bluuebunny/crossref_metadata_2025_split huggingface dataset:
Probabilistic Deconvolution of SS waves for Imaging Fine Mantle Stratification (SHARP-SS)
Authors: Zhang Ziqi , Olugboji Tolulope , Kim Doyeon 
Published: February 2025

Summary
Long-period underside SS wave reflections have been widely used to furnish global constraints on the presence and depth of mantle discontinuities and to document evidence for their origins, e.g., mineral phase-transformations in the transition zone, compositional changes in the mid-mantle, and dehydration-induced melting above and below the transition zone. For higher-resolution imaging, it is necessary to separate the signature of the source wavelet (SS arrival) from that of the distortion caused by the mantle reflectivity (SS precursors). Classical solutions to the general deconvolution problem include frequency-domain or time-domain deconvolution. However, these algorithms do not easily generalize when (1) the reflectivity series is of a much shorter period compared to the source wavelet, (2) the bounce point sampling is sparse, or (3) the source wavelet is noisy or hard to estimate. To address these problems, we propose a new technique called SHARP-SS: Sparse High-Resolution Algorithm for Reflection Profiling with SS waves. SHARP-SS is a Bayesian deconvolution algorithm that makes minimal a-priori assumptions on the noise model, source signature, and reflectivity structure. We test SHARP-SS using real data examples beneath the NoMelt Pacific Ocean region. We recover a low-velocity discontinuity at a depth of ∼69 ± 4 km which marks the base of the oceanic lithosphere, consistent with previous work derived from surface waves, body wave conversions, and ScS reverberations. We anticipate high-resolution fine mantle stratification imaging using SHARP-SS at locations where seismic stations are sparsely distributed.
Research on intelligent detection method of high-voltage circuit breaker based on PSO–CNN
Authors: Hua Xufen , Liu Zhigang 
Published: February 2025

Abstract
Through the intelligent detection of high-voltage circuit breakers, equipment faults can be detected and diagnosed in time, which can effectively prevent power accidents and ensure the safe operation of the power grid. In this paper, a detection method based on particle swarm optimization (PSO) and convolutional neural network (CNN) is proposed, and the parameter setting of CNN is optimized by PSO. The feature of vibration signal is extracted and the feature sample set is constructed. On this basis, the sample set is constructed and the experiment is carried out. The experimental results show that the proposed method has significant advantages in precision, recall rate, and F1 score compared with the traditional method; the precision can reach 0.926, the recall can reach 0.913, and the F1 score can reach 0.919. Future research could optimize the training process to reduce running time, as well as introduce more advanced data preprocessing techniques to improve data quality.
Research on industrial carbon emission prediction method based on CNN–LSTM under dual carbon goals
Authors: Xia Xuwei , Zhu Dongge , Sha Jiangbo , Ma Rui , Kang Wenni 
Published: February 2025

Abstract
In order to achieve the dual carbon goal, a prediction method of industrial carbon emissions based on CNN–LSTM was studied. The extended Kaya identity is used to measure the emissions, and the LMDI decomposition method is used to determine the influencing factors. The model inputs historical emission data, extracts spatial features through CNN, and then makes time series prediction by LSTM, and finally outputs the prediction results. Experiments show that this method can effectively predict carbon emissions in different scenarios and provide support for the goal of double carbon.
Implementation of artificial intelligence techniques in electric vehicles for battery management system
Authors: Sudhapriya K , Jaisiva S 
Published: February 2025

Abstract
The hybrid AI-based battery management system (HAI-BMS) is proposed to solve the complex problem of electric vehicle (EV) battery management. It combines conventional manipulation processes with system-gaining knowledge of neural networks and reinforcement learning algorithms. This simulation showcases the capability of AI-based BMS to transform electric-powered transportation by demonstrating substantial improvements to battery performance, lifespan, and average vehicle efficiency. By incorporating AI techniques into the BMSs of electric automobiles, the HAI-BMS is paving the manner for future transportation options that are sensible, bendy, and eco-friendly.
Research on the optimization strategy for shared energy storage configuration in new energy stations considering hydrogen production from renewable energy
Authors: Zhang Huaqing , Yang Yu , Qun Li , Zhe Zhang , Kuiyuan Liu , Di Chang 
Published: February 2025

Abstract
Renewable energy development and advanced storage technologies are key to reducing fossil fuel dependence and enabling the green transition. This study proposes a shared energy storage strategy for renewable energy station clusters to address fossil fuel dependence and support the green energy transition. By leveraging the spatiotemporal complementarities of storage demands, the approach improves system performance and output tracking. A cooperative investment model accommodates various energy storage technologies, reducing costs and enhancing efficiency. Case studies show the model strengthens station alliances, optimizes energy storage, and offers a cost-effective solution for renewable energy integration and increased hydrogen production profitability.
Architectural design of high-density urban vacant land plots on the basis of thermal environment analysis: the case of Macau BT8 and BT9 plots
Authors: Yang Shuai , Chen Yile , Zheng Liang 
Published: February 2025

Abstract
The purpose of this study is to use thermal environment analysis to investigate architectural design strategies for vacant land in high-density cities. Researchers used Rhino to construct a 3D model of the Macau BT8 and BT9 plots and imported the collected data, including terrain, meteorology, and surrounding buildings, into the model to accurately replicate the actual spatial environment. Moreover, we used Ladybug analysis to determine the sunshine distribution of the plot at a specific time. Finally, a design solution with a visual rendering effect was proposed by combining the upper-level planning constraints with the thermal environment simulation analysis results.
StomaGAN: Improving image-based analysis of stomata through Generative Adversarial Networks
Authors: Gibbs Jonathon A , Gibbs Alexandra J 
Published: February 2025

Abstract
Stomata regulate gas exchange between plants and the atmosphere, but analysing their morphology is challenging due to anatomical variability and artifacts during image acquisition. Deep learning (DL) can address these challenges but often requires large and diverse datasets, which are costly and error prone to produce. Generative adversarial networks (GANs) offer a solution by generating artificial data via unsupervised learning. However, GANs often suffer from problems including mode collapse, vanishing gradients, and network failure, particularly with small datasets. Here, we present StomaGAN, a deep convolutional GAN (DCGAN) with tailored modifications to address common GAN issues. We collected 559 stomatal impressions of field, or faba bean (Vicia faba) consisting of ~3,000 stoma, 80% of which were used to train StomaGAN. Evaluation metrics, including generator and discriminator loss progression and a mean Fréchet Inception Distance (FID) score of 61.4 across eight experimental runs confirms successful training. To validate StomaGAN, we generated artificial images to train a deep convolutional neural network (DCNN) based on the DeepLabV3 framework for stomata detection from real, unseen images. The DCNN achieved a mean Interception over Union (IoU) of 0.95 on artificial training images and a 0.91 on real, unseen, images across varying magnifications. Our results demonstrate that StomaGAN effectively generates high-quality synthetic datasets, enabling reliable stomatal detection and enhancing phenotypic analysis. This approach reduces the need for extensive manual data collection and simplifies complex morphological assessments.
Wide-ranging organic nitrogen diets of freshwater picocyanobacteria
Authors: Druce Elliot , Maberly Stephen C , Sánchez-Baracaldo Patricia 
Published: February 2025

Abstract
Freshwater picocyanobacteria (Syn/Pro clade) contribute substantially to the primary production of inland waters, especially when nitrogen is limiting or co-limiting. Nevertheless, they remain poorly understood ecologically and genomically, with research on their nitrogen acquisition mainly focused on inorganic sources. However, dissolved organic nitrogen is often a major component of the freshwater nitrogen pool and it is increasingly evident that many forms are bioavailable. Comparative genomic analyses, axenic growth assays, and proteomic analyses were used here to investigate organic nitrogen acquisition mechanisms in the Syn/Pro clade. Comparative analysis of the genomes of 295 freshwater and marine strains of picocyanobacteria identified a large diversity of amino acid transporters, the absence of degradation pathways for five amino acids (asparagine, phenylalanine, serine, tryptophan, and tyrosine), and alternative mechanisms for chitin assimilation (direct chitin catabolism vs initial acetylation to chitosan and subsequent degradation). Growth assays demonstrated the widespread bioavailability of amino acids, including basic amino acids though the known basic amino acid transporter is not encoded. This suggests further genetic components are involved, either through extracellular catabolism or the presence of novel transporters. Proteomic analysis demonstrates the dual utilization of nitrogen and carbon from the amino acid substrate and provides evidence for a mild stress response through the up-regulation of lysine biosynthesis and FtsH1, potentially caused by accumulation of secondary metabolites. Our results are relevant to understanding how picocyanobacteria have come to thrive in dissolved organic nitrogen-rich oligotrophic environments and explores how their different molecular capabilities may influence communities between habitats.
Carbon dioxide enhances Akkermansia muciniphila fitness and anti-obesity efficacy in high-fat diet mice
Authors: Wang Xiangfeng , Yang Qianqian , Shi Changping , Wang Yuyang , Guo Dingming , Wan Xuchun , Dong Pengyuan , Zhang Qianyao , Hu Yueyan , Zhang Ruilin , Yang Hongju , Chen Weihua , Liu Zhi 
Published: February 2025

Abstract
Numerous studies and clinical applications have underscored the therapeutic potential of the indigenous gut bacterium Akkermansia muciniphila in various diseases. However, our understanding of how A. muciniphila senses and responds to host gastrointestinal signals remains limited. Here, we demonstrate that A. muciniphila exhibits rapid growth, facilitated by its self-produced carbon dioxide, with key enzymes such as glutamate decarboxylase, carbonic anhydrase, and pyruvate ferredoxin oxidoreductase playing pivotal roles. Additionally, we design a novel delivery system, comprising calcium carbonate, inulin, A. muciniphila, and sodium alginate, which enhances A. muciniphila growth and facilitates the expression of part probiotic genes in mice intestinal milieu. Notably, the administration of this delivery system induces weight loss in mice fed high-fat diets. Furthermore, we elucidate the significant impact of carbon dioxide on the composition and functional genes of the human gut microbiota, with genes encoding carbonic anhydrase and amino acid metabolism enzymes exhibiting heightened responsiveness. These findings reveal a novel mechanism by which gut commensal bacteria sense and respond to gaseous molecules, thereby promoting growth. Moreover, they suggest the potential for designing rational therapeutic strategies utilizing live bacterial delivery systems to enhance probiotic growth and ameliorate gut microbiota-related diseases.
Promoter choice for XKS1 overexpression impacts xylose metabolism in Saccharomyces cerevisiae
Authors: de Souza Brenda Cristina , Vargas Beatriz de Oliveira , Seguchi Gustavo , Carazzolle Marcelo F , Guimarães Pereira Gonçalo Amarante , de Mello Fellipe da Silveira Bezerra 
Published: February 2025

Abstract

Aims
The impact of promoter selection on the overexpression of the XKS1 gene in Saccharomyces cerevisiae is investigated with a focus on optimizing xylose metabolism for second-generation ethanol production. The goal was to identify how different promoters affect the fermentation performance of laboratory and industrial yeast strains under various media conditions.

Methods and Results
Four constitutive promoters—TEF1p, ADH1p, PGK1p, and TDH3p—were tested to overexpress XKS1 in two strains of S. cerevisiae, one laboratory strain (BY4742) and one industrial strain (PE-2B), both engineered with a heterologous xylose isomerase pathway. The strains were evaluated in defined (YNB) and complex (YPDX) media, as well as a synthetic sugarcane hydrolysate, over a 144-hour fermentation period. Promoter choice significantly influenced cell growth, xylose consumption and ethanol production. In the laboratory strain, TEF1p yielded the highest ethanol production in YPDX, while TDH3p promoted higher biomass formation. In the industrial strain, ADH1p, TEF1p, and PGK1p led to high ethanol yields in YPDX, with ADH1p showing superior performance in the synthetic hydrolysate. RT-qPCR reveals lower XKS1 expression levels render a better trait for BY4742, while the opposite is observed for PE-2B.

Conclusions
It is demonstrated that promoter selection is crucial for optimizing XKS1 expression and xylose metabolism in S. cerevisiae. Promoters must be carefully tailored to the yeast strain and fermentation conditions to maximize ethanol production, providing strategic insights for enhancing the industrial fermentation of lignocellulosic biomass.
Karyotype evolution and speciation in Orthoptera
Authors: Palacios-Gimenez Octavio M , Castillo Elio R D , Schielzeth Holger 
Published: February 2025

Abstract
Karyotype evolution might fuel speciation and can thereby contribute to species diversity. To test the hypothesis that speciation and karyotype change are linked, we estimated anagenetic and cladogenetic rates of karyotype evolution as well as speciation rates in Orthoptera. We compiled the male diploid chromosome number and the number of visible chromosome arms (the fundamental number) from published sources for 1,541 species. Chromosome-associated speciation rates were estimated by jointly modeling cladogenetic and anagenetic character evolution and the phylogenetic birth-death process in a Bayesian statistical framework using a subset of 516 species from 14 families. Our findings unveiled heterogeneity among orthopteran families in the pace of karyotype evolution and whether it was linked to speciation. In 6/14 clades we found evidence supporting speciation-associated (cladogenetic) karyotype changes, while in 6/14 clades karyotype evolution was primarily anagenetic. The remaining clades (2/14) showed uncertainty in favor of either model. We further analysed whether flightless phenotype, and thus less mobile species, showed higher rates of karyotype evolution. We showed that the flightless phenotype is associated with the rate of chromosome loss. The finding indicates contrasting patterns of karyotype evolution within specific orthopteran lineages, thus emphasizing substantial diversity in the pace of this evolutionary process. It also implies that substantial changes in chromosome number, arising from instances of chromosomal gains and losses, are recurring events in orthopterans that are associated with reproductive isolation and speciation, at least in some groups.
Mandated Sick Pay: Coverage, Utilization, and Crowding-In
Authors: Maclean Johanna Catherine , Pichler Stefan , Ziebarth Nicolas R 
Published: February 2025

Abstract
Using the National Compensation Survey from 2009 to 2022 and difference-in-differences methods, we find that state-level sick pay mandates are effective in broadening access for U.S. workers. Increases in coverage reach 30ppt from a 63% baseline five years post-mandate. Mandates have more bite in jobs with low pre-mandate coverage. Further, mandates reduce inequality in access to paid sick leave substantially, both across and within firms. Covid-19 reinforced existing positive trends in coverage and take-up. Sick leave use increases linearly, whereas costs plateau after five years. Finally, we find crowding-in of non-mandated benefits which we label “job upscaling” by firms to differentiate jobs and attract labor.
Complexity and Time
Authors: Enke Benjamin , Graeber Thomas , Oprea Ryan 
Published: February 2025

Abstract
A large literature shows that people’s valuation of delayed financial rewards violates exponential discounting, exhibiting a hyperbolic pattern: high short-run impatience that strongly decreases in the length of the delay. We test the hypothesis that the hyperbolic pattern in measured discount rates reflects mistakes driven by the complexity of evaluating delayed payoffs. We document that hyperbolicity (i) is strongly associated with choice inconsistency and cognitive uncertainty, (ii) increases in overt complexity manipulations and (iii) arises nearly identically in computationally similar tasks that involve no actual payoff delays. Our results suggest that even if people had exponential discount functions, complexity-driven mistakes would cause them to make hyperbolic choices. We examine which experimental techniques to estimate present bias are (not) confounded by information-processing constraints.
MyD88 determines T cell fate through BCAP-PI3K signaling
Authors: Bayer Abraham L , Magri Zoie , Muendlein Hayley , Poltorak Alexander , Alcaide Pilar 
Published: February 2025

Abstract
The life cycle of effector T cells is determined by signals downstream of the T cell receptor (TCR) that induce activation and proinflammatory activity, or death as part of the process to resolve inflammation. We recently reported that T cell myeloid differentiation primary response 88 (MyD88) tunes down TCR activation and limits T cell survival in the cardiac and tumor inflammatory environments, in contrast to its proinflammatory role in myeloid cells upon toll-like receptor (TLR) recognition of pathogen- and damage-associated molecular patterns. However, the molecular mechanism remains unknown. Here, we report a central regulatory role for MyD88 in T cell apoptosis after TCR activation and Fas ligation through an association with the B cell adaptor for phosphoinositide 3-kinase (B cell activating protein [BCAP]). We show that TCR engagement upregulates MyD88 and BCAP and promotes their interaction, thereby limiting availability of BCAP for downstream TCR-BCAP-PI3K-AKT signaling required for T cell activation and survival, which are enhanced in MyD88−/− activated T cells. Further, MyD88 and BCAP association and localization to the TCR was prevented by lipopolysaccharide (LPS) activation of TLR4 and restored T cell survival in wild-type cells. The enhanced T cell activation markers, proinflammatory signals, and survival advantage observed in MyD88−/− T cells was fully eliminated upon BCAP knockdown in T cells. Our data demonstrate that MyD88 acts downstream of the TCR to regulate T cell fate through its association with BCAP and elucidate a novel molecular mechanism for MyD88 in T cell biology that could be targeted to fine-tune T cell effector function and survival therapeutically.
Differential shaping of T cell responses elicited by heterologous ChAd68/self-amplifying mRNA SIV vaccine in macaques in combination with αCTLA4, αPD-1, or FLT3R agonist
Authors: Rappaport Amy R , Bekerman Elena , Boucher Gregory R , Sung Janette , Carr Brian , Corzo Cesar A , Larson Heather , Kachura Melissa A , Scallan Ciaran D , Geleziunas Romas , SenGupta Devi , Jooss Karin 
Published: February 2025

Abstract
While therapeutic vaccines are a promising strategy for inducing human immunodeficiency virus (HIV) control, HIV vaccines tested to date have offered limited benefit to people living with HIV. The barriers to success may include the use of vaccine platforms and/or immunogens that drive weak or suboptimal immune responses, immune escape and/or immune dysfunction associated with chronic infection despite effective antiretroviral therapy. Combining vaccines with immune modulators in a safe manner may address some of the challenges and thus increase the efficacy of therapeutic HIV vaccines. We evaluated the immunogenicity of a ChAd68/samRNA-based simian immunodeficiency virus (SIV) vaccine regimen alone and in combination with a series of immune modulators in a preclinical rhesus macaque (M. mulatta) model. The vaccine was co-delivered with the checkpoint inhibitors αPD-1 or αCTLA-4, or with a FLT3 receptor agonist (FLT3Ra) shown to differentiate and expand dendritic cells and improve T cell priming. We demonstrate that the magnitude, breadth and functionality of SIV-specific vaccine-elicited CD8+ T cell responses were enhanced by combination with either αPD-1, αCTLA-4, or FLT3Ra. Combination with FLT3Ra also expanded polyfunctional CD4+ T cell responses. Our data demonstrate enhanced and distinct shaping of vaccine-elicited immune responses by immune modulators with implications for developing a functional HIV cure.
Domain Stabilization for Model-Free Option Implied Moment Estimation
Authors: Lee Geul , Ryu Doojin , Yang Li 
Published: February 2025

Abstract
We propose a new method, domain stabilization (DStab), to enhance the return predictive and forecasting ability of model-free option-implied moment estimators. Analyzing S&P 500 options data from January 2015 to December 2021, we show that DStab improves moment estimation consistency by stabilizing the integration domain, leading to better predictive and forecasting performance. When the options data characteristics are appropriately considered, DStab enhances both in-sample predictive and out-of-sample forecasting abilities of implied moments. DStab’s out-of-sample forecasting ability surpasses other treatment methods.
Preparation protocol for epifluorescence microscopy when working close to the detection limit
Authors: McCerery R , Hall L , Omanakuttan J , Chai H Z , Lawrence J , Woodward J , Pearce D A 
Published: February 2025

Abstract
Working with low density, low biomass material can be challenging, especially when working near the detection limit. Although background contamination is a universal consideration in microbiological research, its impact is increased when the cells under assessment approach the same concentration as the background contamination. The aim of this work was to identify and remove laboratory sources of background contamination in the cell mounting process for epifluorescence microscopy to improve the reliability of cell counting for low biomass samples. Microscope slides and coverslips were assessed before and after autoclaving, washing with detergent and rinsing with ethanol solution. The solutions used in sample mounting; 4′,6-diamidino-2-phenylindole, phosphate buffered saline and immersion oil, were tested before and after autoclaving as well as both single and triple filtering with a 0.2 µm membrane filter. Using a combination of detergent and ethanol rinses of glassware and triple filtering of all solutions, we were able to reduce the background contamination by almost two orders of magnitude, down from 1×104(±4.3×103) cells to 302(±312) cells per filter paper. This method was then validated with low biomass glacial sediment samples from Renardbreen, Svalbard with cell concentrations of 1.8×105(±2.9×104) cells g−1, close to the reported detection limit of epifluorescence microscopy.