bluuebunny/crossref_metadata_2025_split huggingface dataset:
A educação de jovens e adultos em hotelaria: um estudo sobre o curso técnico em hospedagem Proeja no Instituto Federal do Maranhão
Authors: Correia Jonilson Costa , Pinheiro Costa Dandara Catarina 
Published: April 2024

We aim with this work to identify the profile of the students of the Proeja Course (Youth and Adult Education) in Hotel Management at IFMA - Historic Center of São Luís - MA, to map the students' conceptions about the course, during the training process, aiming to present the main demands of this process. This was qualitative research supported by quantitative elements. A questionnaire was administered on Google Forms, as it was a remote class period. The research revealed that despite advances in Youth and Adult Education in Brazil and Maranhão, there is still a lot to do. Today, students still face and overcome many problems such as reconciling study time with work and caring for their families, (re)adaptation to school writing and reading activities. But there are positive points such as the awakening of a feeling of belonging to the history of São Luís, the motivation to continue studying and enter the job market. In other words, there is a stimulus for the personal and professional growth of students.
Modeling of Chemical Tracers for Two-Phase Flow in Advective-Dominated Porous Media at Core Scale
Authors: Velasco-Lozano Moises , Balhoff Matthew T. , Diaz-Paulino Luis E. , Lopez-Ramirez Simon , Galvan-Castro Ramiro 
Published: April 2024

Summary
Chemical tracer modeling in porous media plays a key role in subsurface applications including oil recovery, aquifer remediation, and geothermal energy production. In oil reservoirs, chemical tracers are critical to quantifying the remaining oil saturation in porous media after displacing processes, enabling the correct evaluation of the sweep efficiency of recovery methods at the field scale. Even though the transport of solutes under single-phase flow has been modeled extensively with numerous solutions, there are no existing mathematical approaches to examine the displacement of solutes in two-phase flow conditions. Therefore, we present in this research work the first analytical solutions derived to model the transport of ideal and partitioning tracers in porous media with mobile water and oil phases.
The models presented are derived from the classic study of fluid displacement by viscous forces and the analysis of dynamic phase distribution in porous media, where key transformation variables are introduced to simplify the nonlinear advection-dispersion equation (ADE) into a conventional partial differential expression. In our derivation process, it is recognized that the dispersion effect can be superimposed onto an ideal concentration front via a singular perturbation expansion, resulting in practical solutions that do not require complex numerical calculations or inversion methods. The solutions derived are verified with numerical simulations and validated with experimental data under different flow conditions for the transport of ideal and partitioning tracers, demonstrating that the complex mechanisms of hydrodynamic dispersion, partitioning, and adsorption are accurately modeled under two-phase flow. Thus, our solutions can be used to rapidly evaluate tracer transport under the existing flow conditions in porous media, significantly reducing the number of experiments and simulations to characterize and select the correct tracer to be used in field applications.
A consensus platform for antibody characterization
Authors: Ayoubi Riham , Ryan Joel , Bolivar Sara Gonzalez , Alende Charles , Moleon Vera Ruiz , Fotouhi Maryam , Southern Kathleen , Alshafie Walaa , Baker Matt R , Ball Alexander R , Callahan Danielle , Cooper Jeffery A , Crosby Katherine , Harvey Kevin J , Houston Douglas W , Kumaran Ravindran , Rego Meghan , Schofield Christine , Wu Hai , Biddle Michael S , Brown Claire M , Kahn Richard A , Bandrowski Anita , Virk Harvinder S , Edwards Aled M , McPherson Peter S , Laflamme Carl 
Published: April 2024

Abstract
Antibody-based research applications are critical for biological discovery. Yet, there are no industry standards to compare the performance of antibodies in various applications. We describe a knockout cell line-based antibody characterization platform, developed, and approved jointly by industry and academic researchers that enables the systematic comparison of antibody performance in western blot, immunoprecipitation, and immunofluorescence procedures. The scalable protocols consist of (i) the identification of appropriate cell lines for antibody characterization studies, (ii) development/contribution of isogenic knockout controls, validated at the protein level, and (iii) a series of antibody characterization procedures focused on the most common uses of antibodies in research. Guidelines for the assessment of antibody performance are presented along with the value of making the resulting data publicly available. This antibody characterization platform can be implemented with minimal technological limitations. The proposed antibody characterization platform may be performed readily on antibodies targeting a protein in roughly one month, with one person working less than full-time. Antibody characterization is needed to meet standards for resource validation and data reproducibility, increasingly required by journals and funding agencies.
Quantitative Molecular Imaging using Deep Magnetic Resonance Fingerprinting
Authors: Vladimirov Nikita , Cohen Ouri , Heo Hye-Young , Zaiss Moritz , Farrar Christian T. , Perlman Or 
Published: April 2024

Abstract
Deep learning-based saturation-transfer magnetic resonance fingerprinting (MRF) is an emerging approach for noninvasive in vivo imaging of proteins, metabolites, and pH. It involves a series of steps, including (i) sample/subject preparation, (ii) image acquisition schedule design, (iii) biophysical model formulation, (iv) artificial intelligence (AI) and computational model training, followed by (v) image acquisition, (vi) deep reconstruction, and (vii) analysis. The saturation-transfer-based molecular MRI field has been slow to reach clinical maturity and adoption for clinical practice due to its technical complexity, semi-quantitative contrast-weighted nature, and impractically long scan times needed for the extraction of quantitative molecular biomarkers. Deep MRF constitutes a remedy for these challenges, providing a quantitative and rapid framework for extracting biologically and/or clinically meaningful molecular information. Here, we define a standardized protocol and a practical, complete guide for quantitative molecular MRI using deep MRF. We describe in-vitro sample preparation, animal and human scan considerations, and provide intuition behind acquisition protocol design and optimization of chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) quantitative imaging. We then extensively describe the building blocks for several AI models and demonstrate their performance for different applications, including cancer monitoring, brain myelin imaging, and pH quantification. Finally, explicit guidelines for further modification and expansion of the pipeline for imaging a variety of other pathologies (such as neurodegeneration, stroke, and cardiac disease) are given, accompanied by detailed open-source code and sample data. The protocol takes ~48 min - 57 hrs to complete by a scientist at the graduate student level.
Cognitive Mapping and Episodic Memory Emerge From Simple Associative Learning Rules
Authors: Gribkova Ekaterina , Chowdhary Girish , Gillette Rhanor 
Published: April 2024

Abstract

Episodic memory enables animals to map contexts and environmental features in space and time but is underused in artificial intelligence (AI). Here we show how simple associative learning rules can be expanded to basic episodic memory in AI. We augment an agent-based foraging simulation, ASIMOV, modeled on the simple neuronal circuitry of an invertebrate forager, by adding a novel computational module for simple episodic memory, the Feature Association Matrix (FAM). The FAM is a set of computationally light, graph learning algorithms which functionally resemble the auto- and hetero-associative circuits of the hippocampus for episodic memory. In simulations, FAM enables highly efficient foraging and navigation and shows how higher-order conditioning mechanisms give rise to spatial cognitive mapping by chaining pair-wise associations and encoding them with
additional contexts. Thus, FAM demonstrates a biologically inspired, bottom-up enhancement of AI for higher-order cognition.
Moving Together - benefits of an online dance program on physical and mental health for older adults: a mixed-method study
Authors: Hansen Rasmus Kopp , Jochum Elizabeth , Egholm Ditte , Villumsen Morten , Hirata Rogerio Pessoto 
Published: April 2024

Abstract

Background Previous studies show that in-person dance training is a beneficial form of physical activity that involves mental, social, and physical dimensions. This exploratory study investigated the benefits of a 12-week online dance training intervention on mental and physical health outcomes for older women.
Methods A convergent parallel mixed-method design was used. Forty-five older adults (74.0 ± 5.3 yrs old, 44 women) were recruited through advertisements at activity and rehabilitation centers in the North Denmark region. The intervention consisted of two weekly 60-min classes of improvisation and salsa delivered online through video call applications. Changes in physical health outcomes (body mass and composition, resting blood pressure, Senior Fitness Test battery) and self-rated health and wellbeing (health-related quality of life (HRQOL), feelings of loneliness) were assessed prior to and after 12 weeks of dancing. Focus group interviews were conducted post-intervention to further explore the benefits as well as the participant’s experience of the intervention. Thematic analysis of the qualitative data was conducted.
Results Thirty-two participants (all women) completed the study. Significant improvements in fitness were found for the number of arm curls performed (baseline: 12.3 ± 3.0; post-intervention: 13.7 ± 3.0, P = 0.005), 2-min step test performance (baseline: 66.5 ± 20.0 reps.; post-intervention: 73.8 ± 22.6 reps., P = 0.016), and chair sit-and-reach (baseline: 0.4 ± 11.3 cm; post-intervention: 5.5 ± 10.1 cm, P < 0.001). There was a significant increase in body mass from baseline to post-intervention (P < 0.015). The themes from the focus groups included (1) Participation, (2) Challenges, (3) Progression, (4) Motivation, (5) Perceived health and wellbeing, and (6) Online dance instruction. No significant changes were reported in HRQOL and loneliness from the quantitative data, although the qualitative data did reveal improved feelings of physical health and wellbeing.
Conclusions The intervention improved several aspects of fitness in older women and improved the participants’ perceptions of their own physical abilities and wellbeing. While most participants found the online intervention enjoyable, several participants missed the feedback from the instructors that naturally occurs with in-person instruction.
Assessing Screening Methods and Machine Learning for Predicting Childhood Overweight and Obesity: A Population-Based Study
Authors: Lior-Sadaka Irit , Melamed Shahar , Grotto Itamar , Sadaka Yair , Eilenberg Roni , Uziel Moshe , Greenberg Dan 
Published: April 2024

Abstract

Background:
This study aimed to assess the effectiveness of different screening methods, including Large for Gestational Age (LGA), macrosomia, and the WHO weight-for-length growth charts, in predicting childhood overweight and obesity. Additionally, we sought to develop a machine learning model utilizing various demographic, birth-related, maternal, and child growth data to evaluate its potential for improving predictive accuracy.
Methods:
We conducted this study using data from the Tipat Halav Israeli Screening (THIS) program, covering approximately 70% of Israeli children. The study included all children born between January 2014 and June 2016, with a minimum follow-up of 18 months, while excluding preterm births, multiple pregnancies, and those lacking growth measurements. Childhood overweight was defined based on WHO recommendations, and we evaluated the performance of existing models (LGA, macrosomia, and WHO percentiles) using different cutoffs. We also developed a machine learning model employing Random Forest and XGBoost algorithms.
Results:
The evaluation of prediction models yielded modest Area Under the Curve (AUC) values, ranging from 0.588 to 0.653. However, these models displayed significant improvement over random sampling. Notably, children selected by these models for post-birth intervention demonstrated up to a 17% likelihood of eventually becoming overweight, with 50% considered at risk.
Conclusions:
Our study underscores the importance of early intervention in addressing childhood overweight and obesity. Screening models, particularly LGA and macrosomia, exhibit promise in identifying newborns at risk. Although our machine learning model did not substantially enhance prediction, future research should explore the incorporation of additional relevant parameters to refine predictive accuracy and facilitate more effective early intervention efforts.
Error Correcting Codes Analysis for Wireless Industrial Communication based on Wavelet Packet Architecture.
Authors: Chehaitly Mouhamad , Tabaa Mohamed , Borras Jorge Querol , Monteiro Fabrice , Chatzinotas Symeon 
Published: April 2024

Abstract

With the evolution of applications, the implementation of processing algorithms has become more difficult due to the requirements in terms of throughput and complexity, which make the existing coding/decoding architectures unsuitable due to their insufficient computing capacity. Our objective in this paper is to analyze/study the integration of error-correcting codes into a functional digital architecture based on the inverse discrete wavelet packet transform (IDWPT) in transmission, and the discrete wavelet packet transform (DWPT) in reception (known as discrete wavelet packet modulation). In our proposed architecture, we have exploited two transmission strategies: (i) one-to-many (OtM) for transmission between a single transmitter and multiple sensors, (ii) and a many-to-one (MtO) mode for transmission between multiple users and a single receiver. To improve the transmission quality with the same configuration, we have integrated corrective codes, in particular the different families of Reed Solomon and convolutional codes. This architecture offers a very high processing power and a versatility in terms of flexibility and configurability adapted to very noisy environments, especially industrial ones. The use of error-correcting codes improves the quality of the transmission compared to the state of the art. Where we have achieved a gain of 4 to 6dB for Reed Solomon coding and convolutional codes used individually, 5 to 10dB with the combination of these two codes and 2 to 4dB more for shortened Reed Solomon codes.
Structural basis of human Nav1.5 gating mechanisms
Authors: Biswas Rupam , López-Serrano Ana , Huang Hsiang-Ling , Ramirez-Navarro Angelina , Grandinetti Giovanna , Heissler Sarah , Deschênes Isabelle , Chinthalapudi Krishna 
Published: April 2024

Abstract

Voltage-gated Nav1.5 channels are central to the generation and propagation of cardiac action potentials1. Aberrations in their function are associated with a wide spectrum of cardiac diseases including arrhythmias and heart failure2-5. Despite decades of progress in Nav1.5 biology6-8, the lack of structural insights into intracellular regions has hampered our understanding of its gating mechanisms. Here we present three cryo-EM structures of human Nav1.5 in previously unanticipated open states, revealing sequential conformational changes in gating charges of the voltage-sensing domains (VSDs) and several intracellular regions. Despite the channel being in the open state, these structures show the IFM motif repositioned in the receptor site but not dislodged. In particular, our structural findings highlight a dynamic C-terminal domain (CTD) and III-IV linker interaction, which regulates the conformation of VSDs and pore opening. Electrophysiological studies confirm that disrupting this interaction results in the fast inactivation of Nav1.5. Together, our structure-function studies establish a foundation for understanding the gating mechanisms of Nav1.5 and the mechanisms underlying CTD-related channelopathies.
Characteristics and driving forces of spatiotemporal use change in urban agglomerations from the perspective of functional zoning - Taking the Central Plains urban agglomeration as an example
Authors: Wang Hua , Zhu Xiaoyan , Niu Jiqiang , Zheng Xiaoyun , Wang Lina 
Published: April 2024

Abstract

Functional zoning is an effective method to disclose the spatial diversity of land and a prerequisite for the optimization of national land space. We explore the spatial and temporal characteristics of land use in the Central Plains urban agglomeration from 1990-2020，and the GWLR model was used to investigate the key driving elements for the land change in the urban agglomeration as a whole and in different land use function zones. The results indicate that (1) the most significant changes occurred on cultivated and construction land, with land use change intensities of 1.42% and 1.36%, respectively. (2) There are obvious differences in changes among different land function zones: The core economic zone has the highest proportion of change from cultivated land to construction land; in the belly of urban zone and the main agricultural production zone, due to the lack of natural forest and grass growing conditions, the change in land type is also manifested as the extension of construction land. (3) There are large differences among the motivating elements of cultivated land and construction land change in varied eras and different land use function zones, and changes in construction land are more influenced by socio-economic factors.
Impact of Chlorination and Chloramination on Regulated Trihalomethane Formation: Exploring DOC Reactions with Disinfectants
Authors: Sohrabifar Asyeh , Asadollahi Amin , Fakhraei Habibollah 
Published: April 2024

Abstract

Dissolved organic carbon (DOC) is a critical parameter in water quality management due to interaction with disinfectants, leading to the formation of potentially carcinogenic disinfection by-products (DBPs) during water treatment. Forest ecosystems are key contributors of DOC to surface waters, stemming from soil leachate. This study focuses on the formation of trihalomethanes (THMs), a common group of DBPs, following the chlorination and chloramination of soil-extracted DOC solutions under varying environmental conditions. For this purpose, soil samples from a densely forested upland watershed in Illinois, United States, were processed to extract DOC. The findings indicate notably higher production of THMs through chlorination compared to chloramination. It was observed that total trihalomethane (TTHM) levels significantly elevated with increases in DOC concentration, reaction time, and the dose of disinfectant in both treatment processes. The presence of bromide in the DOC influenced the concentration of brominated THMs (Br-THMs) during chlorination, whereas pH and temperature exhibited a moderate effect on TTHM formation. Furthermore, the study revealed that, except for pH and Specific Ultraviolet Absorbance (SUVA), all tested water quality indicators (DOC, total dissolved solids, turbidity, and UV254) exhibited a strong positive correlation with TTHM levels during chlorination. In contrast, these parameters displayed a moderate to weak correlation with TTHM levels in the chloramination process, suggesting their predictive value for TTHM formation is more pronounced in chlorination treatments.
Cortex Lycii attenuates night sweat in Yin-deficient rat by downregulating β 2 AR/cAMP signaling: A proteomic approach
Authors: Huang Meixia , Hong Zhiyuan , Wang Jie , Liu Xiangxiang , Wang Yingzheng , Wang Yinghao 
Published: April 2024

Abstract

Purpose: To investigate the therapeutic effect of Cortex Lycii on the sweat secretion of YDH rats and its underlying mechanism.
Methods: Male SD rats (N=36) were randomly assigned into Control, YDH, and DI groups (N=12 each group). YDH model rats were constructed by oral administration of thyroxin and reserpine. After two-weeks treatment of Cortex Lycii, the pharmacodynamics was assessed then the protein profile was obtained using a tandem mass tag proteomic approach. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis was used for bioinformatics analysis of differential expressed proteins (DEPs), critical DEPs were validated by Western blot. The receptor involved in the Cortex Lycii were confirmed by using β2AR receptor agonist (isoproterenol)- and M3R receptor agonist (pilocarpine)-induced sweat secretion in YDH rats.
Results: Cortex Lycii attenuates sweat secretion in YDH rats, and down-regulated the expression of AQP5 and β2-receptor on paw pads of YDH rats. Proteomics analysis revealed 189 differential proteins between the model and control groups, of which 124 differential proteins were remarkably reversed by Cortex Lycii. The overlapping DEPs in the model and Cortex Lycii groups were mainly enriched in the β2AR/cAMP pathway. Moreover, Cortex Lycii considerably attenuated the agonistic effect of isoprenaline induced sweat secretion.
Conclusion: This study demonstrated the effect of Cortex Lycii on sweat secretion in Yin-deficient rats. The mechanism on the ground may refer to the inhibition of β2AR/cAMP, followed by the suppression of the downstream PKA/ezrin/CFTR signaling pathway to reduce ion exchange and the inhibition of IP3/Ca2+/AQP5. The finding may provide new research on the mechanism of night sweats in TCM and their amelioration by Cortex Lycii.
Comparison of human thermal loads on foggy and cloudless mornings
Authors: Ács Ferenc , Kristóf Erzsébet , Zsákai Annamária 
Published: April 2024

Abstract

We investigated the human thermal load in Martonvásár (Hungarian lowland, Carpathian region) in anticyclonic weather conditions in the morning, when a) the sky was completely clear and on the other hand, when b) there was fog. A customizable clothing thermal resistance-operative temperature model was used. The BMI and Mb values of the person in the simulations were 25 kgm− 2 and 40 Wm− 2, respectively. During the observations, weather data was provided by the automatic station of the HungaroMet company and it was accessible on the company's website. We had 77 observations in foggy weather, while we had 46 observations under clear sky conditions in the period between 2019–2023. The following main results should be highlighted: 1) rcl varied between 0.5–2.5 clo in the case of fog, while in clear-sky cases rcl was between 0.9–3.5 clo. Based on our data analysis, we concluded that the warming effect of the morning fog was around 1-1.5 clo. 3) We also showed that the effect of inter-personal variability on rcl was significant when the heat deficit was high (rcl ≥ 2.5 clo) and at this time it was comparable with the degree of the warming effect of fog. It should be mentioned that the analysis of typical weather situations from the point of view of human thermal load is a new field of research, since there is little information available on this subject.