bluuebunny/crossref_metadata_2025 huggingface dataset:
Herpesvirus‐Entry Mediator Inhibits the NF‐κB Pathway Activated by IL‐17 and Fosters the Osteogenic Differentiation of Allogeneic Mesenchymal Stem Cells
Authors: Rong Zhigang , Xi Yuhang , Zhang Chengmin , Dai Wei , Xue Hao , Luo Fei , Xu Jianzhong , Dai Fei
Published: August 2024

The challenge in developing tissue‐engineered bones (TEBs) for clinical applications lies in the constraints associated with the source and availability of autologous mesenchymal stem cells (MSCs) derived from the bone marrow, which creates a bottleneck. While allogeneic MSCs have shown promise in TEB applications, their ability to promote bone growth is notably diminished because of the inflammatory reaction at the transplant site and the inherent immune response triggered by allogeneic MSCs. Hence, there is a pressing need to develop methods that enhance the osteogenic differentiation of allogeneic MSCs during transplantation. Previous studies have found that IL‐17 is a key proinflammatory factor in initiating inflammation and cascade amplification in the early stages of an inflammatory response, and proinflammatory cytokines such as TNF‐α and IL‐17 can inhibit the osteogenic differentiation of MSCs in an immune environment. In this study, MSCs expressing HVEM were successfully constructed by viral transfection and further reconfirmed that IL‐17 can inhibit the in vivo and in vitro osteogenesis of allogeneic MSCs through in vitro experiments and mouse calvarial bone defect (diameter about 3 mm) model, while MSCs that express herpesvirus‐entry mediator (HVEM) exhibit the capacity to suppress immune responses and sustain strong osteogenic potential. We further pointed out that the mechanism by which HVEM promotes the osteogenesis of allogeneic MSCs is related to its inhibition of the IκB kinase (IKK)‐NF‐κB signaling pathway activated by IL‐17 in the immune environment, which can significantly inhibit the ubiquitination and degradation of β‐catenin in MSCs induced by the IKK‐NF‐κB pathway, upregulate the expression of β‐catenin, and promote bone formation. Hence, this research provides an initial connection between the Wnt/β‐catenin signaling pathway and the IKK‐NF‐κB pathway during allogeneic MSC transplantation, offering new avenues for investigation and establishing a theoretical foundation for the potential use of HVEM‐expressing MSCs in clinical treatments for bone defects.
Improved Risk Prediction of Acute Myocardial Infarction in Patients With Stable Coronary Artery Disease Using an Amino Acid‐Assisted Model
Authors: Zhao Yi-Jing , Li Yong , Wang Feng-Xiang , Lv Hao , Qu Yaoyao , Qi Lian-Wen , Xiao Pingxi
Published: August 2024

Patients with stable coronary artery disease (CAD) are at an increased risk of acute myocardial infarction (AMI), particularly among older individuals. Developing a reliable model to predict AMI occurrence in these patients holds the potential to expedite early diagnosis and intervention. This study is aimed at establishing a circulating amino acid‐assisted model, incorporating amino acid profiles alongside clinical variables, to predict AMI risk. A cohort of 874 CAD patients from two independent centers was analyzed. Plasma amino acid levels were quantified using liquid chromatography tandem mass spectrometry (LC‐MS/MS) employing a targeted metabolomics approach. This methodology incorporated 13C isotope‐labeled internal standards for precise quantification of 27 amino acids. Univariate logistic regression was applied to identify differentially expressed amino acids that distinguished between stable CAD and AMI patients. To assess prediction performance, receiver operating characteristic (ROC) curve and nomogram analyses were utilized. Five amino acids—lysine, methionine, tryptophan, tyrosine, and N6‐trimethyllysine—emerged as potential biomarkers (p < 0.05), exhibiting significant differences in their expression levels across the two centers when comparing stable CAD with AMI patients. For AMI risk prediction, the base model, utilizing 12 clinical variables, achieved areas under the curve (AUC) of 0.7387 in the discovery phase (n = 623) and 0.8205 in the external validation set (n = 251). Notably, the integration of these five amino acids into the prediction model significantly enhanced its performance, increasing the AUC to 0.7651 in the discovery phase (Delong’s test, p = 1.43e‐02) and to 0.8958 in the validation set (Delong’s test, p = 8.91e‐03). In conclusion, the circulating amino acid‐assisted model effectively enhances the prediction of AMI risk among CAD patients, indicating its potential clinical utility in facilitating early detection and intervention.
Figure S3 from Loss of STK11 Suppresses Lipid Metabolism and Attenuates KRAS-Induced Immunogenicity in Patients with Non–Small Cell Lung Cancer
Authors: Principe Daniel R. , Pasquinelli Mary M. , Nguyen Ryan H. , Munshi Hidayatullah G. , Hulbert Alicia , Aissa Alexandre F. , Weinberg Frank
Published: August 2024

Figure S3. KRAS mutation is not associated with additional alterations to tumor immunogenicity
Figure S2 from Characterization of Non-Monotonic Relationships between Tumor Mutational Burden and Clinical Outcomes
Authors: Anaya Jordan , Kung Julia , Baras Alexander S.
Published: August 2024

Model variability across folds. A, mean-normalized model fits for each training fold for an FCN model with simulated linear data. B, mean-normalized model fits for each training fold for an FCN model with simulated non-monotonic data.
Supplementary Figure 5 from KRAS Promotes GLI2-Dependent Transcription during Pancreatic Carcinogenesis
Authors: Sigafoos Ashley N. , Tolosa Ezequiel J. , Carr Ryan M. , Fernandez-Barrena Maite G. , Almada Luciana L. , Pease David R. , Hogenson Tara L. , Raja Arul Glancis L. , Mousavi Fatemeh , Sen Sandhya , Vera Renzo E. , Marks David L. , Flores Luis F. , LaRue-Nolan Kayla C. , Wu Chen , Bamlet William R. , Vrabel Anne M. , Sicotte Hugues , Schenk Erin L. , Smyrk Thomas C. , Zhang Lizhi , Rabe Kari G. , Oberg Ann L. , Zaphiropoulos Peter G. , Chevet Eric , Graham Rondell P. , Hagen Catherine E. , di Magliano Marina P. , Elsawa Sherine F. , Pin Christopher L. , Mao Junhao , McWilliams Robert R. , Fernandez-Zapico Martin E.
Published: August 2024

Supplementary Figure 5 shows the IHC results looking at CD4 and CD8 expression in KC and KCRG mice. The results show no difference in the immune landscape between these mouse models.
Data from KRAS Promotes GLI2-Dependent Transcription during Pancreatic Carcinogenesis
Authors: Sigafoos Ashley N. , Tolosa Ezequiel J. , Carr Ryan M. , Fernandez-Barrena Maite G. , Almada Luciana L. , Pease David R. , Hogenson Tara L. , Raja Arul Glancis L. , Mousavi Fatemeh , Sen Sandhya , Vera Renzo E. , Marks David L. , Flores Luis F. , LaRue-Nolan Kayla C. , Wu Chen , Bamlet William R. , Vrabel Anne M. , Sicotte Hugues , Schenk Erin L. , Smyrk Thomas C. , Zhang Lizhi , Rabe Kari G. , Oberg Ann L. , Zaphiropoulos Peter G. , Chevet Eric , Graham Rondell P. , Hagen Catherine E. , di Magliano Marina P. , Elsawa Sherine F. , Pin Christopher L. , Mao Junhao , McWilliams Robert R. , Fernandez-Zapico Martin E.
Published: August 2024

Abstract
Aberrant activation of GLI transcription factors has been implicated in the pathogenesis of different tumor types including pancreatic ductal adenocarcinoma. However, the mechanistic link with established drivers of this disease remains in part elusive. In this study, using a new genetically engineered mouse model overexpressing constitutively active mouse form of GLI2 and a combination of genome-wide assays, we provide evidence of a novel mechanism underlying the interplay between KRAS, a major driver of pancreatic ductal adenocarcinoma development, and GLI2 to control oncogenic gene expression. These mice, also expressing KrasG12D, show significantly reduced median survival rate and accelerated tumorigenesis compared with the KrasG12D only expressing mice. Analysis of the mechanism using RNA sequencing demonstrate higher levels of GLI2 targets, particularly tumor growth–promoting genes, including Ccnd1, N-Myc, and Bcl2, in KrasG12D mutant cells. Furthermore, chromatin immunoprecipitation sequencing studies showed that in these cells KrasG12D increases the levels of trimethylation of lysine 4 of the histone 3 (H3K4me3) at the promoter of GLI2 targets without affecting significantly the levels of other major active chromatin marks. Importantly, Gli2 knockdown reduces H3K4me3 enrichment and gene expression induced by mutant Kras. In summary, we demonstrate that Gli2 plays a significant role in pancreatic carcinogenesis by acting as a downstream effector of KrasG12D to control gene expression.
Forecasting research influence: a recurrent neural network approach to citation prediction
Authors: Jamal Naser , Alauthman Mohammad , Malhis Muhannad , Ishtaiwi Abdelraouf M.
Published: August 2024

As the volume of scientific publications continues to proliferate, effective evaluation tools to determine the impact and quality of research articles are increasingly necessary. Citations serve as a widely utilized metric for gauging scientific impact. However, accurately prognosticating the long-term citation impact of nascent published research presents a formidable challenge due to the intricacy and unpredictability innate to the scientific ecosystem. Sophisticated machine learning methodologies, particularly recurrent neural networks (RNNs), have recently demonstrated promising potential in addressing this task. This research proposes an RNN architecture leveraging encoder-decoder sequence modeling capabilities to ingest historical chronicles and predict succeeding evolution via latent temporal dynamics learning. Comparative analysis between the RNN approach and baselines, including random forest, support vector regression, and multi-layer perceptron, demonstrate superior performance on unseen test data and rigorous k-fold cross-validation. On a corpus from Petra University, the RNN methodology attained the lowest errors (root mean squared error (RMSE) 1.84) and highest accuracy (0.91), area under the curve (AUC) (0.96), and F1-score (0.92). Statistical tests further verify significant improvements. The findings validate our deep learning solution's efficacy, robustness, and real-world viability for long-term scientific impact quantification to aid stakeholders in research evaluation. The findings intimate that RNN-based predictive modeling constitutes a potent technology for citation-driven scientific impact quantification.
Improved search method for classified reusable components on cloud computing
Authors: Rawashdeh Adnan , Alkasassbeh Mouhammd , Dwairi Radwan , Abu-Salem Hani , Al-Mattarneh Hashem
Published: August 2024

Expanding development environments to accommodate huge amounts of reusable components along with associated maintenance and evolution responsibilities has become difficult and costly for software organizations to cope with, while benefits are limited to owner organizations. The challenge of organizing reusable assets so that finding the right component needed has always been a big challenge. The literature of software reuse lacks a comprehensive search method that is efficient and covers the entire system development lifecycle (SDLC). This research work attempts to make an efficient use of the cloud computing advantages and thus, encourages the migration of reusable components to the clouds. The maintenance, the search process and cost-related problems encountered with traditional in-house development environments can be resolved conclusively on the cloud. This research work proposes a multi-classification and clusters approach to migrate reusable components to the cloud. Accordingly, it applies indexing process to classified reusable components achieving efficient search. In addition, the proposed approach adopts a comprehensive SDLC-based classification to organize reusable components so that searching and finding an appropriate component becomes an easy task due to the fact it is bound to the particular undergoing phase. Cloud computing provides more storage and resources with low cost, compared to traditional in-house development environments.
Utilizing association rule mining for enhancing sales performance in web-based dashboard application
Authors: Teja Nursasongka Raden Mas , Fahrurrozi Imam , Oktiawati Unan Yusmaniar , Taufiq Umar , Farooq Umar , Alfian Ganjar
Published: August 2024

Data is increasingly recognized as a valuable asset for generating new insights and information. Given the importance of data, businesses must always look for ways to get more value from data generated from sales transactions. In data mining, association rule mining is a good standard technique and is widely used to find interesting relationships in databases. Association rule is closely related to market basket analysis to find items that often appear together in one transaction. This study proposes the frequent pattern growth (FP-Growth) algorithm in finding association rules on sales transaction data. Our methodology includes dataset preparation for modeling, evaluation of model performance, and subsequent integration into a web-based platform. We conducted a comparative analysis of the FP-Growth algorithm against the Apriori algorithm, finding that FP-Growth outperformed Apriori in efficiency. Using the same dataset and constraint level, both algorithms produce the same number of frequent itemsets. However, in terms of computation time, FP-Growth excels by taking 2.89 seconds while Apriori takes 5.29 seconds. We integrated trained FP-Growth algorithm into a web-based dashboard application using the streamlit framework. This system is anticipated to simplify the process for businesses to identify customer purchasing patterns and improve sales.
Optimizing network lifetime in wireless sensor networks: a hierarchical fuzzy logic approach with LEACH integration
Authors: Dadhirao Chandrika , Reddy Sadi Ram Prasad , Prabhakar Rao B V A N S S , Terlapu Panduranga Vital
Published: August 2024

Wireless sensor networks (WSNs) are of significant importance in many applications; nevertheless, their operational efficiency and longevity might be impeded by energy limitations. The low energy adaptive clustering hierarchy (LEACH) protocol has been specifically developed with the objective of achieving energy consumption equilibrium and regularly rotating cluster heads (CHs). This study presents a novel technique, namely the hierarchical fuzzy logic controller (HFLC), which is integrated with the LEACH protocol to enhance the process of CH selection and effectively prolong the network's operational lifespan. The HFLC system employs fuzzy logic as a means to address the challenges posed by uncertainty and imprecision. It assesses many aspects, including residual energy, node proximity, and network density, in order to make informed decisions. The combination of HFLC with LEACH demonstrates superior performance compared to the conventional LEACH protocol in terms of energy efficiency, stability, and network durability. This study emphasizes the potential of intelligent and adaptive mechanisms in improving the performance of WSNs by improving the survivability of nodes by reducing the energy consumption of the nodes during the communication of network process. It also paves the way for future research that integrates soft computing approaches into network protocols.
Convolutional neural networks breast cancer classification using Palestinian mammogram dataset
Authors: Saadah Hanin , Owda Amani Yousef , Owda Majdi
Published: August 2024

Breast cancer is widespread across the globe. It’s the primary cause of death in cancer fatalities. According to the Palestinian Ministry of Health annual report, it ranked as the third reported death of all reported cancer deaths in the West Bank. Mammogram screening is the most common technique to diagnose breast abnormalities, but there is a challenge in the lack of skilled experts able to accurately interpret mammograms. Machine learning plays an important role in medical image processing particularly in early detection when the treatment is less expensive and available. In this paper we proposed different convolutional neural network (CNN) models to detect breast abnormalities with promising results. Six CNN models were used in this research on a unique (first-hand) dataset collected from the Palestinian Ministry of Health. The models are VGG16, VGG19, DenseNet121, ResNet50, Xception, and EfficientNetB7. Consequently, DenseNet121 outperformed other models with 0.83 and 0.85 for testing accuracy and area under curve (AUC) respectively. As a future work, the outperformed model can be combined with other patient data like genetic information, medical history, and lifestyle factors to evaluate the risk of developing specific diseases. This would increase the survival rate and enable proactive measures.
Enhancing surface water quality prediction efficiency in northeastern thailand using machine learning
Authors: Uypatchawong Surasit , Chanamarn Nipaporn
Published: August 2024

Water is the most vital resource for life and is necessary for most living creatures, including humans, to survive. Three rivers’ surface water quality has been predicted by this study: the Chi river, the Mun river, and the Songkhram river. In the northeastern region of Thailand. The dataset is 881 samples and 13 factors. This study investigated various machine learning methods for predicting water quality, including neural networks (NN), support vector machines (SVM), decision trees (DT), Naive Bayes (NB), and K-nearest neighbors (KNN). Furthermore, this study was conducted to find suitable factors using correlation based feature selection, correlation coefficient, and information gain. And optimize the prediction model using the Bagging Approach. The result is found that the bagging model using the DT technique (BaggingDT) has better performance than all models with an accuracy value equal to 98.64%, precision value equal to 98.70%, recall value equal to 98.60%, F-measure value equal to 98.60% and RMSE value equal to 0.0961. The obtained factors and the most appropriate model can be used to develop a surface water quality standard predicting system.
Parameter tuning for enhancing performance of a variant of particle swarm optimization algorithm
Authors: Kumar Ashok , Kumar Sheo , Tiwari Rajesh , Saxena Shalya , Singh Anurag
Published: August 2024

There is dependably an extraordinary requirement for new types of algorithms in the population-based improvement algorithm. These algorithms improve the execution of the current algorithm. Parameter change approach assumes an essential job in improving the execution of the PSO algorithm. A new algorithm called particle acceleration-based particle swarm optimization (PA-PSO) has been proposed. In this algorithm a particle acceleration parameter is tuned. This algorithm significantly improves the performance of the PSO–time varying acceleration coefficients (PSO-TVAC) algorithm. This algorithm reduces the time varying weight of inertia and the nonlinear acceleration coefficients in the equation of the PSO-TVAC velocity vector in each iteration. Particle movements in the n-dimensional search space are governed by the kinetics of the second motion equation. Experiments demonstrate that the proposed PA-PSO algorithm outperforms the existing PSO-TVAC algorithm on five well-known reference test functions. The algorithm possesses adequate control over the local as well as global optimums.
Enhancing diagonal comprehension with advanced topic modeling technique: DIAG-LDA
Authors: Sifi Fatima-Zahrae , Sabbar Wafae , El Mzabi Amal
Published: August 2024

With the speed increase of reviews or other forms of text, natural language has the ability to convey large and complex amounts of information in relatively small communications. This capability is being leveraged by the machine-learning algorithm known as latent dirichlet allocation (LDA), which can be utilized to discover latent topics within documents. LDA can be also used to generate summaries or abstracts from a given set of documents. However, LDA can struggle to identify topics in short documents or in data with high levels of noise. This article will introduce a new method for topic modeling with LDA based on diagonal reading for sentences (DIAG-LDA). Primarily, the features are selected using the TF-IDF algorithm, and the highest relevant features are extracted using the confidence value. Besides, the classification step is executed utilizing the LDA classifier. Ultimately, we evaluate our model using the convolutional neural network algorithm. The experiment results show that DIAG-LDA performs well in identifying features from text data, achieving a 94.4%, and 89.5% in accuracy for the datasets on international economics and the political economy.
Improvement of electricity reliability on the 330 kV Nigeria transmission network with static synchronous compensators
Authors: Omeje Luke Uwakwe , Ohanu Chibuike Peter , Anyaka Boniface Onyemaechi , Sutikno Tole
Published: August 2024

The increasing demand for power has caused distortions in Nigeria’s 330 kV transmission network. This is a result of the bulk of the lines being heavily loaded at the moment, which leads to voltage drops and inconsistent electrical delivery. To ensure system reliability, it is therefore crucial to make sure that the system maintains a constant state under specific conditions. This research presents the use of static synchronous compensators (STATCOM) in the Nigerian 330 kV transmission network to reduce power loss and improve the voltage profile. To solve the problem of insufficient voltage and power losses, a three-phase network is simulated using the MATLAB/Simulink software. A three-level, 48- pulse STATCOM was employed to rectify the problem after weak buses were identified through load flow analysis. A 48- pulse converter that handled the STATCOM was used to control harmonic distortions in the system. The outcomes show how crucial the reactive power control mechanism is for regulating the system’s harmonics. However, the method was able to achieve real and reactive power losses of 12.5%. The STATCOM’s 3-level 48- pulse converter also resulted in a total 4.64% reduction in total harmonic distortion (THD).
An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model
Authors: Amaidi Maria , Zaaraoui Lassaad , Mansouri Ali
Published: August 2024

The future power of photovoltaic systems (PVS) is gaining significant attention due to its rising potential. This has resulted in a substantial amount of research emphasizing the importance of optimizing the PVS efficiency. However, the identification of PV cell model parameters remains a challenging task, mainly due to the characteristics of PV cells and their dependence on varying meteorological conditions. In this work, we present a novel methodology based on an improved new multi objective particle swarm optimization (NMOPSO) algorithm for the PV cell parameters identification. The main goal is to minimize the root mean square error (RMSE) and to calculate the series resistance (Rs) by means of its non-linear equation form. The applied algorithm uses an evolving and adaptive search strategy to enhance both speed of convergence for the parameter identification process precision. Through extensive simulations, we demonstrate that proposed approach outperforms current methods in terms of accuracy, precision, and PV parameters extraction.
Development of an IoT-based sleep pattern monitoring system for sleep disorder detection
Authors: Md Shahrum Muhammad Nur Ikhwan , Md Isa Ida Syafiza , Mohd Shaari Azyze Nur Latif Azyze , Nasir Haslinah Mohd , Sutikno Tole
Published: August 2024

Inadequate sleep can cause various health problems including heart disease and obesity. In this work, a sleep monitoring system that monitors human sleep patterns is developed using the internet of things (IoT) and Raspberry Pi. The system is designed to record any detected movements and process the data using machine learning to provide valuable insight into a person’s sleep patterns including sleep duration, the time taken to fall asleep, and the frequency of waking up. This information is very useful to provide the sleep disorder diagnostics of an individual including restless leg, parasomnia and insomnia syndrome besides giving recommendations to improve their sleep quality. Also, the system allows the processed data to be stored in the cloud database which can be accessed through a mobile application or web interface. The performance of the system is evaluated in terms of its accuracy and reliability in detecting sleep order diagnostics. Based on the confusion matrix, the results show the accuracy of the system is 90.32%, 91.80%, and 91.80% in detecting the restless leg, parasomnia and insomnia syndrome, respectively. Meanwhile, the system showed high reliability in monitoring 10 participants for 8 hours and updated the recorded data and its analysis in the cloud.
Electrocardiogram reconstruction based on Hermite interpolating polynomial with Chebyshev nodes
Authors: Ray Shashwati , Chouhan Vandana
Published: August 2024

Electrocardiogram (ECG) signals generate massive volume of digital data, so they need to be suitably compressed for efficient transmission and storage. Polynomial approximations and polynomial interpolation have been used for ECG data compression where the data signal is described by polynomial coefficients only. Here, we propose approximation using hermite polynomial interpolation with chebyshev nodes for compressing ECG signals that consequently denoises them too. Recommended algorithm is applied on various ECG signals taken from MIT-BIH arrhythmia database without any additional noise as the signals are already contaminated with noise. Performance of the proposed algorithm is evaluated using various performance metrics and compared with some recent compression techniques. Experimental results prove that the proposed method efficiently compresses the ECG signals while preserving the minute details of important morphological features of ECG signal required for clinical diagnosis.
Enhanced Bengali audio categorization using audio segmentation and deep learning
Authors: Khan Niaz Ashraf , Bin Hafiz Md. Ferdous
Published: August 2024

This paper presents an enhanced approach for classifying Bengali songs into different genres by leveraging feature importance analysis and deep learning techniques. The research addresses the challenge of limited data points in the Bengali Song Dataset by employing strategies, including audio segmentation and feature importance analysis, to enhance model performance. Multiple machine learning and deep learning architectures are evaluated to identify the most effective models for Bengali song classification. Additionally, this research conducts feature importance analysis to identify significant audio features contributing to classification accuracy. The best-performing deep learning model achieves an impressive validation accuracy of 94.17%, showcasing the project efficacy of the proposed methodology. Our findings highlight the effectiveness of our proposed methodology, demonstrating significant improvements in classification accuracy and contributing to advancements in Bengali music classification research.
Emojis as Communication Medium Among College Students in a State-Run Institution
Authors: Aranas Kylie Kate S. , Marte Marifel B. , Valentin Christian Jay S. , Biray Ersyl T.
Published: August 2024

Emojis are small images conveying meaning, emotions, and tone embedded in electronic text messages that allow people to express their feelings and emotions in nonverbal and informal conversations. This study aimed to find out the role of emojis as a communication medium among college students in a state-run institution. Specifically, it sought to identify the popularly-used emojis, the social media platforms where these are used, determine their frequency of use, and the instances where these are used. Sophomore college students enrolled in a teacher education degree program specializing in the English language at a state-run university supplied the data collected using a validated survey questionnaire transformed and sent through the Google messenger application. Results revealed that the topmost popular emojis used by college students in their nonverbal communication were the heart, face with tears of joy, and loudly crying face. Participants stated that they always use emojis in their informal communication with friends on popular social media platforms, such as Facebook and Messenger. It was also established that emojis were used when messaging friends or family members expressing their feelings, and having nothing else to reply to in a conversation. These findings attest that the use of technology-driven trends has evolved in the communication media, particularly among the younger generation.
Análise epidemiológica da Sífilis Congênita no Brasil entre 2019 - 2022
Authors: Abrão Roberta Maria , Mendes Ellen Teodora Coelho , Pinheiro Danielle Ivy de Almeida , Albani Pedro Pavesi Simão , Cimini Christiane Corrêa Rodrigues
Published: August 2024

Objetivo: Realizar estudo epidemiológico acerca dos casos notificados de sífilis congênita entre os anos de 2019 a 2022. Metodologia: Estudo quantitativo e retrospectivo, a partir de dados coletados no Sistema de Informações e Agravos de Notificação (SINAN) referentes ao período de  2019  a  2022,  disponível  nos  Departamento  de  Informática  do  Sistema  Único  de  Saúde (DATASUS).  As informações obtidas foram: estratificação dos casos por região brasileira, faixa etária, raça, escolaridade, realização do pré-natal, tratamento e evolução clínica da criança. Resultados:  Foram notificados 102.356 casos de sífilis congênita em crianças menores de 1 ano de idade confirmados por meio do SINAN no Brasil, entre 2019 e 2022, no qual destaca-se maior concentração de casos na região Sudeste cerca de 28,57% dos casos. Houve uma maior proporção de casos entre mães com idades entre 20 e 29 anos (57,10%), pardas (58,63%) e com ensino médio completo (19,88%). A proporção de realização do pré-natal se manteve acima de 80%, no entanto, o percentual de mães com tratamento inadequado ou não realizado também permaneceu acima de 80%. A proporção de crianças que sobreviveram foi superior a 87%. Conclusão: Evidencia-se tendência preocupante à persistência dos casos de sífilis congênita no Brasil, diante desse cenário, é imperioso o incentivo à adoção de medidas abrangentes que visem o acesso aos serviços de saúde e conscientização da população, a fim de fortalecer os sistemas de vigilância epidemiológica.
Fatores associados ao near miss e mortalidade materna no brasil: uma revisão sistemática
Authors: Holanda Anna Luísa de Souza , Araujo Raimundo Antonio Batista de , Mendes Joana Karollyne de Siqueira , Oliveira e Silva Vanessa de , Guedes Idrys Henrique Leite , Brito Vicente Castor , Tomé Danusa Petra Barbosa , Ramos Katarina Venâncio Antunes Romeu , Silva Klleydson Figueiredo da , Gomes Vilessa Lilian de Araújo
Published: August 2024

Introdução: Os eventos de Near Miss Materno (NMM) são importantes indicadores da saúde materna. Diante disso, é importante a observação das principais causas de complicações gestacionais, Mortalidade Materna (MM) e, principalmente, do NMM no Brasil, tendo em vista sua relação com fatores sociodemográficos e de assistência à saúde. Objetivo: Descrever os fatores associados ao NMM e a MM no Brasil. Métodos: Trata-se de uma revisão sistemática com análise de artigos indexados nas bases PubMed, BVS, SciELO, ScienceDirect e Scopus. A coleta de dados foi realizada em duplo cego por dois revisores independentes e contou com auxílio de um terceiro revisor para analisar as discrepâncias. Inicialmente, os artigos foram selecionados por títulos e resumos e, posteriormente, pelo texto na íntegra. Resultados: Analisando-se as principais condições relacionadas ao NMM e MM, pode-se observar que os estudos elencaram principalmente: hemorragia (10/17), hipertensão (8/17), pré-eclâmpsia (7/17), sepse (6/17), eclâmpsia (5/17) e infecção (5/17). Além disso, vale destacar que outras variáveis diferentes como cor da pele, renda, presença do companheiro também influenciaram nas taxas de NMM e MM, segundo um artigo analisado. Conclusões: As principais condições clínicas associadas a MM e NMM foram: hemorragia, hipertensão e pré-eclâmpsia. As limitações do estudo foram: divergência entre os critérios de avaliação das condições relatadas e poucos estudos multicêntricos.
O regente/maestro de orquestra e sua atuação na música popular: um panorama brasileiro
Authors: Correa Marcio Guedes , Santiago Abdenaldo Aurelio Alves de Lima Muniz
Published: August 2024

Na primeira metade do século XX, ocorreu significativa orquestração de canções brasileiras. Gêneros musicais passaram a ser apresentados com arranjos para orquestra sinfônica, além do instrumental original das big bands estadunidenses. Tal fenômeno, sem dúvida, ocorreu em grande medida pela chegada dos discos norte americanos em território brasileiro que continham canções do jazz interpretadas por cantores e cantoras acompanhados por big bands e grandes orquestras. Dessa demanda de se orquestrar a música popular emerge a necessidade da presença do maestro ou regente orquestral nos meios de produção da canção popular. Diante desse fato, surge uma inquietação: quais são as características do regente orquestral que irá atuar na interpretação da canção popular? Buscaremos encontrar respostas para essa questão por meio de uma pesquisa de abordagem qualitativa e natureza básica, objetivando descrever e explicar a atuação do maestro/regente na música popular brasileira. Quanto aos procedimentos, essa é uma pesquisa bibliográfica e documental.
Relationship Between Cardiac Structure and Function With Renal Function Trajectory and Outcomes in Patients With Heart Failure: Insights From the PARAGON-HF Trial
Authors: Lu Henri , Chatur Safia , Lee Sahmin , Inciardi Riccardo M. , Abanda Martin , Mc Causland Finnian R. , Kalayci Arzu , Taheri Kimia Karimi , Shah Amil M. , Cikes Maja , Claggett Brian L. , Prasad Narayana , Lam Carolyn S.P. , O’Meara Eileen , Wang Xiaowen , McMurray John J.V. , Pfeffer Marc A. , Hegde Sheila M. , Solomon Scott D. , Skali Hicham
Published: August 2024

BACKGROUND:

Renal dysfunction is common and associated with a poor prognosis in patients with heart failure. However, the association of cardiac structure and function with decline in kidney function in this population is unknown. We aimed to assess the association between individual measures of cardiac structure and function with changes in renal function and renal outcomes in patients with heart failure with preserved ejection fraction.

METHODS:

Patients enrolled in the PARAGON-HF (Prospective Comparison of Angiotensin Receptor–Neprilysin Inhibitor With Angiotensin-Receptor Blockers Global Outcomes in Heart Failure With Preserved Ejection Fraction) echocardiographic substudy were included. The association between each echocardiographic parameter (expressed in standardized units) and changes over time in estimated glomerular filtration rate was calculated with repeated-measures mixed-effect models. Multivariable Cox proportional hazards models were used to identify individual cardiac parameters associated with the composite renal outcome (≥50% decline in estimated glomerular filtration rate relative to baseline, development of end-stage renal disease, or death attributable to renal causes), after adjusting for covariates.

RESULTS:

Among 1097 patients (mean age 74±8 years and 53% women), over a median follow-up of 2.9 years, 28 composite renal events (0.9 per 100 person-years) occurred. Higher left ventricular (LV) mass index and higher E/average e′ ratio were associated with significantly more profound annual decline in estimated glomerular filtration rate (for both, −0.4 [95% CI, −0.7 to −0.1] mL/min/1.73 m
2
/y per 1 higher SD). Higher LV mass index, LV end-diastolic volume index, right ventricular end-diastolic area, and a lower right ventricular fractional area change were each associated with a significantly higher risk for the composite renal outcome.

CONCLUSIONS:

In the PARAGON-HF echocardiographic substudy, higher LV mass and filling pressures were independently associated with more profound kidney function decline, and higher LV mass and volume, as well as impaired right ventricular structure and function, were each associated with renal events. Assessing these parameters may help identify patients with heart failure with preserved ejection fraction at higher risk for adverse renal events and indicate potential therapeutic targets.

REGISTRATION:

URL:
https://www.clinicaltrials.gov
; Unique identifier: NCT01920711.
Jews and Non-Jews in the Budapest Bar Association
Authors: Buchmüller Peter
Published: August 2024

Legal experts and lawyers in particular, had a uniquely important role in the process of state-building and modernization of the Habsburg Empire. After the collapse of the Monarchy, their contribution to the transition from a multinational empire to a nation-state was crucial, too. New states with different political contexts had various impacts on the profession itself and its inner life as well. Chambers were struggling to maintain their autonomy against the state, and at the same time, radically anti-Semitic ideologies were emerging within the profession and eventually came into power. This group of professionals could serve as an excellent proxy to study the complex process of social, political, and cultural developments of the elites from the imperial to interwar years and also to explore encounters between Jews and non-Jews and their changes. This paper aims to reexamine the success of the social integration of Jews in the legal profession, particularly among lawyers and the Budapest Bar Association.
The Philosophy of Chinese Moral Cultivation: Justification and Educational Implications
Authors: YOU Zhuran , RUD A.G.
Published: August 2024

This article discusses the theme of moral cultivation in ancient China and proposes to consider the relevant philosophical discussions a subfield of traditional Chinese philosophy. We argue that Chinese classics throughout history were freighted with frameworks and concepts concerning moral cultivation, contributing to a philosophical system about people’s daily moral development. The metaphysical justification, the interpenetration of what is beyond form and what is within form, ideas about the heart-mind and human nature further substantiate the claim from academic and cultural perspectives. This philosophy echoes Pierre Hadot’s idea of “philosophy as a way of life”, or an art of living, a pragmatic discipline that is both a meta-philosophy and a methodological approach in addressing pedagogical and psychological concerns (Hadot & Davidson, 1995). The justifications warrant the needs of perceiving traditional moral cultivation philosophically and discussing its educational implications for today.
Synoptic Influences on the Diurnal Cycle of Rainfall over Western Puerto Rico
Authors: Sakaeda Naoko , Martinez-Lopez Kevin , Wu Shun-Nan , Martin Elinor , Rios-Berrios Rosimar , Jimenez-González Héctor J. , Bedka Kristopher , Lambrigtsen Bjorn , Nehrir Amin , Wong Sun , Rodriguez Monje Raquel , Collins James , Barton-Grimley Rory , Schreier Mathias , Sy Ousmane O.
Published: August 2024

The objective of this study is to understand rainfall processes over tropical islands by identifying synoptic conditions that influence the diurnal cycle of rainfall over western Puerto Rico. Summer rainfall over Puerto Rico is dominated by its afternoon peak, yet there is large variability in its behavior that remains challenging to predict. We use radiosonde and airborne data collected through the NASA Convective Processes Experiment—Aerosols and Winds (CPEX-AW) field campaign (August–September 2021) to achieve our objective, in addition to the network of surface station data over the island. We find that the background wind speed and humidity have strong influences on afternoon rainfall through different mechanisms. A stronger background wind inhibits afternoon rainfall likely by reducing land–sea thermal contrast and weakening sea-breeze convergence over the island. At the same time, an inversion layer often forms with a stronger background wind that further inhibits deep convection. When the background wind is weak and sea breezes are prominent, afternoon rainfall increases exclusively over the island, while limited rainfall appears over the surrounding ocean. However, enhanced rainfall still occurs over the island with weak sea breezes if humidity is high, accompanied by enhanced rainfall over the surrounding ocean due to the offshore movement and development of convective storms. The sources of variability in background wind and humidity are mostly independent, resulting in a wide range of synoptic conditions and associated effects on the island rainfall. This expanded understanding of the mechanisms causing variability of diurnal rainfall can lead to improved forecasts over Puerto Rico and other tropical islands.
Consciousness Torn Asunder? Racial Elevation of “South” Koreans, US Camptown Prostitution, and K-Pop Girl Groups in the 1950s–60s
Authors: Kim Veda Hyunjin
Published: August 2024

This article is grounded in the racial elevation of a “South” Korean people (not Korean without “South” prefix) implicated in US imperialism and the intersectional implications of this ascent. Contributing a new application of Du Bois’s concept of “double consciousness” to the racial capitalism literature, I historically analyze a case in which the elevation of a distinct racial group status entailed (1) the subimperial adherence of the imperially subjected and (2) a postcolonial homoerotic triangle in which racialized men adhered to a hegemonic empire-state and co-constituted a structure of expropriation of racialized-sexualized women. During the 1950s–1960s, the two industries of camptown prostitution and early K-pop girl groups were inextricable. Those industries boomed around US military bases, the war zones where South Korean soldiers proved the “South” Korean race’s corporeal values. Due to its subimperial project of enrollment in US hegemony, the South Korean government actively maintained these industries. South Korean soldiers negotiated their racialized status by partaking in the postcolonial homoerotic triangle with US soldiers, consuming racialized-sexualized Korean women’s bodies. Against this backdrop of the intense sexualization of Korean women’s bodies, a couple of the early K-pop girl groups acquired fame in the mainland US.
A study on the effect mechanism of moisture content of the mixed material on denitrification in the sintering process
Authors: Zhao Xinfeng , Li Jie , Hao Xintao , Qi Xiwei , Yang Aimin , Wang Liya , Zhang Zunqian
Published: August 2024

The moisture content was a key factor affecting the permeability of the sintering granulation and the bed layer, and it promoted denitrification and combustion. This study investigated the influence of moisture content on denitrification, bed permeability, and the emissions of O2 and CO in the sintering process through industrial trials on-site. By analyzing sintering reaction mechanisms and SPSS data, and combining CHEMKIN simulation, the mechanism of moisture content on NO emissions during sintering was obtained. The results showed that the moisture content of the sintering mixture could affect the emission of NO in the sintering flue gas by influencing the permeability of the bed and the O2 and CO in the flue gas. As the moisture content of the mixture increased, the NO content in the sintering flue gas first decreased and then increased, while the permeability and CO content showed an initial increase followed by a decrease, and the volume fraction of O2 in the flue gas gradually decreased. Eight factors, including permeability index, O2 concentration, CO concentration, negative pressure, water vapor volume fraction, flue gas temperature, sintering trolley speed, and dust concentration, were significantly correlated with NO emissions, and a generalized linear model with NO emissions as the dependent variable was established. For a specific sintering industrial production, the moisture content of the sintering mixture determined the permeability. Apart from O2 concentration, permeability had the greatest impact and was negatively correlated with NO emissions. Water vapor in the flue gas could promote the CO-NO reaction, and the higher the water vapor volume fraction, the higher the reduction rate of NO, but excessively high levels were detrimental to permeability. Taking into account the permeability of the sintering bed, O2 and CO in the flue gas, the optimal moisture content (mass fraction) of the sintering mixture for denitrification during the sintering process ranged between 6.71% and 8.04%. In actual industrial sintering production, a water vapor volume fraction in the flue gas between 16% and 18% was most favorable for denitrification during the sintering process. These findings provided theoretical references for denitrification processes during sintering.
Why the political party that supports social welfare programs is advantaged in campaign fundraising
Authors: Mark Noah P
Published: August 2024

This study examines a collective action contest between two groups of charitably-minded, rational altruists. The groups are supporters of two political parties that oppose each other in their support for a redistributive tax and social welfare benefit. Analysis identifies an asymmetry in the incentive structure that creates a fundraising advantage for the pro-redistributive party. Big government altruists (BGAs) choose between contributing to charity (i.e., individual voluntary redistribution) and contributing to the political party that supports compulsory redistribution from all of the rich to the poor. Small government altruists (SGAs) choose between contributing to charity and contributing to the political party that tries to stop compulsory redistribution from all of the rich to the poor. In other words, while BGAs choose between individual voluntary redistribution and supporting universal compulsory redistribution, SGAs choose between individual voluntary redistribution and opposing universal compulsory redistribution. Because of this asymmetry, BGAs can advance their redistributive goals (as well as their political goals) via campaign contributing while SGAs cannot. This difference creates a fundraising advantage for the pro-redistributive party. Relative to the other group, BGAs optimize by giving more to party and less to charity and SGAs optimize by giving more to charity and less to party.
Interpretable Models for Near-real-time Prediction of Team Cognitive Workload in Complex Sociotechnical Environments Using Behavioral and Physiological Data
Authors: Naher Nurun , Amon Mary Jean , Fiore Stephen M.
Published: August 2024

This work develops interpretable models to predict near-real-time cognitive workload (CWL) in teams operating in complex environments. Existing approaches using neurological sensors are impractical for field use. Our approach integrates multimodal data from non-invasive behavioral and physiological sensors to robustly detect CWL changes. We apply multidimensional recurrence quantification analysis (MdRQA) with a novel pattern analysis extension to identify recurring multimodal signatures indicative of different CWL states. A multiparty dataset with fNIRS, behavioral, and physiological measures from teams performing a gamified search and rescue mission and individual resting state tasks were used. The findings indicate that the multimodal patterns derived from non-invasive measures were significantly associated with a neurological measure of CWL within 10s time slices. Moreover, the multimodal patterns were predictive of individual and team performance over-and-above the neurological measure of CWL. This can enable timely interventions by intelligent systems to optimally manage team CWL and enhance human-machine teaming in demanding environments.