bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automation Bias in AI-Assisted Detection of Cerebral Aneurysms on Time-of-Flight MR-Angiography
Authors: Kim Su Hwan , Schramm Severin , Riedel Evamaria Olga , Schmitzer Lena , Rosenkranz Enrike , Kertels Olivia , Bodden Jannis , Paprottka Karolin , Sepp Dominik , Renz Martin , Kirschke Jan , Baum Thomas , Maegerlein Christian , Boeckh-Behrens Tobias , Zimmer Claus , Wiestler Benedikt , Hedderich Dennis M. 
Published: June 2024

AbstractBackgroundAI systems have the potential to support in detecting cerebral aneurysms. Yet, the role of automation bias (inclination of humans to overly rely on automated decision-making systems) in AI-assisted cerebral aneurysm detection remains unclear.PurposeTo determine how automation bias can affect radiologists with varying experience levels when reading time-of-flight magnetic resonance angiography (TOF-MRA) studies with the assistance of an AI system for cerebral aneurysm detection.MethodsIn this prospective experiment, nine radiologists with varying levels of experience evaluated twenty TOF-MRA exams for the presence of anterior circulation aneurysms, with each arterial segment rated on a 4-point Likert scale, and provided follow-up recommendations. Every case was evaluated twice (with or without assistance by the AI software © mdbrain), with a washout-period of at least four weeks between the two sessions. Ten out of twenty cases included at least one false-positive AI finding. Aneurysm ratings, follow-up recommendations, and reading times were assessed using the Wilcoxon signed-rank test. A thematic analysis was performed to summarize reader feedback and observations.ResultsFalse-positive AI results led to significantly higher suspicion of aneurysm findings (p = 0.01). Inexperienced readers further recommended significantly more aggressive follow-up examinations when presented with false-positive AI findings (p = 0.005). Reading times were significantly shorter with AI assistance in inexperienced (164.1 vs 228.2 seconds; p < 0.001), moderately experienced (126.2 vs 156.5 seconds; p < 0.009), and very experienced (117.9 vs 153.5 seconds; p < 0.001) readers alike.ConclusionOur results demonstrate susceptibility of radiology readers to automation bias in detecting cerebral aneurysms in TOF-MRA studies when encountering false-positive AI findings. In inexperienced readers, this behavior further translated into more aggressive follow-up recommendations. AI assistance resulted in significantly shorter reading times across experience levels. While AI systems for cerebral aneurysm detection can provide benefits, challenges in human-AI interaction need to be mitigated to ensure safe and effective adoption.
Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning
Authors: Mehdi Rana Raza , Kadivar Nikhil , Mukherjee Tanmay , Mendiola Emilio A. , Shah Dipan J. , Karniadakis George , Avazmohammadi Reza 
Published: June 2024

ABSTRACTMyocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.
Protein Coronas Derived from Cerebrospinal Fluid Enhance the Interactions Between Nanoparticles and Brain Cells
Authors: Morshed Nabila , Rennie Claire , Faria Matthew , Collins-Praino Lyndsey , Care Andrew 
Published: June 2024

AbstractNeuronanomedicine harnesses nanoparticle technology for the treatment of neurological disorders. An unavoidable consequence of nanoparticle delivery to biological systems is the formation of a protein corona on the nanoparticle surface. Despite the well-established influence of the protein corona on nanoparticle behavior and fate, as well as FDA approval of neuro-targeted nanotherapeutics, the effect of a physiologically relevant protein corona on nanoparticle-brain cell interactions is insufficiently explored. Indeed, less than 1% of protein corona studies have investigated protein coronas formed in cerebrospinal fluid (CSF), the fluid surrounding the brain. Herein, we utilize two clinically relevant polymeric nanoparticles (PLGA and PLGA-PEG) to evaluate the formation of serum and CSF protein coronas. LC-MS analysis revealed distinct protein compositions, with selective enrichment/depletion profiles. Following incubation with brain cells, serum and CSF coronas on PLGA particles showed enhanced associations with all cell types as compared to their corresponding corona on PLGA-PEG particles. CSF-derived protein coronas on PLGA nanoparticles, specifically, showed the greatest nanoparticle-cell interactions, with Pearson’s correlation analysis revealing that proteins associated with enhanced nanoparticle-cell interactions were exclusively enriched in this protein corona. This study demonstrates the importance of correct choice of physiologically relevant biological fluids, and its influence on the formation of the protein corona, subsequent nanoparticle-cell interactions.
Natural killer cell migration in HIV-infected individuals is inhibited by impairment of HIF-1α-mediated glycolysis
Authors: Yu Xiaowen , Zhou Jie , Lei Jie , Ge Hongchi , Zhang Zining , Fu Yajing , Han Xiaoxu , Hu Qinghai , Ding Haibo , Geng Wenqing , Shang Hong , Jiang Yongjun 
Published: June 2024

AbstractNatural killer (NK) cells serve as the first line of defense of the immune system and play a crucial role in fighting against HIV infection. The effective function of NK cells is closely related to their migration ability, but the status of NK cell migration in HIV-infected individuals and the regulation mechanism for NK cell migration remains unknown. Here, we found that NK cell migration was significantly impaired in HIV-infected individuals, lower in immune non-responders (INRs) compared with immune responders (IRs), and positively correlated with CD4+T cell counts. Further investigations showed that the decreased NK cell migration in HIV infection was caused by the impairment of glycolysis. Mechanistically, we found that NK cell migration was regulated by HIF-1α pathway, and inhibitory receptor TIGIT restrained HIF-1α expression by inhibiting PI3K/AKT/mTORC1 or ERK signaling pathway, consequently weakening the glycolysis of NK cells in HIV-infected individuals, and ultimately leading to down-regulation of migration. Collectively, we uncovered a mechanism of reduced NK cell migration in HIV infection and provided a new insight for immunotherapy in HIV infection.In BriefThe effective function of NK cells is closely related to its migration ability. The authors show that impaired NK cell migration in HIV-infected individuals is caused by TIGIT inhibiting HIF-1α-mediated glycolysis via PI3K/AKT/mTORC1 or ERK pathway.
Inequality, heterogeneity, and chance: Multiple factors and their interactions
Authors: Caswell Hal , van Daalen Silke F. 
Published: June 2024

AbstractA heterogeneous population is a mixture of groups differing in vital rates. In such a population, some of the variance in demographic outcomes (e.g., longevity, lifetime reproduction) is due to heterogeneity and some is the result of stochastic demographic processes. Many studies have partitioned variance into its between-group and within-group components, but have focused on single factors. Especially for longevity, variance due to stochasticity is far greater than that due to heterogeneity. Here we extend analysis to multiple-factor studies, making it possible to calculate the contributions to variance of each factor and each of the interactions among factors. We treat the population as a mixture and use the marginal mixing distributions to compute variance components. Examples are presented: longevity as a function of sex, race, and U.S. state of residence, lifetime reproduction among set of developed countries and as a result of resource availability and pesticide exposure.
Stick-slip unfolding favors self-association of expandedHTTmRNA
Authors: O’Brien Brett M. , Moulick Roumita , Jiménez-Avalos Gabriel , Rajasekaran Nandakumar , Kaiser Christian M. , Woodson Sarah A. 
Published: June 2024

ABSTRACTIn Huntington’s Disease (HD) and related disorders, expansion of CAG trinucleotide repeats produces a toxic gain of function in affected neurons. Expandedhuntingtin(expHTT) mRNA forms aggregates that sequester essential RNA binding proteins, dysregulating mRNA processing and translation. The physical basis of RNA aggregation has been difficult to disentangle owing to the heterogeneous structure of the CAG repeats. Here, we probe the folding and unfolding pathways of expHTTmRNA using single-molecule force spectroscopy. Whereas normalHTTmRNAs unfold reversibly and cooperatively, expHTTmRNAs with 20 or 40 CAG repeats slip and unravel non-cooperatively at low tension. Slippage of CAG base pairs is punctuated by concerted rearrangement of adjacent CCG trinucleotides, trapping partially folded structures that readily base pair with another RNA strand. We suggest that the conformational entropy of the CAG repeats, combined with stable CCG base pairs, creates a stick-slip behavior that explains the aggregation propensity of expHTTmRNA.
OneSC: A computational platform for recapitulating cell state transitions
Authors: Peng Da , Cahan Patrick 
Published: June 2024

AbstractComputational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high- resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico ‘synthetic’ cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.
The joint evolution of separate sexes and sexual dimorphism
Authors: Lesaffre Thomas , Pannell John R. , Mullon Charles 
Published: June 2024

AbstractDioecious plants are frequently sexually dimorphic. Such dimorphism, which reflects responses to selection acting in opposite directions for male and female components of fitness, is commonly thought to emerge after separate sexes evolved from hermaphroditism. But associations between allocation to male and female function and traits under sexual conflict may well also develop in hermaphroditic ancestors. Here, we show that variation in sex allocation and a trait under sexual conflict inevitably generates an advantage to sexual specialisation, fueling the transition to dioecy. In the absence of constraints, this leads to the joint evolution of separate sexes and sexual dimorphism through the build-up of an association between sex allocation and the conflict trait, such that eventually the population consists of unisexuals expressing their sex-specific optima. We then investigate how such association might materialise genetically, either via recombination suppression or via sex-dependent expression, and show that the genetic architecture of sex allocation and the conflict trait readily evolves to produce the association favoured by selection. Finally and in agreement with previous theory, we demonstrate that limited dispersal and self-fertilisation, which are pervasive ecological characteristics of flowering plants, can offset the advantage of sexual specialisation generated by sexual conflict and thus maintain hermaphroditism. Taken together, our results indicate that advantages to sexual specialisation are inevitable when there is conflict between sexual functions in hermaphrodites, but these advantages can be counterbalanced by ecological benefits of hermaphroditism.
Decoding chromosome organization using CheC-PLS: chromosome conformation by proximity labeling and long-read sequencing
Authors: Xu Kewei , Zhang Yichen , Baldwin-Brown James , Sasani Thomas A. , Phadnis Nitin , Miller Matthew P. , Rog Ofer 
Published: June 2024

AbstractGenomic approaches have provided detailed insight into chromosome architecture. However, commonly deployed techniques do not preserve connectivity-based information, leaving large-scale genome organization poorly characterized. Here, we developed CheC-PLS: a proximity-labeling technique that indelibly marks, and then decodes, protein-associated sites. CheC-PLS tethers dam methyltransferase to a protein of interest, followed by Nanopore sequencing to identify methylated bases - indicative ofin vivoproximity - along reads >100kb. As proof-of-concept we analyzed, in budding yeast, a cohesin-based meiotic backbone that organizes chromatin into an array of loops. Our data recapitulates previously obtained association patterns, and, importantly, exposes variability between cells. Single read data reveals cohesin translocation on DNA and, by anchoring reads onto unique regions, we define the internal organization of the ribosomal DNA locus. Our versatile technique, which we also deployed on isolated nuclei with nanobodies, promises to illuminate diverse chromosomal processes by describing thein vivoconformations of single chromosomes.
SpaNorm: spatially-aware normalisation for spatial transcriptomics data
Authors: Salim Agus , Bhuva Dharmesh D , Chen Carissa , Yang Pengyi , Davis Melissa J , Yang Jean YH 
Published: June 2024

AbstractLibrary size normalisation is necessary to enable comparisons between observations in transcriptomic datasets. Numerous methods have been developed to normalise these effects with sample and gene specific adjustments. However, in spatial transcriptomics data, normalisation is complicated by the fact that spatial region-specific library size confounds biology. The most popular approach of adapting methods developed for single-cell RNA-seq data has been shown to excessively remove biological signals associated with spatial domains and thus results in poorer downstream domain identification. To this end, we propose the first spatially-aware normalisation method, SpaNorm. SpaNorm concurrently models spatial library size effects and the underlying smooth biology, to tease apart these effects, and thereby remove library size effects without removing biology. This is achieved through optimal decomposition of spatially smooth variation into those related and unrelated to library size and the use of location-specific scaling factors. Using 27 tissue samples from 6 datasets spanning 4 spatial platforms, we show that SpaNorm outperforms current state of the art methods at retaining biological information in the form of spatial domains and spatially variable genes (SVGs) better than 4 commonly used single-cell normalisation approaches. SpaNorm is versatile and it can be used for both spot-based and subcellular spatial transcriptomics data. Notably, the benefit of using SpaNorm is more pronounced for the latter data such as those from Xenium, STOmics and CosMx platforms for which the proportion of genes exhibiting region-specific library size effect is higher. SpaNorm works equally well with segmented cell-level data and spot-based data where each spot contains multiple cells.
Genetic Ethnicity and Hypertension Epistatic Interaction Underlying Racial Disparities in US Multiple Myeloma Susceptibility
Authors: Dumont Emmanuel LP , Han Luke , Kodali Srisundesh , Aptekmann Ariel , Carter-Bawa Lisa , Feinman Rena , Tycko Benjamin , Siegel David S. , Goy Andre , Kaplan Peter , Do Catherine 
Published: June 2024

AbstractBackgroundMultiple myeloma (MM), a malignant plasma cell disorder, exhibits pronounced racial disparities in incidence and patient outcomes. The Centers for Disease Control and Prevention (CDC) reports that MM is twice as common in Black Americans as in White Americans. Understanding these racial disparities is paramount to addressing potential healthcare biases and developing targeted interventions to ensure equitable patient care and outcomes.MethodsUsing the ‘All of Us’ database from the National Institute of Health, we performed a retrospective study on 413,457 participants. Of these, 1,430 were diagnosed with MM. We examined the factors contributing to racial disparities in MM risk using multivariable statistical analysis, including interaction effects.ResultsTo comprehensively account for the multidimensional aspects of self-reported race followed by the CDC, we incorporated genetic ethnicity, demographics (age, gender), body mass index, social determinants of health (zipcode’s deprivation index, and health insurance status), and common pre-existing comorbidities (hypertension, diabetes, congestive heart failure - CHF, and chronic obstructive pulmonary disease) into our analysis. Our findings reveal that the racial disparities in health outcomes between non-Hispanic Black and non-Hispanic White individuals, as reported by the CDC, are driven by a synergistic epistatic interaction between having African as a predominant genetic ethnicity and being diagnosed or treated for hypertension (OR: 2.92, 95% CI: 1.54 to 5.57, P = 0.001). This interaction is also true for individuals whose primary genetic ancestry is Ad Mixed American (OR: 2.31, 95% CI: 1.02 to 5.2, P = 0.044). The other variables significantly associated with MM risk are having a predominant genetic ancestry of Ad Mixed American (OR: 0.41, 95% CI: 0.2 to 0.85, P = 0.017), the lack of health insurance (OR: 0.67, 95% CI: 0.48 to 0.93, P = 0.017), zipcode’s deprivation index being above the US median (OR: 1.26, 95% CI: 1.04 to 1.53, P = 0.018), being diagnosed with CHF before MM (OR: 2.06, 95% CI: 1.56 to 2.72, P < 1.e-3), being male (OR: 1.22, 95% CI: 1.02 to 1.46, P = 0.031), and being over the age of 65 (OR: 1.65, 95% CI: 1.36 to 2, P < 1.e-3).ConclusionThese findings reveal a previously unknown epistatic interaction between an individual’s predominant genetic ancestry and hypertension, responsible for the CDC-reported higher risk of the African-American population for MM. In other words, hypertension serves as a surrogate marker for a genetic predisposition in individuals with a predominant African genetic ancestry. This insight could improve the screening and identification of minority individuals at risk for MM.
Anomaly detection for high-content image-based phenotypic cell profiling
Authors: Shpigler Alon , Kolet Naor , Golan Shahar , Weisbart Erin , Zaritsky Assaf 
Published: June 2024

AbstractHigh-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell’s physiological state. Classical representations of the phenotypic profile can not capture the full underlying complexity in cell organization, while recent weakly machine-learning based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and use it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility, Mechanism of Action classification, and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology.
In vivoElectroChromic Shift measurements of photosynthetic activity in far-red absorbing cyanobacteria
Authors: Sellés Julien , Alric Jean , Rutherford A. William , Davis Geoffry A. , Viola Stefania 
Published: June 2024

AbstractSome cyanobacteria can do photosynthesis using not only visible but also far-red light that is unused by most other oxygenic photoautotrophs because of its lower energy content. These species have a modified photosynthetic apparatus containing red-shifted pigments. The incorporation of red-shifted pigments decreases the photochemical efficiency of photosystem I and, especially, photosystem II, and it might affect the distribution of excitation energy between the two photosystems with possible consequences on the activity of the entire electron transport chain. To investigate thein vivoeffects on photosynthetic activity of these pigment changes, we present here the adaptation of a spectroscopic method, based on a physical phenomenon called ElectroChromic Shift (ECS), to the far-red absorbing cyanobacteriaAcaryochloris marinaandChroococcidiopsis thermalisPCC7203. ECS measures the electric field component of the trans-thylakoid proton motive force generated by photosynthetic electron transfer. We show that ECS can be used in these cyanobacteria to investigatein vivothe stoichiometry of photosystem I and photosystem II and their absorption cross-section, as well as the overall efficiency of light energy conversion into electron transport. Our results indicate that both species use visible and far-red light with similar efficiency, despite significant differences in their light absorption characteristics. ECS thus represents a new non-invasive tool to study the performance of naturally occurring far-red photosynthesis.