Vol 19, No 3 (2024)

The development of single-cell RNA sequencing (scRNA-seq) technology provides an excellent opportunity to explore cell heterogeneity and diversity. With the growing application of scRNA-seq data, many computational clustering methods have been developed to further uncover cell subgroups, and cell dynamics at the group level. Due to the characteristics of high dimension, high sparsity and high noise of the scRNA-seq data, it is challenging to use traditional clustering methods. Fortunately, deep learning technologies characterize the properties of scRNA-seq data well and provide a new perspective for data analysis. This work reviews the most popular computational clustering methods and tools based on deep learning technologies, involving comparison, data collection, code acquisition, results evaluation, and so on. In general, such a presentation points out some progress and limitations of the existing methods and discusses the challenges and directions for further research, which may give new insight to address a broader range of new challenges in dealing with single-cell sequencing data and downstream analysis.

Spatial transcriptomics (ST) can provide vital insights into tissue function with the spatial organization of cell types. However, most technologies have limited spatial resolution, i.e., each measured location contains a mixture of cells, which only quantify the average expression level across many cells in the location. Recently developed algorithms show the promise to overcome these challenges by integrating single-cell and spatial data. In this review, we summarize spatial transcriptomic technologies and efforts at cell-type deconvolution. Importantly, we propose a unified probabilistic framework, integrating the details of the ST data generation process and the gene expression process simultaneously for modeling and inferring spatial transcriptomic data.

Background:Recent rumen microbiome studies are progressive due to the advent of nextgeneration sequencing technologies, computational models, and gene referencing databases. Rumen metagenomics enables the linking of the genetic structure and composition of the rumen microbial community to the functional role it plays in the ecosystem. Systematic investigations of the rumen microbiome, including its composition in cattle, have revealed the importance of microbiota in rumen functions. Various research studies have identified different types of microbiome species that reside within the rumen and their relationships, leading to a greater understanding of their functional contribution.

Objective:The objective of this scoping review was to highlight the role of the phylogenetic and functional composition of the microbiome in cattle functions. It is driven by a natural assumption that closely related microbial genes/operational taxonomical units (OTUs)/amplicon sequence variants (ASVs) by phylogeny are highly correlated and tend to have similar functional traits.

Methods:PRISMA approach has been used to conduct the current scoping review providing state-ofthe- art studies for a comprehensive understanding of microbial genes’ phylogeny in the rumen microbiome and their functional capacity.

Results:44 studies have been included in the review, which has facilitated phylogenetic advancement in studying important cattle functions and identifying key microbiota. Microbial genes and their interrelations have the potential to accurately predict the phenotypes linked to ruminants, such as feed efficiency, milk production, and high/low methane emissions. In this review, a variety of cattle have been considered, ranging from cows, buffaloes, lambs, Angus Bulls, etc. Also, results from the reviewed literature indicate that metabolic pathways in microbiome genomic groupings result in better carbon channeling, thereby affecting methane production by ruminants.

Conclusion:The mechanistic understanding of the phylogeny of the rumen microbiome could lead to a better understanding of ruminant functions. The composition of the rumen microbiome is crucial for the understanding of dynamics within the rumen environment. The integration of biological domain knowledge with functional gene activity, metabolic pathways, and rumen metabolites could lead to a better understanding of the rumen system.

Background:In many medically developed applications, the process of early diagnosis in cases of pulmonary disease does not exist. Many people experience immediate suffering due to the lack of early diagnosis, even after becoming aware of breathing difficulties in daily life. Because of this, identifying such hazardous diseases is crucial, and the suggested solution combines computer vision and communication processing techniques. As computing technology advances, a more sophisticated mechanism is required for decision-making.

Objective:The major objective of the proposed method is to use image processing to demonstrate computer vision-based experimentation for identifying lung illness. In order to characterize all the uncertainties that are present in nodule segments, an improved support vector machine is also integrated into the decision-making process.

Methods:As a result, the suggested method incorporates an Improved Support Vector Machine (ISVM) with a clear correlation between various margins. Additionally, an image processing technique is introduced where all impacted sites are marked at high intensity to detect the presence of pulmonary syndrome. Contrary to other methods, the suggested method divides the image processing methodology into groups, making the loop generation process much simpler.

Results:Five situations are taken into account to demonstrate the effectiveness of the suggested technique, and test results are compared with those from existing models.

Conclusion:The proposed technique with ISVM produces 83 percent of successful results.