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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.

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.

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.

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.

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.

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.

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.

Posttraumatic stress disorder (PTSD) is a mental disorder that can translate into severe economic problems. Lycopene is an aliphatic hydrocarbon carotenoid extracted from plants, including papayas, tomatoes, and water melons. Previous studies have shown that lycopene can produce antidepressant-like effects in rodent models of depression. However, little is known about its anti-PTSD-like effect. This was addressed in the present study by using the single prolonged stress (SPS) protocol to induce PTSD-like behavioral deficits in mice. Our results showed that 12 days of lycopene treatment at the dose of 10 and 20 mg/kg, but not at 5 mg/kg, ameliorated the PTSD-like phenotype induced by SPS, including the increase in freezing time in contextual fear paradigm, the decrease in time and entries in open arms in elevated plus maze test, and the decrease in distance and time in the central area of the open field test, without affecting the mouse locomotor activity. Mechanistic studies revealed that lycopene treatment (20 mg/kg, 12 days) could suppress the SPS-induced increase in levels of interleukin-6 (IL-6), IL-1β, tumor necrosis factor-α (TNF-α), and nitrite in the hippocampus and prefrontal cortex in mice, as well as the increased markers that indicate high levels of oxido-nitrosative stress in the hippocampus and prefrontal cortex in SPS mice. Lycopene treatment (20 mg/kg, 12 days) also suppressed the SPS-induced decrease in brain derived neurotrophic factor (BDNF) levels in the hippocampus and prefrontal cortex in mice. Overall, the anti-PTSD-like effect of lycopene may be associated with its anti-neuroinflammation and anti-oxidative stress activities.

Power plants in the competitive market will face volatile generation cost and volatile spot price, and risk management will become crucial. We address quantity risk faced by power plants with quadratic cost, which produce electricity with variable cost and sell it at spot price. By innovatively modifying existing models that provide optimal quantity hedging schemes, we find out the optimal zero-cost hedging portfolio for both CARA and mean-variance utilitarian. Next, we demonstrate how to implement the modified hedging strategies. Finally, by analyzing the sensitivity of the quadratic coefficient, decision making differentiation between CARA and mean-variance utilitarian is revealed.

This paper investigates the trade-off between distribution effect and production effect of monetary policy when there exist unobservable idiosyncratic liquidity shocks. In the absence of risk-sharing arrangements such as a credit market, monetary policy serves to provide ex post insurance to smooth consumption. Specifically, issuing interest-bearing bonds restores credit transactions on money through bond-money exchanges. Such a policy has a positive distribution effect, but the resulting inflation hampers production efficiency. It is demonstrated that the trade-off between distribution efficiency gain and production efficiency loss would result in net welfare enhancement if consumers are relative-risk-averse enough. Politique monetaire optimale: efficacite dans la distribution versus efficacite dans la production. Ce memoire examine la relation d'equivalence entre l'effet sur la distribution et l'effet sur la production de la politique monetaire quand existent des chocs de liquidite idiosyncratiques non-observables. En l'absence d'arrangement pour le partage des risques, tel qu'un marche du credit, la politique monetaire sert d'assurance ex post pour lisser les variations dans les niveaux de consommation. Specifiquement, emettre des obligations qui paient des interets restore les transactions monetaires de credit via les echanges obligations-monnaie. Une telle politique a un effet positif sur la distribution mais l'inflation qui en resulte nuit a l'efficacite de la production. On montre que la relation d'equivalence entre les gains d'efficacite dans la distribution et la perte d'efficacite dans la production resultent en une amelioration du niveau net de bien-etre, si les consommateurs ont une aversion suffisamment grande au risque relatif.

Foxtail millet (Setaria italica) provides food and fodder in semi-arid regions and infertile land. Resequencing of 184 foxtail millet recombinant inbred lines (RILs) was carried out to aid essential research on foxtail millet improvement. Bin map were constructed based on the RILs’ recombination data. By anchoring some unseated scaffolds and filling gaps, we update two original millet reference genomes Zhanggu and Yugu to produce second editions. Gene mapping of nine agronomic traits were done based on this RIL population. The genome resequencing and QTL mapping provided important tools for foxtail millet research and breeding. Resequencing of the RILs could also provide an effective way for high quantity genome assembly and gene identification.

Before an aerial image can be used to support a site-specific application it is essential to perform the geometric corrections and geocoding. This research discusses the development of an automatic aerial image georeferencing method for an unmanned aerial vehicle (UAV) image data acquisition platform that does not require use of ground control points (GCP). An onboard navigation system is capable of providing continuous estimates of the position and attitude of the UAV. Based on a navigation data and a camera lens distortion model, the image collected by an onboard multispectral camera can be automatically georeferenced. When compared with 16 presurveyed ground reference points, image automatic georeferenced results indicated that position errors were less than 90 cm. A large field mosaic image can be generated according to the individual image georeferenced information. A 56.9 cm mosaic error was achieved. This accuracy is considered sufficient for most of the intended precision agriculture applications.

For machine vision and remote sensing systems, image quality is the major factor directly affecting the final image processing results. Especially under natural lighting conditions, image color varies significantly as the lighting conditions change. Unfortunately, changing lighting conditions are inevitable in crop field remote sensing applications. This study was undertaken to develop an artificially intelligent controller based on an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The aim of the implemented controller is to automatically adjust multispectral camera parameters, such as "gain" and "exposure time," to compensate for changing natural lighting conditions and to acquire white-balanced images. A high-resolution digital multispectral camera was used as the image sensor. A calibration panel with 56% reflectivity over the visible and near-infrared bands was used to provide the white-balance reference. The real-time white-balancing of the image was achieved for three channels of the camera under natural lighting conditions. It was shown through experiments that the developed algorithm was able to complete multispectral camera parameter control within three iterations for each channel. The convergence speed was faster than with conventional control methods.

This study discusses the development of an autonomous helicopter system to collect agricultural remote sensing. The paper demonstrates an overview of the whole system and its applications about aerial image acquisition. The system consists of on-board part and ground station part. An embedded linux based single computer board (SCB) is used as main controller on board. Multithread technology is used for software development in the SCB, major threads include sensor fusion thread using Extended Kalman Filter (EKF) to estimate navigation parameters, uplink thread to communicate with ground station and control thread to accomplish flight control and camera mount stabilization. As for ground station, a map based customer software is developed for mission planning and flight monitor.

This research discusses the development of an automatic and no ground control points needed aerial image georeferencing method for the unmanned aerial vehicle (UAV) image data acquisition platform using a low cost integrated navigation system. An Extended Kalman Filter (EKF) based UAV navigation system was designed and implemented to fuse measurements from the inertial sensors (accelerometer and gyro), magnetometer and GPS. The navigation system was capable of providing continuous estimates of UAV position and orientation at 50 Hz even in the case of GPS signal outage. Based on the navigation data, the image collected by an onboard multispectral camera was automatically georeferenced and the ortho-rectified image was produced. Compared to eight presurveyed ground reference points, image automatic georeferencing results indicated that total position errors were less than 40 cm. This accuracy is sufficient for most of precision agricultural applications.

Remote sensing (RS) is a very promising data collection tool for precision agriculture and has been widely used for many decades. However, the relative low spatial and temporal resolution are the major drawbacks for traditional RS platforms (such as satellites and piloted aircrafts). This study discusses the development of agricultural remote sensing system using autonomous unmanned helicopter to acquire the crop field image at the right time and right place with high image resolution. The helicopter was equipped with a global positioning system (GPS), an inertial measurement units (IMU) and a geomagnetic sensor to detect the position, attitude and velocity of the helicopter. An autonomous controller was used to control helicopter to reach desired positions. A multi-spectral camera was mounted on the helicopter using a pan/tilt platform which can adjust the camera posture according to attitude of the helicopter to avoid the image distortion. A ground station computer was used to communicate with the helicopter in real-time to monitor flight parameters and send out control command. The images captured from this system are suitable for multi-purpose agricultural RS research.

A spray system based on fuzzy control and machine vision, which could perform sitespecific pesticide application, was developed and tested. The system could integrate information of the tree crown with the distance between the tree and the nozzles to judge the tree size, and then to select different nozzle group, as well as to control the flow rate and spray range. Nozzles opened when the camera captured a tree and closed when no tree appeared in the camera. Meanwhile, flow rate and spray range could be controlled automatically. In this way, an intelligent pesticide delivery was achieved. Compared to traditional methods, the system could greatly save pesticide so that the environment is the biggest beneficiary. In the future researches, more attentions should be paid to adjust the spray range and maintain the spray pattern at the same time when flow rate varies in a large range.

For machine-vision systems, the image quality is the major factor directly affects the final image processing results. Especially under the natural lighting conditions, image quality varies significantly as the light source condition changes. This light source condition changing is inevitable in crop field remote sensing applications. In this paper, an artificial intelligent algorithm was developed to realize automatic adjustment of the camera settings, such as the “gain” and “exposure time” to compensate the unstable ambient illumination for improving image quality. Therefore, we could detect as much information of the target as we desire. Using a DuncanTech MS4100 highresolution digital color and multispectral camera as the vision sensor, we implemented the real-time white balance of the image among three channels (blue/green, red, near-infrared) under different natural lighting conditions.

The pesticide application is a double-edged sword for agriculture and forestry. There exist pollutions for ecosystem as pesticide manufacturing, circulating and application. A life-cycle pesticide management (LPM) concept was put forward which could be benefit to the environment ecosystem, especially in rural regions. A sound pesticide management legal system, a nationwide pesticide application training network, a web-based life-cycle pesticide management system could be set up. The registration, manufacturing, packaging, distribution, application, supervision of pesticides and the punishment for violence against the legal system were included in the legal system. The training system was analyzed. In the web-based LPM system, the manufacturing, circulating and application were put into the systematic network for fully controlling the whole pesticide life cycle. Several operator-friendly advanced pesticide applications were analyzed.

The paper introduced briefly the concepts of precision pesticide application and proposed the strategy of developing precision pesticide application in China, which will reduce the impact of pesticides on environments, increase the ecosystem sustainability, and protect the operators. The tree image processing, tree crown recognition and smart spray execution system were developed. The tree image processing system included a CCD camera, an image grabber, a computer, and an experimental set-up. The software was developed to realize the functions as follows: capturing, extracting and processing the key frame of tree image from video stream, segmenting the green target from its background by a classifier called relative color indices, measuring the segmented image by statistic method to obtain morphological and location features of the target, which provides into for intelligent understanding and pattern recognition. The tree crown recognition system based on BP neural networks was developed as the toward-target spraying pesticides depended greatly on the tree crown types. 6 typical tree crowns, that is, cone, oval, spherical, cylindrical, umbellate and truncate, could be determined. The spray execution system composed of spraying table, nozzles, solenoid valves and relays was developed, which will realize the data communications and convert spraying decision making results to instructions for controlling the spray execution system. The results of experiments showed that the developed systems were practical and suitable to recognize the tree images, the tree crown types. The non-target or off-target pesticide contamination could be decreased through precisely spraying the pesticide on the desired tree target.

The paper mainly studied the technology of acquisition and recognition of indoor simulated tree image based on machine vision instead of the human eyes. A tree image processing and analyzing system were developed, which included a CCD camera, an image grabber, a computer, and an experimental set-up based on the conveyer belt for simulating the outside conditions. First, the real-time simulated tree images were captured continually and the single frame image was elicited from the video images in a certain interval according to the relative speed between the camera and the simulated trees. At the same time, this single frame image was converted from the RGB image to the chroma image. Furthermore, the green channel of the chroma image was extracted for further processing. Then, the tree image was segmented by the iterative thresholding method. Based on the segmented image, the boundary of the tree image was extracted and the geometrical features were measured such as area, perimeter, roundness, shape factor, inscribed circle diameter, barycenter. Using the two-classification method, the features of the tree image were classified according to the thresholding of each geometrical feature. The results of indoor and outdoor tests showed that the system was suitable to recognize the tree images. According to the recognition results, the non-or off-target pesticide contamination could be decreased by accurately spraying the pesticide on the desired target.