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Background and purposeStroke may cause extensive after-effects such as motor function impairments and disorder of consciousness (DoC). Detecting these after-effects of stroke and monitoring their changes are challenging jobs currently undertaken via traditional clinical examinations. These behavioural examinations often take a great deal of manpower and time, thus consuming significant resources. Computer-aided examinations of the electroencephalogram (EEG) microstates derived from bedside EEG monitoring may provide an alternative way to assist medical practitioners in a quick assessment of the after-effects of stroke.MethodsIn this study, we designed a framework to extract microstate maps and calculate their statistical parameters to input to classifiers to identify DoC in ischemic stroke patients automatically. As the dataset is imbalanced with the minority of patients being DoC, an ensemble of support vector machines (EOSVM) is designed to solve the problem that classifiers always tend to be the majority classes in the classification on an imbalanced dataset.ResultsThe experimental results show EOSVM get better performance (with accuracy and F1-Score both higher than 89%), improving sensitivity the most, from lower than 60% (SVM and AdaBoost) to higher than 80%. This highlighted the usefulness of the EOSVM-aided DoC detection based on microstates parameters.ConclusionTherefore, the classifier EOSVM classification based on features of EEG microstates is helpful to medical practitioners in DoC detection with saved resources that would otherwise be consumed in traditional clinic checks.

Alpine skiing, as an outdoor winter sport, is particularly vulnerable to the variation of meteorological conditions. Scattered and multi-source big data cannot be fully utilized to conduct effective decision analyses by conventional data analysis methods. Presently, knowledge graphs are the most advanced organization form of knowledge base, which can make explicit the complex relationships among different objects. Thus, introducing knowledge graph to the event management of alpine skiing is significant to improve the ability of risk prediction and decision-making. In this research, we analyze the components and dynamic characteristics of alpine skiing, and construct an “Object-Characteristic-Relation” representation model to express multi-level knowledge. Moreover, we propose a “Characteristic-value- Relationship” representation method based on the multi-source data, to construct the knowledge graph of alpine skiing. With the proposed method, comprehensive relationships between meteorological conditions and alpine skiing can be represented clearly, and support further knowledge reasoning for the event management under meteorological conditions. We have tested the utility of the proposed method in a case study of 2018 Winter Olympics in PyeongChang. The case study realizes an semi-automatic construction of knowledge graph for alpine skiing, provides decision supports for event risk managements, according to different meteorological conditions, and grounds a foundation for future knowledge graph construction of other large-scale sport events.

Fecal microbiota transplantation has been proposed as a therapeutic approach for chronic constipation. This randomized, controlled trial aimed to compare the effects of conventional treatment alone (control) with additional treatment with FMT (intervention) in patients with slow-transit constipation (STC). Adults with STC were randomized to receive intervention or control treatment. The control group received education, behavioral strategies, and oral laxatives. The intervention group was additionally provided 6 days of FMT. The primary endpoint was the clinical cure rate (proportion of patients achieving a mean of ≥ three complete spontaneous bowel movements [CSBMs] per week]. Secondary outcomes and safety parameters were assessed throughout the study. Sixty patients were randomized to either conventional treatment alone (n = 30) or FMT (n = 30) through a nasointestinal tube. There were significant differences between the intervention group and control group in the clinical improvement rate (intention-to-treat [ITT]: 53.3% vs. 20.0%, P = 0.009), clinical cure rate (ITT: 36.7% vs. 13.3%, P = 0.04), mean number of CSBMs per week (ITT: 3.2 ± 1.4 vs. 2.1 ± 1.2, P = 0.001), and the Wexner constipation score (ITT: 8.6 ± 1.5 vs. 12.7 ± 2.5, P < 0.00001). Compared with the control group, the intervention group showed better results in the stool consistency score (ITT: 3.9 vs. 2.4, P < 0.00001) and colonic transit time (ITT: 58.5 vs. 73.6 h, P < 0.00001). The intervention group had more treatment-related adverse events than did the control group (50 vs. 4 cases). FMT was significantly more effective (30% higher cure rate) for treatment of STC than conventional treatment. No serious adverse events were observed.

To use Z-scores to compare different charts of femur length (FL) applied to our population with the aim of identifying the most appropriate chart.A retrospective study was conducted in Beijing. Fifteen hospitals in Beijing were chosen as clusters using a systemic cluster sampling method, in which 15,194 pregnant women delivered from June 20th to November 30th, 2013. The measurements of FL in the second and third trimester were recorded, as well as the last measurement obtained before delivery. Based on the inclusion and exclusion criteria, we identified FL measurements from 19996 ultrasounds from 7194 patients between 11 and 42 weeks gestation. The FL data were then transformed into Z-scores that were calculated using three series of reference equations obtained from three reports: Leung TN, Pang MW et al (2008); Chitty LS, Altman DG et al (1994); and Papageorghiou AT et al (2014). Each Z-score distribution was presented as the mean and standard deviation (SD). Skewness and kurtosis and were compared with the standard normal distribution using the Kolmogorov-Smirnov test. The histogram of their distributions was superimposed on the non-skewed standard normal curve (mean = 0, SD = 1) to provide a direct visual impression. Finally, the sensitivity and specificity of each reference chart for identifying fetuses 95th percentile (based on the observed distribution of Z-scores) were calculated. The Youden index was also listed. A scatter diagram with the 5th, 50th, and 95th percentile curves calculated from and superimposed on each reference chart was presented to provide a visual impression.The three Z-score distribution curves appeared to be normal, but none of them matched the expected standard normal distribution. In our study, the Papageorghiou reference curve provided the best results, with a sensitivity of 100% for identifying fetuses with measurements < 5th and > 95th percentile, and specificities of 99.9% and 81.5%, respectively.It is important to choose an appropriate reference curve when defining what is normal. The Papageorghiou reference curve for FL seems to be the best fit for our population. Perhaps it is time to change our reference curve for femur length.

Analysis of mutants and gene expression patterns provides a powerful approach for investigating genes involved in key stages of plant fiber development. In this study, lintless-fuzzless XinWX and linted-fuzzless XinFLM with a single genetic locus difference for lint were used to identify differentially expressed genes. Scanning electron microscopy showed fiber initiation in XinFLM at 0 days post anthesis (DPA). Fiber transcriptional profiling of the lines at three initiation developmental stages (-1, 0, 1 DPA) was performed using an oligonucleotide microarray. Loop comparisons of the differentially expressed genes within and between the lines was carried out, and functional classification and enrichment analysis showed that gene expression patterns during fiber initiation were heavily associated with hormone metabolism, transcription factor regulation, lipid transport, and asparagine biosynthetic processes, as previously reported. Further, four members of the allene-oxide cyclase (AOC) family that function in jasmonate biosynthesis were parallel up-regulation in fiber initiation, especially at -1 DPA, compared to other tissues and organs in linted-fuzzed TM-1. Real time-quantitative PCR (RT-qPCR) analysis in different fiber mutant lines revealed that AOCs were up-regulated higher at -1 DPA in lintless-fuzzless than that in linted-fuzzless and linted-fuzzed materials, and transcription of the AOCs was increased under jasmonic acid (JA) treatment. Expression analysis of JA biosynthesis-associated genes between XinWX and XinFLM showed that they were up-regulated during fiber initiation in the fuzzless-lintless mutant. Taken together, jasmonic acid-associated metabolism was related to cotton fiber initiation. Parallel up-regulation of AOCs expression may be important for normal fiber initiation development, while overproduction of AOCs might disrupt normal fiber development.

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

Effects of different pretreatment methods on sludge dewaterability and polycyclic aromatic hydrocarbons (PAHs) degradation during petrochemical sludge anaerobic digestion were studied. Results showed that the total biogas production volume in the thermal pretreatment system was 4 and 5 times higher than that in the ultrasound pretreatment and in the control system, and the corresponding volatile solid removal efficiencies reached 28%, 15%, and 8%. Phenanthrene, paranaphthalene, fluoranthene, benzofluoranthene, and benzopyrene removal rates reached 43.3%, 55.5%, 30.6%, 42.9%, and 41.7%, respectively, in the thermal pretreatment system, which were much higher than those in the ultrasound pretreatment and in the control system. Moreover, capillary suction time (CST) of sludge increased after pretreatment, and then reduced after 20 days of anaerobic digestion, indicating that sludge dewaterability was greatly improved after anaerobic digestion. The decrease of protein and polysaccharide in the sludge could improve sludge dewaterability during petrochemical sludge anaerobic digestion. This study suggested that thermal pretreatment might be a promising enhancement method for petrochemical sludge solubilization, thus contributing to degradation of the PAHs, biogas production, and improvement of dewaterability during petrochemical sludge anaerobic digestion.

Extracellular polymeric substances (EPS) play important roles in regulating the dewaterability of sludge. This study sought to elucidate the influence of EPS on the dewaterability of sludge during bioleaching process. Results showed that, in bioleaching system with the co-inoculation of Acidithiobacillus thiooxidans TS6 and Acidithiobacillus ferrooxidans LX5 (A. t+A. f system), the capillary suction time (CST) of sludge reduced from 255.9 s to 25.45 s within 48 h, which was obviously better than the controls. The correlation analysis between sludge CST and sludge EPS revealed that the sludge EPS significantly impacted the dewaterability of sludge. Sludge CST had correlation with protein content in slime and both protein and polysaccharide contents in TB-EPS and Slime+LB+TB layers, and the decrease of protein content in slime and decreases of both protein and polysaccharide contents in TB-EPS and Slime+LB+TB layers improved sludge dewaterability during sludge bioleaching process. Moreover, the low sludge pH (2.92) and the increasing distribution of Fe in the solid phase were another two factors responsible for the improvement of sludge dewaterability during bioleaching. This study suggested that during sludge bioleaching the growth of Acidithiobacillus species resulted in the decrease of sludge pH, the increasing distribution of Fe in the solid phase, and the decrease of EPS content (mainly including protein and/or polysaccharide) in the slime, TB-EPS, and Slime+LB+TB layers, all of which are helpful for sludge dewaterability enhancement.

Heme oxygenase-1 (HO-1) has been implicated in cardiac dysfunction, oxidative stress, inflammation, apoptosis and autophagy associated with heart failure, and atherosclerosis, in addition to its recognized role in metabolic syndrome and diabetes. Numerous studies have presented contradictory findings about the role of HO-1 in diabetic cardiomyopathy (DCM). In this study, we explored the role of HO-1 in myocardial dysfunction, myofibril structure, oxidative stress, inflammation, apoptosis and autophagy using a streptozotocin (STZ)-induced diabetes model in mice systemically overexpressing HO-1 (Tg-HO-1) or mutant HO-1 (Tg-mutHO-1). The diabetic mouse model was induced by multiple peritoneal injections of STZ. Two months after injection, left ventricular (LV) function was measured by echocardiography. In addition, molecular biomarkers related to oxidative stress, inflammation, apoptosis and autophagy were evaluated using classical molecular biological/biochemical techniques. Mice with DCM exhibited severe LV dysfunction, myofibril structure disarray, aberrant cardiac oxidative stress, inflammation, apoptosis, autophagy and increased levels of HO-1. In addition, we determined that systemic overexpression of HO-1 ameliorated left ventricular dysfunction, myofibril structure disarray, oxidative stress, inflammation, apoptosis and autophagy in DCM mice. Furthermore, serine/threonine-specific protein kinase (Akt) and AMP-activated protein kinase (AMPK) phosphorylation is normally inhibited in DCM, but overexpression of the HO-1 gene restored the phosphorylation of these kinases to normal levels. In contrast, the functions of HO-1 in DCM were significantly reversed by overexpression of mutant HO-1. This study underlines the unique roles of HO-1, including the inhibition of oxidative stress, inflammation and apoptosis and the enhancement of autophagy, in the pathogenesis of DCM.

Chronic psychological stress has been shown to adversely impact immune system functions and compromise host defenses against various infections. However, the underlying mechanisms remain elusive. Recent studies have demonstrated that myeloid-derived suppressor cells (MDSCs) play an important role in regulating immunity. It is of interest to explore whether or not chronic psychological stress plays immunosuppressive functions partially by inducing MDSCs accumulation. In this work, we report that chronic psychological stress led to the accumulation of CD11b+Gr1+ cells in the bone marrow of BALB/c mice. Repeated β-agonist infusion showed no such effect. However, β-adrenergic blockade, but not glucocorticoids blockade, partially reversed the accumulation of CD11b+Gr1+ cells under the condition of chronic psychological stress, suggesting catecholamines collaborate with other factors to induce the accumulation. Further exploration indicates that cyclooxygenase 2 (COX-2)-prostaglandin E2 (PGE2) loop might act downstream to induce the accumulation. A majority of the accumulated CD11b+Gr1+ cells were Ly6G+Ly6C(low) immature neutrophils, which inhibited cytokine release of macrophages as well as T cell responsiveness. Moreover, the accumulated CD11b+Gr1+ cells under the condition of chronic psychological stress expressed multiple inhibitory molecules. Taken together, our data demonstrate for the first time that chronic psychological stress induces MDSCs accumulation in mice, which can contribute to immunosuppression.