Southwest Minzu University

Object detection techniques are the foundation for the artificial intelligence field. This research paper gives a brief overview of the You Only Look Once (YOLO) algorithm and its subsequent advanced versions. Through the analysis, we reach many remarks and insightful results. The results show the differences and similarities among the YOLO versions and between YOLO and Convolutional Neural Networks (CNNs). The central insight is the YOLO algorithm improvement is still ongoing.This article briefly describes the development process of the YOLO algorithm, summarizes the methods of target recognition and feature selection, and provides literature support for the targeted picture news and feature extraction in the financial and other fields. Besides, this paper contributes a lot to YOLO and other object detection literature.

Genome-wide association study (GWAS) and genomic prediction/selection (GP/GS) are the two essential enterprises in genomic research. Due to the great magnitude and complexity of genomic and phenotypic data, analytical methods and their associated software packages are frequently advanced. GAPIT is a widely-used genomic association and prediction integrated tool as an R package. The first version was released to the public in 2012 with the implementation of the general linear model (GLM), mixed linear model (MLM), compressed MLM (CMLM), and genomic best linear unbiased prediction (gBLUP). The second version was released in 2016 with several new implementations, including enriched CMLM (ECMLM) and settlement of MLMs under progressively exclusive relationship (SUPER). All the GWAS methods are based on the single-locus test. For the first time, in the current release of GAPIT, version 3 implemented three multi-locus test methods, including multiple loci mixed model (MLMM), fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK). Additionally, two GP/GS methods were implemented based on CMLM (named compressed BLUP; cBLUP) and SUPER (named SUPER BLUP; sBLUP). These new implementations not only boost statistical power for GWAS and prediction accuracy for GP/GS, but also improve computing speed and increase the capacity to analyze big genomic data. Here, we document the current upgrade of GAPIT by describing the selection of the recently developed methods, their implementations, and potential impact. All documents, including source code, user manual, demo data, and tutorials, are freely available at the GAPIT website (http://zzlab.net/GAPIT).

Carbon dots, generally referring to small carbon nanoparticles with various levels of surface passivation, have emerged as a new class of quantum dot-like fluorescent nanomaterials. Since the original report in 2006, carbon dots have been investigated by many research groups worldwide, with major advances already made in their syntheses, structural and mechanistic understandings, and evaluations for biocompatibilities and potential bio-applications. In this article, representative studies responsible for these advances in the development and understanding of carbon dots are reviewed, and those targeting the use of carbon dots as high-performance yet nontoxic fluorescence agents for optical bioimaging in vitro and in vivo are highlighted and discussed.

Streptococcus agalactiae and Staphylococcus aureus are two pathogenetic agents of several infective diseases in humans. Biocidal effects and cellular internalization of ZnO nanoparticles (NPs) on two bacteria are reported, and ZnO NPs have a good bacteriostasis effect. ZnO NPs were synthesized in the EG aqueous system through the hydrolysis of ionic Zn2+ salts. Particle size and shape were controlled by the addition of the various surfactants. Bactericidal tests were performed in an ordinary broth medium on solid agar plates and in liquid systems with different concentrations of ZnO NPs. The biocidal action of ZnO materials was studied by transmission electron microscopy of bacteria ultrathin sections. The results confirmed that bactericidal cells were damaged after ZnO NPs contacted with them, showing both gram-negative membrane and gram-positive membrane disorganization. The surface modification of ZnO NPs causes an increase in membrane permeability and the cellular internalization of these NPs whereas there is a ZnO NP structure change inside the cells.

Logs are imperative in the development and maintenance process of many software systems. They record detailed runtime information that allows developers and support engineers to monitor their systems and dissect anomalous behaviors and errors. The increasing scale and complexity of modern software systems, however, make the volume of logs explodes. In many cases, the traditional way of manual log inspection becomes impractical. Many recent studies, as well as industrial tools, resort to powerful text search and machine learning-based analytics solutions. Due to the unstructured nature of logs, a first crucial step is to parse log messages into structured data for subsequent analysis. In recent years, automated log parsing has been widely studied in both academia and industry, producing a series of log parsers by different techniques. To better understand the characteristics of these log parsers, in this paper, we present a comprehensive evaluation study on automated log parsing and further release the tools and benchmarks for easy reuse. More specifically, we evaluate 13 log parsers on a total of 16 log datasets spanning distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. We report the benchmarking results in terms of accuracy, robustness, and efficiency, which are of practical importance when deploying automated log parsing in production. We also share the success stories and lessons learned in an industrial application at Huawei. We believe that our work could serve as the basis and provide valuable guidance to future research and deployment of automated log parsing.

Root exudates are an essential carrier for material cycling, energy exchange, and information transfer between the belowground parts of plants and the soil. We synthesize current properties and regulators of root exudates and their role in the belowground ecosystem as substances cycle and signal regulation. We discussed the composition and amount of root exudates and their production mechanism, indicating that plant species, growth stage, environmental factors, and microorganisms are primary influence factors. The specific mechanisms by which root secretions mobilize the soil nutrients were summarized. First, plants improve the nutrient status of the soil by releasing organic acids for acidification and chelation. Then, root exudates accelerated the SOC turnover due to their dual impacts, forming and destabilizing aggregates and MASOC. Eventually, root exudates mediate the plant-plant interaction and plant-microbe interaction. Additionally, a summary of the current collection methods of root exudates is presented.

The measurement scales, consistency index, inconsistency issues, missing judgment estimation and priority derivation methods have been extensively studied in the pairwise comparison matrix (PCM). Various approaches have been proposed to handle these problems, and made great contributions to the decision making. This paper reviews the literature of the main developments of the PCM. There are plenty of literature related to these issues, thus we mainly focus on the literature published in 37 peer reviewed international journals from 2010 to 2015 (searched via ISI Web of science). We attempt to analyze and classify these literatures so as to find the current hot research topics and research techniques in the PCM, and point out the future directions on the PCM. It is hoped that this paper will provide a comprehensive literature review on PCM, and act as informative summary of the main developments of the PCM for the researchers for their future research.

Clustering technology has important applications in data mining, pattern recognition, machine learning and other fields. However, with the explosive growth of data, traditional clustering algorithm is more and more difficult to meet the needs of big data analysis. How to improve the traditional clustering algorithm and ensure the quality and efficiency of clustering under the background of big data has become an important research topic of artificial intelligence and big data processing. The density-based clustering algorithm can cluster arbitrarily shaped data sets in the case of unknown data distribution. DBSCAN is a classical density-based clustering algorithm, which is widely used for data clustering analysis due to its simple and efficient characteristics. The purpose of this paper is to study DBSCAN clustering algorithm based on density. This paper first introduces the concept of DBSCAN algorithm, and then carries out performance tests on DBSCAN algorithm in three different data sets. By analyzing the experimental results, it can be concluded that DBSCAN algorithm has higher homogeneity and diversity when it performs personalized clustering on data sets of non-uniform density with broad values and gradually sparse forwards. When the DBSCAN algorithm's neighborhood distance eps is 1000, 26 classes are generated after clustering.

Carbon dots (or carbon quantum dots in some literature reports), generally small carbon nanoparticles with various surface passivation effects, have attracted widespread attention in recent years, with a rapidly increasing number of research publications. The reported studies covered many aspects of carbon dots, from the development of many new synthetic methodologies to an improved mechanistic elucidation and to the exploration of application opportunities, especially for those in the fluorescence imaging of cells and tissues. There have also been significant advances in the establishment of a shared mechanistic framework for carbon dots and other carbon-based quantum dots, graphene quantum dots in particular. In this article, representative recent studies for more efficient syntheses of better-performing carbon dots are highlighted along with results from explorations of their various bioimaging applications in vitro and in vivo. Similar fluorescence properties and potential imaging uses of some graphene quantum dots are also discussed, toward a more consistent and uniform understanding of phenomenologically different carbon-based quantum dots.

This technical note addresses the stability problem of delayed positive switched linear systems whose subsystems are all positive. Both discrete-time systems and continuous-time systems are studied. In our analysis, the delays in systems can be unbounded. Under certain conditions, several stability results are established by constructing a sequence of functions that are positive, monotonically decreasing, and convergent to zero as time tends to infinity (additionally continuous for continuous-time systems). It turns out that these functions can serve as an upper bound of the systems' trajectories starting from a particular region. Finally, a numerical example is presented to illustrate the obtained results.

Abstract Developing an effective theranostic nanoplatform remains a great challenge for cancer diagnosis and treatment. Here, BiOI@Bi 2 S 3 @BSA (bovine serum albumin) semiconductor heterojunction nanoparticles (SHNPs) for triple‐combination radio/photodynamic/photothermal cancer therapy and multimodal computed tomography/photoacoustic (CT/PA) bioimaging are reported. On the one hand, SHNPs possess strong X‐ray attenuation capability since they contain high‐Z elements, and thus they are anticipated to be a very competent candidate as radio‐sensitizing materials for radiotherapy enhancement. On the other hand, as a semiconductor, the as‐prepared SHNPs offer an extra approach for reactive oxygen species generation based on electron–hole pair under the irradiation of X‐ray through the photodynamic therapy process. This X‐ray excited photodynamic therapy obviously has better penetration depth in bio‐tissue. What's more, the SHNPs also possess well photothermal conversion efficiency for photothermal therapy, because Bi 2 S 3 is a thin band semiconductor with strong near‐infrared absorption that can cause local overheat. In vivo tumor ablation studies show that synergistic radio/photodynamic/photothermal therapy achieves more significant therapeutic effect than any single treatment. In addition, with the strong X‐ray attenuation and high near‐infrared absorption, the as‐obtained SHNPs can also be applied as a multimodal contrast agent in CT/PA imaging.

The protein-nanoparticle (NP) interface is a current frontier of multiple disciplines, full of challenges and opportunities. The unique behaviors of nanomaterials (NMs) bring many exciting applications, and also raise safety concerns. Beyond bioapplications, various NMs could also enter human bodies from the environment. When entering human bodies, NPs interact with various biomolecules, especially proteins, forming a protein corona. This protein-NP complex is what the biosystems 'see' and 'respond to'. Therefore, understanding how NPs interact with proteins is crucial for both bioapplications and the biosafety of NMs. In this review, the current understanding of protein-NP interactions is summarized, including the theoretical background, experimental results, and computational progresses. Guidelines for improving bioapplication performance and reducing the potential biosafety hazard of NMs by designing the protein-NP interactions are discussed, along with future directions and challenges in this exciting field.

The gut microbiota represents a large community of microorganisms that play an important role in immune regulation and maintenance of homeostasis. Living bacteria receive increasing interest as potential therapeutics for gut disorders, because they inhibit the colonization of pathogens and positively regulate the composition of bacteria in gut. However, these treatments are often accompanied by antibiotic administration targeting pathogens. In these cases, the efficacy of therapeutic bacteria is compromised by their susceptibility to antibiotics. Here, we demonstrate that a single-cell coating composed of tannic acids and ferric ions, referred to as 'nanoarmor', can protect bacteria from the action of antibiotics. The nanoarmor protects both Gram-positive and Gram-negative bacteria against six clinically relevant antibiotics. The multiple interactions between the nanoarmor and antibiotic molecules allow the antibiotics to be effectively absorbed onto the nanoarmor. Armored probiotics have shown the ability to colonize inside the gastrointestinal tracts of levofloxacin-treated rats, which significantly reduced antibiotic-associated diarrhea (AAD) resulting from the levofloxacin-treatment and improved some of the pre-inflammatory symptoms caused by AAD. This nanoarmor strategy represents a robust platform to enhance the potency of therapeutic bacteria in the gastrointestinal tracts of patients receiving antibiotics and to avoid the negative effects of antibiotics in the gastrointestinal tract.

Abstract Rapid industrialization leads to increased wastewater discharge encompassing hexavalent chromium (Cr(VI)), which leads to serious environmental problems of toxicity and potential carcinogenicity. Removal of these species is normally carried out by ion‐exchange, precipitation, membrane filtration, sorption, photocatalytic reduction, etc. This review mainly focuses on the photocatalytic and photoelectrocatalytic (PEC) reduction of Cr (VI), because of their advantages over other methods such as reduced risk of secondary pollution by non‐reduced Cr (VI), no sludge formation, no need for a large amount of chemical reagents, clean and easy installation. The main factors influencing the photocatalytic reduction efficiency of Cr (VI) such as catalyst activity, solution pH, Cr adsorption on the catalyst and additives, are briefly discussed. Finally, a special emphasis is provided to the photoelectrocatalytic (PEC) reduction of Cr (VI).

Cardinal and ordinal inconsistencies are important and popular research topics in the study of decision making with pair-wise comparison matrices (PCMs). Few of the currently-employed tactics are capable of simultaneously dealing with both cardinal and ordinal inconsistency issues in one model, and most are heavily dependent on the method chosen for weight (priorities) derivation or the obtained closest matrix by optimization method that may change many of the original values. In this paper, we propose a Hadamard product induced bias matrix model, which only requires the use of the data in the original matrix to identify and adjust the cardinally inconsistent element(s) in a PCM. Through graph theory and numerical examples, we show that the adapted Hadamard model is effective in identifying and eliminating the ordinal inconsistencies. Also, for the most inconsistent element identified in the matrix, we develop innovative methods to improve the consistency of a PCM. The proposed model is only dependent on the original matrix, is independent of the methods chosen to derive the priority vectors, and preserves most of the original information in matrix A since only the most inconsistent element(s) need(s) to be modified. Our method is much easier to implement than any of the existing models, and the values it recommends for replacement outperform those derived from the literature. It significantly enhances matrix consistency and improves the reliability of PCM decision making.

Time series forecasting uses data from the past periods of time to predict future information, which is of great significance in many applications. Existing time series forecasting methods still have problems such as low accuracy when dealing with some non-stationary multivariate time series data forecasting. Aiming at the shortcomings of existing methods, in this paper we propose a new time series forecasting model LSTM-attention-LSTM. The model uses two LSTM models as the encoder and decoder, and introduces an attention mechanism between the encoder and decoder. The model has two distinctive features: first, by using the attention mechanism to calculate the interrelationship between sequence data, it overcomes the disadvantage of the coder-and-decoder model in that the decoder cannot obtain sufficiently long input sequences; second, it is suitable for sequence forecasting with long time steps. In this paper we validate the proposed model based on several real data sets, and the results show that the LSTM-attention-LSTM model is more accurate than some currently dominant models in prediction. The experiment also assessed the effect of the attention mechanism at different time steps by varying the time step.

Radioresistance is one of the undesirable impediments in hypoxic tumors, which sharply diminishes the therapeutic effectiveness of radiotherapy and eventually results in the failure of their treatments. An attractive strategy for attenuating radioresistance is developing an ideal radiosensitization system with appreciable radiosensitization capacity to attenuate tumor hypoxia and reinforce radiotherapy response in hypoxic tumors. Therefore, we describe the development of Gd-containing polyoxometalates-conjugated chitosan (GdW 10 @CS nanosphere) as a radiosensitization system for simultaneous extrinsic and intrinsic radiosensitization, by generating an overabundance of cytotoxic reactive oxygen species (ROS) using high-energy X-ray stimulation and mediating the hypoxia-inducible factor-1a (HIF-1a) siRNA to down-regulate HIF-1α expression and suppress broken double-stranded DNA self-healing. Most importantly, the GdW 10 @CS nanospheres have the capacity to promote the exhaustion of intracellular glutathione (reduced GSH) by synergy W 6+ -triggered GSH oxidation for sufficient ROS generation, thereby facilitating the therapeutic efficiency of radiotherapy. As a result, the as-synthesized GdW 10 @CS nanosphere can overcome radioresistance of hypoxic tumors through a simultaneous extrinsic and intrinsic strategy to improve radiosensitivity. We have demonstrated GdW 10 @CS nanospheres with special radiosensitization behavior, which provides a versatile approach to solve the critical radioresistance issue of hypoxic tumors.

This experiment was conducted to investigate the efficacy of Lactobacillus acidophilus, Bacillus subtilis, and Clostridium butyricum supplementation in broilers. A total of 400 one-day-old mixed sex Ross 308 broilers with an initial average BW of 46 ± 0.5 g were randomly allotted into 4 treatments with 5 replicate pens per treatment and 20 broilers in each pen for 35 d. Dietary treatments were (1) an antibiotic-free diet (CON), (2) CON + 5 mg/kg of avilamycin, (3) CON + 1 × 10(5) cfu of multistrain probiotics/kg of diet (P1), and (4) CON + 2 × 10(5) cfu of multistrain probiotics/kg of diet (P2). Broilers fed the P1 and P2 diets had greater BW gain than broilers fed the CON diet during d 22 to 35 (P = 0.01) and overall (P = 0.02). Feed conversion ratios in P1 and P2 were decreased (P = 0.03) compared with that in CON from d 22 to 35. Ileal digestibility of most essential amino acids, with the exception of His and Phe, were increased (P < 0.05) in P1 and P2 compared with CON. Serum IgA and IgM concentrations in P2 were higher (P < 0.05) than those in CON. The cecal Lactobacillus numbers were increased (P = 0.02), and the counts of Escherichia coli were decreased (P = 0.03) in P1 and P2 compared with CON. Dietary supplementation with multistrain probiotics decreased (P < 0.05) the excreta NH3 content compared with the CON. In conclusion, dietary supplementation with multistrain probiotics improved broiler growth performance, ileal amino acids digestibility, and humoral immunity. Furthermore, the probiotics decreased the cecal numbers of E. coli and decreased the NH3 content of excreta.

Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing.

Electronic health record (EHR) has recorded the process of occurrence, development, and treatment of diseases. So it has high medical value. Owing to the private and sensitive nature of medical data for patients, the data sharing and privacy preservation are critical issues in EHR. Blockchain technology may be a promising solution for the problems above since it holds the features of decentralization and tamper resistance. In the paper, we propose a medical data sharing and protection scheme based on the hospital’s private blockchain to improve the electronic health system of the hospital. Firstly, the scheme can satisfy various security properties such as decentralization, openness, and tamper resistance. A reliable mechanism is created for the doctors to store medical data or access the historical data of patients while meeting privacy preservation. Furthermore, a symptoms-matching mechanism is given between patients. It allows patients who get the same symptoms to conduct mutual authentication and create a session key for their future communication about the illness. The proposed scheme is implemented by using PBC and OpenSSL libraries. Finally, the security and performance evaluation of the proposed scheme is given.