456 research outputs found

    Microbial dynamics during various activities in residential areas of Lahore, Pakistan

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    Bioaerosols are ubiquitous in the atmosphere with their levels affected by a variety of environmental factors as well as type of activities being carried out at any specific time. The present study investigated how indoor activities influence bioaerosol concentrations in five residential houses of Lahore. Agar coated petri plates were exposed face upwards for twenty minutes in kitchens and living rooms during activity and non-activity periods. The temperature and relative humidity levels were noted as well. The bioaerosol concentrations in kitchens during the activity time ranged between 1022 to 4481 cfu/m3 and in living rooms from 1179 to 3183 cfu/m3 . Lower values were observed during non-activity periods. A paired-t test revealed a significant difference in bacterial loads during activity and non-activity times in both micro-environments (p = 0.038 in kitchen and p = 0.021 in living room). The predominant species identified were Micrococcus spp., Staphylococcus spp., and Bacillus spp. which are a common constituent of the indoor environment and are known to be opportunistic pathogens as well

    Effect of Contaminant Flow-rate and Applied Voltage on the Current Density and Electric Field of Polymer Tracking Test

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    Electrical failure due to surface discharge on the insulation material will cause material degradation and eventually lead to system failure. The flow of leakage current (LC) on the insulator surface under wet contamination is used to determine the material degradation level. According to IEC 60587 standard, LC exceeding 60 mA for more than two seconds is considered as failure. In this study, the  electric field and current density distributions on the linear low-density polyethylene (LLDPE) and natural rubber blend material have been analyzed using finite element method (FEM) analysis. The physical parameters used in FEM simulation were applied with voltage and contaminant flow rate, in accordance to contaminant conductivity. Tracking test condition according to IEC 60587 standard has been applied as proposed by the reference work in simulation using QuickField FEM software. The results show that the electric field and current density would become critical in higher applied voltage and contaminant flow rate. The highest average and highest maximum current density and electric field are found in both applied voltage of 6 kV and contaminant flow rate of 0.90 mlmin-1

    Total cholesterol and triglycerides status in autistic spectrum disorder children: a case-control study on Bangladeshi children

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    Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by challenges in social interaction, communication, and repetitive behaviors. The association between lipid profiles, particularly total cholesterol and triglycerides, and ASD in children is a growing focus in pediatric health research. This study aimed to assess the total cholesterol and triglycerides status in autistic spectrum disorder children. Methods: This cross-sectional study was conducted in the department of physiology, Bangabandhu Sheikh Mujib Medical University, Dhaka from March 2014 to January 2015 with 100 male children, half in a healthy control group (group A) and the other half diagnosed with autism spectrum disorder (group B). Results: In this study, no significant correlation was found between the groups for age (p=0.94) or BMI (p=0.29). The mean (±SE) serum total cholesterol levels were 146±1.70 mg/dl in group A and 145.00±3.77 mg/dl in group B, showing no significant difference between the two groups (p=0.885). But, the mean (±SE) serum triglyceride levels were 86.14±3.28 mg/dl in group A and 107.74±7.91 mg/dl in group B, with significantly higher levels compared to group A (p<0.01). Conclusions: Although there is no significant difference in serum total cholesterol levels between healthy children and those with autistic spectrum disorder, there is a significant difference in serum triglyceride levels. Therefore, further studies are needed to provide a clearer understanding of the lipid profile comparison

    Performance evaluation of multiple-beam free space optics in tropical rainy weather

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    Free space optics (FSO) has the potential to replace optical fiber as a solution for the last mile problem. FSO is favored because of its cheap maintenance costs and quick deployment time as compared to other communication system such as fiber optics. Atmospheric attenuation is a problem for FSOs with classic single beam systems, especially when it rains heavily specially in tropical region like Malaysia. As a result, a multibeam FSO transceiver system has become popular as a solution to this problem. The purpose of this study is to compare the standard single beam FSO system with the suggested multibeam FSO system. At a bit error rate (BER) of 10-9, the comparison is made in terms of received optical power, channel distance, and geometrical losses. Rain intensity data is gathered over a six-month period. To evaluate the performance of both systems, the average rain attenuation is calculated from this data and exposed to single beam and multibeam FSO systems. The multibeam FSO approach was found to increase the system's performance. The results show that employing up to four beams improves the quality of received power and increases the channel distance to 1150 m when compared to a single beam FSO system with a channel distance of only 830 m

    Sociodemographic factors associated with IgG and IgM seroprevalence for human cytomegalovirus infection in adult populations of Pakistan: A seroprevalence survey

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    Background: The seroprevalence of human cytomegalovirus (HCMV) infection ranges from 30 to 90 % in developed countries. Reliable estimates of HCMV seroprevalence are not available for Pakistan. This study determined the seroprevalence and sociodemographic factors associated with HCMV infection in adult populations of Karachi, Pakistan. Methods: A seroprevalence survey was conducted on 1000 adults, including residents of two semi-urban communities, and visitors to a government and a private hospital. Questionnaire-based interviews were conducted. Sera were analysed for HCMV-specific IgG and IgM. Chi-square or Fisher’s exact test was used for comparing sociodemographic variables against seropositivity of HCMV-IgG or IgM. Multiple logistic regression modeling was performed for IgG seroprevalence and adjusted odds ratios were computed. Results: The seroprevalence of HCMV-IgG and IgM was 93.2 and 4.3 % respectively. 95.3 % of individuals who were IgM seropositive were also seropositive for IgG. Around 6 % (15/250) of women of childbearing age remained uninfected and were therefore susceptible to primary infection. HCMV-IgG seroprevalence was associated with being female (p = 0.001), increasing age (p = 0.002) and crowding index (p = 0.003) and also with lower levels of both education (p \u3c 0.001) and income (p = 0.008). Seroprevalence also differed significantly by marital status (p = 0.008) and sampling location (p \u3c 0.001). A logistic regression model for HCMV-IgG seroprevalence showed associations with being female (OR = 1.89; 95 % CI: 1.10–3.25), increasing age (OR = 3.95; 95 % CI: 1.79–8.71) and decreasing income (OR = 0.72; 95 % CI: 0.54–0.96). A strong association was observed between increased seroprevalence of HCMV-IgM and decreasing household size (p = 0.008).Conclusions: Seroprevalence of HCMV is very high in Pakistan, although 6 % of women of childbearing age remain at risk of primary infection. The IgM seropositivity observed in some individuals living in small household size (1–3 individuals) with persistent HCMV infection could have resulted from a recurrent HCMV infection. Future longitudinal research in pregnant women and neonates is required to study the trends in HCMV seroprevalence over time in Pakistan for the development of a potential HCMV prevention and vaccination programme

    Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images

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    Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection. SC-CNN regresses the likelihood of a pixel being the center of a nucleus, where high probability values are spatially constrained to locate in the vicinity of the center of nuclei. For classification of nuclei, we propose a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei. The proposed approaches for detection and classification do not require segmentation of nuclei. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated nuclei belonging to four different classes. Our results show that the joint detection and classification of the proposed SC-CNN and NEP produces the highest average F1 score as compared to other recently published approaches. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and could potentially lead to a better understanding of cancer

    Supp. 2)

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    ABSTRACT Heavy metals in outdoor and indoor airborne particulate matter (PM) and dust in different residential built environmentsat two rural and one urban site in Pakistan were analysed. An eight stage non-viable impactor (Thermo Fisher Scientific Inc., USA) loaded with EMP 2000 glass microfiber filter papers (Whatman, England) was used to collect airborne PM.The indoordust samples (settled dust) were collected from different indoor surfaces (floor, cupboards) in living rooms and kitchens from houses at rural sites. The outdoor samples were collected from courtyards of the houses.At the urban site dust samples were also collected by the roads at 27 different locations around Lahoreand at a background site (University of Veterinary and Animal Sciences).Additionally, samples of dung cake, used as solid fuel, at one of the rural sites were taken.Heavy metals (Si, Al, Zn, Mn, Cu, Ni, Cd, Pb, Co and As) were determined by Graphite Furnace Atomic Absorption Spectrophotometer. At rural site I, in general, the concentrations of metals were higher outdoors than indoors, except for slightly higher indoor levels of Cu (0.85μg/m 3 indoor: 0.56 outdoorμg/m 3 ), Si (3.31μg/m 3 indoor: 3.17 outdoorμg/m 3 ) and Pb(11.99 ng/m 3 indoor: 9.32 outdoor ng/m 3 ). At the rural site II the mean concentration were higher outdoors than indoors, excluding Ni which was considerably higher indoors (55.68 ng/m 3 ) than outdoors (31.91 ng/m 3 ).At the urban site,outdoors, Si had the highest concentration (3.46 μg/m 3 ) followed by Al, Zn, Mn, Cu, Ni, Cd, Pb and Co. Similarly, the indoor levels had a maximum contribution from Si (12.30 μg/m 3 ) followed by Al, Mn, Zn, Cu, Cd, As, Pb, Ni and Co.With reference to dust at rural site I the top five metals outdoors were Si (708 mg/kg), Al, Cu, Zn and Pb (52 mg/kg) while, indoors Al was highest (281 mg/kg), followed by Si, Cu, Zn and Pb (57 mg/kg). At rural site II, both outdoors and indoors, Al (274 mg/kg -outdoor: 266 mg/kg -indoor), Si, Zn, Cu and Pb (61 mg/kg -outdoor: 80 mg/kg -indoor) were the five most abundant metals.The main five metals in decreasing order of their concentration in the road dust around Lahore were Si (686 mg/kg), Al, Cu, Zn and Pb (81 mg/kg). On the other hand, the dust samples from the background site showed Si (345 mg/kg) > Al >Pb> Cu > Zn (73 mg/kg). The airborne metal concentration of Pb was within the guideline value of WHO (0.5 μg/m 3 ) but the levels of Mn, Cd and Ni were higher at all sites than the guidelines proposed by European Commission and WHO highlighting the risk of exposure to toxic metals in non-occupational environments

    Microcontroller-Based Sun Path Tracking System

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    The objective of this paper is to design and construct a solar tracking system based on a microcontroller. The system design depends on some mathematical equations to send three signals to drive circuit to change the position of the solar cell by changing the polarity of two motors. These mathematical equations are used to compute the solar height angle (elevation) and the solar horizon angle (azimuth), whereas the usage of the fixed solar cells does not accomplish the desired object, that means the use of fixed solar cells does not grant a suitable output during a day and a season, where the sun position differs at the morning to the noon and at the setting of the sun, this is due to the spherical shape of Earth and to its rotation around the sun. The solar cell is controlled vertically and horizontally at period equal to one hour, whereas the stored data that denote of the sun position is computed each hour from the sunrise to the sunset, the amount of the stored data is different from a day to another, this variation is produced by the difference of day length during the year, whereas the amount of computed data at the summer is more than the amount of computed data at the winter. The microcontrollers vouch for processing the data and issue the commands to actuators to change the orientation of the solar cell. All obtained results are very acceptable, when the system has tested in certain days. By using the microcontroller, the project efficiency is improved, and the cost of hardware is reduced

    Cell Maps Representation For Lung Adenocarcinoma Growth Patterns Classification In Whole Slide Images

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    Lung adenocarcinoma is a morphologically heterogeneous disease, characterized by five primary histologic growth patterns. The quantity of these patterns can be related to tumor behavior and has a significant impact on patient prognosis. In this work, we propose a novel machine learning pipeline capable of classifying tissue tiles into one of the five patterns or as non-tumor, with an Area Under the Receiver Operating Characteristic Curve (AUCROC) score of 0.97. Our model's strength lies in its comprehensive consideration of cellular spatial patterns, where it first generates cell maps from Hematoxylin and Eosin (H&E) whole slide images (WSIs), which are then fed into a convolutional neural network classification model. Exploiting these cell maps provides the model with robust generalizability to new data, achieving approximately 30% higher accuracy on unseen test-sets compared to current state of the art approaches. The insights derived from our model can be used to predict prognosis, enhancing patient outcomes
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