Machine Learning Methods for Brain Image Analysis

Understanding how the brain functions and quantifying compound interactions between complex synaptic networks inside the brain remain some of the most challenging problems in neuroscience. Lack or abundance of data, shortage of manpower along with heterogeneity of data following from various species all served as an added complexity to the already perplexing problem. The ability to process vast amount of brain data need to be performed automatically, yet with an accuracy close to manual human-level performance. These automated methods essentially need to generalize well to be able to accommodate data from different species. Also, novel approaches and techniques are becoming a necessity to reveal the correlations between different data modalities in the brain at the global level. In this dissertation, I mainly focus on two problems: automatic segmentation of brain electron microscopy (EM) images and stacks, and integrative analysis of the gene expression and synaptic connectivity in the brain. I propose to use deep learning algorithms for the 2D segmentation of EM images. I designed an automated pipeline with novel insights that was able to achieve state-of-the-art performance on the segmentation of the \textit{Drosophila} brain. I also propose a novel technique for 3D segmentation of EM image stacks that can be trained end-to-end with no prior knowledge of the data. This technique was evaluated in an ongoing online challenge for 3D segmentation of neurites where it achieved accuracy close to a second human observer. Later, I employed ensemble learning methods to perform the first systematic integrative analysis of the genome and connectome in the mouse brain at both the regional- and voxel-level. I show that the connectivity signals can be predicted from the gene expression signatures with an extremely high accuracy. Furthermore, I show that only a certain fraction of genes are responsible for this predictive aspect. Rich functional and cellular analysis of these genes are detailed to validate these findings

Evaluation of intralesional propranolol for periocular capillary hemangioma

Ahmed Awadein, Mohamed A FakhryCairo University Faculty of Medicine, Cairo, EgyptBackground: The purpose of this study was to evaluate the use of intralesional propranolol injection in the management of periocular capillary hemangioma.Methods: A prospective study was performed in 22 consecutive patients with periocular hemangioma. Twelve patients underwent intralesional propranolol injection and ten patients underwent intralesional triamcinolone injection. The size of the lesion was measured serially every week during the first month, every 2 weeks for the second month, and then monthly for another 2 months. The refractive error and degree of ptosis if present were measured before injection and at the end of the study.Results: There was reduction in the size of hemangioma, astigmatic error, and degree of ptosis in both groups. The difference in outcome between both groups was not statistically significant. Rebound growth occurred in 25% of the propranolol group and 30% of the steroid group but responded to reinjection. No adverse effects were reported during or after intralesional propranolol injection.Conclusion: Intralesional propranolol injection is an alternative and effective method for treatment of infantile periocular hemangioma.Keywords: propranolol, intralesional, periocular capillary hemangiom

Factors for failure of nonoperative management of blunt hepatosplenic trauma in children

Background Trauma is major cause of morbidity and mortality in children with blunt abdominal trauma; the most commonly injured organs are the liver and the spleen. A high rate of operative complications caused a shift from operative to nonoperative management (NOM) in patients suffering from hemodynamically stable blunt abdominal trauma. The aim is to evaluate factors for failure of NOM for blunt abdominal trauma that caused injuries of the liver and the spleen in children.Patients and methods This study included 142 patients with blunt abdominal trauma with either hepatic or splenic injuries that were hemodynamically stable and treated initially by NOM. Patients had undergone a contrast computed tomography (CT) scan for grading injuries, contrast blush, and hemoperitoneum.Results There were 17 patients with high-grade hepatic or splenic injury. Six of these 17 patients and two patients with low-grade injuries failed NOM. Moderate and large volumes of hemoperitoneum have been reported in 42 and nine patients, respectively, with failure rates of 7.1 and 44.4%. Fourteen patients had CT blush on CT scan; five of them failed NOM (failure rate of 35.7%). Two other patients needed laparotomy for intestinal injuries. Thus, the overall success rate of NOM was 93% (132 patients); 10 (7%) patients failed NOM.Conclusion High-grade injuries, large hemoperitoneum, and contrast blush on the CT scan increase the risk of failure of NOM in patients with blunt hepatosplenic injuries. Nevertheless, most of these patients can be successfully managed with NOM. However, other than hemodynamic instability, the other factors mentioned above deserve further evaluation to determine their ability to aid in the decision between operative and NOM for blunt hepatosplenic injuries in children.Keywords: hepatosplenic injury, nonoperative, blun

Assessment of The Nurse Interns' Medication Administration Safety Performance

Contents: Medication administration is an integral part of delivering quality nursing care. The nurse intern should follow the specific guidelines to enhance their medication administration safety.Aim: The study aims to assess the nurse intern's medication administration safety performance.Methods: The study was conducted at Ain Shams University Hospitals using the descriptive design on 90 nurse interns by using three tools, namely, the medication administration knowledge questionnaire, the observational checklist for nurse interns' safety performance, and the Medication Administration safety Attitude Rating Scale. Results: Findings of the study revealed that the minority of nurse interns (4.3%) had satisfactory total knowledge, 39% had adequate total practice, and around two-thirds of them (62.6%) had a positive attitude.Conclusion: It is concluded that the nurse interns had unsatisfactory knowledge of the medication administration safety, and their related practices are mostly inadequate, although the attitude tends to be high. The study recommended that nurse internship programs emphasize medication administration safety knowledge and practice, focusing on identified gaps and deficiencies. Further research is proposed to assess the effect of training strategies on the nurse intern's medication administration safety

Deep Models for Brain EM Image Segmentation: Novel Insights and improved Performance

Motivation: Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation. Results: In this work, we proposed a novel design of DNNs for this task. We trained a pixel classifier that operates on raw pixel intensities with no preprocessing to generate probability values for each pixel being a membrane or not. Although the use of neural networks in image segmentation is not completely new, we developed novel insights and model architectures that allow us to achieve superior performance on EM image segmentation tasks. Our submission based on these insights to the 2D EM Image Segmentation Challenge achieved the best performance consistently across all the three evaluation metrics. This challenge is still ongoing and the results in this paper are as of June 5, 2015

Mitral valve annuloplasty with a homemade single-sized polytetrafluoroethylene band in degenerative mitral regurgitation

Background: Although mitral annuloplasty is an essential component of mitral repair, there remains little agreement on the ideal device to be used or the ideal sizing method.Objectives: The purpose of this study was to report the early clinical and echocardiographic outcomes of patients undergoing repair for degenerative mitral regurgitation using a homemade single-sized (65 mm) Polytetrafluoroethylene band, and comparing it to the use of commercially available complete rigid rings.Patients and methods: This is a retrospective study including 106 patients, who underwent mitral repair for degenerative mitral regurgitation at Cairo University Hospitals between February 2013 and July 2019. These patients were divided into 2 groups. Group (A) included 69 patients who underwent repair with a single-sized band, and group (B) included 37 patients whose repair included the use of a commercial rigid ring. The primary endpoint was freedom from significant mitral regurgitation at one-year follow-up. Secondary endpoints included mean mitral valve gradient measured postoperatively, and freedom from reoperation at one year.Results: There was no statistically significant difference between both groups in any of the above-mentioned endpoints being examined.Conclusion: The use of a single-sized Polytetrafluoroethylene band for annuloplasty in degenerative mitral disease showed satisfactory results comparable to the commercial rigid rings. Further studies with longer follow-up are needed to confirm the durability of mitral repair using this technique

GENER: A Parallel Layer Deep Learning Network To Detect Gene-Gene Interactions From Gene Expression Data

Detecting and discovering new gene interactions based on known gene expressions and gene interaction data presents a significant challenge. Various statistical and deep learning methods have attempted to tackle this challenge by leveraging the topological structure of gene interactions and gene expression patterns to predict novel gene interactions. In contrast, some approaches have focused exclusively on utilizing gene expression profiles. In this context, we introduce GENER, a parallel-layer deep learning network designed exclusively for the identification of gene-gene relationships using gene expression data. We conducted two training experiments and compared the performance of our network with that of existing statistical and deep learning approaches. Notably, our model achieved an average AUROC score of 0.834 on the combined BioGRID&DREAM5 dataset, outperforming competing methods in predicting gene-gene interactions

Global Analysis of Gene Expression and Projection Target Correlations in the Mouse Brain

Recent studies have shown that projection targets in the mouse neocortex are correlated with their gene expression patterns. However, a brain-wide quantitative analysis of the relationship between voxel genetic composition and their projection targets is lacking to date. Here we extended those studies to perform a global, integrative analysis of gene expression and projection target correlations in the mouse brain. By using the Allen Brain Atlas data, we analyzed the relationship between gene expression and projection targets. We first visualized and clustered the two data sets separately and showed that they both exhibit strong spatial autocorrelation. Building upon this initial analysis, we conducted an integrative correlation analysis of the two data sets while correcting for their spatial autocorrelation. This resulted in a correlation of 0.19 with significant p value. We further identified the top genes responsible for this correlation using two greedy gene ranking techniques. Using only the top genes identified by those techniques, we recomputed the correlation between these two data sets. This led to correlation values up to 0.49 with significant p values. Our results illustrated that although the target specificity of neurons is in fact complex and diverse, yet they are strongly affected by their genetic and molecular compositions

Improving the Dielectric Properties of High Density Polyethylene by Incorporating Clay-Nanofiller

Polymer nanocomposites have been used for various important industrial applications. The preparation of high density polyethylene composed with Na-montmorillonite nanofiller using melt compounding method for different concentrations of clay-nano filler of 0%, 2%, 6%, 10%, and 15% has been successfully done. The morphology of the obtained samples was optimized and characterized by scanning electron microscope showing the formation of the polymer nanocomposites. The thermal stability and dielectric properties were measured for the prepared samples. Thermal gravimetric analysis results show that thermal stability in polymer nanocomposites is more than that in the base polymer. It has been shown that the polymer nanocomposites exhibit some very different dielectric characteristics when compared to the base polymer. The dielectric breakdown strength is enhanced by the addition of clay-nano filler. The dielectric constant (εr) and dissipation factor (Tan δ) have been studied in the frequency range 200 Hz to 2 MHz at room temperature indicating that enhancements have been occurred in εr and Tan δ by the addition of clay-nano filler in the polymer material when compared with the pure material

An Eclectic Model for the Stylistic Exploration of Mind Style in Fiction

Grounded in Halliday’s systemic functional approach to the study of language and his three metafunctions of language, a new model called an Eclectic Model of Mind Style (EMMS) is presented in this thesis. Its building involved the examination of existing research on mind style and further systematic incorporation of some of the existing concepts, approaches and methodologies into one overarching model that could assist scholars in a more comprehensive understanding of the character’s mind style. The goal of the model is to provide an analytical tool for stylistic analysis of the fictional characters’ mind styles by demonstrating various stylistic effects used by authors in depiction of fictional characters in their novels. The building of the model involved two stages: the first stage comprised the detailed review of the scholarly research on the notion of mind style with the focus on the workings of the deviant minds. During this process, the outlines of the new model including its major categories have gradually emerged and finally, the EMMS has been built to be used as an inclusive analytical tool for stylistic analysis. Testing the proposed model by applying it to the analysis of the two selected fictional characters has become the next logical step bringing forth the second stage of the thesis writing process. During this stage, the two novels and their main characters have been chosen, namely: Christopher Boone in Mark Haddon’s (2003) The Curious Incident of the Dog in the Night-Time and Don Tillman in Graeme Simsion’s (2013) The Rosie Project. The primary focus of the analysis has been on exemplifying application of the EMMS categories to identifying the foregrounded use of stylistic features by the two characters and testing the EMMS analytical potential. The findings show the EMMS analytical potential for stylistic research and possible use in other areas of language studies, as well as the necessity for its further testing