Mapping Change in Spatial Extent and Density of Mangrove Forest at Karachi Coast Using Object Based Image Analysis

Karachi shoreline is more than 135 Km long significant for marine fishery breeding and spawning. During 2005 to 2018 the mangrove forest areas in Karachi increased in extent but declined in density. The main cause of mangrove cover change in this region are coastal region development (port building, industrial area and waterfront project). This study aims to monitor both extent and density changes of mangrove forest at Karachi coast. For this purpose, the Landsat imagery was used of the years 2005 and 2018 covering a span of 14 years. The imageries were processed through Normalized Difference Vegetation Index (NDVI) analysis. Simultaneously, random sample locations were identified for mapping and validation of mangrove forest extent and density during 2005 to 2018. The sample locations were categorized as dense, normal and sparse classes. In the next step, sample locations were plotted on NDVI images to determine mean, minimum and maximum values for each class of mangrove forest. In the final step, the accuracy assessment was done using Kappa statistics. Results show that overall accuracy of 2018 imagery is better than 2005 Landsat imagery. The overall extent of mangrove forest increased in the past years

Brainstem encephalitis with Kikuchi-Fujimoto disease

A case of young woman is described who developed clinical and MRI features of brainstem encephalitis in the setting of fever and cervical lymphadenopathy. Lymph node biopsy revealed histiocytic necrotizing lymphadenitis (Kikuchi-Fujimoto disease), which may reflect host response to an unspecified immune insult

High heterotrophic counts in potable water and antimicrobial resistance among indicator organisms in two peri-urban communities of Karachi, Pakistan

Objective: Fecal contamination of potable water leads to unsafe water supply. Although many urban areas of large metropolitan cities receive safe water, peri-urban areas are often not monitored by public health authorities and water supply and quality remain unknown. We assessed microbiological quality and rates of antimicrobial resistance in viable indicator bacteria in two peri-urban communities of Karachi, Pakistan. Water samples were collected over 5 months (October 2015 to February 2016) from these peri-urban communities and samples were processed for microbiological quality as per Standing Committee of Analysts, United Kingdom and World Health Organization guidelines and criteria for drinking water.Result: Both communities received unimproved water. Potable water samples collected from 100 households showed that 96% of samples were unsafe for consumption. Extended spectrum beta lactamases production was found in 29.2% of fecal indicator organisms (coliforms). Use of unimproved water sources and unsafe potable water quality in peri-urban Karachi deserve immediate attention and upgrade. The study is instrumental in attracting the attention of authorities to the state of water resources in peri-urban communities in Karachi with a view to influence improvement of services and effects on human health

Diagnostic accuracy of machine learning models to identify congenital heart disease: A meta-analysis

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD. Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to generate the hierarchical Summary ROC (HSROC) curve. Results: We included 16 studies (1217 participants) that used ML algorithm to diagnose CHD. Neural networks were used in seven studies with overall sensitivity of 90.9% (95% CI 85.2-94.5%) and specificity was 92.7% (95% CI 86.4-96.2%). Other ML models included ensemble methods, deep learning and clustering techniques but did not have sufficient number of studies for a meta-analysis. Majority (n=11, 69%) of studies had a high risk of patient selection bias, unclear bias on index test (n=9, 56%) and flow and timing (n=12, 75%) while low risk of bias was reported for the reference standard (n=10, 62%). Conclusion: ML models such as neural networks have the potential to diagnose CHD accurately without the need for trained personnel. The heterogeneity of the diagnostic modalities used to train these models and the heterogeneity of the CHD diagnoses included between the studies is a major limitation

Strength estimation of evaporitic rocks using different testing methods

Rock strength is defined as the limit of the ability of a rock to resist stress or deformation without breaking. Testing methods recommended by ISRM (International Society of Rock Mechanics) and ASTM (American Standards Testing Material) include unconfined compressive strength (UCS), point load index (PLI), indirect tensile strength (ITS), Schmidt hammer rebound (SHR), sonic velocity (Vp and Vs), and slake durability index 2nd cycle (Id2). This contribution compares the results of these methods and explores the influence of rock composition and texture on Lower Miocene evaporites from Al Ain city, United Arab Emirates (UAE). These sedimentary rocks are common in the Arabian Peninsula as exposures or in the subsurface where they may constitute the foundations of buildings. A large number of UCS, PLI, ITS, SHR, SV, and Id2 tests were carried out on both core samples and rock blocks according to ASTM Standards. Examination of compositional and textural characteristics of representative rock samples was performed using XRD, XRF, polarized-light microscopy, and SEM. The results reveal variable correlations between the rock strength parameters with specific significant values between 0.53 and 0.72. The effect of composition and texture of the evaporitic rocks on their strength behavior is related to impurities such as clay minerals and celestite and grain interlocking textures. Despite the limited compositional variability of the evaporitic rocks (5–10%), the textural variability may present a challenging feature in rock strength testing and should be taken as a primary factor for consideration during applications

Exercise Modes and their Association with Hypoglycaemia Episodes in Adults with Type 1 Diabetes Mellitus: A Systematic Review

Objective: Type 1 diabetes mellitus rates are rising worldwide. Health benefits of physical exercise in this condition are many, but more than 60% do not participate, mainly from fear of hypoglycaemia. This systematic review explores effects of physical exercise modes on blood glucose levels in adults for hypoglycaemia prevention. Research Design and Methods: Predefined inclusion criteria were randomised or non-randomized crossover trials of healthy non-obese adults with type 1 diabetes mellitus. Exercise interventions used standardised protocols of intensity and timing. Outcomes included hypoglycaemia during or after exercise, and acute glycaemic control. Medline, CINAHL, AMED, SportDiscus, CENTRAL (1990–11/01/2018), Embase, (1988-09/04/2018) were searched using keywords and MeSH terms. Inclusions, data-extraction, and quality assessment using CASP checklists, were by one researcher and checked by a second. Meta-analysis used Revman (version 5.3) where four or more outcomes were reported. PROSPERO registration CRD42018068358. Results: From 5459 citations, we included 15 small crossover studies (3 non-randomised), 13 assessing aerobic (intermittent high-intensity exercise (IHE) versus continuous, or continuous versus rest) and 2 assessing resistance exercise versus rest. Study quality was good, and all outcome measures reported. Thirteen gave hypoglycaemia results, of which 5 had no episodes. Meta-analysis of hypoglycaemia during or after IHE compared to continuous exercise showed no significant differences (N=5,OR=0.68(95%CI0.16-2.86),I2=56%). For blood glucose there was little difference between groups at any time point. Conclusion: IHE may be safer than continuous exercise because of lesser decline in blood glucose, but more research needs to demonstrate if this would be reflected in hypoglycaemic episode rates

Mapping Turbidity Levels in the Lake’s Water using Satellite Remote Sensing Technique

Haleji lake, Thatta, Sindh, has been affected by water pollution in the past decades. This study focuses on mapping water pollution at Haleji lake using turbidity as the pollution indicator. In this study, an algorithm was developed by correlating satellite reflectance data and in-situ turbidity measurements using regression analysis. The determination coefficient R2 of the developed algorithm showed a value of 0.83 that is evidence of a good correlation between field-based and mapped turbidity. Moreover, a temporal analysis was carried out using the same algorithm for the years 1999 and 2011. Results of temporal analysis confirmed that the turbidity levels in Haleji lake have increased from below 5 NTU to around 15 – 30 NTU. This is a clear sign of lake pollution in the interim of the past twelve years

Discrimination of Seasonal Snow Cover in Astore Basin, Western Himalaya using Fuzzy Membership Function of Object-Based Classification

The temporal mapping of seasonal snow cover is generally being delineated through low resolution MODIS data (250-500m resolution) due to daily frequency of image acquisition; however, it sometimes compromises the mapping accuracies. In this study, the time-series of high resolution satellite imagery was used to evaluate the spatio-temporal changes in the snow covered area of Astore basin during summer and winter seasons from 1990 to 2017. The Object Based Image Analysis (OBIA) technique was applied on multi-spectral images of Landsat (TM and OLI sensors) of respective years (1990, 2000, 2010 and 2017) in order to discriminate the snow covered area in both seasons. Although OBIA is a strong technique that has been successfully applied in numerous research problems of remote sensing regarding cryosphere, but due to hindrances (i.e. Clouds and haze), it is sometimes not highly efficient to detect the snow accurately, therefore, Normalized Difference Snow Index (NDSI) has been calculated to distinguish snow covered area from snow free areas. The range of 0.4-1.0 was used as a threshold value for fuzzy membership function in OBIA to delineate the snow cover more precisely. The study suggested that the snow covered area is gradually increasing in winters during past few decades in the basin; however, in summer season as compared to winters, no specific trend has been observed