Elimination of human rabies in Goa, India through an integrated One Health approach

Dog-mediated rabies kills tens of thousands of people each year in India, representing one third of the estimated global rabies burden. Whilst the World Health Organization (WHO), World Organization for Animal Health (OIE) and the Food and Agriculture Organization of the United Nations (FAO) have set a target for global dog-mediated human rabies elimination by 2030, examples of large-scale dog vaccination programs demonstrating elimination remain limited in Africa and Asia. We describe the development of a data-driven rabies elimination program from 2013 to 2019 in Goa State, India, culminating in human rabies elimination and a 92% reduction in monthly canine rabies cases. Smartphone technology enabled systematic spatial direction of remote teams to vaccinate over 95,000 dogs at 70% vaccination coverage, and rabies education teams to reach 150,000 children annually. An estimated 2249 disability-adjusted life years (DALYs) were averted over the program period at 526 USD per DALY, making the intervention ‘very cost-effective’ by WHO definitions. This One Health program demonstrates that human rabies elimination is achievable at the state level in India

Anxiety and depression in patients with gastrointestinal cancer: does knowledge of cancer diagnosis matter?

<p>Abstract</p> <p>Background</p> <p>Gastrointestinal cancer is the first leading cause of cancer related deaths in men and the second among women in Iran. An investigation was carried out to examine anxiety and depression in this group of patients and to investigate whether the knowledge of cancer diagnosis affect their psychological distress.</p> <p>Methods</p> <p>This was a cross sectional study of anxiety and depression in patients with gastrointestinal cancer attending to the Tehran Cancer Institute. Anxiety and depression was measured using the Hospital Anxiety and Depression Scale (HADS). This is a widely used valid questionnaire to measure psychological distress in cancer patients. Demographic and clinical data also were collected to examine anxiety and depression in sub-group of patients especially in those who knew their cancer diagnosis and those who did not.</p> <p>Results</p> <p>In all 142 patients were studied. The mean age of patients was 54.1 (SD = 14.8), 56% were male, 52% did not know their cancer diagnosis, and their diagnosis was related to esophagus (29%), stomach (30%), small intestine (3%), colon (22%) and rectum (16%). The mean anxiety score was 7.6 (SD = 4.5) and for the depression this was 8.4 (SD = 3.8). Overall 47.2% and 57% of patients scored high on both anxiety and depression. There were no significant differences between gender, educational level, marital status, cancer site and anxiety and depression scores whereas those who knew their diagnosis showed a significant higher degree of psychological distress [mean (SD) anxiety score: knew diagnosis 9.1 (4.2) vs. 6.3 (4.4) did not know diagnosis, P < 0.001; mean (SD) depression score: knew diagnosis 9.1 (4.1) vs. 7.9 (3.6) did not know diagnosis, P = 0.05]. Performing logistic regression analysis while controlling for demographic and clinical variables studied the results indicated that those who knew their cancer diagnosis showed a significant higher risk of anxiety [OR: 2.7, 95% CI: 1.1–6.8] and depression [OR: 2.8, 95% CI: 1.1–7.2].</p> <p>Conclusion</p> <p>Psychological distress was higher in those who knew their cancer diagnosis. It seems that the cultural issues and the way we provide information for cancer patients play important role in their improved or decreased psychological well-being.</p

Leptospirosis in the Asia Pacific region

<p>Abstract</p> <p>Background</p> <p>Leptospirosis is a worldwide zoonotic infection that has been recognized for decades, but the problem of the disease has not been fully addressed, particularly in resource-poor, developing countries, where the major burden of the disease occurs. This paper presents an overview of the current situation of leptospirosis in the region. It describes the current trends in the epidemiology of leptospirosis, the existing surveillance systems, and presents the existing prevention and control programs in the Asia Pacific region.</p> <p>Methods</p> <p>Data on leptospirosis in each member country were sought from official national organizations, international public health organizations, online articles and the scientific literature. Papers were reviewed and relevant data were extracted.</p> <p>Results</p> <p>Leptospirosis is highly prevalent in the Asia Pacific region. Infections in developed countries arise mainly from occupational exposure, travel to endemic areas, recreational activities, or importation of domestic and wild animals, whereas outbreaks in developing countries are most frequently related to normal daily activities, over-crowding, poor sanitation and climatic conditions.</p> <p>Conclusion</p> <p>In the Asia Pacific region, predominantly in developing countries, leptospirosis is largely a water-borne disease. Unless interventions to minimize exposure are aggressively implemented, the current global climate change will further aggravate the extent of the disease problem. Although trends indicate successful control of leptospirosis in some areas, there is no clear evidence that the disease has decreased in the last decade. The efficiency of surveillance systems and data collection varies significantly among the countries and areas within the region, leading to incomplete information in some instances. Thus, an accurate reflection of the true burden of the disease remains unknown.</p

Apple fruit size estimation using a 3D machine vision system

Estimation of fruit size in tree fruit crops is essential for selective robotic harvesting and crop-load estimation. Machine vision systems for fruit detection and localization have been studied widely for robotic harvesting and crop-load estimation. However, only a few studies have been carried out to estimate fruit size in orchards using machine vision systems. This study was carried out to develop a machine vision system consisting of a color CCD camera and a time-of-flight (TOF) light-based 3D camera for estimating apple size in tree canopies. As a measure of fruit size, the major axis (longest axis) was estimated based on (i) the 3D coordinates of pixels on corresponding apple surfaces, and (ii) the 2D size of individual pixels within apple surfaces. In the 3D coordinates-based method, the distance between pairs of pixels within apple regions were calculated using 3D coordinates, and the maximum distance between all pixel pairs within an apple region was estimated to be the major axis. The accuracy of estimating the major axis using 3D coordinates was 69.1%. In the pixel-size-based method, the physical sizes of pixels were estimated using a calibration model developed based on pixel coordinates and the distance to pixels from the camera. The major axis length was then estimated by summing the size of individual pixels along the major axis of the fruit. The accuracy of size estimation increased to 84.8% when the pixel size-based method was used. The results showed the potential for estimating fruit size in outdoor environments using a 3D machine vision system. Keywords: Machine vision, Fruit identification, 3D registration, Size estimation, Major axi

Strategies for selecting best approach direction for a sweet-pepper harvesting robot

An autonomous sweet pepper harvesting robot must perform several tasks to successfully harvest a fruit. Due to the highly unstructured environment in which the robot operates and the presence of occlusions, the current challenges are to improve the detection rate and lower the risk of losing sight of the fruit while approaching the fruit for harvest. Therefore, it is crucial to choose the best approach direction with least occlusion from obstacles. The value of ideal information regarding the best approach direction was evaluated by comparing it to a method attempting several directions until successful harvesting is performed. A laboratory experiment was conducted on artificial sweet pepper plants using a system based on eye-in-hand configuration comprising a 6DOF robotic manipulator equipped with an RGB camera. The performance is evaluated in laboratorial conditions using both descriptive statistics of the average harvesting times and harvesting success as well as regression models. The results show roughly 40–45% increase in average harvest time when no a-priori information of the correct harvesting direction is available with a nearly linear increase in overall harvesting time for each failed harvesting attempt. The variability of the harvesting times grows with the number of approaches required, causing lower ability to predict them. Tests show that occlusion of the front of the peppers significantly impacts the harvesting times. The major reason for this is the limited workspace of the robot often making the paths to positions to the side of the peppers significantly longer than to positions in front of the fruit which is more open

Detection of tomato flowers from greenhouse images using colorspace transformations

In this paper we propose an image analysis method for detecting and counting tomato flowers from images taken in a greenhouse. Detecting and locating flowers is useful information for tomato growers and breeders, for phenotyping, yield prediction, and for automating procedures such as pollination and spraying. Since the tomato flowers are yellow, we first apply a set of grayscale transformations in which yellow regions stand out, and then threshold and combine them by a logical binary AND operation. Using more than one transform reduces the possibility of spurious detections due to non-flower regions of the image appearing yellow due to illumination conditions. Connected regions larger than a certain threshold are selected as instances belonging to the class flower. Experimental results over images acquired in a greenhouse using a Realsense camera show that this approach could detect flowers with a recall of 0.79 and precision of 0.77, which are comparable to the values reported in literature with higher resolution cameras closer to the flowers being imaged