bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Deep Learning Pipeline for Analysis of the 3D Morphology of the Cerebral Small Perforating Arteries from Time-of-Flight 7 Tesla MRI
Authors: Li Rui , Chatterjee Soumick , Jiaerken Yeerfan , Radhakrishna Chethan , Benjamin Philip , Nannoni Stefania , Tozer Daniel J. , Markus Hugh S. , Rodgers Christopher T. 
Published: October 2024

AbstractThe lenticulostriate arteries (LSA) supply important subcortical structures in the brain and are affected in cerebral small vessel disease (CSVD), leading to changes in their morphology. 7 Tesla Time-of-Flight magnetic resonance angiography (7T-TOF-MRA) now allows their visualisation in humans, but current analysis of LSA morphology largely relies on manual tracing on 2D coronal maximum-intensity-projection (MIP) images, which discards significant information from the third spatial dimension. We aimed to develop a semi-automatic pipeline for quantifying the 3D morphology of LSAs from 7T-TOF-MRA in patients with CSVD.We used contrast-enhanced 7T-TOF-MRA data from 15 subjects enrolled in a local CSVD study. Our pipeline consists of two main stages: vessel segmentation and LSA quantification. For segmentation, we fine-tuned a state-of-the-art deep learning model, “DS6”, for vessel segmentation and compared its performance against a classical Frangi filter-based pipeline, Multi-Scale Frangi Diffusive Filter (MSFDF). Both methods were evaluated against manually labelled ground-truth masks in LSA regions. In the LSA quantification stage, the user defines a region-of-interest around LSAs and checks the segmentation. Based on this, the LSA centrelines are extracted, and branch counts, length, tortuosity, and curvature are computed. Additionally, we conducted the traditional LSA analysis using 2D coronal MIPs, and we evaluated the correlation between the results from the 2D and 3D analyses.For vessel segmentation, the fine-tuned DS6 model achieved a mean Dice similarity coefficient (DSC) of 0.814±0.029 during testing, outperforming MSFDF on DSC, sensitivity, and balanced average Hausdorff distance in terms of both mean value and stability. Visual inspection confirmed that DS6 was more sensitive in detecting LSA branches with weak signals. On average, the 15 subjects had 5.9±1.6 LSA stems and 28.7±9.9 branches. The mean length of an LSA branch was 42.5±5.7mm, and mean tortuosity was 1.9±0.2. Finally, the branch counts from 2D and 3D analyses correlated well (ρ=0.741,p=2.816e-06), whereas the stem count, branch length and tortuosity measurements were significantly different, showing the insufficiency of MIP analysis (stem:ρ=0.230,p=2.207e-01; length:r=0.565,p=1.153e-03; tortuosity:r=0.400,p=2.847e-02).We have developed an open-source semi-automatic pipeline using deep learning for evaluating the 3D morphology of LSAs in CSVD patients from 7T-TOF-MRA. We show that analysing LSA morphology in 3D reveals previously inaccessible aspects of morphology. Our pipeline offers a valuable tool for clinical research studies to characterise the 3D morphology of LSAs in CSVD.
A multi-omics census reveals obesity-associated microRNAmiR-let-7as novel instigator of adipose mitochondrial dysfunction and of intergenerational metabolic decline
Authors: Huang Chien , Park Joo Hyun , Altintas Ali , Stanic Natasa , Batarlar Hande Topel , Malmros Rocio Valdebenito , Havelund Jesper , Færgeman Nils Joakim , Marcher Larsen Bjørk Ditlev , Szczepanowska Karolina , Lackmann Jan-Wilm , Trifunovic Aleksandra , Hejbøl Eva Kildall , Detlefsen Sönke , Jepsen Ida Engberg , Kolstrup Stefanie Hansborg , Laguna-Barraza Ricardo , Gonzalez Javier-Martin , Khodosevich Konstantin , Barrès Romain , Kornfeld Jan-Wilhelm 
Published: October 2024

AbstractWe here describe that obesity and weight loss in male mice causes reversible abnormalities in glucose and lipid metabolism, serum metabolomes and reduced expression of genes controlling mitochondrial energy dissipation in white adipose tissue. When mating obese male mice with lean females, we observed concordant reductions in adipose mitochondrial gene expression and translation in offspring (F1) that resemble those observed in the paternal (F0) generation. When mapping miRNA differential expression across somatic organs and the germline (i.e., liver, adipose tissue, sperm) and generations, we found that obesity and weight loss reversible affected miRNA levels, and thatmiR-let7isoforms were induced in adipose tissues of obese F0and F1adipose tissue and sperm of obese F0mice. OverexpressingmiR-let-7in adipocytes silenced DICER1, a cellular rheostat required for adipose tissue adaptation in obesity as evidenced by deficiencies in mitochondrial function following DICER1 loss in adipocytes. Delivery ofmiR-let-7into lean oocytes elicited glucose intolerance and impediments in adipose mitochondrial gene expression in mice sired from miRNA-injected embryos, thus phenocopying important aspects of obesity heredity. When performing single-cell RNA-Seq of miRNA-injected embryos,miR-let7impaired mitochondrial gene expression, suggesting altered energy metabolism following alterations in zygotic miRNAs. When studying miRNA alterations in human semen, lifestyle-induced weight loss downregulatedMIR-LET-7in human subjects, suggesting similar roles for humanMIR-LET-7in gametic epigenomes and embryogenesis.
Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity
Authors: McDermott MeiLu , Mehta Riddhee , Roussos Torres Evanthia T. , MacLean Adam L. 
Published: October 2024

AbstractEpithelial-mesenchymal transition (EMT) is a developmental cell state transition co-opted by cancer to drive metastasis and resistance. Stable EMT intermediate states play a particularly important role in cell state plasticity and confer metastatic potential. To explore the dynamics of EMT and identify marker genes of highly metastatic intermediate cells, we analyzed EMT across multiple tumor types and stimuli via mathematical modeling with single-cell RNA-sequencing (scRNA-seq) data. We identified pan-cancer genes consistently expressed or upregulated in EMT intermediate states, most of which were not previously annotated as markers of EMT. Using Bayesian parameter inference, we fit a simple mathematical model to scRNA-seq data, revealing tumor-specific transition rates. This mathematical model offers a framework to quantify EMT progression. A consensus analysis of differential intermediate expression, regulation, and model-derived dynamics identified marker genes associated with persistence of the intermediate EMT state.SFNandNRG1emerged as genes with the strongest evidence for their role influencing intermediate EMT dynamics. Through analysis of an independent cell line, we verified the role ofSFNas a marker intermediate EMT transition. Modeling and inference of genes associated with EMT dynamics offer means to find biomarkers and to identify therapeutic approaches to harness or reverse tumor-promoting cell state transitions driven by EMT.
Conflict neurons in cingulate cortex of macaques
Authors: Corrigan Benjamin W. , Errington Steven P. , Sajad Amirsaman , Schall Jeffrey D. 
Published: October 2024

AbstractConflict—the magnitude of co-activation of mutually incompatible response processes—was proposed to explain how cognitive control is invoked (Botvinick et al. 2001) and continues to engage debate (Becker et al. 2024). Original observations consistent with this construct emphasized the primary contribution of cingulate cortex (CC) based on human functional imaging (Botvinick et al., 1999; Carter et al., 2000) and electroencephalogram (Yeung, Botvinick, & Cohen, 2004). In countermanding tasks conflict arises through co-activation of competing GO and STOP processes (Boucher et al. 2007; Schall & Boucher 2007; Sajad et al. 2022). Single neuron activity representing conflict has been described in the supplementary motor cortex of human epilepsy patients (Fu et al., 2019; Sheth et al., 2012) and of macaque monkeys (Sajad, Errington, & Schall, 2022; Stuphorn, Taylor, & Schall, 2000) and in human cingulate cortex (Fu et al., 2019; Sheth et al., 2012) but not in monkey cingulate cortex (Ebitz & Platt, 2015; Ito, Stuphorn, Brown, & Schall, 2003; Nakamura, Roesch, & Olson, 2005). This lack of homology generated debate about the utility of macaques for investigation of cognitive control (Cole et al. 2009; Schall & Emeric 2010). With higher-resolution, less-biased samples, we re-examined the presence of a conflict signal in cingulate cortex of monkeys. Neurons modulating specifically when response conflict was maximal were found in cingulate cortex— more commonly in the dorsal than the ventral bank. However, such neurons were much more common in supplementary motor cortex. These data confirm the presence of a conflict signal in medial frontal cortex and demonstrate that it can be found in a small fraction of neurons in cingulate cortex. Further research is needed to determine if the weak response conflict signal in cingulate cortex is sufficient or negligible.
RUVBL1 and RUVBL2 are druggable MYCN regulators in neuroblastoma
Authors: Siaw Joachim Tetteh , Claeys Arne , Lai Wei-Yun , Borenäs Marcus , Hilgert Elien , Bekaert Sarah-Lee , Sanders Ellen , Kaya Irem , Dorpe Jo Van , Speleman Frank , Durinck Kaat , Hallberg Bengt , Palmer Ruth H. , Van den Eynden Jimmy 
Published: October 2024

ABSTRACTHigh-risk neuroblastoma is characterized byMYCNamplification and highMYCNorMYCgene expression. These patients have a poor prognosis and there is an urgent need for more effective drugs. While strategies to develop inhibitors that directly target the MYC proteins have remained largely unsuccessful, recent preclinical studies have identified ATR, a key protein of the DNA damage response, as a promising alternative therapeutic target. Here we identified a strong RUVBL1 and RUVBL2 signature in transcriptomics data derived from differentMYCN-driven mice tumors treated with ATR inhibitors. The RUVBL proteins form a complex with ATPase activity that has broad cellular functions and we demonstrate that pharmacological inhibition of this protein complex results in a strong reduction of MYC signaling, cell cycle arrest, DNA damage and apoptosis. We confirmed the association withMYCNand identified theRUVBLgenes as independent prognosticators in human primary neuroblastoma data.
Enhanced Pooling for Weakly Supervised Gigapixel WSI Training Improves Classification and Lesion Localization
Authors: Chen Chi-Chung , Yeh Yi-Chen , Lin Matthew MY , Yeh Chao-Yuan 
Published: October 2024

AbstractA robust artificial intelligence-assisted workflow for tumor assessment in pathology requires not only accurate classification but also precise lesion localization. While current weakly supervised learning methods significantly reduce the need for extensive annotations and leverage large quantities of annotation-free whole-slide images (WSIs) to enhance classification robustness, they often fall short in segmentation accuracy. We attribute this limitation to the optimization goals in classification, which tend to focus solely on the most representative features—an approach that is particularly inefficient for WSIs with gigapixel resolution. To address this challenge, we introduce a novel approach based on streaming convolution, an end-to-end method for WSI training. Our contributions include the Rectified LogSumExp pooling method and adaptive pseudo annotation generation for self-training, both designed to encourage models to learn from sub-representative features. Using only slide-level annotations from the CAMELYON16 dataset, our method achieves a significant improvement in metastasis localization, with a recall from 49.85% to 71.33% at a precision of 90%. This conclusion also holds true for a 3,024-LN dataset used in the assessment of lung cancer lymph node metastasis with a recall improved from 28.31% to 50.82%.
Spatial and network principles behind neural generation of locomotion
Authors: Komi Salif , Winther August , Houser Grace A. , Topilko Thomas , Sørensen R.J.F. , Larsen Silas Dalum , Adamsson Bonfils Madelaine C. , Li Guanghui , Berg Rune W. 
Published: October 2024

ABSTRACTGeneration of locomotion is a fundamental function of the spinal cord, yet the underlying principles remain unclear. In particular, the relationship between neuronal cell types, networks and functions has been difficult to establish1,2. Here, we propose principles by which functions arise primarily from spatial features of the cord. First, we suggest that projections of distinct cell types constitute an asymmetrical “Mexican hat” topology, i.e. local excitation and surrounding inhibition with dissimilar length of projection along the rostro-caudal axis. Second, this projection topology constitutes the mechanism of rhythm- and pattern generation of mammalian locomotion. Third, the role of segregation of cell types in the transversal plane is for descending fibers to find appropriate targets. Modulation of these targets allows control of motor activity by adjusting the symmetry of the projection topology. We extract these principles via a model of the mouse spinal cord, where networks are constructed by probabilistic sampling of synaptic connections from cell-specific projection patterns, which are based on previous studies3, 4. The cell-type distributions are derived from single-cell RNA sequencing combined with spatial transcriptomics5. We find that essential aspects of locomotion are readily reproduced and controlled without requiring parameter optimization, and several experimental observations can now be explained mechanistically. Further, two main features are predicted: propagating bumps of neural activity during rhythmical activity and formation of static bumps during arrest and posture. Besides linking cell types, structure and function, we propose our approach as a new theoretical framework for motor control.
Lung CD4+resident memory T cells use airway secretory cells to stimulate and regulate neutrophilic allergic airways disease
Authors: Ravi Vijay Raaj , Korkmaz Filiz T. , De Ana Carolina Lyon , Lu Lu , Shao Feng-Zhi , Odom Christine V. , Barker Kimberly A. , Ramanujan Aditya , Niszczak Emma N. , Goltry Wesley N. , Martin Ian M.C. , Ha Catherine T. , Quinton Lee J. , Jones Matthew R. , Fine Alan , Welch Joshua D. , Chen Felicia , Belkina Anna C. , Mizgerd Joseph P. , Shenoy Anukul T. 
Published: October 2024

ABSTRACTNeutrophilic asthma is a vexing disease, but mechanistic and therapeutic advancement will require better models of allergy-induced airway neutrophilia. Here, we find that periodic ovalbumin (OVA) inhalation in sensitized mice elicits allergic airway inflammation and pathophysiology mimicking neutrophilic asthma. OVA-experienced murine lungs harbor diverse clusters of CD4+resident memory TRMcells, including unconventional RORγtnegative/lowTH17 cells. Acute OVA challenge instigates IL-17A secretion from these TRMcells, driving CXCL5 production from Muc5achighairway secretory cells, leading to destructive airway neutrophilia. The TRM- and epithelial-cell signals discovered herein are also observed in adult human asthmatic airways. Epithelial antigen presentation regulates this biology by skewing TRMcells towards TH2 and TH1 fates, so that the TH1-related IFN-γ suppresses IL-17A-driven, CXCL5-mediated airway neutrophilia. Concordantly,in vivoIFN-γ supplementation improves disease outcomes. Thus, using our model of neutrophilic asthma we identify lung epithelial-CD4+TRMcell crosstalk as a key rheostat of allergic airway neutrophilia.HighlightsRecurrent OVA inhalation experience predisposes mice to allergic airways neutrophiliaNeutrophil-prone lungs harbor CD4+TRMcells including RORγtnegative/lowTH17 cellsMuc5achighsecretory cells instruct CD4+TRMfates and neutrophilia via MHC-II and CXCL5, respectivelyProphylactic or therapeutic delivery of IFN-γ curbs allergic airway neutrophilia
Data-driven model discovery and model selection for noisy biological systems
Authors: Wu Xiaojun , McDermott MeiLu , MacLean Adam L. 
Published: October 2024

AbstractBiological systems exhibit complex dynamics that differential equations can often adeptly represent. Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction: differential equation models can be learnt from data via model discovery using sparse identification of nonlinear dynamics (SINDy). However, SINDy struggles with realistic levels of biological noise and does not incorporate prior knowledge of the system. We propose a data-driven framework for model discovery and model selection using hybrid dynamical systems: partial models containing missing terms. Neural networks are used to approximate the unknown dynamics of a system, enabling the denoising the data while simultaneously learning the latent dynamics. Simulations from the fitted neural network are then used to infer models using SINDy. We show, via model selection, that model discovery in SINDy with hybrid dynamical systems outperforms alternative approaches. We find it possible to infer models correctly up to high levels of biological noise of different types. We demonstrate the potential to learn models from sparse, noisy data in application to a canonical cell state transition using data derived from single-cell transcriptomics. Overall, this approach provides a practical framework for model discovery in biology in cases where data are noisy and sparse, of particular utility when the underlying biological mechanisms are partially but incompletely known.
Aging at scale: Younger dogs and larger breeds from the Dog Aging Project show accelerated epigenetic aging
Authors: McCoy Brianah M. , Mariner Blaise L. , Cheng Claire F. , Slikas Elizabeth , Adjangba Christine , Greenier Ashlee , Brassington Layla , Marye Abbey , Harrison Benjamin R. , Partida-Aguilar Maria , Bamberger Tal , Algavi Yadid , Muller Efrat , Harris Adam , Rout Emily ,   The Dog Aging Project Consortium, Avery Anne , Borenstein Elhanan , Promislow Daniel , Snyder-Mackler Noah 
Published: October 2024

AbstractDogs exhibit striking within-species variability in lifespan, with smaller breeds often living more than twice as long as larger breeds. This longevity discrepancy also extends to health and aging–larger dogs show higher rates of age-related diseases. Despite this well-established phenomenon, we still know little about the biomarkers and molecular mechanisms that might underlie breed differences in aging and survival. To address this gap, we generated an epigenetic clock using DNA methylation from over 3 million CpG sites in a deeply phenotyped cohort of 864 companion dogs from the Dog Aging Project, including some dogs sampled annually for 2-3 years. We found that the largest breed size tends to have epigenomes that are, on average, 0.37 years older per chronological year compared to the smallest breed size. We also found that higher residual epigenetic age was significantly associated with increased mortality risk, with dogs experiencing a 34% higher risk of death for each year increase in residual epigenetic age. These findings not only broaden our understanding of how aging manifests within a diverse species but also highlight the significant role that demographic factors play in modulating the biological mechanisms underlying aging. Additionally, they highlight the utility of DNA methylation as both a biomarker for healthspan-extending interventions, a mortality predictor, and a mechanism for understanding inter-individual variation in aging in dogs.
TheC. eleganshomolog of Sjögren’s Syndrome Nuclear Antigen 1 is required for the structural integrity of the centriole and bipolar mitotic spindle assembly
Authors: Pfister Jason A. , Agostini Lorenzo , Bournonville Lorène , Sankaralingam Prabhu , Bell Zachary G. , Hamel Virginie , Guichard Paul , Biertümpfel Christian , Mizuno Naoko , O’Connell Kevin F. 
Published: October 2024

AbstractCentrioles play central roles in ciliogenesis and mitotic spindle assembly. Once assembled, centrioles exhibit long-term stability, a property essential for maintaining numerical control. How centriole stability is achieved and how it is lost in certain biological contexts are still not completely understood. In this study we show that SSNA-1, theCaenorhabditis elegansortholog of Sjogren’s Syndrome Nuclear Antigen 1, is a centriole constituent that localizes close to the microtubule outer wall, while also exhibiting a developmentally regulated association with centriole satellite-like structures. A complete deletion of thessna-1gene results in an embryonic lethal phenotype marked by the appearance of extra centrioles and spindle poles. We show that SSNA-1 genetically interacts with the centriole stability factor SAS-1 and is required post assembly for centriole structural integrity. In SSNA-1’s absence, centrioles assemble but fracture leading to extra spindle poles. However, if the efficiency of cartwheel assembly is reduced, the absence of SSNA-1 results in daughter centriole loss and monopolar spindle formation, indicating that the cartwheel and SSNA-1 cooperate to stabilize the centriole during assembly. Our work thus shows that SSNA-1 contributes to centriole stability during and after assembly, thereby ensuring proper centriole number.
Dissecting autonomous enzyme variability in single cells
Authors: Gnann Christian , Cesnik Anthony , Sigaeva Alina , Le Trang , Sariyar Sanem , Mahdessian Diana , Schutten Rutger , Raghavan Preethi , Leonetti Manuel D. , Lindskog Cecilia , Uhlén Mathias , Axelsson Ulrika , Lundberg Emma 
Published: October 2024

AbstractMetabolic enzymes perform life-sustaining functions in various cellular compartments. Anecdotally, metabolic activity is observed to vary between genetically identical cells, which impacts drug resistance, differentiation, and immune cell activation. However, no large-scale resource systematically reporting metabolic cellular heterogeneity exists. Here, we leverage imaging-based single-cell spatial proteomics to reveal the extent of non-genetic variability of the human enzymatic proteome, as a proxy for metabolic states. Nearly two fifths of enzymes exhibit cell-to-cell variable expression, and half localize to multiple cellular compartments. Metabolic heterogeneity arises largely autonomously of cell cycling, and individual cells reestablish these myriad metabolic phenotypes over several cell divisions. These results establish that cell-to-cell enzymatic heterogeneity is an organizing principle of cell biology and one that may rewire our understanding of drug resistance, treatment design, and other aspects of medicine.