This study identified Vibrio parahaemolyticus in oyster and seawater samples collected from Delaware Bay from June through October of 2016. Environmental parameters including water temperature, salinity, dissolved oxygen, pH, and chlorophyll a were measured per sampling event. Oysters homogenate and seawater samples were 10-fold serially diluted and directly plated on CHROMagarᵀᴹ Vibrio medium. Presumptive V. parahaemolyticus colonies were counted and at least 20% of these colonies were selected for molecular chracterization. V. parahaemolyticus isolates (n = 165) were screened for the presence of the species-specific thermolabile hemolysin (tlh) gene, the pathogenic thermostable direct hemolysin (tdh)/ thermostable related hemolysin (trh) genes, the regulatory transmembrane DNA-binding gene (toxR), and V. parahaemolyticus metalloprotease (vpm) gene using a conventional PCR. The highest mean levels of the presumptive V. 4-Octyl solubility dmso parahaemolyticus were 9.63×103 CFU/g and 1.85×103 CFU/mL in the oyster and seawater samplet hemolysin (TDH) protein can be of significance. The outcomes of this study will provide some foundation for future studies regarding pathogenic Vibrio dynamics in relation to environmental quality.Adoption of a new technology depends on many factors. Marketing, advertising, social interactions, and personal convictions are relevant features when deciding to adopt, or not, a new technology. Thus, it is very important to determine the relative weight of these factors when introducing a new technology. Here we discuss an agent based model to investigate the behavior of agents exposed to advertising and social contacts. Agents may follow the social pressure, or maybe contrarians, acting against the majority, to decide if they adopt or not a new technology. First, we solve analytically the model that relies on the above quoted factors. Then, we compare the theoretical results with empirical data concerning the adoption of innovations by American households during the 20th century. The analysis of the diffusion dynamics process is done either for the whole period, or by periods based on the so-called technical-economic paradigms, according to Freeman and Perez. Three different periods are considered before 1920, from 1920 to 1970, and after 1970. We study the evolution of the model parameters for each technical-economic period. Finally, by adjusting the key parameters we are able to collapse all the data into a universal curve that describes all the adoption processes.Patent Citation Analysis has been gaining considerable traction over the past few decades. In this paper, we collect extensive information on patents and citations and provide a perspective of citation network analysis of patents from a statistical viewpoint. We identify and analyze the most cited patents, the most innovative and the highly cited companies along with the structural properties of the network by providing in-depth descriptive analysis. Furthermore, we employ Exponential Random Graph Models (ERGMs) to analyze the citation networks. ERGMs enables understanding the social perspectives of a patent citation network which has not been studied earlier. We demonstrate that social properties such as homophily (the inclination to cite patents from the same country or in the same language) and transitivity (the inclination to cite references’ references) together with the technicalities of the patents (e.g., language, categories), has a significant effect on citations. We also provide an in-depth analysis of citations for sectors in patents and how it is affected by the size of the same. Overall, our paper delves into European patents with the aim of providing new insights and serves as an account for fitting ERGMs on large networks and analyzing them. ERGMs help us model network mechanisms directly, instead of acting as a proxy for unspecified dependence and relationships among the observations.This study describes, for the first time, the experimental and computational investigations for evaluation of kolliphor RH 40 as a fluorescence enhancer surfactant in development of a spectrofluorimetric method for determination of lapatinib (LAP), a tyrosine kinase-inhibitor drug approved for targeted therapy of breast cancer. The investigations involved the ability of kolliphor RH 40 to form micelles with LAP and its enhancing effect on the weak native fluorescence of LAP at 420 nm after its excitation at 292 nm. Different variables were experimentally investigated types of organized media, diluting solvent, buffer type and its pH value. The optimum values of the most influencing variables on the interaction of kolliphor RH 40 with LAP were refined by the computational response surface methodology (RSM). Under the optimized conditions, it was found that kolliphor RH 40 forms micelles with LAP, and its fluorescence enhancing ability was higher than other surfactants tested by ~ 10-folds. This micellar-enhanced effect of kolliphor RH 40 was employed in the development of a new sensitive spectrofluorimetric method for the accurate determination of LAP. The method was validated according to the guidelines of the International Conference on Harmonization (ICH) for validation of analytical procedures. The relative fluorescence intensity (RFI) was in excellent linear relationship (correlation coefficient was 0.998) with the LAP concentrations in the range of 50-1000 ng/mL. The method limit of detection (LOD) was 27.31 ng/mL and its accuracy was ≥ 99.82%. The method was successfully applied to the determination of LAP in its pharmaceutical tablets, tablets dissolution testing and content uniformity. The method application was extended to the determination of LAP in urine samples with an accuracy of 99.82 ± 3.45%. The method is considered as an eco-friendly green approach and more efficient alternative method to the existing analytical methodologies for determination of LAP.The extent to which brain functions are localized or distributed is a foundational question in neuroscience. In the human brain, common fMRI methods such as cluster correction, atlas parcellation, and anatomical searchlight are biased by design toward finding localized representations. Here we introduce the functional searchlight approach as an alternative to anatomical searchlight analysis, the most commonly used exploratory multivariate fMRI technique. Functional searchlight removes any anatomical bias by grouping voxels based only on functional similarity and ignoring anatomical proximity. We report evidence that visual and auditory features from deep neural networks and semantic features from a natural language processing model, as well as object representations, are more widely distributed across the brain than previously acknowledged and that functional searchlight can improve model-based similarity and decoding accuracy. This approach provides a new way to evaluate and constrain computational models with brain activity and pushes our understanding of human brain function further along the spectrum from strict modularity toward distributed representation.