The Effect of Nutrients Mixture on The Biomass and Lipid Production from Microalgae Botryococcus braunii Mutated by UV-C Rays

Nutrient is one of the most important factors in the growth of microalgae. This research was conducted to study the effect of nutrient mixture on the biomass and lipid production of Botryococcus braunii. Microalgae B. braunii was cultivated in the commercial nutrient medium of agricultural fertilizer combinations of ammonium sulphate (ZA), urea, and triple superphosphate (TSP). Before the cultivation process, B. braunii was exposed to UV-C rays (254 nm) for 3 minutes. The concentration and type of fertilizer as a nitrogen source divided into four types of mixtures, namely FM-1, FM-2, FM-3, and FM-4 were compared with Walne nutrients to study their effects on microalgae growth and lipids. FM-1 consisting of 150 mg/L of ZA, 7.5 mg/L of urea, and 25 mg/L of TSP led to the best growth for native and mutated microalgae strains compared to Walne nutrients and other nutrient mixtures. The mutated microalgae showed less growth than the native microalgae strains. However, the mutation process significantly increased the lipid content in the microalgae. In native microalgae strains, FM-4 consisting of 136.3 mg/L of urea and 50 mg/L of TSP produced the lowest lipid at 8.96%. After being exposed to UV-C rays, the lipids in FM-4 medium increased to 55.11%. The results show that the use of commercial fertilizers and exposure to UV-C rays on microalgae have high potential in preparing lipids as raw material for biodiesel which can be effectively applied in large-scale microalgae cultivation.

Dynamic Zero-Covid strategy curtails mutagenesis and emergence of new variants of the SARS-CoV-2

Within the local outbreak period of SARS-CoV-2 Delta variant in Nanjing and Yangzhou, China, we analyzed the mutation process of the Delta variants in 520 cases, as well as the production, spread and elimination of new mutant strains under the non-pharmaceutical interventions (NPI) strategy. The investigation on distribution of COVID-19 cases and phylogenetic analysis of SARS-CoV-2 genome sequences attributed to tracking the transmission chains, transmission chains were terminated by the isolation of the COVID-19 patients and quarantine of close-contracts, suggesting the importance of NPI in prompting some mutations to disappear and stopping the transmission of new variants. Dynamic zero-Covid strategy has been implemented successfully to against the second-largest local epidemic caused by an imported COVID-19 case in China.

Italian Heritage and the Experience of Migration and Nostos in Canadian Poetry

The article addresses the theme of nostos by referring to the journeys of three authors of Italian heritage: Pier Giorgio Di Cicco, Mary di Michele, and Gianna Patriarca. Their poetry allows the possibility to revisit their journeys and to consider migration as a source of knowledge, and positive change, despite the many challenges involved in the mutation process, and the difficult hermeneutic of losses, necessary to reach awareness and a new sense of belonging.

The infinite alleles model revisited: a Gibbs sampling approach

The SARS-CoV-2 outbreak started in late 2019 in the Hubei province in China and the first viral sequence was made available to the scientific community on early January 2020. From there, viral genomes from all over the world have followed at an outstanding rate, reaching already more than 10^5 on early May 2020, and more than 10^6 by early March 2021. Phylodynamics methods have been designed in recent years to process such datasets and infer population dynamics and sampling intensities in the past. However, the unprecedented scale of the SARS-CoV-2 dataset now calls for new methodological developments, relying e.g. on simplifying assumptions of the mutation process. In this article, I build on the infinite alleles model stemming from the field of population genetics to develop a new Bayesian statistical method allowing the joint reconstruction of the outbreak's effective population sizes and sampling intensities through time. This relies on prior conjugacy properties that prove useful both to develop a Gibbs sampler and to gain intuition on the way different parameters of the model are linked and inferred. I finally illustrate the use of this method on SARS-CoV-2 genomes sequenced during the first wave of the outbreak in four distinct European countries, thus offering a new perspective on the evolution of the sampling intensity through time in these countries from genetic data only.

A Dynamic Invasive Weeds Optimization Applied to Null Control of Linear Antenna Arrays with Constrained DRR

In the present work, a dynamic stochastic method is proposed and used for the synthesis of uniform linear antenna arrays. The proposed method combines the classical invasive weeds (IWO) and the mutation process, which makes it robust, simple and shows flexibility to be adapted. The dynamic IWO applies the mutation process in the calculation of standard deviation during the spatial dispersal process of produced seeds while keeping the mean at the parent plants. In the mutation process, if special conditions were achieved, the standard deviation would be re-initialized. This proposed method tries to achieve an optimal array pattern by acting on the relative amplitude excitation of each element in the linear array for an optimal inter-element spacing. The optimal array pattern has deep or broad nulls in some directions of interferences with low sidelobes level. The objective of the synthesis is to get amplitude excitations with low dynamic range ratio (DRR), which facilitates the design of beamforming feed network. To illustrate the robustness of the proposed method, numerical examples are presented and compared with the obtained results using bees algorithm (BA), bacterial foraging algorithm (BFA), real genetic algorithm (RGA), and the corresponding reference array pattern for each example.

The statistics of k-mers from a sequence undergoing a simple mutation process without spurious matches

K-mer-based methods are widely used in bioinformatics, but there are many gaps in our understanding of their statistical properties. Here, we consider the simple model where a sequence S (e.g. a genome or a read) undergoes a simple mutation process whereby each nucleotide is mutated independently with some probability r, under the assumption that there are no spurious k-mer matches. How does this process affect the k-mers of S? We derive the expectation and variance of the number of mutated k-mers and of the number of islands (a maximal interval of mutated k-mers) and oceans (a maximal interval of non-mutated k-mers). We then derive hypothesis tests and confidence intervals for r given an observed number of mutated k-mers, or, alternatively, given the Jaccard similarity (with or without minhash). We demonstrate the usefulness of our results using a few select applications: obtaining a confidence interval to supplement the Mash distance point estimate, filtering out reads during alignment by Minimap2, and rating long read alignments to a de Bruijn graph by Jabba.

An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers

The fast development of connected vehicles with support for various V2X (vehicle-to-everything) applications carries high demand for quality of edge services, which concerns microservice deployment and edge computing. We herein propose an efficient resource scheduling strategy to containerize microservice deployment for better performance. Firstly, we quantify three crucial factors (resource utilization, resource utilization balancing, and microservice dependencies) in resource scheduling. Then, we propose a multi-objective model to achieve equilibrium in these factors and a multiple fitness genetic algorithm (MFGA) for the balance between resource utilization, resource utilization balancing, and calling distance, where a container dynamic migration strategy in the crossover and mutation process of the algorithm is provided. The simulated results from Container-CloudSim showed the effectiveness of our MFGA.

Analysis of the Influence of Conflict and Mutations on Employee’s Passion

Excellent human resources are the most important assets for the organization. Human resources in the organizations that have high morale can increase work productivity which also affects organizational performance. Employee passion can be increased with the support of a good environment. A good environment can be formed with minimal conflicts that occur and the mutation process is carried out appropriately. This analysis aimed to examine the various previous literatures that discuss the effects of conflict and mutations on work passion. This analysis used the literature study method which noted previous findings related to conflicts, mutations and morale, and then analyzes them coherently. The results of the literature review show that, 1) there is a negative influence between conflict and morale; 2) there is a positive influence between mutations and employee passion.

Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan

The lecture schedule is a problem that belongs to the NP-Hard problem and multi-objective problem because it has several variables that affect the preparation of the schedule and has limitations that must be met. One solution that has been found is using a Genetic Algorithm (GA). GA has been proven to be able to provide a schedule that can meet limitations in scheduling. Besides, it also found a new concept of thought from GA, namely the Fluid Genetic Algorithm (FGA). The most visible difference between FGA and GA is that there is no mutation process in each iteration. FGA has a new stage, namely individual born and new constants, namely global learning rate, individual learning rate, and diversity rate. This concept of thinking was tested in previous studies and found that FGA is superior to GA for the problem of finding the optimum value of a predetermined function, but this function is not included in the multi-objective problem. In this study, the testing and comparison of FGA and GA were conducted for the problem of scheduling lectures at STMIK XYZ. Based on the results obtained, FGA can produce a schedule without any hard constraint violations. FGA can be used to solve multi-objective problems. FGA has a smaller number of generations than GA. However, overall GA is superior in producing schedules without any problems.