Assessment Method for a Power Analysis to Identify Differentially Expressed Pathways

Gene expression data can provide a very rich source of information for elucidating the biological function on the pathway level if the experimental design considers the needs of the statistical analysis methods. The purpose of this paper is to provide a comparative analysis of statistical methods for detecting the differentially expression of pathways (DEP). In contrast to many other studies conducted so far, we use three novel simulation types, producing a more realistic correlation structure than previous simulation methods. This includes also the generation of surrogate data from two large-scale microarray experiments from prostate cancer and ALL. As a result from our comprehensive analysis of [Image: see text] parameter configurations, we find that each method should only be applied if certain conditions of the data from a pathway are met. Further, we provide method-specific estimates for the optimal sample size for microarray experiments aiming to identify DEP in order to avoid an underpowered design. Our study highlights the sensitivity of the studied methods on the parameters of the system

Preparedness for Data-Driven Business Model Innovation:A Knowledge Framework for Incumbent Manufacturers

This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized into three levels: Enablers, value creators, and outcomes. This categorization aims to assess incumbent manufacturers’ preparedness for DDBMI. Additionally, a knowledge framework is developed based on the identified nine key topics of DDBMI to aid incumbent manufacturers in enhancing their understanding of DDBMI, thereby facilitating the practical application and interpretation of data-driven approaches to business model innovation.</p

Cross-Impact Analysis of Entrepreneurial Failure and Business Model Innovation:Navigating the Impact of Societal Perceptions

Failed entrepreneurs often encounter negative societal perceptions that impede their ability to learn from failure, take risks, and innovate business models. Reducing this stigma appears crucial to support entrepreneurship and foster innovation. However, the precise relationship between stigma reduction and desired outcomes remains uncertain. This study addresses this gap by examining the variables influencing the perception of business failure. Through a systematic literature review and content analysis, we identified 20 variables within the network. A subsequent cross-impact analysis helped delineate these variables as critical, influential, dependent, inert, or neuter. Stigma emerged as the critical variable, exerting significant influence. Culture, bankruptcy laws, social capital, the frequency of business failures, and entrepreneurial attributes played pivotal roles as influential variables. Dependent variables encompassed the rate of entrepreneurship, entrepreneurial intention, and learning from failure. This study underscores the importance of comprehending the interplay between these variables and their impact on entrepreneurial outcomes. Although the influence of societal perceptions on business model innovation proved minimal, failed entrepreneurs displayed resilience in defying stigma and engaging in innovative endeavors. Our findings shed light on the significance of societal perceptions within entrepreneurial ecosystems and the adaptability of entrepreneurs in innovating existing business models. This study lays a foundation for further research into the dynamics of these influences.</p

Assessing the current landscape of AI and sustainability literature:Identifying key trends, addressing gaps and challenges

The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities and implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, and data-driven methods offer potential solutions for optimizing resources, integrating different aspects of sustainability, and informed decision-making. Sustainability research surrounds various local, regional, and global challenges, emphasizing the need to identify emerging areas and gaps where AI and data-driven models play a crucial role. The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data. The outcomes of the analyses highlight the importance of collaborative and inclusive research that bridges regional differences, the interconnection of AI, technology, and sustainability topics, and the major research themes related to sustainability. It further emphasizes the significance of developing hybrid approaches combining AI, data-driven techniques, and expert knowledge for multi-level, multi-dimensional decision-making. Furthermore, the study recognizes the necessity of addressing ethical concerns and ensuring the sustainable use of AI and big data in sustainability research.</p

Constrained Covariance Matrices With a Biologically Realistic Structure: Comparison of Methods for Generating High-Dimensional Gaussian Graphical Models

High-dimensional data from molecular biology possess an intricate correlation structure that is imposed by the molecular interactions between genes and their products forming various different types of gene networks. This fact is particularly well-known for gene expression data, because there is a sufficient number of large-scale data sets available that are amenable for a sensible statistical analysis confirming this assertion. The purpose of this paper is two fold. First, we investigate three methods for generating constrained covariance matrices with a biologically realistic structure. Such covariance matrices are playing a pivotal role in designing novel statistical methods for high-dimensional biological data, because they allow to define Gaussian graphical models (GGM) for the simulation of realistic data; including their correlation structure. We study local and global characteristics of these covariance matrices, and derived concentration/partial correlation matrices. Second, we connect these results, obtained from a probabilistic perspective, to statistical results of studies aiming to estimate gene regulatory networks from biological data. This connection allows to shed light on the well-known heterogeneity of statistical estimation methods for inferring gene regulatory networks and provides an explanation for the difficulties inferring molecular interactions between highly connected genes

Unleashing the power of Virtual Reality to manage LAZY EYE-A silent public health problem: A case study from India

Amblyopia, also known as "lazy eye," is a childhood vision disorder characterized by impaired coordination between the brain and the eye, resulting in decreased vision in one eye. It is not caused by any structural abnormalities in the eye but rather by a lack of effective coordination between the eye and brain during the crucial period of visual development in early childhood.(1) Amblyopia is a significant global public health issue, with an estimated prevalence of 1-5% of the global population according to the World Health Organization (WHO). Developing countries face a higher burden of amblyopia due to limited access to early diagnosis and treatment. If left untreated, amblyopia can result in permanent vision loss and impairment, negatively impacting an individual's quality of life, educational achievements, and career prospects.(1) A 2017 study explored the prevalence and impact of amblyopia and strabismus in Indian children. The study found a prevalence rate of 1.67% for amblyopia among children aged 5-15 years, with a higher occurrence in rural areas compared to urban areas.(2)  In another study conducted by Gupta et al. found the percentage of amblyopia was 8.6% (n=31) among 5-15 years children in uttrakhand.(3) Another study conducted by Ganekal et al. revealed the prevalence of amblyopia was 1.1% (n?=?44) among 5-15 years of students.(4) The research emphasized the importance of enhanced awareness, early detection, and improved access to suitable treatment options to alleviate the burden of amblyopia in India. Furthermore, the All India Ophthalmological Society (AIOS) conducted a study in 2019 titled "AIOS Guidelines for Amblyopia Management." This study aimed to provide evidence-based guidelines for managing amblyopia in India. It highlighted the lack of awareness and limited availability of eye care services in certain regions, leading to delayed diagnosis and treatment of amblyopia. The guidelines emphasized the significance of early screening, timely intervention, and appropriate treatment approaches to prevent long-term visual impairment in children.(5

MicroGISAXS of Langmuir–Blodgett protein films: effect of temperature on long-range order. Addendum

n/

Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance.

Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is challenging due to the large number of GO terms, their hierarchical and connected organization as directed acyclic graphs (DAGs) and the lack of tools allowing to exploit this structural information explicitly. For this reason, we developed the R package GOxploreR. The main features of GOxploreR are (I) easy and direct access to structural features of GO, (II) structure-based ranking of GO-terms, (III) mapping to reduced GO-DAGs including visualization capabilities and (IV) prioritizing of GO-terms. The underlying idea of GOxploreR is to exploit a graph-theoretical perspective of GO as manifested by its DAG-structure and the containing hierarchy levels for cumulating semantic information. That means all these features enhance the utilization of structural information of GO and complement existing analysis tools. Overall, GOxploreR provides exploratory as well as confirmatory tools for complementing any kind of analysis resulting in a list of GO-terms, e.g., from differentially expressed genes or gene sets, GWAS or biomarkers. Our R package GOxploreR is freely available from CRAN