bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pooled multicolour tagging for visualizing subcellular protein dynamics
Authors: Reicher Andreas , Reiniš Jiří , Ciobanu Maria , Růžička Pavel , Malik Monika , Siklos Marton , Kartysh Victoria , Tomek Tatjana , Koren Anna , Rendeiro André F. , Kubicek Stefan 
Published: April 2024

AbstractImaging-based methods are widely used for studying the subcellular localization of proteins in living cells. While routine for individual proteins, global monitoring of protein dynamics following perturbation typically relies on arrayed panels of fluorescently tagged cell lines, limiting throughput and scalability. Here, we describe a strategy that combines high-throughput microscopy, computer vision and machine learning to detect perturbation-induced changes in multicolour tagged visual proteomics cell (vpCell) pools. We use genome-wide and cancer-focused intron-targeting sgRNA libraries to generate vpCell pools and a large, arrayed collection of clones each expressing two different endogenously tagged fluorescent proteins. Individual clones can be identified in vpCell pools by image analysis using the localization patterns and expression level of the tagged proteins as visual barcodes, enabling simultaneous live-cell monitoring of large sets of proteins. To demonstrate broad applicability and scale, we test the effects of antiproliferative compounds on a pool with cancer-related proteins, on which we identify widespread protein localization changes and new inhibitors of the nuclear import/export machinery. The time-resolved characterization of changes in subcellular localization and abundance of proteins upon perturbation in a pooled format highlights the power of the vpCell approach for drug discovery and mechanism-of-action studies.
Deep CO2 release and the carbon budget of the central Apennines modulated by geodynamics
Authors: Erlanger Erica , Bufe Aaron , Paris Guillaume , D’Angeli Ilenia , Pisani Luca , Kemeny Preston Cosslett , Stammeier Jessica , Haghipour Negar , Hovius Niels 
Published: April 2024

AbstractRecent studies increasingly recognize the importance of critical-zone weathering during mountain building for long-term CO2 drawdown and release. However, the focus on near-surface weathering reactions commonly does not account for CO2 emissions from the crust, which could outstrip CO2 drawdown where carbonates melt and decarbonize during subduction and metamorphism. We analyse water chemistry from streams in Italy’s central Apennines that cross a gradient in heat flow and crustal thickness with relatively constant climatic conditions. We quantify the balance of inorganic carbon fluxes from near-surface weathering processes, metamorphism and the melting of carbonates. We find that, at the regional scale, carbon emissions from crustal sources outpace near-surface fluxes by two orders of magnitude above a tear in the subducting slab characterized by heat flow greater than 150 mW m–2 and crustal thickness of less than 25 km. By contrast, weathering processes dominate the carbon budget where crustal thickness exceeds 40 km and heat flow is lower than 30 mW m–2. The observed variation in metamorphic fluxes is one to two orders of magnitude larger than that of weathering fluxes. We therefore suggest that geodynamic modulations of metamorphic melting and decarbonation reactions are an efficient process by which tectonics can regulate the inorganic carbon cycle.
Penning-trap measurement of the Q value of electron capture in 163Ho for the determination of the electron neutrino mass
Authors: Schweiger Christoph , Braß Martin , Debierre Vincent , Door Menno , Dorrer Holger , Düllmann Christoph E. , Enss Christian , Filianin Pavel , Gastaldo Loredana , Harman Zoltán , Haverkort Maurits W. , Herkenhoff Jost , Indelicato Paul , Keitel Christoph H. , Kromer Kathrin , Lange Daniel , Novikov Yuri N. , Renisch Dennis , Rischka Alexander , Schüssler Rima X. , Eliseev Sergey , Blaum Klaus 
Published: April 2024

AbstractThe investigation of the absolute scale of the effective neutrino mass remains challenging due to the exclusively weak interaction of neutrinos with all known particles in the standard model of particle physics. At present, the most precise and least-model-dependent upper limit on the electron antineutrino mass is set by the Karlsruhe Tritium Neutrino Experiment (KATRIN) from the analysis of the tritium β-decay. Another promising approach is the electron capture in 163Ho, which is under investigation using microcalorimetry by the Electron Capture in Holmium (ECHo) and HOLMES collaborations. An independently measured Q value for this process is vital for the assessment of systematic uncertainties in the neutrino mass determination. Here we report a direct, independent determination of this Q value by measuring the free-space cyclotron frequency ratio of highly charged ions of 163Ho and 163Dy in the Penning-trap experiment PENTATRAP. Combining this ratio with atomic physics calculations of the electronic binding energies yields a Q value of 2,863.2 ± 0.6 eV c−2, which represents a more than 50-fold improvement over the state of the art. This will enable the determination of the electron neutrino mass on a sub-electronvolt level from the analysis of the electron capture in 163Ho.
Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence
Authors: Louis Thomas , Lucia François , Cousin François , Mievis Carole , Jansen Nicolas , Duysinx Bernard , Le Pennec Romain , Visvikis Dimitris , Nebbache Malik , Rehn Martin , Hamya Mohamed , Geier Margaux , Salaun Pierre-Yves , Schick Ulrike , Hatt Mathieu , Coucke Philippe , Lovinfosse Pierre , Hustinx Roland 
Published: April 2024

AbstractThe primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset.
Predictors of chronic kidney disease survival in type 2 diabetes: a 12-year retrospective cohort study utilizing estimated glomerular filtration rate
Authors: Jairoun Ammar Abdulrahman , Ping Chong Chee , Ibrahim Baharudin 
Published: April 2024

AbstractPredicting the course of kidney disease in individuals with both type 1 and type 2 diabetes mellitus (DM) is a significant clinical and policy challenge. In several regions, DM is now the leading cause of end-stage renal disease. The aim of this study to identify both modifiable and non-modifiable risk factors, along with clinical markers and coexisting conditions, that increase the likelihood of stage 3–5 chronic kidney disease (CKD) development in individuals with type 2 DM in the United Arab Emirates (UAE). This was a single-center retrospective cohort study based on data derived from electronic medical records of UAE patients with DM who were registered at outpatient clinics at Tawam Hospital in Al Ain, UAE, between January 2011 and December 2021. Type 2 DM patients aged ≥ 18 years who had serum HbA1c levels ≥ 6.5% were included in the study. Patients with type 1 DM, who had undergone permanent renal replacement therapy, who had under 1 year of follow-up, or who had missing or incomplete data were excluded from the study. Factors associated with diabetic patients developing stage 3–5 CKD were identified through Cox regression analysis and a fine and gray competing risk model to account for competing events that could potentially hinder the development of CKD. A total of 1003 patients were recruited for the study. The mean age of the study cohort at baseline was 70.6 ± 28.2 years. Several factors were found to increase the risk of developing stage 3–5 CKD: advancing age (HR 1.005, 95% CI 1.002–1.009, p = 0.026), a history of hypertension (HR 1.69, 95% CI 1.032–2.8, p = 0.037), a history of heart disease (HR 1.49, 95% CI 1.16–1.92, p = 0.002), elevated levels of serum creatinine (HR 1.006, 95% CI 1.002–1.010, p = 0.003), decreased levels of estimated glomerular filtration rate (eGFR) (HR 0.943, 95% CI, 0.938–0.947; p < 0.001), and the use of beta-blockers (HR 139, 95% CI 112–173, p = 0.003). Implementing preventative measures, initiating early interventions, and developing personalized care plans tailored to address specific risk factors are imperative for reducing the impact of CKD. Additionally, the unforeseen findings related to eGFR highlight the ongoing need for research to deepen our understanding of the complexities of kidney disease.
Calculating and analyzing time delay in zigzag graphene nanoscrolls based complementary metal-oxide-semiconductors
Authors: Sadeqian Ali , Ahmadi Mohammad Taghi , Bodaghzadeh Morteza , Abazari Amir Musa 
Published: April 2024

AbstractGraphene Nano Scrolls (GNSs) and Zigzag graphene nanoscrolls (ZGNSs) are semi-one-dimensional materials with exceptional electrical and optical properties, making them attractive to be used in nanoelectronics and complementary metal–oxide–semiconductor (CMOS) technology. With in CMOS device technology, time delay is a crucial issue in the design and implementation of CMOS based ZGNSs. Current paper focus is on ZGNSs application in the channel area of metal–oxide–semiconductor field-effect transistors (MOSFETs) in CMOS technology. We studied analytically, the importance of different parameters on time delay reduction, resulting in faster switching and higher frequency in integrated circuits (ICs). The results of this research demonstrates that, the ZGNS-based CMOS proves considerable variations in the current due to the geometrical parameters, such as chirality number, channel length, and nanoscroll length which can be engineered to produce faster ICs.
Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach
Authors: Hemati Mohammadali , Mahdianpari Masoud , Nassar Ray , Shiri Hodjat , Mohammadimanesh Fariba 
Published: April 2024

AbstractMonitoring methane emissions is crucial in mitigating climate change as it has a relatively short atmospheric lifetime of about 12 years and a significant radiative forcing impact. To measure the impact of methane-controlling policies and techniques, a deep understanding of methane emissions is of great importance. Remote sensing offers scalable approaches for monitoring methane emissions at various scales, from point-source high-resolution monitoring to regional and global estimates. The TROPOMI satellite instrument provides daily XCH4 data globally, offering the opportunity to monitor methane at a moderate spatial resolution with an acceptable level of sensitivity. To infer emissions from TROPOMI data, we used the prior emission estimates from global and national inventories and the GEOS-Chem chemical transport model to simulate atmospheric methane along with actual observations of TROPOMI. In this study, methane emissions from Toronto, Montreal, New York, Los Angeles, Houston, and Mexico City have been estimated using the analytical solution of Bayesian inversion using the cloud-based Integrated Methane Inversion (IMI) framework. Using the result from ensemble inversions, and city boundaries, the average total emissions were as follows: Toronto 230.52 Gg a−1, Montreal 111.54 Gg a−1, New York 144.38 Gg a−1, Los Angeles 207.03 Gg a−1, Houston 650.16 Gg a−1, and Mexico City 280.81 Gg a−1. The resulting gridded scale factors ranged from 0.22 to 6.2, implying methane prior emission underestimations in most of these cities. As such, this study underscores the key role of remote sensing in accurately assessing urban methane emissions, informing essential climate mitigation efforts.
Maternal and foetal complications of pregestational and gestational diabetes: a descriptive, retrospective cohort study
Authors: Oros Ruiz Miriam , Perejón López Daniel , Serna Arnaiz Catalina , Siscart Viladegut Júlia , Àngel Baldó Joan , Sol Joaquim 
Published: April 2024

AbstractGestational diabetes is characterized by hyperglycaemia diagnosed during pregnancy. Gestational and pregestational diabetes can have deleterious effects during pregnancy and perinatally. The baby's weight is frequently above average and might reach macrosomia (≥ 4 kg), which can reduce pregnancy time causing preterm births, and increase foetal-pelvic disproportion which often requires delivery by caesarean section. Foetal-pelvic disproportion due to the baby’s weight can also cause foetal distress resulting in lower Apgar scores. To analyse the association between pregestational and gestational diabetes with maternal and foetal risk. We conducted a retrospective cohort study in women pregnant between 2012 and 2018 in the region of Lleida. Regression coefficients and 95% confidence intervals (CI) were used. The multivariate analysis showed statistically significant associations between pregestational diabetes and: prematurity (OR 2.4); caesarean section (OR 1.4); moderate (OR 1.3), high (OR 3.3) and very high (OR 1.7) risk pregnancies; and birth weight ≥ 4000 g (macrosomia) (OR 1.7). In getational diabetes the multivariate analysis show significant association with: caesarean section (OR 1.5); moderate (OR 1.7), high (OR 1.7) and very high (OR 1.8) risk pregnancies and lower 1-minuto Apgar score (OR 1.5). Pregestational and gestational diabetes increase: pregnancy risk, caesarean sections, prematurity, low Apgar scores, and macrosomia.
De novo reconstruction of a functional in vivo-like equine endometrium using collagen-based tissue engineering
Authors: Santiviparat Sawita , Swangchan-Uthai Theerawat , Stout Tom A. E. , Buranapraditkun Supranee , Setthawong Piyathip , Taephatthanasagon Teeanutree , Rodprasert Watchareewan , Sawangmake Chenphop , Tharasanit Theerawat 
Published: April 2024

AbstractTo better understand molecular aspects of equine endometrial function, there is a need for advanced in vitro culture systems that more closely imitate the intricate 3-dimensional (3D) in vivo endometrial structure than current techniques. However, development of a 3D in vitro model of this complex tissue is challenging. This study aimed to develop an in vitro 3D endometrial tissue (3D-ET) with an epithelial cell phenotype optimized by treatment with a Rho-associated protein kinase (ROCK) inhibitor. Equine endometrial epithelial (eECs) and mesenchymal stromal (eMSCs) cells were isolated separately, and eECs cultured in various concentrations of Rock inhibitor (0, 5, 10 µmol) in epithelial medium (EC-medium) containing 10% knock-out serum replacement (KSR). The optimal concentration of Rock inhibitor for enhancing eEC proliferation and viability was 10 µM. However, 10 µM Rock inhibitor in the 10% KSR EC-medium was able to maintain mucin1 (Muc1) gene expression for only a short period. In contrast, fetal bovine serum (FBS) was able to maintain Muc1 gene expression for longer culture durations. An in vitro 3D-ET was successfully constructed using a collagen-based scaffold to support the eECs and eMSCs. The 3D-ET closely mimicked in vivo endometrium by displaying gland-like eEC-derived structures positive for the endometrial gland marker, Fork headbox A2 (FOXA2), and by mimicking the 3D morphology of the stromal compartment. In addition, the 3D-ET expressed the secretory protein MUC1 on its glandular epithelial surface and responded to LPS challenge by upregulating the expression of the interleukin-6 (IL6) and prostaglandin F synthase (PGFS) genes (P < 0.01), along with an increase in their secretory products, IL-6 (P < 0.01) and prostaglandin F2alpha (PGF2α) (P < 0.001) respectively. In the future, this culture system can be used to study both normal physiology and pathological processes of the equine endometrium.
Serum micro-RNAs with mutation-targeted RNA modification: a potent cancer detection tool constructed using an optimized machine learning workflow
Authors: Liao Wei , Xu Yuyan , Pan Mingxin , Chen Huanwei 
Published: April 2024

AbstractRNA modifications affect fundamental biological processes and diseases and are a research hotspot. Several micro-RNAs (miRNAs) exhibit genetic variant-targeted RNA modifications that can greatly alter their biofunctions and influence their effect on cancer. Therefore, the potential role of these miRNAs in cancer can be implicated in new prevention and treatment strategies. In this study, we determined whether RMvar-related miRNAs were closely associated with tumorigenesis and identified cancer-specific signatures based on these miRNAs with variants targeting RNA modifications using an optimized machine learning workflow. An effective machine learning workflow, combining least absolute shrinkage and selection operator analyses, recursive feature elimination, and nine types of machine learning algorithms, was used to screen candidate miRNAs from 504 serum RMvar-related miRNAs and construct a diagnostic signature for cancer detection based on 43,047 clinical samples (with an area under the curve value of 0.998, specificity of 93.1%, and sensitivity of 99.3% in the validation cohort). This signature demonstrated a satisfactory diagnostic performance for certain cancers and different conditions, including distinguishing early-stage tumors. Our study revealed the close relationship between RMvar-related miRNAs and tumors and proposed an effective cancer screening tool.
Development and validation of a nomogram to predict cancer-specific survival of elderly patients with unresected gastric cancer who received chemotherapy
Authors: Wang Qi , Shen Kexin , Fei Bingyuan , Wei Mengqiang , Ge Xinbin , Xie Zhongshi 
Published: April 2024

AbstractThis investigation aimed to explore the prognostic factors in elderly patients with unresected gastric cancer (GC) who have received chemotherapy and to develop a nomogram for predicting their cancer-specific survival (CSS). Elderly gastric cancer patients who have received chemotherapy but no surgery in the Surveillance, Epidemiology, and End Results Database between 2004 and 2015 were included in this study. Cox analyses were conducted to identify prognostic factors, leading to the formulation of a nomogram. The nomogram was validated using receiver operating characteristic (ROC) and calibration curves. The findings elucidated six prognostic factors encompassing grade, histology, M stage, radiotherapy, tumor size, and T stage, culminating in the development of a nomogram. The ROC curve indicated that the area under curve of the nomogram used to predict CSS for 3, 4, and 5 years in the training queue as 0.689, 0.708, and 0.731, and in the validation queue, as 0.666, 0.693, and 0.708. The calibration curve indicated a high degree of consistency between actual and predicted CSS for 3, 4, and 5 years. This nomogram created to predict the CSS of elderly patients with unresected GC who have received chemotherapy could significantly enhance treatment accuracy.
Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations
Authors: Gadár László , Abonyi János 
Published: April 2024

AbstractIn real-world classification problems, it is important to build accurate prediction models and provide information that can improve decision-making. Decision-support tools are often based on network models, and this article uses information encoded by social networks to solve the problem of employer turnover. However, understanding the factors behind black-box prediction models can be challenging. Our question was about the predictability of employee turnover, given information from the multilayer network that describes collaborations and perceptions that assess the performance of organizations that indicate the success of cooperation. Our goal was to develop an accurate prediction procedure, preserve the interpretability of the classification, and capture the wide variety of specific reasons that explain positive cases. After a feature engineering, we identified variables with the best predictive power using decision trees and ranked them based on their added value considering their frequent co-occurrence. We applied the Random Forest using the SMOTE balancing technique for prediction. We calculated the SHAP values to identify the variables that contribute the most to individual predictions. As a last step, we clustered the sample based on SHAP values to fine-tune the explanations for quitting due to different background factors.
VDR is a potential prognostic biomarker and positively correlated with immune infiltration: a comprehensive pan-cancer analysis with experimental verification
Authors: Xia Xuedi , Xu Feng , Dai Dexing , Xiong An , Sun Ruoman , Ling Yali , Qiu Lei , Wang Rui , Ding Ya , Lin Miaoying , Li Haibo , Xie Zhongjian 
Published: April 2024

Abstract
The vitamin D receptor (VDR) is a transcription factor that mediates a variety of biological functions of 1,25-dihydroxyvitamin D3. Although there is growing evidence of cytological and animal studies supporting the suppressive role of VDR in cancers, the conclusion is still controversial in human cancers and no systematic pan-cancer analysis of VDR is available. We explored the relationships between VDR expression and prognosis, immune infiltration, tumor microenvironment, or gene set enrichment analysis (GSEA) in 33 types of human cancers based on multiple public databases and R software. Meanwhile, the expression and role of VDR were experimentally validated in papillary thyroid cancer (PTC). VDR expression decreased in 8 types and increased in 12 types of cancer compared with normal tissues. Increased expression of VDR was associated with either good or poor prognosis in 13 cancer types. VDR expression was positively correlated with the infiltration of cancer-associated fibroblasts, macrophages, or neutrophils in 20, 12, and 10 cancer types respectively and this correlation was experimentally validated in PTC. Increased VDR expression was associated with increased percentage of stromal or immune components in tumor microenvironment (TME) in 24 cancer types. VDR positively and negatively correlated genes were enriched in immune cell function and energy metabolism pathways, respectively, in the top 9 highly lethal tumors. Additionally, VDR expression was increased in PTC and inhibited cell proliferation and migration. In conclusion, VDR is a potential prognostic biomarker and positively correlated with immune infiltration as well as stromal or immune components in TME in multiple human cancers.