A Novel Neural Network-based Multi-objective Evolution Lower Upper Bound Estimation Method for Electricity Load Interval Forecast

Currently, an interval prediction model, lower and upper bounds estimation (LUBE) which constructs the prediction intervals (PIs) by using the double outputs of the neural network (NN) is growing popular. However, existing LUBE researches have two problems. One is that the applied NNs are flawed: feedforward NN (FNN) cannot map the dynamic relationship of data and recurrent NN (RNN) is computationally expensive. The other is that most LUBE models are built under a single-objective frame in which the uncertainty cannot be fully quantified. In this article, a novel wavelet NN (WNN) with direct input–output links (DLWNN) is proposed to obtain PIs in a multiobjective LUBE frame. Different from WNN, the proposed DLWNN adds the direct links from the input layer to output layer which can make full use of the information of time series data. Besides, a niched differential evolution nondominated fast sort genetic algorithm (NDENSGA) is proposed to optimize the prediction model, so as to achieve a balance between estimation accuracy and the average width of the PIs. NDENSGA modifies the traditional population renewal mechanism to increase population diversity and adopts a new elite selection strategy for obtaining more extensive and uniform solutions. The effectiveness of DLWNN and NDENSGA is evaluated through a series of experiments with real electricity load data sets. The results show that the proposed model has better performance than others in terms of convergence and diversity of obtained nondominated solutions

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature

Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network

The electricity consumption forecasting is a challenging task, because the predictive accuracy is easily affected by multiple external factors, such as society, economics, environment, as well as the renewable energy, including hydro power, wind power and solar power. Particularly, in the smart grid with large amount of data, how to extract valuable information of those external factors timely is the key to the success of electricity consumption forecasting. A method of probability density forecasting based on Least Absolute Shrinkage and Selection Operator-Quantile Regression Neural Network (LASSO-QRNN) is proposed in this paper. First, important features are extracted from external factors affecting the electricity consumption forecasting by LASSO regression. Then, the LASSO-QRNN model is constructed to predict annual electricity consumption. The results of electricity consumption forecasting under different quantiles in the next several years are evaluated. Besides, we introduce kernel density estimation into our LASSO-QRNN model, which can give a probability distribution instead of a single-valued prediction. The prediction accuracy is evaluated through the empirical analyses from the Guangdong province dataset in China and the California dataset in the United States. The simulation results demonstrate that the proposed method provides better performance for electricity consumption forecasting, in comparison with existing quantile regression neural network (QRNN), back-propagation of errors neural network (BP), radial basis function neural network (RBF), quantile regression (QR) and nonlinear quantile regression (NLQR). LASSO-QRNN can not only better learn the high-dimensional data in electricity consumption forecasting, but also provide more precise results

Biases during DNA extraction affect characterization of the microbiota associated with larvae of the Pacific white shrimp, Litopenaeus vannamei

For in-depth characterization of the microbiota associated with shrimp larvae, careful selection of DNA isolation procedure is paramount for avoiding biases introduced in community profiling. Four E.Z.N.A.™ DNA extraction kits, i.e., Bacterial, Mollusc, Stool, and Tissue DNA Kits, abbreviated as Ba, Mo, St, and Ti, respectively, were initially evaluated with zoea 2 (Z2) larvae of the Pacific white shrimp (Litopenaeus vannamei) by 16S amplicon sequencing on a Illumina MiSeq platform. Further characterization of additional larval samples, specifically nauplii 5 (N5), mysis 1 (M1), and postlarvae 1 (P1), was performed with Ba and St kits to examine the changing microbiota profile during shrimp hatchery period. The results from the Z2 samples showed that DNA yields from the four kits varied significantly (P < 0.05), whereas no significant differences were detected in the α-diversity metrics of the microbiota. By contrast, the St kit, with the lowest DNA yield and quality, successfully recovered DNA from Gram-positive and gut-associated bacterial groups, whereas the Ba kit, which showed maximal microbiota similarity with the Mo kit, manifested the best reproducibility. Notably, significant differences were observed in relative abundances of most dominant taxa when comparing results from the Ba and St kits on Z2, M1, and P1 samples. In addition, the bacterial community identified shifted markedly with larval development regardless of the DNA extraction kits. The DNA recovery biases arising from the larval microbiota could be due to different protocols for cell lysis and purification. Therefore, combined application of different DNA extraction methods may facilitate identification of some biologically important groups owing to their complementary effects. This approach should receive adequate attention for a thorough understanding of the larvae-associated microbiota of the penaeid shrimp

Increased Corticomuscular Coherence and Brain Activation Immediately After Short-Term Neuromuscular Electrical Stimulation

Neuromuscular Electrical Stimulation (NMES) is commonly used in motor rehabilitation for stroke patients. It has been verified that NMES can improve muscle strength and activate the brain, but the studies on how NMES affects the corticomuscular connection are limited. Some studies found an increased corticomuscular coherence (CMC) after a long-term NMES. However, it is still unknown about CMC during NMES, as relatively pure EMG is very difficult to obtain with the contamination of NMES current pulses. In order to approach the condition during NMES, we designed an experiment with short-term NMES and immediately captured data within 100 s. The repetition of wrist flexion was used to realize static muscle contractions for CMC calculation and dynamic contractions for event-related desynchronization (ERD). The result of 13 healthy participants showed that maximal values (p = 0.0020) and areas (p = 0.0098) of CMC and beta ERD were significantly increased immediately after NMES. It was concluded that a short-term NMES can still reinforce corticomuscular functional connection and brain activation related to motor task. This study verified the immediate strengthen of corticomuscular changes after NMES, which was expected to be the basis of long-term neural plasticity induced by NMES

Virome and metagenomic analysis reveal the distinct distribution of microbiota in human fetal gut during gestation

Studies have shown that fetal immune cell activation may result from potential exposure to microbes, although the presence of microbes in fetus has been a controversial topic. Here, we combined metagenomic and virome techniques to investigate the presence of bacteria and viruses in fetal tissues (small intestine, cecum, and rectum). We found that the fetal gut is not a sterile environment and has a low abundance but metabolically rich microbiome. Specifically, Proteobacteria and Actinobacteria were the dominant bacteria phyla of fetal gut. In total, 700 species viruses were detected, and Human betaherpesvirus 5 was the most abundant eukaryotic viruses. Especially, we first identified Methanobrevibacter smithii in fetal gut. Through the comparison with adults’ gut microbiota we found that Firmicutes and Bacteroidetes gradually became the main force of gut microbiota during the process of growth and development. Interestingly, 6 antibiotic resistance genes were shared by the fetus and adults. Our results indicate the presence of microbes in the fetal gut and demonstrate the diversity of bacteria, archaea and viruses, which provide support for the studies related to early fetal immunity. This study further explores the specific composition of viruses in the fetal gut and the similarities between fetal and adults’ gut microbiota, which is valuable for understanding human fetal immunity development during gestation

Molecular Engineering of Potent Sensitizers for Very Efficient Light Harvesting in Thin-Film Solid-State Dye-Sensitized Solar Cells

Dye-sensitized solar cells (DSSCs) have shown significant potential for indoor arid building integrated photovoltaic applications. Herein we present three new D-A-pi-A organic sensitizers, XY1, XY2, and XY3, that exhibit high molar extinction coefficients and a broad absorption range. Molecular modifications of these dyes, featuring a benzothiadiazole (BTZ) auxiliary acceptor, were achieved by introducing a thiophene heterocycle as well as by shifting the, position of BTZ on the conjugated bridge. The ensuing high molar absorption coefficients enabled the fabrication of highly efficient thin-film solid-state DSSCs with only 1.3 mu m mesoporous TiO2 layer. XY2 with a molar extinction coefficient of 6.66 X 10(4) M-1 cm(-1) at 578 nm led to the best photovoltaic performance of 7.51%

The Clinicopathologic and Prognostic Significance of Programmed Cell Death Ligand 1 (PD-L1) Expression in Patients With Prostate Cancer: A Systematic Review and Meta-Analysis

Background: Programmed cell death ligand 1 (PD-L1) expression has been shown to correlate with poor prognosis in diverse human cancers. However, limited data exist on the prognostic and clinicopathologic significance of PD-L1 expression in prostate cancers (PCa), and the curative effect of anti-PD-1/PD-L1 therapy remains controversial. In this systematic review and meta-analysis, we aimed to evaluate the prognostic and clinicopathologic value of PD-L1 in PCa.Methods: We performed a systematic literature search in the PubMed, Cochrane Library, EMBASE, Web of Science, and SCOPUS databases up to July 21st, 2018. Pooled prevalence of PD-L1 in PCa was calculated using Freeman-Tukey double arcsine transformation by R software version 3.5.0. The data from the studies were examined by a meta-analysis using Review Manager software 5.3 to calculate pooled hazard ratios (HRs) and pooled odds ratios (ORs) with 95% confidence intervals (CIs) to estimate the prognostic and clinicopathologic value of PD-L1 in PCa. Heterogeneity was tested by the Chi-squared test and I2 statistic.Results: Five studies with 2,272 patients were included in this meta-analysis. The pooled prevalence of PD-L1 in PCa was 35% (95% CI 0.32 to 0.37). Both PD-L1 expression (HR = 1.78; 95% CI 1.39 to 2.27; p &lt; 0.00001) and PD-L1 DNA methylation (HR = 2.23; 95% CI 1.51 to 3.29; p &lt; 0.0001) were significantly associated with poor biochemical recurrence-free survival (BCR-FS). PD-L1 tended to have high expression levels in high Gleason score cases (OR = 1.54; 95% CI, 1.17 to 2.03; P = 0.002) and androgen receptor-positive cases (OR = 2.42, 95% CI 1.31 to 4.50; P = 0.005). However, PD-L1 had relatively weak correlation with age, pathologic stage, lymph node metastasis and preoperative PSA level.Conclusions: This meta-analysis confirms the negative prognostic significance of PD-L1 expression and mPD-L1 in PCa patients. Additionally, PD-L1 has a statistically significant correlation with Gleason score and androgen receptor status, while the correlations with age, pathologic stage, lymph node metastasis, and preoperative PSA level were not statistically significant. However, the number of included studies is too small to make the conclusions more convincing, so more retrospective large-cohort studies are expected for the further confirmation of these findings

The Prognostic and Clinicopathological Roles of PD-L1 Expression in Colorectal Cancer: A Systematic Review and Meta-Analysis

Background: Studies evaluating the prognostic significance of programmed death-ligand 1 (PD-L1) expression in colorectal cancer (CRC) are limited and remain controversial. This meta-analysis was conducted in order to evaluate the clinicopathological and prognostic significance of PD-L1 expression in CRC patients.Methods: A comprehensive search was performed against the Medline/PubMed, Embase, Cochrane Library, Web of Science (WoS) and Scopus databases. Data were extracted with name of the first author, year of publication, country of origin, tumor type, number of cases, staining method, cut-off values, PD-L1 positive expression, clinicopathological parameters, outcome, and quality assessment score, and statistical analysis was conducted using Review Manager Version 5.3 (Revman the Cochrane Collaboration; Oxford, England) and STATA version 14 (Stata Corporation; College Station, TX, USA).Results: Ten studies were included in this meta-analysis, in which the pooled hazard ratio (HR) showed that PD-L1 expression in tumor cells was significantly associated with a poor overall survival (HR = 1.50, 95% CI 1.05–2.13, P = 0.03). The pooled HR for disease-free survival (DFS) indicated that PD-L1 expression was significantly associated with shorter DFS (HR = 2.57, 95% CI 1.40–4.75, P = 0.002). The pooled odds ratios (ORs) showed that PD-L1 expression was associated with poor differentiation (OR = 3.47, 95% CI 1.37–8.77, P = 0.008) and right colon cancer (OR = 2.38, 95% CI 1.57–3.60, P &lt; 0.0001). However, the expression of PD-L1 was independent of gender, age, tumor size, tumor stage, lymph node metastasis, and tumor-node metastasis stage.Conclusion: This meta-analysis indicated that a high level of PD-L1 expression might be a biomarker for a poor prognosis in CRC patients. This information may be helpful for clinicians to stratify CRC patients for anti-PD-1/PD-L1 therapy, particularly patients with microsatellite instability high (MSI-H)