bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reduced dorsal fronto-striatal connectivity at rest in anorexia nervosa
Authors: Muratore Alexandra F. , Foerde Karin , Lloyd E. Caitlin , Touzeau Caroline , Uniacke Blair , Aw Natalie , Semanek David , Wang Yun , Walsh B. Timothy , Attia Evelyn , Posner Jonathan , Steinglass Joanna E. 
Published: March 2024

AbstractBackgroundAnorexia nervosa (AN) is a serious psychiatric illness that remains difficult to treat. Elucidating the neural mechanisms of AN is necessary to identify novel treatment targets and improve outcomes. A growing body of literature points to a role for dorsal fronto-striatal circuitry in the pathophysiology of AN, with increasing evidence of abnormal task-based fMRI activation within this network among patients with AN. Whether these abnormalities are present at rest and reflect fundamental differences in brain organization is unclear.MethodsThe current study combined resting-state fMRI data from patients with AN (n = 89) and healthy controls (HC; n = 92) across four studies, removing site effects using ComBat harmonization. First, the a priori hypothesis that dorsal fronto-striatal connectivity strength – specifically between the anterior caudate and dlPFC – differed between patients and HC was tested using seed-based functional connectivity analysis with small-volume correction. To assess specificity of effects, exploratory analyses examined anterior caudate whole-brain connectivity, amplitude of low-frequency fluctuations (ALFF), and node centrality.ResultsCompared to HC, patients showed significantly reduced right, but not left, anterior caudate-dlPFC connectivity (p = 0.002) in small-volume corrected analyses. Whole-brain analyses also identified reduced connectivity between the right anterior caudate and left superior frontal and middle frontal gyri (p = 0.028) and increased connectivity between the right anterior caudate and right occipital cortex (p = 0.038). No group differences were found in analyses of anterior caudate ALFF and node centrality.ConclusionsDecreased coupling of dorsal fronto-striatal regions indicates that circuit-based abnormalities persist at rest and suggests this network may be a potential treatment target.
Precision psychiatry: predicting predictability
Authors: van Dellen Edwin 
Published: March 2024

AbstractPrecision psychiatry is an emerging field that aims to provide individualized approaches to mental health care. An important strategy to achieve this precision is to reduce uncertainty about prognosis and treatment response. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, and consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.
Divine psychology and cosmic fine-tuning
Authors: Donahue Miles K. 
Published: March 2024

AbstractAfter briefly outlining the fine-tuning argument (FTA), I explain how it relies crucially on the claim that it is not improbable that God would design a fine-tuned universe. Against this premise stands the divine psychology objection: the contention that the probability that God would design a fine-tuned universe is inscrutable. I explore three strategies for meeting this objection: (i) denying that the FTA requires any claims about divine psychology in the first place, (ii) defining the motivation and intention to design a fine-tuned universe into the theistic hypothesis, and (iii) providing arguments that the relevant probability is not terribly low. While I reject the first two, I conclude, in line with the third, that considerations about life's objective value establish that it is not absurdly improbable that God would design a fine-tuned universe, whether one regards the FTA as an inference merely to a cosmic designer, or to theism proper. Accordingly, the divine psychology objection fails.
L2 learning outcomes of a research-based digital app for Japanese children
Authors: Bang Hee Jin , Setoguchi Eric , Mackey Alison , Fujii Akiko 
Published: March 2024

AbstractDigital educational game-based apps can be effective in helping young children develop language skills, particularly when paired with formal instruction. However, we need to know more about how educational games benefit learning in the absence of formal instruction, given children’s proficiency with and willingness to use mobile devices anytime, anywhere. This study uses a randomized controlled trial design to investigate the impact of a digital app—ABCmouse English—on L2 learning of seven- and eight-year-old Japanese children over a 16-week period. Pre- and post-assessments of the children’s English proficiency, together with an analysis of when and how they played with the app, were used to shed light on the relationship between the children’s in-app game choices and their language learning outcomes. Surveys and interviews with parents provide qualitative insights and information about the experiences of children and their families while using the app and its impact on their development as language learners.
The Causal Effects of a Trump Endorsement on Voter Preferences in a General Election Scenario
Authors: Blatte Scott , Piccoli Danielle , Zachem Matthew 
Published: March 2024

ABSTRACTFormer President Trump’s persistent influence over Republican politics divides those who argue that he mobilizes otherwise apathetic voters against those contending he mobilizes Democrats at down-ballot Republicans’ expense. Scholars and pundits alike question whether policy still matters in the face of increasingly strong personas like the former president’s. Using a survey experiment, we find suggestive evidence that Trump’s endorsement in a general election reduces the likelihood of voting for a hypothetical Republican candidate. We also test the effect of policy stances and find evidence that Republican respondents value policy stances over an endorsement, but Democrats show no signs of prioritizing one more than the other. However, when shown a hypothetical candidate with unorthodox policy stances, the mere mention of a Trump endorsement leads members of both parties to demonstrate significant changes in the likelihood of voting for that candidate. Ultimately, we show that elite signals can attenuate support derived from policies.
A Unified Phenomenological Model Captures Water Equilibrium and Kinetic Processes in Soil
Authors: Zhang Yong , van Genuchten Martinus Th. , Zhou Dongbao , Zhang Golden J. , Sun HongGuang 
Published: March 2024

AbstractSoil water sustains life on Earth, and how to quantify water equilibrium and kinetics in soil remains a challenge for over a century despite significant efforts. For example, various models were proposed to interpret non‐Darcian flow in saturated soils, but none of them can capture the full range of non‐Darcian flow. To unify the different models into one overall framework and improve them if needed, this technical note proposes a theory based on the tempered stable density (TSD) assumption for the soil‐hydraulic property distribution, recognizing that the underlying hydrologic processes all occur in the same, albeit very complex and not measurable at all the relevant scales, soil‐water system. The TSD assumption forms a unified fractional‐derivative equation (FDE) using subordination. Preliminary applications show that simplified FDEs, with proposed hydrological interpretations and TSD distributed properties, effectively capture core equilibrium and kinetic water processes, spanning non‐Darcian flow, water retention, moisture movement, infiltration, and wetting/drying, in the soil‐water system with various degrees and scales of system heterogeneity. Model comparisons and evaluations suggest that the TSD may serve as a unified density for the properties of a broad range of soil‐water systems, driving multi‐rate mass, momentum, and energy equilibrium/kinetic processes often oversimplified by classical models as single‐rate processes.
Unveiling the relationship between WWOX and BRCA1 in mammary tumorigenicity and in DNA repair pathway selection
Authors: Bidany-Mizrahi Tirza , Shweiki Aya , Maroun Kian , Abu-Tair Lina , Mali Bella , Aqeilan Rami I. 
Published: March 2024

AbstractBreast cancer is the leading cause of cancer-related deaths in women worldwide, with the basal-like or triple-negative breast cancer (TNBC) subtype being particularly aggressive and challenging to treat. Understanding the molecular mechanisms driving the development and progression of TNBC is essential. We previously showed that WW domain-containing oxidoreductase (WWOX) is commonly inactivated in TNBC and is implicated in the DNA damage response (DDR) through ATM and ATR activation. In this study, we investigated the interplay between WWOX and BRCA1, both frequently inactivated in TNBC, on mammary tumor development and on DNA double-strand break (DSB) repair choice. We generated and characterized a transgenic mouse model (K14-Cre;Brca1fl/fl;Wwoxfl/fl) and observed that mice lacking both WWOX and BRCA1 developed basal-like mammary tumors and exhibited a decrease in 53BP1 foci and an increase in RAD51 foci, suggesting impaired DSB repair. We examined human TNBC cell lines harboring wild-type and mutant BRCA1 and found that WWOX expression promoted NHEJ repair in cells with wild-type BRCA1. Our findings suggest that WWOX and BRCA1 play an important role in DSB repair pathway choice in mammary epithelial cells, underscoring their functional interaction and significance in breast carcinogenesis.
Retinal sensitivity changes in early/intermediate AMD: a systematic review and meta-analysis of visual field testing under mesopic and scotopic lighting
Authors: Trinh Matt , Kalloniatis Michael , Khuu Sieu K. , Nivison-Smith Lisa 
Published: March 2024

AbstractVisual fields under mesopic and scotopic lighting are increasingly being used for macular functional assessment. This review evaluates its statistical significance and clinical relevance, and the optimal testing protocol for early/intermediate age-related macular degeneration (AMD). PubMed and Embase were searched from inception to 14/05/2022. All quality assessments were performed according to GRADE guidelines. The primary outcome was global mean sensitivity (MS), further meta-analysed by: AMD classification scheme, device, test pattern, mesopic/scotopic lighting, stimuli size/chromaticity, pupil dilation, testing radius (area), background luminance, adaptation time, AMD severity, reticular pseudodrusen presence, and follow-up visit. From 1489 studies screened, 42 observational study results contributed to the primary meta-analysis. Supported by moderate GRADE certainty of the evidence, global MS was significantly reduced across all devices under mesopic and scotopic lighting with large effect size (−0.9 [−1.04, −0.75] Hedge’s g, P < 0.0001). The device (P < 0.01) and lighting (P < 0.05) used were the only modifiable factors affecting global MS, whereby the mesopic MP-1 and MAIA produced the largest effect sizes and exceeded test-retest variabilities. Global MS was significantly affected by AMD severity (intermediate versus early AMD; −0.58 [−0.88, −0.29] Hedge’s g or −2.55 [3.62, −1.47] MAIA-dB) and at follow-up visit (versus baseline; −0.62 [−0.84, −0.41] Hedge’s g or −1.61[−2.69, −0.54] MAIA-dB). Magnitudes of retinal sensitivity changes in early/intermediate AMD are clinically relevant for the MP-1 and MAIA devices under mesopic lighting within the central 10° radius. Other factors including pupil dilation and dark adaptation did not significantly affect global MS in early/intermediate AMD.
Genetic and geographic population structure in the malaria vector, Anopheles farauti, provides a candidate system for pioneering confinable gene-drive releases
Authors: Ambrose Luke , Allen Scott L. , Iro’ofa Charlie , Butafa Charles , Beebe Nigel W. 
Published: March 2024

AbstractIndoor insecticide applications are the primary tool for reducing malaria transmission in the Solomon Archipelago, a region where Anopheles farauti is the only common malaria vector. Due to the evolution of behavioural resistance in some An. farauti populations, these applications have become less effective. New malaria control interventions are therefore needed in this region, and gene-drives provide a promising new technology. In considering developing a population-specific (local) gene-drive in An. farauti, we detail the species’ population genetic structure using microsatellites and whole mitogenomes, finding many spatially confined populations both within and between landmasses. This strong population structure suggests that An. farauti would be a useful system for developing a population-specific, confinable gene-drive for field release, where private alleles can be used as Cas9 targets. Previous work on Anopheles gambiae has used the Cardinal gene for the development of a global population replacement gene-drive. We therefore also analyse the Cardinal gene to assess whether it may be a suitable target to engineer a gene-drive for the modification of local An. farauti populations. Despite the extensive population structure observed in An. farauti for microsatellites, only one remote island population from Vanuatu contained fixed and private alleles at the Cardinal locus. Nonetheless, this study provides an initial framework for further population genomic investigations to discover high-frequency private allele targets in localized An. farauti populations. This would enable the development of gene-drive strains for modifying localised populations with minimal chance of escape and may provide a low-risk route to field trial evaluations.
Switch of innate to adaptative immune responses in the brain of patients with Alzheimer’s disease correlates with tauopathy progression
Authors: Costa Marcos R. 
Published: March 2024

AbstractNeuroinflammation is a key feature of Alzheimer’s disease (AD). In this work, analysis of single- cell RNA-sequencing (scRNA-seq) data obtained from the brain of patients with AD provides evidence supporting a switch from an innate to an adaptative immune response during tauopathy progression, with both disease-associated microglia (DAM) and CD8+ T cells becoming more frequent at advanced Braak stages.
Principal component analysis enables the design of deep learning potential precisely capturing LLZO phase transitions
Authors: You Yiwei , Zhang Dexin , Wu Fulun , Cao Xinrui , Sun Yang , Zhu Zi-Zhong , Wu Shunqing 
Published: March 2024

AbstractThe development of accurate and efficient interatomic potentials using machine learning has emerged as an important approach in materials simulations and discovery. However, the systematic construction of diverse, converged training sets remains challenging. We develop a deep learning-based interatomic potential for the Li7La3Zr2O12 (LLZO) system. Our interatomic potential is trained using a diverse dataset obtained from databases and first-principles simulations. We propose using the coverage of the training and test sets as the convergence criteria for the training iterations, where the coverage is calculated by principal component analysis. This results in an accurate LLZO interatomic potential that can describe the structure and dynamical properties of LLZO systems meanwhile greatly reducing computational costs compared to density functional theory calculations. The interatomic potential accurately describes radial distribution functions and thermal expansion coefficient consistent with experiments. It also predicts the tetragonal-to-cubic phase transition behaviors of LLZO systems. Our work provides an efficient training strategy to develop accurate deep-learning interatomic potential for complex solid-state electrolyte materials, providing a promising simulation tool to accelerate solid-state battery design and applications.
Machine intelligence-accelerated discovery of all-natural plastic substitutes
Authors: Chen Tianle , Pang Zhenqian , He Shuaiming , Li Yang , Shrestha Snehi , Little Joshua M. , Yang Haochen , Chung Tsai-Chun , Sun Jiayue , Whitley Hayden Christopher , Lee I-Chi , Woehl Taylor J. , Li Teng , Hu Liangbing , Chen Po-Yen 
Published: March 2024

AbstractOne possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model’s prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.
The composition of the bacterial communities collected from the PM10 samples inside the Seoul subway and railway station
Authors: Sharma Shambhavi , Jahanzaib Muhammad , Bakht Ahtesham , Kim Min-Kyung , Lee Hyunsoo , Park Duckshin 
Published: March 2024

AbstractHealth implications of indoor air quality (IAQ) have drawn more attention since the COVID epidemic. There are many different kinds of studies done on how IAQ affects people’s well-being. There hasn’t been much research that looks at the microbiological composition of the aerosol in subway transit systems. In this work, for the first time, we examined the aerosol bacterial abundance, diversity, and composition in the microbiome of the Seoul subway and train stations using DNA isolated from the PM10 samples from each station (three subway and two KTX stations). The average PM10 mass concentration collected on the respective platform was 41.862 µg/m3, with the highest average value of 45.95 µg/m3 and the lowest of 39.25 µg/m3. The bacterial microbiomes mainly constituted bacterial species of soil and environmental origin (e.g., Acinetobacter, Brevundimonas, Lysinibacillus, Clostridiodes) with fewer from human sources (Flaviflexus, Staphylococcus). This study highlights the relationship between microbiome diversity and PM10 mass concentration contributed by outdoor air and commuters in South Korea’s subway and train stations. This study gives insights into the microbiome diversity, the source, and the susceptibility of public transports in disease spreading.
Anti-proliferative and apoptotic effect of cannabinoids on human pancreatic ductal adenocarcinoma xenograft in BALB/c nude mice model
Authors: Le Trung Quang , Meesiripan Nuntana , Sanggrajang Suleeporn , Suwanpidokkul Nuntakan , Prayakprom Piyaporn , Bodhibukkana Chatchada , Khaowroongrueng Vipada , Suriyachan Kankanit , Thanasitthichai Somchai , Srisubat Attasit , Surawongsin Pattamaporn , Rungsipipat Anudep , Sakarin Siriwan , Rattanapinyopituk Kasem 
Published: March 2024

AbstractHuman pancreatic ductal adenocarcinoma (PDAC) is a highly malignant and lethal tumor of the exocrine pancreas. Cannabinoids extracted from the hemp plant Cannabis sativa have been suggested as a potential therapeutic agent in several human tumors. However, the anti–tumor effect of cannabinoids on human PDAC is not entirely clarified. In this study, the anti–proliferative and apoptotic effect of cannabinoid solution (THC:CBD at 1:6) at a dose of 1, 5, and 10 mg/kg body weight compared to the negative control (sesame oil) and positive control (5-fluorouracil) was investigated in human PDAC xenograft nude mice model. The findings showed that cannabinoids significantly decreased the mitotic cells and mitotic/apoptotic ratio, meanwhile dramatically increased the apoptotic cells. Parallelly, cannabinoids significantly downregulated Ki-67 and PCNA expression levels. Interestingly, cannabinoids upregulated BAX, BAX/BCL-2 ratio, and Caspase-3, meanwhile, downregulated BCL-2 expression level and could not change Caspase-8 expression level. These findings suggest that cannabinoid solution (THC:CBD at 1:6) could inhibit proliferation and induce apoptosis in human PDAC xenograft models. Cannabinoids, including THC:CBD, should be further studied for use as the potent PDCA therapeutic agent in humans.
PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies
Authors: Ounissi Mehdi , Latouche Morwena , Racoceanu Daniel 
Published: March 2024

AbstractQuantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases’ characterization. https://github.com/ounissimehdi/PhagoStat.