Diffuse steatonecrosis - presenting as an obscure abdominal mass - a diagnostic dilemma

Diffuse steatonecrosis secondary to acute / gangrenous pancreatitis presenting as vague abdominal mass is difficult to diagnose and requires special method / techniques for demonstration of fatty acid crystalloids. We report a case of acute abdomen with palpable lump. On emergency exploratory laprotomy a large thick jumbled up omental mass was revealed adhered to parietal wall along with complete pancreatic necrosis. Biopsy show diffuse fat necrosis of pericolic fat, omentum / mesentry with involvement of gut submucosa and birefringent saponified fatty acid crystalloids were demonstrated. Steatonecrosis may cause diagnostic dilemma and should be considered as differential diagnosis in appropriate clinical setting

An Innovative Strategy of Energy Generation using Piezoelectric Materials: A Review

Certain material when strained produce electric potential over their surface which is directly proportional to the amount of mechanical stress applied. These materials are known as piezoelectric materials and this effect is referred as a direct piezoelectric effect. Piezoelectricity is intensely used in the working of transducers, actuators, surface acoustic wave devices, frequency controls, etc. Use of piezoelectric material for power generation is now becoming a new promising area of its usage. Many countries like Japan, Israel India have already moved ahead in this direction with its wide range of experimentation and testing on using the material as a source for power generation. Also, with the advancement in the manufacturing and production capabilities of these materials the aspects like performance, affordability, reliability, easy implantation and longevity have greatly enhanced. This paper focuses on using the piezoelectric material as a power generating source and extension of its use in various areas

National report on the status of shelters for urban homeless

"This report...outlines the progress of construction of shelters, and provisioning of amenities and allied services to meet the needs of homeless persons in different states of the country...Based on field visit reports to several cities as well as other data from state governments and non-governmental organizations (NGOs), this report attempts to present issues for future actions by state and central governments and by civil society organizations involved in developing the programme of shelters for urban homeless

Diastema-The Treatment Dilemma

Spacing between adjacent teeth is known as Diastema, many of the patients seek closure of diastema for aesthetic reasons. Diastema less than 2 mm close spontaneously ,if they do not do so then they should not be straightaway corrected rather a thorough clinical and radiographic examination should done to  determine the underlying cause and to rule out anomalies, such as the presence of any supernumerary  tooth or odontoma’s which should be ruled out before going on for orthodontic therapy. The purpose of this article is to present and discuss the case of a 9-year old child with the chief complaint of spacing between the maxillary right permanent central incisor and right permanent lateral incisor. Radioopaque calcified masses were seen in the radiograph and was diagnosed with compound odontome followed by the surgical removal of the calcified masses

Web Based Data Visualization and Data Preprocessing Tool

The escalating adoption of Machine Learning techniques has given a bigger picture to newbies trying to explore the usage of it. Our tool deals with the idea of helping them, in order to make their lives easier. Various visualizations and algorithms have been developed to help Machine Learning enthusiasts decide upon the best model. Deciding the perfect model needs enough time which now can be reduced by the solution provided in this tool. This interactive technique helps users with some expertise to explore and validate predictive as well as classification models. Once the user provides the dataset, the visualization techniques discussed in this tool lets the user decide to select the features that are most suitable for training the model. It allows one to decide upon the importance of a particular feature and know the dataset predictions across various algorithms used for regression and classification. The accuracy percentage or the precision, recall score for regression and classification models respectively can be seen in order to know the best model

ALL RIGHTS RESERVEDSCIENCE IN HIGH DIMENSIONS: MULTIPARAMETER MODELS AND BIG DATA

Complex multiparameter models such as in climate science, economics, systems biology, materials science, neural networks and machine learning have a large-dimensional space of undetermined parameters as well as a large-dimensional space of predicted data. These high-dimensional spaces of inputs and outputs pose many challenges. Recent work with a diversity of nonlinear predictive models, microscopic models in physics, and analysis of large datasets, has led to important insights. In particular, it was shown that nonlinear fits to data in a variety of multiparameter models largely rely on only a few stiff directions in parameter space. Chapter 2 explores a qualitative basis for this compression of parameter space using a model nonlinear system with two time scales. A systematic separation of scales is shown to correspond to an increasing insensitivity of parameter space directions that only affect the fast dynamics. Chapter 3 shows with the help of microscopic physics models that emergent theories in physics also rely on a sloppy compression of the parameter space where macroscopically relevant variables form the stiff directions. Lastly, in chapter 4, we will learn that the data space of historical daily stock returns of US public companies has an emergent simplex structure that makes it amenable to a low-dimensional representation. This leads t

Detecting Damage Building Using Real-time Crowdsourced Images and Transfer Learning

After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision

Reply to Comment on “Sloppy models, parameter uncertainty, and the role of experimental design"

available in PMC 2012 November 10.We welcome the commentary from Chachra, Transtrum, and Sethna1 regarding our paper “Sloppy models, parameter uncertainty, and the role of experimental design,”2 as their intriguing work shaped our thinking in this area.3 Sethna and colleagues introduced the notion of sloppy models, in which the uncertainty in the values of some combinations of parameters is many orders of magnitude greater than others.4 In our work we explored the extent to which large parameter uncertainties are an intrinsic characteristic of systems biology network models, or whether uncertainties are instead closely related to the collection of experiments used for model estimation. We were gratified to find the latter result –– that parameters are in principle knowable, which is important for the field of systems biology. The work also showed that small parameter uncertainties can be achieved and that the process can be greatly accelerated by using computational experimental design approaches5–9 deployed to select sets of experiments that effectively exercise the system in complementary directions