The COVID-19 Pandemic and Physical Activity

The SARS-CoV-2-caused COVID-19 pandemic has resulted in a devastating threat to human society in terms of health, economy, and lifestyle. Although the virus usually first invades and infects the lung and respiratory track tissue, in extreme cases, almost all major organs in the body are now known to be negatively impacted often leading to severe systemic failure in some people. Unfortunately, there is currently no effective treatment for this disease. Pre-existing pathological conditions or comorbidities such as age are a major reason for premature death and increased morbidity and mortality. The immobilization due to hospitalization and bed rest and the physical inactivity due to sustained quarantine and social distancing can downregulate the ability of organs systems to resist to viral infection and increase the risk of damage to the immune, respiratory, cardiovascular, musculoskeletal systems and the brain. The cellular mechanisms and danger of this "second wave" effect of COVID-19 to the human body, along with the effects of aging, proper nutrition, and regular physical activity, are reviewed in this editorial article

Mmwave massive MIMO: one joint beam selection combining cuckoo search and ant colony optimization

Abstract In order to degrade the inter-user interference caused by the same beam selected for different users in mmWave massive MIMO systems, this paper proposes a joint beam selection combining cuckoo search (CS) and ant colony optimization (ACO) (referred to as CSACO). Differently from the existing interference-aware beam selection, a candidate beam set (CBS) for all users is created according to the power distribution of the beamspace channel, thereby all users can be classified into non-interfering users (NIUs) and interfering users (IUs), and NIUs will be assigned the beams with large power directly, while for IUs, the beams are selected by the CSACO; in the proposed CSACO, all beams for IUs are regarded as an optimizable individual, which is continuously evolved towards the direction of sum-rate maximization. Simulation results verify that the proposed beam selection can obtain the higher sum-rate and energy efficiency compared with the existing ones

Use of genomic selection and breeding simulation in cross prediction for improvement of yield and quality in wheat (Triticum aestivum L.)

In wheat breeding, it is a difficult task to select the most suitable parents for making crosses aimed at the improvement of both grain yield and grain quality. By quantitative genetics theory, the best cross should have high progeny mean and large genetic variance, and ideally yield and quality should be less negatively or positively correlated. Usefulness is built on population mean and genetic variance, which can be used to select the best crosses or populations to achieve the breeding objective. In this study, we first compared five models (RR-BLUP, Bayes A, Bayes B, Bayes ridge regression, and Bayes LASSO) for genomic selection (GS) with respect to prediction of usefulness of a biparental cross and two criteria for parental selection, using simulation. The two parental selection criteria were usefulness and midparent genomic estimated breeding value (GEBV). Marginal differences were observed among GS models. Parental selection with usefulness resulted in higher genetic gain than midparent GEBV. In a population of 57 wheat fixed lines genotyped with 7588 selected markers, usefulness of each biparental cross was calculated to evaluate the cross performance, a key target of breeding programs aimed at developing pure lines. It was observed that progeny mean was a major determinant of usefulness, but the usefulness ratings of quality traits were more influenced by their genetic variances in the progeny population. Near-zero or positive correlations between yield and major quality traits were found in some crosses, although they were negatively correlated in the population of parents. A selection index incorporating yield, extensibility, and maximum resistance was formed as a new trait and its usefulness for selecting the crosses with the best potential to improve yield and quality simultaneously was calculated. It was shown that applying the selection index improved both yield and quality while retaining more genetic variance in the selected progenies than the individual trait selection. It was concluded that combining genomic selection with simulation allows the prediction of cross performance in simulated progenies and thereby identifies candidate parents before crosses are made in the field for pure-line breeding programs. Keywords: Breeding simulation, Cross prediction, Genomic selection, Parental selection, Usefulnes

Emergence of norovirus GII.P16-GII.2 strains in patients with acute gastroenteritis in Huzhou, China, 2016–2017

Abstract Background In late 2016, an uncommon recombinant NoV genotype called GII.P16-GII.2 caused a sharp increase in outbreaks of acute gastroenteritis in different countries of Asia and Europe, including China. However, we did not observe a drastic increase in sporadic norovirus cases in the winter of 2016 in Huzhou. Therefore, we investigate the prevalence and genetic diversity of NoVs in the sporadic acute gastroenteritis (AGE) cases from January 2016 to December 2017 in Huzhou City, Zhejiang, China. Methods From January 2016 to December 2017, a total of 1001 specimens collected from patients with AGE were screened for NoV by real-time RT-PCR. Partial sequences of the RNA-dependent RNA polymerase (RdRp) and capsid gene of the positive samples were amplified by RT-PCR and sequenced. Genotypes of NoV were confirmed by online NoV typing tool and phylogenetic analysis. Complete VP1 sequences of GII.P16-GII.2 strains detected in this study were further obtained and subjected into sequence analysis. Results In total, 204 (20.4%) specimens were identified as NoV-positive. GII genogroup accounted for most of the NoV-infected cases (98.0%, 200/204). NoV infection was found in all age groups tested (60 years), with the 5–15 year age group having the highest detection rate (17/49, 34.7%). Higher activity of NoV infection could be seen in winter-spring season. The predominant NoV genotypes have changed from GII.Pe-GII.4 Sydney2012 and GII.P17-GII.17 in 2016 to GII.P16-GII.2, GII.Pe-GII.4 Sydney2012 and GII.P17-GII.17 in 2017. Phylogenetic analyses revealed that 2016–2017 GII.P16-GII.2 strains were most closely related to Japan 2010–2012 cluster in VP1 region and no common mutations were found in the amino acids of the HBGA-binding sites and the predicted epitopes. Conclusions We report the emergence of GII.P16-GII.2 strains and characterize the molecular epidemiological patterns NoV infection between January 2016 and December 2017 in Huzhou. The predominant genotypes of NoV during our study period are diverse. VP1 amino acid sequences of 2016–2017 GII.P16-GII.2 strains remain static after one year of circulation

A Working Fluid Assessment for a Biomass Organic Rankine Cycle under Different Conditions

Many thermal resources are not reasonably used in the chemical industry’s production process. To recover the waste heat from organic waste residue-calcium carbonate (CaCO3), which is added to inhibit hydrogen production, an organic Rankine cycle (ORC) system is applied in this research. An ORC system can reuse the low-temperature waste heat that is not fully utilized. In this study, the mathematical model of the biomass ORC power generation system is constructed. Five organic working fluids, R11, R113, R123, R141b, and R245fa, were selected from the physical characteristics and safety of working fluids. The system application case is the low-temperature heat absorption in a chemical industry’s production process. The system is simulated by Aspen Plus V11 software, so as to study and analyze the influence of different working fluids and working conditions on the system performance and to obtain the preferred working fluids under different working conditions. At the same time, the economic evaluation and entropy method of the system are evaluated by using the investment profit rate PRI from different angles. It can be found that R11 and R141b have advantages, but R11 does not have advantages in environmental aspects. Through research, it is found that it is difficult to have a working fluid that can adapt to the biomass ORC power generation system under any working conditions. This paper can provide a basis for the subsequent research and selection of working fluids in the biomass ORC system

A Working Fluid Assessment for a Biomass Organic Rankine Cycle under Different Conditions

Many thermal resources are not reasonably used in the chemical industry’s production process. To recover the waste heat from organic waste residue-calcium carbonate (CaCO3), which is added to inhibit hydrogen production, an organic Rankine cycle (ORC) system is applied in this research. An ORC system can reuse the low-temperature waste heat that is not fully utilized. In this study, the mathematical model of the biomass ORC power generation system is constructed. Five organic working fluids, R11, R113, R123, R141b, and R245fa, were selected from the physical characteristics and safety of working fluids. The system application case is the low-temperature heat absorption in a chemical industry’s production process. The system is simulated by Aspen Plus V11 software, so as to study and analyze the influence of different working fluids and working conditions on the system performance and to obtain the preferred working fluids under different working conditions. At the same time, the economic evaluation and entropy method of the system are evaluated by using the investment profit rate PRI from different angles. It can be found that R11 and R141b have advantages, but R11 does not have advantages in environmental aspects. Through research, it is found that it is difficult to have a working fluid that can adapt to the biomass ORC power generation system under any working conditions. This paper can provide a basis for the subsequent research and selection of working fluids in the biomass ORC system