The Relationship between Self-Regulated Strategies and Burnout: A Teacher Analysis in the EFL Context of Iran

The deleterious effects of teacher burnout (academic-related stress) on academic outcomes have been previously established. However, teacher burnout’s relationship with self-regulated strategies as well as underlying factors contributing to their potential relationship is less understood. Consequently, the present study examined the link between Iranian EFL teachers’ self-regulation and burnout at Kerman English language institutes. For this aim, a total of 101 English language teachers teaching in fifteen language institutes in Kerman took part in this study. The research participant selection was according to the convenience sampling. They completed two questionnaires: Teachers’ Self-Regulation Questionnaire (TSRQ) designed by Yesim et al. (2009), based on the model proposed by Zimmerman’s self-regulation (2000), and Teachers’ Burnout Questionnaire (TBQ) extracted from Pines et al. (1981) Burnout Scale. This study was a quantitative correlation survey of issue in which the relationship between predictor variable (self-regulation) and criterion variable (burnout) was analyzed. The findings yielded via correlation analysis documented that there was a significant negative relationship between applying self-regulated strategies and burnout. Subsequent data analyses showed that among the components of self-regulated strategies, goal setting was the best predictor of burnout. It means that EFL teachers who establish goals for their teaching and attempt to accomplish them will be rarely at the risk of burnout

5G and beyond networks

This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications

Assessing Predicted HIV-1 Replicative Capacity in a Clinical Setting

HIV-1 replicative capacity (RC) provides a measure of within-host fitness and is determined in the context of phenotypic drug resistance testing. However it is unclear how these in-vitro measurements relate to in-vivo processes. Here we assess RCs in a clinical setting by combining a previously published machine-learning tool, which predicts RC values from partial pol sequences with genotypic and clinical data from the Swiss HIV Cohort Study. The machine-learning tool is based on a training set consisting of 65000 RC measurements paired with their corresponding partial pol sequences. We find that predicted RC values (pRCs) correlate significantly with the virus load measured in 2073 infected but drug naïve individuals. Furthermore, we find that, for 53 pairs of sequences, each pair sampled in the same infected individual, the pRC was significantly higher for the sequence sampled later in the infection and that the increase in pRC was also significantly correlated with the increase in plasma viral load and with the length of the time-interval between the sampling points. These findings indicate that selection within a patient favors the evolution of higher replicative capacities and that these in-vitro fitness measures are indicative of in-vivo HIV virus load

Exploring the Complexity of the HIV-1 Fitness Landscape

Although fitness landscapes are central to evolutionary theory, so far no biologically realistic examples for large-scale fitness landscapes have been described. Most currently available biological examples are restricted to very few loci or alleles and therefore do not capture the high dimensionality characteristic of real fitness landscapes. Here we analyze large-scale fitness landscapes that are based on predictive models for in vitro replicative fitness of HIV-1. We find that these landscapes are characterized by large correlation lengths, considerable neutrality, and high ruggedness and that these properties depend only weakly on whether fitness is measured in the absence or presence of different antiretrovirals. Accordingly, adaptive processes on these landscapes depend sensitively on the initial conditions. While the relative extent to which mutations affect fitness on their own (main effects) or in combination with other mutations (epistasis) is a strong determinant of these properties, the fitness landscape of HIV-1 is considerably less rugged, less neutral, and more correlated than expected from the distribution of main effects and epistatic interactions alone. Overall this study confirms theoretical conjectures about the complexity of biological fitness landscapes and the importance of the high dimensionality of the genetic space in which adaptation takes place

Estimating the Fitness Cost of Escape from HLA Presentation in HIV-1 Protease and Reverse Transcriptase

Human immunodeficiency virus (HIV-1) is, like most pathogens, under selective pressure to escape the immune system of its host. In particular, HIV-1 can avoid recognition by cytotoxic T lymphocytes (CTLs) by altering the binding affinity of viral peptides to human leukocyte antigen (HLA) molecules, the role of which is to present those peptides to the immune system. It is generally assumed that HLA escape mutations carry a replicative fitness cost, but these costs have not been quantified. In this study, we assess the replicative cost of mutations which are likely to escape presentation by HLA molecules in the region of HIV-1 protease and reverse transcriptase. Specifically, we combine computational approaches for prediction of in vitro replicative fitness and peptide binding affinity to HLA molecules. We find that mutations which impair binding to HLA-A molecules tend to have lower in vitro replicative fitness than mutations which do not impair binding to HLA-A molecules, suggesting that HLA-A escape mutations carry higher fitness costs than non-escape mutations. We argue that the association between fitness and HLA-A binding impairment is probably due to an intrinsic cost of escape from HLA-A molecules, and these costs are particularly strong for HLA-A alleles associated with efficient virus control. Counter-intuitively, we do not observe a significant effect in the case of HLA-B, but, as discussed, this does not argue against the relevance of HLA-B in virus control. Overall, this article points to the intriguing possibility that HLA-A molecules preferentially target more conserved regions of HIV-1, emphasizing the importance of HLA-A genes in the evolution of HIV-1 and RNA viruses in general

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Recombination accelerates adaptation on a large-scale empirical fitness landscape in HIV-1

Recombination has the potential to facilitate adaptation. In spite of the substantial body of theory on the impact of recombination on the evolutionary dynamics of adapting populations, empirical evidence to test these theories is still scarce. We examined the effect of recombination on adaptation on a large-scale empirical fitness landscape in HIV-1 based on in vitro fitness measurements. Our results indicate that recombination substantially increases the rate of adaptation under a wide range of parameter values for population size, mutation rate and recombination rate. The accelerating effect of recombination is stronger for intermediate mutation rates but increases in a monotonic way with the recombination rates and population sizes that we examined. We also found that both fitness effects of individual mutations and epistatic fitness interactions cause recombination to accelerate adaptation. The estimated epistasis in the adapting populations is significantly negative. Our results highlight the importance of recombination in the evolution of HIV-I

Disturbed lipid and amino acid metabolisms in COVID-19 patients.

The Coronavirus disease 2019 (COVID-19) pandemic is overwhelming the healthcare systems. Identification of systemic reactions underlying COVID-19 will lead to new biomarkers and therapeutic targets for monitoring and early intervention in this viral infection. We performed targeted metabolomics covering up to 630 metabolites within several key metabolic pathways in plasma samples of 20 hospitalized COVID-19 patients and 37 matched controls. Plasma metabolic signatures specifically differentiated severe COVID-19 from control patients. The identified metabolic signatures indicated distinct alterations in both lipid and amino acid metabolisms in COVID-19 compared to control patient plasma. Systems biology-based analyses identified sphingolipid, tryptophan, tyrosine, glutamine, arginine, and arachidonic acid metabolism as mostly impacted pathways in COVID-19 patients. Notably, gamma-aminobutyric acid (GABA) was significantly reduced in COVID-19 patients and GABA plasma levels allowed for stratification of COVID-19 patients with high sensitivity and specificity. The data reveal large metabolic disturbances in COVID-19 patients and suggest use of GABA as potential biomarker and therapeutic target for the infection

Contribution of the main and epistatic effects to the recombination effect on adaptation on the HIV-1 fitness landscape.

<p>The x and y axes show the values of the constants by which the elements of the main and epistatic effects, respectively, are multiplied in the hierarchical fitness landscape. The plot shows the logarithm (base 10) of the ratio of mean fitness of the recombining to that of the non-recombining populations at generation 100, averaged across 100 simulations. Parameters take values: and .</p