Monozygotic twins discordant for phacomatosis pigmentovascularis: evidence for the concept of twin spotting.

The term phacomatosis pigmentovascularis (PPV) refers to the occurrence of vascular nevi with melanocytic or epidermal nevi. We report on monozygotic twins (MZTs) discordant for phacomatosis cesioflammea (PPV type II) providing evidence for the mechanism of twin spotting in the development of PPV. The affected twin had a nevus flammeus on the right arm and the right maxilla, and a pigmented area on the trunk in keeping with a persistent, aberrant Mongolian spot. The affected twin had bilateral ocular melanocytosis with abnormal scleral pigmentation, iris mamillations, increased pigmentation of the trabecular meshwork, and increased fundal pigmentation and secondary glaucoma. DNA testing confirmed monozygosity. This case of MZTs discordant for PPV supports the hypothesis that PPV results from mosaicism due to a post-zygotic mutational event and the concept of twin spotting.

Efficient Processing of Spatial Group Keyword Queries

With the proliferation of geo-positioning and geo-tagging techniques, spatio-textual objects that possess both a geographical location and a textual description are gaining in prevalence, and spatial keyword queries that exploit both location and textual description are gaining in prominence. However, the queries studied so far generally focus on finding individual objects that each satisfy a query rather than finding groups of objects where the objects in a group together satisfy a query.

We define the problem of retrieving a group of spatio-textual objects such that the group's keywords cover the query's keywords and such that the objects are nearest to the query location and have the smallest inter-object distances. Specifically, we study three instantiations of this problem, all of which are NP-hard. We devise exact solutions as well as approximate solutions with provable approximation bounds to the problems. In addition, we solve the problems of retrieving top-k groups of three instantiations, and study a weighted version of the problem that incorporates object weights. We present empirical studies that offer insight into the efficiency of the solutions, as well as the accuracy of the approximate solutions.

Temporal Spatial-Keyword Top-k publish/subscribe

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale. These geo-textual data cover a wide range of topics. Users are interested in receiving up-to-date tweets such that their locations are close to a user specified location and their texts are interesting to users. For example, a user may want to be updated with tweets near her home on the topic “food poisoning vomiting.” We consider the Temporal Spatial-Keyword Top-k Subscription (TaSK) query. Given a TaSK query, we continuously maintain up-to-date top-k most relevant results over a stream of geo-textual objects (e.g., geo-tagged Tweets) for the query. The TaSK query takes into account text relevance, spatial proximity, and recency of geo-textual objects in evaluating its relevance with a geo-textual object. We propose a novel solution to efficiently process a large number of TaSK queries over a stream of geotextual objects. We evaluate the efficiency of our approach on two real-world datasets and the experimental results show that our solution is able to achieve a reduction of the processing time by 70-80% compared with two baselines.

SOPS: A System for Efficient Processing of Spatial-Keyword Publish/Subscribe

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale. These geo-textual data cover a wide range of topics. Users are interested in receiving up-to-date geo-textual objects (e.g., geo-tagged Tweets) such that their locations meet users’ need and their texts are interesting to users. For example, a user may want to be updated with tweets near her home on the topic “dengue fever headache.” In this demonstration, we present SOPS, the Spatial-Keyword Publish/Subscribe System, that is capable of efficiently processing spatial keyword continuous queries. SOPS supports two types of queries: (1) Boolean Range Continuous (BRC) query that can be used to subscribe the geo-textual objects satisfying a boolean keyword expression and falling in a specified spatial region; (2) Temporal Spatial-Keyword Top-k Continuous (TaSK) query that continuously maintains up-to-date top-k most relevant results over a stream of geo-textual objects. SOPS enables users to formulate their queries and view the real-time results over a stream of geotextual objects by browser-based user interfaces. On the server side, we propose solutions to efficiently processing a large number of BRC queries (tens of millions) and TaSK queries over a stream of geo-textual objects.

On-line Emotion Recognition in a 3-D Activation-Valence-Time Continuum using Acoustic and Linguistic Cues

For many applications of emotion recognition, such as virtual agents, the system must select responses while the user is speaking. This requires reliable on-line recognition of the user’s affect. However most emotion recognition systems are based on turnwise processing. We present a novel approach to on-line emotion recognition from speech using Long Short-Term Memory Recurrent Neural Networks. Emotion is recognised frame-wise in a two-dimensional valence-activation continuum. In contrast to current state-of-the-art approaches, recognition is performed on low-level signal frames, similar to those used for speech recognition. No statistical functionals are applied to low-level feature contours. Framing at a higher level is therefore unnecessary and regression outputs can be produced in real-time for every low-level input frame. We also investigate the benefits of including linguistic features on the signal frame level obtained by a keyword spotter.

Protein kinase B/Akt regulation in diabetic kidney disease

Many reviews have been written on protein kinase B/Akt focusing on its history dating back from the isolation of the Akt8 transforming murine leukemia virus by Staal in 1977, to the co-discovery of the Akt1 gene by the three groups in 1991 (reviewed in 7). There are currently over 22,000 publications in the PubMed database with "Akt" as a keyword - these publications describe a wealth of diverse data on the physiological functions of Akt isoforms. Many of these publications describe roles of Akt ranging from its requirement for cellular processes such as glucose uptake, cell survival and angiogenesis to roles in diseases such as cancer and ischaemia (22). This review will focus on the evidence for Akt signaling in different kidney cells during diabetes, or diabetic nephropathy (DN).

Geological fakes and frauds

Some geological fakes and frauds are carried out solely for financial gain (mining fraud), whereas others maybe have increasing aesthetic appeal (faked fossils) or academic advancement (fabricated data) as their motive. All types of geological fake or fraud can be ingenious and sophisticated, as demonstrated in this article. Fake gems, faked fossils and mining fraud are common examples where monetary profit is to blame: nonetheless these may impact both scientific theory and the reputation of geologists and Earth scientists. The substitution or fabrication of both physical and intellectual data also occurs for no direct financial gain, such as career advancement or establishment of belief (e.g. evolution vs. creationism). Knowledge of such fakes and frauds may assist in spotting undetected geological crimes: application of geoforensic techniques helps the scientific community to detect such activity, which ultimately undermines scientific integrity.

Comparison of techniques to screen and characterize bacteria-specific hybridomas for high-quality monoclonal antibodies selection

Antibodies are are very important materials for diagnostics. A rapid and simple hybridoma screening method will help in delivering specific monoclonal antibodies. In this study, we systematically developed the first antibody array to screen for bacteria-specific monoclonal antibodies using Listeria monocytogenes as a bacteria model. The antibody array was developed to expedite the hybridoma screening process by printing hybridoma supernatants on a glass slide coated with an antigen of interest. This screening method is based on the binding ability of supernatants to the coated antigen. The bound supernatants were detected by a fluorescently labeled anti-mouse immunoglobulin. Conditions (slide types, coating, spotting, and blocking buffers) for antibody array construction were optimized. To demonstrate its usefulness, antibody array was used to screen a sample set of 96 hybridoma supernatants in comparison to ELISA. Most of the positive results identified by ELISA and antibody array methods were in agreement except for those with low signals that were undetectable by antibody array. Hybridoma supernatants were further characterized with surface plasmon resonance to obtain additional data on the characteristics of each selected clone. While the antibody array was slightly less sensitive than ELISA, a much faster and lower cost procedure to screen clones against multiple antigens has been demonstrated. (C) 2011 Elsevier Inc. All rights reserved.

Enhanced approach for latent semantic indexing using wavelet transform

Latent semantic indexing (LSI) is a technique used for intelligent information retrieval (IR). It can be used as an alternative to traditional keyword matching IR and is attractive in this respect because of its ability to overcome problems with synonymy and polysemy. This study investigates various aspects of LSI: the effect of the Haar wavelet transform (HWT) as a preprocessing step for the singular value decomposition (SVD) in the key stage of the LSI process; and the effect of different threshold types in the HWT on the search results. The developed method allows the visualisation and processing of the term document matrix, generated in the LSI process, using HWT. The results have shown that precision can be increased by applying the HWT as a preprocessing step, with better results for hard thresholding than soft thresholding, whereas standard SVD-based LSI remains the most effective way of searching in terms of recall value.

Efficient Algorithms for Answering the m-Closest Keywords Query

As an important type of spatial keyword query, the m-closest keywords (mCK) query finds a group of objects such that they cover all query keywords and have the smallest diameter, which is defined as the largest distance between any pair of objects in the group. The query is useful in many applications such as detecting locations of web resources. However, the existing work does not study the intractability of this problem and only provides exact algorithms, which are computationally expensive.

In this paper, we prove that the problem of answering mCK queries is NP-hard. We first devise a greedy algorithm that has an approximation ratio of 2. Then, we observe that an mCK query can be approximately answered by finding the circle with the smallest diameter that encloses a group of objects together covering all query keywords. We prove that the group enclosed in the circle can answer the mCK query with an approximation ratio of 2 over 3. Based on this, we develop an algorithm for finding such a circle exactly, which has a high time complexity. To improve efficiency, we propose another two algorithms that find such a circle approximately, with a ratio of 2 over √3 + ε. Finally, we propose an exact algorithm that utilizes the group found by the 2 over √3 + ε)-approximation algorithm to obtain the optimal group. We conduct extensive experiments using real-life datasets. The experimental results offer insights into both efficiency and accuracy of the proposed approximation algorithms, and the results also demonstrate that our exact algorithm outperforms the best known algorithm by an order of magnitude.