Search Results (7)

Phytases of the PhyD class according to their pH optimum (7.0–7.8) and high thermal stability can claim to be used in the production of feed supplements. However, today they have no practical application in feed production because there are no suitable producers sufficient for its biotechnological production compared to the PhyA and PhyC class ones. Moreover, in most cases, the technologies with the enzymes produced in secretory form are preferable for the production of phytases, though upon microencapsulation in yeast-producing cells, the phytase thermal stability increases significantly compared to the extracellular form, which improves its compatibility with spray drying technology. In this study, we assayed the intracellular heterologous expression of PhyD phytase from Bacillus species in the Yarrowia lipolytica yeast cells. While the technology has been successfully used to synthesize PhyC phytase from Obesumbacterium proteus, PhyD phytase tends to aggregate upon intracellular accumulation. Furthermore, we evaluated the prospects for the production of encapsulated phytase of the PhyD class of high enzymatic activity when it accumulates in the cell cytoplasm of the Y. lipolytica extremophile yeast, a highly effective platform for the production of recombinant proteins. Full article

Plants are sessile organisms forced to adapt to environmental variations recurring in a day–night cycle. Extensive research has uncovered the transcriptional control of plants’ inner clock and has revealed at least some part of the intricate and elaborate regulatory mechanisms that govern plant diel responses and provide adaptation to the ever-changing environment. Here, we analyzed the proteome of the Arabidopsis thaliana mutant genotypes collected in the middle of the day and the middle of the night, including four mutants in the phytochrome (phyA, phyB, phyC, and phyD) and the circadian clock protein LHY. Our approach provided a novel insight into the diel regulations, identifying 640 significant changes in the night–day protein abundance. The comparison with previous studies confirmed that a large portion of identified proteins was a known target of diurnal regulation. However, more than 300 were novel oscillations hidden under standard growth chamber conditions or not manifested in the wild type. Our results indicated a prominent role for ROS metabolism and phytohormone cytokinin in the observed regulations, and the consecutive analyses confirmed that. The cytokinin signaling significantly increased at night, and in the mutants, the hydrogen peroxide content was lower, and the night–day variation seemed to be lost in the phyD genotype. Furthermore, regulations in the lhy and phyB mutants were partially similar to those found in the catalase mutant cat2, indicating shared ROS-mediated signaling pathways. Our data also shed light on the role of the relatively poorly characterized Phytochrome D, pointing to its connection to glutathione metabolism and the regulation of glutathione S-transferases. Full article

The COVID-19 pandemic caused serious health problems that affected people around the globe. This study aims to understand the physical distress (PhyD), psychological distress (PsyD), and coping experiences among people infected with COVID-19, develop a grounded theory, and examine PhyD, PsyD, and coping among people infected with COVID-19. A sequential exploratory mixed methods strategy is employed. A qualitative procedure is based on a grounded theory; data collection includes observation and in-depth interviews with 25 participants, aged 18 years and above. The quantitative one included 180 participants. Content analysis was applied using the Strauss and Corbin method, and ATLAS.ti software. Descriptive statistics, Pearson’s correlation, and the independent t-test were used. Results: The six major themes, including (1) severity of COVID-19 symptoms, (2) death anxiety, (3) uncertainty, (4) barrier to healthcare access, (5) compliance and self-regulation coping (6) post-COVID-19 effects. PhyD, PsyD, and coping were all at a moderate level. The relationship between PhyD, PsyD, and coping was positive. The prevalence in post-COVID-19 effects was 70% (95% CI 63.3-76.4%). There were higher amounts in women than men. The most frequent residual symptoms were decreased activity tolerance (40%), fatigue (33.3%), anxiety and fear of abnormal lungs (33.3%), dyspnea (27.8%), allergy (24.4%), and lung impairment (22.2%). Moreover, the prevalence of more than two symptoms was 54% (95% CI 47.2–61.7%). This study considers that the healthcare providers should be concerned with sufficient healthcare services. Interventions are needed for supporting their recovery from COVID-19 effects. Full article

Real-time coaching programs are designed to give feedback on driving behavior to usage-based motor insurance users; they are often general purpose programs that aim to promote smooth driving. Here, we investigated the effect of different on-board real-time coaching programs on the driving behavior on highway deceleration lanes with a driving simulator experiment. The experiment was organized into two trials. The first was a baseline trial, in which participants drove without receiving any feedback; a cluster analysis was then performed to divide participants into two groups, based on their observed driving style. One month later, a second trial was carried out, with participants driving on the same path as the first trial, this time receiving contingent feedback related to their braking/acceleration behavior. Four feedback systems were tested; overall, there were eight experimental groups, depending on the clustered driving style (aggressive and defensive), feedback modality (visual and auditory), and feedback valence (positive and negative). Speed, deceleration, trajectory, and lateral control variables, collected before and onto the deceleration lane, were investigated with mixed ANOVAs, which showed that the real-time coaching programs significantly reduced speeds and maximum deceleration values, while improving lateral control. A change toward a safer exit strategy (i.e., entering the lane before starting to decelerate) was also observed in defensive drivers. Full article

With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of ‘marker density’ seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of ‘marker density’ based on high-density SNP or whole-genome sequence data is important for the field of GP. In this study, we constructed different SNP panels with certain SNP numbers (e.g., 1 k) by using the physical distance (PhyD), genetic distance (GenD) and random distance (RanD) between SNPs respectively based on the high-density SNP data of a Germany Holstein dairy cattle population. Therefore, there are three different panels at a certain SNP number level. These panels were used to construct GP models to predict fat percentage, milk yield and somatic cell score. Meanwhile, the mean ($\overline{\mathrm{d}}$) and variance (${\sigma }_{\mathrm{d}}^2$) of the physical distance between SNPs and the mean ($\overline{r^2}$) and variance (${\sigma }_{r^2}^2$) of the genetic distance between SNPs in each panel were used as marker density-related measurements and their influence on the GEBV accuracy was investigated. At the same SNP number level, the $\overline{\mathrm{d}}$ of all panels is basically the same, but the ${\sigma }_{\mathrm{d}}^2$, $\overline{r^2}$ and ${\sigma }_{r^2}^2$ are different. Therefore, we only investigated the effects of ${\sigma }_{\mathrm{d}}^2$, $\overline{r^2}$ and ${\sigma }_{r^2}^2$ on the GEBV accuracy. The results showed that at a certain SNP number level, the GEBV accuracy was negatively correlated with ${\sigma }_{\mathrm{d}}^2$, but not with $\overline{r^2}$ and ${\sigma }_{r^2}^2$. Compared with GenD and RanD, the ${\sigma }_{\mathrm{d}}^2$ of panels constructed by PhyD is smaller. The low and moderate-density panels (< 50 k) constructed by RanD or GenD have large ${\sigma }_{\mathrm{d}}^2$, which is not conducive to genomic prediction. The GEBV accuracy of the low and moderate-density panels constructed by PhyD is 3.8~34.8% higher than that of the low and moderate-density panels constructed by RanD and GenD. Panels with 20–30 k SNPs constructed by PhyD can achieve the same or slightly higher GEBV accuracy than that of high-density SNP panels for all three traits. In summary, the smaller the variation degree of physical distance between adjacent SNPs, the higher the GEBV accuracy. The low and moderate-density panels construct by physical distance are beneficial to genomic prediction, while pruning high-density SNP data based on genetic distance is detrimental to genomic prediction. The results provide suggestions for the development of SNP panels and the research of genome prediction based on whole-genome sequence data. Full article

The aim of the present study was to analyze the behavior of players in a standard small-sided game (SSG) according to the role played (offensive (OF), defensive (DF), and wildcard (W)) and its relationship with physical demands (PHYD), technical performance (TP), and internal load (RPE). A total of 24 young highly trained male soccer players (under 16: n = 12; under 19: n = 12) participated. During the SSG, the players alternated the three roles (OF, DF, and W). The duration of each repetition was 4 min with a passive rest of 3 min between them. Furthermore, it emphasized the high demand in all defensive parameters. In addition, DF roles showed higher values in PHYD and RPE, followed by the OF roles, and finally by the W roles. A complementary, positive moderate correlation was found between PHYD and RPE in the U16 dataset (r = 0.45, p < 0.006). Very large positive correlations were also found between PHYD and RPE in the U19 and merged dataset (r = 0.78, p < 0.001 and r = 0.46, p < 0.63, respectively). This information could be useful for coaches in order to structure the roles in SSGs and control training load. Full article

The transcriptomic datasets of the plant model organism Arabidopsis thaliana grown in the International Space Station provided by GeneLab have been mined to isolate the impact of spaceflight microgravity on gene expressions related to root growth. A set of computational tools is used to identify the hub genes that respond differently in spaceflight with controlled lighting compared to on the ground. These computational tools based on graph-theoretic approaches are used to infer gene regulatory networks from the transcriptomic datasets. The three main algorithms used for network analyses are Least Absolute Shrinkage and Selection Operator (LASSO), Pearson correlation, and the Hyperlink-Induced Topic Search (HITS) algorithm. Graph-based spectral analyses reveal distinct properties of the spaceflight microgravity networks for the Wassilewskija (WS), Columbia (Col)-0, and mutant phytochromeD (phyD) ecotypes. The set of hub genes that are significantly altered in spaceflight microgravity are mainly involved in cell wall synthesis, protein transport, response to auxin, stress responses, and catabolic processes. Network analysis highlights five important root growth-regulating hub genes that have the highest outdegree distribution in spaceflight microgravity networks. These concerned genes coding for proteins are identified from the Gene Regulatory Networks (GRNs) corresponding to spaceflight total light environment. Furthermore, network analysis uncovers genes that encode nucleotide-diphospho-sugar interconversion enzymes that have higher transcriptional regulation in spaceflight microgravity and are involved in cell wall biosynthesis. Full article