High Resolution Image Reconstruction of Polymer Composite Materials Using Neural Networks

A neural network is an artificial intelligence technique inspired by a simplistic model of biological neurons and their connectivity. A neural network has the ability to learn an input-output function without a priori knowledge of the relationship between them. Typically a neural network consists of layers of neurons, whereby each neuron in a given layer is fully connected to neurons in adjacent layers. Figure 1 shows such an arrangement with three layers, called the input, hidden and output layers. The connection strengths between neurons, often referred to as weights, are modified by a training phase. The training phase used here utilizes an error back propagation algorithm [1]. During training the neural network is presented with input which propagates through the network producing a corresponding output. A comparison of the actual output with the desired or target output generates an error which is used to adjust the neural network’s weights according to an error gradient descent technique [2]. This procedure is repeated for many different input and desired output pairs allowing the neural network to learn the input-output function

Spoken Discourse Assessment and Analysis in Aphasia: An International Survey of Current Practices.

Purpose Spoken discourse analysis is commonly employed in the assessment and treatment of people living with aphasia, yet there is no standardization in assessment, analysis, or reporting procedures, thereby precluding comparison/meta-analyses of data and hindering replication of findings. An important first step is to identify current practices in collecting and analyzing spoken discourse in aphasia. Thus, this study surveyed current practices, with the goal of working toward standardizing spoken discourse assessment first in research settings with subsequent implementation into clinical settings. Method A mixed-methods (quantitative and qualitative) survey was publicized to researchers and clinicians around the globe who have collected and/or analyzed spoken discourse data in aphasia. The survey data were collected between September and November 2019. Results Of the 201 individuals who consented to participate, 189 completed all mandatory questions in the survey (with fewer completing nonmandatory response questions). The majority of respondents reported barriers to utilizing discourse including transcription, coding, and analysis. The most common barrier was time (e.g., lack of time). Respondents also indicated that there was a lack of, and a need for, psychometric properties and normative data for spoken discourse use in the assessment and treatment of persons with aphasia. Quantitative and qualitative results are described in detail. Conclusions The current survey study evaluated spoken discourse methods in aphasia across research and clinical settings. Findings from this study will be used to guide development of process standardization in spoken discourse and for the creation of a psychometric and normative property database. Supplemental Material https://doi.org/10.23641/asha.166395100

Interaction imaging with amplitude-dependence force spectroscopy

Knowledge of surface forces is the key to understanding a large number of processes in fields ranging from physics to material science and biology. The most common method to study surfaces is dynamic atomic force microscopy (AFM). Dynamic AFM has been enormously successful in imaging surface topography, even to atomic resolution, but the force between the AFM tip and the surface remains unknown during imaging. Here, we present a new approach that combines high accuracy force measurements and high resolution scanning. The method, called amplitude-dependence force spectroscopy (ADFS) is based on the amplitude-dependence of the cantilever's response near resonance and allows for separate determination of both conservative and dissipative tip-surface interactions. We use ADFS to quantitatively study and map the nano-mechanical interaction between the AFM tip and heterogeneous polymer surfaces. ADFS is compatible with commercial atomic force microscopes and we anticipate its wide-spread use in taking AFM toward quantitative microscopy

Discriminative structural approaches for enzyme active-site prediction

<p>Abstract</p> <p>Background</p> <p>Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far.</p> <p>Results</p> <p>This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis.</p> <p>Conclusions</p> <p>This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.</p

Reproducibility of exhaled nitric oxide in smokers and non-smokers: relevance for longitudinal studies

<p>Abstract</p> <p>Background</p> <p>Currently, there is much interest in measuring fractional exhaled nitric oxide (<b>FE_NO</b>) in populations. We evaluated the reproducibility of <b>FE_NO</b>in healthy subjects and determined the number of subjects necessary to carry out a longitudinal survey of <b>FE_NO</b>in a population containing smokers and non-smokers, based on the assessed reproducibility.</p> <p>Methods</p> <p>The reproducibility of <b>FE_NO</b>was examined in 18 healthy smokers and 21 non-smokers. <b>FE_NO</b>was assessed once at 9 AM on five consecutive days; in the last day this measurement was repeated at 2 PM. Respiratory symptoms and medical history were assessed by questionnaire. The within- and between-session repeatability of <b>FE_NO</b>and log-transformed <b>FE_NO</b>was described. The power of a longitudinal study based on a relative increase in <b>FE_NO</b>was estimated using a bilateral t-test of the log-transformed <b>FE_NO</b>using the between-session variance of the assay.</p> <p>Results</p> <p><b>FE_NO</b>measurements were highly reproducible throughout the study. <b>FE_NO</b>was significantly higher in males than females regardless of smoking status. <b>FE_NO</b>was positively associated with height (p < 0.001), gender (p < 0.034), smoking (p < 0.0001) and percent FEV₁/FVC (p < 0.001) but not with age (p = 0.987). The between-session standard deviation was roughly constant on the log scale. Assuming the between-session standard deviation is equal to its longitudinal equivalent, either 111 or 29 subjects would be necessary to achieve an 80% power in detecting a 3% or a 10% increase in <b>FE_NO</b>respectively.</p> <p>Conclusion</p> <p>The good reproducibility of <b>FE_NO</b>is not influenced by gender or smoking habits. In a well controlled, longitudinal study it should allow detecting even small increases in <b>FE_NO</b>with a reasonable population size.</p

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs which represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.Comment: 34 pages, 7 figure

FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

The impact of ageing reveals distinct roles for human dentate gyrus and CA3 in pattern separation and object recognition memory

© 2017 The Author(s). Both recognition of familiar objects and pattern separation, a process that orthogonalises overlapping events, are critical for effective memory. Evidence is emerging that human pattern separation requires dentate gyrus. Dentate gyrus is intimately connected to CA3 where, in animals, an autoassociative network enables recall of complete memories to underpin object/event recognition. Despite huge motivation to treat age-related human memory disorders, interaction between human CA3 and dentate subfields is difficult to investigate due to small size and proximity. We tested the hypothesis that human dentate gyrus is critical for pattern separation, whereas, CA3 underpins identical object recognition. Using 3 T MR hippocampal subfield volumetry combined with a behavioural pattern separation task, we demonstrate that dentate gyrus volume predicts accuracy and response time during behavioural pattern separation whereas CA3 predicts performance in object recognition memory. Critically, human dentate gyrus volume decreases with age whereas CA3 volume is age-independent. Further, decreased dentate gyrus volume, and no other subfield volume, mediates adverse effects of aging on memory. Thus, we demonstrate distinct roles for CA3 and dentate gyrus in human memory and uncover the variegated effects of human ageing across hippocampal regions. Accurate pinpointing of focal memory-related deficits will allow future targeted treatment for memory loss

The influence of semantic and phonological factors on syntactic decisions: An event-related brain potential study

During language production and comprehension, information about a word's syntactic properties is sometimes needed. While the decision about the grammatical gender of a word requires access to syntactic knowledge, it has also been hypothesized that semantic (i.e., biological gender) or phonological information (i.e., sound regularities) may influence this decision. Event-related potentials (ERPs) were measured while native speakers of German processed written words that were or were not semantically and/or phonologically marked for gender. Behavioral and ERP results showed that participants were faster in making a gender decision when words were semantically and/or phonologically gender marked than when this was not the case, although the phonological effects were less clear. In conclusion, our data provide evidence that even though participants performed a grammatical gender decision, this task can be influenced by semantic and phonological factors