A side-effect free method for identifying cancer drug targets

Identifying efective drug targets, with little or no side efects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side efect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identifcation of efective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying efective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as efective candidates for drug development

Controlling Delegations in Liquid Democracy

In liquid democracy, agents can either vote directly or delegate their vote to a different agent of their choice. This results in a power structure in which certain agents possess more voting weight than others. As a result, it opens up certain possibilities of vote manipulation, including control and bribery, that do not exist in standard voting scenarios of direct democracy. Here we formalize a certain kind of election control -- in which an external agent may change certain delegation arcs -- and study the computational complexity of the corresponding combinatorial problem.Comment: Accepted in 23rd International Conference on Autonomous Agents and Multiagent Systems(AAMAS 2024

Ensemble approach for fake news classification using machine learning

During the covid 19 outbreak, fake news has grown highly, affecting people’s mental and physical health. There is a wide range of solutions for fake news classification which are machine learning-based proposed models. Research shows that the existing proposed models have less accuracy, and they are only text-based models. In our research paper, we are focused on different algorithms, and we are comparing these algorithms in our proposed model in this research paper. We are considering the title author and text in the proposed model. Based on our experiments, Logistic Regression has high accuracy, recall, and precision score values. This research paper suggests using a logistic regression model to classify fake news

Biomarker Potential of Vimentin in Oral Cancers

Oral carcinogenesis is a multistep process. As much as 5% to 85% of oral tumors can develop from potentially malignant disorders (PMD). Although the oral cavity is accessible for visual examination, the ability of current clinical or histological methods to predict the lesions that can progress to malignancy is limited. Thus, developing biological markers that will serve as an adjunct to histodiagnosis has become essential. Our previous studies comprehensively demonstrated that aberrant vimentin expression in oral premalignant lesions correlates to the degree of malignancy. Likewise, overwhelming research from various groups show a substantial contribution of vimentin in oral cancer progression. In this review, we have described studies on vimentin in oral cancers, to make a compelling case for vimentin as a prognostic biomarker

Role of a patatin-like phospholipase in Plasmodium falciparum gametogenesis and malaria transmission

International audienceTransmission of Plasmodium falciparum involves a complex process that starts with the ingestion of gametocytes by female Anopheles mosquitoes during a blood meal. Activation of gametocytes in the mosquito midgut triggers "rounding up" followed by egress of both male and female gametes. Egress requires secretion of a perforin-like protein, PfPLP2, from intracellular vesicles to the periphery, which leads to destabilization of peripheral membranes. Male gametes also develop flagella, which assist in binding female gametes for fertilization. This process of gametogenesis, which is key to malaria transmission, involves extensive membrane remodeling as well as vesicular discharge. Phospholipase A2 enzymes (PLA2) are known to mediate membrane remodeling and vesicle secretion in diverse organisms. Here, we show that a P. falciparum patatin-like phospholipase (PfPATPL1) with PLA2 activity plays a key role in gametogenesis. Conditional deletion of the gene encoding PfPATPL1 does not affect P. falciparum blood stage growth or gametocyte development but reduces efficiency of rounding up, egress, and exflagellation of gametocytes following activation. Interestingly, deletion of the PfPATPL1 gene inhibits secretion of PfPLP2, reducing the efficiency of gamete egress. Deletion of PfPATPL1 also reduces the efficiency of oocyst formation in mosquitoes. These studies demonstrate that PfPATPL1 plays a role in gametogenesis, thereby identifying PLA2 phospholipases such as PfPATPL1 as potential targets for the development of drugs to block malaria transmission

A critical review on the state-of-the-art and future prospects of Machine Learning for Earth Observation Operations

The continuing Machine Learning (ML) revolution indubitably has had a significant positive impact on the analysis of downlinked satellite data. Other aspects of the Earth Observation industry, despite being less susceptible to widespread application of Machine Learning, are also following this trend. These applications, actual use cases, possible prospects and difficulties, as well as anticipated research gaps, are the focus of this review of Machine Learning applied to Earth Observation Operations. A wide range of topics are covered, including mission planning, fault diagnosis, fault prognosis and fault repair, optimization of telecommunications, enhanced GNC, on-board image processing, and the use of Machine Learning models on platforms with constrained compute and power capabilities, as well as recommendations in the respective areas of research. The review tackles all on-board and off-board applications of machine learning to Earth Observation with one notable exception: it omits all post-processing of payload data on the ground, a topic that has been studied extensively by past authors. In addition, this review article discusses the standardization of Machine Learning (i.e., Guidelines and Roadmaps), as well as the challenges and recommendations in Earth Observation operations for the purpose of building better space missions