Scientific Data Research Articles

Tech Business Analytics in Quaternary Industry Sector

Purpose: The knowledge-based segment of the economy is referred to as the "quaternary sector," which comprises businesses like information technology, telecommunications, research and development, and other professional services. Businesses in this industry may find that technology-driven business analytics greatly aids in helping them to make data-driven decisions, optimize workflows, and enhance overall performance. Utilizing technology to analyse business analytics can significantly improve market trends, consumer behaviour, and an organization's operational performance. Through analysis of this data, companies can make more informed decisions that support expansion and competitiveness. Analytics tools assist companies in identifying inefficiencies in their processes and operations so they can make changes that reduce expenses, boost output, and ultimately boost revenue. Customer loyalty and satisfaction may rise as a result of this. Information regarding emerging technologies and their integration with data science and business analytics may support to prediction of market trends and could present companies with chances for growth and innovation. Methodology: There are particular potential and challenges for business analytics in the Quaternary industry sector because of its emphasis on knowledge-based activities, innovation, and cutting-edge technology. Here, we present a methodical strategy for using technology for analytics in this industry, allowing businesses to obtain useful information for long-term planning calculations. This approach gives businesses in the Quaternary industry sector a methodical framework for utilizing technology-driven analytics. This framework helps them obtain competitive advantages in an increasingly data-driven business environment by helping them access important insights and spur innovation. Findings/Result: The study looks at how digital business analytics have been used to control growth in the Quaternary sector from the birth of the industry to the present. Originality/Value: An explanation of how tech business analytics differs from traditional business analytics within the Quaternary industry. It also includes a general design that can be used for technical purposes, and it examines thirty recently submitted research recommendations related to Tech Business Analytics in Quaternary industries. Paper Type: Exploratory research.

Consequence relations and data science: From Galois mappings to data interpretation

The concepts of a consequence relation and operation, though very abstract and theoretical, may be related to specific categories of information systems (i.e. mathematical frontends of data tables); as it has been demonstrated by D. Vakarelov, there exist correspondence between Pawlak information systems and Scott as well as Tarski consequence operations. This line of research goes (via representation) from abstract concepts to data. In this paper we would like to take the opposite direction: from data (via construction) to consequence relations. The main emphasis is laid here not on general categories of consequence relations (e.g. Scott or Tarski ones) but on concrete operators that can be retrieved from information systems (e.g. different examples of Scott consequence). To this end, we employ Galois connections and adjunctions (en masse called Galois mappings) and study the consequence relations that can be built via these maps. The main novelty of our research comes from the investigation of consequence relations induced by adjunctions rather than monotone Galois connections, which have been the main subject of studies so far. Surprisingly, the operations obtained from adjunctions possess a number of counter-intuitive properties, which (in turn) request some intelligible interpretations. And this is our next objective: to make sense of these consequence relations in the context of information processing.

Data science in healthcare: techniques, challenges and opportunities

Data science in healthcare: techniques, challenges and opportunities

Optical versus radiographic imaging and tomography: introduction to the ROADS feature issue

Optical imaging is an ancient branch of imaging dating back to thousands of years. Radiographic imaging and tomography (RadIT), including the first use of X-rays by Wilhelm Röntgen, and then, γ-rays, energetic charged particles, neutrons, etc. are about 130 years young. The synergies between optical and radiographic imaging can be cast in the framework of these building blocks: Physics, Sources, Detectors, Methods, and Data Science, as described in Appl. Opt. 61, RDS1 (2022)APOPAI0003-693510.1364/AO.455628. Optical imaging has expanded to include three-dimensional (3D) tomography (including holography), due in to part the invention of optical (including infrared) lasers. RadIT are intrinsically 3D because of the penetrating power of ionizing radiation. Both optical imaging and tomography (OIT) and RadIT are evolving into even higher dimensional regimes, such as time-resolved tomography (4D) and temporarily and spectroscopically resolved tomography (4D+). Further advances in OIT and RadIT will continue to be driven by desires for higher information yield, higher resolutions, and higher probability models with reduced uncertainties. Synergies in quantum physics, laser-driven sources, low-cost detectors, data-driven methods, automated processing of data, and artificially intelligent data acquisition protocols will be beneficial to both branches of imaging in many applications. These topics, along with an overview of the Radiography, Applied Optics, and Data Science virtual feature issue, are discussed here.

Molecular Pathways and Treatment Updates on Huntington’s Disease with Special Preference to Juvenile Phase: A Comprehensive Review

Abstract:: A degenerative brain ailment called Huntington's disease (HTD) causes irritable behavior, emotional distress, cognition, etc. It is also known as Huntington's chorea. Compared to men, women are more likely to get HTD. However, in India, if 3 to 5% of Europeans are affected, it is difficult to estimate the disease's prevalence. The full pathophysiological status, several molecular pathways, and pharmaceutical and non-pharmacological treatments for Huntington's disease are covered in this article. Google, PubMed, Scopus, Bentham, Elsevier, and other significant web resources were used to gather the scientific data. Moreover, this review article may serve as the foundation for future study, particularly on Huntington's illness.

A reliable knowledge processing framework for combustion science using foundation models

This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG) framework, the study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The multifaceted nature of combustion research emphasizes the critical role of knowledge processing in navigating and extracting valuable information from a vast and diverse pool of sources. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. It incorporates prompt engineering and offline open-source LLMs, offering user autonomy in selecting base models. The study provides a thorough examination of text segmentation strategies, conducts comparative studies between LLMs, and explores various optimized prompts to demonstrate the effectiveness of the framework. By incorporating an external vector database, the framework outperforms a conventional LLM in generating accurate responses and constructing robust arguments. Additionally, the study delves into the investigation of optimized prompt templates for the purpose of efficient extraction of scientific literature. Furthermore, we present a targeted scaling study to quantify the algorithmic performance of the framework as the number of prompt tokens increases. The research addresses concerns related to hallucinations and false research articles by introducing a custom workflow developed with a detection algorithm to filter out inaccuracies. Despite identified areas for improvement, the framework consistently delivers accurate domain-specific responses with minimal human oversight. The prompt-agnostic approach introduced holds promise for future improvements. The study underscores the significance of integrating LLMs and knowledge processing techniques in scientific research, providing a foundation for advancements in data assimilation and utilization.

"We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning

Organizations rely on machine learning engineers (MLEs) to deploy models and maintain ML pipelines in production. Due to models' extensive reliance on fresh data, the operationalization of machine learning, or MLOps, requires MLEs to have proficiency in data science and engineering. When considered holistically, the job seems staggering---how do MLEs do MLOps, and what are their unaddressed challenges? To address these questions, we conducted semi-structured ethnographic interviews with 18 MLEs working on various applications, including chatbots, autonomous vehicles, and finance. We find that MLEs engage in a workflow of (i) data preparation, (ii) experimentation, (iii) evaluation throughout a multi-staged deployment, and (iv) continual monitoring and response. Throughout this workflow, MLEs collaborate extensively with data scientists, product stakeholders, and one another, supplementing routine verbal exchanges with communication tools ranging from Slack to organization-wide ticketing and reporting systems. We introduce the 3Vs of MLOps: velocity, visibility, and versioning --- three virtues of successful ML deployments that MLEs learn to balance and grow as they mature. Finally, we discuss design implications and opportunities for future work.

Characterizing Usability Issue Discussions in Open Source Software Projects

Usability is a crucial factor but one of the most neglected concerns in open source software (OSS). While far from an ideal approach, a common practice that OSS communities adopt to collaboratively address usability is through discussions on issue tracking systems (ITSs). However, there is little knowledge about the extent to which OSS community members engage in usability issue discussions, the aspects of usability they frequently target, and the characteristics of their collaboration around usability issue discussions. This knowledge is important for providing practical recommendations and research directions to better support OSS communities in addressing this important topic and improve OSS usability in general. To help achieve this goal, we performed an extensive empirical study on issues discussed in five popular OSS applications: three data science notebook projects (Jupyter Lab, Google Colab, and CoCalc) and two code editor projects (VSCode and Atom). Our results indicated that while usability issues are extensively discussed in the OSS projects, their scope tended to be limited to efficiency and aesthetics. Additionally, these issues are more frequently posted by experienced community members and display distinguishable characteristics, such as involving more visual communication and more participants. Our results provide important implications that can inform the OSS practitioners to better engage the community in usability issue discussion and shed light on future research efforts toward collaboration techniques and tools for discussing niche topics in diverse communities, such as the usability issues in the OSS context.

An orchestra of machine learning methods reveals landmarks in single-cell data exemplified with aging fibroblasts.

In this work, a Python framework for characteristic feature extraction is developed and applied to gene expression data of human fibroblasts. Unlabeled feature selection objectively determines groups and minimal gene sets separating groups. ML explainability methods transform the features correlating with phenotypic differences into causal reasoning, supported by further pipeline and visualization tools, allowing user knowledge to boost causal reasoning. The purpose of the framework is to identify characteristic features that are causally related to phenotypic differences of single cells. The pipeline consists of several data science methods enriched with purposeful visualization of the intermediate results in order to check them systematically and infuse the domain knowledge about the investigated process. A specific focus is to extract a small but meaningful set of genes to facilitate causal reasoning for the phenotypic differences. One application could be drug target identification. For this purpose, the framework follows different steps: feature reduction (PFA), low dimensional embedding (UMAP), clustering ((H)DBSCAN), feature correlation (chi-square, mutual information), ML validation and explainability (SHAP, tree explainer). The pipeline is validated by identifying and correctly separating signature genes associated with aging in fibroblasts from single-cell gene expression measurements: PLK3, polo-like protein kinase 3; CCDC88A, Coiled-Coil Domain Containing 88A; STAT3, signal transducer and activator of transcription-3; ZNF7, Zinc Finger Protein 7; SLC24A2, solute carrier family 24 member 2 and lncRNA RP11-372K14.2. The code for the preprocessing step can be found in the GitHub repository https://github.com/AC-PHD/NoLabelPFA, along with the characteristic feature extraction https://github.com/LauritzR/characteristic-feature-extraction.

How environmental perception affects players’ in-game behaviors? Towards developing games in compliance with sustainable development goals

Several research studies have pointed out that players’ in-game behaviors might be affected by different individual differences, such as player type, personality, etc. However, limited information is found about whether players’ environmental perceptions might impact their in-game behaviors towards the environment while playing. To address this research gap, this study analyzes the in-game behaviors data of 640 players within Nintendo’s Animal Crossing: New Horizons (ACNH), as well as their environmental perceptions based on the revised New Environmental Paradigm (NEP) scale, using open data sourced and adapted from Vuong et al. [53] from the Science Data Bank repository. The obtained results revealed that players’ environmental perceptions might impact their in-game behaviors, however this is not always true; the obtained results also revealed that players might behave within the game against their beliefs and perceptions just to level-up in the game. For instance, anti-anthropocentrism players, who are environment-caring, are found to harm the nature within the game. The findings of this study can help to understand how people with different environmental perceptions might behave, hence promoting ecological behaviors. Additionally, they can also help to reveal how to design games that can emphasize environment-friendly behaviors, hence helping to achieve the sustainable development goals of United Nations (UN).

Esophageal food bolus impaction in pediatric age.

Esophageal food impaction (EFI) is the sudden onset of dysphagia that occurs when a food bolus becomes lodged in the esophagus, requiring endoscopic removal. Scientific data on the prevalence and causes of EFI in children is lacking. The aim of this study was to provide further insights into EFI episodes in children. We have prospectively enrolled all children admitted for a first episode of EFI between March 2018 and March 2023. A fluoroscopic contrast study was performed in all patients to confirm the boluses and assess their position. Boluses were extracted by esophagogastroduodenoscopies, and esophageal biopsies were routinely obtained for histologic evaluation. Over the study period, 41 children were admitted for a first episode of food impaction. Drooling was the most commonly reported symptom. Half children experiencing a first episode of food bolus were diagnosed with EoE (20/41, 48.8%). Almost a fourth of the episodes subtended a different condition, such as esophageal anastomotic, peptic or congenital strictures, stricturing caustic esophagitis, esophageal duplication,and achalasia. In the last fourth of patients the cause of EFI was not identified and thus probably related to quick eating and inadequate chewing of food. Our study represents the largest known series of pediatric patients evaluated for food bolus impaction. Our main finding is the high frequency of EoE, which accounts for a half of EFI episodes in pediatric age, especially in older children. This finding highlights the importance of obtaining esophageal biopsies after the endoscopic bolus removal in children with EFI to provide a complete diagnostic evaluation.

Prevalence and genetic diversity of rotavirus among children under 5 years of age in China: a meta-analysis.

This meta-analysis was performed to assess the prevalence and circulating strains of rotavirus (RV) among Chinese children under 5 years of age after the implantation of the RV vaccine. Studies published between 2019 and 2023, focused on RV-based diarrhea among children less than 5 years were systematically reviewed using PubMed, Embase, Web of Science, CNKI, Wanfang and SinoMed Data. We synthesized their findings to examine prevalence and genetic diversity of RV after the RV vaccine implementation using a fixed-effects or random-effects model. Seventeen studies met the inclusion criteria for this meta-analysis. The overall prevalence of RV was found to be 19.00%. The highest infection rate was noted in children aged 12-23months (25.79%), followed by those aged 24-35 months (23.91%), and 6-11 months (22.08%). The serotype G9 emerged as the most predominant RV genotype, accounting for 85.48% of infections, followed by G2 (7.70%), G8 (5.74%), G1 (4.86%), and G3 (3.21%). The most common P type was P[8], representing 64.02% of RV cases. Among G-P combinations, G9P[8] was the most frequent, responsible for 78.46% of RV infections, succeeded by G8P[8] (31.22%) and G3P[8] (8.11%). Despite the variation of serotypes observed in China, the G1, G2, G3, G8 and G9 serotypes accounted for most RV strains. The genetic diversity analysis highlights the dynamic nature of RV genotypes, necessitating ongoing surveillance to monitor changes in strain distribution and inform future vaccine strategies.

Climate Justice and Climate Adaptation in California: Indigenous Community Climate Adaptation Leadership and Opportunities for Scientific Collaboration

Abstract Climate and weather-related disasters in California illustrate the need for immediate climate change action - both mitigation to reduce impacts and adaptation to protect our communities, relatives, and the ecosystems we depend upon. Indigenous frontline communities face even greater threats from climate impacts due to historical and political legacies of environmental injustice. Climate change adaptation actions have proven challenging to implement as communities struggle to access necessary climate data at appropriate scales, identify effective strategies that address community priorities, and obtain resources to act, at a whole-community level. In this paper, we present three examples of Indigenous communities in California that have used a climate justice approach to climate change adaptation. These communities are drawing upon community knowledge and expertise to address the challenges of adaptation planning, and taking actions that center community priorities. The three cases address emergency preparation and response, cultural burning and fire management, and community organizing and social cohesion. Across these spheres, they illustrate the ways in which a community-based and climate justice-focused approach to adaptation can be effective in addressing current threats, while also addressing the legacy of imposed, socially constructed vulnerability and environmental injustices. Because we recognize the need for multiple knowledges and skills in adaptation actions, we include recommendations that have emerged based on what’s been learned through these long-standing and engaged participatory research collaborations for climate scientists who wish to contribute to climate justice-focused adaptation efforts by using scientific data to support – not supplant – community efforts, target funding toward genuine community engagement and adaptation actions, and become aware of the historical and political legacies that created the climate vulnerabilities and injustices evident today.

Focus group interviews as part of a clinical research immersion experience

Background Research is an essential component of the dental profession and should be integrated into dental education and training curricula. Opportunities to participate in clinical research studies can enrich the learning experience of those involved. Methods This paper reports a new educational method utilized to enhance the experiences of novice members of a research study team in conducting Focus Group Interviews (FGIs) as part of a double-blind clinical trial exploring an oral probiotic intervention. Study team members participated in a series of in-person FGIs conducted at the beginning and toward the end of a University-based clinical trial, which took place from October 2021 to October 2022. Results Study team members consisting of faculty, staff, and pre-doctoral dental students help plan and conduct interviews, curate, and qualitatively analyze the data. Study team members acquired experience with regard to interpersonal communication skills, organization, research subject communication, the application of human subject protection, FGI methodology, data analysis, developing presentations, and preparing scientific data presentation and publications. Conclusion Exposing dental faculty and students to clinical research studies in university settings can offer opportunities for experiential, multidimensional learning and the development of new research knowledge and skills.

The tale of the sea turtle flipper tagging technique, a global environmental impact assessment

AbstractAlthough sea turtle flipper tagging is a tenet field technique generating irreplaceable scientific data worldwide since the inception of sea turtle conservation in 1920s along the Great Barrier Reef, the procedure was reported to trigger outbreaks of sea turtle fibropapillomatosis (FP) in 2018. This global environmental impact assessment performed during 2020–2023 meta‐analyzed three impacts on sea turtles health and conservation of the presence of visible flipper tags: a risk of clinical expression of FP lesions at the tag insertion point, a deterrent effect of the presence of flipper tags on sea turtle fishing, and a risk of entanglement of sea turtles in fishing gears. This essay concluded that the risk of expression of a FP lesion at the flipper tag insertion point is most probably correlated to a direct inoculation of ChHV5 via infected tagging equipment, and that the risk of entanglement in fishing gears can be mitigated by the choice of metallic flipper tags over plastic flipper tags. This essay also strongly recalls the sea turtle flipper tagging technique represents a remedy to knowledge gaps relevant to climate velocity research and can be a deterrent on sea turtle fishing in a frame of strong partnerships with indigenous people, independently of communities' socio‐cultural backgrounds.

Data Science Corps (NSF)

Data Science Corps (NSF)

AI-enabled prediction of sim racing performance using telemetry data

Despite the emerging and rapid progress of esports, approaches for ensuring high-quality analytics and training among professional and amateur esports teams are lacking. In this paper, we demonstrate how the application of data science techniques and Machine Learning (ML) approaches in esports, particularly in sim racing science, can illuminate the most important in-game metrics that dictate performance. Thus, using a professional racing simulator and MoTec i2 Pro (v1.1.5, Australia), we gathered extensive telemetry data from 174 participants, who completed 1327 laps on the Brands-Hatch circuit in the Assetto Corsa Competizione (v1.9, KUNOS Simulazioni). We clustered the obtained laps based on performance (lap-time), and then identified driving behaviors within performance groups. We also analyzed the feature subset obtained from a hybrid feature selection approach using two correlation analyses and three ML models.The best model achieved a prediction accuracy of 97.19%, demonstrating that the model effectively captured the critical factors that influenced driving performance during a lap. The results confirm that average speed is the most important metric, followed by lateral acceleration, steering angle, and lane deviation. Our analyses offer key metrics for refining training tools and techniques in sim racing performance improvement.

Therapeutic Potential of Dicentrine in Human Disorders: A Review On Medicinal Importance and Pharmacological Activities of Aporphine Alkaloid

Backgrounds: Plants have always been an important source of food and drugs, in addition to their application in medical and cosmetic sectors. The popularity of herbal remedies has increased in many countries in recent decades. Herbal medicines contain numerous active phytocomponents with different biological activities. Alkaloid class phytochemicals have diverse chemical structures and pharmacological activities in nature. Additionally, Aporphinoids are an important class of plant secondary metabolites that have been used for the treatment of numerous human disorders for a long time in traditional medicine. Dicentrine is an aporphine class phytochemical isolated from numerous medicinal plants, including Actinodaphne sesquipedalis and Lindera megaphylla. Methods: The biological potential of dicentrine, an aporphine alkaloid derivative, has been described in the present work. Scientific data on dicentrine were collected here from different scientific databases and presented in this paper in order to know the biological importance of dicentrine in medicine for the treatment of human complications. Further, detailed pharmacological activities and scientific data on dicentrine were also analyzed in order to determine its therapeutic potential in medicine. Moreover, its analytical aspects were also described in this work to understand its separation and isolation methods. Results: The present work described the biological potential of dicentrine in medicine and its analytical aspects. It signified the biological potential of dicentrine in cancer, breast cancer, oral squamous cell carcinoma, lung adenocarcinoma, blood pressure, inflammatory disorders, hyperlipidaemia, arrhythmia, stomach muscle, and glomerulonephropathy. Further, its effectiveness in medicine was found to be mainly because of its antiplatelet, alpha 1-adrenoceptor, epidermal growth factor, antiprotozoal, larvicidal, antimicrobial, topoisomerase II, and acetylcholinesterase inhibitory potential. Moreover, other scientific data also signified its metabolism and pharmacokinetic parameters in terms of its analytical aspects in medicine. Conclusion: The present review gives us an updated summary of the scientific information for pharmacological activities and analytical aspects of dicentrine in medicine. It also signified the potential contribution of dicentrine in medicine for the development of a newer class of drug molecules for human disorders.

First person – Joshua Hack

ABSTRACT First Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping researchers promote themselves alongside their papers. Joshua Hack is first author on ‘ Machine learning models reveal distinct disease subgroups and improve diagnostic and prognostic accuracy for individuals with pathogenic SCN8A gain-of-function variants’, published in BiO. Joshua is a Data Scientist in the lab of Dr Michael Hammer at the University of Arizona, investigating the mechanisms of diseases and expanding the phenotypic landscapes of rare genetic diseases towards the goal of improving treatment strategies on an individual basis.

Financial Technology Revolution in India: An Appraisal

Role of artificial intelligence has increased tremendously in the field of finance. Traditional banking and financial players are challenged by technological start-ups with the help of services like international money transfer, online portfolio management, crowd funding platforms, and mobile payment solutions. To promote digital financial services in India main foundation stones are Unified Payment Interface (UPI), JAM trinity i.e. JAN-Dhan, Adhar and Mobile. So, to study FinTech revolution in Indian context this research article has been planned. Further, this paper has been divided into three parts to achieve two objectives. First objective is to study the evolution of FinTech worldwide, and its relationship with data science and artificial intelligence. Secondly, to analyse FinTech revolution, trends, problems and prospects in Indian Context. A descriptive research approach is adopted to examine FinTech Industry status in India. Various sources like research articles, papers, government reports, and websites have been considered to gather various data for this work. Further, chart and table has been used to explain data. For more precise explanation, a comparison has also been done with global trends. The study revealed some of the gap areas in Financial Inclusion in financially underserved India and these are low insurance penetration, lack of strong telecom network, lack of access to formal credit, lack of collateral for loans, building a cashless economy, low financial literacy, lack of acceptable identification, high brokerage cost and low penetration. Finally, the research article also highlighted the regulatory framework for FinTech industry in India.