Assessment of Dental Materials’ Catalogs Based on Safety and Protection Items in Dental School of Kerman

Objectives Dental materials are potentially hazardous and can negatively affect the health of patients, dental staff, and the surrounding environment. Thus, it is important to be aware and comply with the information provided in the material safety data sheets (MSDSs). Therefore, it seems necessary to review the dental material safety sheets in order to determine their consistency with the standard safety items required for dental materials. This study aimed to evaluate the MSDSs of dental materials consumed in Kerman Dental School to determine their compliance with the standard safety items. Methods In this cross-sectional study, 106 dental materials were selected from 12 clinical departments of Kerman Dental School. The MSDSs were assessed in order to determine their consistency with the standard safety items. Data were analyzed with SPSS version 21, and t-test was used for statistical analysis. Statistical significance level was set at P<0.05. Results Among the 15 items considered necessary according to the standard MSDSs, the item “necessary measures in case of possible leakage and spillage” had been least frequently stated in the assessed MSDSs. Also, the mean safety score of the materials with MSDSs was significantly higher compared with materials that had no MSDSs(P=0.0001). Conclusion Evaluation of the MSDSs of dental materials consumed in Kerman Dental School regarding the required standard items revealed that they did not meet the defined standard levels

Assessment of Landslide-Induced Geomorphological Changes in Hítardalur Valley, Iceland, Using Sentinel-1 and Sentinel-2 Data

Publisher's version (útgefin grein)Landslide mapping and analysis are essential aspects of hazard and risk analysis. Landslides can block rivers and create landslide-dammed lakes, which pose a significant risk for downstream areas. In this research, we used an object-based image analysis approach to map geomorphological features and related changes and assess the applicability of Sentinel-1 data for the fast creation of post-event digital elevation models (DEMs) for landslide volume estimation. We investigated the Hítardalur landslide, which occurred on the 7 July 2018 in western Iceland, along with the geomorphological changes induced by this landslide, using optical and synthetic aperture radar data from Sentinel-2 and Sentinel-1. The results show that there were no considerable changes in the landslide area between 2018 and 2019. However, the landslide-dammed lake area shrunk between 2018 and 2019. Moreover, the Hítará river diverted its course as a result of the landslide. The DEMs, generated by ascending and descending flight directions and three orbits, and the subsequent volume estimation revealed that-without further post-processing-the results need to be interpreted with care since several factors influence the DEM generation from Sentinel-1 imagery.This research has been supported by the Austrian Science Fund (FWF) through the project MORPH (Mapping, monitoring and modelling the spatio-temporal dynamics of land surface morphology; FWF-P29461-N29) and the Doctoral Collage GIScience (DKW1237-N23), as well as by the Austrian Academy of Sciences (?AW) through the project RiCoLa (Detection and analysis of landslide-induced river course changes and lake formation).Peer Reviewe

Candidate Biomarkers for Targeting in Type 1 Diabetes; A Bioinformatic Analysis of Pancreatic Cell Surface Antigens

Objective: Type 1 diabetes (T1Ds) is an autoimmune disease in which the immune system invades and destroysinsulin-producing cells. Nevertheless, at the time of diagnosis, about 30-40% of pancreatic beta cells are healthy andcapable of producing insulin. Bi-specific antibodies, chimeric antigen receptor regulatory T cells (CAR-Treg cells), andlabeled antibodies could be a new emerging option for the treatment or diagnosis of type I diabetic patients. The aimof the study is to choose appropriate cell surface antigens in the pancreas tissue for generating an antibody for type Idiabetic patients.Materials and Methods: In this bioinformatics study, we extracted pancreas-specific proteins from two largedatabases; the Human Protein Atlas (HPA) and Genotype-Tissue Expression (GTEx) Portal. Pancreatic-enrichedgenes were chosen and narrowed down by Protter software for the investigation of accessible extracellular domains.The immunohistochemistry (IHC) data of the protein atlas database were used to evaluate the protein expression ofselected antigens. We explored the function of candidate antigens by using the GeneCards database to evaluate thepotential dysfunction or activation/hyperactivation of antigens after antibody binding.Results: The results showed 429 genes are highly expressed in the pancreas tissue. Also, eighteen genes encodedplasma membrane proteins that have high expression in the microarray (GEO) dataset. Our results introduced fourstructural proteins, including NPHS1, KIRREL2, GP2, and CUZD1, among all seventeen candidate proteins.Conclusion: The presented antigens can potentially be used to produce specific pancreatic antibodies that guide CARTreg,bi-specific, or labeling molecules to the pancreas for treatment, detection, or other molecular targeted therapyscopes for type I diabetes

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Comparison of Independent Component Analysis, Principal Component Analysis, and Minimum Noise Fraction Transformation for Tree Species Classification Using APEX Hyperspectral Imagery

Hyperspectral imagery provides detailed spectral information that can be used for tree species discrimination. The aim of this study is to assess spectralspatial complexity reduction techniques for tree species classification using an airborne prism experiment (APEX) hyperspectral image. The methodology comprised the following main steps: (1) preprocessing (removing noisy bands) and masking out non-forested areas; (2) applying dimensionality reduction techniques, namely, independent component analysis (ICA), principal component analysis (PCA), and minimum noise fraction transformation (MNF), and stacking the selected dimensionality-reduced (DR) components to create new data cubes; (3) super-pixel segmentation on the original image and on each of the dimensionality-reduced data cubes; (4) tree species classification using a random forest (RF) classifier; and (5) accuracy assessment. The results revealed that tree species classification using the APEX hyperspectral imagery and DR data cubes yielded good results (with an overall accuracy of 80% for the APEX imagery and an overall accuracy of more than 90% for the DR data cubes). Among the classification results of the DR data cubes, the ICA-transformed components performed best, followed by the MNF-transformed components and the PCA-transformed components. The best class performance (according to producers and users accuracy) belonged to Picea abies and Salix alba. The other classes (Populus x (hybrid), Alnus incana, Fraxinus excelsior, and Quercus robur) performed differently depending on the different DR data cubes used as the input to the RF classifier.(VLID)340696

Genome-wide association study of therapeutic response to statin drugs in cardiovascular disease

Abstract Cardiovascular disease (CVD) is one of the main causes of death in the world. The increased level of blood cholesterol is significantly correlated to CVD incidents. Statins are a group of drugs that decrease the synthesis of cholesterol in the liver by inhibiting the final enzyme of the pathway named HMG-CoA reductase. Several investigations showed that different patients give different responses to the administration of statin drugs according to their genetic background. In this research study, using Genome-Wide Association Studies (GWAS) data analysis methods, such as the SimpleM statistical approach and genomic connection matrix, we tried to discover the novel candidate SNPs that were involved in response to statin drugs. The investigation was carried out using 3,221 cardiovascular patients' data about genotypes and phenotypes of two important parameters including total cholesterol, and LDL level, in response to statin administration. Functional annotation of nearest genes to candidate SNPs was also carried out by using comprehensive databases and tools such as BioMart-Ensembl, UCSC, NCBI, and WebGestalt software. Our results represented eight novel SNPs (rs10820084, rs4803750, rs10989887, rs1966503, rs17502794, rs10785232, rs484071, rs4785621) significantly associated with statin response in different individual cardiovascular patients for the first time. In addition, the groups of genes that are close to the SNPs were also represented and evaluated in detail. Our results illustrated that some of the genes such as BAAT, BCL3, and CMTM6 have a direct functional impact on cholesterol level or LDL biosynthesis which confirmed the effects of neighbor SNPs on the response to statin drugs. Today, finding the loci, genes, and molecular mechanisms involved in the response to drugs is of great importance in pharmacogenomics and personalized medicine

Scale matters : a survey of the concepts of scale used in spatial disciplin

Scale is a critical factor when studying patterns and the processes that cause them. A variety of approaches have been used to define the concept of scale but confusion and ambiguities remain regarding scale types and their definitions. The objectives of this study were therefore (1) to review existing types and definitions of scale, and (2) to systematically investigate the ambiguities in scale definitions and to determine the applicability of the various scale types and definitions. Through a comprehensive literature review, we identified seven types of scales and designed a survey for the seven definitions of scale and interviewed 150 scientists. The results show that the more cartography related types of scale are relatively well known while the more abstract dimensions are less known and are most ambiguous. Based on graphical examples, participants were asked which spatial scales are most relevant for their work. Surprisingly, composite objects such as a forest stand were most relevant followed by individual objects such as single trees and, lastly, more generalized categorizes or meta-objects such as “forested area”. We have drawn some conclusions that will help to clarify the different types of scale in regard to their practical use.(VLID)377541

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences / Applicability of multi-seasonal X-band SAR imagery for multiresolution segmentation : a case study in a riparian mixed forest

he increasing availability of synthetic aperture radar (SAR) data from a range of different sensors necessitates efficient methods for semi-automated information extraction at multiple spatial scales for different fields of application. The focus of the presented study is two-fold: 1) to evaluate the applicability of multi-temporal TerraSAR-X imagery for multiresolution segmentation, and 2) to identify suitable Scale Parameters through different weighing of different homogeneity criteria, mainly colour variance. Multiresolution segmentation was used for segmentation of multi-temporal TerraSAR-X imagery, and the ESP (Estimation of Scale Parameter) tool was used to identify suitable Scale Parameters for image segmentation. The validation of the segmentation results was performed using very high resolution WorldView-2 imagery and a reference map, which was created by an ecological expert. The results of multiresolution segmentation revealed that in the context of object-based image analysis the TerraSAR-X images are applicable for generating optimal image objects. Furthermore, ESP tool can be used as an indicator for estimation of Scale Parameter for multiresolution segmentation of TerraSAR-X imagery. Additionally, for more reliable results, this study suggests that the homogeneity criterion of colour, in a variance based segmentation algorithm, needs to be set to high values. Setting the shape/colour criteria to 0.005/0.995 or 0.00/1 led to the best results and to the creation of adequate image objects.(VLID)446983

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences / Assessment of landslide-induced morphology changes using an object-based image analysis approach : a case study of Hítardalur, Iceland

On July 7, 2018, a large landslide occurred at the eastern slope of the Fagraskógarfjall Mountain in Hítardalur valley in West Iceland. The landslide dammed the river, led to the formation of a lake and, consequently, to a change in the river course. The main focus of this research is to develop a knowledge-based expert system using an object-based image analysis (OBIA) approach for identifying morphology changes caused by the Hítardalur landslide. We use synthetic aperture radar (SAR) and optical remote sensing data, in particular from Sentinel-1/2 for detection of the landslide and its effects on the river system. We extracted and classified the landslide area, the landslide-dammed lake, other lakes and the river course using intensity information from S1 and spectral information from S2 in the object-based framework. Future research will focus on further developing this approach to support mapping and monitoring of the spatio-temporal dynamics of surface morphology in an object-based framework by combining SAR and optical data. The results can reveal details on the applicability of different remote sensing data for the spatio-temporal investigation of landslides, landslide-induced river course changes and lake formation and lead to a better understanding of the impact of large landslides on river systems.(VLID)459038