Simulation of Evapotranspiration at a 3-Minute Time Interval Based on Remote Sensing Data and SEBAL Model

Using remote sensing to estimate evapotranspiration minute frequency is the basis for accurately calculating hourly and daily evapotranspiration from the regional scale. However, from the existing research, it is difficult to use remote sensing data to estimate evapotranspiration minute frequency. This paper uses GF-4 and moderate-resolution imaging spectroradiometer (MODIS) data in conjunction with the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET at a 3-min time interval in part of China and South Korea, and compares those simulation results with that from field measured data. According to the spatial distribution of ET derived from GF-4 and MODIS, the texture of ET derived from GF-4 is more obvious than that of MODIS, and GF-4 is able to express the variability of the spatial distribution of ET. Meanwhile, according to the value of ET derived from both GF-4 and MODIS, results from these two satellites have significant linear correlation, and ET derived from GF-4 is higher than that from MODIS. Since the temporal resolution of GF-4 is 3 min, the land surface ET at a 3-min time interval could be obtained by utilizing all available meteorological and remote sensing data, which avoids error associated with extrapolating instantaneously from a single image

Time Series Clustering Based on Singularity

With relevant theories on time series clustering, the thesis makes research into similarity clustering process of time series from the perspective of singularity and proposes the time series clustering based on singularity applying K-means and DBScan clustering algorithms according to the shortage of traditional clustering algorithm. In accordance with the general clustering process of time series, time series clustering based on singularity and K-means are made respectively to get different clustering results and make a comparison, thus proving that similarity clustering research of time series from the perspective of singularity can better find out people's concern on time series

Etiologic subtype predicts outcome in mild stroke: prospective data from a hospital stroke registry

BACKGROUND: Few studies on whether etiologic subtype can predict outcome in mild stroke are available. The study aim to explore the effect of different etiologic subtype on prognosis of these patients. METHODS: We prospectively registered consecutive cases of acute ischemic stroke from September. 01, 2009 to August. 31, 2011. Patients with National Institute of Health Stroke Scale (NIHSS) ≦3 and within 30 days of symptom onset were included. All cause death or disability (defined as modified Rankin Scale >2) were followed up at 3 months. The multivariate logistical regression model was used to analyse relationship between etiologic subtype and clinical outcomes. RESULTS: We included 680 cases, which accounted for 41.1% (680/1655) of the total registered cases. Mean age were 62.54 ± 13.51 years, and males were 65.4%. The median time of symptoms onset to admission was 72 hours. 3.8% (26/680) of cases admitted within 3 hours and 4.7% (32/680) admitted within 4.5 hours. However, no patient received intravenous thrombolysis. Of included patients, 21.5% large-artery atherosclerosis, 40.6% small-vessel disease, 7.5% cardioembolisms, 2.2% other causes and 28.2% undetermined causes. The rate of case fatality and death/disability was 2.2% and 10.1% respectively at 3 months. After adjustment of potential confounders, such as age, sex, NIHSS on admission and vascular risk factors et al., cardioembolism (RR = 3.395;95%CI 1.257 ~ 9.170) was the predictor of death or disability at 3 months and small vessel occlusion (RR = 0.412;95%CI 0.202 ~ 0.842) was the protective factor of death or disability at 3 months. CONCLUSION: Different etiologic subtype can predict the outcome in patients with mild stroke and it can help to stratify these patients for individual decision-making

Understanding the Mechanism of Deep Learning Frameworks in Lesion Detection for Pathological Images with Breast Cancer

With the advances of scanning sensors and deep learning algorithms, computational pathology has drawn much attention in recent years and started to play an important role in the clinical workflow. Computer-aided detection (CADe) systems have been developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing misdetections. In this study, we conducted four experiments to demonstrate that the features learned by deep learning models are interpretable from a pathological perspective. In addition, classifiers such as the support vector machine (SVM) and random forests (RF) were used in experiments to replace the fully connected layers and decompose the end-to-end framework, verifying the validity of feature extraction in the convolutional layers. The experimental results reveal that the features learned from the convolutional layers work as morphological descriptors for specific cells or tissues, in agreement with the diagnostic rules in practice. Most of the properties learned by the deep learning models summarized detection rules that agree with those of experienced pathologists. The interpretability of deep features from a clinical viewpoint not only enhances the reliability of AI systems, enabling them to gain acceptance from medical experts, but also facilitates the development of deep learning frameworks for different tasks in pathological analytics

Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis

Bacteraemia has attracted great attention owing to its serious outcomes, including deterioration of the primary disease, infection, severe sepsis, overwhelming septic shock or even death. Candidemia, secondary to bacteraemia, is frequently seen in hospitalised patients, especially in those with weak immune systems, and may lead to lethal outcomes and a poor prognosis. Moreover, higher morbidity and mortality associated with candidemia. Owing to the complexity of patient conditions, the occurrence of candidemia is increasing. Candidemia-related studies are relatively challenging. Because candidemia is associated with increasing mortality related to invasive infection of organs, its pathogenesis warrants further investigation. We collected the relevant clinical data of 367 patients with concomitant candidemia and bacteraemia in the first hospital of China Medical University from January 2013 to January 2018. We analysed the available information and attempted to obtain the undisclosed information. Subsequently, we used machine learning to screen for regulators such as prognostic factors related to death. Of the 367 patients, 231 (62.9%) were men, and the median age of all patients was 61 years old (range, 52–71 years), with 133 (36.2%) patients aged >65 years. In addition, 249 patients had hypoproteinaemia, and 169 patients were admitted to the intensive care unit (ICU) during hospitalisation. The most common fungi and bacteria associated with tumour development and Candida infection were Candida parapsilosis and Acinetobacter baumannii, respectively. We used machine learning to screen for death-related prognostic factors in patients with candidemia and bacteraemia mainly based on integrated information. The results showed that serum creatinine level, endotoxic shock, length of stay in ICU, age, leukocyte count, total parenteral nutrition, total bilirubin level, length of stay in the hospital, PCT level and lymphocyte count were identified as the main prognostic factors. These findings will greatly help clinicians treat patients with candidemia and bacteraemia

Machine‑learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study

Background Invasive candidal infection combined with bacterial bloodstream infection is one of the common nosocomial infections that is also the main cause of morbidity and mortality. The incidence of invasive Candidal infection with bacterial bloodstream infection is increasing year by year worldwide, but data on China is still limited. Methods We included 246 hospitalised patients who had invasive candidal infection combined with a bacterial bloodstream infection from January 2013 to January 2018; we collected and analysed the relevant epidemiological information and used machine learning methods to find prognostic factors related to death (training set and test set were randomly allocated at a ratio of 7:3). Results Of the 246 patients with invasive candidal infection complicated with a bacterial bloodstream infection, the median age was 63 years (53.25–74), of which 159 (64.6%) were male, 109 (44.3%) were elderly patients (> 65 years), 238 (96.7%) were hospitalised for more than 10 days, 168 (68.3%) were admitted to ICU during hospitalisation, and most patients had records of multiple admissions within 2 years (167/246, 67.9%). The most common blood index was hypoproteinemia (169/246, 68.7%), and the most common inducement was urinary catheter use (210/246, 85.4%). Moreover, the most frequently infected fungi and bacteria were Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis by machine learning method are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, C-Reactive protein (CRP), leukocyte count, neutrophil count, Procalcitonin (PCT), and total bilirubin level. Conclusion Our results showed that the most common candida and bacteria infections were caused by Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, CRP, leukocyte count, neutrophil count, PCT and total bilirubin level

Assessment of dietary intake among pregnant women in a rural area of western China

<p>Abstract</p> <p>Background</p> <p>Adequate maternal nutrient intake during pregnancy is important to ensure satisfactory birth outcomes. There are no data available on the usual dietary intake among pregnant women in rural China. The present study describes and evaluates the dietary intake in a cohort of pregnant women living in two counties of rural Shaanxi, western China.</p> <p>Methods</p> <p>1420 pregnant women were recruited from a trial that examined the effects of micronutrient supplementation on birth outcomes. Dietary information was collected at the end of their trimester or after delivery with an interviewed-administrated semi-quantitative food frequency questionnaire (FFQ). Nutrients intake was calculated from the FFQ and compared to the Estimated Average Requirements (EAR). The EAR cut-offs based on the Chinese Nutrition Society Dietary Reference Intakes (DRIs) were used to assess the prevalence of inadequate dietary intakes of energy, protein, calcium, zinc, riboflavin, vitamin C and folate. Mann-Whitney U and Kruskal Wallis tests were used to compare nutrient intakes across subgroups.</p> <p>Results</p> <p>The mean nutrient intakes assessed by the FFQ was similar to those reported in the 2002 Chinese National Nutrition and Health Survey from women living in rural areas except for low intakes of protein, fat, iron and zinc. Of the participants, 54% were at risk of inadequate intake of energy. There were high proportions of pregnant women who did not have adequate intakes of folate (97%) and zinc (91%). Using the "probability approach", 64% of subjects had an inadequate consumption of iron.</p> <p>Conclusion</p> <p>These results reveal that the majority of pregnant women in these two counties had low intakes of nutrients that are essential for pregnancy such as iron and folate.</p> <p>Trial registration</p> <p>ISRCTN08850194.</p

System Noise Removal for Gaofen-4 Area-Array Camera

Gaofen-4 is a geostationary orbit area array imaging satellite. Due to the difficulty of the on-orbit radiometric calibration of area array cameras, there is system noise in the images. This paper analyzes the source of the system noise, constructs a noise model of Gaofen-4, and proposes a practical method to remove the system noise using multiple images. Gaussian filtering is used to remove radiometric characteristics, and the Grubbs criterion is used to remove gradient characteristics, thereby transforming the images into noise images. System noise can be removed using correction coefficients obtained by superimposing multiple noise images. Using a variety of denoising methods to perform contrast experiments, the results show that the proposed method can effectively maintain image edge details and texture information while removing image noise