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Reconfigurable Intelligent surface (RIS) can enhance the desired signals and suppress the undesired signals by adjusting the phase shifts of its reflector elements. This letter considers secure multicast communication with the help of RIS. A multiple-antennas transmitter multicasts the common messages to a group of single-antenna receivers in the presence of a single-antenna eavesdropper, where an RIS is set up to enhance the desired signals for the receivers and suppress the signals for the eavesdropper. First, the equivalent multicast and wiretap channels are analyzed, and a secrecy capacity maximization problem is formulated. Next, this letter obtains the necessary conditions that the optimal solution needs to satisfy. Finally, a logarithmic barrier algorithm is proposed to obtain a local optimal solution.

Recent advances in face recognition mostly concentrate on designing more discriminative loss functions or adding normalization on features/weights to make a single feature more accurate. In this work, inspired by the frequently used multi-patch ensemble method for face recognition and part-based models for person re-identification, we propose a novel training strategy to enhance the discriminability of deeply learned feature from another perspective, namely learning with dense supervision. The main idea is to apply multiple classification losses on top of multiple component features extracted from a single network. Ideally, each component feature is expected to be accurate and have low correlation with the others. To this end, we first design a metric called feature consistency to evaluate the correlation between one component feature and the others, which is defined as the sum of distances between one component feature and the others, where the distance here is measured with KL divergence between corresponding softmax probabilities. Then we use feature consistency to select which component features to sample for one learning pass by importance sampling. The dense supervision significantly outperforms the single supervision baseline and even performs on par with its multi-patch ensemble counterpart which has much more parameters (9×). Our experimental results match state-of-the-art performance on LFW, YTF, MegaFace and surpass the others on LFW BLUFR and VGGFace2 pose protocol, thereby achieving state-of-the-art. Specially, results on VGGFace2 also show the superiority of the dense supervision on cross-pose face matching.

The co‐evolving tumour cells and the systemic immune environment are mutually dysregulated. Tumours affect the immune response in a complex manner. For example, although lymphocytes are mobilized in response to tumours, their function is impaired by tumour progression. This study aimed to explore how the baseline and dynamic renal cell carcinoma (RCC) tumour burdens affect the T‐cell repertoire, and whether the baseline T‐cell receptor β‐chain (TCRB) diversity predicts prognosis. To characterise the TCRB repertoire, the baseline and follow‐up peripheral TCRB repertoires of 45 patients with RCC and 2 patients with benign renal disease patients were examined using high‐throughput TCRB sequencing. To explain the significance of TCRB diversity, 56 peripheral leukocyte samples from 28 patients before and after surgery were subjected to transcriptome sequencing. To validate the results, an advanced RCC patient's sample was subjected to single‐cell RNA sequencing (scRNA, 10x Genomics). Higher TCRB diversity was found to be correlated with a higher lymphocyte‐to‐neutrophil ratio, especially indicating more naïve T cells. High‐baseline TCRB diversity predicted a better prognosis for stage IV patients, and different tumour burdens exerted distinct effects on the immune status. The pre‐operative TCRB diversity was significantly higher in benign and stage I (low tumour burden) RCC patients than in stage IV (high tumour burden) patients. After the tumour burden of advanced patients was mostly relieved, we observed that the TCRB diversity was restored, T‐cell exhaustion was reduced, and naïve T‐cells were mobilized. It was demonstrated that the circulating TCRB repertoire could reflect the immune status and predict prognosis, and to some extent that cytoreductive nephrectomy (CN) reduces the burden of the immune system in advanced patients, which might provide a good opportunity for immunotherapy. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

The objective of this study was to assess the impact of different tillage and organic fertiliser regimes on soil carbon fractions and bacterial community composition within a maize–wheat cropping s...

Hypothesis: Nomogram can be built to predict the pathological T3a upstaging from clinical T1a in patients with localized renal cell carcinoma before surgery. Purpose: Renal cell carcinoma (RCC) patients with clinical T1a (cT1a) disease who are upstaged to pathological T3a (pT3a) have reduced survivals after partial nephrectomy. We aimed to develop a nomogram-based model predicting pT3a upstaging in RCC patients with preoperative cT1a based on multiple preoperative blood indexes and oncological characteristics. Materials and Methods: Between 2010 and 2019, 510 patients with cT1a RCC were individually matched according to pT3a upstaging and pathological T1a (pT1a) at a 1:4 ratio using clinicopathologic features. Least absolute shrinkage and selection operator regression analysis was used to identify the most important risk factor from 40 peripheral blood indicators, and a predictive model was established. Multivariate logistic regression analysis was performed with the screened blood parameters and clinical data to identify significant variables. Harrell’s concordance index (C-index) was applied to evaluate the accuracy of the model for predicting pT3a upstaging in patients with cT1a RCC. Results: Out of 40 blood indexes, the top ranked predictor was fibrinogen (FIB). Age, the ratio of the tumor maximum and minimum diameter (ROD), FIB, and tumor size were all independent risk factors for pT3a upstaging in multivariate analysis. A predictive ARFS model (Age, ROD, FIB, tumor Size) was established, and the C-index was 0.756 (95% CI, 0.681-0.831) and 0.712 (95% CI, 0.638-0.785) in the training and validation cohorts, respectively. Conclusions: Older age, higher ROD, increased FIB level, and larger tumor size were independent risk factors for upstaging. The ARFS model has a high prediction efficiency for pT3a upstaging in patients with cT1a RCC.

Background Several models and markers were developed and found to predict outcome of advanced renal cell carcinoma. This study aimed to evaluate the prognostic value of the ratio of maximum to minimum tumor diameter (ROD) in metastatic clear cell renal cell carcinoma (mccRCC). Methods Patients with mccRCC (n = 213) treated with sunitinib from January 2008 to December 2018 were identified. Cutoff value for ROD was determined using receiver operating characteristic. Patients with different ROD scores were grouped and evaluated. Survival outcomes were estimated by Kaplan–Meier method. Results The optimal ROD cutoff value of 1.34 was determined for progression free survival (PFS) and overall survival (OS). Patients in ROD ≥ 1.34 group had shorter PFS (9.6 versus 17.7 months, p Conclusion Primary tumor with higher ROD was an independently prognostic factor for both PFS and OS in patients with mccRCC who received targeted therapy. Higher ROD was also associated with high pT stage, high WHO/ISUP grade, sarcomatoid features, tumor necrosis, poor MSKCC and IMDC risk score.

The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis rate (0.0248), was significantly higher than the pathologists’ misdiagnosis rate (p p > 0.01). However, the other low-accuracy group included most of the misdiagnoses of DNN models. Our research shows that the misdiagnosis from deep learning model can be further enriched by our method, and that the low-accuracy WSIs must be selected for pathologists to review and the high-accuracy ones may be ready for pathologists to give diagnostic reports.

A novel Halbach permanent magnet array with rectangle section and trapezoid section is proposed and optimized in this paper. The analytical model of the premanent magnet segment is established based on the surface current method, which is numerically efficient and can be utilized to evaluate the magnetic field closely with the premanent magnet segment’s configurations. The analytical model of the Halbach array is acquired based on the superposition principle and coordinate transformation. The fundamental component of the magnetic flux density and the sinusoidal distortion rate are chosen as the optimization object. And the optimization is executed on the Halbach array with one specific set of dimensions by the genetic algorithm in global scale. The effectiveness of the optimization is validated by the finite element analysis. Compared to the traditional Halbach array with rectangle section, the magnetic field created by the optimized proposed Halbach array in this paper owns better performance.

Background We aimed to build a common terminology in the domain of cervical cancer, named Cervical Cancer Common Terminology (CCCT), that will facilitate clinical data exchange, ensure quality of data and support large scale data analysis. Methods The standard concepts and relations of CCCT were collected from ICD-10-CM Chinese Version, ICD-9-PC Chinese Version, officially issued commonly used Chinese clinical terms, Chinese guidelines for diagnosis and treatment of cervical cancer and Chinese medical book Lin Qiaozhi Gynecologic Oncology. 2062 cervical cancer electronic medical records (EMRs) from 16 hospitals, belong to different regions and hospital tiers, were collected for terminology enrichment and building common terms and relations. Concepts hierarchies, terms and relationships were built using Protégé. The performance of natural language processing results was evaluated by average precision, recall, and F1-score. The usability of CCCT were evaluated by terminology coverage. Results A total of 880 standard concepts, 1182 common terms, 16 relations and 6 attributes were defined in CCCT, which organized in 6 levels and 11 classes. Initial evaluation of the natural language processing results demonstrated average precision, recall, and F1-score percentages of 96%, 72.6%, and 88.5%. The average terminology coverage for three classes of terms, clinical manifestation, treatment, and pathology, were 87.22%, 92.63%, and 89.85%, respectively. Flexible Chinese expressions exist between regions, traditions, cultures, and language habits within the country, linguistic variations in different settings and diverse translation of introduced western language terms are the main reasons of uncovered terms. Conclusions Our study demonstrated the initial results of CCCT construction. This study is an ongoing work, with the update of medical knowledge, more standard clinical concepts will be added in, and with more EMRs to be collected and analyzed, the term coverage will be continuing improved. In the future, CCCT will effectively support clinical data analysis in large scale.

Approximate nearest neighbor search (ANNs) plays an important role in many applications ranging from information retrieval, recommender systems to machine translation. Several ANN indexes, such as hashing and quantization, have been designed to update for the evolving database, but there exists a remarkable performance gap between them and retrained indexes on the entire database. To close the gap, we propose an online additive quantization algorithm (online AQ) to dynamically update quantization codebooks with the incoming streaming data. Then we derive the regret bound to theoretically guarantee the performance of the online AQ algorithm. Moreover, to improve the learning efficiency, we develop a randomized block beam search algorithm for assigning each data to the codewords of the codebook. Finally, we extensively evaluate the proposed online AQ algorithm on four real-world datasets, showing that it remarkably outperforms the state-of-the-art baselines.

Learning informative representations for educational questions is a fundamental problem in online learning systems, which can promote many applications, e.g., difficulty estimation. Most solutions integrate all information of one question together following a supervised manner, where the representation results are unsatisfactory sometimes due to the following issues. First, they cannot ensure the presentation ability due to the scarcity of labeled data. Then, the label-dependent representation results have poor feasibility to be transferred. Moreover, aggregating all information into the unified may introduce some noises in applications since it cannot distinguish the diverse characteristics of questions. In this paper, we aim to learn the disentangled representations of questions. We propose a novel unsupervised model, namely DisenQNet, to divide one question into two parts, i.e., a concept representation that captures its explicit concept meaning and an individual representation that preserves its personal characteristics. We achieve this goal via mutual information estimation by proposing three self-supervised estimators in a large unlabeled question corpus. Then, we propose another enhanced model, DisenQNet+, that transfers the representation knowledge from unlabeled questions to labeled questions in specific applications by maximizing the mutual information between both. Extensive experiments on real-world datasets demonstrate that DisenQNet can generate effective and meaningful disentangled representations for questions, and furthermore, DisenQNet+ can improve the performance of different applications.

The contexts and cultures have a direct impact on student learning by affecting student's implicit cognitive states, such as the preference and the proficiency on specific knowledge. Motivated by the success of context-aware modeling in various fields, such as recommender systems, in this paper, we propose to study how to model context-aware features and adapt them for more precisely diagnosing student's knowledge proficiency. Specifically, by analyzing the characteristics of educational contexts, we design a two-stage framework ECD (Educational context-aware Cognitive Diagnosis), where a hierarchical attentive network is first proposed to represent the context impact on students and then an adaptive optimization is used to achieve diagnosis enhancement by aggregating the cognitive states reflected from both educational contexts and students' historical learning records. Moreover, we give three implementations of general ECD framework following the typical cognitive diagnosis solutions. Finally, we conduct extensive experiments on nearly 52 million records of the students sampled by PISA (Programme for International Student Assessment) from 73 countries and regions. The experimental results not only prove that ECD is more effective in student performance prediction since it can well capture the impact from educational contexts to students' cognitive states, but also give some interesting discoveries regarding the difference among different educational contexts in different countries and regions.

Recent years have witnessed the booming of venture capital industry, which may not only bring huge benefits to investors, startups and emerging industries, but also lead to potential risk due to the difficulty to find proper investment targets, especially for inexperienced investors. Therefore, the investment target recommendation task has long been treated as a valuable issue to help the investors for the next investments based on their historical behavior and the market trend. However, due to the sequential features of historical invest records, which could be severely disturbed by the time-varying factors like market trend or short-term invest preference, it will be extremely difficult to accurately reveal the potential rule of investment. Moreover, the latent relationships between investors and targets (i.e., startups) or brands, which formed a heterogeneous graph to reflect the mutual influence within investors, have been largely ignored. To that end, in this paper, we propose a novel paradigm for investment target recommendation with integrating the sequential records and heterogeneous relation graph. Specifically, we aggregate information at the micro level through a time segmentation method, and then extract trends at the macro level. Meanwhile, we use relational graph convolutional networks to propagate the latent preferences through the various relationships. Based on the validations on the realworld dataset, by fusing above information, the Investment Target Recommendation System (ITRS) we proposed has achieved impressive results compared with competitive baseline methods, which indicates the significance of both sequential and structural information.

Purpose Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient-specific anatomy but also on physicians' preferred trade-offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Therefore, it is desirable to allow physicians to fine-tune the dose distribution predicted based on patient anatomy. In this work, we developed a DL model to predict the individualized 3D dose distributions by using not only the patient's anatomy but also the desired PTV/OAR trade-offs, as represented by a dose volume histogram (DVH), as inputs. Methods In this work, we developed a modified U-Net network to predict the 3D dose distribution by using patient PTV/OAR masks and the desired DVH as inputs. The desired DVH, fine-tuned by physicians from the initially predicted DVH, is first projected onto the Pareto surface, then converted into a vector, and then concatenated with feature maps encoded from the PTV/OAR masks. The network output for training is the dose distribution corresponding to the Pareto optimal DVH. The training/validation datasets contain 77 prostate cancer patients, and the testing dataset has 20 patients. Results The trained model can predict a 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the input desired DVH. We calculated the difference between the predicted dose distribution and the optimized dose distribution that has a DVH closest to the desired one for the PTV and for all OARs as a quantitative evaluation. The largest absolute error in mean dose was about 3.6% of the prescription dose, and the largest absolute error in the maximum dose was about 2.0% of the prescription dose. Conclusions In this feasibility study, we have developed a 3D U-Net model with the patient's anatomy and the desired DVH curves as inputs to predict an individualized 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the desired one. The predicted dose distributions can be used as references for dosimetrists and physicians to rapidly develop a clinically acceptable treatment plan.

Cognitive diagnosis (CD) is a fundamental issue in intelligent educational settings, which aims to discover the mastery levels of students on different knowledge concepts. In general, most previous works consider it as an inter-layer interaction modeling problem, e.g., student-exercise interactions in IRT or student-concept interactions in DINA, while the inner-layer structural relations, such as educational interdependencies among concepts, are still underexplored. Furthermore, there is a lack of comprehensive modeling for the student-exercise-concept hierarchical relations in CD systems. To this end, in this paper, we present a novel Relation map driven Cognitive Diagnosis (RCD) framework, uniformly modeling the interactive and structural relations via a multi-layer student-exercise-concept relation map. Specifically, we first represent students, exercises and concepts as individual nodes in a hierarchical layout, and construct three well-defined local relation maps to incorporate inter- and inner-layer relations, including a student-exercise interaction map, a concept-exercise correlation map and a concept dependency map. Then, we leverage a multi-level attention network to integrate node-level relation aggregation inside each local map and balance map-level aggregation across different maps. Finally, we design an extendable diagnosis function to predict students' performance and jointly train the networks. Extensive experimental results on real-world datasets clearly show the effectiveness and extendibility of our RCD in both diagnosis accuracy improvement and relation-aware representation learning.

BackgroundTesticular sex cord stromal tumours (TSCSTs) are rare, with few studies focusing on the metastatic TSCST prognosis. The value of treatments, including radical orchiectomy (RO) and retroperitoneal lymph node dissection (RPLND), in preventing metastasis is controversial. Additionally, metastatic TSCSTs are resistant to chemotherapy. We aimed to assess the effectiveness and safety of immunotherapy in metastatic TSCSTs after first-line chemotherapy.MethodsWe retrospectively screened patients with testicular tumours undergoing testis surgery between January 2005 and January 2019. Patients with TSCSTs who had undergone testis-sparing surgery (TSS) or RO were identified. The malignant type was defined as metastasis confirmed by pathology. Treatment responses, progression-free survival (PFS), overall survival (OS) and safety were analysed.ResultsAmong the 494 testicular tumour patients who received TSS or RO, 11 (2.2%) patients with histologically proven TSCSTs were identified. At the last follow-up, 7 patients survived without tumours, and 4 patients developed metastasis and received first-line cisplatin-based chemotherapy, with 1 of them achieving an objective response. Their PFS times were 1.5, 2.2, 9.0, and 17.0 months, respectively. Two patients received immune checkpoint inhibitors (ICIs) after developing chemotherapy resistance and achieved a partial response up to the last follow-up; one of them experienced Grade 1 adverse events, and the other experienced Grade 2 adverse events during immunotherapy. The median OS time of the 4 patients with metastatic TSCSTs was 32 months.ConclusionsTSCSTs are rare, and most are benign with a good prognosis. ICIs represent a promising option for improving clinical outcomes in metastatic TSCSTs.

Cooperative jamming (CJ) is considered as one of the crucial means in physical-layer security (PLS) for defending against eavesdropping. With the aid of the CJ signal from a friendly jammer, the eavesdropper’s channel quality will be intentionally degraded. Unfortunately, the CJ signal also propagates into the legitimate receiver (RX) as the jamming-interference (JI), which should be estimated, rebuilt, and cancelled accordingly. In this paper, we first model the JI after considering practical imperfections existed in the transmit-receive link that incorporates the power amplifier nonlinearity, the carrier frequency offset, and imperfect time-frequency alignments. Then, we formulate an advanced time-frequency misalignments tolerant memory polynomial (TFMT-MP) model to rebuild and cancel the received JI in the legitimate RX. Numerical simulations show that the proposed solution provides the excellent JI cancellation performance and exhibits the strong tolerance of imperfect time-frequency alignments, over the existing MP model solution.

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 a rationale for therapeutic targeting of EGFR intron 1 enhancers through perturbation of AP-1 and BET in EGFR-positive malignancies.

In this letter, the energy efficiency (EE) for a three-node full-duplex (FD) relay channel with the amplify-and-forward (AF) scheme is studied. In particular, both the residual self-interference (RSI) and the circuit power consumption for imperfect self-interference cancellation (SIC) are considered and modeled as linear functions over the relay transmission power. Under this setup, the EE maximization problem for the AF FD relay channel is formulated and efficiently solved. Moreover, the EE performance of the direct transmission (DT), half-duplex (HD), and FD modes are compared under different channel conditions.

Recently, N2O, CO2, and CH4 have been gaining attention as major greenhouse gases (GHGs) that contribute to global warming. Agricultural water-saving irrigation technology has become more and more important and is widely applied to the arid areas. These influences have important and strategic significances for reducing greenhouse effects. However, there is limit information regarding the influence of irrigation methods on soil gases emissions. We investigated the effect of mulched drip irrigation (MDI), drip filtration irrigation (DFI), and conventional irrigation (CK) on N2O, CO2, CH4 emissions, affecting factors and their correlations in a greenhouse soil. Observations were performed from April to August in 2015 on the entire tomato growth season. We monitored the flux of GHG emissions by the static chamber-gas chromatograph method under three irrigation conditions: mulched drip irrigation (MDI), drip filtration irrigation (DFI), and conventional irrigation (CK). To investigate the effects of affecting factors on soil GHG emissions, we also measured soil temperature (5 cm), soil moisture, and air temperature. Compared with the cumulative emission of GHGs (N2O and CH4 calculation in the form of CO2), we created the concept of the ratio of yield and gas (crop yield and soil accumulated GHG emissions). Tomato yield of MDI and DFI is significantly higher than CK. Soil moisture is the main factor affecting N2O emissions and CH4 exchange during the tomato-growing season. N2O emission fluxes have an extremely significant correlation with water-filled pore space (WFPS) at suitable temperatures. CO2 emissions demonstrated a significant correlation with environment temperature (soil and air). CO2 emissions gradually increased and had seasonal variations with rising temperatures and plant growth in each irrigation method. CH4 exchange fluxes showed a significant correlation with WFPS. MDI obtains high yields, is cost-efficient, conserves water, and produces the least amount of integrated GHGs. It helps to mitigate global warming and it would be the best irrigation method.

Mammography is the first option for screening breast cancer. However, some lesions may be missed due to superimposition of breast parenchymal patterns/tissues. Current enhancement methods can highlight some specific tissues, but the edges are weakened or extra noise is added. For improving the solution, this paper proposed a multiscale contrast enhancement for mammogram using dynamic unsharp masking (UM) in Laplacian pyramid. Laplacian pyramid is utilized to preserve the fine structure at each scale. Dynamic UM is presented to adaptively enhance details and suppress noise simultaneously. The proposed method mainly consists of five steps: 1) down-sample images; 2) calculate the weight of dynamic UM; 3) utilize dynamic UM enhancement for each scale image; 4) subtract the enhancement results for each scale image to acquire Laplacian pyramid images; and 5) restore the final enhanced image. To evaluate the proposed method qualitatively and quantitatively, one phantom and three clinical mammography cases were evaluated. Results showed the proposed method can provide much clearer details of the mammary gland. Quantitatively, information entropy and peak signal-to-noise ratio are increased by 0.96 and 3.89 at most compared with the state-of-the-art method. The proposed method has demonstrated that different types of regions are enhanced with the help of a regional adaptive evolution, which has great potential for adaptively enhancing the details and relatively suppressing the noise.

Purpose:To investigate the clinical significance of serum procalcitonin (PCT) concentrations and related indicators of infection in the early diagnosis and prognosis of severe surgical patients with infection. Methods: This study included 77 critically ill patients taken from the Surgery Department to the Intensive Care unit between June 2015 and July 2017. Patients were divided into control, sepsis and septic shock groups, and their serum concentrations of PCT and related indicators of infection were compared. Results: PCT levels increased significantly from the control to the sepsis group and from the sepsis to the septic shock group (P0.05). Receiver operating curve (ROC) analysis showed that the areas under the curve (AUC) for PCT, WBC count, neutrophil percentage and body temperature were 0.949, 0.657, 0.640 and 0.656, respectively. PCT, with 0.52 µg/L as the cut-off concentration, had the highest performance in the diagnosis of severe surgical sepsis, with a sensitivity of 96.1%, a specificity of 92.3% and a Youden index of 0.884. Conclusion: PCT concentration is diagnostic of infection in severe surgical patients, has high specificity in the early diagnosis of sepsis, and can reflect the severity of infection.

Background Anlotinib is a tyrosine kinase inhibitor inhibiting angiogenesis. This multicenter, randomized phase II trial aimed to investigate the efficacy and safety of anlotinib in comparison with sunitinib as first-line treatment for patients with metastatic renal cell carcinoma (mRCC). Materials and Methods Patients with mRCC from 13 clinical centers were randomly assigned in a 2:1 ratio to receive anlotinib (n = 90) or sunitinib (n = 43). Anlotinib was given orally at a dose of 12 mg once daily (2 weeks on/1 week off), and sunitinib was given orally at 50 mg once daily (4 weeks on/2 weeks off). The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety. Results The median PFS was similar with anlotinib and sunitinib (17.5 vs. 16.6 months, p > .05). The median OS (30.9 vs. 30.5 months, p > .05), ORR (30.3% vs. 27.9%), and 6-week DCR (97.8% vs. 93.0%) were similar in the two groups. Adverse events (AEs) of grade 3 or 4 were significantly less frequent with anlotinib than with sunitinib (28.9% vs. 55.8%, p Conclusion The clinical efficacy of anlotinib was similar to that of sunitinib as the first-line treatment for mRCC, but with a more favorable safety profile.

Background Previous related studies have mainly focused on renal cell carcinoma (RCC) with venous tumor thrombus, specifically inferior vena cava tumor thrombus with renal vein tumor thrombus (RVTT). However, only a few studies have focused on postoperative long-term survival of RCC patients exclusively with RVTT. Our aim was to investigate the independent prognostic factors for locally advanced RCC with RVTT in China. Methods Patients with locally advanced RCC with RVTT were enrolled for the study from January 2000 to December 2015. All patients underwent radical nephrectomy. Survival analysis was estimated using Kaplan-Meier. Univariable and multivariable survival analyses were performed using COX. Patients were divided into high-risk, middle-risk, and low-risk groups based on independent prognostic factors and then analyzed for survival. Results One hundred twenty-eight consecutive patients (103 men & 25 women) were enrolled with a median age of 61 years. Thrombi were all graded 0 using the Mayo system, of which 23 were friable. None of the thrombi detached during surgery. 121 patients were successfully followed up, with a median follow-up period of 47 months. Median overall survival was 127 months (95%CI: 101–153). The 5-year and 10-year cancer-specific survival (CSS) rate was 67.9 and 57.0%. 59 patients had recurrence with median time of 40 months. Friable thrombus, paraneoplastic syndrome (PNS), modified Fuhrman grade 3/4 and perirenal fat invasion were independent prognostic factors (p

In recent years, wheat (Triticum aestivum L.) yields have declined as a result of heavy metal pollution. In this study, hydroponics experiments were conducted to assess the genotypic differences in copper (Cu) tolerance and glutathione metabolism in wheat. Thirty-seven wheat cultivars from different primary cultivation areas in China were selected and exposed to Cu stress for 48 h. Six growth and physiological indexes in the young roots were measured, and a comprehensive evaluation of the resistance of wheat to Cu was achieved using correlation analysis, subordinate function analysis, and the median clustering method. Based on the average subordinate function values, the 37 wheat cultivars could be classified into a high Cu-resistant, medium Cu-resistant, and sensitive group. The root transcriptome of ‘Jimai 22’ (high Cu-resistant) and ‘Luomai 23’ (sensitive) under Cu stress was further sequenced using lllumina sequencing technology. The results showed that a total of 49,611 and 43,195 genes were discovered in Jimai 22 and Luomai 23, respectively, in response to Cu. Gene Ontology term enrichment combined with pathway analysis indicated that 26 overrepresented up-regulated DEGs were associated with glutathione metabolism in Jimai 22, while 17 DEGs associated with glutathione metabolism were identified in Luomai 23. Eight of the 13 unique DEGs in Jimai 22 encoded glutathione S-transferases (GST). In addition, the activity of enzymes, including glutathione reductase (GR), glutathione-S-transferase (GST) and ascorbate peroxidase (APX) were measured in the roots. Furthermore, the contents of ascorbate (ASC), reduced glutathione (GSH), oxidized glutathione (GSSG), and some nutrients, including Cu, potassium, calcium, magnesium, sodium, manganese, and zinc, were also determined. The results clearly indicated that excess Cu affected the concentrations of non-enzymatic antioxidants and nutrients (except for Cu). Besides that, APX activity in wheat was affected by excess Cu and it was higher in Jimai 22. On the contrary, GST and GR activity increased with Cu stress treatment for 48 h.

Inhibition of the DNA damage response (DDR) pathway is an attractive approach to target cancers cells. Unlike normal cells which relay on two cell cycle checkpoints to repair DNA damage, most tumor cells only depend on the G2 checkpoint because their G1 checkpoint is defective. Wee1 regulates the G2/M checkpoint and abrogating the G2 checkpoint by inhibiting Wee-1 in cancer cells with DNA damage can preferentially sensitize cancer cells to undergo mitotic catastrophe. We have developed and characterized ZN-c3, a potent and selective Wee-1 inhibitor with a differentiated kinase selectivity and pharmacokinetics profile. ZN-c3 was evaluated in ER-positive, triple negative, triple negative PARP inhibitor resistant, and Her-2 positive trastuzumab resistant breast cancers. In the ER-positive MCF-7 efficacy model, ZN-c3 demonstrated anti-tumor activity as a single agent as well as in combinations with a CDK4/6 inhibitor (Palbociclib) or ZN-c5, a selective estrogen receptor degrader (SERD). In the MDA-MB-436, a triple negative breast cancer tumor xenograft model, ZN-c3 demonstrated efficacy as a single agent and in combination with the PARP inhibitors Olaparib or Niraparib. We extended these combination studies by evaluating the efficacy of Niraparib in combination with ZN-c3 in patient-derived xenograft (PDX) triple negative breast tumors. ZN-c3 was efficacious as a single agent and the efficacy was enhanced in the combination with Niraparib. PARP inhibitors are effective and have been approved for treatment of some cancer types. However, most patients develop resistance to these agents. We sought to investigate whether ZN-c3 can overcome the acquired resistance to PARP inhibitors (Olaparib and Niraparib). We generated PARP inhibitor resistant breast tumor xenograft models. A significant tumor growth inhibition was observed in these resistant tumor xenograft models when treated with ZN-c3. These data suggest that ZN-c3 as a single agent can overcome the acquired PARP inhibitor resistance. Finally, we also evaluated ZN-c3 activity in a HER-2 positive trastuzumab resistant breast tumor xenograft model (JIMT-1) and ZN-c3 showed anti-tumor activity alone and in combination with trastuzumab. In summary, these results demonstrate that ZN-c3 is effective as a single agent and in combination with other targeted therapies in breast cancer models. Moreover, ZN-c3 can overcome the acquired resistance to PARP inhibitors and trastuzumab. ZN-c3 is currently in clinical trials for several indications and has demonstrated promising clinical activity and good tolerability. Citation Format: Jiali Li, Jianhui Ma, Petrus R. de Jong, Hooman Izadi, Tiffany Hoang, Noah Ibrahim, Brant Boren, Sayee Hegde Hegde, Fernando Doñate, Ahmed A. Samatar. ZN-c3, a potent and selective Wee-1 inhibitor demonstrates anti-tumor activities in combination with other targeted therapies and overcomes PARP inhibitor resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2606.

Background: Wee1 is a crucial cell cycle checkpoint kinase that regulates the G2/M checkpoint in response to DNA damage, and its inhibition can cause mitotic catastrophe and apoptosis in tumor cells. Inhibitors of BCL-2 have been shown to also induce apoptosis. ZN-d5 and ZN-c3, currently in clinical development for the treatment of cancer, are highly selective and potent inhibitors of Bcl-2 and Wee1, respectively. The combination of ZN-d5 with ZN-c3 induced robust tumor regressions in preclinical models and the mechanism of action of this activity is presented. Methods: Cell proliferation in tumor cell lines was measured using CellTiter-Glo® (Promega). Cleaved caspase-3, cleaved PARP, phosphorylated CDK1 (p-CDK1), γH2AX, Wee1, RRM2 and other markers were measured by Western blotting, IHC and/or flow cytometry. Anti-tumor efficacy was determined in several xenograft models. Results: The combination of ZN-c3 and ZN-d5 resulted in additive or synergistic anti-proliferative activity in several tumor cell lines. This enhancement of anti-proliferative activity was also observed at concentrations of ZN-d5 that induced caspase activation, but minimal apoptosis. Treatment of tumor cell lines with ZN-d5 alone at these sublethal concentrations caused induction of γH2AX, a marker of double-stranded DNA breaks, and degradation of caspase-sensitive proteins relevant to the DNA Damage Response (DDR), such as RRM2 (a ribonucleotide reductase subunit) and Wee1. Degradation of Wee1 correlated with decreases in p-CDK1, a marker of Wee1 inhibition. These effects were mainly reversible and were inhibited by pre-treatment with caspase inhibitors. Mechanistically, the combination of ZN-c3 and ZN-d5 resulted in increased levels of γH2AX, enhanced inhibition of p-CDK1, and increased levels of apoptotic markers compared to single agent treatment. Similar results were obtained when ZN-c3 was combined with inhibitors of other BCL-2 family proteins such as inhibitors of MCL-1 and BCL-xL. Finally, the combination of ZN-c3 with ZN-d5 or other BH3 mimetics showed synergistic anti-tumor activity in several xenograft models further supporting the rationale for combining these agents. Conclusions: ZN-d5 or other BH3 mimetics caused caspase-mediated DNA damage and degraded relevant proteins such as Wee1 and RRM2 resulting in additive or synergistic anti-tumor activity when combined with ZN-c3. These results support a novel use of BH3 mimetics to activate caspase activity, independently of apoptosis induction, to significantly enhance the activity of DDR inhibitors such as ZN-c3 Citation Format: Hooman Izadi, Noah Ibrahim, Tiffany Hoang, Jianhui Ma, Petrus R. de Jong, Joseph Pinchman, Kevin D. Bunker, Ahmed A. Samatar, Fernando Doñate. BH3 mimetics synergize with the Wee1 inhibitor ZN-c3 by activating caspases which induce DNA damage and degrade key proteins [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2605.

Background: The Bcl-2 inhibitor venetoclax is approved in combination with hypomethylating agents such as azacitidine for the treatment of newly diagnosed elderly patients with AML; however, most patients eventually relapse, especially those with TP53 mutations who have poor prognosis. Wee1 is a crucial cell cycle checkpoint kinase that regulates the G2/M checkpoint in response to DNA damage, and its inhibition can cause mitotic catastrophe and apoptosis. ZN-d5 and ZN-c3 are highly selective and potent inhibitors of Bcl-2 and Wee1, respectively. We show here that the combination is highly active in preclinical models of AML. Methods: Cell proliferation in tumor cell lines and AML patient’s samples were measured using CellTiter-Glo® (Promega). Relevant markers were measured by Western blotting. Anti-tumor efficacy was determined in cell line derived subcutaneous xenograft models of acute myeloid leukemia (AML) and in an AML patient-derived systemic xenograft (PDX) model. Flow cytometry was used for analysis of the PDX model to measure human AML blast populations. Results: The in vitro combination of ZN-d5 and ZN-c3 was tested in AML cell lines and showed synergistic or additive anti-proliferative activity which was independent of BCL-2 sensitivity in some cases. In addition, ZN-d5 in combination with ZN-c3 had a synergistic anti-tumor activity in vivo in cell line-derived AML models, such as MV4-11 and HL-60. Also, the triple combination of ZN-d5 + ZN-c3 + azacitidine in the HL-60 in vivo model resulted in synergistic anti-tumor efficacy and significant tumor growth inhibition compared to single- and double-agent treatments. The ZN-d5 + ZN-c3 combination was also tested in vitro in 29 AML patient-derived samples. Response to ZN-c3 alone was observed in 23/29 samples (IC50 < 450 nM), independent of TP53 mutation status. Response to ZN-d5 alone was observed in 17/29 samples (IC50 10 μM). Combination of ZN-d5 and ZN-c3 was also highly active in an AML patient-derived PDX model, resulting in a 99.5% decrease in human CD45+ blast population in the bone marrow. Finally, the combination of ZN-d5 and ZN-c3 was active in 3/3 ex vivo experiments of patients derived AML samples who had progressed on venetoclax. Conclusions: These results justify testing the ZN-d5 and ZN-c3 combination or possibly the triple combination including azacitidine in AML patients independently of TP53 status. Due to synergism observed with these combination(s) and activity in samples from patients resistant to venetoclax, activity may be seen in patients with low sensitivity to Bcl-2 inhibitors or even in patients who progressed on venetoclax plus azacitidine. Citation Format: Hooman Izadi, Tiffany Hoang, Noah Ibrahim, Joseph Pinchman, Brant C. Boren, Jianhui Ma, Petrus R. de Jong, Jiali Li, Kevin D. Bunker, Ahmed A. Samatar, Fernando Doñate. Combination of the BCL-2 inhibitor ZN-d5 with the WEE1 inhibitor ZN-c3 shows additive or synergistic anti-tumor activity in acute myeloid leukemia (AML) models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2591.

Background: The relationship between the size of the primary tumor and the prognosis of patients with metastatic renal cell carcinoma (mRCC) is unclear. In this study, we aimed to investigate the significance of the size of the primary tumor in mRCC. Methods: We retrospectively reviewed the data of patients with mRCC who underwent cytoreductive nephrectomy (CN) from 2006 to 2013 in a Chinese center (n = 96) and those in the Surveillance, Epidemiology, and End Results (SEER) database (from 2004 to 2015, n = 4403). Tumors less than 4 cm in size were defined as small. Prognostic factors were analyzed using univariate and multivariate Cox proportional hazards regression analyses. Results: Patients with small tumors had a longer overall survival than other patients, both in the Chinese cohort (median, 30.0 vs 24.0 months, P = 0.026) and the SEER cohort (median, 43.0 vs 23.0 months, P < 0.001). After adjusting for other significant prognostic factors, small tumor size was still an independent protective factor in the Chinese cohort (adjusted hazard ratio [HR], 0.793; 95% confidence interval [CI]: 0.587–0.998, P = 0.043). In the SEER cohort, multivariate analysis showed that small tumor size was also an independent protective factor (HR, 0.880; 95% CI: 0.654–0.987, P = 0.008). In addition, as a continuous variable, a 1 cm elevation in tumor size translated into a 3.8% higher risk of death (HR, 1.038; 95% CI, 1.029–1.046; P < 0.001). Conclusion: Patients with small tumors may have a favorable prognosis after CN for mRCC. Although CN is not a standard protocol in mRCC, small tumor size may be a candidate when we are deciding to perform CN because of the potential benefit for OS.

BackgroundFinding effective prognostic signatures is of great urgency due to the high risk of recurrence and progression of bladder cancer (BC). Although a lot of genetic alterations are involved in the carcinogenesis, none of them were referred in the current risk group stratifications. In this study, we aimed to find significant copy number variations (CNVs) to predict prognosis for BC patients.MethodsCNVs with high aberration frequencies in BC were explored by array-based comparative genomic hybridization in 65 tumor samples. Candidates were validated in independent groups of BC tumor samples (n=219) and urine samples (n=123). 3D digital PCR was applied for detecting accurate gene copy numbers in BC urine. In order to explore the prognostic value of candidate CNVs, all enrolled patients were followed up for the disease-free survival (DFS). Cox proportional hazards regression analysis was performed to find the independent prognostic factors for DFS.ResultsCNVs of CEP63, FOSL2 and PAQR6 with high aberration frequencies (67.7%, 56.9% and 60.0%, respectively) were found in BC tumors. Copy numbers of CEP63, FOSL2 and PAQR6 were gained in 219 tumor samples. CNVs of CEP63 and FOSL2 were correlated with advanced tumor stage and high grade. Retrospective analysis (median follow-up time: 69 months) revealed that CNVs of CEP63 and FOSL2 were independent prognostic factors for DFS of non-muscle-invasive bladder cancer (NMIBC) patients, while CNVs of FOSL2 and PAQR6 were independent prognostic factors for DFS of muscle-invasive bladder cancer (MIBC) patients. Models for predicting DFS were constructed based on CNVs of three genes. Patients with high prognostic indexes tended to have poor DFS. Prognostic index can also help to identify those with worse outcomes among high risk NMIBC patients. Copy number gains of CEP63 and FOSL2 in urine were found to be significantly correlated with poor DFS of NMIBC patients.ConclusionsCNVs of CEP63, FOSL2 and PAQR6 were capable of predicting DFS and may serve as promising signatures for prognosis of BC.

BACKGROUND: The biological behaviors, clinical treatment, prognosis of non-muscle-invasive bladder cancers (NMIBCs) and muscle-invasive bladder cancers (MIBCs) are distinct. Accurate staging is pivotal in optimal therapy planning for bladder cancers (BCs). However, it is insufficient for urologists in preoperative determining whether the tumor has invaded within the muscularis propria through cystoscope and imaging methods (CT or MRI). Therefore, searching for ideal biomarkers from the tumor tissues and urine is important for identifying the MIBCs preoperatively. METHODS: Differentially expressed genes between NMIBCs and MIBCs were identified by microarray analysis and validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemical analysis. The correlation between cysteine-rich angiogenic inducer 61 (CYR61) expression and Kaplan-Meier test evaluated patients’ overall survival (OS). CYR61 protein levels were measured using enzyme-linked immunosorbent assay (ELISA) in preoperatively collected urine samples from BC patients. The receiver-operating characteristic (ROC) curve analyzed the diagnostic accuracy of uric CYR61. The siRNA mediated silencing of CYR61 in bladder carcinoma cells was performed using Lipofectamine 2000. Cell migration and invasion were assessed using wound healing and transwell assay, respectively. RESULTS: Differential gene expression analysis using microarray between 14 MIBCs and 16 NMIBCs human tumor samples revealed a significant increase (P

The NAC genes, a large plant-specific family of transcription factors, regulate a wide range of pathways involved in development and response to biotic and abiotic stress. In this study, the NAC transcription factors were identified in 27 green plants, and the results showed that NAC transcription factors in plants undergo an appearance stage from water to land and a number expansion stage from gymnosperm to angiosperm. Investigating the evolutionary process of the NAC transcription factors from diploid species to hexaploid wheat revealed that tandem replications during the polyploidization process is an important event for increasing the number of NAC transcription factors in wheat. Then, the molecular characteristics, phylogenetic relationships, and expression patterns of 462 NAC transcription factors of hexaploid wheat (TaNACs) were analyzed. The protein structure results showed that TaNAC was relatively conservative at the N-terminal that contains five subdomains. All these TaNACs were divided into Group I and Group II by phylogenetic analysis, and the TaNACs in Group I should undergo strong artificial selection based on single nucleotide polymorphism (SNP) analysis. Through genome synteny and phylogenetic analysis, these TaNACs were classified into 88 groups and 9 clusters. The biased expression results of these TaNACs showed that there are 24 groups and 67 groups of neofunctionalization genes under biotic and abiotic stress, respectively, and 16 groups and 59 groups of subfunctionalization genes. This shows that neofunctionalization plays an important role in coping with different stresses. Our study provides new insights into the evolution of NAC transcription factors in hexaploid wheat.

Background Glioblastoma (GBM) is the most common primary brain tumor in adults with a median survival of approximately 15 months; therefore, more effective treatment options for GBM are required. To identify new drugs targeting GBMs, we performed a high-throughput drug screen using patient-derived neurospheres cultured to preferentially retain their glioblastoma stem cell (GSC) phenotype. Methods High-throughput drug screening was performed on GSCs followed by a dose-response assay of the 5 identified original “hits.” A PI3K/mTOR dependency to a proteasome inhibitor (carfilzomib), was confirmed by genetic and pharmacologic experiments. Proteasome Inhibition Response Signatures were derived from proteomic and bioinformatic analysis. Molecular mechanism of action was determined using three-dimensional (3D) GBM-organoids and preclinical orthotopic models. Results We found that GSCs were highly sensitive to proteasome inhibition due to an underlying dependency on an increased protein synthesis rate, and loss of autophagy, associated with PTEN loss and activation of the PI3K/mTOR pathway. In contrast, combinatory inhibition of autophagy and the proteasome resulted in enhanced cytotoxicity specifically in GSCs that did express PTEN. Finally, proteasome inhibition specifically increased cell death markers in 3D GBM-organoids, suppressed tumor growth, and increased survival of mice orthotopically engrafted with GSCs. As perturbations of the PI3K/mTOR pathway occur in nearly 50% of GBMs, these findings suggest that a significant fraction of these tumors could be vulnerable to proteasome inhibition. Conclusions Proteasome inhibition is a potential synthetic lethal therapeutic strategy for GBM with proteasome addiction due to a high protein synthesis rate and autophagy deficiency.

Purpose Reduced quality of life after cystectomy has made bladder preservation a popular research topic for muscle-invasive bladder cancer (MIBC). Previous research has indicated significant tumor downstaging after neoadjuvant chemotherapy (NAC). However, maximal transurethral resection of bladder tumor (TURBT) was performed before NAC to define the pathology, impacting the real evaluation of NAC. This research aimed to assess real NAC efficacy without interference from TURBT and apply combined modality therapies guided by NAC efficacy.Materials and Methods Patients with cT2-4aN0M0 MIBC were confirmed by cystoscopic biopsy and imaging. NAC efficacy was assessed by imaging, urine cytology, and cystoscopy with multidisciplinary team discussion. Definite responders (≤ T1) underwent TURBT plus concurrent chemoradiotherapy. Incomplete responders underwent radical cystectomy or partial cystectomy if feasible. The primary endpoint was the bladder preservation rate.Results Fifty-nine patients were enrolled, and the median age was 63 years. Patients with cT3-4 accounted for 75%. The median number of NAC cycles was three. Definite responders were 52.5%. The complete response (CR) was 10.2%, and 59.3% of patients received bladder-sparing treatments. With a median follow-up of 44.6 months, the 3-year overall survival (OS) was 72.8%. Three-year OS and relapse-free survival were 88.4% and 60.0% in the bladder-sparing group but only 74.3% and 37.5% in the cystectomy group. The evaluations of preserved bladder function were satisfactory.Conclusion After stratifying MIBC patients by NAC efficacy, definite responders achieved a satisfactory bladder-sparing rate, prognosis, and bladder function. The CR rate reflected the real NAC efficacy for MIBC. This therapy is worth verifying through multicenter research.

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature. However, despite being extensively studied, these sequential methods still suffer from three limitations. First, existing methods mostly utilize attention on the behavior of users, which is not always suitable for CTR prediction, because users often click on new products that are irrelevant to any historical behaviors. Second, in the real scenario, there exist numerous users that have operations a long time ago, but turn relatively inactive in recent times. Thus, it is hard to precisely capture user's current preferences through early behaviors. Third, multiple representations of user's historical behaviors in different feature subspaces are largely ignored. To remedy these issues, we propose a Multi-Interactive Attention Network (MIAN) to comprehensively extract the latent relationship among all kinds of fine-grained features (e.g., gender, age and occupation in user-profile). Specifically, MIAN contains a Multi-Interactive Layer (MIL) that integrates three local interaction modules to capture multiple representations of user preference through sequential behaviors and simultaneously utilize the fine-grained user-specific as well as context information. In addition, we design a Global Interaction Module (GIM) to learn the high-order interactions and balance the different impacts of multiple features. Finally, Offline experiment results from three datasets, together with an Online A/B test in a large-scale recommendation system, demonstrate the effectiveness of our proposed approach., 9 pages, 6 figures, WSDM2021, accepted

This paper introduced PGTWR model as the base model of the study and adopted thirteen prefecture-level cities as individuals of cross section and conducted spatial and temporal heterogeneity study of the converted influencing factors of carbon emissions in Beijing-Tianjin-Hebei region with the time period from the year 2013 to 2018 as panel data. From the perspective of time and space as a whole, the regression coefficient of each influencing factor of carbon emission in Hebei Province has obvious heterogeneity. Relatively speaking, the heterogeneity of influencing factors of carbon emission in Beijing-Tianjin-Hebei region is mainly reflected in time dimension. In the period of study, the impact of industrial structure, the level of urbanization, energy intensity and the level of economic development on carbon emission was on a decline curve while the impact of population size and the level of opening up on carbon emission was on the rise, which indicates that the former four factors that reflect the level of economy and technology are not the focus of consideration when making the policy of carbon emission reduction and that more attention should be paid to the latter two factors for the time to come. From the perspective of space, the differences in the impact of industrial structure and energy intensity on carbon emission vary significantly. As a result, these differences should be attached importance to when making the policy of carbon emission reduction.

In this paper, a three-phase switched reluctance motor (SRM) controller is designed to solve the problem of large torque ripple and narrow high efficiency area for electric vehicle. Firstly, according to the linear model of switched reluctance motor, the starting control strategy based on Hall state is proposed which can make the starting torque of motor as large as possible. At low speed, the current chopper control (CCC) strategy based on current distribution coefficient is adopted to make the instantaneous torque of three-phase output approximately equal and reduce the torque ripple. At high speed, the angle position control (APC) method is adopted. The opening angle is adjusted according to the speed PI, and the turning off angle is determined by experiment according to the principle of optimal efficiency. Finally, the advantages of the designed drive system are verified by testing the dynamometer of the experimental prototype.

The two-dimensional model of SRM is established in the Rmxprt module of Ansoft electromechanical system, and the simulation is performed in Rmxprt to obtain the curve of the speed, torque and current of the switched reluctance motor; importing the model to Maxwell2D, mading relevant settings such as model grouping, material settings, boundaries and excitation source settings, made a finite element analysis based on static field, obtained the motor torque characteristic curve, inductance characteristic curve, magnetization characteristic curve. The characteristics of the motor at different times are analyzed through the simulation model, which provides an effective theoretical basis for the subsequent control analysis of the motor.

The improvement of living standards has driven people’s demand for food quality, and the performance requirements of dough mixers have also increased accordingly. The paper introduces a motor controller of three-phase 6/4 pole switched reluctance motors used for dough mixers. The control chip uses STM32F103R8T6 microcontroller. The paper theoretically analyzes the basic composition and working principle of SRD, and then establishes mathematical equations; designing the controller hardware circuit, including the chip peripheral interface, power drive circuit, power conversion circuit, status display circuit, position detection circuit, etc.; developing corresponding software programs according to the hardware working environment, which adopts PID control algorithm to ensure automatic adjustment and reliable operation of the system.

Recent years have witnessed a booming increase of patent applications, which provides an open chance for revealing the inner law of innovation, but in the meantime, puts forward higher requirements on patent mining techniques. Considering that patent mining highly relies on patent document analysis, this paper makes a focused study on constructing a technology portrait for each patent, i.e., to recognize technical phrases concerned in it, which can summarize and represent patents from a technology angle. To this end, we first give a clear and detailed description about technical phrases in patents based on various prior works and analyses. Then, combining characteristics of technical phrases and multi-level structures of patent documents, we develop an Unsupervised Multi-level Technical Phrase Extraction (UMTPE) model. Particularly, a novel evaluation metric called Information Retrieval Efficiency (IRE) is designed to evaluate the extracted phrases from a new perspective, which greatly supplements traditional metrics like Precision and Recall. Finally, extensive experiments on real-world patent data show the effectiveness of our UMTPE model.

Point-of-Interest (POI) recommendation helps users find their interested places to visit based on the time and user location. Unlike traditional recommendation tasks, POI recommendation is personalized, spatial-aware, and temporally dependent. Although many previous works have tried modeling spatial and temporal characteristics, most of them suffer from the following two limitations: For the spatial aspect, existing works only consider the user-POI distance or POI-POI distance. However, we find that a user prefers different regions at different times, which is known as user-region periodic pattern. For the temporal aspect, most works treat user and time as two independent factors. However, different users may prefer the same POI in different time periods, which is known as user-POI periodic pattern. To address the limitation of existing works, we propose a novel Spatial-Temporal aware Graph Convolutional Neural Network (STGCN) for POI recommendation. Specifically, we first design a user record multigraph to fuse all the context information into a unified graph. Then, we propose a time-based neighborhood sampling algorithm and take advantage of the flexible propagation mechanism of GCNs to learn the representations of each node at a specific time. Furthermore, multiple scoring functions are proposed to exploit user-region periodic pattern and user-POI periodic pattern, respectively. We also develop a time smoothing strategy to alleviate the data sparsity problem. Extensive experiments are conducted on two real-world datasets, and the experimental results demonstrate the effectiveness of our method.

In education systems, Finding Similar Exercises (FSE) is the key step for both exercise retrieval and duplicate detection. Recently, more and more attention has been drawn into this area and several works have been proposed, to utilize the exercise content (e.g., texts or images) or the labeled knowledge concepts. Such approaches, however, have failed to take knowledge hierarchy into account. To this end, we advance a novel knowledge-aware multimodal network, namely KnowNet, for finding similar exercises in large-scale online education systems by integrating the knowledge hierarchy into the heterogeneous exercise data and learning a relation-aware semantic representation. Specifically, we first propose a Content Representation Layer (CRL) to learn a unified semantic representation of the heterogeneous exercise content. Then, we design a Hierarchy Fusion Layer (HFL) to exploit the knowledge hierarchy. By combining the knowledge hierarchy, HFL can not only retrieve the relation-aware semantic representation but also provide an interpretable view to investigate the similarity of exercises. Finally, we adopt a Similarity Score Layer (SSL) for returning similar exercises. Extensive experiments demonstrate the effectiveness and interpretability of KnowNet.

In online learning systems, measuring the similarity between educational videos and exercises is a fundamental task with great application potentials. In this paper, we explore to measure the fine-grained similarity by leveraging multimodal information. The problem remains pretty much open due to several domain-specific characteristics. First, unlike general videos, educational videos contain not only graphics but also text and formulas, which have a fixed reading order. Both spatial and temporal information embedded in the frames should be modeled. Second, there are semantic associations between adjacent video segments. The semantic associations will affect the similarity and different exercises usually focus on the related context of different ranges. Third, the fine-grained labeled data for training the model is scarce and costly. To tackle the aforementioned challenges, we propose VENet to measure the similarity at both video-level and segment-level by just exploiting the video-level labeled data. Extensive experimental results on real-world data demonstrate the effectiveness of VENet.

Background β3‐αC loop is a highly conserved structural domain across oncogene families, which is a switch for kinase activity. There have been numerous researches on mutations within β3‐αC loop in EGFR, but relatively less in ERBB2, BRAF, and MAP2K1. In addition, previous studies mainly focus on β3‐αC deletion in EGFR, which is the most common type affecting kinase activity and driving lung cancer. Other mutation types are not well studied. Methods Here we analyzed the profile of β3‐αC loop mutations in a total of 10,000 tumor biopsy and/or ctDNA patient samples using hybridization capture‐based next‐generation sequencing. Results We identified 1616 mutations within β3‐αC loop in this cohort. Most mutations were located in EGFR, with less percentage in ERBB2, BRAF, and MAP2K1. EGFR β3‐αC deletions occurred at a high percentage of 96.7% and were all drug‐relevant. We also detected rare EGFR β3‐αC insertions and point mutations, most of which were related to EGFR TKIs resistance. ERBB2 β3‐αC deletions were only found in breast cancers and sensitive to EGFR/ERBB2 inhibitor. Moreover, BRAF and MAP2K1 mutations within β3‐αC loop also demonstrated drugs relevance. Conclusion Our study showed that oncogenic mutations within the β3‐αC loop of ERBB2, MAP2K1, and BRAF are analogous to that of EGFR, which have profound effect on drug response. Understanding the mutation profile within the β3‐αC loop is critical for targeted therapies., Oncogenic alterations within the kinase domain β3‐αC loop, which is highly conserved across oncogene families, are very common in human cancers. Therefore, understanding the spectrum of β3‐αC loop mutations within these genes is critical for target therapies. Motivated by this, we analyzed tumor biopsy and/or ctDNA samples of a large cohort of patients (10,000) using hybridization capture‐based next‐generation sequencing and summarized different alterations in this region and the possible response to target therapy. Our findings provided a comprehensive instruction for target therapies for cancer patients.