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Existing data on COPD prevalence are limited or totally lacking in many regions of Europe. The geographic information system inverse distance weighted (IDW) interpolation technique has proved to be an effective tool in spatial distribution estimation of epidemiological variables, when real data are few and widely separated. Therefore, in order to represent cartographically the prevalence of COPD in Europe, an IDW interpolation mapping was performed. The point prevalence data provided by 62 studies from 19 countries (21 from 5 Northern European countries, 11 from 3 Western European countries, 14 from 5 Central European countries, and 16 from 6 Southern European countries) were identified using validated spirometric criteria. Despite the lack of data in many areas (including all regions of the eastern part of the continent), the IDW mapping predicted the COPD prevalence in the whole territory, even in extensive areas lacking real data. Although the quality of the data obtained from some studies may have some limitations related to different confounding factors, this methodology may be a suitable tool for obtaining epidemiological estimates that can enable us to better address this major public health problem.

\"Facebook and Conversation Analysis investigates the structure and organization of comments on a major social media platform, Facebook, using applied conversation analysis methods. Providing previously undocumented insights into the structure of comment threads, this book demonstrates that they have a meaningful organization, rather than casually following one another. Although normally used to explore the structure of spoken conversations, in recent years conversation analysis approaches have been successfully applied to examine online interactions on Twitter, discussion forums and email exchanges. By turning this approach towards Facebook comments, Matteo Farina provides clear and important insights into the organization of this type of social interaction. Supported by a large sample of data, with findings based on a corpus of 213 comment threads, with over 1,200 comments exchanged by 266 contributors, this book makes an important contribution to our understanding of the way people communicate on Facebook.\"--Description from publisher.

In the three decades since its introduction, resource selection analysis (RSA) has become a widespread method for analyzing spatial patterns of animal relocations obtained from telemetry studies. Recently, mechanistic home range models have been proposed as an alternative framework for studying patterns of animal space-use. In contrast to RSA models, mechanistic home range models are derived from underlying mechanistic descriptions of individual movement behavior and yield spatially explicit predictions for patterns of animal space-use. In addition, their mechanistic underpinning means that, unlike RSA, mechanistic home range models can also be used to predict changes in space-use following perturbation. In this paper, we develop a formal reconciliation between these two methods of home range analysis, showing how differences in the habitat preferences of individuals give rise to spatially explicit patterns of space-use. The resulting unified framework combines the simplicity of resource selection analysis with the spatially explicit and predictive capabilities of mechanistic home range models.

Multiomics data integration approaches offer a comprehensive functional understanding of biological systems, with significant applications in disease therapeutics. However, the quantitative integration of multiomics data presents a complex challenge, requiring highly specialized computational methods. By providing deep insights into disease-associated molecular mechanisms, multiomics facilitates precision medicine by accounting for individual omics profiles, enabling early disease detection and prevention, aiding biomarker discovery for diagnosis, prognosis, and treatment monitoring, and identifying molecular targets for innovative drug development or the repurposing of existing therapies. AI-driven bioinformatics plays a crucial role in multiomics by computing scores to prioritize available drugs, assisting clinicians in selecting optimal treatments. This review will explain the potential of AI and multiomics data integration for disease understanding and therapeutics. It highlight the challenges in quantitative integration of diverse omics data and clinical workflows involving AI in cancer genomics, addressing the ethical and privacy concerns related to AI-driven applications in oncology. The scope of this text is broad yet focused, providing readers with a comprehensive overview of how AI-powered bioinformatics and integrative multiomics approaches are transforming precision oncology. Understanding bioinformatics in Genomics, it explore the integrative multiomics strategies for drug selection, genome profiling and tumor clonality analysis with clinical application of drug prioritization tools, addressing the technical, ethical, and practical hurdles in deploying AI-driven genomics tools.

International strategies vary in their potential to exploit and augment a firm's resources, especially its knowledge base. Resource-based analysis suggests clustering the diverse entry modes in terms of their exploitation and augmentation characteristics. We thus introduce a new categorization of entry modes based on their potential to augment the resources of an entrant. We then explore the antecedents of these modes, and advance testable propositions delimiting for which firms and in which circumstances each mode maximizes long-term value creation. Finally, we outline how our resource-based framework complements transaction-cost-based frameworks.

During the past decade, compositional analysis (CA) has been used widely in wildlife habitat and resource selection studies. However, critical aspects of CA have not been tested for potential systematic biases such as an inflated Type I error rate. We used computer-simulated data based on known habitat use and availability parameters and found that compositional analysis could result in large Type I error rates. These inflated Type I errors occurred when available habitat types that were not used by animals were included in the resource selection analysis. These error rates arise because of the recommended substitution of an arbitrarily small value, such as 0.01, for each 0% utilization value for any animal. We observed, based on a series of computer-simulation analyses, that progressively larger Type I error rates in CA resulted from substituting progressively smaller positive values for each 0% utilization of a habitat category. The Type I error rate in CA also increased when the number of experimental animals was increased for a fixed number of observations per animal. Two other resource selection analysis methods (Neu et al. [1974] and the Euclidean distance-based analysis [DA] method of Conner and Plowman [2001]) did not exhibit inflated Type I error rates for the same simulated data. Our computer simulations cause us to question the veracity of CA habitat selection analyses that include habitat patches or categories with relatively small areas of availabilities and 0% use.

The south west coast of Kanyakumari district in Tamil Nadu, India, is significantly affected by seawater intrusion and diffusion of pollutants into the aquifers due to unregulated beach placer mining and other anthropogenic activities. The present study investigates the vulnerability of the coastal aquifers using Geographic Information System (GIS)-based DRASTIC model. The seven DRASTIC parameters have been analyzed using the statistical equation of this model to demarcate the vulnerable zones for aquifer contamination. The vulnerability index map is prepared from the weighted spatial parameters, and an accounting of total index value ranged from 85 to 213. Based on the categorization of vulnerability classes, the high vulnerable zones are found near the beach placer mining areas between Manavalakurichi and Kodimanal coastal stretches. The aquifers associated with settlements and agricultural lands in the middle–eastern part have experienced high vulnerability due to contaminated water bodies. Similarly, the coastal areas of Thengapattinam and Manakudi estuary and around the South Tamaraikulam have also been falling under high vulnerability condition due to backwater and saltpan. In general, the nearshore region except the placer mining zone and the backwater has a moderately vulnerable condition, and the vulnerability index values range from 149 to180. Significantly, the northern and northeastern uplands and some parts of deposition zones in the middle–south coast have been identified as low to no vulnerable conditions. They are structurally controlled by various geological features such as charnockite, garnet biotite gneiss and granites, and sand dunes, respectively. The aquifer vulnerability assessment has been cross-verified by geochemical indicators such as total dissolved solids (TDS), Cl⁻, HCO₃⁻, and Cl⁻/HCO₃⁻ratio. The high ranges of TDS (1,842––3,736 mg/l) and Cl⁻(1,412––2,112 mg/l) values are well correlated with the observed high vulnerable zones in the study area. The Cl⁻/HCO₃⁻ratio (7.13 to 12.18) of the high vulnerable zone obviously indicates deterioration of the aquifer contamination. Sensitivity analysis has also been performed to evaluate sensitivity of the individual DRASTIC parameters to aquifer vulnerability. This reveals the net recharge rate and groundwater table depth are becoming more sensitive to aquifer contamination. It is realized that the GIS is an effective platform for aquifer vulnerability mapping with reliable accuracy, and hence, the study is more useful for sustainable water resource management and the aquifer conservation.

We review 87 articles published in the Journal of Wildlife Management from 2000 to 2004 to assess the current state of practice in the design and analysis of resource selection studies. Articles were classified into 4 study designs. In design 1, data are collected at the population level because individual animals are not identified. Individual animal selection may be assessed in designs 2 and 3. In design 2, use by each animal is recorded, but availability (or nonuse) is measured only at the population level. Use and availability (or unused) are measured for each animal in design 3. In design 4, resource use is measured multiple times for each animal, and availability (or nonuse) is measured for each use location. Thus, use and availability measures are paired for each use in design 4. The 4 study designs were used about equally in the articles reviewed. The most commonly used statistical analyses were logistic regression (40%) and compositional analysis (25%). We illustrate 4 problem areas in resource selection analyses: pooling of relocation data across animals with differing numbers of relocations, analyzing paired data as though they were independent, tests that do not control experiment wise error rates, and modeling observations as if they were independent when temporal or spatial correlations occurs in the data. Statistical models that allow for variation in individual animal selection rather than pooling are recommended to improve error estimation in population-level selection. Some researchers did not select appropriate statistical analyses for paired data, or their analyses were not well described. Researchers using one-resource-at-a-time procedures often did not control the experiment wise error rate, so simultaneous inference procedures and multivariate assessments of selection are suggested. The time interval between animal relocations was often relatively short, but existing analyses for temporally or spatially correlated data were not used. For studies that used logistic regression, we identified the data type employed: single sample, case control (used–unused), use–availability, or paired use–availability. It was not always clear whether studies intended to compare use to nonuse or use to availability. Despite the popularity of compositional analysis, we do not recommend it for multiple relocation data when use of one or more resources is low. We illustrate that resource selection models are part of a broader collection of statistical models called weighted distributions and recommend some promising areas for future development.

While wind assessment periods commonly range from 1 to several years, this is typically based on experience and industry norms. In this investigation, we perform an analysis of the error that can be expected in a wind resource assessment of various lengths of time. In contrast to earlier work measuring the uncertainty of predicted wind speeds, the uncertainty in this evaluation is measured directly in terms of energy and revenue production. As the wind assessment period increased from 30 days to 1 year, the average error increased slightly. However, when the wind assessment period was increased to 2 years, the average error decreased significantly. Simultaneously, the standard deviation of the error distributions decreased and the magnitude of the maximum experimentally obtained error decreased. By understanding how the energy production uncertainty decreases with increasing assessment time, the length of the assessment period can be tailored to match a developer’s risk tolerance.