Data Analysis Framework Research Articles

An optimization and discrete event simulation framework for evaluating delivery performance in Swedish wood supply chains under stochastic weather variations

ABSTRACT Delivery performance in wood supply chains is affected by carrying capacity on forest roads. Stochastic weather variations affect carrying capacity of roads and accessibility of harvested wood on landings. In Sweden, annual harvest scheduling is usually based on four seasons and road accessibility classes are set to match these seasons. In reality, road accessibility changes depend on weather variations not season. This causes a risk of poor delivery performance. Delivery performance is evaluated using two performance indicators, backorders (orders not delivered on time) and lead time (time from harvesting to delivery to mill). A data-analytic framework, combining optimization and simulation, is proposed to assess and evaluate these performance indicators for the wood supply chain under stochastic weather variations. An optimization model is used to generate a feasible harvest schedule for expected weather. This is thereafter evaluated given various weather scenarios using a discrete-event simulation model. The combination of optimization and discrete-event simulation is used to assess delivery performance over time, and changes in road accessibility caused by weather variation. An expected decrease in road accessibility implies a stable delivery performance, while an unexpected decrease causes a reduced delivery performance.

Chemoproteomic analysis of the pharmacological properties of vitamin В12 derivatives

Background. Chemical derivatives of vitamin B12 are characterized by a wide range of pharmacological effects. It is important to learn how to establish relationships between changes of the corrin ring structure in vitamin B12 derivatives and changes in pharmacological properties.Objective: to evaluate the interaction of six vitamin B12 derivatives (aquacobalamin, diaquacobinamide, aquacyano-forms of heptaethanolamine, heptaethylenediamine, heptamethyl and heptabutyl cobyrinates) with human proteome proteins.Material and methods. Using the method of chemoinformational (chemoproteomic) analysis, implemented within the framework of algebraic recognition theory and topological data analysis, the constants of half-maximal inhibitory concentration (IC50) and half-maximal effective concentration (EC50) of human proteome proteins were assessed.Results. Significant differences were found in the interactions of the studied molecules with 1200 proteins. It was shown that the chemoproteomic profiles of each of the compounds form three groups of molecules with similar proteomic properties: (1) aquacobalamin, (2) diaquacobinamide, aquacyano-forms of heptaethanolamine and heptaethylenediamine cobyrinates, (3) aquacyano-forms of heptamethyl and heptabutyl cobyrinates. A more detailed analysis of the chemoproteomic profiles of the studied compounds using the GO (Gene Ontology) nomenclature of biological functions of proteins made it possible to identify functional GO categories indicating differences in the biological effects of the studied compounds: neuroprotective regulation of neurotransmitter activity (serotonin receptor activity, cholinergic synapses, regulation of dopamine secretion, receptor thyroid hormones), reduction of inflammation (inhibition of cytokine biosynthesis, including tumor necrosis factor alpha and interleukin 1 beta, I-kappa-B kinases / nuclear factor kappa В, leukocyte migration), etc.Conclusion. The profiles of differences in the pharmacological properties of the studied compounds with respect to their effects on neuroprotection, neurotransmitter metabolism, and inflammation were identified and described.

The pronunciation constraints of syllable stress-coloration in Pakistani English

Pakistani English speakers struggle with pronunciation issues due to unfamiliarity with the morphological and phonological aspects of the English language. The challenging aspect of teaching English pronunciation in an ESL environment in Pakistan is pronunciation. The study looks at Pakistani ESL students' pronunciation problems and aims to pinpoint Pakistani English speakers’ pronunciation issues in an ESL setting. This is a descriptive study, and information from ten ESL students is acquired through recording. The study uses the PRAAT as a framework for data analysis and points to the syllable break and stress marking in PE. Based on L1, the speakers were chosen, and only Kashmiri speakers of Pakistani English were included. The study concludes that studying English helps ESL students speak effectively with people from different linguistic and cultural backgrounds, promoting intercultural understanding and fostering international collaboration. The lack of excitement, self-doubt, willingness, shyness, and language proficiency are the main causes of pronunciation problems. L1 also affects English pronunciation. According to the study, students’ L1 influences lead them to struggle with stress marking, syllabification, and transcription of English words. Additionally, epenthesis, the insertion of a short vowel at the beginning, and improper stress marking were prevalent issues.

A crowdsensing-based framework for sound and vibration data analysis in smart urban environments

In this paper, we introduce a framework tailored to the precise analysis of sound and vibration data in the dynamic context of smart cities. By harnessing the power of crowdsensing data, our system offers a robust and highly adaptable solution for the real-time monitoring and comprehensive assessment of acoustic and vibrational parameters within urban environments. The rise of smart cities, fueled by sensor tech and data analysis, demands advanced tools to tackle urban environmental issues and improve residents’ quality of life. Our framework empowers city stakeholders, offering insights for informed decisions in urban planning, transportation, infrastructure maintenance, and public health. Key features of our system include data acquisition through mobile crowdsensing application, advanced signal processing algorithms for noise and vibration identification, and the integration of geospatial information to provide location-specific context. Furthermore, our framework supports scalable and adaptable data analytics, ensuring the efficient utilization of resources and the effective management of urban environments. The presented framework addresses the challenges of noise pollution, structural health monitoring, transportation optimization, and public safety in smart cities. By exploiting crowdsourced data, it promotes a collaborative approach to data collection, analysis, and decision-making, fostering an environment where cities can continuously evolve and adapt to the evolving needs of their residents. This paper offers a detailed exploration of the system’s architecture, and showcases its practical implementation, affirming its potential as a powerful starting point for advancing the science of smart cities.

Perioperative care to patients living with HIV by theatre nurses at a South African tertiary hospital

Background Human Immunodeficiency Virus (HIV) is one of the leading health challenges worldwide that influences the provision of quality patient care. Stigma and discrimination around this condition and the health care needs affect the health care provision. This study aimed to gain an in-depth understanding of theatre nurses’ experiences in providing perioperative care to patients living with HIV at a tertiary hospital in South Africa. Methods This study was conducted at a tertiary hospital in Tshwane district, South Africa. The study was underpinned by an interpretative phenomenological analysis design. Data were collected from 10 criterion purposively selected theatre nurses using in-depth individual interviews. Data were thematically analyzed and guided by an interpretive phenomenological analysis framework for data analysis. Results The study revealed that theatre nurses work in an under-resourced environment. The resources highlighted are human, materials, including personal protective equipment and life support. The situation negatively affects the perioperative care of HIV-positive patients, who are always prone to perioperative complications, such as profuse bleeding, and even death. The situation increases the occupational risk to the nurses. Conclusions The researchers proposed several recommendations targeted at improving the resources needed by theatre nurses when caring for people living with HIV perioperatively at South African tertiary hospitals. Findings will add to the body of knowledge of the Nursing profession about offering perioperative care to persons living with HIV.

An Analytical Inquiry into the Impact of International Trade on Poverty Reduction in Nigeria

This work is propelled by the alarming rate at which poverty has continued to increase, thereby aiding socio-economic challenges in Nigeria. The work through the application of the ARDL and ECM framework of data analysis on secondary data sourced from the Central Bank of Nigeria, calibrated the impact of international trade on poverty reduction in Nigeria from 1981-2021. The result of the study has proven that the relationship between the nation's non-oil export and poverty is negative. However, evidence from the study more so alludes that the country's export of crude oil overtime has not had any meaningful effect of reducing poverty in Nigeria, rather the rate of poverty has continued to rise with the increase in oil exports. Empirical evidence from the study further revealed that Nigeria's imports within the scope of the work has had a significant negative relationship with poverty, which negates the theoretical condition of their expected relationship. Hence, to overturn this positive trend between oil exports and poverty rate, the Nigerian government needs to train and equip the unskilled poor Nigerian with requisite skills and knowledge that would make them gainfully employed in the oil industry, more so the country is admonished to restrict its imports to just commodities that cannot be produced locally.

One down, fifty to go: managers' perceptions of their workload and how they cope with it to maintain their psychological health.

Like many other countries, healthcare services in Canada face numerous organizational changes with the main objective of doing more with less. The approach taken within different healthcare networks has brought about a reform in healthcare facilities in Quebec, leading to several mergers and eliminating over 1,000 managerial positions. As a result, this has placed a progressively heavier workload on the shoulders of the remaining managers. Research on mental health in the workplace has mainly focused with the workforce and generally neglects managers. However, studies have shown that workload is a risk factor for managers. Therefore, the objectives of our study are to (1) better understand the elements that make up a manager's workload and the factors that influence it and (2) identify the coping strategies used by managers to deal with their workloads. Employing a qualitative approach, we analyzed 61 semistructured interviews through an abductive method, utilizing diverse frameworks for data analysis. The participants came from the same Quebec healthcare establishment. Our findings align with the notion that workload is a multifaceted phenomenon that warrants a holistic analysis. The workload mapping framework we propose for healthcare network managers enables pinpointing those factors that contribute to the burden of their workload. Ultimately, this workload can detrimentally impact the psychological wellbeing of employees. In conclusion, this study takes a comprehensive look at workload by using a holistic approach, enabling a more comprehensive understanding of this phenomenon. It also allows for the identification of coping strategies used by managers to deal with their workloads. Finally, our results can provide valuable guidance for the interventions aimed at addressing workload issues among healthcare network managers in Quebec by utilizing the specific elements we have identified.

Assessment of acceptability of black wheat flour products and factors affecting it among Anganwadi beneficiaries and workers: A mixed-method prospective observational study.

Malnutrition is very common in India and black wheat might be an acceptable solution to this problem. The aim of the study was to assess acceptability of black wheat flour products and factors affecting it among Anganwadi beneficiaries and workers. This was a mixed-method prospective observational study. All the family members enrolled for supplementary nutrition and Anganwadi workers/helpers of three randomly selected Anganwadi centers were taken in the study. For qualitative data, in-depth interview was done, and for quantitative data, 9-point hedonic scale was administered. Braun and Clarke's six-phase data analysis framework was used for qualitative data. A total of 16 pregnant females, 14 lactating females, 16 children, 2 Anganwadi workers, and 3 Anganwadi helpers participated in the study. Thematic analysis of the data revealed five significant themes. It included characteristics of black wheat flour, the process of making the product (experience of making the product), family acceptability, availability, and hygiene. Participants expressed that the black color appearance is one of the negative influencers in the acceptability of black wheat flour. Most of the participants liked the taste as well as the texture. However, kneading, rolling, and puffing were more challenging than traditional wheat flour. On the hedonic scale, the mean rank of acceptability is lowest for color (3.03), followed by puffing (3.49) and highest for texture (4.87) and taste (4.60). Our study results revealed that black wheat is acceptable to the Anganwadi beneficiaries and workers.

Case study of Er- and Dy-doped boron-aluminosilicate glasses on radiation shielding performance in the energy range 0.015–10 MeV with MCNP6.2

The use of radiation-shielding materials has significantly increased with the advancement of new technologies. The present work aims to demonstrate that the nuclear shielding properties of boron-aluminosilicate glasses can be enhanced by doping with rare earth elements (REEs) such as erbium (Er) and dysprosium (Dy). Testing was performed by mixing different ratios of dopants into the glass system. This procedure was implemented on 13Al2O3–21B2O3-35.4SiO2-(30.6-X) Er2O3-X(Dy2O3) glass, where X = 0, 15.3, 22.1, and 30.6. MCNP6.2, SRIM, PSD, ROOT software, and XCOM databases were used as tools. MCNP6.2 software and XCOM databases were used to calculate, simulate, and validate the mass attenuation cross-sections for 15 selected gamma energies ranging from 0.01 MeV to 10 MeV. On the other hand, SRIM was used to obtain information about the mass stopping power (MSP) and stopping distance, the so-called projected range (PR), for charged particles of H+1 and He+2. Likewise, the PSD was only used to obtain a fast neutron removal cross-section (FNRCS) per density value for each element. This is referred to as the macroscopic neutron cross-section for fast neutron removal in the literature. Finally, the data analysis framework ROOT was used for calculations and graphical representations. To validate this study, the radiation-shielding properties of the material were compared with the globally accepted mass attenuation coefficient database of XCOM using MCNP6.2 simulation results. The proximity between these two is promising. The accuracy of the study was determined by examining the relative error values. The results indicate a strong correlation between the database and theoretical data. Radiation shielding properties such as mass and linear attenuation coefficients (MAC and LAC), half and tenth value layers (HVL and TVL), mean free path (MFP), effective atomic number (Zeff), effective electron density (Neff), effective conductivity (Ceff), and fast neutron removal cross-sections (FNRCS) against gamma rays suggested that the lowest X ratio indicated the highest protection. However, the MSP and PR values for the charged particles at most energies indicate the lowest protection with the lowest X ratio in the glass mixture. Finally, the glass shield with the lowest X ratio provided the highest neutron protection, similar to gamma rays. In summary, dysprosium and erbium doping in boron-aluminosilicate glasses can be tailored to specify different requirements for gamma-ray and particle attenuation, as well as neutron absorption. This study aims to fill a gap in the literature by providing a basis for future research on shielding materials based on doped boron-aluminosilicate glasses using high-atomic-number particles, such as REEs, as a radiation protection medium.

Observation of Coherent Symmetry-Breaking Vibration by Polarization-Dependent Femtosecond Spectroscopy.

Understanding photoinduced chemical reactions beyond the Born-Oppenheimer paradigm requires a comprehensive examination of vibronic interactions. Although femtosecond studies have unveiled the influence of vibrational modes strongly coupled to ultrafast intramolecular reactions in the excited state, they often lack direct observations of how vibrations modulate electronic properties due to the rapid disappearance of reactants. To address this gap, our research investigates the dynamics of photoexcited molecules that do not react. Specifically, we focus on the coherent librational motion of molecular transition dipole moments, discovering that the coherent libration primarily originates from symmetry-breaking components in vibronically excited vibrational modes. Symmetry breaking motion can significantly impact the excited-state dynamics of highly symmetric molecules, potentially leading to nonadiabatic transitions. In essence, the data analysis framework introduced in this study can be harnessed to uncover potential reactivity in photoexcited molecules, further enhancing our understanding of the mechanisms governing these reactions.

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene-gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene-gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.

A predictive analytics framework for sensor data using time series and deep learning techniques

IoT devices convert billions of objects into data-generating entities, enabling them to report status and interact with their surroundings. This data comes in various formats, like structured, semi-structured, or unstructured. In addition, it can be collected in batches or in real time. The problem now is how to benefit from all of this data gathered by sensing and monitoring changes like temperature, light, and position. In this paper, we propose a predictive analytics framework constructed on top of open-source technologies such as Apache Spark and Kafka. The framework focuses on forecasting temperature time series data using traditional and deep learning predictive analytics methods. The analysis and prediction tasks were performed using Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and a novel hybrid model based on Convolution Neural Network (CNN) and LSTM. The purpose of this paper is to determine whether and how recently developed deep learning-based models outperform traditional algorithms in the prediction of time series data. The empirical studies conducted and reported in this paper demonstrate that deep learning-based models, specifically LSTM and CNN-LSTM, exhibit superior performance compared to traditional-based algorithms, ARIMA and SARIMA. More specifically, the average reduction in error rates obtained by LSTM and CNN-LSTM models were substantial when compared to other models indicating the superiority of deep learning. Moreover, the CNN-LSTM-based deep learning model exhibits a higher degree of closeness to the actual values when compared to the LSTM-based model.

Automated Data Transformation and Feature Extraction for Oxygenation-Sensitive Cardiovascular Magnetic Resonance Images.

Oxygenation-sensitive cardiovascular magnetic resonance (OS-CMR) is a novel, powerful tool for assessing coronary function in vivo. The data extraction and analysis however are labor-intensive. The objective of this study was to provide an automated approach for the extraction, visualization, and biomarker selection of OS-CMR images. We created a Python-based tool to automate extraction and export of raw patient data, featuring 3336 attributes per participant, into a template compatible with common data analytics frameworks, including the functionality to select predictive features for the given disease state. Each analysis was completed in about 2min. The features selected by both ANOVA and MIC significantly outperformed (p < 0.001) the null set and complete set of features in two datasets, with mean AUROC scores of 0.89eatures f 0.94lete set of features in two datasets, with mean AUROC scores that our tool is suitable for automated data extraction and analysis of OS-CMR images.

A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy

The mass production of plastic waste has caused an urgent worldwide public health crisis. Although government policies and industrial innovation are the driving forces to meet this challenge, trying to understand public attitudes may improve the efficiency of this process. Social media has become the main ways for the public to obtain information and express opinions and feelings. This motivated us to mine the perceptions and behavioral responses towards plastic usage using social media data. In this paper, we proposed a framework for data collection and analysis based on mainstream media in the UK to obtain public opinions on plastics. An unsupervised machine learning model based on Latent Dirichlet Allocation (LDA) has been employed to analyse and cluster the topics to deal with the lack of annotation of the data contents. An additional dictionary method was then proposed to evaluate the sentiment of the comments. The framework also provides tools to visualise the model and results to stimulate insightful understandings. We validated the framework's effectiveness by applying it to analyse three mainstream social media, where 6 first-level topic categories and 13 second-level topic categories from the comment texts related to plastics have been identified. The results show that public sentiment towards plastic products is generally stable. The spatiotemporal distribution of each topic's sentiment is highly correlated with the number of occurrences.

A Report on Investment Differs from Gambling: A Comprehensive Study on Technical Analysis in Investment Decision-Making

This research paper conducts a thorough examination with primary objectives focused on acquiring a comprehensive understanding of scrutinizing portfolio analysis, navigating the complexities of the stock market, addressing risk management considerations, and exploring the psychological facets of trading, the study aims to provide valuable insights for investors and researchers. Employing a descriptive research design, the paper establishes a structured framework for data collection, analysis, and interpretation, ensuring the reliability and validity of the study's outcomes. The research emphasizes technical analysis, specifically delving into candlestick patterns, and systematically evaluates indicators, oscillators, and trading strategies prevalent in financial markets. Distinguishing investing from trading and illuminating the divergence from speculative activities like gambling are additional focal points. In terms of data collection, the paper opts for a secondary method, utilizing existing information from literature, articles, and databases. This pragmatic choice aligns with considerations of accessibility, cost-effectiveness, and time efficiency, facilitating an in-depth analysis of the company's financial landscape and investment dynamics. Ultimately, the research contributes a nuanced understanding of financial markets and investment strategies, offering insights crucial for investors, analysts, and researchers navigating the complex terrain of corporate finance.

Augmented Differential Privacy Framework for Data Analytics

Differential privacy has emerged as a popular privacy framework for providing privacy preserving noisy query answers based on statistical properties of databases. It guarantees that the distribution of noisy query answers changes very little with the addition or deletion of any tuple. Differential enjoys popular reputation that providing privacy without building any assumptions about the data and protecting against attackers who know all but one record. Differential privacy is a relatively new field of research. Most users have a limited experience in managing differential privacy parameters and achieving a suitable level of privacy without affecting the quality of the analysis. A vast majority of users is still learning how to effectively apply differential privacy in practice. In this paper, we discussed: on the proposed augmented framework which enables the differential privacy data of any given query, the various differential privacy techniques, metrics for the privacy &amp; utility tradeoff of the data and efficacy of the framework. Discussed state of the art of different differential privacy techniques defined in the framework Laplace, Laplace bounded, Randomized response and Exponential for different data types. The augmented framework consists of three parts one on privacy parameter inputs to control interactively and iteratively on the querying the data , the various differential privacy techniques, the metrics to measure privacy and utility threshold which allows the data analyst to evaluate the accuracy of the privacy safe data for selecting the privacy guaranteed data within the given privacy budget. The framework takes any dataset as input and, generates another dataset which is structurally and statistically very similar original dataset. The newly generated dataset has much stronger privacy guarantee on the selected sensitive and non-sensitive datatypes. We have also demonstrated analytical models developed using the privacy safe data from the framework as substitute to the models developed on the original datasets. We have demonstrated the framework and analytical model with sample data sets to present the similarity of original and differential privacy safe datasets.

Metamorphosis of human health risk assessment with artificial intelligence (AI) - a new paradigm in pharmaco-toxicological sciences

Toxicological Science, especially in the last five decades, has witnessed rapid evolution of different tools and techniques developed to address diverse issues related to studies dealing with adverse health effects of a variety of poisons, drugs, chemicals,ever-growing list of xenobiotics and human diseases. Traditionally these studies are performed using suitable animal (in vivo) models. There was a time when toxicologists/pharmacologists were searching models alternate to animal toxicity testing (Doke and Dhawale, 2015). Improved cell culture techniques, knowledge on stem cells and other microbiological systems led to the development of in vitro toxicology. It was soon followed by DNA chips, micro fluidics, in silico toxicology , toxicogenomics and computational toxicology. Several platforms are now discussing machine learning (ML) and artificial intelligence (AI) together as future tools of computational toxicology. For decades, quantitative structure-activity relationship (QSAR) methods have been employed to study the effects of drugs/chemicals (Cai et al., 2022). However, AI methods for toxicity assessment ranging from ADMEtox to AI4TOX provide evidence to the immense potential of AI. Intriguingly, a few problems between theoretical developments and practice of AI by end users have been recognized. AI is now being employed in cancer care. According to WHO (2022), cancer is responsible for 9.3 million deaths per year. AI is being used for cancer grading, classification, follow up services and diagnostic accuracy. However, certain limitations viz. testing, validation, certification and auditing need to be addressed (Cabral et al., (2023). Potential of AI in diabetic care and management has recently been recognized. The huge burden of diabetic patients in India can be managed through AI tools. Diabetic risk can be predicted using genomic data, to diagnose diabetes using EHR data and to identify diabetes related complications i.e. retinopathy and nephropathy (Singhla et al., 2019). Application of AI in the management of cardiovascular diseases like myocardial infarction has been highlighted with special reference to Chinese medicine (Chen et al., 2022). There exists experimental evidence that AI tools can be used to assess, monitor and manage Parkinsons' disease (Bounsall et al., 2023). Perspectives of the application of AI in complimentary and alternative medicine were reviewed by Chu et al. (2022). Several regulatory agencies are now adopting the concept of 3R ie., replacement, reduction and refinement of animal testing (EU REACH/3R principles; Toxicology 21 of U.S. Government) ( Maestri, 2021).The application of AI in clinical toxicology through converging data resources, algorithms, real world information from sensors and health records has also been discussed (Sinha et al. 2021). Plausibility of toxicity prediction using AI tools was recently reviewed by Santin et al. (2021). Application of AI in recently emerged science of nanotoxicology is also being sought. The need for nanotoxicity databases, powerful nano descriptors, new modeling approaches, molecular mechanism analyses and designing of next generation nanomaterials are being debated ( Jha et al., 2014; Yan et al., 2023). U.S. Food and Drug Administration (USFDA) has recently initiated and AI program in Toxicology known as AI4TOX. This program mainly consists of four initiatives- AnimalGAN- to predict animal toxicology data for untested chemicals ( /about –fda/nctr-research-focus-areas/animalgan-initiative); SafetAI- to develop novel deep learning methods for toxicological endpoints (/about-fda/nctr-research-focus-areas/safetai-initiative); BERTox- to develop the most advanced AI powered Natural Language Processing (NPL) (/about-fda/nctr-research-focus-areas/bertox initiative) and PathologAI- to develop an effective and accurate framework for analysis of histopathological data from animal studies (/ about-fda/nctr-research-focus-areas/pathologai-initiative). Recently, Society of Toxicology (SOT) annual meeting held at Nashville from March 19-23, focused on a question-“How could AI be used for risk assessment?” There exists some skepticism weather AI may be used in human health risk assessment? How AI could be applied -to prioritize pharmaceutical/environmental chemicals, to identify potential off targets and decipher the mechanisms of toxicity and detect pathological effects? A Symposium session devoted to AI summarized international collaborative computational projects like CERaPP, COmPara and CATOMOS that have been designed to streamline the regulatory and safety assessments (Hartung, 2023). High throughput screening data (HTS) to predict drug induced liver injury (DILI) using AI is also being generated. AI can identify mechanisms for off target effects in drug development. AI can also be plausibly used to predict genotoxicity. Can AI tools automate the analysis of developmental or physiologically based assays? These discussions held during the symposium indicate exciting potential of AI in health risk assessment. It is speculated that, tools of nanotechnology hybridized with AI can metamorphose human health risk assessment to an extent that has never been achieved before.

Hypothesis-driven mediation analysis for compositional data: an application to gut microbiome

Sequencing read-count data often exhibit sparsity (zero-count inflation) and overdispersion. As most sequencing techniques provide an arbitrary total count, taxon-specific counts should be treated under the compositional data-analytic framework. There is increasing interest in the role of gut microbiome composition in mediating the effects of exposures on health. Previous compositional mediation approaches have focused on identifying mediating taxa among a number of candidates. We here consider compositional causal mediation when a priori knowledge is available about the hierarchy for a restricted number of taxa, building on a single hypothesis structured as contrasts between appropriate sub-compositions. Based on the assumed causal graph and the theory of multiple contemporaneous mediators, we define non-parametric estimands for overall and coordinate-wise mediation effects and show how they are estimated based on parametric linear models. The mediators have straightforward and coherent interpretations, related to causal questions about interrelationships between the sub-compositions. We perform a simulation study focusing on the impact of sparsity on estimation. While unbiased, the estimators' precision depends on sparsity and the relative magnitudes of exposure-to-mediator and mediator-to-outcome effects in a complex manner. In the empirical application we find an inverse association of fibre intake on insulin level, mainly attributable to the direct effects.

Unveiling the Unseen: Exploring the Lived Experiences of Family Caregivers of Young Adults with Chronic Mental Disorders: A Qualitative Phenomenological Study

Introduction: One of India’s most valuable resources is its young people, and this generation needs to be nurtured for the bright future of the nation. However, when the young generation is affected by chronic mental disorders, families find it difficult to take care of them and are burdened with the responsibility for several years. Aim: To explore the lived experiences of Family Caregivers (FCGs) of young adults with chronic mental disorders. Materials and Methods: This qualitative study was conducted at the selected mental inpatient and outpatient Department of a selected mental health facility in Urban Bangaluru, Karnataka, India from April 2021 to December 2021. The authors used a phenomenological qualitative study to explore the lived experiences of family caregivers of young adults with chronic mental disorders {mania, depression, Bipolar Affective Disorder (BPAD), schizophrenia} aged between 18-40 years. The Family Caregivers of young adults undergoing treatment were approached for written informed consent and recruited through purposive sampling. Data were collected using face-to-face, indepth, semi-structured interviews. Each interview lasted around 45 minutes to one hour, and data saturation was achieved with 15 samples. Interviews were conducted over a period of nine months. The audio-recorded interviews were transcribed, interpreted, and investigated using Colaizzi’s data analysis framework. Baseline variables were analysed using descriptive statistics. Results: In the present, the family caregivers’ ages ranged from 28 to 60 years, with the majority (67%) being females and employed. One of the family members was affected by a mental disorder. A rehabilitation facility was unavailable to the majority (67%) of them near their residence. Regarding the young adults with chronic mental disorders, the majority (74%) were unemployed and diagnosed with affective disorders. The maximum duration of illness was 20 years, with onset typically at the age of 17-21 years. From the analysis, six main themes and 36 subthemes emerged. The main themes were as follows: family caregivers experience difficulty in identifying the responsible person and support system, providing physical care, tackling emergencies, problems with medication and early identification of relapse, problems with indecent behaviour, and knowledge deficit related to mental disorders and their management. Conclusion: The study concludes that family caregivers of young adults with chronic mental disorders experience various psychosocial and economic issues and problems while caring for a young adult with chronic mental disorders. The knowledge generated from the present study can be utilised by mental health professionals to plan home-based care strategies to improve the quality of life and reduce the burden of care for FCGs.

Standardized Workflow for Mass-Spectrometry-Based Single-Cell Proteomics Data Processing and Analysis Using the scp Package.

Mass-spectrometry (MS)-based single-cell proteomics (SCP) explores cellular heterogeneity by focusing on the functional effectors of the cells-proteins. However, extracting meaningful biological information from MS data is far from trivial, especially with single cells. Currently, data analysis workflows are substantially different from one research team to another. Moreover, it is difficult to evaluate pipelines as ground truths are missing. Our team has developed the R/Bioconductor package called scp to provide a standardized framework for SCP data analysis. It relies on the widely used QFeatures and SingleCellExperiment data structures. In addition, we used a design containing cell lines mixed in known proportions to generate controlled variability for data analysis benchmarking. In this chapter, we provide a flexible data analysis protocol for SCP data using the scp package together with comprehensive explanations at each step of the processing. Our main steps are quality control on the feature and cell level, aggregation of the raw data into peptides and proteins, normalization, and batch correction. We validate our workflow using our ground truth data set. We illustrate how to use this modular, standardized framework and highlight some crucial steps.