Deraining and Desnowing Algorithm on Adaptive Tolerance and Dual-tree Complex Wavelet Fusion

Severe weather conditions such as rain and snow often reduce the visual perception quality of the video image system, the traditional methods of deraining and desnowing usually rarely consider adaptive parameters. In order to enhance the effect of video deraining and desnowing, this paper proposes a video deraining and desnowing algorithm based on adaptive tolerance and dual-tree complex wavelet. This algorithm can be widely used in security surveillance, military defense, biological monitoring, remote sensing and other fields. First, this paper introduces the main work of the adaptive tolerance method for the video of dynamic scenes. Second, the algorithm of dual-tree complex wavelet fusion is analyzed and introduced. Using principal component analysis fusion rules to process low-frequency sub-bands, the fusion rule of local energy matching is used to process the high-frequency sub-bands. Finally, this paper used various rain and snow videos to verify the validity and superiority of image reconstruction. Experimental results show that the algorithm has achieved good results in improving the image clarity and restoring the image details obscured by raindrops and snows

Advances in understanding of health-promoting benefits of medicine and food homology using analysis of gut microbiota and metabolomics

The health-promoting benefits of medicine and food homology (MFH) are known for thousands of years in China. However, active compounds and biological mechanisms are unclear, greatly limiting clinical practice of MFH. The advent of gut microbiota analysis and metabolomics emerge as key tools to discover functional compounds, therapeutic targets, and mechanisms of benefits of MFH. Such studies hold great promise to promote and optimize functional efficacy and development of MFH-based products, for example, foods for daily dietary supplements or for special medical purposes. In this review, we summarized pharmacological effects of 109 species of MFH approved by the Health and Fitness Commission in 2015. Recent studies applying genome sequencing of gut microbiota and metabolomics to explain the activity of MFH in prevention and management of health consequences were extensively reviewed. We discussed the potentiality in future to decipher functional activities of MFH by applying metabolomics-based polypharmacokinetic strategy and multiomics technologies. The needs for personalized MFH recommendations and comprehensive databases have also been highlighted. This review emphasizes current achievements and challenges of the analysis of gut microbiota and metabolomics as a new avenue to understand MFH

Dynamic characteristics and drivers of the regional household energy-carbon-water nexus in China

Being a node of the energy-water consumer and carbon dioxide (CO2) emitter, the household is one key sector to pilot integrated energy-carbon-water (ECW) management. This study developed an integrated framework to explore China’s provincial household ECW nexus as well as their drivers from the years 2000 through 2016. The absolute amount and growth rate of household energy use (HEU), household CO2 emissions (HCE), and household water use (HWU) were abstracted to reveal the dynamic characteristics of the household ECW nexus. Efficiency advance, income growth, urbanization, family size, and household number were defined to explain the changes in the household ECW nexus. This study revealed that there is a huge regional heterogeneity in China’s household ECW nexus. Developed regions such as Zhejiang, Jiangsu, Guangdong, and Shanghai are the most important household ECW nexus nodes with larger amounts and growth rates of household ECW. Income growth overwhelmingly increases ECW, while efficiency advance effectively curbs its growth. Comparatively, household number, family size, and urbanization have small effects. Therefore, implementing differentiated management and focusing on the synergy of socioeconomic factors are the keys to achieving integrated household ECW management. And the analytical framework can be used to analyze ECW nexus from a sector, city, or country perspective

Aberrant hippocampal subregion networks associated with the classifications of aMCI subjects: a longitudinal resting-state study

Background: Altered hippocampal structure and function is a valuable indicator of possible conversion from amnestic type mild cognitive impairment (aMCI) to Alzheimer’s disease (AD). However, little is known about the disrupted functional connectivity of hippocampus subregional networks in aMCI subjects. Methodology/Principal Findings: aMCI group-1 (n = 26) and controls group-1 (n = 18) underwent baseline and after approximately 20 months follow up resting-state fMRI scans. Integrity of distributed functional connectivity networks incorporating six hippocampal subregions (i.e. cornu ammonis, dentate gyrus and subicular complex, bilaterally) was then explored over time and comparisons made between groups. The ability of these extent longitudinal changes to separate unrelated groups of 30 subjects (aMCI-converters, n = 6; aMCI group-2, n = 12; controls group-2, n = 12) were further assessed. Six longitudinal hippocampus subregional functional connectivity networks showed similar changes in aMCI subjects over time, which were mainly associated with medial frontal gyrus, lateral temporal cortex, insula, posterior cingulate cortex (PCC) and cerebellum. However, the disconnection of hippocampal subregions and PCC may be a key factor of impaired episodic memory in aMCI, and the functional index of these longitudinal changes allowed well classifying independent samples of aMCI converters from non-converters (sensitivity was 83.3%, specificity was 83.3%) and controls (sensitivity was 83.3%, specificity was 91.7%). Conclusions/Significance: It demonstrated that the functional changes in resting-state hippocampus subregional networks could be an important and early indicator for dysfunction that may be particularly relevant to early stage changes and progression of aMCI subjects

Urinary Aromatic Amino Acid Metabolites Associated With Postoperative Emergence Agitation in Paediatric Patients After General Anaesthesia: Urine Metabolomics Study

Background: Emergence agitation (EA) is very common in paediatric patients during recovery from general anaesthesia, but underlying mechanisms remain unknown. This prospective study was designed to profile preoperative urine metabolites and identify potential biomarkers that can predict the occurrence of EA.Methods: A total of 224 patients were screened for recruitment; of those, preoperative morning urine samples from 33 paediatric patients with EA and 33 non-EA gender- and age-matched patients after being given sevoflurane general anaesthesia were analysed by ultra-high-performance liquid chromatography (UHPLC) coupled with a Q Exactive Plus mass spectrometer. Univariate analysis and orthogonal projection to latent structures squares-discriminant analysis (OPLS-DA) were used to analyse these metabolites. The least absolute shrinkage and selection operator (LASSO) regression was used to identify predictive variables. The predictive model was evaluated through the receiver operating characteristic (ROC) analysis and then further assessed with 10-fold cross-validation.Results: Seventy-seven patients completed the study, of which 33 (42.9%) patients developed EA. EA and non-EA patients had many differences in preoperative urine metabolic profiling. Sixteen metabolites including nine aromatic amino acid metabolites, acylcarnitines, pyridoxamine, porphobilinogen, 7-methylxanthine, and 5′-methylthioadenosine were found associated with an increased risk of EA, and they all exhibited higher levels in the EA group than in the non-EA group. The main metabolic pathways involved in these metabolic changes included phenylalanine, tyrosine and tryptophan metabolisms. Among these potential biomarkers, L-tyrosine had the best predictive value with an odds ratio (OR) (95% CI) of 5.27 (2.20–12.63) and the AUC value of 0.81 (0.70–0.91) and was robust with internal 10-fold cross-validation.Conclusion: Urinary aromatic amino acid metabolites are closely associated with EA in paediatric patients, and further validation with larger cohorts and mechanistic studies is needed.Clinical Trial Registration:clinicaltrials.gov, identifier NCT0480799

Establishment of a Bernard-Soulier syndrome model in zebrafish

Platelets play an essential role in thrombosis and hemostasis. Abnormal hemostasis can cause spontaneous or severe post-traumatic bleeding. Bernard-Soulier syndrome (BSS) is a rare inherited bleeding disorder caused by a complete quantitative deficiency in the GPIb-IX-V complex. Multiple mutations in GP9 lead to the clinical manifestations of BSS. Understanding the roles and underlying mechanisms of GP9 in thrombopoiesis and establishing a proper animal model of BSS would be valuable to understand the disease pathogenesis and to improve its medical management. Here, by using CRISPR-Cas9 technology, we created a zebrafish gp9SMU15 mutant to model human BSS. Disruption of zebrafish gp9 led to thrombocytopenia and a pronounced bleeding tendency, as well as an abnormal expansion of progenitor cells. The gp9SMU15 zebrafish can be used as a BSS animal model as the roles of GP9 in thrombocytopoiesis are highly conserved from zebrafish to mammals. Utilizing the BSS model, we verified the clinical GP9 mutations by in vivo functional assay and tested clinical drugs for their ability to increase platelets. Thus, the inherited BSS zebrafish model could be of benefit for in vivo verification of patient-derived GP9 variants of uncertain significance and for the development of potential therapeutic strategies for BSS

Effects of BIS-MEP on Reversing Amyloid Plaque Deposition and Spatial Learning and Memory Impairments in a Mouse Model of β-Amyloid Peptide- and Ibotenic Acid-Induced Alzheimer’s Disease

Alzheimer’s disease (AD) is the main type of dementia and is characterized by progressive memory loss and a notable decrease in cholinergic neuron activity. As classic drugs currently used in the clinic, acetylcholinesterase inhibitors (AChEIs) restore acetylcholine levels and relieve the symptoms of AD, but are insufficient at delaying the onset of AD. Based on the multi-target-directed ligand (MTDL) strategy, bis-(-)-nor-meptazinol (BIS-MEP) was developed as a multi-target AChEI that mainly targets AChE catalysis and the β-amyloid (Aβ) aggregation process. In this study, we bilaterally injected Aβ oligomers and ibotenic acid (IBO) into the hippocampus of ICR mice and then subcutaneously injected mice with BIS-MEP to investigate its therapeutic effects and underlying mechanisms. According to the results from the Morris water maze test, BIS-MEP significantly improved the spatial learning and memory impairments in AD model mice. Compared with the vehicle control, the BIS-MEP treatment obviously inhibited the AChE activity in the mouse brain, consistent with the findings from the behavioral tests. The BIS-MEP treatment also significantly reduced the Aβ plaque area in both the hippocampus and cortex, suggesting that BIS-MEP represents a direct intervention for AD pathology. Additionally, the immunohistochemistry and ELISA results revealed that microglia (ionized calcium-binding adapter molecule 1, IBA1) and astrocyte (Glial fibrillary acidic protein, GFAP) activation and the secretion of relevant inflammatory factors (TNFα and IL-6) induced by Aβ were decreased by the BIS-MEP treatment. Furthermore, BIS-MEP showed more advantages than donepezil (an approved AChEI) as an Aβ intervention. Based on our findings, BIS-MEP improved spatial learning and memory deficits in AD mice by regulating acetylcholinesterase activity, Aβ deposition and the inflammatory response in the brain

Arbuscular Mycorrhiza Enhances Biomass Production and Salt Tolerance of Sweet Sorghum

Arbuscular mycorrhizal (AM) fungi (AMF) are widely known to form a symbiosis with most higher plants and enhance plant adaptation to a series of environmental stresses. Sweet sorghum (Sorghum bicolor (L.) Moench) is considered a promising alternative feedstock for bioalcohol production because of its sugar-rich stalk and high biomass. However, little is known of AMF benefit for biomass production and salt tolerance of sweet sorghum. Here, we investigated the effects of Acaulospora mellea ZZ on growth and salt tolerance in two sweet sorghum cultivars (Liaotian5 and Yajin2) under different NaCl addition levels (0, 0.5, 1, 2, and 3 g NaCl/kg soil). Results showed AMF colonized the two cultivars well under all NaCl addition levels. NaCl addition increased mycorrhizal colonization rates in Yajin2, but the effects on Liaotian5 ranged from stimulatory at 0.5 and 1 g/kg to insignificant at 2 g/kg, and even inhibitory at 3 g/kg. High NaCl addition levels produced negative effects on both AM and non-AM plants, leading to lower biomass production, poorer mineral nutrition (N, P, K), higher Na+ uptake, and lower soluble sugar content in leaves. Compared with non-AM plants, AM plants of both cultivars had improved plant biomass and mineral uptake, as well as higher K+/Na+ ratio, but only Yajin2 plants had a low shoot/root Na ratio. AM inoculation increased the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), and soluble sugar content in leaves. Overall, both cultivars benefited from mycorrhization, and Yajin2 with less salt tolerance showed higher mycorrhizal response. In conclusion, AMF could help to alleviate the negative effects caused by salinity, and thus showed potential in biomass production of sweet sorghum in saline soil

Enhanced Oil Recovery for ASP Flooding Based on Biorthogonal Spatial-Temporal Wiener Modeling and Iterative Dynamic Programming

Because of the mechanism complexity, coupling, and time-space characteristic of alkali-surfactant-polymer (ASP) flooding, common methods are very hard to be implemented directly. In this paper, an iterative dynamic programming (IDP) based on a biorthogonal spatial-temporal Wiener modeling method is developed to solve the enhanced oil recovery for ASP flooding. At first, a comprehensive mechanism model for the enhanced oil recovery of ASP flooding is introduced. Then the biorthogonal spatial-temporal Wiener model is presented to build the relation between inputs and states, in which the Wiener model is expanded on a set of spatial basis functions and temporal basis functions. After inferring the necessary condition of solutions, these basis functions are determined by the snapshot method. Furthermore, a theorem is proved to identify parameters in the Wiener model. Combined with the least square estimation (LSE), all unknown parameters are determined. In addition, the ARMA model is applied to build the model between states and outputs, whose parameters are identified by recursive least squares (RLS). Thus, the whole modeling process for ASP flooding is finished. At last, the IDP algorithm is applied to solve the enhanced oil recovery problem for ASP flooding based on the identification model to obtain the optimal injection strategy. Simulations on the ASP flooding with four injection wells and nine production wells show the accuracy and effectiveness of the proposed method