Automatic Detection of Fake Profiles in Online Social Networks

In the present generation, the social life of everyone has become associated with the online social networks. These sites have made a drastic change in the way we pursue our social life. Making friends and keeping in contact with them and their updates has become easier. But with their rapid growth, many problems like fake profiles, online impersonation have also grown. There are no feasible solution exist to control these problems. In this project, we came up with a framework with which automatic detection of fake profiles is possible and is efficient. This framework uses classification techniques like Support Vector Machine, Naïve Bayes and Decision trees to classify the profiles into fake or genuine classes. As, this is an automatic detection method, it can be applied easily by online social networks which has millions of profile whose profiles can not be examined manually

Rare Occurrence of Primary Gastric Lymphoma:

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Paraoxanase-1 Modulates Cardiotonic Steroid Induced Cardiac Inflammation and Fibrosis in Dahl Salt Sensitive Model of Chronic Kidney Disease

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Dynamic Wetting and Dewetting: Comparison of Experiment with Theories

The dynamics wetting/dewetting of a metal surface by distilled water drop was studied experimentally. The advancing and receding dynamic contact angles were obtained as a function of a contact line speed. The hydrodynamic and molecular-kinetic models have been applied to the experimental data to interpret the obtained results. The independent variables of the molecular-kinetic and hydrodynamic models, and the determination coefficient were determined by fitting procedure. The receding contact angles are found to be fitted better to the wetting models in comparison with the advancing dynamic contact angles

Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways.

The molecular and cellular processes that lead to renal damage and to the heterogeneity of lupus nephritis (LN) are not well understood. We applied single-cell RNA sequencing (scRNA-seq) to renal biopsies from patients with LN and evaluated skin biopsies as a potential source of diagnostic and prognostic markers of renal disease. Type I interferon (IFN)-response signatures in tubular cells and keratinocytes distinguished patients with LN from healthy control subjects. Moreover, a high IFN-response signature and fibrotic signature in tubular cells were each associated with failure to respond to treatment. Analysis of tubular cells from patients with proliferative, membranous and mixed LN indicated pathways relevant to inflammation and fibrosis, which offer insight into their histologic differences. In summary, we applied scRNA-seq to LN to deconstruct its heterogeneity and identify novel targets for personalized approaches to therapy

Inferior mesenteric vein thrombophlebitis secondary to acute diverticulitis

Inferior mesenteric vein thrombophlebitis is an uncommon condition. Most cases of portal-mesenteric thrombophlebitis affect either the portal vein or superior mesenteric vein; it is not known why the inferior mesenteric vein is less affected. Thrombophlebitis typically occurs following inflammatory intra-abdominal processes, such as diverticulitis. Diverticulitis is a common condition in the Western world, with several common complications, such as fistula formation and bowel wall perforation. However, although diverticulitis is a common cause of portal-mesenteric thrombophlebitis, thrombophlebitis is still a rare complication of diverticulitis. We present a case of diverticulitis complicated with interior mesenteric vein thrombophlebitis with gas extension into the portal vein

Case report of rare primary gastric large B-cell lymphoma

Gastric large B-cell lymphoma is rare and can be challenging to diagnose due to its nonspecific presentation. Primary gastric large B-cell lymphoma is rare, especially compared to systemic disease with gastric involvement. In this case, an 85-year-old female was brought to the ER with abdominal pain, as well as a history of nausea, constipation, and weight loss. CT imaging showed thickening of the anterior wall of the stomach accompanied by inflammatory changes. Esophagogastroduodenoscopy revealed a 7-8 cm “half circumferential necrotic” ulcer suggestive of malignancy. Biopsy confirmed this to be gastric large B-cell lymphoma. Subsequent PET-CT showed no metastasis. This case illustrates the value of imaging in diagnosing this unusual condition

Mercury poisoning from artisanal gold mining equipment

Artisanal and small-scale gold mining uses mercury to isolate gold from ore. Although uncommon in the United States, it is more common in poor and undeveloped countries. This practice requires heating mercury, which vaporizes into an odorless gas that can be inspired and absorbed into the blood. Inspired mercury vapors place individuals at risk of acute mercury toxicity and its subsequent chronic sequelae. We report a case of incidentally detected mercury foreign bodies in a 56-year-old male with a prior history of accidental mercury poisoning due to prior contact with artisanal gold mining equipment

An Efficient Practical Non-Blocking PageRank Algorithm for Large Scale Graphs

PageRank algorithm is a benchmark for many graph analytics and is the underlying kernel for link predictions, recommendation systems. It is an iterative algorithm that updates ranks of pages until the value converges. Implementation of PageRank algorithm on a shared memory architecture while taking advantage of fine-grained parallelism using large-scale graphs is a challenging task. In this paper, We present parallel algorithms for computing the PageRank suitable to the shared memory systems. Initially, we present parallel implementations of page-rank algorithms using barrier and lock variants. Later, we propose new approaches which are lock-free and are barrier-less synchronization to overcome the issues of lock based methods.A detailed experimental analysis of our approach is carried out using real-world web graphs from SNAP and Synthetic Graphs from RMAT on an Intel(R) Xeon E5-2660 v4 processor architecture with 56 threads using the POSIX thread library

An Improved/Optimized Practical Non-Blocking PageRank Algorithm for Massive Graphs*

PageRank kernel is a standard benchmark addressing various graph processing and analytical problems. The PageRank algorithm serves as a standard for many graph analytics and a foundation for extracting graph features and predicting user ratings in recommendation systems. The PageRank algorithm is an iterative algorithm that continuously updates the ranks of pages until it converges to a value. However, implementing the PageRank algorithm on a shared memory architecture while taking advantage of fine-grained parallelism with large-scale graphs is hard to implement. The experimental study and analysis of the parallel PageRank metric on large graphs and shared memory architectures using different programming models have been studied extensively. This paper presents the asynchronous execution of the PageRank algorithm to leverage the computations on massive graphs, especially on shared memory architectures. We evaluate the performance of our proposed non-blocking algorithms for PageRank computation on real-world and synthetic datasets using POSIX Multithreaded Library on a 56 core Intel(R) Xeon processor. We observed that our asynchronous implementations achieve 10 × to 30 × speed-up with respect to sequential runs and 5 × to 10 × improvements over synchronous variants. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature