Prestoncooke1888

The Army Marches on its Stomach". To provide nutritious, wholesome, safe and quality food to the large Indian Armed Forces, spread over various terrains, ranging from the icy Himalayas to the burning deserts of Rajasthan or the humid jungles of the North East and over various platforms like tanks, ships and aircraft is a challenge. The major issue in this is to procure and supply the food while ensuring that it is safe and retains its quality till it is cooked. This vital part of the supply chain viz from farm to the military cook house is the responsibility of the Army Service Corps (ASC) supported by the Army Medical Corps (AMC) and the Remount and Veterinary Corps (RVC). The Food Inspection Organization of the ASC lays down the best practices to be followed in terms of inspection, sampling, analysis, dispatch and issue of both fresh and processed edible foodstuff. The Armed Forces have their own network of Composite Food Laboratories for sampling and analysis of the food items. To ensure superior quality the Defence Food Specifications are much higher than legislated by the Food Standards and Safety Authority of India (FSSAI) for the general public. This paper highlights the best practices followed to ensure food safety and quality control in the Indian Armed Forces.Stove stacking (concurrent use of multiple stoves and/or fuels) is a poorly quantified practice in regions where efforts to transition household energy to cleaner stoves/or fuels are on-going. Using biomass-burning stoves alongside clean stoves undermines health and environmental goals. This review synthesizes stove stacking data gathered from eleven case studies of clean cooking programs in low/middle-income country settings. Analyzed data are from ministry and program records, research studies, and informant interviews. Thematic analysis identify key drivers of stove stacking behavior in each setting. Significant (28%-100%) stacking with traditional cooking methods was observed in all cases. Reason for traditional fuel use includes costs of clean fuel; mismatches between cooking technologies and household needs; and unreliable fuel supply. National household surveys often focus on 'primary' cookstoves and miss stove stacking data. Thus more attention should be paid to discontinuation of traditional stove use, not solely adoption of cleaner stoves/fuels. Future energy policies and programs should acknowledge the realities of stacking and incorporate strategies at the design stage to transition away from polluting stoves/fuels. Seven principles for clean cooking system program design and policy are presented, focused on a shift toward "cleaner stacking" that could yield household air pollution reductions approaching WHO targets.Squamous cell carcinoma (SCC) comprises over 90 percent of tumors in the head and neck. The diagnosis process involves performing surgical resection of tissue and creating histological slides from the removed tissue. Pathologists detect SCC in histology slides, and may fail to correctly identify tumor regions within the slides. In this study, a dataset of patches extracted from 200 digitized histological images from 84 head and neck SCC patients was used to train, validate and test the segmentation performance of a fully-convolutional U-Net architecture. The neural network achieved a pixel-level segmentation AUC of 0.89 on the testing group. The average segmentation time for whole slide images was 72 seconds. The training, validation, and testing process in this experiment produces a model that has the potential to help segment SCC images in histological images with improved speed and accuracy compared to the manual segmentation process performed by pathologists.The purpose of this study is to develop hyperspectral imaging (HSI) for automatic detection of head and neck cancer cells on histologic slides. A compact hyperspectral microscopic system is developed in this study. Histologic slides from 15 patients with squamous cell carcinoma (SCC) of the larynx and hypopharynx are imaged with the system. The proposed nuclei segmentation method based on principle component analysis (PCA) can extract most nuclei in the hyperspectral image without extracting other sub-cellular components. Both spectra-based support vector machine (SVM) and patch-based convolutional neural network (CNN) are used for nuclei classification. CNNs were trained with both hyperspectral images and pseudo RGB images of extracted nuclei, in order to evaluate the usefulness of extra information provided by hyperspectral imaging. The average accuracy of spectra-based SVM classification is 68%. The average AUC and average accuracy of the HSI patch-based CNN classification is 0.94 and 82.4%, respectively. Roblitinib The hyperspectral microscopic imaging and classification methods provide an automatic tool to aid pathologists in detecting SCC on histologic slides.We developed a reliable and repeatable process to create hyper-realistic, kidney phantoms with tunable image visibility under ultrasound (US) and CT imaging modalities. A methodology was defined to create phantoms that could be produced for renal biopsy evaluation. The final complex kidney phantom was devised containing critical structures of a kidney kidney cortex, medulla, and ureter. Simultaneously, some lesions were integrated into the phantom to mimic the presence of tumors during biopsy. The phantoms were created and scanned by ultrasound and CT scanners to verify the visibility of the complex internal structures and to observe the interactions between material properties. The result was a successful advancement in knowledge of materials with ideal acoustic and impedance properties to replicate human organs for the field of image-guided interventions.Cardiac magnetic resonance (CMR) imaging is considered the standard imaging modality for volumetric analysis of the right ventricle (RV), an especially important practice in the evaluation of heart structure and function in patients with repaired Tetralogy of Fallot (rTOF). In clinical practice, however, this requires time-consuming manual delineation of the RV endocardium in multiple 2-dimensional (2D) slices at multiple phases of the cardiac cycle. In this work, we employed a U-Net based 2D convolutional neural network (CNN) classifier in the fully automatic segmentation of the RV blood pool. Our dataset was comprised of 5,729 short-axis cine CMR slices taken from 100 individuals with rTOF. Training of our CNN model was performed on images from 50 individuals while validation was performed on images from 10 individuals. Segmentation results were evaluated by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Use of the CNN model on our testing group of 40 individuals yielded a median DSC of 90% and a median 95th percentile HD of 5.