Zhongkai University of Agriculture and Engineering

Simultaneous measurements of atmospheric organic and elemental carbon (OC and EC) were taken during winter and summer seasons at 2003 in 14 cities in China. Daily PM 2.5 samples were analyzed for OC and EC by the Interagency Monitoring of Protected Visual Environments (IMPROVE) thermal/optical reflectance protocol. Average PM 2.5 OC concentrations in the 14 cities were 38.1 μ g m −3 and 13.8 μ g m −3 for winter and summer periods, and the corresponding EC were 9.9 μ g m −3 and 3.6 μ g m −3 , respectively. OC and EC concentrations had summer minima and winter maxima in all the cities. Carbonaceous matter (CM), the sum of organic matter (OM = 1.6 × OC) and EC, contributed 44.2% to PM 2.5 in winter and 38.8% in summer. OC was correlated with EC ( R 2 : 0.56–0.99) in winter, but correlation coefficients were lower in summer ( R 2 : 0.003–0.90). Using OC/EC enrichment factors, the primary OC, secondary OC and EC accounted for 47.5%, 31.7% and 20.8%, respectively, of total carbon in Chinese urban environments. More than two thirds of China's urban carbon is derived from directly emitted particles. Average OC/EC ratios ranged from 2.0 to 4.7 among 14 cities during winter and from 2.1 to 5.9 during summer. OC/EC ratios in this study were consistent with a possible cooling effect of carbonaceous aerosols over China.

As an economic crop, pepper satisfies people's spicy taste and has medicinal uses worldwide. To gain a better understanding of Capsicum evolution, domestication, and specialization, we present here the genome sequence of the cultivated pepper Zunla-1 (C. annuum L.) and its wild progenitor Chiltepin (C. annuum var. glabriusculum). We estimate that the pepper genome expanded ∼0.3 Mya (with respect to the genome of other Solanaceae) by a rapid amplification of retrotransposons elements, resulting in a genome comprised of ∼81% repetitive sequences. Approximately 79% of 3.48-Gb scaffolds containing 34,476 protein-coding genes were anchored to chromosomes by a high-density genetic map. Comparison of cultivated and wild pepper genomes with 20 resequencing accessions revealed molecular footprints of artificial selection, providing us with a list of candidate domestication genes. We also found that dosage compensation effect of tandem duplication genes probably contributed to the pungent diversification in pepper. The Capsicum reference genome provides crucial information for the study of not only the evolution of the pepper genome but also, the Solanaceae family, and it will facilitate the establishment of more effective pepper breeding programs.

High oil and protein content make tetraploid peanut a leading oil and food legume. Here we report a high-quality peanut genome sequence, comprising 2.54 Gb with 20 pseudomolecules and 83,709 protein-coding gene models. We characterize gene functional groups implicated in seed size evolution, seed oil content, disease resistance and symbiotic nitrogen fixation. The peanut B subgenome has more genes and general expression dominance, temporally associated with long-terminal-repeat expansion in the A subgenome that also raises questions about the A-genome progenitor. The polyploid genome provided insights into the evolution of Arachis hypogaea and other legume chromosomes. Resequencing of 52 accessions suggests that independent domestications formed peanut ecotypes. Whereas 0.42-0.47 million years ago (Ma) polyploidy constrained genetic variation, the peanut genome sequence aids mapping and candidate-gene discovery for traits such as seed size and color, foliar disease resistance and others, also providing a cornerstone for functional genomics and peanut improvement.

Traditionally, fungal taxonomy was based on observable phenotypic characters. Recent advances have driven taxonomic conclusions towards DNA-based approaches and these techniques have corresponding pros and cons. Species concepts must therefore rely on incorporated approaches of genotypic, phenotypic and physiological characters and chemotaxonomy. Examination and interpretation of morphological characters however vary from person to person. Standardized procedures are used in the taxonomic study of fungi and general practices of phenotypic approaches are herein outlined. It is not possible to detail all techniques for all fungi and thus, this paper emphasizes on microfungi. Specimen collection is the initial step in any

The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described.

This paper provides an updated classification of the Kingdom Fungi (including fossil fungi) and fungus-like taxa. Five-hundred and twenty-three (535) notes are provided for newly introduced taxa and for changes that have been made since the previous outline. In the discussion, the latest taxonomic changes in Basidiomycota are provided and the classification of Mycosphaerellales are broadly discussed. Genera listed in Mycosphaerellaceae have been confirmed by DNA sequence analyses, while doubtful genera (DNA sequences being unavailable but traditionally accommodated in Mycosphaerellaceae) are listed in the discussion. Problematic genera in Glomeromycota are also discussed based on phylogenetic results.

In the past decade, with the rapid development of wearable electronics, medical health monitoring, the Internet of Things, and flexible intelligent robots, flexible pressure sensors have received unprecedented attention. As a very important kind of electronic component for information transmission and collection, flexible pressure sensors have gained a wide application prospect in the fields of aerospace, biomedical and health monitoring, electronic skin, and human-machine interface. In recent years, MXene has attracted extensive attention because of its unique 2D layered structure, high conductivity, rich surface terminal groups, and hydrophilicity, which has brought a new breakthrough for flexible sensing. Thus, it has become a revolutionary pressure-sensitive material with great potential. In this work, the recent advances of MXene-based flexible pressure sensors are reviewed from the aspects of sensing type, sensing mechanism, material selection, structural design, preparation strategy, and sensing application. The methods and strategies to improve the performance of MXene-based flexible pressure sensors are analyzed in details. Finally, the opportunities and challenges faced by MXene-based flexible pressure sensors are discussed. This review will bring the research and development of MXene-based flexible sensors to a new high level, promoting the wider research exploitation and practical application of MXene materials in flexible pressure sensors.

The traditional production paradigm of large batch production does not offer flexibility toward satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multivariety and small-batch customized production modes. For this, artificial intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are: self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This article focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, that is, machine learning, multiagent systems, Internet of Things, big data, and cloud-edge computing, are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

In recent years, UAV remote sensing has gradually attracted the attention of scientific researchers and industry, due to its broad application prospects. It has been widely used in agriculture, forestry, mining, and other industries. UAVs can be flexibly equipped with various sensors, such as optical, infrared, and LIDAR, and become an essential remote sensing observation platform. Based on UAV remote sensing, researchers can obtain many high-resolution images, with each pixel being a centimeter or millimeter. The purpose of this paper is to investigate the current applications of UAV remote sensing, as well as the aircraft platforms, data types, and elements used in each application category; the data processing methods, etc.; and to study the advantages of the current application of UAV remote sensing technology, the limitations, and promising directions that still lack applications. By reviewing the papers published in this field in recent years, we found that the current application research of UAV remote sensing research can be classified into four categories according to the application field: (1) Precision agriculture, including crop disease observation, crop yield estimation, and crop environmental observation; (2) Forestry remote sensing, including forest disease identification, forest disaster observation, etc.; (3) Remote sensing of power systems; (4) Artificial facilities and the natural environment. We found that in the papers published in recent years, image data (RGB, multi-spectral, hyper-spectral) processing mainly used neural network methods; in crop disease monitoring, multi-spectral data are the most studied type of data; for LIDAR data, current applications still lack an end-to-end neural network processing method; this review examines UAV platforms, sensors, and data processing methods, and according to the development process of certain application fields and current implementation limitations, some predictions are made about possible future development directions.

Characterization of genetic regulatory variants acting on livestock gene expression is essential for interpreting the molecular mechanisms underlying traits of economic value and for increasing the rate of genetic gain through artificial selection. Here we build a Cattle Genotype–Tissue Expression atlas (CattleGTEx) as part of the pilot phase of the Farm animal GTEx (FarmGTEx) project for the research community based on 7,180 publicly available RNA-sequencing (RNA-seq) samples. We describe the transcriptomic landscape of more than 100 tissues/cell types and report hundreds of thousands of genetic associations with gene expression and alternative splicing for 23 distinct tissues. We evaluate the tissue-sharing patterns of these genetic regulatory effects, and functionally annotate them using multiomics data. Finally, we link gene expression in different tissues to 43 economically important traits using both transcriptome-wide association and colocalization analyses to decipher the molecular regulatory mechanisms underpinning such agronomic traits in cattle. The cattle Genotype–Tissue Expression atlas of expression and splicing QTLs is generated from 7,180 uniformly re-processed RNA-seq samples. Integration with GWAS identifies candidate genes and variants associated with economically important traits.

Significance Aromatic hydrocarbons account for 20 to 30% of volatile organic compounds and contribute importantly to ozone and secondary organic aerosol (SOA) formation in urban environments. The oxidation of toluene, the most abundant aromatic compound, is believed to occur mainly via OH addition, primary organic peroxy radical (RO 2 ) formation, and ring cleavage, leading to ozone and SOA. From combined experimental and theoretical studies, we show that cresol formation is dominant, while primary RO 2 production is negligible. Our work reveals that the formation and subsequent reactions of cresols regulate the atmospheric impacts of toluene oxidation, suggesting that its representation in current atmospheric models should be reassessed for accurate determination of ozone and SOA formation. The results from our study provide important constraints and guidance for future modeling studies.

Effective therapy lies in achieving a therapeutic amount of drug to the proper site in the body and then maintaining the desired drug concentration for a sufficient time interval to be clinically effective for treatment. The blood-brain barrier (BBB) hinders most drugs from entering the central nervous system (CNS) from the blood stream, leading to the difficulty of delivering drugs to the brain via the circulatory system for the treatment, diagnosis and prevention of brain diseases. Several brain drug delivery approaches have been developed, such as intracerebral and intracerebroventricular administration, intranasal delivery and blood-to-brain delivery, as a result of transient BBB disruption induced by biological, chemical or physical stimuli such as zonula occludens toxin, mannitol, magnetic heating and ultrasound, but these approaches showed disadvantages of being dangerous, high cost and unsuitability for most brain diseases and drugs. The strategy of vector-mediated blood-to-brain delivery, which involves improving BBB permeability of the drug-carrier conjugate, can minimize side effects, such as being submicrometre objects that behave as a whole unit in terms of their transport and properties, nanomaterials, are promising carrier vehicles for direct drug transport across the intact BBB as a result of their potential to enter the brain capillary endothelial cells by means of normal endocytosis and transcytosis due to their small size, as well as their possibility of being functionalized with multiple copies of the drug molecule of interest. This review provids a concise discussion of nano carriers for drug transport across the intact BBB, various forms of nanomaterials including inorganic/solid lipid/polymeric nanoparticles, nanoemulsions, quantum dots, nanogels, liposomes, micelles, dendrimers, polymersomes and exosomes are critically evaluated, their mechanisms for drug transport across the BBB are reviewed, and the future directions of this area are fully discussed.

Object detection in unmanned aerial vehicle (UAV) imagery is a meaningful foundation in various research domains. However, UAV imagery poses unique challenges, including large image sizes, small sizes detection objects, dense distribution, overlapping instances, and insufficient lighting impacting the effectiveness of object detection. In this article, we propose Drone-YOLO, a series of multi-scale UAV image object detection algorithms based on the YOLOv8 model, designed to overcome the specific challenges associated with UAV image object detection. To address the issues of large scene sizes and small detection objects, we introduce improvements to the neck component of the YOLOv8 model. Specifically, we employ a three-layer PAFPN structure and incorporate a detection head tailored for small-sized objects using large-scale feature maps, significantly enhancing the algorithm’s capability to detect small-sized targets. Furthermore, we integrate the sandwich-fusion module into each layer of the neck’s up–down branch. This fusion mechanism combines network features with low-level features, providing rich spatial information about the objects at different layer detection heads. We achieve this fusion using depthwise separable evolution, which balances parameter costs and a large receptive field. In the network backbone, we employ RepVGG modules as downsampling layers, enhancing the network’s ability to learn multi-scale features and outperforming traditional convolutional layers. The proposed Drone-YOLO methods have been evaluated in ablation experiments and compared with other state-of-the-art approaches on the VisDrone2019 dataset. The results demonstrate that our Drone-YOLO (large) outperforms other baseline methods in the accuracy of object detection. Compared to YOLOv8, our method achieves a significant improvement in mAP0.5 metrics, with a 13.4% increase on the VisDrone2019-test and a 17.40% increase on the VisDrone2019-val. Additionally, the parameter-efficient Drone-YOLO (tiny) with only 5.25 M parameters performs equivalently or better than the baseline method with 9.66M parameters on the dataset. These experiments validate the effectiveness of the Drone-YOLO methods in the task of object detection in drone imagery.

Abstract The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.

Increasing evidence suggests that phenylalanine ammonia-lyase (PAL, EC 4.3.1.5) is associated with low temperature stress in plant tissues. Banana fruit are highly susceptible to chilling injury. However, little is known about the role of PAL (i.e. gene expression, protein level and activity) in fruit chilling. In this work, the involvement of PAL induced by heat treatment (38 degrees C for 3 days) prior to storage (8 degrees C) in chilling tolerance was investigated. The PAL inhibitor 2-aminoindan-2-phosphonic acid (AIP) was also used to further study the role of PAL in the chilling tolerance. The results showed that mRNA transcripts (MaPAL1 and MaPAL2) and PAL protein levels increased during storage at chilling temperature. Heat treatment prior to storage alleviated chilling injury and enhanced PAL activity, protein amount and MaPAL1 and MaPAL2 transcript levels. The increases in parameters of PAL upon heat pretreatment were all inhibited by AIP treatment, which resulted in aggravation of chilling injury. Thus, these findings indicate that the induction of PAL by heat pretreatment was regulated at both the transcriptional and the translational levels and that PAL may play a role in heat pretreatment-induced chilling tolerance of banana fruit.

At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing.

With the increasing demand for intelligent transportation systems, short-term traffic flow prediction has become an important research direction. The memory unit of a Long Short-Term Memory (LSTM) neural network can store data characteristics over a certain period of time, hence the suitability of this network for time series processing. This paper uses an improved Gate Recurrent Unit (GRU) neural network to study the time series of traffic parameter flows. The LSTM short-term traffic flow prediction based on the flow series is first investigated, and then the GRU model is introduced. The GRU can be regarded as a simplified LSTM. After extracting the spatial and temporal characteristics of the flow matrix, an improved GRU with a bidirectional positive and negative feedback called the Bi-GRU prediction model is used to complete the short-term traffic flow prediction and study its characteristics. The Rectified Adaptive (RAdam) model is adopted to improve the shortcomings of the common optimizer. The cosine learning rate attenuation is also used for the model to avoid converging to the local optimal solution and for the appropriate convergence speed to be controlled. Furthermore, the scientific and reliable model learning rate is set together with the adaptive learning rate in RAdam. In this manner, the accuracy of network prediction can be further improved. Finally, an experiment of the Bi-GRU model is conducted. The comprehensive Bi-GRU prediction results demonstrate the effectiveness of the proposed method.

This is the opening paper in the special issue of Fungal Diversity, which collates the data on defining species. Defining and recognizing species has long been a controversial issue. Since Darwin's proposed origin of species, over 30 species criteria have been brought forth and used to define species boundaries. In recent times, phylogenetic analyses based on multiple loci have been extensively used as a method to define species boundaries. However, only a few mycologists are aware that phylogenetic species criteria can mask discordances among fungal groups, leading to inaccurately defined species boundaries. In the current review, we discuss species recognition criteria, how and where these criteria can be applied along with their limitations and derived alternatives. In order to delimit fungal species, authors need to take into account not only the phylogenetic and phenotypic coherence, but also the timing of events that lead to fungal speciation and subsequent diversifications. Variations in the rate of phenotypic diversifications and convergent fungal evolution make it difficult to establish a universal species recognition criterion. The best practice can only be defined in the context of each fungal group. In this review, we provide a set of guidelines, encouraging an integrative taxonomic approach for species delimitation that can be used to define fungal species boundaries in the future. The other papers in this special issue deal with fungal speciation in Ascomycota, Dothideomycetes, Basidiomycota, basal fungi, lichen-forming fungi, plant pathogenic fungi, and yeasts.

Cobalt is a transition metal located in the fourth row of the periodic table and is a neighbor of iron and nickel. It has been considered an essential element for prokaryotes, human beings, and other mammals, but its essentiality for plants remains obscure. In this article, we proposed that cobalt (Co) is a potentially essential micronutrient of plants. Co is essential for the growth of many lower plants, such as marine algal species including diatoms, chrysophytes, and dinoflagellates, as well as for higher plants in the family Fabaceae or Leguminosae . The essentiality to leguminous plants is attributed to its role in nitrogen (N) fixation by symbiotic microbes, primarily rhizobia. Co is an integral component of cobalamin or vitamin B 12 , which is required by several enzymes involved in N 2 fixation. In addition to symbiosis, a group of N 2 fixing bacteria known as diazotrophs is able to situate in plant tissue as endophytes or closely associated with roots of plants including economically important crops, such as barley, corn, rice, sugarcane, and wheat. Their action in N 2 fixation provides crops with the macronutrient of N. Co is a component of several enzymes and proteins, participating in plant metabolism. Plants may exhibit Co deficiency if there is a severe limitation in Co supply. Conversely, Co is toxic to plants at higher concentrations. High levels of Co result in pale-colored leaves, discolored veins, and the loss of leaves and can also cause iron deficiency in plants. It is anticipated that with the advance of omics, Co as a constitute of enzymes and proteins and its specific role in plant metabolism will be exclusively revealed. The confirmation of Co as an essential micronutrient will enrich our understanding of plant mineral nutrition and improve our practice in crop production.

The recent realistic estimate of fungal numbers which used various algorithms was between 2.2 and 3.8 million. There are nearly 100,000 accepted species of Fungi and fungus-like taxa, which is between 2.6 and 4.5% of the estimated species. Several forums such as Botanica Marina series, Fungal Diversity notes, Fungal Biodiversity Profiles, Fungal Systematics and Evolution—New and Interesting Fungi, Mycosphere notes and Fungal Planet have enhanced the introduction of new taxa and nearly 2000 species have been introduced in these publications in the last decade. The need to define a fungal species more accurately has been recognized, but there is much research needed before this can be better clarified. We address the evidence that is needed to estimate the numbers of fungi and address the various advances that have been made towards its understanding. Some genera are barely known, whereas some plant pathogens comprise numerous species complexes and numbers are steadily increasing. In this paper, we examine ten genera as case studies to establish trends in fungal description and introduce new species in each genus. The genera are the ascomycetes Colletotrichum and Pestalotiopsis (with many species or complexes), Atrocalyx, Dothiora, Lignosphaeria, Okeanomyces, Rhamphoriopsis, Thozetella, Thyrostroma (relatively poorly studied genera) and the basidiomycete genus Lepiota. We provide examples where knowledge is incomplete or lacking and suggest areas needing further research. These include (1) the need to establish what is a species, (2) the need to establish how host-specific fungi are, not in highly disturbed urban areas, but in pristine or relatively undisturbed forests, and (3) the need to establish if species in different continents, islands, countries or regions are different, or if the same fungi occur worldwide? Finally, we conclude whether we are anywhere near to flattening the curve in new species description.