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Exploring the spatiotemporal dynamic evolution of local climate zones (LCZ) associated with changes in land surface temperature (LST) can help urban planners deeply understand urban climate. Firstly, we monitored the evolution of 3D urban spatial form in Chengdu City, Sichuan Province, China from 2010 to 2020, used the ordinary least squares model to fit the dynamic correlation (DR) between the changes in urban spatial patterns and changes in LST, and revealed the changes of urban spatial patterns closely related to the rise in LST. Secondly, the spatiotemporal patterns of LST were examined by the integration of the Space–Time Cube model and emerging hotspot analysis. Finally, a prediction model based on curve fitting and random forest was integrated to simulate the LST of study area in 2025. Results show the following: the evolution of the urban spatial form consists of three stages: initial incremental expansion, midterm incremental expansion and stock renewal, and late stock renewal and ecological transformation. The influence of the built environment on the rise of LST is greater than that of the natural environment, and the building density has a greater effect than the building height. The overall LST shows a warming trend, and the seven identified LST spatiotemporal patterns are dominated by oscillating and new hotspots patterns, accounting for 51.99 and 11.44% of the study area, respectively. The DR between urban spatial form and LST varies across different time periods and built environment types, whereas the natural environment is always positively correlated with LST. The thermal environment of the city will warm up in the future, and the area affected by the heat island will shift to the central of the city.

Agricultural products usually contain a large number of spoilage bacteria and endogenous enzymes, which can adversely affect the quality of agricultural products. Radio frequency is a kind of dielectric heating technology, which has fast heating speed and high efficiency, can effectively inactivate microorganisms and endogenous enzymes in agricultural products and has little influence on the quality of agricultural products， such as color, texture and nutritional composition, etc. This review summarized the mechanism of action of radiofrequency sterilization blunt enzyme technology and its application in agricultural products processing in recent years, and the shortcomings and solutions of radiofrequency sterilization blunt enzyme technology was pointed out. Moreover, the future application of radiofrequency sterilization blunt enzyme technology in agricultural products and food was also prospected.

BackgroundThe impact of COVID-19 on the world is still ongoing, and it is currently under regular management. Although most infected people have flu-like symptoms and can cure themselves, coexisting pathogens in COVID-19 patients should not be taken lightly. The present study sought to investigate the coexisting pathogens in SARS-CoV-2 infected patients and identify the variety and abundance of dangerous microbes to guide treatment strategies with a better understanding of the untested factors.MethodsWe extracted total DNA and RNA in COVID-19 patient specimens from nasopharyngeal swabs to construct a metagenomic library and utilize Next Generation Sequencing (NGS) to discover chief bacteria, fungi, and viruses in the body of patients. High-throughput sequencing data from Illumina Hiseq 4000 were analyzed using Krona taxonomic methodology for species diversity.ResultsWe studied 56 samples to detect SARS-CoV-2 and other pathogens and analyzed the species diversity and community composition of these samples after sequencing. Our results showed some threatening pathogens such as Mycoplasma pneumoniae, Klebsiella pneumoniae, Streptococcus pneumoniae, and some previously reported pathogens. SARS-CoV-2 combined with bacterial infection is more common. The results of heat map analysis showed that the abundance of bacteria was mostly more than 1000 and that of viruses was generally less than 500. The pathogens most likely to cause SARS-CoV-2 coinfection or superinfection include Streptococcus pneumoniae, Haemophilus influenzae, Staphylococcus aureus, Klebsiella pneumoniae, and Human gammaherpesvirus 4.ConclusionsThe current coinfection and superinfection status is not optimistic. Bacteria are the major threat group that increases the risk of complications and death in COVID-19 patients and attention should be paid to the use and control of antibiotics. Our study investigated the main types of respiratory pathogens prone to coexisting or superinfection in COVID-19 patients, which is valuable for identifying and treating SARS-CoV-2.

In order to clarify the quality change of indica-japonica hybrid rice in different packaging，this experiment was conducted to study the effects of four packaging, woven bags, PE bags, vacuum packages and kraft paper bags, on the rice and steamed rice quality at room temperature (20~25 ℃) with YongYu 15 rice as raw material. The results showed that, with the extension of storage time, the fatty acid value, amylose content and rice hardness of indica japonica hybrid rice increased gradually, while the indexes of rice freshness value, taste value, viscosity and taste value of rice decreased gradually. The correlation between the quality indexes was significant (P PE bag > woven bag > kraft paper bag. The above results show that, the vacuum and barrier packaging could effectively delay the decrease of the quality of indica-japonica hybrid rice and its cooked rice, and improve its storage quality.

Fusarium oxysporum f. sp. capsici is the specific pathogen of pepper Fusarium wilt and causes a significant reduction in pepper yield. Its narrow host specificity has led to the concept of formae speciales. This interesting phenomenon has great potential and needs to be analyzed at the molecular level. In this study, we obtained the draft genome sequence of F. oxysporum f. sp. capsici, using the Oxford Nanopore sequencing technology. The long read–based assembly consisted of 34 contigs, with a total length of 54,516,562 bp. The contig N50 was 4,962,668 bp and the GC content was 47.6%. Our genome assembly of F. oxysporum f. sp. capsici provides a valuable resource for the study of pepper Fusarium wilt, and the comparative genomic study of F. oxysporum.

Abstract Background Post-database search is a key procedure in peptide identification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of peptide identification, the challenge remains on large-scale datasets and datasets with a distribution of unbalanced PSMs. A more efficient learning strategy is required for improving the accuracy of peptide identification on challenging datasets. While complex learning models have larger power of classification, they may cause overfitting problems and introduce computational complexity on large-scale datasets. Kernel methods map data from the sample space to high dimensional spaces where data relationships can be simplified for modeling. Results In order to tackle the computational challenge of using the kernel-based learning model for practical peptide identification problems, we present an online learning algorithm, OLCS-Ranker, which iteratively feeds only one training sample into the learning model at each round, and, as a result, the memory requirement for computation is significantly reduced. Meanwhile, we propose a cost-sensitive learning model for OLCS-Ranker by using a larger loss of decoy PSMs than that of target PSMs in the loss function. Conclusions The new model can reduce its false discovery rate on datasets with a distribution of unbalanced PSMs. Experimental studies show that OLCS-Ranker outperforms other methods in terms of accuracy and stability, especially on datasets with a distribution of unbalanced PSMs. Furthermore, OLCS-Ranker is 15–85 times faster than CRanker.

Due to the cost of acquiring and labeling remote sensing images, only a limited number of images with the target objects are obtained and labeled in some practical applications, which severely limits the generalization capability of typical deep learning networks. Self-supervised learning can learn the inherent feature representations of unlabeled instances and is a promising technique for marine ship detection. In this work, we design a more-way CutPaste self-supervised task to train a feature representation network using clean marine surface images with no ships, based on which a two-stage object detection model using Mask R-CNN is improved to detect marine ships. Experimental results show that with a limited number of labeled remote sensing images, the designed model achieves better detection performance than supervised baseline methods in terms of mAP. Particularly, the detection accuracy for small-sized marine ships is evidently improved.

“Concept drift”makes learning from streaming data fundamentally different from traditional batch learning. Focusing on the regression task on streaming data, this paper presents an efficient online learning algorithm, i.e., budgeted online kernel ridge regression (BOKRR). It is a budget version kernel ridge regression algorithm coupled with minimum contribution criterion to maintain the budget of the active set. BOKRR employs low-rank correction technology and the Sherman-Morrison-Woodbury formula to update the dynamic KRR model with the computational complexity of only O(B2) with B learning samples (Budget size of an active set). Limited storage burden and efficient computational ability make the proposed BOKRR algorithm an ideal candidate to process streaming data. The experimental results on benchmark and real-world datasets further demonstrate the validity and efficiency of the proposed algorithms.

Oil slicks from ships or oil platforms cause serious damage to the marine and coastal environment and ecosystems. To monitor such spill events, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been widely used in improving oil spillage detection performance of the image segmentation methods and machine learning methods. A key factor that greatly affects the performance of the existing methods is the use of prior knowledge, such as the manual labels of oil slicks and clean seawater. In some scenarios, a large number of trustworthy manual labels of oil slicks have been accumulated. In other scenarios, however, there are few manual labels. To efficiently utilize the manual labels in various scenarios, we propose an oil spillage identification method, named RampOC. We design cost-sensitive risk minimization model to tackle the problem of imbalance of oil slicks and seawater, employ the ramp loss function to deal with various cases of the manual labels, and design a kernel-based CCCP algorithm to train a classifier by solving a series of convex dual programming. The trained classifier is applicable to various cases, including the normal case that a certain number of the labels of oil slicks are labeled, the case that a large proportion of labels have gone missing or incorrect, and the case that no labels of oil slicks are known. In all of these cases, RampOC has proven to be effective to detect the ocean oil spillage regions. All the MATLAB source codes could be freely downloaded from https://github.com/Isaac-QiXing/rampOC.

The entomopathogenic fungus Beauveria pseudobassiana strain RGM 2184 can reach a maximum efficacy of 80% against the quarantine pest Lobesia botrana in field assays. In this study, the RGM 2184 genome was sequenced, and genome mining analyses were performed to predict the factors involved in its insecticidal activity. Additionally, the metabolic profiling of the RMG 2184 culture’s supernatants was analyzed by mass spectrometry, and the insecticidal activity from one of these extracts was evaluated in Galleria mellonella larvae. The genome analysis resulted in 114 genes encoding for extracellular enzymes, four biosynthetic gene clusters reported as producers of insecticidal and bactericidal factors (oosporein, beauvericin, desmethylbassianin, and beauveriolide), 20 toxins, and at least 40 undescribed potential biocontrol factors (polyketides and nonribosomal peptides). Comparative genomic analysis revealed that 65–95% of these genes are Beauveria genus-specific. Metabolic profiling of supernatant extracts from RGM 2184 cultures exhibited secondary metabolites such as beauveriolide, oosporein, inflatin C, and bassiatin. However, a number of detected metabolites still remain undescribed. The metabolite extract caused 79% mortality of Galleria mellonella larvae at 28 days. The results of this research lay the groundwork for the study of new insecticidal molecules.

The new diagnostic items such as fuel system, VVT and cold start emission reduction strategy are tested for several vehicles meeting the requirements of the China 6 emission standards of light-duty vehicles. The test results show that the fuel system fault will increase the emissions of NMHC, NOx, CO and other pollutants by 0.7-7.5 times, and the emissions of lean mixture, rich mixture and uneven cylinder to cylinder faults will increase to different degrees; VVT fault will increase NMHC and CO emissions by 1.2-2.9 times and NOx by 1.6-6.1 times; The failure of cold start emission reduction strategy will increase NMHC, NOx and CO emissions by 4.2-9.0 times and NOx by 2.1-2.2 times; It is necessary to strengthen the monitoring of these diagnostic items and tighten the requirements in the future emission standards.

A 6.2 L diesel engine that meets China VI emission standard was selected to run world harmonized transient cycle (WHTC) and California Low Load Cycle (LLC) to measure the pollutants. The brake specific emission under the two cycles were compared, and nitrogen oxides (NOx) emission were analyzed carefully. The results show that the NOx emission of the LLC of this engine is about 15 times than that of the hot WHTC, and 4.7 times than that of the limitation of current China VI emission standard. The low inlet temperature of selective catalytic reduction (SCR) is the predominant reason for the high NOx emission in the LLC. The SCR inlet temperature of lower than 200°C in LLC accounts for 52% of the total time with an average inlet temperature of only 190°C. While the inlet temperature of lower than 200°C of the hot WHTC only accounts for 7% with the average inlet temperature of 275°C. The average SCR catalytic efficiency calculated by NOx sensor amounted on the upstream and downstream of LLC is only 75%, while the efficiency is 98% for hot WHTC. It is necessary to focus on thermal management of the aftertreatment system and the SCR catalytic performance at low temperature.

The actual road diesel emissions of heavy-duty vehicle is very high, which has been a concern around the world. In 2018, Chinese government promulgated the "China VI" regulations containing real-driving emission test requirements and limits, requiring vehicle manufacturers to effectively control vehicle emissions to meet the requirements of China VI. This article takes a heavy-duty tractor equipped with a "China VI" engine as the research object, and performs actual road emission tests after loading 10%, 25%, 50%, 75%, and 100% respectively. The results show that NOx emissions are higher at low loads and PN emissions are higher at high loads, and CO emissions are not sensitive to load changes.

A test fleet including 47 gasoline vehicles with 6 different technical types was organized, and the adaptability of these vehicles under China 6 standard was studied. By analyzing the performance of the vehicles tested in laboratory and in real-world, technical types that could simultaneously reconcile emission and fuel consumption performance were proposed. The results show that all vehicles were well performed under laboratory conditions, but in real-world conditions, 50% direct injection vehicles without GPF would fail the RDE test. On the other hand, vehicles with GPF had better performance, all of which passed the RDE tests even if the conformity factor decreased from 2.1 to 1. In addition, it is found that the fuel consumption of the DI vehicles were lower than that of the PFI vehicles. As the DI vehicles with GPF showed better performance on the balance of emission and fuel consumption, indicating that they may have better potentials to deal with the increasing tightened emission and fuel consumption standards at the same time.

This interview with Tobias Biancone, Director General of the International Theatre Institute (ITI), was conducted on the afternoon of July 8, 2014 at Café & Deli of Shanghai Theatre Academy (STA). Mr. Biancone was invited as keynote at the Performance Studies international Conference 20 (PSi20) : he addressed the theme of “Avant-garde, Tradition, Community,” and talked about his vision of contemporary Chinese theatre.

The proliferation of remote sensing imagery motivates a surge of research interest in image processing such as feature extraction and scene recognition, etc. Among them, scene recognition (classification) is a typical learning task that focuses on exploiting annotated images to infer the category of an unlabeled image. Existing scene classification algorithms predominantly focus on static data and are designed to learn discriminant information from clean data. They, however, suffer from two major shortcomings, i.e., the noisy label may negatively affect the learning procedure and learning from scratch may lead to a huge computational burden. Thus, they are not able to handle large-scale remote sensing images, in terms of both recognition accuracy and computational cost. To address this problem, in the paper, we propose a noise-resilient online classification algorithm, which is scalable and robust to noisy labels. Specifically, ramp loss is employed as loss function to alleviate the negative affect of noisy labels, and we iteratively optimize the decision function in Reproducing Kernel Hilbert Space under the framework of Online Gradient Descent (OGD). Experiments on both synthetic and real-world data sets demonstrate that the proposed noise-resilient online classification algorithm is more robust and sparser than state-of-the-art online classification algorithms.

OBJECTIVE: To describe the Single Photon Emission Microscope (SPEM), a state-of-the-art instrument for small animal SPECT imaging, and characterize its performance presenting typical images of different animal organs. METHODS: SPEM consists of two independent imaging devices based on high resolution scintillators, high sensitivity and resolution Electron-Multiplying CCDs and multi-pinhole collimators. During image acquisition, the mouse is placed in a rotational vertical holder between the imaging devices. Subsequently, an appropriate software tool based on the Maximum Likelihood algorithm iteratively produces the volumetric image. Radiopharmaceuticals for imaging kidneys, heart, thyroid and brain were used. The mice were injected with 74 to 148 MBq/0,3mL and scanned for 40 to 80 minutes, 30 to 60 minutes afterwards. During this procedure, the animals remained under ketamine/xilazine anesthesia. RESULTS: SPEM images of different mouse organs are presented, attesting the imaging capabilities of the instrument. CONCLUSION: SPEM is an innovative technology for small animal SPECT imaging providing high resolution images with appropriate sensitivity for pre-clinical research. Its use with appropriate radiotracers will allow translational investigation of several animal models of human diseases, their pharmacological treatment and the development of potential new therapeutic agents.

Objective: To describe the Single Photon Emission Microscope(SPEM), a state-of-the-art instrument for small animal SPECT imaging,and characterize its performance presenting typical images of differentanimal organs. Methods: SPEM consists of two independent imagingdevices based on high resolution scintillators, high sensitivity andresolution Electron-Multiplying CCDs and multi-pinhole collimators.During image acquisition, the mouse is placed in a rotational verticalholder between the imaging devices. Subsequently, an appropriatesoftware tool based on the Maximum Likelihood algorithm iterativelyproduces the volumetric image. Radiopharmaceuticals for imagingkidneys, heart, thyroid and brain were used. The mice were injectedwith 74 to 148 MBq/0,3mL and scanned for 40 to 80 minutes, 30to 60 minutes afterwards. During this procedure, the animalsremained under ketamine/xilazine anesthesia. Results: SPEM imagesof different mouse organs are presented, attesting the imagingcapabilities of the instrument. Conclusion: SPEM is an innovativetechnology for small animal SPECT imaging providing high resolutionimages with appropriate sensitivity for pre-clinical research. Its usewith appropriate radiotracers will allow translational investigationof several animal models of human diseases, their pharmacologicaltreatment and the development of potential new therapeutic agents.

In this paper, we consider a single-index varying-coefficient model with application to longitudinal data. In order to accommodate the within-group correlation, we apply the block empirical likelihood procedure to longitudinal single-index varying-coefficient model, and prove a nonparametric version of Wilks’ theorem which can be used to construct the block empirical likelihood confidence region with asymptotically correct coverage probability for the parametric component. In comparison with normal approximations, the proposed method does not require a consistent estimator for the asymptotic covariance matrix, making it easier to conduct inference for the model's parametric component. Simulations demonstrate how the proposed method works.

The solution of least squares support vector machines (LS-SVMs) is characterized by a specific linear system, that is, a saddle point system. Approaches for its numerical solutions such as conjugate methods Sykens and Vandewalle (1999) and null space methods Chu et al. (2005) have been proposed. To speed up the solution of LS-SVM, this paper employs the minimal residual (MINRES) method to solve the above saddle point system directly. Theoretical analysis indicates that the MINRES method is more efficient than the conjugate gradient method and the null space method for solving the saddle point system. Experiments on benchmark data sets show that compared with mainstream algorithms for LS-SVM, the proposed approach significantly reduces the training time and keeps comparable accuracy. To heel, the LS-SVM based on MINRES method is used to track a practical problem originated from blast furnace iron-making process: changing trend prediction of silicon content in hot metal. The MINRES method-based LS-SVM can effectively perform feature reduction and model selection simultaneously, so it is a practical tool for the silicon trend prediction task.