Particulate Matter Inhalation Exposure Chambers and Parameters Affecting Their Performance: A Systematic Review Study

Exposure to inhalation aerosols and particulate matter (PM) in different concentrations can increase the risk of respiratory, cardiovascular, and other related diseases. The inhalation exposure studies are implemented to assess the biological effects of these hazardous agents in human or animal models, in whole-body (WB) or nose/head-only conditions. Several factors can affect the performance of the inhalation exposure chambers and if left uncontrolled, the results may not be desirable. The current study reviewed the characteristics, structures, and factors affecting the performance of the WB chambers, especially the ones designed for small animal exposure to the PM. At the primary stage, the criteria and the search strategy were determined and the keywords were searched in the scientific electronic databases. Totally, 1051 articles were extracted in the first stage, and finally seven articles were adopted. The technical and design details, materials, coefficient variations (CVs) of concentration, assessment methods, type and number of laboratory animals, procedure, and animals housing conditions were extracted from the selected articles. Then the most desirable WB inhalation exposure chamber was determined based on the criteria for assessing the presented exposure chambers such as the animal housing and least CVs of the concentration in the respiratory zones of the animals under study. It was concluded that the Kimmel design was the best and the most desirable chamber structurally and geometrically, since the concentration of the particle (NaCl) injected into the chamber varied from 3.5% to 5.2%, under standard conditions. Keywords:Inhalation Chamber; Whole-Body; Inhalation Exposure; Particulate Matter

Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology

Recently, deep learning has started to play an essential role in healthcare applications, including image search in digital pathology. Despite the recent progress in computer vision, significant issues remain for image searching in histopathology archives. A well-known problem is AI bias and lack of generalization. A more particular shortcoming of deep models is the ignorance toward search functionality. The former affects every model, the latter only search and matching. Due to the lack of ranking-based learning, researchers must train models based on the classification error and then use the resultant embedding for image search purposes. Moreover, deep models appear to be prone to internal bias even if using a large image repository of various hospitals. This paper proposes two novel ideas to improve image search performance. First, we use a ranking loss function to guide feature extraction toward the matching-oriented nature of the search. By forcing the model to learn the ranking of matched outputs, the representation learning is customized toward image search instead of learning a class label. Second, we introduce the concept of sequestering learning to enhance the generalization of feature extraction. By excluding the images of the input hospital from the matched outputs, i.e., sequestering the input domain, the institutional bias is reduced. The proposed ideas are implemented and validated through the largest public dataset of whole slide images. The experiments demonstrate superior results compare to the-state-of-art.Comment: Under Review for publicatio

Malaria or kalimbe: how to choose?

Should the Kalimbe (a traditional Amerindian loincloth) be banned, based on its association with an increased risk of malaria? Studies on malaria conducted on Amerindian children in the Oyapock region, French Guiana suggest that there is an argument for replacing the Kalimbe with a modern alternative. However, the wider issue of how the positive (risk reduction and related benefits) and negative effects (exacerbation of acculturation processes and associated consequences) should be assessed needs to be considered before suggesting a change in ancestral behaviour for medical purposes. A multidisciplinary approach is needed, together with caution and humility from epidemiologists

Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images

One of the main obstacles of adopting digital pathology is the challenge of efficient processing of hyperdimensional digitized biopsy samples, called whole slide images (WSIs). Exploiting deep learning and introducing compact WSI representations are urgently needed to accelerate image analysis and facilitate the visualization and interpretability of pathology results in a postpandemic world. In this paper, we introduce a new evolutionary approach for WSI representation based on large-scale multi-objective optimization (LSMOP) of deep embeddings. We start with patch-based sampling to feed KimiaNet , a histopathology-specialized deep network, and to extract a multitude of feature vectors. Coarse multi-objective feature selection uses the reduced search space strategy guided by the classification accuracy and the number of features. In the second stage, the frequent features histogram (FFH), a novel WSI representation, is constructed by multiple runs of coarse LSMOP. Fine evolutionary feature selection is then applied to find a compact (short-length) feature vector based on the FFH and contributes to a more robust deep-learning approach to digital pathology supported by the stochastic power of evolutionary algorithms. We validate the proposed schemes using The Cancer Genome Atlas (TCGA) images in terms of WSI representation, classification accuracy, and feature quality. Furthermore, a novel decision space for multicriteria decision making in the LSMOP field is introduced. Finally, a patch-level visualization approach is proposed to increase the interpretability of deep features. The proposed evolutionary algorithm finds a very compact feature vector to represent a WSI (almost 14,000 times smaller than the original feature vectors) with 8% higher accuracy compared to the codes provided by the state-of-the-art methods

Evaluation of photoionization detector performance for measuring the airborne toluene

Background and aims: In the field of chemical agents at workplaces, traditional measurement method for assessing the volatile organic compounds (VOCs) concentration is using a gas chromatograph generally equipped with a flame ionization detector (GC-FID). However, there are some limitations in working with this equipment including equipment accessibility, necessity of highly trained operators, and the high cost of sample analysis. The aim of this study was to evaluate the performance of photoionization detector (PID) as a substitution for GC-FID in the measurement of toluene as a representative of the VOCs in experimental studies. Methods: This study was carried out by an experimental set up for generating toluene known concentrations at 5, 20, 50, 100, 200, 500 and 1000 ppm with relative humidity 13 ±2. The concentration values were measured with PID as well as the National Institute of Occupational Safety and Health (NIOSH) 1501 reference method and results were compared. Results: The results showed a significant difference between the two methods at concentrations higher than 50 ppm while there was no significant difference at 5 ppm and 20 ppm. The correlation coefficient of the toluene concentrations at 5 to 1000 ppm was 0.999. The correction factor for the PID was 1.05 at the studied concentration range. Conclusion: Although the results presented by PID were different from those extracted from the NIOSH reference method, the response was linear. Thus, in studies of measuring airborne concentrations of toluene using this type of detector; the reading values must be corrected by the calculated correction factor

Structural Modeling of Safety Performance in Construction Industry

Background: With rapid economic development and industrialization, the construction industry continues to rank among the most hazardous industries in the world. Therefore, construction safety is always a significant concern for both practitioners and researchers. The objective of this study was to create a structural modeling of components that influence the safety performance in construction projects. Methods: We followed a two-stage Structural Equation Model based on a questionnaire study (n=230). In the first stage, we applied the Structural Equation Model to the proposed model to test the validity of the observed variables of each latent variable. In the next stage, we modified the proposed model. The LISREL 8.8 software was used to conduct the analysis of the structural model. Results: A good-fit structural model (Goodness of Fit Index=0.92; Root Mean Square Residual=0.04; Root Mean Square Error of Approximation=0.04; Comparative Fit Index=0.98; Normalized Fit Index=0.96) indicated that social and organizational constructs influence safety performance via the general component of the safety climate. Conclusion: The new structural model can be used to provide better understanding of the links between safety performance indicators and contributing components, and make stronger recommendations for effective intervention in construction projects

Photochemical of Polychlorinated biphenyl by the photolysis and solvent

Polychlorinated biphenyls (PCBs) are one group of persistent organic pollutants (POPs) that are of international concern because of global distribution, persistence, and toxicity. Removal of these compounds from the environment remains a very difficult challenge because the compounds are highly hydrophobic and have very low solubility in water. The photochemical reactor was of annular geometry with a cylindrical low-pressure mercury lamp. The whole Lamp was immersed in a reactor thermostat controlling the temperature at 32 ± 2 °C. The Polychlorinated biphenyls (PCBs) were analyzed by GC/ECD. The degradation of PCBs in terms of one, two and three lamp was 91.9%, 92.7% and 93% respectively. The degradation of PCBs in terms of use of 10% and 20% of total volume of solution of H2O2 were 88.8% and 93% respectively. The degradation of PCBs in terms of ratio to ethanol with oil transformer in 1:1, 2:1 and 3:1 was 83.4%, 92.5% and 93% respectively. The experiments show that UVC-photolysis of H2O2 leads to a degradation efficiency of PCBs in the presence of ethanol. @ JASEMJ. Appl. Sci. Environ. Manage. December, 2010, Vol. 14 (4) 107 - 11

The Effects of Aerobic Exercise on NF-κB and TNF-α in Lung Tissue of Male Rat

Background:Regular aerobic exercise improves theBackground: Regular aerobic exercise improves the inflammatory status in different lung diseases. However, the effects of long-term aerobic exercise on the lung response have not been investigated. The present study evaluated the effect of aerobic exercise on the lung inflammatory.Materials and Methods: 12 adult male Wistar rats were divided to 2 groups: A: control (n=6), B: aerobic exercise (five times per week for 4 week; n=6). The gene expression of NF-κB and TNF-α were analyzed in lung tissue by Real time–PCR. In order to determine the significant differences between groups independent t-test were used.Results: Aerobic exercise inhibited the gene expression of NF-κB and TNF-α. But there was no significant difference between A and B groups for TNF-α and NF-κB.Conclusion: We conclude that four week aerobic exercise decrease inflammatory status in lung tissue. Our results indicate a need for human studies that evaluate the lung responses to aerobic exercise

Designing Artificial Neural Network Using Particle Swarm Optimization: A Survey

Neural network modeling has become a special interest for many engineers and scientists to be utilized in different types of data as time series, regression, and classification and have been used to solve complicated practical problems in different areas, such as medicine, engineering, manufacturing, military, business. To utilize a prediction model that is based upon artificial neural network (ANN), some challenges should be addressed that optimal designing and training of ANN are major ones. ANN can be defined as an optimization task because it has many hyper parameters and weights that can be optimized. Metaheuristic algorithms such as swarm intelligence-based methods are a category of optimization methods that aim to find an optimal structure of ANN and to train the network by optimizing the weights. One of the commonly used swarm intelligence-based algorithms is particle swarm optimization (PSO) that can be used for optimizing ANN. In this study, we review the conducted research works on optimizing the ANNs using PSO. All studies are reviewed from two different perspectives: optimization of weights and optimization of structure and hyper parameters