Search

This article discusses the use of theoretical analysis to measure the performance of algorithms and investigates its shortcomings using the field of sequencing bioinformatics. The author posits that theoretical analysis applied to algorithm performance has fallen short of its stated goals of predicting the empirical performance of an algorithm and being a yardstick to drive the design of new algorithms.

Toxoplasmosis is a significant public health concern with limited therapeutic options. The medicines for malaria venture (MMV) developed the pandemic response box (PRB) containing 400 drug-like molecules with broad pathogen activity. The aim of this work is to evaluate PRB compounds for their anti- Toxoplasma gondii activity and identify promising candidates for further evaluation. Screening identified 42 selective compounds with half effective concentration (EC50) ranging from 2.4 to 913.1 nm and half cytotoxic concentration (CC50) ranging from 6 μm to >50 μm. Selectivity index (SI) values (CC50/EC50) ranged from 11 to 17 708. Based on its in silico and in vitro profile and its commercial availability, RWJ-67657 was selected for further studies. Molecular docking analysis showed RWJ-67657 is predicted to bind to T. gondii p38 mitogen-activated protein kinase (TgMAPK). Oral administration of RWJ-67657 (20 mg kg day−1/10 days) significantly reduced parasite burden in chronically infected mice compared to mock-treated group (P < 0.01). These findings highlight the PRB as a promising source for anti- T. gondii compounds, with several showing favourable drug properties, including MMV1634492, MMV002731, MMV1634491, MMV1581551, MMV011565, MMV1581558, MMV1578577, MMV233495 and MMV1580482, firstly described here as anti- T. gondii agents. RWJ-67657 emerges as a valuable drug candidate for experimental chronic cerebral toxoplasmosis therapy. [ABSTRACT FROM AUTHOR]

The low amount of metabolites isolated from natural products is one of the challenges preventing their biological evaluation. The modulation of biosynthetic pathways by stimulating stress-induced responses in plants was proven to be a valuable tool for diversification of already known natural products. Recently, we reported the dramatic effect of methyl jasmonate (MeJA) on Vinca minor alkaloids distribution. In this study, three compounds identified as 9-methoxyvincamine, minovincinine, and minovincine are successfully isolated in good yield and subjected to several bioassays based on a network pharmacology study. The extracts and isolated compounds show weak to moderate antimicrobial and cytotoxic activities. Also, they are found to significantly promote wound healing in scratch assay, and transforming growth factor- β (TGF- β) modulation is suggested to be the potential pathway based on bioinformatic analysis. Hence, Western blotting is used to assess the expression of several markers related to this pathway and wound healing. The extracts and isolated compounds are able to increase the expression of Smad3 and Phosphatidylinositol-3-kinase (PI3K), while downregulating the levels of cyclin D1 and the mammalian target of rapamycin (mTOR) except for minovincine, which increases the mTOR expression, inferring that it might act through a different mechanism. Molecular docking is used to give insights on the ability of isolated compounds to bind with different active sites in mTOR. Collectively, the integrated phytochemical, in silico , and molecular biology approach reveal that V. minor and its metabolite could be repurposed for the management of dermatological disorders where these markers are dysregulated, which opens the gate to develop new therapeutics in the future. [ABSTRACT FROM AUTHOR]

N4-methylcytosine (4mC), as a DNA modification, plays a crucial role in epigenetic regulation. However, the existing experimentation methods for accurately identifying 4mC sites are inefficient and highly consumable, making them difficult to implement. Although a variety of new identification methods are continuously being proposed, existing techniques are not yet fully mature. Compared to traditional 4mC site predictors, based on support vector machine or convolutional neural network, we present an alternative computational approach. In this study, we propose a method based on a kernelized higher-order fuzzy inference system (KHFIS) and deep multiple kernel learning, called DMKL-HFIS, to improve the accuracy of 4mC site identification DNA sequences. We use PSTNP to process the benchmark datasets, and then apply KHFIS to obtain multiple fuzzy kernel matrices. A deep neural network is used to fuse multiple fuzzy kernel matrices. Finally, the predicted value is derived from the fused matrix. Our approach was compared with existing mainstream computational methods. On the benchmark datasets (G. subterraneus, D. melanogaster, E. coli, A. thaliana, and C. elegans), the accuracy of our approach exceeded that of a state-of-the-art method by 0.4%, 0.44%, 1.51%, 0.55%, and 0.25%, respectively. Compared to mainstream methods, our approach exhibits a higher level of accuracy and can therefore be considered an effective prediction tool. • Prediction of N4-methylcytosine sites via kernelized higher-order fuzzy inference system and deep multiple kernel learning. • Multiple fuzzy kernel matrices fusion through a deep neural network. • Our proposed method improves the accuracy of 4mC sites identification against SOTA. [ABSTRACT FROM AUTHOR]

Cohesive group extraction is an important task in many applications such as graph visualization, system science, and bioinformatics. The k -vertex-connected component (k -VCC), which is a connected graph that remains connected whenever fewer than k vertices are removed, is a well-studied model for this task. However, the use of k -VCC model may cause resolution issues; thus, we use the k -relaxed-vertex connected component (k -RVCC) model, which effectively eliminates the problem. A k -RVCC is also a connected graph, but to disconnect it, at least n - k vertices must be removed, n is the vertex number of the graph. In this study, we investigate efficient practical algorithms for listing all maximal k -RVCCs from a given graph. Specifically, we first adapted the celebrate Bron-Kerbosch tree-search algorithm from listing cliques to listing k -RVCCs. We then design new branching rules to reduce the number of branches and vertex-cut-based strategies to recursively partition the input graph into smaller independent pieces. Implementation techniques that accelerate the aforementioned algorithms were also investigated. Experiments on various large real networks validated the effectiveness of the proposed algorithms and demonstrated the efficiency of our algorithms in mining cohesive groups. [ABSTRACT FROM AUTHOR]