As SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) continues to inflict chaos globally, a new variant officially known as B.1.1.529 was reported in South Africa and was found to harbor 30 mutations in the spike protein. It is too early to speculate on transmission and hospitalizations. Hence, more analyses are required, particularly to connect the genomic patterns to the phenotypic attributes to reveal the binding differences and antibody response for this variant, which can then be used for therapeutic interventions. Given the urgency of the required analysis and data on the B.1.1.529 variant, we have performed a detailed investigation to provide an understanding of the impact of these novel mutations on the structure, function, and binding of RBD to hACE2 and mAb to the NTD of the spike protein. The differences in the binding pattern between the wild type and B.1.1.529 variant complexes revealed that the key substitutions Asn417, Ser446, Arg493, and Arg498 in the B.1.1.529 RBD caused additional interactions with hACE2 and the loss of key residues in the B.1.1.529 NTD resulted in decreased interactions with three CDR regions (1–3) in the mAb. Further investigation revealed that B.1.1.529 displayed a stable dynamic that follows a global stability trend. In addition, the dissociation constant (K D ), hydrogen bonding analysis, and binding free energy calculations further validated the findings. Hydrogen bonding analysis demonstrated that significant hydrogen bonding reprogramming took place, which revealed key differences in the binding. The total binding free energy using MM/GBSA and MM/PBSA further validated the docking results and demonstrated significant variations in the binding. This study is the first to provide a basis for the higher infectivity of the new SARS-CoV-2 variants and provides a strong impetus for the development of novel drugs against them.

Health systems are expected to serve the population needs in an effective, efficient and equitable manner. The factors determining the health behaviors may be seen in various contexts physical, socio-economic, cultural and political. Therefore, the utilization of a health care system, public or private, formal or non-formal, may depend on socio-demographic factors, social structures, level of education, cultural beliefs and practices, gender discrimination, status of women, economic and political systems environmental conditions, and the disease pattern and health care system itself. Policy makers need to understand the drivers of health seeking behavior of the population in an increasingly pluralistic health care system. Also a more concerted effort is required for designing behavioral health promotion campaigns through inter-sectoral collaboration focusing more on disadvantaged segments of the population. The paper reviews the health care providers, the national policies emphasizing health services as well as health care systems in Pakistan and the role of the pharmacist in health care system of Pakistan, health and economics of Pakistan and current budgeting policies and the importance of non government organizations in health care system of Pakistan.

This paper concerns the design space exploration (DSE) of Reconfigurable Multi- Processor System-on- Chip (MPSoC) architectures. Reconfiguration allows users to allocate optimum system resources for a specific application in such a way to improve the energy and throughput balance. To achieve the best balance between power consumption and throughput performance for a particular application domain, typical design space parameters for a multi-processor architecture comprise the cache size, the number of processor cores and the operating frequency. The exploration of the design space has always been an offline technique, consuming a large amount of time. Hence, the exploration has been unsuitable for reconfigurable architectures, which require an early runtime decision. This paper presents Approximate Computing DSE (AC-DSE), an online technique for the DSE of MPSoCs by means of approximate computing. In AC-DSE, design space solutions are first obtained from a set of optimization algorithms, which in turn are used to train a neural network (NN). From then on, the NN can be used to rapidly return its own solutions in the form of design space parameters for a desired energy and throughput performance, without any further training.

Emergence of modern multicore architectures has made runtime reconfiguration of system resources possible. All reconfigurable system resources constitute a design space and the proper selection of configuration of these resources to improve the system performance is known as Design Space Exploration (DSE). This reconfiguration feature helps in appropriate allocation of system resources to improve the efficiency in terms of performance, energy consumption, throughput, etc. Different techniques like exhaustive search of design space, architect’s experience, etc. are used for optimization of system resources to achieve desired goals. In this work, we hybridized two optimization algorithms, i.e., Genetic Algorithm (GA) and Estimation of Distribution Algorithm (EDA) for DSE of computer architecture. This hybrid algorithm achieved optimal balance between two objectives (minimal energy consumption and maximal throughput) by using decision variables such as number of cores, cache size and operating frequency. The final set of optimal solutions proposed by this GA–EDA hybrid algorithm is explored and verified by running different benchmark applications derived from SPLASH-2 benchmark suite on a cycle level simulator. The significant reduction in energy consumption without extensive impact on throughput in simulation results validate the use of this GA–EDA hybrid algorithm for DSE of multicore architecture. Moreover, the simulation results are compared with that of standalone GA, EDA and fuzzy logic to show the efficiency of GA–EDA hybrid algorithm.