bluuebunny/crossref_metadata_2025_split huggingface dataset:
Delirium Severity and Physical Function in Hospitalized Persons Living With Dementia
Authors: Kuzmik Ashley , Boltz Marie , Resnick Barbara , McPherson Rachel , Rodriguez Marleny , Drazich Brittany F. , Galik Elizabeth 
Published: January 2024

Objective:
This study investigated whether demographic characteristics (age, sex, and race) moderated delirium severity as a predictor of physical function in hospitalized persons living with dementia.

Methods:
The sample consisted of 351 patients enrolled in a randomized controlled trial (Function Focused Care for Acute Care Using the Evidence Integration Triangle). Preliminary analysis was conducted to assess the main effect, and multiple linear regression was used to examine the moderating effect of demographic characteristics between delirium severity and physical function.

Results:
Both age and sex were found to have significant moderating effects on the relationship between delirium severity and physical function (β = 2.22; P = 0.02 and β = 1.34; P = 0.04, respectively). Older adults aged 85 years or older with higher levels of delirium severity reported lower levels of physical function compared with older adults aged 65 to 84 years. Males with higher levels of delirium severity reported lower levels of physical function compared with females. Race did not significantly moderate the association between delirium severity and physical function (β = 0.22; P = 0.90).

Conclusions:
Our findings suggest that age and sex may have differential effects on physical function across different levels of delirium severity in hospitalized persons living with dementia.
The Technology in Caring Questionnaire
Authors: Kiselica Andrew M. , Lin Shayne S.H. , Ranum Rylea , Mikula Cynthia M. , Hermann Greta , Boone Anna , Scullin Michael , Mechanic-Hamilton Dawn , Wolf Timothy , Stevens Alan , Benge Jared F. 
Published: January 2024

Objective:
We developed the Technology in Caring Questionnaire (TCQ) to assess the use of technology-based strategies by dementia caregivers.

Methods:
One hundred caregivers completed a survey that included TCQ items along with measures of technology proficiency and patient and caregiver-centered outcomes.

Results:
The final 34-item TCQ scale had adequate to excellent internal consistency (raw Cronbach alpha = 0.75; standardized Cronbach alpha = 0.95; Guttman lambda-6 = 0.97). TCQ scores demonstrated modest convergent associations with scores from measures of smartphone (r = 0.265, P < 0.01) and computer proficiency (r = 0.230, P < 0.05) but a strong association with overall technology experience scores (r = 0.578, P < 0.001). Elevated TCQ scores were associated with reduced informant-reported cognitive symptoms (B = −0.003, P < 0.05), increased ability of caregivers to find support and information (B = 0.03, P < 0.001), and increased direct care strain (B = 0.03, P < 0.05), after controlling for dementia severity and demographics.

Conclusion:
The TCQ has good psychometric properties for the assessment of technology-based care strategies among dementia caregivers. Findings imply that the use of technologies may aid in symptom management and finding support and information but may also increase caregiver strain.
Citizen Science Data on Urban Forageable Plants: A Case Study in Brazil
Authors: Soares Filipi Miranda , Pires LuÍs Ferreira , Garcia Maria Carolina , Coradin Lidio , Ghilardi-Lopes Natalia Pirani , Silva Rubens Rangel , de Carvalho Aline Martins , Gavai Anand , Bouzembrak Yamine , dos Santos Maculan Benildes Coura Moreira , Koffler Sheina , Montedo Uiara Bandineli , Drucker Debora Pignatari , Santiago Raquel , de Carvalho Maria Clara Peres , Silva Lima Ana Carolina da , Elias Gabriel Hillary Dandara , de França Stephanie Gabriele Mendonça , de Almeida Karoline Reis , dos Santos Bárbara Junqueira , Saraiva Antonio Mauro 
Published: January 2024

AbstractThis paper presents two key data sets derived from thePomar Urbanoproject. The first data set is a comprehensive catalog of edible fruit-bearing plant species, native or introduced in Brazil. The second data set, sourced from the iNaturalist platform, tracks the distribution and monitoring of these plants within urban landscapes across Brazil. The study encompasses data from all 27 Brazilian state capitals, focusing on the ten cities that contributed the most observations as of August 2023. The research emphasizes the significance of citizen science in urban biodiversity monitoring and its potential to contribute to various fields, including food and nutrition, creative industry, study of plant phenology, and machine learning applications. We expect the data sets to serve as a resource for further studies in urban foraging, food security, cultural ecosystem services, and environmental sustainability.
Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks
Authors: Deng Chao , Li Hong-Dong , Zhang Li-Shen , Liu Yi-Wei , Li Yaohang , Wang Jianxin 
Published: January 2024

AbstractMotivationIdentifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited.ResultsHere, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes.AvailabilityDISHyper is freely available for download athttps://github.com/genemine/DISHyper.Contactjxwang@mail.csu.edu.cn
Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM
Authors: Sasmal Subarna , Pal Triasha , Hocky Glen M. , McCullagh Martin 
Published: January 2024

AbstractQuantifying the conformational ensembles of biomolecules is fundamental to describing mechanisms of processes such as ligand binding and allosteric regulation. Accurate quantification of these ensembles remains a challenge for all but the simplest molecules. One such challenge is insufficient sampling which enhanced sampling approaches, such as metadynamics, were designed to overcome; however, the non-uniform frame weights that result from many of these approaches present an additional challenge to ensemble quantification techniques such as Markov State Modeling or structural clustering. Here, we present rigorous inclusion of non-uniform frame weights into a structural clustering method entitled shapeGMM. The shapeGMM method fits a Gaussian mixture model to particle positions, and here we advance that approach by incorporating nonuniform frame weights in the estimates of all parameters of the model. The resulting models are high dimensional probability densities for the unbiased systems from which we can compute important thermodynamic properties such as relative free energies and configurational entropy. The accuracy of this approach is demonstrated by the quantitative agreement between GMMs computed by Hamiltonian reweighting and direct simulation of a coarse-grained helix model system. Furthermore, the relative free energy computed from a high dimensional probability density of alanine dipeptide reweighted from a metadynamics simulation quantitatively reproduces the metadynamics free energy in the basins. Finally, the method identifies hidden structures along the actin globular to filamentous-like structural transition from a metadynamics simulation on a linear discriminant analysis coordinate trained on GMM states, demonstrating the broad applicability of combining our prior and new methods, and illustrating how structural clustering of biased data can lead to biophysical insight. Combined, these results demonstrate that frame-weighted shapeGMM is a powerful approach to quantify biomolecular ensembles from biased simulations.
Comprehensive epitope mutational scan database enables accurate T cell receptor cross-reactivity prediction
Authors: Banerjee Amitava , Pattinson David J , Wincek Cornelia L. , Bunk Paul , Axhemi Armend , Chapin Sarah R. , Navlakha Saket , Meyer Hannah V. 
Published: January 2024

ABSTRACTPredicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database, called BATCAVE, encompassing complete single amino acid mutational assays of more than 22,000 TCR-peptide pairs, centered around 25 immunogenic human and mouse epitopes, across both major histocompatibility complex classes, against 151 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. We also developed an active learning version of BATMAN, which can efficiently learn the binding profile of a novel TCR by selecting an informative yet small number of peptides to assay. When validated on our database, BATMAN outperforms existing methods and reveals important biochemical predictors of TCR-peptide interactions. Finally, we demonstrate the broad applicability of BATMAN, including for predicting off-target effects for TCR-based therapies and polyclonal T cell responses.
Genetic network analysis uncovers spatial variation in diversity and connectivity of a species presenting a continuous distribution
Authors: Fournier Cory , Manseau Micheline , Redquest Bridget , Andrew Leon , Kelly Allicia , Hervieux Dave , Hegel Troy , Pittoello Gigi , Trim Vicki , Brannen Dennis , Wilson Paul 
Published: January 2024

ABSTRACTThe conservation of genetic diversity and connectivity is essential for the long-term persistence and adaptive ability of a species. Recent calls have been made for the inclusion of genetic diversity and differentiation measures in the assessment, management, and conservation of species. However, the literature often lacks direction on how to do so for species with continuous distributions or no distinct breaks in genetic connectivity. There are many considerations to overcome when investigating genetic diversity and connectivity of such species. We combine multiple genetic network methodologies with more traditional population genetic analyses within a single framework to address the challenges of investigating population structure and quantifying variation in genetic diversity and connectivity of wide-ranging species with continuous distributions. We demonstrate the efficacy and applicability of our framework through a study on woodland caribou (Rangifer tarandus) occupying the boreal forest of Canada; a species of significant conservation concern. The dataset consisted of 4911 unique individuals genotyped at 9 microsatellite loci, which were subsequently partitioned into 103 spatial nodes to create a population-based genetic network. The Walktrap community detection algorithm was used to detect hierarchical population genetic structure within the study area and node-based network metrics such as mean inverse edge weight and clustering coefficient were used to quantify the variation in genetic connectivity across the range. Lastly, genetic diversity was assessed by calculating allelic richness and heterozygosity of the nodes making up the network. The community detection analysis identified two communities at the coarsest scale to nine communities at the optimal partition. A strong pattern of Isolation by Distance (IBD) was found across the range at multiple scales. Furthermore, signs of genetic erosion along the study area’s southern boundaries were depicted by nodes presenting low genetic diversity and low centrality values. These results are important to the species status assessments in providing previously unavailable information on connectivity and diversity within and beyond the current local population units used in management. Our approach to quantify the patterns and extent of connectivity across the boreal range is comprehensive and could easily be adapted to other species. The results are robust and provide a solid foundation for the continued monitoring and recovery of the species.
Mitochondrial uncoupler and retinoic acid synergistically induce differentiation and inhibit proliferation in neuroblastoma
Authors: Jiang Haowen , Tiche Sarah Jane , He Clifford JiaJun , Jedoui Mohamed , Forgo Balint , Zhao Meng , He Bo , Li Yang , Li Albert M. , Truong Anh T. , Ho Jestine , Simmermaker Cathyrin , Yang Yanan , Zhou Meng-Ning , Hu Zhen , Cuthbertson Daniel J. , Svensson Katrin J. , Hazard Florette K. , Shimada Hiroyuki , Chiu Bill , Ye Jiangbin 
Published: January 2024

AbstractNeuroblastoma is a leading cause of death in childhood cancer cases. Unlike adult malignancies, which typically develop from aged cells through accumulated damage and mutagenesis, neuroblastoma originates from neural crest cells with disrupted differentiation. This distinct feature provides novel therapeutic opportunities beyond conventional cytotoxic methods. Previously, we reported that the mitochondrial uncoupler NEN (niclosamide ethanolamine) activated mitochondria respiration to reprogram the epigenome, promoting neuronal differentiation. In the current study, we further combine NEN with retinoic acid (RA) to promote neural differentiation bothin vitroandin vivo. The treatment increased the expression of RA signaling and neuron differentiation-related genes, resulting in a global shift in the transcriptome towards a more favorable prognosis. Overall, these results suggest that the combination of a mitochondrial uncoupler and the differentiation agent RA is a promising therapeutic strategy for neuroblastoma.
Right-to-Left Shunt-associated Brain Functional Changes in Migraine: Evidences from a Resting-state FMRI Study
Authors: Cao Wenfei , Jiao Lei , Zhou Huizhong , Zhong Jiaqi , Wang Nizhuan , Yang Jiajun 
Published: January 2024

AbstractBackgroundMigraine, a neurological disorder under perpetual investigation, has an elusive etiology. An potential association with Right-to-Left Shunt (RLS) exists, yet the precise nature of this connection remains unclear. This study employs the resting-state functional magnetic resonance imaging (rs-fMRI) technique to examine brain functional differences between the migraine patients with and without RLS, aiming at exploring RLS associated alterations in functional segregation and integration.MethodsThis study included 32 migraine patients (14 patients with RLS and 18 without RLS), each undergoing rs-fMRI data acquisition. The amplitude of low-frequency fluctuation (ALFF) was employed to investigate functional segregation. Functional connectivity (FC) analysis was conducted to explore the functional integration across distinct brain regions. Graph theory-based network analysis was utilized to assess functional networks in migraine patients with RLS. Pearson correlation analysis further explored the relationship between RLS severity and various functional metrics..ResultsCompared with migraine patients without RLS, migraine patients with RLS exhibited a significant increase in the ALFF in the left middle occipital and superior occipital gyrus; As to FC, the reduced connectivity between the left rolandic operculum and the right middle cingulate gyrus was observed in migraine patients with RLS; Based on the brain networks analysis, migraine patients with RLS displayed higher values of the normalized clustering coefficient and greater betweenness centrality in specific regions, including the left precuneus, right insula, and right inferior temporal gyrus. Further, the study found positive correlations between ALFF values in the temporal lobes, thalamus, left middle occipital, and superior occipital gyrus and RLS severity. Conversely, negative correlations emerged between ALFF values in the right inferior frontal gyrus, middle frontal gyrus, and insula and RLS grading. Finally, the study identified a positive correlation between angular gyrus betweenness centrality and RLS severity.ConclusionRLS-associated brain functional alterations in migraine consisted of local brain regions, connectivity, and networks involved in pain conduction and regulation did exist in migraine with RLS.
High-performing Multi-task Model of Urinary Tract Dilation (UTD) Classification for Neonatal Ultrasound Reports Through Natural Language Processing
Authors: Hua Yining , Mukkamala Anudeep , Estrada Carlos , Li Michael Lingzhi , Wang Hsin-Hsiao Scott 
Published: January 2024

ABSTRACTObjectiveThe urinary tract dilation (UTD) classification system provides objective assessment relevant to hydronephrosis management for children. However, the lack of uniform language regarding UTD in radiology reports leads to significant difficulty in both clinical management and research. We seek to develop a unified multi-task/multi-class model that can effectively extract UTD components and classifications from early postnatal ultrasound (US) reports.MethodsRadiology records from our institution were reviewed to identify infants aged 0-90 days undergoing early ultrasound for antenatal UTD. The report and images were reviewed by the study team to create the ground truth of UTD classification and components (primary outcome). Bio_ClinicalBERT, a variant of the Bidirectional Encoder Representations from Transformers (BERT) model, was used as the embedding layers of the classification model. The model was fine-tuned with 11 linear classification layers. All but the last BERT layer were frozen during the fine-tuning process. The model performance was evaluated with five-fold cross-validation with an 80:20 train-test ratio.Results2460 early (0-90 days) US reports were included. The five-fold cross-validated model performance is satisfactory (Weighted F1 > 0.9 for all UTD components). We report the weighted F1 scores, accuracies, and standard deviations for all 11 tasks and their average performance.ConclusionsBy applying deep state-of-the-art NLP neural networks, we developed a high-performing, efficient, and scalable solution to extract UTD components from unstructured ultrasound reports using one single multi-task model. This can potentially help standardize and facilitate large-scale computer vision research for pediatric hydronephrosis. Key Words: machine learning, efficiency, ambulatory care, forecasting
The Validation of Non-Invasive Pressure-Volume Loop Indices in Severe Aortic Stenosis
Authors: Aldalati Omar , Eskandari Mehdi , Ali Montasir H , Cabaco Rita , Byrne Jonathan , Monaghan Mark , Lukban Bobit , Kennedy Nicola , Shah Ajay , Dworakowski Rafal , MacCarthy Philip 
Published: January 2024

AbstractBackgroundStudies utilizing invasive pressure-volume loops offer valuable insights into left ventricular (LV) contractility, yet their availability remains limited. Conversely, non-invasive indices are accessible and reproducible; however, their validation in aortic stenosis (AS) is lacking. We sought to validate the non-invasive indices of PVL studies in a group of symptomatic severe AS.MethodsWe recruited patients with symptomatic severe AS admitted for trans-catheter aortic valve replacement (TAVR) to undergo invasive PVL studies. Non-invasive PVL indices were measured with three-dimensional (3D) echocardiography with a pre-specified protocol. The agreement between invasive and non-invasive calculation methods were assessed.ResultsEleven patients (11) were recruited for this pilot study. The non-invasive end-systolic pressure-volume relationship (ESPVR) Kelly’s method (Ees(sb)= 0.9 × systolic blood pressure/end-systolic volume (ESV)) had the best agreement with invasive ESPVR (limits of agreement - 1.7 to 2.1 with a percentage error of 24%, one sample T-test p =0.504). Systolic blood pressure, as measured by the brachial blood pressure cuff, had the best agreement with end-systolic pressure in severe aortic stenosis (limits of agreement −60 to 60 with a percentage error of 3%, one sample T-test p =0.959).ConclusionMeasurement of the single-beat estimate of ventricular elastance (Ees(sb)) is possible in patients with severe aortic stenosis. Kelly’s method (Ees(sb)= 0.9 × SBP /ESV) has the best agreement with the invasive measurement of left ventricular elastance (Ees). Systolic blood pressure, as measured by the brachial blood pressure cuff, has the best agreement with end-systolic pressure in severe aortic stenosis.
Instability of excitatory synapses in experimental autoimmune encephalomyelitis and the outcome for excitatory circuit inputs to individual cortical neurons
Authors: Gillani Rebecca L. , Kironde Eseza N. , Whiteman Sara , Zwang Theodore J. , Bacskai Brian J. 
Published: January 2024

AbstractSynapses are lost on a massive scale in the brain and spinal cord of people living with multiple sclerosis (PwMS), and this synaptic loss extends far beyond demyelinating lesions. Post-mortem studies show the long-term consequences of multiple sclerosis (MS) on synapses but do not inform on the early impacts of neuroinflammation on synapses that subsequently lead to synapse loss. How excitatory circuit inputs are altered across the dendritic tree of individual neurons under neuroinflammatory stress is not well understood. Here, we directly assessed the structural dynamics of labeled excitatory synapses in experimental autoimmune encephalomyelitis (EAE) as a model of immune-mediated cortical neuronal damage. We used in vivo two-photon imaging and a synthetic tissue-hydrogel super-resolution imaging technique to reveal the dynamics of excitatory synapses, map their location across the dendritic tree of individual neurons, and examine neurons at super-resolution for synaptic loss. We found that excitatory synapses are destabilized but not lost from dendritic spines in EAE, starting with the earliest imaging session before symptom onset. This led to dramatic changes in excitatory circuit inputs to individual cells. In EAE, stable synapses are replaced by synapses that appear or disappear across the imaging sessions or repeatedly change at the same location. These unstable excitatory inputs occur closer to one another in EAE than in healthy controls and are distributed across the dendritic tree. When imaged at super-resolution, we found that a small proportion of dendritic protrusions lost their presynapse and/or postsynapse. Our finding of diffuse destabilizing effects of neuroinflammation on excitatory synapses across cortical neurons may have significant functional consequences since normal dendritic spine dynamics and clustering are essential for learning and memory.