bluuebunny/crossref_metadata_2025_split huggingface dataset:
Anthropogenetic study of the Arabic - speaking population of Chaouia Ouardigha (Morocco) based on autosomal STRs
Authors: Essoubaiy Othmane , Gazzaz Bouchaïb , Yahia Hakima , EL Ossmani Hicham , Talbi Jalal , El Houate Brahim , Fechtali Taoufiq 
Published: April 2024

Abstract
Background
The Chaouia - Ouradigha is a historical and ethno-geographic region of Morocco. Despite its historical relevance, this region has not received sufficient attention in terms of population genetic exploration. The present study aims to provide new data on the genetic background of the Chaouia-Ouardigha population, the Arabic-speaking North African population in the western center of Morocco, and to examine the relationship between this population and other reference populations based on our dataset as well as previously published population data. Genetic exploration will enable the reconstruction of past gene flows that have influenced the genetic makeup of this particular population. We utilized the Investigator® IDplex Plus PCR amplification kit (Qiagen, Germany) to establish the allelic frequencies and forensic statistical parameters of 15 autosomal Short Tandem Repeats (STRs) included in the kit in 153 random healthy unrelated individuals from the Chaouia-Ourdigha region.

Results
The Chaouia-Ouardigha population, which speaks Arabic, exhibits a high degree of genetic diversity with heterozygosity values ranging from 0.670 to 0.879. The loci with the highest allele frequencies, namely D7S820 and TPOX, have the lowest heterozygosity values (0.670 and 0.672, respectively), resulting in lower exclusion power (PE) and typical relatedness index (TPI) values (PE = 0.427 and TPI = 1.66 for TPOX; PE = 0.442 and TPI = 1.72 for D7S820). Phylogenetic analysis showed that the Arabic-speaking population of Chaouia-Ouardigha was found to be distinct from Sub-Saharan, East Asian, and Latin American populations.

Conclusions
Our study provides insights into the genetic connections of the Chaouia Ouardigha population, highlighting how historical events, socio-cultural influences, and geographical proximity have contributed to shaping their genetic structure. It demonstrates the substantial impact of historical events and geographical closeness in fostering affinities between the Chaouia Ouardigha community and neighboring populations in North Africa, Southern Europe, and the Middle East. Additionally, the study has generated valuable data confirming the effectiveness of these 15 genetic markers for forensic applications across the broader population.
Synthetic modulation of ROS scavenging during host—Sclerotinia sclerotiorum interaction: a new strategy for the development of highly resistant plants
Authors: Ding Yijuan , Yan Baoqin , Zhao Siqi , Chen Yangui , Wan Huafang , Qian Wei 
Published: April 2024

AbstractSclerotinia sclerotiorum is a widespread fungal pathogen responsible for significant crop losses across the globe. The challenge of breeding resistant varieties is exacerbated by the fungus's sophisticated pathogenic mechanisms. A pivotal factor in the host-pathogen interaction is the regulation of reactive oxygen species (ROS) within both the fungi and the host plants. However, there is currently no efficient strategy to leverage this interaction mechanism for developing disease-resistant crop varieties. Here, we introduce an engineered ROS scavenging system designated as syn-ROS for impairing ROS neutralization in S. sclerotiorum while concurrently fortifying it in the host. The syn-ROS system comprises gene silencing constructs targeting the S. sclerotiorum Cu/Zn superoxide dismutase (SsSOD) and its copper chaperone (SsCCS), alongside overexpression constructs for the Arabidopsis thaliana AtSOD1 and AtCCS. Transgenic plants carrying the syn-ROS system demonstrated a marked enhancement in resistance to S. sclerotiorum. Upon infection, the expression of SsSOD and SsCCS was reduced, while the expression of AtSOD1 and AtCCS was enhanced in syn-ROS transgenic plants. Moreover, the infected syn-ROS plants showed decreased Cu/Zn SOD enzyme activity and elevated ROS concentrations within the fungal cells. In contrast, the cells of A. thaliana manifested increased Cu/Zn SOD enzyme activity and lowered ROS levels. Collectively, these findings suggest a novel and promising approach for contriving plants with robust resistance by synthetically manipulating ROS scavenging activities in the interaction between the host and S. sclerotiorum.
Automated analysis of pectoralis major thickness in pec-fly exercises: evolving from manual measurement to deep learning techniques
Authors: Cai Shangyu , Lin Yongsheng , Chen Haoxin , Huang Zihao , Zhou Yongjin , Zheng Yongping 
Published: April 2024

AbstractThis study addresses a limitation of prior research on pectoralis major (PMaj) thickness changes during the pectoralis fly exercise using a wearable ultrasound imaging setup. Although previous studies used manual measurement and subjective evaluation, it is important to acknowledge the subsequent limitations of automating widespread applications. We then employed a deep learning model for image segmentation and automated measurement to solve the problem and study the additional quantitative supplementary information that could be provided. Our results revealed increased PMaj thickness changes in the coronal plane within the probe detection region when real-time ultrasound imaging (RUSI) visual biofeedback was incorporated, regardless of load intensity (50% or 80% of one-repetition maximum). Additionally, participants showed uniform thickness changes in the PMaj in response to enhanced RUSI biofeedback. Notably, the differences in PMaj thickness changes between load intensities were reduced by RUSI biofeedback, suggesting altered muscle activation strategies. We identified the optimal measurement location for the maximal PMaj thickness close to the rib end and emphasized the lightweight applicability of our model for fitness training and muscle assessment. Further studies can refine load intensities, investigate diverse parameters, and employ different network models to enhance accuracy. This study contributes to our understanding of the effects of muscle physiology and exercise training.
Systems analysis and improvement approach to optimize tuberculosis (SAIA-TB) screening, treatment, and prevention in South Africa: a stepped-wedge cluster randomized trial
Authors: van de Water Brittney J. , Brooks Meredith B. , Matji Refiloe , Ncanywa Betty , Dikgale Freck , Abuelezam Nadia N. , Mzileni Bulelwa , Nokwe Miyakazi , Moko Singilizwe , Mvusi Lindiwe , Loveday Marian , Gimbel Sarah 
Published: April 2024

Abstract
Background
The use of systems engineering tools, including the development and use of care cascades using routinely collected data, process mapping, and continuous quality improvement, is used for frontline healthcare workers to devise systems level change. South Africa experiences high rates of tuberculosis (TB) infection and disease as well as HIV co-infection. The Department of Health has made significant gains in HIV services over the last two decades, reaching their set “90–90-90” targets for HIV. However, TB services, although robust, have lagged in comparison for both disease and infection. The Systems Analysis and Improvement Approach (SAIA) is a five-step implementation science method, drawn from systems engineering, to identify, define, and implement workflow modifications using cascade analysis, process mapping, and repeated quality improvement cycles within healthcare facilities.

Methods
This stepped-wedge cluster randomized trial will evaluate the effectiveness of SAIA on TB (SAIA-TB) cascade optimization for patients with TB and high-risk contacts across 16 clinics in four local municipalities in the Sarah Baartman district, Eastern Cape, South Africa. We hypothesize that SAIA-TB implementation will lead to a 20% increase in each of: TB screening, TB preventive treatment initiation, and TB disease treatment initiation during the 18-month intervention period. Focus group discussions and key informant interviews with clinic staff will also be conducted to determine drivers of implementation variability across clinics.

Discussion
This study has the potential to improve TB screening, treatment initiation, and completion for both active disease and preventive measures among individuals with and without HIV in a high burden setting. SAIA-TB provides frontline health care workers with a systems-level view of their care delivery system with the aim of sustainable systems-level improvements.

Trial registration
Clinicaltrials.gov, NCT06314386. Registered 18 March 2024, https://clinicaltrials.gov/study/NCT06314386. NCT06314386.
On the electromagnetic interaction and the anomalous term in the Duffin-Kemmer-Petiau theory
Authors: Jirón Andrés G. , Castro Luis B. , de Castro Antonio S. , Obispo Angel E. 
Published: April 2024

Abstract
The problem of vectorial mesons embedded in an electromagnetic field via Duffin-Kemmer-Petiau (DKP) formalism is reinvestigated. Considering the electromagnetic interaction as a minimal coupling, an incorrect value

is identified for the gyromagnetic factor (g-factor). Furthermore, it is shown that it is cumbersome to find analytical solutions due to the presence of the so-called anomalous term for the spin-1 sector of the DKP theory. Suspecting that the anomalous term results from an incomplete version of the DKP equation to describe the electromagnetic interaction, we consider the addition of a non-minimal coupling. This leads to the correct g-factor

and, as a consequence, the anomalous term becomes proportional to an external four current. As an application, the DKP equation with a static uniform magnetic field is considered, yielding the corresponding Landau levels.
The Spectrum of GH Excess in Carney Complex and Genotype-phenotype Correlations
Authors: Tatsi Christina , Pitsava Georgia , Faucz Fabio R , Keil Meg , Stratakis Constantine A 
Published: April 2024

Abstract

Context
Carney complex (CNC) is a familial neoplasia syndrome associated with GH excess (GHE).

Objective
To describe the frequency of GHE in a large cohort of patients with CNC and to identify genotype-phenotype correlations.

Methods
Patients with CNC with at least 1 biochemical evaluation of GH secretion at our center from 1995 to 2021 (n = 140) were included in the study. Diagnosis of GHE was based on levels of IGF-1, GH suppression during oral glucose tolerance test, GH stimulation after thyrotropin administration and overnight GH secretion.

Results
Fifty patients (35.7%) had GHE, and 28 subjects (20%) had symptomatic acromegaly, with median age at diagnosis of 25.3 and 26.1 years, respectively. Most of the patients (99.3%) had a PRKAR1A gene defect. There was a higher risk of GHE in patients harboring a variant that led to no expression of the affected allele [hazard risk (HR): 3.06, 95% confidence interval (CI): 1.2-7.8] and for patients harboring the hotspot variant c.491_492delTG (HR: 2.10, 95% CI: 1.1-4.1). Almost half of patients with CNC had an abnormal finding on pituitary imaging. CNC patients with abnormal pituitary imaging had a higher risk of GHE (HR: 2.94, 95% CI: 1.5-5.8), especially when single or multiple adenoma-like lesions were identified. Management of patients with symptomatic acromegaly involved surgical and medical approaches.

Conclusion
Dysregulation of GH secretion is a common finding in CNC. Knowing the clinical spectrum of this disorder and its association with genetic and imaging characteristics of the patient make more likely its prompt diagnosis and better management.
Prevalence of Dysbetalipoproteinemia in the UK Biobank According to Different Diagnostic Criteria
Authors: Paquette Martine , Trinder Mark , Guay Simon-Pierre , Brunham Liam R , Baass Alexis 
Published: April 2024

Abstract

Context
Dysbetalipoproteinemia (DBL) is a multifactorial disorder that disrupts the normal metabolism of remnant lipoproteins, causing increased risk of cardiovascular disease. However, establishing a proper diagnosis is difficult, and the true prevalence of the disease in the general population remains unknown.

Objective
The objectives were to study the prevalence of the disease and to validate the performance of different clinical diagnostic criteria in a large population-based cohort.

Methods
This study included 453 437 participants from the UK Biobank. DBL was established in participants having an ε2ε2 genotype with mixed dyslipidemia or lipid-lowering therapy use (n = 964). The different diagnostic criteria for DBL were applied in individuals without lipid-lowering medication (n = 370 039, n = 534 DBL), to compare their performance.

Results
Overall, 0.6% of participants had an ε2ε2 genotype, of which 36% were classified as DBL, for a disease prevalence of 0.2% (1:469). The prevalence of DBL was similar between the different genetic ancestries (≤0.2%). Several diagnostic criteria showed good sensitivity for the diagnosis of DBL (&gt;90%), but they suffered from a very low positive predictive value (0.6-15.4%).

Conclusion
This study reported for the first time the prevalence of DBL in the UK Biobank according to genetic ancestry. Furthermore, we provided the first external validation of different diagnostic criteria for DBL in a large population-based cohort and highlighted the fact that these criteria should not be used to diagnose DBL alone but should rather be used as a first screening step to determine which individuals may benefit from genetic testing to confirm the diagnosis.
Twin Pregnancy Complicated by Gestational Diabetes Mellitus: Maternal and Neonatal Outcomes
Authors: Das Devika , Christie Hannah E , Hegazi Moustafa , Takawy Marina , Pone Karina A , Vella Adrian , Egan Aoife M 
Published: April 2024

Abstract

Context
The risk of gestational diabetes mellitus (GDM) in twin pregnancies is more than double that of singleton pregnancies. Although twin pregnancies present unique challenges for fetal growth and prenatal management, the approach to GDM diagnosis and treatment is the same regardless of plurality. Data on pregnancy outcomes for individuals with GDM and a twin pregnancy are limited and conflicting.

Objective
To describe the maternal characteristics associated with GDM in twin pregnancies and to assess the associated pregnancy outcomes compared to twin pregnancies unaffected by GDM.

Methods
A retrospective cohort study was conducted at Mayo Clinic, Rochester, Minnesota, USA, and included predominantly Causasian women aged 18 to 45 years who received prenatal care for a twin pregnancy from 2017-2022. Maternal characteristics and a broad spectrum of pregnancy outcomes were evaluated. Universal GDM screening involved a 50 g oral glucose challenge test +/− a 100 g oral glucose tolerance test.

Results
GDM was diagnosed in 23% pregnancies (n = 104/452). Compared to those without, women with GDM had known risk factors including a higher prepregnancy body mass index (31.1vs 26.3 kg/m2; P &lt; .01) and a prior history of GDM (21.7 vs 5.9%; P &lt; .01). There were no differences in maternal pregnancy complications or neonatal outcomes between groups. Attendance at postpartum glucose testing among women with GDM was poor at 27.9% (29/104).

Conclusion
These data suggest that women with twin pregnancies share a similar GDM risk profile to those with singleton pregnancies and provide reassuring evidence that current management for GDM twin pregnancies produces similar outcomes to twin pregnancies without GDM.
Spatio-Temporal Time Series Forecasting Using an Iterative Kernel-Based Regression
Authors: Hen Ben , Rabin Neta 
Published: April 2024

Spatio-temporal time series analysis is a growing area of research that includes different types of tasks, such as forecasting, prediction, clustering, and visualization. In many domains, like epidemiology or economics, time series data are collected to describe the observed phenomenon in particular locations over a predefined time slot and predict future behavior. Regression methods provide a simple mechanism for evaluating empirical functions over scattered data points. In particular, kernel-based regressions are suitable for cases in which the relationship between the data points and the function is not linear. In this work, we propose a kernel-based iterative regression model, which fuses data from several spatial locations for improving the forecasting accuracy of a given time series. In more detail, the proposed method approximates and extends a function based on two or more spatial input modalities coded by a series of multiscale kernels, which are averaged as a convex combination. The proposed spatio-temporal regression resembles ideas that are present in deep learning architectures, such as passing information between scales. Nevertheless, the construction is easy to implement, and it is also suitable for modeling data sets of limited size. Experimental results demonstrate the proposed model for solar energy prediction, forecasting epidemiology infections, and future number of fire events. The method is compared with well-known regression techniques and highlights the benefits of the proposed model in terms of accuracy and flexibility. The reliable outcome of the proposed model and its nonparametric nature yield a robust tool to be integrated as a forecasting component in wide range of decision support systems that analyze time series data. History: Kwok-Leung Tsui served as the senior editor for this article. Funding: This research was supported by the Israel Science Foundation [Grant 1144/20] and partly supported by the Ministry of Science and Technology, Israel [Grant 5614]. Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/6417440/tree and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2023.0019 ).
Systematic review and network meta-analysis of different non-steroidal anti-inflammatory drugs for juvenile idiopathic arthritis
Authors: Zeng Tao , Ye Jian-Zhong , Qin Hui , Xu Qian-Qian 
Published: April 2024

BACKGROUND
Various non-steroidal anti-inflammatory drugs (NSAIDs) have been used for juvenile idiopathic arthritis (JIA). However, the optimal method for JIA has not yet been developed.
AIM
To perform a systematic review and network meta-analysis to determine the optimal instructions.
METHODS
We searched for randomized controlled trials (RCTs) from PubMed, EMBASE, Google Scholar, CNKI, and Wanfang without restriction for publication date or language at August, 2023. Any RCTs that comparing the effectiveness of NSAIDs with each other or placebo for JIA were included in this network meta-analysis. The surface under the cumulative ranking curve (SUCRA) analysis was used to rank the treatments. P value less than 0.05 was identified as statistically significant.
RESULTS
We included 8 RCTs (1127 patients) comparing 8 different instructions including meloxicam (0.125 qd and 0.250 qd), Celecoxib (3 mg/kg bid and 6 mg/kg bid), piroxicam, Naproxen (5.0 mg/kg/d, 7.5 mg/kg/d and 12.5 mg/kg/d), inuprofen (30-40 mg/kg/d), Aspirin (60-80 mg/kg/d, 75 mg/kg/d, and 55 mg/kg/d), Tolmetin (15 mg/kg/d), Rofecoxib, and placebo. There were no significant differences between any two NSAIDs regarding ACR Pedi 30 response. The SUCRA shows that celecoxib (6 mg/kg bid) ranked first (SUCRA, 88.9%), rofecoxib ranked second (SUCRA, 68.1%), Celecoxib (3 mg/kg bid) ranked third (SUCRA, 51.0%). There were no significant differences between any two NSAIDs regarding adverse events. The SUCRA shows that placebo ranked first (SUCRA, 88.2%), piroxicam ranked second (SUCRA, 60.5%), rofecoxib (0.6 mg/kg qd) ranked third (SUCRA, 56.1%), meloxicam (0.125 mg/kg qd) ranked fourth (SUCRA, 56.1%), and rofecoxib (0.3 mg/kg qd) ranked fifth (SUCRA, 56.1%).
CONCLUSION
In summary, celecoxib (6 mg/kg bid) was found to be the most effective NSAID for treating JIA. Rofecoxib, piroxicam, and meloxicam may be safer options, but further research is needed to confirm these findings in larger trials with higher quality studies.
Active visual continuous seam tracking based on adaptive feature detection and particle filter tracking
Authors: Fan Rong , Zhang Peng , Guo Fengyun , Rong Jie , Lian Xupeng 
Published: April 2024

Welding seam tracking based on online programming is the future trend of intelligent
production. However, most of the existing image processing methods
have certain limitations in the adaptability, accuracy, and robustness
of weld feature point detection. The online welding method of gas
metal arc welding (GMAW) based on active vision sensing is studied in
this paper. The Steger sub-pixel detection method is used to guarantee
the accuracy of feature point extraction, and a self-adaptive search
window and self-adaptive slope extraction are proposed on this basis.
The self-adaptive window is generated according to the linear
information of the weld area, and the scale factor and range threshold
constraint are added to realize the real-time detection of the weld
feature information. Screening the center pixel of the laser stripe in
the self-adaptive window of the current frame by the initial slope or
the self-adaptive slope of the previous frame, the linear information
of the weld area is obtained. The self-adaptive slope of the current
frame is fitted by the random sampling consistency method, and the
pixel margin is retained to adapt to the linear detection of different
continuous welds. When arc light and other serious interference make
it difficult to obtain weld information, a particle filter is used to
make the best prediction of the weld position. Finally, the welding
robot platform based on laser vision sensing was built to test various
continuous welds of the butt weld, fillet weld, and lap weld.
Experimental results show that the detection speed is 27 ms,
and the accuracy of detection and tracking can respectively reach
0.03 mm and 0.78 mm, which meets the requirements of
weld detection and tracking.
Ab initio-simulated optical response of hot electrons in gold and ruthenium
Authors: Akhmetov Fedor , Vorberger Jan , Milov Igor , Makhotkin Igor , Ackermann Marcelo 
Published: April 2024

Optical femtosecond pump-probe experiments allow to measure the dynamics of ultrafast heating of metals with high accuracy. However, the theoretical analysis of such experiments is often complicated because of the indirect connection of the measured signal and the desired temperature transients. Establishing such a connection requires an accurate model of the optical constants of a metal, depending on both the electron temperature T
e
and the lattice temperature T
l
. In this paper, we present first-principles simulations of the two-temperature scenario with T
e
≫ T
l
, showing the optical response of hot electrons to laser irradiation in gold and ruthenium. Comparing our simulations with the Kubo-Greenwood approach, we discuss the influence of electron-phonon and electron-electron scattering on the intraband contribution to optical constants. Applying the simulated optical constants to the analysis of ultrafast heating of ruthenium thin films we highlight the importance of the latter scattering channel to understand the measured heating dynamics.