bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics
Authors: Bifarin Olatomiwa O. , Fernández Facundo M. 
Published: November 2023

AbstractMotivationMetabolomics generates complex data necessitating advanced computational methods for generating biological insight. While machine learning (ML) is promising, the challenges of selecting the best algorithms and tuning hyperparameters, particularly for nonexperts, remain. Automated machine learning (AutoML) can streamline this process; however, the issue of interpretability could persist. This research introduces a unified pipeline that combines AutoML with explainable AI (XAI) techniques to optimize metabolomics analysis.ResultsWe tested our approach on two datasets: renal cell carcinoma (RCC) urine metabolomics and ovarian cancer (OC) serum metabolomics. AutoML, using auto-sklearn, surpassed standalone ML algorithms such as SVM and random forest in differentiating between RCC and healthy controls, as well as OC patients and those with other gynecological cancers (Non-OC). Autosklearn employed a mix of algorithms and ensemble techniques, yielding a superior performance (AUC of 0.97 for RCC and 0.85 for OC). Shapley Additive Explanations (SHAP) provided a global ranking of feature importance, identifying dibutylamine and ganglioside GM(d34:1) as the top discriminative metabolites for RCC and OC, respectively. Waterfall plots offered local explanations by illustrating the influence of each metabolite on individual predictions. Dependence plots spotlighted metabolite interactions, such as the connection between hippuric acid and one of its derivatives in RCC, and between GM3(d34:1) and GM3(18:1_16:0) in OC, hinting at potential mechanistic relationships. Through decision plots, a detailed error analysis was conducted, contrasting feature importance for correctly versus incorrectly classified samples. In essence, our pipeline emphasizes the importance of harmonizing AutoML and XAI, facilitating both simplified ML application and improved interpretability in metabolomics data science.Availabilityhttps://github.com/obifarin/automl-xai-metabolomicsContact:facundo.fernandez@chemistry.gatech.edu
Reproductive character displacement explains strengthening of mechanical barriers in damselflies
Authors: Ballen-Guapacha Andrea Viviana , Ospina-Garces Sandra M. , Sanchez-Guillen Rosa Ana 
Published: November 2023

ABSTRACTReinforcing natural selection against maladaptive hybrids can favor the strengthening of premating reproductive isolation driving a pattern of Reproductive Character Displacement (RCD). In a recent study conducted in North-West (NW) (older) Spanish hybrid zone, was detected an asymmetric reinforcement of the mechanical isolation in the reciprocal cross direction betweenI. graellsiimales andI. elegansfemales. Furthermore, in the North-Central and Mediterranean (NCM) (younger) hybrid zone was also detected a similar strengthening of the mechanical isolation, consistent with a pattern of asymmetric reinforcement in this hybrid zone as well. In this study, we did geometric morphometrics analyses, shape, and Centroid Size (CS), on male and female secondary sexual traits to investigate whether reinforcement has generated a pattern of RCD of these traits in both hybrid zones. We detected, in the NW hybrid zone, unidirectional RCD (CS) of the male caudal appendages ofI. graellsii, and bidirectional RCD (shape) of the female prothorax. Consistently with the prediction that the signal of reinforcement may diminish rapidly once reinforcement ceases to operate, a stronger signal of RCD was detected in the NCM than in the NW hybrid region. In this region, was detected unidirectional RCD (CS) of the male caudal appendages which was consistent with the lock-and-key mechanism of genital coevolution, as well as RCD (shape) of the female prothorax ofI. elegans.Interestingly, our study highlights the importance of using geometric morphometrics to deal with the complexity of reproductive structures and controlling for environmental and geographic factors to investigate RCD.
Deep-Learning-Enabled Differentiation between Intraprostatic Gold Fiducial Markers and Calcification in Quantitative Susceptibility Mapping
Authors: Stewart Ashley Wilton , Goodwin Jonathan , Richardson Matthew , Robinson Simon Daniel , O’Brien Kieran , Jin Jin , Barth Markus , Bollmann Steffen 
Published: November 2023

AbstractPurposeInterest is growing in MR-only radiotherapy (RT) planning for prostate cancer (PCa) due to the potential reductions in cost and patient exposure to radiation, and a more streamlined work-flow and patient imaging pathway. However, in MRI, the gold fiducial markers (FMs) used for target localization appear as signal voids, complicating differentiation from other void sources such as calcifications and bleeds. This work investigates using Quantitative Susceptibility Mapping (QSM), an MRI phase post-processing technique, to aid in the differentiation task. It also presents deep learning models that capture nuanced information and automate the segmentation task, facilitating a streamlined approach to MR-only RT.MethodsCT and MRI, including GRE and T1-weighted imaging, were acquired from 26 PCa patients, each with three implanted gold FMs. GRE data were post-processed into QSM,T2*, andR2*maps using QSMxT’s body imaging pipeline. Statistical analyses were conducted to investigate the quantitative differentiation of FMs and calcification in each contrast. 3D U-Nets were developed using fastMONAI to automate the segmentation task using various combinations of MR-derived contrasts, with a model trained on CT used as a baseline. Models were evaluated using precision and recall calculated using a leave-one-out cross-validation scheme.ResultsSignificant differences were observed between FM and calcification regions in CT, QSM andT2*, though overlap was observed in QSM andT2*. The baseline CT U-Net achieved an FM-level precision of ≈ 98% and perfect recall. The best-performing QSM-based model achieved precision and recall of 80% and 90%, respectively, while conventional MRI had values below 70% and 80%, respectively. The QSM-based model produced segmentations with good agreement with the ground truth, including a challenging FM that coincided with a bleed.ConclusionThe model performance highlights the value of using QSM over indirect measures in MRI, such as signal voids in magnitude-based contrasts. The results also indicate that a U-Net can capture more information about the presentation of FMs and other sources than would be possible using susceptibility quantification alone, which may be less reliable due to the diverse presentation of sources across a patient population. In our study, QSM was a reliable discriminator of FMs and other sources in the prostate, facilitating an accurate and streamlined approach to MR-only RT.
Increasing spectral DCM flexibility and speed by leveraging Julia’s ModelingToolkit and automated differentiation
Authors: Hofmann David , Chesebro Anthony G. , Rackauckas Chris , Mujica-Parodi Lilianne R. , Friston Karl J. , Edelman Alan , Strey Helmut H. 
Published: November 2023

AbstractUsing neuroimaging and electrophysiological data to infer neural parameter estimations from theoretical circuits requires solving the inverse problem. Here, we provide a new Julia language package designed to i) compose complex dynamical models in a simple and modular way with ModelingToolkit.jl, ii) implement parameter fitting based on spectral dynamic causal modeling (sDCM) using the Laplace approximation, analogous to MATLAB implementation in SPM12, and iii) leverage Julia’s unique strengths to increase accuracy and speed by employing Automatic Differentiation during the fitting procedure. To illustrate the utility of our flexible modular approach, we provide a method to improve correction for fMRI scanner field strengths (1.5T, 3T, 7T) when fitting models to real data.
Multi-echo Acquisition and Thermal Denoising Advances Precision Functional Imaging
Authors: Moser Julia , Nelson Steven M. , Koirala Sanju , Madison Thomas J. , Labonte Alyssa K. , Morales Carrasco Cristian , Feczko Eric , Moore Lucille A. , Lundquist Jacob T. , Weldon Kimberly B. , Grimsrud Gracie , Hufnagle Kristina , Ahmed Weli , Myers Michael J. , Adeyemo Babatunde , Snyder Abraham Z. , Gordon Evan M. , Dosenbach Nico U. F. , Tervo-Clemmens Brenden , Larsen Bart , Moeller Steen , Yacoub Essa , Vizioli Luca , Uğurbil Kamil , Laumann Timothy O. , Sylvester Chad M. , Fair Damien A. 
Published: November 2023

AbstractThe characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development. Precise characterization of systems organization during periods of high plasticity is likely to be essential for discoveries promoting lifelong health. Obtaining precision fMRI data during development has unique challenges that highlight the importance of establishing new methods to improve data acquisition, processing, and analysis. Here, we investigate two methods that can facilitate attaining this goal: multi-echo (ME) data acquisition and thermal noise removal with Noise Reduction with Distribution Corrected (NORDIC) principal component analysis. We applied these methods to precision fMRI data from adults, children, and newborn infants. In adults, both ME acquisitions and NORDIC increased temporal signal to noise ratio (tSNR) as well as the split-half reliability of functional connectivity matrices, with the combination helping more than either technique alone. The benefits of NORDIC denoising replicated in both our developmental samples. ME acquisitions revealed longer and more variable T2* relaxation times across the brain in infants relative to older children and adults, leading to major differences in the echo weighting for optimally combining ME data. This result suggests ME acquisitions may be a promising tool for optimizing developmental fMRI, albeit application in infants needs further investigation. The present work showcases methodological advances that improve Precision Functional Mapping in adults and developmental populations and, at the same time, highlights the need for further improvements in infant specific fMRI.
3D-printed, Citrate-based Bioresorbable Vascular Scaffolds for Coronary Artery Angioplasty
Authors: Ding Yonghui , Warlick Liam , Chen Mian , Taddese Eden , Collins Caralyn , Fu Rao , Duan Chongwen , Wang Xinlong , Ware Henry , Sun Cheng , Ameer Guillermo 
Published: November 2023

AbstractFully bioresorbable vascular scaffolds (BVSs) were designed to overcome the limitations of metallic drug-eluting stents (DESs). However, current polymer-based BVSs, such as Abbott’s Absorb, the only US FDA-approved BVS, struggle with increased strut thickness (150 μm for Absorb) and exacerbated tissue inflammation, leading to inferior clinical performance compared to metallic DESs. Here we develop a drug-eluting BVS (DE-BVS) through the innovative use of photopolymerizable, citrate-based materials and high-precision additive manufacturing process. Bare BVS with a clinically relevant strut thickness of 62 μm can be produced in a high-throughput manner, i.e. one BVS per minute. By modulating the coating polymer and structure, we achieve a controlled release of anti-restenosis drug of everolimus from DE-BVSs. We show the mechanical competence of DE-BVS and the successful deployment in swine coronary arteries using a custom-built balloon catheter delivery system. We further demonstrate that BVS and DE-BVS remain safe and effective to keep the vessel patency, induce limited inflammation, and facilitate the recovery of smooth muscle and endothelial tissues over 28 days implantation in swine coronary arteries. All these evaluated pre-clinical performances are largely comparable to the commercial XIENCE™ DES (Abbott Vascular).
MetaGate: Interactive Analysis of High-Dimensional Cytometry Data with Meta Data Integration
Authors: Ask Eivind Heggernes , Tschan-Plessl Astrid , Hoel Hanna Julie , Kolstad Arne , Holte Harald , Malmberg Karl-Johan 
Published: November 2023

AbstractFlow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level, widely used in basic research and routine clinical diagnostics. Traditionally, data analysis is carried out using manual gating, in which cut-offs are defined manually for each marker. Recent technical advances, including the introduction of mass cytometry, have increased the number of proteins that can be simultaneously assessed in each cell. To tackle the resulting escalation in data complexity, numerous new analysis algorithms have been developed. However, many of these show limitations in terms of providing statistical testing, data sharing, cross-experiment comparability integration with clinical data. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of clinical meta data. MetaGate allows manual gating to take place in traditional cytometry analysis software, while providing a combinatorial gating system for simple and transparent definition of biologically relevant cell populations. We demonstrate the utility of MetaGate through a comprehensive analysis of peripheral blood immune cells from 28 patients with diffuse large B-cell lymphoma (DLBCL) along with 17 age- and sex-matched healthy controls using two mass cytometry panels made of a total of 55 phenotypic markers. In a two-step process, raw data from 143 FCS files is first condensed through a data reduction algorithm and combined with information from manual gates, user-defined cellular populations and clinical meta data. This results in one single small project file containing all relevant information to allow rapid statistical calculation and visualization of any desired comparison, including box plots, heatmaps and volcano plots. Our detailed characterization of the peripheral blood immune cell repertoire in patients with DLBCL corroborate previous reports showing expansion of monocytic myeloid-derived suppressor cells, as well as an inverse correlation between NK cell numbers and disease progression.
Molecular evaluation ofConogethes punctiferalis(Guenée, 1854): Species status and intraspecific divergence
Authors: An Jin , Yao Ya-Lin , Gao Ping , Xiu Minghua , Shi Cheng-Min 
Published: November 2023

AbstractSpecies are not only the fundamental units of taxonomy but also the basic units of pest management. Insects ofConogethesare important agricultural and forestry pests. However, species boundaries withinConogethesoften appear obscure. In the present study, we re-evaluated the species status ofConogethesby applying three species delimitation approaches based on the mitochondrial DNA sequences, with particular emphasis on the yellow peach mothC. punctiferalis(Guenée, 1854). We first optimized species delimitation and inter-species genetic divergence threshold using a DNA barcoding dataset. Our results revealed that several nominal species ofConogethesspecies harbored deeply diverged mitochondrial lineages which were recognized as independent species by the species delimitation methods. The p-distance between the delimited putative species ranged from 0.0159 to 0.1321 with a mean of 0.0841. Then we refined the species status ofC. punctiferalisusing the smallest interspecific distance threshold based on a geographically comprehensive population-scale dataset. This procedure narrowed the species concept ofC. punctiferalisto a genetically coherent unit. Further investigation of its intraspecific divergence in the geographic context revealed that the refinedC. punctiferaliswas still widely distributed with the same or highly similar mitochondrial haplotypes occurring across South and East Asia.
Diverse and flexible behavioral strategies arise in recurrent neural networks trained on multisensory decision making
Authors: Wierda Thomas , Dora Shirin , Pennartz Cyriel M. A. , Mejias Jorge F. 
Published: November 2023

AbstractBehavioral variability across individuals leads to substantial performance differences during cognitive tasks, although its neuronal origin and mechanisms remain elusive. Here we use recurrent neural networks trained on a multisensory decision-making task to investigate inter-subject behavioral variability. By uniquely characterizing each network with a random synaptic-weights initialization, we observed a large variability in the level of accuracy, bias and decision speed across these networks, mimicking experimental observations in mice. Performance was generally improved when networks integrated multiple sensory modalities. Additionally, individual neurons developed modality-, choice- or mixed-selectivity, these preferences were different for excitatory and inhibitory neurons, and the concrete composition of each network reflected its preferred behavioral strategy: fast networks contained more choice- and mixed-selective units, while accurate networks had relatively less choice-selective units. External modulatory signals shifted the preferred behavioral strategies of networks, suggesting an explanation for the recently observed within-session strategy alternations in mice.
Nicotine in adolescence freezes dopamine circuits in an immature state
Authors: Reynolds Lauren M. , Gulmez Aylin , Fayad Sophie L. , Campos Renan Costa , Rigoni Daiana , Nguyen Claire , Borgne Tinaïg Le , Topilko Thomas , Rajot Domitille , Franco Clara , Marti Fabio , Heck Nicolas , Mourot Alexandre , Renier Nicolas , Barik Jacques , Faure Philippe 
Published: November 2023

AbstractNicotine use during adolescence is largely associated with negative long-term outcomes, including addiction to nicotine in adulthood. How nicotine acts on developing neurocircuitry in adolescence remains largely unknown, but may hold the key for informing more effective intervention efforts. We found transient nicotine exposure in early adolescence was sufficient for adult mice to show a marked vulnerability to nicotine. Brain-wide activity mapping showed that these mice had an enhanced response to an acute nicotine injection and widespread disruption of functional connectivity in comparison to controls, particularly within dopaminergic networks. Neurophysiological analysis further revealed that their ventral tegmental area (VTA) dopamine neurons show an immature basal plasticity signature and an adolescent-like imbalance in nicotine-induced activity between nucleus accumbens (NAc) and amygdala (AMG)-projecting pathways, known to respectively produce the reinforcing and anxiogenic effects of nicotine. The anxiogenic effect of nicotine is abolished in adult mice treated with nicotine in adolescence, strongly resembling the normal phenotype of young mice. Together these results suggest that nicotine exposure in adolescence somehow “froze” both their neural circuit and behavioral reaction to nicotine, carrying an adolescent-like vulnerability to the drug into adulthood. Finally, we are able to “thaw” the behavioral response to acute nicotine in adolescent-exposed mice by chemogenetically resetting the balance between the underlying NAc- and AMG-projecting dopamine circuits, restoring a mature anxiety-like response to acute nicotine. Together, our results highlight how diverse dopamine pathways can be impacted by experience in adolescence, and further suggest that the perseverance of a developmental imbalance between dopamine pathways may alter vulnerability profiles for later dopamine-dependent psychopathologies.
RNA isoform expression landscape of the human dorsal root ganglion (DRG) generated from long read sequencing
Authors: Arendt-Tranholm Asta , Mwirigi Juliet M. , Price Theodore J. 
Published: November 2023

AbstractSplicing is a post-transcriptional RNA processing mechanism that enhances genomic complexity by creating multiple isoforms from the same gene. Diversity in splicing in the mammalian nervous system is associated with neuronal development, synaptic function and plasticity, and is also associated with diseases of the nervous system ranging from neurodegeneration to chronic pain. We aimed to characterize the isoforms expressed in the human peripheral nervous system, with the goal of creating a resource to identify novel isoforms of functionally relevant genes associated with somatosensation and nociception. We used long read sequencing (LRS) to document isoform expression in the human dorsal root ganglia (hDRG) from 3 organ donors. Isoforms were validatedin silicoby confirming expression in hDRG short read sequencing (SRS) data from 3 independent organ donors. 19,547 isoforms of protein-coding genes were detected using LRS and validated with SRS and strict expression cutoffs. We identified 763 isoforms with at least one previously undescribed splice-junction. Previously unannotated isoforms of multiple pain-associated genes, includingASIC3,MRGPRX1andHNRNPKwere identified. In the novel isoforms ofASIC3, a region comprising ∼35% of the 5’UTR was excised. In contrast, a novel splice-junction was utilized in isoforms ofMRGPRX1to include an additional exon upstream of the start-codon, consequently adding a region to the 5’UTR. Novel isoforms ofHNRNPKwere identified which utilized previously unannotated splice-sites to both excise exon 14 and include a sequence in the 5’ end of exon 13. The insertion and deletion in the coding region was predicted to excise a serine-phosphorylation site favored by cdc2, and replace it with a tyrosine-phosphorylation site potentially phosphorylated by SRC. We also independently confirm a recently reported DRG-specific splicing event in WNK1 that gives insight into how painless peripheral neuropathy occurs when this gene is mutated. Our findings give a clear overview of mRNA isoform diversity in the hDRG obtained using LRS. Using this work as a foundation, an important next step will be to use LRS on hDRG tissues recovered from people with a history of chronic pain. This should enable identification of new drug targets and a better understanding of chronic pain that may involve aberrant splicing events.
Deep Learning-Based Prediction Model of Heart Failure with Improved Ejection Fraction
Authors: Zhang Yan , Pan Yanbing , Zhang Yanzi , Wang Yongle , Jia Qiuting , Kong Yihui 
Published: November 2023

AbstractBackgroundThis study developed a deep learning model to predict improvement of left ventricular ejection fraction in patients with heart failure.MethodsAn internal database comprising clinical, laboratory and echocardiographic features obtained from the First Affiliated Hospital of Harbin Medical University was used to construct and train the deep learning model.ResultsA total of 422 cases were included in this study. 122 (28.9%) were patients with HFimpEF, 300 (71.0%) were patients with HFrEF. Multivariable analyses showed that smaller baseline left atrial anterior-posterior diameter (LAD) and left ventricular end-diastolic diameter (LVEDD), higher baseline interventricular septal thickness at diastole (IVSD) and levels of prealbumin were the independent clinical predictors of LVEF improvement. Deep learning model demonstrated an overall predict accuracy of 96% in the validation set and 89% in the training set.ConclusionsIndependent predictors of LVEF improvement were smaller baseline LVEDD, LAD, higher baseline IVSD and baseline levels of prealbumin. Our deep learning model had shown acceptable performance in predicting improvement of left ventricular ejection fraction in patients with heart failure.RegistrationURL:https://www.clinicaltrials.gov; Unique identifier:NCT06070506.