Data Sources Research Articles

A Prerequisite for need of Harmonized Nutrient Reference Values & its Standardized Terminology for Better access of Nutritional Supplements Globally

Abstract: To reduce the risk of insufficient nutrient intake, nutrient reference values for humans are set at the average nutrient need level without a safety margin. The vast majority of nations and areas in the globe set nutritional intake guidelines for their citizens. Currently, no harmonized nutrient reference value has been published, as well as the development of standards cannot be achieved through the use of formalized frameworks or terminology. The study's objective was to create recommendations for appropriate and secure micronutrient intake ranges that could be used to inform dietary advice. As relevant to the study, we examined micronutrient data from food intake surveys from representative nations. The comparison of national standards illustrates that the level of reference values for nutrient for individual countries is dependent on variables such as methodology, data sources, physical activity exemptions, standard body weights, and age range. In the current study, iron, calcium, magnesium, vitamin A, B12, D and folate were identified as the most commonly under-consumed micronutrients. In this article, the level of six countries' standards are compared to the current reference values for nutrients. Further, numerous words for different dietary standards components are discussed, and a final set of harmonized reference nutrient intake for the dietary advice is provided.

Exploring the Relationship between Consumer Protection and Product Liability: Civil and Islamic Perspectives

Consumer protection is an aspect which aims to ensure that consumers are always given adequate protection from a legal perspective and also to increase the level of consumers’ trust in the quality of goods and services, which will, in turn, contribute to the economic growth of a nation. However, today, issues surrounding product liability cases are no longer uncommon, where consumers or third parties face the risk of suffering injury or property damage due to the existence of defects in a particular product. Thus, the main objective of this study is to analyze the relationship between consumer protection and product liability from both civil and Islamic perspectives. The type of research being carried out is doctrinal legal research. The data sources, including case law and statutory provision, are analyzed using content and critical analysis methods. The results show that according to civil and Islamic perspectives, consumer protection in the context of product liability plays a crucial role in conferring legal protection to consumers who are adversely affected by the use of particular products that pose a risk to health and safety. Further, the authors expect that the results of this study shall contribute new and significant knowledge by proving that the aspect of consumer protection is not only related to product liability issues from a civil perspective, but it is also can be evaluated from the Islamic perspective, which the earlier researchers have rarely discussed.

A decentralized federated learning-based cancer survival prediction method with privacy protection

BackgroundSurvival prediction is one of the crucial goals in precision medicine, as accurate survival assessment can aid physicians in selecting appropriate treatment for individual patients. To achieve this aim, extensive data must be utilized to train the prediction model and prevent overfitting. However, the collection of patient data for disease prediction is challenging due to potential variations in data sources across institutions and concerns regarding privacy and ownership issues in data sharing. To facilitate the integration of cancer data from different institutions without violating privacy laws, we developed a federated learning-based data integration framework called AdFed, which can be used to evaluate patients’ survival while considering the privacy protection problem by utilizing the decentralized federated learning technology and regularization method. ResultsAdFed was tested on different cancer datasets that contain the patients’ information from different institutions. The experimental results show that AdFed using distributed data can achieve better performance in cancer survival prediction (AUC=0.605) than the compared federated-learning-based methods (average AUC=0.554). Additionally, to assess the biological interpretability of our method, in the case study we list 10 identified genes related to liver cancer selected by AdFed, among which 5 genes have been proved by literature review. ConclusionsThe results indicate that AdFed outperforms better than other federated-learning-based methods, and the interpretable algorithm can select biologically significant genes and pathways while ensuring the confidentiality and integrity of data.

Antenatal care coverage and early childhood mortality in Zimbabwe: new interpretations from nationally representative household surveys.

Zimbabwe has implemented universal antenatal care (ANC) policies since 1980 that have significantly contributed to improvements in ANC access and early childhood mortality rates. However, Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), two of Zimbabwe's main sources of health data and evidence, often provide seemingly different estimates of ANC coverage and under-five mortality rates. This creates confusion that can result in disparate policies and practices, with potential negative impacts on mother and child health in Zimbabwe. We conducted a comparability analysis of multiple DHS and MICS datasets to enhance the understanding of point estimates, temporal changes, rural-urban differences and reliability of estimates of ANC coverage and neonatal, infant and under-five mortality rates (NMR, IMR and U5MR, separately) from 2009 to 2019 in Zimbabwe. Our two samples z-tests revealed that both DHS and MICS indicated significant increases in ANC coverage and declines in IMR and U5MR but only from 2009 to 2015. NMR neither increased nor declined from 2009 to 2019. Rural-urban differences were significant for ANC coverage (2009-15 only) but not for NMR, IMR and U5MR. We found that there is a need for more precise DHS and MICS estimates of urban ANC coverage and all estimates of NMR, IMR and U5MR, and that shorter recall periods provide more reliable estimates of ANC coverage in Zimbabwe. Our findings represent new interpretations and clearer insights into progress and gaps around ANC coverage and under-five mortality rates that can inform the development, implementation, monitoring and evaluation of policy and practice responses and further research in Zimbabwe.

Oculomics: A Crusade Against the Four Horsemen of Chronic Disease.

Chronic, non-communicable diseases present a major barrier to living a long and healthy life. In many cases, early diagnosis can facilitate prevention, monitoring, and treatment efforts, improving patient outcomes. There is therefore a critical need to make screening techniques as accessible, unintimidating, and cost-effective as possible. The association between ocular biomarkers and systemic health and disease (oculomics) presents an attractive opportunity for detection of systemic diseases, as ophthalmic techniques are often relatively low-cost, fast, and non-invasive. In this review, we highlight the key associations between structural biomarkers in the eye and the four globally leading causes of morbidity and mortality: cardiovascular disease, cancer, neurodegenerative disease, and metabolic disease. We observe that neurodegenerative disease is a particularly promising target for oculomics, with biomarkers detected in multiple ocular structures. Cardiovascular disease biomarkers are present in the choroid, retinal vasculature, and retinal nerve fiber layer, and metabolic disease biomarkers are present in the eyelid, tear fluid, lens, and retinal vasculature. In contrast, only the tear fluid emerged as a promising ocular target for the detection of cancer. The retina is a rich source of oculomics data, the analysis of which has been enhanced by artificial intelligence-based tools. Although not all biomarkers are disease-specific, limiting their current diagnostic utility, future oculomics research will likely benefit from combining data from various structures to improve specificity, as well as active design, development, and optimization of instruments that target specific disease signatures, thus facilitating differential diagnoses.

Consumption prediction with privacy concern: Application and evaluation of Federated Learning

In this paper, we propose privacy-friendly electricity consumption prediction models based on Federated Learning (FL). Federated Learning provides a novel framework for Artificial Neural Network training. It decouples data storage from model training from decentralized data sources. A central server does not need to collect raw training data from individual clients but only the model parameters, like the gradients or weights updates. The sensitive raw data can be safely stored and used by end-users themselves. We improve the accuracy of the FL model by first clustering households, and training a personalized model for each cluster. We also analyze the Deep Leakage from Gradients (DLG) attack in our study case with three scenarios. Our simulations suggest that the DLG attack can barely succeed in consumption prediction. In this way, the FL model can guarantee clients’ privacy by design. We use a large-scale dataset that contains 3590 households’ 1.5 years of consumption to test the FL model’s performance. Several clustering algorithms are tested for the following experiments. To comprehensively test the FL model’s performance, we propose several popular neural network models: the simple Deep Neural Network, Long Short-Term Memory, Convolutional Neural Network, and WaveNet. These models are both trained under the centralized and federated framework. The federated trained models slightly sacrifice the model’s accuracy while guaranteeing the client’s data privacy. Meanwhile, the federated model shows remarkable scalability. Under the FL framework, new clients can obtain their prediction 6 times faster. Moreover, the federated model has strong robustness against missing or damaged training data. With a certain percentage of missing data in the training set, the centralized model’s accuracy gets 27% worse, while the federated model’s accuracy only gets 2% worse.

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

Community Policing in Bangladesh: An Overview

Community policing is a proactive policing tactic that is employed by the police of Bangladesh. The primary principle of community policing is to create a harmonious communal life via joint efforts on the part of both the police and the community. The purpose of this paper is to analyze the role of community policing in Bangladesh in terms of its philosophy, based on secondary data sources. The data were described using descriptive and qualitative methodologies. The study found that many different methods of community policing have been applied in Bangladesh, to date, but that, in its contemporary form, community policing was first deployed in 1990 in Bangladesh’s Mymensing district. Currently, community policing is being practiced in many districts throughout Bangladesh, without any reference to the philosophical aspects of community policing, such as collaboration, organizational reform, and problem-solving. To secure the safety of the community, as well as to maintain order with in, the philosophical component of community policing in Bangladesh should be examined.

A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets

Integrating field rainfall data with satellite data improves data accuracy and overcomes rainfall data limitations for rain thresholds. Integration can involve field rainfall data, satellite rainfall data, or a different satellite dataset. Merging these rainfall data sources provides more spatial coverage of satellite data. To determine how well rainfall thresholds predict rainfall-triggered landslides, the threshold model must be validated. This study will evaluate satellite rainfall data before and after integration in developing a rainfall threshold model for landslide prediction in Badung Regency. To do so, the study used a cumulative rainfall threshold over 3, 7, 15, and 30 days and two rainfall satellite products (integrated merged multi-satellite retrievals (IMERG) and precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)). Median, first, and third quartiles were used to set thresholds. The area under the curve (AUC) was calculated to validate rainfall threshold outcomes using receiver operating characteristic (ROC) curves. Analysis showed that integrating satellite rainfall data into the rainfall threshold model for landslide prediction yields better results than other methods. An AUC value of 0.903 (90.3%) for the 30-day cumulative rainfall thresholds supports this claim. This model could be a good input for a landslide early warning system in Badung Regency.

A Bayesian hierarchical small area population model accounting for data source specific methodologies from American Community Survey, Population Estimates Program, and Decennial census data

A Bayesian hierarchical small area population model accounting for data source specific methodologies from American Community Survey, Population Estimates Program, and Decennial census data

Analyzing electroencephalograph signals for early Alzheimer’s disease detection: deep learning vs. traditional machine learning approaches

Alzheimer’s disease (AD) stands as a progressive neurodegenerative disorder with a significant global public health impact. It is imperative to establish early and accurate diagnoses of AD to facilitate effective interventions and treatments. Recent years have witnessed the emergence of machine learning (ML) and deep learning (DL) techniques, displaying promise in various medical domains, including AD diagnosis. This study undertakes a comprehensive contrast between conventional machine learning methods and advanced deep learning strategies for early AD diagnosis. Conventional ML algorithms like support vector machines, decision trees, and K nearest neighbor have been extensively employed for AD diagnosis through relevant feature extraction from heterogeneous data sources. Conversely, deep learning techniques such as multilayer perceptron (MLP) and convolutional neural networks (CNNs) have demonstrated exceptional aptitude in autonomously uncovering intricate patterns and representations from unprocessed data like EEG data. The findings reveal that while traditional ML methods may perform adequately with limited data, deep learning techniques excel when ample data is available, showcasing their potential for early and precise AD diagnosis. In conclusion, this research paper contributes to the ongoing discourse surrounding the choice of appropriate methodologies for early Alzheimer’s disease diagnosis.

Proactive System Maintenance: Machine Learning Models for Predicting and Preventing Potential System Failures

Abstract: In modern industrial systems, the prevention of failures and downtime is of paramount importance for ensuring efficiency and productivity. Proactive system maintenance approaches leverage machine learning (ML) models to predict potential failures before they occur, enabling pre-emptive actions to be taken [1]. In this paper, we present a comprehensive review of existing research on proactive system maintenance, focusing on the development and application of ML algorithms for fault prediction and prevention. We discuss various machine learning techniques, data sources, feature engineering methods, and evaluation metrics employed in this domain [8]. Furthermore, we propose novel algorithms and strategies for enhancing the effectiveness of proactive maintenance systems. Through experimentation and case studies, we demonstrate the feasibility and benefits of utilizing machine learning for proactive maintenance in diverse industrial settings.

A Comprehensive Analysis of various Covid-19 Detection and Pandemic Prevention Systems and Methods Using Data Science

Abstract: The paper proposes a technique for early Covid-19 test prediction using AI and data science. The study analyzes various data sources to understand the patterns and trends of the pandemic and the impact of various interventions on the spread of the virus. The proposed system integrates data from multiple sources to identify potential cases and can also track the spread of the virus in real-time. The system has high accuracy in predicting positive cases and has the potential to improve our ability to detect and predict Covid-19 cases, ultimately saving lives and reducing the economic impact of the pandemic. The proposed technique for early Covid-19 test prediction using AI and data science can be a game- changer in our fight against the pandemic. With the integration of multiple data sources, including individual health records, social media, and geographic data, the system can identify potential cases even before symptoms appear. This early detection can help individuals get timely medical attention and prevent the spread of the virus to others.

MAAS - Monitoring as a Service through Payment Gateway

Abstract: This paper describes the creation of a cloud-based Monitoring as a Service (MaaS) platform designed specifically for thorough and safe payment gateway monitoring. Scalability, strong security, and real-time transaction insights are provided by the MaaS project through the use of the ELK Stack (Elasticsearch, Logstash, Kibana). Flexibility in monitoring modules allows for the smooth integration of the ELK Stack for effective analysis and display of vital payment gateway data. An interface that is easy to use enables operators to keep an eye on transactions and spot possible problems. Further integration with internal security and fraud detection tools is made easier by thoroughly described APIs. The goal of this MaaS platform is to improve oversight capabilities for crucial payment gateway operations by providing a consolidated, secure monitoring solution. Future development will involve adding machine learning for anomaly detection and extending data source support to cover other payment channels.

Exploring Sentiment Analysis across Text, Audio, and Video: A Comprehensive Approach and Future Directions

Abstract: This study presents a comprehensive exploration of sentiment analysis techniques across text, audio, and video modalities. Leveraging natural language processing (NLP), speech recognition, and computer vision algorithms, the research demonstrates the versatility and adaptability of sentiment analysis across diverse data sources. The necessity of such an approach lies in its ability to provide deeper insights into user emotions and opinions expressed in various mediums, including written text, spoken language, and visual content. Moreover, the study highlights the importance of sentiment analysis in understanding customer feedback, market trends, social media sentiments, and sentiment-aware recommendation systems. Future directions include advancing algorithmic accuracy and efficiency, integrating multimodal fusion techniques, and exploring applications in diverse domains, thereby paving the way for enhanced sentiment analysis capabilities and broader realworld applications

A Retrospective Database Analysis to Investigate if Electrolytes in Venous Blood are Equivalent to the Levels in Arterial Blood.

In a critically ill patient, when an arterial blood sample is processed on an arterial blood gas (ABG) analyzer, it also measures electrolytes apart from analyzing the blood gases. The turnaround time for ABG analysis is way too less compared to the conventional electrolyte analysis with a serum sample. This study intends to investigate whether values of electrolytes estimated in arterial blood can substitute the routinely practiced method. This is a retrospective cross-sectional study. The source of data is patients' reports of serum electrolytes and ABG analysis from the Clinical Biochemistry laboratory, CIMS Teaching Hospital, Chamarajanagar between January and June 2021. The electrolytes report of 200 patients from whom both arterial and venous blood samples were sent to the Clinical Biochemistry laboratory on the same day and at the same time for analysis were selected. The data was compiled, compared, and correlated using a suitable statistical tool. The mean and standard deviation of sodium (135.62 ± 5.20 in venous vs 134.08 ± 8.49 in arterial blood), potassium (4.20 ± 0.64 vs 3.80 ± 0.75), and chloride (102.28 ± 4.99 vs 96.33 ± 8.11) were observed. However, when the concordance correlation coefficient and Bland-Altman plot analysis were made there was no agreement between electrolytes analyzed on serum in an autoanalyzer with that of ABG analyzer. We conclude that the electrolytes measured by a conventional autoanalyzer on a serum sample cannot be replaced by values analyzed on a blood gas analyzer. Devaki RN, Kasargod P, Roopa Urs AN, Chandrika N. A Retrospective Database Analysis to Investigate if Electrolytes in Venous Blood are Equivalent to the Levels in Arterial Blood. Indian J Crit Care Med 2024;28(5):442-446.

Culture and local wisdom of the lampung indigenous people on the Pesisir Barat in the utilization of the Repong Damar Agroforestry System

Forest resources have a very important role in people's lives, both directly and indirectly. Agroforestry can be an interesting example in the production of timber and non-timber forest products. Agroforestry is an intensive land management system that combines forestry plants and agricultural plants to achieve optimal results, while still paying attention to aspects of land conservation and carrying out cultivation in accordance with the needs of local communities. Indigenous communities on the Pesisir Barat inherit the values of Repong Damar through unique management, which is based on local wisdom of the local area. This research was conducted using a qualitative approach using the research desk method. This study approach seeks to explore data from various sources as secondary data so that the main source of research data is obtained using the research desk method or Secondary Data Analysis (SDA) with qualitative data. Indigenous communities in Pesisir Barat Regency manage Repong Damar by applying traditional values, namely regulated through customary law, inheritance of Repong Damar, and values in managing Repong Damar as a guideline for not destroying forests and continuing to manage them properly. Therefore, granting utilization permits, collaborative implementation, partnerships, and cooperation in empowering indigenous communities through local culture and wisdom in utilizing agroforestry systems carried out around the Repong Damar customary forest can help maintain sustainability and the ecosystem within it.

Advancements and characteristics of scholar profiling research in China

This study analyzes the development and characteristics of scholar profiling research in China from 2016 to 2022, utilizing content analysis of 38 core journal articles. Scholar profiling, adapted from user profiling in big data, involves extracting and analyzing academic data to build models that describe scholars' characteristics and behaviors. Our findings highlight the use of diverse data sources, including commercial databases and social platforms, and emphasize the importance of data preprocessing and modeling techniques. While applied research dominates, the field faces challenges such as the lack of standardized data collection and depth in model construction. The study underscores the need for robust profiling tools and broader application scenarios to enhance the effectiveness of scholar profiling systems.

The Effectiveness of Hammock Positioning in Regulating Behavioral Patterns in Preterm: A Systematic Review

Background- Preterm newborns benefit from supportive developmental techniques such as hammock placement, which give them a sense of being in the womb that promotes calm, relaxation, and deep restful sleep. Objective- To identify and synthesize the available evidence on the effect of hammock positioning in regulating behavioral patterns and sleep in preterm infants admitted to the NICU Data Sources: We searched the Google Scholar, Pubmed, and Scopus databases for reports of primary research studies using a three-step strategy. We also searched for and reviewed the reference lists of retrieved articles. Methods: We included data from randomized controlled trials, Quasi-experimental and Comparative study measuring the effects of hammock positioning on physiological stress outcomes, defined as oxygen saturation, body temperature, heart rate and respiratory rate, Sleep-wakefulness, and behavioral pattern. The PRISMA model was used to conduct data extraction. We performed a narrative synthesis of all studies when data were available from multiple studies. Results: Five studies were eligible for inclusion. Infants who received hammock positioning has shown to have a higher mean heart rate, oxygen saturation and temperature, Sleep-wakefulness and improved behavioral patterns. Conclusions: Current evidence suggests that hammock positioning in the neonatal intensive care unit setting is a safe method that may significantly affect some of the physiological parameters of stress, improved behavioral patterns and Sleep-wakefulness in preterm infants. However, due to clinical heterogeneity, further studies are needed to assess the effects of physiological stress in the neonatal intensive care unit on the development of preterm infants.

Ultraconserved elements from transcriptome and genome dataprovide insight into the phylogenomics of Sternorrhyncha (Insecta: Hemiptera).

Sternorrhyncha, one of the four major suborders of Hemiptera, is a phytophagous taxon inclusive of nearly 18 000 described species. The phylogenetic relationships within the taxon and the earliest-branching lineage of its infraorders remain incompletely understood. This study attempted to illuminate the phylogenetic relationships within Sternorrhyncha through the use of maximum likelihood, Bayesian inference and maximum parsimony analyses, employing ultraconserved element (UCE) data from 39 genomic and 62 transcriptomic datasets and thereby representing most families within the taxon. The probe set Hemiptera 2.7Kv1 was used to recover a total of 2731 UCE loci: from 547 to 1699 (with an average of 1084) across all genomic datasets and from 108 to 849 (with an average of 329) across all transcriptomic datasets. All three types of phylogenetic analyses employed in this study produced robust statistical support for Sternorrhyncha being a monophyletic group. The different methods of phylogenetic analysis produced inconsistent descriptions of topological structure at the infraorder level: while maximum likelihood and Bayesian inference analyses produced strong statistical evidence (100%) indicating the clade Psylloidea + Aleyrodoidea to be a sister of the clade Aphidoidea (Aphidomorpha) + Coccoidea (Coccomorpha), the maximum parsimony analysis failed to recover a similar result. Our results also provide detail on the phylogenetic relationships within each infraorder. This study presents the first use of UCE data to investigate the phylogeny of Sternorrhyncha. It also shows the viability of amalgamating genomic and transcriptomic data in studies of phylogenetic relationships, potentially highlighting a resource-efficient approach for future inquiries into diverse taxa through the integration of varied data sources.