An Autonomous Sailboat for Environment Monitoring

The marine environment is constantly at risk from coastal urbanization. The deterioration of coastal and marine environments is evidenced by the decline of mangroves and the biodiversity of such environments and increasing recurrences of algal and jellyfish blooms. There is a lack of environmental data especially in developing countries such as Malaysia to determine the sustainability and impact of the current development on coastal resources. We developed an autonomous sailboat that utilizes the Internet of things technology to collect and analyze ocean water quality data for local authorities to obtain insights into the sustainable development of coastal resources. The USV is equipped with sensors, microcontrollers, and a wireless communication module based on ZigBee standards to allow sending water quality data to a gateway located at the shore. The data collected by the USV will be processed by a cloud server and visualized through user applications

Respiratory viral pathogens among Singapore military servicemen 2009 - 2012: Epidemiology and clinical characteristics

10.1186/1471-2334-14-204BMC Infectious Diseases141-BIDM

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial segments extracted from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlying arterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs

Automated Assessment for Bone Age of Left Wrist Joint in Uyghur Teenagers by Deep Learning

Objective To realize the automated bone age assessment by applying deep learning to digital radiography （DR） image recognition of left wrist joint in Uyghur teenagers, and explore its practical application value in forensic medicine bone age assessment. Methods The X-ray films of left wrist joint after pretreatment, which were taken from 245 male and 227 female Uyghur nationality teenagers in Uygur Autonomous Region aged from 13.0 to 19.0 years old, were chosen as subjects. And AlexNet was as a regression model of image recognition. From the total samples above, 60％ of male and female DR images of left wrist joint were selected as net train set, and 10％ of samples were selected as validation set. As test set, the rest 30％ were used to obtain the image recognition accuracy with an error range in ±1.0 and ±0.7 age respectively, compared to the real age. Results The modelling results of deep learning algorithm showed that when the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the net train set was 81.4% and 75.6% in male, and 80.5% and 74.8% in female, respectively. When the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the test set was 79.5% and 71.2% in male, and 79.4% and 66.2% in female, respectively. Conclusion The combination of bone age research on teenagers’ left wrist joint and deep learning, which has high accuracy and good feasibility, can be the research basis of bone age automatic assessment system for the rest joints of body

Sex Estimation of Typical Adult Vertebrae Morphology in Central China Based on CT Technique

Objective The morphological data of the second thoracic vertebra and the third lumbar vertebra were measured by computerized tomography （CT）. The sex differences were analyzed and the discrimination equation was obtained. Methods The data of 274 adults （203 cases from experimental group and 69 cases from validation group） from central China were collected. Four linear data （maximum transverse length of vertebral body, maximum longitudinal length of vertebral body, maximum transverse length of vertebral foramen, maximum longitudinal length of vertebral foramen）, one angle data （angle between spinous processes） and two area（vertebral foramen area, total cross-sectional area of vertebral body） data of the second thoracic vertebra and the third lumbar vertebra were collected, respectively. Then three ratios［maximum transverse length/ maximum longitudinal length of vertebral body, maximum transverse length/ maximum longitudinal length of vertebral foramen, vertebral foramen area/ （total cross-sectional area of vertebral body-vertebral foramen area）］ and one angle （angle between spinous processes） were obtained. The discriminant equation was established for sexual discriminant analysis. Results The morphology of the second thoracic vertebra and the third lumbar vertebra was related with gender. Four single index discriminant formulae and eleven multi- index discriminant formulae were established. The 69 validation group samples were substituted into the formula for testing, and the maximum discriminant accuracy rate of the single-index discriminant formula was 75%. The maximum discriminant accuracy rate of multi-index discriminant formula was 83%. Conclusion It is feasible to conduct individual sex analysis by the morphological indexes of second thoracic vertebra and the third lumbar vertebra. The indexes have important application values in practice

Medication adherence among diabetic and hypertensive patients in Al-Qassim region of Saudi Arabia

Non-adherence to medication is often an unrecognized risk factor that contributes to failure of the therapeutic plan. The purpose of the study was to identify factors related to high, medium and low medication adherence among adult Saudi patients with hypertension and diabetes mellitus. This study is designed as a descriptive cross sectional survey and was conducted in three tertiary care hospitals of Al-Qassim province of Saudi Arabia. The data was collected using the 8-item Morisky Medication Adherence Scale (MMAS-8) and analyzed by SPSS. Three levels of adherence were considered based on the following scores: 0 to <6 (low); 6 to <8 (medium); 8 (high). Of the 396 patients interviewed, 52% reported low adherence to prescribed medication. Multinomial logistic regression analysis was conducted. Gender, age, literacy level, duration of illness and type of chronic disease were negatively associated with medication adherence. The study shows very high proportion of low and medium adherence on long term medication, which may be responsible for the failure of achieving therapeutic outcome. Further investigation is required to evaluate the applicability of MMAS-8 as a tool of measuring medication adherence among Saudi patients with chronic diseases. Adherence enhancing strategies should also be evaluated in separate patients group

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Tropical field stations yield high conservation return on investment

Conservation funding is currently limited; cost‐effective conservation solutions are essential. We suggest that the thousands of field stations worldwide can play key roles at the frontline of biodiversity conservation and have high intrinsic value. We assessed field stations’ conservation return on investment and explored the impact of COVID‐19. We surveyed leaders of field stations across tropical regions that host primate research; 157 field stations in 56 countries responded. Respondents reported improved habitat quality and reduced hunting rates at over 80% of field stations and lower operational costs per km$^{2}$ than protected areas, yet half of those surveyed have less funding now than in 2019. Spatial analyses support field station presence as reducing deforestation. These “earth observatories” provide a high return on investment; we advocate for increased support of field station programs and for governments to support their vital conservation efforts by investing accordingly

Diagnostic techniques for inflammatory eye disease: past, present and future: a review

Investigations used to aid diagnosis and prognosticate outcomes in ocular inflammatory disorders are based on techniques that have evolved over the last two centuries have dramatically evolved with the advances in molecular biological and imaging technology. Our improved understanding of basic biological processes of infective drives of innate immunity bridging the engagement of adaptive immunity have formed techniques to tailor and develop assays, and deliver targeted treatment options. Diagnostic techniques are paramount to distinguish infective from non-infective intraocular inflammatory disease, particularly in atypical cases. The advances have enabled our ability to multiplex assay small amount of specimen quantities of intraocular samples including aqueous, vitreous or small tissue samples. Nevertheless to achieve diagnosis, techniques often require a range of assays from traditional hypersensitivity reactions and microbe specific immunoglobulin analysis to modern molecular techniques and cytokine analysis. Such approaches capitalise on the advantages of each technique, thereby improving the sensitivity and specificity of diagnoses. This review article highlights the development of laboratory diagnostic techniques for intraocular inflammatory disorders now readily available to assist in accurate identification of infective agents and appropriation of appropriate therapies as well as formulating patient stratification alongside clinical diagnoses into disease groups for clinical trials