Abstract Purpose Early detection of infectious disease outbreaks is crucial to protecting the public health of a society. Online news articles provide timely information on disease outbreaks ...worldwide. In this study, we investigated automated detection of articles relevant to disease outbreaks using machine learning classifiers. In a real-life setting, it is expensive to prepare a training data set for classifiers, which usually consists of manually labeled relevant and irrelevant articles. To mitigate this challenge, we examined the use of randomly sampled unlabeled articles as well as labeled relevant articles. Methods Naïve Bayes and Support Vector Machine (SVM) classifiers were trained on 149 relevant and 149 or more randomly sampled unlabeled articles. Diverse classifiers were trained by varying the number of sampled unlabeled articles and also the number of word features. The trained classifiers were applied to 15 thousand articles published over 15 days. Top-ranked articles from each classifier were pooled and the resulting set of 1337 articles was reviewed by an expert analyst to evaluate the classifiers. Results Daily averages of areas under ROC curves (AUCs) over the 15-day evaluation period were 0.841 and 0.836, respectively, for the naïve Bayes and SVM classifier. We referenced a database of disease outbreak reports to confirm that this evaluation data set resulted from the pooling method indeed covered incidents recorded in the database during the evaluation period. Conclusions The proposed text classification framework utilizing randomly sampled unlabeled articles can facilitate a cost-effective approach to training machine learning classifiers in a real-life Internet-based biosurveillance project. We plan to examine this framework further using larger data sets and using articles in non-English languages.
Urban air quality monitoring plays an important role due to high concentration of particle sources and a large population exposed to elevated particle concentrations. Continuous ground based ...measurements of black carbon (BC) aerosol; carbon monoxide (CO) and ozone (O
3) were carried out in the tropical urban region of Hyderabad, India, during the forest fire season. Julian day variation of BC, CO and ozone showed high values on certain days. In order to ascertain the additional sources for observed high concentration of BC and CO, DMSP-OLS nighttime satellite data over the Indian region were processed for occurrence of forest fires. Results of the analysis suggested a higher incidence of forest fires on days with higher concentrations of BC and CO and a spatial distribution of forest fires; wind trajectories were observed to have a bearing on the higher values of BC, CO and ozone. Results are discussed in the paper.
Monitoring and management of forest fires is very important in countries like India where 55% of the total forest cover is prone to fires annually. The present study aims at effective monitoring of ...forest fires over the Indian region using Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime satellite data and to evaluate the active fire detection capabilities of the sensor. Nightly DMSP-OLS fire products were generated from February to May 2005 (peak fire season) and analyzed to study the occurrence and behavior of fires over different forest physiognomies in Indian region. Fire products generated from DMSP-OLS were validated with ground observations of fire records from state forest departments to evaluate the accuracy of fire products. Further, inter-comparison of the DMSP-OLS derived fire products with contemporary fire products from Moderate resolution Imaging Spectroradiometer (MODIS) (both daytime and nighttime products) in addition to fires and burnt areas derived from Indian Remote sensing Satellite (IRS-P6) Advanced Wide Field Sensor (AWiFS) data has been done to analyze spatial agreement of fire locations given by the above sensors.
Results from the DMSP-OLS fire products (derived from February to May 2005) over Indian region showed high forest fires in southern dry deciduous forests during February–March; central Indian dry and mixed deciduous forests during March–April; northeastern tropical forests during February–April and northern pine forests during May. Spatial pattern in fires showed a typical seasonal shift in fire activity from the southern dry deciduous forests to the northern pine forests and temperate forests as the fire season progressed. Statistical evaluation of DMSP-OLS fire products with ground observations showed an over all accuracy of 98%. Comparison of DMSP-OLS derived fires with consecutive MODIS and AWiFS derived fires for individual days indicated that 69% of the fires continued from current day (DMSP-OLS pass around ∼
7
pm to ∼
10
pm local time) to the next day (MODIS and AWiFS pass ∼
10:30
am local time). Comparison of DMSP-OLS derived fires with burnt areas estimated from AWiFS showed that 98% of DMSP-OLS derived fires on the current day fell within the burnt area of AWiFS on subsequent day. Since the worst forest fires are those that extend from the current to the consecutive days, DMSP-OLS derived fires provide a valuable augmentation to the fires derived from other sensors operating in daytime.
Every year during winter months (December-January) fog formation over Indo-Gangetic plains (IGP) of Indian region is believed to create numerous hazards. The present study addresses variations in ...aerosol optical properties, aerosol mass concentration and their impact on solar irradiance for pre-during-post fog conditions of December 2004 over IGP, India. Continuous measurements on aerosol optical depth (AOD), total aerosol mass concentration, black carbon (BC) aerosols, UVery and UVA were carried out for pre, during and post fog periods over study site of Allahabad, India, during December 2004 as a part of Aerosol Land Campaign-II conducted by Indian Space Research Organization (ISRO). High aerosol mass concentrations were observed during fog and post-fog periods. Accumulation mode particle loading was found to be high during pre-fog period and coarse mode particle loading was observed to be high during fog and post-fog periods. Considerable reduction in UVery and UVA irradiance was observed during fog period compared with pre and post-fog periods. Analysis of NOAA-HYSPLIT model runs suggested that enhanced biomass burning episodes down-wind to the study area increased the concentration of AOD and BC.
Left or right is right? Ectopic gallbladder Chand, Jithin; Tharakan, George; Sebastian, George
Formosan journal of surgery : the official publication of the Surgical Association ... et al.,
09/2022, Letnik:
55, Številka:
5
Journal Article
Recenzirano
Odprti dostop
Ectopic gallbladder (GB) is a rare entity with an incidence of <1%. True left-sided GB (LSG), i.e., GB seen under the left lobe of the liver and to the left of the falciform ligament, presents as a ...surgical problem on table, due to its unusual presentation and lack of findings on imaging. What we may perceive as an LSG on the table, may in fact be a normally located GB under the right lobe but with the ligamentum teres being attached to the right lobe, giving the appearance of a left-sided gallbladder. Here, we are presenting a case of a true LSG which was discovered on table. By minimizing the use of electrocautery and placing an additional port along with the traditional ports in laparoscopic cholecystectomy, we were able to perform a successful cholecystectomy.
The present paper gives an account of potential of Environment Satellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) C-band data in forest parameter retrieval and forest type classification over ...deciduous forests of Tadoba Andhari Tiger Reserve (TATR), central India. Ground data on phyto-sociology and Leaf Area Index (LAI) over the study area was collected in 23 sampling points (20m×20m) over the study area. Phyto-sociological data collected over the study area was used to compute plot-wise biometric parameters like basal area, volume, stem density and dominant height. ENVISAT ASAR data covering the study area, pertaining to 24 November 2005, has been geo-referenced and digital number (DN) values were converted to radar backscatter values. Regression analysis between backscatter and the retrieved biometric variables has been done to explain the relationships between SAR backscatter and forest parameters. Analysis showed a significant correlation between backscatter and biometric parameters and backscatter values typically increased with increase in basal area, volume, stem density and dominant height. The scatter observed between ASAR backscatter and stem density, basal area and dominant height suggested limitation of C-band data in estimating biometric variables in heterogeneous forest systems. Further, ASAR data was used in conjunction with Indian Remote sensing Satellite (IRS-P6)-Linear Imaging Self Scanner (LISS) III data of 16 October 2004 to classify the study area into different land use/land cover (LU/LC) classes. Various texture and adaptive filters were applied on ASAR image to reduce speckle noise and enhance image features. An attempt is made to merge ASAR image with LISS-III to enhance feature discrimination. Training sets corresponding to the ground data have been used to derive confusion matrices for the ASAR and LISS-III images. Results suggested better discrimination of vegetation types in the merged data suggesting the possible use of ASAR data in forest type discrimination.
Tropical forests contribute to approximately 40 % of the total carbon found in terrestrial biomass. In this context, forest/non-forest classification and estimation of forest above ground biomass ...over tropical regions are very important and relevant in understanding the contribution of tropical forests in global biogeochemical cycles, especially in terms of carbon pools and fluxes. Information on the spatio-temporal biomass distribution acts as a key input to Reducing Emissions from Deforestation and forest Degradation Plus (REDD+) action plans. This necessitates precise and reliable methods to estimate forest biomass and to reduce uncertainties in existing biomass quantification scenarios. The use of backscatter information from a host of allweather capable Synthetic Aperture Radar (SAR) systems during the recent past has demonstrated the potential of SAR data in forest above ground biomass estimation and forest / nonforest classification. In the present study, Advanced Land Observing Satellite (ALOS) / Phased Array L-band Synthetic Aperture Radar (PALSAR) data along with field inventory data have been used in forest above ground biomass estimation and forest / non-forest classification over Odisha state, India. The ALOSPALSAR 50 m spatial resolution orthorectified and radiometrically corrected HH/HV dual polarization data (digital numbers) for the year 2010 were converted to backscattering coefficient images (Schimada et al., 2009). The tree level measurements collected during field inventory (2009–'10) on Girth at Breast Height (GBH at 1.3 m above ground) and height of all individual trees at plot (plot size 0.1 ha) level were converted to biomass density using species specific allometric equations and wood densities. The field inventory based biomass estimations were empirically integrated with ALOS-PALSAR backscatter coefficients to derive spatial forest above ground biomass estimates for the study area. Further, The Support Vector Machines (SVM) based Radial Basis Function classification technique was employed to carry out binary (forest-non forest) classification using ALOSPALSAR HH and HV backscatter coefficient images and field inventory data. The textural Haralick’s Grey Level Cooccurrence Matrix (GLCM) texture measures are determined on HV backscatter image for Odisha, for the year 2010. PALSAR HH, HV backscatter coefficient images, their difference (HHHV) and HV backscatter coefficient based eight textural parameters (Mean, Variance, Dissimilarity, Contrast, Angular second moment, Homogeneity, Correlation and Contrast) are used as input parameters for Support Vector Machines (SVM) tool. Ground based inputs for forest / non-forest were taken from field inventory data and high resolution Google maps. Results suggested significant relationship between HV backscatter coefficient and field based biomass (R2 = 0.508, p = 0.55) compared to HH with biomass values ranging from 5 to 365 t/ha. The spatial variability of biomass with reference to different forest types is in good agreement. The forest / nonforest classified map suggested a total forest cover of 50214 km2 with an overall accuracy of 92.54 %. The forest / non-forest information derived from the present study showed a good spatial agreement with the standard forest cover map of Forest Survey of India (FSI) and corresponding published area of 50575 km2. Results are discussed in the paper.
This paper gives an account of day-night active forest fire monitoring conducted over the sub-tropical and moist temperate forests of the Uttaranchal State, India, during 2005 using the Defence ...Meteorological Satellite Program - Operational Line Scan system (DMSP-OLS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The state experienced heavy fire episodes during May-June 2005 and daily datasets of DMSP-OLS (night-time) and selected cloud-free MODIS (daytime) datasets were used in mapping active fire locations. DMSP-OLS collects data in visible (0.5 to 0.9 µm) and thermal (10.5 to 12.5 µm) bands and detects dim sources of lighting on the earth's surface, including fires. The enhanced fire algorithm for active fire detection (version 4) was used in deriving fire products from MODIS datasets. Fire locations derived from DMSP-OLS and MODIS data were validated with limited ground data from forest department and media reports. Results of the study indicated that the state experienced heavy fire episodes, most of them occurring during night-time rather than daytime. Validation of satellite-derived fires with ground data showed a high degree of spatial correlation.
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