Enhancing the functional content of protein interaction networks

Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, they face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we explore the use of the concept of common neighborhood similarity (CNS), which is a form of local structure in networks, to address these issues. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of S. cerevisiae interactions, and a set of 136 GO terms, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the $HC.cont$ measure proposed here performs particularly well for this task. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures, especially $HC.cont$, to prune out noisy edges and introduce new links between functionally related proteins

PBES: PCA Based Exemplar Sampling Algorithm for Continual Learning

We propose a novel exemplar selection approach based on Principal Component Analysis (PCA) and median sampling, and a neural network training regime in the setting of class-incremental learning. This approach avoids the pitfalls due to outliers in the data and is both simple to implement and use across various incremental machine learning models. It also has independent usage as a sampling algorithm. We achieve better performance compared to state-of-the-art methods

DSS: A Diverse Sample Selection Method to Preserve Knowledge in Class-Incremental Learning

Rehearsal-based techniques are commonly used to mitigate catastrophic forgetting (CF) in Incremental learning (IL). The quality of the exemplars selected is important for this purpose and most methods do not ensure the appropriate diversity of the selected exemplars. We propose a new technique "DSS" -- Diverse Selection of Samples from the input data stream in the Class-incremental learning (CIL) setup under both disjoint and fuzzy task boundary scenarios. Our method outperforms state-of-the-art methods and is much simpler to understand and implement

RTRA: Rapid Training of Regularization-based Approaches in Continual Learning

Catastrophic forgetting(CF) is a significant challenge in continual learning (CL). In regularization-based approaches to mitigate CF, modifications to important training parameters are penalized in subsequent tasks using an appropriate loss function. We propose the RTRA, a modification to the widely used Elastic Weight Consolidation (EWC) regularization scheme, using the Natural Gradient for loss function optimization. Our approach improves the training of regularization-based methods without sacrificing test-data performance. We compare the proposed RTRA approach against EWC using the iFood251 dataset. We show that RTRA has a clear edge over the state-of-the-art approaches

Cloud Computing: Applications, Challenges and Open Issues

Cloud computing is one of the innovative computing, which deals with storing and accessing data and programs over the Internet [1]. It is the delivery of computing resources and services, such as storing of data on servers and databases, providing networking facilities and software development platforms over the Internet. It provides the flexibility of resources for everyone. These services are provided via data centers, which are located in various parts of the world [2, 3]. Cloud computing makes access to these resources to everyone on a global scale at a very minimal cost and significantly higher speed. These servers provide services to the users, which would have cost a lot of computational power to them if they had to buy them. The first mention of cloud computing was referenced in a Compaq internal document released in 1996 [4]. Cloud computing was then commercialized in 2006 when Amazon released elastic compute cloud (EC2). Furthermore, Google released Google app engine in 2008 and Microsoft Azure services were launched in October 2008, which increased the competition in the area of cloud computing. Since then these companies have done a lot of development in cloud computing

Mefenamic acid and diclofenac in the treatment of menorrhagia and dysmenorrhea in dysfunctional uterine bleeding: a randomized comparative study

Background: There is a perception that Mefenamic Acid should be the preferred NSAID for menorrhagia. However, there are insufficient evidences to prove this. Further RCTs are required to compare individual NSAIDs.Purposes of the study were to assess and compare the efficacy of mefenamic acid and diclofenac in control of menorrhagia in patients with DUB, to assess and compare their analgesic effects in dysmenorrhea associated with DUB and to study their adverse effects.Methods: Sixty-eight patients were randomized into either Mefenamic Acid (n=34) or Diclofenac (n=34) group. Efficacy variables (Pictorial Blood loss Assessment Chart quantification, Number of pads used, Number of days of menstrual bleeding, Visual Analog Scale score) and adverse effects were recorded over three menstrual cycles.Results: The median reduction of menorrhagia with Mefenamic Acid was 43.39% (Range: 2.86% to 94.4%) and for Diclofenac was 57.5% (Range: 9.9% to 93.58%). The Diclofenac group showed a statistically significant decrease in median bleeding volume, median number of pads used and median number of days of bleeding compared to the Mefenamic Acid group (p<0.05, CI = 95%) but did not show a statistically significant decrease in median VAS score compared to the Mefenamic Acid group. Adverse effects with both groups were mild.Conclusions: Mefenamic Acid and Diclofenac individually managed to significantly reduce excessive bleeding compared to baseline. Diclofenac fared better than Mefenamic Acid in terms of control of excessive menstrual bleeding. Both these agents were able to reduce the menstrual pain and on comparison, were found to be equi-efficacious

An Unusual Cause of Camptocormia

Background: Camptocormia is defined as forward flexion of the spine that manifests during walking and standing and disappears in recumbent position. The various etiologies include idiopathic Parkinson’s disease, multiple system atrophy, myopathies, degenerative joint disease, and drugs. Case Report: A 67-year-old diabetic female presented with bradykinesia and camptocormia that started 1 year prior to presentation. Evaluation revealed levosulpiride, a dopamine receptor blocker commonly used for dyspepsia, to be the culprit. Discussion: It is well known that dopamine receptor blockers cause parkinsonism and tardive syndromes. We report a rare and unusual presentation of camptocormia attributed to this commonly used gastrointestinal drug in the Asian population