Circumcision with the Plastibell Device in Hooded Prepuce or Glanular Hypospadias

Purpose. To retrospectively review our experience in infants with glanular hypospadias or hooded prepuce without meatal anomaly, who underwent circumcision with the plastibell device. Although circumcision with the plastibell device is well described, there are no reported experiences pertaining to hooded prepuce or glanular hypospadias that have been operated on by this technique. Materials and Methods. Between September 2002 and September 2008, 21 children with hooded prepuce (age 1 to 11 months, mean 4.6 months) were referred for hypospadias repair. Four of them did not have meatal anomaly. Their parents accepted this small anomaly and requested circumcision without glanuloplasty. In all cases, the circumcision was corrected by a plastibell device. Results. No complications occurred in the circumcised patients, except delayed falling of bell in one case that was removed by a surgeon, after the tenth day. Conclusion. Circumcision with the plastibell device is a suitable method for excision of hooded prepuce. It can also be used successfully in infants, who have miniglanular hypospadias, and whose parents accepted this small anomaly

Near-Optimal Straggler Mitigation for Distributed Gradient Methods

Modern learning algorithms use gradient descent updates to train inferential models that best explain data. Scaling these approaches to massive data sizes requires proper distributed gradient descent schemes where distributed worker nodes compute partial gradients based on their partial and local data sets, and send the results to a master node where all the computations are aggregated into a full gradient and the learning model is updated. However, a major performance bottleneck that arises is that some of the worker nodes may run slow. These nodes a.k.a. stragglers can significantly slow down computation as the slowest node may dictate the overall computational time. We propose a distributed computing scheme, called Batched Coupon's Collector (BCC) to alleviate the effect of stragglers in gradient methods. We prove that our BCC scheme is robust to a near optimal number of random stragglers. We also empirically demonstrate that our proposed BCC scheme reduces the run-time by up to 85.4% over Amazon EC2 clusters when compared with other straggler mitigation strategies. We also generalize the proposed BCC scheme to minimize the completion time when implementing gradient descent-based algorithms over heterogeneous worker nodes

Up-regulation of uPARAP/Endo180 during culture activation of rat hepatic stellate cells and its presence in hepatic stellate cell lines from different species

<p>Abstract</p> <p>Background</p> <p>The urokinase plasminogen activator receptor associated protein (uPARAP)/Endo180 is a novel endocytic receptor that mediates collagen uptake and is implicated to play a role in physiological and pathological tissue-remodelling processes by mediating intracellular collagen degradation.</p> <p>Result</p> <p>This study investigates the expression of uPARAP/Endo180 protein and messenger RNA in primary rat hepatic stellate cell (HSC) cultures. The results show that uPARAP/Endo180 protein is not expressed in freshly isolated HSCs or during the first few days of culture while the cells still display quiescent features. In contrast, uPARAP/Endo180 protein is expressed early during HSC activation when cells are transdifferentiated into myofibroblast-like cells. Very low levels of uPARAP/Endo180 mRNA are detectable during the first days of culture but uPARAP/Endo180 mRNA is strongly up-regulated with increasing time in culture. Moreover, endocytic uptake of denatured collagen increases as transdifferentiation proceeds over time and correlates with increased expression of uPARAP/Endo180. Finally, analysis of uPARAP/Endo180 expression in four hepatic stellate cell lines from three different species showed that all these cell lines express uPARAP/Endo180 and are able to take up denatured collagen efficiently.</p> <p>Conclusion</p> <p>These results demonstrate that uPARAP/Endo180 expression by rat HSCs is strongly up-regulated during culture activation and identify this receptor as a feature common to culture-activated HSCs.</p

Deep Learning Based Forecasting of COVID-19 Hospitalisation in England:A Comparative Analysis

In the midst of the COVID-19 pandemic, it was essential to accurately forecast the demand for hospitalisation resources to achieve an effective allocation of healthcare resources. This paper explores the potential of various Deep Learning (DL) models, namely basic Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRU), Bidirectional RNNs, and Sequence-to-Sequence architectures with the inclusion of attention mechanisms, to forecast the demand for hospitalisation resources (mechanical ventilators) in England during the COVID-19 pandemic. The implementation of simulated annealing (SA) as a hyperparameter tuning method produced certain model structures and good results in terms of prediction accuracy. Our findings show that the LSTM-based models (LSTM_SA), achieved the lowest mean average error (MAE), outperforming other architectures used in this study. The results of this study show the potential of DL models to forecast the demand for resources and could help inform the distribution of hospitalisation resources in England during the COVID-19 pandemic.</p

Intracranial Aneurysms: Review of Current Treatment Options and Outcomes

Intracranial aneurysms are present in roughly 5% of the population, yet most are often asymptomatic and never detected. Development of an aneurysm typically occurs during adulthood, while formation and growth are associated with risk factors such as age, hypertension, pre-existing familial conditions, and smoking. Subarachnoid hemorrhage, the most common presentation due to aneurysm rupture, represents a serious medical condition often leading to severe neurological deficit or death. Recent technological advances in imaging modalities, along with increased understanding of natural history and prevalence of aneurysms, have increased detection of asymptomatic unruptured intracranial aneurysms (UIA). Studies reporting on the risk of rupture and outcomes have provided much insight, but the debate remains of how and when unruptured aneurysms should be managed. Treatment methods include two major intervention options: clipping of the aneurysm and endovascular methods such as coiling, stent-assisted coiling, and flow diversion stents. The studies reviewed here support the generalized notion that endovascular treatment of UIA provides a safe and effective alternative to surgical treatment. The risks associated with endovascular repair are lower and incur shorter hospital stays for appropriately selected patients. The endovascular treatment option should be considered based on factors such as aneurysm size, location, patient medical history, and operator experience

Lagrange Coded Computing: Optimal Design for Resiliency, Security, and Privacy

We consider a scenario involving computations over a massive dataset stored distributedly across multiple workers, which is at the core of distributed learning algorithms. We propose Lagrange Coded Computing (LCC), a new framework to simultaneously provide (1) resiliency against stragglers that may prolong computations; (2) security against Byzantine (or malicious) workers that deliberately modify the computation for their benefit; and (3) (information-theoretic) privacy of the dataset amidst possible collusion of workers. LCC, which leverages the well-known Lagrange polynomial to create computation redundancy in a novel coded form across workers, can be applied to any computation scenario in which the function of interest is an arbitrary multivariate polynomial of the input dataset, hence covering many computations of interest in machine learning. LCC significantly generalizes prior works to go beyond linear computations. It also enables secure and private computing in distributed settings, improving the computation and communication efficiency of the state-of-the-art. Furthermore, we prove the optimality of LCC by showing that it achieves the optimal tradeoff between resiliency, security, and privacy, i.e., in terms of tolerating the maximum number of stragglers and adversaries, and providing data privacy against the maximum number of colluding workers. Finally, we show via experiments on Amazon EC2 that LCC speeds up the conventional uncoded implementation of distributed least-squares linear regression by up to 13.43×, and also achieves a 2.36×-12.65× speedup over the state-of-the-art straggler mitigation strategies