Combining Machine Learning and Hierarchical Indexing Structures for Text Categorization

This paper presents a method that exploits the hierarchical structure of an indexing vocabulary to guide the development and training of machine learning methods for automatic text categorization. We present the design of a hierarchical classifier based on the divide and conquer principle. The method is evaluated using backpropagation neural networks, as the machine learning algorithm, that learn to assign MeSH categories to a subset ofMEDLINE records. Comparisons with traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers are provided. The results indicate that the use ofhierarchical structures improves performance significantly

Automatic Text Categorization Using Neural Networks

This paper presents the results obtained from a series of experiments in automatic text categorization of MEDLINE articles. The main goal ofthis research is to build a counter propagation network and to train it in assigning MeSH phrases based on term frequency of single words from title and abstract. The experiments compare the performance of the counterpropagation network against a backpropagation neural network trained for the same purpose. Results obtained by using a set of 2,344 MEDLINE documents are presented and discussed

The Effects of Non-Nutritive Sweeteners on the Health of Youth in United States

Obesity results from a multitude of problems from depression, an imbalance in hormones, genetics, environmental factors, and can also result from a poor diet, where we are focusing our attention. A strong correlation is made with low income and obesity in children. In 2014, 14.5% of patients ages 2-4 were obese. From 2011 to 2014 17% of adolescents experienced obesity and this affects around 12.7 million people. Statistics have shown the as the age increases in adolescents, so does the prevalence of obesity

Pleomorphic Adenoma of the Hard Palate:A Multidisciplinary Approach

Pleomorphic adenoma is the most common salivary gland tumor accounting for 80% of all major salivary gland tumors. It is a benign salivary gland neoplasm that constitutes 3% to 10% of the neoplasms in the head and neck region.1 Salivary gland neoplasms represents less than 1% of all tumors. This article is being showcased as a special case due to the fact it was done at a Taluk Hospital and also because ENT and oromaxillofacial surgeons were involved during the surgery

3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries

Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resulting meshes are suitable for finite element simulations. This work is an important step towards making physical simulations of biological processes in realistic geometries routine. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery at the interface of geometry and cellular processes. We posit that a new frontier at the intersection of computational technologies and single cell biology is now open.Comment: 39 pages, 14 figures. High resolution figures and supplemental movies available upon reques

Combining Machine Learning and Hierarchical Indexing Structures for Text Categorization

This paper presents a method that exploits the hierarchical structure of an indexing vocabulary to guide the development and training of machine learning methods for automatic text categorization. We present the design of a hierarchical classifier based on the divide and conquer principle. The method is evaluated using backpropagation neural networks, as the machine learning algorithm, that learn to assign MeSH categories to a subset ofMEDLINE records. Comparisons with traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers are provided. The results indicate that the use ofhierarchical structures improves performance significantly

Initial collateral measurements of some properties of Calanus finmarchicus

In general, acoustic quantification of zooplankton such as Calanus finmarchicus requires the use of models, among other reasons, to aid in the interpretations of data collected on animals whose scattering properties change with development stage, season, and other environmentally linked factors. In conjunction with a project to determine acoustic scattering signatures of zooplankton and fish, a study is being performed to measure physical, morphometric, and biochemical properties of selected euphausiid species and Calanus finmarchicus. An important feature of this study is the performance of a suite of measurements on animals collected at the same time and place. The measurement methods being used to study Calanus are presented here together with results from the initial field experiment. The criticism of interested parties is solicited

Highly efficient transfection of human induced pluripotent stem cells using magnetic nanoparticles.

PurposeThe delivery of transgenes into human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (hiPSC-CMs) represents an important tool in cardiac regeneration with potential for clinical applications. Gene transfection is more difficult, however, for hiPSCs and hiPSC-CMs than for somatic cells. Despite improvements in transfection and transduction, the efficiency, cytotoxicity, safety, and cost of these methods remain unsatisfactory. The objective of this study is to examine gene transfection in hiPSCs and hiPSC-CMs using magnetic nanoparticles (NPs).MethodsMagnetic NPs are unique transfection reagents that form complexes with nucleic acids by ionic interaction. The particles, loaded with nucleic acids, can be guided by a magnetic field to allow their concentration onto the surface of the cell membrane. Subsequent uptake of the loaded particles by the cells allows for high efficiency transfection of the cells with nucleic acids. We developed a new method using magnetic NPs to transfect hiPSCs and hiPSC-CMs. HiPSCs and hiPSC-CMs were cultured and analyzed using confocal microscopy, flow cytometry, and patch clamp recordings to quantify the transfection efficiency and cellular function.ResultsWe compared the transfection efficiency of hiPSCs with that of human embryonic kidney (HEK 293) cells. We observed that the average efficiency in hiPSCs was 43%±2% compared to 62%±4% in HEK 293 cells. Further analysis of the transfected hiPSCs showed that the differentiation of hiPSCs to hiPSC-CMs was not altered by NPs. Finally, robust transfection of hiPSC-CMs with an efficiency of 18%±2% was obtained.ConclusionThe difficult-to-transfect hiPSCs and hiPSC-CMs were efficiently transfected using magnetic NPs. Our study offers a novel approach for transfection of hiPSCs and hiPSC-CMs without the need for viral vector generation