Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Efficient and Accurate Neural Network Based Internal Combustion Engine Modeling and Prediction

Traditionally the internal combustion engines and their subsystems are modeled purely based on their physical/mathematical principles. Such modeling techniques usually require deep prior knowledge of the internal combustion engine, which is often too difficult for many non-engine experts. In addition, the modeling process is usually very complicated and time-consuming. In some cases, the models may not be useful for many real-world applications due to oversimplified modeling assumptions. In recent years, with the rise of artificial intelligence technologies, the neural network based internal combustion engine modeling techniques have gained increasing popularity. In contrast to the traditional internal combustion engine modeling approaches, the neural network based methods can create the models directly from the system data instead of from the complicated physical/mathematical equations. This type of approach is easier to handle and often has fewer parameters to tune. This dissertation presents an extreme learning machine based neural network modeling technique for gasoline engine torque prediction. The technique utilizes a single-hidden layer feedforward neural-network structure that has the potential to approximate any continuous function with high accuracy. To verify the robustness of this technique, over 3300 data points collected from a real-world gasoline engine were used to train and test the model. The data points spanned from 1000 rpm to 4500 rpm engine speed, idle to full engine load, which mirrored the full map of normal engine operating conditions. The experimental results demonstrate that the created model predicts the gasoline engine torque with high accuracy. Furthermore, this research proposed a weight factor approach to further improve the model accuracy in the desired data regions without modifying the input data set. The model evaluation showed that the weight factor approach could reduce the overall prediction errors in the desired regions significantly. This feature is particularly useful in tuning the performance of the model when the significance of the individual data points varies, or when the distribution of the data points is imbalanced. Moreover, an innovative form of extreme learning (referred to as progressive extreme learning machine) was proposed and evaluated. It was capable of gradually improving the estimation accuracy with recursions. The new algorithm maintained the random weights generation feature of the traditional extreme learning machine and upheld the training speed advantage over many other competing algorithms. The experimental evaluation results show that progressive extreme learning machine has higher accuracy and superior generalization than many other extreme learning machine based algorithms. Furthermore, its performance was also compared with some nonlinear machine learning algorithms using the publicly available data sets. The experimental evaluation results showed that the progressive learning machine outperformed the support vector regression and had comparable performance with Levenberg-Marquardt Algorithm

Increasing the Use of Urban Greenways in Developing Countries: A Case Study on Wutong Greenway in Shenzhen, China

Given the benefits of urban greenways on the health and well-being of urban populations, the increased use of urban greenways has garnered increasing attention. Studies on urban greenways, however, have been mostly conducted in Western countries, whereas there is limited knowledge on greenway use in urban areas in developing countries. To address this shortcoming, the present study selected Wutong Greenway in Shenzhen, China, as a case study and focused on the use pattern and factors that influence the frequency and duration of urban greenway use in developing countries. An intercept survey of greenway users was conducted, and 1257 valid questionnaires were obtained. Multiple logistic regression analysis was used to examine the relationship between potential predictors and greenway use. Results showed that visitors with a varied sociodemographic background use Wutong Greenway with high intensity. Various factors affect the use of urban greenways, including individual and environmental factors and greenway use patterns. Unlike previous studies, we found that accommodation type, length of stay at present residence and mode of transportation to the greenway are important factors that affect greenway use. In contrast with studies conducted in Western countries, less-educated and low-income respondents visit the Wutong greenway even more frequently than others. Thus, the greenway is an important public asset that promotes social equity and that all residents can freely use. To better serve citizens, we suggest that the greenway network should be extended to other areas and that its environmental quality should be improved

Proteomic analysis by iTRAQ-MRM of soybean resistance to Lamprosema Indicate

Abstract Background Lamprosema indicate is a major leaf feeding insect pest to soybean, which has caused serious yield losses in central and southern China. To explore the defense mechanisms of soybean resistance to Lamprosema indicate, a highly resistant line (Gantai-2-2) and a highly susceptible line (Wan 82–178) were exposed to Lamprosema indicate larval feedings for 0 h and 48 h, and the differential proteomic analyses of these two lines were carried out. Results The results showed that 31 differentially expressed proteins (DEPs) were identified in the Gantai-2-2 when comparing 48 h feeding with 0 h feeding, and 53 DEPs were identified in the Wan 82–178. 28 DEPs were identified when comparing Gantai-2-2 with Wan 82–178 at 0 h feeding. The bioinformatic analysis results showed that most of the DEPs were associated with ribosome, linoleic acid metabolism, flavonoid biosynthesis, phenylpropanoid biosynthesis, peroxisome, stilbenoid, diarylheptanoid and gingerol biosynthesis, glutathione metabolism, pant hormone signal transduction, and flavone and flavonol biosynthesis, as well as other resistance related metabolic pathways. The MRM analysis showed that the iTRAQ results were reliable. Conclusions According to the analysis of the DEPs results, the soybean defended or resisted the Lamprosema indicate damage by the induction of a synthesis of anti-digestive proteins which inhibit the growth and development of insects, reactive oxygen species scavenging, signaling pathways, secondary metabolites synthesis, and so on

Clinical and metabolic characteristics of endometrial lesions in polycystic ovary syndrome at reproductive age

Abstract Background We aimed to explore the clinical and metabolic characteristics in polycystic ovary syndrome (PCOS) patients with different endometrial lesions. Methods 234 PCOS patients who underwent hysteroscopy and endometrial biopsy were categorized into four groups: (1) normal endometrium (control group, n = 98), (2) endometrial polyp (EP group, n = 92), (3) endometrial hyperplasia (EH group, n = 33), (4) endometrial cancer (EC group, n = 11). Serum sex hormone levels, 75 g oral glucose tolerance test, insulin release test, fasting plasma lipid, complete blood count and coagulation parameters were measured and analyzed. Results Body mass index and triglyceride level of the EH group were higher while average menstrual cycle length was longer in comparison with the control and EP group. Sex hormone-binding globulin (SHBG) and high density lipoprotein were lower in the EH group than that in the control group. 36% of the patients in the EH group suggested obesity, higher than the other three groups. Using multivariant regression analysis, patients with free androgen index > 5 had higher risk of EH (OR 5.70; 95% CI 1.05–31.01), while metformin appeared to be a protective factor for EH (OR 0.12; 95% CI 0.02–0.80). Metformin and hormones (oral contraceptives or progestogen) were shown to be protective factors for EP (OR 0.09; 95% CI 0.02–0.42; OR 0.10; 95% CI 0.02–0.56). Hormones therapy appeared to be a protective factor for EC (OR 0.05; 95% CI 0.01–0.39). Conclusion Obesity, prolonged menstrual cycle, decreased SHBG, and dyslipidemia are risk factors for EH in patients with PCOS. Oral contraceptives, progestogen and metformin are recommended for prevention and treatment of endometrial lesions in PCOS patients

Parameter study on characteristic pulse diagram of polycystic ovary syndrome based on logistic regression analysis

This study aimed to explore the parameters of the independent predictive characteristic pulse diagram of polycystic ovary syndrome (PCOS) by analysing the pulse characteristics between healthy women and the PCOS group. A total of 278 women were recruited for this study. Pulse wave parameters were collected by the pulse spectrum analyser. The single-factor analysis of the pulse diagram parameters was used to identify significant indicators, and the logistic regression analysis was carried out on the above indicators with statistical differences to obtain independent predictors. According to the single-factor and multi-factor analyses, h1, h5, h3/h1, t, t1 and t5 were independent predictors of PCOS diagnosis. The results showed that PCOS patients had a faster heart rate, decreased left ventricular systolic function and decreased aortic compliance compared to healthy individuals. These findings suggested that the characteristic pulse parameters screened out are valuable for the diagnosis of PCOS.IMPACT STATEMENTWhat is already known on this subject? Polycystic ovary syndrome (PCOS) is a common gynecological reproductive endocrine and metabolic disease, which is significant for screening and early intervention in the disease. However, due to the lack of pulse’s diagnostic evidence of PCOS, there is still an unknown area in the research on the correlation between PCOS and pulse diagram parameters.What do the results of this study add? This study fills the gap between the research on PCOS and pulse wave. The study also shows that the pulse characteristic parameters h1, h5, h3/h1, t, t1, and t5 are independent predictors of PCOS, suggesting that the patients have a higher heart rate, lower ventricular systolic function, and aortic compliance than healthy individuals.What are the implications of these findings for clinical practice and/or further research? Prominent risk factors for pulse parameters associated with the occurrence of PCOS facilitate early screening and diagnosis of the disease. The objectification of pulse diagnosis helps to establish a health management model, which can be used for the accurate assessment and treatment of PCOS by traditional Chinese medicine (TCM). It provides a clinical reference for the study of pulse diagnosis objectification. What is already known on this subject? Polycystic ovary syndrome (PCOS) is a common gynecological reproductive endocrine and metabolic disease, which is significant for screening and early intervention in the disease. However, due to the lack of pulse’s diagnostic evidence of PCOS, there is still an unknown area in the research on the correlation between PCOS and pulse diagram parameters. What do the results of this study add? This study fills the gap between the research on PCOS and pulse wave. The study also shows that the pulse characteristic parameters h1, h5, h3/h1, t, t1, and t5 are independent predictors of PCOS, suggesting that the patients have a higher heart rate, lower ventricular systolic function, and aortic compliance than healthy individuals. What are the implications of these findings for clinical practice and/or further research? Prominent risk factors for pulse parameters associated with the occurrence of PCOS facilitate early screening and diagnosis of the disease. The objectification of pulse diagnosis helps to establish a health management model, which can be used for the accurate assessment and treatment of PCOS by traditional Chinese medicine (TCM). It provides a clinical reference for the study of pulse diagnosis objectification.</p