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On-chip optical communications are in increasingly demand for low-loss, small-footprint and power-efficient waveguiding solutions in the telecom band. However, most integrated optical circuits suffer from high propagation loss and low integration degree. Through manipulating the valley-dependent topological phase of light, we have experimentally demonstrated both robust optical transport and electrical modulation of lightwaves at telecom wavelengths in the valley photonic crystals. With the adoption of valley kink states, the 25 Gbit/s optical signal at 1550 nm is successfully transmitted through a highly twisted interface. Furthermore, an extreme high data rate of 100 Gbit/s is demonstrated with such topological waveguide by wavelength division multiplexing. The electrical tunability of the topological modulators based on thermo-optic effect is also verified, opening a novel route towards active valley kink photonic devices. Our study shows a great possibility of making use of the topological protection in building up high-speed datalinks on a chip. Published version This work was supported by the Innovation and Entrepreneurship Program of Jiangsu Province (JSSCBS20210467), the Natural Science Foundation of the Jiangsu Higher Institution of China (21KJB140012), the Natural Science Foundation of Jiangsu Province (SBK2021041180), the National Natural Science Foundation of China (62105158, 62075038).

Systematically tracking the tumor immunophenotype is required to understand the mechanisms of cancer immunity and improve clinical benefit of cancer immunotherapy. However, progress in current research is hindered by the lack of comprehensive immune activity resources and easy-to-use tools for biologists, clinicians, and researchers to conveniently evaluate immune activity during the “cancer-immunity cycle.” We developed a user-friendly one-stop shop web tool called TIP to comprehensively resolve tumor immunophenotype. TIP has the capability to rapidly analyze and intuitively visualize the activity of anticancer immunity and the extent of tumor-infiltrating immune cells across the seven-step cancer-immunity cycle. Also, we precalculated the pan-cancer immunophenotype for 11,373 samples from 33 The Cancer Genome Atlas human cancers that allow users to obtain and compare immunophenotype of pan-cancer samples. We expect TIP to be useful in a large number of emerging cancer immunity studies and development of effective immunotherapy biomarkers. TIP is freely available for use at http://biocc.hrbmu.edu.cn/TIP/.Significance:TIP is a one-stop shop platform that can help biologists, clinicians, and researchers conveniently evaluate anticancer immune activity with their own gene expression data.See related commentary by Hirano, p. 6536

Leukocyte signature matrix (LM14), including gene expression matrix and source data.

Supplementary figures and tables

Limited by large transmission loss, the development of transverse electric (TE) mode silicon-on-insulator (SOI) based on-chip long length chirped grating waveguide faces many difficulties now. To overcome this problem, multi-mode waveguide with a measured transmission loss of 0.7 dB/cm is applied in this paper, and a chirped spiral Bragg grating waveguide (SBGW) is proposed and experimentally demonstrated. The length of the chirped SBGW reaches 2.7 cm, which is the longest SOI based grating reported so far. The total group delay is measured to be 628 ps, with a structure size of only 0.3 mm2 due to the application of spiral configuration. The slope of the linear dispersion is -27.7 ps/nm. This integrated chirped SBGW shows great compatibility with frequently used TE mode SOI devices and has great potential for applications in microwave photonics requiring dispersion control.

A compact and broadband silicon mode-order converter (MOC) scheme by employing reciprocal mode evolution between asymmetric input/output taper and bricked subwavelength gratings (BSWG) is proposed. In the proposed MOC, a quasi-TE0 mode is generated in the BSWG region, which can be regarded as an effective bridge between the two TE modes to be converted. Flexible mode conversion can be realized by only choosing appropriate structure parameters for specific mode transitions between input/output modes and the quasi-TE0 mode. By combing 3D finite difference time domain (FDTD) and particle swarm optimization (PSO) method, TE0-TE1 and TE0-TE2 MOCs are optimal designed, which can efficiently convert TE0 mode to TE1 and TE2 modes with lengths of 9.39 µm and 11.27 µm, respectively. Results show that the insertion losses of 0-TE1 and TE0-TE2 MOCs, the corresponding working bandwidth are 128 nm (1511∼1639 nm) and 126 nm (1527∼1653 nm), respectively. Additionally, the MOCs can be fabricated with only single etch step with minimum feature size of 145 nm.

BackgroundUnderstanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown.MethodsWe constructed a powerful computational framework to characterize the host genetics-influenced immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets (N = 606,534 cells).ResultsWe found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly-expressed genetics-risk genes increased with the severity of COVID-19. Three cell-subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were specifically expressed in these three cell types, respectively. CCR1+CD16+monocytes and ABO+ megakaryocytes with significant up-regulated genes including S100A12, S100A8, S100A9, and IFITM1 confer higher risk to the cytokine storms among severe patients. CXCR6+ memory CD8+ T cells exhibit a notable polyfunctionality of multiple immunologic features, including elevation of proliferation, migration, and chemotaxis. Moreover, we observed a prominent increase in cell-cell interactions of both CCR1+ CD16+monocytes and CXCR6+ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues, and elevated interactions with epithelial cells could contribute to enhance the resident to lung airway for against COVID-19 infection.ConclusionsWe uncover a major genetics-modulated immunological shift between mild and severe infection, including an increase in up-regulated genetic-risk genes, excessive secreted inflammatory cytokines, and functional immune cell subsets contributing high risk to severity, which provides novel insights in parsing the host genetics-influenced immune cells for severe COVID-19.

Summary Controlling morphology of nanocrystals is one of the central tasks of nanoscience. In this work, we studied nanoscale metal-organic frameworks (nMOFs) from Hf-oxo clusters and linear dicarboxylate ligands with the aid of machine-learning methods for data analysis. Ligand solubility and modulator concentration were found to quantitatively predict the growth of nMOFs with a specific morphology, such as ultrathin two-dimensional film, hexagonal nanoplate, octahedron, cuboctahedron, concave octahedron, or hollow octahedron morphology. With these insights, we use epitaxy growth sequences to design nMOFs of desirable nanostructures with enhanced substrate transport and, hence, increased activities for catalytic olefin hydrogenation. This work highlights new opportunities in using machine learning to guide morphology engineering of nMOFs and other nanomaterials.

We propose and experimentally demonstrate a scheme for the photonic generation of pulsed microwave signals with selectable frequency based on spectral shaping and wavelength-to-time mapping (WTTM) technique. The frequency selectivity is realized by channel switching on an integrated silicon-on-insulator (SOI) spectral shaping chip. The incident signal is spectrally shaped by the asymmetric Mach-Zehnder interferometer (MZI) in the selected channel, and an optical spectrum with uniform free spectral range (FSR) can be generated in a broad bandwidth up to dozens of nanometers, implying large microwave signal duration after WTTM if a pulse light source with matched bandwidth is available. Microwave pulses of frequency from 3.6 GHz to 28.4 GHz with a fixed interval are experimentally generated respectively. The realization of eight microwave frequencies selectable with only one shared dispersive element (DE) required indicates high expansibility in the frequency cover range of our scheme by tuning the dispersion value in WTTM.

Background Primary biliary cholangitis (PBC) is a classical autoimmune disease, which is highly influenced by genetic determinants. Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with PBC susceptibility. However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear. Results We constructed a powerful computational framework to integrate GWAS summary statistics with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. Based on our multi-omics integrative analysis, 29 risk genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. By combining GWAS summary statistics with scRNA-seq data, we found that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals (Permuted P ORMDL3 showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). The ORMDL3+ cholangiocytes have prominently higher metabolism activity score than ORMDL3− cholangiocytes (P = 1.38 × 10–15). Compared with ORMDL3− cholangiocytes, there were 77 significantly differentially expressed genes among ORMDL3+ cholangiocytes (FDR ORMDL3+ cholangiocytes exhibited relatively high communications with macrophage and monocyte. Compared with ORMDL3− cholangiocytes, the VEGF signaling pathway is specific for ORMDL3+ cholangiocytes to interact with other cell populations. Conclusions To the best of our knowledge, this is the first study to integrate genetic information with single cell sequencing data for parsing genetics-influenced liver cells for PBC risk. We identified that ORMDL3+ cholangiocytes with higher metabolism activity play important immune-modulatory roles in the etiology of PBC. Graphical Abstract

The genetic origins of nanoscale extracellular vesicles in our body fluids remains unclear. Here, we perform a tracking analysis of urinary exosomes via RNA sequencing, revealing that urine exosomes mostly express tissue-specific genes for the bladder and have close cell-genetic relationships to the endothelial cell, basal cell, monocyte, and dendritic cell. Tracking the differentially expressed genes of cancers and corresponding enrichment analysis show urine exosomes are intensively involved in immune activities, indicating that they may be harnessed as reliable biomarkers of noninvasive liquid biopsy in cancer genomic diagnostics and precision medicine.

ImportancePrimary biliary cholangitis (PBC) is a classical autoimmune disease, which is highly influenced by genetic determinants. Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with PBC susceptibility. However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear.ObjectiveTo identify genetics-modulated functional liver cell subsets involved in the pathogenesis of PBC.Design, Setting, and ParticipantsIn this present study, 13,239 European participants were collected from IEU open GWAS project on PBC. There were 1,124,241 qualified SNPs used for GWAS analysis. Expression quantitative trait loci (eQTL) data across 49 tissues were downloaded from the GTEx database. Two single cell RNA sequencing (scRNA-seq) profiles and two bulk-based RNA transcriptomes were downloaded from the GEO database. Data collection and analyses were performed from August 2020 to June 2021.Main outcomes and measuresWe constructed a powerful computational framework to integrate GWAS summary statistics with scRNA-seq data to uncover genetics-modulated liver cell subpopulations.ResultsBased on our multi-omics integrative analysis, we found that 29 risk genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. Gene-property analysis revealed that four immune cell types, including Cst3+ dendritic cell, Chil3+ macrophage, Trbc2+ T cell, and Gzma+ T cell, were significantly enriched by PBC-risk genes. By combining GWAS summary statistics with scRNA-seq data, we found that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals (Permuted P < 0.05). The risk gene of ORMDL3 showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). Compared with ORMDL3+ cholangiocytes, there were 71 significantly highly-expressed genes among ORMDL3- cholangiocytes (FDR < 0.05), such as inflammatory cytokine genes CXCL8, CCL3, IFI16, and IRF1. These highly-expressed genes were significantly enriched in numerous biological pathways and functional terms associated with autoimmune diseases (FDR < 0.05).Conclusions and relevanceTo the best of our knowledge, this is the first study to integrate genetic information with single cell sequencing data for parsing genetics-influenced liver cells for PBC risk. We identified that ORMDL3- cholangiocytes play important immune-modulatory roles in the etiology of PBC.Key pointsQuestionAre genetics factors influenced liver cell subpopulations and its immune microenvironment for PBC?FindingsIn this comprehensive genomics study based on multi-omics data, genetic determinants were significantly enriched in cholangiocytes and immune cells including subsets of macrophage, dendritic cells, and T cells. ORMDL3- cholangiocytes have crucial immune-modulatory roles in developing PBC.MeaningFindings suggest that integration of single cell sequencing data with GWAS summary statistics contribute to pinpoint PBC-relevant cell types and risk genes.

Background Understanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown. Methods We constructed a computational framework to characterize the host genetics that influence immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets containing healthy controls and patients with mild, moderate, and severe symptom (N = 606,534 cells). We collected 10 predefined gene sets including inflammatory and cytokine genes to calculate cell state score for evaluating the immunological features of individual immune cells. Results We found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly expressed genes increased with the severity of COVID-19. Three cell subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were highly expressed in these three cell types, respectively. CCR1+CD16+monocytes and ABO+ megakaryocytes with significantly up-regulated genes, including S100A12, S100A8, S100A9, and IFITM1, confer higher risk to the dysregulated immune response among severe patients. CXCR6+ memory CD8+ T cells exhibit a notable polyfunctionality including elevation of proliferation, migration, and chemotaxis. Moreover, we observed an increase in cell-cell interactions of both CCR1+ CD16+monocytes and CXCR6+ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues. The enhanced interactions of CXCR6+ memory CD8+T cells with epithelial cells facilitate the recruitment of this specific population of T cells to airways, promoting CD8+T cell-mediated immunity against COVID-19 infection. Conclusions We uncover a major genetics-modulated immunological shift between mild and severe infection, including an elevated expression of genetics-risk genes, increase in inflammatory cytokines, and of functional immune cell subsets aggravating disease severity, which provides novel insights into parsing the host genetic determinants that influence peripheral immune cells in severe COVID-19.

PbZr0.52Ti0.48O3 nanocrystals were synthesized by a hydrothermal method. The effect of NaOH concentration, reaction temperature and time on nucleation and growth of PbZr0.52Ti0.48O3 nanocrystals was investigated. As the 0.05 mol/L PbZr0.52Ti0.48O3 precursors were heated at 200 °C for 21 h with NaOH concentration of 0.5 mol/L, the tetragonal PbZr0.52Ti0.48O3 nanocrystals were formed, and the grain size was more than 20 nm. With increasing the NaOH concentration from 0.5 to 1.5 mol/L, the grain size of PbZr0.52Ti0.48O3 nanocrystals decreased. When the precursors were heated at different temperatures (140 °C to 200 °C) for 21 h with 1.0 mol/L NaOH, single-phase PbZr0.52Ti0.48O3 nanocrystals were obtained at 160 °C to 200 °C. With increasing the reaction temperature from 160 °C to 200 °C, the grains size of PbZr0.52Ti0.48O3 nanocrystals increased from 5 nm to 9 nm. When the precursors were heated at 160 °C in different reaction times from 6 h to 21 h, the evolution from amorphous to crystalline PbZr0.52Ti0.48O3 nanocrystals in correlation with the reaction time was observed. Single crystalline PbZr0.52Ti0.48O3 nanocrystals with narrow size distribution (from 5 nm to 9 nm) were synthesized by controlling the NaOH concentration, reaction temperature and time. The obtained results can find potential application in preparing PbZr0.52Ti0.48O3 thin films on flexible substrates.

The accumulation of somatic driver mutations in the human genome enables cells to gradually acquire a growth advantage and contributes to tumor development. Great efforts on protein-coding cancer drivers have yielded fruitful discoveries and clinical applications. However, investigations on cancer drivers in non-coding regions, especially long non-coding RNAs (lncRNAs), are extremely scarce due to the limitation of functional understanding. Thus, to identify driver lncRNAs integrating multi-omics data in human cancers, we proposed a computational framework, DriverLncNet, which dissected the functional impact of somatic copy number alteration (CNA) of lncRNAs on regulatory networks and captured key functional effectors in dys-regulatory networks. Applying it to 5 cancer types from The Cancer Genome Atlas (TCGA), we portrayed the landscape of 117 driver lncRNAs and revealed their associated cancer hallmarks through their functional effectors. Moreover, lncRNA RP11-571M6.8 was detected to be highly associated with immunotherapeutic targets (PD-1, PD-L1, and CTLA-4) and regulatory T cell infiltration level and their markers (IL2RA and FCGR2B) in glioblastoma multiforme, highlighting its immunosuppressive function. Meanwhile, a high expression of RP11-1020A11.1 in bladder carcinoma was predictive of poor survival independent of clinical characteristics, and CTD-2256P15.2 in lung adenocarcinoma responded to the sensitivity of methyl ethyl ketone (MEK) inhibitors. In summary, this study provided a framework to decipher the mechanisms of tumorigenesis from driver lncRNA level, established a new landscape of driver lncRNAs in human cancers, and offered potential clinical implications for precision oncology. Keywords: cancer drivers, long non-coding RNAs, copy number alterations, cancer hallmarks, immunosuppression

Systematically tracking the tumor immunophenotype is required to understand the mechanisms of cancer immunity and improve clinical benefit of cancer immunotherapy. However, progress in current research is hindered by the lack of comprehensive immune activity resources and easy-to-use tools for biologists, clinicians, and researchers to conveniently evaluate immune activity during the “cancer-immunity cycle.” We developed a user-friendly one-stop shop web tool called TIP to comprehensively resolve tumor immunophenotype. TIP has the capability to rapidly analyze and intuitively visualize the activity of anticancer immunity and the extent of tumor-infiltrating immune cells across the seven-step cancer-immunity cycle. Also, we precalculated the pan-cancer immunophenotype for 11,373 samples from 33 The Cancer Genome Atlas human cancers that allow users to obtain and compare immunophenotype of pan-cancer samples. We expect TIP to be useful in a large number of emerging cancer immunity studies and development of effective immunotherapy biomarkers. TIP is freely available for use at http://biocc.hrbmu.edu.cn/TIP/. Significance: TIP is a one-stop shop platform that can help biologists, clinicians, and researchers conveniently evaluate anticancer immune activity with their own gene expression data. See related commentary by Hirano, p. 6536

With the continuous improvement of the power system and the deepening of electricity market reform, the trend of users’ active participation in power distribution is more and more significant. Demand response has become the promising focus of smart grid research. Providing reasonable incentive strategies for power grid companies and demand response strategies for customers plays a crucial role in maximizing the benefits of different participants. To meet different expectations of multiple agents in the same environment, deep reinforcement learning was adopted. The generative model of residential demand response strategy under different incentive policies can be trained iteratively through real-time interactions with the environmental conditions. In this paper, a novel optimization model of residential demand response strategy, based on a deep deterministic policy gradient (DDPG) algorithm, was proposed. The proposed work was validated with the actual electricity consumption data of a certain area in China. The results showed that the DDPG model could optimize residential demand response strategy under certain incentive policies. In addition, the overall goal of peak load-cutting and valley filling can be achieved, which reflects promising prospects of the electricity market.

Simple Summary The aim of our study was to evaluate the predictive performance of transcriptomic biomarkers to immune response. The study collected 22 transcriptomic biomarkers and constructed multiple benchmark datasets to evaluate their predictive performance of immune checkpoint blockade (ICB) response in pre-treatment patients with distinct ICB agents in diverse cancers. We found “Immune-checkpoint molecule” biomarkers PD-L1, PD-L2, CTLA-4 and IMPRES and the “Effector molecule” biomarker CYT showed significant associations with ICB response and clinical outcomes. These immune-checkpoint biomarkers and another immune effector IFN-gamma presented predictive ability in melanoma, urothelial cancer and clear cell renal-cell cancer. Interestingly, for anti-PD-1 therapy and anti-CTLA-4 therapy, the top-performing response biomarkers were usually mutually exclusive even though in the same biomarker category and most of biomarkers with outstanding predictive power were observed in patients with combined anti-PD-1 and anti-CTLA-4 therapy. Abstract Background: Immune checkpoint blockade (ICB) therapy has yielded successful clinical responses in treatment of a minority of patients in certain cancer types. Substantial efforts were made to establish biomarkers for predicting responsiveness to ICB. However, the systematic assessment of these ICB response biomarkers remains insufficient. Methods: We collected 22 transcriptome-based biomarkers for ICB response and constructed multiple benchmark datasets to evaluate the associations with clinical response, predictive performance, and clinical efficacy of them in pre-treatment patients with distinct ICB agents in diverse cancers. Results: Overall, “Immune-checkpoint molecule” biomarkers PD-L1, PD-L2, CTLA-4 and IMPRES and the “Effector molecule” biomarker CYT showed significant associations with ICB response and clinical outcomes. These immune-checkpoint biomarkers and another immune effector IFN-gamma presented predictive ability in melanoma, urothelial cancer (UC) and clear cell renal-cell cancer (ccRCC). In non-small cell lung cancer (NSCLC), only PD-L2 and CTLA-4 showed preferable correlation with clinical response. Under different ICB therapies, the top-performing biomarkers were usually mutually exclusive in patients with anti-PD-1 and anti-CTLA-4 therapy, and most of biomarkers presented outstanding predictive power in patients with combined anti-PD-1 and anti-CTLA-4 therapy. Conclusions: Our results show these biomarkers had different performance in predicting ICB response across distinct ICB agents in diverse cancers.

Improving energy efficiency management has become an important task for current electricity market participating entities, and monitoring consumption of pivotal appliances plays an important role in many applications. This paper focuses on detecting whether a residence hold a certain type appliance based on their electricity consumption. We propose a data-driven deep learning approach with attention mechanism to detect residential appliances from low-resolution aggregate energy consumption data. Firstly, the historical consumption profile of each user is divided into a specific length and labeled with the status of an appliance to generate training and test samples; Then a deep convolutional neural network model with attention mechanism is trained, and the trained model is utilized to classify the test samples; Finally, we obtain appliance status in a residence based on classification of multiple samples. Experiments are conducted on a low-frequency smart meter data set sampled once every 30 minutes, whose results show our proposed model performs better than hidden Markov model based algorithms and has good application prospects.

Post evaluation of power grid investment and construction project is an important part of power grid construction project. The effect of post evaluation will directly affect the quality level of project construction. Therefore, this paper proposes an improved post evaluation system based on rough set and fuzzy multi-level extension. Firstly, the main factors affecting the function effect of power grid construction project are analyzed. Then, the improved fuzzy multi-level extension evaluation model is constructed by combining fuzzy comprehensive evaluation theory with multi-level extension evaluation method. Finally, a 220 kV substation construction project is taken as an example to verify the validity and rationality of the model.

High functional heterogeneity of cancer cells poses a major challenge for cancer research. Single-cell sequencing technology provides an unprecedented opportunity to decipher diverse functional states of cancer cells at single-cell resolution, and cancer scRNA-seq datasets have been largely accumulated. This emphasizes the urgent need to build a dedicated resource to decode the functional states of cancer single cells. Here, we developed CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/ or http://202.97.205.69/CancerSEA/), the first dedicated database that aims to comprehensively explore distinct functional states of cancer cells at the single-cell level. CancerSEA portrays a cancer single-cell functional state atlas, involving 14 functional states (including stemness, invasion, metastasis, proliferation, EMT, angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation and quiescence) of 41 900 cancer single cells from 25 cancer types. It allows querying which functional states are associated with the gene (or gene list) of interest in different cancers. CancerSEA also provides functional state-associated PCG/lncRNA repertoires across all cancers, in specific cancers, and in individual cancer single-cell datasets. In summary, CancerSEA provides a user-friendly interface for comprehensively searching, browsing, visualizing and downloading functional state activity profiles of tens of thousands of cancer single cells and the corresponding PCGs/lncRNAs expression profiles.

One of the most fundamental questions in biology is what types of cells form different tissues and organs in a functionally coordinated fashion. Larger-scale single-cell sequencing and biology experiment studies are now rapidly opening up new ways to track this question by revealing substantial cell markers for distinguishing different cell types in tissues. Here, we developed the CellMarker database (http://biocc.hrbmu.edu.cn/CellMarker/ or http://bio-bigdata.hrbmu.edu.cn/CellMarker/), aiming to provide a comprehensive and accurate resource of cell markers for various cell types in tissues of human and mouse. By manually curating over 100 000 published papers, 4124 entries including the cell marker information, tissue type, cell type, cancer information and source, were recorded. At last, 13 605 cell markers of 467 cell types in 158 human tissues/sub-tissues and 9148 cell makers of 389 cell types in 81 mouse tissues/sub-tissues were collected and deposited in CellMarker. CellMarker provides a user-friendly interface for browsing, searching and downloading markers of diverse cell types of different tissues. Furthermore, a summarized marker prevalence in each cell type is graphically and intuitively presented through a vivid statistical graph. We believe that CellMarker is a comprehensive and valuable resource for cell researches in precisely identifying and characterizing cells, especially at the single-cell level.

Substantial cancer genome sequencing efforts have discovered many important driver genes contributing to tumorigenesis. However, very little is known about the genetic alterations of long non‐coding RNAs (lncRNAs) in cancer. Thus, there is a need for systematic surveys of driver lncRNAs. Through integrative analysis of 5918 tumors across 11 cancer types, we revealed that lncRNAs have undergone dramatic genomic alterations, many of which are mutually exclusive with well‐known cancer genes. Using the hypothesis of functional redundancy of mutual exclusivity, we developed a computational framework to identify driver lncRNAs associated with different cancer hallmarks. Applying it to pan‐cancer data, we identified 378 candidate driver lncRNAs whose genomic features highly resemble the known cancer driver genes (e.g. high conservation and early replication). We further validated the candidate driver lncRNAs involved in ‘Tissue Invasion and Metastasis’ in lung adenocarcinoma and breast cancer, and also highlighted their potential roles in improving clinical outcomes. In summary, we have generated a comprehensive landscape of cancer candidate driver lncRNAs that could act as a starting point for future functional explorations, as well as the identification of biomarkers and lncRNA‐based target therapy.

Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids.

Characterizing functions of long noncoding RNAs (lncRNAs) remains a major challenge, mostly due to the lack of lncRNA-involved regulatory relationships. A wide array of genome-wide expression profiles generated by gene perturbation have been widely used to capture causal links between perturbed genes and response genes. Through annotating more than 600 gene perturbation profiles, over 354,000 causal relationships between perturbed genes and lncRNAs were identified. This large-scale resource of causal relations inspired us to develop a novel computational approach LnCAR for inferring lncRNAs' functions, which showed a higher accuracy than the co-expression based approach. By application of LnCAR to the cancer hallmark processes, we identified 38 lncRNAs involved in distinct carcinogenic processes. The "activating invasion & metastasis" related lncRNAs were strongly associated with metastatic progression in various cancer types and could act as a predictor of cancer metastasis. Meanwhile, the "evading immune destruction" related lncRNAs showed significant associations with immune infiltration of various immune cells and, importantly, can predict response to anti-PD-1 immunotherapy, suggesting their potential roles as biomarkers for immune therapy. Taken together, our approach provides a novel way to systematically reveal functions of lncRNAs, which will be helpful for further experimental exploration and clinical translational research of lncRNAs. Funding: This work was supported in part by the National High Technology Research and Development Program of China [863 Program, Grant Nos. 2014AA021102], the National Program on Key Basic Research Project [973 Program, Grant Nos. 2014CB910504], the National Natural Science Foundation of China [Grant Nos. 91439117, 61473106, 61573122], the China Postdoctoral Science Foundation (2016M600260), Wu lien-teh youth science fund project of Harbin medical university [Grant Nos. WLD-QN1407], Special funds for the construction of higher education in Heilongjiang Province [Grant Nos. UNPYSCT-2016049], and the Heilongjiang Postdoctoral Foundation (LBH-Z16098). Declaration of Interest: We declare that we have no competing financial interests regarding this work. There are no relationships with industry.

MicroRNAs (miRNAs) are widely expressed in organisms and are implicated in the regulation of most biological functions. The present study investigated the association of plasma miRNAs with the clinical outcomes of dual antiplatelet therapy in coronary artery disease (CAD) patients who underwent percutaneous coronary intervention (PCI). Plasma miRNA levels were screened using high-throughput Illumina sequencing to evaluate the antiplatelet efficacy of clopidogrel and aspirin. Six plasma miRNAs (miR-126, miR-130a, miR-27a, miR-106a, miR-21, and miR-142) were associated with clopidogrel-treated platelet aggregation. These miRNAs were validated in a prospective cohort of 1230 CAD patients using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). High plasma miR-142 levels were associated with a high risk of major adverse cardiovascular events (MACE), with a hazard ratio (95% confidence interval) of 1.83 (1.30–2.59) at a false discovery rate of

At present, the management systems and enterprises demand have already changed because of the environments-changing, which puts new requirements and directions for grid enterprises. This research comprehensively analyzes the current situation and problems of the project management, and then explores the profound impact of the new situation to the project management system in power grid enterprises. After that, this research also summarizes the reforms and innovation experiences of the power grid enterprises in the project management, and extracts the project management characteristics of the grid enterprises under the new situation. At the same time, based on the project management practices, we provide a new method and implementation path for grid enterprises. This method and path provide theoretical and methodological support for relevant grid enterprises to improve their management efficiency and improve organizational performance.

In this paper, theoretical modeling and numerical simulations of a high-performance polarization beam splitter (PBS) based on hetero-anisotropic metamaterials are proposed on the lithium-niobate-on-insulator (LNOI) platform. The hetero-anisotropic metamaterials constructed by sub-wavelength gratings (SWGs) can be regarded as effective anisotropy medium, which exhibits strong birefringence without breaking the geometrical symmetry, contributing to the formation of PBS. Rather than the principle of PBS based on beat-length difference of transverse electric (TE) polarization and transverse magnetic (TM) polarization, the device can realize polarization beam splitting in single beat length, and the footprint of the proposed PBS can be reduced to 8 µm × 160 µm (with S-bend). The simulation results show that the bandwidth is 185 nm (1450∼1634 nm) for TE polarization while the bandwidth is 85 nm (1490∼1575 nm) for TM polarization when the polarization extinction ratio is >20 dB. Furthermore, the insertion loss is less than 1 dB in the range of 1450 to 1650 nm, for both TE and TM polarization. Additionally, the proposed device proves strong robustness of the fabrication tolerance.

Bound states in the continuum (BICs) have become a new trend in the area of metaoptics and nanophotonics. Strong interactions in electromagnetic fields are analogous to electron transitions in atoms, giving rise to BICs with vanishing radiative losses. However, it is still a great challenge to realize BICs in the lossy plasmonic systems. For this problem, we propose a supercavity-like plasmonic nanocavity consisting of an Au nanorod deposited inside an Au symmetric split ring, and explore the possibility of exciting quasi-BICs that own finite but high quality (Q) factors. In such hybrid configuration, the excited resonances can be easily engineered by modifying the rotation angle or the length of the Au nanorod. With the integration of such nanocavity in silicon (Si) waveguides, sharp transmission spectra could be achieved with fiber-chip in-parallel excitations and detections. Besides, the ultracompact geometry of this plasmonic nanocavity provides a route to boost enhanced electric fields, thus improving sensing performances significantly. Our study not only offers a novel platform for the realization of chip-scale quasi-BICs, but extends functionalities of photonic-plasmonic hybrid circuits.

Despite the complex vascular effects of dexmedetomidine (DEX), its actions on human pulmonary resistance arteries remain unknown. The present study tested the hypothesis that DEX inhibits vascular tension in human pulmonary arteries through the endothelial nitric oxide synthase (eNOS) mediated production of nitric oxide (NO). Pulmonary artery segments were obtained from 62 patients who underwent lung resection. The direct effects of DEX on human pulmonary artery tension and changes in vascular tension were determined by isometric force measurements recorded on a myograph. Arterial contractions caused by increasing concentrations of serotonin with DEX in the presence or absence of L-NAME (endothelial nitric oxide synthase inhibitor), yohimbine (α2-adrenoceptor antagonist) and indomethacin (cyclooxygenase inhibitor) as antagonists were also measured. DEX had no effect on endothelium-intact pulmonary arteries, whereas at concentrations of 10(-8)~10(-6) mol/L, it elicited contractions in endothelium-denuded pulmonary arteries. DEX (0.3, 1, or 3×10(-9) mmol/L) inhibited serotonin-induced contraction in arteries with intact endothelium in a dose-dependent manner. L-NAME and yohimbine abolished DEX-induced inhibition, whereas indomethacin had no effect. No inhibitory effect was observed in endothelium-denuded pulmonary arteries. DEX-induced inhibition of vasoconstriction in human pulmonary arteries is mediated by NO production induced by the activation of endothelial α2-adrenoceptor and nitric oxide synthase.