Impacts of oxbow lake evolution on sediment microbial community structure in the Yellow River source region.

Oxbow lake formation and evolution have significant impacts on the fragile Yellow River Basin ecosystem. However, the effects of different oxbow lake evolutionary stages on sediment microbial community structure are not yet understood comprehensively. Therefore, microbial community structure in three stages of oxbow lake succession, namely, lotic lake (early stage), semi-lotic lake (middle stage), and lentic lake (late stage), was investigated in the present study in the Yellow River Basin on the Qinghai-Tibet Plateau. Amplicon sequencing was employed to reveal differences in microbial community diversity and composition. The bacterial and fungal communities in sediment were significantly different among the three succession stages and were driven by different environmental factors. In particular, bacterial community structure was influenced primarily by nitrate-nitrogen (N), microbial biomass phosphorus, and total carbon (C) and organic C in the early, middle, and late stages, respectively. Conversely, fungal community structure was influenced primarily by ammonium-N in the early stage and by moisture content in the middle and late stages. However, the predicted functions of the microbial communities did not exhibit significant differences across the three succession stages. Both bacteria and fungi were influenced significantly by stochastic factors. Homogeneous selection had a high relative contribution to bacteria community assembly in the middle stage, whereas the relative contributions of heterogeneous selection processes to fungal community assembly increased through the three stages. As succession time increased, the total number of keystone species increased gradually, and the late succession stage had high network complexity and the highest network stability. The findings could facilitate further elucidation of the evolution mechanisms of oxbow lake source area, high-altitude river evolution dynamics, in addition to aiding a deeper understanding of the long-term ecological evolution patterns of source river ecosystems.

An interpretable time series machine learning method for varying forecast and nowcast lengths in wastewater-based epidemiology.

Wastewater-based epidemiology has emerged as a viable tool for monitoring disease prevalence in a population. This paper details a time series machine learning (TSML) method for predicting COVID-19 cases from wastewater and environmental variables. The TSML method utilizes a number of techniques to create an interpretable, hypothesis-driven framework for machine learning that can handle different nowcast and forecast lengths. Some of the techniques employed include:•Feature engineering to construct interpretable features, like site-specific lead times, hypothesized to be potential predictors of COVID-19 cases.•Feature selection to identify features with the best predictive performance for the tasks of nowcasting and forecasting.•Prequential evaluation to prevent data leakage while evaluating the performance of the machine learning algorithm.

Establishing a quantitative assessment methodology framework of water conservation based on the water balance method under spatiotemporal and different discontinuous ecosystem scales.

Water conservation (WC) is an essential terrestrial ecosystem service that mitigates surface runoff and replenishes groundwater, which has received considerable attention under the dual pressures of climate change and human activity. However, there is insufficient understanding of the trends in WC changes on temporal (annual, monthly, daily), spatial, and ecosystem scales. This study proposed a quantitative assessment methodology framework (QAMF) for analyzing the spatiotemporal variation of WC under different discontinuous ecosystems. The QAMF mainly used models and methods such as the hydrological model (SWAT), calibration and uncertainty program (SWAT-CUP), WC calculation formula (water balance method), and spatial analysis method (empirical orthogonal function and wavelet analysis). It was applied to the source region of the Yellow River (SRYR), where the ecological landscape pattern underwent varying degrees of degradation, and WC capacity decreased. The results show that: Firstly, the constructed SWAT in the SRYR had high accuracy, and the proposed formula for calculating WC was suitable for multi-temporal scale analysis of WC in spatially distributed discontinuous basins. Secondly, the annual and monthly WC were respectively 81.00-184.13 mm and -28.58-107.64 mm, and daily WC was positive during extreme precipitation periods and negative during dry periods. The regulating effect of WC was fully reflected on the daily scale, partially reflected on the monthly scale, and absent on the annual scale. Third, the crucial WC area was mainly distributed in the southeast, and there was a significant primary yearly cycle of WC in the SRYR. Finally, different ecosystems exhibited different WC capabilities, and protecting the diversity of ecosystems played an essential role in maintaining and improving the WC function in the SRYR. This project has great scientific significance and technological support for scientifically evaluating the WC capacity in the SRYR.

A phenomenological neural network powered by the National Wastewater Surveillance System for estimation of silent COVID-19 infections.

Although wastewater-based epidemiology (WBE) has emerged as an inexpensive and non-intrusive method in contrast to clinical testing to track public health at community levels, there is a lack of structured interpretative criteria to translate the SARS-CoV-2 concentrations in wastewater to COVID-19 infection cases. The difficulties lie in the uncertainties of the amount of virus shed by an infected individual to wastewater as documented in clinical studies. This situation is even worse considering the existence of a population of silent infections and many other confounding factors. In this research, a quantitative framework of a phenomenological neural network (PNN) was developed to compute silent infections. The PNN was trained using the WBE data from the National Wastewater Surveillance System (NWSS) - a program launched by the CDC of the United States in 2020. It is found that the PNN excelled with superior interpretability and reduced overfitting. A big-data perspective on virus shedding by an infected population revealed more deterministic virus-shedding dynamics compared to the clinical studies perspective on virus shedding by an infected individual. With such characteristics employed as the theoretical basis for the estimation of the silent infections, a ratio of silent to reported infections was found to be 5.7 as the national median during the studied period. The study also noted the influence of temperature, sewershed population, and per-capita flow rates on the computation of silent infections. It is expected that the proposed framework in this work would facilitate public health actions guided by the SARS-CoV-2 concentrations in wastewater. In case of a new wave emergence or a new virus disease outbreak like COVID-19, the PNN powered by the NWSS would outline consolidated and systematic information that would enable rapid deployment of public health actions.

A time series based machine learning strategy for wastewater-based forecasting and nowcasting of COVID-19 dynamics.

Monitoring COVID-19 infection cases has been a singular focus of many policy makers and communities. However, direct monitoring through testing has become more onerous for a number of reasons, such as costs, delays, and personal choices. Wastewater-based epidemiology (WBE) has emerged as a viable tool for monitoring disease prevalence and dynamics to supplement direct monitoring. The objective of this study is to intelligently incorporate WBE information to nowcast and forecast new weekly COVID-19 cases and to assess the efficacy of such WBE information for these tasks in an interpretable manner. The methodology consists of a time-series based machine learning (TSML) strategy that can extract deeper knowledge and insights from temporal structured WBE data in the presence of other relevant temporal variables, such as minimum ambient temperature and water temperature, to boost the capability for predicting new weekly COVID-19 case numbers. The results confirm that feature engineering and machine learning can be utilized to enhance the performance and interpretability of WBE for COVID-19 monitoring, along with identifying the different recommended features to be applied for short-term and long-term nowcasting and short-term and long-term forecasting. The conclusion of this research is that the proposed time-series ML methodology performs as well, and sometimes better, than simple predictions that assume available and accurate COVID-19 case numbers from extensive monitoring and testing. Overall, this paper provides an insight into the prospects of machine learning based WBE to the researchers and decision-makers as well as public health practitioners for predicting and preparing the next wave of COVID-19 or the next pandemic.

On forecasting the community-level COVID-19 cases from the concentration of SARS-CoV-2 in wastewater.

The building of an effective wastewater-based epidemiological model that can translate SARS-CoV-2 concentrations in wastewater to the prevalence of virus shedders within a community is a significant challenge for wastewater surveillance. The objectives of this study were to investigate the association between SARS-CoV-2 wastewater concentrations and the COVID-19 cases at the community-level and to assess how SARS-CoV-2 wastewater concentrations should be integrated into a wastewater-based epidemiological statistical model that can provide reliable forecasts for the number of COVID-19 infections and the evolution over time as well. Weekly variations on the SARS-CoV-2 wastewater concentrations and COVID-19 cases from April 29, 2020 through February 17, 2021 were obtained in Borough of Indiana, PA. Vector autoregression (VAR) model with different data forms were fitted on this data from April 29, 2020 through January 27, 2021, and the performance in three weeks ahead forecasting (February 3, 10, and 17) were compared with measures of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). A stationary block bootstrapping VAR method was also presented to reduce the variability in the forecasting values. Our results demonstrate that VAR(1) estimated with the logged data has the best interpretation of the data, but a VAR(1) estimated with the original data has a stronger forecasting ability. The forecast accuracy, measured by MAPE, for 1 week, 2 weeks, and 3 weeks in the future can be as low as 11.85%, 8.97% and 21.57%. The forecasting performance of the model on a short time span is unfortunately not very impressive. Also, a single increase in the SARS-CoV-2 concentration can impact the COVID-19 cases in an inverted-U shape pattern with the maximum impact occur in the third week after. The flexibility of this approach and easy-to-follow explanations are suitable for many different locations where the wastewater surveillance system has been implemented.

Intervention Treatment for Myocardial Infarction With Tai Chi: A Systematic Review and Meta-analysis.

OBJECTIVE: To assess the efficacy of Tai Chi (TC) in patients with myocardial infarction and provide up-to-date evidence for its application. DATA SOURCES: Three English databases (PubMed, Embase, and Cochrane Central Register of Controlled Trials) and 3 Chinese databases (China Knowledge Resource Integrated, Wanfang, Weipu) were screened for the time period between January 1, 1976 and May 31, 2019. STUDY SELECTION: Seven randomized and controlled experiments were included. DATA EXTRACTION: Two independent researchers under 2 independent advisors extracted and classified the data from all relevant studies based on the prespecified inclusion criteria and rules for data extraction. DATA SYNTHESIS: A total of 615 patients were included in this study. The TC group was comprised of 294 patients, and the control group included 261 patients. The results revealed that TC has significant effects on the outcomes of the 6-minute walk (standardized mean difference, 1.30; 95% confidence interval, 0.50-2.11) and left ventricular ejection fraction (standardized mean difference, 1; 95% confidence interval, 0.43-1.57) compared with no or low-density exercise. Also, TC positively affected the quality of life, pro-B type natriuretic peptide, and short form-36. However, TC did not significantly affect activities of daily living (P=.060), sense of coherence-13 (P=.057) and N-terminal-pro-brain natriuretic peptide (P=.081). A moderate to high heterogeneity was observed across all comparisons. CONCLUSIONS: Compared with no exercise or other types of low-intensity physical activities, TC improved the outcome of the 6-minute walk, left ventricular ejection fraction, quality of life, and short form-36 scores, but reduced the outcome of pro-B type natriuretic peptide in patients with myocardial infarction. Therefore, TC could be an effective exercise option for cardiac rehabilitation. More research should be done to identify the effects of TC on academic functioning and to determine ways of motivating patients to use preventive TC interventions.

Microbial assessments of soil with a 40-year history of reclaimed wastewater irrigation.

The long-term effects on soil microorganisms from 40 years of irrigating soil with reclaimed wastewater was investigated by determining the quantity, composition, and inter-species connection of microorganisms. No significant difference in microbial quantity and composition were identified in the reclaimed wastewater- and groundwater-irrigated soils. The dominant bacterial phylum in both irrigation water sources and soils was Proteobacteria, which commonly exists in soil. From the analysis of four (4) alpha diversity metrics, including the observed number of operational taxonomic units (OTUs), Chao1, and the Shannon and Simpson diversity, there was no significant difference between the reclaimed wastewater- and groundwater-irrigated soils. Three zones (shallow, medium and deep) were identified in the reclaimed wastewater- and groundwater-irrigated soils based on the taxonomic networks and clusters generated by graphical lasso and random walk algorithm. The cluster profiles (shallow, medium and deep zones) appear to be different in the reclaimed wastewater- and groundwater-irrigated soils. Soil irrigated with reclaimed wastewater showed less depth of clustered profile in medium zone than that in soil irrigated with groundwater (20-60 cm of reclaimed wastewater-irrigated soil compared to 20-100 cm of groundwater-irrigated soil), although the significance of such a variance (the depth of medium zone of reclaimed wastewater-irrigated soil decreased 40 cm than that of groundwater-irrigated soil) is not clear at this time. Positive influence has been identified in the growth and yield of eggplant, tomato and cucumber between the reclaimed wastewater- and groundwater-irrigated soils, suggesting that reclaimed wastewater irrigation can potentially substitute groundwater irrigation, despite the variance in inter-species clustering profiles in soil microbes in certain soil zones. Nevertheless, the possible negative influence of pathogens, organic compounds and pharmaceuticals should be seriously considered during the reclaimed wastewater irrigation.

Cathepsin K knockout alleviates aging-induced cardiac dysfunction.

Aging is a major risk factor for cardiovascular disease. It has previously been shown that protein levels of cathepsin K, a lysosomal cysteine protease, are elevated in the failing heart and that genetic ablation of cathepsin K protects against pressure overload-induced cardiac hypertrophy and contractile dysfunction. Here we test the hypothesis that cathepsin K knockout alleviates age-dependent decline in cardiac function. Cardiac geometry, contractile function, intracellular Ca(2+) properties, and cardiomyocyte apoptosis were evaluated using echocardiography, fura-2 technique, immunohistochemistry, Western blot and TUNEL staining, respectively. Aged (24-month-old) mice exhibited significant cardiac remodeling (enlarged chamber size, wall thickness, myocyte cross-sectional area, and fibrosis), decreased cardiac contractility, prolonged relengthening along with compromised intracellular Ca(2+) release compared to young (6-month-old) mice, which were attenuated in the cathepsin K knockout mice. Cellular markers of senescence, including cardiac lipofuscin, p21 and p16, were lower in the aged-cathepsin K knockout mice compared to their wild-type counterpart. Mechanistically, cathepsin K knockout mice attenuated an age-induced increase in cardiomyocyte apoptosis and nuclear translocation of mitochondrial apoptosis-inducing factor (AIF). In cultured H9c2 cells, doxorubicin stimulated premature senescence and apoptosis. Silencing of cathepsin K blocked the doxorubicin-induced translocation of AIF from the mitochondria to the nuclei. Collectively, these results suggest that cathepsin K knockout attenuates age-related decline in cardiac function via suppressing caspase-dependent and caspase-independent apoptosis.

Quality of life after children undergo a radical arterial switch operation at an older age.

OBJECTIVE: The goal of the study was to evaluate the quality of life of children after the older corrective arterial switch operation (ASO) by means of the Pediatric Quality of Life Inventory (PedsQL), version 4.0. METHODS: The records of 86 patients who had complete transposition of the great arteries plus a nonrestrictive ventricular septal defect, or a Taussig-Bing anomaly, and severe pulmonary arterial hypertension, and who underwent a corrective ASO at an older age (>6 months) between May 2000 and October 2008 were reviewed retrospectively. Eighty survivors were followed up, and the health-related quality of life of the survivors was evaluated with the PedsQL, version 4.0. RESULTS: There were 6 hospital deaths. The mean (SD) follow-up interval was 3.5 ± 2.3 years, and the mean age at last visit was 7.0 ± 1.2 years. Two late deaths occurred, and 8 patients were lost to follow-up. Patients who underwent a corrective ASO at an older age showed acceptable scores for all scales, and they were all comparable with those of a healthy population. CONCLUSIONS: Our data suggest that the quality of life of children who undergo a corrective ASO at an older age (>6 months) is acceptable, compared with that of healthy children in China.