Attribute-Graph: A Graph based approach to Image Ranking

We propose a novel image representation, termed Attribute-Graph, to rank images by their semantic similarity to a given query image. An Attribute-Graph is an undirected fully connected graph, incorporating both local and global image characteristics. The graph nodes characterise objects as well as the overall scene context using mid-level semantic attributes, while the edges capture the object topology. We demonstrate the effectiveness of Attribute-Graphs by applying them to the problem of image ranking. We benchmark the performance of our algorithm on the 'rPascal' and 'rImageNet' datasets, which we have created in order to evaluate the ranking performance on complex queries containing multiple objects. Our experimental evaluation shows that modelling images as Attribute-Graphs results in improved ranking performance over existing techniques.Comment: In IEEE International Conference on Computer Vision (ICCV) 201

Risk Factors Effecting Mortality in Acute Mesenteric Vascular Occlusion: A Single Experience

INTRODUCTION : Acute Mesenteric Vascular Occlusion is an infrequent but complicated, life-threatening condition, mostly seen in elderly patients. Despite the advances in diagnosis of Acute Mesenteric Vascular Occlusion, morbidity and mortality rates remain high. Atypical presenting symptoms, presence of predisposing diseases, delayed surgical intervention due to diagnostic difficulties and due to delayed presentation, and in most cases, elderly patients who have cardiac problems, these may be some of the factors for higher mortality rates. Intestinal blood flow is impaired as a result of mesenteric vascular insufficiency, which evolves due to underlying causes such as atherosclerosis, mesenteric artery embolism, generalize vasospasm, and mesenteric vein thrombosis. Duration of ischemia, grade of mesentery artery occlusion, and proportion of collateral flow are determining factors of intestinal damage, after acute arterial occlusion. The objective of this study is to discuss the effective factors or morbidity & mortality in patients who were operated on for acute mesenteric vascular occlusion. Between Jan 2014 and Jan 2015 about 25 patients underwent emergent surgery for acute mesenteric vascular occlusion, were analyzed retrospectively for factors effecting mortality. AIM OF STUDY : OBJECTIVES : 1. To identify risk factors effecting mortality in acute mesenteric vascular occlusion. 2. To study the level and extent of bowel involvement in acute mesenteric vascular occlusion. 3. To study the morbidity and mortality after surgical. intervention for acute mesenteric vascular occlusion. MATERIALS AND METHODS : Source of Data: 25 patients admitted in Coimbatore Medical College and Hospital with Acute Mesenteric Vascular Occlusion. Study Place: Coimbatore Medical College and Hospital Study Design: Prospective Observational Study Sample Size: 25 Patients. Study Period: September 2014 – September 2015. Inclusive Criteria: 1. Patients admitted with suspected Mesenteric Vascular Occlusion. 2. Age > 18 yrs. Exclusion Criteria: Patients admitted with bowel gangrene due to other causes. 25 patients admitted in general surgery department in Coimbatore Medical College with a probable diagnosis of Acute Mesenteric Vascular Occlusion will be evaluated as follows. Demographic features (age, gender, time elapsed to laparotomy), serum values of leukocytes, amylase, alkaline phosphatase, and urea, liver enzyme levels, radiologic imaging techniques, surgical techniques, complications, mortality, and hospitalization period were evaluated. Acute Mesenteric Vascular Occlusion was diagnosed on clinical examination supported with laboratory and imaging techniques. Elapsed time between the onset of symptoms and the surgery is defined as 24 hours and more than 24 hours. All Patients underwent emergent laparotomy. This was determined by prediagnositic contributory techniques. Patients were allocated to three groups according to the place of the necrosis. Resection and Anastamosis was done according to bowel viability. Comparing the factors effecting mortality between groups, the data on the group with total necrosis were disregarded because all patients died. CONCLUSION : 1. Highest incidence was seen in 5th decade. 2. Males are more predominantly affected than females. 3. Smoking and alcoholism are strong predisposing factors for the disease. 4. Advanced age is an unfavourable factor for good prognosis. 5. High leucocyte levels correlates with the disease sevearity and high mortality. 6. Increased elapsed time between the symptoms and the surgical intervention has an increasing effect on mortality. 7. Superior mesenteric vein thrombosis is more common than arterial thrombosis. 8. Superior mesenteric vein thrombosis carries a good prognosis when compared to arterial thrombosis or emboli. 9. Mortality is directly proportional to the length of the bowel involved. 10. Mortality is high in both intestinal and colon gangrene when compared to small bowel alone

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm

Identity parameters on traditionally used Antiurolithiatic Herb - Scoparia Dulcis Linn.

Introduction: Scoparia dulcis Linn. locally known as Manithumbe Gida belongs to Scophularaceae family and used in medicine by the traditional practitioners for the treatment of urinary calculi. Materials and Methods: Matured plants are collected from Udupi district and authenticated. Macromicroscopic features, physico-chemical standards, HPTLC and secondary metabolites were recorded as per standard guidelines. Result: TS of leaf has shown the presence of mesophyll and bi-collateral vascular bundles. Outer cork tissue, a layer of cortex and conjoint collateral closed vascular bundles and central pith are inclusions of stem TS. Pitted and reticulate vessels are characteristic features of plant powder. Physico-chemical standards and presence of alkaloids, carbohydrates, tannin and coumarins were indicative of its chemical nature. HPTLC fingerprints are a record of its different chemical constituents. Thus the quality monograph prepared on this drug beneficial in future research

The wound healing property of ethanolic extract of Michelia champaca flowers in diabetic rats

Background: The plant Michelia champaca (MC) is widely used in the treatment of inflammation, constipation, dysmenorrhea, ulcers, wounds, fever, and cough. The aim was to evaluate the wound healing property of ethanolic extract of MCflowers in streptozotocin-induced diabetes in rats.Methods: Wound healing activity was assessed by incision and excision wound models. Five groups of n=6 rats and n=14 rats were used for incision and excision wound models, respectively. Group I rats, non-diabetic control and Group II rats diabetic control, received 1 ml of 0.5% caboxymethylcellolose, which was used to prepare a suspension of ethanolic extracts of MC. Group III, IV and V rats were given MC extract the suspension of 100 mg/kg, 200 mg/kg and 300 mg/kg respectively. Parameters observed were breaking strength of incision wound and wound contraction, epithelialization, hydroxyproline content of excision wound respectively. Results were analyzed using one-way analysis of variance, followed by Tukey’s post-hoc test.Results: Breaking strength, rate of wound contraction and hydroxyproline content were significantly increased, and the period of epithelialization was significantly reduced in Group IV and V rats respectively.Conclusion: Oral administration of ethanolic extract of MC promotes wound healing in diabetic rats. Hence, further study in humans is suggested

Drug utilization study and prescribing patterns in psychiatry patients at a tertiary care hospital

Background: The Drug utilization research (DUR) compares drug use between different countries and regions and is used to assess the rationality of prescribing pattern of the drug therapy. With this background we decided to evaluate antipsychotic drugs prescribing pattern in the psychiatric patients in a tertiary care hospital.Methods: The study was carried out at Department of Psychiatry, DSMCH. It was open label, cross - sectional, prescribed Documents based study. Duration of the study was one month (May-2017). Out-Patient number, age, sex, diagnosis, prescribed generic name, brand name, dose, route of administration, duration of therapy obtained from the Prescription register of Out - Patient Department of the Psychiatry.Results: The clinical experiences of the Psychiatrist I, II and III were 17 years, 35 years and 10 years respectively. The Psychiatrist I, II and III prescribed treatment for 36 (31.9%), 61 (54%) and 16 (14.2%) patients respectively. Among overall (n=113) patients (average age 38.9 years), male n=56 (49.6%) and female=57 (50.4%) were treated by all the three psychiatrists. The percentage of prescription of various drugs used were: Escitalopram (15.7%), Clonazepam (14.6%), Sertraline (8.7%), Risperidone (7.5%), Propranolol (6.7%), Olanzapine (6.3%), Quetiapine (5.9%), Trihexyphenidyl (5.5%), Amitriptyline (5.1%) and Other prescribed drugs, were between (0.4 to 2.8%).Conclusions: From this study, it can conclude that rational usage of drugs were followed in this study. All three prescribers (Psychiatrist I, II, and III) prescriptions were found to be rationale

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%

Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis

Abstract The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings

PANC Study (Pancreatitis: A National Cohort Study): national cohort study examining the first 30 days from presentation of acute pancreatitis in the UK

Abstract Background Acute pancreatitis is a common, yet complex, emergency surgical presentation. Multiple guidelines exist and management can vary significantly. The aim of this first UK, multicentre, prospective cohort study was to assess the variation in management of acute pancreatitis to guide resource planning and optimize treatment. Methods All patients aged greater than or equal to 18 years presenting with acute pancreatitis, as per the Atlanta criteria, from March to April 2021 were eligible for inclusion and followed up for 30 days. Anonymized data were uploaded to a secure electronic database in line with local governance approvals. Results A total of 113 hospitals contributed data on 2580 patients, with an equal sex distribution and a mean age of 57 years. The aetiology was gallstones in 50.6 per cent, with idiopathic the next most common (22.4 per cent). In addition to the 7.6 per cent with a diagnosis of chronic pancreatitis, 20.1 per cent of patients had a previous episode of acute pancreatitis. One in 20 patients were classed as having severe pancreatitis, as per the Atlanta criteria. The overall mortality rate was 2.3 per cent at 30 days, but rose to one in three in the severe group. Predictors of death included male sex, increased age, and frailty; previous acute pancreatitis and gallstones as aetiologies were protective. Smoking status and body mass index did not affect death. Conclusion Most patients presenting with acute pancreatitis have a mild, self-limiting disease. Rates of patients with idiopathic pancreatitis are high. Recurrent attacks of pancreatitis are common, but are likely to have reduced risk of death on subsequent admissions. </jats:sec