Human Action Recognition Employing 2Dpca And Vq In The Spatio-Temporal Domain

In this paper a novel algorithm for human action recognition is presented. This approach is based on Two-Dimensional Principal Component Analysis (2DPCA) and Vector Quantization (VQ) in the spatial-temporal domain. This method reduces computational complexity by a factor of 98, while maintaining the storage requirement and the recognition accuracy, compared with some of the most recent approaches in the field. Experimental results applied on the Weizmann dataset confirm the excellent properties of the proposed algorithm. © 2010 IEEE

Human Action Recognition Employing Td2Dpca And Vq

A novel algorithm for human action recognition in the transform domain is presented. This approach is based on Two- Dimensional Principal Component Analysis (2DPCA) and Vector Quantization (VQ). This technique reduces the computational complexity and the storage requirement by at least a factor of 45.27, and 12 respectively, while achieving the highest recognition accuracy, compared with the most recently published approaches. Experimental results applied on the Weizmann dataset confirm the excellent properties of the proposed algorithm, which lends itself to real-time economic implementation. © 2010 IEEE

Highly Efficient Human Action Recognition Using Compact 2Dpca-Based Descriptors In The Spatial And Transform Domains

Human action recognition is considered as a challenging problem in the field of computer vision. Most of the reported algorithms are computationally expensive. In this paper, a novel system for human action recognition based on Two-Dimensional Principal Component Analysis (2DPCA) is presented. This method works directly on the optical flow and / or silhouette extracted from the input video in both the spatial domain and the transform domain. The algorithm reduces the computational complexity and storage requirements, while achieving high recognition accuracy, compared with the most recent reports in the field. Experimental results performed on the Weizmann action and the INIRIA IXMAS datasets confirm the excellent properties of the proposed algorithm. © 2011 IEEE

Simultaneous Human Detection And Action Recognition Employing 2Dpca-Hog

In this paper a novel algorithm for Human detection and action recognition in videos is presented. The algorithm is based on Two-Dimensional Principal Components Analysis (2DPCA) applied to Histogram of Oriented Gradients (HOG). Due to simultaneous Human detection and action recognition employing the same algorithm, the computational complexity is reduced to a great deal. Experimental results applied to public datasets confirm these excellent properties compared to most recent methods. © 2011 IEEE

Molecular Characterization of Carbapenem/Colistin-Resistant Acinetobacter baumannii Clinical Isolates from Egypt by Whole-Genome Sequencing

Purpose: The rise of carbapenem-resistant A. baumannii (CRAB) is considered a public health problem limiting the treatment options. Our current work studied the emergence and mechanisms of colistin-resistance among CRAB isolates in Egypt. Materials and Methods: Seventeen clinically recovered A. baumannii were identified and screened for their antimicrobial susceptibilities using VITEK-2 system. Colistin susceptibility was evaluated using broth microdilution, and characterization of carbapenem/colistin resistance determinants was performed using whole-genome sequencing (Illumina MiSeq). Results: About 52.9% (9/17) were colistin-resistant. PCR results revealed that all isolates carried bla(OXA-51-like genes), bla(OXA-23-like) was detected in 82.3% (14/17) and bla(NDM) in 23.5% (4/17). Two isolates harboured bla(GES-35) and bla(OXA-23). Furthermore, genome analysis of seven isolates revealed six belonged to international clone 2 (IC2) while the remaining isolate was a singleton (ST158), representing a clone circulating in Mediterranean/Middle Eastern countries. Conclusion: The emergence and high incidence of colistin-resistance among CRAB clinical isolates in Egypt are alarming because it further limits therapy options and requires prudent antimicrobial stewardship and stringent infection control measures. Whole-genome sequence analyses suggest that the resistance to colistin was associated with multiple mutations in the pmrCAB genes. The high incidence of the high-risk lineage IC2 harbouring bla(OXA-23-like) as well as bla(NDM) is also of concern

Preoperative predictive parameters for accurate detection of stage IV endometriosis

Abstract Background Surgery is the main line of treatment of endometriosis. Patients with stage IV endometriosis have more extensive adhesions, which make the surgery difficult. There are no accurate non-invasive predictive preoperative parameters of stage IV endometriosis and no consensus has been reached. Therefore, the aim of the present study was to evaluate and detect preoperative non-invasive parameters for the detection of stage IV endometriosis. Patients and methods In the present study, we included 150 females admitted for surgical removal of endometriosis. We scored and classified endometriosis into four stages according to the revised ASRM classification. We compared between baseline characteristics of patients with different stages of endometriosis, and then we selected the best combination of diagnostic and predictive parameters of stage IV endometriosis. Results Predictors of stage IV endometriosis and indicators for safety surgery were as follows: VAS ≥ 4 (p < 0.001), fixed uterus (p = 0.005), fixed ovarian cysts (p < 0.001), tender uterosacral ligament nodule (p < 0.001), tender rectovaginal septum nodule (p = 0.003), bilateral endometriosis (p < 0.001), and sum of sizes of endometriotic nodules (p < 0.001). Conclusion Fixed uterus, fixed ovarian cysts, tender uterosacral ligament nodule, tender rectovaginal septum nodule, bilateral endometriosis, and indications for surgery were significantly considered adequate predictive markers for stage IV endometriosis

Antidiabetic and Antioxidant Impacts of Desert Date (Balanites aegyptiaca) and Parsley (Petroselinum sativum) Aqueous Extracts: Lessons from Experimental Rats

Medicinal plants are effective in controlling plasma glucose level with minimal side effects and are commonly used in developing countries as an alternative therapy for the treatment of type 1 diabetes mellitus. The aim of this study is to evaluate the potential antidiabetic and antioxidant impacts of Balanites aegyptiaca and Petroselinum sativum extracts on streptozotocin-induced diabetic and normal rats. The influences of these extracts on body weight, plasma glucose, insulin, total antioxidant capacity (TAC), malondialdehyde (MDA) levels, and liver-pyruvate kinase (L-PK) levels were assessed. Furthermore, the weight and histomorphological changes of the pancreas were studied in the different experimental groups. The herbal preparations significantly reduced the mean plasma glucose and MDA levels and significantly increased the mean plasma insulin, L-PK, and TAC levels in the treated diabetic groups compared to the diabetic control group. An obvious increase in the weight of the pancreas and the size of the islets of Langerhans and improvement in the histoarchitecture were evident in the treated groups compared to untreated ones. In conclusion, the present study provides a scientific evidence for the traditional use of these extracts as antidiabetic and antioxidant agents in type 1 diabetes mellitus

A guide for evaluation of online learning in medical education: a qualitative reflective analysis

Abstract Background With the strike of Covid-19, an unprecedented rapid shift to remote learning happened worldwide with a paradigm shift to online learning from an institutional adjuvant luxury package and learner choice into a forced solo choice. This raises the question of quality assurance. While some groups have already established standards for online courses, teaching and programs yet very little information is included on methodology of their development and very little emphasis is placed on the online learning experience. Nevertheless, no work has been done specifically for medical education institutions. Aim To develop a set of descriptors for best practice in online learning in medical education utilizing existing expertise and needs. Methods This work utilizes a qualitative multistage approach to identify the descriptors of best practice in online learning starting with a question guided focus group, thematic analysis, Delphi technique and an expert consensus session done simultaneously for triangulation. This was done involving 32 institution in 19 countries. Results This materialized into the development of a set of standards, indicators, and development of a checklist for each standard area. The standard areas identified were organizational capacity, educational effectiveness, and human resources each of which listed a number of standards. Expert consensus sessions identified the need for qualification of data and thus the development of indicators for best practice. Conclusion Standards are needed for online learning experience and their development and redesign is situational and needs to be enhanced methodologically in axes that are pertaining to the needs of the education community. Taking such axes into consideration by educators and institutions will lead to planning and implementing successful online learning activities, while taking them into consideration by the evaluators will help them conduct comprehensive audits and provide stakeholders with highly informative evaluation reports