bluuebunny/crossref_metadata_2025_split huggingface dataset:
Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey
Authors: Frost Emma , O’Shaughnessy Pauline , Steel David , Braunack-Mayer Annette , James Aquino Yves Saint , Carter Stacy M. 
Published: February 2023

ABSTRACTApplications of AI (artificial intelligence) have the potential to improve aspects of healthcare. However, studies have shown that healthcare AI algorithms also have the potential to perpetuate existing inequities in healthcare, performing less effectively for marginalised populations. Studies on public attitudes toward AI outside of the healthcare field have tended to show higher levels of support for AI amongst socioeconomically advantaged groups that are less likely to be sufferers of algorithmic harms. We aimed to examine the sociodemographic predictors of support for scenarios related to healthcare AI.The AVA-AI survey was conducted in March 2020 to assess Australians’ attitudes toward artificial intelligence in healthcare. An innovative weighting methodology involved weighting a non-probability web-based panel against results from a shorter omnibus survey distributed to a representative sample of Australians. We used multinomial logistic regression to examine the relationship between support for AI and a suite of sociodemographic variables in various healthcare scenarios.Where support for AI was predicted by measures of socioeconomic advantage such as education, household income, and SEIFA index, the same variables were not predictors of support for the scenarios presented. Variables associated with support for healthcare AI across all three scenarios included being male, having computer science or programming experience, and being aged between 18 and 34 years. Other Australian studies suggest that this group have a higher level of perceived familiarity with AI. Our findings suggest that while support for AI in general is predicted by indicators of social advantage, these same indicators do not predict support for healthcare AI.WHAT IS ALREADY KNOWN ON THIS TOPICArtificial intelligence has the potential to perpetuate existing biases in healthcare datasets, which may be more harmful for marginalised populations. Support for the development of artificial intelligence tends to be higher amongst more socioeconomically privileged groups.WHAT THIS STUDY ADDSWhilst general support for the development of artificial intelligence was higher amongst socioeconomically privileged groups, support for the development of healthcare artificial intelligence was not. Groups that were more likely to support healthcare artificial intelligence were males, those with computer science experience, and younger people.HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICYHealthcare artificial intelligence is becoming more relevant for the public as new applications are developed and implemented. Understanding how public attitudes differ amongst sociodemographic subgroups is important for future governance of healthcare AI.
midiaPASEF maximizes information content in data-independent acquisition proteomics
Authors: Distler Ute , Łącki Mateusz Krzysztof , Startek Michał Piotr , Teschner David , Brehmer Sven , Decker Jens , Schild Thilo , Krieger Jonathan , Krohs Florian , Raether Oliver , Hildebrandt Andreas , Tenzer Stefan 
Published: February 2023

AbstractData-independent acquisition (DIA) approaches provide comprehensive records of all detectable pre-cursor and fragment ions. Here we introduce midiaPASEF, a novel DIA scan mode using mobility-specific micro-encoding of overlapping quadrupole windows to optimally cover the ion population in the ion mobility-mass to charge plane. Using overlapping ion mobility-encoded quadrupole windows, midiaPASEF maximizes information content in DIA acquisitions which enables the determination of the precursor m/z of each fragment ion with a precision of less than 2 Th. The Snakemake-based MIDIAID pipeline integrates algorithms for multidimensional peak detection and for machine-learning-based classification of precursor-fragment relationships. The MIDIAID pipeline enables fully automated processing and multidimensional deconvolution of midia-PASEF files and exports highly specific DDA-like MSMS spectra which are suitable forde novosequencing and can be searched directly with established tools including PEAKS, FragPipe and Mascot. midiaPASEF acquisition identifies over 40 unique peptides per second and provides powerful library-free DIA analyses including phosphopeptidome and immunopeptidome samples.
When do longer reads matter? A benchmark of long read de novo assembly tools for eukaryotic genomes
Authors: Cosma Bianca-Maria , Zade Ramin Shirali Hossein , Jordan Erin Noel , van Lent Paul , Peng Chengyao , Pillay Stephanie , Abeel Thomas 
Published: February 2023

AbstractBackgroundAssembly algorithm choice should be a deliberate, well-justified decision when researchers create genome assemblies for eukaryotic organisms from third-generation sequencing technologies. While third-generation sequencing by Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) have overcome the disadvantages of short read lengths specific to next-generation sequencing (NGS), third-generation sequencers are known to produce more error-prone reads, thereby generating a new set of challenges for assembly algorithms and pipelines. Since the introduction of third-generation sequencing technologies, many tools have been developed that aim to take advantage of the longer reads, and researchers need to choose the correct assembler for their projects.ResultsWe benchmarked state-of-the-art long-readde novoassemblers, to help readers make a balanced choice for the assembly of eukaryotes. To this end, we used 13 real and 72 simulated datasets from different eukaryotic genomes, with different read length distributions, imitating PacBio CLR, PacBio HiFi, and ONT sequencing to evaluate the assemblers. We include five commonly used long read assemblers in our benchmark: Canu, Flye, Miniasm, Raven and Redbean. Evaluation categories address the following metrics: reference-based metrics, assembly statistics, misassembly count, BUSCO completeness, runtime, and RAM usage. Additionally, we investigated the effect of increased read length on the quality of the assemblies, and report that read length can, but does not always, positively impact assembly quality.ConclusionsOur benchmark concludes that there is no assembler that performs the best in all the evaluation categories. However, our results shows that overall Flye is the best-performing assembler, both on real and simulated data. Next, the benchmarking using longer reads shows that the increased read length improves assembly quality, but the extent to which that can be achieved depends on the size and complexity of the reference genome.
Jointly looking to the past and the future in visual working memory
Authors: Liu Baiwei , Alexopoulou Zampeta-Sofia , van Ede Freek 
Published: February 2023

ABSTRACTWorking memory enables us to bridge past sensory information to upcoming future behaviour. Accordingly, by its very nature, working memory is concerned with two components: the past and the future. Yet, in conventional laboratory tasks, these two components are often conflated, such as when sensory information in working memory is encoded and tested at the same location. We developed a task in which we dissociated the past (encoded location) and future (to-be-tested location) attributes of visual contents in working memory. This enabled us to independently track the utilisation of past and future memory attributes through gaze, as observed during mnemonic selection. Our results reveal the joint consideration of past and future locations. This was prevalent even at the single-trial level of individual saccades that were jointly biased to the past and future. This uncovers the rich nature of working memory representations, whereby both past and future memory attributes are retained and can be accessed together when memory contents become relevant for behaviour.
Power analysis for human melatonin suppression experiments
Authors: Spitschan Manuel , Vidafar Parisa , Cain Sean W. , Phillips Andrew , Lambert Ben C. 
Published: February 2023

AbstractIn humans, the nocturnal secretion of melatonin by the pineal gland is suppressed by ocular exposure to light. In the laboratory, melatonin suppression is a convenient biomarker for this neural pathway. Recent work has found that individuals differ substantially in their melatonin-suppressive response to light, with the most sensitive individuals being up to 60 times more sensitive than the least sensitive individuals. Planning experiments with melatonin suppression as an outcome needs to incorporate these individual differences, particularly in common resource-limited scenarios where running within-subjects studies at multiple light levels is expensive and not feasible with respect to participant compliance. Here, we present a novel framework for virtual laboratory melatonin suppression experiments, incorporating a Bayesian statistical model. We provide a Shiny web app for power analyses that allows users to modify various experimental parameters (sample size, individual-level heterogeneity, statistical significance threshold, light levels), and simulate a systematic shift in sensitivity (e.g., due to a pharmacological or other intervention). Our framework helps experimenters to design compelling and robust studies, offering novel insights into the underlying biological variability relevant for practical applications.
Soil legacies of extreme droughts enhance the performance of invading plants
Authors: Thakur Madhav P. , van der Sloot Maartje A. , Wilschut Rutger A. , Hannula S. Emilia , Hooven Freddy ten , Geisen Stefan , Quist Casper W. , Steinauer Katja , van der Putten Wim H. 
Published: February 2023

AbstractExtreme droughts can weaken the biotic resistance of native plant communities against the establishment of invading plants. However, we know little about the underlying mechanisms. Using a plant-soil feedback approach, we tested how an extreme drought event alters the soil-mediated biotic resistance of resident native plant communities against invading plant species from native and non-native ranges, namely non-resident natives, native range-expanders, and alien plants. We show that all three types of invading plants performed better in soils with a legacy of extreme drought independent of resident native plant diversity. Path models revealed that extreme drought effects on non-resident natives were mediated by the root biomass of resident native plants and endophytic fungal pathogens during drought, whereas alien plant performance was mediated onlyviathe root biomass of resident native plants also during drought. Our results highlight that the performance of resident native plants during extreme drought and subsequent effects on soil fungi determine the performance of invading plants from native and non-native origins after extreme droughts.
Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: age, sex, and behavioral associations
Authors: Di Xin , Xu Ting , Uddin Lucina Q. , Biswal Bharat B. 
Published: February 2023

AbstractSpatially remote brain regions exhibit dynamic functional interactions across various task conditions. While time-varying functional connectivity during movie watching shows sensitivity to movie content, stationary functional connectivity remains relatively stable across videos. These findings suggest that dynamic and stationary functional interactions may represent different aspects of brain function. However, the relationship between individual differences in time-varying and stationary connectivity and behavioral phenotypes remains elusive. To address this gap, we analyzed an open-access functional MRI dataset comprising participants aged 5 to 22 years, who watched two cartoon movie clips. We calculated regional brain activity, time-varying connectivity, and stationary connectivity, examining associations with age, sex, and behavioral assessments. Model comparison revealed that time-varying connectivity was more sensitive to age and sex effects compared with stationary connectivity. The preferred age models exhibited quadratic log age or quadratic age effects, indicative of inverted-U shaped developmental patterns. In addition, females showed higher consistency in regional brain activity and time-varying connectivity than males. However, in terms of behavioral predictions, only stationary connectivity demonstrated the ability to predict full-scale intelligence quotient. These findings suggest that individual differences in time-varying and stationary connectivity may capture distinct aspects of behavioral phenotypes.
Multichannel Modulation of Depolarizing and Repolarizing Ion Currents Increases the Positive Rate Dependence of Action Potential Prolongation
Authors: Cabo Candido 
Published: February 2023

ABSTRACTA pharmacological approach to prevent reentrant tachycardias is to prolong the action potential duration (APD) of the myocytes that provide the substrate for the arrhythmia. For such an anti-arrhythmic approach to be effective, APD prolongation should be positive rate dependent, that is, it should maximize APD prolongation at the fast excitation rates of tachycardia and minimize APD prolongation at slow excitation rates. APD prolongation by current anti-arrhythmic agents is either reverse (larger APD prolongation at slow rates than at fast rates) or neutral (similar APD prolongation at slow and fast rates), which may not result in an effective anti-arrhythmic action. In this report we show that, in computer models of the human ventricular action potential, the combined modulation of both depolarizing and repolarizing ion currents (INaL, ICaL, IKs, IKrand IK1) results in a stronger positive rate dependent APD prolongation than modulation of just repolarizing potassium currents (IKs, IKrand IK1); modulation of a single repolarizing current (IKr) results in reverse rate dependence. A robust positive rate dependent APD prolongation correlates with an acceleration of phase 2 repolarization and a deceleration of phase 3 repolarization, which leads to a triangulation of the action potential shape. A positive rate dependent APD prolongation decreases the repolarization reserve with respect to control at slow excitation rates, which can be managed by interventions that prolong APD at fast excitation rates and shorten APD at slow excitation rates. Feature importance analysis shows that, for both computer models of the action potential, ICaLand IK1are the most important ion currents to achieve a positive rate dependent APD prolongation. In conclusion, multichannel modulation of depolarizing and repolarizing ion currents, with ion channel activators and blockers, results in a robust APD prolongation at fast excitation rates, which should be anti-arrhythmic, while minimizing APD prolongation at slow heart rates, which should reduce potential pro-arrhythmic risks.
The defects of the hippocampal ripples and theta rhythm in depression, and the effects of physical exercise on their amelioration
Authors: Koketsu Shinnosuke , Matsubara Kohki , Ueki Yoshino , Shinohara Yoshiaki , Inoue Koichi , Murakami Satona , Ueki Takatoshi 
Published: February 2023

ABSTRACTAccumulated evidence demonstrate that environmental stress affects the hippocampus, functioning in cognition and sociality, and causes various depressive symptoms. In addition, recent findings showed that environmental stress influenced the hippocampal activity correlated with neuroinflammation, and impaired the hippocampal sharp wave ripples (SWRs), pattens of spike sequences, and the theta rhythms, a strong oscillation observed in the hippocampus. The involvement of the electrophysiological alterations in the etiology of depression has not been appreciated especially in the hippocampus. Furthermore, the pathological markers associated with such alterations have not been identified. In the present study, therefore, the impairment of the SWRs and the theta rhythms in the hippocampus of the restraint stress-induced depression model of mice was analyzed. In the model mice the hippocampal SWRs and theta rhythms were impaired in depression, while physical exercise significantly reverted them. As previously reported, chronic stress induced inflammation in the affected hippocampus in parallel with defects of adult neurogenesis, on the other hand physical exercise ameliorated those pathological conditions of the bran in depression. In conclusion, this study demonstrated the implications of impairment of the hippocampal SWRs and theta rhythms in the etiology of depression and their usefulness as diagnostic markers of depression.
Mass Spectrometry Imaging of Hair Identifies Daily Maraviroc Adherence in HPTN 069/ACTG A5305
Authors: Rosen Elias P. , White Nicole , Gilliland William M. , Gerona Roy R. , Gandhi Monica , Amico K. Rivet , Mayer Kenneth H. , Gulick Roy M. , Kashuba Angela DM 
Published: February 2023

AbstractObjective measures of adherence for antiretrovirals used as pre-exposure prophylaxis (PrEP) are critical for improving preventative efficacy in both clinical trials and real-world application. Current objective adherence measures either reflect only recent behavior (eg days for plasma or urine) or cumulative behavior (eg months for dried blood spots). We measured the accumulation of the antiretroviral drug maraviroc (MVC) in hair strands by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) mass spectrometry imaging (MSI) to evaluate adherence behavior longitudinally at high temporal resolution. An MSI threshold for classifying daily adherence was established using clinical samples from healthy volunteers following directly observed dosing of 1 to 7 doses MVC/week. We then used the benchmarked MSI assay to classify adherence to MVC-based PrEP regimens in hair samples collected throughout the 49-week HPTN069/ACTGA5305 study. We found that only ~32% of investigated hair samples collected during the study’s active dosing period showed consistent daily PrEP adherence throughout a retrospective period of 30 days, and also found that profiles of daily individual adherence from MSI hair analysis could identify when patients were and were not taking study drug. The assessment of adherence from MSI hair strand analysis was 62% lower than adherence classified using paired plasma samples, the latter of which may be influenced by white-coat adherence. These findings demonstrate the ability of MSI hair analysis to examine daily variability of adherence behavior over a longer-term measurement and offer the potential for longitudinal comparison with risk behavior to target patient-specific adherence interventions and improve outcomes.
EstroGene database reveals diverse temporal, context-dependent and directional estrogen receptor regulomes in breast cancer
Authors: Li Zheqi , Li Tianqin , Yates Megan E. , Wu Yang , Ferber Amanda , Chen Lyuqin , Brown Daniel D. , Carroll Jason S. , Sikora Matthew J. , Tseng George C. , Oesterreich Steffi , Lee Adrian V. 
Published: February 2023

AbstractAs one of the most successful cancer therapeutic targets, estrogen receptor-α (ER/ESR1) has been extensively studied in decade-long. Sequencing technological advances have enabled genome-wide analysis of ER action. However, reproducibility is limited by different experimental design. Here, we established the EstroGene database through centralizing 246 experiments from 136 transcriptomic, cistromic and epigenetic datasets focusing on estradiol-treated ER activation across 19 breast cancer cell lines. We generated a user-friendly browser (https://estrogene.org/) for data visualization and gene inquiry under user-defined experimental conditions and statistical thresholds. Notably, documentation-based meta-analysis revealed a considerable lack of experimental details. Comparison of independent RNA-seq or ER ChIP-seq data with the same design showed large variability and only strong effects could be consistently detected. We defined temporal estrogen response metasignatures and showed the association with specific transcriptional factors, chromatin accessibility and ER heterogeneity. Unexpectedly, harmonizing 146 transcriptomic analyses uncovered a subset of E2-bidirectionally regulated genes, which linked to immune surveillance in the clinical setting. Furthermore, we defined context dependent E2 response programs in MCF7 and T47D cell lines, the two most frequently used models in the field. Collectively, the EstroGene database provides an informative resource to the cancer research community and reveals a diverse mode of ER signaling.
A k-mer based transcriptomics analysis for NPM1-mutated AML
Authors: Silva Raíssa , Riedel Cédric , Guibert Benoit , Ruffle Florence , Boureux Anthony , Commes Thérèse 
Published: February 2023

AbstractMotivationAcute Myeloid Leukemia is a highly heterogeneous disease. Although current classifications are well-known and widely adopted, many patients experience drug resistance and disease relapse. New biomarkers are needed to make classifications more reliable and propose personalized treatment.ResultsWe performed tests on a large scale in 3 AML cohorts, 1112 RNAseq samples. The accuracy to distinguish NPM1 mutant and non-mutant patients using machine learning models achieved more than 95% in three different scenarios. Using our approach, we found already described genes associated with NPM1 mutations and new genes to be investigated. Furthermore, we provide a new view to search for signatures/biomarkers and explore diagnosis/prognosis, at the k-mer level.AvailabilityCode available athttps://github.com/railorena/npm1amlandhttps://osf.io/4s9tc/. The cohorts used in this article were authorized for use.Contact*therese.commes@inserm.fr
Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state
Authors: Matthews Lynn T , Long Dustin M , Pratt Madeline C , Yuan Ya , Heath Sonya L , Levitan Emily B , Grooms Sydney , Creger Thomas , Rana Aadia , Mugavero Michael J , Judd Suzanne E 
Published: February 2023

AbstractBackgroundThe U.S. Southeast has a high burden of SARS-CoV-2 infections and COVID-19 disease. We used public data sources and community engagement to prioritize county selections for a precision population health intervention to promote a SARS-CoV-2 testing intervention in rural Alabama during October 2020 and March 2021.MethodsWe modeled factors associated with county-level SARS-CoV-2 percent positivity using covariates thought to associate with SARS-CoV-2 acquisition risk, disease severity, and risk mitigation practices. Descriptive epidemiologic data were presented to scientific and community advisory boards to prioritize counties for a testing intervention.ResultsIn October 2020, SARS-CoV-2 percent positivity was not associated with any modeled factors. In March 2021, premature death rate (aRR 1.16, 95% CI 1.07, 1.25), percent Black residents (aRR 1.00, 95% CI 1.00, 1.01), preventable hospitalizations (aRR 1.03, 95% CI 1.00, 1.06), and proportion of smokers (aRR 0.231, 95% CI 0.10, 0.55) were associated with average SARS-CoV-2 percent positivity. We then ranked counties based on percent positivity, case fatality, case rates, and number of testing sites using individual variables and factor scores. Top ranking counties identified through factor analysis and univariate associations were provided to community partners who considered ongoing efforts and strength of community partnerships to promote testing to inform intervention.ConclusionsThe dynamic nature of SARS-CoV-2 proved challenging for a modelling approach to inform a precision population health intervention at the county level. Epidemiological data allowed for engagement of community stakeholders implementing testing. As data sources and analytic capacities expand, engaging communities in data interpretation is vital to address diseases locally.