bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Need for Artificial Intelligence Curriculum in Military Medical Education
Authors: Spirnak Jonathan R , Antani Sameer 
Published: October 2023

ABSTRACT
The success of deep-learning algorithms in analyzing complex structured and unstructured multidimensional data has caused an exponential increase in the amount of research devoted to the applications of artificial intelligence (AI) in medicine in the past decade. Public release of large language models like ChatGPT the past year has generated an unprecedented storm of excitement and rumors of machine intelligence finally reaching or even surpassing human capability in detecting meaningful signals in complex multivariate data. Such enthusiasm, however, is met with an equal degree of both skepticism and fear over the social, legal, and moral implications of such powerful technology with relatively little safeguards or regulations on its development. The question remains in medicine of how to harness the power of AI to improve patient outcomes by increasing the diagnostic accuracy and treatment precision provided by medical professionals. Military medicine, given its unique mission and resource constraints,can benefit immensely from such technology. However, reaping such benefits hinges on the ability of the rising generations of military medical professionals to understand AI algorithms and their applications. Additionally, they should strongly consider working with them as an adjunct decision-maker and view them as a colleague to access and harness relevant information as opposed to something to be feared. Ideas expressed in this commentary were formulated by a military medical student during a two-month research elective working on a multidisciplinary team of computer scientists and clinicians at the National Library of Medicine advancing the state of the art of AI in medicine. A motivation to incorporate AI in the Military Health System is provided, including examples of applications in military medicine. Rationale is then given for inclusion of AI in education starting in medical school as well as a prudent implementation of these algorithms in a clinical workflow during graduate medical education. Finally, barriers to implementation are addressed along with potential solutions. The end state is not that rising military physicians are technical experts in AI; but rather that they understand how they can leverage its rapidly evolving capabilities to prepare for a future where AI will have a significant role in clinical care. The overall goal is to develop trained clinicians that can leverage these technologies to improve the Military Health System.
The MASSIVE survey – XVIII. Deep wide-field K-band photometry and local scaling relations for massive early-type galaxies
Authors: Quenneville Matthew E , Blakeslee John P , Ma Chung-Pei , Greene Jenny E , Gwyn Stephen D J , Ciccone Stephanie , Nyiri Blanka 
Published: October 2023

ABSTRACT
We present wide-field, deep K-band photometry of 98 luminous early-type galaxies (ETGs) from the MASSIVE survey based on observations taken with the WIRCam instrument on the Canada–France–Hawaii Telescope. Using these images, we extract accurate total K-band luminosities (LK) and half-light radii (Re) for this sample of galaxies. We use these new values to explore the size–luminosity and Faber–Jackson relations for massive ETGs. Within this volume-limited sample, we find clear evidence for curvature in both relations, indicating that the most luminous galaxies tend to have larger sizes and smaller velocity dispersions than expected from a simple power-law fit to less luminous galaxies. Our measured relations are qualitatively consistent with the most massive elliptical galaxies forming largely through dissipationless mergers. When the sample is separated into fast and slow rotators, we find the slow rotators to exhibit similar changes in slope with increasing LK, suggesting that low-mass and high-mass slow rotators have different formation histories. The curvatures in the Re–LK and σ–LK relations cancel, leading to a relation between dynamical mass and luminosity that is well described by a single power law: Reσ2 ∝ LKb with b ≈ 1.2. This is consistent with the tilt of the fundamental plane observed in lower mass elliptical galaxies.
Conserved cobalamin acquisition protein 1 is essential for vitamin B12 uptake in both Chlamydomonas and Phaeodactylum
Authors: Sayer Andrew P , Llavero-Pasquina Marcel , Geisler Katrin , Holzer Andre , Bunbury Freddy , Mendoza-Ochoa Gonzalo I , Lawrence Andrew D , Warren Martin J , Mehrshahi Payam , Smith Alison G 
Published: October 2023

Abstract
Microalgae play an essential role in global net primary productivity and global biogeochemical cycling. Despite their phototrophic lifestyle, over half of algal species depend for growth on acquiring an external supply of the corrinoid vitamin B12 (cobalamin), a micronutrient produced only by a subset of prokaryotic organisms. Previous studies have identified protein components involved in vitamin B12 uptake in bacterial species and humans. However, little is known about its uptake in algae. Here, we demonstrate the essential role of a protein, cobalamin acquisition protein 1 (CBA1), in B12 uptake in Phaeodactylum tricornutum using CRISPR-Cas9 to generate targeted knockouts and in Chlamydomonas reinhardtii by insertional mutagenesis. In both cases, CBA1 knockout lines could not take up exogenous vitamin B12. Complementation of the C. reinhardtii mutants with the wild-type CBA1 gene restored B12 uptake, and regulation of CBA1 expression via a riboswitch element enabled control of the phenotype. When visualized by confocal microscopy, a YFP-fusion with C. reinhardtii CBA1 showed association with membranes. Bioinformatics analysis found that CBA1-like sequences are present in all major eukaryotic phyla. In algal taxa, the majority that encoded CBA1 also had genes for B12-dependent enzymes, suggesting CBA1 plays a conserved role. Our results thus provide insight into the molecular basis of algal B12 acquisition, a process that likely underpins many interactions in aquatic microbial communities.
An investigation into the effect of exposing XMM–Newton’s Optical Monitor to the light of Jupiter
Authors: Sullivan S , Page M J , Breeveld A A , Rosen S , Talavera A 
Published: October 2023

Abstract
After nearly 18 yr of successful and safe operations, on 2017 July 17 and 18, XMM–Newton’s Optical Monitor (OM) observed Jupiter – an object 10 magnitudes brighter than safe brightness limits – in its visual (V) filter. The object was exposed 40 arcsec from the nominal European Photon Imaging Cameras pn boresight in the negative Y direction, creating a patch of depleted sensitivity. Two exposures of 4000 s each in the V filter left the sensitivity depleted in an area 70 by 40 arcsec; with the decrease in throughput varying from 34 per cent in the V filter to 15 per cent in the ultraviolet bands, but reaching a depth of 45 per cent in flat-field exposures. The wavelength dependency suggests the majority of the detector damage is due to loss of sensitivity in the photocathode from damage inflicted by ion feedback, while up to 15 per cent could be due to gain depletion of the MCP. The physical mechanisms causing the damage to the detector are discussed as well as possible solutions and opportunities that exist for the future operation of the OM.
Trends in clinical features and severity of Plasmodium vivax malaria among children at tertiary care center in North India
Authors: Arya Aditi , Meena Shyam Sundar , Matlani Monika , Chaudhry Shewta , Singh Vineeta 
Published: October 2023

Abstract

Background
Malaria is a significant cause of morbidity and mortality in adults and children. Plasmodium falciparum is the primary cause of severe malaria, but recently Plasmodium vivax is also recognized to cause severe malaria-associated morbidity and mortality. The study focuses on determining the mortality related to severity parameters in individuals under 12 years and their critical presentation in P.vivax malaria-infected children.

Methods
A prospective cross-sectional hospital-based study was conducted at Safdarjung Hospital, New Delhi, and ICMR-NIMR, New Delhi. All clinically suspected cases were admitted for screening. Exclusion criteria (rapid malaria antigen test, microscopy and medication history) were applied to all the admitted patients (n = 221) to obtain P.vivax patients only. Patients aged ≤ 12 years were included in the study. DNA was extracted from dried blood spots and amplified by nested PCR, followed by visualization on gel electrophoresis.

Result
A total of 221 clinically suspected cases of malaria were screened for P.vivax. After implementing various exclusion criteria, 45/221 cases were enrolled for the study, among which 44.4% (20/45) of children had the symptoms of severe malaria in terms of cerebral malaria, thrombocytopenia, anemia, pancytopenia, acute respiratory distress syndrome and hemophagocytic lymphohistiocytosis.

Conclusion
Plasmodium vivax mono-infection can cause severe manifestation and must be treated as P.falciparum without any delay because it may lead to increased morbidity and mortality. A changing trend in clinical symptoms has shown in P.vivax which was an earlier phenomenon of P.falciparum.
Mapping Pathways to Neuronal Atrophy in Healthy, Mid-aged Adults: From Chronic Stress to Systemic Inflammation to Neurodegeneration?
Authors: Schaefer Julia K , Engert Veronika , Valk Sofie L , Singer Tania , Puhlmann Lara MC 
Published: October 2023

AbstractGrowing evidence implicates systemic inflammation in the loss of structural brain integrity in natural ageing and disorder development. Chronic stress and glucocorticoid exposure can potentiate inflammatory processes and have also been linked to neuronal atrophy, particularly in the hippocampus and the human neocortex. To improve understanding of emerging maladaptive interactions between stress and inflammation, this study examined evidence for glucocorticoid- and inflammation-mediated neurodegeneration in healthy mid-aged adults.N=169 healthy adults (mean age = 39.4, 64.5% female) were sampled from the general population in the context of the ReSource Project. Stress, inflammation and neuronal atrophy were quantified using physiological indices of chronic stress (hair cortisol and cortisone concentration), systemic inflammation (interleukin-6, high-sensitive C-reactive protein), the systemic inflammation index (SII), hippocampal volume (HCV) and cortical thickness (CT) in regions of interest. Structural equation models were used to examined evidence for pathways from stress and inflammation to neuronal atrophy.Model fit indices indicated good representation of stress, inflammation, and neurological data through the constructed models (CT model: robust RMSEA = 0.041, robustχ2= 910.90; HCV model: robust RMSEA < 0.001, robustχ2= 40.95). We replicated typical negative age-cortical thickness associations (Anterior cingulate cortex (β =-0.51, p < .001), Parahippocampal Cortex (β = −0.50, p = .012), Frontal Lobe (β = −0.56, p < .001) and Temporal Lobe (β = −0.61, p < .001). Among inflammatory indices, only the SII was positively associated with hair cortisol as one indicator of chronic stress (β = 0.18, p<.05). Direct and indirect pathways from chronic stress and systemic inflammation to cortical thickness or hippocampal volume were non-significant.We identify the SII as a potential marker of systemic inflammation in human psychobiological studies. More generally, these data suggest that neurophysiological associations found in at-risk populations are not detectable in healthy, mid-aged populations. We conclude that inflammation and glucocorticoid-mediated neurodegeneration may only emerge during advanced ageing and disorder processes and may thus have limited use as early risk markers. Future work should examine these pathways in prospective longitudinal designs, for which the present investigation serves as a baseline.
Short-stranded zein fibers for muscle tissue engineering in alginate-based hydrogels
Authors: Melzener Lea , Spaans Sergio , Hauck Nicolas , Pötgens André J. G. , Flack Joshua E. , Post Mark J. , Doğan Arın 
Published: October 2023

AbstractCultivated meat is a nascent technology that aims to produce an environmentally and animal-friendly alternative to conventional meat. Producing skeletal muscle tissue in an animal-free system allowing for high levels of myofusion and maturation is important for the nutritional and sensorial value of cultivated meat. Alginate is an attractive biomaterial to support muscle formation as it is food-safe, sustainable, cheap, and can be cross-linked using non-toxic methods. Although alginate can be functionalized to promote cell attachment, limitations in its mechanical properties, including form, viscosity and stress relaxation, hinder cellular capacity for myogenic differentiation and maturation in alginate-based hydrogels. Here, we show that the addition of electrospun short-stranded zein fibers increased hydrogel degradation, resulting in faster compaction, improved cell-gel interaction and enhanced alignment of bovine muscle precursor cells. We conclude that fiber-hydrogel composites are a promising approach to support optimal formation of 3D constructs, by improving tissue stability and thus prolonging culture duration. Together, this improves muscle-related protein content by facilitating myogenic differentiation and priming muscle organoids for maturation.
Refining the resolution of the yeast genotype-phenotype map using single-cell RNA-sequencing
Authors: N’Guessan Arnaud , Tong Wen Yuan , Heydari Hamed , Nguyen Ba Alex N 
Published: October 2023

ABSTRACTGenotype-phenotype mapping (GPM), or the association of trait variation to genetic variation, has been a long-lasting problem in biology. The existing approaches to this problem allowed researchers to partially understand within– and between-species variation as well as the emergence or evolution of phenotypes. However, traditional GPM methods typically ignore the transcriptome or have low statistical power due to challenges related to dataset scale. Thus, it is not clear to what extent selection modulates transcriptomes and whether cis-or trans-regulatory elements are more important. To overcome these challenges, we leveraged the cost efficiency and scalability of single-cell RNA sequencing (scRNA-seq) by collecting data from 18,233 yeast cells from 4,489 F2 segregants derived from an F1 cross between the laboratory strain BY4741 and the vineyard strain RM11-1a. More precisely, we performed eQTL mapping with the scRNA-seq data to identify single-cell eQTL (sc-eQTL) and transcriptome variation patterns associated with fitness variation inferred from the segregant bulk fitness assay. Due to the larger scale of our dataset and its multidimensionality, we could recapitulate results from decades of work in GPM from yeast bulk assays while revealing new associations between phenotypic and transcriptomic variations at a broad scale. We evaluated the strength of the association between phenotype variation and expression variation, revealed new hotspots of gene expression regulation associated to trait variation, revealed new gene function with high expression heritability and highlighted the larger aggregate effect of trans-regulation compared to cis-regulation Altogether these results suggest that integrating large-scale scRNA-seq data into GPM improves our understanding of trait variation in the context of transcriptomic regulation.
CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks
Authors: Bhatt Roshni , Koes David Ryan , Durrant Jacob D. 
Published: October 2023

AbstractWe present a novel and interpretable approach for predicting small-molecule binding affinities using context explanation networks (CENs). Given the specific structure of a protein/ligand complex, our CENsible scoring function uses a deep convolutional neural network to predict the contributions of pre-calculated terms to the overall binding affinity. We show that CENsible can effectively distinguish active vs. inactive compounds for many systems. Its primary benefit over related machine-learning scoring functions, however, is that it retains interpretability, allowing researchers to identify the contribution of each pre-calculated term to the final affinity prediction, with implications for subsequent lead optimization.
Evolutionary Rate Covariation is a reliable predictor of co-functional interactions but not necessarily physical interactions
Authors: Little Jordan , Chikina Maria , Clark Nathan 
Published: October 2023

AbstractCo-functional proteins tend to have rates of evolution that covary over time. This correlation between evolutionary rates can be measured over the branches of a phylogenetic tree through methods such as evolutionary rate covariation (ERC), and then used to construct gene networks by the identification of proteins with functional interactions. The cause of this correlation has been hypothesized to result from both compensatory coevolution at physical interfaces and non-physical forces such as shared changes in selective pressure. This study explores whether coevolution due to compensatory mutations has a measurable effect on the ERC signal. We examined the difference in ERC signal between physically interacting protein domains within complexes as compared to domains of the same proteins that do not physically interact. We found no generalizable relationship between physical interaction and high ERC, although a few complexes ranked physical interactions higher than non-physical interactions. Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC, and we hypothesize that the stronger signal instead comes from selective pressures on the protein as a whole and maintenance of the general function.
NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage
Authors: Li Yi , Frederick Rebecca A. , George Daniel , Cogan Stuart F. , Pancrazio Joseph J. , Bleris Leonidas , Hernandez-Reynoso Ana G. 
Published: October 2023

AbstractObjectiveThe safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters.ApproachA literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation.Main ResultsWe compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%.SignificanceThis novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.
The combination of topological data analysis and mathematical modeling improves sleep stage prediction from consumer-grade wearables
Authors: Lee Minki P. , Kim Dae Wook , Walch Olivia , Forger Daniel B. 
Published: October 2023

AbstractWearable devices have become commonplace tools for tracking behavioral and physiological parameters in real-world settings. Nonetheless, the practical utility of these data for clinical and research applications, such as sleep analysis, is hindered by their noisy, large-scale, and multidimensional characteristics. Here, we develop a neural network algorithm that predicts sleep stages by tracking topological features (TFs) of wearable data and model-driven clock proxies reflecting the circadian propensity for sleep. To evaluate its accuracy, we apply it to motion and heart rate data from the Apple Watch worn by subjects undergoing polysomnography (PSG) and compare the predicted sleep stages with the corresponding ground truth PSG records. We find that TFs and clock proxies can improve the overall performance of wake/REM/NREM sleep classification, particularly in identifying REM and NREM sleep (AUROC/AUPRC improvements > 9% and REM/NREM accuracy improvement “ 12%). We find that this improvement is mainly attributed to the heart rate TFs. To confirm this, we compare the heart rate TFs between two groups, expected to have different cardiovascular conditions: younger, healthy subjects from the Apple Watch cohort and elderly subjects from the Multi-ethnic Study of Atherosclerosis cohort. Indeed, TFs largely vary across REM and NREM sleep in younger individuals while the variations disappear in elderly individuals, explaining the enhanced improvements in REM or NREM sleep classification problems only in the younger individuals. This study demonstrates the benefits of combining topological data analysis and mathematical modeling to extract hidden inputs of neural networks from puzzling wearable data.
Deletion of NuRD componentMta2in nephron progenitor cells causes developmentally programmed FSGS
Authors: Basta Jeannine , Robbins Lynn , Stout Lisa , Brennan Michelle , Shapiro John , Chen Mary , Denner Darcy , Baldan Angel , Messias Nidia , Madhavan Sethu , Parikh Samir V. , Rauchman Michael 
Published: October 2023

ABSTRACTLow nephron endowment at birth is a risk factor for chronic kidney disease. The prevalence of this condition is increasing due to higher survival rates of preterm infants and children with multi- organ birth defect syndromes that affect the kidney and urinary tract. We created a mouse model of congenital low nephron number due to deletion ofMta2in nephron progenitor cells.Mta2is a core component of the Nucleosome Remodeling and Deacetylase (NuRD) chromatin remodeling complex. These mice developed albuminuria at 4 weeks of age followed by focal segmental glomerulosclerosis (FSGS) at 8 weeks, with progressive kidney injury and fibrosis. Our studies reveal that altered mitochondrial metabolism in the post-natal period leads to accumulation of neutral lipids in glomeruli at 4 weeks of age followed by reduced mitochondrial oxygen consumption. We found that NuRD cooperated with Zbtb7a/7b to regulate a large number of metabolic genes required for fatty acid oxidation and oxidative phosphorylation. Analysis of human kidney tissue also supported a role for reduced mitochondrial lipid metabolism and ZBTB7A/7B in FSGS and CKD. We propose that an inability to meet the physiological and metabolic demands of post-natal somatic growth of the kidney promotes the transition to CKD in the setting of glomerular hypertrophy due to low nephron endowment.