bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synaptic alterations in pyramidal cells following genetic manipulation of neuronal excitability in monkey prefrontal cortex
Authors: Gonzalez Burgos Guillermo , Miyamae Takeaki , Nishihata Yosuke , Krimer Olga L , Wade Kirsten , Fish Kenneth N , Arion Dominique , Cai Zhao-Lin , Xue Mingshan , Stauffer William R , Lewis David A 
Published: June 2024

AbstractIn schizophrenia, layer 3 pyramidal neurons (L3PNs) in the dorsolateral prefrontal cortex (DLPFC) are thought to receive fewer excitatory synaptic inputs and to have lower expression levels of activity-dependent genes and of genes involved in mitochondrial energy production. In concert, these findings from previous studies suggest that DLPFC L3PNs are hypoactive in schizophrenia, disrupting the patterns of activity that are crucial for working memory, which is impaired in the illness. However, whether lower PN activity produces alterations in inhibitory and/or excitatory synaptic strength has not been tested in the primate DLPFC. Here, we decreased PN excitability in rhesus monkey DLPFCin vivousing adeno-associated viral vectors (AAVs) to produce Cre recombinase-mediated overexpression of Kir2.1 channels, a genetic silencing tool that efficiently decreases neuronal excitability. In acute slices prepared from DLPFC 7-12 weeks post-AAV microinjections, Kir2.1-overexpressing PNs had a significantly reduced excitability largely attributable to highly specific effects of the AAV-encoded Kir2.1 channels. Moreover, recordings of synaptic currents showed that Kir2.1-overexpressing DLPFC PNs had reduced strength of excitatory synapses whereas inhibitory synaptic inputs were not affected. The decrease in excitatory synaptic strength was not associated with changes in dendritic spine number, suggesting that excitatory synapse quantity was unaltered in Kir2.1-overexpressing DLPFC PNs. These findings suggest that, in schizophrenia, the excitatory synapses on hypoactive L3PNs are weaker and thus might represent a substrate for novel therapeutic interventions.Significance StatementIn schizophrenia, dorsolateral prefrontal cortex (DLPFC) pyramidal neurons (PNs) have both transcriptional and structural alterations that suggest they are hypoactive. PN hypoactivity is thought to produce synaptic alterations in schizophrenia, however the effects of lower neuronal activity on synaptic function in primate DLPFC have not been examined. Here, we used, for the first time in primate neocortex, adeno-associated viral vectors (AAVs) to reduce PN excitability with Kir2.1 channel overexpression and tested if this manipulation altered the strength of synaptic inputs onto the Kir2.1-overexpressing PNs. Recordings in DLPFC slices showed that Kir2.1 overexpression depressed excitatory (but not inhibitory), synaptic currents, suggesting that, in schizophrenia, the hypoactivity of PNs might be exacerbated by reduced strength of the excitatory synapses they receive.
Leveraging single cell multiomic analyses to identify factors that drive human chondrocyte cell fate
Authors: Venkatasubramanian Divya , Senevirathne Gayani , Capellini Terence D. , Craft April M. 
Published: June 2024

AbstractCartilage plays a crucial role in skeletal development and function, and abnormal development contributes to genetic and age-related skeletal disease. To better understand how human cartilage developsin vivo, we jointly profiled the transcriptome and open chromatin regions in individual nuclei recovered from distal femurs at 2 fetal timepoints. We used these multiomic data to identify transcription factors expressed in distinct chondrocyte subtypes, link accessible regulatory elements with gene expression, and predict transcription factor-based regulatory networks that are important for growth plate or epiphyseal chondrocyte differentiation. We developed a human pluripotent stem cell platform for interrogating the function of predicted transcription factors during chondrocyte differentiation and used it to testNFATC2. We expect new regulatory networks we uncovered using multiomic data to be important for promoting cartilage health and treating disease, and our platform to be a useful tool for studying cartilage developmentin vitro.Statement of SignificanceThe identity and integrity of the articular cartilage lining our joints are crucial to pain-free activities of daily living. Here we identified a gene regulatory landscape of human chondrogenesis at single cell resolution, which is expected to open new avenues of research aimed at mitigating cartilage diseases that affect hundreds of millions of individuals world-wide.
Patch and matrix striatonigral neurons differentially regulate locomotion
Authors: Dong Jie , Wang Lupeng , Sullivan Breanna T. , Sun Lixin , Chang Lisa , Martinez Smith Victor M. , Ding Jinhui , Le Weidong , Gerfen Charles R. , Cai Huaibin 
Published: June 2024

AbstractStriatonigral neurons, known to promote locomotion, reside in both the patch and matrix compartments of the dorsal striatum. However, their compartment-specific contributions to locomotion remain largely unexplored. Using molecular identifierKremen1andCalb1, we showed in mouse models that patch and matrix striatonigral neurons exert opposite influences on locomotion. Matrix striatonigral neurons reduced their activity before the cessation of self-paced locomotion, while patch striatonigral neuronal activity increased, suggesting an inhibitory function. Indeed, optogenetic activation of patch striatonigral neurons suppressed ongoing locomotion with reduced striatal dopamine release, contrasting with the locomotion-promoting effect of matrix striatonigral neurons, which showed an initial increase in dopamine release. Furthermore, genetic deletion of the GABA-B receptor in Aldehyde dehydrogenase 1A1-positive (ALDH1A1+) nigrostriatal dopaminergic neurons completely abolished the locomotion-suppressing effect of patch striatonigral neurons. Our findings unravel a compartment-specific mechanism governing locomotion in the dorsal striatum, where patch striatonigral neurons suppress locomotion by inhibiting ALDH1A1+nigrostriatal dopaminergic neurons.
Capped black hole in five dimensions
Authors: Suzuki Ryotaku , Tomizawa Shinya 
Published: June 2024

We present the first non-BPS (Bogomol’nyi-Prasad-Sommerfield) exact solution of an asymptotically flat, stationary spherical black hole having domain of outer communication with nontrivial topology in five-dimensional minimal supergravity. It describes a charged rotating black hole capped by a disk-shaped bubble. The existence of the “capped black hole” shows the nonuniqueness of spherical black holes.

Published by the American Physical Society
2024
Standard form of master equations for general non-Markovian jump processes: The Laplace-space embedding framework and asymptotic solution
Authors: Kanazawa Kiyoshi , Sornette Didier 
Published: June 2024

We present a standard form of master equations (MEs) for general one-dimensional non-Markovian (history-dependent) jump processes, complemented by an asymptotic solution derived from an expanded system-size approach. The ME is obtained by developing a general Markovian embedding using a suitable set of auxiliary field variables. This Markovian embedding uses a Laplace-convolution operation applied to the velocity trajectory. We introduce an asymptotic method tailored for this ME standard, generalizing the system-size expansion for these jump processes. Under specific stability conditions tied to a single noise source, upon coarse graining, the generalized Langevin equation (GLE) emerges as a universal approximate model for point processes in the weak-coupling limit. This methodology offers a unified analytical tool set for general non-Markovian processes, reinforcing the universal applicability of the GLE founded in microdynamics and the principles of statistical physics.

Published by the American Physical Society
2024
Post-compulsory schooling of youth in Turkey: a case of pro-cyclical enrollment
Authors: Demirci Murat , Poyraz Meltem 
Published: June 2024

PurposeThis study investigates the effect of business cycles on school enrollment in Turkey. During recessions, school enrollment might increase as opportunity cost of schooling declines, yet it might also decrease because of reduced income households have for education. Which effect dominates depends on the context. We empirically explore this in a context displaying canonical features of developing countries.Design/methodology/approachUsing the Turkish Household Labor Force Survey data for a period covering the Great Recession, we estimate the effect of unemployment rate separately for enrollments in general and vocational high schools and in undergraduate programs. To understand the cyclicality, we use a probit model with the regional and time variations in unemployment rates. We also build a simple theoretical model of work-schooling choice to interpret the findings.FindingsWe find that the likelihood of enrolling in general high schools and undergraduate programs declines with higher adult unemployment rates, but the likelihood of enrollment in vocational high schools increases. Confronting these empirical findings with the theoretical model suggests that the major factor in enrollment cyclicality in Turkey is how parental resources allocated to education change during recessions by schooling type.Originality/valueOur finding of pro-cyclical enrollment in academically oriented programs is in contrast with counter-cyclicality documented for similar programs in developed countries, which highlights the importance of income related factors in developing-country contexts. Our heterogeneous findings for general and vocational high schools are also novel.
Network meta‐analysis: Comparative onset of early effect of biologics and small molecules in moderately to severely active luminal Crohn's disease
Authors: Attauabi Mohamed , Steenholdt Casper , Poulsen Anja , Gubatan John , Burisch Johan , Nielsen Ole Haagen , Seidelin Jakob Benedict 
Published: June 2024

SummaryIntroductionRapidity of effect of advanced therapies for patients with Crohn's disease (CD) can be an essential decision parameter; however, comparative evaluation is lacking. We aimed to compare early response for advanced CD therapies in a network meta‐analysis (NMA).MethodsWe searched systematically MEDLINE, Embase, and CENTRAL up to 19 February 2024, for randomised controlled trials. The co‐primary outcomes were induction of clinical remission (Crohn's Disease Activity Index (CDAI) ≤150) and clinical response (≥100‐point reduction in CDAI) within the first 6 weeks of treatment. We incorporated any assessment within this time point in a Bayesian random‐effects NMA following PRISMA‐NMA guidance (PROSPERO ID: CRD42022368509).ResultsTwenty‐five studies, comprising 7414 patients, were included. Infliximab combined with azathioprine or monotherapy ranked highest for induction of clinical remission within 6 weeks and was significantly superior to certolizumab, ustekinumab, guselkumab, vedolizumab, and upadacitinib. However, superiority over risankizumab 600 mg and adalimumab 160/80 mg was non‐significant. Accordingly, infliximab in combination with azathioprine and guselkumab 600 mg ranked highest in the corresponding analysis of clinical response with no statistical significance demonstrated. Among bio‐exposed patients, none of whom received infliximab, upadacitinib, and risankizumab induced the highest clinical responses. On the other hand, vedolizumab, certolizumab, and ustekinumab ranked lowest across the analyses.ConclusionsWe found infliximab to be ranked highest and superior to all other agents but risankizumab and adalimumab, demonstrating the highest probability of early induction of remission. Upadacitinib and risankizumab induced the highest clinical responses in bio‐exposed patients. However, infliximab was not investigated in this population.
An alternative in deriving subjective probabilities in the futures wagering market
Authors: Ryan Matt E. 
Published: June 2024

AbstractImplied subjective probabilities from futures wagering markets are typically determined using a system known as “fair odds.” Here, an alternative system of calculating subjective probabilities—“quasi‐parimutuel”—is offered. Instead of considering individual lines independent of the remainder of the wagering opportunities, the quasi‐parimutuel method considers the ratios of wagers required between lines such that a constant return is generated for every line. As a result, relative to the fair odds system, favorites (underdogs) have higher (lower) subjective probabilities, and expected returns across all wagers vary.
Identifying indicators, empathy, and anthropomorphism in zoo visitors' perceptions of animal wellbeing through qualitative interviews
Authors: Sherman Wilson C. , Terry Ashley J. A. , Bowers Alison W. 
Published: June 2024

AbstractTo achieve their conservation missions, zoos and aquariums must understand how their audiences make judgments about animal wellbeing, as public trust and learning outcomes hinge on the extent to which animals living in these institutions seem well cared for. While previous research has investigated public perceptions of animal wellbeing, the majority of this work has focused on specific species or programs, with few studies employing qualitative research methods. Using qualitative analysis of data from 37 semi‐structured interviews with visitors to the Oakland Zoo, this study explores the indicators visitors used to assess animal wellbeing and the ways visitors employ empathy and anthropomorphism in their thinking about animal wellbeing. Our findings describe the suite of factors influencing visitors' perceptions of animal wellbeing: animal activity, animal habitats, interactions with zoo personnel, and animal health. We also describe the ways interviewees negotiate empathy and anthropomorphism with varying degrees of accuracy and reflectiveness.
EEG markers of successful allocentric spatial working memory maintenance in humans
Authors: Meziane Hadj Boumediene , Jabès Adeline , Klencklen Giuliana , Banta Lavenex Pamela , Lavenex Pierre 
Published: June 2024

AbstractSeveral brain regions in the frontal, occipital and medial temporal lobes are known to contribute to spatial information processing. In contrast, the oscillatory patterns contributing to allocentric spatial working memory maintenance are poorly understood, especially in humans. Here, we tested twenty‐three 21‐ to 32‐year‐old and twenty‐two 64‐ to 76‐year‐old healthy right‐handed adults in a real‐world, spatial working memory task and recorded electroencephalographic (EEG) activity during the maintenance period. We established criteria for designating recall trials as perfect (no errors) or failed (errors and random search) and identified 8 young and 13 older adults who had at least 1 perfect and 1 failed trial amongst 10 recall trials. Individual alpha frequency–based analyses were used to identify oscillatory patterns during the maintenance period of perfect and failed trials. Spectral scalp topographies showed that individual theta frequency band relative power was stronger in perfect than in failed trials in the frontal midline and posterior regions. Similarly, gamma band (30–40 Hz) relative power was stronger in perfect than in failed trials over the right motor cortex. Exact low‐resolution brain electromagnetic tomography in the frequency domain identified greater theta power in perfect than in failed trials in the secondary visual area (BA19) and greater gamma power in perfect than in failed trials in the right supplementary motor area. The findings of this exploratory study suggest that theta oscillations in the occipital lobe and gamma oscillations in the secondary motor cortex (BA6) play a particular role in successful allocentric spatial working memory maintenance.
Barzilai Borwein Incremental Grey Polynomial Regression for train delay prediction
Authors: Singh Ajay , Dhanaraj Rajesh Kumar , Kadry Seifedine 
Published: June 2024

AbstractThe swift societal evolution and ceaseless advancement of human value of life have been set forth for reliability as well as rapidity of railway transportation. Latest advances in machine learning approaches as well as surging accessibility of numerous information sources is produced state‐of‐the‐art probabilities for significant, precise train delay identification. In this method called, Barzilai Borwein Incremental Grey Polynomial Regression (BBI‐GPR) is introduced for predicting train arrival/departure delays, which utilized for later delay management in an accurate manner with this method comprised into three sections such as, pre‐processing, feature selection and classification. First, with the raw ETA train delay dataset as input, Barzilai–Borwein Feature Rescaling‐based Pre‐processing is applied to model computationally efficient feature rescaled and normalized values. Second with processed features as input, Incremental Maximum Relevance Minimum Redundant‐based Feature Selection is applied to select error minimized optimal features. Finally, with optimal features selected as input, Grey Polynomial Regression‐based Prediction algorithm is employed to analyse train delay. For confirming proposed BBI‐GPR, as well as analyse its performance, compare standard train delay prediction method with existing machine learning‐based regression method. Results show that new variants outperform existing train delay prediction method by minimizing train delay prediction time, error rate by 25% and 27% respectively, with improved accuracy rate of 7%, therefore paving ways for efficient train delay prediction.
When geoscience meets generative AI and large language models: Foundations, trends, and future challenges
Authors: Hadid Abdenour , Chakraborty Tanujit , Busby Daniel 
Published: June 2024

AbstractGenerative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi‐criteria decision‐making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super‐resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data‐driven modelling and uncertainty quantification.
Different prognosis in cutaneous early‐onset and late‐onset Merkel cell carcinoma: a population‐based retrospective study
Authors: Li Zhujun , Hu Bozhi , Kang Lin , Zeng Songlu , Xiao Yiding , Yu Nanze , Huang Jiuzuo , Long Xiao 
Published: June 2024

AbstractBackgroundMerkel cell carcinoma (MCC) is a rare and highly aggressive form of skin cancer. However, there is limited research on the clinicopathological features of early‐onset MCC (EOMCC) and the differences between EOMCC and late‐onset MCC (LOMCC). Our objective was to evaluate the clinicopathological features and cancer‐specific survival (CSS) of EOMCC.MethodsOur cohort study analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database from January 1, 2018, to December 31, 2020. Data from 1941 patients who were diagnosed with primary cutaneous MCC were included. We then divided the patients with MCC into two groups: those with EOMCC (526 patients) and those with LOMCC (1415 patients). CSS is used as the primary outcome.ResultsThe EOMCC group exhibited trends toward advanced tumor progression, an expanded surgical scope, increased lymph node retrieval, intensified radiotherapy, greater utilization of systemic therapy, and a better prognosis. Multivariate analysis revealed that LOMCC (HR 3.305 [2.002–5.456], P < 0.001), advanced T stage (HR 1.430 [1.139–1.797], P = 0.002), advanced N stage (HR 1.522 [1.221–1.897], P < 0.001), M1 stage (HR 2.587 [1.480–4.521], P < 0.001), and radiation (HR 0.586 [0.410–0.837], P = 0.003) were significantly associated with CSS. Among these factors, EOMCC/LOMCC was most strongly associated with CSS, indicating that LOMCC is an independent risk factor for CSS. Interestingly, we found that regional EOMCC and localized or in situ LOMCC had almost completely overlapping survival curves (Plog‐rank = 0.620). Additionally, we observed that the TNM staging + age model was a more accurate predictor of CSS among MCC patients than using TNM staging alone.ConclusionsWe found that EOMCC has distinct clinicopathological features compared to LOMCC. EOMCC is associated with better CSS. The combination of TNM staging and age was more accurate for predicting patient outcomes than TNM staging alone.
Contract adoption of motivating green technological innovation in a supply chain
Authors: Li Bo , Wen Shuning , Li Xiufeng , Cheng Yuepeng 
Published: June 2024

AbstractGreen technological innovation has been widely recognized around the world; however, consumer environmental awareness may result in uncertain market demand growth and lead to manufacturers’ risk‐averse attitude towards green technological innovation. This paper focuses on the contract adoption problem of a risk‐averse manufacturer who is responsible for green technological innovation, through a resell contract (RC) or agency contract (AC) with a retail platform. Stackelberg models are built, and the conditional value at risk method is used to measure the manufacturer's risk‐averse behavior. The differences between two contracts are compared. We find that when the commission rate is less than some threshold, the green technological level under AC is greater than that under RC regardless of the fixed cost coefficient, otherwise the green technological level under RC is higher. Further, there is the dual excellent region for AC and RC where the manufacturer can not only obtain a higher level of green technology but also achieve better utility. Finally, consumer surplus and social welfare under two contracts are performed through numerical experiments.