bluuebunny/crossref_metadata_2025_split huggingface dataset:
Variable parameters memory-type control charts for simultaneous monitoring of the mean and variability of multivariate multiple linear regression profiles
Authors: Sabahno Hamed , Eriksson Marie 
Published: April 2024

AbstractVariable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and two use an SS (squared sum) operator to create the final statistic. Moreover, two of the charts monitor the regression parameters, and the other two monitor the residuals. After developing the VP control charts, we developed a computer algorithm with which the charts' time-to-signal and run-length-based performances can be measured. Then, we perform extensive numerical analysis and simulation studies to evaluate the charts’ performance and the result shows significant improvements by using the VP schemes. Finally, we use real data from the national quality register for stroke care in Sweden, Riksstroke, to illustrate how the proposed control charts can be implemented in practice.
Three decades of nearshore surveys reveal long-term patterns in gray whale habitat use, distribution, and abundance in the Northern California Current
Authors: Barlow Dawn R. , Strong Craig S. , Torres Leigh G. 
Published: April 2024

AbstractThe nearshore waters of the Northern California Current support an important seasonal foraging ground for Pacific Coast Feeding Group (PCFG) gray whales. We examine gray whale distribution, habitat use, and abundance over 31 years (1992–2022) using standardized nearshore (< 5 km from shore) surveys spanning a large swath of the PCFG foraging range. Specifically, we generated density surface models, which incorporate detection probability into generalized additive models to assess environmental correlates of gray whale distribution and predict abundance over time. We illustrate the importance of coastal upwelling dynamics, whereby increased upwelling only yields higher gray whale density if interspersed with relaxation events, likely because this combination optimizes influx and retention of nutrients to support recruitment and aggregation of gray whale prey. Several habitat features influence gray whale distribution, including substrate, shelf width, prominent capes, and river estuaries. However, the influence of these features differs between regions, revealing heterogeneity in habitat preferences throughout the PCFG foraging range. Predicted gray whale abundance fluctuated throughout our study period, but without clear directional trends, unlike previous abundance estimates based on mark-recapture models. This study highlights the value of long-term monitoring, shedding light on the impacts of variable environmental conditions on an iconic nearshore marine predator.
Influence of helical pitch and gantry rotation time on image quality and file size in ultrahigh-resolution photon-counting detector CT
Authors: Feldle Philipp , Grunz Jan-Peter , Huflage Henner , Kunz Andreas Steven , Ergün Süleyman , Afat Saif , Gruschwitz Philipp , Görtz Lukas , Pennig Lenhard , Bley Thorsten Alexander , Conrads Nora 
Published: April 2024

AbstractThe goal of this experimental study was to quantify the influence of helical pitch and gantry rotation time on image quality and file size in ultrahigh-resolution photon-counting CT (UHR-PCCT). Cervical and lumbar spine, pelvis, and upper legs of two fresh-frozen cadaveric specimens were subjected to nine dose-matched UHR-PCCT scan protocols employing a collimation of 120 × 0.2 mm with varying pitch (0.3/1.0/1.2) and rotation time (0.25/0.5/1.0 s). Image quality was analyzed independently by five radiologists and further substantiated by placing normed regions of interest to record mean signal attenuation and noise. Effective mAs, CT dose index (CTDIvol), size-specific dose estimate (SSDE), scan duration, and raw data file size were compared. Regardless of anatomical region, no significant difference was ascertained for CTDIvol (p ≥ 0.204) and SSDE (p ≥ 0.240) among protocols. While exam duration differed substantially (all p ≤ 0.016), the lowest scan time was recorded for high-pitch protocols (4.3 ± 1.0 s) and the highest for low-pitch protocols (43.6 ± 15.4 s). The combination of high helical pitch and short gantry rotation times produced the lowest perceived image quality (intraclass correlation coefficient 0.866; 95% confidence interval 0.807–0.910; p < 0.001) and highest noise. Raw data size increased with acquisition time (15.4 ± 5.0 to 235.0 ± 83.5 GByte; p ≤ 0.013). Rotation time and pitch factor have considerable influence on image quality in UHR-PCCT and must therefore be chosen deliberately for different musculoskeletal imaging tasks. In examinations with long acquisition times, raw data size increases considerably, consequently limiting clinical applicability for larger scan volumes.
Analysis of factors associated with depressive symptoms in stroke patients based on a national cross-sectional study
Authors: Xiao Wenhui , Liu Ying , Huang Jinglin , Huang Li-an , Bian Ying , Zou Guanyang 
Published: April 2024

AbstractPost-stroke depression is commonly experienced by stroke survivors and has a significant negative impact on the physical, cognitive, and social functioning of those affected. This study aims to investigate the prevalence of depressive symptoms and their associated factors in Chinese stroke patients. Research samples were selected from the China Health and Retirement Longitudinal Study 2018 survey. Depression was evaluated using the 10-item Center for Epidemiological Studies Depression Scale, with a score ≥ 10 defined as depression. Univariate and multivariable analyses were performed to examine the associations of depressive symptoms with demographics, family relationships, health status, and lifestyle. A total of 963 stroke patients were included and 57.8% of them had depressive symptoms. Depressive symptoms were significantly associated with female sex (OR 1.762, 95% CI 1.235–2.514), lower education level (non-formal education: OR 2.148, 95% CI 1.235–3.737, primary to secondary school education: OR 1.964, 95% CI 1.272–3.033), dissatisfaction with spouse (OR 1.912, 95% CI 1.075–3.401), dissatisfaction with life (OR 1.779, 95% CI 1.080–2.931), dissatisfaction with health (OR 1.592, 95% CI 1.138–2.226), pain (OR 1.392, 95% CI 1.005–1.928) and abnormal sleep (OR 1.557, 95% CI 1.126–2.152). The findings suggest the need for regular depression screening and evaluation after a stroke, and that a well-functioning support system, effective health management, and lifestyle modifications could potentially improve the mental state of stroke patients.
Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data
Authors: Rosenberg Carina A. , Rodrigues Matthew A. , Bill Marie , Ludvigsen Maja 
Published: April 2024

AbstractMyelodysplastic syndrome is primarily characterized by dysplasia in the bone marrow (BM), presenting a challenge in consistent morphology interpretation. Accurate diagnosis through traditional slide-based analysis is difficult, necessitating a standardized objective technique. Over the past two decades, imaging flow cytometry (IFC) has proven effective in combining image-based morphometric analyses with high-parameter phenotyping. We have previously demonstrated the effectiveness of combining IFC with a feature-based machine learning algorithm to accurately identify and quantify rare binucleated erythroblasts (BNEs) in dyserythropoietic BM cells. However, a feature-based workflow poses challenges requiring software-specific expertise. Here we employ a Convolutional Neural Network (CNN) algorithm for BNE identification and differentiation from doublets and cells with irregular nuclear morphology in IFC data. We demonstrate that this simplified AI workflow, coupled with a powerful CNN algorithm, achieves comparable BNE quantification accuracy to manual and feature-based analysis with substantial time savings, eliminating workflow complexity. This streamlined approach holds significant clinical value, enhancing IFC accessibility for routine diagnostic purposes.
ANTXR1 deficiency promotes fibroblast senescence: implications for GAPO syndrome as a progeroid disorder
Authors: Przyklenk Matthias , Karmacharya Shreya , Bonasera Debora , Pasanen-Zentz Arthur-Lauri , Kmoch Stanislav , Paulsson Mats , Wagener Raimund , Liccardi Gianmaria , Schiavinato Alvise 
Published: April 2024

AbstractANTXR1 is one of two cell surface receptors mediating the uptake of the anthrax toxin into cells. Despite substantial research on its role in anthrax poisoning and a proposed function as a collagen receptor, ANTXR1’s physiological functions remain largely undefined. Pathogenic variants in ANTXR1 lead to the rare GAPO syndrome, named for its four primary features: Growth retardation, Alopecia, Pseudoanodontia, and Optic atrophy. The disease is also associated with a complex range of other phenotypes impacting the cardiovascular, skeletal, pulmonary and nervous systems. Aberrant accumulation of extracellular matrix components and fibrosis are considered to be crucial components in the pathogenesis of GAPO syndrome, contributing to the shortened life expectancy of affected individuals. Nonetheless, the specific mechanisms connecting ANTXR1 deficiency to the clinical manifestations of GAPO syndrome are largely unexplored. In this study, we present evidence that ANTXR1 deficiency initiates a senescent phenotype in human fibroblasts, correlating with defects in nuclear architecture and actin dynamics. We provide novel insights into ANTXR1's physiological functions and propose GAPO syndrome to be reconsidered as a progeroid disorder highlighting an unexpected role for an integrin-like extracellular matrix receptor in human aging.
County land use carbon emission and scenario prediction in Mianyang Science and Technology City New District, Sichuan Province, China
Authors: Wei Tianyi , Yang Bin , Wang Guangyu , Yang Kun 
Published: April 2024

AbstractThe role of carbon emissions resulting from land use change in the compilation of national greenhouse gas emission inventories is of paramount significance. This study is centered on the Mianyang Science and Technology City New Area located in Sichuan Province, China. We used the CLUE-S model and Sentinel-2A remote sensing data from 2017 to simulate and validate land use changes in 2022. Based on this validation, we established three simulation scenarios: a baseline scenario, an agricultural development scenario, and a construction development scenario. Using remote sensing data from 2022, we projected the land use for 2030. We also used CO2 concentration data collected in 2022 and 2023, processed the data using ArcGIS and Python, and conducted a quantitative analysis of carbon emissions under each scenario. Ultimately, the accuracy of both measured and predicted CO2 values for 2023 was juxtaposed and authenticated, thus concluding the investigative cycle of this study. Key findings include: (1) The accuracy of the CLUE-S model in the study area was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. In 2022, the overall accuracy is 98.19%, the quantity disagreement is 1.7%, and the allocation disagreement is 2.2%. (2) Distinct land resource utilization characteristics in scenarios, highlighting potential impacts on economic development and pollution. (3) Increased carbon emissions across scenarios, with construction development showing the highest rise (4.170%) and agricultural development the lowest (0.766%). (4) The predictive accuracy of the validation group's CO2 concentration values can reach 99.5%. This study proposes precise CO2 prediction at the county level, thus laying the groundwork for future research endeavors. Such findings are indispensable for informing carbon policy formulation and promoting low-carbon development strategies.
A pancreatic cancer organoid-in-matrix platform shows distinct sensitivities to T cell killing
Authors: Lahusen Anton , Cai Jierui , Schirmbeck Reinhold , Wellstein Anton , Kleger Alexander , Seufferlein Thomas , Eiseler Tim , Lin Yuan-Na 
Published: April 2024

AbstractPoor treatment responses of pancreatic ductal adenocarcinoma (PDAC) are in large part due to tumor heterogeneity and an immunosuppressive desmoplastic tumor stroma that impacts interactions with cells in the tumor microenvironment (TME). Thus, there is a pressing need for models to probe the contributions of cellular and noncellular crosstalk. Organoids are promising model systems with the potential to generate a plethora of data including phenotypic, transcriptomic and genomic characterization but still require improvements in culture conditions mimicking the TME. Here, we describe an INTERaction with Organoid-in-MatriX ("InterOMaX") model system, that presents a 3D co-culture-based platform for investigating matrix-dependent cellular crosstalk. We describe its potential to uncover new molecular mechanisms of T cell responses to murine KPC (LSL-KrasG12D/+27/Trp53tm1Tyj/J/p48Cre/+) PDAC cells as well as PDAC patient-derived organoids (PDOs). For this, a customizable matrix and homogenously sized organoid-in-matrix positioning of cancer cells were designed based on a standardized agarose microwell chip array system and established for co-culture with T cells and inclusion of stromal cells. We describe the detection and orthogonal analysis of murine and human PDAC cell populations with distinct sensitivity to T cell killing that is corroborated in vivo. By enabling both identification and validation of gene candidates for T cell resistance, this platform sets the stage for better mechanistic understanding of cancer cell-intrinsic resistance phenotypes in PDAC.
A new method based on YOLOv5 and multiscale data augmentation for visual inspection in substation
Authors: Chen Junjie , Pan Siqi , Chan Yanping , Ni Yuedong , Ye Donghua 
Published: April 2024

AbstractArtificial intelligence has demonstrated notable advancements in the realm of visual inspection and defect detection in substations. Nevertheless, practical application presents challenges, with issues arising from the dynamic shooting environment and limited dataset resulting in suboptimal defect identification accuracy and instability. To address these concerns, a pioneering approach based on hybrid pruning YOLOv5 and multiscale data augmentation is proposed for enhancing defect detection in substations. Initially, an enhanced multiscale data augmentation method is proposed. The improved multiscale data augmentation mitigates the impact of the time-varying shooting environment on recognition accuracy and enhances defect detection precision. Subsequently, YOLOv5 is employed for training and detecting defects within multi-scale image data. To alleviate the potential destabilizing effects of YOLOv5’s large-scale parameters on model stability, a new model pruning method is implemented. This method strategically prunes parameters to bolster the model’s defect identification accuracy. The efficacy of the proposed methodology is evaluated through testing on substation defect images, confirming its effectiveness in enhancing defect detection capabilities.
Simultaneous boost radiotherapy versus conventional dose radiotherapy for patients with newly diagnosed glioblastoma: a multi-institutional analysis
Authors: Takano Seiya , Tomita Natsuo , Kuno Mayu , Niwa Masanari , Torii Akira , Takaoka Taiki , Kita Nozomi , Okazaki Dai , Yamamoto Shintaro , Kawai Tatsuya , Sugie Chikao , Ogawa Yasutaka , Matsumoto Kenichi , Uchiyama Kaoru , Otsuka Shinya , Matsui Tooru , Miyakawa Akifumi , Mizuno Tomoki , Iida Masato , Tanikawa Motoki , Mase Mitsuhito , Hiwatashi Akio 
Published: April 2024

AbstractWe compared survival outcomes of high-dose concomitant boost radiotherapy (HDCBRT) and conventional dose radiotherapy (CRT) for newly diagnosed glioblastoma (GB). Patients treated with intensity-modulated radiation therapy for newly diagnosed GB were included. In HDCBRT, specific targets received 69, 60, and 51 Gy in 30 fractions, while 60 Gy in 30 fractions was administered with a standard radiotherapy method in CRT. Overall survival (OS) and progression-free survival (PFS) were compared using the Log-rank test, followed by multivariate Cox analysis. The inverse probability of treatment weighting (IPTW) method was also applied to each analysis. Among 102 eligible patients, 45 received HDCBRT and 57 received CRT. With a median follow-up of 16 months, the median survival times of OS and PFS were 21 and 9 months, respectively. No significant differences were observed in OS or PFS in the Kaplan–Meier analyses. In the multivariate analysis, HDCBRT correlated with improved OS (hazard ratio, 0.49; 95% confidence interval, 0.27–0.90; P = 0.021), and this result remained consistent after IPTW adjustments (P = 0.028). Conversely, dose suppression due to the proximity of normal tissues and IMRT field correlated with worse OS and PFS (P = 0.008 and 0.049, respectively). A prospective study with a stricter protocol is warranted to validate the efficacy of HDCBRT for GB.
Design and analysis of solitary AC–AC converter using reduced components for efficient power generation system
Authors: Nandakumar K. , Mohan V. , Alsaif Faisal , Senthilkumar S. 
Published: April 2024

AbstractConsidering different applications that require varied power and voltage conversion levels between AC grids and AC loads, AC–AC power conversion between AC grids has become an inevitable technology of energy management systems. An isolated converter for performing AC-to-AC transmission is proposed with minimal components for reduced losses and enhanced system efficiency. Single-phase direct buck-boost AC to AC converter with minimum components constituted with two dual IGBT control units (IGBT 1–IGBT 4), inductor (Lf), and capacitor (Cf) is proposed in this work. The MATLAB/Simulink platform is used to provide in-depth analysis of the circuit and components along with the design guidelines, and simulation outcomes of this proposed model. The voltage gains of G = 2.13, power factor of 0.97, and overall efficiency of 98% are achieved in the proposed system with minimum components of 4 switches, 2 conductors, and 1 capacitor and inductor respectively. The obtained results are compared with existing technology to evaluate the proposed system.
Dual-branch feature encoding framework for infrared images super-resolution reconstruction
Authors: Zhang Yuke , Zhou Peizi , Chen Lizhu 
Published: April 2024

AbstractInfrared thermal imaging is a passive non-contact detection and identification technology, which is not subject to electromagnetic infection and good concealment, is widely used in military and commercial fields. However, due to the limitations of the existing infrared imaging system mechanisms, the spatial resolution of the acquired infrared images is low and the edge details are blurred, which in turn leads to poor performance in downstream missions based on infrared images. In this paper, in order to better solve the above problems, we propose a new super-resolution reconstruction framework for infrared images, called DBFE, which extracts and retains abundant structure and textual information for robust infrared image high-resolution reconstruction with a novel structure-textual encoder module. Extensive experiment demonstrates that our proposed method achieves significantly superior contraband high-resolution reconstruction results on the multiple dataset compared to progressive methods for high resolution infrared image reconstruction, effectively proving the practicability of the method proposed in this paper.
People are surprisingly hesitant to reach out to old friends
Authors: Aknin Lara B. , Sandstrom Gillian M. 
Published: April 2024

AbstractSocial relationships provide one of the most reliable paths to happiness, but relationships can fade for various reasons. While it does not take much to reinitiate contact, here we find that people are surprisingly reluctant to do so. Specifically, most people reported losing touch with an old friend yet expressed little interest in reaching out (Studies 1-2, Ns = 401 and 199). Moreover, fewer than one third of participants sent a message to an old friend, even when they wanted to, thought the friend would be appreciative, had the friend’s contact information, and were given time to draft and send a message (Studies 3-4, Ns = 453 and 604). One reason for this reluctance may be that old friends feel like strangers. Supporting this possibility, participants were no more willing to reach out to an old friend than they were to talk to a stranger (Study 5, N = 288), and were less willing to contact old friends who felt more like strangers (Study 6, N = 319). Therefore, in Study 7 (N = 194), we adapted an intervention shown to ease anxieties about talking to strangers and found that it increased the number of people who reached out to an old friend by two-thirds.
Traditional fault diagnosis methods for mineral oil‐immersed power transformer based on dissolved gas analysis: Past, present and future
Authors: Nanfak Arnaud , Samuel Eke , Fofana Issouf , Meghnefi Fethi , Ngaleu Martial Gildas , Hubert Kom Charles 
Published: April 2024

AbstractA key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent‐based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA‐based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA‐based methods for mineral oil‐immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA‐based diagnostic methods are also presented.