bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ambient temperature exposure and rapid infant weight gain
Authors: Alterman Neora , Nevo Daniel , Calderon-Margalit Ronit , Youssim Iaroslav , Weinstein Bar , Kloog Itai , Hauzer Michael , Raz Raanan 
Published: October 2024

Abstract

Background
Childhood obesity is a major public health concern, and the global rate is rising. Rapid infant weight gain is a risk factor for later overweight. Studies have linked prenatal ambient temperature exposure to fetal growth, and preliminary evidence suggests postnatal exposure may be associated with infant weight gain.

Methods
Using a population-based historical cohort study including 1 100 576 infants born 2011–2019, we assessed the relationship between prenatal and one-month postnatal ambient temperature exposure and rapid infant weight gain. We used a hybrid spatiotemporal model to assess temperatures at the family’s recorded residence at birth. Repeated weight measurements between birth and 15 months were used to model the outcome using the SuperImposition by Translation and Rotation (SITAR) method. We employed generalized linear models and distributed lag models to estimate the association between prenatal and postnatal exposure and rapid infant weight gain, defined as the upper tertile of the SITAR growth velocity.

Results
Overall, higher ambient temperatures were associated with rapid infant weight gain. The cumulative adjusted relative risk for the highest exposure quintile during pregnancy compared with the lowest quintile was 1.33 [95% confidence interval (CI): 1.25, 1.40], and the corresponding association for the first postnatal month was 1.19 (95% CI: 1.15, 1.23). Exposure to high ambient temperature during early and mid-pregnancy, as well as the first postnatal month, was associated with rapid weight gain, while during late pregnancy, exposure to low temperatures was associated with this outcome.

Conclusions
Prenatal and postnatal ambient temperatures are associated with rapid infant weight gain.
Effect of diffusivity of amyloid beta monomers on the formation of senile plaques
Authors: Kuznetsov Andrey V 
Published: October 2024

Abstract
Alzheimer’s disease (AD) presents a perplexing question: why does its development span decades, even though individual amyloid beta (Aβ) deposits (senile plaques) can form rapidly in as little as 24 hours, as recent publications suggest? This study investigated whether the formation of senile plaques can be limited by factors other than polymerization kinetics alone. Instead, their formation may be limited by the diffusion-driven supply of Aβ monomers, along with the rate at which the monomers are produced from amyloid precursor protein and the rate at which Aβ monomers undergo degradation. A mathematical model incorporating the nucleation and autocatalytic process (via the Finke–Watzky model), as well as Aβ monomer diffusion, was proposed. The obtained system of partial differential equations was solved numerically, and a simplified version was investigated analytically. The computational results predicted that it takes approximately 7 years for Aβ aggregates to reach a neurotoxic concentration of 50 μM. Additionally, a sensitivity analysis was performed to examine how the diffusivity of Aβ monomers and their production rate impact the concentration of Aβ aggregates.
Windings of Prime Geodesics
Authors: Burrin Claire , von Essen Flemming 
Published: October 2024

Abstract
The winding of a closed oriented geodesic around the cusp of the modular orbifold is computed by the Rademacher symbol, a classical function from the theory of modular forms. In this article, we introduce a new construction of winding numbers to record the winding of closed oriented geodesics about a prescribed cusp of a general cusped hyperbolic orbifold. For various arithmetic families of surfaces, this winding number can again be expressed by a Rademacher symbol, and access to the spectral theory of automorphic forms yields statistical results on the distribution of closed (primitive) oriented geodesics with respect to their winding.
PP2A negatively regulates NK cell T-bet expression and anti-tumor effector function
Authors: Shinzawa Yui , Hara Daisuke , Shinguryo Yuki , Yokoyama Satoru , Kawada Manabu , Hayakawa Yoshihiro 
Published: October 2024

Abstract
The transcription factor T-bet is essential for the anti-tumor effector function of natural killer (NK) cells, but the mechanism regulating its expression in NK cells remains unclear. In this study, we aimed to identify an NK cell-intrinsic regulator that controls T-bet expression. Using T-bet-luciferase reporter assay screening, we identified a protein phosphatase inhibitor as a potential activator of T-bet expression. A series of protein phosphatase 2A (PP2A)-specific inhibitors (PP2Ai) or PP2A siRNA induced the expression of T-bet. In PP2Ai-treated mice, the expression of T-bet and its downstream effector molecules, granzyme B and IFN-γ, was also upregulated in NK cells. Mechanistically, PP2Ai increased the phosphorylation of mTOR and ribosomal protein S6 in NK cells, and mTOR inhibitor canceled the effects of PP2Ai in NK cells. Importantly, NK cells isolated from PP2Ai-treated mice showed higher cytotoxicity and IFN-γ production; therefore, they increased the anti-tumor effector function of NK cells. Accordingly, PP2Ai treatment inhibited lung metastasis of B16 melanoma by NK cell- and mTOR-dependent mechanisms. These results suggest that PP2A negatively regulates NK cell T-bet expression and effector function by an mTOR-dependent mechanism.
Metagenomic and -transcriptomic analyses of microbial nitrogen transformation potential, and gene expression in Swiss lake sediments
Authors: Baumann Kathrin B L , Mazzoli Alessandra , Salazar Guillem , Ruscheweyh Hans-Joachim , Müller Beat , Niederdorfer Robert , Sunagawa Shinichi , Lever Mark A , Lehmann Moritz F , Bürgmann Helmut 
Published: October 2024

Abstract
The global nitrogen (N) cycle has been strongly altered by anthropogenic activities, including increased input of bioavailable N into aquatic ecosystems. Freshwater sediments are hotspots with regards to the turnover and elimination of fixed N, yet the environmental controls on the microbial pathways involved in benthic N removal are not fully understood. Here, we analyze the abundance and expression of microbial genes involved in N transformations using metagenomics and -transcriptomics across sediments of 12 Swiss lakes that differ in sedimentation rates and trophic regimes. Our results indicate that microbial N loss in these sediments is primarily driven by nitrification coupled to denitrification. N-transformation gene compositions indicated three groups of lakes: agriculture-influenced lakes characterized by rapid depletion of oxidants in the sediment porewater, pristine-alpine lakes with relatively deep sedimentary penetration of oxygen and nitrate, and large, deep lakes with intermediate porewater hydrochemical properties. Sedimentary organic matter (OM) characteristics showed the strongest correlations with the community structure of microbial N-cycling communities. Most transformation pathways were expressed, but expression deviated from gene abundance and did not correlate with benthic geochemistry. Cryptic N-cycling may maintain transcriptional activity even when substrate levels are below detection. Sediments of large, deep lakes generally showed lower in-situ N gene expression than agriculture-influenced lakes, and half of the pristine-alpine lakes. This implies that prolonged OM mineralization in the water column can lead to the suppression of benthic N gene expression.
Deep learning transient electromagnetic inversion for seawater intrusion
Authors: Wang Liang , Dai Yunfeng , Liu Wei , Zhou Sheng , Long Xia , Xi Zhenzhu , Xue Junping , Wang Wei 
Published: October 2024

Abstract
To enhance the capability of the transient electromagnetic method (TEM) in detecting seawater intrusion and delineating the boundaries in coastal areas, we developed a deep learning inversion method for TEM data based on the Swin Transformer model in this study. First many standardized resistivity models were designed and generated to describe the subsurface resistivity structures associated with seawater intrusion in coastal areas. Then, TEM forward modeling was performed to compute the corresponding TEM responses, thereby constructing a seawater intrusion-oriented training dataset. Next, the robust Swin Transformer model was employed as the backbone network to build a deep learning inversion model, named SITEMNet, to derive a direct nonlinear transformation that maps TEM responses to subsurface resistivity models. The proposed SITEMNet inversion technique was validated using simulated data scenarios and actual field TEM measurements, showing great promise in accurately identifying seawater intrusion interface and geological formations.
Optimization of ore pillar recovery based on weighting combined with uncertainty measurement theory
Authors: Qian Jun , Xu Wenqiang , Lan Chunlu , Wang Chunlai , Li Changfeng , Yu Qingjun , Li Yuze , Tang Yutao , Zhang Jianwei , Wang Guannan , Zhao Dongsheng , Qu Zhanpeng , Zhao Jiangbo 
Published: October 2024

Abstract
Given the multi-objective nature and inherent uncertainty in evaluating ore pillar mining schemes, the analytic hierarchy process and entropy method were applied to determine factor weights. A new combined weighting method was proposed to obtain more objective and accurate results. Seven quantitative and four qualitative indicators were selected based on various mining performance factors and grading standards. A combined weighting–uncertainty measurement theory evaluation model was then constructed, integrating weight values and confidence levels from the recognition criteria. This model was applied to evaluate three proposed schemes, determining their relative merits and ranking. The evaluation demonstrated that the combined weighting–uncertainty measurement model outperformed both the fuzzy theory and the TOPSIS method in delivering comprehensive, objective, and reliable results for assessing ore pillar mining schemes.
Comparison of torsional and SH-waves in isotropic and anisotropic media based on laboratory measurements
Authors: Ding Pinbo , Li Xiang-yang , Zhang Feng , Chen Haitao 
Published: October 2024

Abstract
Utilization of the torsional wave signal has been reported in medical testing and engineering detection. A torsional wave is a shear-wave train whose vibrating direction (polarization) forms a torsional motion. However, there is a lack of understanding of its characteristics and potential for subsurface investigations. Here, compressional (P), shear (S), and torsional (T) transducers are constructed using different piezoelectric-transformer (PZT) chips in the laboratory, and the characteristics of the torsional (T) wave propagating in isotropic and fracture-induced anisotropic media are analyzed in line with conventional P- and S-waves, covering waveform, amplitude, arrival time, and velocity at different propagating angles. The results indicate that the velocity and anisotropy values measured using the T transducer are almost the same as the SH-wave. For propagation in isotropic and anisotropic media, the wavefield generated by the T transducer is very simple with no mode conversions and source-generated interferences, which is similar to the SH-wavefield, while the SV-wavefield comprises a series of complex events from mode conversions and interferences. For propagating in fluid-saturated fractured rocks, the torsional wavefield also comprises a fast and slow wave train, which is referred to as torsional wave splitting, and is similar to the shear-wave splitting from S transducers. Furthermore, the anisotropy parameters measured using P, S, and T transducers show a good correlation with the fracture density, and the P-wave anisotropy is higher in air-saturated rocks than water-saturated rocks, while the shear-wave anisotropy measured by the S and T transducers is sensitive to fracture density. Like the P transducer, the T transducer is polarization-independent, or directional invariant. Therefore, utilizing the T transducer may simplify the field logistics during 3D shear-wave surveys. However, field torsional wave generation is a major issue that needs further study.
Frequencies of house fly proto-Y chromosomes across populations are predicted by temperature heterogeneity within populations
Authors: Foy Patrick D , Loetzerich Sara R , Boxler David , Burgess Edwin R , Trout Fryxell R T , Gerry Alec C , Hinkle Nancy C , Machtinger Erika T , Olds Cassandra , Tarone Aaron M , Watson Wes , Scott Jeffrey G , Meisel Richard P 
Published: October 2024

Abstract
Sex chromosomes often differ between closely related species and can even be polymorphic within populations. Species with multifactorial sex determination segregate for multiple different sex-determining loci within populations, making them uniquely informative of the selection pressures that drive the evolution of sex chromosomes. The house fly (Musca domestica) is a model species for studying multifactorial sex determination because male-determining genes have been identified on all six of the chromosomes, which means that any chromosome can be a “proto-Y.” Natural populations of house flies also segregate for a recently derived female-determining locus, meaning house flies also have a proto-W chromosome. The different proto-Y chromosomes are distributed along latitudinal clines on multiple continents, their distributions can be explained by seasonality in temperature, and they have temperature-dependent effects on physiological and behavioral traits. It is not clear, however, how the clinal distributions interact with the effect of seasonality on the frequencies of house fly proto-Y and proto-W chromosomes across populations. To address this question, we measured the frequencies of house fly proto-Y and proto-W chromosomes across nine populations in the United States of America. We confirmed the clinal distribution along the eastern coast of North America, but it is limited to the eastern coast. In contrast, the annual mean daily temperature range predicts proto-Y chromosome frequencies across the entire continent. Our results therefore suggest that temperature heterogeneity can explain the distributions of house fly proto-Y chromosomes in a way that does not depend on the cline.
Targeting HPV for the prevention, diagnosis, and treatment of Cervical Cancer
Authors: Ni Huiling , Huang Canhua , Ran Zhi , Li Shan , Kuang Chunmei , Zhang Yu , Yuan Kai 
Published: October 2024

Abstract
Despite advances in screening and prevention, cervical cancer (CC) remains an unresolved public health issue and poses a significant global challenge, particularly for women in low-income regions. Human papillomavirus (HPV) infection, especially with the high-risk strains, is a primary driver of cervical carcinogenesis. Emerging evidence indicates that integrating HPV testing with existing approaches, such as cervical cytology and visual inspection, offers enhanced sensitivity and specificity in CC screening. HPV infection-associated biomarkers, including HPV E6/E7 oncogenes, p16^INK4a, DNA methylation signatures, and non-coding RNAs, offer valuable insights into disease progression and the development of personalized interventions. Preventive and therapeutic vaccination against HPV, along with tertiary prevention strategies such as the use of antiviral and immune-modulating drugs for HPV-related lesions, show great clinical potential. At the mechanistic level, single-cell RNA sequencing analysis and the development of organoid models for HPV infection provide new cellular and molecular insights into HPV-related CC pathogenesis. This review focuses on the crucial roles of HPV in the prevention, diagnosis, and treatment of CC, with particular emphasis on the latest advancements in screening and disease intervention.
Massive chest wall bleeding 3 days after lung wedge resection caused by a protruding staple
Authors: Fang Jincheng , Liu Quan , Wu Chuangyan , Li Jinsong 
Published: October 2024

Abstract
We report a case of massive chest wall bleeding after lung wedge resection caused by a protruding staple. On the third postoperative day, the patient experienced sudden left posterior back pain without any apparent trigger, accompanied by signs of shock. Computed tomography imaging revealed a significant accumulation of blood in the pleural cavity on the side of the surgery. A reoperation was performed, during which we identified active arterial bleeding from a small vessel at the second intercostal space on the posterior chest wall. Hemostasis was achieved using electrocautery. Further examination revealed a protruding staple at the left upper lobe resection margin, which we speculated was likely causing abrasion against the chest wall and leading to the bleeding. This case reveals the potential risk posed by protruding staples. Appropriate precautions should be taken to prevent this rare but dangerous occurrence.
Enhancing Semantic Segmentation in High-Resolution TEM Images: A Comparative Study of Batch Normalization and Instance Normalization
Authors: Kazimi Bashir , Sandfeld Stefan 
Published: October 2024

Abstract
Integrating deep learning into image analysis for transmission electron microscopy (TEM) holds significant promise for advancing materials science and nanotechnology. Deep learning is able to enhance image quality, to automate feature detection, and to accelerate data analysis, addressing the complex nature of TEM datasets. This capability is crucial for precise and efficient characterization of details on the nano—and microscale, e.g., facilitating more accurate and high-throughput analysis of nanoparticle structures. This study investigates the influence of batch normalization (BN) and instance normalization (IN) on the performance of deep learning models for semantic segmentation of high-resolution TEM images. Using U-Net and ResNet architectures, we trained models on two different datasets. Our results demonstrate that IN consistently outperforms BN, yielding higher Dice scores and Intersection over Union metrics. These findings underscore the necessity of selecting appropriate normalization methods to maximize the performance of deep learning models applied to TEM images.
Control of Grain Boundary Formation in Atomically Resolved Nanocrystalline Carbon Monolayers: Dependence on Electron Energy
Authors: Leist Christopher , Makurat Max , Jiao Andy , Liu Xue , Schneider Grégory F , Kaiser Ute 
Published: October 2024

Abstract
In this study, we explore the dynamics of grain boundaries in nanocrystalline carbon monolayers, focusing on their variation with electron beam energy and electron dose rate in a spherical and chromatic aberration-corrected transmission electron microscope. We demonstrate that a clean surface, a high-dose rate, and a 60 keV electron beam are essential for precise local control over the dynamics of grain boundaries. The structure of these linear defects has been evaluated using neural network-generated polygon mapping.
Using two- and three-dimensional landmark data to examine the shape variation of Elliptio complanata from two heavily impounded rivers in northern New York (USA)
Authors: Nagel-Myers Judith , Walrath Krista A , Bowdish Maeve C 
Published: October 2024

ABSTRACT
Despite vast improvements in the quantification of shape variation with geometric morphometric methods, evaluating morphological variation in unionids has remained difficult due to the lack of measurable landmarks on shells and their overall morphological plasticity. In this study, we supplement traditional two-dimensional landmark data with three-dimensional landmark data. Using photogrammetry, we generate three-dimensional models that are landmarked. This approach adds a new, third dimension to capture shape changes, providing a more detailed understanding of the shell topography. With this method, we explore the differences in shell shape along river stretches of two heavily impounded rivers in northern New York (USA). We ask whether isolation by distance, presence of dams and/or difference in land use around sampling localities impact the shell morphologies of Elliptio complanata populations. Our data suggest that, although separated for almost a century, these unionid populations do not show a pattern of increased morphological difference with distance. But we find that broad, rounded morphs that are posteriorly extended occur more frequently in reservoir environments while unaltered river habitats have a higher number of slim and more elongated variants.
Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA
Authors: Wang Meng-Guo , Chen Luonan , Zhang Xiao-Fei 
Published: October 2024

Abstract
Sequencing-based spatial transcriptomics technologies have revolutionized our understanding of complex biological systems by enabling transcriptome profiling while preserving spatial context. However, spot-level expression measurements often amalgamate signals from diverse cells, obscuring potential heterogeneity. Existing methods aim to deconvolute spatial transcriptomics data into cell type proportions for each spot using single-cell RNA sequencing references but overlook cell-type-specific gene expression, essential for uncovering intra-type heterogeneity. We present PANDA (ProbAbilistic-based decoNvolution with spot-aDaptive cell type signAtures), a novel method that concurrently deciphers spot-level gene expression into both cell type proportions and cell-type-specific gene expression. PANDA integrates archetypal analysis to capture within-cell-type heterogeneity and dynamically learns cell type signatures for each spot during deconvolution. Simulations demonstrate PANDA’s superior performance. Applied to real spatial transcriptomics data from diverse tissues, including tumor, brain, and developing heart, PANDA reconstructs spatial structures and reveals subtle transcriptional variations within specific cell types, offering a comprehensive understanding of tissue dynamics.
bcRflow: a Nextflow pipeline for characterizing B cell receptor repertoires from non-targeted transcriptomic data
Authors: Schlegel Brent T , Morikone Michael , Mu Fangping , Tang Wan-Yee , Kohanbash Gary , Rajasundaram Dhivyaa 
Published: October 2024

Abstract
B cells play a critical role in the adaptive recognition of foreign antigens through diverse receptor generation. While targeted immune sequencing methods are commonly used to profile B cell receptors (BCRs), they have limitations in cost and tissue availability. Analyzing B cell receptor profiling from non-targeted transcriptomics data is a promising alternative, but a systematic pipeline integrating tools for accurate immune repertoire extraction is lacking. Here, we present bcRflow, a Nextflow pipeline designed to characterize BCR repertoires from non-targeted transcriptomics data, with functional modules for alignment, processing, and visualization. bcRflow is a comprehensive, reproducible, and scalable pipeline that can run on high-performance computing clusters, cloud-based computing resources like Amazon Web Services (AWS), the Open OnDemand framework, or even local desktops. bcRflow utilizes institutional configurations provided by nf-core to ensure maximum portability and accessibility. To demonstrate the functionality of the bcRflow pipeline, we analyzed a public dataset of bulk transcriptomic samples from COVID-19 patients and healthy controls. We have shown that bcRflow streamlines the analysis of BCR repertoires from non-targeted transcriptomics data, providing valuable insights into the B cell immune response for biological and clinical research. bcRflow is available at https://github.com/Bioinformatics-Core-at-Childrens/bcRflow.