52. Cardiac injury in infants with acute gastroenteritis: Is it ischemia or rota associated carditis

Reports suggested that rotavirus could be found in extra-intestinal tissues including the heart following infection and fatal rotavirus myocarditis has been recently reported in 2 children. We hypothesized that rotavirus may have a direct injurious effect on the myocardium of infants and this injury can be detected by the presence of cardiac troponin I (TnI). Methods: Over 8 weeks period, 50 of 150 infants(5–18 months) with acute gastroenteritis were found to have human rotavirus (HRV) gastroenteritis with rotavirus antigenemia. Sera of 150 infants were analyzed for TnI. If TnI value was above the screening limit (0.05 ng/ml), electrocardiogram (ECG) and cardiac ultrasound were performed. Infants with primary conditions associated with elevated TnI were excluded. Results: Thirty four infants (22.6%) had elevated TnI (0.06–2.5 ng/ml), 16 (47%) of them had HRV-GE (p = 0.054). However, none of them had any sign of myocarditis or ischemia in their ECG or cardiac ultrasound scan and their TnI levels normalized within 24–72 h after correction of dehydration. Infants less than 1 year ,and those with dehydration, anemia or acidosis were more prone to have elevated cTnI (p = 0.008, 0.009, 0.006, 0.001, respectively). Multivariate logistic regression analysis, showed that severe dehydration and acidosis are still significantly associated with elevated TnI levels (adjusted OR, 95% CI = 22.9, 2.19–239 and 20.76, 6.15–70, respectively. Conclusion: Our study is the first pediatric study to show that myocardial injury occurs in infants with gastroenteritis and this injury was precipitated by transient ischemia which may go unnoticed on the ECG. However, we could not document rotavirus myocarditi

The Degree to Which Kindergarten Principals in the Green Line Possess Administrative Competencies and Their Relationship to the Level of Quality Management Implementation درجة امتلاك مديرات رياض الأطفال داخل الخط الأخضر للكفايات الإدارية وعلاقتها بمستوى تطبيق إدارة الجودة

Abstract: The study aimed to shed light on the degree to which kindergarten principals within the Green Line possess administrative competencies, and to identify the correlation between the availability of administrative competencies among kindergarten principals within the Green Line, and the level of application of quality management in their work. To achieve the aims of the study and to answer its questions, a relational descriptive methodology was used, and the study sample was chosen in a simple random way representing the study population and included (600) kindergarten principals, (60) complementary nurses, (59) female counselors and (27) educational supervisors, who showed The results showed that the degree of kindergarten principals within the Green Line possess the administrative competencies necessary for their work from the point of view of the sample came to a large degree, and the results also showed that the level of application of quality management to kindergarten principals within the Green Line from their point of view came to a large degree, as the results showed a positive relationship statistically significant between estimates of the study sample individuals to own kindergarten principals within the Green Line administrative efficiencies on the one hand and the level of application of quality management in their work on the other hand, where the link between them reached coefficient (0.73). ملخص: تهدف الدراسة إلى تسليط الضوء على درجة امتلاك مديرات رياض الأطفال داخل الخط الأخضر للكفايات الإدارية, والتعرف إلى العلاقة الارتباطية بين توافر الكفايات الإدارية لدى مديرات رياض الأطفال داخل الخط الأخضر, ومستوى تطبيق إدارة الجودة في عملهن. لتحقيق أهداف الدراسة وللإجابة عن أسئلتها تم استخدام المنهج الوصفي الارتباطي, وتم اختيار عينة الدراسة بالطريقة العشوائية البسيطة ممثلة لمجتمع الدراسة واشتملت على (600) مديرة روضة, و(60) مربية مكملة, و(59) مرشدة و(27) مشرفة تربوية, أظهرت النتائج أن درجة امتلاك مديرات رياض الأطفال داخل الخط الأخضر للكفايات الإدارية اللازمة لعملهن من وجهة نظر العينة جاءت بدرجة كبيرة, كما أظهرت النتائج أن مستوى تطبيق إدارة الجودة لدى مديرات رياض الأطفال داخل الخط الأخضر من وجهة نظرهن جاءت بدرجة كبيرة أيضا, كما أظهرت النتائج وجود علاقة موجبة ذات دلالة إحصائية بين تقديرات أفراد عينة الدراسة على امتلاك مديرات رياض الأطفال داخل الخط الأخضر للكفايات الإدارية من جهة ومستوى تطبيق إدارة الجودة في عملهن من جهة اخرى, إذ بلغ معامل الارتباط بينهما (0.73 (

Digital Hebrew Paleography: Script Types and Modes

Paleography is the study of ancient and medieval handwriting. It is essential for understanding, authenticating, and dating historical texts. Across many archives and libraries, many handwritten manuscripts are yet to be classified. Human experts can process a limited number of manuscripts; therefore, there is a need for an automatic tool for script type classification. In this study, we utilize a deep-learning methodology to classify medieval Hebrew manuscripts into 14 classes based on their script style and mode. Hebrew paleography recognizes six regional styles and three graphical modes of scripts. We experiment with several input image representations and network architectures to determine the appropriate ones and explore several approaches for script classification. We obtained the highest accuracy using hierarchical classification approach. At the first level, the regional style of the script is classified. Then, the patch is passed to the corresponding model at the second level to determine the graphical mode. In addition, we explore the use of soft labels to define a value we call squareness value that indicates the squareness/cursiveness of the script. We show how the graphical mode labels can be redefined using the squareness value. This redefinition increases the classification accuracy significantly. Finally, we show that the automatic classification is on-par with a human expert paleographer

Understanding Unsupervised Deep Learning for Text Line Segmentation

We propose an unsupervised feature learning approach for segmenting text lines of handwritten document images with no labelling effort. Humans can easily group local text line features to global coarse patterns. We leverage this coherent visual perception of text lines as a supervising signal by formulating the feature learning as a global pattern differentiation task. The machine is trained to detect whether a document patch contains a similar global text line pattern with its identity or neighbours, and a different global text line pattern with its 90-degree-rotated identity or neighbours. Clustering the central windows of document image patches using their extracted features, forms blob lines which strike through the text lines. The blob lines guide an energy minimization function for extracting text lines in a binary image and guide a seam carving function for detecting baselines in a colour image. In identifying the aspect of the input patch that supports the actual prediction and clustering, we contribute toward the understanding of input patch functionality. We evaluate the method on several variants of text line segmentation datasets to demonstrate its effectiveness, visualize what it has learned, and enable it to comprehend its clustering strategy from a human perspective

Understanding Unsupervised Deep Learning for Text Line Segmentation

We propose an unsupervised feature learning approach for segmenting text lines of handwritten document images with no labelling effort. Humans can easily group local text line features to global coarse patterns. We leverage this coherent visual perception of text lines as a supervising signal by formulating the feature learning as a global pattern differentiation task. The machine is trained to detect whether a document patch contains a similar global text line pattern with its identity or neighbours, and a different global text line pattern with its 90-degree-rotated identity or neighbours. Clustering the central windows of document image patches using their extracted features, forms blob lines which strike through the text lines. The blob lines guide an energy minimization function for extracting text lines in a binary image and guide a seam carving function for detecting baselines in a colour image. In identifying the aspect of the input patch that supports the actual prediction and clustering, we contribute toward the understanding of input patch functionality. We evaluate the method on several variants of text line segmentation datasets to demonstrate its effectiveness, visualize what it has learned, and enable it to comprehend its clustering strategy from a human perspective

Text Line Extraction in Historical Documents Using Mask R-CNN

Text line extraction is an essential preprocessing step in many handwritten document image analysis tasks. It includes detecting text lines in a document image and segmenting the regions of each detected line. Deep learning-based methods are frequently used for text line detection. However, only a limited number of methods tackle the problems of detection and segmentation together. This paper proposes a holistic method that applies Mask R-CNN for text line extraction. A Mask R-CNN model is trained to extract text lines fractions from document patches, which are further merged to form the text lines of an entire page. The presented method was evaluated on the two well-known datasets of historical documents, DIVA-HisDB and ICDAR 2015-HTR, and achieved state-of-the-art results. In addition, we introduce a new challenging dataset of Arabic historical manuscripts, VML-AHTE, where numerous diacritics are present. We show that the presented Mask R-CNN-based method can successfully segment text lines, even in such a challenging scenario