On Steam Pipe Network Modeling and Flow Rate Calculation

AbstractThe paper demonstrates the method to set up the pipe network hydraulic-thermal synthetic mode by applying hydraulic and thermal models of single pipe, and proposes the algorithm based on searching for the problem that iterative calculation sometimes cannot derive convergent reasonable result as well. Compared the calculated values with the measurements, it shows the validation of the model and effectiveness of the algorithm

Expression and aberrant promoter methylation of Wnt inhibitory factor-1 in human astrocytomas

<p>Abstract</p> <p>Background</p> <p>Wnt inhibitory factor-1(WIF-1) acts as a Wnt-antagonists and tumor suppressor, but hypermethylation of WIF-1 gene promoter and low expression activate Wnt signaling aberrantly and induce the development of various human tumors. With this work we intended to investigate the expression and promoter methylation status of WIF-1 gene in human astrocytomas.</p> <p>Methods</p> <p>The tissue samples consisted of 53 astrocytomas and 6 normal brain tissues. The expression levels of WIF-1 were determined by immunohistochemistry and semiquantitative RT-PCR. The results were analyzed in correlation with clinicopathological data. Methylation status of WIF-1 gene promoter was investigated using methylation specific PCR. The relationship between methylation and expression of the genes was analyzed.</p> <p>Results</p> <p>The average expression levels of WIF-1 protein and mRNA in astrocytomas were decreased significantly compared with normal control tissues. The protein and mRNA expression of WIF-1 gene in astrocytomas was decreased with the increase of pathological grade. Furthermore, WIF-1 promoter methylation was observed by MS-PCR in astrocytomas which showed significant reduction of WIF-1 expression. The WIF-1 promoter hypermethylation was associated with reduced expression of WIF-1 expression.</p> <p>Conclusion</p> <p>Our results demonstrate that the WIF-1 gene is frequently down-regulated or silenced in astrocytomas by aberrant promoter methylation. This may be an important mechanism in astrocytoma carcinogenesis.</p

Characterization of membranes with X-ray ultramicroscopy

Non-invasive characterization and observation of synthetic membranes is an important practice to monitor the performance of membrane process. Primarily there are two techniques&mdash;optical and non-optical for this purpose. Among them, X-ray computed tomography, as a non-optical technique, has been extensively used for the measurement of fibre distribution and air pockets trapped in the modules. However, the micro resolution of most commercial systems has limited its application which can hardly be used for the sub-micro characterization of membrane processes. A novel micro and nano characterization method is introduced in the current work by exploring an innovative development of the X-ray ultramicroscope (XuM) and micro-tomographic techniques. The XuM, based on using a scanning electron microscope as host, provides a new approach to X-ray projection microscopy. It has demonstrated the ability to characterize very small features in objects, down to of order 100 nm, including the use for dry, wet and even liquid samples. It can also distinguish objects with very subtle difference in density.<br /

A smart community energy management scheme considering user dominated demand side response and P2P trading

This paper proposed a Peer-to-Peer (P2P) local community energy pool and a User Dominated Demand Side Response (UDDSR) that can help energy sharing and reduce energy bills of smart community. The proposed UDDSR allows energy users within the community to submit flexible Demand Response (DR) bids to Community Energy Management Scheme (EMS) with flexible start time, stop time and response durations with regarding to users' comfort zones for electric heating systems, electric vehicles and other home appliances, which gives maximum freedom to the DR participants. The scheduling of the DR bids, originally a multi-objective optimization problem (maximize the total flexible demand and the flexible demand in every interval during the whole DR duration), is transferred to a single objective optimization problem (maximize the total demand with penalty for demand imbalance during the whole DR duration) that can significantly decrease the computational complexity. Furthermore, to facilitate efficient energy usage among neighbourhoods, a local energy pool is also proposed to enable the energy trading among users aiming to facilitate the usage of surplus energy within the community. The electricity price of energy pool is determined by the real-time demand/supply ratio, and upper/lower limit for the price is configured to ensure the profitability for all the participants within the pool. To evaluate the performance of proposed UDDSR and local energy pool, comprehensive numerical analysis is conducted. It is found that the energy pool participants without PV can get at least 6.16% savings on electricity bill (when PV penetration level equals to 20%). The energy pool participants with PV can get much better return (at least 13.4% profit increase) on the PV generation compared to the conventional Feed-in-Tariff. If energy users join the UDDSR scheme, the participants can get further return, and the proposed UDDSR can provide a constant load reduction/increase during the every time interval of the whole DR event. If Battery Energy Storage System (BESS) is included in the DR operation, the usage efficiency of customers' flexible loads can achieve more than 85%

Home appliances classification based on multi-feature using ELM

With the development of science and technology, the application in artificial intelligence has been more and more popular, as well as smart home has become a hot topic. And pattern recognition adapting to smart home attracts more attention, while the improvement of the accuracy of recognition is an important and difficult issue of smart home. In this paper, the characteristics of electrical appliances are extracted from the load curve of household appliances, and a fast and efficient home appliance recognition algorithm is proposed based on the advantage of classification of ELM (Extreme Learning Machine). At the same time, the sampling frequency with low rate is mentioned in this paper, which can obtain the required data through intelligent hardware directly, as well as reduce the cost of investment. And the intelligent hardware isdesigned by our team, which is wireless sensor network (WSN) composed by a lot of wireless sensors. Experiments in this paper show that the proposed method can accurately determine theusing electrical appliances. And greatly improve the accuracy of identification, which can further improve the popularity of smart home

A novel oxidative stress-related genes signature associated with clinical prognosis and immunotherapy responses in clear cell renal cell carcinoma

BackgroundOxidative stress plays a significant role in the tumorigenesis and progression of tumors. We aimed to develop a prognostic signature using oxidative stress-related genes (ORGs) to predict clinical outcome and provide light on the immunotherapy responses of clear cell renal cell carcinoma (ccRCC).MethodsThe information of ccRCC patients were collected from the TCGA and the E-MTAB-1980 datasets. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) were conducted to screen out overall survival (OS)-related genes. Then, an ORGs risk signature was built by multivariate Cox regression analyses. The performance of the risk signature was evaluated with Kaplan-Meier (K-M) survival. The ssGSEA and CIBERSORT algorithms were performed to evaluate immune infiltration status. Finally, immunotherapy responses was analyzed based on expression of several immune checkpoints.ResultsA prognostic 9-gene signature with ABCB1, AGER, E2F1, FOXM1, HADH, ISG15, KCNMA1, PLG, and TEK. The patients in the high risk group had apparently poor survival (TCGA: p &lt; 0.001; E-MTAB-1980: p &lt; 0.001). The AUC of the signature was 0.81 at 1 year, 0.76 at 3 years, and 0.78 at 5 years in the TCGA, respectively, and was 0.8 at 1 year, 0.82 at 3 years, and 0.83 at 5 years in the E-MTAB-1980, respectively. Independent prognostic analysis proved the stable clinical prognostic value of the signature (TCGA cohort: HR = 1.188, 95% CI =1.142-1.236, p &lt; 0.001; E-MTAB-1980 cohort: HR =1.877, 95% CI= 1.377-2.588, p &lt; 0.001). Clinical features correlation analysis proved that patients in the high risk group were more likely to have a larger range of clinical tumor progression. The ssGSEA and CIBERSORT analysis indicated that immune infiltration status were significantly different between two risk groups. Finally, we found that patients in the high risk group tended to respond more actively to immunotherapy.ConclusionWe developed a robust prognostic signature based on ORGs, which may contribute to predict survival and guide personalize immunotherapy of individuals with ccRCC

Machine learning-based early diagnosis of autism according to eye movements of real and artificial faces scanning

BackgroundStudies on eye movements found that children with autism spectrum disorder (ASD) had abnormal gaze behavior to social stimuli. The current study aimed to investigate whether their eye movement patterns in relation to cartoon characters or real people could be useful in identifying ASD children.MethodsEye-tracking tests based on videos of cartoon characters and real people were performed for ASD and typically developing (TD) children aged between 12 and 60 months. A three-level hierarchical structure including participants, events, and areas of interest was used to arrange the data obtained from eye-tracking tests. Random forest was adopted as the feature selection tool and classifier, and the flattened vectors and diagnostic information were used as features and labels. A logistic regression was used to evaluate the impact of the most important features.ResultsA total of 161 children (117 ASD and 44 TD) with a mean age of 39.70 ± 12.27 months were recruited. The overall accuracy, precision, and recall of the model were 0.73, 0.73, and 0.75, respectively. Attention to human-related elements was positively related to the diagnosis of ASD, while fixation time for cartoons was negatively related to the diagnosis.ConclusionUsing eye-tracking techniques with machine learning algorithms might be promising for identifying ASD. The value of artificial faces, such as cartoon characters, in the field of ASD diagnosis and intervention is worth further exploring

CRISPR-Cas and catalytic hairpin assembly technology for target-initiated amplification detection of pancreatic cancer specific tsRNAs

Transfer RNA-derived small RNAs (tsRNAs) tRF-LeuCAG-002 (ts3011a RNA) is a novel class of non-coding RNAs biomarker for pancreatic cancer (PC). Reverse transcription polymerase chain reaction (RT-qPCR) has been unfit for community hospitals that are short of specialized equipment or laboratory setups. It has not been reported whether isothermal technology can be used for detection, because the tsRNAs have rich modifications and secondary structures compared with other non-coding RNAs. Herein, we have employed a catalytic hairpin assembly (CHA) circuit and clustered regularly interspaced short palindromic repeats (CRISPR) to develop an isothermal and target-initiated amplification method for detecting ts3011a RNA. In the proposed assay, the presence of target tsRNA triggers the CHA circuit that transforms new DNA duplexes to activate collateral cleavage activity of CRISPR-associated proteins (CRISPR-Cas) 12a, achieving cascade signal amplification. This method showed a low detection limit of 88 aM at 37 °C within 2 h. Moreover, it was demonstrated for the first time that, this method is less likely to produce aerosol contamination than RT-qPCR by simulating aerosol leakage experiments. This method has good consistency with RT-qPCR in the detection of serum samples and showed great potential for PC-specific tsRNAs point-of-care testing (POCT)

Meeting the 24-h Movement Guidelines and Health-Related Outcomes Among Youth With Autism Spectrum Disorder: A Seven-Country Observational Study

Background: Meeting daily guidelines for physical activity, screen time, and sleep duration is associated with a host of health indicators for youth. In this cross-sectional observational study, we investigated the associations between adherence to the movement guidelines and health-related outcomes among youth with autism spectrum disorder (ASD). Methods: Parents of youth with ASD (10-17 years) from seven countries and regions were invited to provide online proxy-reports for child\u27s movement behaviors (i.e., physical activity, sleep and screen time), and health-related outcomes (i.e., body mass index [BMI], general health, and quality of life). A series of multiple linear regression analyses were used to examine the associations between meeting movement guidelines and health-related outcomes, adjusted for covariates. Results: The final sample consisted of 1165 youth with ASD. Compared with youth meeting all three guidelines, a higher BMI z-score was observed in those who met no guidelines (B = 0.62, P = 0.04), sedentary time only (B = 0.60, P = 0.047), and physical activity plus sleep only (B = 0.85, P = 0.04). Compared with meeting all three guidelines, meeting no guidelines was associated with poorer general health (B = - 0.46, P = 0.02). Further, compared with youth meeting all three guidelines, a lower quality of life score was observed in those who met no guidelines (B = - 0.47, P = 0.02) and physical activity only (B = - 0.62, P = 0.03). Lastly, there were dose-response associations between the number of guidelines met and all three health-related outcomes (all P(trend) \u3c 0.05). Conclusions: In conclusion, meeting more 24-h movement guidelines was generally associated with more favorable health-related outcomes in youth with ASD. The low level of adherence to all three guidelines (2.0%) suggests the urgent need to promote the adoption of all the guidelines in this group

Network Pharmacology Based Research on the Combination Mechanism Between Escin and Low Dose Glucocorticoids in Anti-rheumatoid Arthritis

Rheumatoid arthritis (RA) is characterized by chronic progressive symmetrical synovitis and destruction of multiple joints. Glucocorticoids (GCs) are widely used in the treatment of RA. However, their adverse effects can be serious. Escin, which is isolated from Aesculus hippocastanum L., has been reported to have anti-inflammatory effects. We investigated the anti-RA effect of Escin combined with low dose GCs (dexamethasone, Dex) and the underlying mechanism. Adjuvant-induced RA rats and lipopolysaccharides (LPS)-injured RAW264.7 cells were used to investigate the anti-RA effects of Escin combined with low dose Dex in vivo and in vitro. The results showed that Escin combined with low-dose Dex significantly decreased arthritic index, serum IL-6 and TNF-α levels, reduced paw swelling, and ameliorated the joint pathology and immune organ pathology. Gene chip results revealed that Nr3c1 (GR) expression was significantly altered, and that GR was activated by Escin and low dose Dex in vivo and in vitro. Additionally, Escin combined with low dose Dex also significantly increased GR mRNA expression. However, when GR expression was suppressed by its specific inhibitor, the anti-RA effect of Escin combined with low-dose Dex was abolished. The data in this study demonstrated that Escin combined with Dex reduced the dose of Dex, and exerted significant anti-RA effects, which could also reduce the adverse effects of Dex. This combination might result from GR activation. This study might provide a new combination of drugs for the treatment of RA