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Baroreflex plays a significant role in modulating blood pressure for the human body. It is known that activation of the vagal nerve related to baroreflex can lead to reductions of blood pressure. However, how the vagal activities quantitatively relate with blood pressure can hardly be achieved. Here fine carbon nanotube yarn (CNTy) electrodes were adopted for recording intrafascicular vagal activities, synchronized with measurement of arterial blood pressure in a rat. Together with a novel algorithm, the results preliminarily quantified that there were six clusters of neural spikes within recorded vagal activities, and the number of individual vagal spikes correspondingly varied with blood pressure. Especially for Cluster 2, the neural activations decreased with arterial blood pressure increasing. This study can shed lights on the quantified neural mechanism underlying the control of vagal activities on blood pressure, and guide the vagal-nerve neuromodulation for treating hypertension.

A novel approach to simultaneous radiofrequency self-interference cancellation (SIC) and frequency down-conversion in a unified system is proposed and demonstrated experimentally based on cascade Mach-Zehnder modulators (MZM). The local oscillator signal is injected into the MZM1, whereas the received RF signal and the reference signal are fed into MZM2 and MZM3. After photoelectrical conversion by a balanced photodetector, the desired intermediate frequency signal with SIC is obtained. The cancellation depth for the single frequency SIC with frequency down-conversion is around 38 dB, and that for a wideband signal is around 19 dB. No electrical delay and attenuation are used in the proposed system, so it can potentially reach a wider frequency band and a higher cancellation depth.

In this article, a reconfigurable mixer based on only one dual-polarization quadrature phase shift -keying modulator (DP-QPSKM) is proposed and comprehensively demonstrated for a radio-over-fiber (RoF) system. By changing the detection unit and adjusting the dc voltages, the single-ended, double-balanced, image (IM)-free, mixing spurs suppressed, and dispersion-immune mixing can be implemented. Experimental results show that the ~60-dB IM rejection ratio and the spur suppression ratio are achieved. Moreover, the dispersion-induced power fading is successfully compensated by the dc voltage adjustment. The spurious-free dynamic range (SFDR) is about 100.56 dB.Hz2/3. Only one optical path is required without extra optical filters or phase-related devices, which makes the structure concise and stable. This structure would be more compact to meet the potential of integration if a dispersion medium with smaller size is available.

Background Gastric intestinal metaplasia (IM) is considered a precancerous lesion, and bile acids (BA) play a critical role in the induction of IM. Ectopic expression of HNF4α was observed in a BA-induced IM cell model. However, the mechanisms underlying the upregulation of the protein in IM cells remains to be elucidated. Methods The effects of HNF4α on gastric mucosal cells in vivo were identified by a transgenic mouse model and RNA-seq was used to screen downstream targets of deoxycholic acid (DCA). The expression of pivotal molecules and miR-1 was detected by immunohistochemistry and in situ hybridization in normal, gastritis and IM tissue slides or microarrays. The transcriptional regulation of HDAC6 was investigated by chromatin immunoprecipitation (ChIP) and luciferase reporter assays. Results The transgenic mouse model validated that HNF4α stimulated the HDAC6 expression and mucin secretion in gastric mucosa. Increased HDAC6 and HNF4α expression was also detected in the gastric IM cell model and patient specimens. HNF4α could bind to and activate HDAC6 promoter. In turn, HDAC6 enhanced the HNF4α protein level in GES-1 cells. Furthermore, miR-1 suppressed the expression of downstream intestinal markers by targeting HDAC6 and HNF4α. Conclusions Our findings show that the HDAC6/HNF4α loop regulated by miR-1 plays a critical role in gastric IM. Blocking the activation of this loop could be a potential approach to preventing BA-induced gastric IM or even gastric cancer (GC).

We performed a meta-analysis to determine safety and efficacy of corticosteroids in SARS-CoV-2, SARS-CoV, and MERS-CoV infections. We searched PubMed, Web of Science, Medline, WanFang Chinese database, and ZhiWang Chinese database using Boolean operators and search terms covering SARS-CoV-2, SARS-CoV, OR MERS-CoV AND corticosteroids to find appropriate studies. Review Manager 5.3 was used to analyze results of meta-analysis. Observational studies were analyzed for quality using the modified Newcastle–Ottawa scale and randomized clinical trials, using the Jadad scale. Subjects were divided into those with severe-only and other (severe and not severe) cohorts based on published criteria. Efficacy endpoints studied included mortality, hospitalization duration, rates of intensive care unit (ICU) admission, use of mechanical ventilation, and a composite endpoint (death, ICU admission, or mechanical ventilation). We included 11 reports including 10 cohort studies and 1 randomized clinical trial involving 5249 subjects (2003–2020). Two discussed the association of corticosteroids and virus clearing and 10 explored how corticosteroids impacted mortality, hospitalization duration, use of mechanical ventilation, and a composite endpoint. Corticosteroid use was associated with delayed virus clearing with a mean difference (MD) = 3.78 days (95% confidence Interval [CI] = 1.16, 6.41 days; I2 = 0%). There was no significant reduction in deaths with relative Risk Ratio (RR) = 1.07 (90% CI = 0.81; 1.42; I2 = 80%). Hospitalization duration was prolonged and use of mechanical ventilation increased. In conclusion, corticosteroid use in subjects with SARS-CoV-2, SARS-CoV, and MERS-CoV infections delayed virus clearing and did not convincingly improve survival, reduce hospitalization duration or ICU admission rate and/or use of mechanical ventilation. There were several adverse effects. Because of a preponderance of observational studies in the dataset and selection and publication biases our conclusions, especially regarding SARS-CoV-2, need confirmation in a randomized clinical trial. In the interim we suggest caution using corticosteroids in persons with COVID-19.

Background Several studies show that autophagy plays an important part in the biological processes of lung adenocarcinoma. Therefore, this work aimed to establish one scoring system on the basis of the expression profiles of differentially expressed autophagy-related genes (DEARGs) in patients with lung adenocarcinoma. Methods The Cancer Genome Atlas (TCGA) was applied to retrieve lung adenocarcinoma data. The overall survival (OS)-associated DEARGs were selected for the DEARG scoring scale. Moreover, the online database Kaplan-Meier Plotter (www.Kmplot.com) was employed to verify the accuracy of our results. Results The expression patterns of DEARG were detected in lung adenocarcinoma as well as normal lung tissues. A gene set related to autophagy was identified, along with 9 genes that showed marked significance in predicting the lung adenocarcinoma prognosis. According to the cox regression results, DEARGs (including ITGB4, BIRC5, ERO1A, and NLRC4) were applied to calculate the DEARGs risk score. Patients with lower DEARGs risk scores were associated with better OS. Moreover, based on analysis with the receiver operating characteristic (ROC) curve, DEARGs accurately distinguished the healthy tissues from lung adenocarcinoma tissues [area under the curve (AUC) value of >0.6]. Conclusions A scoring system is constructed based on the primary DEARGs, which accurately predicts the outcomes of lung adenocarcinoma.

Aiming at the problem that the petroleum industry is a high-occurrence industry with many occupational hazards, in order to comprehensively evaluate the hazards in coal mines, the number of supervisors for petroleum occupational hazards is small, the number of objects to be supervised, the frequency of supervision, and the scope of supervision are large, combined with cloud computing Based on the characteristics of cloud computing, an occupational hazard supervision system for petroleum companies based on cloud computing is proposed. Based on relevant theories, 15 occupational hazard identification indicators such as lifestyle, average working age, cultural level, professional skills, and health level were selected from massive data to establish an occupational hazard evaluation index system. In this paper, the analytic hierarchy process is used to determine the weight of the occupational hazard identification index, and then a BPNN neural network prediction model is established based on the determined weight index system. The qualitative prediction of the input and output of the index is achieved through model solving and model training. The experimental results show that the training error accuracy of BPNN neural network is less than 5%, and the model accuracy meets the requirements. Compared with the error of AHP, the average evaluation error of BPNN is 0.637% lower than that of AHP. The comprehensive evaluation system based on BPNN accords with the actual situation of the enterprise is greater than 97%, so it has certain guiding significance in the evaluation of petroleum occupational diseases.

With the development of medical digitization technology, artificial intelligence and big data technology, the medical model is gradually changing from treatment-oriented to prevention-oriented. In recent years, with the rise of artificial neural networks, especially deep learning, great achievements have been made in realizing image classification, natural language processing, text processing and other fields. Combining artificial intelligence and big data technology for disease risk prediction is a research focus in the field of intelligent medicine. Blood lipids are the main risk factors of cardiovascular and cerebrovascular diseases. If early prediction of abnormal blood lipids in iron and steel workers can be carried out, early intervention can be carried out, which is beneficial to protect the health of iron and steel workers. This paper around the steel workers dyslipidemia prediction problem for further study, firstly analyzes the influence factors of the steel workers dyslipidemia, discusses the commonly used method for prediction of disease, and then studied deep learning related theory, this paper introduces the two deep learning algorithms of RNN (Recurrent Neural Network) and LSTM (Long Short-Term Memory). Use the basic principle of Python language and the TensorFlow deep learning framework, establishes a prediction model based on two deep learning networks, and makes an example analysis. Experimental results show the LSTM prediction effect is superior to traditional RNN network, It provides scientific basis for the prevention of iron and steel dyslipidemia.

Arterial hypertension has a large prevalence in the general population and as a major hypertensive target organ, the involvement of kidney is usually hard to avoid and gradually develops into chronic kidney disease (CKD). Acute hypertension is defined as a blood pressure greater than 180/120, also known as hypertensive emergency (HE). In acute severe hypertension, the pathophysiology damage to the kidney tends to worsen on the basis of chronic damage, and accounts for more significant mortality. However, the mechanisms of renal injury induced by acute hypertension remain unclear. This review summarizes the clinical and histopathological features of hypertensive renal injury by using “in vivo cyrotechnique” and focusses on the interplay of distinct systemic signaling pathways, which drive glomerular podocyte injury. A thorough understanding of the cellular and molecular mechanisms of kidney damage and repair in hypertension will provide significant insight into the development of new research methods and therapeutic strategies for global CKD progression.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide. Although dysbiosis of the lung and gut microbiota have been associated with NSCLC, their relative contributions are unclear; in addition, their roles in distant metastasis (DM) are still illusive. We recruited in total 121 participants, including 87 newly diagnosed treatment-naive NSCLC patients of various stages and 34 healthy volunteers, and surveyed their fecal and sputum microbiota. We compared the microbial profiles between groups, identified microbial biomarkers, and generated machine learning models for distinguishing healthy individuals from patients with NSCLC and patients of various stages. We found significant perturbations of gut and sputum microbiota in patients with NSCLC and DM. A machine learning model combining both microbiota (combined model) performed better than an individual data set in patient stratification, with the highest area under the curve (AUC) value of 0.896. Sputum and gut microbiota both contributed to the combined model; in most cases, sputum-only models performed similar to the combined models. Several microbial biomarkers were shared by both microbiotas, indicating their similar roles at distinct body sites. Microbial biomarkers of distinct disease stages were mostly shared, suggesting biomarkers for DM could be acquired early. Furthermore, Pseudomonas aeruginosa, a species previously associated with wound infections, was significantly more abundant in brain metastasis, indicating that distinct types of DMs could have different microbes. Our results indicate that alterations of the sputum microbiota have stronger relationships with NSCLC and DM than the gut and strongly support the feasibility of metagenome-based noninvasive disease diagnosis and risk evaluation. (This study has been registered at ClinicalTrials.gov under registration no. NCT03454685). IMPORTANCE Our survey on gut and sputum microbiota revealed that both were significantly disturbed in non-small cell lung cancer (NSCLC) and associated with distant metastasis (DM) while only the sputum microbiota was associated with non-DM NSCLC. The lung microbiota could therefore have a stronger association with (and thus may contribute more to) disease development than the gut microbiota. Mathematic models using both microbiotas performed better in patient stratification than using individual microbiota. Sputum models, however, performed similar to the combined models, suggesting a convenient, noninvasive diagnostic for NSCLC. Microbial biomarkers of distinct disease stages were mostly shared, suggesting that the same set of microbes were underlying disease progression, and the signals for distant metastasis could be acquired at early stages of the disease. Our results strongly support the feasibility of noninvasive diagnosis of NSCLC, including distant metastasis, are of clinical importance, and should warrant further research on the underlying molecular mechanisms.

INTRODUCTION AND OBJECTIVE:The survival of patients with metastatic renal cell carcinoma (mRCC) is < 10% at 5-years, despite treatment. Hyaluronic acid (HA) promotes tumor growth and metastasis thr...

The investigation aimed to evaluate the effects of Mcc950, an inhibitor of the NLRP3 inflammasome, on diabetic retinopathy (DR) mice. The general physiological condition of each group of mice was recorded. Retinal blood vessels were stained for observation of the density of blood vessels, and retinas were used for further morphological examination and fluorescent staining after the intravitreal injection of Mcc950. Mcc950 partially reversed hyperglycemia-induced vascular damage and had reduced histological changes compared to DR mice. IL-1β production in mice retinas in the diabetic model (DM) group increased, but pretreatment with Mcc950 significantly reversed these changes. Additionally, Mcc950 engineered reduced FITC dextran extravasation and vascular leakage. Therefore, it played an apparent protective role in DR and could be a new treatment strategy for DR.

Background Hypertension is prevalent in the general population and is regarded as the second leading cause of renal damage and dysfunction, outnumbered only by diabetes. However, the mechanisms remain unclear. Aim To investigate podocyte injury induced by hypertension in the early course without massive proteinuria or renal dysfunction. Methods The hypertension group comprised 18 patients with hypertension accompanied by microalbuminuria, diagnosed with hypertensive renal injury according to biopsy results. For a comparison of pathological changes in renal tissue, control group 1 comprised 10 healthy volunteers, and control group 2 comprised 16 patients who underwent surgery for renal trauma. Results The hypertension group had significantly higher blood pressure (P = 0.000) and microalbuminuria (P = 0.000) compared with control group 1. In the hypertension group, urinary podocytes were detected following positive staining of podocyte-specific nephrin and/or CD2-associated protein (CD2AP) in urine sediment. Podocyte foot process fusion and a significant decrease in nephrin and/or CD2AP expression in glomeruli were observed in the hypertension group compared with control group 2. This indicated that hypertension caused podocyte injury and detachment from the glomerular basement membrane, which was consistent with urinary detection of podocytes. Conclusion Our results suggest that podocyturia appears early in the course of hypertensive renal injury, and may be a sensitive marker for early prediction of hypertensive renal injury.

BACKGROUND Differential expression of chemokines/chemokine receptors in colorectal cancer (CRC) may enable molecular characterization of patients' tumors for predicting clinical outcome. OBJECTIVE To evaluate the prognostic ability of these molecules in a CRC cohort and the CRC TCGA-dataset. METHODS Chemokine (CXCL-12α, CXCL-12β, IL-17A, CXCL-8, GM-CSF) and chemokine receptor (CXCR-4, CXCR-7) transcripts were analyzed by RT-qPCR in 76 CRC specimens (normal: 27, tumor: 49; clinical cohort). RNA-Seq data was analyzed from the TCGA-dataset (n= 375). Transcript levels were correlated with outcome; analyses: univariate, multivariable, Kaplan-Meier. RESULTS In the clinical cohort, chemokine/chemokine receptor levels were elevated 3-10-fold in CRC specimens (P⩽ 0.004) and were higher in patients who developed metastasis (P= 0.03 - < 0.0001). CXCR-4, CXCR-7, CXCL-12α, CXCL-8, IL-17 and GM-CSF levels predicted metastasis (P⩽ 0.0421) and/or overall survival (OS; P⩽ 0.0373). The CXCR-4+CXCR-7+CXCL-12 marker (CXCR-4/7+CXCL-12 (α/b) signature) stratified patients into risk for metastasis (P= 0.0014; OR, 2.72) and OS (P= 0.0442; OR, 2.7); sensitivity: 86.67%, specificity: 97.06%. In the TCGA-dataset, the CXCR-4/7+CXCL-12 signature predicted metastasis (P= 0.011; OR, 2.72) and OS (P= 0.0006; OR: 4.04). In both datasets, the signature was an independent predictor of clinical outcome. CONCLUSIONS Results of 451 specimens from both cohorts reveal that the CXCR-4/7+CXCL-12 signature potentially predicts outcome in CRC patients and may allow earlier intervention.

We analyzed reports on safety and efficacy of JAK-inhibitors in patients with coronavirus infectious disease-2019 (COVID-19) published between January 1st and March 6th 2021 using the Newcastle-Ottawa and Jadad scales for quality assessment. We used disease severity as a proxy for time when JAK-inhibitor therapy was started. We identified 6 cohort studies and 5 clinical trials involving 2367 subjects treated with ruxolitinib (N = 3) or baricitinib 45 (N = 8). Use of JAK-inhibitors decreased use of invasive mechanical ventilation (RR = 0.63; [95% Confidence Interval (CI), 0.47, 0.84]; P = 0.002) and had borderline impact on rates of intensive care unit (ICU) admission (RR = 0.24 [0.06, 1.02]; P = 0.05) and acute respiratory distress syndrome (ARDS; RR = 0.50 [0.19, 1.33]; P = 0.16). JAK-inhibitors did not decrease length of hospitalization (mean difference (MD) -0.18 [-4.54, 4.18]; P = 0.94). Relative risks of death for both drugs were 0.42 [0.30, 0.59] (P < 0.001), for ruxolitinib, RR = 0.33 (0.13, 0.88; P = 0.03) and for baricitinib RR = 0.44 (0.31, 0.63; P < 0.001). Timing of JAK-inhibitor treatment during the course of COVID-19 treatment may be important in determining impact on outcome. However, these data are not consistently reported.

Migrate the local color information of the reference image and the target image, and on this basis, maintain the color consistency of the transferred part and the untransferred part; 1. Based on the color channel statistical histogram, Use the Newton difference method to fit the histogram and segment the histogram; 2. Extract the color histogram information of the region of interest, this part mainly matches the color histogram information of the two regions; 3. Complete the local color After the migration, adjust the color information of the regional boundary to make the effect after the migration more natural. For scenes of different complexity, local color migration can be realized. The color distribution of the image is matched and transmitted based on the slice method of the optimal transmission theory, which greatly improves the efficiency of the algorithm; the method of dividing the channel image histogram is very good Realize the extraction of local area information; The experiment proves that the proposed slice method can realize the local color migration of the image more accurately and efficiently.

Background The objective of this study was to determine the causal relationship between habitual alcohol consumption with meals and lung cancer. Methods Public genetic summary data from two large consortia [the Neale Lab and the International Lung Cancer Consortium (ILCCO)] were used for analysis. As the instrumental variables of habitual alcohol consumption with meals, data on genetic variants were retrieved from Neale Lab. Additionally, genetic data from other consortia [Global Lipid Genetics Consortium (GLGC), Tobacco, Alcohol and Genetics (TAG), Genetic Investigation of Anthropocentric Traits (GIANT)] were utilized to determine whether alcohol could causally alter some general risk factors for lung cancer. The primary outcome was the risk of lung cancer (11,348 cases and 15,861 controls in the ILCCO). The R package TwoSampleMR was used for analysis. Results Based on the inverse variance weighted method, the results of the two-sample Mendelian randomization (MR) analyses indicated that commonly consuming alcohol with meals was a protective factor, reducing lung cancer risk [odds ratio (OR) 0.175, 95% confidence interval (CI): 0.045-0.682, P=0.012]. The heterogeneity analysis revealed that the causal relationship analyses of different types of lung cancer all had low heterogeneity (P>0.05). The horizontal pleiotropic study showed that major bias was unlikely. The MR assumptions did not seem to be violated. The causal relationship analyses between habitual alcohol consumption with meals and some risk factors for cancers showed that this alcohol consumption habit was a beneficial factor for reducing body mass index (BMI) and the number of cigarettes smoked per day. Conclusions Habitual appropriate alcohol consumption with meals is a protective factor for the development of lung cancer.

Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints.

To investigate the relationships between financial constraints, government subsidies, and corporate innovation, a semi-logarithmic fixed-effect panel model and mediation effect test were applied, based on the data of Chinese listed companies from 2007 to 2017. We find that (1) financial constraints suppress corporate innovation. (2) Government subsidies are targeted at bailing out firms facing financial constraints. (3) Government subsidies promote corporate innovation (4) Government subsidies partially offset the suppression of financial constraints on innovation. We contribute to the fields of public finance, corporate finance, and corporate innovation by: (1) justifying the government subsidies target strategy as a bailout of corporate financial constraints, (2) verifying the corporate-innovation promotion of government subsidies, thus justifying the efficiency of government subsidies, and (3) showing that different types of innovation benefit differently from subsidies, thus justifying subsidies as a structural innovation engine.

Osteosarcoma is a malignant tumor most commonly arising in children and adolescents and associated with poor prognosis. In recent years, some prognostic models have been constructed to assist clinicians in the treatment of osteosarcoma. However, the prognosis and treatment of patients with osteosarcoma remain unsatisfactory. Notably, super-enhancer (SE)-associated genes strongly promote the progression of osteosarcoma. In the present study, we constructed a novel effective prognostic model using SE-associated genes from osteosarcoma. Five SE-associated genes were initially screened through the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, as well as univariate and multivariate Cox regression analyses. Meanwhile, a risk score model was constructed using the expression of these five genes. The excellent performance of the five-SE-associated-gene-based prognostic model was determined via time-dependent receiver operating characteristic (ROC) curves and Kaplan–Meier curves. Inferior outcome of overall survival (OS) was predicted in the high-risk group. A nomogram based on the polygenic risk score model was further established to validate the performance of the prognostic model. It showed that our prognostic model performed outstandingly in predicting 1-, 3-, and 5-year OS of patients with osteosarcoma. Meanwhile, these five genes also belonged to the hub genes associated with survival and necrosis of osteosarcoma according to the result of weighted gene co-expression network analysis based on the dataset of GSE39058. Therefore, we believe that the five-SE-associated-gene-based prognostic model established in this study can accurately predict the prognosis of patients with osteosarcoma and effectively assist clinicians in treating osteosarcoma in the future.

The intriguing opportunities enabled by the use of living components in biological machines have spurred the development of a variety of muscle-powered bio-hybrid robots in recent years. Among them, several generations of bio-hybrid walkers have been established as reliable platforms to study untethered locomotion. However, despite these advances, such technology is not mature yet, and major challenges remain. This study takes steps to address two of them: the lack of systematic design approaches, common to bio-hybrid robotics in general, and in the case of bio-hybrid walkers specifically, the lack of maneuverability. We then present here a dual-ring biobot, computationally designed and selected to exhibit robust forward motion and rotational steering. This dual-ring biobot consists of two independent muscle actuators and a 4-legged scaffold asymmetric in the fore/aft direction. The integration of multiple muscles within its body architecture, combined with differential electrical stimulation, allows the robot to maneuver. The dual-ring robot design is then fabricated and experimentally tested, confirming computational predictions and turning abilities. Overall, our design approach based on modeling, simulation, and fabrication exemplified in this robot represents a route to efficiently engineer biological machines with adaptive functionalities.

Background Several works suggest the importance of autophagy during esophageal carcinoma development. The aim of the study is to construct a scoring system according to the expression profiles of major autophagy-related genes (ARGs) among esophageal carcinoma cases. Methods The Cancer Genome Atlas was employed to obtain the esophageal carcinoma data. Thereafter, the online database Oncolnc (http://www.oncolnc.org/) was employed to verify the accuracy of our results. According to our results, the included ARGs were related to overall survival (OS). Results We detected the expression patterns of ARG within esophageal carcinoma and normal esophageal tissues. In addition, we identified the autophagy related gene set, including 14 genes displaying remarkable significance in predicting the esophageal carcinoma prognosis. The cox regression results showed that, 7 ARGs (including TBK1, ATG5, HSP90AB1, VAMP7, DNAJB1, GABARAPL2, and MAP2K7) were screened to calculate the ARGs scores. Typically, patients with higher ARGs scores were associated with poorer OS. Moreover, the receiver operating characteristic (ROC) curve analysis suggested that, ARGs accurately distinguished the healthy people from esophageal carcinoma patients, with the area under curve (AUC) value of > 0.6. Conclusion A scoring system is constructed in this study based on the main ARGs, which accurately predicts the outcomes for esophageal carcinoma.

Multiple myeloma (MM) is a malignant plasma cell tumor with high heterogeneity, characterized by anemia, hypercalcemia, renal failure, and lytic bone lesions. Although various powerful prognostic factors and models have been exploited, the development of more accurate prognosis and treatment for MM patients is still facing many challenges. Given the essential roles of super-enhancer (SE) associated genes in the tumorigenesis of MM, we tried to initially screen and identify the significant prognostic factors from SE associated genes in MM by the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, univariate and multivariate Cox regression analysis using GSE24080 and GSE9782 datasets. Risk score model of five genes including CSGALNACT1, FAM53B, TAPBPL, REPIN1, and DDX11, was further constructed and the Kaplan-Meier (K-M) curves showed that the low-risk group seems to have better clinical outcome of survival compared to the high-risk group. Time-dependent receiver operating characteristic (ROC) curves presented the favorable performance of the model. An interactive nomogram consisting of the five-gene risk group and eleven clinical traits was established and identified by calibration curves. Therefore, the risk score model of SE associated five genes developed here could be used to predict the prognosis of MM patients, which may assist the clinical treatment of MM patients in the future.

Background & aims Patients with obstructive jaundice (OJ) are considered to be prothrombotic with increased risk of thromboembolism complications. The role of neutrophil extracellular traps (NETs) in procoagulant activity (PCA) and thrombosis risk in patients with OJ is unclear. In this study, we investigated NETs formation in OJ patients and the role of elevated unconjugated bilirubin (UCB) in inducing NETs, resulting in enhanced PCA and endothelial injury. Methods NETs of OJ patients and healthy controls were measured. NETs PCA was assessed via coagulation time (CT), fibrin formation and purified coagulation complex production assays. Visualization of NETs and mitochondrial reactive oxygen species (MitoROS) were performed with a fluorescence microscope. We further used confocal microscopy to quantify the exposure of phosphatidylserine (PS), fibrin strands and FVa/Xa on Human umbilical vein endothelial cells (HUVECs). Results Assessment of NETs components levels revealed greater NETs production in OJ patients than in healthy controls. Importantly, OJ-NETs were responsible for enhanced PCA. UCB induced NETs formation via MitoROS accumulation and mitochondrial mobilization. HUVECs cocultured with OJ NETs lost their cell-cell junctions and consequently converted to a procoagulant phenotype. The PCA was attenuated by using DNase I alone or in combination with lactadherin. Conclusions Our results suggest that UCB-induced NETs play a prominent role in promoting the hypercoagulable and prothrombotic state in OJ patients. The increased MitoROS accumulation in neutrophils initiated NETosis. NETs are promising targets for indicating or improving coagulation disorders in OJ patients.

Radiation-induced brain injury (RIBI) is a serious complication in cancer patients receiving brain radiotherapy, and accumulating evidence suggests that microglial activation plays an important role in its pathogenesis. Fractalkine (FKN) is a crucial mediator responsible for the biological activity of microglia. In this study, the effect of FKN on activated microglial after irradiation and RIBI was explored and the underlying mechanisms were investigated. Our study demonstrated treatment with exogenous FKN diminished radiation-induced production of pro-inflammatory factors, such as IL1-β and TNFα, promoted transformation of microglial M1 phenotype to M2 phenotype after irradiation, and partially recovered the spatial memory of irradiated mice. Furthermore, upregulation of FKN/CX3CR1 via FKN lentivirus promoted radiation-induced microglial M2 transformation in the hippocampus and diminished the spatial memory injury of irradiated mice. Furthermore, while inhibiting the expression of CX3CR1, which exclusively expressed on microglia in the brain, the regulatory effect of FKN on microglia and cognitive ability of mice disappeared after radiation. In conclusion, the FKN could attenuate RIBI through the microglia polarization toward M2 phenotype by binding to CX3CR1 on microglia. Our study unveiled an important role of FKN/CX3CR1 in RIBI, indicating that promotion of FKN/CX3CR1 axis could be a promising strategy for the treatment of RIBI. Electronic supplementary material The online version of this article (10.1007/s12035-020-02138-3) contains supplementary material, which is available to authorized users.

Background. The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people's health.In this paper, three kinds of risk prediction models applicable to the metabolic syndrome of oil workers were established, and the optimal models were found through comparison. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome.Methods. A total of 1,468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models.Results. The results showed that the accuracy of the three models in the training set was 83.45%, 94.21% and 86.34%, the sensitivity was 78.47%, 94.62% and 81.30%, the F1 score was 0.79, 0.93 and 0.83, and the area under the ROC curve was 0.894, 0.987 and 0.935, respectively. In the test set, the accuracy was 76.72%, 80.66% and 78.69%, the sensitivity was 70.00%, 77.50% and 68.33%, the F1 score was 0.70, 0.76 and 0.71, and the area under the ROC curve was 0.797, 0.861 and 0.855, respectively.Conclusions. The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

Introduction: Chronic obstructive pulmonary disease (COPD) is a common high-burden and highly disabling lung disease. The quality of life and exercise endurance of patients with COPD is often low because of atrophy of the respiratory and skeletal muscles. Although recommended by the global initiative for chronic obstructive lung disease guidelines, pulmonary rehabilitation (PR) has not been used widely because of its inherent limitations. Tuna-Hui-Chun-Gong (TNHCG) is a popular traditional exercise used to treat COPD in China. We aim to evaluate the safety and efficacy of TNHCG for PR of COPD. Methods: The provided protocol is for a single-blind randomized controlled trial in which 120 COPD patients will be randomly and equally divided into the experimental or control group. The control group will be treated with standard COPD drugs while the experimental group will perform TNHCG exercises apart from standard drug treatment. The duration of treatment will be 24 weeks and a follow-up for 48 weeks. The primary outcome will be the 6-Minute Walk Test. The secondary outcomes will include the pulmonary function test, St George's respiratory questionnaire, COPD assessment test, modified medical research council dyspnea scale, Hospital Anxiety and Depression Scale, and exacerbation frequency. A safety assessment will also be performed during the trial. Discussion: Our study will provide evidence to support TNHCG exercise as an additional measure for PR of COPD. Trial registration: ChiCTR1900028332, Registered December 29, 2019. Ethics and dissemination: Ethics approval has been granted by the Sichuan Traditional Chinese Medicine Regional Ethics Review Committee (No. 2019KL-050).

Tomato is the first vegetable consumed in the world. It is grown in very different conditions and areas, mainly in field for processing tomatoes while fresh-market tomatoes are often produced in greenhouses. Tomato faces many environmental stresses, both biotic and abiotic. Today many new genomic resources are available allowing an acceleration of the genetic progress. In this chapter, we will first present the main challenges to breed climate-smart tomatoes. The breeding objectives relative to productivity, fruit quality, and adaptation to environmental stresses will be presented with a special focus on how climate change is impacting these objectives. In the second part, the genetic and genomic resources available will be presented. Then, traditional and molecular breeding techniques will be discussed. A special focus will then be presented on ecophysiological modeling, which could constitute an important strategy to define new ideotypes adapted to breeding objectives. Finally, we will illustrate how new biotechnological tools are implemented and could be used to breed climate-smart tomatoes.

The 5-year survival rate of patients with metastatic renal cell carcinoma (mRCC) is

Objectives: Dendrobium officinale polysaccharide (DOP), a traditional Chinese medicine, has several pharmacological actions in hepatoprotection, cancer chemotherapy, hypoglycemia, anti-fatigue effect, and gastric ulcer protection, among others. However, the effect of DOP in obesity-associated insulin resistance and lipid metabolism remains unknown. This study aimed to investigate the role of DOP in insulin resistance and abnormal lipid metabolism in obese mice. Materials and Methods: The insulin resistance models were established using 3T3-L1 adipocytes, C2C12 myocytes, and primary cultured hepatocytes incubated in palmitate acid. After treatment with DOP, insulin-stimulated glucose uptake of 3T3-L1 adipocytes and C2C12 myocytes and glucose output of primary cultured hepatocytes were detected. In addition, the insulin-stimulated phosphorylation of AKT was confirmed by western blot. Related mechanism studies were confirmed by qRT-PCR and western blotting. DOP was orally administrated in obese mice. Fasting blood glucose, fasting serum insulin, glucose tolerance test (GTT), and insulin tolerance test (ITT) were measured to evaluate insulin sensitivity of mice. Lipid analysis was conducted to evaluate the effect of DOP on lipid metabolism in obese mice. Results: In vitro, DOP treatment ameliorated palmitic acid-induced insulin resistance in adipocytes, myocytes, and hepatocytes. Mechanistically, DOP regulated cellular insulin sensitivity via peroxisome proliferator-activated receptor-γ (PPAR-γ). Furthermore,administration of DOP significantly reduced the insulin resistance of obese mice, as seen by decreased levels of fasting glucose, fasting insulin, and homeostasis model assessment of insulin resistance (HOMA-IR). In addition, DOP treatment attenuated high fat diet(HFD)-induced liver lipid accumulation by reducing liver triglycerides (TG), plasma free fatty acid (FFA), serum cholesterol (TC), TG, and low-density lipoprotein cholesterol (LDL-C) levels, and increasing HDL-C levels. Conclusions: DOP could improve obesity-associated insulin resistance and abnormal lipid metabolism by acting on PPAR-γ, which may serve as a potential therapeutic agent for obesity-associated insulin resistance and lipid metabolism disorder. Funding Statement: This work was supported by the Natural Science Foundation of Hunan Province (2020JJ4799). Declaration of Interests: There are no potential conflicts of interest to declare. Ethics Approval Statement: All protocols of animal care and experiment were reviewed and approved by the Institutional Animal Care and Use Committee of the Laboratory Animal Research Center at Xiangya Medical School of Central South University, China.

The practice and research of ecological civilization is a focus of current planning and design, as well as a scientific strategy under the current situation of resource constraint, environmental degradation and ecosystem degradation. Urban elements such as buildings, green land, farmland,water systems and mountains can be connected by ecological corridors into a green ecological system design.At present, many ecological and environmental problems, such as urban heat island effect, fog and haze, automobile exhaust have a negative effect on the construction of social ecological environment. In order to build a new modern city with prosperous economy, beautiful environment and social civilization, scientific and efficient ecological corridors should be designed to improve the environmental quality of the eco-city, and promote the construction and development of ecological civilization and green cities. Based on the relevant research and specific practices of ecological corridors at home and abroad,combine the needs of the planning and construction of the Yuzhong Ecological Innovation City, and discuss on the connotation and characteristics of ecological corridors, and discuss the key elements of ecological corridor planning.This article will take the ecological corridor planning of Yuzhong Eco Innovation City as an example. We design ecological corridor based on field investigation, literature and geographic information system..The planning and design of the ecological corridor in the planning area proposed in this paper can provide positive suggestion on the planning and design of the ecological corridor in other ecological innovation cities.

Inflammation is a primary defense and immune response. However, under pathological conditions, the inflammation processes always become uncontrolled and lead to chronic diseases. Bufotenine, as a natural component from toad venom, showed great potential for development as a novel anti-inflammation and analgesia agent. This study aimed to investigate the therapeutic effects of bufotenine against inflammation and pain on animal models with a focus on lipid metabolism. In pharmacological studies, bufotenine significantly inhibited the swelling rates on formalin-induced paw edema model, and increased paw withdrawal mechanical thresholds (PWMTs) in von Frey test and thermal pain thresholds (TPTs) in hot-plate test. High-sensitivity lipidomics analysis revealed the effects might be related to the down-regulation of inflammatory mediators from cyclooxygenase (COX), lipoxygenase (LOX), cytochrome P450 (CYP450), linoleic acid (LA), docosahexaenoic acid (DHA) and other pathways. The activities might result from the binding of bufotenine and its receptors, including sigma-1 receptor and 5-Hydroxytryptamine receptor 3A, thus regulating lipid metabolism pathway. The research provided a systemic evidence for the actions and mechanism of bufotenine. It suggested that the natural compound might be a potential candidate for reducing inflammatory pain disorders.

Taking the agricultural situation of Hubei Province as the research object, this paper uses the time series ARIMA model, ARMA model and deep learning LSTM neural network model to explore the impact of COVID-19 on the agriculture of Hubei Province. Three main indicators are screened out to measure the development of agricultural economy, that is, gross regional product, gross output value of agriculture, forestry, animal husbandry and fishery, agricultural product production price index. Based on the quarterly data of indicators from 2001 to 2019 from the National Bureau of Statistics, three indicators in Hubei Province in the first quarter and the second quarter of 2020 are predicted by using the deep learning-based time series ARIMA model, ARMA model and LSTM neural network model. By comparing the predicted data with the real data, the impact of COVID-19 is measured on the agricultural situation of Hubei Province. It was found that COVID-19 had a great impact on the agricultural situation of Hubei Province in both of the first and second quarters of 2020, with the impact in the first quarter being greater than that in the second quarter. At the same time, the prediction accuracy of the two methods is compared to find that the time series model is more effective and reliable in predicting the agricultural product price index. The LSTM neural network model with a long and short term memory has a good prediction effect on the regional gross product and the total output value of agriculture, forestry, animal husbandry and fishery.

Port shoreline resources are the basis of port and shipping development, and its assessment method has become one of the hot issues in port research. On the basis of constructing a reasonable index evaluation system, this paper constructs the fuzzy evaluation matrix based on the triangular fuzzy analytic hierarchy process and constructs the fuzzy evaluation matrix by using the fuzzy comprehensive evaluation method, and obtains the maximum membership degree of the port shoreline resources. Compared with the traditional port shoreline Resource evaluation methods, the new one got more advantages in objective and quantitative. Finally, Combined with the Nanjing section of the Yangtze River as a case for verification, the results show that the model can accurately solve the problem of resource evaluation of port shoreline.

Purpose With prospective clinical sequencing of tumors emerging as a mainstay in cancer care, an urgent need exists for a clinical support tool that distills the clinical implications associated with specific mutation events into a standardized and easily interpretable format. To this end, we developed OncoKB, an expert-guided precision oncology knowledge base. Methods OncoKB annotates the biologic and oncogenic effects and prognostic and predictive significance of somatic molecular alterations. Potential treatment implications are stratified by the level of evidence that a specific molecular alteration is predictive of drug response on the basis of US Food and Drug Administration labeling, National Comprehensive Cancer Network guidelines, disease-focused expert group recommendations, and scientific literature. Results To date, > 3,000 unique mutations, fusions, and copy number alterations in 418 cancer-associated genes have been annotated. To test the utility of OncoKB, we annotated all genomic events in 5,983 primary tumor samples in 19 cancer types. Forty-one percent of samples harbored at least one potentially actionable alteration, of which 7.5% were predictive of clinical benefit from a standard treatment. OncoKB annotations are available through a public Web resource ( http://oncokb.org ) and are incorporated into the cBioPortal for Cancer Genomics to facilitate the interpretation of genomic alterations by physicians and researchers. Conclusion OncoKB, a comprehensive and curated precision oncology knowledge base, offers oncologists detailed, evidence-based information about individual somatic mutations and structural alterations present in patient tumors with the goal of supporting optimal treatment decisions.

Background: Molecular markers of clinical outcome may aid in designing targeted treatments for bladder cancer. However, only a few bladder cancer biomarkers have been examined as therapeutic targets. Methods: Data from The Cancer Genome Atlas (TCGA) and bladder specimens were evaluated to determine the biomarker potential of the hyaluronic acid (HA) family of molecules – HA synthases, HA receptors and hyaluronidase. The therapeutic efficacy of 4-methylumbelliferone (4MU), a HA synthesis inhibitor, was evaluated in vitro and in xenograft models. Results: In clinical specimens and TCGA data sets, HA synthases and hyaluronidase-1 levels significantly predicted metastasis and poor survival. 4-Methylumbelliferone inhibited proliferation and motility/invasion and induced apoptosis in bladder cancer cells. Oral administration of 4MU both prevented and inhibited tumour growth, without dose-related toxicity. Effects of 4MU were mediated through the inhibition of CD44/RHAMM and phosphatidylinositol 3-kinase/AKT axis, and of epithelial–mesenchymal transition determinants. These were attenuated by HA, suggesting that 4MU targets oncogenic HA signalling. In tumour specimens and the TCGA data set, HA family expression correlated positively with β-catenin, Twist and Snail expression, but negatively with E-cadherin expression. Conclusions: This study demonstrates that the HA family can be exploited for developing a biomarker-driven, targeted treatment for bladder cancer, and 4MU, a non-toxic oral HA synthesis inhibitor, is one such candidate.

Credit risk management (CRM) is to identify, measure, monitor, and control risk arising from the possibility of default in payments. Existing CRM tools available for large financial institutions do not meet the requirements of rural commercial banks (RCBs) because their main customers are SMEs and farming households whose financial data and credit rating records are not available. RCBs in China also expose specific risks connected to rural commercial banking business and in particular farming-related loans and services. Adopting a qualitative analysis approach to identify key factors contributing to failures of RCBs’ customers, we endeavour to develop a CRM framework for RCBs in China. The framework, which is based on the identification of business failures of RCBs’ customers and factors contributing to failures of SMEs and farming households, incorporates financial and non-financial variables. Using nonfinancial variables along with financial variables as predictors of company failure significantly improves credit analysis quality and accuracy. Also, this study recognises guanxi1 as risk potentials and includes guanxi risks in the framework. This study has made contributions to the extant literature on CRM of banks in general and RCBs in particular.

Hypertension is a severe threat to human being's health due to its association with many comorbidities. Many research works have explored hypertension's prevalence and treatment. However, few considered impact of patient's socioeconomic status and geographical disparities. We intended to fulfill that research gap by analyzing the association of the prevalence of hypertension and three important comorbidities with various socioeconomic and geographical factors. We also investigated the prevalence of those comorbidities if the patient has been diagnosed with hypertension. We obtained a large collection of medical records from 29 hospitals across China. We utilized Bayes' Theorem, Pearson's chi-squared test, univariate and multivariate regression methods and geographical detector methods to analyze the association between disease prevalence and risk factors. We first attempted to quantified and analyzed the spatial stratified heterogeneity of the prevalence of hypertension comorbidities by q-statistic using geographical detector methods. We found that the demographic and socioeconomic factors, and hospital class and geographical factors would have an enhanced interactive influence on the prevalence of hypertension comorbidities. Our findings can be leveraged by public health policy makers to allocate medical resources more effectively. Healthcare practitioners can also be benefited by our analysis to offer customized disease prevention for populations with different socioeconomic status.

The DC charging can achieve rapid energy supply of electric vehicles. The performance of the DC charging coupler is a crucial factor for the safety and reliability of DC charging. This paper focus on the test and research of the DC charging coupler in the terminal configuration, metal-coated material and crimping type, a detailed comparative study of the different terminal design has been made, and gives the advise of the design selection for the terminal of the DC charging coupler. The results are useful for the design of EV DC Charging Coupler and the revise of correlative standards.

How people divide their attention across their friends can help to understand key issues in the realm of geopolitics. Such attention exploration allows us to compare people who focus a large portion of their attention on a small set of close friends with those disperse their attention more widely. Using 2.5 million twitter data written by 130 thousands users, we find the balance of attention is a relatively stable property of people across different modalities of interaction. It displays subtle variation across people with different characteristics and different modalities of interaction. Specifically, people’s attention is more focused in mention interactions, while those active in socialization tend to allocate higher portion of total attention to their close friends. Besides external interactions, people’s inner interests also affect their attention allocation. People spreading multiple memes tend to be focused, and those with more even distribution of memes are focused on their intimate friends. Finally, people’s relationships also plays an important role in their attention allocation. People are more likely to focus their attention on those most like them, and this similarity sequentially enhances the intimate relationship between them.

Healthcare-seeking behavior (HSB) is the motivation of formulating, developing and changing healthcare policy and medical insurance system, also the important reference of reforming and improving healthcare systems. This paper mainly focused on exploring ‘Patterns-Drivers’ of HSB using a case study on Beijing Hand-Foot-Mouth Disease (HFMD) in China. We extracted the index for HSB and constructed the networks using the heterogeneous information from patients’ records, clarified the spatial-temporal distribution of HSB and examined spatial association between HSB and socioeconomic factors using Geographically Weighted Regression. It was found that HFMD morbidity, the spatial distribution of kindergarten, the scale of the public infrastructures such as the park and the toilet was the important local drivers of HSB. The outcome based on the application of complex networks, geographic information system and spatial statistical techniques would provide valuable information supporting the optimal allocation of health resources and decision-making in disease control and prevention.

Outcome-oriented predictive process monitoring plays an important role for business process management, which is to predict the most likely outcome class (positive or negative) for an on-going case given the event log consisting of completed business process instances with labeled outcome classes. Unfortunately, the existing traditional machine learning methods for this problem require a lot of manual intervention and long time in real-time prediction applications. In order to construct a predictive classification model automatically, we propose an effective approach based on deep learning techniques, called Att-Bi-LSTM, which is combined with the bidirectional long short-term memory network and the attention-mechanism. This approach can capture and optimize the features that have a decisive effect on the outcome for a completed case so as to construct a predictive model with good performance. The extensive experiments conducted on twelve real datasets show that our approach outperforms the state-of-the-art ones in terms of prediction accuracy, earliness, and time performance.

Studies indicate that molecular subtypes in muscle invasive bladder cancer predict the clinical outcome. We evaluated whether subtyping by a simplified method and established classifications could predict the clinical outcome.We subtyped institutional cohort 1 of 52 patients, including 39 with muscle invasive bladder cancer, an Oncomine™ data set of 151 with muscle invasive bladder cancer and TCGA (The Cancer Genome Atlas) data set of 402 with muscle invasive bladder cancer. Subtyping was done using simplified panels (MCG-1 and MCG-Ext) which included only transcripts common in published studies and were analyzed for predicting metastasis, and cancer specific, overall and recurrence-free survival. TCGA data set was further analyzed using the Lund taxonomy, the Bladder Cancer Molecular Taxonomy Group Consensus and TCGA 2017 mRNA subtype classifications.Muscle invasive bladder cancer specimens from cohort 1 and the Oncomine data set showed intratumor heterogeneity for transcript and protein expression. MCG-1 subtypes did not predict the outcome on univariate or Kaplan-Meier analysis. On multivariate analysis N stage (p ≤0.007), T stage (p ≤0.04), M stage (p=0.007) and/or patient age (p=0.01) predicted metastasis, cancer specific and overall survival, and/or the cisplatin based adjuvant chemotherapy response. In TCGA data set publications showed that subtypes risk stratified patients for overall survival. Consistently the MCG-1 and MCG-Ext subtypes were associated with overall but not recurrence-free survival on univariate and Kaplan-Meier analyses. TCGA data set included 21 low grade specimens of the total of 402 and subtypes associated with tumor grade (p=0.005). However, less than 1% of muscle invasive bladder cancer cases are low grade. In only high grade specimens the MCG-1 and MCG-Ext subtypes could not predict overall survival. On univariate analysis subtypes according to the Bladder Cancer Molecular Taxonomy Group Consensus, TCGA 2017 and the Lund taxonomy were associated with tumor grade (p0.0001) and overall survival (p=0.01 to0.0001). Regardless of classification, subtypes had about 50% to 60% sensitivity and specificity to predict overall and recurrence-free survival. On multivariate analyses N stage and lymphovascular invasion consistently predicted recurrence-free and overall survival (p=0.039 and 0.003, respectively).Molecular subtypes reflect bladder tumor heterogeneity and are associated with tumor grade. In multiple cohorts and subtyping classifications the clinical parameters outperformed subtypes for predicting the outcome.

INTRODUCTION AND OBJECTIVES:Despite many available treatments, >90% of metastatic renal cell carcinoma (mRCC) patients die within 5 years. Due to its modest efficacy, Sorafenib (SF) is a second lin...

The intriguing opportunities enabled by the use of living components in biological machines have spurred the development of a variety of muscle‐powered biohybrid robots in recent years. Among them, several generations of tissue‐engineered biohybrid walkers have been established as reliable platforms to study untethered locomotion. However, despite these advances, such technology is not mature yet, and major challenges remain. Herein, steps are taken to address two of them: the lack of systematic design approaches, common to biohybrid robotics in general, and in the case of biohybrid walkers specifically, the lack of maneuverability. A dual‐ring biobot is presented which is computationally designed and selected to exhibit robust forward motion and rotational steering. This dual‐ring biobot consists of two independent muscle actuators and a four‐legged scaffold asymmetric in the fore/aft direction. The integration of multiple muscles within its body architecture, combined with differential electrical stimulation, allows the robot to maneuver. The dual‐ring robot design is then fabricated and experimentally tested, confirming computational predictions and turning abilities. Overall, a design approach based on modeling, simulation, and fabrication exemplified in this versatile robot represents a route to efficiently engineer complex biological machines with adaptive functionalities.

Background: Hypertension, an important risk factor for the health of human being, is often accompanied by various comorbidities. However, the incidence patterns of those comorbidities have not been widely studied. Aim: Applying big-data techniques on a large collection of electronic medical records, we investigated sex-specific and age-specific detection rates of some important comorbidities of hypertension, and sketched their relationships to reveal the risk for hypertension patients. Methods: We collected a total of 6,371,963 hypertension-related medical records from 106 hospitals in 72 cities throughout China. Those records were reported to a National Center for Disease Control in China between 2011 and 2013. Based on the comprehensive and geographically distributed data set, we identified the top 20 comorbidities of hypertension, and disclosed the sex-specific and age-specific patterns of those comorbidities. A comorbidities network was constructed based on the frequency of co-occurrence relationships among those comorbidities. Results: The top four comorbidities of hypertension were coronary heart disease, diabetes, hyperlipemia, and arteriosclerosis, whose detection rates were 21.71% (21.49% for men vs 21.95% for women), 16.00% (16.24% vs 15.74%), 13.81% (13.86% vs 13.76%), and 12.66% (12.25% vs 13.08%), respectively. The age-specific detection rates of comorbidities showed five unique patterns and also indicated that nephropathy, uremia, and anemia were significant risks for patients under 39 years of age. On the other hand, coronary heart disease, diabetes, arteriosclerosis, hyperlipemia, and cerebral infarction were more likely to occur in older patients. The comorbidity network that we constructed indicated that the top 20 comorbidities of hypertension had strong co-occurrence correlations. Conclusions: Hypertension patients can be aware of their risks of comorbidities based on our sex-specific results, age-specific patterns, and the comorbidity network. Our findings provide useful insights into the comorbidity prevention, risk assessment, and early warning for hypertension patients.

Owing to their superior physical properties, carbon nanotubes (CNT) films are excellent candidates of next-generation multifunctional textile. To integrate functionalities, guest materials can be introduced onto or inside CNT films. Here an ultra-thin, soft, and strong textile with superior abilities of dual-mode thermal regulation and electromagnetic interference shielding is prepared by depositing Cu nanolayers on CNT film. The composite textile also exhibits an excellent anti-fatigue property, and can be water vapor transmissible easily via laser perforation. This provides a new strategy to develop multifunctional textile materials.

With the limitations of the network resources and battery energy of wireless sensors, the competition of resources in the process of communication will increase the network energy consumption and reduce the Quality of Service (QoS), resulting in that the application of Multi-Radio Multi-Channel (MRMC) Wireless Sensor Networks (WSNs) face many challenges. In this paper, we concentrate on the resource allocation of joint time slot assignment, channel allocation and power control for MRMC WSNs. Due to the diversity of research objectives and the computational complexity of the non-convex problem, this paper develops a two-stage resource allocation optimization algorithm by analyzing the interdependence of various resources. Specifically, to exchange information with conflict-free transmission among all sensors, a graph coloring algorithm for time slot assignment is designed firstly. Then based on the first stage of this algorithm, the problem of joint power control and channel allocation is studied and formulated as a multi-objective optimization problem to achieve the trade-off between energy efficiency and network capacity maximization under the constraints of link interference and load balance. Multi-objective hybrid particle swarm optimization is introduced to obtain the Pareto optimal solutions. The simulation results show that the proposed algorithm significantly performs better in terms of achieving the trade-off of multi-performance.