bluuebunny/crossref_metadata_2025_split huggingface dataset:
Overcoming Pitfalls when Estimating Variation in Parental Care: A Simulation Study
Authors: Mundry R. , Dal Pesco F. , Avilès de Diego A. , Fischer J. 
Published: October 2023

Abstract1.BackgroundIn many animal species, parents allocate resources to enhance offspring survival and thereby reproductive success (‘parental investment’). Parental care behaviors are a crucial part of parental investment, and one critical question in behavioral ecology is to which degree variation in parental care affects offspring development and survival. Crucially, parental care behaviors vary with offspring age, which creates non-trivial problems for statistical analyses, specifically when sampling is incomplete or biased.2.MethodsWe conducted a simulation study to illustrate the problem when analyzing average parental care behaviors while not correcting for offspring age (‘conventional approach’), and propose a modeling approach to correct for offspring age and sampling biases. We simulated a set of different scenarios, varying with regard to sample size, sampling scheme, and variation in parental behavior. To model parent behavior as a function of offspring age, we fitted a Generalized Linear Mixed Model to each simulated data set. We included offspring age as a fixed effect and extracted Best Linear Unbiased Predictors (BLUPs) for the random intercept of parent identity. These BLUPs represent the parental behavior corrected for offspring age. Finally, we assessed how our proposed modeling approach compares to the conventional approach.3.ResultsThe proposed modeling approach was superior to the conventional approach. More specifically, the conventional approach clearly overestimated the variation between parents, and even diagnosed appreciable variation when there was none in the simulated data. This bias was exacerbated in incomplete or unbalanced sampling schemes.4.Conclusion.Our simulation study clearly demonstrates the necessity of correcting for offspring age in analyses of parental behavior. We strongly suggest including random slopes of offspring age within parent-offspring dyads, because doing so can reveal parent-specific trajectories of the modeled behavior against offspring age. Our approach allows for the inclusion of further categorical and continuous predictor variables that may affect parental behavior, such as parity or ecological conditions. In summary, the proposed modeling approach has several advantages over conventional methods.
Cell type specificity of mosaic chromosome 1q gain resolved by snRNA-seq in a case of epilepsy with hyaline protoplasmic astrocytopathy
Authors: Leng Kun , Cadwell Cathryn R. , Devine W. Patrick , Tihan Tarik , Qi Zhongxia , Singhal Nilika , Glenn Orit , Kamiya Sherry , Wiita Arun , Berger Amy , Shieh Joseph T. , Titus Erron W. , Paredes Mercedes F. , Upadhyay Vaibhav 
Published: October 2023

STRUCTURED ABSTRACTIntroductionMosaic gain of chromosome 1q (chr1q) has been associated with malformation of cortical development (MCD) and epilepsy. Hyaline protoplasmic astrocytopathy (HPA) is a rare neuropathological finding seen in cases of epilepsy with MCD. The cell-type specificity of mosaic chr1q gain in the brain and the molecular signatures of HPA are unknown.MethodsWe present a child with pharmacoresistant epilepsy who underwent epileptic focus resections at age 3 and 5 years and was found to have mosaic chr1q gain and HPA. We performed single-nuclei RNA-sequencing (snRNA-seq) of brain tissue from the second resection.ResultssnRNA-seq showed increased expression of chr1q genes specifically in subsets of neurons and astrocytes. Differentially expressed genes associated with inferred chr1q gain includedAKT3and genes associated with cell adhesion or migration. A subpopulation of astrocytes demonstrated marked enrichment for synapse-associated transcripts, possibly linked to the astrocytic inclusions observed in HPA.DiscussionsnRNA-seq may be used to infer the cell type-specificity of mosaic chromosomal copy number changes and identify associated gene expression alterations, which in the case of chr1q gain may involve aberrations in cell migration. Future studies using spatial profiling could yield further insights on the molecular signatures of HPA.
Structural covariation between cerebellum and cerebral cortex is atypically modulated by thalamus in autism spectrum disorder
Authors: d’Oleire Uquillas Federico , Sefik Esra , Li Bing , Trotter Matthew A. , Steele Kara , Seidlitz Jakob , Gesue Rowen , Latif Mariam , Fasulo Tristano , Zhang Veronica , Kislin Mikhail , Verpeut Jessica L. , Cohen Jonathan D. , Sepulcre Jorge , Wang Samuel S.-H. , Gomez Jesse 
Published: October 2023

AbstractDespite its strong interconnectivity with the cerebral cortex, the influence of the human cerebellum on neocortical structure and its role in the development of neuropsychiatric disorders is unclear. Because cerebellar damage in early postnatal life creates a high risk for autism spectrum disorder (ASD), we investigated inter-relationships in cerebello-cerebral morphology. Leveraging a large structural brain MRI dataset in neurotypical children (n=375) and those diagnosed with ASD (n=373), we examined whether volumetric variation in cerebellar structure across individuals was correlated with neocortical variation during development, modeling the thalamus as a moderating coupling factor. We found negative covariation between cerebellar cortical regions and thalamic/sensorimotor neocortical regions, and positive covariation between thalamic and sensorimotor neocortical regions. This pattern aligned with the major disynaptic path of cerebellar inhibition to thalamocortical excitation. Examining the dependence of this structural covariation on ASD diagnosis, we found that neurotypical and ASD children displayed inverted hemispheric biases. In ASD, the thalamus moderated structural associations between the left cerebellum and right sensorimotor cortex. For neurotypical children, right cerebellum and left sensorimotor cortex were coupled. Notably, structural coupling between cerebellum, thalamus, and neocortex was strongest in younger childhood and waned by early adolescence, a time during which behavioral differences were smallest between typically developing and autistic children. In addition to the sensorimotor cortex, cerebellar dentate nuclei in ASD displayed greater coupling to broad neocortical regions, and these nuclei related to cognitive function differently such that greater dentate nuclear volume was associated with greater behavioral impairment in ASD but not in controls. Graph analyses demonstrated that the cerebello-thalamocortical network was more densely and less prolifically interconnected in ASD than in typical development. Taken as a whole, our study reveals a developmental interplay between the cerebellum, thalamus, and neocortex which differs in ASD from neurotypical children. This pattern is consistent with a model of ASD in which early developmental influences of cerebellar output on brain maturation are specifically moderated by cerebello-thalamocortical pathways.
Modeling the roles of cohesotaxis, cell-intercalation, and tissue geometry in collective cell migration ofXenopusmesendoderm
Authors: Comlekoglu Tien , Dzamba Bette J. , Pacheco Gustavo G. , Shook David R. , Sego T.J. , Glazier James A. , Peirce Shayn M. , DeSimone Douglas W. 
Published: October 2023

ABSTRACTCollectively migrating Xenopus mesendoderm cells are arranged into leader and follower rows with distinct adhesive properties and protrusive behaviors. In vivo, leading row mesendoderm cells extend polarized protrusions and migrate along a fibronectin matrix assembled by blastocoel roof cells. Traction stresses generated at the leading row result in the pulling forward of attached follower row cells. Mesendoderm explants removed from embryos provide an experimentally tractable system for characterizing collective cell movements and behaviors, yet the cellular mechanisms responsible for this mode of migration remain elusive. We introduce an agent-based computational model of migrating mesendoderm in the Cellular-Potts computational framework to investigate the relative contributions of multiple parameters specific to the behaviors of leader and follower row cells. Sensitivity analyses identify cohesotaxis, tissue geometry, and cell intercalation as key parameters affecting the migration velocity of collectively migrating cells. The model predicts that cohesotaxis and tissue geometry in combination promote cooperative migration of leader cells resulting in increased migration velocity of the collective. Radial intercalation of cells towards the substrate is an additional mechanism to increase migratory speed of the tissue.Summary StatementWe present a novel Cellular-Potts model of collective cell migration to investigate the relative roles of cohesotaxis, tissue geometry, and cell intercalation on migration velocity ofXenopusmesendoderm
Noninvasive detection of Phenylalanine in the human brain with MRS at 7T
Authors: Salibi Nouha , Denney Thomas S , Deshpande Gopikrishna 
Published: October 2023

AbstractWe set out to measure phenylalanine in the human brain using magnetic resonance spectroscopy (MRS) in a ultra-high field 7T MRI scanner. Phenylalanine is a precursor to Norepinephrine, a neurotransmitter important for attention and arousal. Depletion in norepinephrine, especially in the locus coeruleus, has been implicated as an etiological factor in Alzheimer’s disease. Therefore, being able to noninvasively measure phenylalaninein vivoin humans has a multitude of translational applications. Using phantom experiments, we first validate and optimize the MRS techniques used for observing phenylalanine in the brain at 7T. However, we failed to detect phenylalanine in human volunteers (N=15). In order to understand the reasons for this failure, we performed experiments in a cat model with external phenylalanine injections to determine the amount of phenylalanine required for it to be detectedin vivoin the brain. This threshold was found to be 3.4 mM. This indicated that phenylalanine concentrations in both healthy and AD patients, that too in a small region such as the locus coeruleus, will likely not meet this threshold. Therefore, we conclude that even with state-of-the-art technologies and 7T MRI, it is not possible to detect phenylalanine in the human brainin vivounder natural conditions.
A Classification-Based Generative Approach to Selective Targeting of Global Slow Oscillations during Sleep
Authors: Alipour Mahmoud , Seok SangCheol , Mednick Sara C. , Malerba Paola 
Published: October 2023

AbstractBackgroundGiven sleep’s crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5-1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability.Objective/HypothesisThis study targets a specific type of SOs, widespread on the electrode manifold in a short delay (“global SOs”), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile.MethodsWe introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance.ResultsOur study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization.ConclusionOur research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.
Combinatorial binding of semantic information through the sharing of neural oscillatory signals
Authors: Noguchi Yasuki 
Published: October 2023

AbstractWe comprehend linguistic inputs (e.g. sentence) by retrieving semantic memory of each element (e.g. word) and integrating them. How semantic information is represented and bound as neural (electric) signals is an unsolved issue. I presently used a simple sentence composed of a noun phrase (NP) and a verb (V), comparing human electroencephalography (EEG) responses to a congruent sentence in which the NP and V were semantically related (e.g. “grass grows”) with those to an incongruent sentence (e.g. “a key grows”). In the left temporo-parietal cortex, neural oscillation patterns (8 – 30 Hz) to the second stimulus (V) shifted toward those to the first stimulus (NP), thereby producing coherent (faster and more regular) neural responses to the congruent sentence. No such NP-V interaction was observed in the incongruent sentence. These results indicate that the “semantic unification” as a linguistic concept actually takes place in neural oscillatory signals of the healthy human brain.
Genome wide association study of clinical duration and age at onset of sporadic CJD
Authors: Hummerich Holger , Speedy Helen , Campbell Tracy , Darwent Lee , Hill Elizabeth , Collins Steven , Stehmann Christiane , Kovacs Gabor G , Geschwind Michael D , Frontzek Karl , Budka Herbert , Gelpi Ellen , Aguzzi Adriano , van der Lee Sven J , van Duijn Cornelia M , Liberski Pawel P , Calero Miguel , Sanchez-Juan Pascual , Bouaziz-Amar Elodie , Laplanche Jean-Louis , Haïk Stéphane , Brandel Jean-Phillipe , Mammana Angela , Capellari Sabina , Poleggi Anna , Ladogana Anna , Pocchiari Maurizio , Zafar Saima , Booth Stephanie , Jansen Gerard H , Areškevičiūtė Aušrinė , Lund Eva Løbner , Glisic Katie , Parchi Piero , Hermann Peter , Zerr Inga , Appleby Brian S , Collinge John , Mead Simon 
Published: October 2023

AbstractHuman prion diseases are rare, transmissible and often rapidly progressive dementias. The most common type, sporadic Creutzfeldt-Jakob disease (sCJD), is highly variable in clinical duration and age at onset. Genetic determinants of late onset or slower progression might suggest new targets for research and therapeutics. We assembled and array genotyped sCJD cases diagnosed in life or at autopsy. Clinical duration (median:4, interquartile range (IQR):2.5-9 (months)) was available in 3,773 and age at onset (median:67, IQR:61-73 (years)) in 3,767 cases. Phenotypes were successfully transformed to approximate normal distributions allowing genome-wide analysis without statistical inflation. 53 SNPs achieved genome-wide significance for the clinical duration; all of which were located at chromosome 20 (top SNP rs1799990, pvalue=3.45×10-36, beta=0.34 for an additive model; rs1799990, pvalue=9.92×10-67, beta=0.84 for a heterozygous model). Fine mapping, conditional and expression analysis suggests that the well-known non-synonymous variant at codon 129 is the obvious outstanding genome-wide determinant of clinical duration. Pathway analysis and suggestive loci are described. No genome-wide significant SNP determinants of age at onset were found, but theHS6ST3gene was significant (pvalue=1.93 × 10-6) in a gene-based test. We found no evidence of genome-wide genetic correlation between case-control (disease risk factors) and case-only (determinants of phenotypes) studies. Relative to other common genetic variants,PRNPcodon 129 is by far the outstanding modifier of CJD survival suggesting only modest or rare variant effects at other genetic loci.
Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: P-CARDIAC model
Authors: Zhou Yekai , Lin Jiaxi , Yu Qiuyan , Blais Joseph Edgar , Wan Eric Yuk Fai , Lee Marco , Wong Emmanuel , Siu David Chung-Wah , Wong Vincent , Chan Esther Wai Yin , Lam Tak-Wah , Chui William , Wong Ian Chi Kei , Luo Ruibang , Chui Celine SL 
Published: October 2023

AbstractThis study aimed to develop and validate a cardiovascular diseases (CVD) risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using Machine-Learning technique.Three cohorts of Chinese patients with established CVD in Hong Kong were included; Hong Kong Island cohort as the derivation cohort, whilst the Kowloon and New Territories cohorts were validation cohorts. The 10-year CVD outcome was a composite of diagnostic or procedure codes for coronary heart disease, ischaemic or haemorrhagic stroke, peripheral artery disease, and revascularization. We estimated incidence of recurrent CVD events for each cohort with reference to the total person-years of each cohort. Multivariate imputation with chained equations (MICE) and XGBoost were applied for the model development. The comparison with TRS-2°P and SMART2 used the validation cohorts with 1000 bootstrap replicates.A total 48,799, 119,672 and 140,533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which, eight classes of medications were considered interactive drug use. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C-statistic of 0·69. Internal validation showed good discrimination and calibration performance with C-statistic over 0·6. P-CARDIAC also showed better performance than TRS-2°P and SMART2.Compared to other risk scores, P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden.Condensed AbstractA CVD risk prediction model named Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events among Chinese adults using Machine-Learning technique was newly developed. It predicted 10-year CVD outcome including a composite of diagnostic or procedure codes for coronary heart disease, ischaemic or haemorrhagic stroke, peripheral artery disease, and revascularization by incidence of recurrent CVD. Model showed satisfying discrimination and calibration with a C-statistic of 0·69. P-CARDIAC also showed better performance than existing risk scores, such as TRS-2°P and SMART2. P-CARDIAC could help predict recurrent CVD risk and reduce the healthcare burden.
Addressing Label Noise for Electronic Health Records: Insights from Computer Vision for Tabular Data
Authors: Yang Jenny , Triendl Hagen , Soltan Andrew A. S. , Prakash Mangal , Clifton David A. 
Published: October 2023

AbstractThe analysis of extensive electronic health records (EHR) datasets often calls for automated solutions, with machine learning (ML) techniques, including deep learning (DL), taking a lead role. One common task involves categorizing EHR data into predefined groups. However, the vulnerability of EHRs to noise and errors stemming from data collection processes, as well as potential human labeling errors, poses a significant risk. This risk is particularly prominent during the training of DL models, where the possibility of overfitting to noisy labels can have serious repercussions in healthcare. Despite the well-documented existence of label noise in EHR data, few studies have tackled this challenge within the EHR domain. Our work addresses this gap by adapting computer vision (CV) algorithms to mitigate the impact of label noise in DL models trained on EHR data. Notably, it remains uncertain whether CV methods, when applied to the EHR domain, will prove effective, given the substantial divergence between the two domains. We present empirical evidence demonstrating that these methods, whether used individually or in combination, can substantially enhance model performance when applied to EHR data, especially in the presence of noisy/incorrect labels. We validate our methods and underscore their practical utility in real-world EHR data, specifically in the context of COVID-19 diagnosis. Our study highlights the effectiveness of CV methods in the EHR domain, making a valuable contribution to the advancement of healthcare analytics and research.
Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: A Scoping Review
Authors: Apakama Donald , Abbott Ethan E , Richardson Lynne , Chan Lili , Carr Brendan G , Nadkarni Girish N 
Published: October 2023

AbstractObjectiveSocial Determinants of Health (SDOH) are critical drivers of health disparities and patient outcomes. However, accessing and collecting patient level SDOH data can be operationally challenging in the emergency department clinical setting requiring innovative approaches. This scoping review examines the potential of artificial intelligence (AI) and data science for modeling, extraction, and incorporation of SDOH data specifically within emergency departments (ED), further identifying areas for advancement and investigation.MethodsWe conducted a standardized search across Medline (Ovid), Embase (Ovid), CINAHL, Web of Science, and ERIC databases for studies published between 2015-2022. We focused on identifying studies employing AI or data science related to SDOH within emergency care contexts or conditions. Two specialized reviewers in Emergency Medicine and clinical informatics independently assessed each article, resolving discrepancies through iterative reviews and discussion. We then extracted data covering study details, methodologies, patient demographics, care settings, and principal outcomes.ResultsOf the 1,047 studies screened, 26 met the inclusion criteria. Notably, 9 out of 26 studies were solely concentrated on ED patients. Conditions studied spanned broad Emergency Medicine complaints and conditions including sepsis, acute myocardial infarction, and asthma. The majority (n=16) explored multiple SDOH domains, with homelessness/housing insecurity and neighborhood/built environment predominating. Machine learning (ML) techniques were utilized in 23 of the studies, natural language processing (NLP) being the most common approach used (n=11). Rule-based (n=5), deep learning (n=2), and pattern matching (n=4) were the most common NLP techniques used. NLP models in the reviewed studies displayed significant predictive performance with outcomes, With F1-scores ranging between 0.40 - 0.75 and specificities nearing 95.9%.ConclusionAlthough in its infancy, the convergence of AI and data science techniques, especially ML and NLP, with SDOH in Emergency Medicine offers transformative possibilities for better usage and integration of social data into clinical care and research. With a significant focus on the ED and notable NLP model performance, there is an imperative to standardize SDOH data collection, refine algorithms for diverse patient groups, and champion interdisciplinary synergies. These efforts aim to harness SDOH data optimally, enhancing patient care and mitigating health disparities. Our research underscores the vital need for continued investigation in this domain.