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In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN models have their own advantages, i.e., each of them puts forward many effective operations and achieves good prediction results in the real applications. If users can effectively utilize and combine these excellent operations integrating the advantages of existing models, then they may obtain more effective STGCN models thus create greater value using existing work. However, they fail to do so due to the lack of domain knowledge, and there is lack of automated system to help users to achieve this goal. In this article, we fill this gap and propose Auto-STGCN algorithm, which makes use of existing models to automatically explore high-performance STGCN model for specific scenarios. Specifically, we design Unified-STGCN framework, which summarizes the operations of existing architectures, and use parameters to control the usage and characteristic attributes of each operation, so as to realize the parameterized representation of the STGCN architecture and the reorganization and fusion of advantages. Then, we present Auto-STGCN, an optimization method based on reinforcement learning, to quickly search the parameter search space provided by Unified-STGCN, and generate optimal STGCN models automatically. Extensive experiments on real-world benchmark datasets show that our Auto-STGCN can find STGCN models superior to existing STGCN models used for search space construction, which demonstrates the effectiveness of our proposed method.

A new strategy for large-scale preparation of topological deformation actuators via the regulation of polyethylene orientation factor is shown. Using the triboelectric effect, the actuator can be applied to object feature recognition.

Thermoplastic polyurethane (TPU) inverse opal structural color fabrics for colorimetric detecting of volatile organic compounds (VOCs) vapor especially DMF, THF, toluene and chloroform.

Three common Apolipoprotein E isoforms, ApoE2, ApoE3, and ApoE4, are key regulators of lipid homeostasis, among other functions. Apolipoprotein E can interact with amyloid proteins. The isoforms differ by one or two residues at positions 112 and 158, and possess distinct structural conformations and functions, leading to isoform-specific roles in amyloid-based neurodegenerative diseases. Over 30 different amyloid proteins have been found to share similar characteristics of structure and toxicity, suggesting a common interactome. The molecular and genetic interactions of ApoE with amyloid proteins have been extensively studied in neurodegenerative diseases, but have not yet been well connected and clarified. Here we summarize essential features of the interactions between ApoE and different amyloid proteins, identify gaps in the understanding of the interactome and propose the general interaction mechanism between ApoE isoforms and amyloid proteins. Perhaps more importantly, this review outlines what we can learn from the interactome of ApoE and amyloid proteins; that is the need to see both ApoE and amyloid proteins as a basis to understand neurodegenerative diseases.

Human-machine interaction plays a significant role in promoting convenience, production efficiency, and usage experience. Because of the universality and characteristics of electroencephalogram (EEG) signals, active EEG interaction is a promising and cutting-edge method for human-machine interaction. The seamless, skin-compliant, and motion-robust human-machine interface (HMI) for active EEG interaction has been in focus. Herein, we report a self-adaptive HMI (PAAS-MXene hydrogel) that can activate rapid gelation (5 s) using MXene cross-linking and conformably self-adapt to the scalp to help improve signal transduction. In addition to exhibiting satisfactory skin compliance, appropriate adhesion, and good biocompatibility, PAAS-MXene has demonstrated electrical performance reliability, such as low impedance (50 Ω) at physiologically relevant frequencies, stable polarization potential (the rate of change is less than 6.5 × 10

Improved cytosine and adenine base editors and an efficient dual editor were applied in targeted evolution of ACETYL COA CARBOXYLASE in rice, resulting in the generation of dozens of herbicide-resistant mutations, at least three of which, W2125L, W2125Q and C2186H, have not been reported previously.

Electronic textiles, inherited with the wearability of conventional clothes, are deemed fundamental for emerging wearable electronics, particularly in the Internet of Things era. However, the electronic waste produced by electronic textiles will further exacerbate the severe pollution in traditional textiles. Here, we develop a large-scale green electronic textile using renewable bio-based polylactic acid and sustainable eutectic gallium-indium alloys. The green electronic textile is extremely abrasion resistant and can degrade naturally in the environment even if abrasion produces infinitesimal amounts of microplastics. The mass loss and performance change rates of the reconstituted green electronic textiles are all below 5.4% after going through the full-cycle recycling procedure. This green electronic textile delivers high physiological comfort (including electronic comfort and thermal-moisture comfort), enables wireless power supply (without constraints by, e.g., wires and ports), has 2 orders of magnitude better air and moisture permeability than the body requires, and can lower skin temperature by 5.2 °C.

Previous diffusion tensor imaging (DTI) studies have mainly focused on dose-dependent white matter (WM) alterations 1 month to 1 year after radiation therapy (RT) with a tract-average method. However, WM alterations immediately after RT are subtle, resulting in early WM alterations that cannot be detected by tract-average methods. Therefore, we performed a study with an along-tract method in patients with brain metastases to explore the early dose-response pattern of WM alterations after RT.Sixteen patients with brain metastases underwent DTI before and 1-3 days after brain RT. DTI metrics, such as fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD), were calculated. Along-tract statistics were then used to resample WM fibre streamlines and generate a WM skeleton fibre tract. DTI metric alterations (post_RT-pre_RT DTI metrics) and the planned doses (max or mean doses) were mapped to 18 WM tracts. A linear fixed model was performed to analyse the main effect of dose on DTI metric alterations.AD alterations in the left hemispheric uncinated fasciculus (UNC_L) were associated with max doses, in which decreased AD alterations were associated with higher doses.Our findings may provide pathological insight into early dose-dependent WM alterations and may contribute to the development of max dose-constrained RT techniques to protect brain microstructure in the UNC_L.

To develop and validate a pretreatment magnetic resonance imaging (MRI)-based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models.We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution.Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901-0.949) and 0.851 (95%CI 0.816-0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature's value affected the feature's impact attributed to model, and SHAP force plot showed the integration of features' impact attributed to individual response.The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner.• Radiomic-clinical model can be useful for assessing the treatment response of WBRT. • SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.

Introduction: Among gynecological cancers, ovarian cancer has a high mortality rate. Cisplatin-based chemotherapy is commonly used for the treatment of ovarian cancer. However, the clinical efficacy of cisplatin in ovarian cancer is limited due to the development of chemo-resistance during treatment. Objective: In the study, we aimed to investigate the synergistic anti-cancer activity and targets of the FDA-approved drug disulfiram combined with cisplatin in ovarian cancer. Methods: The cell viability was determined by Celltier-Glo luminescent assay. The synergistic anti-cancer activity was assessed by combination index. Cell cycle and apoptosis were detected by flow cytometry. The in vivo anti-tumor activity and side effects were evaluated using a xenografted mice model. The synergistic anti-cancer targets were identified by a mass spectrometry-based proteomics analysis. Results: In this study, we first found that disulfiram synergistically enhanced the anti-tumor activity of cisplatin in chemo-resistant ovarian cancer cells, which was accompanied by the enhanced induction of cellular apoptosis. Secondly, the in vivo study demonstrated that the combination treatment of disulfiram and cisplatin dramatically inhibited tumor growth and had no apparent side effects in ovarian cancer xenografted mice. Finally, proteomics analysis identified SMAD3 as a potential target of disulfiram-cisplatin combined treatment, and the down-regulation of SMAD3 could increase cisplatin-induced cell death in ovarian cancer. Conclusion: Combination treatment of disulfiram and cisplatin synergistically inhibited the growth of ovarian cancer through down-regulating SMAD3. As a repurposed drug, disulfiram could be quickly transformed into a clinic to overcome cisplatin resistance for the treatment of ovarian cancer.

Background Small cell lung cancer (SCLC) is a very malignant tumor with rapid growth and early metastasis. Platinum-based chemo-resistance is the major issue for SCLC treatment failure. Identifying a new prognostic model will help to make an accurate treatment decision for SCLC patients. Methods Using the genomics of drug sensitivity in cancer (GDSC) database, we identified cisplatin resistance-related lncRNAs in SCLC cells. Based on the competing endogenous RNA (ceRNA) network, we identified the mRNAs correlated with the lncRNAs. Using Cox and LASSO regression analysis, a prognostic model was established. The survival prediction accuracy was evaluated by receiver operating characteristic (ROC) curve and Kaplan–Meier analysis. GSEA, GO, KEGG and CIBERSORT tools were used for functional enrichment and immune cells infiltration analysis. Results We first screened out 10 differentially expressed lncRNAs between cisplatin resistant and sensitive SCLC cells from GDSC database. Based on ceRNA network, 31 mRNAs were identified with a correlation with the 10 lncRNAs. Furthermore, two genes (LIMK2 and PI4K2B) were identified by Cox and LASSO regression analysis to construct a prognostic model. Kaplan–Meier analysis indicated that the high-risk group had a poor overall survival compared with the low-risk group. The predicted area under the ROC curve (AUC) was 0.853 in the training set, and the AUC was 0.671 in the validation set. In the meanwhile, the low expression of LIMK2 or the high expression of PI4K2B in SCLC tumors was also significantly associated with poor overall survival in both training and validation sets. Functional enrichment analysis showed that the low-risk group was enriched in the apoptosis pathway and high immune infiltration of T cells. Finally, an apoptosis-related gene Cathepsin D (CTSD) was identified to be up-regulated in the low-risk group, and its higher expression correlated with better overall survival in SCLC. Conclusion We established a prognostic model and potential biomarkers (LIMK2, PI4K2B and CTSD), which could help to improve the risk stratification of SCLC patients.

The emergence of ionic-junction devices has attracted growing interests due to the potential of serving as signal transmission and translation media between electronic devices and biological systems using ions. Among them, fiber-shaped iontronics possesses a great advantage in implantable applications owing to the unique one-dimensional geometry. However, fabricating stable ionic-junction on curved surfaces remains a challenge. Here, we developed a polyelectrolyte based ionic-junction fiber via an integrated opposite charge grafting method capable of large-scale continuous fabrication. The ionic-junction fibers can be integrated into functions such as ionic diodes and ionic bipolar junction transistors, where rectification and switching of input signals are implemented. Moreover, synaptic functionality has also been demonstrated by utilizing the fiber memory capacitance. The connection between the ionic-junction fiber and sciatic nerves of the mouse simulating end-to-side anastomosis is further performed to realize effective nerve signal conduction, verifying the capability for next-generation artificial neural pathways in implantable bioelectronics.

Supplementary Figure S1 shows synthesis and characterization of PLSWT7-DMI. (A) Visible absorption spectrum of PLSWT7-DMI. Similar absorption peaks were observed for PLSWT7-DMI and IRDye800CW (~774 nm). (B) Absorbance stability of PLSWT7-DMI. The absorbance dropped to 0.05). (D) Fluorescence intensity of RT112 and EJ cells incubated with various concentrations of PLSWT7-DMI. Cells incubated with 180 pmol/mL PLSWT7-DMI produced the highest fluorescence. Supplementary Figure S2 shows fluorescence microscopy images of SV-HUC-1 cells incubated with PLSWT7-DMI. Minimal fluorescence was observed after incubation. Supplementary Figure S3 shows metabolic profiles of PLSWT7-DMI post-intravenous-injection via the tail vein. (A) IVIS imaging of subcutaneous tumor-bearing mice injected with cPLSWT7-DMI and PLSWT7-DMI. Red circle indicates the tumor region. (B) Fluorescence intensity of the tumor site in (A). PLSWT7-DMI-treated groups exhibited higher fluorescence intensity than that of cPLSWT7-DMI-treated groups. (C) Comparison of TBR profiles of PLSWT7-DMI and cPLSWT7-DMI. TBR of PLSWT7-DMI-treated groups peaked 8 h post-injection and was higher than the TBR of control groups. Values are presented as means {plus minus} SD of 5 animals. Supplementary Figure S4 shows ex vivo imaging of mouse organs at 6-h post-instillation of PLSWT7-DMI. (A) A bright signal was observed in RT112 tumors, whereas signals in organs were minimal. (B) Quantitative analysis of the fluorescence intensity observed in (A). Values are presented as means {plus minus} SD of 5 animals. Supplementary Figure S5 shows orthotopic BC mice imaging. (A) Luciferase imaging showing tumor growth in the orthotopic BC model. (B) NIR imaging of orthotopic BC mice post-instillation of the probes. Bladders of orthotopic BC mice instilled with PBS, cPLSWT7-DMI, PLSWT7-DMI with blockade, or PLSWT7-DMII, followed by imaging using a homemade NIR-imaging system at 30-min post-instillation. Tumors in PLSWT7-DMI groups were clearly visualized; however, the blockade of PLSWT7-DMI binding to the tumor was observed following PLSWT7 pretreatment. The yellow-dashed circle indicates the tumor site. (C) Quantitative analysis of the mean fluorescence density shown in (B). Values are presented as means {plus minus} SD of 5 animals. *p < 0.05 vs. PLSWT7-DMI+blockade groups. Supplementary Figure S6 shows PAI of subcutaneous tumor-bearing mice after intravenous injection via the tail vein. (A) PAI of subcutaneous tumor-bearing mice. Subcutaneous tumor-bearing mice were injected with PBS, cPLSWT7-DMI, PLSWT7-DMI with blockade, or PLSWT7-DMI via tail vein, followed by PAI. PLSWT7-DMI-treated groups exhibited enhanced contrast compared with other groups. The red-dashed circle indicates the tumor site. (B) Quantification of the average PA-signal amplitude of subcutaneous tumor-bearing mice in (A). The PLSWT7-DMI groups exhibited higher signals than those of other groups. Values present means {plus minus} SD of 5 animals. *p < 0.05 vs. PLSWT7-DMI + blockade groups. Supplementary Figure S7 shows ex vivo human bladder imaging post-administration of cPLSWT7-DMI. No optical contrast between cancer and normal tissues was observed post-administration of cPLSWT7-DMI.

Oleanolic acid exhibits extensive pharmacologic activities and takes significant antitumor effects. Its pharmacologic mechanism, however, still remained to be further clarified. In this study, we demonstrated that oleanolic acid attenuated the migration and invasion abilities, resulting in the suppression of the epithelial–mesenchymal transition (EMT) process in liver cancer cells, and inhibited the tumor growth of the peritoneal lymphocytes–bearing mice. We further proved that inducible nitric oxide synthase (iNOS) may be the potential target of oleanolic acid. We confirmed that oleanolic acid could promote the dimerization of iNOS, activating it, and subsequently increasing the production of nitric oxide. Further experiments indicated that oleanolic acid promoted the nitration of specific proteins and consequently suppressed their EMT-related biological functions. Furthermore, it has been confirmed that oleanolic acid enhanced the antitumor effects of regorafenib in liver cancer treatment. These results deepened our understanding of the pharmacologic mechanism of the antitumor effect oleanolic acid, and the importance of nitric oxide synthetase as a therapeutic target for liver cancer treatment.

Bladder cancer is a common human malignancy. Conventional ultrasound and white-light cystoscopy are often used for bladder cancer diagnosis and resection, but insufficient specificity results in a high bladder cancer recurrence rate. New strategies for the diagnosis and resection of bladder cancer are needed. In this study, we developed a highly specific peptide-based probe for bladder cancer photoacoustic imaging (PAI) diagnosis and near-infrared (NIR)-imaging-guided resection after instillation. A bladder cancer–specific peptide (PLSWT7) was selected by in vivo phage-display technology and labeled with IRDye800CW to synthesize a bladder cancer–specific dual-modality imaging (DMI) probe (PLSWT7-DMI). The feasibility of PLSWT7-DMI–based dual-modality PAI-NIR imaging was assessed in vitro, in mouse models, and ex vivo human bladders. An air-pouch bladder cancer (APBC) model suitable for probe instillation was established to evaluate the probe-based bladder cancer PAI diagnosis and NIR-imaging–guided resection. Human bladders were used to assess whether the PLSWT7-DMI–based DMI strategy is a translatable approach for bladder cancer detection and resection. The probe exhibited excellent selectivity and specificity both in vitro and in vivo. Postinstillation of the probe, tumors ex vivo NIR imaging of human bladders indicated that PLSWT7-DMI–based imaging would potentially allow precise resection of bladder cancer in clinical settings. This PLSWT7-DMI–based DMI strategy was a translatable approach for bladder cancer diagnosis and resection and could potentially lower the bladder cancer recurrence rate. Mol Cancer Ther; 17(10); 2100–11. ©2018 AACR.

Table S1 shows the nitrified proteins identified by mass spectrometry. Figure S1 shows the cytotoxicity assay of OA in HepG2, PLC and SMMC7721 cell lines.

Due to respiratory motion, precise tracking of lung nodule movement is a persistent challenge for guiding percutaneous lung biopsy during image-guided intervention. We developed an automated image-guided system incorporating effective and robust tracking algorithms to address this challenge. Accurate lung motion prediction and personalized image-guided intervention are the key technological contributions of this work.A patient-specific respiratory motion model is developed to predict pulmonary movements of individual patients. It is based on the relation between the artificial 4D CT and corresponding positions tracked by position sensors attached on the chest using an electromagnetic (EM) tracking system. The 4D CT image of the thorax during breathing is calculated through deformable registration of two 3D CT scans acquired at inspiratory and expiratory breath-hold. The robustness and accuracy of the image-guided intervention system were assessed on a static thorax phantom under different clinical parametric combinations.Real 4D CT images of ten patients were used to evaluate the accuracy of the respiratory motion model. The mean error of the model in different breathing phases was 1.59 ± 0.66 mm. Using a static thorax phantom, we achieved an average targeting accuracy of 3.18 ± 1.2 mm across 50 independent tests with different intervention parameters. The positive results demonstrate the robustness and accuracy of our system for personalized lung cancer intervention.The proposed system integrates a patient-specific respiratory motion compensation model to reduce the effect of respiratory motion during percutaneous lung biopsy and help interventional radiologists target the lesion efficiently. Our preclinical studies indicate that the image-guided system has the ability to accurately predict and track lung nodules of individual patients and has the potential for use in the diagnosis and treatment of early stage lung cancer.

In China, drastic losses in the economy have been caused by the Tembusu virus (TMUV), the causative agent of the egg-drop syndrome, to the duck-raising industry. To succeed in preventing and controlling infections, extant techniques must be upgraded to achieve fast detection of viruses. This work is the first attempt to present the development of a recombinase polymerase amplification (RPA)-based clustered regularly interspaced short palindromic repeats (CRISPRs)-Cas13a approach for the TMUV infection diagnosis, where the CRISPR-Cas13a system is exploited