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Fault detection is a necessary component to perform ongoing monitoring of photovoltaic plants and helps in their safety, maintainability, and productivity with the desired performance. In this study, an innovative technique is introduced by amalgamating Latent Variable Regression (LVR) methods, namely Principal Component Regression (PCR) and Partial Least Square (PLS), and the Triple Exponentially Weighted Moving Average (TEWMA) statistical monitoring scheme. The TEWMA scheme is known for its sensitivity to uncovering changes of small magnitude. Nevertheless, TEWMA can only be utilized for monitoring single variables and ignoring the correlation among monitored variables. To alleviate this difficulty, the LVR methods (i.e., PCR and PLS) are used as residual generators. Then, the TEWMA is applied to the obtained residuals for fault detection purposes, where the detection threshold is computed via kernel density estimation to improve its performance and widen its applicability in practice. Real data with different fault scenarios from a 9.54 kW photovoltaic plant has been used to verify the efficiency of the proposed schemes. Results revealed the superior performance of the PLS-TEWMA chart compared to the PLS-TEWMA chart, particularly in detecting anomalies with small changes. Moreover, they have almost comparable performance for large anomalies.

Acute skin damage caused by burns or cuts occurs frequently in people’s daily lives. Such wounds are difficult to heal normally and have persistent inflammation. Wound dressings not only improve the speed of wound healing, but also protect and cover the wound well. Hydrogels have the characteristics of good flexibility, high water content, and good biocompatibility, and are widely used in biomedicine and other fields. Common hydrogels are mainly natural hydrogels and synthetic hydrogels. Hydrogels cross-linked using different raw materials and different methods have different performance characteristics. Natural hydrogels prepared using polysaccharides are simple to obtain and have good biocompatibility, but are inferior to synthetic hydrogels in terms of mechanical properties and stability, and a single polysaccharide hydrogel cannot meet the component requirements for wound healing. Therefore, functional composite hydrogels with high mechanical properties, high biocompatibility, and high antibacterial properties are the current research hotspots. In this review, several common polysaccharides for hydrogel synthesis and the synthesis methods of polysaccharide hydrogels are introduced, and functional composite hydrogel dressings from recent years are classified. It is hoped that this can provide useful references for relevant research in this field.

Objects of modern technology located in the rain zone experience additional loads and can be destroyed due to water droplet erosion. With a significant number of successive impacts in a certain period of time, rain causes damage to the surface of materials or structures. It should be noted that supersonic water droplet impact has a low probability of occurrence; however, the peak pressure impulse of the water hammer (up to GPa level) far exceeds the strength of many materials, and a small number of impacts are enough to damage the material or structure. Therefore, it is very interesting to determine the external load caused by a water droplet’s impact and its response to various obstacles. In this work, the external load is determined on the basis of experimental studies. To carry out such tests, a single-jet generator is most widely used, which, with a certain ease of operation, makes it possible to investigate the mechanisms of damage to materials and the effect of water droplet impact erosion on structural elements. Based on the obtained research results, mathematical modeling of the droplet impact with an obstacle is provided. The examples are considered.

The evolved acetolactate synthase (ALS) inhibiting herbicide-resistant redroot amaranth has been confirmed in China and caused a great loss in soybean production. This study was conducted to evaluate the growth and competitiveness of ALS-resistant (R) and ALS-susceptible (S) redroot amaranth biotypes. Seeds of both R and S biotypes were subjected to different temperature regimes. Data revealed that the germination percentage and seedling vigor of both biotypes did not differ largely from each other at 10/20 to 30/40 °C. Under noncompetitive conditions, there were no significant leaf number, plant height, or dry weight differences between the R and S biotypes. Moreover, replacement series experiment results indicated that the R and S biotypes have a similar competitive ability. This study shows that there are no significant differences in growth or competitiveness between the R and S redroot amaranth biotypes regarding the physiological characteristics evaluated. Therefore, the proportion and distribution of the R biotype will not be affected in the absence of the ALS-inhibiting herbicide. Some other effective management practices should be adopted to cope with this troublesome weed.

Simultaneous localization and mapping (SLAM) technology can be used to locate and build maps in unknown environments, but the constructed maps often suffer from poor readability and interactivity, and the primary and secondary information in the map cannot be accurately grasped. For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. Our proposed method can not only reduce the absolute positional errors (APE) and improve the positioning performance of the system but also construct the object-oriented dense semantic point cloud map and output point cloud model of each object to reconstruct each object in the indoor scene. In fact, eight categories of objects are used for detection and semantic mapping using coco weights in our experiments, and most objects in the actual scene can be reconstructed in theory. Experiments show that the number of points in the point cloud is significantly reduced. The average positioning error of the eight categories of objects in Technical University of Munich (TUM) datasets is very small. The absolute positional error of the camera is also reduced with the introduction of semantic constraints, and the positioning performance of the system is improved. At the same time, our algorithm can segment the point cloud model of objects in the environment with high accuracy.

Over the past few years, there has been a significant increase in the interest in and adoption of solar energy all over the world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed to numerous anomalies. If not detected accurately and in a timely manner, anomalies in PV plants may degrade the desired performance and result in severe consequences. Hence, developing effective and flexible methods capable of early detection of anomalies in PV plants is essential for enhancing their management. This paper proposes flexible data-driven techniques to accurately detect anomalies in the DC side of the PV plants. Essentially, this approach amalgamates the desirable characteristics of ensemble learning approaches (i.e., the boosting (BS) and bagging (BG)) and the sensitivity of the Double Exponentially Weighted Moving Average (DEWMA) chart. Here, we employ ensemble learning techniques to exploit their capability to enhance the modeling accuracy and the sensitivity of the DEWMA monitoring chart to uncover potential anomalies. In the ensemble models, the values of parameters are selected with the assistance of the Bayesian optimization algorithm. Here, BS and BG are adopted to obtain residuals, which are then monitored by the DEWMA chart. Kernel density estimation is utilized to define the decision thresholds of the proposed ensemble learning-based charts. The proposed monitoring schemes are illustrated via actual measurements from a 9.54 kW PV plant. Results showed the superior detection performance of the BS and BG-based DEWMA charts with non-parametric threshold in uncovering different types of anomalies, including circuit breaker faults, inverter disconnections, and short-circuit faults. In addition, the performance of the proposed schemes is compared to that of BG and BS-based DEWMA and EWMA charts with parametric thresholds.

The nonlinear bending of the sandwich plates with graphene nanoplatelets (GPLs) reinforced porous composite (GNRPC) core and two metal skins subjected to different boundary conditions and various loads, such as the concentrated load at the center, linear loads with different slopes passing through the center, linear eccentric loads, uniform loads, and trapezoidal loads, has been presented. The popular four-unknown refined theory accounting for the thickness stretching effects has been employed to model the mechanics of the sandwich plates. The governing equations have been derived from the nonlinear Von Karman strain–displacement relationship and principle of virtual work with subsequent solution by employing the classical finite element method in combination with the Newton downhill method. The convergence of the numerical results has been checked. The accuracy and efficiency of the theory have been confirmed by comparing the obtained results with those available in the literature. Furthermore, a parametric study has been carried out to analyze the effects of load type, boundary conditions, porosity coefficient, GPLs weight fraction, GPLs geometry, and concentrated load radius on the nonlinear central bending deflections of the sandwich plates. In addition, the numerical results reveal that the adopted higher order theory can significantly improve the simulation of the transverse deflection in the thickness direction.

African swine fever (ASF) is a highly contagious and fatal disease caused by the African swine fever virus. Recently, the multigene family and CD2v gene-deleted ASF vaccine candidate HLJ/18-7GD was found to be safe and effective in laboratory and clinical trials. However, the immune-protective mechanisms underlying the effects of HLJ/18-7GD remain unclear. We assessed samples from pigs immunized with a single dose of 106 TCID50 HLJ/18-7GD. We found that pigs immunized with HLJ/18-7GD showed high levels of specific antibodies. T lymphocyte subsets (helper T cells (Th); cytotoxic T lymphocytes (CTL); double-positive T cells (DP-T cells)) were temporarily increased in peripheral blood mononuclear cells (PBMCs) after HLJ/18-7GD immunization. Once the HLJ/18-7GD-immunized pigs had been challenged with virulent HLJ/18, the percentage of Th, CTL, and DP-T cells increased significantly. PBMCs extracted from the pigs induced higher levels of CD8+ T cells after infection with the HLJ/18 strain in vitro. The levels of GM-CSF, IFN-γ, and TNF-α were upregulated at 7 days post-inoculation; this finding was contrary to the results obtained after HLJ/18 or HLJ/18ΔCD2v infection. The immune protection from HLJ/18-7GD resulted from many synergies, which could provide a theoretical basis for HLJ/18-7GD as a safe and effective ASF vaccine.

In the background of artificial intelligence, the realization of smooth communication between people and machines has become the goal pursued by people. Mel spectrograms is a common method used in speech emotion recognition, focusing on the low-frequency part of speech. In contrast, the inverse Mel (IMel) spectrogram, which focuses on the high-frequency part, is proposed to comprehensively analyze emotions. Because the convolutional neural network-stacked sparse autoencoder (CNN-SSAE) can extract deep optimized features, the Mel-IMel dual-channel complementary structure is proposed. In the first channel, a CNN is used to extract the low-frequency information of the Mel spectrogram. The other channel extracts the high-frequency information of the IMel spectrogram. This information is transmitted into an SSAE to reduce the number of dimensions, and obtain the optimized information. Experimental results show that the highest recognition rates achieved on the EMO-DB, SAVEE, and RAVDESS datasets were 94.79%, 88.96%, and 83.18%, respectively. The conclusions are that the recognition rate of the two spectrograms was higher than that of each of the single spectrograms, which proves that the two spectrograms are complementary. The SSAE followed the CNN to get the optimized information, and the recognition rate was further improved, which proves the effectiveness of the CNN-SSAE network.

The present study aimed to explore the effects of ultra-high pressure (UHP) on the cathepsin (B, D, H, and L) activities, protein oxidation, and degradation properties as well as quality characteristics of iced shrimp (Litopenaeus vannamei). Fresh shrimps were vacuum-packed, treated with UHP (100–500 MPa for 5 min), and stored at 0 °C for 15 days. The results showed that the L* (luminance), b* (yellowness), W (whiteness), ΔE (color difference), hardness, shear force, gumminess, chewiness, and resilience of shrimp were significantly improved by UHP treatment. Moreover, the contents of surface hydrophobicity, myofibril fragmentation index (MFI), trichloroacetic acid (TCA)-soluble peptides, carbonyl, dityrosine, and free sulfhydryl of myofibrillar protein (MP) were significantly promoted by UHP treatment. In addition, UHP (above 300 MPa) treatment enhanced the mitochondrial membrane permeability but inhibited the lysosomal membrane stability, and the cathepsin (B, D, H, and L) activities. UHP treatment notably inhibited the activities of cathepsins, delayed protein oxidation and degradation, as well as texture softening of shrimp during storage. Generally, UHP treatment at 300 MPa for 5 min effectively delayed the protein and quality deterioration caused by endogenous enzymes and prolonged the shelf life of shrimp by 8 days.

Hazel (Corylus spp.) is an economically important nut species with a unique biological characteristic of ovary differentiation and development initiating from the ovary primordium after pollination. Auxin participates in ovary initiation and has an essential impact on hazel fruit yield and quality. The regulation of auxin in ovary development is thought to be related to auxin response factors (ARFs); however, its detailed regulatory mechanism remains unclear. The spatiotemporal expression pattern of C. heterophylla ARF3 (ChARF3) was accessed via ARF gene family member identification and expression abundance analysis as well as immunohistochemistry. ChARF3 target genes were identified via chromatin immunoprecipitation followed by next-generation sequencing (ChIP-Seq). In total, 14 ChARF members containing at least B3 and Auxin_resp domains were found to be distributed on 9 of 11 chromosomes, and the protein molecular weights were predicted to range from 70.93–139.22 kD. Among eight differentially expressed ChARFs, ChARF3 showed the most significant differences over four ovary developmental stages. Immunohistochemical analysis revealed that ChARF3 was expressed in the ovary primordium and funiculus, integument, endosperm, radicle, and cotyledon indicating its potential regulatory roles in ovary differentiation and development. In total, 3,167 ChARF3 target genes were identified through ChIP-Seq in four ovary developmental stages and were significantly enriched in the biosynthesis of secondary metabolites (ko01110), phenylpropanoid biosynthesis (ko00940), and phytohormone signal transduction (ko04075). ChARF3 was hypothesized to be involved in the regulation of auxin-induced genes and the transcription factors MADS, AP2/ERF, TCP, FT, and LFY. These results suggest that ChARF3 may regulate ovary initiation and ovule development by mediating genes related to auxin biosynthesis and transport, cell division and proliferation, and flower and fruit development. This study provides new insights into the molecular mechanism of hazel yield formation.

Summary: The endoplasmic reticulum (ER)-resident transmembrane protein kinase/RNase Ire1 is a conserved sensor of the cellular unfolded protein response and has been implicated in lipid homeostasis, including lipid synthesis and transport, across species. Here we report a novel catabolic role of Ire1 in regulating lipid mobilization in Drosophila. We found that Ire1 is activated by nutrient deprivation, and, importantly, fat body-specific Ire1 deficiency leads to increased lipid mobilization and sensitizes flies to starvation, whereas fat body Ire1 overexpression results in the opposite phenotypes. Genetic interaction and biochemical analyses revealed that Ire1 regulates lipid mobilization by promoting Xbp1s-associated FoxO degradation and suppressing FoxO-dependent lipolytic programs. Our results demonstrate that Ire1 is a catabolic sensor and acts through the Xbp1s-FoxO axis to hamper the lipolytic response during chronic food deprivation. These findings offer new insights into the conserved Ire1 regulation of lipid homeostasis.

AimsSerine/threonine/tyrosine kinase 1 (STYK1) has been previously shown to have oncogenic properties, and emerging evidence suggests that STYK1 expression correlates with epithelial-mesenchymal transition (EMT). However, the mechanism of STYK1 involvement in oncogenesis remains unknown. The present study aimed to elucidate how STYK1 expression level relates to the metastasis, migration, invasion, and EMT in non-small cell lung cancer (NSCLC) and to determine the molecular mechanism of STYK1 effects.MethodsSerine/threonine/tyrosine kinase 1 (STYK1) expression level and its relationship with the prognosis of NSCLC were determined using the ONCOMINE database and clinical cases. Non-small cell lung cancer cell lines with the overexpression or knockdown of STYK1 were established to determine whether STYK1 promotes cell migration, invasion, and EMT in vitro and in vivo. In addition, a constitutively active FoxO1 mutant (FoxO1AAA) was used to examine the role of FoxO1 in the STYK1-mediated upregulation of metastasis and EMT in NSCLC.ResultsSerine/threonine/tyrosine kinase 1 (STYK1) was upregulated in NSCLC tissues and cell lines, and its overexpression correlated with poor prognosis in patients with NSCLC after surgery. Enhanced expression of STYK1 potentiated the migration, invasion, and EMT in SW900 cells, thereby promoting metastasis, whereas knockdown of STYK1 inhibited these cellular phenomena in Calu-1 cells. Furthermore, STYK1 expression was positively related to the level of phosphorylated-FoxO1, whereas the constitutively active FoxO1 mutant protected against the positive effect of STYK1 overexpression on cell migration, invasion, and EMT.ConclusionSerine/threonine/tyrosine kinase 1 (STYK1) was upregulated in NSCLC and correlated with poor clinical outcomes. In addition, STYK1 suppressed FoxO1 functions, thereby promoting metastasis and EMT in NSCLC.

Cadmium (Cd), a biologically non-essential heavy metal, is widespread in the environment, including the air, water, and soil, and is widely present in foods and quantum dot preparations. Cd enters the body primarily through inhalation and ingestion. Its biological half-life in humans is 10–35 years; therefore, Cd poses long-term health risks. While most studies on Cd toxicity have focused on organ and tissue damage, the immunotoxicity of Cd has drawn increasing attention recently. Cd accumulates in immune cells, modulates the function of the immune system, triggers immunological responses, and leads to diverse health problems. Cd acts as an immunotoxic agent by regulating the activity and apoptosis of immune cells, altering the secretion of immune cytokines, inducing reactive oxygen species (ROS) production and oxidative stress, changing the frequency of T lymphocyte subsets, and altering the production of selective antibodies in immune cells. This review summarizes the immunological toxicity of Cd, elucidates the mechanisms underlying Cd toxicity in terms of innate immunity and adaptive immunity, and discusses potential strategies to alleviate the adverse effects of Cd on the immune system.

In humans, taste genes are responsible for perceiving at least 5 different taste qualities. Human taste genes’ evolutionary mechanisms need to be explored. We compiled a list of 69 human taste-related genes and divided them into 7 functional groups. We carried out comparative genomic and evolutionary analyses for these taste genes based on 8 vertebrate species. We found that relative to other groups of human taste genes, human TAS2R genes have a higher proportion of tandem duplicates, suggesting that tandem duplications have contributed significantly to the expansion of the human TAS2R gene family. Human TAS2R genes tend to have fewer collinear genes in outgroup species and evolve faster, suggesting that human TAS2R genes have experienced more gene relocations. Moreover, human TAS2R genes tend to be under more relaxed purifying selection than other genes. Our study sheds new insights into diverse and contrasting evolutionary patterns among human taste genes.

Abstract Based on the observational dataset SA‐OBS and model outputs from the Community Earth System Model Large Ensemble project, this study investigates heatwaves in Southeast Asia in the current and future warmer climate. A heatwave is detected when the daily maximum temperature exceeds the 90th percentile threshold at each grid for at least three consecutive days. Three characteristics describing the frequency, duration, and amplitude of heatwaves are examined, including the sum of heatwave days per year (HWF) satisfying the heatwave definition, the length of the longest yearly heatwave event (HWD), and the hottest amplitude of the hottest yearly heatwave event (HWA). Results indicate that increased global warming is associated with substantial changes in heatwave characteristics over Southeast Asia, with more frequent heatwaves, longer heatwave duration, and higher extreme temperatures. The increase in HWA has a linear growth against global warming levels with distinct regional differences between the Maritime Continent and the Indochina Peninsula due to their different heat content of lower atmospheric boundaries. In contrast, those in HWF and HWD have nonlinear growth characteristics. The projected warmer future tends to be associated with a higher risk ratio value with the occurrence of rarer extreme heatwaves relative to the current climate. These results reiterate the potential risks of extreme regional heatwaves if global warming is unrestricted.

BackgroundTo assess the prevalence, risk factors and prognostic significance of retropharyngeal lymph node (RPLN) metastasis diagnosed by magnetic resonance imaging (MRI) in patients with hypopharyngeal squamous cell carcinoma (HPSCC).Methods259 patients from three cancer institutions in China from Jan 2010 to Dec 2018 were analyzed, retrospectively. All the patients had been given pre-treatment magnetic resonance imaging (MRI) of head and neck and were then treated with definitive radiotherapy with or without chemotherapy. Pretreatment diagnostic MRIs were reviewed by a dedicated head and neck radiologist, for the presence or absence of radiographically positive RPLN, cervical LN and tumor invasion.Demographic variables were analysed by descriptive statistics using SPSS 20.0. Predictors of the presence of RPLN and its prognostic significance were examined.ResultsRPLN metastasis was discovered in 44 patients (17%). Logistic analysis showed that posterior pharyngeal wall (PPW) primary tumor; PPW invasion; N2-3; multiple cervical lymph node (LN) involvement (>2 LNs) were associated with RPLN metastasis, with metastasis rates 37%, 30%, 31% and 33% respectively. Patients with RPLN metastasis had a significantly reduced 5-year overall survival (OS) and disease-free survival (DFS) compared to the non-RPLN metastasis group (OS 28% vs. 48%, p=0.001; DFS 25% vs. 41%, p=0.040).ConclusionsRPLN metastasis was not uncommon in HPSCC patients. Risk factors were: PPW primary tumor, PPW invasion and cervical LN status. RPLN metastasis is a poor prognosticator for survival.

Combination therapy using Western and traditional Chinese medicines has shown notable effects on coronavirus disease 2019 (COVID-19). The He-Jie-Shen-Shi decoction (HJSS), composed of Bupleurum chinense DC., Scutellaria baicalensis Georgi, Pinellia ternata (Thunb.) Makino, Glycyrrhiza uralensis Fisch. ex DC., and nine other herbs, has been used to treat severe COVID-19 in clinical practice. The aim of this study was to compare the clinical efficacies of HJSS combination therapy and Western monotherapy against severe COVID-19 and to study the potential action mechanism of HJSS. From February 2020 to March 2020, 81 patients with severe COVID-19 in Wuhan Tongji Hospital were selected for retrospective cohort study. Network pharmacology was conducted to predict the possible mechanism of HJSS on COVID-19-related acute respiratory distress syndrome (ARDS). Targets of active components in HJSS were screened using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) and PharmMapper databases. The targets of COVID-19 and ARDS were obtained from GeneCards and Online Mendelian Inheritance in Man databases. The key targets of HJSS in COVID-19 and ARDS were obtained based on the protein–protein interaction network (PPI). Kyoto Encyclopedia of Genes and Genomes analysis (KEGG) was conducted to predict the pathways related to the targets of HJSS in COVID-19 and ARDS. A “herb-ingredient-target-pathway” network was established using Cytoscape 3.2.7. Results showed that the duration of the negative conversion time of nucleic acid was shorter in patients who received HJSS combination therapy. HJSS combination therapy also relieved fever in patients with severe COVID-19. Network pharmacology analysis identified interleukin (IL) 6, tumor necrosis factor (TNF), vascular endothelial growth factor A (VEGFA), catalase (CAT), mitogen-activated protein kinase (MAPK) 1, tumor protein p53 (TP53), CC-chemokine ligand (CCL2), MAPK3, prostaglandin-endoperoxide synthase 2 (PTGS2), and IL1B as the key targets of HJSS in COVID-19-related ARDS. KEGG analysis suggested that HJSS improved COVID-19-related ARDS by regulating hypoxia-inducible factor (HIF)-1, NOD-like receptor, TNF, T cell receptor, sphingolipid, PI3K-Akt, toll-like receptor, VEGF, FoxO, and MAPK signaling pathways. In conclusion, HJSS can be used as an adjuvant therapy on severe COVID-19. The therapeutic mechanisms may be involved in inhibiting viral replication, inflammatory response, and oxidative stress and alleviating lung injury. Further studies are required to confirm its clinical efficacies and action mechanisms.

Abstract Background In plant science, the study of salinity tolerance is crucial to improving plant growth and productivity under saline conditions. Since quantile regression is a more robust, comprehensive and flexible method of statistical analysis than the commonly used mean regression methods, we applied a set of quantile analysis methods to barley field data. We use univariate and bivariate quantile analysis methods to study the effect of plant traits on yield and salinity tolerance at different quantiles. Results We evaluate the performance of barley accessions under fresh and saline water using quantile regression with covariates such as flowering time, ear number per plant, and grain number per ear. We identify the traits affecting the accessions with high yields, such as late flowering time has a negative impact on yield. Salinity tolerance indices evaluate plant performance under saline conditions relative to control conditions, so we identify the traits affecting the accessions with high values of indices using quantile regression. It was observed that an increase in ear number per plant and grain number per ear in saline conditions increases the salinity tolerance of plants. In the case of grain number per ear, the rate of increase being higher for plants with high yield than plants with average yield. Bivariate quantile analysis methods were used to link the salinity tolerance index with plant traits, and it was observed that the index remains stable for earlier flowering times but declines as the flowering time decreases. Conclusions This analysis has revealed new dimensions of plant responses to salinity that could be relevant to salinity tolerance. Use of univariate quantile analyses for quantifying yield under both conditions facilitates the identification of traits affecting salinity tolerance and is more informative than mean regression. The bivariate quantile analyses allow linking plant traits to salinity tolerance index directly by predicting the joint distribution of yield and it also allows a nonlinear relationship between the yield and plant traits.

Abstract Background To identify thyroid dose‐volume thresholds for radiotherapy (RT)‐related hypothyroidism (HT) in patients with nasopharyngeal carcinoma (NPC) treated with intensity‐modulated RT (IMRT). In this way, we desired to guide the design of treatment plans and, finally, lower HT prevalence. Methods In total, 345 NPC patients treated with IMRT were evaluated retrospectively during a median follow‐up of 45.2 (range, 11.3‐64.9) months. Serum‐based assessments of thyroid function before and after IMRT were monitored periodically. Thyroid dose‐volume parameters were analyzed for their association with HT risk. Results In total, 44.1% of patients (152/345) developed primary HT. Analyses of thyroid dose‐volume parameters identified a stringent dose‐volume histogram (DVH) threshold defined by V25Gy (the percentage thyroid volume that receives >25 Gy, not the absolute volume) ≤60%, V35Gy ≤ 55%, and V45Gy ≤ 45%. Patients whose thyroid DVHs satisfied these constraints had a lower prevalence of 2‐year HT compared with the overall prevalence (13.2% vs 25.8%, P 95%, V35Gy > 90%, and V45Gy > 75%, and patients whose thyroid DVHs satisfied with these constraints had a higher prevalence of 2‐year HT than the overall incidence (36.0% vs 25.8%, P 95%, V35Gy > 90%, and V45Gy > 75% as the “inhibition” DVH line, under the precondition of not compromising the target coverage. These findings could help in the design of individual treatment plans and, eventually, to lowering of HT prevalence.

Abstract Background Immunotherapy, especially immune checkpoint inhibition, has provided powerful tools against cancer. We aimed to detect the expression of common immune checkpoints and evaluate their prognostic values in nasopharyngeal carcinoma (NPC). Methods The expression of 9 immune checkpoints consistent with 13 features was detected in the training cohort (n = 208) by immunohistochemistry and quantified by computational pathology. Then, the LASSO cox regression model was used to construct an immune checkpoint-based signature (ICS), which was validated in a validation cohort containing 125 patients. Results High positive expression of PD-L1 and B7-H4 was observed in tumour cells (TCs), whereas PD-L1, B7-H3, B7-H4, IDO-1, VISTA, ICOS and OX40 were highly expressed in tumour-associated immune cells (TAICs). Eight of the 13 immune features were associated with patient overall survival, and an ICS classifier consisting of 5 features (B7-H3TAIC, IDO-1TAIC, VISTATAIC, ICOSTAIC, and LAG3TAIC) was established. Patients with high-risk scores in the training cohort had shorter overall (P

Abstract Background In locoregionally advanced nasopharyngeal carcinoma (LANPC) patients, variance of tumor response to induction chemotherapy (ICT) was observed. We developed and validated a novel imaging biomarker to predict which patients will benefit most from additional ICT compared with chemoradiotherapy (CCRT) alone. Methods All patients, including retrospective training (n = 254) and prospective randomized controlled validation cohorts (a substudy of NCT01245959, n = 248), received ICT+CCRT or CCRT alone. Primary endpoint was failure-free survival (FFS). From the multi-parameter magnetic resonance images of the primary tumor at baseline, 819 quantitative 2D imaging features were extracted. Selected key features (according to their interaction effect between the two treatments) were combined into an Induction Chemotherapy Outcome Score (ICTOS) with a multivariable Cox proportional hazards model using modified covariate method. Kaplan-Meier curves and significance test for treatment interaction were used to evaluate ICTOS, in both cohorts. Results Three imaging features were selected and combined into ICTOS to predict treatment outcome for additional ICT. In the matched training cohort, patients with a high ICTOS had higher 3-year and 5-year FFS in ICT+CCRT than CCRT subgroup (69.3% vs. 45.6% for 3-year FFS, and 64.0% vs. 36.5% for 5-year FFS; HR = 0.43, 95% CI = 0.25–0.74, p = 0.002), whereas patients with a low ICTOS had no significant difference in FFS between the subgroups (p = 0.063), with a significant treatment interaction (p interaction

Abstract Background Acute infection leads to substantial mortality in the nonagenarian population. However, the predictive efficacies of functional status and biochemical indexes for in-hospital mortality in these patients remain to be determined. Methods A single-center, retrospective cohort study was performed. Consecutive nonagenarian patients who were admitted to our department from January 1, 2014 to December 31, 2016 for acute infectious diseases were included. Baseline data for medical history, functional status, and biochemical indexes were obtained on admission. The outcomes of these patients during hospitalization were recorded. Predictors of in-hospital mortality were identified via logistic regression analyses. Results A total of 162 patients were included, and 46 patients died (17.2%) during hospitalization. Univariate analysis showed that the prevalence rates of atrial fibrillation (32.1%) and malignant disease (26.5%) were higher in nonagenarian patients who died during hospitalization than in those who discharged. Multivariate logistic regression analyses identified malignant disease (odds ratio [OR] 2.73, 95% confidence interval [CI]: 1.10–6.78), ADL category (OR 0.82, 95% CI: 0.75–0.89) and serum albumin (OR 0.86, 95%CI 0.78–0.95) as independent predictors of in-hospital mortality in nonagenarian patients hospitalized for acute infection. Conclusions Functional impairment as well as serum albumin may be independent predictors of in-hospital mortality in nonagenarian patients hospitalized for acute infectious diseases. Stratification of patients according to Barthel Index score and serum albumin is very necessary.

Liquid biopsies are emerging as a useful method for diagnosis and prognosis in cancer. Here, the authors show the prognostic value of monitoring the level of circulating Epstein-barr virus DNA throughout induction chemotherapy and chemo-radiotherapy and its potential utility for risk-adapted individualised therapy in nasopharyngeal carcinomapatients.

Abstract Background Cotton is a leading natural fiber crop. Beyond its fiber, cottonseed is a valuable source of plant protein and oil. Due to the much higher value of cotton fiber, there is less consideration of cottonseed quality despite its potential value. Though some QTL controlling cottonseed quality have been identified, few of them that warrant further study are known. Identifying stable QTL controlling seed size, oil and protein content is necessary for improvement of cottonseed quality. Results In this study, a recombinant inbred line (RIL) population was developed from a cross between upland cotton cultivars/lines Yumian 1 and M11. Specific locus amplified fragment sequencing (SLAF-seq) technology was used to construct a genetic map that covered 3353.15 cM with an average distance between consecutive markers of 0.48 cM. The seed index, together with kernel size, oil and protein content were further used to identify QTL. In total, 58 QTL associated with six traits were detected, including 13 stable QTL detected in all three environments and 11 in two environments. Conclusion A high resolution genetic map including 7033 SNP loci was constructed through specific locus amplified fragment sequencing technology. A total of 13 stable QTL associated with six cottonseed quality traits were detected. These stable QTL have the potential for fine mapping, identifying candidate genes, elaborating molecular mechanisms of cottonseed development, and application in cotton breeding programs.

Abstract The standard of care for patients with locoregionally advanced nasopharyngeal carcinoma is concurrent platinum-based chemoradiotherapy. Existing literature have demonstrated that the addition of gemcitabine and cisplatin as induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma may have promising efficacy but were from phase 2 clinical trials. Stronger evidence-based data in forms of phase 3 clinical trial investigating the survival benefits of adding gemcitabine and cisplatin induction chemotherapy for such patients have been urgently warranted. In one of our recent studies published in the New England Journal of Medicine, “Gemcitabine and cisplatin induction chemotherapy in nasopharyngeal carcinoma”, 480 locoregionally advanced nasopharyngeal carcinoma patients from 12 hospitals across China were randomly assigned in a 1:1 ratio to receive either chemoradiotherapy alone or gemcitabine plus cisplatin and chemoradiotherapy. Our findings evinced that, as compared to chemoradiotherapy alone, the addition of induction chemotherapy comprising of gemcitabine plus cisplatin to concurrent cisplatin-radiotherapy to patients with locoregionally advanced nasopharyngeal carcinoma was safe, demonstrated improved recurrence-free survival, overall survival, and distant recurrence-free survival, and marginally superior locoregional recurrence-free survival.

Early warning during sky survey provides a crucial opportunity to detect low-mass, free-floating planets. In particular, to search short-timescale microlensing (ML) events from high-cadence and wide- field survey in real time, a hybrid method which combines ARIMA (Autoregressive Integrated Moving Average) with LSTM (Long-Short Time Memory) and GRU (Gated Recurrent Unit) recurrent neural networks (RNN) is presented to monitor all observed light curves and identify ML events at their early stages. Experimental results show that the hybrid models perform better in accuracy and less time consuming of adjusting parameters. ARIMA+LSTM and ARIMA+GRU can achieve improvement in accuracy by 14.5% and 13.2%, respectively. In the case of abnormal detection of light curves, GRU can achieve almost the same result as LSTM with less time by 8%. Same models are also applied to MIT-BIH Arrhythmia Databases ECG dataset with similar abnormal pattern and we yield from both sets that we can reduce up to 40% of time consuming for researchers to adjust the model to 90% accuracy.

The amygdala regulates multiple behaviors and emotions by projecting to multiple brain regions. However, the topographical distribution of amygdala neurons projecting to specific brain areas is still unclear. In the present study, we focus on determining whether single amygdala neurons project to the brain stem ventrolateral periaqueductal grey (vlPAG) and to the medial prefrontal cortex (mPFC). The mPFC neurons are involved in detecting emotional content while the vlPAG neurons are involved in regulating muscle tone. In VGAT-Cre mice a cre-inducible retrograde AAV tracer expressing tdTomato was microinjected into the ventrolateral periaqueductal grey matter (vlPAG), while a second retrograde AAV tracer with generic expression of GFP was delivered into the medial prefrontal cortex (mPFC). The results identified a subgroup of bifurcating GABAergic neurons in the central nucleus (CeA) and basolateral amygdala (BLA) that projects to vlPAG and mPFC. Based on these projections we suggest that amygdala GABA neurons may be involved in triggering emotionally-induced cataplexy in the sleep disorder, narcolepsy. Keywords: AAV retro, GFP, tdTomato, CeA, BLA, VGAT-Cre mice

Background: Little is known about the vaccine effectiveness (VE) in attenuating the influenza-associated symptoms in children during the 2014–2015 influenza season in Beijing, China, in which there was a mismatch between the vaccine and circulating strain. Methods: This study included 210 laboratory-confirmed influenza cases among children, who were enrolled in 2014, from November 1 to December 31. Standard demographic information and clinical symptoms were recorded. Influenza vaccination was confirmed via a vaccination registry. Univariate and multivariate analyses were used to estimate the odds of presenting with clinical symptoms among vaccinated and unvaccinated groups, adjusting for sex, age, area, BMI level, and chronic conditions. Results: Among the 210 laboratory-confirmed influenza cases, 170 (81.0%) presented with fever ≥38°C. The other most common symptoms were cough (78.1%), sore throat (46.7%), nasal congestion/rhinorrhea (38.6%), headache (34.8%), fatigue (24.8%) and myalgia/arthralgia (16.2%). Approximately 9.0% (19/210) exhibited nausea/vomiting, and 2.4% (5/210) exhibited diarrhea/abdominal pain. Respiratory complications occurred in 5.7% (12/210) of the confirmed influenza cases. In 210 laboratory-confirmed cases, univariate and multivariate conducted after adjusted for the aforementioned characteristics suggested that the odds of fever ≥ 38°C were significantly reduced in vaccinated children (odds ratio [OR]: 0.42, 95% CI: 0.19–0.93; P = 0.033). Conclusions: Influenza vaccination may reduce the clinical symptoms of laboratory-confirmed influenza cases potentially even in the mismatching season.

Abstract Background Intensity‐modulated radiotherapy (IMRT) provides excellent local control in nasopharyngeal carcinoma (NPC). We investigated whether simplifying 8th American Joint Committee on Cancer staging system T categories improves prognostic value. Methods We used 2191 NPC patients as a training set and 414 patients separately as an independent, external validation cohort. Results In the training set, local relapse‐free survival (LRFS), disease‐free survival (DFS), and overall survival (OS) were not significantly different between the 8th edition T2/T3 (P = 0.610, 0.380 and 0.353, respectively). Merging T2 and T3 to proposed T2 (proT2) provided significant differences in LRFS, DFS, and OS between proposed T categories. Proposed T categories had similar c‐indices for LRFS, DFS, and OS (vs the 8th edition), which was validated in the external cohorts. Moreover, for DFS, the adjusted HRs of the proT2N0 (3.8), proT1N1 (3.8), and proT2N1 (6.0) subsets were similar; the adjusted HRs of the proT3N0 (7.0), proT3N1 (11.4), proT1N2 (11.0), proT2N2 (11.6), and proT3N2 (13.3) subsets were similar; the adjusted HRs of the proT1N3 (17.8), proT2N3 (15.3), and proT3N3 (26.4) subsets were similar; the results of the adjusted HRs for OS had the same rule. Defining proT1N0 as stage I; proT1N1/proT2N0‐1 as stage II; proT3N0‐2/proT1‐2N2 as stage III; and proT1‐3N3 as stage IVa generated orderly, significant differences in DFS and OS between stages in the training set and external validation cohort. Conclusions In the IMRT era, three T categories are more reasonable (merging T2/T3 into T2) and proT3N0‐2 (the 8th edition T4N0‐2) should be down‐staged to stage III.

Abstract. Background. Gestational diabetes mellitus (GDM) is usually diagnosed between 24th and 28th gestational week using the 75-g oral glucose tolerance test (OGTT). It is difficult to predict GDM before 24th gestational week because fast plasma glucose (FPG) decreases as the gestational age increases. It is controversial that if FPG ≥5.1 mmol/L before 24th gestational week should be intervened or not. The aim of this study was to evaluate the value of FPG to screen GDM before 24th gestational week in women with different pre-pregnancy body mass index (BMI). Methods. This was a multi-region retrospective cohort study in China. Women who had a singleton live birth between June 20, 2013 and November 30, 2014, resided in Beijing, Guangzhou and Chengdu, and received prenatal care in 21 selected hospitals, were included in this study. Pre-pregnancy BMI, FPG before the 24th gestational week, and one-step GDM screening with 75 g-OGTT at the 24th to 28th gestational weeks were extracted from medical charts and analyzed. The pregnant women were classified into four groups based on pre-pregnancy BMI: Group A (underweight, BMI < 18.5 kg/m2), Group B (normal, BMI 18.5–23.9 kg/m2), Group C (overweight, BMI 24.0–27.9 kg/m2) and Group D (obesity, BMI ≥28.0 kg/m2). The trend of FPG before 24th week of gestation was described, and the sensitivity and specificity of using FPG before the 24th gestational week to diagnose GDM among different pre-pregnancy BMI groups were reported. Differences in the means between groups were evaluated using independent sample t-test and analysis of variance. Pearson Chi-square test was used for categorical variables. Results. The prevalence of GDM was 20.0% (6806/34,087) in the study population. FPG decreased gradually as the gestational age increased in all pre-pregnancy BMI groups until the 19th gestational week. FPG was higher in women with higher pre-pregnancy BMI. FPG before the 24th gestational week and pre-pregnancy BMI could be used to predict GDM. The incidence of GDM in women with FPG ≥5.10 mmol/L in the 19th to 24th gestational weeks and pre-pregnancy overweight or obesity was significantly higher than that in women with FPG ≥5.10 mmol/L and pre-pregnancy BMI

Background: To identify a radiomics signature to predict local recurrence in patients with non-metastatic T4 nasopharyngeal carcinoma (NPC). Methods: A total of 737 patients from Sun Yat-sen University Cancer Center (training cohort: n = 360; internal validation cohort: n = 120) and Wuzhou Red Cross Hospital (external validation cohort: n = 257) underwent feature extraction from the largest axial area of the tumor on pretreatment magnetic resonance imaging scans. Feature selection was based on the prognostic performance and feature stability in the training cohort. Radscores were generated using the Cox proportional hazards regression model with the selected features in the training cohort and then validated in the internal and external validation cohorts. We also constructed a nomogram for predicting local recurrence-free survival (LRFS). Findings: Eleven features were selected to construct the Radscore, which was significantly associated with LRFS. For the training, internal validation, and external validation cohorts, the Radscore (C-index: 0.741 vs. 0.753 vs. 0.730) outperformed clinical prognostic variables (C-index for primary gross tumor volume: 0.665 vs. 0.672 vs. 0.577; C-index for age: 0.571 vs. 0.629 vs. 0.605) in predicting LRFS. The generated radiomics nomogram, which integrated the Radscore and clinical variables, exhibited a satisfactory prediction performance (C-index: 0.810 vs. 0.807 vs. 0.753). The nomogram-defined high-risk group had a shorter LRFS than did the low-risk group (5-year LRFS: 73.6% vs. 95.3%, P

DNA methylation is an important epigenetic change in carcinogenesis. However, the function and mechanism of DNA methylation dysregulation in nasopharyngeal carcinoma (NPC) is still largely unclear. Our previous genome-wide microarray data showed that NFAT1 is one of the most hypermethylated transcription factor genes in NPC tissues. Here, we found that NFAT1 hypermethylation contributes to its down-regulation in NPC. NFAT1 overexpression inhibited cell migration, invasion, and epithelial-mesenchymal transition in vitro and tumor metastasis in vivo. We further established that the tumor suppressor effect of NFAT1 is mediated by its inactivation of ITGA6 transcription. Our findings suggest the significance of activating NFAT1/ITGA6 signaling in aggressive NPC, defining a novel critical signaling mechanism that drives NPC invasion and metastasis and providing a novel target for future personalized therapy.

AIM: To explore the relationship between insulin treatment and diabetic retinopathy in patients with type 2 diabetes mellitus.METHODS: We collected data from 415 patients with type 2 diabetes who were treated at the Yantai Yuhuangding Hospital endocrinology unit from 2016 to 2018. The patients were divided into 6 groups based on the therapies they received and were divided into 3 groups based on the results of fundus angiography. All patients' general information were collected. Then we gathered the morning fasting venous blood and measured the fasting blood glucose, glycosylated hemoglobin, C peptide and analyzed the differences in fasting blood glucose, HbA1c and fasting c-peptide in patients with different drugs and retinopathy groups and the relationship between insulin dosage and the severity of DR.RESULTS: There were differences in the proportion of diabetic retinopathy among different treatment groups, and the dosage of insulin was different among different DR groups. The cut point affect the occurrence of retinopathy of insulin is 35.7 U/d, 4.5 years.CONCLUSION: The effect of different hypoglycemic regimens on diabetic retinopathy were different. Insulin treatment is positively correlated with the incidence and severity of type 2 diabetic retinopathy.

Fog radio access network (F‐RAN) architectures provide markedly improved performance compared to conventional approaches. In this paper, an efficient genetic algorithm‐based content distribution scheme is proposed that improves the throughput and reduces the transmission delay of a F‐RAN. First, an F‐RAN system model is presented that includes a certain number of randomly distributed fog access points (F‐APs) that cache popular content from cloud and other sources. Second, the problem of efficient content distribution in F‐RANs is described. Third, the details of the proposed optimal genetic algorithm‐based content distribution scheme are presented. Finally, simulation results are presented that show the performance of the proposed algorithm rapidly approaches the optimal throughput. When compared with the performance of existing random and exhaustive algorithms, that of the proposed method is demonstrably superior.

Abstract Background Depression symptom questionnaires are commonly used to assess symptom severity and as screening tools to identify patients who may have depression. They are not designed to ascertain diagnostic status and, based on published sensitivity and specificity estimates, would theoretically be expected to overestimate prevalence. Meta-analyses sometimes estimate depression prevalence based on primary studies that used screening tools or rating scales rather than validated diagnostic interviews. Our objectives were to determine classification methods used in primary studies included in depression prevalence meta-analyses, if pooled prevalence differs by primary study classification methods as would be predicted, whether meta-analysis abstracts accurately describe primary study classification methods, and how meta-analyses describe prevalence estimates in abstracts. Methods We searched PubMed (January 2008–December 2017) for meta-analyses that reported pooled depression prevalence in the abstract. For each meta-analysis, we included up to one pooled prevalence for each of three depression classification method categories: (1) diagnostic interviews only, (2) screening or rating tools, and (3) a combination of methods. Results In 69 included meta-analyses (81 prevalence estimates), eight prevalence estimates (10%) were based on diagnostic interviews, 36 (44%) on screening or rating tools, and 37 (46%) on combinations. Prevalence was 31% based on screening or rating tools, 22% for combinations, and 17% for diagnostic interviews. Among 2094 primary studies in 81 pooled prevalence estimates, 277 (13%) used validated diagnostic interviews, 1604 (77%) used screening or rating tools, and 213 (10%) used other methods (e.g., unstructured interviews, medical records). Classification methods pooled were accurately described in meta-analysis abstracts for 17 of 81 (21%) prevalence estimates. In 73 meta-analyses based on screening or rating tools or on combined methods, 52 (71%) described the prevalence as being for “depression” or “depressive disorders.” Results were similar for meta-analyses in journals with impact factor ≥ 10. Conclusions Most meta-analyses combined estimates from studies that used screening tools or rating scales instead of diagnostic interviews, did not disclose this in abstracts, and described the prevalence as being for “depression” or “depressive disorders ” even though disorders were not assessed. Users of meta-analyses of depression prevalence should be cautious when interpreting results because reported prevalence may exceed actual prevalence.

The mechano-properties of the Extracellular Matrix (ECM) are important for tumorigenesis. Here, the authors show that the stiffening of the ECM promotes translocation of the focal adhesion protein—Kindlin-2—to the mitochondria, where it interacts with the proline synthesis enzyme PYCR1, stimulating proline synthesis and cell proliferation.

For the flange end of the internal combustion engine crankshaft, the position of threaded holes and pinhole relative to the journal reference plane affects the dynamic balance and thermal efficiency of the engine, and therefore, this parameter measurement has a positive significance in the crankshaft manufacture. This paper proposes a system for quickly measuring the relative position of the flange-end holes, which uses a pneumatic linear variable differential transformer to form measuring benches for getting the position of the journal reference plane, and the high precision coordinates of the flange-end holes are obtained by multi-camera. The coordinates of the flange-end holes are merged into the global coordinate system of the journal reference plane, and the experimental result shows that the position uncertainty is better than 0.1 mm. This method has the advantage of convenient loading and unloading, fast measuring speed, and high accuracy and is easily implemented in the flexible manufacturing line for a full inspection of the semi-finished crankshaft.

A fully electronic time reversal mirror (TRM) system, which can make signal time reversal transformation, is proposed in this paper. The high-order asymmetric all-pass equalizer (APE) is proposed to provide a prescribed group delay for the TRM system. Finally, two APEs are designed to be used in a demonstrated TRM, which has a time duration of 10 ns and a bandwidth of 1 GHz.

To ensure the integrity of cloud storage, it is necessary for data owners to launch data integrity auditing (DIA) periodically. To fit more application scenarios, the research direction is shifted to design identity-based DIA (ID-DIA) protocols without expensive public key infrastructure, which can resist the quantum computing attacks simultaneously. However, the existing ID-DIA protocols are usually based on heavy anti-quantum computing cryptographies, which might severely limit their practical deployments. To address this problem, we explore three ways to design RLWE-based ID-DIA (ID-DIA-RLWE) protocols. The first is to improve the existing LWE-based ID-DIA protocols. The second is to enhance the existing RLWE-based DIA protocols. The third is to instantiate the proposed semantic ID-DIA protocol. Among these three proposed protocols, the third one is the most efficient due to its simplest construction. We also propose a new security model of ID-DIA protocols, including correctness assurance, security guarantee, and privacy protection. Then, we provide theoretical analysis of all proposed ID-DIA protocols under the given security model. Finally, we conduct extensive performance evaluation and comparison, which further validate that the third protocol has superiority in the perspectives of computation, communication, and storage costs.

Urban intelligence is an emerging concept which guides a series of infrastructure developments in modern smart cities. Human-computer interaction (HCI) is the interface between residents and the smart cities, it plays a key role in bridging the gap in applicating information technologies in modern cities. Hand gestures have been widely acknowledged as a promising HCI method, recognition human hand gestures using surface electromyogram (sEMG) is an important research topic in the application of sEMG. However, state-of-the-art signal processing technologies are not robust in feature extraction and pattern recognition with sEMG signals, several technical problems are still yet to be solved. For example, how to maintain the availability of myoelectric control in intermittent use, since pattern recognition qualities are greatly affected by time variability, but it is unavoidable during daily use. How to ensure the reliability and effectiveness of myoelectric control system also important in developing a good human-machine interface. In this paper, linear discriminant analysis (LDA) and extreme learning machine (ELM) are implemented in hand gesture recognition system, which is able to reduce the redundant information in sEMG signals and improve recognition efficiency and accuracy. The characteristic map slope (CMS) is extracted by using the feature re-extraction method because CMS can strengthen the relationship of features cross time domain and enhance the feasibility of cross-time identification. This study is focusing on optimizing the time differences in sEMG pattern recognition, the experimental results are beneficial to reducing the time differences in gesture recognition based on sEMG. The recognition framework proposed in this paper can enhance the generalization ability of HCI in the long term use and it also simplifies the data collection stage before training the device ready for daily use, which is of great significance to improve the time generalization performance of an HCI system.

Discovering the behavior pattern of urban high-risk driving is of great significance for national economy development and social stability. With increasing traffic accidents and crimes on current urban roads, the government endeavors to reduce the traffic violation rate. Exploring the behavior pattern of urban high-risk driving is an effective approach to prevent traffic accidents. In this paper, we explore the urban-scale high-risk driving behavior pattern in China. In order to achieve this goal, we analyze the death records of traffics among more than 300 cities throughout the country. In particular, we first build a city network, in which a node represents the properties of the traffic violation for the discovery of the urban-scale high-risk driving behavior pattern. Six urban-scale high-risk driving behavior patterns about overspeeding, nonlicense, drunk, and violation of the traffic rules are discovered from the traffic violation city network data. The research result leads to three key conclusions. First, urban networks based on death records information show strong spatial dependence and hierarchical characteristics and are largely coupled with the space distribution of major cities in China, reflecting the spatial relationship and core-periphery structure of high-risk driving behavior on the urban-scale. Second, to a large extent, there is a frequent occurrence of death record information around or in major domestic cities, economic belts, and important coastal ports, such as Beijing, Tianjin, Shanghai, the Yangtze River Delta Economic Belt, and the Pearl River Delta Economic Belt. This phenomenon demonstrates the attractiveness of large cities and regions as a domestic economic and political center. Third, the geographical location of the city has an impact on the pattern of high-risk driving behavior. For example, Tangshan city, the largest production base of mineral resources in China, possessing several transportation routes and heavy vehicle flows on the road, shows an increasing probability of violation of the traffic law.

This paper investigates the two problems of generalized chaotic synchronization. In one case, the Chen system is treated as the drive system and the controlled Rucklidge system as the response system. In the other case, we choose the Rucklidge system as the drive system and the controlled Chen system as the response system. For each case, a scalar control input and a state transformation are proposed by using the differential geometric approach when a simple condition holds. Under the scalar control input and the state transformation, the trajectory of the response system can track the trajectory of the drive system no matter what initial values of the two systems. The two generalized synchronizations realized in this paper reveal the Rucklidge system and the Chen system are similar in some sense although their system equations are quite different. Finally, several numerical simulations are studied to demonstrate the control schemes given in this paper.

For D2D-based vehicular communication networks, how to effectively and quickly allocate frequency resources is a big challenge. In this paper, a resource allocation scheme based on immune algorithm for D2D-based vehicular communication networks is proposed. Firstly, the D2D-based vehicular communication system model is described. Then the resource allocation issue in D2D-based vehicular communication networks is formulated and analyzed. Next, an efficient resource allocation scheme based on immune algorithm is presented. Finally, the analysis and simulation results validate the high efficiency of the proposed scheme compared with other algorithms. This result can provide the guidance for the deployment of VUE communication system.

Falls are an important healthcare problem for vulnerable persons like seniors. Response to potential emergencies can be fastened timely detection and classification of falls. This paper addresses the detection of human falls using relevant pixel-based features reflecting variations in body shape. Specifically, the human body is divided into five partitions that correspond to five partial occupancy areas. For each frame, area ratios are calculated and used as input data for fall detection and classification. First, the detection of falls is addressed from a statistical point of view as an anomaly detection problem. Towards this end, an integrated approach merging a detection step with a classification step is proposed for enabling efficient human fall detection in a home environment. In this regard, an effective fall detection approach using generalized likelihood ratio (GLR) scheme is designed. However, a GLR scheme cannot discriminate between true falls and like-fall events, such as lying down. To mitigate this limitation, the support vector machine algorithm has been successfully applied on features of the detected fall to recognize the type of fall. Tests on two publicly available datasets show the effectiveness of the proposed approach to appropriately detecting and identifying falls. Compared with the neural network, k -nearest neighbor, decision tree and naïve Bayes procedures, the two steps approach achieved better detection performance.

To operate wastewater treatment plants (WWTPs) with optimized efficiency, influent conditions (ICs) as initial states of inflow fed to WWTPs were monitored to identify potential anomalies that would trigger adverse events or system crash. To employ voluminous measurements for data-driven decisions, the non-linear, non-Gaussian, non-stationary, auto-correlated, cross-correlated, hetero-skedastic, case-specific nature of multivariate environmental datasets must be considered. This research proposed kernel machine learning models, the kernel principal components analysis based one-class support vector machine (KPCA-OCSVM) with various kernels, to learn anomaly-free training set then classify the testing set. A seven-years multivariate ICs time series was introduced with exploratory analysis performed to reveal temporal behaviors and statistical properties. KPCA with polynomial kernels sufficiently output representative features, based on which OCSVM with Gaussian kernels sensitively and specifically identified anomalies in ICs that were previously omitted by WWTP operators. The proposed kernel algorithms surpassed previous linear PCA-based K-nearest-neighbors models, and improved outcomes with limited increase in computation cost. Without requiring linear, Gaussian, stationary, independent, and homo-skedastic qualities from data, the proposed flexible environmental data science approach could be transferred, rebuilt, and tuned conveniently for ICs from different WWTPs.

Aggregation is a vital behavior when performing complex tasks in most of the swarm systems, such as swarm robotics systems. In this paper, three new aggregation methods, namely the distance-angular, the distance-cosine, and the distance-Minkowski k-nearest neighbor (k-NN) have been introduced. These aggregation methods are mainly built on well-known metrics: the cosine, angular, and Minkowski distance functions, which are used here to compute distances among robots' neighbors. Relying on these methods, each robot identifies its k-nearest neighborhood set that will interact with. Then, in order to achieve the aggregation, the interactions sensing capabilities among the set members are modeled using a virtual viscoelastic mesh. Analysis of the results obtained from the ARGoS simulator shows a significant improvement in the swarm aggregation performance compared to the conventional distance-weighted k-NN aggregation method. Also, the aggregation performance of the methods is reported to be robust to partially faulty robots and accurate under noisy sensors.

Abstract Background Findings remain unclear whether neutrophil-to-lymphocyte ratio (NLR) detrimentally affects advanced nasopharyngeal carcinoma (NPC) prognosis. We aim to evaluate the prognostic value of NLR in patients with NPC based on a large-scale cohort from an endemic area. Methods We selected patients retrospectively from a cohort examining long-term cancer outcomes following diagnosis. Neutrophil counts and lymphocyte counts were assessed prior to treatment. Kaplan–Meier method and log-rank test were used to calculate and compare survival outcomes. Additionally, Cox proportional hazards model was utilized to carry out univariate and multivariate analyses. Results Between October 2009 and August 2012, we enrolled 1550 consecutive NPC patients staged II-IVB. The median value of NLR was 2.27 (interquartile range [IQR], 1.71–3.12). Determined by operating characteristic curve using overall survival (OS) as an endpoint, the cutoff value for NLR was 2.50. At 5 years, NLR > 2.50 was associated with inferior OS (90.3% vs 82.5%; P

Shiitake mushrooms were processed by hot-air drying (HD) and freeze-drying (FD) to investigate the taste compounds. The total free amino acids in HD samples treated at 60°C was the highest as compared to the other three dried samples. Although cysteine was not detected in fresh shiitake mushroom (FSM), it was released during the drying process and its content increased with the temperature increase from 50°C to 70°C. The content of total organic acids was the highest in the HD sample treated at 60°C (56579.6 µg/g DW). Based on the obtained moisture contents, all compounds were calculated to their contents in respective FSMs, the content of Glu, citric acid, and succinic acid increased during drying as temperature increase from 50°C to 70°C. It indicated that new taste compounds were produced during the HD process. The content of total nucleotides was the highest in HD samples treated at 50°C, and the lowest in FD sample. The HD samples treated at 70°C showed the highest equivalent umami concentrations (EUC) (25.2 g MSG/100 g), followed by at 60°C (19.7 g MSG/100 g), 50°C (14.7 g MSG/100 g) and FD samples (12.0 g MSG/100 g). Our results indicated that different drying treatments led to different physiochemical properties and nutritional contents in Shiitake mushrooms.