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Mechanistic targets for BPH and prostate cancer-a review

Abhishek Shah, Aarti Abhishek Shah, Nandakumar K, Richard Lobo
2020 Reviews on Environmental Health  
All men, almost, suffer from prostatic disorders in average life expectancy. In the year of 1950s, the first autopsy of prostate gland discovered the link between Benign prostatic hyperplasia (BPH) and Prostate Cancer (PCa). After that, many histology, biochemistry, epidemiology studies explained the association and associated risk factor for the same. From the various scientific evidence, it is proved that both diseases share some common transcription factors and signalling pathways. Still,
more » ... cannot be considered as the first step of PCa progression. To define, the relationship between both of the diseases, a well-defined large epidemiological study is needed. Along with androgen signalling, imbalanced apoptosis, oxidative stress, and microbial infection also crucial factors that significantly affect the pathogenesis of BPH. Various signalling pathways are involved in the progression of BPH. Androgen signalling is the driving force for the progress of PCa. In PCa androgen signalling is upregulated as compared to a healthy prostate. Some dominant Androgen-regulated genes and their functions have been discussed in this work.
doi:10.1515/reveh-2020-0051 pmid:32960781 fatcat:peps3oqnkndubnkfgazdvwhqvu

Conditional Hybrid GAN for Sequence Generation [article]

Yi Yu, Abhishek Srivastava, Rajiv Ratn Shah
2020 arXiv   pre-print
Conditional sequence generation aims to instruct the generation procedure by conditioning the model with additional context information, which is a self-supervised learning issue (a form of unsupervised learning with supervision information from data itself). Unfortunately, the current state-of-the-art generative models have limitations in sequence generation with multiple attributes. In this paper, we propose a novel conditional hybrid GAN (C-Hybrid-GAN) to solve this issue. Discrete sequence
more » ... ith triplet attributes are separately generated when conditioned on the same context. Most importantly, relational reasoning technique is exploited to model not only the dependency inside each sequence of the attribute during the training of the generator but also the consistency among the sequences of attributes during the training of the discriminator. To avoid the non-differentiability problem in GANs encountered during discrete data generation, we exploit the Gumbel-Softmax technique to approximate the distribution of discrete-valued sequences.Through evaluating the task of generating melody (associated with note, duration, and rest) from lyrics, we demonstrate that the proposed C-Hybrid-GAN outperforms the existing methods in context-conditioned discrete-valued sequence generation.
arXiv:2009.08616v1 fatcat:lg57jaoavzb2hkvuqqpaqlewg4

Panacea of challenges in real-world application of big data analytics in healthcare sector

Grishma Shah, Abhishek Shah, Manan Shah
2019 Journal of Data, Information and Management  
Furthermore, the usage of big data is widely espoused in personalized healthcare which provides an individual centric approach (Shah et al. 2015) .  ... 
doi:10.1007/s42488-019-00010-1 fatcat:v4wnkuw5rfddbefawhlwqfzg5u

Impact of Irrigation on India's Dairy Economy

Abhishek Rajan, Tushaar Shah
2020 Agriculture  
Shah et al. [33] have highlighted the abundant replenishable groundwater available for future irrigation development in this region.  ...  Shah [26] reported that dairy production, supported by groundwater-irrigated green fodder cultivation, emerged as a major farm diversification activity in the dry-land regions of Gujarat, Rajasthan,  ... 
doi:10.3390/agriculture10030053 fatcat:xp7vxabn5fdvpalfsqvy3ak5ay

Anti-acne activity of Achillea Moonshine petroleum ether extract

Shah Rahul, Patel Abhishek, Shah Mamta, Peethambaran Bela
2015 Journal of Medicinal Plants Research  
Achillea millefolium (yarrow) is a traditionally used plant to treat wounds. The present study was conducted to evaluate the anti-acne activity of Achillea 'Moonshine', a hybrid variety of Achillea. The plant was extracted in four solvents -petroleum ether, ethyl acetate, ethanol and water. These extracts were screened for anti-microbial, free radical scavenging, anti-tyrosinase, anti-inflammatory activity and cytotoxicity assays necessary to characterize its anti-acne activity. The most
more » ... ng activity was determined in the petroleum ether extract. The minimum inhibitory concentration (MIC) value for the petroleum ether extract was 0.83 mg/ml against Propioni bacterium acnes and 0.37 mg/ml against Staphylococcus. The minimum bactericidal concentration (MBC) value for petroleum ether was 0.83 and 0.75 mg/ml for P. acnes and Staphylococcus epidermidis, respectively. Though the ethyl acetate had a high flavonoid and phenolic content it was observed that the IC 50 values for the petroleum ether extract for free radical scavenging activity was 64.81 µg/ml, which was higher than ethyl acetate. Petroleum ether also showed tyrosinase inhibition at 0.033 mg/ml. The extract was also able to decrease the inflammatory cytokines like TNF-α and IL-8, and showed no cytotoxicity against dermal fibroblasts. These results suggest presence of active anti-acne phytochemicals in the petroleum ether extract, making it a novel plant candidate for the treatment of acne.
doi:10.5897/jmpr2015.5792 fatcat:hl3yuu3nbfgrzkybcslsqm64ue

Iterative Data Programming for Expanding Text Classification Corpora [article]

Neil Mallinar, Abhishek Shah, Tin Kam Ho, Rajendra Ugrani, Ayush Gupta
2020 arXiv   pre-print
Real-world text classification tasks often require many labeled training examples that are expensive to obtain. Recent advancements in machine teaching, specifically the data programming paradigm, facilitate the creation of training data sets quickly via a general framework for building weak models, also known as labeling functions, and denoising them through ensemble learning techniques. We present a fast, simple data programming method for augmenting text data sets by generating
more » ... ased weak models with minimal supervision. Furthermore, our method employs an iterative procedure to identify sparsely distributed examples from large volumes of unlabeled data. The iterative data programming techniques improve newer weak models as more labeled data is confirmed with human-in-loop. We show empirical results on sentence classification tasks, including those from a task of improving intent recognition in conversational agents.
arXiv:2002.01412v1 fatcat:soan2jxci5f2ddoank4bruvfya

Graphical Password Authentication System Using Modified Intuitive Approach

Abhishek Narayan Shah, Dipti Anand, Sabyasachi Samanta, Dipankar Dey
2021 Zenodo  
B. Tech Student, Haldia Institute of Technology, Dept. of IT, Haldia, WB, India Haldia Institute of Technology, Dept. of IT, Haldia, WB, India Global Institute of Science and Technology, Haldia, WB, India In today's world everything we use needs security, especially when we are using something on internet. For security purpose, we use text passwords as it is the most popular user authentication method, but have security and user friendly problems. To address this problem, some researchers have
more » ... eveloped authentication methods that use pictures as passwords. Graphical passwords offer another alternative, and are the focus of this project. Graphical password authentication systems are a type of Image-based authentication that attempt to understand the human memory for visual information. In Pass Points, passwords consist of sequence pixel click-points on a given image. Users may choose one pixels in that image as click-points for their password. To log in process, they repeat the sequence of clicks in the same order. In this paper, we conduct a modified intuitive approach of the graphical password techniques.
doi:10.5281/zenodo.5893205 fatcat:glyghn3pvbfz7knjakx5v7c2vm

Object-Aware Cropping for Self-Supervised Learning [article]

Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, Dilip Krishnan
2021 arXiv   pre-print
A core component of the recent success of self-supervised learning is cropping data augmentation, which selects sub-regions of an image to be used as positive views in the self-supervised loss. The underlying assumption is that randomly cropped and resized regions of a given image share information about the objects of interest, which the learned representation will capture. This assumption is mostly satisfied in datasets such as ImageNet where there is a large, centered object, which is highly
more » ... likely to be present in random crops of the full image. However, in other datasets such as OpenImages or COCO, which are more representative of real world uncurated data, there are typically multiple small objects in an image. In this work, we show that self-supervised learning based on the usual random cropping performs poorly on such datasets. We propose replacing one or both of the random crops with crops obtained from an object proposal algorithm. This encourages the model to learn both object and scene level semantic representations. Using this approach, which we call object-aware cropping, results in significant improvements over scene cropping on classification and object detection benchmarks. For example, on OpenImages, our approach achieves an improvement of 8.8% mAP over random scene-level cropping using MoCo-v2 based pre-training. We also show significant improvements on COCO and PASCAL-VOC object detection and segmentation tasks over the state-of-the-art self-supervised learning approaches. Our approach is efficient, simple and general, and can be used in most existing contrastive and non-contrastive self-supervised learning frameworks.
arXiv:2112.00319v1 fatcat:uaj7oexio5flde66ubcq5ybxmi

Thermal Analysis of disc Brakes Rotor: A comparative Report

Shah E Alam, Yuvraj Vidhyadhar, Prashant Sharma, Abhishek Jain
2015 Zenodo  
In this paper, authors present our results of thermal analysis of disc brake rotor used by two-wheelers in India. The aim of this paper is to realize the purpose of the holes in the disk brake. Thermal analysis is done for two different models of rotors. One is a simple rotor without vents and holes and the other perforated (consists of holes). Researchers have tried to analyze the heat loss from a rotor which is considered to be heated by disc brake friction when in use. Researchers analyze
more » ... heat loss taking into account convection and radiation. The results are compared for both the discs. The initial condition assumed here is that the vehicle has stopped completely by application of brakes. Both the rotors are of same dimensions. The geometry of disc brake rotor is made in Solid-Works. The heat transfer analysis is done using ANSYS software. The analysis helps us to understand which of the two models is better in terms of performance, heat loss and manufacturing cost and hence extensively used in motorcycles in real world.
doi:10.5281/zenodo.3968799 fatcat:4wqull3ga5eyrmkagu5npmcrpu

Multilingual BERT Post-Pretraining Alignment [article]

Lin Pan, Chung-Wei Hang, Haode Qi, Abhishek Shah, Saloni Potdar, Mo Yu
2021 arXiv   pre-print
We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved zero-shot cross-lingual transferability of the pretrained models. Using parallel data, our method aligns embeddings on the word level through the recently proposed Translation Language Modeling objective as well as on the sentence level via contrastive learning and random input shuffling. We also perform sentence-level code-switching with English when finetuning on downstream tasks. On
more » ... XNLI, our best model (initialized from mBERT) improves over mBERT by 4.7% in the zero-shot setting and achieves comparable result to XLM for translate-train while using less than 18% of the same parallel data and 31% less model parameters. On MLQA, our model outperforms XLM-R_Base that has 57% more parameters than ours.
arXiv:2010.12547v2 fatcat:nbjvz5vmcvgvvhpav6vtbbnksq

Complement System in Alzheimer's Disease

Akash Shah, Uday Kishore, Abhishek Shastri
2021 International Journal of Molecular Sciences  
Alzheimer's disease is a type of dementia characterized by problems with short-term memory, cognition, and difficulties with activities of daily living. It is a progressive, neurodegenerative disorder. The complement system is an ancient part of the innate immune system and comprises of more than thirty serum and membrane-bound proteins. This system has three different activating pathways and culminates into the formation of a membrane attack complex that ultimately causes target cell lysis
more » ... ally pathogens) The complement system is involved in several important functions in the central nervous system (CNS) that include neurogenesis, synaptic pruning, apoptosis, and neuronal plasticity. Here, we discuss how the complement system is involved in the effective functioning of CNS, while also contributing to chronic neuroinflammation leading to neurodegenerative disorders such as Alzheimer's disease. We also discuss potential targets in the complement system for stopping its harmful effects via neuroinflammation and provide perspective for the direction of future research in this field.
doi:10.3390/ijms222413647 pmid:34948444 pmcid:PMC8705098 fatcat:eh2hz7kq65bgdeo7vlk43jdkfa

On the Robustness of Human Pose Estimation [article]

Sahil Shah, Naman Jain, Abhishek Sharma, Arjun Jain
2021 arXiv   pre-print
This paper provides a comprehensive and exhaustive study of adversarial attacks on human pose estimation models and the evaluation of their robustness. Besides highlighting the important differences between well-studied classification and human pose-estimation systems w.r.t. adversarial attacks, we also provide deep insights into the design choices of pose-estimation systems to shape future work. We benchmark the robustness of several 2D single person pose-estimation architectures trained on
more » ... tiple datasets, MPII and COCO. In doing so, we also explore the problem of attacking non-classification networks including regression based networks, which has been virtually unexplored in the past. \par We find that compared to classification and semantic segmentation, human pose estimation architectures are relatively robust to adversarial attacks with the single-step attacks being surprisingly ineffective. Our study shows that the heatmap-based pose-estimation models are notably robust than their direct regression-based systems and that the systems which explicitly model anthropomorphic semantics of human body fare better than their other counterparts. Besides, targeted attacks are more difficult to obtain than un-targeted ones and some body-joints are easier to fool than the others. We present visualizations of universal perturbations to facilitate unprecedented insights into their workings on pose-estimation. Additionally, we show them to generalize well across different networks. Finally we perform a user study about perceptibility of these examples.
arXiv:1908.06401v2 fatcat:rf5q2gabuvgohhl47yguajqdju

Adaptive process monitoring via multichannel EIV lattice filters

Weihua Li, Abhishek Bhargava, Sirish L. Shah
2002 AIChE Journal  
Shah. Ž . Ku et al., 1995 ; however, its recursive variant is not avail-Ž . able yet.  ...  The Ž . interested readers are referred to Li and Shah 2000 . Instrumental ©ariable methods Ž .  ... 
doi:10.1002/aic.690480413 fatcat:kjzkkulkgzbw3akruagpe4fffe

Posterior nutcracker syndrome with left renal vein duplication: An uncommon cause of hematuria

Deepa Shah, Xiang Qiu, Abhishek Shah, Dianbo Cao
2013 International journal of surgery case reports  
Posterior Nutcracker syndrome (NCS) is a rare anomaly in which the left renal vein passes behind the aorta which compresses it against the vertebral column, restricting the venous drainage of the left kidney. A 46 year-old lady presented with intermittent painless hematuria for 6 years. Urinalysis showed microscopic hematuria. An abdominal CT scan showed left renal vein duplication with the retroaortic branch trapped between the vertebral column and the aorta at the level of the aortic
more » ... on, suggestive of posterior NCS. There were multiple small cortical cysts, sand-like stones in the left kidney and duplication of both right and left renal arteries. Posterior NCS in a patient with a duplicated left renal vein may not show all the clinical features of a typical NCS as the elevated pressure due to compression is dissipated through the pre-aortic branch of the duplicated renal vein. CT Angiography can be helpful in such a patient with multiple abnormalities. Management can range from simple surveillance to nephrectomy depending on the symptoms and renocaval pressure gradient. Although posterior NCS is a rare anomaly of the left renal vein, it should be considered in the differential diagnosis of haematuria.
doi:10.1016/j.ijscr.2013.10.012 pmid:24270287 pmcid:PMC3860029 fatcat:w3uyetpacfhd3bg5wmnbesufc4

The Ingredients of Real-World Robotic Reinforcement Learning [article]

Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine
2020 arXiv   pre-print
The success of reinforcement learning for real world robotics has been, in many cases limited to instrumented laboratory scenarios, often requiring arduous human effort and oversight to enable continuous learning. In this work, we discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world. We propose a particular instantiation of such a system, using dexterous manipulation as our case study.
more » ... we investigate a number of challenges that come up when learning without instrumentation. In such settings, learning must be feasible without manually designed resets, using only on-board perception, and without hand-engineered reward functions. We propose simple and scalable solutions to these challenges, and then demonstrate the efficacy of our proposed system on a set of dexterous robotic manipulation tasks, providing an in-depth analysis of the challenges associated with this learning paradigm. We demonstrate that our complete system can learn without any human intervention, acquiring a variety of vision-based skills with a real-world three-fingered hand. Results and videos can be found at
arXiv:2004.12570v1 fatcat:j3sqvq52ajdd5c3to5ujkew7vq
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