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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.

Enhancing and expanding the manipulated range of room-temperature valley polarization at off-resonance wavelength is extremely crucial to developing various functional valleytronic devices. Although these have been realized through the double-resonance strategy or twist-angle engineering, the demand for electrical control over the concepts remains elusive. Here, we fabricate a gate-tunable double-resonance chiral microstructure using a molybdenum disulfides (MoS

The multimode power splitter plays an essential role in on-chip mode-division multiplexing systems. In this work, a broadband dual-mode optical power splitter based on the self-imaging effect in the multimode interference (MMI) engineered by bricked subwavelength gratings (BSWGs) is proposed. The dual-mode power splitter is optimized by combining the 3D finite difference time domain (FDTD) and the particle swarm optimization (PSO) method. For the input T E 0 mode, the insertion loss (IL) and the imbalance (IB) are lower than 0.55 dB and 0.65 dB, while the IL and IB for the input T E 1 mode are, respectively, lower than 0.7 dB and 0.37 dB over 150 nm bandwidth in the wavelength range of 1.5–1.65 µm. In addition, the cross talk (CT) is lower than − 25 d B for the two modes over this wavelength range. Furthermore, the minimum feature size of the gratings is as large as 135 nm, and only a single etch step is required, which is beneficial for the fabrication.

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.

Hydrogel is a kind of new functional polymer material with application value and economic benefit. It has wide application in biomedical field because of its good biocompatibility and hydrophilicity.In this work, chitosan hydrogel was prepared by free radical graft copolymerization using natural biodegradable polymer chitosan as the carrier, temperature sensitive N-isopropylacrylamide (NIPAm) and pH sensitive 2‑acrylamido-2-methylpropane (AMPS) as monomers. The adsorption and drug release properties of chitosan hydrogel are discussed emphatically.The adsorption performance of chitosan hydrogel on copper ions showed that the temperature and the concentration of copper ions had influence on the adsorption. When the concentration of copper ions was 800 mg/L at 25°C, the equilibrium adsorption capacity was 225.8 mg/g, quasi-second order kinetic equation was suitable for describing the adsorption process. With the methylene blue as the drug model, the controlled release results showed that the temperature and the initial drug dosage had great effect on controlled release performance, and the cumulative release rate of 48 h was about 70%, which could achieve sustained release and controlled release to a certain extent.The experimental results also showed that the chitosan hydrogel can be completely degraded for about 20 days with excellent degradation performance. In conclusion, chitosan hydrogel has broad application prospects in drug controlled release, tissue engineering, environmental engineering and so on.

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.

Tomato picking is a relatively complex production link in the overall process of tomato production. It is the labor-intensive work and it is urgent to use automated picking of tomato fruits. Therefore, this article mainly uses the depth camera and the six-degree-of-freedom manipulator to study the construction of the visual servo system, the modeling and kinematics simulation of the manipulator in the automatic tomato picking robot. We have established a robot visual servo system that can be directly grasped based on the robot operating system(ROS). The system acquires fruit targets through visual sensors, uses the depth and color information of the targets, and uses coordinated control strategies for hands and eyes at different distances. The tomato picking space is divided into multiple picking sub-regions, and the picking path planning is combined with the obstacle avoidance algorithm to improve the overall efficiency of tomato picking.

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.

A refractive index sensor based on all-grating racetrack resonators is proposed and demonstrated in this paper. The structure consists of two subwavelength grating (SWG) waveguide-based racetrack resonators, a straight SWG bus waveguide and a SWG waveguide-based open racetrack. A sharp Fano resonance can be obtained by the mutual coupling of the two SWG racetrack resonators. Theoretical analysis and numerical simulations show that the introduction of grating can enhance the interaction between the sensor and the measured liquid analyte, thus increasing the sensitivity of the device. Calculated sensitivity of 500 nm/RIU around wavelength 1550 nm is achieved, and the figure of merit is 2000 RIU−1. The extinction ratio of this device can reach 21.53 dB.

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

Copy number alterations (CNAs) of lincRNAs act as one of important mechanisms in disrupting lincRNA expression which may play critical roles during tumorigenesis in lung adenocarcinoma (LUAD). The copy number alterations of lincRNAs can mark the spectrum of cancer progression and may serve as biomarkers for prognosis in LUAD, however it is rarely studied. We analyzed RNASeq data for 488 LUAD patients from TCGA portal and 58 healthy subjects to identify prognostic lincRNAs predictive of patient survival. Computational analysis entailing integration of expression and copy number alteration data revealed five prognostic lincRNAs: RBPMS-AS1, TDRKH-AS1, LINC00578, RP11-470 M17.2 and LINC00941. The copy number alterations in the LINC00578 and RP11-470 M17.2 genes were positively associated with the longer overall survival of LUAD patients. The CNA in LINC00941 was negatively associated with the longer overall survival. Copy number amplification significantly correlated with increased expression of TDRKH-AS1, which regulates telomere organization and EZH2-mediated epigenetic silencing of CDKN1A, CDKN1B and IL24. Decreased survival of LUAD patients was associated with high LINC00941 expression. The LINC00941 regulates the PI3K-AKT signaling pathway, focal adhesion by influencing potential targets, such as KRAS proto-oncogene GTPase and VEGFC. These lincRNA-based prognostic biomarkers may destroy important cancer-related biological processes contributing to LUAD prognosis. In summary, we demonstrate the prognostic potential of four differentially expressed lincRNAs with copy number alterations (RBPMS-AS1, TDRKH-AS1, LINC00578 and RP11-470 M17.2) that are positively associated with longer overall survival of LUAD patients. One differentially expressed lincRNA LINC00941 with copy number alterations was negatively associated with longer overall survival of LUAD patients.

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.

Hypertension is one of the most common complications of chronic kidney disease (CKD). Some research has indicated that changes in large artery function especially caused by thromboxane A2 (TXA2) may be a novel factor acting to induce hypertension in CKD. We studied the 5/6 nephrectomy rat model and measured serum levels of creatinine (Cr), calcium (Ca), phosphorus (P), TXA2-stable metabolites (thromboxane B2, TXB2), and caudal artery pressure after nephrectomy. The tension variations in thoracic aortas were measured after stimulating by vasoconstrictor/vasodilator using the cumulative concentration administration method and then tested the expression of TXA2 receptors in the thoracic aortas through western blots. The CKD rats developed uremia, electrolyte imbalances，and hypertension. They also exhibited a significant increase in TXB2 concentration. The aortic rings of CKD rats showed an increased contraction response to U46619 (a TXA2 analogue) and the expression of TXA2 receptors also enhanced. In the meanwhile, the diastolic function decreased in the CKD group. Our results demonstrate that the impairment of artery contractile function caused by the increase of TXA2 receptors on the wall of aortic rings may be involved in hypertension in CKD rats.

A silicon on-chip spectral shaper based on a Sagnac loop incorporating a chirped multi-mode waveguide Bragg grating (WBG) for linearly chirped microwave waveform generation is fabricated and demonstrated. The transmission spectrum of the spectral shaper displays low insertion loss characteristic due to the application of edge coupling taper and multi-mode waveguide based grating. An up-chirped microwave waveform with bandwidth as large as 44 GHz is generated by mapping the spectrum profile of the spectral shaper to the temporal domain through a dispersion fiber. The instantaneous frequency of the generated signal shows good linearity benefiting from the weak modulation strength in the multi-mode WBG. The low insertion loss performance as well as the low dispersion value required in our design presents feasibility in further integration with on-chip dispersion.

Being a new class of materials, transition metal dichalcogenides are paving the way for applications in atomically thin optoelectronics. However, the intrinsically weak light-matter interaction and the lack of manipulation ability has lead to poor light emission and tunable behavior. Here, we investigate the fluorescence characteristic of monolayer molybdenum disulfide on a metal narrow-slit grating, where a highly efficient, 471 times photoluminescence enhancement are realized, based on the hybrid surface plasmon polaritons resonances and the decreased influence of substrate. Moreover, the emitted intensity and polarization are controllable due to the polarization-dependent characteristic and anisotropy of grating. The manipulations of light-matter interactions in this special system provide a new insight into the fluorescent emission process and open a new avenue for high-performance low dimensional materials devices designs.

With the development of distributed energy, many novel electricity market mechanisms are emerging. The consumer-centric electricity market, which includes the peer-to-peer (P2P) model and the community-based market, arouses more attention in academia and industry area. This paper applies the Deep Q-Learning (DQN) for autonomous agents in the consumer-centric electricity market. Both the local energy priority transactions and public shared energy facilities are taking into consideration. We generated a test dataset and compared results in 5 different scenarios. This study verifies that the applied data-driven methods can handle the peer-to-peer (P2P) decision-making problem as well as promote the profitability of the whole community in the electricity market. Furthermore, multi-agent cooperation with public resources is more appropriate than other situations.

Engineered organoids by sequential introduction of key mutations could help modeling the dynamic cancer progression. However, it remains difficult to determine gene paths which were sufficient to capture cancer behaviors and to broadly explain cancer mechanisms. Here, as a case study of colorectal cancer (CRC), functional and dynamic characterizations of five types of engineered organoids with different mutation combinations of five driver genes (APC, SMAD4, KRAS, TP53, and PIK3CA) showed that sequential introductions of all five driver mutations could induce enhanced activation of more hallmark signatures, tending to cancer. Comparative analysis of engineered organoids and corresponding CRC tissues revealed sequential introduction of key mutations could continually shorten the biological distance from engineered organoids to CRC tissues. Nevertheless, there still existed substantial biological gaps between the engineered organoid even with five key mutations and CRC samples. Thus, we proposed an integrative strategy to prioritize gene cascading paths for shrinking biological gaps between engineered organoids and CRC tissues. Our results not only recapitulated the well-known adenoma–carcinoma sequence model (e.g., AKST-organoid with driver mutations in APC, KRAS, SMAD4, and TP53), but also provided potential paths for delineating alternative pathogenesis underlying CRC populations (e.g., A-organoid with APC mutation). Our strategy also can be applied to both organoids with more mutations and other cancers, which can improve and innovate mechanism across cancer patients for drug design and cancer therapy.

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.