Deep Language Space Neural Network for Classifying Mild Cognitive Impairment and Alzheimer-Type Dementia

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. It has been quite a challenge to diagnose Mild Cognitive Impairment due to Alzheimer\u27s disease (MCI) and Alzheimer-type dementia (AD-type dementia) using the currently available clinical diagnostic criteria and neuropsychological examinations. As such we propose an automated diagnostic technique using a variant of deep neural networks language models (DNNLM) on the verbal utterances of affected individuals. Motivated by the success of DNNLM on natural language tasks, we propose a combination of deep neural network and deep language models (D2NNLM) for classifying the disease. Results on the DementiaBank language transcript clinical dataset show that D2NNLM sufficiently learned several linguistic biomarkers in the form of higher order n-grams to distinguish the affected group from the healthy group with reasonable accuracy on very sparse clinical datasets

Nanostructured Bimetallic Block Copolymers as Precursors to Magnetic FePt Nanoparticles

Phase-separated block copolymers (BCPs) that function as precursors to arrays of FePt nanoparticles (NPs) are of potential interest for the creation of media for the next-generation high-density magnetic data storage devices. A series of bimetallic BCPs has been synthesized by incorporating a complex containing Fe and Pt centers into the coordinating block of four different poly­(styrene-<i>b</i>-4-vinylpyridine)­s (PS-<i>b</i>-P4VPs, <b>P1–P4</b>). To facilitate phase separation for the resulting metalated BCPs (<b>PM1–PM4</b>), a loading of the FePt-bimetallic complex corresponding to ca. 20% was used. The bulk and thin-film self-assembly of these BCPs was studied by transmission electron microscopy (TEM) and atomic force microscopy, respectively. The spherical and cylindrical morphologies observed for the metalated BCPs corresponded to those observed for the metal-free BCPs. The products from the pyrolysis of the BCPs in bulk were also characterized by TEM, powder X-ray diffraction, and energy-dispersive X-ray spectroscopy, which indicated that the FePt NPs formed exist in an fct phase with average particle sizes of ca. 4–8 nm within a carbonaceous matrix. A comparison of the pyrolysis behavior of the metalated BCP (<b>PM3</b>), the metalated <b>P4VP</b> homopolymer (<b>PM5</b>), and the molecular model organometallic complex revealed the importance of using a nanostructured BCP approach for the synthesis of ferromagnetic FePt NPs with a smaller average NP size and a close to 1:1 Fe/Pt stoichiometric ratio

Patterning of L10 FePt Nanoparticles with Ultra-High Coercivity for Bit-Patterned Media

L1(0)-ordered FePt nanoparticles (NPs) with ultra-high coercivity were directly prepared from a new metallopolyyne using a one-step pyrolysis method. The chemical ordering, morphology and magnetic properties of the as-synthesized FePt NPs have been studied. Magnetic measurements show the coercivity of these FePt NPs is as high as 3.6 T. Comparison of NPs synthesized under the Ar and Ar/H-2 atmospheres shows that the presence of H-2 in the annealing environment influences the nucleation and promotes the growth of L1(0)-FePt NPs. Application of this metallopolymer for bit-patterned media was also demonstrated using nanoimprint lithography.Department of Applied PhysicsDepartment of Applied Biology and Chemical Technolog

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Native language identification incorporating syntactic knowledge

"This dissertation is presented for the degree of Doctor of Philosophy""October 2013"Includes bibliographical references.1. Introduction -- 2. Literature review -- 3. A preliminary error-based analysis -- 4. Using statistical parsing for syntactic structures -- 5. Exploring topic modeling -- 6. Conclusion -- Appendices."Inferring characteristics of authors from their textual data, often termed authorship profiling, is typically treated as a classification task, where an author is classified with respect to characteristics including gender, age, native language, and so on. This profile information is often of interest to marketing organisations for product promotional reasons as well as governments for crime investigation purposes. The thesis focuses on the specific task of inferring the native language of an author based on texts written in a second language, typically English; this is referred as native language identification (NLI). Since the seminal work of Koppel et al. in 2005, this task has been primarily tackled as a text classification task using supervised machine learning techniques. Lexical features, such as function words, character n-grams, and part-of-speech (PoS) n-grams, have been proven to be useful in NLI. Syntactic features, on the other hand, in particular those that capture grammatical errors, which might potentially be useful for this task, have received little attention. The thesis explores the relevance of concepts from the field of second language acquisition, with a focus on those which postulate that constructions of the native language lead to some form of characteristic errors or patterns in a second language. In the first part of the thesis, an experimental study is conducted to determine the native language of seven different groups of authors in a specially constructed corpus of non-native English learners (International Corpus of Learner English). Three commonly observed syntactic errors that might be attributed to the transfer effects from the native language are examined - namely, subject-verb disagreement, noun-number disagreement, and misuse of determiners. Based on the results of a statistical analysis, it is demonstrated that these features generally have some predictive power, but that they do not improve the level of best performance of the supervised classification, in comparison with a baseline using lexical features. In the second part, a second experimental study aims to learn syntax-based errors from syntactic parsing, with the purpose of uncovering more useful error patterns in the form of parse structures which might characterise language-specific ungrammaticality. The study demonstrates that parse structures, represented by context-free grammar (CFG) production rules and parse reranking features, are useful in general sentence grammaticality judgement. Consequently, adapting these syntactic features to NLI, with the use of parse production rules in particular, a statistically significant improvement over the lexical features is observed in the overall classification performance. The final part of the thesis takes a Bayesian approach to NLI through topic modeling in two ways. Topic modeling, using a probabilistic CFG formulation, is first taken as a feature clustering technique to discover coherent latent factors (known as 'topics') that might capture predictive features for individual native languages. The topics, rather than the word n-grams that are typical of topic modeling, consist of bi-grams over part of speech. While there is some evidence of topic cluster coherence, this does not improve the classification performance. The second approach explores adaptor grammars, a hierarchical non-parametric extension of probabilistic CFGs (and also interpretable as an extension of topic modeling), for feature selection of useful collocations. Adaptor grammars are extended to identify n-gram collocations of arbitrary length over mixtures of PoS and function words, using both maxent and induced syntactic language model approaches to NLI classification. It is demonstrated that the learned collocations used as features can also improve over the baseline (lexical) performance, although success varies with the approach taken.Mode of access: World Wide Web.1 online resource (xvii, 177 pages) illustrations (some coloured