Search

Accelerated global warming is leading to the loss of plant species diversity, and ex situ preservation of seeds is becoming an increasingly important aspect of species conservation. However, information on dormancy and germination is lacking in many endangered species. Amsonia elliptica (Apocynaceae) is the only Amsonia species native to Korea, and the South Korean Ministry of Environment has designated it Class II endangered wildlife. Nevertheless, the dormancy class and the dormancy breaking method for seeds of this species for germination are not precisely known. We identified the structure of A. elliptica seeds and the causes of dormancy, which inhibits germination. In addition, we tried to develop an effective germination promotion method by testing the wet stratified condition, which breaks dormancy, and the form of gibberellin that can replace it. Fresh seeds of A. elliptica imbibe water, but the covering layers (endosperm and seed coat) inhibit germination by mechanically restricting the embryo. Initial germination tests confirmed low embryo growth potential and physiological dormancy (PD). Restriction due to the covering layer was eliminated by seed scarification, and abnormal germination was observed. After 12 weeks of cold moist stratification at 4°C, only 12% of seeds germinated. However, 68.8% of seeds subjected to 8 weeks of warm moist stratification followed by 12 weeks of cold stratification germinated, indicating that warm stratification pretreatment before cold stratification is effective in breaking dormancy. A. elliptica seeds exhibited intermediate PD. Furthermore, 61.3% of seeds soaked in 500 mg/L GA4+7 for 14 days and incubated at 25/15°C germinated. Therefore, GA4+7 rapidly broke the dormancy of A. elliptica seeds compared with warm plus cold stratification treatment, thus providing an efficient method for seedling production.

Introduction: SARS-CoV-2 antigen tests can complement and substitute for RT-PCR tests. Centralized laboratory automated SARS-CoV-2 antigen tests that can be scaled to process a large number COVID-19 cases simultaneously are now available. We have evaluated the new Roche Elecsys SARS-CoV-2 antigen electro-chemiluminescent immunoassay. Methods: The Roche SARS-CoV-2 antigen assay is a double-antibody sandwich electro-chemiluminescent immunoassay, which reports a cut-off index (COI) (COI ≥ 1.0 considered positive). We assessed assay precision and linearity, and confirmed the reactivity limit. We determined the assay sensitivity and specificity with a verification group (289 controls and 61 RT-PCR positive COVID-19 cases). Assay performance was also validated against the consecutive samples we received (7657 controls and 17 cases) for SARS-CoV-2 antigen testing from June to October 2021. Result: The assay had a within-run precision CV of 3.0% at COI 0.68, and a CV of 1.5% at COI 3.49. Between-run precision was 3.0% at COI 0.68 and 1.8% at COI 3.49. The assay was linear from COI 0.65 to 7.84. All 35 C50 ± 20% test results performed over 7 days were positive/negative, respectively. In the verification group, overall sensitivity was 42.6% (26/61 positive, 95% CI 30.0–55.9), and specificity was 99.7% (1/289 positive, 95% CI 98.1–100). The agreement between the SARS-CoV-2 antigen and the RT-PCR cycle threshold (Ct) count was good (r = 0.90). In cases with Ct counts ≤ 30, the antigen assay sensitivity improved to 94.7% (18/19 positive, 95% CI 74.0–99.9). In our validation group, antigen sensitivity was 62.5% (5/8 antigen positive, 95% CI 24.5–91.5) within the first week of disease onset, but no cases were reactive after the first week of disease onset. Conclusion: The Elecsys SARS-CoV-2 antigen assay has good performance within manufacturer specifications. The sensitivity of the Roche antigen assay was greatest when used in patients with lower RT-PCR Ct values (≤30) and within the first week of disease onset.

Abstract Background The impact of different classes of microbial pathogens on mortality in severe community-acquired pneumonia is not well elucidated. Previous studies have shown significant variation in the incidence of viral, bacterial and mixed infections, with conflicting risk associations for mortality. We aimed to determine the risk association of microbial aetiologies with hospital mortality in severe CAP, utilising a diagnostic strategy incorporating molecular testing. Our primary hypothesis was that respiratory viruses were important causative pathogens in severe CAP and was associated with increased mortality when present with bacterial pathogens in mixed viral-bacterial co-infections. Methods A retrospective cohort study from January 2014 to July 2015 was conducted in a tertiary hospital medical intensive care unit in eastern Singapore, which has a tropical climate. All patients diagnosed with severe community-acquired pneumonia were included. Results A total of 117 patients were in the study. Microbial pathogens were identified in 84 (71.8%) patients. Mixed viral-bacterial co-infections occurred in 18 (15.4%) of patients. Isolated viral infections were present in 32 patients (27.4%); isolated bacterial infections were detected in 34 patients (29.1%). Hospital mortality occurred in 16 (13.7%) patients. The most common bacteria isolated was Streptococcus pneumoniae and the most common virus isolated was Influenza A. Univariate and multivariate logistic regression showed that serum procalcitonin, APACHE II severity score and mixed viral-bacterial infection were associated with increased risk of hospital mortality. Mixed viral-bacterial co-infections were associated with an adjusted odds ratio of 13.99 (95% CI 1.30–151.05, p = 0.03) for hospital mortality. Conclusions Respiratory viruses are common organisms isolated in severe community-acquired pneumonia. Mixed viral-bacterial infections may be associated with an increased risk of mortality.

Objectives: The aim of this study was to determine the prevalence of the colistin-resistance gene (mcr-1) and the antibiotic-susceptibility profile of mcr-1 positive, colistin-resistant isolates in stool specimens of patients attending a tertiary care hospital in Singapore. Methods: 201 diarrheal stool specimens of patients attending the Changi General Hospital between May to August 2017 were collected and screened for the presence of mcr-1 by culture and molecular methods. Antibiotic-susceptibility profile of mcr-1 positive isolates was determined using the polymyxin B and colistin E-tests and the VITEK 2 system. Results: We observed an unexpectedly high prevalence of mcr-1 in patients attending a tertiary care hospital in Singapore, i.e 6.0% and 8.0% estimated by stool culture and direct stool PCR, respectively. The mcr-1 gene was detected predominantly in Escherichia coli. Antibiotic-susceptibility testing on 12 mcr-1 positive Enterobacteriaceae isolates revealed variable susceptibility profiles with no detection of carbapenem-resistant Enterobacteriaceae. Conclusions: This is the first report of the prevalence of human faecal carriage of mcr-1 in Singapore. Our findings highlight the potential risk of mcr-1 spread among our patient cohort. The mcr-1 gene detection combined with the detection of other resistance gene targets of clinical importance is recommended to pre-empt the spread mcr-1 in our patients. Keywords: mcr-1, human faecal carriage, Stool specimen, Colistin resistance, Singapore

Background/Purpose: Screening for vancomycin-resistant enterococci (VRE) by culture takes days to generate results, while polymerase chain reaction (PCR) testing directly from clinical specimens lacks specificity. The aims of this study were to develop a real-time PCR to detect and identify Enterococcus faecium, Enterococcus faecalis, and vanA and vanB genes, and to evaluate the impact of this PCR on test-reporting times when performing it directly from suspect VRE isolates present on screening chromogenic media. Methods: The tetraplex PCR primers were designed to amplify E. faecium, E. faecalis, and vanA and vanB genes, with melt-curve analysis of PCR products. Following analytical and clinical validation of the molecular assay, PCR testing was performed for target colonies present on VRE chromogenic media. PCR results were evaluated against conventional phenotypic identification and susceptibility testing, with the time to result being monitored for both modalities. Results: A total of 519 colonies from clinical specimens were tested concurrently by real-time PCR and phenotypic methods. In all, 223 isolates were identified with phenotypic vancomycin resistance (vanA, n = 108; vanB, n = 105; non-vanA/vanB = 10), with complete agreement between PCR and phenotypic testing for vancomycin-resistant E. faecium and E. faecalis. The majority (88.6%) of PCR results were reported, on average, 24.8 hours earlier than those of phenotypic testing, with 68% reduction in total costs. Conclusion: The use of culture on selective media, followed by direct colony PCR confirmation allows faster and economical VRE screening.

The effect of nonsinusoidal oscillation at different modification ratios (α) on slag lubrication was investigated during mold oscillation. A validated and reliable multiphase model was employed, which involved flow and solidification of the molten steel and mold slag. The main results revealed that a large amount of liquid slag at the entrance of the mold–strand channel reflowed into the slag pool at the middle of the negative strip period. The phenomenon was more distinct, with an increase in the modification ratio. The modification ratio had no obvious effect on the average thickness of the liquid film at different depths below the meniscus. A modification ratio of 0.5 caused less fluctuation of the transient liquid film. Quantitative prediction of slag consumption indicated that as the modification ratio increased from 0.2 to 0.5 to 0.8, the average values were 0.278, 0.286, and 0.279 kg/m2, respectively. Shell solidification and growth near the meniscus mainly occurred when the mold was descending, which not only depended on the heat flux, but also on the liquid slag flow, the pressure driven by slag rim, and the mold oscillation. Optimization of the modification ratio of nonsinusoidal oscillation could be an alternative to delay growth of the initial shell towards the molten steel. A modification ratio of 0.5 had the least robust shell tip at the meniscus, thereby reducing entrapment of inclusions and bubbles by the shell tip.

The number of leaves in maize plant is one of the key traits describing its growth conditions. It is directly related to plant development and leaf counts also give insight into changing plant development stages. Compared with the traditional solutions which need excessive human interventions, the methods of computer vision and machine learning are more efficient. However, leaf counting with computer vision remains a challenging problem. More and more researchers are trying to improve accuracy. To this end, an automated, deep learning based approach for counting leaves in maize plants is developed in this paper. A Convolution Neural Network(CNN) is used to extract leaf features. The CNN model in this paper is inspired by Google Inception Net V3, which using multi-scale convolution kernels in one convolution layer. To compress feature maps generated from some middle layers in CNN, the Fisher Vector (FV) is used to reduce redundant information. Finally, these encoded feature maps are used to regress the leaf numbers by using Random Forests. To boost the related research, a relatively single maize image dataset (Different growth stage with 2845 samples, which 80% for train and 20% for test) is constructed by our team. The proposed algorithm in single maize data set achieves Mean Square Error (MSE) of 0.32.

With the rapid advances in remote-sensing technologies and the larger number of satellite images, fast and effective object detection plays an important role in understanding and analyzing image information, which could be further applied to civilian and military fields. Recently object detection methods with region-based convolutional neural network have shown excellent performance. However, these two-stage methods contain region proposal generation and object detection procedures, resulting in low computation speed. Because of the expensive manual costs, the quantity of well-annotated aerial images is scarce, which also limits the progress of geospatial object detection in remote sensing. In this paper, on the one hand, we construct and release a large-scale remote-sensing dataset for geospatial object detection (RSD-GOD) that consists of 5 different categories with 18,187 annotated images and 40,990 instances. On the other hand, we design a single shot detection framework with multi-scale feature fusion. The feature maps from different layers are fused together through the up-sampling and concatenation blocks to predict the detection results. High-level features with semantic information and low-level features with fine details are fully explored for detection tasks, especially for small objects. Meanwhile, a soft non-maximum suppression strategy is put into practice to select the final detection results. Extensive experiments have been conducted on two datasets to evaluate the designed network. Results show that the proposed approach achieves a good detection performance and obtains the mean average precision value of 89.0% on a newly constructed RSD-GOD dataset and 83.8% on the Northwestern Polytechnical University very high spatial resolution-10 (NWPU VHR-10) dataset at 18 frames per second (FPS) on a NVIDIA GTX-1080Ti GPU.