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Catalase is present in prokaryotic and eukaryotic organisms and is important for the protective effects of the antioxidant system against free radicals. Many studies have confirmed that catalase is required for the growth, development, and pathogenesis of bacteria, plants, animals, and fungi. However, there has been relatively little research on the catalases in oomycetes, which form an important group of fungus‐like eukaryotes that produce zoosporangia. In this study, we detected two Phytophthora infestans genes encoding catalases, but only PiCAT2 exhibited catalase activity in the sporulation stage and was highly produced during asexual reproduction and in the late infection stage. Compared with the wild‐type strain, the PiCAT2‐silenced P. infestans transformants were more sensitive to abiotic stress, were less pathogenic, and had a lower colony expansion rate and lower PiMPK7, PiVPS1, and PiGPG1 expression levels. In contrast, the PiCAT2‐overexpressed transformants were slightly less sensitive to abiotic stress. Interestingly, increasing and decreasing PiCAT2 expression from the normal level inhibited sporulation, germination, and infectivity, and down‐regulated PiCdc14 expression, but up‐regulated PiSDA1 expression. These results suggest that PiCAT2 is required for P. infestans mycelial growth, asexual reproduction, abiotic stress tolerance, and pathogenicity. However, a proper PiCAT2 level is critical for the formation and normal function of sporangia. Furthermore, PiCAT2 affects P. infestans sporangial formation and function, pathogenicity, and abiotic stress tolerance by regulating the expression of cell cycle‐related genes (PiCdc14 and PiSDA1) and MAPK pathway genes. Our findings provide new insights into catalase functions in eukaryotic pathogens., PiCAT2 is required for P. infestans asexual reproduction, abiotic stress tolerance, and pathogenicity; reducing or elevating catalase activity suppresses sporangia formation, germination, and appressoria formation.

We provide an efficient and accurate method for converting Hartree-Fock-Bogoliubov wave functions into matrix product states (MPSs). These wave functions, also known as Bogoliubov vacua, exhibit a peculiar entanglement structure that the eigenvectors of the reduced density matrix are also Bogoliubov vacua. We exploit this important feature to obtain their optimal MPS approximation and derive an explicit formula for corresponding MPS matrices. The performance of our method is benchmarked with the Kitaev chain and the Majorana-Hubbard model on the honeycomb lattice. The approach facilitates the applications of Hartree-Fock-Bogoliubov wave functions and is ideally suited for combining with the density-matrix renormalization group method., Comment: 5 pages, 3 figures, published version

Countries in the Greater Mekong Subregion have committed to eliminate Plasmodium falciparum malaria by 2025. Subclinical malaria infections that can be detected by highly sensitive polymerase chain reaction (PCR) testing in asymptomatic individuals represent a potential impediment to this goal, although the extent to which these low-density infections contribute to transmission is unclear. To understand the temporal dynamics of subclinical malaria in this setting, a cohort of 2,705 participants from three epidemiologically distinct regions of Myanmar was screened for subclinical P. falciparum and P. vivax infection using ultrasensitive PCR (usPCR). Standard rapid diagnostic tests (RDTs) for P. falciparum were also performed. Individuals who tested positive for malaria by usPCR were followed for up to 12 weeks. Regression analysis was performed to estimate whether the baseline prevalence of infection and the count of repeated positive tests were associated with demographic, behavioral, and clinical factors. At enrollment, the prevalence of subclinical malaria infection measured by usPCR was 7.7% (1.5% P. falciparum monoinfection, 0.3% mixed P. falciparum and P. vivax, and 6.0% P. vivax monoinfection), while P. falciparum prevalence measured by RDT was just 0.2%. Prevalence varied by geography and was higher among older people and in those with outdoor exposure and travel. No difference was observed in either the prevalence or count of subclinical infection by time of year, indicating that even in low-endemicity areas, a reservoir of subclinical infection persists year-round. If low-density infections are shown to represent a significant source of transmission, identification of high-risk groups and locations may aid elimination efforts.

We perform a tensor network simulation of the (1+1)-dimensional O(3) nonlinear σ-model with θ=π term. Within the Hamiltonian formulation, this field theory emerges as the finite-temperature partition function of a modified quantum rotor model decorated with magnetic monopoles. Using the monopole harmonics basis, we derive the matrix representation for this modified quantum rotor model, which enables tensor network simulations. We employ our recently developed continuous matrix product operator method [Tang et al., Phys. Rev. Lett. 125, 170604 (2020)] to study the finite-temperature properties of this model and reveal its massless nature. The central charge as a function of the coupling constant is directly extracted in our calculations and compared with field theory predictions.

Background Tobacco use is one of the most important risk factors for health, and China is the largest producer and consumer of tobacco in the world. Monitoring and controlling the tobacco epidemic is an important issue. However, the motivation underlying smoking behavior is complex and specific to the individual. The Habit, Reward and Fear Scale (HRFS) is a feasible tool to evaluate this complex motivation. Objectives To validate the psychometric properties of the HRFS Chinese version (HRFS‐C) and to assess the relationship between motivation and smoking behavior. Method We recruited 967 participants through social media and assessed their smoking behavior with three instruments: the Fagerstrom Test for Nicotine Dependence‐Chinese version (FTND‐C), the Questionnaire on Smoking Urges‐Brief Scale‐Chinese version (QSU‐brief‐C), and the HRFS‐C. Ultimately, we retained 700 valid data points. Cronbach's α and split‐half tests were used to evaluate the reliability. Confirmatory factor analysis, Pearson's r and an analysis of variance (ANOVA) were used to evaluate the validity. In addition, linear regression was used to explore the relationship among the three instruments. The HRFS‐C showed good homogeneity (α = 0.965), concurrent validity, and discriminant validity. A significant linear relationship was observed among the FTND‐C, QSU‐brief‐C, and HRFS‐C (p, HRFS‐C is a good and potential instrument for measuring the motivation of substance use disorder. Motivation measured by HRFS‐C can significantly predict nicotine dependence and tobacco craving.

Background Knowledge of COVID-19 epidemiology remains incomplete and crucial questions persist. We aimed to examine risk factors for COVID-19 death. Methods A total of 80 543 COVID-19 cases reported in China, nationwide, through April 8, 2020 were included. Risk factors for death were investigated by Cox proportional hazards regression and stratified analyses. Results Overall national case fatality ratio (CFR) was 5.64%. Risk factors for death were older age (≥80: adjusted hazard ratio [aHR]=12.58, 95% confidence interval [CI]=6.78-23.33), presence of underlying disease (aHR=1.33, CI=1.19-1.49), worse case severity (severe: aHR=3.86, CI=3.15-4.73; critical: aHR=11.34, CI=9.22-13.95), and near-epicenter region (Hubei: aHR=2.64, CI=2.11-3.30; Wuhan: aHR=6.35, CI=5.04-8.00). CFR increased from 0.35% (30-39 years) to 18.21% (≥70 years) without underlying disease. Regardless of age, CFR increased from 2.50% for no underlying disease to 7.72% for 1, 13.99% for 2, and 21.99% for ≥3. CFR increased with worse case severity from 2.80% (mild), to 12.51% (severe) and 48.60% (critical) regardless of region. Compared to other regions, CFR was much higher in Wuhan regardless of case severity (mild: 3.83% versus 0.14% in Hubei and 0.03% elsewhere; moderate: 4.60% versus 0.21% and 0.06%; severe: 15.92% versus 5.84% and 1.86%; and critical: 58.57% versus 49.80% and 18.39%). Conclusions Older patients regardless of underlying disease and patients with underlying disease regardless of age were at elevated risk of death. Higher death rates near the outbreak epicenter and during the surge of cases reflect the deleterious effects of allowing health systems to become overwhelmed.

Objective: To investigate and verify the efficiency and effectiveness the the value of nomogram based on radiomics label in predicting the treatment of lumbar disc herniation (LDH). Methods: The clinical medical records and imaging data of 200 patients with LDH diagnosed in the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine in the past 3 years have been analyzed retrospectively. The collected cases are randomly divided into a training group (n=140) and a test group (n=60) with ratio of 7:3. Two radiologists with experience in reading orthopaedics images are independently segmented ROI, the whole intervertebral disc with the most obvious protrusion in the sagittal plane T2WI of lumbar MRI as a mask (ROI) are sketched. The LASSO algorithm is used to filter the features after extracting the radiomics features. The multivariate Logistic regression model is used to construct a quantitative imaging Rad‑Score for the selected features with non-zero coefficients. The radiomics labels and nomogram were evaluated using the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The calibration curve is used to evaluate the consistency between the nomogram prediction and the actual treatment plan. The DCA decision curve is used to evaluate the clinical applicability of nomogram. Result: Following the feature extraction, 11 radiomics features are used to construct the radiomics label for predicting the treatment plan of LDH, The nomogram was then constructed. The AUC was 0.930 (95%CI:0.865-0.995) with a sensitivity of 96.8% and a specificity of 90%. The calibration curve shows that there is a good consistency between the prediction and the actual observation. The DCA decision curve analysis shows that the nomogram of imaging group has a great potential for clinical application when the risk threshold is over 10%. Conclusion: The nomogram based on radiomics label has a good predictive value for the treatment of LDH and therefore can be used as a reference for clinical decision-making.