MOTIVATION: Spatial transcriptomics (ST) can reveal the existence and extent of spatial variation of gene expression in complex tissues. Such analyses could help identify spatially localized processes underlying a tissue's function. Existing tools to detect spatially variable genes assume a constant noise variance across spatial locations. This assumption might miss important biological signals when the variance can change across locations. RESULTS: In this article, we propose NoVaTeST, a framework to identify genes with location-dependent noise variance in ST data. NoVaTeST models gene expression as a function of spatial location and allows the noise to vary spatially. NoVaTeST then statistically compares this model to one with constant noise and detects genes showing significant spatial noise variation. We refer to these genes as "noisy genes." In tumor samples, the noisy genes detected by NoVaTeST are largely independent of the spatially variable genes detected by existing tools that assume constant noise, and provide important biological insights into tumor microenvironments. AVAILABILITY AND IMPLEMENTATION: An implementation of the NoVaTeST framework in Python along with instructions for running the pipeline is available at https://github.com/abidabrar-bracu/NoVaTeST.

MOTIVATION: Spatial domain identification is a very important problem in the field of spatial transcriptomics. The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement. RESULTS: In this article, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network based approach. Named "ScribbleDom", our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial components: similarity in gene expression profiles and adherence to the valuable input of a human annotator through scribbles on histology images, providing prior knowledge about spot labels. The spatial continuity of the tissue domains is taken into account by extracting information on the spot microenvironment through convolution filters of varying sizes, in the form of "Inception" blocks. By leveraging this semi-supervised approach, ScribbleDom significantly improves the quality of spatial domains, yielding superior results both quantitatively and qualitatively. Our experiments on several benchmark datasets demonstrate the clear edge of ScribbleDom over state-of-the-art methods-between 1.82% to 169.38% improvements in adjusted Rand index for 9 of the 12 human dorsolateral prefrontal cortex samples, and 15.54% improvement in the melanoma cancer dataset. Notably, when the expert input is absent, ScribbleDom can still operate, in a fully unsupervised manner like the state-of-the-art methods, and produces results that remain competitive. AVAILABILITY AND IMPLEMENTATION: Source code is available at Github (https://github.com/1alnoman/ScribbleDom) and Zenodo (https://zenodo.org/badge/latestdoi/681572669).

Epileptic seizure is a neurological disorder that is prevalent in both males and females of all age ranges. Detection of epileptic seizure serves as an important role for epileptic patients as it allows the initiation of systems to prevent injuries and limiting the possibilities of risk by providing targeted therapy by anticipating their onset prior to presentation. Electroencephalography (EEG) plays an important role in seizure detection and is one of the most well-known techniques for determining stages of epilepsy. Since, EEG is a non-stationary signal it can be quite difficult to differentiate amongst seizure activity and normal neural activity. In this paper we have proposed an epilepsy detection method based on five different feature extraction methods and followed by that the original domain of the extracted features were transformed using Discrete Wavelet Transform (DWT) and three different classifiers- Decision Tree, Random Forest and KNN to classify into seizure and non-seizure stages. Results demonstrated in this paper have outperformed the existing state-of-the-art methods with 97.22%, 100% and 83.33% for 2 class classification and 91.67%, 91.67% and 80.56% for 4 class classification for the aforementioned classification techniques accordingly.

This paper proposes a system for converting handwritten words and numbers into a text file. Our system uses A CNN based method to identify letter and digits. This Automatic system requires image preprocessing, classifying into letters and digits and saving the letter into a text file. The system consists of a touchscreen as a user interface, an Arduino board (microcontroller ATmega 2560) and MATLAB. Any type of handwriting is tested with the classifying process; we got 89% accuracy using our own dataset. Both letter and digits can be recognized and converted into text file using this process. The proposed system will lessen the labor of creating electronic documents and provide easy preservation of data.