bluuebunny/crossref_metadata_2025_split huggingface dataset:
Generation and characterization of human induced pluripotent stem cell lines from patients with autism spectrum disorder and SCN2A variants
Authors: Santos John Lenon de Souza , Paredes Bruno Diaz , Adanho Corynne Stephanie Ahouefa , Nonaka Carolina Kymie Vasques , Silva Kátia Nunes da , Santos Ian Marinho , Rocha Clarissa Araújo Gurgel , Souza Bruno Solano de Freitas 
Published: July 2024

Abstract

Autism spectrum disorders (ASD) comprise a group of complex neurodevelopmental disorders that affect communication and social interactions. Over a thousand genes have been associated with ASD, with SCN2A standing out due to its critical role in neuronal function and development. Induced pluripotent stem cells (iPSCs) derived from individuals with ASD have become invaluable in vitro models for investigating the cellular and molecular mechanisms underlying the disorder. In this study, we generated and characterized four iPSC clones from peripheral blood mononuclear cells (PBMCs) of two ASD patients carrying loss-of-function variants in the SCN2A gene. These iPSC lines underwent comprehensive characterization through multiple assays. Reverse transcription polymerase chain reaction (RT-PCR), flow cytometry, and immunofluorescence analyses confirmed the presence of pluripotency markers. An embryoid body formation assay demonstrated their potential to differentiate into the three germ layers. Sequencing analysis confirmed the SCN2A variants, while short tandem repeat (STR) analysis authenticated the cell lines, and karyotype analysis ensured chromosomal integrity. The iPSCs exhibited typical morphological characteristics, including large nuclei with prominent nucleoli, a high nucleus-to-cytoplasm ratio, densely packed cells, and well-defined borders. These cells maintained pluripotency markers, demonstrated the ability to differentiate into the three germ layers, and showed a normal karyotype. Furthermore, we successfully generated cerebral organoids from these cells. Our study establishes a robust platform for further exploration of the pathophysiological mechanisms of ASD, particularly those involving SCN2A.
Diversity-aware Population Models: Quantifying Associations between Socio-Spatial Factors and Cognitive Development in the ABCD Cohort
Authors: Osayande Nicole , Marotta Justin , Aggarwal Shambhavi , Kopal Jakub , Holmes Avram , Yip Sarah , Bzdok Danilo 
Published: July 2024

Abstract

Population-level analyses are inherently complex due to a myriad of latent confounding effects that underlie the interdisciplinary topics of research interest. Despite the mounting demand for generative population models, the limited generalizability to underrepresented groups hinders their widespread adoption in downstream applications. Interpretability and reliability are essential for clinicians and policymakers, while accuracy and precision are prioritized from an engineering standpoint. Thus, in domains such as population neuroscience, the challenge lies in determining a suitable approach to model population data effectively. Notably, the traditional strata-agnostic nature of existing methods in this field reveals a pertinent gap in quantitative techniques that directly capture major sources of population stratification. The emergence of population-scale cohorts, like the Adolescent Brain Cognitive Development℠ (ABCD) Study, provides unparalleled opportunities to explore and characterize neurobehavioral and sociodemographic relationships comprehensively. We propose diversity-aware population modeling, a framework poised to standardize systematic incorporation of diverse attributes, structured with respect to intrinsic population stratification to obtain holistic insights. Here, we leverage Bayesian multilevel regression and poststratification, to elucidate inter-individual differences in the relationships between socioeconomic status (SES) and cognitive development. We constructed 14 varying-intercepts and varying-slopes models to investigate 3 cognitive phenotypes and 5 sociodemographic variables (SDV), across 17 US states and 5 race subgroups. SDVs exhibited systemic socio-spatial effects that served as fundamental drivers of variation in cognitive outcomes. Low SES was disproportionately associated with cognitive development among Black and Hispanic children, while high SES was a robust predictor of cognitive development only among White and Asian children, consistent with the minorities’ diminished returns (MDRs) theory. Notably, adversity-susceptible subgroups demonstrated an expressive association with fluid cognition compared to crystallized cognition. Poststratification proved effective in correcting group attribution biases, particularly in Pennsylvania, highlighting sampling discrepancies in US states with the highest percentage of marginalized participants in the ABCD Study©. Our collective analyses underscore the inextricable link between race and geographic location within the US. We emphasize the importance of diversity-aware population models that consider the intersectional composition of society to derive precise and interpretable insights across applicable domains.
A scoping review to identify opportunities and challenges for communities of South Asian (SA) origin in accessing mental health services and support in high- income countries
Authors: Menon Gayathri , Sarma Haribondhu , Bestman Amy , O'Callaghan Cathy , Yadav Uday Narayan 
Published: July 2024

Abstract

Introduction:
Communities of South Asian (SA) origin in high-income countries experience the highest prevalence of mental health (MH) disorders amongst culturally and linguistically diverse (CALD) community groups. Emerging evidence highlights community experiences of socio-cultural and systemic challenges to MH care access; however, there is no comprehensive qualitative evidence regarding these opportunities and challenges. To address this knowledge gap, this review aimed at exploring the lived experiences of SA CALD communities through opportunities and challenges experienced in accessing MH care across four high-income nations (the United Kingdom, Australia, the United States, and Canada).
Methods
This scoping review followed reporting guidelines for scoping reviews outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A search was conducted to identify studies published in English between 2000- January 2023 in three electronic databases: PubMed, Web of Science, and Scopus. The search strategy included keywords related to SA CALD communities, MH and access to health care in four high-income countries. Extracted qualitative data were analysed using a thematic analysis approach.
Results
Analysis of the data from 25 eligible studies across all four high-income identified interlinked opportunities and challenges informing their ability to access MH care that supports their needs. Findings have been structured according to the five dimensions of accessibility of services (1) approachability, 2) acceptability, 3) availability and accommodation, 4) affordability, and 5) appropriateness). Our study identified myriad challenges faced by SA CALD communities in four high-income nations, including inflexible accessibility to diverse MH care, difficult family dynamics, stigma and acculturative issues hindering MH care usage and poor MH care experiences informing perceptions of MH care and re-engagement.
Conclusion
The research finds that access to MH care for CALD communities requires a comprehensive and nuanced framework that addresses population-specific factors. Recommendations include increasing cultural safety, facilitating flexible models of care and service delivery in practice alongside policy and funding reflective of systemic MH care access issues, and implementation research to assess the effectiveness of such recommendations.
Integrated Biophysical and Biochemical Analyses for Profiling Lipids and Carotenoids in Rhodotorula mucilaginosa
Authors: Gedela Ravi , Dasu Prof. Veeranki Venkata , Pakshirajan Prof. Kannan 
Published: July 2024

Abstract

The oleaginous yeast species Rhodotorula mucilaginosa is distinguished by its genetic, ecological, physiological, and morphological variety. The biotechnological potential of R. mucilaginosa, which was isolated from petroleum oil refinery resources in Guwahati, Assam, India, specifically for the potential producing of lipids, carotenoids, and β-carotene were examined. In this study to describe carotenoid pigments in R. mucilaginosa, determine targets for metabolic products, and comprehend the precise lipid composition profile. R. mucilaginosa alive cells were analysed using a variety of biochemical and biophysical techniques, such as gas chromatography for lipid and thin-layer chromatography for neutral lipid composition profiling, Raman spectroscopy for lipid and carotenoid analysis, Field Effect Scanning Electron Microscopy (FESEM) for lipid visualization, and fluorescence spectroscopy for lipid detection using the Nile red assay. The results of these tests offer a thorough understanding of R. mucilaginosa's capacity to produce carotenoid and lipids. This precise identification of oleaginous yeast R. mucilaginosa needed for the commercial production of carotenoids, high-value biofuel, and value-added merchandises. Raman spectroscopy, fluorescence spectroscopy, FESEM, TLC, and LC-MS provided valuable insights into the lipid and carotenoid production capabilities of R. mucilaginosa. Moreover, any of these analytical techniques can be used to confirm the presence of lipids and carotenoids in yeast species. These comprehensive results help reduce the time required for selecting new oleaginous yeast species.
Drowsiness Detection for Drivers Using Convolutional Neural Networks (CNNs)
Authors: Ali  
Published: July 2024

Abstract

Drowsy driving is a huge risk to road safety, resulting in serious accidents. Drowsiness detection in drivers can assist prevent accidents by providing timely alarms and actions. In this research, we look at how deep learning Convolutional Neural Networks (CNNs) can be used to identify tiredness. We discuss our technique, experimental results, and conclusions about the efficacy of deep learning CNNs in solving this essential road safety issue. In this work, we employed two convolutional neural networks, one is built from scratch and another one that is a pre-trained model (ResNet). Our findings show that the pre-trained outperformed the one built from scratch achieving an accuracy of 95.6%
Unique Binary representation of Bases and Symmetry in The Standard Genetic Code
Authors: Mohanty Bijan Bihari 
Published: July 2024

Abstract

As a sequel to the extraction of unique two-bit binary codes of four nucleotide bases Adenine(A), Guanine(G), Uracil / Thymine(U/T) and Cytosine(C) by the author [1] and fixation of numerical values of all the 64 codons of L- amino acids(Appendix-1a), various numerical symmetries are discovered amongst codons and codon blocks in the Standard Genetic Code(SGC) table for the first time and their implication are studied. Also , 20 canonical amino acids are assigned specific numerical values based on an integrated formula where the only variable is the integer value ranging from 1 to 16 which is the total number of cells in Genetic code table. Consequently, the AA can be converted to 8-bit binaries. Rumer transform is applied on the codons and polar – nonpolar bilateral numerical symmetry spontaneously emerges.
A Comprehensive Review of Intelligent Navigation of Mobile Robots Using Reinforcement Learning with A Comparative Analysis of a modified Q-Learning Method and DQN in Simulated Gym Environment
Authors: Khlif Nessrine 
Published: July 2024

Abstract

Purpose: The field of autonomous mobile robots (AMRs) has experienced significant growth in recent years, propelled by advancements in autonomous driving and unmanned aerial vehicles (UAVs). The integration of intelligence into robotic systems necessitates addressing various research challenges, with naviga- tion emerging as a pivotal aspect of mobile robotics. This paper explores the three fundamental questions central to the navigation problem: localization (determin- ing the robot’s position), mapping (creating a representation of the environment), and path planning (determining the optimal route to the destination). The pro- posed solution to the mobile robot navigation problem involves the seamless integration of these three foundational navigation components.
Methods: Our comparative analysis between the Q-learning modified method and a deep Q-network (DQN) in simulated gym pathfinding tasks reveals the efficacy of this approach. The modified Q-learning algorithm consistently outperforms DQN, demonstrating its superior ability to navigate complex environments and achieve optimal solutions. The transition from a definite environment to a simulated gym environment serves as a valuable validation of the method’s applicability in real-world scenarios. By rigorously evaluating our algorithm in a controlled setting, we can ensure its robustness and effectiveness across a broader range of applications.
Results: In essence, our study establishes the modified Q-learning algorithm as a promising new approach to addressing the exploration-exploitation dilemma in reinforcement learning. Its superior performance in simulated gym environments suggests its potential for real-world applications in various domains, including robotics, autonomous navigation, and game development.
Conclusion: The paper furnishes a comprehensive overview of research on autonomous mobile robot navigation. It begins with a succinct introduction to the diverse facets of navigation, followed by an examination of the roles of machine learning and reinforcement learning in the realm of mobile robotics. Subsequently, the paper delves into various path planning techniques. In the end, this paper presents a comparative analysis of two path planning methods for mobile robots: Q-learning with an enhanced exploration strategy and Deep Q-Network (DQN). Through a comprehensive simulation study in a gym environment, the superior performance of the proposed Q-learning approach is firmly established.
Gini calculation and rule performance interpretation
Authors: Badki Meenal 
Published: July 2024

Abstract

The Gini coefficient is a widely used measure of income inequality within a population. This paper investigates the application of the Gini coefficient concept to calculate fraud risk based transaction amount losses. The paper is essentially divided into two parts: (1) Interpretation and implementation of Lorenz curve in calculating fraud risk in a real-time scenario for online e-commerce merchants/customers for various industries. Implementing Gini is useful for verifying fraud-based metrics or saving fraud losses after a machine learning model has been implemented and deployed in production. (2) The rules automation system is helpful in optimizing and improving strategies required for deployment on a real-time transaction based platform in the financial services world where the real-time decisioning system for a customer on board plays an important role. The study concludes that Gini coefficient remains a robust tool for evaluating risk based losses, thus helping financial institutions make informed strategy-driven decisions, and further also explores the business rule sets that help to optimize the strategic decisions.
Inventory management through Economic Order Quantity Model:A Bibliometric and Scientometric analysis
Authors: Dey Agniva , Ray Partha , Mallik Ajoy , Seal Somen 
Published: July 2024

Abstract

Inventory management employs the economic order quantity (EOQ) model and the economic production quantity (EPQ) model to determine the optimal order quantities for purchasing and manufacturing. The developed EOQ model was predicated on the constants of demand rate, holding cost, purchasing cost, and ordering cost.The EPQ model is predicated on the same assumptions as the EOQ model, namely constant holding costs and constant demand. This study aims to identify the citation impact of the most productive countries and journals based on the number of publications on inventory models in a fuzzy environment. About 3510 publications were obtained from the Web of Science between 2004 and 2023. In addition, this study uses “co-word cluster network analysis” to determine how this topic has evolved thematically over time. Additionally, this study used a thematic evolution map, a keywords overlapping map, and strategic maps to quantify and visualise the distribution and impact of this topic’s research fronts at successive periods.
Glycine-modified Pencil Graphite Electrode as a Sensor for Caffeine Determination: A Voltammetric Study
Authors: Korkmaz Şeyma , Bosnali Wael , Sharifi Hafizullah , Mülazımoğlu İbrahim Ender , Mülazımoğlu Ayşen Demir 
Published: July 2024

Background:
Caffeine (CAF) is a widely used chemical in foods and pharmaceuticals that
is consumed by a lot of people every day. Overconsumption of caffeine can lead to some undesirable
symptoms. However, CAF is considered a crucial nervous system stimulant.

Aims:
This study aimed to find an accurate and effective route for determining caffeine using voltammetric
techniques.

Objective:
In this work, for the first time, a glycine (Gly) modified pencil graphite electrode
(Gly/PGE) was used for the determination of caffeine (CAF) by Square Wave Adsorptive Stripping
Voltammetry (SWAdSV) technique.

Methods:
The modification procedure of PGE by Gly was accomplished by performing the cyclic
voltammetry (CV) technique for 10 cycles at a potential range of 0.8 to 1.8 V. Moreover, the characterization
of Gly/PGE was done by applying the CV technique in aqueous and non-aqueous media
and electrochemical impedance spectroscopy (EIS) in addition to the SEM technique. The movement
type of CAF toward Gly/PGE was proven to be a diffusion-controlled process by conducting some
measurements at different scan rates. The supporting electrolyte for the determination of CAF was
studied, where 100 mM of sulfuric acid (H2SO4) provided the best result. Optimization of accumulation
time, pulse size, and wave frequency were implemented and found to be 60 s accumulation time,
50 mV pulse size, and 25 Hz wave frequency. The stability of Gly/PGE was studied in different mediums,
such as air, ultrapure water, and acetonitrile (CH3CN).

Results:
The determination of CAF under optimum conditions was within the linear concentration
range of 0.1–75 μM, with a correlation coefficient of R2 = 0.9990. The limit of detection (LOD) and
limit of quantification (LOQ) were 0.070 and 0.231 μM, respectively.

Conclusion:
An effective voltammetric methodology was suggested to determine CAF. This methodology
has great promise to be applied in different matrices to determine CAF.
РАК ЩИТОПОДІНОЇ ЗАЛОЗИ: СУЧАСНА КЛАСИФІКАЦІЯ, НАПРЯМКИ МОРФОЛОГІЧНОЇ ДІАГНОСТИКИ, НАНОТЕХНОЛОГІЇ
Authors: Кіндратів Ельвіра , Гурик Зоряна , Чуйко Наталія 
Published: July 2024

Вивчення проблеми раку щитоподіної залози (РЩЗ) і розробка ефективних методів його діагностики й лікування є одним із пріоритетних напрямів сучасної ендокринної хірургії, ендокринології, онкології та патологічної анатомії. Проаналізувати структуру злоякісних пухлин щитоподібної залози, сучасні аспекти їх морфологічної діагностики та можливості застосування нанотехнологій. Для досягнення мети на базі ПАВ КНП «Обласна клінічна лікарня Івано-Франківської обласної ради» опрацьовано 804 направлення на гістологічне дослідження щитовидної залози (ЩЗ) хворих на різні захворювання ЩЗ в період з 2018 по 2022 рр. Проведений аналіз вітчизняних та закордонних наукових праць щодо сучасних методів діагностики РЩЗ. Результати проведеного дослідження вказують на те, що злоякісними новоутвореннями щитоподібної залози частіше хворіють жінки у віковому діапазоні від 40 до 65 років, середній вік 52,89 років. Серед новоутворень найбільша частка припадає на папілярну карциному, яка спостерігається 82,7% на фоні хронічного тиреоїдиту. Важливим у ранній діагностиці та визначені гістологічного варіанту РЩЗ є дослідження молекулярних біомаркерів за допомогою молекулярних методів, таких як полімеразна ланцюгова реакція (ПЛР), секвенування геному, мас-спектрометрія і інші. Найважливішими молекулярними маркерами РЩЗ є визначення мутаціїї генів RET, BRAF і RAS. Застосування нанотехнологій у діагностиці РЩЗ може покращити можливості виявлення цього захворювання на ранніх стадіях та забезпечити більш точне та ефективне лікування. Однак важливо зазначити, що багато з цих технологій все ще перебувають у стадії досліджень, і їх клінічне впровадження може зайняти час. Використання нанотехнологій у біопсії може сприяти підвищенню точності та ефективності цього процесу, а також зменшити ризики ускладнень для пацієнтів. Однак, перед впровадженням таких технологій в клінічну практику, потрібні додаткові дослідження та клінічні випробовування для оцінки безпеки і ефективності.
A Machine Learning Model for Predicting In-Hospital Mortality in Chinese Patients With ST-Segment Elevation Myocardial Infarction: Findings From the China Myocardial Infarction Registry
Authors: Yang Jingang , Li Yingxue , Li Xiang , Tao Shuiying , Zhang Yuan , Chen Tiange , Xie Guotong , Xu Haiyan , Gao Xiaojin , Yang Yuejin 
Published: July 2024

Background
Machine learning (ML) risk prediction models, although much more accurate than traditional statistical methods, are inconvenient to use in clinical practice due to their nontransparency and requirement of a large number of input variables.

Objective
We aimed to develop a precise, explainable, and flexible ML model to predict the risk of in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI).

Methods
This study recruited 18,744 patients enrolled in the 2013 China Acute Myocardial Infarction (CAMI) registry and 12,018 patients from the China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE)-Retrospective Acute Myocardial Infarction Study. The Extreme Gradient Boosting (XGBoost) model was derived from 9616 patients in the CAMI registry (2014, 89 variables) with 5-fold cross-validation and validated on both the 9125 patients in the CAMI registry (89 variables) and the independent China PEACE cohort (10 variables). The Shapley Additive Explanations (SHAP) approach was employed to interpret the complex relationships embedded in the proposed model.

Results
In the XGBoost model for predicting all-cause in-hospital mortality, the variables with the top 8 most important scores were age, left ventricular ejection fraction, Killip class, heart rate, creatinine, blood glucose, white blood cell count, and use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). The area under the curve (AUC) on the CAMI validation set was 0.896 (95% CI 0.884-0.909), significantly higher than the previous models. The AUC for the Global Registry of Acute Coronary Events (GRACE) model was 0.809 (95% CI 0.790-0.828), and for the TIMI model, it was 0.782 (95% CI 0.763-0.800). Despite the China PEACE validation set only having 10 available variables, the AUC reached 0.840 (0.829-0.852), showing a substantial improvement to the GRACE (0.762, 95% CI 0.748-0.776) and TIMI (0.789, 95% CI 0.776-0.803) scores. Several novel and nonlinear relationships were discovered between patients’ characteristics and in-hospital mortality, including a U-shape pattern of high-density lipoprotein cholesterol (HDL-C).

Conclusions
The proposed ML risk prediction model was highly accurate in predicting in-hospital mortality. Its flexible and explainable characteristics make the model convenient to use in clinical practice and could help guide patient management.

Trial Registration
ClinicalTrials.gov NCT01874691; https://clinicaltrials.gov/study/NCT01874691