RETRACTED: Effect of supplemental parenteral nutrition on all-cause mortality in critically Ill adults: A meta-analysis and subgroup analysis

ObjectiveSeveral observational studies have demonstrated that increased nutritional delivery by supplemental parenteral nutrition (SPN) plus enteral nutrition (EN) reduces the rate of all-cause mortality in critically ill patients. Therefore, we aimed to compare and evaluate the effect of SPN plus EN on all-cause mortality in critically ill adults.MethodsRandomized controlled trials were retrieved from PubMed, Embase, Google Scholar, Cochrane Library, and Sinomed (up to May 2021). Adults with severe illness treated with SPN plus EN or with EN alone were enrolled. The risk of bias was evaluated using the Newcastle–Ottawa scale, and a meta-analysis was conducted using Stata software. The primary outcome was all-cause mortality and was evaluated by pooled odds ratio (OR) with the fixed-effects model. Required information size was also calculated using trial sequential analysis.ResultsWe identified 10 randomized controlled trials, with a total of 6,908 patients. No significant differences in rate of all-cause mortality (OR = 0.96, 95% CI: 0.84–1.09, P = 0.518), intensive care unit (ICU) mortality (OR = 0.90, 95% CI: 0.75–1.07, P = 0.229), and hospital mortality (OR = 0.95, 95% CI: 0.82–1.10, P = 0.482) were found between the SPN plus EN and EN alone groups. SPN plus EN support was associated with a significantly decreased risk of infection (OR = 0.83, 95% CI: 0.74–0.93, P = 0.001), although the duration of mechanical ventilation [standardized mean difference (SMD) = − 0.20], length of hospital stay (SMD = 0.12), and ICU stay (SMD = − 0.57) were similar between the two groups (all P > 0.05). Meta-regression analyses showed no significant correlations between all-cause mortality and baseline clinical factors, including patients’ age, the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, time of SPN initiation, and follow-up duration (all P > 0.05). Subgroup analysis showed that SPN plus EN support was associated with a trend toward decreased rate of all-cause mortality in studies with follow-up < 30 days (OR = 0.61, 95% CI: 0.36–1.02, P = 0.058). Trial sequence analysis showed that the required information size for all-cause mortality was 16,972, and the cumulative Z-curve indicated no significant differences in the risk of all-cause mortality between the two groups (P > 0.05).ConclusionSPN plus EN support can significantly reduce the risk of infection, although it has no significant effect on all-cause mortality among critically ill patients. More studies are warranted to confirm these findings

Changes in DNA methylation assessed by genomic bisulfite sequencing suggest a role for DNA methylation in cotton fruiting branch development

Cotton plant architecture, including fruit branch formation and flowering pattern, influences plant light exploitation, cotton yield and planting cost. DNA methylation has been widely observed at different developmental stages in both plants and animals and is associated with regulation of gene expression, chromatin remodelling, genome protection and other functions. Here, we investigated the global epigenetic reprogramming during the development of fruiting branches and floral buds at three developmental stages: the seedling stage, the pre-squaring stage and the squaring stage. We first identified 22 cotton genes which potentially encode DNA methyltransferases and demethylases. Among them, the homologous genes of CMT, DRM2 and MET1 were upregulated at pre-squaring and squaring stages, suggesting that DNA methylation is involved in the development of floral buds and fruit branches. Although the global methylation at all of three developmental stages was not changed, the CHG-type methylation of non-expressed genes was higher than those of expressed genes. In addition, we found that the expression of the homologous genes of the key circadian rhythm regulators, including CRY, LHY and CO, was associated with changes of DNA methylation at three developmental stages

Tectonic Features of the Wufeng–Longmaxi Formation in the Mugan Area, Southwestern Sichuan Basin, China, and Implications for Shale Gas Preservation

AbstractShale gas resources in mainland China and its commercial exploitation has been widely focused on the Wufeng–Longmaxi Formation organic-matter-rich shale in the Sichuan Basin. However, whether southwestern margin of the Sichuan Basin can produce high-quality shale gas has not been well resolved, which might be related to the poor understanding of the relationship between Cenozoic tectonic deformation and shale gas preservation. To answer the aforementioned scientific question, we conducted a detailed work in the Mugan area to show geologic structures and gas contents in the area through seismic profiles and geochemistry analysis. Specifically, the stable Mugan syncline shows a high gas content (>2.6 m3/t measured at three boreholes D1, D2, and D3), whereas its periphery presents a poor gas content (about 0.6 m3/t measured at two boreholes X1 and Y1). Moreover, oblique fracture density and dissolved pores are much higher at boreholes X1 and Y1 than that at the other three boreholes. We propose an opposite-verging thrust fault model to explain the different gas contents and tectonic features in the Mugan area, which might indicate that regions in the southwestern Sichuan Basin with similar tectonic and stratigraphic characteristics as those in the Mugan syncline are likely to produce high-yield shale gas. This finding provides new insights into the exploration theory of shale gas in the Tibetan Plateau

A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series

Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and current deep learning models. A novel hybrid end-to-end deep learning framework named Time-distributed ConvLSTM model (TDConvLSTM) is proposed in the paper for machine health monitoring, which works directly on raw multi-sensor time series. In TDConvLSTM, the normalized multi-sensor data is first segmented into a collection of subsequences by a sliding window along the temporal dimension. Time-distributed local feature extractors are simultaneously applied to each subsequence to extract local spatiotemporal features. Then a holistic ConvLSTM layer is designed to extract holistic spatiotemporal features between subsequences. At last, a fully-connected layer and a supervised learning layer are stacked on the top of the model to obtain the target. TDConvLSTM can extract spatiotemporal features on different time scales without any handcrafted feature engineering. The proposed model can achieve better performance in both time series classification tasks and regression prediction tasks than some state-of-the-art models, which has been verified in the gearbox fault diagnosis experiment and the tool wear prediction experiment