A novel no-sensors 3D model reconstruction from monocular video frames for a dynamic environment.

Occlusion awareness is one of the most challenging problems in several fields such as multimedia, remote sensing, computer vision, and computer graphics. Realistic interaction applications are suffering from dealing with occlusion and collision problems in a dynamic environment. Creating dense 3D reconstruction methods is the best solution to solve this issue. However, these methods have poor performance in practical applications due to the absence of accurate depth, camera pose, and object motion.This paper proposes a new framework that builds a full 3D model reconstruction that overcomes the occlusion problem in a complex dynamic scene without using sensors' data. Popular devices such as a monocular camera are used to generate a suitable model for video streaming applications. The main objective is to create a smooth and accurate 3D point-cloud for a dynamic environment using cumulative information of a sequence of RGB video frames. The framework is composed of two main phases. The first uses an unsupervised learning technique to predict scene depth, camera pose, and objects' motion from RGB monocular videos. The second generates a frame-wise point cloud fusion to reconstruct a 3D model based on a video frame sequence. Several evaluation metrics are measured: Localization error, RMSE, and fitness between ground truth (KITTI's sparse LiDAR points) and predicted point-cloud. Moreover, we compared the framework with different widely used state-of-the-art evaluation methods such as MRE and Chamfer Distance. Experimental results showed that the proposed framework surpassed the other methods and proved to be a powerful candidate in 3D model reconstruction.

A conflicts' classification for IoT-based services: a comparative survey.

Recently, Internet of Things (IoT)-based systems, especially automation systems, have become an indispensable part of modern-day lives to support the controlling of the networked devices and providing context-aware and intelligent environments. IoT-based services/apps developed by the end-users interact with each other and share concurrent access to devices according to their preferences, which increases safety, security, and correctness issues in IoT systems. Due to the critical impacts resulting from these issues, IoT-based apps require a customized type of compilers or checking tools that capable of analyzing the structures of these apps and detecting different types of errors and conflicts either in intra-IoT app instructions or in inter-IoT apps interactions. A plethora of approaches and frameworks have been proposed to assist the best practices for end-users in developing their IoT-based apps and mitigate these errors and conflicts. This paper focuses on conflict classification and detection approaches in the context of IoT systems by investigating the current research techniques that provided conflicts' classification or detection in IoT systems (published between 2014 and 2020). A classification of IoT-based apps interaction conflicts is proposed. The proposed conflicts' classification provides a priori conflicts detection method based on the analysis of IoT app instructions' relationships with utilizing the state-of-the-art Satisfiability Modulo Theories (SMT) model checking and formal notations. The current detection approaches are compared with each other according to the proposed conflicts' classification to determine to which extend they cover different conflicts. Based on this comparison, we provide evidence that the existing approaches have a gap in covering different conflicts' levels and types which yields to minimize the correctness and safety of IoT systems. We point out the need to develop a safety and security compiler or tool for IoT systems. Also, we recommend using a hybrid approach that combines model checking with a variety of languages and semantic technologies in developing future IoT-based apps verification frameworks to cover all levels and types of conflicts to guarantee and increase the safety, security, and correctness of IoT systems.

Assessment of Circulating miRNA-17 and miRNA-222 Expression Profiles as Non-Invasive Biomarkers in Egyptian Patients with Non-Small-Cell Lung Cancer.

Background: Lung cancer is one of the main human health threats. Survival of lung cancer patients depends on the timely detection and diagnosis. Among the genetic irregularities that control cancer development and progression, there are microRNAs (miRNAs). This study aimed to assess the plasma level of circulating miRNA-17 and miRNA-222 as non-invasive markers in non-small-cell lung cancer (NSCLC) patients. Patients and methods: A total of 40 patients with NSCLC and 20 healthy controls who were matched in terms of age and sex with the patient group were included in this case-control study.. Estimation of miRNA-17 and miRNA-222 expression profiles in the plasma was done using quantitative real-time PCR (qRT-PCR). The relationship between both markers and their clinicopathological features were also determined. Receiver operating characteristic (ROC) curve analysis was done to evaluate the role of these microRNAs in NSCLC diagnosis and follow-up. Results: MiRNA-17 and miRNA-222 levels were significantly upregulated in NSCLC patients compared with controls (48.32±12.35 vs 1.16±0.19 and 34.53±3.1 vs 1.22±0.14) (P=0.000). Plasma miRNA-17 level was increased, and the miRNA-222 level was decreased across different stages of the disease; however, these differences d were not statistically significant (P=0.4, P=0.5, respectively). The miRNA-17 levels were higher in the lung cancer patients with metastasis , but miRNA-222 levels were lower patients without metastasis. We found no statistically significant difference in this regard(P=0.4 vs P=0.3, respectively). ROC curve analysis showed that the sensi¬tivity and specificity of miRNA-17 were 77.78% and 87.50% , and of miRNA-222 were 50% and 88.89%. Conclusion: MiRNA-17 and miRNA-222 can be considered as non-invasive biomarkers for detection of early lung carcinogenesis and metastasis in patients with NSCLC, hence providing a basis for the development of novel therapeutic approaches.

Circulating miRNA-21 and miRNA-23a Expression Signature as Potential Biomarkers for Early Detection of Non-Small-Cell Lung Cancer.

BACKGROUND AND AIM: Lung Cancer (LC) is a major cancer killer worldwide, and 5-yr survival is extremely poor (≤15%), accentuating the need for more effective diagnostic and therapeutic strategies. Studies have shown cell-free microRNAs (miRNAs) circulating in the serum and plasma with specific expression in cancer, indicating the potential of using miRNAs as biomarkers for cancer diagnosis and therapy. This study aimed to identify differentially-expressed two miRNAs in the plasma of Non-Small Cell Lung Cancer (NSCLC) patients that might be a clinically useful tool for lung cancer early detection. miRNA-21 is one of the most abundant oncomirs. miRNA-23a functions as an oncogene in several human cancers, however, its clinical value has not been investigated in NSCLC. MATERIALS AND METHODS: A case-control study was conducted in Assiut University Hospital, Egypt, from 2017 to 2018. Plasma samples were obtained from 45 NSCLC patients. The expression level of miR-21 and miRNA-23a was detected by qRT-PCR and compared to 40 healthy control subjects. The relation between both miRNAs and clinicopathological parameters was evaluated. RESULTS: The expression level of miR-21 and miRNA-23a was significantly up-regulated (36.9 ± 18.7 vs. 1.12 ± 0.84 and 24.7 ± 19.09 vs. 1.16 ± 0.45) in NSCLC compared to matched controls (P<0.0001each). There was a significant difference in the level of plasma miRNA-21 and miRNA- 23a expression between the different grades of the disease (P = 0.032 and P = 0.001, respectively). The plasma miRNA-21 and miRNA-23a levels in the lung cancer patients with distant metastasis (n = 20) were significantly higher than those in the patients without metastasis (n = 25) (P<0.0001 each), the expression of miR-21 and miRNA-23a was significantly associated with tumor size (P = 0.001, P = 0.0001, respectively), but not significantly related to lymph node metastasis (P = 0.687 and 0.696, respectively). A positive correlation was observed between miRNA-21 and miRNA-23a (r = 0.784, P<0.01), There was no significant difference in the plasma miRNA-21 and miRNA-23a levels in the lung cancer patients with different histopathological types. CONCLUSION: miR-21 and miR-23a might play an oncogenic role in LC and is a poor prognostic factor. Switching off miRNA-21 and miRNA-23a may improve the treatment of LC. Our results must be verified by large-scale prospective studies with standardized methodology.

Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

BACKGROUND AND OBJECTIVE: T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. METHODS: In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. RESULTS: The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95.125% and an AUC of 0.987 on the HLA-DRB1*0101 allele of the Wang benchmark dataset. CONCLUSIONS: The results indicate that the proposed prediction technique "GAPES" is a promising technique that will help researchers and scientists to predict the protein structure and it will assist them in the intelligent design of new epitope-based vaccines.

Upper Egypt experience in bladder preservation using concurrent chemoradiotherapy.

OBJECTIVE: To share our experience in bladder preservation in Upper Egypt, Assiut and Sohag Universities, using different treatment protocols. In Sohag study patients with operable muscle invasive bladder cancer were included and underwent transurethral resection followed by radiochemotherapy (5- fluorouracil and Cisplatin) for bladder preservation. In Assiut study after maximum safe resection of bladder tumor, patients received combined chemo-radiotherapy, 60 Gy of fractionated radiotherapy over 6 weeks, with Cisplatin and Gemcitabine. RESULTS: In Sohag study the age of patients ranged from 35-72ys with Median 56 years, 24 patients were male (80%) and 6 patients were female (20%). In Assiut study the mean of age was 57.30 years, median 58.5 years with peak incidence in 7th decade (9 cases) then in 6th decade 7 cases (23.33%). Performance status was represented as following, 23 patients (76.6%) were scale 1 and seven patients (23.3%) were scale 2. In Assiut study, 90% of patients were disease free at the time of cystoscopic reevaluation. Of concern is that within 18 months of follow up in Assiut study, 7 of 27 (74%) complete responding patients have had local recurrence and 66.7% of all cases. The recurrence free survival in Sohag study at the median follow up (17 months) was 84% and at the end of follow up (30 months) was 70%. The overall survival at the median follow up was 95%, and at the end of follow up was 84%. The disease free survival in Assiut study was 66.7% and the overall Survival in Assiut study was 76.7. CONCLUSION: Three significant prognostic factors were detected for overall survival, performance status, tumor size and residual of tumor and two significant prognostic factors were detected for disease free survival, tumor size and residual of tumor in Assiut study. And it was nearly similar to that reported by Sohag study as they found the completeness of TUR and early stage of the tumor had the strongest impact in response to treatment.