Exploratory Survey for the Emerald Ash Borer, \u3ci\u3eAgrilus Planipennis\u3c/i\u3e (Coleoptera: Buprestidae), and Its Natural Enemies in China

An exploratory survey for the emerald ash borer, Agrilus planipennis, and its natural enemies was conducted in China during October and November 2003. We examined 29 field plots in six provinces. We visually inspected living Fraxinus chinensis, F. mandshurica, F. pennsylvanica, F. rhynchophylla, and F. velutina then peeled off the bark in search of A. planipennis and associated natural enemies. We found active A. planipennis infestations in nine of the 29 field plots, including plots in the provinces of Hebei, Heilongjiang, Jilin, Liaoning, and the provincial level city of Tianjin. Signs of past A. planipennis infestations were found in five of the 20 plots where no active infestations were present. The distribution of A. planipennis was patchy within the forest, and larval densities varied greatly among trees and at different heights within the same tree. Agrilus planipennis densities ranged from 0 to 284 larvae/m2 of bark surface for 1-m log sections. The Nearctic ash species, F. pennsylvanica and F. velutina, planted in China were apparently more susceptible to A. planipennis attack than were the native Chinese ash species. Similarly, ash trees growing along streets or in plantations or city parks were more susceptible to A. planipennis infestation compared with trees in natural forests. We identified two species of natural enemies attacking A. planipennis during this survey. In Changchun City, Jilin Province and Guangang District, Tianjin City, we found a previously reported but undescribed species of Spathius sp. (Braconidae) parasitizing an average of 6.3% A. planipennis larvae in individual trees, ranging from 0 to 50%. In Changchun City, Jilin Province and in Benxi County, Liaoning Province, we discovered a previously unknown gregarious endoparasitoid of A. planipennis larvae, Tetrastichus nov. sp. (Eulophidae), with a total parasitism rate of 6.6% in individual trees, ranging from 0 to 50%. We discussed the potential role of natural enemies in the management of A. planipennis in North America

Time-Series Classification Based on Fusion Features of Sequence and Visualization

For the task of time-series data classification (TSC), some methods directly classify raw time-series (TS) data. However, certain sequence features are not evident in the time domain and the human brain can extract visual features based on visualization to classify data. Therefore, some researchers have converted TS data to image data and used image processing methods for TSC. While human perceptionconsists of a combination of human senses from different aspects, existing methods only use sequence features or visualization features. Therefore, this paper proposes a framework for TSC based on fusion features (TSC-FF) of sequence features extracted from raw TS and visualization features extracted from Area Graphs converted from TS. Deep learning methods have been proven to be useful tools for automatically learning features from data; therefore, we use long short-term memory with an attention mechanism (LSTM-A) to learn sequence features and a convolutional neural network with an attention mechanism (CNN-A) for visualization features, in order to imitate the human brain. In addition, we use the simplest visualization method of Area Graph for visualization features extraction, avoiding loss of information and additional computational cost. This article aims to prove that using deep neural networks to learn features from different aspects and fusing them can replace complex, artificially constructed features, as well as remove the bias due to manually designed features, in order to avoid the limitations of domain knowledge. Experiments on several open data sets show that the framework achieves promising results, compared with other methods

a-Methylacyl coenzyme A racemase is highly expressed in the intestinal-type adenocarcinoma and high-grade dysplasia lesions of the stomach

To study a-Methylacyl coenzyme A racemase (AMACR) expression in gastric intestinal-type adenocarcinoma and its precursors, we performed an immunohistochemical assay (using an avidin-biotinperoxidase complex method) on 106 paraffin-embedded gastric mucosal biopsy samples and 25 gastrectomy samples (37 negative for dysplasia; 30 indefinite for dysplasia; 22 low-grade dysplasia; 25 high-grade dysplasia; and 34 invasive intestinal adenocarcinoma). The results showed that AMACR staining was uniformly negative in the groups negative for dysplasia and indefinite for dysplasia. Only 1 of 22 (4.5%) low-grade dysplasia showed weak staining for AMACR. In the groups of high-grade dysplasia and invasive intestinaltype adenocarcinoma, however, 19 of 25 (76%) and 18 of 34 (52.9%) were positive for AMACR respectively. Expression of AMACR was not correlated with location, H. Pylori infection or intestinal metaplasia. These results suggested that AMACR may play a role in the intermediate stage of gastric carcinogenesis. The high level expression of AMACR in high-grade dysplasia and carcinoma suggests that it may be a useful biomarker in distinguishing high-grade dysplasia and carcinoma from low-grade dysplasia

Characteristics and risk factors for renal recovery after acute kidney injury in critically ill patients in cohorts of elderly and non-elderly: a multicenter retrospective cohort study

AbstractBackground The purpose of this study was to explore the risk factors for renal nonrecovery among elderly and nonelderly patients with acute kidney injury (AKI) in critically ill patients.Methods A multicenter retrospective cohort of 583 critically ill patients with AKI was examined. We found the best cutoff value for predicting renal recovery by age was 63 years old through logistic regression. All patients were divided into two cohorts, age <63 and age ≥63-years old; on the basis of renal recovery at 30 days after AKI, the two patient cohorts were further divided into a renal recovery group and a renal nonrecovery group. Multivariate logistic regression was used to analyze the risk factors affecting renal recovery in the two cohorts.Results The 30-day renal recovery rate of patients aged <63 years was 70.0% (198/283), multivariate analysis showed that the independent risk factors affecting renal nonrecovery in age <63 years old included AKI stage, blood lactate level and hemoglobin level. The 30-day renal recovery rate of patients aged ≥63 years was 28.7% (86/300), multivariate analysis showed that the independent risk factors for renal nonrecovery in age ≥63-years old included diabetes mellitus, surgery with general anesthesia, AKI stage, APACHE II score, eGFR, and hemoglobin level.Conclusions The renal nonrecovery after AKI in critically ill patients in patients aged ≥63 years was more strongly affected by multiple risk factors, such as diabetes mellitus, surgery with general anesthesia, eGFR, and APACHE II score, in addition to hemoglobin and AKI stage

Principal component analysis (PCA) of poplar genes during infection.

<p>The graph illustrates the distribution of each sample in the space of the first two principal components (PCs). The samples infected with weakly active fungi are clustered together, whereas the distribution of the highly active group is relatively scattered.</p

More poplar DR genes were up-regulated in the highly resistant 895/highly active fungus group during infection (especially during the early stage).

<p>(A) The differentially expressed poplar disease-resistant genes between different time points in three groups. (B) The overlapping DE-DR genes among the three groups.</p

A Comprehensive Analysis of the Transcriptomes of <i>Marssonina brunnea</i> and Infected Poplar Leaves to Capture Vital Events in Host-Pathogen Interactions

<div><p>Background</p><p>Understanding host-pathogen interaction mechanisms helps to elucidate the entire infection process and focus on important events, and it is a promising approach for improvement of disease control and selection of treatment strategy. Time-course host-pathogen transcriptome analyses and network inference have been applied to unravel the direct or indirect relationships of gene expression alterations. However, time series analyses can suffer from absent time points due to technical problems such as RNA degradation, which limits the application of algorithms that require strict sequential sampling. Here, we introduce an efficient method using independence test to infer an independent network that is exclusively concerned with the frequency of gene expression changes.</p><p>Results</p><p>Highly resistant NL895 poplar leaves and weakly resistant NL214 leaves were infected with highly active and weakly active <i>Marssonina brunnea</i>, respectively, and were harvested at different time points. The independent network inference illustrated the top 1,000 vital fungus-poplar relationships, which contained 768 fungal genes and 54 poplar genes. These genes could be classified into three categories: a fungal gene surrounded by many poplar genes; a poplar gene connected to many fungal genes; and other genes (possessing low degrees of connectivity). Notably, the fungal gene M6_08342 (a metalloprotease) was connected to 10 poplar genes, particularly including two disease-resistance genes. These core genes, which are surrounded by other genes, may be of particular importance in complicated infection processes and worthy of further investigation.</p><p>Conclusions</p><p>We provide a clear framework of the interaction network and identify a number of candidate key effectors in this process, which might assist in functional tests, resistant clone selection, and disease control in the future.</p></div