Computationally Efficient Modeling and Data Assimilation of Near-Surface Variability

Near-surface (< 20m) ocean exhibits high variability due to coupled interactions, for e.g., with the atmosphere, sea ice, land, etc. Here we focus on atmospheric heat and momentum (wind) forcing, which are known to cause diurnal variability within the mixed layer. Only recently with a combination of sufficiently high vertical/horizontal resolution (75L, 1/4deg) and sub-daily atmospheric forcing fields, ocean models are starting to resolve this diurnal variability. However, the computation expense of such a high vertical resolution is burdensome in the context of coupled modeling and data assimilation. An alternative approach is to parameterize this diurnal variability with a prognostic model, that is embedded into the ocean model.In the first part of this presentation, we will demonstrate results with the above two approaches, by comparing them to profiles of near-surface temperature and salinity. In the context of data assimilation and reanalysis, this modeling capability opens the door to re-examine and perhaps improve specification of background (or, ensemble) error characteristics. The second half of this talk will focus on illustrating diurnally varying errors within an ensemble DA, and possible approaches to improve localization (horizontal/vertical) to extract maximum possible observational information content from in-situ and satellite observations of sea surface temperature

A Randomized Kernel-Based Secret Image Sharing Scheme

This paper proposes a ($k,n$)-threshold secret image sharing scheme that offers flexibility in terms of meeting contrasting demands such as information security and storage efficiency with the help of a randomized kernel (binary matrix) operation. A secret image is split into $n$ shares such that any $k$ or more shares ($k\leq n$) can be used to reconstruct the image. Each share has a size less than or at most equal to the size of the secret image. Security and share sizes are solely determined by the kernel of the scheme. The kernel operation is optimized in terms of the security and computational requirements. The storage overhead of the kernel can further be made independent of its size by efficiently storing it as a sparse matrix. Moreover, the scheme is free from any kind of single point of failure (SPOF).Comment: Accepted in IEEE International Workshop on Information Forensics and Security (WIFS) 201

Biomechanics of Smooth Muscle Cell Differentiation: Experimental Study using an Innovative in vitro Mechanical System

poster abstractIdentifying mechanisms that regulate different smooth muscle cell (SMC) gene expressions is critical for understanding the SMC phenotype and genotype in both physiological and pathological conditions, as SMCs’ primary role is to control the slow, involuntary movement of hollow organs such as blood vessels, airways, gastrointestinal, urinary and reproductive tracks. Previous in vitro studies indicated that specific genes were lost and there was a slight change in the physical structure of the SMCs. This was due to the overwhelming complexity of the in vivo environment which could not be accurately simulated in vitro. It is hypothesized that if SMCs are cultured in vitro by subjecting them to controlled mechanical stresses (cyclic strains at various frequencies and time durations), they will retain the same level of gene expression as in vivo. The objective is to evaluate subsequent changes in the SMC lineage based on gene expression changes. To accomplish this, a novel cell stretching device is being developed that will stimulate cultured SMCs by allowing both culturing and stretching of cells on the same unit. This also effectively reduces the working time needed by researchers to complete each run. The expected outcome will be the effects of different mechanical stresses on cell survival over time. Specifically, SMC lineage assessment and western blot analysis will be done. The results will hopefully prove that in vivo conditions of SMCs can be successfully simulated in vitro. The research will help in comparing the oxidative stresses, hyperglycemia, lipotoxicity and calcification responses on specific SMC types in vitro, and offer new insights into the genetic and environmental bases of SMC diseases. This is critical for research in areas such as drug screening and tissue engineering. For future research, co-culture systems may be studied as the device is capable of culturing two cell-types in the same environment

Natural Language Query in the Biochemistry and Molecular Biology Domains Based on Cognition Search&#x2122;

Motivation: With the tremendous growth in scientific literature, it is necessary to improve upon the standard pattern matching style of the available search engines. Semantic NLP may be the solution to this problem. Cognition Search (CSIR) is a natural language technology. It is best used by asking a simple question that might be answered in textual data being queried, such as MEDLINE. CSIR has a large English dictionary and semantic database. Cognition&#x2019;s semantic map enables the search process to be based on meaning rather than statistical word pattern matching and, therefore, returns more complete and relevant results. The Cognition Search engine uses downward reasoning and synonymy which also improves recall. It improves precision through phrase parsing and word sense disambiguation.&#xd;&#xa;Result: Here we have carried out several projects to &#x22;teach&#x22; the CSIR lexicon medical, biochemical and molecular biological language and acronyms from curated web-based free sources. Vocabulary from the Alliance for Cell Signaling (AfCS), the Human Genome Nomenclature Consortium (HGNC), the United Medical Language System (UMLS) Meta-thesaurus, and The International Union of Pure and Applied Chemistry (IUPAC) was introduced into the CSIR dictionary and curated. The resulting system was used to interpret MEDLINE abstracts. Meaning-based search of MEDLINE abstracts yields high precision (estimated at &#x3e;90%), and high recall (estimated at &#x3e;90%), where synonym information has been encoded. The present implementation can be found at http://MEDLINE.cognition.com. &#xd;&#xa

FoodNet: Recognizing Foods Using Ensemble of Deep Networks

In this work we propose a methodology for an automatic food classification system which recognizes the contents of the meal from the images of the food. We developed a multi-layered deep convolutional neural network (CNN) architecture that takes advantages of the features from other deep networks and improves the efficiency. Numerous classical handcrafted features and approaches are explored, among which CNNs are chosen as the best performing features. Networks are trained and fine-tuned using preprocessed images and the filter outputs are fused to achieve higher accuracy. Experimental results on the largest real-world food recognition database ETH Food-101 and newly contributed Indian food image database demonstrate the effectiveness of the proposed methodology as compared to many other benchmark deep learned CNN frameworks.Comment: 5 pages, 3 figures, 3 tables, IEEE Signal Processing Letter

NetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Figure 1

Figure 1 demonstrates a series of training experiments with the Naïve Bayes classifier using different neighborhoods for contextual features, different sizes of positive and negative training examples and evaluated the resulting classifiers with our annotated gold standard corpus. The data sets are the results of running NetiNeti on subset of 136 PubMedCentral tagged open access articles and with no stop list.A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information.We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org we present the comparison results of various machine learning algorithms on our annotated corpus. Naïve Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms