To contain multidrug-resistant Plasmodium falciparum, malaria elimination in the Greater Mekong subregion needs to be accelerated while current antimalarials remain effective. We evaluated the ...safety, effectiveness, and potential resistance selection of dihydroartemisinin–piperaquine mass drug administration (MDA) in a region with artemisinin resistance in Myanmar.
We did a cluster-randomised controlled trial in rural community clusters in Kayin (Karen) state in southeast Myanmar. Malaria prevalence was assessed using ultrasensitive quantitative PCR (uPCR) in villages that were operationally suitable for MDA (villages with community willingness, no other malaria control campaigns, and a population of 50–1200). Villages were eligible to participate if the prevalence of malaria (all species) in adults was greater than 30% or P falciparum prevalence was greater than 10% (or both). Contiguous villages were combined into clusters. Eligible clusters were paired based on P falciparum prevalence (estimates within 10%) and proximity. Community health workers provided routine malaria case management and distributed long-lasting insecticidal bed-nets (LLINs) in all clusters. Randomisation of clusters (1:1) to the MDA intervention group or control group was by public coin-flip. Group allocations were not concealed. Three MDA rounds (3 days of supervised dihydroartemisinin–piperaquine target total dose 7 mg/kg dihydroartemisinin and 55 mg/kg piperaquine and single low-dose primaquine target dose 0·25 mg base per kg) were delivered to intervention clusters. Parasitaemia prevalence was assessed at 3, 5, 10, 15, 21, 27, and 33 months. The primary outcomes were P falciparum prevalence at months 3 and 10. All clusters were included in the primary analysis. Adverse events were monitored from the first MDA dose until 1 month after the final dose, or until resolution of any adverse event occurring during follow-up. This trial is registered with ClinicalTrials.gov, NCT01872702.
Baseline uPCR malaria surveys were done in January, 2015, in 43 villages that were operationally suitable for MDA (2671 individuals). 18 villages met the eligibility criteria. Three villages in close proximity were combined into one cluster because a border between them could not be defined. This gave a total of 16 clusters in eight pairs. In the intervention clusters, MDA was delivered from March 4 to March 17, from March 30 to April 10, and from April 27 to May 10, 2015. The weighted mean absolute difference in P falciparum prevalence in the MDA group relative to the control group was −10·6% (95% CI −15·1 to −6·1; p=0·0008) at month 3 and −4·5% (−10·9 to 1·9; p=0·14) at month 10. At month 3, the weighted P falciparum prevalence was 1·4% (0·6 to 3·6; 12 of 747) in the MDA group and 10·6% (7·0 to 15·6; 56 of 485) in the control group. Corresponding prevalences at month 10 were 3·2% (1·5 to 6·8; 34 of 1013) and 5·8% (2·5 to 12·9; 33 of 515). Adverse events were reported for 151 (3·6%) of 4173 treated individuals. The most common adverse events were dizziness (n=109) and rash or itching (n=20). No treatment-related deaths occurred.
In this low-transmission setting, the substantial reduction in P falciparum prevalence resulting from support of community case management was accelerated by MDA. In addition to supporting community health worker case management and LLIN distribution, malaria elimination programmes should consider using MDA to reduce P falciparum prevalence rapidly in foci of higher transmission.
The Global Fund to Fight AIDS, Tuberculosis and Malaria.
The spread of artemisinin-resistance in Plasmodium falciparum is a threat to current global malaria control initiatives. Targeted malaria treatment (TMT), which combines mass anti-malarial ...administration with conventional malaria prevention and control measures, has been proposed as a strategy to tackle this problem. The effectiveness of TMT depends on high levels of population coverage and is influenced by accompanying community engagement activities and the local social context. The article explores how these factors influenced attitudes and behaviours towards TMT in Kayin (Karen) State, Myanmar.
Semi-structured interviews were conducted with villagers from study villages (N = 31) and TMT project staff (N = 14) between March and July 2015.
Community engagement consisted of a range of activities to communicate the local malaria situation (including anti-malarial drug resistance and asymptomatic malaria), the aims of the TMT project, and its potential benefits. Community engagement was seen by staff as integral to the TMT project as a whole and not a sub-set of activities. Attitudes towards TMT (including towards community engagement) showed that developing trusting relationships helped foster participation. After initial wariness, staff received hospitality and acceptance among villagers. Offering healthcare alongside TMT proved mutually beneficial for the study and villagers. A handful of more socially-mobile and wealthy community members were reluctant to participate. The challenges of community engagement included time constraints and the isolation of the community with its limited infrastructure and a history of conflict.
Community engagement had to be responsive to the local community even though staff faced time constraints. Understanding the social context of engagement helped TMT to foster respectful and trusting relationships. The complex relationship between the local context and community engagement complicated evaluation of the community strategy. Nonetheless, the project did record high levels of population coverage.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Pneumonia is a serious and potentially life-threatening disease that predominantly affects the elderly population. Early diagnosis plays a crucial role in saving lives. Medical imaging, particularly ...the use of deep learning techniques, has shown promise in aiding the detection and classification of pneumonia in patients based on their chest X-rays. This paper presents a novel system for pneumonia detection and classification, utilizing a deep learning snapshot ensemble model. The EfficientNet-B0 technique is employed to address the pneumonia identification problems. The system is trained on the chest X-ray images (pneumonia) Kermany dataset, which is a well-known dataset in the field. The proposed system demonstrates impressive performance, achieving an accuracy of 97% for pneumonia classification.
Plant disease classification has been associated with the production of essential food crops and human society. In this paper, we classify tomato plant disease using two different features: texture ...and color. For a texture feature, we extract statistical texture information (shape, scale and location) of an image from Scale invariant Feature Transform (SIFT) feature. As a main contribution, a new approach is introduced to model the Scale Invariant Feature Transform (SIFT) texture feature by Johnson SB distribution for statistical texture information of an image. The moment method is used to estimate the parameters of Johnson SB distribution. The mathematical representation of SIFT feature is matrix representation and too complex to be applied in image classification. Therefore, we propose a new statistical feature to represent the image in few numbers of dimensions. For a color feature, we extract statistical color information of an image from RGB color channel. The color statistics feature is the combination of mean, standard deviation and moments from degree three to five for each RGB color channel. Our proposed feature is a combination of statistical texture and color features to classify tomato plant disease. The experimental performance on PlantVillage database is compared with state-of-art feature vectors to highlight the advantages of the proposed feature.
The current focus of our research is to detect and classify the plant disease in agricultural domain, by implementing image processing techniques. We aim to propose an innovative set of statistical ...texture features for classification of plant diseases images of leaves. The input images are taken by various mobile cameras. The Scale-invariant feature transform (SIFT) features used as texture feature and it is invariant to scaling, rotation, noise and illumination. But the exact mathematical model of SIFT texture descriptor is too complex and take high computing time in training and classification. The model-based statistical features are calculated from SIFT descriptor to represent the features of an image in a small number of dimensions. We derive texture information probability density function called Generalized Pareto Distributions from SIFT texture feature. The main focus of our proposed feature is to reduce computational cost of mobile devices. In our experiment, 10-Fold cross validation with SVM classifiers are applied to show that our experiment has no data bias and exclude theoretically derived values.
A variety of automated gait-based human identification system is a biometric technology designed to identify people by analyzing how they walk. However, "intra-class variability" including carrying ...bag, clothing, and view angle variation has a significant influence on gait recognition performance. Speed Up Robust Features (SURF) is a scale and rotation -invariant interest point detector and descriptor but this algorithm might not sufficiently return a sufficient number of features. Therefore, this paper proposes statistical features extraction based on Speed Up Robust Features (SURF). The proposed system contains four parts are subject motion detection, human silhouette extraction, gait feature extraction and gait classification. A moving subject is first identified from the input video sequence. We extract human silhouettes using background subtraction method. For feature extraction, first and second order statistical features and gray level co-occurrence matrix statistical features calculated from the result of Speed Up Robust Features (SURF). Then, Meta-sample based sparse representation method (MSRC) method uses to classify the proposed features for human identification. The experimental result is evaluated on CASIA-B (multi-view gait database). According to our results, our approach is able to significantly improve gait recognition accuracy and decrease computation time.
Action recognition has been an active research area in computer vision community during the recent years. However, it is still a challenging task due to the difficulties mainly resulted from the ...background clutter, illumination changes, large intra-class variation and noise. In this paper, we aim to develop an action recognition approach by navigating focus of attention (action region) with saliency detection and introducing a feature descriptor, namely Histogram of Accumulated Changing Gradient Orientation (HACGO). We firstly detect saliency in each video frame by computing pattern and color distinctness to localize action region. Then, we extract appearance and motion features using proposed HACGO, and existing HOG and HOF feature descriptors. Finally, a multi-class SVM classifier is applied to recognize different actions. The experiments were conducted on the standard UCF Sports action dataset. As experimental results, our action recognition approach achieved high recognition accuracy with a new combination of feature descriptors.
Action recognition has been a growing research topic in computer vision due to its great potentials for real-world applications. In this paper, we develop an effective action recognition approach ...based on salient object detection and propose a new feature descriptor to represent the changes of edge orientation. Firstly, we detect salient objects from each frame of a video sequence and generate edge maps for those detected salient objects. Then, we extract features on developed edge maps, using a combination of proposed Histogram of Changing Edge Orientation (HCEO) feature descriptor and existing Histogram of Optical Flow (HOF) feature descriptor. Finally, supervised multi-class support vector machine (SVM) classifier is used for recognizing various actions. The experiments were carried out on the standard UCF-Sports action dataset. As experimental results, our proposed action recognition approach is achieved with a significant improvement in recognition accuracy.
Over the past decades, pool boiling heat transfer of water has been investigated extensively by many scientists and researchers at system pressures varying from atmospheric to near critical pressure. ...However, at sub-atmospheric pressures conditions there is a dearth of data, particularly when the vapour pressures are less than 10
kPa. The authors have conducted a detailed study of pool boiling of water in an evaporator where its system pressure was about 1.8
kPa. The heat flux for pool boiling was derived from an uniform radiant heaters up to 5
W/cm
2 (or a total heating rate of 125
W within an area of 25
cm
2), a region that is of interest for the cooling of CPUs.
We introduce a set of statistical features and propose the SIFT texture features descriptor model on statistical image processing. The proposed feature is applied to plant disease classification with ...PlantVillage image dataset. The input is plant leaf image taken by phone camera whereas the output is the plant disease name. The input image is preprocessed to remove background. The SIFT features are extracted from the preprocessed image. As a main contribution, the extracted SIFT features are model by Generalized Extreme Value (GEV) Distribution to represent an image information in a small number of dimensions. We focus on the statistical feature and model-based texture features to minimize the computational time and complexity of phone image processing. The propose features aim to be significantly reduced in computational time for plant disease recognition for mobile phone. The experimental result shows that the proposed features can compare with other previous statistical features and can also distinguish between six tomato diseases, including Leaf Mold, Septoria Leaf Spot, Two Spotted Spider Mite, Late Blight, Bacterial Spot and Target Spot.
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