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In real-world express systems, couriers need to satisfy not only the delivery demands but also the pick-up demands of customers. Delivery and pickup tasks are usually mixed together within integrated routing plans. Such a mixed routing problem can be abstracted and formulated as Vehicle Routing Problem with Mixed Delivery and Pickup (VRPMDP), which is an NP-hard combinatorial optimization problem. To solve VRPMDP, there are three major challenges as below. (a) Even though successive pickup and delivery tasks are independent to accomplish, the inter-influence between choosing pickup task or delivery task to deal with still exists. (b) Due to the two-way flow of goods between the depot and customers, the loading rate of vehicles leaving the depot affects routing decisions. (c) The proportion of deliveries and pickups will change due to the complex demand situation in real-world scenarios, which requires robustness of the algorithm. To solve the challenges above, we design an encoder-decoder based framework to generate high-quality and robust VRPMDP solutions. First, we consider a VRPMDP instance as a graph and utilize a GNN encoder to extract the feature of the instance effectively. The detailed routing solutions are further decoded as a sequence by the decoder with attention mechanism. Second, we propose a Coordinated Decision of Loading and Routing (CDLR) mechanism to determine the loading rate dynamically after the vehicle returns to the depot, thus avoiding the influence of improper loading rate settings. Finally, the model equipped with a GNN encoder and CDLR simultaneously can adapt to the changes in the proportion of deliveries and pickups. We conduct the experiments to demonstrate the effectiveness of our model. The experiments show that our method achieves desirable results and generalization ability.

Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods are proposed for spatio-temporal modeling, they ignore the dynamic characteristics of correlations among locations on road network. Meanwhile, most Recurrent Neural Network based works are not efficient enough due to their recurrent operations. Additionally, there is a severe lack of fair comparison among different methods on the same datasets. To address the above challenges, in this article, we propose a novel traffic prediction framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In DGCRN, hyper-networks are designed to leverage and extract dynamic characteristics from node attributes, while the parameters of dynamic filters are generated at each time step. We filter the node embeddings and then use them to generate dynamic graph, which is integrated with pre-defined static graph. As far as we know, we are first to employ a generation method to model fine topology of dynamic graph at each time step. Furthermore, to enhance efficiency and performance, we employ a training strategy for DGCRN by restricting the iteration number of decoder during forward and backward propagation. Finally, a reproducible standardized benchmark and a brand new representative traffic dataset are opened for fair comparison and further research. Extensive experiments on three datasets demonstrate that our model outperforms 15 baselines consistently. Source codes are available at https://github.com/tsinghua-fib-lab/Traffic-Benchmark .

Compound 1 with two oxygen bridges between C-16/C-23 and C-18/C-23, together with four 23-spirocholestane derivatives, were isolated from Ypsilandra thibetica.

Middle East respiratory syndrome coronavirus (MERS-CoV) and several bat coronaviruses use dipeptidyl peptidase-4 (DPP4) as an entry receptor1–4. However, the receptor for NeoCoV—the closest known MERS-CoV relative found in bats—remains unclear5. Here, using a pseudotype virus entry assay, we found that NeoCoV and its close relative, PDF-2180, can efficiently bind to and use specific bat angiotensin-converting enzyme 2 (ACE2) orthologues and, less favourably, human ACE2 as entry receptors through their receptor-binding domains (RBDs) on the spike (S) proteins. Cryo-electron microscopy analysis revealed an RBD–ACE2 binding interface involving protein–glycan interactions, distinct from those of other known ACE2-using coronaviruses. We identified residues 337–342 of human ACE2 as a molecular determinant restricting NeoCoV entry, whereas a NeoCoV S pseudotyped virus containing a T510F RBD mutation efficiently entered cells expressing human ACE2. Although polyclonal SARS-CoV-2 antibodies or MERS-CoV RBD-specific nanobodies did not cross-neutralize NeoCoV or PDF-2180, an ACE2-specific antibody and two broadly neutralizing betacoronavirus antibodies efficiently inhibited these two pseudotyped viruses. We describe MERS-CoV-related viruses that use ACE2 as an entry receptor, underscoring a promiscuity of receptor use and a potential zoonotic threat.

Hexavalent chromium pollution is one of the most serious types of site pollution. In this study, a microorganism was screened to remove most hexavalent chromium from soil by leaching in 24 h. After ITS sequencing, the microorganism was identified as belonging to the genus Fusarium solani. The optimization experiment of leaching conditions determined that the removal rate reached the maximum 80% when the rotation speed was 200 rpm, the liquid-soil ratio was 15:1, the temperature was 35℃, and the pH was 7. The study has also shown that tea saponin can effectively strengthen the leaching of Fusarium solani to remove hexavalent chromium from the soil. Compared with tea saponin, the strengthening effect of glucose and rhamnolipid was relatively small. The removal rate of hexavalent chromium reached 85% when the added amount of tea saponin was 0.02 g/mL. The leaching solution destroyed part of the iron-manganese nodule structure of the soil, and its hydroxyl, carboxyl, and other groups complexed metal ions into the solution to achieve the purpose of removing hexavalent chromium. However, since the main crystal of the soil was SiO

This study is aimed at evaluating the feasibility of a screening method for the pulmonary adenocarcinoma nodules through surface-enhanced Raman spectroscopy (SERS). Objective. Using SERS to measure serum from pulmonary nodules and healthy subjects, intraoperative biopsy pathological diagnosis was regarded as the gold standard for labeling serum samples. To explore the application value of SERS in the differential diagnosis of pulmonary adenocarcinoma nodules, benign nodules, and healthy, we build a machine learning model. Method. We collected 116 serum samples from patients. Radiographically confirmed nodules less than 3 cm in maximum diameter in all patients, including 58 cancer (pathologic diagnosis: adenocarcinoma nodules, labeled as cancer) patients, 58 pathologic diagnoses as benign nodule (labeled as benign) patients, and 63 healthy (labeled as normal) people from the clinical laboratory of Sichuan Cancer Hospital. Gold nanorods were employed as SERS substrates. Support vector machine (SVM) was used to classify the normal, benign, and cancer sample groups, and SVM model evaluated using cross-validation. Results. The average SERS spectra of serum were significantly different between the normal group and the cancer/benign group. While the average SERS spectra of the cancer group and the benign group differed slightly, for the cancer, benign, and normal groups, SVM models can predict with 93.33% accuracy. Conclusion. This exploratory study demonstrates that the SERS technique based on nanoparticles in conjunction with SVM has great potential as a clinical auxiliary diagnosis and screening for pulmonary adenocarcinoma nodules.

It is essential to predict crowd flow precisely in a city, which is practically partitioned into irregular regions based on road networks and functionality. However, prior works mainly focus on grid-based crowd flow prediction, where a city is divided into many regular grids. Although Convolutional Neural Netwok (CNN) is powerful to capture spatial dependence from grid-based Euclidean data, it fails to tackle non-Euclidean data, which reflect the correlations among irregular regions. Besides, prior works fail to jointly capture the hierarchical spatio-temporal dependence from both regular and irregular regions. Finally, the correlations among regions are time-varying and functionality-related. However, the combination of dynamic and semantic attributes of regions are ignored by related works. To address the above challenges, in this article, we propose a novel model to tackle the flow prediction task for irregular regions. First, we employ CNN and Graph Neural Network (GNN) to capture micro and macro spatial dependence among grid-based regions and irregular regions, respectively. Further, we think highly of the dynamic inter-region correlations and propose a location-aware and time-aware graph attention mechanism named Semantic Graph Attention Network (Semantic-GAT), based on dynamic node attribute embedding and multi-view graph reconstruction. Extensive experimental results based on two real-life datasets demonstrate that our model outperforms 10 baselines by reducing the prediction error around 8%.

Background Immune checkpoint inhibitors (ICI)-based combination strategies have improved the survival outcomes in advanced non-small cell lung cancers; however, data regarding their efficacy remains limited for uncommon histological types, including large-cell carcinoma (LCC) and large-cell neuroendocrine carcinoma (LCNEC). Methods We retrospectively analyzed a total of 60 patients with advanced LCC and LCNEC – 37 treatment-naïve and 23 pre-treated – who received pembrolizumab with or without chemotherapy. Treatment and survival outcomes were analyzed. Results Of the 37 treatment-naïve patients who received first-line pembrolizumab combined with chemotherapy, the 27 patients with LCC had an overall response rate (ORR) of 44.4% (12/27) and a disease control rate (DCR) of 88.9% (24/27); whereas 10 patients with LCNEC had an ORR of 70% (7/10) and DCR of 90% (9/10). The median progression-free survival (mPFS) was 7.0 months (95% confidence intervals [CI]: 2.2–11.8) and median overall survival (mOS) was 24.0 months (95%CI: 0.0–50.1) for first-line pembrolizumab plus chemotherapy of LCC (n = 27), whereas mPFS was 5.5 months (95%CI: 2.3–8.7) and mOS was 13.0 months (95%CI: 11.0–15.0) for first-line pembrolizumab plus chemotherapy of LCNEC (n = 10). Of the 23 pre-treated patients who received subsequent-line pembrolizumab with or without chemotherapy, mPFS was 2.0 months (95% CI: 0.6–3.4) and mOS was 4.5 months (95% CI: 0.0–9.0) for LCC and mPFS was 3.8 months (95% CI: 0.0–7.6) and mOS was not reached for LCNEC. Conclusion Our study provides real-world clinical evidence of the anti-tumor activity of pembrolizumab plus chemotherapy in advanced LCC and LCNEC, indicating that this regimen could serve as a treatment option, particularly as first-line therapy, for improving the survival outcomes of patients with these rare histological subtypes of lung cancer. Trial registration NCT05023837(ESPORTA, 27/08/2021).

Background The aim of this study was to construct a predictive signature integrating tumour-mutation- and copy-number-variation-associated features using machine learning to precisely predict early relapse and survival in patients with resected stage I–II pancreatic ductal adenocarcinoma. Methods Patients with microscopically confirmed stage I–II pancreatic ductal adenocarcinoma undergoing R0 resection at the Chinese PLA General Hospital between March 2015 and December 2016 were enrolled. Whole exosome sequencing was performed, and genes with different mutation or copy number variation statuses between patients with and without relapse within 1 year were identified using bioinformatics analysis. A support vector machine was used to evaluate the importance of the differential gene features and to develop a signature. Signature validation was performed in an independent cohort. The associations of the support vector machine signature and single gene features with disease-free survival and overall survival were assessed. Biological functions of integrated genes were further analysed. Results Overall, 30 and 40 patients were included in the training and validation cohorts, respectively. Some 11 genes with differential patterns were first identified; using a support vector machine, four features (mutations of DNAH9, TP53, and TUBGCP6, and copy number variation of TMEM132E) were further selected and integrated to construct a predictive signature (the support vector machine classifier). In the training cohort, the 1-year disease-free survival rates were 88 per cent (95 per cent c.i. 73 to 100) and 7 per cent (95 per cent c.i. 1 to 47) in the low-support vector machine subgroup and the high-support vector machine subgroup respectively (P < 0.001). Multivariable analyses showed that high support vector machine was significantly and independently associated with both worse overall survival (HR 29.20 (95 per cent c.i. 4.48 to 190.21); P < 0.001) and disease-free survival (HR 72.04 (95 per cent c.i. 6.74 to 769.96); P < 0.001). The area under the curve of the support vector machine signature for 1-year disease-free survival (0.900) was significantly larger than the area under the curve values of the mutations of DNAH9 (0.733; P = 0.039), TP53 (0.767; P = 0.024), and TUBGCP6 (0.733; P = 0.023), the copy number variation of TMEM132E (0.700; P = 0.014), TNM stage (0.567; P = 0.002), and differentiation grade (0.633; P = 0.005), suggesting higher predictive accuracy for prognosis. The value of the signature was further validated in the validation cohort. The four genes included in the support vector machine signature (DNAH9, TUBGCP6, and TMEM132E were novel in pancreatic ductal adenocarcinoma) were significantly associated with the tumour immune microenvironment, G protein-coupled receptor binding and signalling, cell–cell adhesion, etc. Conclusion The newly constructed support vector machine signature precisely and powerfully predicted relapse and survival in patients with stage I–II pancreatic ductal adenocarcinoma after R0 resection.

Synbiotics can ameliorate liver injury and improve liver function by reducing hepatic inflammation and pyroptosis. Our data indicate that our new Syn not only reverses gut dysbiosis by increasing beneficial bacteria and decreasing lipopolysaccharide (LPS)-bearing Gram-negative bacteria but also maintains intestinal barrier integrity.

Travel time estimation (TTE) is a crucial task in intelligent transportation systems, which has been widely used in navigation and route planning. In recent years, several deep learning frameworks have been proposed to capture the dynamic features of road segments or intersections for travel time estimation. However, most existing works do not consider the joint features of the intersections and road segments. Moreover, most deep neural networks for TTE are designed based on empirical knowledge. Since the independent and joint features of intersections and road segments commonly vary with different datasets, the empirical deterministic neural architectures have limited adaptability to different scenarios. To tackle the above problems, we propose a novel automated deep learning framework, namely Automated Spatio-Temporal Dual Graph Convolutional Networks (Auto-STDGCN), for travel time estimation. Specifically, we propose to construct the node-wise graph and edge-wise graph to characterize the spatio-temporal features of intersections and road segments respectively. In order to capture the joint spatio-temporal correlations of the dual graphs, a hierarchical neural architecture search approach is introduced, whose search space is composed of internal and external search space. In the internal search space, spatial graph convolution and temporal convolution operations are adopted to capture the respective spatio-temporal correlations of the dual graphs. Further, we design the external search space including the node-wise and edge-wise graph convolution operations from the internal architecture search to capture the interaction patterns between the intersections and road segments. We evaluate our proposed model Auto-STDGCN on three real-world datasets, which demonstrates that our model is significantly superior to the state-of-art methods. In addition, we also conduct case studies to visualize and explain the neural architectures learned by our model.

Background Immunotherapy in combination with platinum-etoposide (EP) chemotherapy has been approved as a first-line treatment for extensive-stage small cell lung cancer (ES-SCLC). However, real-world (RW) data regarding the use of immune checkpoint inhibitors (ICIs) in ES-SCLC are lacking. We aimed to assess the differences between programmed death protein 1 (PD-1) inhibitors and programmed death ligand 1 (PD-L1) inhibitors plus EP chemotherapy as first-line treatment for ES SCLC. Methods We conducted a real-world, multicenter, retrospective cohort, controlled study to compare the prognosis, efficacy, and safety of PD-1 and PD-L1 inhibitors along with chemotherapy for patients with ES-SCLC. Each patient received up to six cycles of etoposide, carboplatin, or cisplatin combined with ICIs drugs, including PD-1 and PD-L1 inhibitors. The primary endpoints were investigator-assessed progression-free survival (PFS) and overall survival (OS). The secondary endpoints were investigator-assessed objective response rate (ORR), disease control rate (DCR), and duration of response (DOR)according to the Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1). Results Between January 2017 and December 2021, 194 patients with ES-SCLC from three clinical centers in a PLA general hospital were included in our study, including 93 patients in the PD-1 group and 101 patients in the PD-L1 group. At the time of data cutoff, progression-free survival in the PD-1 group (median PFS, 6.8months; 95%CI, 5.3-8.1) was similar to the PD-L1 group (median PFS, 6.4months; 95%CI, 5.5-7.5); the stratified hazard ratio for PFS was 1.12 (95%CI, 0.83-1.53; P=0.452). The median OS was similar in the PD-1 and PD-L1 group (15.8m vs 17.7m, P = 0.566); the hazard ratio was 0.90 (95% CI, 0.62-1.30, P=0.566). The two groups had comparable investigator-assessed confirmed objective response rates (ORR) (76.3% vs 76.2%) and median duration of response (DOR) (6.2m vs 6.1m). Adverse effects (AEs) related discontinuation occurred in 4(4.3%) patients in the PD-1 group and 2(2.0%) patients in the PD-L1 group. Deaths due to AEs of any cause occurred in 2(2.2%) patients in the PD-1 inhibitor group and 1(1.0%) patient in the PD-L1 inhibitor group, separately. Conclusions Our research revealed that no significant differences in efficacy or prognosis were observed between PD-1 inhibitor + EP chemotherapy and PD-L1 inhibitor + EP chemotherapy. The two groups seemed to have comparable safety profiles, but the number of discontinuation or death events is too few to draw a firm conclusion.

Closely spaced modes commonly observed in long-span suspension bridges can greatly increase the difficulty of identifying and tracking modal parameters. Most existing studies generally focus on identifying the closely spaced modes and quantifying the uncertainties based on numerical and experimental models. Further research focusing on full-scale long-span bridges is still required. A case study on identifying the closely spaced modes of the Qixiashan Yangtze River Bridge, a long-span suspension bridge with a main span of 1 418 m, is conducted in this paper. The effectiveness of the generalized fast Bayesian fast Fourier transform (GFBFFT) method is verified by both the simulated and monitoring data. The results show that a larger coefficient of variation (COV) and higher uncertainty is typically contained in the closely spaced modes than the separated modes. Compared with the FDD and SSI methods, the GFBFFT method guarantees higher identification accuracy of modal parameters and can serve as a reliable tool to identify the closely spaced modes.

Supplementary Figure S1. Survivin and EMT markers were stained in sections of ovarian tumor of survivin KO and control mice; Supplementary Figure S2. Ovarian cancer cell migration and invasion following inhibition of apoptosis; Supplementary Figure S3. Cell viability in the upper chamber following treatment of MX106 in the migration assay; Supplementary Figure S4. Cell viability in the upper chamber following treatment of MX106 in the invasion assay; Supplementary Figure S5. SMAD dependent reporter gene luciferase activity; Supplementary Figure S6. TGFβ did not activate non-SMAD pathway in ovarian cancer cells; Supplementary Figure S7. Immunostaining for survivin and EMT markers in sections of ovarian tumor tissue following MX106 or vehicle treatment.

Survivin, a member of the inhibitor of apoptosis family, is upregulated in multiple cancers including ovarian cancer, but is rarely detectable in normal tissues. We previously reported that survivin promoted epithelial-to-mesenchymal transition (EMT) in ovarian cancer cells, suggesting that survivin may contribute to ovarian tumor metastasis and chemoresistance. In this study, we tested whether knockout or pharmacologic inhibition of survivin overcomes chemoresistance and suppresses tumor metastasis. The genetic loss of survivin suppressed tumor metastasis in an orthotopic ovarian cancer mouse model. To pharmacologically test the role of survivin on ovarian tumor metastasis, we treated chemo-resistant ovarian cancer cells with a selective survivin inhibitor, MX106, and found that MX106 effectively overcame chemoresistance in vitro. MX106 inhibited cell migration and invasion by attenuating the TGFβ pathway and inhibiting EMT in ovarian cancer cells. To evaluate the efficacy of MX106 in inhibiting ovarian tumor metastasis, we treated an orthotopic ovarian cancer mouse model with MX106, and found that MX106 efficiently inhibited primary tumor growth in ovaries and metastasis in multiple peritoneal organs as compared with vehicle-treated control mice. Our data demonstrate that inhibition of survivin using either genetic knockout or a novel inhibitor MX106 suppresses primary ovarian tumor growth and metastasis, supporting that targeting survivin could be an effective therapeutic approach in ovarian cancer.

This supplementary data file contains the detailed methods section and six figures. Figure S1 shows cell-matrix adhesion induces tyrosine phosphorylation of Lats1, and is related to Figure 1. Figure S2 shows Src phosphorylates Lats1/2, and is related to Figure 2. Figure S3 shows the effect of CSK on Lats1 tyrosine phosphorylation, and is related to Figure 3. Figure S4 shows the identification of Lats1 residues phosphorylated by Src, and is related to Figure 4. Figure S5 shows that Src activates YAP through inhibition of Lats1, and is related to Figure 5. Figure S6 provides essential controls to Figure 6.

The Hippo pathway regulates cell proliferation, apoptosis, and stem cell self-renewal, and its inactivation in animal models causes organ enlargement followed by tumorigenesis. Hippo pathway deregulation occurs in many human cancers, but the underlying mechanisms are not fully understood. Here, we report tyrosine phosphorylation of the Hippo pathway tumor suppressor LATS1 as a mechanism underlying its regulation by cell adhesion. A tyrosine kinase library screen identified Src as the kinase to directly phosphorylate LATS1 on multiple residues, causing attenuated Mob kinase activator binding and structural alteration of the substrate-binding pocket in the kinase domain. Cell matrix adhesion activated the Hippo pathway effector transcription coactivator YAP partially through Src-mediated phosphorylation and inhibition of LATS1. Aberrant Src activation abolished the tumor suppressor activity of LATS1 and induced tumorigenesis in a YAP-dependent manner. Protein levels of Src in human breast cancer tissues correlated with accumulation of active YAP dephosphorylated on the LATS1 target site. These findings reveal tyrosine phosphorylation of LATS1 by Src as a novel mechanism of Hippo pathway regulation by cell adhesion and suggest Src activation as an underlying reason for YAP deregulation in tumorigenesis. Cancer Res; 77(18); 4868–80. ©2017 AACR.

PURPOSE Immune checkpoint inhibitors (ICIs) exert robust antitumor activity in non–small-cell lung cancer (NSCLC) without actionable mutations. Apart from isolated case reports, the efficacy of PD-1 blockade in ROS1-rearranged NSCLC is currently unknown. METHODS This retrospective cohort study included 23 patients with ROS1-rearranged advanced lung adenocarcinoma who received ICI plus chemotherapy regardless of the treatment setting. ICI plus chemotherapy was received as a later-line regimen by 14 patients, as the first-line regimen by six patients, and after chemoradiotherapy by three patients. RESULTS All three patients who received chemoradiotherapy followed by ICI plus chemotherapy achieved partial response (PR) and had a progression-free survival (PFS) of >17.9 months. Of the six patients who received first-line ICI plus chemotherapy, five patients achieved PR and one had stable disease (SD), with a median PFS of 24.3 months (95% CI, 4.9 to 43.7). Of the 14 previously treated patients who received later-line ICI plus chemotherapy, the Objective Response Rate (ORR) was 28.6%, the Disease Control Rate (DCR) was 92.9%, and the median PFS was 5.8 months (95% CI, 0.2 to 9.4). The median time on ICI therapy was 10.0 months (95% CI, 1.5 to 32.5). The duration of response was 24.3 months (95% CI, 5.4 to 43.2) and 4.8 months (95% CI, 2.3 to 12.7) for first-line (n = 5) and subsequent-line (n = 4) ICI plus chemotherapy, respectively. Of the 10 patients with brain metastasis before receiving ICI plus chemotherapy, four patients achieved intracranial PR and five patients achieved intracranial SD, achieving an intracranial ORR of 40.0% and an intracranial DCR of 90.0%. Conclusion Our retrospective study provides real-world clinical evidence that ROS1-rearranged NSCLCs benefit from ICI plus chemotherapy in any treatment setting, including patients who present with brain metastasis.

Background. The application of immunotherapy is gradually increasing in advanced bile tract carcinoma (BTC), but only some patients could benefit from it. Validated biomarkers can screen out the beneficiaries. Therefore, the objective of this research is aimed at exploring the predictive value of lung immune prognostic index (LIPI) in advanced BTC patients receiving immunotherapy. Methods. This study was conducted on 110 BTC patients. The cut-off value of the derived neutrophil-to-lymphocyte (dNLR) ratio was obtained by the ROC curves to predict the tumor progression rate at the 6th month. The high levels of dNLR (≥the cut-off value) and lactate dehydrogenase (≥the upper limit of normal) were considered to be two risk factors for LIPI. Based on these two risk factors, patients were categorized into 3 groups based on risk factors: 0 for the good group, 1 for the intermediate group, and 2 for the poor group. Due to the limited number of patients in the poor group, it was integrated into the intermediate group to be the intermediate/poor group. Finally, the subjects were divided into two groups: LIPI-good and LIPI-intermediate/poor. Results. The results shed light on the 110 BTC patients’ LIPI in advanced BTC patients receiving immunotherapy, indicating that the cut-off value of dNLR was 1.74. According to the risk stratification, 38 (34.5%) patients had a good LIPI score, whereas the LIPI score was intermediate/poor in 72 (65.5%). In addition, patients with good LIPI were related to longer progression-free survival (PFS) and overall survival (OS), compared to those with intermediate/poor LIPI (12.17 months vs. 3.17 months; 20.2 months vs. 8.7 months). According to multivariate analysis, the intermediate/poor LIPI group was independently correlated with over 2.3 times greater risk of tumor progression ( HR = 2.301 ; 95% CI, 1.395-3.796; P = 0.001 ) and over 1.8 times greater risk of death ( HR = 1.877 ; 95% CI, 1.076-3.275; P = 0.027 ) than the good group. Moreover, the result also revealed that there were significant differences of DCR for patients of the good group and the intermediate/poor group (86.8% vs. 65.3%; P = 0.012 ). Conclusion. Finally, this study verifies, for the first time, that LIPI is an independent factor affecting the survival and clinical efficacy of advanced BTC patients receiving immunotherapy. It may be difficult for patients with intermediate/poor LIPI to benefit from immunotherapy.

The educational data mining (EDM) methods are increasingly diversified. In this research, a hybrid method of multikernel learning (MKL), least-squares support vector machine (LSSVM), and genetic algorithm (GA) is employed to evaluate teaching quality through nine indicators; the reliability of our proposed method is evaluated by confidence interval and prediction interval. First, English teaching quality samples occurring from three age groups at Guizhou Normal University are collected. Next, an intelligent method MK-LSSVM is proposed. Finally, the test sets are regression by the proposed model, and regression results are evaluated by confidence interval, prediction interval, and several error calculation methods; we also develop an ablation experiment for our proposed model. The experiment indicates that the MKL-LSSVM-GA outperforms other benchmark methods at three age-group levels. Additionally, at all three age-group levels, the experiment indicates that three indicators are crucial for the evaluation of teaching quality. Therefore, the proposed model in this paper can evaluate the English teaching quality effectively.

In order to improve the anticorrosive performance of waterborne epoxy (WEP) coating, small amounts of polyaniline (PANI) were blended under ultrasonic irradiation to obtain PANI/WEP composite coatings with high dispersibility of PANI particles. The PANI/WEP composite coatings were characterized by Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (FESEM). Their adhesive force level and hardness grade were tested based on the Chinese National Standard GB/T9286-1998 and GB/T6739-1996, respectively. These results indicated that, compared to the pristine WEP coating, the addition of PANI with suitable content could completely fill the micropores or microcracks and remarkably improve the hardness grade of composite coatings. The electrochemical impedance spectra (EIS) and Tafel polarization curves revealed that the addition of PANI not only could increase the impedance arc, but also could increase the impedance modulus at low frequency. Then, the salt spray tests were employed to observe the anticorrosive performance of PANI/WEP composite coatings. Finally, the enhanced anticorrosive mechanism of WEP coating by the addition of small amounts of PANI was proposed and discussed. The addition of PANI with suitable content could play an important role of physical and chemical barriers to improve the anticorrosive performance of waterborne epoxy coating.

In recent years, people’s acceptance of online shopping has increased markedly with the gradual maturing of e-commerce. The furniture industry in China, along with many other countries, is paying increased attention to the online retail business. The furniture online consumption experience has attracted attention both in academic and industrial fields. The purpose of this paper is to provide a literature review of the furniture online consumption with an aim to extend the concept of consumer experience to the context of online furniture consumption. The paper offers three important contributions for both academics and practitioners. First, it analyzes the main influencing factors of the consumer experience concerning wood furniture online consumption in China. And secondly, it proposes a conceptual framework of furniture online consumer experience (FOCE), which divides online consumption experience into three dimensions: perceived risk experience, emotional experience, and new technology interactive experience. Finally, from a managerial perspective, the authors put forward constructive strategies in terms of furniture online sales. The findings of this study afford practical implications for the improvement of the online shopping experience of consumers for furniture companies.

Sarcanolide C (1) with an unusual orthoformate unit, together with two new lindenane-type sesquiterpenoid dimers were isolated from the roots of Sarcandra glabra.

SARS-CoV-2 continues to accumulate mutations to evade immunity, leading to breakthrough infections after vaccination. How researchers can anticipate the evolutionary trajectory of the virus in advance in the design of next-generation vaccines requires investigation. Here, we performed a comprehensive study of 11,650,487 SARS-CoV-2 sequences, which revealed that the SARS-CoV-2 spike (S) protein evolved not randomly but into directional paths of either high infectivity plus low immune resistance or low infectivity plus high immune resistance. The viral infectivity and immune resistance of variants are generally incompatible, except for limited variants such as Beta and Kappa. The Omicron variant has the highest immune resistance but showed high infectivity in only one of the tested cell lines. To provide cross-clade immunity against variants that undergo diverse evolutionary pathways, we designed a new pan-vaccine antigen (S pan ). S pan was designed by analyzing the homology of 2675 SARS-CoV-2 S protein sequences from the NCBI database before the Delta variant emerged. The refined S pan protein harbors high-frequency residues at given positions that reflect cross-clade generality in sequence evolution. Compared with a prototype wild-type (S wt ) vaccine, which, when administered to mice, induced serum with decreased neutralization activity against emerging variants, S pan vaccination of mice elicited broad immunity to a wide range of variants, including those that emerged after our design. Moreover, vaccinating mice with a heterologous S pan booster conferred complete protection against lethal infection with the Omicron variant. Our results highlight the importance and feasibility of a universal vaccine to fight against SARS-CoV-2 antigenic drift.

SARS-CoV-2 continues to accumulate mutations to evade immunity, leading to breakthrough infections after vaccination. How researchers can anticipate the evolutionary trajectory of the virus in advance in the design of next-generation vaccines requires investigation. Here, we performed a comprehensive study of 11,650,487 SARS-CoV-2 sequences, which revealed that the SARS-CoV-2 spike (S) protein evolved not randomly but into directional paths of either high infectivity plus low immune resistance or low infectivity plus high immune resistance. The viral infectivity and immune resistance of variants are generally incompatible, except for limited variants such as Beta and Kappa. The Omicron variant has the highest immune resistance but showed high infectivity in only one of the tested cell lines. To provide cross-clade immunity against variants that undergo diverse evolutionary pathways, we designed a new pan-vaccine antigen (S

This study investigated the characteristics of dredger fill after preloading at the DaLian. Granulometric composition test and X-ray diffraction were employed to determine the composition of dredger fill. Mercury intrusion porosimetry was combined to determine the related features of the pore. This paper also discussed the changes in porosity, pore diameter distribution, and morphological characteristics of the pore. Preloading influence depth was established based on pore changes. The results show that: the dredger fill contained numerous clay minerals, mainly illite and illite–smectite mixed layer; and the influence depth established based on pore changes of the area was almost 11 m. Within the influence depth, porosity decreased rapidly as the depth increased, and the main scope of pore volume fraction changes from the scope of 0.06–10 μm to the scope of 0.1–1 μm. When depth was greater than the influence depth, porosity slowly changed, pores with diameters greater than 1 μm show an increasing trend. Thus, the soil was compacted.

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs., Comment: Accepted by IEEE THMS

Using a set of Chinese economic data and a structural vector autoregression (SVAR) model, this paper investigates the transmission channels of fiscal policy to bank credit in China. We find that increases in tax revenue can increase bank credit through external financing premium channel, collateral channel, and bank liquidity channel. We also find that increases in government spending can reduce bank credit through bank liquidity channel and increase bank credit through external financing premium channel and collateral channel.

Video accounts for a large proportion of traffic on the Internet. Understanding its geographical viewing patterns is extremely valuable for the design of Internet ecosystems for content delivery, recommendation and ads. While previous works have addressed this problem at coarse-grain scales (e.g., national), the urban-scale geographical patterns of video access have never been revealed. To this end, this article aims to investigate the problem that whether there exists distinct viewing patterns among the neighborhoods of a large-scale city. To achieve this, we need to address several challenges including unknown of patterns profiles, complicate urban neighborhoods, and comprehensive viewing features. The contributions of this article include two aspects. First, we design a framework to automatically identify geographical video viewing patterns in urban neighborhoods. Second, by using a dataset of two months real video requests in Shanghai collected from one major ISP of China, we make a rigorous analysis of video viewing patterns in Shanghai. Our study reveals the following important observations. First, there exists four prevalent and distinct patterns of video access behavior in urban neighborhoods, which are corresponding to four different geographical contexts: downtown residential, office, suburb residential and hybrid regions. Second, there exists significant features that distinguish different patterns, e.g., the probabilities of viewing TV plays at midnight, and viewing cartoons at weekends can distinguish the two viewing patterns corresponding to downtown and suburb regions.

COVID-19 pandemic has severely impacted the public health and social economy worldwide. A safe, effective, and affordable vaccine against SARS-CoV-2 infections/diseases is urgently needed. We have been developing a recombinant vaccine based on a prefusion-stabilized spike trimer of SARS-CoV-2 and formulated with aluminium hydroxide and CpG 7909. The spike protein was expressed in Chinese hamster ovary (CHO) cells, purified, and prepared as a stable formulation with the dual adjuvant. Immunogenicity studies showed that candidate vaccines elicited robust neutralizing antibody responses and substantial CD4+ T cell responses in both mice and non-human primates. And vaccine-induced neutralizing antibodies persisted at high level for at least 6 months. Challenge studies demonstrated that candidate vaccine reduced the viral loads and inflammation in the lungs of SARS-CoV-2 infected golden Syrian hamsters significantly. In addition, the vaccine-induced antibodies showed cross-neutralization activity against B.1.1.7 and B.1.351 variants. These data suggest candidate vaccine is efficacious in preventing SARS-CoV-2 infections and associated pneumonia, thereby justifying ongoing phase I/II clinical studies in China (NCT04982068 and NCT04990544).

A new method of FeCl2-catalyzed [4+2] annulation of α,β-unsaturated ketoxime acetates with N-acetyl enamides in batch and flow is reported. The current strategy features low-cost catalytic system, use of electron-rich olefins, operational simplicity, and broad substrate scope, thus providing a facile and efficient access to substituted pyridines in moderate to good yields.

Alcoholic liver disease (ALD) is a worldwide health threaten lack of effective treatment. Gut dysbiosis and concomitant augmented intestinal permeability are strongly implicated in the pathogenesis and progression of ALD. Research on the protective effect of probiotics on ALD is limited, and more effective intestinal microecological regulators and the related mechanisms still need to be further explored. In the present study, the protective effects and mechanisms of a compound probiotic against acute alcohol-induced liver injury in vivo were explod. It was showed that the compound probiotic ameliorated liver injury in acute ALD mice and stabilized the levels of ALT, AST, and TG in serum. The compound probiotic reversed acute alcohol-induced gut dysbiosis and maintained the intestinal barrier integrity by upregulating the production of mucus and the expression of tight junction (TJ) proteins and thus reduced LPS level in liver. Meanwhile, the compound probiotic reduced inflammation level by inhibiting TLR4/NF-κB signaling pathway and suppressed oxidative stress level in liver. Furthermore, the compound probiotic alleviated liver lipid accumulation by regulating fatty acid metabolism-associated genes and AMPK-PPARα signaling pathway. Noteworthy, fecal microbiota transplantation (FMT) realized comparable protective effect with that of compound probiotic. In conclusion, present study demonstrates the beneficial effects and underlying mechanism of the compound probiotic against acute alcohol-induced liver injury. It provides clues for development of novel strategy for treatment of ALD.