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Finding and matching drugs to treat subsets of cancers remains a daunting challenge of precision medicine. Neddylation inhibition, affecting posttranslational protein function and turnover, is a promising therapeutic approach to cancer including glioblastoma (GBM). We report vulnerability to neddylation inhibition by MLN4924 in a subset of GBM preclinical models and identify mechanisms underlying this differential response. MLN4924 treatment of GBM cells with intact PTEN die due to DNA damage and re-replication events. Loss of PTEN drives resistance to MLN4924. Time-course transcriptomics elevates PTEN signaling, DNA replication, and chromatin instability pathways as significant differentiators between MLN4924-sensitive and resistant models. Shotgun proteomics corroborates these findings and also identifies elevated TOP2A in resistant models. TOP2A inhibitors combined with MLN4924 prove synergistic. We show that PTEN status serves as both a novel biomarker for MLN4924 response in GBM and reveals a vulnerability to TOP2A inhibitors in combination with MLN4924. Summary Neddylation inhibition kills glioma cells by DNA damage and re-replication events which are dependent on wild-type nuclear PTEN.

Few studies have independently investigated the population of patients with synchronous metastatic renal cell carcinoma (smRCC). In this study, we evaluated programmed death protein-ligand 1 (PD-L1) and vascular endothelial growth factor receptor 2 (VEGFR-2) expression in primary tumor tissue of smRCC.A total of 96 patients with smRCC who were treated with cytoreductive nephrectomy followed by targeted therapy from January 2006 to January 2013 were identified. PD-L1 and VEGFR-2 expression were evaluated by immunohistochemistry. Kaplan-Meier and Cox methods were used for analysis.PD-L1 and VEGFR-2 protein immunopositivity were observed in 39.6% (38 of 96) and 58.3% (56 of 96) of patients, respectively. A significant correlation was detected between VEGFR-2 and PD-L1 expression (P = 0.030). Based on PD-L1 and VEGFR-2 expression, patients with intermediate-risk disease (n = 63) were divided into 4 subgroups including patients who were PD-L1 (+) VEGFR-2 (+) (n = 21), PD-L1 (+) VEGFR-2 (-) (n = 11), PD-L1 (-) VEGFR-2 (+) (n = 15) and PD-L1 (-) VEGFR-2 (-) (n = 16). Compared to the PD-L1 (-) VEGFR-2 (+), PD-L1 (+) VEGFR-2 (+) and PD-L1 (-) VEGFR-2 (-) groups, patients in the PD-L1 (+) VEGFR-2 (-) group had shorter progression-free survival (median, 9.0 vs. 20.0, 16.0 and 15.5 months, P0.05) and overall survival (median, 14.0 vs. 33.0, 24.0 and 26.5 months, P0.05).Intermediate-risk smRCC patients with PD-L1-positive and VEGFR-2-negative expression who were treated with targeted therapy following cytoreductive nephrectomy had poor prognoses. We suggest that other treatments beyond sunitinib or sorafenib may be explored in this subgroup.

Aim Radium-223, a targeted alpha therapy, is approved widely for the treatment of patients with metastatic castrate-resistant prostate cancer, based on a pivotal phase 3 study in predominantly white patients. We investigated the efficacy and safety of radium-223 in Asian patients with castrate-resistant prostate cancer and metastatic bone disease. Methods This multicenter, prospective, single-arm, open-label phase 3 trial evaluated the efficacy and safety of the standard radium-223 regimen (55 kBq/kg every 4 weeks for six cycles) in patients from Asian countries. The primary endpoints were the safety and overall survival. Results A total of 226 patients were enrolled and received at least one dose of radium-223. Median overall survival was 14.0 months (95% confidence interval [CI], 11.2-17.4). Median time to total alkaline phosphatase and prostate-specific antigen progression were 7.5 (95% CI, 6.8-7.7) and 3.6 (95% CI, 3.1-3.7) months, respectively. Median skeletal-related event-free survival was 26.0 months (95% CI, 12.6-not reached). Grade ≥3 treatment-emergent adverse events were reported in 103 (46%) of 226 patients, with anemia being the most common event (34 [15%] patients). Grade ≥3 drug-related treatment-emergent adverse events occurred in 39 (17%) of 226 patients. Serious treatment-emergent adverse events were reported in 65 (29%) of 226 patients. Seven (3%) patients had an adverse event leading to death; none were considered to be related to radium-223. Conclusion The results of this study support the use of the standard radium-223 regimen for the treatment of Asian patients with castrate-resistant prostate cancer and symptomatic bone metastases.

Introduction: Sequential therapy with vascular endothelial growth factor receptor-tyrosine kinase inhibitors (VEGFR-TKIs) is effective in some patients with metastatic renal cell carcinoma (mRCC) progressed from or were intolerant to a prior TKIs. Anlotinib is a multi-kinase inhibitor targeting VEGFR1/2/3, PDGFR and FGFR, which has demonstrated efficacy and safety in first-line treatment of mRCC. This study assessed the potential of anloitnib as second-line treatment for patients with mRCC after prior one VEGFR-TKI. Methods: This is a single-arm, open-label, phase 2 study. Patients progressed after or were intolerant to sorafenib or sunitinib were enrolled. Anlotinib was administrated orally 12 mg once daily for 14 days every 3 weeks. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS), objective response rate (ORR), safety and quality of life (QoL). Results: Forty three patients were enrolled and 42 received anlotinib, of whom 32 progressed after and 10 were intolerant to sorafenib or sunitinib. Median PFS were 14.0 months (95% CI 8.3–20.3) and 8.5 months (95% CI 5.6–16.6) for overall population and patients progressed after a previous VEGFR-TKI, respectively. Median OS was 21.4 months (95% CI 16.0–34.5), confirmed ORR and DCR were 16.7 and 83.3% in overall population. The most common adverse events included diarrhea (47.6%), hypertension (45.2%), hand and foot syndrome (42.9%), and fatigue (40.5%). Grade 3 hematological adverse events occurred in four cases, while no grade 4 hematological adverse events was observed. Conclusions: Anlotinib showed promising efficacy as well as favorable safety as second-line treatment for patients with mRCC. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT02072044.

INTRODUCTION AND OBJECTIVE:Previous studies have reported that cytoreductive nephrectomy (CN) is not suitable for patients with poor-risk synchronous metastatic renal cell carcinoma (smRCC). Whethe...

Many cellular models aimed at elucidating cancer biology do not recapitulate pathobiology including tumor heterogeneity, an inherent feature of cancer that underlies treatment resistance. Here we introduce a cancer modeling paradigm using genetically engineered human pluripotent stem cells (hiPSCs) that captures authentic cancer pathobiology. Orthotopic engraftment of the neural progenitor cells derived from hiPSCs that have been genome-edited to contain tumor-associated genetic driver mutations revealed by The Cancer Genome Atlas project for glioblastoma (GBM) results in formation of high-grade gliomas. Similar to patient-derived GBM, these models harbor inter-tumor heterogeneity resembling different GBM molecular subtypes, intra-tumor heterogeneity, and extrachromosomal DNA amplification. Re-engraftment of these primary tumor neurospheres generates secondary tumors with features characteristic of patient samples and present mutation-dependent patterns of tumor evolution. These cancer avatar models provide a platform for comprehensive longitudinal assessment of human tumor development as governed by molecular subtype mutations and lineage-restricted differentiation., The dearth of glioblastoma model systems that accurately recapitulate the disease remains a challenge. Here, the authors develop cancer avatars using genetically engineered human induced pluripotent cells.

Technological change and innovation are vitally important, especially for high-tech companies. However, factors influencing their future research and development (R&D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for high-tech companies. To this end, in this paper, we develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company. Specially, DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network. For one thing, PCR and CTR aim to capture competitive relations among enterprises and collaborative relations among technologies, respectively. For another, DTT is designed for modeling dynamic interactions between companies and technologies with the above relations involved. Finally, we evaluate our DTF framework on real-world patent data, and the experimental results clearly prove that DTF can precisely help to prospect future technology emphasis of companies by exploiting hybrid factors., 6 pages, 7 figures

Purpose: Previous reports showed that some patients with renal cell carcinoma (RCC) and renal vein tumor thrombus (RVTT) were misdiagnosed pre-operatively. To improve the accuracy of this diagnosis, the clinical characteristics of RCC with missed RVTT diagnosis were analyzed. Methods: We retrospectively reviewed RCC patients with RVTT between January 2000 and December 2015. The survival analysis was estimated using the Kaplan–Meier method. The Cox proportional hazard models were applied to identify risk factors. Results: The missed diagnosis rate of RVTT in RCC was 30.5%. In multivariate analysis, maximal tumor diameter, tumor located in the middle part, renal vein contrast agents filling insufficiently and tumor with collateral vessels (odds ratio = 1.22, 1.35, 1.25, 1.22; and p = .034, .003, .015 and .037, respectively) were independent predictors of missed RVTT diagnosis. A missed-diagnosis score was presented as area under curve of 0.852 (p p = .025). Conclusions: This was the first study exploring clinical features as predictors of missed RVTT diagnosis. The possibility of complicating tumor thrombus should be considered when there is pre-operative presence of tumor with large diameter, renal tumor in the middle part, renal tumor with collateral vessels and renal vein contrast agents filling insufficiently. Patients with three points in missed-diagnosis scoring suggested a high possibility of missed RVTT diagnosis, and tumor with collateral vessels indicated poor prognosis.

For the sake of measuring the reliability of actual face recognition system with continuous variables, after analyzing system structure, common failures, influencing factors of reliability, and maintenance data of a public security face recognition system in use, we propose a reliability evaluation model based on Continuous Bayesian Network. We design a Clique Tree Propagation algorithm to reason and solve the model, which is realized by R programs, and as a result, the reliability coefficient of the actual system is obtained. Subsequently, we verify the Continuous Bayesian Network by comparing its evaluation results with those of traditional Bayesian Network and Ground Truth. According to these evaluation results, we find out some weaknesses of the system and propose some optimization strategies by the way of finding the right remedies and filling in blanks. In this paper, we synthetically apply a variety of methods, such as qualitative analysis, quantitative analysis, theoretical analysis, and empirical analysis, to solve the unascertained causal reasoning problem. The evaluation method is reasonable and valid, the results are consistent with realities and objective, and the proposed strategies are very operable and targeted. This work is of theoretical significance to research on reliability theory. It is also of practical significance to the improvement of the system’s reliability and the ability of public order maintenance.

Ionizing radiation (IR) and chemotherapy are standard of care treatments for glioblastoma (GBM) patients and both result in DNA damage, however, the clinical efficacy is limited due to therapeutic resistance. We identified a mechanism of such resistance mediated by phosphorylation of PTEN on tyrosine 240 (pY240-PTEN) by FGFR2. pY240-PTEN is rapidly elevated and bound to chromatin through interaction with Ki-67 in response to IR treatment and facilitates the recruitment of RAD51 to promote DNA repair. Blocking Y240 phosphorylation confers radiation sensitivity to tumors and extends survival in GBM preclinical models. Y240F-Pten knock-in mice showed radiation sensitivity. These results suggest that FGFR-mediated pY240-PTEN is a key mechanism of radiation resistance and is an actionable target for improving radiotherapy efficacy.

20-Hydroxyecdysone (20E), a molting hormone of insects, is the most abundant phytoecdysteroid (PE) produced by plants, where it represents a protective molecule against insect herbivores. The objective of the investigation is to determine the effect of 20E on the growth, physiological and biochemical characteristics and the transcript levels of antioxidant enzymes of wheat seedlings under salt stress. Our results showed that exogenous 20E was able to significantly alleviate salt-induced oxidative damage in wheat seedlings. It most likely acts by decreasing the concentration of malondialdehyde (MDA) and the rate of superoxide radical (O 2 •− ) generation, and by increasing the activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), concentrations of ascorbic acid (AsA) and glutathione (GSH), and the gene expression levels of SOD , POD , CAT . It was suggested that foliar spraying with 20E could efficiently protect wheat seedlings against salt-induced oxidative stress. The results also show that 20E had a positive physiological effect on the growth of salt-stressed seedlings. This is the first report dealing with the effect of 20E pretreatment in the enhancing of wheat seedling tolerance under salt stress. https://doi.org/10.2298/ABS170722056L Received: July 22, 2017; Revised: November 14, 2017; Accepted: December 11, 2017; Published online: December 22, 2017 How to cite this article: Li J, Han X, Tang L, Wang C, Zhang W, Ma J. 20-hydroxyecdysone protects wheat seedlings from salt stress. Arch Biol Sci. 2018;70(2):379-86.

Digital breast tomosynthesis (DBT) mammography is a promising imaging technique for detecting early-stage breast cancers. However, DBT imaging usually contains truncation artifacts around the chest wall area, where many recurring breast tumors or masses are located. Extrapolation or interpolation and weighting techniques are used to suppress artifacts in existing methods, but these are inappropriate for DBT due to its high spatial resolution requirement. To solve this problem, we propose a practical truncation correction method, which can realize simultaneous truncation artifact reduction and image reconstruction (STARIR) for DBT. In contrast to extrapolation or interpolation estimations and weighting strategy, this integrated method renews each truncated voxel by considering the information of all measured projections rather than that of one projection at each reconstruction iteration. Qualitative and quantitative studies were performed on simulated and realistic DBT data to validate the proposed method. Results showed that STARIR reduces truncation artifacts and preserves tissue details in reconstructed images more effectively than do the current methods. The proposed method can be used for simultaneous and accurate truncation artifact reduction and image reconstruction in DBT.

Neddylation is a specific pathway within the ubiquitin/proteasome system that is overactive in GBM, and whose upregulation has been associated with glioma progression and worse survival. Pevonedistat (MLN4924) is a first-in-class small-molecule neddylation inhibitor shown to inhibit growth of GBM cells by impacting protein degradation in culture and orthotopic xenografts. However, the determinants of vulnerability are not fully understood. Because the molecular heterogeneity within and across GBM patients obscures therapeutic targets and obfuscates signals of efficacy in clinical trials, we pursue the use of molecular “signatures of vulnerability” to targeted agents in subsets of preclinical models. Selective vulnerability to pevonedistat was shown in a subset of GBM; notably, models with mutations or copy number deletions of PTEN are associated with de novo resistance to pevonedistat. Time-course studies of sensitive and non-sensitive GBM cells using transcriptomics and proteomics/phosphoproteomics uncovered additional determinants of response to pevonedistat. Our results demonstrate that in GBM, resistance to pevonedistat is driven by reduced PTEN-chromatin binding (loss-of-function or lower expression) that is also independent of PTEN’s lipid phosphatase activity (i.e., PI3K/AKT signaling). Across 25 glioma cell lines, we found that PTEN signaling, DNA replication, and chromatin instability pathways are the most significant differentiators between pevonedistat sensitive vs. non-sensitive models. In GBM models with modest to low sensitivity to pevonedistat, TOP2A expression was elevated. Combination treatment with the TOP2A inhibitor, etoposide, proved synergistic with pevonedistat. We report that PTEN status both serves as a novel biomarker for GBM sensitivity to pevonedistat and reveals a synergistic vulnerability to TOP2A inhibitors in combination with pevonedistat. Paired use of GBM PDX models of varying sensitivity with drug development testing allows the advancement of a promising agent as well as a patient-enrollment “signature of vulnerability” likely to increase the likelihood of demonstrating therapeutic efficacy in early stage clinical trials.

Small molecule tyrosine kinase inhibitors against the epidermal growth factor (EGFR) have made significant impact in the treatment of advanced non-small-cell lung cancer (NSCLC) with activating EGFR mutations. Although the first and second-generation EGFR inhibitors have remarkably improved survival in advanced EGFR mutant NSCLC patients, most of the patients develop acquired resistance. The gatekeeper mutation (T790M) is the most common acquired resistance mechanism to the first and second-generation EGFR inhibitors. To overcome the T790M acquired resistance, third-generation EGFR inhibitors, such as osimertinib, were developed. However, additional mutant selective EGFR inhibitors with improved toxicity profile are still needed as osimertinib treatment produced incidence of adverse events, such as diarrhea and skin rush. Here, we describe the identification and characterization of ZN-e4, an orally bioavailable, selective, irreversible third generation EGFR inhibitor. ZN-e4 selectively inhibited the kinase activity of several mutated forms of EGFR, including L858R, T790M/L858R, and Exon19 del in biochemical assay. In cell-based assays, ZN-e4 demonstrated a 20-40-fold selectivity ratio for mutant EGFR forms over the wild-type form thus minimizing the potential for on target toxicity. ZN-e4 potently inhibited the proliferation of NSCLC cell lines harboring EGFR activating and T790M mutation and induced tumor regression in small and large tumors with activating EGFR mutations in preclinical human NSCLC xenograft models. Taken together, our data suggest that ZN-e4 is a potent and selective third generation EGFR inhibitor. Currently, ZN-e4 is in a Phase I clinical trial in patients with EGFR mutant NSCLC showing clinical activity and encouraging toxicity profile. Citation Format: Jiali Li, Sunny Abraham, Jianhui Ma, Peter Q. Huang, Kevin D. Bunker, Fernando Doñate, Ahmed A. Samatar. Discovery of ZN-e4, an irreversible EGFR-TKI with potent anti-tumor activity in EGFR mutant non-small-cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2423.

In transmitted X-ray tomography imaging, the acquired projections may be corrupted for various reasons, such as defective detector cells and beam-stop array scatter correction problems. In this study, we derive a consistency condition for cone-beam projections and propose a method to restore lost data in corrupted projections. In particular, the relationship of the geometry parameters in circular trajectory cone-beam computed tomography (CBCT) is utilized to convert an ultra-hyperbolic partial differential equation (PDE) into a second-order PDE. The second-order PDE is then transformed into a first-order ordinary differential equation in the frequency domain. The left side of the equation for the newly derived consistency condition is the projection derivative of the current and adjacent views, whereas the right side is the projection derivative of the geometry parameters. A projection restoration method is established based on the newly derived equation to restore corrupted data in projections in circular trajectory CBCT. The proposed method is tested in beam-stop array scatter correction, metal artifact reduction, and abnormal pixel correction cases to evaluate the performance of the consistency condition and corrupted projection restoration method. Qualitative and quantitative results demonstrate that the present method has considerable potential in restoring lost data in corrupted projections.

For many decades it has been known that tumor DNA is shed into the blood. As a consequence of technological limitations, researchers were unable to comprehensively characterize circulating DNA. The advent of ultrasensitive and highly specific molecular assays has provided a comprehensive profile of the molecular characteristics and dynamics of circulating DNA in healthy subjects and cancer patients. With these new tools in hand, significant interest has been provoked for an innovative type of tumor biopsy termed a "liquid biopsy". Liquid biopsies are obtained by minimal invasive blood draws from cancer patients. Circulating cancer cells, exosomes and a variety of molecules contained within the liquid biopsy including cell-free circulating tumor DNA (ctDNA) can serve as promising tools to track cancer evolution. Attractive features of ctDNA are that ctDNA isolation is straightforward, ctDNA levels increase or decrease in response to the degree of tumor burden and ctDNA contains DNA mutations found in both primary and metastatic lesions. Consequently, the analysis of circulating DNA for cancer-specific mutations might prove to be a valuable tool for cancer detection. Moreover, the capacity to screen for ctDNA in serial liquid biopsies offers the possibility to monitor tumor progression and responses to therapy and to influence treatment decisions that ultimately may improve patient survival. Here we focus on mutation detection in ctDNA and provide an overview of the characteristics of ctDNA, detection methods for ctDNA and the feasibility of ctDNA to monitor tumor dynamics. Current challenges associate with ctDNA will also be discussed.

The traditional PI controller parameters are mostly fixed, do not have online self-tuning ability, and it is difficult to satisfy the requirements of the speed control system for Permanent Magnet Synchronous Motor(PMSM). Moment of inertia is a very important parameter in controller parameter selfo-tuning. In this paper, the moment of inertia is identified in real time, combined with the relationship between the PI parameter of the controller speed loop and the moment of inertia, the appropriate control parameters are directly calculated to realize the online self-tuning of the controller parameters. The simulation model is built in Matlab/Simulink. The experimental results show that the proposed algorithm can realize the fast and accurate identification of the moment of inertia. At the same time, the controller PI parameter self-tuning can effectively reduce the system overshoot, the system response speed is fast, and the anti-interference ability is strong, greatly improving the performance of the speed control system.

Industrial robots usually divide some functions into modules in the form of multiple CAN nodes, set and use the same parameters to achieve specific motion control functions, and generally perform parameter learning through diagnostic services. In order to avoid all the CAN nodes to implement complex diagnostic protocols, the diagnostic protocols related to parameter learning are implemented on a single node, and the parameter learning and learning result verification of other nodes are dominated by dedicated CAN messages and protocols.It avoids the problem of repeated development of diagnostic protocols for multiple nodes. It is not necessary to modify the design of this method when adding or subtracting CAN nodes. It is also suitable for parameter collaborative learning of multiple CAN nodes in other CAN networks, and has strong versatility.

The Industrial Internet of Things (IIoT) concentrates on the interconnection and utilization of big and powerful data in manufacturing environments. Data from different devices and different protocols needs to be converted for making these devices IIoT-ready. Today a large number of serial Modbus-based devices still exist in industrial applications. MODBUS RTU is the most widely used industrial protocol for connecting industrial devices and almost all major industrial instrumentation and automation equipment vendors continue to support it in new products. Modbus protocol follows a master– slave model. MQTT is a messaging protocol suitable for the Internet of things (IoT). The MQTT protocol is based on the principles of publishing messages and subscribing to topics commonly known as the publish / subscribe model. In this paper, we have designed a Modbus to MQTT gateway for Industrial IoT cloud applications using inexpensive Raspberry Pi single-board computer and a RS485 add-on board with the necessary integrated circuits for data communication. It supports two-way communication between field devices and cloud applications, and supplies a Modbus RTU interface on RS485 to communicate with on-site field devices. It contains smart-agent at the gateway side to process and convert data sent from the field level of different data formats into the IoT-standard MQTT protocol and pass periodically the unified data to the public or private cloud service applications (Amazon AWS, Alibaba Cloud, or MQTT Broker etc.) for future analyses. Based on embedded web server, the configuration of the gateway is performed through web pages, and updates can be performed over the air when necessary. The proposal gateways provide an easy and secure way to build the IIoT applications now and in the future. Moreover, it can be added to a system and take advantage of the IIoT world while the existing operational technology stays in place.

Concept map is an useful tool to help people organize and improve knowledge. Particularly in educational domain, it is beneficial for students and teachers to improve the learning and teaching quality. Traditionally, manual educational concept maps, provided by teachers, are quite time-consuming and limited to teachers' experience. Thus, it is meaningful to automatically construct high-quality concept maps. However, existing data-driven solutions only focus on either separate data source or single pedagogic relationship, which are not sufficient to satisfy actual demands. To this end, we propose a novel framework, named Extracting Multiple Relationships Concept Map (EMRCM), to construct multiple relations concept maps from Multi-source Data. Specifically, we design various targeted evidences to explore diverse information of multi-source data from different perspectives. Then, we employ three classic classifiers to bulid the predictive model for extracting key concepts and multiple concept relationships using the proposed evidences. We create a real dataset for empirically studying this problem. Extensive experiments on a real-world dataset show the effectiveness of our method.

Background: In the recent years, the increasing health care needs and the stricter learning requirements for medical students have led to a mismatch between what is taught at medical school and the actual skills to provide health care service in China,which also have brought challenges to medical teachers and students.The Chinese medical educators, thus, are pursuing new teaching methods to overcome the mismatch and Problem-based learning(PBL) responds to the situation. The current model of medical education is urgently needed to change. Methods: In this paper, we count the number of published articles according to PBL from mainland China and summary the current adoption situations and the typical challenges of PBL in medical education in mainland China. Results:The number of published articles according to PBL from mainland China increases with time in this decade.There are still unresolved application issues from both tutors and students regarding the adjustment to this highly resource-intensive pedagogy Conclusions: The number of colleges adopting PBL has been gradually increasing. This new learning model places hopes on Chinese medical educators who would optimize the educational outcomes based on a clear understanding of the PBL process, principle and practice. We expect that PBL can resolve the medical educational challenges in mainland China.

In order to evaluate and improve the effectiveness of the public security face recognition systems reasonably and scientifically in real scenes, we create an effectiveness evaluation index system and the improved Unascertained C-means model. The data of indicators are obtained through measuring practical application systems of five public security bureaus. Then we carry out data statistics and pretreatment. The improved Unascertained C-means algorithm is realized by programming in MATLAB. After inputting the data into the model, we obtain the evaluation results. By comparing the real achievements of each system during the evaluation cycle, we verify the consistency between the evaluation results and realities. On the basis of the comparative analysis of the evaluation results, we identify the weaknesses of a system, and put forward some optimization strategies of the system effectiveness. In the paper, we synthetically apply various methods, such as qualitative analysis, quantitative analysis, theoretical analysis and experimental analysis, to study the uncertainty problem of the effectiveness evaluation of the public security face recognition systems. The evaluation process is objective and impartial, the results are valid and reasonable, and the strategies are targeted and operable. This paper is of certain theoretical significance to the research on effectiveness evaluation, and it is of certain practical significance to effectiveness improvement of the practical application systems.

The epidermal growth factor receptor (EGFR) is overexpressed in numerous solid tumors and is the subject of extensive therapeutic efforts. Much of the research on EGFR is focused on protein dynamics and downstream signaling, however few studies have explored its transcriptional regulation. Here, we identified two enhancers (CE1 and CE2) present within the first intron of the EGFR gene in models of glioblastoma (GBM) and head and neck squamous cell carcinoma (HNSCC). CE1 and CE2 contain open chromatin and H3K27Ac histone marks, enhance transcription in reporter assays, and interact with the EGFR promoter. Enhancer genetic deletion by CRISPR/Cas9 significantly reduces EGFR transcript levels, with double deletion exercising an additive effect. Targeted repression of CE1 and CE2 by dCas9-KRAB demonstrates repression of transcription similar to that of genomic deletion. We identify AP-1 transcription factor family members in concert with BET bromodomain proteins as modulators of CE1 and CE2 activity in HNSCC and GBM through de novo motif identification and validate their presence. Genetic inhibition of AP-1 or pharmacologic disruption of BET/AP-1 binding results in downregulated EGFR protein and transcript levels, confirming a role for these factors in CE1 and CE2. Our results identify and characterize these novel enhancers, shedding light on the role that epigenetic mechanisms play in regulating EGFR transcription in EGFR-dependent cancers. IMPLICATIONS: We identify critical constituent enhancers present in the first intron of the EGFR gene, and provide rationale for therapeutic targeting of EGFR intron 1 enhancers through perturbation of AP-1 and BET in EGFR-positive malignancies.

Purpose: Previously, we have found some renal cell carcinoma (RCC) patients with renal vein tumor thrombus (RVTT) were misdiagnosed preoperatively. To improve the accuracy of diagnosis of RVTT in RCC, the clinical characteristics of RCC with missed diagnosis of RVTT were analyzed. Methods: We retrospectively reviewed RCC patients with RVTT between January 2000 and December 2015. Survival analysis was estimated using Kaplan-Meier. Cox proportional hazard models were applied to identify risk factors. Results: Missed diagnosis rate of RVTT in RCC was 30.5%. In multivariate analysis, maximal tumor diameter (OR=1.218, p=0.034)，tumor located in the middle part (OR=1.354, p=0.003), renal vein contrast agents filling not well (OR=1.252, p=0.015) and tumor with collateral vessels (OR=1.218, p=0.037) were independent predictors of missed diagnosis of RVTT. A missed-diagnosis score presented an AUC of 0.852 (p

Many current cellular models aimed at elucidating cancer biology do not recapitulate pathobiology including tumor heterogeneity, an inherent feature of cancer that underlies treatment resistance. Here we introduce a new cancer modeling paradigm using genetically engineered human pluripotent stem cells (hiPSCs) that capture authentic cancer pathobiology. Orthotopic engraftment of neural progenitor cells derived from hiPSCs that have been genome-edited to contain tumor-associated genetic driver mutations revealed by The Cancer Genome Atlas project for glioblastoma (GBM) result in formation of high-grade gliomas. As observed in GBM patient samples, these models harbor inter-tumor heterogeneity resembling different GBM molecular subtypes, and intra-tumor heterogeneity. Further, re-engraftment of primary tumor neurospheres generates secondary tumors with features characteristic of patient samples and present mutation-dependent patterns of tumor evolution. Thus, these cancer avatar models provide a platform for a comprehensive longitudinal assessment of human tumor development as governed by molecular subtype mutations and lineage-restricted differentiation.

In cancer radiotherapy, inverse treatment planning is a multi-objective optimization problem. There exists a set of plans with various trade-offs on Pareto surface which are referred as Pareto optimal plans. Currently exploring such trade-offs, i.e., physician preference is a trial and error process and often time-consuming. Therefore, it is desirable to predict desired Pareto optimal plans in an efficient way before treatment planning. The predicted plans can be used as references for dosimetrists to rapidly achieve a clinically acceptable plan. Clinically the dose volume histogram (DVH) is a useful tool that can visually indicate the specific dose received by each certain volume percentage which is supposed to describe different trade-offs. Consequently, we have proposed a deep learning method based on patient’s anatomy and DVH information to predict the individualized 3D dose distribution. Qualitative measurements have showed analogous dose distributions and DVH curves compared to the true dose distribution. Quantitative measurements have demonstrated that our model can precisely predict the dose distribution with various trade-offs for different patients, with the largest mean and max dose differences between true dose and predicted dose for all critical structures no more than 1.7% of the prescription dose.

Aspirin, one of the most commonly used nonsteroidal anti-inflammatory drugs (NAIDS), not only shows cancer chemoprevention effects but also improves cancer therapeutic effects when combined with other therapies. Studies that focus on aspirin regulation of the hallmarks of cancer and the associated molecular mechanisms facilitate a more thorough understanding of aspirin in mediating chemoprevention and may supply additional information for the development of novel cancer therapeutic agents. The relevant literatures from PubMed have been reviewed in this article. Current studies have revealed that aspirin regulates almost all the hallmarks of cancer. Within tumor tissue, aspirin suppresses the bioactivities of cancer cells themselves and deteriorates the tumor microenvironment that supports cancer progression. In addition to tumor tissues, blocking of platelet activation also contributes to the ability of aspirin to inhibit cancer progression. In terms of the molecular mechanism, aspirin targets oncogenes and cancer-related signaling pathways and activates certain tumor suppressors. Beyond a chemopreventive agent, aspirin is a master regulator of the hallmarks of cancer.

Particle distribution estimation is an important issue in medical diagnosis. In particular, photon scattering in some medical devices extremely degrades image quality and causes measurement inaccuracy. The Monte Carlo (MC) algorithm is regarded as the most accurate particle estimation approach but is still time-consuming, even with graphic processing unit (GPU) acceleration. The goal of this work is to develop an automatic scatter estimation framework for high-efficiency photon distribution estimation. Specifically, a GPU-based MC simulation initially yields a raw scatter signal with a low photon number to hasten scatter generation. In the proposed method, assume that the scatter signal follows Poisson distribution, where an optimization objective function fused with sparse feature penalty is modeled. Then, an over-relaxation algorithm is deduced mathematically to solve this objective function. For optimizing the parameters in the over-relaxation algorithm, the deep Q -network in the deep reinforcement learning scheme is built to intelligently interact with the over-relaxation algorithm to accurately and rapidly estimate a scatter signal with the large range of photon numbers. Experimental results demonstrated that our proposed framework can achieve superior performance with structural similarity > 0.94 , peak signal-to-noise ratio > 26.55 dB , and relative absolute error < 5.62 % , and the lowest computation time for one scatter image generation can be within 2 s.

Background: The metastatic renal cell carcinoma (mRCC) patients treated with upfront cytoreductive nephrectomy combined with α-interferon yields additional overall survival (OS) benefits. It is unclear whether mRCC patients treated with vascular endothelial growth factor receptor-tyrosine kinase inhibitor (VEGFR-TKI) will benefit from such cytoreductive nephrectomy either. The aim of the study was to identify variables for selection of patients who would benefit from upfront cytoreductive nephrectomy for mRCC treated with VEGFR-TKI. Methods: Clinical data on 74 patients enrolled in 5 clinical trials conducted in Cancer Hospital (Institute), Chinese Academy of Medical Sciences from January 2006 to January 2014 were reviewed retrospectively. The survival analysis was performed by the Kaplan–Meier method. Comparisons between patient groups were performed by Chi-square test. A Cox regression model was adopted for analysis of multiple factors affecting survival, with a significance level of α = 0.05. Results: Fifty-one patients underwent cytoreductive nephrectomy followed by targeted therapy (cytoreductive nephrectomy group) and 23 patients were treated with targeted therapy alone (noncytoreductive nephrectomy group). The median OS was 32.2 months and 23.0 months in cytoreductive nephrectomy and noncytoreductive nephrectomy groups, respectively ( P = 0.041). Age ≤45 years ( P = 0.002), a low or high body mass index (BMI 30 kg/m 2 ) ( P = 0.008), a serum lactate dehydrogenase (LDH) concentration >1.5 × upper limit of normal ( P = 0.025), a serum calcium concentration >10 mg/ml ( P = 0.034), and 3 or more metastatic sites ( P = 0.023) were independent preoperative risk factors for survival. The patients only with 0–2 risk factors benefited from upfront cytoreductive nephrectomy in terms of OS when compared with the patients treated with targeted therapy alone (40.0 months vs. 23.2 months, P = 0.042), while those with more than 2 risk factors did not. Conclusions: Five risk factors (age, BMI, LDH, serum calcium, and number of metastatic sites) seemed to be helpful for selecting patients who would benefit from undergoing upfront cytoreductive nephrectomy.

NAC (NAM, ATAF, and CUC) is one of the largest transcription factor families in plants, and its members play various roles in plant growth, development, and the response to biotic and abiotic stresses. Currently, 77 NAC genes have been reported in cotton (Gossypium hirsutum L.). And GhNAC12 showed up-regulation during leaf senescence, but its role in this process is poorly understood. In the present study, a preliminary function analysis of GhNAC12 was performed during leaf senescence. qRT-PCR analysis indicated that GhNAC12 expression increased during the early-aging process and the aging of cotyledons. Additionally, we observed that overexpression of GhNAC12 in Arabidopsis led to early senescence (early aging). Our findings suggest that GhNAC12 is a candidate gene for early aging in upland cotton cultivars. Neutrality tests suggested that there was no selection pressure imposed on GhNAC12 during the domestication of upland cotton.

This paper proposes Halbach permanent magnet arrays with convex type section. The magnetic field is modeled according to Ampere’s hypothesis of molecular current. Based on the model, the fundamental component of the magnetic flux density and the sinusoidal distortion rate are selected as evaluation indexes. Compared with these of Halbach permanent magnet array with normal square section, the Halbach permanent magnet arrays with specific configuration own better performance, which provide direction for further optimization.

Majority of breast cancers express the estrogen receptor alpha (ERα) and the two major strategies for therapeutic targeting of ER-alpha positive breast cancer are aromatase inhibitors (which deprives estrogen) and inhibition of the estrogen receptor alpha. Although these strategies can be effective, many patients develop resistance which ultimately leads to disease progression which continues to rely on estrogen receptor signaling. One of the resistance mechanisms is the acquisition of activating mutations in the ER gene (ESR1) that allow tumors to proliferate without depending on estrogen. One approach to overcome resistance is to develop high affinity small molecules that degrade the estrogen receptor and effectively shut down ER signaling. Fulvestrant, an ER antagonist and a selective estrogen receptor degrader (SERD) is the only small molecule that is approved for the treatment of ER+/HER2− metastatic breast cancer. However, fulvestrant's limitation include low oral bioavailability and administration via intramuscular injection, which limits its exposure and leads to sub-optimal estrogen receptor degradation. The poor pharmacokinetic properties of fulvestrant has fueled the interest in developing an orally bioavailable small molecule estrogen receptor antagonist and degrader that could potentially benefit breast cancer patients. Here we describe the discovery of Zn-c5 a novel, small molecule with potent antagonism and degradative properties against the estrogen receptor both in vitro and in vivo. ZN-c5 showed a high oral bioavailability across several preclinical species as compared to other SERDs. To test if the high oral bioavailability can be translated to potent efficacy in vivo, we evaluated the anti-tumor activity of ZN-c5 in MCF-7 orthotopic tumor xenograft model. Oral ZN-c5 treatment at 5 mg/kg and 10 mg/kg resulted in 89% and 102% tumor growth inhibition respectively. Combination of ZN-c5 with cell cycle inhibitors such as CDK4/6 inhibitors or PI3K inhibitors results in enhanced antitumor activity. In addition to MCF-7 model, we evaluated the activity of ZN-c5 in ER mutant models including WHIM20, a Y537S ESR1 patient derived xenograft model. Treatment with ZN-c5 at 40 mg/kg induced 64% tumor growth inhibition while fulvestrant at 200 mg/kg (exposures 8-fold higher than that achieved in the clinic) resulted in 13% tumor growth inhibition. These data indicate that ZN-c5 has improved antitumor activity over fulvestrant in human tumor xenograft models. Zn-c5 is currently in clinical trials as a single agent and in combination studies. The PK profile of ZN-c5 in breast cancer patients indicates that Zn-c5 has greater than 5-fold exposure than fulvestrant. We believe that the high exposure of ZN-c5 coupled with its potency and degradative properties could therapeutic benefit estrogen receptor positive breast cancer patients. Citation Format: Ahmed A. Samatar, Jiali Li, Sayee Hegde, Peter Huang, Jianhui Ma, Kevin Bunker, Robert Winkler, Fernando Donate, Masha Sergeeva. Discovery of ZN-c5, a novel potent and oral selective estrogen receptor degrader [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4373.

INTRODUCTION AND OBJECTIVE:Previous researches indicated Neoadjuvant Chemotherapy (NAC) achieved optimistic tumor degrading and bladder preservation rates in muscle-invasive bladder cancer (MIBC). ...

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We propose and demonstrate a high efficiency broadband near infrared single-photon upconversion and detection with a broadband pump laser based on sum frequency conversion in the PPLN crystal. By using a pump laser centered at 1040 nm with a spectral bandwidth of 10 nm, the signal single-photons centered at 1562 nm with a broadband bandwidth up to 7.2 nm are frequency-converted from the near infrared to the visible regime. A maximum conversion efficiency of 18.8% is achieved, while the background noise is measured to be only 1.2 × 10−3 counts/pulse. The corresponding spectral linewidth of the upconverted photons is 0.2 nm. This scheme of broadband infrared single-photon upconversion and detection provides potential solutions in infrared laser ranging, broadband infrared imaging and quantum key distribution.

The implicit feedback based collaborative filtering (CF) has attracted much attention in recent years, mainly because users implicitly express their preferences in many real-world scenarios. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proved to be helpful to exploit binary relevance data, but lead to either not address the ranking problem, or not specifically focus on top-k recommendation. Although there exists the listwise method maximizes the Mean Reciprocal Rank (MRR), it has low efficiency and is not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely Collaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we smooth another well-known rank-biased measure called Mean Average Precision (MAP), and respectively combine two rank-biased metrics (MAP, MRR) with the pairwise objective function to capture the performance of top-k recommendation. Furthermore, the sampling scheme for CLAPF is discussed to accelerate the convergence speed. Our CLAPF framework is a new hybrid model that provides an idea of utilizing rank-biased measures in a pairwise way on implicit feedback. Empirical studies demonstrated CLAPF outperforms state-of-the-art approaches on real-world datasets.

One of the most common genetic alterations in glioblastoma (30–40%) occurs in the PTEN (phosphatase and tensin homolog) tumor suppressor gene, where loss of function has been mechanistically linked to increased tumor cell invasion, and to a lack of radio- and chemo-therapy response. To identify new drug compounds that target PTEN-deficient brain tumors we performed a high throughput drug screen using patient-derived GBM spheres and found that PTEN-deficient samples were highly sensitive to proteasome inhibition. We confirmed this sensitivity in GBM spheres and iNPCs (inducible Neuronal Progenitor Cells) by genetically over-expressing or deleting PTEN, where PTEN over-expression decreased and PTEN-deletion increased sensitivity to the drug, respectively. Additionally, proteasome inhibition specifically suppressed tumor growth in mice of orthotopically engrafted human glioblastoma samples. Mechanistically, we determined that PTEN-deficient cells are more sensitive to proteasome inhibition due to an increase in protein synthesis rate and loss of autophagy activity associated with activation of the PI3K/mTOR pathway. This study reveals that proteasome inhibition is a targeted-therapeutic strategy for PTEN-deficient brain cancer.

One of the most common genetic alterations in glioblastoma (30–40%) occurs in the PTEN (phosphatase and tensin homolog) tumor suppressor gene, where loss of function has been mechanistically linked to increased tumor cell invasion, and to a lack of radio- and chemo-therapy response. To identify new drug compounds that target PTEN-deficient brain tumors we performed a high throughput drug screen using patient-derived GBM spheres and found that PTEN-deficient samples were highly sensitive to proteasome inhibition. We confirmed this sensitivity in GBM spheres and iNPCs (inducible Neuronal Progenitor Cells) by genetically over-expressing or deleting PTEN, where PTEN over-expression decreased and PTEN-deletion increased sensitivity to the drug, respectively. Additionally, proteasome inhibition specifically suppressed tumor growth in mice of orthotopically engrafted human glioblastoma samples. Mechanistically, we determined that PTEN-deficient cells are more sensitive to proteasome inhibition due to an increase in protein synthesis rate and loss of autophagy activity associated with activation of the PI3K/mTOR pathway. This study reveals that proteasome inhibition is a targeted-therapeutic strategy for PTEN-deficient brain cancer.

Objective Breast cancer is the most common malignant tumor among women. The diagnosis and treatment information of breast cancer patients is abundant in multiple types of clinical fields, including clinicopathological data, genotype and phenotype information, treatment information, and prognosis information. However, current studies are mainly focused on extracting information from one specific type of clinical field. This study defines a comprehensive information model to represent the whole-course clinical information of patients. Furthermore, deep learning approaches are used to extract the concepts and their attributes from clinical breast cancer documents by fine-tuning pretrained Bidirectional Encoder Representations from Transformers (BERT) language models. Materials and methods The clinical corpus that was used in this study was from one 3A cancer hospital in China, consisting of the encounter notes, operation records, pathology notes, radiology notes, progress notes and discharge summaries of 100 breast cancer patients. Our system consists of two components: a named entity recognition (NER) component and a relation recognition component. For each component, we implemented deep learning-based approaches by fine-tuning BERT, which outperformed other state-of-the-art methods on multiple natural language processing (NLP) tasks. A clinical language model is first pretrained using BERT on a large-scale unlabeled corpus of Chinese clinical text. For NER, the context embeddings that were pretrained using BERT were used as the input features of the Bi-LSTM-CRF (Bidirectional long-short-memory-conditional random fields) model and were fine-tuned using the annotated breast cancer notes. Furthermore, we proposed an approach to fine-tune BERT for relation extraction. It was considered to be a classification problem in which the two entities that were mentioned in the input sentence were replaced with their semantic types. Results Our best-performing system achieved F1 scores of 93.53% for the NER and 96.73% for the relation extraction. Additional evaluations showed that the deep learning-based approaches that fine-tuned BERT did outperform the traditional Bi-LSTM-CRF and CRF machine learning algorithms in NER and the attention-Bi-LSTM and SVM (support vector machines) algorithms in relation recognition. Conclusion In this study, we developed a deep learning approach that fine-tuned BERT to extract the breast cancer concepts and their attributes. It demonstrated its superior performance compared to traditional machine learning algorithms, thus supporting its uses in broader NER and relation extraction tasks in the medical domain.

Glioblastoma (GBM) is the most lethal type of human brain cancer, where deletions and mutations in the tumour suppressor gene PTEN (phosphatase and tensin homolog) are frequent events and are associated with therapeutic resistance. Herein, we report a novel chromatin-associated function of PTEN in complex with the histone chaperone DAXX and the histone variant H3.3. We show that PTEN interacts with DAXX and, in turn PTEN directly regulates oncogene expression by modulating DAXX-H3.3 association on the chromatin, independently of PTEN enzymatic activity. Furthermore, DAXX inhibition specifically suppresses tumour growth and improves the survival of orthotopically engrafted mice implanted with human PTEN-deficient glioma samples, associated with global H3.3 genomic distribution changes leading to upregulation of tumour suppressor genes and downregulation of oncogenes. Moreover, DAXX expression anti-correlates with PTEN expression in GBM patient samples. Since loss of chromosome 10 and PTEN are common events in cancer, this synthetic growth defect mediated by DAXX suppression represents a therapeutic opportunity to inhibit tumorigenesis specifically in the context of PTEN deletion., PTEN mutations are frequent in glioblastoma and often are associated with therapeutic resistance. Here, the authors demonstrate that PTEN regulates gene expression at the chromatin level by interacting with the histone chaperone DAXX and H3.3, and that DAXX inhibition inhibits PTEN-deficient GBM growth in vivo.

Bread wheat is one of the most important crops in the world. However, osmotic stress significantly inhibits wheat growth and development, and reduces crop yield and quality. Plants respond to osmotic stress mainly through abscisic acid (ABA)-dependent and -independent pathways. In this study, root transcriptome profiles of wheat seedlings exposed to osmotic stress and exogenous ABA were analysed to identify osmotic-responsive genes belonging to the ABA-dependent or -independent pathways. We found that osmotic stress promoted proline biosynthesis in the ABA-dependent pathway, and trehalose biosynthesis is likely promoted among soluble sugars to maintain protein bioactivity under osmotic stress. In wheat roots subjected to osmotic stress, calcium ions, and glutathione exert their functions mainly through calcium-binding protein (CaM/CML) and glutathione-S-transferase, respectively, depending on both pathways. In addition, a complex relationship among phytohormones signal transduction was observed in response to osmotic stress. The findings of this study deepen our understanding of the molecular mechanisms of osmotic-stress resistance, and provide several candidate osmotic-responsive genes for further study.

The mammography is the first choice of breast cancer screening, which has proven to be the most effective screening method. An antiscatter grid is usually employed to enhance the contrast of image by absorbing unexpected scattered signals. However, the grid pattern casts shadows and grid artifacts, which severely degrade the image quality. To solve the problem, we propose the patch based frequency signal filtering for fast grid artifacts suppressing. As opposed to whole image processing synchronously, the proposed method divides image into a number of blocks for tuning filter simultaneously, which reduces the frequency interference among image blocks and saves computation time by multithread processing. Moreover, for mitigating grid artifacts more precisely, characteristic peak detection is employed in each block automatically, which can accurately identify the location of the antiscatter grid and its motion pattern. Qualitative and quantitative studies were performed on simulation and real machine data to validate the proposed method. The results show great potential for fast suppressing grid artifacts and generating high quality of digital mammography.

We demonstrate broad bandwidth single-photon upconversion of femtosecond pulses at telecom wavelength in periodically poled lithium niobate (PPLN) bulk with high conversion efficiency by pumping with a broad bandwidth laser.

Male sterility is a common phenomenon in flowering plants, and it has been successfully developed in several crops by taking advantage of heterosis. Cotton (Gossypium hirsutum L.) is an important economic crop, used mainly for the production of textile fiber. Using a space mutation breeding technique, a novel photosensitive genetic male sterile mutant CCRI9106 was isolated from the wild-type upland cotton cultivar CCRI040029. To use CCRI9106 in cotton hybrid breeding, it is of great importance to study the molecular mechanisms of its male sterility. Here, histological and iTRAQ-facilitated proteomic analyses of anthers were performed to explore male sterility mechanisms of the mutant. Scanning and transmission electron microscopy of the anthers showed that the development of pollen wall in CCRI9106 was severely defective with a lack of exine formation. At the protein level, 6121 high-confidence proteins were identified and 325 of them showed differential expression patterns between mutant and wild-type anthers. The proteins up- or down-regulated in MT anthers were mainly involved in exine formation, protein degradation, calcium ion binding,etc. These findings provide valuable information on the proteins involved in anther and pollen development, and contribute to elucidate the mechanism of male sterility in upland cotton.

As a superfamily of transcription factors, the tryptophan-arginine-lysine-tyrosine (WRKY) transcription factors have been found to be essential for abiotic and biotic stress responses in plants. Currently, only 76 WRKY transcription factors in wheat could be identified in the NCBI database, among which only a few have been functionally analyzed. Herein, a total of 188 WRKY transcription factors were identified from the wheat genome database, which included 123 full-length coding sequences, and all of them were used for detailed evolution studies. By bioinformatics analysis, a WRKY transcription factor, named TaWRKY146, was found to be the homologous gene of AtWRKY46, overexpression of which leads to hypersensitivity to drought and salt stress in Arabidopsis. Consequently, the full length of TaWRKY146 was cloned, and the expression levels of TaWRKY146 were found significantly up-regulated in the leaves and roots of wheat seedlings, which were subjected to osmotic stress. Overexpression of TaWRKY146 in Arabidopsis was shown to enhance drought tolerance by the induction of stomatal closure that reduced the transpiration rate. All these results provide a firm foundation for further identification of WRKY transcription factors with important functions in wheat.

The design scheme of a wireless code update system based on the tire pressure transmitter is presented by the introduction of tire pressure monitoring transmitter working environment and structure. The scheme consists of microprocessor, low frequency LF transmitter and high frequency RF receiver. The binary data of low frequency LF transmitter is responsible for sending flash, high frequency RF receiver is responsible for recording monitoring results of the transmitter. The key technologies in the hardware and software design of the system are discussed in detail.