li Yi 
Published: August 2024

AbstractTillering and shade tolerance are important traits in turfgrass, influenced by environmental factors, nutrients, and hormones. Shade stress negatively affects tillering. In this study, two dwarf mutants,shadow-1andshadow-2, developed via Gamma-ray and fast-neutron mutagenesis, respectively, showed significantly higher tillering than the wild-type under greenhouse conditions. Both mutants demonstrated shade tolerance in plant height, grass quality, and color under 85% and 95% shade conditions, while shade-induced inhibition of tillering was observed in both the mutants and the wild-type. In comparison to wild-type plants under 95% shade conditions, we observed that the cytokinin biosynthetic geneIPT8is upregulated, while the cytokinin inactivating geneCKX2is downregulated inshadow-1. Similarly, the GA biosynthetic genesCPS1, GA2ox3, and GA20ox1are upregulated, while the GA inactivating geneGA20ox8is downregulated in theshadow-1mutant. Furthermore, the ethylene biosynthetic genesACSandACOare also downregulated in theshadow-1mutant. Consistently, we observed that wild-type plants exhibit increased GA and reduced CK levels, whileshadow-1mutant plants have reduced GA but increased CK levels. This explains theshadow-1mutant’s shade tolerance in terms of plant height, grass quality, and color. Conversely, the tillering inhibitor genesCRY1, MAX2,andSnRK1are upregulated in both wild-type andshadow-1mutant plants. Our results provide novel insights into the mechanisms behind tillering and shade tolerance in turfgrasses under shade conditions.
The glycolytic reaction PGAM unexpectedly restrains Th17 pathogenicity and Th17-dependent autoimmunity
Authors: Wang Chao , Wagner Allon , Fessler Johannes , DeTomaso David , Zaghouani Sarah , Zhou Yulin , Pierce Kerry , Sobel Raymond A. , Clish Clary , Yosef Nir , Kuchroo Vijay K. 
Published: August 2024

ABSTRACTGlucose metabolism is a critical regulator of T cell function, largely thought to support their activation and effector differentiation. Here, we investigate the relevance of individual glycolytic reactions in determining the pathogenicity of T helper 17 (Th17) cells using single-cell RNA-seq and Compass, an algorithm we previously developed for estimating metabolic flux from single-cell transcriptomes. Surprisingly, Compass predicted that the metabolic shunt between 3-phosphoglycerate (3PG) and 2-phosphoglycerate (2PG) is inversely correlated with pathogenicity in these cells, whereas both its upstream and downstream reactions were positively correlated. Perturbation of phosphoglycerate mutase (PGAM), an enzyme required for 3PG to 2PG conversion, resulted in an increase in protein expression of IL2, IL17, and TNFa, as well as induction of a pathogenic gene expression program. Consistent with PGAM playing a pro-regulatory role, inhibiting PGAM in Th17 cells resulted in exacerbated autoimmune responses in the adoptive transfer model of experimental autoimmune encephalomyelitis (EAE). Finally, we further investigated the effects of modulating glucose concentration on Th17 cells in culture. Th17 cells differentiated under high- and low-glucose conditions substantially differed in their metabolic and effector transcriptomic programs, both central to Th17 function. Importantly, the PGAM-dependent gene module marks the least pathogenic state of Th17 cells irrespective of glucose concentration. Overall, our study identifies PGAM, contrary to other glycolytic enzymes, as a negative regulator of Th17 pathogenicity.
Universal high-throughput image quantification of subcellular structure dynamics and spatial distributions within cells
Authors: Martin Andrew , Zhang Sue , Williamson Amanda , Tingley Brett , Pickus Mira , Zurakowski David , Nia Hadi T. , Shirihai Orian , Han Xue , Grinstaff Mark W. 
Published: August 2024

AbstractImage analysis of subcellular structures and biological processes relies on specific, context-dependent pipelines, which are labor-intensive, constrained by the intricacies of the specific biological system, and inaccessible to broader applications. Here we introduce the application of dispersion indices, a statistical tool traditionally employed by economists, to analyze the spatial distribution and heterogeneity of subcellular structures. This computationally efficient high-throughput approach, termed GRID (Generalized Readout of Image Dispersion), is highly generalizable, compatible with open-source image analysis software, and adaptable to diverse biological scenarios. GRID readily quantifies diverse structures and processes to include autophagic puncta, mitochondrial clustering, and microtubule dynamics. Further, GRID is versatile, applicable to both 2D cell cultures and 3D multicellular organoids, and suitable for high-throughput screening and performance metric measurements, such as half-maximal effective concentration (EC50) values. The approach enables mechanistic analysis of critical subcellular structure processes of relevance for diseases ranging from metabolic and neuronal diseases to cancer as well as a first-pass screening method for identifying biologically active agents for drug discovery.Statement of SignificanceCurrent methods for image analysis in microscopy are tailored to specific biological contexts, which creates challenges in implementation and efficiency for researchers studying a diverse range of subcellular processes. Our application of dispersion indices, traditionally used in economics, offers a universal framework for high throughput quantification of biological structures, enabling easier and more consistent analysis across various biological contexts. By transforming pixel intensity and count into meaningful statistical measures, our method simplifies the quantification of subcellular structures such as autophagic puncta, microtubule dynamics, and mitochondrial clustering. This approach accelerates the quantitative analysis of sub-cellular processes in disease as well as imaged-based drug discovery.Classification:Bioengineering, Cell Biology, Applied Biological Sciences
Metadichol, a modulator that controls expression of Toll Like Receptors in Cancer Cell Lines
Authors: Raghavan P.R. 
Published: August 2024

AbstractToll-like receptors are essential players in the innate immune signaling system and play critical roles in recognizing pathogen-associated and damage-associated molecular patterns. Its presence in various cancer cells, including breast, ovarian, cervical, colon, hepatocellular carcinoma, prostate, lung cancer, melanoma, and neuroblastoma cells, has been associated with cancer progression, immune evasion, apoptosis, and chemoresistance. Despite extensive research, no small molecules can induce the expression of all TLRs in cancer cells have been identified. This study investigated the effects of metadichol, a nanoemulsion of long-chain alcohols, on the expression of TLR receptor families 1–10 in cancer cell lines. Using quantitative polymerase chain reaction (qPCR) and other molecular biology techniques, we assessed the concentration-dependent effects of metadichol on TLR expression, which ranged from 1 picogram per ml to 100 nanograms per ml. Our results show that metadichol can modulate TLR expression in a cancer cell type-specific manner, with TLR4 being upregulated in lung cancer cells, enhancing antitumor effects but downregulated in other cancer lines, reducing inflammation.Furthermore, TLR7, TLR8, and TLR9 are upregulated in certain cell lines. Importantly, metadichol induced the downregulation of MYD88, TRAF6, and IRAK4 across various cell lines, suggesting considerable potential to inhibit tumor growth, enhance apoptosis, and reduce metastasis. This broad-spectrum gene activation supports a multitarget therapeutic approach, making metadichol the first molecule to target the entire TLR family without the need for complex biological methods such as CRISPR—alternatively, virus grafting techniques. Our findings underscore the potential of metadichol in treating complex diseases involving multiple pathways, such as cancer, chronic inflammatory pathways, and infectious diseases, highlighting noteworthy advancements in cancer therapy.
Identifiability of heterogeneous phenotype adaptation from low-cell-count experiments and a stochastic model
Authors: Browning Alexander P , Crossley Rebecca M , Villa Chiara , Maini Philip K , Jenner Adrianne L , Cassidy Tyler , Hamis Sara 
Published: August 2024

AbstractAdaptive resistance contributes significantly to treatment failure in many cancers. Despite the increased prevalence of experimental studies that interrogate this phenomenon, there remains a lack of applicable quantitative tools to characterise data, and importantly to distinguish between resistance as a discrete phenotype and a (potentially heterogeneous) continuous distribution of phenotypes. To address this, we develop a stochastic individual-based model of adaptive resistance in low-cell-count proliferation assays. That our model corresponds probabilistically to common partial differential equation models of resistance allows us to formulate a likelihood that captures the intrinsic noise ubiquitous to such experiments. We apply our framework to assess the identifiability of key model parameters in several population-level data collection regimes; in particular, parameters relating to the adaptation velocity and within-population heterogeneity. Significantly, we find that heterogeneity is practically non-identifiable from both cell count and proliferation marker data, implying that population-level behaviours may be well characterised by homogeneous ordinary differential equation models. Additionally, we demonstrate that population-level data are insufficient to distinguish resistance as a discrete phenotype from a continuous distribution of phenotypes. Our results inform the design of both future experiments and future quantitative analyses that probe adaptive resistance in cancer.
Mycn regulates vascular development through PI3K signaling pathway in zebrafish
Authors: Zhao Guo-Qin , Cheng Tao , Wang Peng-Yun , Mo Jing , Yu Feng , Dong Yang , Li Yun-Fei , Feng Yu , Xu Peng-Fei , Shu Li-Ping 
Published: August 2024

AbstractMycn, a MYC gene family member, is implicated in both carcinogenesis through amplification and Feingold syndrome through its deficiency. Previous studies have indicated that increased Mycn expression enhances vascularization in human neuroblastomas, yet its precise role in vascular development remains elusive. In this study, we utilized single-cell RNA-seq and live imaging analyses to confirm thatmycnis expressed during zebrafish vasculogenesis. We investigated vascular development in zebrafish using a genetically engineeredmycnmutation. Our findings reveal thatmycn-deficient zebrafish exhibit reduced intersegmental vessels and malformed subintestinal vessels, primarily due to decreased cell proliferation in vascular cells. Importantly, we discovered that activation of PI3K signaling significantly ameliorates these vascular abnormalities.
Relationship between teacher ideals and attitudes toward peers with autism spectrum disorder among teacher college students
Authors: Suzuki Kota , Oi Yuhei , Mitsuhashi Shota , Hirata Shogo , Goto Takaaki 
Published: August 2024

Purpose
This study aims to examine the relationship between teacher ideals and attitudes toward peers with autism spectrum disorder (ASD) among teacher college students, and the effect of knowledge of ASD identification on this relationship.

Design/methodology/approach
This study used a quantitative approach and included 341 teacher college students. A vignette was used to assess social and professional attitudes toward peers with ASD. The participants were divided into identification and nonidentification groups based on how accurately they identified the peer depicted in the vignette as having ASD. A teacher ideals questionnaire consisting of two subscales indicating different ideal teachers − a dedicated/engaging teacher (e.g. a teacher who can work with enthusiasm) and a skillful teacher (e.g. a teacher who is knowledgeable) − was developed and administered to evaluate the participants’ teacher ideals and self-perceptions of a teacher.

Findings
In the nonidentification group, ideals of a skillful teacher were significantly correlated with social attitudes toward peers with ASD. The identification group exhibited no significant correlations between ideals and social attitudes. Furthermore, the self-perceptions of being a dedicated/engaging teacher was significantly correlated with professional attitudes toward peers with ASD in the identification group.

Originality/value
To the best of the authors’ knowledge, this study is the first to examine the relationship between teacher ideals and attitudes toward peers with ASD. Teacher ideals were related to negative social attitudes toward peers with ASD. While knowledge of ASD identification weakens this relationship, it may drive the relationship between self-perceptions of a teacher and professional attitudes toward peers with ASD. Therefore, ASD education is important for reducing the stigma toward ASD in teacher education.