The Computational Thematic Analysis Toolkit

As online communities have grown, Computational Social Science has rapidly developed new techniques to study them. However, these techniques require researchers to become experts in a wide variety of tools in addition to qualitative and computational research methods. Studying online communities also requires researchers to constantly navigate highly contextual ethical and transparency considerations when engaging with data, such as respecting their members' privacy when discussing sensitive or stigmatized topics. To overcome these challenges, we developed the Computational Thematic Analysis Toolkit, a modular software package that supports analysis of online communities by combining aspects of reflexive thematic analysis with computational techniques. Our toolkit demonstrates how common analysis tasks like data collection, cleaning and filtering, modelling and sampling, and coding can be implemented within a single visual interface, and how that interface can encourage researchers to manage ethical and transparency considerations throughout their research process.

Global Characterization of Metabolic Genes Regulating Survival and Immune Infiltration in Osteosarcoma

Background: The alterations in metabolic profile of tumors have been identified as one of the prognostic hallmarks of cancers, including osteosarcoma. These alterations are majorly controlled by groups of metabolically active genes. However, the regulation of metabolic gene signatures in tumor microenvironment of osteosarcoma has not been well explained.Objectives: Thus, we investigated the sets of previously published metabolic genes in osteosarcoma patients and normal samples.Methods: We applied computational techniques to identify metabolic genes involved in the immune function of tumor microenvironment (TME) and survival and prognosis of the osteosarcoma patients. Potential candidate gene PAICS (phosphoribosyl aminoimidazole carboxylase, phosphoribosyl aminoimidazole succino carboxamide synthetase) was chosen for further studies in osteosarcoma cell lines for its role in cell proliferation, migration and apoptosis.Results: Our analyses identified a list of metabolic genes differentially expressed in osteosarcoma tissues. Next, we scrutinized the list of genes correlated with survival and immune cells, followed by clustering osteosarcoma patients into three categories: C1, C2, and C3. These analyses led us to choose PAICS as potential candidate gene as its expression showed association with poor survival and negative correlation with the immune cells. Furthermore, we established that loss of PAICS induced apoptosis and inhibited proliferation, migration, and wound healing in HOS and MG-63 cell lines. Finally, the results were supported by constructing and validating a prediction model for prognosis of the osteosarcoma patients.Conclusion: Here, we conclude that metabolic genes specifically PAICS play an integral role in the immune cell infiltration in osteosarcoma TME, as well as cancer development and metastasis.

NNeurite: artificial neuronal networks for the unsupervised extraction of axonal and dendritic time-lapse signals

Fluorescence microscopy of Ca2+ transients in small neurites of the behaving mouse provides an unprecedented view of the micrometer-scale mechanisms supporting neuronal communication and computation, and therefore opens the way to understanding their role in cognition. However, the exploitation of this growing and precious experimental data is impeded by the scarcity of methods dedicated to the analysis of images of neurites activity in vivo. We present NNeurite, a set of mathematical and computational techniques specialized for the analysis of time-lapse microscopy images of neurite activity in small behaving animals. Starting from noisy and unstable microscopy images containing an unknown number of small neurites, NNeurite simultaneously aligns images, denoises signals and extracts the location and the temporal activity of the sources of Ca2+ transients. At the core of NNeurite is a novel artificial neuronal network(NN) which we have specifically designed to solve the non-negative matrix factorization (NMF)problem modeling source separation in fluorescence microscopy images. For the first time, we have embedded non-rigid image alignment in the NMF optimization procedure, hence allowing to stabilize images based on the transient and weak neurite signals. NNeurite processing is free of any human intervention as NN training is unsupervised and the unknown number of Ca2+ sources is automatically obtained by the NN-based computation of a low-dimensional representation of time-lapse images. Importantly, the spatial shapes of the sources of Ca2+ fluorescence are not constrained in NNeurite, which allowed to automatically extract the micrometer-scale details of dendritic and axonal branches, such dendritic spines and synaptic boutons, in the cortex of behaving mice. We provide NNeurite as a free and open-source library to support the efforts of the community in advancing in vivo microscopy of neurite activity.

Compressive Fatigue Behaviour of High-Strength Concrete and Mortar: Experimental Investigations and Computational Modelling

A high-strength concrete and mortar subjected to compressive fatigue loading were comparatively investigated using experimental and computational techniques. The focus of the investigations was on the influence of the coarse aggregate in high-strength concrete. Accordingly, the fatigue behaviour was analysed experimentally using the macroscopic damage indicators strain, stiffness and acoustic emission hits. The results clearly show differences in the fatigue behaviour between the concrete and the mortar, especially at the lower stress level investigated. The basalt coarse aggregate here improves the fatigue behaviour of the concrete. Indication of a negative effect can be seen at the higher stress level. A finite element approach with a gradient-enhanced equivalent strain-based damage model combined with a fatigue model was used for the computational simulation of the fatigue behaviour. The damage model includes a differentiation between tension and compression. The fatigue model follows the assumption of the reduction in the material strength based on the accumulated gradient-enhanced equivalent strains. A random distribution of spherically shaped basalt aggregates following a given particle size distribution curve is used for the simulation of concrete. The comparison of the experimentally and computationally determined strain developments of the concrete and mortar shows very good agreement.

A deep learning approach based on stochastic gradient descent and least absolute shrinkage and selection operator for identifying diabetic retinopathy

More than eighty-five to ninety percentage of the diabetic patients are affected with diabetic retinopathy (DR) which is an eye disorder that leads to blindness. The computational techniques can support to detect the DR by using the retinal images. However, it is hard to measure the DR with the raw retinal image. This paper proposes an effective method for identification of DR from the retinal images. In this research work, initially the Weiner filter is used for preprocessing the raw retinal image. Then the preprocessed image is segmented using fuzzy c-mean technique. Then from the segmented image, the features are extracted using grey level co-occurrence matrix (GLCM). After extracting the fundus image, the feature selection is performed stochastic gradient descent, and least absolute shrinkage and selection operator (LASSO) for accurate identification during the classification process. Then the inception v3-convolutional neural network (IV3-CNN) model is used in the classification process to classify the image as DR image or non-DR image. By applying the proposed method, the classification performance of IV3-CNN model in identifying DR is studied. Using the proposed method, the DR is identified with the accuracy of about 95%, and the processed retinal image is identified as mild DR.

Epistemology of the Digital

The debate on the consequences that big data and computational techniques have generated in social sciences has developed from two opposite extremes. A consistent group of scholars today supports an active commitment of sociologists in dealing with the technological dimension of social investigation. The works developed by these “digital sociologists” focus on the definition of a method of social research that adopts a critical posture on the role that digital technology must have in scientific research but, at the same time, creative on the possibilities offered by technology to research. This posture requires great attention to the epistemology of the digital.

A NUMERICAL APPROACH TO THE CONTACT OF NOMINALLY FLAT SURFACES

Machined surfaces can be described by heights and wavelengths of the surface asperities that show a statistical variation. Considering that a regular wavy surface with a sinusoidal profile is the crudest model for a rough surface, studying the contact of regular wavy surfaces is a good approximation for the contact of nominally flat surfaces. Such contact problems exhibit periodicity that can be simulated with the aid of computational techniques derived for contact mechanics in the frequency domain. The displacement calculation, which is a necessary step in the resolution of the contact problem, is mathematically a convolution product that can be calculated in the frequency domain with increased computational efficiency. The displacement induced by a unit surface load can be expressed in the frequency domain by the frequency response functions, which are counterparts of the space domain solutions to half-space fundamental problems such as the Boussinesq problem. The displacement induced by a periodic pressure distribution can be computed by executing the convolution product between the frequency response function and pressure on a single period. It should be noted that the convolution calculation in the spectral domain implies that the contributions of all neighbouring pressure periods are accounted for. The need to treat numerically only a single period results in remarkable computational efficiency, allowing for high density meshes that can capture the essential features of any textured real surface. The displacement calculation promotes the solution of the contact problem by an iterative approach. The advanced method is benchmarked against existing analytical solutions for the 3D contact of surfaces possessing two-dimensional waviness. This essentially deterministic model, supported by a direct numerical solution that can be obtained for samples of real rough surfaces, presents itself as a worthy alternative to the existing statistical models for rough contact interaction.