The objective of this study was to explore the effects of plant growth-promoting rhizobacteria (PGPR), strain Bacillus licheniformis, with softwood biochar amendment on potato growth and water use efficiency (WUE) under a deficit irrigation (DI) regime. A pot experiment was conducted in a greenhouse. The results showed that PGPR improved leaf gas exchange rates, including photosynthesis rate, stomatal conductance and transpiration rate at early seedling stage, while tended to depress these parameters gradually until final harvest. The effects of biochar on plant leaf physiology, plant growth and WUE were not evident. Plants were more affected by DI than PGPR inoculation and biochar amendment. DI significantly decreased leaf gas exchange rates after exposure to water treatment for around three weeks, and the negative effect was eliminated at final harvest. At final harvest, DI significantly decreased leaf area, specific leaf area, dry mass of leaf and stem, total dry mass, dry mass increment and plant water use. The synergistical effect of PGPR strain Bacillus licheniformis and DI on plant growth and WUE were not observed in our study. WUE was solely improved by DI, indicating that, compared to PGPR inoculation, DI was a more effective measure to enhance plant WUE.
Exogenous intestinal alkaline phosphatase (IAP), an enzyme produced endogenously at the brush edge of the intestinal mucosa, may mitigate the increase in aberrant intestinal permeability increased during sepsis. The aim of this study was to test the efficacy of the inhibitory effect of IAP on acute intestinal inflammation and to study the molecular mechanisms underlying IAP in ameliorating intestinal permeability. We used an in vivo imaging method to evaluate disease status and the curative effect of IAP. Two Escherichia coli (E.coli) B21 strains, carrying EGFP labeled enhanced green fluorescent protein (EGFP) and RFP labeled red fluorescent protein (RFP), were constructed as tracer bacteria and were administered orally to C57/B6N mice to generate an injection peritonitis (IP) model. The IP model was established by injecting inflammatory lavage fluid. C57/B6N mice bearing the tracer bacteria were subsequently treated with (IP+IAP group), or without IAP (IP group). IAP was administered to the mice via tail vein injections. The amount of tracer bacteria in the blood, liver, and lungs at 24 h post-injection was analyzed via flow cytometry (FCM), in vivo imaging, and Western blotting. Intestinal barrier function was measured using a flux assay with the macro-molecule fluorescein isothiocyanate dextran, molecular weight 40kD, (FD40). To elucidate the molecular mechanism underlying the effects of IAP, we examined the levels of ERK phosphorylation, and the expression levels of proteins in the ERK-SP1-VEGF and ERK-Cdx-2-Claudin-2 pathways. We observed that IAP inhibited the expression of Claudin-2, a type of cation channel-forming protein, and VEGF, a cytokine that may increase intestinal permeability by reducing the levels of dephosphorylated ERK. In conclusion, exogenous IAP shows a therapeutic effect in an injection peritonitis model. This including inhibition of bacterial translocation. Moreover, we have established an imaging methodology for live-animals can effectively evaluate intestinal permeability and aberrant bacterial translocation in IP models.
Abstract Over the past four decades, the decrease in Arctic sea ice has driven significant growth in vessel traffic through the Arctic passages. A precise and quantitative sea ice risk assessment would be the cornerstone of route planning for Arctic ships. Taking the chokepoint of Arctic Northeast Passage, the Vilkitsky Strait, as an example, the temporal and spatial characteristics of ice conditions in the strait were analyzed based on simulated ice thickness and observed sea ice concentration data from 2012 to 2021. Additionally, navigation risk in the strait was assessed based on the Polar Operational Limit Assessment Risk Indexing System (POLARIS). The results showed that the strait experienced 100% sea ice coverage from December to May, peaking in thickness of nearly 2 m in May, receding starting in June, and presenting ice-free passages by August and September. A significant interannual variability is evident in the timing of sea ice melting and freezing. Moreover, the average navigability of the strait was 365 days for vessels with an ice class of PC3, 72 days for PC6 and fewer than 64 days for those below B1. Remarkably, in 2013, 2014, and 2021, vessels below PC5 had less than 30 navigable days in the strait.
Irritable bowel syndrome (IBS) is a functional bowel disease characterized by abdominal pain or discomfort associated with altered bowel habits. Several clinical studies have demonstrated the effectiveness of acupuncture and moxibustion for IBS. Many systematic reviews of acupuncture and moxibustion for IBS have been published in recent years, but their results are not entirely consistent. To evaluate the methodological, reporting, and evidence quality of systematic reviews of acupuncture and moxibustion for IBS. Search strategy: Systematic reviews of acupuncture and moxibustion for IBS published before February 20, 2023 were searched in eight databases: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang Data, VIP Database for Chinese Technical Periodicals, and China Biology Medicine. The keywords used for literature search were acupuncture, moxibustion, systematic review, meta-analysis, and irritable bowel syndrome. Inclusion criteria: Systematic reviews and meta-analyses of randomized controlled trials of acupuncture and moxibustion for IBS were included. Data extraction and analysis: Relevant information was independently extracted by two investigators. The A MeaSurement Tool to Assess systematic Reviews 2 (AMSTAR 2), Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020), and Grading of Recommendations Assessment, Development and Evaluation (GRADE) were used to evaluate the methodological quality, reporting quality and evidence quality, respectively. A total of 342 studies were retrieved and 15 systematic reviews were included. The results of AMSTAR 2 showed low methodological quality in 2 studies and very low methodological quality in the remaining 13 studies, with main issues being failure to register a protocol, incomplete search strategy, not providing a list of excluded studies, incomplete consideration of the risk of bias in the included studies, and a failure to assess the publication bias. The results of PRISMA 2020 showed seriously deficient reporting quality of 2 studies, somewhat deficient reporting quality of 12 studies, and relatively complete reporting quality of 1 study, with the main problems being lack of a complete search strategy, non-availability of a list of excluded studies with justification for their exclusion, not conducting heterogeneity and sensitivity analyses, not evaluating the credibility of the evidence, and not registering the protocol. The results of GRADE showed that the quality of the evidence is low or very low. Most included systematic reviews interpreted findings to suggest that acupuncture and moxibustion have benefits for IBS. However, there is a need to improve the methodological, reporting and evidence quality of the systematic reviews. Larger, multicenter, rigorously designed randomized controlled trials and high-quality systematic reviews are required to obtain more robust evidence. Please cite this article as: Ma YY, Hao Z, Chen ZY, Shen YX, Liu HR, Wu HG, Bao CH. Acupuncture and moxibustion for irritable bowel syndrome: An umbrella systematic review. J Integr Med. 2023; Epub ahead of print. Received June 12, 2023; Accepted November 9, 2022.
Abstract The eddy‐covariance (EC) method assumes a homogeneous underlying surface. However, recent studies increasingly examining on EC measurements across diverse surfaces, raising concerns about measurement precision and accuracy. This study evaluates the impacts of altering the emission height and rate on the EC measurements through utilizing an artificial source emission system. The results demonstrated a significant impact of changes in the emission height and rate on the EC measurements. Higher emission height may lead to the underestimation of the measured EC fluxes, attributed to the variations in the footprint area and turbulent transport. Traditional data quality control methods may discard effective EC data during sudden changes in the emission rate. Therefore, to secure effective data and accurately observe emissions, it was practical to analyze the auxiliary factors, such as environmental factors, such as vapor pressure deficit (VPD). An unresolved issue would persist with the correction of the EC method for accurately capturing the actual emission signals when there was a sudden increase in the data range or deviation. Furthermore, comparing the footprint model estimations with the actual emissions demonstrated the necessity of footprint analyses, offering a valuable reference for the data calibration when the uncertainties arose owing to inhomogeneous underlying surfaces. Although EC fluxes across the three averaging periods indicated no significant differences, the footprint model suggested that 15‐min interval was the optimal. Further validation experiments are required for the EC measurements in locations with complex source conditions to enhance our understanding of land‐atmosphere flux exchange.
Abstract Hail, an intense convective catastrophic weather, is seriously hazardous to people’s lives and properties. This article proposes a multi-step cyclone hail weather recognition model, called long short-term memory (LSTM)-C3D, based on radar images, integrating attention mechanism and network voting optimization characteristics to achieve intelligent recognition and accurate classification of hailstorm weather based on long short-term memory networks. Based on radar echo data in the strong-echo region, LSTM-C3D can selectively fuse the long short-term time feature information of hail meteorological images and effectively focus on the significant features to achieve intelligent recognition of hail disaster weather. The meteorological scans of 11 Doppler weather radars deployed in various regions of the Hunan Province of China are used as the specific experimental and application objects for extensive validation and comparison experiments. The results show that the proposed method can realize the automatic extraction of radar reflectivity image features, and the accuracy of hail identification in the strong-echo region reaches 91.3%. It can also effectively realize the prediction of convective storm movement trends, laying the theoretical foundation for reducing the misjudgment of extreme disaster weather.
Acupuncture treatment (AT) of depressive insomnia by traditional Chinese medicine has the advantages of fewer side effects, quicker results, and lower prices compared to medication and psychological and cognitive therapy. Clinicians often select multiple acupoints, such as Bai Hui (GV20), San Yin Jiao (SP6), and Shen Men (HT7), for combined treatment in a single AT session to improve sleep quality. Since the ancient literature on AT often only records the general order of acupoints, there needs to be more discussion on the influence of the multiple acupoint sequence on the priority of efficacy for a specific disease. At the same time, determining the ranking of acupoints in-patient treatment in clinical practice is mainly dependent on the treatment experience of practitioners, and there is no transparent quantitative model or evaluation method for generating credible acupoint sequences from a small and limited scale of cases. Therefore, it is essential to explore the optimization of the order of multiple acupoints in treating depressive insomnia by Traditional Chinese Medicine acupuncture both for the symptom relief of depressive insomnia patients and for the efficient use of national health care resources. This paper proposes a reinforcement learning-based method for optimizing the acupoint sequence for depressive insomnia AT to address these issues. This paper provides a post-AT EEG signal prediction model with related interpretable models to construct a reinforcement learning framework to represent the state transfer of the AT environment and a quantitative EEG signal-based AT efficacy model to represent the reward function. Finally, 30 patients with depressive insomnia were recruited to collect EEG signals during AT for depressive insomnia, and the case data were used to quantify the efficacy of AT and to model the post-AT EEG signal prediction. The above two models were applied to optimize the acupoint sequence based on reinforcement learning. Satisfactory results were obtained, verifying the effectiveness and feasibility of the method proposed in this paper.
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