3D Region Research Articles

Damage inspection for road markings based on images with hierarchical semantic segmentation strategy and dynamic homography estimation

This study proposes a computer vision-based damage inspection system for road markings. In order to evaluate the degree of damage of a marking objectively, the proposed system estimates its damage ratio according to the marking's damaged part and the marking's region. A hierarchical semantic segmentation strategy is proposed which employs a series of convolutional neural networks to recognize the 2D bounding box, damaged part and region of a marking. Specifically, this strategy can effectively identify the original region of a marking through an improved U-Net even if the marking is significantly damaged. The damage ratio estimation is enhanced by integrating information from multiple images based on object tracking and dynamic homography estimation. The experimental results confirm that the proposed system is effective in automating the inspection of road markings and producing objective damage assessments that should significantly assist road managers in prioritizing maintenance operations.

Mid-Holocene monsoons in South and Southeast Asia: dynamically downscaled simulations and the influence of the Green Sahara

Abstract. Proxy records suggest that the Northern Hemisphere during the mid-Holocene (MH), to be assumed herein to correspond to 6000 years ago, was generally warmer than today during summer and colder in the winter due to the enhanced seasonal contrast in the amount of solar radiation reaching the top of the atmosphere. The complex orography of both South and Southeast Asia (SA and SEA), which includes the Himalayas and the Tibetan Plateau (TP) in the north and the Western Ghats mountains along the west coast of India in the south, renders the regional climate complex and the simulation of the intensity and spatial variability of the MH summer monsoon technically challenging. In order to more accurately capture important regional features of the monsoon system in these regions, we have completed a series of regional climate simulations using a coupled modeling system to dynamically downscale MH global simulations. This regional coupled modeling system consists of the University of Toronto version of the Community Climate System Model version 4 (UofT-CCSM4), the Weather Research and Forecasting (WRF) regional climate model, and the 3D Coastal and Regional Ocean Community model (CROCO). In the global model, we have taken care to incorporate Green Sahara (GS) boundary conditions in order to compare with standard MH simulations and to capture interactions between the GS and the monsoon circulations in India and SEA. Comparison of simulated and reconstructed climates suggest that the dynamically downscaled simulations produce significantly more realistic anomalies in the Asian monsoon than the global climate model, although they both continue to underestimate the inferred changes in precipitation based upon reconstructions using climate proxy information. Monsoon precipitation over SA and SEA is also greatly influenced by the inclusion of a GS, with a large increase particularly being predicted over northern SA and SEA, and a lengthening of the monsoon season. Data–model comparisons with downscaled simulations outperform those with the coarser global model, highlighting the crucial role of downscaling in paleo data–model comparison.

Three-dimensional temperature and velocity measurements in fluids using thermographic phosphor tracer particles

Many flows of technical and scientific interest are intrinsically three-dimensional. Extracting slices using planar measurement techniques allows only a limited view into the flow physics and can introduce ambiguities while investigating the extent of 3D regions. Nowadays, thanks to tremendous progress in the field of volumetric velocimetry, full 3D-3C velocity information can be gathered using tomographic PIV or PTV hence eliminating many of these ambiguities (Discetti and Coletti, 2018; Westerweel et al., 2013). However, for scalar quantities like temperature, 3D measurements remain challenging. Previous approaches for coupled 3D thermometry and velocimetry combined astigmatism PTV with encapsulated europium chelates particles (Massing et al., 2018) or tomographic PIV with thermochromic liquid crystals particles (Schiepel et al., 2021). Here we present a new technique based on solid thermographic phosphor tracer particles, which have been extensively used for planar fluid temperature and velocity measurements (Abram et al., 2018) and are applicable in a wide range of temperatures. The particles are seeded into a gas flow where their 3D positions are retrieved by triangulation from multiple views and their temperatures are derived from two-colour luminescence ratio imaging. In the following, the experimental setup and key processing steps are described before a demonstration of the concept in a turbulent heated jet is shown.

Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors

This study aims to investigate the influence of interobserver manual segmentation variability on the reproducibility of 2D and 3D unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Thirty patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors, 10 chondrosarcomas) were retrospectively included. Three radiologists independently performed manual contour-focused segmentation on unenhanced CT and T1-weighted and T2-weighted MRI by drawing both a 2D region of interest (ROI) on the slice showing the largest tumor area and a 3D ROI including the whole tumor volume. Additionally, a marginal erosion was applied to both 2D and 3D segmentations to evaluate the influence of segmentation margins. A total of 783 and 1132 features were extracted from original and filtered 2D and 3D images, respectively. Intraclass correlation coefficient ≥ 0.75 defined feature stability. In 2D vs. 3D contour-focused segmentation, the rates of stable features were 74.71% vs. 86.57% (p < 0.001), 77.14% vs. 80.04% (p = 0.142), and 95.66% vs. 94.97% (p = 0.554) for CT and T1-weighted and T2-weighted images, respectively. Margin shrinkage did not improve 2D (p = 0.343) and performed worse than 3D (p < 0.001) contour-focused segmentation in terms of feature stability. In 2D vs. 3D contour-focused segmentation, matching stable features derived from CT and MRI were 65.8% vs. 68.7% (p = 0.191), and those derived from T1-weighted and T2-weighted images were 76.0% vs. 78.2% (p = 0.285). 2D and 3D radiomic features of cartilaginous bone tumors extracted from unenhanced CT and MRI are reproducible, although some degree of interobserver segmentation variability highlights the need for reliability analysis in future studies.

​Pathological and Molecular Investigation of Porcine Kobuvirus (PKV) from Indian Pigs

Background: The porcine kobuvirus (PKV) is a single-stranded, positive-sense, RNA virus of the family Picornaviridae which composed of small, non-enveloped viruses. The PKV is suspected to cause large outbreaks of diarrhoea and dehydration along with vomition in suckling piglets with a high morbidity rate. Despite of its economical importance very few studies are available on the pathology of PKV. The present study was conducted with the aim to investigate PKV in naturally infected Indian pigs. Methods: A total of 41 intestinal content and tissue samples from dead pigs were collected from post-mortem facility of, ICAR-IVRI during April, 2019 to July 2020. All the carcasses were subjected to detailed necropsy examination and appropriate tissue samples were collected for histopathological examination and molecular investigation. The amplification of 3D region of PKV was carried out via RT-PCR using the specific primers. The Genetic characterization of Indian isolate of the PKV was done on the basis of viral 3D gene of PKV. Result: A total of six out of 41 necropsied piglets were found positive for the PKV. The diarrhoea and dehydration were observed as the main clinical signs. The thickening and clouding of brain meninges, congestion of brain, pneumonia along with catarrhal and haemorrhagic enteritis was observed as common gross lesions. Microscopic lesions observed in brain were mild to moderate perivascular cuffing, congestion of meningeal and cerebral vessels, focal gliosis and neuronophagia. Mild to severe interstitial pneumonia and emphysema was noticed in lungs. Intestinal mucosa revealed severe inflammatory changes along with sloughing of villi. The genetic characterization revealed maximum identity of 91-93% with Indian isolate of PKV MH394282 and Chinese strain of PKV FJ459905. This study reported the first pathological investigation of PKV from Indian pigs and described the molecular findings in detail. Further explorative surveillance along with epidemiological studies are highly warranted to find out the real impact of the PKV on Indian pigs.

Computational design of weingarten surfaces

In this paper we study Weingarten surfaces and explore their potential for fabrication-aware design in freeform architecture. Weingarten surfaces are characterized by a functional relation between their principal curvatures that implicitly defines approximate local congruences on the surface. These symmetries can be exploited to simplify surface paneling of double-curved architectural skins through mold re-use. We present an optimization approach to find a Weingarten surface that is close to a given input design. Leveraging insights from differential geometry, our method aligns curvature isolines of the surface in order to contract the curvature diagram from a 2D region into a 1D curve. The unknown functional curvature relation then emerges as the result of the optimization. We show how a robust and efficient numerical shape approximation method can be implemented using a guided projection approach on a high-order B-spline representation. This algorithm is applied in several design studies to illustrate how Weingarten surfaces define a versatile shape space for fabrication-aware exploration in freeform architecture. Our optimization algorithm provides the first practical tool to compute general Weingarten surfaces with arbitrary curvature relation, thus enabling new investigations into a rich, but as of yet largely unexplored class of surfaces.

To Better Generate Organoids, What Can We Learn From Teratomas?

The genomic profile of animal models is not completely matched with the genomic profile of humans, and 2D cultures do not represent the cellular heterogeneity and tissue architecture found in tissues of their origin. Derived from 3D culture systems, organoids establish a crucial bridge between 2D cell cultures and in vivo animal models. Organoids have wide and promising applications in developmental research, disease modeling, drug screening, precision therapy, and regenerative medicine. However, current organoids represent only single or partial components of a tissue, which lack blood vessels, native microenvironment, communication with near tissues, and a continuous dorsal-ventral axis within 3D culture systems. Although efforts have been made to solve these problems, unfortunately, there is no ideal method. Teratoma, which has been frequently studied in pathological conditions, was recently discovered as a new in vivo model for developmental studies. In contrast to organoids, teratomas have vascularized 3D structures and regions of complex tissue-like organization. Studies have demonstrated that teratomas can be used to mimic multilineage human development, enrich specific somatic progenitor/stem cells, and even generate brain organoids. These results provide unique opportunities to promote our understanding of the vascularization and maturation of organoids. In this review, we first summarize the basic characteristics, applications, and limitations of both organoids and teratomas and further discuss the possibility that in vivo teratoma systems can be used to promote the vascularization and maturation of organoids within an in vitro 3D culture system.

Molecular characterization of a porcine sapelovirus strain isolated in China.

Porcine sapelovirus (PSV) infections have been associated with a wide spectrum of symptoms, ranging from asymptomatic infection to clinical signs including diarrhoea, pneumonia, reproductive disorders, and polioencephalomyelitis. Although it has a global distribution, there have been relatively few studies on PSV in domestic animals. We isolated a PSV strain, SHCM2019, from faecal specimens from swine, using PK-15 cells. To investigate its molecular characteristics and pathogenicity, the genomic sequence of strain SHCM2019 was analysed, and clinical manifestations and pathological changes occurring after inoculation of neonatal piglets were observed. The virus isolated using PK-15 cells was identified as PSV using RT-PCR, transmission electron microscopy (TEM), and immunofluorescence assay (IFA). Sequencing results showed that the full-length genome of the SHCM2019 strain was 7,567 nucleotides (nt) in length, including a 27-nucleotide poly(A) tail. Phylogenetic analysis demonstrated that this virus was a PSV isolate belonging to the Chinese strain cluster. Recombination analysis indicated that there might be a recombination breakpoint upstream of the 3D region of the genome. Pathogenicity experiments demonstrated that the virus isolate could cause diarrhoea and pneumonia in piglets. In breif, a recombinant PSV strain, SHCM2019, was isolated and shown to be pathogenic. Our results may provide a reference for future research on the pathogenic mechanism and evolutionary characteristics of PSV.

Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer.

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

Classification and Diagnosis of Pulmonary Nodules in Thoracic Surgery Using CT Image Segmentation Algorithm

This study was aimed at studying the pulmonary nodule (PN) classification and diagnosis through computed tomography (CT) images based on segmentation algorithms. 120 PN patients were taken as research subjects. Linear filter fine segmentation algorithm under 3D region growth was compared with the initial segmentation algorithm and applied to images of PN patients. The results showed that the segmentation effect of the proposed algorithm was at the upper-middle level. The cases of patients with smoking history were greatly more than those without (χ2 = 1.256, P &lt; 0.05 ). Benign and malignant PNs were classified, and morphological features included rough ones and round-like ones. The size characteristics included edge length and area. The gray-scale features included the uniformity of the gray-scale value and the mean value of the gray-scale value. The operation time of pulmonary lobectomy (76.2 ± 23.1 min) was obviously longer than that of pulmonary wedge resection (27.5.2 ± 4.5 min) ( P &lt; 0.05 ). The surgical blood loss of patients who underwent pulmonary lobectomy (125 ± 42 mL) was remarkably higher versus patients who underwent pulmonary wedge resection (51.6 ± 13.8 mL) ( P &lt; 0.05 ). After the operation, the length of stay of patients who underwent lobectomy (8.6 ± 1.4 days) was evidently longer than that of patients who underwent wedge resection (6.4 ± 1.2 days) ( P &lt; 0.05 ). The classification of benign and malignant PNs can effectively obtain the shape and size characteristics of PNs. Preoperative positioning surgery based on classification can shorten the operation time, reduce the amount of bleeding during the operation, and help improve the success rate of surgical resection.

The potential anticancer activities of platinum(II) complexes with tridentate N'N'N' pincer ligands

Treatment of cis/trans -[PtCl 2 (N≡CR) 2 ] 1 (R = CH 3 ( 1a ), C 2 H 5 ( 1b ), C 6 H 5 ( 1c ), CH 2 C 6 H 4 ( p -CH 3 ) ( 1d )) with 1,3-diiminoisoindoline 2 gives access to the corresponding symmetrical (1,3,5,7,9-pentaazanona-1,3,6,8-tetraenato) platinum(II) complexes [PtCl{ N H=C(R)N=C(C 6 H 4 ) N C=NC(R)= N H}] 3a-d , in good yields (65–77%). The compounds 3a-d have been characterized by IR, 1 H, 13 C and DEPT-135 NMR spectroscopies, ESI-MS and elemental analyses. GIAO/DFT studies have been performed to confirm the molecular structure of the platinum(II)-pincer 3d by comparing the experimental and theoretical 1 H and 13 C NMR chemical shifts, and it has shown good correlations between experimental and calculated chemical shifts for proton and carbon with correlation coefficients of 0.9947 and 0.9968, respectively. Molecular electrostatic potential is used to investigate the nucleophilic or electrophilic regions in the molecule 3d . The antimicrobial activities of compounds 3a-d are determined against different bacterial pathogens and yeasts. No toxicity is recorded against Artemia saline as a test organism for 3a-c , while moderate toxicity is found for 3d at 0.62 µM. Comparable antitumor activities are found for 3a-d against human colon HCT116 and human breast (MCF-7) cancer cell lines. The complexes 3a-d have shown good binding affinities to ct-DNA in the range of 6.00´10 5 to 8.33´10 5 and the conducted molecular docking studies suggest an intercalation mode of binding with DNA by the isoindole fragment of the ligands. Overall, this class of tridentate ligands have shown good potential in designing platinum(II) complexes with promising biological and anticancer activities. Moreover, the presence of the side chains on the ligands provides great design flexibility by introducing some chemical and/or physical characteristics.

De Novo Identification and Visualization of Important Cell Populations for Classic Hodgkin Lymphoma Using Flow Cytometry and Machine Learning.

Automated classification of flow cytometry data has the potential to reduce errors and accelerate flow cytometry interpretation. We desired a machine learning approach that is accurate, is intuitively easy to understand, and highlights the cells that are most important in the algorithm's prediction for a given case. We developed an ensemble of convolutional neural networks for classification and visualization of impactful cell populations in detecting classic Hodgkin lymphoma using two-dimensional (2D) histograms. Data from 977 and 245 clinical flow cytometry cases were used for training and testing, respectively. Seventy-eight nongated 2D histograms were created per flow cytometry file. Shapley additive explanation (SHAP) values were calculated to determine the most impactful 2D histograms and regions within histograms. SHAP values from all 78 histograms were then projected back to the original cell data for gating and visualization using standard flow cytometry software. The algorithm achieved 67.7% recall (sensitivity), 82.4% precision, and 0.92 area under the receiver operating characteristic. Visualization of the important cell populations for individual predictions demonstrated correlations with known biology. The method presented enables model explainability while highlighting important cell populations in individual flow cytometry specimens, with potential applications in both diagnosis and discovery of previously overlooked key cell populations.

Thermal convection in a cubical region saturated with a temperature-dependent viscosity fluid under the non-uniform temperature profile at vertical wall

Convective heat transfer is a major phenomenon in different technical and natural systems. This process can be found in enclosures under the different temperature distributions at bounded walls. In this research, the thermogravitation energy transport of a non-constant viscosity medium inside a 3D region under the non-uniform or uniform temperature profile at one of the vertical walls is addressed. The opposite border is at a low constant temperature, and the remaining borders are well insulated. The control equations with the border restrictions are reduced into a non-dimensional form and then integrated by the finite difference procedures. The control characteristics are the Rayleigh number and viscosity changeable magnitude. The impacts of these characteristics on the energy transference and flow structures are studied.

Two-dimensional (2D) morphologic measurements can quantify the severity of liver disease in children with autosomal recessive polycystic kidney disease (ARPKD).

To evaluate the correlation of 2D shape-based features with magnetic resonance elastography (MRE)-derived liver stiffness and portal hypertension (pHTN) in children with ARPKD-associated congenital hepatic fibrosis. In a prospective IRB-approved study, 14 children with ARPKD (mean age ± SD = 13.8 ± 5.8years) and 14 healthy controls (mean age ± SD = 13.7 ± 3.9years) underwent liver MRE. A 2D region of interest (ROI) outlining the left liver lobe at the level of the abdominal aorta was drawn on sagittal T2-weighted images. Eight shape features (perimeter, major axis length, maximum diameter, perimeter to surface ratio (PSR), elongation, sphericity, minor axis length, and mesh surface) describing the 2D-ROI were calculated. Spearman's correlation was calculated between shape features and MRE-derived liver stiffness (kPa) (n = 28). Shape features were compared between participants with ARPKD with pHTN (splenomegaly and thrombocytopenia), (n = 4) and without pHTN (n = 8) using the Mann Whitney U test. Receiver operating characteristic (ROC) curves were generated to examine the diagnostic accuracy of shape features in identifying cases with liver stiffness > 2.9kPa. In ARPKD participants and healthy controls, all eight shape features, except elongation, showed moderate to strong correlation with liver stiffness (kPa); the perimeter surface ratio had the strongest correlation (rho = - 0.75, p < 0.001). In ROC analysis, a cut-off of PSR ≤ 0.057mm-1 gave 100% (95% CI: 59.0-100.0) sensitivity and 100% (95% CI: 83.9-100.0) specificity in identifying ARPKD participants with liver stiffness > 2.9kPa, with an area under the ROC curve (AUC) of 1.0 (95% CI: 0.88-1.00). Individuals with pHTN had a lower median PSR (mean ± SD = 0.05 ± 0.01) than those without (0.07 ± 0.01; p = 0.027) with an AUC of 0.91 (95% CI: 0.60-0.99) in differentiating the participants with and without pHTN. Shape-based features of the left liver lobe show potential as non-invasive biomarkers of liver fibrosis and portal hypertension in children with ARPKD.

Geometric and dosimetric impact of 3D generative adversarial network-based metal artifact reduction algorithm on VMAT and IMPT for the head and neck region

BackgroundWe investigated the geometric and dosimetric impact of three-dimensional (3D) generative adversarial network (GAN)-based metal artifact reduction (MAR) algorithms on volumetric-modulated arc therapy (VMAT) and intensity-modulated proton therapy (IMPT) for the head and neck region, based on artifact-free computed tomography (CT) volumes with dental fillings.MethodsThirteen metal-free CT volumes of the head and neck regions were obtained from The Cancer Imaging Archive. To simulate metal artifacts on CT volumes, we defined 3D regions of the teeth for pseudo-dental fillings from the metal-free CT volumes. HU values of 4000 HU were assigned to the selected teeth region of interest. Two different CT volumes, one with four (m4) and the other with eight (m8) pseudo-dental fillings, were generated for each case. These CT volumes were used as the Reference. CT volumes with metal artifacts were then generated from the Reference CT volumes (Artifacts). On the Artifacts CT volumes, metal artifacts were manually corrected for using the water density override method with a value of 1.0 g/cm3 (Water). By contrast, the CT volumes with reduced metal artifacts using 3D GAN model extension of CycleGAN were also generated (GAN-MAR). The structural similarity (SSIM) index within the planning target volume was calculated as quantitative error metric between the Reference CT volumes and the other volumes. After creating VMAT and IMPT plans on the Reference CT volumes, the reference plans were recalculated for the remaining CT volumes.ResultsThe time required to generate a single GAN-MAR CT volume was approximately 30 s. The median SSIMs were lower in the m8 group than those in the m4 group, and ANOVA showed a significant difference in the SSIM for the m8 group (p < 0.05). Although the median differences in D98%, D50% and D2% were larger in the m8 group than the m4 group, those from the reference plans were within 3% for VMAT and 1% for IMPT.ConclusionsThe GAN-MAR CT volumes generated in a short time were closer to the Reference CT volumes than the Water and Artifacts CT volumes. The observed dosimetric differences compared to the reference plan were clinically acceptable.

Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images

Pelvic fracture is one of the leading causes of death in the elderly, carrying a high risk of death within 1 year of fracture. This study proposes an automated method to detect pelvic fractures on 3-dimensional computed tomography (3D-CT). Deep convolutional neural networks (DCNNs) have been used for lesion detection on 2D and 3D medical images. However, training a DCNN directly using 3D images is complicated, computationally costly, and requires large amounts of training data. We propose a method that evaluates multiple, 2D, real-time object detection systems (YOLOv3 models) in parallel, in which each YOLOv3 model is trained using differently orientated 2D slab images reconstructed from 3D-CT. We assume that an appropriate reconstruction orientation would exist to optimally characterize image features of bone fractures on 3D-CT. Multiple YOLOv3 models in parallel detect 2D fracture candidates in different orientations simultaneously. The 3D fracture region is then obtained by integrating the 2D fracture candidates. The proposed method was validated in 93 subjects with bone fractures. Area under the curve (AUC) was 0.824, with 0.805 recall and 0.907 precision. The AUC with a single orientation was 0.652. This method was then applied to 112 subjects without bone fractures to evaluate over-detection. The proposed method successfully detected no bone fractures in all except 4 non-fracture subjects (96.4%).

Knowledge and Attitude of Dentists toward Cone-Beam Computed Tomography in and Around Salem District: A Questionnaire Survey

Introduction: Cone-beam computed tomography (CBCT) is a revolutionary technique that was first created in 1982 for angiography and subsequently extended to maxillofacial imaging. It uses a divergent or "cone-" shaped ionizing radiation source and a two-dimensional (3D) region detector mounted on a rotating portal. During maxillofacial exposure, the radiation exposure dosage from CBCT is 10 times smaller than from conventional computed tomography scans. 3D perception of dental structures is growing quickly and it would not have been conceivable without the accessibility of advanced digital imaging. Materials and Methodology: A questionnaire study was done on 204 participants. The questions were self-designed, structured, and validated e questionnaire. Twenty-five multiple choice e questions were given to the participants. Results: Results were analyzed which shows that the vast majority of the members utilize advanced imaging in their daily schedule. Easy to store information and shorter time was the primary motivation to utilize CBCT than contrasted with less radiation dose. Advantages were data reconstruction and lower radiation dose followed shorter scan time, free from processing of radiograph. Discussion: Easy to store information and shorter time was the primary motivation to utilize CBCT. The most significant factor for preferring CBCT over digital imaging was the reduced radiation dose. The advantages were data reconstruction and lower radiation dose followed shorter scan time. Conclusion: In evaluating oral and maxillofacial pathologies with a reduction in radiation exposure, CBCT has a major role. Dental specialists should support CBCT imaging only as they expect that diagnostic performance can favor the still mind, improve persistent well-being, or significantly improve health outcomes. CBCT is one of the most notable recent developments in today's dentistry.

Epidemiology, Genetic Characterization, and Evolution of Hunnivirus Carried by Rattus norvegicus and Rattus tanezumi: The First Epidemiological Evidence from Southern China.

Hunnivirus is a novel member of the family Picornaviridae. A single species, Hunnivirus A, is currently described. However, there is limited information on the identification of Hunnivirus to date, and thereby the circulation of Hunnivirus is not fully understood. Thus, the objective of this study was to investigate the prevalence, genomic characteristics, and evolution of rat hunnivirus in southern China. A total of 404 fecal samples were subjected to detection of Hunnivirus from urban rats (Rattus norvegicus and Rattus tanezumi) using PCR assay based on specific primers targeted to partial 3D regions, with the prevalence of 17.8% in Rattus norvegicus and 15.6% in Rattus tanezumi. An almost full-length rat hunnivirus sequence (RatHuV/YY12/CHN) and the genome structure were acquired in the present study. Phylogenetic analysis of the P1 coding regions suggested the RatHuV/YY12/CHN sequence was found to be within the genotype of Hunnivirus A4. The negative selection was further identified based on analysis of non-synonymous to synonymous substitution rates. The present findings suggest that hunniviruses are common in urban rats. Further research is needed for increased surveillance and awareness of potential risks to human health.

Genetic analysis of Enterovirus D68 associated with pneumonia in children from South India.

EV-D68 is an emerging enterovirus infection associated with severe acute respiratory illness (SARI), acute flaccid myelitis (AFM) and acute flaccid paralysis (AFP). While EV-D68 outbreaks and sporadic cases are reported globally, a single case has been reported from India. The present study aims to investigate the molecular epidemiology and clinical characteristics of EV-D68-associated SARI cases from South India. We screened influenza-negative archived throat swab specimens from Influenza-Like Illness (ILI) and SARI cases (n=959; 2016 to 2018 period) for enteroviruses by pan-enterovirus real-time RT-PCR. Thirteen samples positive for enteroviruses were typed by PCR and sequencing based on VPI, VP2 and/or 5'NCR regions. One EV-D68 RNA sample was subjected to next-generation sequencing for whole genome characterisation. Among 13 enterovirus cases, four were ECHO-11, three EV-D68, two CV-A16 and one each EV-71, CV-B1, CV-B2 and CV-A9. All three cases of EV-D68 infection were reported in children below 2 years of age from Kerala state of South India during June and July 2017. The patients developed pneumonia without any neurological complications. Sequencing based on VPI and 5'NCR regions showed that EV-D68 strains belong to the novel subclade B3. The EV-D68 complete genome identified with two unique amino acid substitutions in VP1 (T-246-I) and 3D (K-344-R) regions. This study reiterates the EV-D68 novel subclade B3 circulation in India and indicates the urgent need for structured EV-D68 surveillance in the country to describe the epidemiology.

Adsorption and activation of CO2 on Pt/CeOx/TiO2(110): Role of the Pt-CeOx interface

The adsorption and dissociation of CO2 on TiO2(110), CeOx/TiO2(110) and Pt/CeOx/TiO2(110) surfaces has been examined using Ambient Pressure X-ray Photoelectron Spectroscopy (AP-XPS). The substrates under study exhibited different degrees of complexity which were tested for the binding of the adsorbate and the cleavage of C-O bonds. The surfaces were prepared by depositing CeOx (0.1 ML) onto TiO2(110) to form a mixed oxide support, onto which Pt nanoparticles (0.2 ML) were deposited. This configuration yields a complex set of interfaces between metal and oxides and we have systematically titrated the active role of each component (Pt 4f, Ce 3d and Ti 2p regions) and the arising surface intermediates (C 1 s and O 1 s regions). CO2 barely bonds to stoichiometric TiO2(110). It heals oxygen vacancies of this oxide surface (CO2,gas → COgas + Oa) and does not form stable carbonates. A stable carbonate was seen upon adsorption of CO2 on CeOx/TiO2(110) and on this type of substrate the adsorbate also removed O vacancies leading to the oxidation of Ti3+and Ce3+ sites. Pt nanoparticles dispersed on CeOx/TiO2(110) were highly effective for the binding and dissociation of CO2, with the formation of CO3, CO, C and CHx species on the Pt/CeOx/TiO2(110) system. The results of theoretical calculations based on density-functional theory (DFT) show that Pt/CeOx/TiO2(110) binds CO2 much stronger than surfaces of bulk platinum {(111), (100), (110)} or other late transition metals. On a Pt-CeOx interface, the molecule adsorbs with a bent configuration (~130° O-C-O bond angle) and with a substantial elongation (~ 0.1 Å) of the C-O bonds, facilitating its transformation into high value chemicals.