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Learn Amazon Web Services in a Month of Lunches guides you through the process of building a robust and secure web application using the core AWS services you really need to know. You'll be amazed by how much you can accomplish with AWS!

Transposable elements (TEs) are grouped into several families with diverse sequences. Owing to their diversity, studies involving the detection, classification, and annotation of TEs are difficult tasks. Moreover, simple comparisons of TEs among different species with different methods can lead to misinterpretations. The genome data of several honey bee (Apis) species are available in public databases. Therefore, we conducted a meta-analysis of TEs, using 11 sets of genome data for Apis species, in order to establish data of “landscape of TEs”. Consensus TE sequences were constructed and their distributions in the Apis genomes were determined. Our results showed that TEs belonged to four to seven TE families among 13 and 15 families of TEs detected in classes I and II respectively mainly consisted of Apis TEs and that more DNA/TcMar-Mariner consensus sequences and copies were present in all Apis genomes tested. In addition, more consensus sequences and copy numbers of DNA/TcMar-Mariner were detected in Apis mellifera than in other Apis species. These results suggest that TcMar-Mariner might exert A. mellifera-specific effects on the host A. mellifera species. In conclusion, our unified approach enabled comparison of Apis genome sequences to determine the TE landscape, which provide novel evolutionary insights into Apis species.

Migration studies include spectacular examples from vertebrates, such as birds, bats, and turtles. However, insect migration studies have lagged due to a restrictive definition of what entails migration and because of constraints in tracking insect movement. This has changed in recent times with studies on migratory butterflies, moths, and dragonflies. However, studies on collective migration by social insects such as honey bees are still largely lacking, despite their impact on ecosystem services, food security, and biodiversity. In this review, we synthesise findings from scattered studies on migration in a subset of honey bee species to better understand this phenomenon and to provide impetus for future research. As a general trend, migration in the genus Apis begins with a shift from a statary to a migratory phase within colonies, characterised by greater scout activity and consensus-building with respect to the direction of departure using migratory waggle dances. Once air-borne, the finer details of a swarm’s movement are unknown for any Apis species. Swarms reportedly make multiple stops, and while temporarily existing as comb-less clusters at these sites, the decision-making process occurs repeatedly, until nest-selection dances occur and the migratory phase finally culminates. We highlight the need for studies into the drivers and mechanisms of honey bee migration, as well as the promise of initiatives such as citizen science and tools such as pollen metabarcoding in studying migration in honey bees. This is particularly needed, given that rapidly changing habitats and climate could affect honey bee migration and the pollination services they provide.

Successful user interfaces need to be visually interesting, fast, and flowing. The React.js JavaScript library supercharges view-heavy web applications by improving data flow between UI components. React sites update visual elements efficiently and smoothly, minimizing page reloads. React is developer friendly, with a strong ecosystem to support the dev process along the full application stack. And becuse it's all JavaScript. React is instantly familiar. React Quickly is the tutorial for web developers who want to get started fast with React.js. Following carefully chosen and clearly explained examples, you'll learn React development using your existing JavaScript and web dev skills. You'll explore a host of different projects as you learn about web components, forms and data.\"--Back cover.

The spatial distribution of the aerosols over 86 Chinese cities was reconstructed from air pollution index (API) records for summer 2000 to winter 2006. PM sub(10) (particulate matter less than or equal to 10 mu m) mass concentrations were calculated for days when PM sub(10) was the principal pollutant, these accounted for 91.6% of the total 150 428 recorded days. The 83 cities in mid-eastern China (100 degree E to 130 degree E) were separated into three latitudinal zones using natural landscape features as boundaries. Areas with high PM sub(10) level in northern China (127 to 192 mu g m super(-3)) included Urumchi, Lanzhou-Xining, Weinan-Xi'an, Taiyuan-Datong-Yangquan-Changzhi, Pingdingshan-Kaifeng, Beijing-Tianjin-Shijiazhuang, Jinan, and Shenyang-Anshan-Fushun; in the middle zone, high PM sub(10) (119-147 mu g m super(-3)) occurred at Chongqing-Chengdu-Luzhou, Changsha-Wuhan, and Nanjing-Hangzhou; in the southern zone, only four cities (Qujing, Guiyang, Guangzhou and Shaoguan) showed PM sub(10) concentration >80 mu g m super(-3). The median PM sub(10) concentration decreased from 108 mu g m super(-3) for the northern cities to 95 mu g m super(-3) and 55 mu g m super(-3) for the middle and southern zones, respectively. PM sub(10) concentration and the APIs both exhibited wintertime maxima, summertime minima, and the second highest values in spring. PM sub(10)showed evidence for a decreasing trend for the northern cities while in the other zones urban PM sub(10) levels fluctuated, but showed no obvious change over time. The spatial distribution of PM sub(10) was compared with the emissions, and the relationship between the surface PM sub(10) concentration and the aerosol optical depth (AOD) was also discussed.

Production of cucurbit crops presents growers with numerous challenges. Several severe pests and diseases can be managed through the use of rotation, trap cropping, mechanical barriers, such as row covers, and chemical applications. However, considerations must also be made for pollinating insects, as adequate pollination affects the quantity and quality of fruit. Insecticides may negatively affect pollinators; a concern enhanced in recent years due to losses in managed Apis melifera L. colonies. Row covers can be used in place of chemical control before pollination, but when removed, pests have access to fields along with the pollinators. If pollination services of native bees could be harnessed for use under continuous row covers, both concerns could be balanced for growers. The potential of two bee species which specialize on cucurbit flowers, Peponapis pruinosa Say and Xenoglossa strenua Cresson, were assessed under continuous row covers, employed over acorn squash. Experimental treatments included plots with either naturally or artificially introduced bees under row covers and control plots with row covers either permanently removed at crop flowering, or employed continuously with no added pollinating insects. Pests in plots with permanently removed row covers were managed using standard practices used in certified organic production. Marketable yields from plots inoculated with bees were indistinguishable from those produced under standard practices, indicating this system would provide adequate yields to growers without time and monetary inputs of insecticide applications. Additionally, application of this technique was investigated for muskmelon production and discussed along with considerations for farm management.

Ranging from dwarfs to giants, the species of honeybees show remarkable differences in body size that have placed evolutionary constrains on the size of sensory organs and the brain. Colonies comprise three adult phenotypes, drones and two female castes, the reproductive queen and sterile workers. The phenotypes differ with respect to tasks and thus selection pressures which additionally constrain the shape of sensory systems. In a first step to explore the variability and interaction between species size-limitations and sex and caste-specific selection pressures in sensory and neural structures in honeybees, we compared eye size, ommatidia number and distribution of facet lens diameters in drones, queens and workers of five species (Apis andreniformis, A. florea, A. dorsata, A. mellifera, A. cerana). In these species, male and female eyes show a consistent sex-specific organization with respect to eye size and regional specialization of facet diameters. Drones possess distinctly enlarged eyes with large dorsal facets. Aside from these general patterns, we found signs of unique adaptations in eyes of A. florea and A. dorsata drones. In both species, drone eyes are disproportionately enlarged. In A. dorsata the increased eye size results from enlarged facets, a likely adaptation to crepuscular mating flights. In contrast, the relative enlargement of A. florea drone eyes results from an increase in ommatidia number, suggesting strong selection for high spatial resolution. Comparison of eye morphology and published mating flight times indicates a correlation between overall light sensitivity and species-specific mating flight times. The correlation suggests an important role of ambient light intensities in the regulation of species-specific mating flight times and the evolution of the visual system. Our study further deepens insights into visual adaptations within the genus Apis and opens up future perspectives for research to better understand the timing mechanisms and sensory physiology of mating related signals.

Leverage the power of Strapi to build self-hosted, customizable, and performant content APIs Key Features Discover how Strapi can help you build APIs quickly and focus on your products and featuresLearn how to put Strapi into practice by implementing it in real-world scenariosUnderstand how to use Strapi's powerful features to customize your APIs Book Description Strapi is a Node.js-based, flexible, open-source headless CMS with an integrated admin panel that anyone can use and helps save API development time. APIs built with Strapi can be consumed using REST or GraphQL from any client. With this book, you'll take a hands-on approach to exploring the capabilities of the Strapi platform and creating a custom API from scratch. This book will help JavaScript developers to put their knowledge to work by guiding them through building powerful backend APIs. You'll see how to effortlessly create content structures that can be customized according to your needs, and gain insights into how to write, edit, and manage your content seamlessly with Strapi. As you progress through the chapters, you'll discover a wide range of Strapi features, as well as understand how to add complex features to the API such as user authentication, data sorting, and pagination. You'll not only learn how to find and use existing plugins from the open-source community but also build your own plugins with custom functionality with the Strapi plugin API and add them to the admin panel. Finally, you'll learn how to deploy the API to Heroku and AWS. By the end of this book, you'll be able to build powerful, scalable, and secure APIs using Strapi. What you will learn Explore Strapi and understand how it worksDefine content types to build APIs quickly and efficientlyUnderstand authentication and authorization in StrapiCreate production-ready APIs with StrapiDeploy the Strapi API to various environments, including Heroku and AWSUse best practices to run the Strapi API in productionSync permissions to access the API between multiple environmentsWrite basic tests for API utilities as well as the endpoint Who this book is for This book is for backend and frontend JavaScript developers. Experienced API developers will learn a new, fast, and flexible way of building APIs, while frontend developers will be able to take a step toward becoming full-stack developers by learning how to leverage Strapi for building APIs quickly. Basic knowledge of JavaScript and REST API concepts is assumed.

Cuticular hydrocarbons (CHC) are known to serve as discrimination cues and will trigger defence behaviour in a plethora of eusocial insects. However, little is known how about nestmate recognition ability selects for CHC diversification. In this study we investigate differences in CHC composition of four major honey bee species with respect to the differences in their nesting behavior. In contrast to A . mellifera , A . cerana and A . florea , the giant honey bee A . dorsata prefers to build their nests in aggregations with very small spatial distances between nests, which increases the probability of intrusions. Thus, A . dorsata exhibits a particularly challenging nesting behavior which we hypothesize should be accompanied with an improved nestmate recognition system. Comparative analyses of the worker CHC profiles indicate that A . dorsata workers exhibit a unique and more complex CHC profile than the other three honey bee species. This increased complexity is likely based on a developmental process that retains the capability to synthesize methyl-branched hydrocarbons as adults. Furthermore, two sets of behavioral experiments provide evidence that A . dorsata shows an improved nestmate discrimination ability compared to the phylogenetically ancestral A . florea , which is also open-nesting but does not form nest aggregations. The results of our study suggest that ecological traits like nesting in aggregation might be able to drive CHC profile diversification even in closely related insect species.