We report the Simons Genome Diversity Project (SGDP) dataset: high quality genomes from 300 individuals from 142 diverse populations. These genomes include at least 5.8 million base pairs that are not present in the human reference genome. Our analysis reveals key features of the landscape of human genome variation, including that the rate of accumulation of mutations has accelerated by about 5% in non-Africans compared to Africans since divergence. We show that the ancestors of some pairs of present-day human populations were substantially separated by 100,000 years ago, well before the archaeologically attested onset of behavioral modernity. We also demonstrate that indigenous Australians, New Guineans and Andamanese do not derive substantial ancestry from an early dispersal of modern humans; instead, their modern human ancestry is consistent with coming from the same source as that in other non-Africans.

In order to explore the diversity and selective signatures of duplication and deletion human copy number variants (CNVs), we sequenced 236 individuals from 125 distinct human populations. We observed that duplications exhibit fundamentally different population genetic and selective signatures than deletions and are more likely to be stratified between human populations. Through reconstruction of the ancestral human genome, we identify megabases of DNA lost in different human lineages and pinpoint large duplications that introgressed from the extinct Denisova lineage now found at high frequency exclusively in Oceanic populations. We find that the proportion of CNV base pairs to single nucleotide variant base pairs is greater among non-Africans than it is among African populations, but we conclude that this difference is likely due to unique aspects of non-African population history as opposed to differences in CNV load.

Objective: To evaluate the efficacy of ivermectin (IVM) as an addition to the standard of care (SOC) treatment in COVID-19 patients with mild and moderate disease Materials and Methods: A randomized clinical trial (Trial registration # NCT04392713) was carried out at Combined Military Hospital Lahore from March 15, 2020, to June 15, 2020. Eighty-six patients with reverse transcriptase-polymerase chain reaction (RT-PCR) proven SARS-CoV-2 infection completed the trial protocol. Patients were stratified via the lottery method into two groups. Group A was administered standard of care (SOC) treatment as per existing hospital guidelines whereas group B was given ivermectin (single dose of 12 milligrams) along with SOC treatment. PCR was repeated at 72 hours, 7th day, and at 14th day of admission for both the groups and the point at which the PCR became negative was noted. Complete blood counts, liver function tests and renal function tests were done at recruitment, 7th day, and 14th day. The primary outcome was the viral clearance, measured as days to achieve PCR negativity. The secondary outcome was the development of any adverse side effects pertinent to ivermectin or derangement in baseline laboratory parameters. Results: In group A, 36 (80%) participants were males, and 9 (20%) were females, whereas in group B, 37 (90.2%) were males and 4 (9.8%) were females. Mean age was 39.0 +/- 12.6 and 42.2 +/- 12.0 years for groups A and B, respectively (p= 0.394). There was early viral clearance in group B as compared to group A (p=0.001). No adverse reaction or derangements in laboratory parameters was noted in the intervention arm during the trial period. Conclusion: In the intervention arm, early viral clearance was observed and no side effects were documented. Therefore ivermectin is a potential addition to the standard care of treatment in COVID-19 patients.

Popularity of metasurfaces has been continuouslygrowing due to their attractive properties including the ability to effectively manipulate electromagnetic (EM) waves. Metasurfaces comprise optimized geometries of unit cells arranged as a periodic lattice to obtain a desired EM response. One of their emerging application areas is the stealth technology, in particular, realization of radar cross section (RCS) reduction. Despite potential benefits, a practical obstacle hindering widespread metasurface utilization is the lack of systematic design procedures. Conventional approaches are largely intuitioninspired and demand heavy designer's interaction while exploring the parameter space and pursuing optimum unit cell geometries. Not surprisingly, these are unable to identify truly optimum solutions. In this article, we introduce a novel machine-learningbased framework for automated and computationally efficient design of metasurfaces realizing broadband RCS reduction. Our methodology is a three-stage procedure that involves global surrogate-assisted optimization of the unit cells, followed by their local refinement. The last stage is direct EM-driven maximization of the RCS reduction bandwidth, facilitated by appropriate formulation of the objective function involving regularization terms. The appealing feature of the proposed framework is that it optimizes the RCS reduction bandwidth directly at the level of the entire metasurface as opposed to merely optimizing unit cell geometries. Computational feasibility of the optimization process, especially its last stage, is ensured by high-quality initial designs rendered during the first two stages. To corroborate the utility of our procedure, it has been applied to several metasurface designs reported in the literature, leading to the RCS reduction bandwidth improvement by 15%-25% when compared with the original designs. Furthermore, it was used to design a novel metasurface featuring over 100% of relative bandwidth. Although the procedure has been used in the context of RCS design, it can be generalized to handle metasurface development for other application areas.

The COVID-19 pandemic caught the world by surprise, causing millions of confirmed cases and hundreds of thousands of deaths. Hence, the Malaysian government announced a Movement Control Order at the start of the containment phase to flatten the epidemiological curve. Universiti Malaysia Sabah (UMS), a public university in Borneo, was accelerated into alert phase because of high risk of case importation from more than 400 China incoming undergraduates. Measures to mitigate the potential COVID-19 outbreaks in its population were taken by using conventional public health measures with special attention to task-shifting and widespread community mental health interventions. A Preparedness and Response Centre was established to overseer the mitigating measures happening inside the university. Measures taken included empowerment of frontline staff, strengthening of restrictions, strengthening university health center, vigorous contact tracing, widespread health education, maintaining cultural sensitivity, and establishment of early standard operating procedures and university continuity plans. Hence, UMS was able to ensure no importation of cases into its campus during both acute and containment phases at the nationwide level.