Microservice Transition and its Granularity Problem: A Systematic Mapping Study

Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. The transition to microservices has been highly motivated by the need for better alignment of technical design decisions with improving value potentials of architectures. Despite microservices' popularity, research still lacks disciplined understanding of transition and consensus on the principles and activities underlying "micro-ing" architectures. In this paper, we report on a systematic mapping study that consolidates various views, approaches and activities that commonly assist in the transition to microservices. The study aims to provide a better understanding of the transition; it also contributes a working definition of the transition and technical activities underlying it. We term the transition and technical activities leading to microservice architectures as microservitization. We then shed light on a fundamental problem of microservitization: microservice granularity and reasoning about its adaptation as first-class entities. This study reviews state-of-the-art and -practice related to reasoning about microservice granularity; it reviews modelling approaches, aspects considered, guidelines and processes used to reason about microservice granularity. This study identifies opportunities for future research and development related to reasoning about microservice granularity.Comment: 36 pages including references, 6 figures, and 3 table

Multiphoton excitation microscopy for the reconstructionand analysis of single neuron morphology

Neurons are the main cellular components of the circuits of the central nervous system (CNS). The dendritic and axonal morphology of individual neurons display marked variability between neurons in different regions of the CNS, and there is evidence that the morphology of a neuron has a strong impact on its function. For studies of structure-function relationships of specific types of neurons, it is important to visualize and quantify the complete neuronal morphology. In addition, realistic and detailed morphological reconstruction is essential for developing compartmental models that can be used for studying neuronal computation and signal processing. Here we describe in detail how multiphoton excitation (MPE) microscopy of dye-filled neurons can be used for visualization and imaging of neuronal morphology, followed by a workflow with digital deconvolution and manual or semiautomatic morphological reconstruction. The specific advantages of MPE structural imaging are low phototoxicity, the ease with which it can be combined with parallel physiological measurements from the same neurons, and the elimination of tissue post-processing and fixation-related artifacts. Because manual morphological reconstruction can be very time-consuming, this chapter also includes a detailed, step-by-step description of a workflow for semiautomatic morphological reconstruction (using freely available software developed in our laboratory), exemplified by reconstruction of a retinal amacrine cell (AII)

Patterned Photostimulation in the Brain

Photostimulation has been instrumental in the past two decades for studying the structural synaptic plasticity and functional connectivity of neuronal circuits. With the advent of optogenetic strategies, this approach has been further expanded and used to identify the neuronal substrates of behavior via monitoring and modulating the activity of specific neuronal types in vivo. To date, however, photostimulation has been mainly implemented via full-field illumination and laser scanning protocols, which suffer from limited selectivity and stop short of generating asynchronous and spatially distributed neuronal firing patterns, characteristic for brain activity