Machine learning approach for segmenting glands in colon histology images using local intensity and texture features

Colon Cancer is one of the most common types of cancer. The treatment is planned to depend on the grade or stage of cancer. One of the preconditions for grading of colon cancer is to segment the glandular structures of tissues. Manual segmentation method is very time-consuming, and it leads to life risk for the patients. The principal objective of this project is to assist the pathologist to accurate detection of colon cancer. In this paper, the authors have proposed an algorithm for an automatic segmentation of glands in colon histology using local intensity and texture features. Here the dataset images are cropped into patches with different window sizes and taken the intensity of those patches, and also calculated texture-based features. Random forest classifier has been used to classify this patch into different labels. A multilevel random forest technique in a hierarchical way is proposed. This solution is fast, accurate and it is very much applicable in a clinical setup

Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoni{\ae} and healthy subjects using Chest X-Ray. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable model

Urban cooling potential and cost comparison of heat mitigation techniques for their impact on the lower atmosphere

Cool materials and rooftop vegetation help achieve urban heating mitigation as they can reduce building cooling demands. This study assesses the cooling potential of different mitigation technologies using Weather Research and Forecasting (WRF)- taking case of a tropical coastal climate in the Kolkata Metropolitan Area. The model was validated using data from six meteorological sites. The cooling potential of eight mitigation scenarios was evaluated for: three cool roofs, four green roofs, and their combination (cool-city). The sensible heat, latent heat, heat storage, 2-m ambient temperature, surface temperature, air temperature, roof temperature, and urban canopy temperature was calculated. The effects on the urban boundary layer were also investigated. The different scenarios reduced the daytime temperature of various urban components, and the effect varied nearly linearly with increasing albedo and green roof fractions. For example, the maximum ambient temperature decreased by 3.6 °C, 0.9 °C, and 1.4 °C for a cool roof with 85% albedo, 100% rooftop vegetation, and their combination. The cost of different mitigation scenarios was assumed to depend on the construction options, location, and market prices. The potential for price per square meter and corresponding temperature decreased was related to one another. Recognizing the complex relationship between scenarios and construction options, the reduction in the maximum and minimum temperature across different cool and green roof cases were used for developing the cost estimates. This estimate thus attempted a summary of the price per degree of cooling for the different potential technologies. Higher green fraction, cool materials, and their combination generally reduced winds and enhanced buoyancy. The surface changes alter the lower atmospheric dynamics such as low-level vertical mixing and a shallower boundary layer and weakened horizontal convective rolls during afternoon hours. Although cool materials offer the highest temperature reductions, the cooling resulting from its combination and a green roof strategy could mitigate or reverse the summertime heat island effect. The results highlight the possibilities for heat mitigation and offer insight into the different strategies and costs for mitigating the urban heating and cooling demands.Dev Niyogi acknowledges fnancial support from US NOAA-NIHHIS (NA21OAR4310146; Program Manager: Hunter Jones); the NASA Interdisciplinary Sciences program (80NSSC20K1262 and 80NSSC20K1268), the U.S. National Science Foundation (1835739, 2051110, and 2228205), and the University of Texas at Austin for William Stamps Farish Chair funds and the Bridging Barriers/Planet Texas 2050 initiativ

Association of respiratory symptoms and lung function with occupation in the multinational Burden of Obstructive Lung Disease (BOLD) study

Background Chronic obstructive pulmonary disease has been associated with exposures in the workplace. We aimed to assess the association of respiratory symptoms and lung function with occupation in the Burden of Obstructive Lung Disease study. Methods We analysed cross-sectional data from 28 823 adults (≥40 years) in 34 countries. We considered 11 occupations and grouped them by likelihood of exposure to organic dusts, inorganic dusts and fumes. The association of chronic cough, chronic phlegm, wheeze, dyspnoea, forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1)/FVC with occupation was assessed, per study site, using multivariable regression. These estimates were then meta-analysed. Sensitivity analyses explored differences between sexes and gross national income. Results Overall, working in settings with potentially high exposure to dusts or fumes was associated with respiratory symptoms but not lung function differences. The most common occupation was farming. Compared to people not working in any of the 11 considered occupations, those who were farmers for ≥20 years were more likely to have chronic cough (OR 1.52, 95% CI 1.19–1.94), wheeze (OR 1.37, 95% CI 1.16–1.63) and dyspnoea (OR 1.83, 95% CI 1.53–2.20), but not lower FVC (β=0.02 L, 95% CI −0.02–0.06 L) or lower FEV1/FVC (β=0.04%, 95% CI −0.49–0.58%). Some findings differed by sex and gross national income. Conclusion At a population level, the occupational exposures considered in this study do not appear to be major determinants of differences in lung function, although they are associated with more respiratory symptoms. Because not all work settings were included in this study, respiratory surveillance should still be encouraged among high-risk dusty and fume job workers, especially in low- and middle-income countries.publishedVersio

Optically Modulated Passive Broadband Daytime Radiative Cooling Materials Can Cool Cities in Summer and Heat Cities in Winter

Broadband passive daytime radiative cooling (PDRC) materials exhibit sub-ambient surface temperatures and contribute highly to mitigating extreme urban heat during the warm period. However, their application may cause undesired overcooling problems in winter. This study aims to assess, on a city scale, different solutions to overcome the winter overcooling penalty derived from using PDRC materials. Furthermore, a mesoscale urban modeling system assesses the potential of the optical modulation of reflectance (ρ) and emissivity (ε) to reduce, minimize, or reverse the overcooling penalty. The alteration of heat flux components, air temperature modification, ground and roof surface temperature, and the urban canopy temperature are assessed. The maximum decrease of the winter ambient temperature using standard PDRC materials is 1.1 °C and 0.8 °C for daytime and nighttime, respectively, while the ρ+ε-modulation can increase the ambient temperature up to 0.4 °C and 1.4 °C, respectively, compared to the use of conventional materials. Compared with the control case, the maximum decrease of net radiation inflow occurred at the peak hour, reducing by 192.7 Wm−2 for the PDRC materials, 5.4 Wm−2 for ρ-modulated PDRC materials, and 173.7 Wm−2 for ε-PDRC materials; nevertheless, the ρ+ε-modulated PDRC materials increased the maximum net radiation inflow by 51.5 Wm−2, leading to heating of the cities during the winter

Optically Modulated Passive Broadband Daytime Radiative Cooling Materials Can Cool Cities in Summer and Heat Cities in Winter

Broadband passive daytime radiative cooling (PDRC) materials exhibit sub-ambient surface temperatures and contribute highly to mitigating extreme urban heat during the warm period. However, their application may cause undesired overcooling problems in winter. This study aims to assess, on a city scale, different solutions to overcome the winter overcooling penalty derived from using PDRC materials. Furthermore, a mesoscale urban modeling system assesses the potential of the optical modulation of reflectance (ρ) and emissivity (ε) to reduce, minimize, or reverse the overcooling penalty. The alteration of heat flux components, air temperature modification, ground and roof surface temperature, and the urban canopy temperature are assessed. The maximum decrease of the winter ambient temperature using standard PDRC materials is 1.1 °C and 0.8 °C for daytime and nighttime, respectively, while the ρ+ε-modulation can increase the ambient temperature up to 0.4 °C and 1.4 °C, respectively, compared to the use of conventional materials. Compared with the control case, the maximum decrease of net radiation inflow occurred at the peak hour, reducing by 192.7 Wm−2 for the PDRC materials, 5.4 Wm−2 for ρ-modulated PDRC materials, and 173.7 Wm−2 for ε-PDRC materials; nevertheless, the ρ+ε-modulated PDRC materials increased the maximum net radiation inflow by 51.5 Wm−2, leading to heating of the cities during the winter

Nutritional monitoring in older people prevention services

Nutritional monitoring is an important aspect of providing a healthy lifestyle and smarter food consumption. Food recognition is a key task in nutrition monitoring applications especially of interest for older people suffering from malnutrition and unhealthy eating. In order to ease the problem of food intake monitoring, recent health apps involve Artificial Intelligence algorithms for food image analysis. Food recognition is a highly challenging task as the dishes could be composed of mixed types, it could have a lower inter-class variance and also the number of dishes to be recognized is quite higher. In this chapter, we present an in-depth analysis and insights into the development of a nutritional monitoring system that would act as a Key Performance Indicator for the older people food intake monitoring. This system is aimed at providing a convenient food logging environment that would assist older people and their relatives as well as geriatrists in monitoring and maintaining the healthy daily dietary nutritional needs

Urban cooling potential and cost comparison of heat mitigation techniques for their impact on the lower atmosphere

Abstract Cool materials and rooftop vegetation help achieve urban heating mitigation as they can reduce building cooling demands. This study assesses the cooling potential of different mitigation technologies using Weather Research and Forecasting (WRF)- taking case of a tropical coastal climate in the Kolkata Metropolitan Area. The model was validated using data from six meteorological sites. The cooling potential of eight mitigation scenarios was evaluated for: three cool roofs, four green roofs, and their combination (cool-city). The sensible heat, latent heat, heat storage, 2-m ambient temperature, surface temperature, air temperature, roof temperature, and urban canopy temperature was calculated. The effects on the urban boundary layer were also investigated. The different scenarios reduced the daytime temperature of various urban components, and the effect varied nearly linearly with increasing albedo and green roof fractions. For example, the maximum ambient temperature decreased by 3.6 °C, 0.9 °C, and 1.4 °C for a cool roof with 85% albedo, 100% rooftop vegetation, and their combination. The cost of different mitigation scenarios was assumed to depend on the construction options, location, and market prices. The potential for price per square meter and corresponding temperature decreased was related to one another. Recognizing the complex relationship between scenarios and construction options, the reduction in the maximum and minimum temperature across different cool and green roof cases were used for developing the cost estimates. This estimate thus attempted a summary of the price per degree of cooling for the different potential technologies. Higher green fraction, cool materials, and their combination generally reduced winds and enhanced buoyancy. The surface changes alter the lower atmospheric dynamics such as low-level vertical mixing and a shallower boundary layer and weakened horizontal convective rolls during afternoon hours. Although cool materials offer the highest temperature reductions, the cooling resulting from its combination and a green roof strategy could mitigate or reverse the summertime heat island effect. The results highlight the possibilities for heat mitigation and offer insight into the different strategies and costs for mitigating the urban heating and cooling demands