Island-based Cuckoo Search with Elite Opposition-based Learning and Multiple Mutation Methods for Solving Discrete and Continuous Optimization Problems

The Island Cuckoo Search ( i CSPM) algorithm is a new variation of Cuckoo Search (CS) that uses the island model and the Highly Disruptive Polynomial (HDP) mutation for solving a broad range of optimization problems. This article introduces an improved i CSPM algorithm called i CSPM with elite opposition-based learning and multiple mutation methods ( i CSPM2). i CSPM2 has three main characteristics. Firstly, it separates candidate solutions into a number of islands (sub-populations) and then divides the islands equally among four improved versions of CS: CS via Le'vy fights (CS1) [1], CS with HDPM mutation (CS10) [2], CS with Jaya mutation (CSJ) and CS with pitch adjustment mutation (CS11) [2]. Secondly, it uses Elite Opposition-based Learning (EOBL) to improve its convergence rate and exploration ability. Finally, it uses the Smallest Position Value (SPV) with scheduling problems to convert continuous candidate solutions into discrete ones. A set of 15 popular benchmark functions was used to compare the performance of iCSPM2 to the performance of the original i CSPM algorithm based on different experimental scenarios. Results indicate that i CSPM2 exhibits improved performance over i CSPM. However, the sensitivity analysis of i CSPM and i CSPM2 to their parameters indicates that their convergence behavior is sensitive to the island model parameters. Further, the single-objective IEEE CEC 2014 functions were used to evaluate and compare the performance of iCSPM2 to four well-known swarm optimization algorithms: DGWO [3], L-SHADE [4], MHDA [5] and FWA-DM [6]. The overall experimental and statistical results suggest that i CSPM2 has better performance than the four well-known swarm optimization algorithms. i CSPM2's performance was also compared to two powerful discrete optimization algorithms (GAIbH [7] and MASC [8]) using a set of Taillard's benchmark instances for the permutation flow shop scheduling problem. The results indicate that i CSPM2 performs better than GAIbH and MASC. The source code of i CSPM2 is publicly available at https://github.com/bilalh2021/iCSPM2

A unified model of human hemoglobin switching through single-cell genome editing

Key mechanisms of fetal hemoglobin (HbF) regulation and switching have been elucidated through studies of human genetic variation, including mutations in the HBG1/2 promoters, deletions in the β-globin locus, and variation impacting BCL11A. While this has led to substantial insights, there has not been a unified understanding of how these distinct genetically-nominated elements, as well as other key transcription factors such as ZBTB7A, collectively interact to regulate HbF. A key limitation has been the inability to model specific genetic changes in primary isogenic human hematopoietic cells to uncover how each of these act individually and in aggregate. Here, we describe a single-cell genome editing functional assay that enables specific mutations to be recapitulated individually and in combination, providing insights into how multiple mutation-harboring functional elements collectively contribute to HbF expression. In conjunction with quantitative modeling and chromatin capture analyses, we illustrate how these genetic findings enable a comprehensive understanding of how distinct regulatory mechanisms can synergistically modulate HbF expression.

Isolation and Molecular Identification of Respiratory Diseases Viruses in Poultry During 2020

Poultry production has affected by multiple respiratory disease triggering serious economic losses in Egypt. In this study, the situation and genetic evolution of respiratory disease in Egypt during 2020 were studied. We collected 53 samples from infected flocks suffered from respiratory signs and variable mortality rate from nine governorates in Egypt during 2020. The collected samples were examined for detection of respiratory disease viruses (Avian influenza virus (AIV), Infectious bronchitis virus (IBV), and Newcastle disease virus (NDV)) by rRT-PCR. The single infection was confirmed in 90.6% (37.7% I. B, 30.2% AIV (H5N8), 9.4% I. B and 5.7% NDV) and co-infection of HPAIV (H5N8) + I.BV and LPAIV (H9N2) +IBV were detected in 3.8% in nine governorates. The HA gene of HPAIV (H5N8) were cluster to clad 2.3.4.4.1b in new branch with characteristic specific mutations especially in T140A in antigenic site A and R72S in the receptor binding site when comparing with A/duck/Egypt/F446/2017 with low A.A identity percent with vaccinal strains (H5N1 and H5N2) reach to 91.9-94% and 84.6% respectively. The HA gene of AIV (H9N2) were belong to A/quail/Hong Kong/G1/97-like virus clustered with group B with specific mutation (212I) that may be effect on human transmission of the virus. The HVRs of S1 gene of IBV cluster to GI23 (Egy Var I) clad with multiple mutation in HVR1, HVR2 when comparing with IBV/CU/4/2014 and low identity percent (68.3%-78.8%) with vaccine strains (H120, M41, 4/91). In conclusion, the respiratory disease continues circulate and rapidly evolved in Egypt during 2020.

An Alternative Molecular View of Evolution: How DNA was Altered over Geological Time

Four natural phenomena are cited for their defiance of conventional neo-Darwinian analysis: human intelligence; cat domesticity; the Cambrian explosion; and convergent evolution. 1. Humans are now far more intelligent than needed in their hunting–gathering days >10,000 years ago. 2. Domestic cats evolved from wildcats via major genetic and physical changes, all occurring in less than 12,000 years. 3. The Cambrian explosion refers to the remarkable expansion of species that mystifies evolutionists, as there is a total lack of fossil evidence for precursors of this abundant new life. 4. Convergent evolution often involves formation of complex, multigene traits in two or more species that have no common ancestor. These four evolutionary riddles are discussed in terms of a proposed “preassembly” mechanism in which genes and gene precursors are collected silently and randomly over extensive time periods within huge non-coding sections of DNA. This is followed by epigenetic release of the genes, when the environment so allows, and by natural selection. In neo-Darwinism, macroevolution of complex traits involves multiple mutation/selections, with each of the resulting intermediates being more favorable to the species than the previous one. Preassembly, in contrast, invokes natural selection only after a partially or fully formed trait is already in place. Preassembly does not supplant neo-Darwinism but, instead, supplements neo-Darwinism in those important instances where the classical theory is wanting.

Moving Molecular Profiling to Routine Clinical Practice: A Way Forward?

Molecular profiling of a patient’s tumor to guide targeted treatment selection offers the potential to advance patient care by improving outcomes and minimizing toxicity (by avoiding ineffective treatments). However, current development of molecular profile (MP) panels is often based on applying institution-specific or subjective algorithms to nonrandomized patient cohorts. Consequently, obtaining reliable evidence that molecular profiling is offering clinical benefit and is ready for routine clinical practice is challenging. In particular, we discuss here the problems with interpreting for clinical utility nonrandomized studies that compare outcomes in patients treated based on their MP vs those treated with standard of care, studies that compare the progression-free survival (PFS) seen on a MP-directed treatment to the PFS seen for the same patient on a previous standard treatment (PFS ratio), and multibasket trials that evaluate the response rates of targeted therapies in specific molecularly defined subpopulations (regardless of histology). We also consider some limitations of randomized trial designs. A two-step strategy is proposed in which multiple mutation-agent pairs are tested for activity in one or more multibasket trials in the first step. The results of the first step are then used to identify promising mutation-agent pairs that are combined in a molecular panel that is then tested in the step-two strategy-design randomized clinical trial (the molecular panel–guided treatment for the selected mutations vs standard of care). This two-step strategy should allow rigorous evidence-driven identification of mutation-agent pairs that can be moved into routine clinical practice.