Dynamics of a stochastic hybrid delay food chain model with jumps in an impulsive polluted environment

In this paper, a stochastic hybrid delay food chain model with jumps in an impulsive polluted environment is investigated. We obtain the sufficient and necessary conditions for persistence in mean and extinction of each species. The results show that the stochastic dynamics of the system are closely correlated with both time delays and environmental noises. Some numerical examples are introduced to illustrate the main results

Metagenomics-based exploration of key soil microorganisms contributing to continuously planted Casuarina equisetifolia growth inhibition and their interactions with soil nutrient transformation

Casuarina equisetifolia (C. equisetifolia) is an economically important forest tree species, often cultivated in continuous monoculture as a coastal protection forest. Continuous planting has gradually affected growth and severely restricted the sustainable development of the C. equisetifolia industry. In this study, we analyzed the effects of continuous planting on C. equisetifolia growth and explored the rhizosphere soil microecological mechanism from a metagenomic perspective. The results showed that continuous planting resulted in dwarfing, shorter root length, and reduced C. equisetifolia seedling root system. Metagenomics analysis showed that 10 key characteristic microorganisms, mainly Actinoallomurus, Actinomadura, and Mycobacterium, were responsible for continuously planted C. equisetifolia trees. Quantitative analysis showed that the number of microorganisms in these three genera decreased significantly with the increase of continuous planting. Gene function analysis showed that continuous planting led to the weakening of the environmental information processing-signal transduction ability of soil characteristic microorganisms, and the decrease of C. equisetifolia trees against stress. Reduced capacity for metabolism, genetic information processing-replication and repair resulted in reduced microbial propagation and reduced microbial quantity in the rhizosphere soil of C. equisetifolia trees. Secondly, amino acid metabolism, carbohydrate metabolism, glycan biosynthesis and metabolism, lipid metabolism, metabolism of cofactors and vitamins were all significantly reduced, resulting in a decrease in the ability of the soil to synthesize and metabolize carbon and nitrogen. These reduced capacities further led to reduced soil microbial quantity, microbial carbon and nitrogen, microbial respiration intensity, reduced soil enzyme nutrient cycling and resistance-related enzyme activities, a significant reduction in available nutrient content of rhizosphere soils, a reduction in the ion exchange capacity, and an impediment to C. equisetifolia growth. This study provides an important basis for the management of continuously planted C. equisetifolia plantations

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.Comment: 12 pages (including references), 17 figures, submitted to WWW 2018: The 2018 Web Conference, April 23--27, 2018, Lyon, France. The contents discarded from the conference version due to the 9-page limitation are also included in this versio

Reasonable deep application of sheep manure fertilizer to alleviate soil acidification to improve tea yield and quality

Soil acidification in Chinese tea plantations is widespread, and it has significantly affected the growth of tea trees; it was important to explore soil remediation of acidified tea plantations in depth for the sustainable development of tea industry. In this study, the effects of sheep manure fertilizer with different application depths on soil acidification, tea yield and quality, and soil nitrogen transformation in tea plantations were analyzed for five consecutive years from 2018 to 2022. The results showed that long-term use of sheep manure fertilizer significantly reduced soil acidification (P< 0.05) in tea plantations, improved soil pH and soil ammonium nitrogen content, enhanced root activity and root nitrogen uptake capacity of tea trees, and thus improved tea yield and quality. The effect of different application depths of sheep manure fertilizer on tea yield and quality was mainly reflected in the transformation ability of soil ammonium nitrogen and nitrate nitrogen, which showed that high transformation ability of soil ammonium nitrogen and high ammonium nitrogen content were beneficial to high tea yield and vice versa, and the best effect was achieved when sheep manure was applied at a depth of 50 cm and 70 cm. The topsis analysis confirmed that sheep manure fertilization had a greater effect on root activity, ammonium nitrogen, ammonia intensity, and nifH gene. This study provided an important practical basis for the restoration of acidified tea plantation soil through sheep manure fertilizer management

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and two vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given

Residential Pesticide Usage in Older Adults Residing in Central California

Information on residential pesticide usage and behaviors that may influence pesticide exposure was collected in three population-based studies of older adults residing in the three Central California counties of Fresno, Kern, and Tulare. We present data from participants in the Study of Use of Products and Exposure Related Behaviors (SUPERB) study (N = 153) and from community controls ascertained in two Parkinson’s disease studies, the Parkinson’s Environment and Gene (PEG) study (N = 359) and The Center for Gene-Environment Studies in Parkinson’s Disease (CGEP; N = 297). All participants were interviewed by telephone to obtain information on recent and lifetime indoor and outdoor residential pesticide use. Interviews ascertained type of product used, frequency of use, and behaviors that may influence exposure to pesticides during and after application. Well over half of all participants reported ever using indoor and outdoor pesticides; yet frequency of pesticide use was relatively low, and appeared to increase slightly with age. Few participants engaged in behaviors to protect themselves or family members and limit exposure to pesticides during and after treatment, such as ventilating and cleaning treated areas, or using protective equipment during application. Our findings on frequency of use over lifetime and exposure related behaviors will inform future efforts to develop population pesticide exposure models and risk assessment

Genomic and Phenotypic Analysis of Salmonella enterica Bacteriophages Identifies Two Novel Phage Species

Bacteriophages (phages) are potential alternatives to chemical antimicrobials against pathogens of public health significance. Understanding the diversity and host specificity of phages is important for developing effective phage biocontrol approaches. Here, we assessed the host range, morphology, and genetic diversity of eight Salmonella enterica phages isolated from a wastewater treatment plant. The host range analysis revealed that six out of eight phages lysed more than 81% of the 43 Salmonella enterica isolates tested. The genomic sequences of all phages were determined. Whole-genome sequencing (WGS) data revealed that phage genome sizes ranged from 41 to 114 kb, with GC contents between 39.9 and 50.0%. Two of the phages SB13 and SB28 represent new species, Epseptimavirus SB13 and genera Macdonaldcampvirus, respectively, as designated by the International Committee for the Taxonomy of Viruses (ICTV) using genome-based taxonomic classification. One phage (SB18) belonged to the Myoviridae morphotype while the remaining phages belonged to the Siphoviridae morphotype. The gene content analyses showed that none of the phages possessed virulence, toxin, antibiotic resistance, type I–VI toxin–antitoxin modules, or lysogeny genes. Three (SB3, SB15, and SB18) out of the eight phages possessed tailspike proteins. Whole-genome-based phylogeny of the eight phages with their 113 homologs revealed three clusters A, B, and C and seven subclusters (A1, A2, A3, B1, B2, C1, and C2). While cluster C1 phages were predominantly isolated from animal sources, cluster B contained phages from both wastewater and animal sources. The broad host range of these phages highlights their potential use for controlling the presence of S. enterica in foods