Evolution of assortative mating following selective introgression of pigmentation genes between two Drosophila species

Adaptive introgression is ubiquitous in animals but experimental support for its role in driving speciation remains scarce. In the absence of conscious selection, admixed laboratory strains of Drosophila asymmetrically and progressively lose alleles from one parental species and reproductive isolation against the predominant parent ceases after 10 generations. Here, we selectively introgressed during one year light pigmentation genes of D. santomea into the genome of its dark sibling D. yakuba, and vice versa. We found that the pace of phenotypic change differed between the species and the sexes, and identified through genome sequencing common as well as distinct introgressed loci in each species. Mating assays showed that assortative mating between introgressed flies and both parental species persisted even after four years (~ 60 generations) from the end of the selection. Those results indicate that selective introgression of as low as 0.5% of the genome can beget morphologically-distinct and reproductively-isolated strains, two prerequisites for the delimitation of new species. Our findings hence represent a significant step towards understanding the genome-wide dynamics of speciation-through-introgression.

Associations Between Canada's Cannabis Legalization and Emergency Department Presentations for Transient Cannabis-Induced Psychosis and Schizophrenia Conditions: Ontario and Alberta, 2015–2019

Objective Cannabis legalization in many jurisdictions worldwide has raised concerns that such legislation might increase the burden of transient and persistent psychotic illnesses in society. Our study aimed to address this issue. Methods Drawing upon emergency department (ED) presentations aggregated across Alberta and Ontario, Canada records (April 1, 2015–December 31, 2019), we employed Seasonal Autoregressive Integrated Moving Average (SARIMA) models to assess associations between Canada's cannabis legalization (via the Cannabis Act implemented on October 17, 2018) and weekly ED presentation counts of the following ICD-10-CA-defined target series of cannabis-induced psychosis (F12.5; n = 5832) and schizophrenia and related conditions (“schizophrenia”; F20-F29; n = 211,661), as well as two comparison series of amphetamine-induced psychosis (F15.5; n = 10,829) and alcohol-induced psychosis (F10.5; n = 1,884). Results ED presentations for cannabis-induced psychosis doubled between April 2015 and December 2019. However, across all four SARIMA models, there was no evidence of significant step-function effects associated with cannabis legalization on post-legalization weekly ED counts of: (1) cannabis-induced psychosis [0.34 (95% CI −4.1; 4.8; P = 0.88)]; (2) schizophrenia [24.34 (95% CI −18.3; 67.0; P = 0.26)]; (3) alcohol-induced psychosis [0.61 (95% CI −0.6; 1.8; P = 0.31); or (4) amphetamine-induced psychosis [1.93 (95% CI −2.8; 6.7; P = 0.43)]. Conclusion Implementation of Canada's cannabis legalization framework was not associated with evidence of significant changes in cannabis-induced psychosis or schizophrenia ED presentations. Given the potentially idiosyncratic rollout of Canada's cannabis legalization, further research will be required to establish whether study results generalize to other settings.

Transfer Printing of Perovskite Whispering Gallery Mode Laser Cavities by Thermal Release Tape

The outstanding optoelectrical properties and high-quality factor of whispering gallery mode perovskite nanocavities make it attractive for applications in small lasers. However, efforts to make lasers with better performance have been hampered by the lack of efficient methods for the synthesis and transfer of perovskite nanocavities on desired substrate at quality required for applications. Here, we report transfer printing of perovskite nanocavities grown by chemical vapor deposition from mica substrate onto SiO2 substrate. Transferred perovskite nanocavity has an RMS roughness of ~ 1.2 nm and no thermal degradation in thermal release process. We further use femtosecond laser to excite a transferred perovskite nanocavity and measures its quality factor as high as 2580 and a lasing threshold of 27.89 μJ/cm2 which is almost unchanged as compared with pristine perovskite nanocavities. This method represents a significant step toward the realization of perovskite nanolasers with smaller sizes and better heat management as well as application in optoelectronic devices.

OffFog: An Approach to Support the Definition of Offloading Policies on Fog Computing

IoT devices deployed in Smart Cities usually have significant resource limitations. For this reason, offload tasks or data to other layers such as fog or cloud is regularly adopted to smooth out this issue. Although data offloading is a well-known aspect of fog computing, the specification of offloading policies is still an open issue due to the lack of clear guidelines. Therefore, we propose OffFog—an approach to guide the definition of data offloading policies in the context of fog computing. In order to evaluate OffFog, we extended the well-known simulator iFogSim and conducted an experimental study based on an urban surveillance system. The results demonstrated the benefits of implementing data offloading based on OffFog recommended policies. Furthermore, we identified the best configuration involving design decisions such as data compression, data criticality, and storage thresholds. The best configuration produced at least 76% improvement in network latency and 5% in the average execution time compared to the iFogSim default strategy. We believe these results represent a significant step towards establishing a systematic decision framework for data offloading policies in the context of fog computing.

Digital business model innovation: toward construct clarity and future research directions

In times of unprecedented change related to the ongoing digital transformation of business and society at large, a pressing contemporary management challenge is recognizing and translating these changes into digital business model innovation (DBMI). Academia potentially has much to offer in aiding this managerial challenge, yet research in the field remains vague with regard to what DBMI is. We detect conceptual ambiguity among scholars as a bottleneck that prevents advancements in the field of DBMI research. In this article, we aim to trace the foundation of key attributes of the DBMI concept and propose a novel definition. Our insights are based on a targeted, state-of-the-art literature review of 57 publications. We conclude with an exploration of avenues for future research, which we closely link to the broader fields of strategic management, information systems, and organization studies, thereby exposing the issue of DBMI to a wider audience. Overall, we aim to make a significant step toward construct clarity in DBMI research.

A Systematic Literature Review on Risk Assessment and Mitigation Approaches in Requirement Engineering

Risk assessment and management practice is an organized way to identify, analyze, and assess the impacts of risks and mitigate them when they arise. Risk can occur in any phase of software development and is a significant step for better supervision of threats. The purpose of this study is to identify and analyze existing risk assessment and management techniques from a historical perspective that address and study risk management and perception of risk. The chapter presents extensive summary of existing literature on various techniques and approaches related to requirements defects, defect taxonomy, its classification, and its potential impact on software development as the main contributions of this research work. The primary objective of this study was to present a systematic literature review of techniques/methods/tools for risk assessment and management. This research successfully identifies and discovers existing risk assessment and management techniques, their limitations, taxonomies, processes, and identifies possible improvements for better defect identification and prevention.

Images Processing for Segmentation Neisseria Bacteria Cells

This paper introduces the segmentation of Neisseria bacterial meningitis images. Images segmentation is an operation of identifying the homogeneous location in a digital image. The basic idea behind segmentation called thresholding, which be classified as single thresholding and multiple thresholding. To perform images segmentation, transformations and morphological operations processes are used to segment the images, as well as image transformation an edge detecting, filling operation, design structure element, and arithmetic operations technique is used to implement images segmentation. The images segmentation represent significant step in extracting images features and diagnoses the disease by computer software applications.

Analysis of the Possibilities for Improving the Environmental Legislation of Kazakhstan

The study describes the results of a study of environmental problems that limit socio-economic development in Kazakhstan. Changes in legislation, management decisions, and strengthening of control over compliance with environmental standards are proposed. The article examines the gaps in the legislative regulation of environmental safety and the policy at the national level. The recommendations outlined in the research will create conditions for ensuring a higher level of environmental protection, introduce a more effective system for the prevention and control of pollution and waste management, which will inevitably lead to a significant step-by-step improvement in the quality of the environment in the medium and long term, and as a result - to reduce the levels of morbidity and mortality, loss and decrease in the working capacity of the population caused by environmental pollution. Such positive social consequences will help reduce the losses in GDP associated with the above factors.

Stain-free nucleus identification in holographic learning flow cyto-tomography

Quantitative Phase Imaging (QPI) has gained popularity because it can avoid the staining step, which in some cases is difficult or impossible. However, QPI does not provide the well-known specificity to various parts of the cell (e.g., organelles, membrane). Here we show a novel computational segmentation method based on statistical inference that bridges the gap between the specificity of Fluorescence Microscopy (FM) and the label-free property of QPI techniques to identify the cell nucleus. We demonstrate application to stain-free cells reconstructed through the holographic learning and in flow cyto-tomography modality. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus-like regions can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal FM data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting the three-dimensional (3D) intracellular specificity directly from the phase-contrast data in a typical flow cytometry configuration.

The Di models method: geological 3-D modeling of detrital systems consisting of varying grain fractions to predict the relative lithological variability for a multipurpose usability

The coexistence of a wide variety of subsurface uses in urban areas requires increasingly demanding geological prediction capacities for characterizing the geological heterogeneities at a small-scale. In particular, detrital systems are characterized by the presence of highly varying sediment mixtures which control the non-constant spatial distribution of properties, therefore presenting a crucial aspect for understanding the small-scale spatial variability of physical properties. The proposed methodology uses the lithological descriptions from drilled boreholes and implements sequential indicator simulation to simulate the cumulative frequencies of each lithological class in the whole sediment mixture. The resulting distributions are expressed by a set of voxel models, referred to as Di models. This solution is able to predict the relative amounts of each grain fraction on a cell-by-cell basis and therefore also derive a virtual grain size distribution. Its implementation allows the modeler to flexibly choose both the grain fractions to be modeled and the precision in the relative quantification. The concept of information entropy is adapted as a measure of the disorder state of the clasts mixture, resulting in the concept of “Model Lithological Uniformity,” proposed as a measure of the degree of detrital homogeneity. Moreover, the “Most Uniform Lithological Model” is presented as a distribution of the most prevailing lithologies. This method was tested in the city of Munich (Germany) using a dataset of over 20,000 boreholes, providing a significant step forward in capturing the spatial heterogeneity of detrital systems and addressing model scenarios for applications requiring variable relative amounts of grain fractions.