bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterizing Liquid Water in Deep Martian Aquifers: A Seismo‐Electric Approach
Authors: Roth N. , Zhu T. , Gao Y. 
Published: May 2024

AbstractDeep Martian aquifers harboring liquid water could hold vital insights for current and past habitability. We show that with seismo‐electric interface responses (IRs) we can quantitatively characterize subsurface water on Mars. Full‐waveform simulations and sensitivity analyses across diverse Martian aquifer scenarios demonstrate the technique's effectiveness. In contrast to how seismo‐electric signals often appear on Earth, Mars' desiccated surface naturally removes co‐seismic fields and exposes useful IRs that allow us to characterize several aquifer properties. Changing the aquifer depth, thickness, or quantity changes the IR arrival times or shape: aquifer depth is a strong control on evanescent IRs, thickness affects the relative timing of IRs, and increasing the number of aquifers introduces more dipole sources to the waveform. Other factors, such as aquifer saturation, chemistry, and salinity, strongly affect IR amplitude but have minimal or no effect on waveform shape. Notably, for a deep low‐porosity aquifer, the salinity and brine chemistry (perchlorate vs. chloride) are the strongest controls on signal amplitude. Analyzing the effects of epicentral distance shows that radiating and evanescent IRs separate at large source‐receiver offset, allowing analyses of both signals and accurate event distance derivation. From this numerical investigation of the sensitivity of IRs to deep Martian aquifers, we anticipate future analyses of electromagnetic data from the InSight lander or future missions to Mars and other planets.
ASS1 inhibits triple-negative breast cancer by regulating PHGDH stability and de novo serine synthesis
Authors: Luo Wensong , Zou Zizheng , Nie Yuan , Luo Junli , Ming Zhengnan , Hu Xiyuan , Luo Tiao , Ouyang Min , Liu Mingquan , Tang Huicheng , Xie Yuanzhu , Peng Kunjian , Chen Ling , Zhou Jiang , Luo Zhiyong 
Published: May 2024

AbstractArgininosuccinate synthase (ASS1), a critical enzyme in the urea cycle, acts as a tumor suppressor in many cancers. To date, the anticancer mechanism of ASS1 has not been fully elucidated. Here, we found that phosphoglycerate dehydrogenase (PHGDH), a key rate-limiting enzyme in serine synthesis, is a pivotal protein that interacts with ASS1. Our results showed that ASS1 directly binds to PHGDH and promotes its ubiquitination-mediated degradation to inhibit serine synthesis, consequently suppressing tumorigenesis. Importantly, the tumor suppressive effects of ASS1 were strongly abrogated by PHGDH knockout. In addition, ASS1 knockout and knockdown partially rescued cell proliferation when serine and glycine were depleted, while the inhibitory effect of ASS1 overexpression on cell proliferation was restored by the addition of serine and glycine. These findings unveil a novel role of ASS1 and suggest that the ASS1/PHGDH serine synthesis pathway is a promising target for cancer therapy.
HIF-1α participates in the regulation of S100A16-HRD1-GSK3β/CK1α pathway in renal hypoxia injury
Authors: Han Shuying , Jin Runbing , Huo Lei , Teng Yunfei , Zhao Lihua , Zhang Kaini , Li Rongfeng , Su Dongming , Liang Xiubin 
Published: May 2024

AbstractS100 calcium-binding protein 16 (S100A16) is implicated in both chronic kidney disease (CKD) and acute kidney injury (AKI). Previous research has shown that S100A16 contributes to AKI by facilitating the ubiquitylation and degradation of glycogen synthase kinase 3β (GSK3β) and casein kinase 1α (CK1α) through the activation of HMG-CoA reductase degradation protein 1 (HRD1). However, the mechanisms governing S100A16-induced HRD1 activation and the upregulation of S100A16 expression in renal injury are not fully understood. In this study, we observed elevated expression of Hypoxia-inducible Factor 1-alpha (HIF-1α) in the kidneys of mice subjected to ischemia-reperfusion injury (IRI). S100A16 deletion attenuated the increased HIF-1α expression induced by IRI. Using a S100A16 knockout rat renal tubular epithelial cell line (NRK-52E cells), we found that S100A16 knockout effectively mitigated apoptosis during hypoxic reoxygenation (H/R) and cell injury induced by TGF-β1. Our results revealed that H/R injuries increased both protein and mRNA levels of HIF-1α and HRD1 in renal tubular cells. S100A16 knockout reversed the expressions of HIF-1α and HRD1 under H/R conditions. Conversely, S100A16 overexpression in NRK-52E cells elevated HIF-1α and HRD1 levels. HIF-1α overexpression increased HRD1 and β-catenin while decreasing GSK-3β. HIF-1α inhibition restored HRD1 and β-catenin upregulation and GSK-3β downregulation by cellular H/R injury. Notably, Chromatin immunoprecipitation (ChIP) and luciferase reporter assays demonstrated HIF-1α binding signals on the HRD1 promoter, and luciferase reporter gene assays confirmed HIF-1α‘s transcriptional regulation of HRD1. Additionally, we identified Transcription Factor AP-2 Beta (TFAP2B) as the upregulator of S100A16. ChIP and luciferase reporter assays confirmed TFAP2B as a transcription factor for S100A16. In summary, this study identifies TFAP2B as the transcription factor for S100A16 and demonstrates HIF-1α regulation of HRD1 transcription within the S100A16-HRD1-GSK3β/CK1α pathway during renal hypoxia injury. These findings provide crucial insights into the molecular mechanisms of kidney injury, offering potential avenues for therapeutic intervention.
CEBPB-mediated upregulation of SERPINA1 promotes colorectal cancer progression by enhancing STAT3 signaling
Authors: Ma Yiming , Chen Ying , Zhan Lei , Dong Qian , Wang Yuanhe , Li Xiaoyan , He Lian , Zhang Jingdong 
Published: May 2024

AbstractColorectal cancer (CRC) is a highly malignant carcinoma associated with poor prognosis, and metastasis is one of the most common causes of death in CRC. Serpin Family A Member 1 (SERPINA1) is a serine protease inhibitor from the Serpin family. Till now, the function and mechanism of SERPINA1 in CRC progression have not been fully illustrated. We established highly metastatic colorectal cancer cells named as RKO-H and Caco2-H by mice liver metastasis model. By integrative bioinformatic approaches, we analyzed the prognostic value and clinical significance of SERPINA1 in CRC, and predicted potential transcription factors. Colony formation, EDU, MTS, Transwell and wound healing assay were performed to evaluate the biological functions of SERPINA1 in CRC in vitro. Experiments in vivo were conducted to explore the effects of SERPINA1 on liver metastasis of CRC. ChIP and luciferase reporter gene assays were performed to identify the transcriptional regulatory mechanism of SERPINA1 by CEBPB. Our results show that SERPINA1 is highly expressed in CRC and correlated with poor clinical outcomes. SERPINA1 promotes the proliferation, migration by activating STAT3 pathway. Mechanistically, CEBPB binds SERPINA1 gene promoter sequence and promotes the transcription of SERPINA1. SERPINA1 drives CEBPB-induced tumor cell growth and migration via augmenting STAT3 signaling. Our results suggest that SERPINA1 is a potential prognostic marker and may serve as a novel treatment target for CRC.
Synthetic molecular switches driven by DNA-modifying enzymes
Authors: Kang Hong , Yang Yuexuan , Wei Bryan 
Published: May 2024

AbstractTaking inspiration from natural systems, in which molecular switches are ubiquitous in the biochemistry regulatory network, we aim to design and construct synthetic molecular switches driven by DNA-modifying enzymes, such as DNA polymerase and nicking endonuclease. The enzymatic treatments on our synthetic DNA constructs controllably switch ON or OFF the sticky end cohesion and in turn cascade to the structural association or disassociation. Here we showcase the concept in multiple DNA nanostructure systems with robust assembly/disassembly performance. The switch mechanisms are first illustrated in minimalist systems with a few DNA strands. Then the ON/OFF switches are realized in complex DNA lattice and origami systems with designated morphological changes responsive to the specific enzymatic treatments.
Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank
Authors: Kemper Kathryn E. , Sidorenko Julia , Wang Huanwei , Hayes Ben J. , Wray Naomi R. , Yengo Loic , Keller Matthew C. , Goddard Michael , Visscher Peter M. 
Published: May 2024

AbstractThe causes of temporal fluctuations in adult traits are poorly understood. Here, we investigate the genetic determinants of within-person trait variability of 8 repeatedly measured anthropometric traits in 50,117 individuals from the UK Biobank. We found that within-person (non-directional) variability had a SNP-based heritability of 2–5% for height, sitting height, body mass index (BMI) and weight (P $$\le$$
≤
2.4 × 10−3). We also analysed longitudinal trait change and show a loss of both average height and weight beyond about 70 years of age. A variant tracking the Alzheimer’s risk APOE-$${{{{{\mathcal{E}}}}}}4$$
E
4
allele (rs429358) was significantly associated with weight loss ($$\beta$$
β
= −0.047 kg per yr, s.e. 0.007, P = 2.2 × 10−11), and using 2-sample Mendelian Randomisation we detected a relationship consistent with causality between decreased lumbar spine bone mineral density and height loss (bxy = 0.011, s.e. 0.003, P = 3.5 × 10−4). Finally, population-level variance quantitative trait loci (vQTL) were consistent with within-person variability for several traits, indicating an overlap between trait variability assessed at the population or individual level. Our findings help elucidate the genetic influence on trait-change within an individual and highlight disease risks associated with these changes.
A conserved N-terminal motif of CUL3 contributes to assembly and E3 ligase activity of CRL3KLHL22
Authors: Wang Weize , Liang Ling , Dai Zonglin , Zuo Peng , Yu Shang , Lu Yishuo , Ding Dian , Chen Hongyi , Shan Hui , Jin Yan , Mao Youdong , Yin Yuxin 
Published: May 2024

AbstractThe CUL3-RING E3 ubiquitin ligases (CRL3s) play an essential role in response to extracellular nutrition and stress stimuli. The ubiquitin ligase function of CRL3s is activated through dimerization. However, how and why such a dimeric assembly is required for its ligase activity remains elusive. Here, we report the cryo-EM structure of the dimeric CRL3KLHL22 complex and reveal a conserved N-terminal motif in CUL3 that contributes to the dimerization assembly and the E3 ligase activity of CRL3KLHL22. We show that deletion of the CUL3 N-terminal motif impairs dimeric assembly and the E3 ligase activity of both CRL3KLHL22 and several other CRL3s. In addition, we found that the dynamics of dimeric assembly of CRL3KLHL22 generates a variable ubiquitination zone, potentially facilitating substrate recognition and ubiquitination. These findings demonstrate that a CUL3 N-terminal motif participates in the assembly process and provide insights into the assembly and activation of CRL3s.
Targeted delivery of the probiotic Saccharomyces boulardii to the extracellular matrix enhances gut residence time and recovery in murine colitis
Authors: Heavey Mairead K. , Hazelton Anthony , Wang Yuyan , Garner Mitzy , Anselmo Aaron C. , Arthur Janelle C. , Nguyen Juliane 
Published: May 2024

AbstractProbiotic and engineered microbe-based therapeutics are an emerging class of pharmaceutical agents. They represent a promising strategy for treating various chronic and inflammatory conditions by interacting with the host immune system and/or delivering therapeutic molecules. Here, we engineered a targeted probiotic yeast platform wherein Saccharomyces boulardii is designed to bind to abundant extracellular matrix proteins found within inflammatory lesions of the gastrointestinal tract through tunable antibody surface display. This approach enabled an additional 24–48 h of probiotic gut residence time compared to controls and 100-fold increased probiotic concentrations within the colon in preclinical models of ulcerative colitis in female mice. As a result, pharmacodynamic parameters including colon length, colonic cytokine expression profiles, and histological inflammation scores were robustly improved and restored back to healthy levels. Overall, these studies highlight the potential for targeted microbial therapeutics as a potential oral dosage form for the treatment of inflammatory bowel diseases.
An adeno-associated virus variant enabling efficient ocular-directed gene delivery across species
Authors: Luo Shuang , Jiang Hao , Li Qingwei , Qin Yingfei , Yang Shiping , Li Jing , Xu Lingli , Gou Yan , Zhang Yafei , Liu Fengjiang , Ke Xiao , Zheng Qiang , Sun Xun 
Published: May 2024

AbstractRecombinant adeno-associated viruses (rAAVs) have emerged as promising gene therapy vectors due to their proven efficacy and safety in clinical applications. In non-human primates (NHPs), rAAVs are administered via suprachoroidal injection at a higher dose. However, high doses of rAAVs tend to increase additional safety risks. Here, we present a novel AAV capsid (AAVv128), which exhibits significantly enhanced transduction efficiency for photoreceptors and retinal pigment epithelial (RPE) cells, along with a broader distribution across the layers of retinal tissues in different animal models (mice, rabbits, and NHPs) following intraocular injection. Notably, the suprachoroidal delivery of AAVv128-anti-VEGF vector completely suppresses the Grade IV lesions in a laser-induced choroidal neovascularization (CNV) NHP model for neovascular age-related macular degeneration (nAMD). Furthermore, cryo-EM analysis at 2.1 Å resolution reveals that the critical residues of AAVv128 exhibit a more robust advantage in AAV binding, the nuclear uptake and endosome escaping. Collectively, our findings highlight the potential of AAVv128 as a next generation ocular gene therapy vector, particularly using the suprachoroidal delivery route.
Platform-directed allostery and quaternary structure dynamics of SAMHD1 catalysis
Authors: Acton Oliver J. , Sheppard Devon , Kunzelmann Simone , Caswell Sarah J. , Nans Andrea , Burgess Ailidh J. O. , Kelly Geoff , Morris Elizabeth R. , Rosenthal Peter B. , Taylor Ian A. 
Published: May 2024

AbstractSAMHD1 regulates cellular nucleotide homeostasis, controlling dNTP levels by catalysing their hydrolysis into 2’-deoxynucleosides and triphosphate. In differentiated CD4+ macrophage and resting T-cells SAMHD1 activity results in the inhibition of HIV-1 infection through a dNTP blockade. In cancer, SAMHD1 desensitizes cells to nucleoside-analogue chemotherapies. Here we employ time-resolved cryogenic-EM imaging and single-particle analysis to visualise assembly, allostery and catalysis by this multi-subunit enzyme. Our observations reveal how dynamic conformational changes in the SAMHD1 quaternary structure drive the catalytic cycle. We capture five states at high-resolution in a live catalytic reaction, revealing how allosteric activators support assembly of a stable SAMHD1 tetrameric core and how catalysis is driven by the opening and closing of active sites through pairwise coupling of active sites and order-disorder transitions in regulatory domains. This direct visualisation of enzyme catalysis dynamics within an allostery-stabilised platform sets a precedent for mechanistic studies into the regulation of multi-subunit enzymes.
Dimensionality reduction beyond neural subspaces with slice tensor component analysis
Authors: Pellegrino Arthur , Stein Heike , Cayco-Gajic N. Alex 
Published: May 2024

AbstractRecent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that neural variability is constrained to a fixed, low-dimensional subspace may overlook higher-dimensional structure, including stereotyped neural sequences or slowly evolving latent spaces. Here we argue that task-relevant variability in neural data can also cofluctuate over trials or time, defining distinct ‘covariability classes’ that may co-occur within the same dataset. To demix these covariability classes, we develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors. In three example datasets, including motor cortical activity during a classic reaching task in primates and recent multiregion recordings in mice, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.
A high-quality chromosome-scale genome assembly of blood orange, an important pigmented sweet orange variety
Authors: Yang Lei , Deng Honghong , Wang Min , Li Shuang , Wang Wu , Yang Haijian , Pang Changqing , Zhong Qi , Sun Yue , Hong Lin 
Published: May 2024

AbstractBlood orange (BO) is a rare red-fleshed sweet orange (SWO) with a high anthocyanin content and is associated with numerous health-related benefits. Here, we reported a high-quality chromosome-scale genome assembly for Neixiu (NX) BO, reaching 336.63 Mb in length with contig and scaffold N50 values of 30.6 Mb. Furthermore, 96% of the assembled sequences were successfully anchored to 9 pseudo-chromosomes. The genome assembly also revealed the presence of 37.87% transposon elements and 7.64% tandem repeats, and the annotation of 30,395 protein-coding genes. A high level of genome synteny was observed between BO and SWO, further supporting their genetic similarity. The speciation event that gave rise to the Citrus species predated the duplication event found within them. The genome-wide variation between NX and SWO was also compared. This first high-quality BO genome will serve as a fundamental basis for future studies on functional genomics and genome evolution.
The relative contributions of traffic and non-traffic sources in ultrafine particle formations in Tehran mega city
Authors: Jafarigol Farzaneh , Yousefi Somayeh , Darvishi Omrani Ali , Rashidi Yousef , Buonanno Giorgio , Stabile Luca , Sabanov Sergei , Amouei Torkmahalleh Mehdi 
Published: May 2024

AbstractEmissions of ultrafine particles (UFPs; diameter < 100 nm) are strongly associated with traffic-related emissions and are a growing global concern in urban environments. The aim of this study was to investigate the variations of particle number concentration (PNC) with a diameter > 10 nm at nine stations and understand the major sources of UFPs (primary vs. secondary) in Tehran megacity. The study was carried out in Tehran in 2020. NOx and PNC were reported from a total of nine urban site locations in Tehran and BC concentrations were examined at two monitoring stations. Data from all stations showed diurnal changes with peak morning and evening rush hours. The hourly PNC was correlated with NOx. PNCs in Tehran were higher compared to those of many cities reported in the literature. The highest concentrations were at District 19 station (traffic) and the lowest was at Punak station (residential) such that the average PNC varied from 8.4 × 103 to 5.7 × 104 cm−3. In Ray and Sharif stations, the average contributions of primary and secondary sources of PNC were 67 and 33%, respectively. Overall, we conclude that a decrease in primary emission leads to a decrease in the total concentration of aerosols, despite an increase in the formation of new particles by photo nucleation.
An adaptable and personalized framework for top-N course recommendations in online learning
Authors: Amin Samina , Uddin M. Irfan , Alarood Ala Abdulsalam , Mashwani Wali Khan , Alzahrani Ahmed Omar , Alzahrani Hamdan Ahmed 
Published: May 2024

AbstractIn recent years, the proliferation of Massive Open Online Courses (MOOC) platforms on a global scale has been remarkable. Learners can now meet their learning demands with the help of MOOC. However, learners might not understand the course material well if they have access to a lot of information due to their inadequate expertise and cognitive ability. Personalized Recommender Systems (RSs), a cutting-edge technology, can assist in addressing this issue. It greatly increases resource acquisition through personalized availability for various people of all ages. Intelligent learning methods, such as machine learning and Reinforcement Learning (RL) can be used in RS challenges. However, machine learning needs supervised data and classical RL is not suitable for multi-task recommendations in online learning platforms. To address these challenges, the proposed framework integrates a Deep Reinforcement Learning (DRL) and multi-agent approach. This adaptive system personalizes the learning experience by considering key factors such as learner sentiments, learning style, preferences, competency, and adaptive difficulty levels. We formulate the interactive RS problem using a DRL-based Actor-Critic model named DRR, treating recommendations as a sequential decision-making process. The DRR enables the system to provide top-N course recommendations and personalized learning paths, enriching the student's experience. Extensive experiments on a MOOC dataset such as the 100 K Coursera course review validate the proposed DRR model, demonstrating its superiority over baseline models in major evaluation metrics for long-term recommendations. The outcomes of this research contribute to the field of e-learning technology, guiding the design and implementation of course RSs, to facilitate personalized and relevant recommendations for online learning students.
Reservoir temperature prediction based on characterization of water chemistry data—case study of western Anatolia, Turkey
Authors: Shi Haoxin , Zhang Yanjun , Yu Ziwang , Yang Yunxing 
Published: May 2024

AbstractReservoir temperature estimation is crucial for geothermal studies, but traditional methods are complex and uncertain. To address this, we collected 83 sets of water chemistry and reservoir temperature data and applied four machine learning algorithms. These models considered various input factors and underwent data preprocessing steps like null value imputation, normalization, and Pearson coefficient calculation. Cross-validation addressed data volume issues, and performance metrics were used for model evaluation. The results revealed that our machine learning models outperformed traditional fluid geothermometers. All machine learning models surpassed traditional methods. The XGBoost model, based on the F-3 combination, demonstrated the best prediction accuracy with an R2 of 0.9732, while the Bayesian ridge regression model using the F-4 combination had the lowest performance with an R2 of 0.8302. This study highlights the potential of machine learning for accurate reservoir temperature prediction, offering geothermal professionals a reliable tool for model selection and advancing our understanding of geothermal resources.