bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fetal-maternal interactions with gluten immunogenic peptides during pregnancy: a new determinant on the coeliac exposome
Authors: Moreno María de Lourdes , González-Rovira María , Martínez-Pancorbo Cristina , Martín-Cameán María , Nájar-Moyano Ana María , Romero Mercedes , Hoz Esther , López-Beltrán Cristina , Mellado Encarnación , Bartha José Luis , Brodin Petter , Rodríguez-Herrera Alfonso , Sainz-Bueno José Luís , Sousa Carolina 
Published: March 2024

Abstract

Background
The increasing incidence of coeliac disease is leading to a growing interest in active search for associated factors, even the intrauterine and early life. The exposome approach to disease encompasses a life course perspective from conception onwards has recently been highlighted. Knowledge of early exposure to gluten immunogenic peptides (GIP) in utero could challenge the chronology of early prenatal tolerance or inflammation, rather than after the infant's solid diet after birth.
Methods
We developed an accurate and specific immunoassay to detect GIP in amniotic fluid (AF) and studied their accumulates, excretion dynamics and foetal exposure resulting from AF swallowing. 119 pregnant women with different gluten diets and gestational ages were recruited.
Results
GIP were detectable in AF from at least the 16th gestational week in gluten-consuming women. Although no significant differences in GIP levels were observed during gestation, amniotic GIP late pregnancy was not altered by maternal fasting, suggesting closed-loop entailing foetal swallowing of GIP-containing AF and subsequent excretion via the foetal kidneys.
Conclusions
The study shows evidence, for the first time, of the fetal exposure to gluten immunogenic peptides, and establish a positive correlation with maternal gluten intake. The results obtained point to a novel physiological concept as they describe a closed-loop circuit entailing fetal swallowing of GIP contained in AF, and its subsequent excretion through the fetal kidneys. The study adds important new information to understanding the coeliac exposome.
Direct counts underestimate mountain ungulate population size
Authors: Peña-Carmona Genís , Escobar-González María , Dobbins Michael Taylor , Conejero Carles , Valldeperes Marta , Lavín Santiago , Pérez Jesús M. , López-Olvera Jorge Ramón , López-Martín Josep M. , Serrano Emmanuel 
Published: March 2024

Abstract

The topography of mountain habitats limits the accuracy of methods to assess the population size of mountain ungulates. This fact hampers decision-making for monitoring and conservation purposes and thus any attempt to evaluate the precision in known-size populations is more than welcome. In this work, we tested the accuracy of direct counts and distance sampling to assess the size of an Iberian ibex (Capra pyrenaica) flock of known size. We evaluated the influence of observer expertise (beginners and experts) on the detection error of female and male ibexes and whether the training of observers contributes to boosting the accuracy of density estimates. The ibex flock was comprised of 18 individuals (9 males, 8 females, and a male yearling) living in a 17 ha enclosure with natural Mediterranean vegetation in the National Game Reserve of Els Ports de Tortosa i Beseit, northeast Spain. After 27 surveys, experts detected 16% more ibexes than beginners. Male ibexes were ~ 13% easier to detect than females, and experts were more accurate than beginners in sexing. Additionally, the detection error in absolute counts was quite similar among beginners, but different among experts (> 10%). Despite the reduction in detection error over increasing effort scenarios, under-detection was greater than 50% in all events (> 85% for beginners and > 67% for experts). This study suggests the systematic underestimation of direct counts and density estimates of mountain ungulate populations in Mediterranean landscapes and the contribution of expertise to the improvement of the direct observation method. Our results show that wildlife managers assessing mountain ungulate populations for managing purposes should consider completing direct counts with alternative methods to minimize this systematic underestimation. Furthermore, surveys of the impact of infectious diseases on ungulate populations by direct observations may also result in the underestimation of the disease's impact on the host population.
Efficient designs of QCA Full-adder and 4-bit QCA RCA Circuits
Authors: Lee Sang-Woong , Rahmani Amir Masoud , Azimi Nemat , Safaiezadeh Behrouz , Hosseinzadeh Mehdi 
Published: March 2024

Abstract

Quantum-dot Cellular Automata (QCA) technology offers several advantages over traditional CMOS technology, including reduced power consumption, higher clock speeds, and increased density. QCA has the potential to overcome the physical limitations of CMOS, making it an attractive choice for the future of VLSI circuits. It uses quantum dots to represent binary information. Digital arithmetic operations are performed by adder circuits. A Ripple Carry Adder (RCA) is a digital circuit used in digital electronics to add multiple binary numbers together. In QCA technology, the implementation of such adders is notably different from traditional CMOS-based implementations due to the unique properties and principles of QCA. It is constructed by interconnecting a series of Full-adders. In this study, we introduce a high-performance single-bit Full-adder as well as 4-bit RCA circuit developed using QCA technology. Our proposed designs are simulated using QCADesigner 2.0.3 in a coplanar crossover way. Our 4-bit RCA design outperforms existing designs by achieving a remarkable 23% reduction in cell counts, 66% and 90% decrease in latency and circuit costs. Furthermore, power dissipation metrics for our single-bit Full-adder were ascertained using the QCAPro tool, with findings showing over 40% energy savings relative to the best current designs.
A Floating Capillary Based Open Microfluidic Device for Stable and On- Demand Generation of Monodispersed Emulsion Droplets
Authors: Deng Yueming , Xie Shuting , Yang Ruizhi , Liu Zhenping , Yao Jiyuan , Wang Haoyu , Li Zhengguang , Wang Jing , Jin Mingliang , Shui Lingling 
Published: March 2024

Abstract

Simple and stable generation of monodispersed droplets with volume from picolitre to nanoliter is one of the key factors in high-throughput quantitative microreactors for chemical and biomedical applications. In this work, a floating capillary based open microfluidic device (FCOMD) is constructed by inserting a capillary with an oblique angle (α) into the continuous phase (CP) to develop a simple method for preparation of monodispersed droplet. The geometrical parameters, including inner diameter and α, present a strong correlation for the controllability of droplet breakage and thus its volume. According to the microfluidic experiments, the mechanism of breakup of dispersed phase (DP) into droplets in the FCOMD was proposed from the aspect of energy. The transition of droplet generating mode between dripping and jetting can be achieved by changing the DP capillary number and α. Water in oil (W/O), oil in water (O/W), double-emulsion, colloidal dispersed droplets and liquid crystal droplets with diameter ranges from 25–800 µm (CVs under 2%) are prepared on-demand by the FCOMD. To improve the generating throughput, different numbers of capillaries are integrated for an integrated FCOMD. This FCOMD shows the features of simple, stable, practicable, and flexible, showing a great potential application in material science, foods, pharmaceuticals and cosmetics.
In vitro matured oocytes have a higher developmental potential than in vivo matured oocytes after hormonal ovarian stimulation in Callithrix jacchus
Authors: Tkachenko Olena Y. , Kahland Tobias , Lindenwald Dimitri , Heistermann Michael , Drummer Charis , Daskalaki Maria , Rüger Nancy , Behr Rüdiger 
Published: March 2024

Abstract
Background.
The common marmoset, Callithrix jacchus, is an invaluable model in biomedical research. Its use includes genetic engineering applications, which require manipulations of oocytes and production of embryos in vitro. To maximize the recovery of oocytes suitable for embryo production and to fulfil the requirements of the 3R principles to the highest degree possible, optimization of ovarian stimulation protocols is crucial. Here, we compared the efficacy of two hormonal ovarian stimulation approaches: 1) stimulation of follicular growth with hFSH followed by triggering of oocyte maturation with hCG (FSH + hCG) and 2) stimulation with hFSH only (FSH-priming).
Methods.
In total, 14 female marmosets were used as oocyte donors in this study. Each animal underwent up to four surgical interventions, with the first three performed as ovum pick-up (OPU) procedures and the last one being an ovariohysterectomy (OvH). In total, 20 experiments were carried out with FSH + hCG stimulation and 18 with FSH-priming. Efficacy of each stimulation protocol was assessed through in vitro maturation (IVM), in vitro fertilization (IVF) and embryo production rates.
Results.
Each study group consisted of two subgroups: the in vivo matured oocytes and the oocytes that underwent IVM. Surprisingly, in the absence of hCG triggering some of the oocytes recovered were at the MII stage, moreover, their number was not significantly lower compared to FSH + hCG stimulation (2.8 vs. 3.9, respectively (ns)). While the IVM and IVF rates did not differ between the two stimulation groups, the IVF rates of in vivo matured oocytes were significantly lower compared to in vitro matured ones in both FSH-priming and FSH + hCG groups. In total, 1.7 eight-cell embryos/experiment (OPU) and 2.1 eight-cell embryos/experiment (OvH) were obtained after FSH + hCG stimulation vs. 1.8 eight-cell embryos/experiment (OPU) and 5.0 eight-cell embryos/experiment (OvH) following FSH-priming. These numbers include embryos obtained from both in vivo and in vitro matured oocytes.
Conclusion.
A significantly lower developmental competence of the in vivo matured oocytes renders triggering of the in vivo maturation with hCG as a part of the currently used FSH-stimulation protocol unnecessary. In actual numbers, between 1 and 7 blastocysts were obtained following each FSH-priming. In the absence of further studies, FSH-priming appears superior to FSH + hCG stimulation in the common marmoset under current experimental settings.
Benchmarking machine learning methods for the identification of mislabeled data
Authors: Nazaretyan Lusiné , Kircher Martin , Leser Ulf 
Published: March 2024

Abstract
Supervised machine learning recently gained growing importance in various fields of research. To train reliable models, data scientists need credible data, which is not always available. A particularly hard and widespread problem deteriorating the performance of methods are mislabeled samples (Northcutt et al, 2021). Common sources of mislabeling are weakly defined classes, labels that change their meaning, unsuitable annotators, or ambiguous guidelines for labeling. Because mislabeling lowers prediction quality, it is essential for scientists to be able to identify wrong labels before actually starting the learning process. For that, numerous algorithms for the identification of noisy instances have been developed. However, so far, a comprehensive empirical comparison of available methods has been missing.
In this paper, we survey and benchmark methods for the identification of mislabled samples in tabular data. We discuss the theoretical background of label noise and how it can lead to mislabeling, review categorizations of identification methods, and briefly introduce 34 specific approaches together with popular data sets. Finally, 20 selected methods are benchmarked using artificially blurred data with controllable mislabling and a new real-life genomic dataset with known errors. We compare methods varying the amount and the type of noise, as well as the sample size and domain of data. We find that most of the methods have the best performance on datasets with a noise level of around 20-30%, whereas the identification of noisy instances in low and high ranges of noise prevalence is more challenging. Furthermore, none of the methods excels over all others in isolation, while ensemble-based methods often outperform individual models. We provide all data sets and analysis code to enable a better handling of mislabeled data and give recommendations on usage of noise filters depending on various dataset parameters.
RISAT-1A (EOS-4) Medium and Fine resolution data utilization for field crop and plantation monitoring
Authors: Haldar Dipanwita , Thulariraman Dharanya , Mitra Mekhala , Singh Raghavendra Pratap 
Published: March 2024

Abstract
The utility of the multidimension-multiparametric Radar data has been demonstrated in the past in a big way for Agricultural applications and more so with the Radar Imaging SATellite (RISAT)-1 data during the last decade. The advancement of the technology in Radar in the recent times was witnessed by the Indian SAR research community in achieving the hybrid/compact and full-polarimetric mode capability in both high resolution FRS (Fine Resolution Stripmap) mode as well as larger swath MRS (Medium Resolution ScanSAR) data. The later is catering to the multifaceted applications from oceanography to glaciers and particularly for the Agriculture and field crops. The latter applications was limited in the earlier satellite of RISAT-1 but immense potential is demonstrated in this study not only for the crop discrimination and mapping but more so utilizing the value added products for crop growth progress and mapping as well as biophysical parameter retrieval. The various stages of the crop both standing and harvested as well as biophysical parameters was found to have strong sensitivity to this data. Apart from field crops, plantation crops which did not receive much attention in the past, is investigated here.
The Retinex Enhancement Algorithm for Low-light Intensity Image Based on Improved Illumination Map
Authors: Weng Ruidi , Zhang Ya , Wu Hanyang , Wang Weiyong , Wang Dongyun 
Published: March 2024

Abstract
Due to the low brightness of the images taken in low-light intensity conditions, the image processing precision will be affected. In this paper, the Gamma Function based on the brightness average and the weighted fusion method according to gray entropy are proposed. They are combined with the improved Retinex algorithm. Firstly, the maximum values of R, G, and B channels in the original image are extracted to form the primary illumination map. Secondly, the illumination map is optimized by the optimization problems and adjusted by using the Gamma correction function based on the average brightness value. Finally, the illumination map and detail layer are fused by a weighted fusion algorithm of gray entropy to obtain the reflection map. Reflection maps are used as enhancement results. The algorithm proposed in this paper can improve the brightness and can maintain light distribution in the original image and has less color distortion.
QSPR analysis of heart attack medications, especially antiplatelet agents and dual antiplatelet therapy (DAPT), through graph labeling based topological indicators and regression models
Authors: S NANDISH N , P USHA , S RAMANANDA H 
Published: March 2024

Abstract
To model and characterize the molecular structure of pharmaceuticals and forecast their physicochemical properties without undergoing time-consuming and arduous laboratory procedures, labeled-based topological indices are highly helpful tools. Using the concepts of graph theory, these indices are numerical descriptors for the molecular structures. Labeled-based topological indices, which offer molecular descriptors to anticipate the features of pharmaceuticals, are essential for the QSPR analysis of heart attack medications, especially antiplatelet agents and dual antiplatelet therapy (DAPT). This research aims to calculate a linear regression model and eight labeled-based topological indices for five antiplatelet agents and dual antiplatelet therapy (DAPT) medications. These drugs are aspirin, clopidogrel, dipyridamole, prasugrel, and ticagrelor. Linear regression analysis and labeled-based topological indices correlate with various physicochemical properties related to drug activities, such as molar refractivity, polar surface, polarizability, molar volume, surface tension, log P, boiling point, and flash point. By shedding light on how the molecular structure affects these characteristics, correlations aid in the development and improvement of novel pharmaceuticals. In this paper, various statistical parameters are used to analyze antiplatelet agents and dual antiplatelet therapy (DAPT) in the case of a heart attack.
Blast Toes Volume Estimation for Post-Blast Efficiency: A Comparative Analysis of hybrid ensemble learning, voting, and base AI-algorithms
Authors: Kahraman Esma , Taiwo Blessing Olamide , Hosseini Shahab , Fissha Yewuhalashet , Jebutu Victor , Akinlabi Adams 
Published: March 2024

Abstract
This study compares base, hybrid, and voting modeling techniques to predict blast toe volume size. The investigation integrates independent models, explores synergies in hybrid approaches, and optimizes accuracy through ensemble voting to offer comprehensive knowledge and more reliable forecasts for blast toe volume estimation in various design. 457 blasting was investigated and data was collected at Anguran lead and zinc mine in Iran. Nine model accuracy indices were used to compare the algorithm's prediction accuracy. The study indicates a significant relationship between toe volume size and explosive charge per delay, as demonstrated by multicollinearity, Spearman, and Kendall correlation analyses. The analysis of the model showed that Light Gradient Boosting Machine (LightGBM) achieved the highest accuracy compared to the other 8 conventional models, with correlation coefficients (R2) of 0.9004 and 0.8625 for the training and testing datasets, respectively. The Hybrid 6 model, which combines LightGBM and CART algorithms, achieved the highest R2 scores of 0.9473 in the training phase and 0.9467 in the testing phase. The Voting 8 model, consisting of LightGBM, GBM, DT, ET, RF, CatBoost, CART, AdaBoost, and XGBoost, had the greatest R2 scores of 0.9876 and 0.97265 in both the training and testing stages. The voting models can reliably forecast toe volume resulting from a blast design pattern, thereby providing a novel tool for simulation.
Bayesian Hierarchical Network Autocorrelation Models for Estimating Direct and Indirect Effects of Peer Hospitals on Outcomes of Hospitalized Patients
Authors: Chen Guanqing , O'Malley A James 
Published: March 2024

Abstract

When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. In such a situation, a patient's own hospital can be thought of as a mediator between the effects of peer hospitals and their outcome. However, as in mediation analyses, there may be interest in allowing the effects of peer units to directly impact patients of other units. To accommodate these possibilities, we develop two hierarchical network autocorrelation models that allow for direct and indirect peer effect pathways between hospitals when modeling individual outcomes of the patients cared for at the hospitals. A Bayesian approach is used for model estimation while a simulation study is used to assess the performance of the models and sensitivity of results to different prior distributions. We construct a United States New England region patient-sharing hospital network and apply our Bayesian hierarchical models to study the diffusion of robotic surgery and hospital peer effects in patient outcomes using a cohort of United States Medicare beneficiaries in 2016 and 2017. The comparative fit of models to the data is assessed using Deviance information criteria tailored to hierarchical models that include peer effects as latent variables.
Application of Physics-Informed Neural Networks for Simulating Mass-Spring-Damper Systems
Authors: Sahoo Arup Kumar , Kumar Sandeep , Chakraverty S. 
Published: March 2024

Abstract
Dynamical problems are generally governed by a set of linear/non-linear differential equations (DEs). A large amount of prior physical information in the form of DEs plays an important role in simulation of dynamical systems. However, the traditional data-hungry machine learning models fail to express insightful scientific information from the data. Most of the implementations of neural networks are to perform non-linear mapping from input space to target space. However, Physics-informed neural networks (PINNs) can bridge the gap between scientific computing and data-hungry models. This paper exploits a new application of PINNs for approximating the behaviour of mass-spring-damper systems, showcasing how PINNs can effectively blend scientific principles with data-driven modeling. In this regard, we present solutions of two realistic application problems using PINNs. The accuracy of the predicted displacements of objects is established through results from literatures.
A New Index for Assessing Quality of Solar X-ray Imaging System and Image Reconstruction
Authors: Li Zhentong , Yu Wenhui , Su Yang , Chen Wei , Gan Weiqun 
Published: March 2024

Abstract

Indirect X-ray modulation imaging has been adopted in a number of solar missions and provided reconstructed X-ray images of solar flares that are of great scientific importance. However, the assessment of the image quality of the reconstruction is still difficult which is particularly useful for scheme design of X-ray imaging systems, improvement and testing of imaging algorithms, and scientific research. Currently, there is no specified method to quantitatively evaluate the quality of X-ray images. In this paper, we first propose percentage proximity degree (PPD) by considering the imaging characteristics of X-ray sources and in particular, side lobes and their effects on imaging quality. After screening a variety of imaging quality assessments through systematic testing of six aspects, we utilized the technique for order preference by similarity to ideal solution to the indices that meet the requirements. The final quality index established for X-ray image reconstruction (QuIX), consists of the selected indices and the new PPD, performs well on all the aspects, including assessment of PSF (point spread function) of ASO-S/HXI and simulation tests under different grid configurations, and imaging tests with RHESSI data. It was also found to be a useful tool in determination of proper imaging algorithm and field of view (FOV) for image reconstruction.