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SummaryT-cell receptor (TCR)-engineered T cells can precisely recognize a broad repertoire of targets derived from both intracellular and surface proteins of tumor cells. TCR-T adoptive cell therapy has shown safety and promising efficacy in solid tumor immunotherapy. However, antigen-specific functional TCR screening is time-consuming and expensive, which limits its application clinically. Here, we developed a novel integrated antigen-TCR screening platform based on droplet microfluidics technology, enabling high-throughput peptide-major histocompatibility complex (pMHC) library-to-TCR library screening with high sensitivity and low background signal. We introduced DNA barcoding technology to label peptide antigen candidate-loaded antigen-presenting cells (APCs) and Jurkat reporter cells to check the specificity of pMHC-TCR candidates. Coupled with the next-generation sequencing pipeline, interpretation of the DNA barcodes and the gene expression level of the Jurkat T-cell activation pathway provided a clear peptide-MHC-TCR recognition relationship. Our proof-of-principle study demonstrates that the platform could achieve unbiased pMHC-TCR library-on-library screening, which is expected to be used in the cross-reactivity and off-target testing of candidate pMHC-TCR libraries in clinical applications.

Cartilaginous fish have three immunoglobulin (Ig) isotypes, IgM, IgW, and IgNAR. IgNAR is a heavy chain homodimer devoid of light chains. It is firstly identified in nurse shark serum and present as multiple clusters in shark genome. The adaptive humoral immune response for sharks is dominated by monomeric IgM and IgNAR. The antigen-binding variable domain of IgNAR is vNAR, a type of single domain antibody. SdAbs are promising candidates in diagnostics and therapeutics. Whitespotted bamboo shark (Chiloscyllium plagiosum) is a small inshore bottom-dwelling shark species (up to 1m of adult length) living in the coast areas of Eastern Pacific and Eastern Indian. Here we identified Immunoglobin new antigen receptor gene (IgNAR) from the genome of Chiloscyllium plagiosum, and investigated the transcription profiling of vNAR immune repertoire to characterize the immune landscape by high throughput sequencing.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the pandemic of coronavirus disease 2019 (COVID-19). Great international efforts have been put into the development of prophylactic vaccines and neutralizing antibodies. However, the knowledge about the B cell immune response induced by the SARS-CoV-2 virus is still limited. Here, we report a comprehensive characterization of the dynamics of immunoglobin heavy chain (IGH) repertoire in COVID-19 patients. By using next-generation sequencing technology, we examined the temporal changes in the landscape of the patient's immunological status and found dramatic changes in the IGH within the patient's immune system after the onset of COVID-19 symptoms. Although different patients have distinct immune responses to SARS-CoV-2 infection, by employing clonotype overlap, lineage expansion, and clonotype network analyses, we observed a higher clonotype overlap and substantial lineage expansion of B cell clones 2-3 weeks after the onset of illness, which is of great importance to B-cell immune responses. Meanwhile, for preferences of V gene usage during SARS-CoV-2 infection, IGHV3-74 and IGHV4-34, and IGHV4-39 in COVID-19 patients were more abundant than those of healthy controls. Overall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development as well as mechanistic research.

As the COVID-19 spreads across the world, many nations impose lockdown measures at the early stage of the pandemic to prevent the spread of the disease. Controversy surrounds the lockdown as it is a choice between economic freedom and public health. The ultimate solution to a pandemic is to vaccinate a massive population to achieve herd immunity. However, the whole vaccination programme is a long and complicated process. The virus and the vaccine will persist for quite a long time. How to gradually ease the lockdown based on vaccination progress is an important issue, as both economic and epidemiological issues are involved. In this paper, we extend the classic SIR model to find optimal decision making to balance between economy and public health in the process of vaccination rollout. The model provides an approach of vaccine value estimation. Our results provide scientific suggestion for policymakers to make important decisions about when to start the lockdown and how strong it should be over the entire vaccination cycle.

Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening medical condition with a high mortality and disability rate. aSAH has an unclear pathogenesis, and limited treatment options are available. Here, we aimed to identify critical genes involved in aSAH pathogenesis using peripheral blood gene expression data of 43 patients with aSAH due to ruptured intracranial aneurysms and 18 controls with headache, downloaded from Gene Expression Omnibus. These data were used to construct a co-expression network using weighted gene co-expression network analysis (WGCNA). The biological functions of the hub genes were explored, and critical genes were selected by combining with differentially expressed genes analysis. Fourteen modules were identified by WGCNA. Among those modules, red, blue, brown and cyan modules were closely associated with aSAH. Moreover, 364 hub genes in the significant modules were found to play important roles in aSAH. Biological function analysis suggested that protein biosynthesis-related processes and inflammatory responses-related processes were involved in the pathology of aSAH pathology. Combined with differentially expressed genes analysis and validation in 35 clinical samples, seven gene (CD27, ANXA3, ACSL1, PGLYRP1, ALPL, ARG1, and TPST1) were identified as potential biomarkers for aSAH, and three genes (ANXA3, ALPL, and ARG1) were changed with disease development, that may provide new insights into potential molecular mechanisms for aSAH.

Currently circulating SARS-CoV-2 Omicron variants feature highly mutated spike proteins with extraordinary abilities in evading acute-infection-induced germline antibodies isolated earlier in the pandemic. We identified that memory B cells from Delta variant breakthrough-infection patients expressed antibodies with more extensive somatic hypermutations (SHMs) allowing isolation of a number of broadly neutralizing antibodies with activities against heterologous variants of concerns (VOCs) including Omicron variant. Structural studies identified that SHM introduced altered amino acids and highly unusual HCDR2 insertions respectively in two representative broadly neutralizing antibodies - YB9-258 and YB13-292. Previously, insertion/deletion were rarely reported for antiviral antibodies except for those induced by HIV-1 chronic infections. Identified SHMs involved heavily in epitope recognition, they broadened neutralization breadth by rendering antibodies resistant to VOC mutations highly detrimental to previously isolated antibodies targeting similar epitopes. These data provide molecular mechanisms for enhanced immunity to heterologous SARS-CoV-2 variants after repeated antigen exposures with implications for future vaccination strategy.

We have reported that SMIP004, an inhibitor of S-phase kinase-associated protein 2 (Skp2), displays antidepressant-like activities in stress-naïve and chronically stressed mice. Here, we investigated the antidepressant-like effect of C1, another inhibitor of Skp2, in mouse models following acute or chronic drug administration at different doses and treatment times by using the tail suspension test (TST), forced swimming test (FST), and social interaction test (SIT). The time- and dose-dependent results showed that the antidepressant-like effect of C1 occurred 8 days after the drug treatment, and C1 produced antidepressant-like activities at the dose of 5 and 10 but not 1 mg/kg in male or female mice. C1 administration (5 mg/kg) also induced antidepressant-like effects in stress-naïve mice in a three-times administration mode within 24 h (24, 5, and 1 h before the test) but not in an acute administration mode (1 h before the test). The C1 and fluoxetine co-administration produced additive effect on depression-like behaviors in stress-naïve mice. The antidepressant-like effect of C1 was not associated with the change in locomotor activity, as no increased locomotor activity was observed in different treatment modes. Furthermore, the long-term C1 treatment (5 mg/kg) was found to ameliorate the depression-like behaviors in chronic social defeat stress-exposed mice, suggesting that C1 can produce antidepressant-like actions in stress conditions. Since C1 is a specific inhibitor of Skp2, our results demonstrate that inhibition of Skp2 might be a potential strategy for the treatment of depression, and Skp2 may be potential target for the development of novel antidepressants.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development. This work is part of the ‘‘SpatioTemporal Omics Consortium’’ (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org. We would like to thank the MOTIC China Group, Rongqin Ke (Huaqiao University, Xiamen, China), Jiazuan Ni (Shenzhen University, Shenzhen, China), Wei Huang (Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China), and Jonathan S. Weissman (Whitehead Institute, Boston, USA) for their help. This work was supported by the grant of Top Ten Foundamental Research Institutes of Shenzhen, the Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), and the Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011); Longqi Liu was supported by the National Natural Science Foundation of China (31900466) and Miguel A. Esteban’s laboratory at the Guangzhou Institutes of Biomedicine and Health by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), National Natural Science Foundation of China (92068106), and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). Sí

The contradiction between the development of urban agglomerations and ecological protection has long been a challenging issue. China has experienced an astonishing expansion of its urban scale in the past 40 years, and nearly 783 million of the nation’s people now live in cities. Beijing–Tianjin–Hebei, the Yangtze River Delta and the Pearl River Delta have been prioritized to become world-class clusters by 2020. The health effects of air pollution in these three urban agglomerations are becoming increasingly formidable. Given these conditions, using the daily mean PM2.5 concentration in 40 cities from January 2014 to December 2016, this research explored the spatial–temporal characteristics of PM2.5 concentrations in these three urban agglomerations. The annual mean PM2.5 concentrations in Beijing–Tianjin–Hebei, the Yangtze River Delta and the Pearl River Delta are 35.39 µg/m3, 53.72 µg/m3 and 78.54 µg/m3, respectively. Compared with the other two urban agglomerations, abundant rainfall causes the Pearl River Delta to have the lowest PM2.5 level. Furthermore, a general regression neural network (GRNN) method is developed to predict the PM2.5 concentration in these clusters on the second day, with inputs including the average, maximum and minimum temperature; average, maximum and minimum atmosphere; total rainfall; average humidity; average and maximum wind speed; and the PM2.5 concentration measured 1 day ahead. The results indicate that the GRNN method can precisely predict the concentration level in these clusters, and it is especially useful for the Pearl River Delta, as the underlying influence mechanism is more specified in this cluster than in the others. Importantly, this 1-day-ahead forecasting of PM2.5 concentrations can raise awareness among the public to improve their precautionary behaviours and help urban planners to provide corresponding support.

It has been reported that pre-stimulation of the innate immune system in animals can prevent chronic stress-induced depression- and anxiety-like behaviors in animals, suggesting the possibility that innate immune stimulants may prevent the pathogenesis of neuropsychiatric disorders. Alcohol use, especially when it begins in adolescence, is a risk factor for the development of neuropsychiatric disorders in adulthood. Preventing the pathological changes induced by alcohol exposure in adolescence could be of great importance for improving human mental health. Here, we investigated whether pre-stimulation of the innate immune system can prevent the behavioral abnormalities in a disease model induced by adolescent intermittent alcohol exposure (AIE). The results showed that a single injection of lipopolysaccharide (LPS) injection (100 μg/kg) one day before alcohol exposure prevented the AIE-induced depression- and anxiety-like behaviors in the tail suspension test, forced swimming test, sucrose preference test, elevated pluz maze test, light-dark test, and open field test in adult mice. Single LPS injection (100 μg/kg) before alcohol exposure also transformed the AIE-induced neuroinflammatory responses in the hippocampus and prefrontal cortex in adult mice to an anti-inflammatory phenotype. Suppression of the innate immune response by minocycline pretreatment abolished the preventive effect of LPS on AIE-induced abnormalities and neuroinflammatory responses in the hippocampus and prefrontal cortex in adult mice. These results indicate that pre-stimulation of the innate immune system may prevent the AIE-induced depression- and anxiety-like behaviors in adult mice by preventing neuroinflammation. This may help to develop new strategies to prevent neuropsychiatric disorders induced by adolescent alcohol exposure.

The field of antibody development is under pressure to meet rising demands for speed, cost-effectiveness, efficacy, reliability, and large-scale production. It is costly and time-consuming to immunize animals and build a single-domain antibody (sdAb) library for each target. Using the variable domain (VHH) of heavy-chain only antibodies (HcAbs) derived from blood samples of 75 non-immunized camelid animals (51 alpacas, 13 llamas, 11 Bactrian camels), and spleens from two Bactrian camels, a naïve sdAb library with extensive megadiversity and reusability was constructed. The library was evaluated using next-generation DNA sequencing (NGS) and was found to contain hundreds of billions of unique clones. To confirm the availability of target-specific VHHs, a naive library was screened for a variety of targets. At least two VHH candidates were extracted for each target using a 20-day selection pipeline. Some binders had ultrahigh potencies, with binding affinities in the nanomolar range. This naïve library, in particular, offers the possibility of acquiring unique antibodies targeting antigens of interest with low feasible dissociation constant (kD) without the time, effort, and price associated in producing antibodies in animals via antigen injection. Overall, the study shows that the megadiverse naïve library provides a rapid, adaptable, and easy platform for antibody creation, emphasizing its therapeutic and diagnostic implications.

Although database search tools originally developed for shotgun proteome have been widely used in immunopeptidomic mass spectrometry identifications, they have been reported to achieve undesirably low sensitivities and/or high false positive rates as a result of the hugely inflated search space caused by the lack of specific enzymic digestions in immunopeptidome. To overcome such a problem, we have developed a motif-guided immunopeptidome database building tool named IntroSpect, which is designed to first learn the peptide motifs from high confidence hits in the initial search and then build a targeted database for refined search. Evaluated on three representative HLA class I datasets, IntroSpect can improve the sensitivity by an average of 80% comparing to conventional searches with unspecific digestions while maintaining a very high accuracy (∼96%) as confirmed by synthetic validation experiments. A distinct advantage of IntroSpect is that it does not depend on any external HLA data so that it performs equally well on both well-studied and poorly-studied HLA types, unlike a previously developed method SpectMHC. We have also designed IntroSpect to keep a global FDR that can be conveniently controlled, similar to conventional database search engines. Finally, we demonstrate the practical value of IntroSpect by discovering neoantigens from MS data directly. IntroSpect is freely available at https://github.com/BGI2016/IntroSpect.

In this paper, we extend the classic SIR model to find an optimal lockdown policy to balance between the economy and people’s health during the outbreak of COVID-19. In our model, we intend to solve a two phases optimisation problem: policymakers control the lockdown rate to maximise the overall welfare of the society; people in different health statuses take different decisions on their working hours and consumption to maximise their utility. We develop a novel method to estimate parameters for the model through various additional sources of data. We use the Cournot equilibrium to model people’s behaviour. The analysis of simulation results provides scientific suggestions for policymakers to make critical decisions on when to start the lockdown and how strong it should be during the whole period of the outbreak.

Consensus algorithm is the core of blockchain and it plays a crucial role in the performance of the blockchain. In general, there are two types of blockchain consensus algorithms: the Bitcoin-like Nakamoto consensus (NC) algorithms and the Byzantine fault tolerance (BFT) consensus algorithms. These two types of consensus algorithms are fundamentally different in forms and hard to be compared. However, currently, they are often used interchangeably for similar blockchains, which naturally raises a question of "given a network, which consensus would have the best performance in practice''. In this paper, we propose AlphaBlock, a theoretical framework for the performance comparison of blockchain consensus algorithms, in particular, NC algorithms and BFT algorithms. To make fair comparisons, AlphaBlock captures the most important advantages and disadvantages of both categories. Moreover, we incorporate some of the key features of the practical blockchain networks. The results show that BFT algorithms have a superior performance over NC algorithms in most cases in both throughput and latency, expect for the low latency region in large networks, where the NC algorithms show strong competence to the best BFT algorithms.

Estimating plant nitrogen concentration (PNC) has been conducted using vegetation indices (VIs) from UAV-based imagery, but color features have been rarely considered as additional variables. In this study, the VIs and color moments (color feature) were calculated from UAV-based RGB images, then partial least square regression (PLSR) and random forest regression (RF) models were established to estimate PNC through fusing VIs and color moments. The results demonstrated that the fusion of VIs and color moments as inputs yielded higher accuracies of PNC estimation compared to VIs or color moments as input; the RF models based on the combination of VIs and color moments (R2ranging from 0.69 to 0.91 and NRMSE ranging from 0.07 to 0.13) showed similar performances to the PLSR models (R2ranging from 0.68 to 0.87 and NRMSE ranging from 0.10 to 0.29); Among the top five important variables in the RF models, there was at least one variable which belonged to the color moments in different datasets, indicating the significant contribution of color moments in improving PNC estimation accuracy. This revealed the great potential of combination of RGB-VIs and color moments for the estimation of rice PNC.

Rice yield is not only influenced by factors of varieties and managements, but also by environmental factors. In this study, agronomic trait data of rice and climate data in eastern China were collected, and rice yields were predicted using a variety of algorithms, including the non-linear tool of feed-forward backpropagation neural networks (FFBN) and the linear model of partial least squares regression (PLSR). The results showed that both the agronomic traits and the climate data were significantly related with rice yield. The PLSR model showed that covariates occurred among the parameters, and modifications should be considered for climate data-based modelling. The FFBN model demonstrated better prediction performance than that of PLSR, in which the relation coefficient (R2) and root mean square error (RMSE) were 0.611 vs. 0.374 and 0.578 vs. 0.865 ton/ha using climate data, respectively, and 0.742 vs. 0.689 and 0.556 vs. 0.608 using agronomic trait data, respectively. When using fused data the R2 and RMSE improved to 0.843 vs. 0.746 and 0.440 vs. 0.549, respectively. The optimum architecture of the FFBN consisted of one hidden layer with 29 neurons. Therefore, the FFBN algorithm is an effective option for the prediction of rice yield in complex systems of rice production.

SUMMARYSpatially resolved transcriptomic technologies are promising tools to study cell fate decisions in a physical microenvironment, which is fundamental for enhancing our knowledge of mammalian development. However, the imbalance between resolution, transcript capture and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation mammalian embryos. Here, we combined DNA nanoball (DNB) patterned arrays and tissue RNA capture to create SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq). This approach allows transcriptomic profiling of large histological sections with high resolution and sensitivity. We have applied Stereo-seq to study the kinetics and directionality of transcriptional variation in a time course of mouse organogenesis. We used this information to gain insight into the molecular basis of regional specification, neuronal migration and differentiation in the developing brain. Furthermore, we mapped the expression of a panel of developmental disease-related loci on our global transcriptomic maps to define the spatiotemporal windows of tissue vulnerability. Our panoramic atlas constitutes an essential resource to investigate longstanding questions concerning normal and abnormal mammalian development.

Innate immune activation has been shown to reduce the severity of nervous system disorders such as brain ischemia and traumatic brain damage. Macrophage-colony stimulating factor (M-CSF), a drug that is used to treat hematological system disease, is an enhancer of the innate immune response. In the present study, we evaluated the effect of M-CSF preconditioning on chronic social defeat stress (CSDS)-induced depression-like behaviors in mice. Results showed that a single M-CSF injection 1 day before stress exposure at the dose of 100 and 500 μg/kg, or a single M-CSF injection (100 μg/kg) 1 or 5 days but not 10 days before stress exposure prevented CSDS-induced depression-like behaviors in mice. Further analysis showed that a second M-CSF injection 10 days after the first M-CSF injection and a 2 × or 4 × M-CSF injections 10 days before stress exposure also prevented CSDS-induced depression-like behaviors. Molecular studies revealed that a single M-CSF injection prior to stress exposure skewed the neuroinflammatory responses in the brain in CSDS-exposed mice towards an anti-inflammatory phenotype. These behavioral and molecular actions of M-CSF were correlated with innate immune stimulation, as pre-inhibiting the innate immune activation by minocycline pretreatment (40 mg/kg) abrogated the preventive effect of M-CSF on CSDS-induced depression-like behaviors and neuroinflammatory responses. These results provide evidence to show that innate immune activation by M-CSF pretreatment may prevent chronic stress-induced depression-like behaviors via preventing the development of neuroinflammatory response in the brain, which may help to develop novel strategies for the prevention of depression.

The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.

As the installation of PV power grid increases, it becomes vital to know the out-door reliability and degradation of the modules. In this study, more than 30 kW modules have been installed in Chinese Academy of Sciences-Shanghai Institute of Microsystem & Information Technology (SIMIT). The outdoor performance of seven different PV modules, i. e. HIT, CIGS, n-type multi-busbar module, PERC, and CdTe were measured from 2016 to 2018. The performance of all of the seven kinds of PV modules is excellent in March, April and May of 2018, owing to the relative low temperature and the intense irradiance. The daily and monthly PR of the PV modules displays distinct seasonal cyclic patterns. The PR calibrated to 25 °C (PRT = 25°C) is more consistent than the uncalibrated PR throughout the whole year. The comparison of the annual average uncalibrated PR is: bifacial HIT (1.0345)＞CIGS (1.0258)＞n-type multi-busbar module (0.9656)＞monofacial HIT (0.9648)＞sc-Si PERC (0.9591)＞mc-Si PERC (0.9346)＞CdTe (0.9067). Moreover, the power degradation after the installation more than 2 years is: mc-Si PERC (5.33%)＞n-type multi-busbar module (3.19%)＞sc-Si PERC (1.32%)＞monofacial HIT (0.25%). The property improvement of monofacial HIT and bifacial HIT after the light soaking was observed and the power increase of bifacial HIT is 0.72% in these two years, owing to the metastability of the amorphous—crystalline silicon heterointerface after the light soaking.

Under the condition that satellite communication resources are limited, how to ensure the high efficiency to ensure satellite communication tasks is one of the key issues facing satellite communication systems. This article mainly introduces satellite communication tasks facing different task levels. Based on the improved GA algorithm, based on the analysis of satellite communication task parameter sets, it constructs satellite communication tasks through operations such as chromosome coding, fitness calculation, selection, crossover, and mutation. The dynamic allocation model and algorithm simulation are performed to compare the dynamic allocation and static fixed allocation methods. The simulation results show that the satellite communication task allocation algorithm based on GA algorithm can well satisfy the satellite communication task guarantees of different levels, and has certain reference value for improving the resource utilization of satellite communication systems.

Meta learning is an advanced field of artificial intelligence where automatic learning algorithms are applied to acquire learning experience for a set of learning algorithms to improve learning performance. One of popular meta learning methodologies is based on cross validation, especially for selection processes among different machine learning models. However, the challenge is that it is very time-consuming to do cross validation among models in large data sets, especially in financial big data with high noise. This article proposes two asymptotic meta learning algorithms (AML-Lin and AML-Xiang), which are ordinal optimization algorithms for meta learning based on cross validation. The numerical experiments and real-world cases are conducted to illustrate its efficiency in cross validation of models in different scenarios, especially for financial data. The method proposed in this article has significant improvement by comparing with those ones in existing algorithms OCBA and IAML (e.g., see the work done by Chen et al. and Lin et al.),8 ,9 and it is new in dealing with financial data.

Meta learning is an advanced field of machine learning where automatic learning algorithms are applied to acquire meta-knowledge for a set of learning algorithms called base learners. One of meta-learning purposes is to select the best base learners for certain kind of data set to support future learning process. Comparing average out-of-sample predictability with data bootstrapping is one of popular meta-learning algorithms to measure the performance of each base learner for time series data. The challenge is that it is a very time-consuming for data analytics, such as quantitative investment time series modeling. In order to complete the meta-learning process on time before new time-series data arrive, we need to optimally allocate the limited computation budget to each learner. In this paper, we propose the Asymptotic Meta Learning (AML) to data bootstrapping process during Meta learning, which is one of asymptotic ordinal optimization algorithm for mean measure of designs with random outputs. The numerical experiments are conducted to illustrate its efficiency.

Background The advent of proprotein convertase subtilisin/kexin type 9 (PCSK9)–inhibiting drugs have provided an effective, but extremely expensive treatment for the management of low density lipoprotein (LDL). Our aim was to explore a cost-effective application of camelid anti-PCSK9 single domain antibodies (sdAbs), which are high variable regions of the camelid heavy chain antibodies (VHHs), as a human PCSK9 (hPCSK9) inhibitor. One female llama was immunized with hPCSK9. Screening of high affinity anti-PCSK9 VHHs was carried out based on surface plasmon resonance (SPR) technology. We reported a lysate kinetic analysis method improving the screening efficiency. To increase the serum half-life and targeting properties, the constant region fragment of the human immunoglobulin gamma sub-type 4 (IgG4 Fc) was incorporated to form a novel llama-human chimeric molecule (VHH-hFc). Results The PCSK9 inhibiting effects of the VHH proteins were analyzed in two human liver hepatocellular cells (HepG2 and Huh7) and in the hPCSK9 transgenic Sprague–Dawley (SD) rat model. The hPCSK9 antagonistic potency of the bivalent VHH-hFc exceeded the monovalent VHH (P

Additional file 4: Table S1. The sequences of the sdAbs.

The contradiction between the development of urban agglomerations and ecological protection has long been a challenging issue. China has experienced an astonishing expansion of its urban scale in the past 40 years, and nearly 783 million of the nation's people now live in cities. Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta have been prioritized to become world-class clusters by 2020. The health effects of air pollution in these three urban agglomerations are becoming increasingly formidable. Given these conditions, using the daily mean PM

Multipath fading has a great impact on the signal transmission of Satellite Communication System. This paper first introduces the multipath fading channel concept of Satellite Communication System. It analyzes the amplitude fading and delay spread for Rayleigh fading and frequency selective fading. Finally, the multi-path fading channel characteristics of Satellite Communication System are simulated and simulated. The results show that multipath fading has a large amplitude fading and delay spread on the signal transmission of Satellite Communication System, resulting in inability to communicate reliably. The simulation results have certain reference value in the channel transmission design of Satellite Communication System.

We recently reported that intraperitoneal injection of a low dose of lipopolysaccharide (LPS) prevents chronic stress-induced depression-like behaviors in mice. In this study, we reported that a single intranasal LPS administration (10 μg/mouse) one day prior to stress exposure produced prophylactic effects on chronic social defeat stress (CSDS)-induced depression-like behaviors, which was indicated by the reduction in social interaction time in the social interaction test and the decrease in immobility time in the tail suspension test and forced swimming test. The single intranasal LPS administration prior to stress exposure was also found to prevent CSDS-induced anxiety-like behaviors, including prevention of CSDS-induced decrease in the time spent in open arms in the elevated plus maze test, decrease in the time spent in lit side in the light-dark test, and decrease in the time spent in central regions in the open field test, along with no changes in locomotor activity. Further analysis showed that the single intranasal LPS administration one day prior to stress exposure prevented CSDS-induced increase in levels of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and IL-1β mRNA in the hippocampus and prefrontal cortex. Inhibition of innate immune stimulation by minocycline pretreatment not only abrogated the preventive effect of intranasal LPS administration on CSDS-induced depression- and anxiety-like behaviors, but also abrogated the preventive effect of intranasal LPS administration on CSDS-induced neuroinflammatory responses in the hippocampus and prefrontal cortex. These results demonstrate that intranasal administration of innate immune stimulants could be a potential approach for the prevention of depression and anxiety.

Extracellular vesicles (EVs) play a role in intercellular communications via exchanging biological molecules, being involved in host-parasite interplay. Little is to date known about E. multilocularis EVs and their biological activities. Here spherical EVs secreted by E. multilocularis metacestodes were shown to range predominately from 34nm to 95nm in diameter. A total of 433 proteins were identified in the EVs, and the proteins involved in binding (42%) and catalytic activity (41%) were most frequently represented. Moreover, the proteins associated with EV biogenesis and trafficking, including annexin, 14-3-3, tetraspanin and heat shock protein 70kDa, were highly enriched. It was shown that the EVs remarkably suppressed NO produced by activated RAW macrophages via downregulation of inducible nitric oxide synthase expression (p

Background Accurate prediction of epitopes presented by human leukocyte antigen (HLA) is crucial for personalized cancer immunotherapies targeting T cell epitopes. Mass spectrometry (MS) profiling of eluted HLA ligands, which provides unbiased, high-throughput measurements of HLA associated peptides in vivo , could be used to faithfully model the presentation of epitopes on the cell surface. In addition, gene expression profiles measured by RNA-seq data in a specific cell/tissue type can significantly improve the performance of epitope presentation prediction. However, although large amount of high-quality MS data of HLA-bound peptides is being generated in recent years, few provide matching RNA-seq data, which makes incorporating gene expression into epitope prediction difficult. Methods We collected publicly available HLA peptidome and matching RNA-seq data of 34 cell lines derived from various sources. We built position score specific matrixes (PSSMs) for 21 HLA-I alleles based on these MS data, then used logistic regression (LR) to model the relationship among PSSM score, gene expression and peptide length to predict whether a peptide could be presented in each of the cell line. Comparing the feature weights and biases across different HLA-I alleles and cell lines, we observed a universal relationship among these three variables. To confirm this, we built a single LR model by pooling PSSM scores, gene expression levels and peptide length features across different HLA alleles and cell lines, and compared its performance with the allele and cell line specific LR models. Indeed, the predictive powers had no significant differences across cell lines and HLA alleles, and both substantially outperformed predictions based on PSSM scores alone. Based on such a finding, we further built a universal LR model, termed Epitope Presentation Integrated prediCtion (EPIC), based on more than 180,000 unique HLA ligands collected from public sources and ∼3,000 HLA ligands generated by ourselves, to predict epitope presentation for 66 common HLA-I alleles. Results When evaluating EPIC on large, independent HLA eluted ligand datasets, it performed substantially better than other popular methods, including MixMHCpred (v2.0), NetMHCpan (v4.0), and MHCflurry (v1.2.2), with an average 0.1% positive predictive value (PPV) of 51.59%, compared to 36.98%, 36.41%, 24.67% and 23.39% achieved by MixMHCpred, NetMHCpan-4.0 (EL), NetMHCpan-4.0 (BA) and MHCflurry, respectively. It is also comparable to EDGE, a recent deep learning-based model that is not yet publicly available, on predicting epitope presentation and selecting immunogenic cancer neoantigens. However, the simplicity and flexibility of EPIC makes it much easier to be applied in diverse situations, especially when users would like to take advantage of emerging eluted ligand data for new HLA alleles. We demonstrated this by generating MS data for the HCC4006 cell line and adding the support of HLA-A*33:03, which has no previous MS or binding affinity data available, to EPIC. EPIC is publicly available at < https://github.com/BGI2016/EPIC >. Conclusions we have developed an easy to use, publicly available epitope prediction tool, EPIC, that incorporates information from both MS and RNA-seq data, and demonstrated its superior performance over existing public methods.

Summary Chondrichthyan (cartilaginous fish) occupies a key phylogenetic position and is important for investigating evolutionary processes of vertebrates. However, limited whole genomes impede our in-depth knowledge of important issues such as chromosome evolution and immunity. Here, we report the chromosome-level genome of white-spotted bamboo shark. Combing it with other shark genomes, we reconstructed 16 ancestral chromosomes of bamboo shark and illustrate a dynamic chromosome rearrangement process. We found that genes on 13 fast-evolving chromosomes can be enriched in immune-related pathways. And two chromosomes contain important genes that can be used to develop single-chain antibodies, which were shown to have high affinity to human disease markers by using enzyme-linked immunosorbent assay. We also found three bone formation-related genes were lost due to chromosome rearrangements. Our study highlights the importance of chromosome rearrangements, providing resources for understanding of cartilaginous fish diversification and potential application of single-chain antibodies., Graphical Abstract, Highlights • Inferred ancestral chromosome karyotypes of cartilaginous fish • Chromosome rearrangements resulted in fast-evolving chromosomes and immune genes • Chromosome rearrangements led to deletion of bone formation-related genes • Proved that single-domain antibodies in shark have great potential application, Biological Sciences; Genetics; Genomics; Phylogenetics; Evolutionary Biology