Epidemiology and clinical course of COVID-19 in Shanghai, China.

Background: Novel coronavirus pneumonia (COVID-19) is prevalent around the world. We aimed to describe epidemiological features and clinical course in Shanghai. Methods: We retrospectively analysed 325 cases admitted at Shanghai Public Health Clinical Center, between January 20 and February 29, 2020. Results: 47.4% (154/325) had visited Wuhan within 2 weeks of illness onset. 57.2% occurred in 67 clusters; 40% were situated within 53 family clusters. 83.7% developed fever during the disease course. Median times from onset to first medical care, hospitalization and negative detection of nucleic acid by nasopharyngeal swab were 1, 4 and 8 days. Patients with mild disease using glucocorticoid tended to have longer viral shedding in blood and feces. At admission, 69.8% presented with lymphopenia and 38.8% had elevated D-dimers. Pneumonia was identified in 97.5% (314/322) of cases by chest CT scan. Severe-critical patients were 8% with a median time from onset to critical disease of 10.5 days. Half required oxygen therapy and 7.1% high-flow nasal oxygen. The case fatality rate was 0.92% with median time from onset to death of 16 days. Conclusion: COVID-19 cases in Shanghai were imported. Rapid identification, and effective control measures helped to contain the outbreak and prevent community transmission

A Review of Imaging Techniques for Plant Phenotyping

Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review

Query adaptive similarity for large scale object retrieval

Many recent object retrieval systems rely on local features for describing an image. The similarity between a pair of images is measured by aggregating the similarity between their corresponding local features. In this paper we present a probabilistic framework for modeling the feature to feature similarity measure. We then derive a query adaptive distance which is appropriate for global similarity evaluation. Furthermore, we propose a function to score the individual contributions into an image to image similarity within the probabilistic framework. Experimental results show that our method improves the retrieval accuracy significantly and consistently. Moreover, our result compares favorably to the state-of-the-art. © 2013 IEEE.Qin D., Wengert C., Van Gool L., ''Query adaptive similarity for large scale object retrieval'', 26th IEEE computer society conference on computer vision and pattern recognition - CVPR 2013, pp. 1610-1617, June 23-28, 2013, Portland, Oregon, USA.status: publishe

Learning to rank bag-of-word histograms for large-scale object retrieval

© 2014. The copyright of this document resides with its authors. Most state-of-the-art object retrieval systems rely on ad-hoc similarities between histograms of quantised local descriptors to find, in their databases, all the images relevant to an image query. In this work, our goal is to replace those similarities with ones that are specifically trained to maximize the retrieval accuracy. We propose to use a simple and very general linear model whose weights directly represent the similarity values. We devise a variant of rank-SVM to learn those weights automatically from training data with fast convergence and we propose techniques to limit the number of parameters of the model and prevent overfitting. Importantly, the flexibility of our model allows us to seamlessly incorporate well-known image retrieval schemes such as burstiness, negative evidence and idf weighting, and still exploit inverted files for efficiency in the large-scale setting. In our experiments, we show that our approach consistently and significantly outperforms the similarities used in several state-of-the-art systems on 4 standard benchmark datasets. In particular, on the Oxford105k dataset, our method outperforms the closest competitor by 6%.Qin D., Chen Y., Guillaumin M., Van Gool L., ''Learning to rank bag-of-word histograms for large-scale object retrieval'', 25th British machine vision conference - BMVC 2014, 12 pp., September 1-5, 2014, Nottingham, UK.status: publishe

Jinlida Granules Improve Dysfunction of Hypothalamic-Pituitary-Thyroid Axis in Diabetic Rats Induced by STZ

Objective. We aim to explore the effects and mechanisms of Jinlida granules on the dysfunction of hypothalamic-pituitary-thyroid (HPT) axis in diabetic rats induced by streptozotocin. Methods. A total of 48 SD rats were randomized into normal control group (NC, n=6) and diabetic group (n=42). Rats in diabetic group were randomly divided into diabetes mellitus (DM) control group, low, medium, and high doses of Jinlida group (JL, JM, and JH), medium dose of Jinlida plus Tongxinluo group (JM + T), metformin group (Met), and Saxagliptin group (Sax) (n=6 in each group). Diabetic rats were obtained by intraperitoneal injection of streptozotocin and sacrificed at 8 weeks to examine the function of HPT axis. Results. Levels of fasting blood glucose (P<0.05), pIκB, TNFα (P<0.05), pNF-κB, and IL-6 (P<0.01) in liver tissue and TSHR mRNA expression (P<0.01) in diabetic group were significantly increased, while levels of serum T3 and T4, thyroid hormone receptor (TR) mRNA and Dio1 mRNA in liver tissue, and sodium iodide symporter (NIS) mRNA in thyroid tissue in diabetic group were significantly decreased compared with those in NC group (P<0.01). Among diabetic groups, level of fasting blood glucose in JH, JM + T and Met group was lower (P<0.05) compared with DM group. However, levels of serum T3 and T4, TR mRNA in liver tissue, TSHR, and NIS mRNA in thyroid tissue in JH, JM + T, Met, and Sax group were significantly increased (P<0.01) compared to DM group. In contrast, levels of Dio1 mRNA, pI-κB in Met and JM + T groups, pNF-κB in JH, JM + T, and Met group, and TNFα and IL-6 in JM, JH, JM + T, and Met group were significantly decreased (P<0.05). HE staining showed reduced thyroid follicular epithelium and follicular area, as well as increased colloid area in DM group, indicating impaired synthesis, reabsorption, and secretory of TH in diabetes, which was significantly improved in JH, JM + T, and Met groups. Conclusion. HPT axis dysfunction in DM could be significantly improved by Jinlida granules. The mechanism might be associated with the anti-inflammatory effects involving NF-κB pathway. Our findings suggested the potential benefit of Jinlida granules for patients with HPT axis dysfunction and DM, which was to be verified by more experimental and clinical studies

Modeling Apple Surface Temperature Dynamics Based on Weather Data

The exposure of fruit surfaces to direct sunlight during the summer months can result in sunburn damage. Losses due to sunburn damage are a major economic problem when marketing fresh apples. The objective of this study was to develop and validate a model for simulating fruit surface temperature (FST) dynamics based on energy balance and measured weather data. A series of weather data (air temperature, humidity, solar radiation, and wind speed) was recorded for seven hours between 11:00–18:00 for two months at fifteen minute intervals. To validate the model, the FSTs of “Fuji” apples were monitored using an infrared camera in a natural orchard environment. The FST dynamics were measured using a series of thermal images. For the apples that were completely exposed to the sun, the RMSE of the model for estimating FST was less than 2.0 °C. A sensitivity analysis of the emissivity of the apple surface and the conductance of the fruit surface to water vapour showed that accurate estimations of the apple surface emissivity were important for the model. The validation results showed that the model was capable of accurately describing the thermal performances of apples under different solar radiation intensities. Thus, this model could be used to more accurately estimate the FST relative to estimates that only consider the air temperature. In addition, this model provides useful information for sunburn protection management

Hello neighbor: accurate object retrieval with k-reciprocal nearest neighbors

This paper introduces a simple yet effective method to improve visual word based image retrieval. Our method is based on an analysis of the k-reciprocal nearest neighbor structure in the image space. At query time the information obtained from this process is used to treat different parts of the ranked retrieval list with different distance measures. This leads effectively to a re-ranking of retrieved images. As we will show, this has two benefits: first, using different similarity measures for different parts of the ranked list allows for compensation of the "curse of dimensionality". Second, it allows for dealing with the uneven distribution of images in the data space. Dealing with both challenges has very beneficial effect on retrieval accuracy. Furthermore, a major part of the process happens offline, so it does not affect speed at retrieval time. Finally, the method operates on the bag-of-words level only, thus it could be combined with any additional measures on e.g. either descriptor level or feature geometry making room for further improvement. We evaluate our approach on common object retrieval benchmarks and demonstrate a significant improvement over standard bag-of-words retrieval. © 2011 IEEE.Qin D., Gammeter S., Bossard L., Quack T., Van Gool L., ''Hello neighbor: accurate object retrieval with k-reciprocal nearest neighbors'', IEEE computer society conference on computer vision and pattern recognition - CVPR2011, pp. 777-783, June 21-23, 2011, Colorado Springs, CO, USA.status: publishe