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

Narrative skills in deaf children who use spoken English: Dissociations between macro and microstructural devices

Previous research has highlighted that deaf children acquiring spoken English have difficulties in narrative development relative to their hearing peers both in terms of macro-structure and with micro-structural devices. The majority of previous research focused on narrative tasks designed for hearing children that depend on good receptive language skills. The current study compared narratives of 6 to 11-year-old deaf children who use spoken English (N=59) with matched for age and non-verbal intelligence hearing peers. To examine the role of general language abilities, single word vocabulary was also assessed. Narratives were elicited by the retelling of a story presented non-verbally in video format. Results showed that deaf and hearing children had equivalent macro-structure skills, but the deaf group showed poorer performance on micro-structural components. Furthermore, the deaf group gave less detailed responses to inferencing probe questions indicating poorer understanding of the story's underlying message. For deaf children, micro-level devices most strongly correlated with the vocabulary measure. These findings suggest that deaf children, despite spoken language delays, are able to convey the main elements of content and structure in narrative but have greater difficulty in using grammatical devices more dependent on finer linguistic and pragmatic skills

A Finite Element Test Bed for Diffraction Tomography

Finite element analysis methods have been successfully applied to the study of ultrasonic wave propagation in elastic solids [1–4]. As a natural part of such numerical solutions. displacements are predicted for every node of the spatial discretization describing the solids geometry and at every instant of time in the temporal discretization used to define the pulse propagation through the material. All of the data constitute a solution to the forward problem and can be used to visualize wavefront propagation and interactions with defects, thus predicting displacement signals at any point in or on the solid

A UMLS-based spell checker for natural language processing in vaccine safety

BACKGROUND: The Institute of Medicine has identified patient safety as a key goal for health care in the United States. Detecting vaccine adverse events is an important public health activity that contributes to patient safety. Reports about adverse events following immunization (AEFI) from surveillance systems contain free-text components that can be analyzed using natural language processing. To extract Unified Medical Language System (UMLS) concepts from free text and classify AEFI reports based on concepts they contain, we first needed to clean the text by expanding abbreviations and shortcuts and correcting spelling errors. Our objective in this paper was to create a UMLS-based spelling error correction tool as a first step in the natural language processing (NLP) pipeline for AEFI reports. METHODS: We developed spell checking algorithms using open source tools. We used de-identified AEFI surveillance reports to create free-text data sets for analysis. After expansion of abbreviated clinical terms and shortcuts, we performed spelling correction in four steps: (1) error detection, (2) word list generation, (3) word list disambiguation and (4) error correction. We then measured the performance of the resulting spell checker by comparing it to manual correction. RESULTS: We used 12,056 words to train the spell checker and tested its performance on 8,131 words. During testing, sensitivity, specificity, and positive predictive value (PPV) for the spell checker were 74% (95% CI: 74–75), 100% (95% CI: 100–100), and 47% (95% CI: 46%–48%), respectively. CONCLUSION: We created a prototype spell checker that can be used to process AEFI reports. We used the UMLS Specialist Lexicon as the primary source of dictionary terms and the WordNet lexicon as a secondary source. We used the UMLS as a domain-specific source of dictionary terms to compare potentially misspelled words in the corpus. The prototype sensitivity was comparable to currently available tools, but the specificity was much superior. The slow processing speed may be improved by trimming it down to the most useful component algorithms. Other investigators may find the methods we developed useful for cleaning text using lexicons specific to their area of interest

Narrow band imaging for the detection of gastric intestinal metaplasia and dysplasia during surveillance endoscopy

Background: Surveillance of premalignant gastric lesions relies mainly on random biopsy sampling. Narrow band imaging (NBI) may enhance the accuracy of endoscopic surveillance of intestinal metaplasia (IM) and dysplasia. We aimed to compare the yield of NBI to white light endoscopy (WLE) in the surveillance of patients with IM and dysplasia. Methods: Patients with previously identified gastric IM or dysplasia underwent a surveillance endoscopy. Both WLE and NBI were performed in all patients during a single procedure. The sensitivity of WLE and NBI for the detection of premalignant lesions was calculated by correlating endoscopic findings to histological diagnosis. Results: Forty-three patients (28 males and 15 females, mean age 59 years) were included. IM was diagnosed in 27 patients; 20 were detected by NBI and WLE, four solely by NBI and three by random biopsies only. Dysplasia was detected in seven patients by WLE and NBI and in two patients by random biopsies only. Sixty-eight endoscopically detected lesions contained IM: 47 were detected by WLE and NBI, 21 by NBI only. Nine endoscopically detected lesions demonstrated dysplasia: eight were detected by WLE and NBI, one was detected by NBI only. The sensitivity, specificity, positive and negative predictive values for detection of premalignant lesions were 71, 58, 65 and 65% for NBI and 51, 67, 62 and 55% for WLE, respectively. Conclusions: NBI increases the diagnostic yield for detection of advanced premalignant gastric lesions compared to routine WLE

The role of parental achievement goals in predicting autonomy-supportive and controlling parenting

Although autonomy-supportive and controlling parenting are linked to numerous positive and negative child outcomes respectively, fewer studies have focused on their determinants. Drawing on achievement goal theory and self-determination theory, we propose that parental achievement goals (i.e., achievement goals that parents have for their children) can be mastery, performance-approach or performance-avoidance oriented and that types of goals predict mothers' tendency to adopt autonomy-supportive and controlling behaviors. A total of 67 mothers (aged 30-53 years) reported their goals for their adolescent (aged 13-16 years; 19.4 % girls), while their adolescent evaluated their mothers' behaviors. Hierarchical regression analyses showed that parental performance-approach goals predict more controlling parenting and prevent acknowledgement of feelings, one autonomy-supportive behavior. In addition, mothers who have mastery goals and who endorse performance-avoidance goals are less likely to use guilt-inducing criticisms. These findings were observed while controlling for the effect of maternal anxiety

Lead exposure in adult males in urban Transvaal Province, South Africa during the apartheid era

Human exposure to lead is a substantial public health hazard worldwide and is particularly problematic in the Republic of South Africa given the country’s late cessation of leaded petrol. Lead exposure is associated with a number of serious health issues and diseases including developmental and cognitive deficiency, hypertension and heart disease. Understanding the distribution of lifetime lead burden within a given population is critical for reducing exposure rates. Femoral bone from 101 deceased adult males living in urban Transvaal Province (now Gauteng Province), South Africa between 1960 and 1998 were analyzed for lead concentration by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Of the 72 black and 29 white individuals sampled, chronic lead exposure was apparent in nearly all individuals. White males showed significantly higher median bone lead concentration (ME = 10.04 µg·g−1), than black males (ME = 3.80 µg·g−1) despite higher socioeconomic status. Bone lead concentration covaries significantly, though weakly, with individual age. There was no significant temporal trend in bone lead concentration. These results indicate that long-term low to moderate lead exposure is the historical norm among South African males. Unexpectedly, this research indicates that white males in the sample population were more highly exposed to lead

A multivariate logistic regression equation to screen for dysglycaemia: development and validation

Aims  To develop and validate an empirical equation to screen for dysglycaemia [impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and undiagnosed diabetes]. Methods  A predictive equation was developed using multiple logistic regression analysis and data collected from 1032 Egyptian subjects with no history of diabetes. The equation incorporated age, sex, body mass index (BMI), post-prandial time (self-reported number of hours since last food or drink other than water), systolic blood pressure, high-density lipoprotein (HDL) cholesterol and random capillary plasma glucose as independent covariates for prediction of dysglycaemia based on fasting plasma glucose (FPG) ≥ 6.1 mmol/l and/or plasma glucose 2 h after a 75-g oral glucose load (2-h PG) ≥ 7.8 mmol/l. The equation was validated using a cross-validation procedure. Its performance was also compared with static plasma glucose cut-points for dysglycaemia screening. Results  The predictive equation was calculated with the following logistic regression parameters: P  = 1 + 1/(1 + e −X ) = where X = −8.3390 + 0.0214 (age in years) + 0.6764 (if female) + 0.0335 (BMI in kg/m 2 ) + 0.0934 (post-prandial time in hours) + 0.0141 (systolic blood pressure in mmHg) − 0.0110 (HDL in mmol/l) + 0.0243 (random capillary plasma glucose in mmol/l). The cut-point for the prediction of dysglycaemia was defined as a probability ≥ 0.38. The equation's sensitivity was 55%, specificity 90% and positive predictive value (PPV) 65%. When applied to a new sample, the equation's sensitivity was 53%, specificity 89% and PPV 63%. Conclusions  This multivariate logistic equation improves on currently recommended methods of screening for dysglycaemia and can be easily implemented in a clinical setting using readily available clinical and non-fasting laboratory data and an inexpensive hand-held programmable calculator. Diabet. Med. 22, 599–605 (2005)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75603/1/j.1464-5491.2005.01467.x.pd

Performativity, border-crossings and ethics in a prison-based creative writing class

We critically reflect on insights from our experiences as female researchers on a creative writing project in a men’s prison, including the emotional impact on the men involved and the ways in which our role as participant researchers impacted deeply on us. Juxtaposed starkly with the physical constraints of the prison, a sense of journeys emerged as significant throughout the study, particularly the symbolic crossing of boundaries. We draw on theories of performativity from both Feminist and Symbolic Interactionist perspectives to frame our understanding of the experience of being participant researchers in prison creative writing workshops, and also consider associated ethical issues

Reduced projection angles for binary tomography with particle aggregation

This paper extends particle aggregate reconstruction technique (PART), a reconstruction algorithm for binary tomography based on the movement of particles. PART supposes that pixel values are particles, and that particles diffuse through the image, staying together in regions of uniform pixel value known as aggregates. In this work, a variation of this algorithm is proposed and a focus is placed on reducing the number of projections and whether this impacts the reconstruction of images. The algorithm is tested on three phantoms of varying sizes and numbers of forward projections and compared to filtered back projection, a random search algorithm and to SART, a standard algebraic reconstruction method. It is shown that the proposed algorithm outperforms the aforementioned algorithms on small numbers of projections. This potentially makes the algorithm attractive in scenarios where collecting less projection data are inevitable