Explore the vast range of titles available.

Pattern Recognition on Oriented Matroids covers a range of innovative problems in combinatorics, poset and graph theories, optimization, and number theory that constitute a far-reaching extension of the arsenal of committee methods in pattern recognition.

In this study we determined protein and gene expression in the caeca of newly hatched chickens inoculated with cecal contents sourced from hens of different ages. Over 250 proteins exhibited modified expression levels in response to microbiota inoculation. The most significant inductions were observed for ISG12-2, OASL, ES1, LYG2, DMBT1-L, CDD, ANGPTL6, B2M, CUZD1, IgM and Ig lambda chain. Of these, ISG12-2, ES1 and both immunoglobulins were expressed at lower levels in germ-free chickens compared to conventional chickens. In contrast, CELA2A, BRT-2, ALDH1A1, ADH1C, AKR1B1L, HEXB, ALDH2, ALDOB, CALB1 and TTR were expressed at lower levels following inoculation of microbiota. When chicks were given microbiota preparations from different age donors, the recipients mounted differential responses to the inoculation which also differed from the response profile in naturally colonised birds. For example, B2M, CUZD1 and CELA2A responded differently to the inoculation with microbiota of 4- or 40-week-old hens. The increased or decreased gene expression could be recorded 6 weeks after the inoculation of newly hatched chickens. To characterise the proteins that may directly interact with the microbiota we characterised chicken proteins that co-purified with the microbiota and identified a range of host proteins including CDD, ANGPTL6, DMBT1-L, MEP1A and Ig lambda. We propose that induction of ISG12-2 results in reduced apoptosis of host cells exposed to the colonizing commensal microbiota and that CDD, ANGPTL6, DMBT1-L, MEP1A and Ig lambda reduce contact of luminal microbiota with the gut epithelium thereby reducing the inflammatory response.

The problem of discriminant analysis under mild conditions is reduced to a system of linear inequalities. However, this system can turn out to be inconsistent, which happens rather frequently. This is when the method of committees is used. The quality of a committee is improved as the number of its members drops. A method of reducing the number of members of committees, if this is fundamentally possible, is considered.

Objective:To create a more culturally relevant developmental assessment tool for use in children in rural Africa.Design:Through focus groups, piloting work and validation, a more culturally appropriate developmental tool, based on the style of the Denver II, was created. Age standardised norms were estimated using 1130 normal children aged 0–6 years from a rural setting in Malawi. The performance of each item in the tool was examined through goodness of fit on logistic regression, reliability and interpretability at a consensus meeting. The instrument was revised with removal of items performing poorly.Results:An assessment tool with 138 items was created. Face, content and respondent validity was demonstrated. At the consensus meeting, 97% (33/34) of gross motor items were retained in comparison to 51% (18/35) of social items, and 86% (69/80) of items from the Denver II or Denver Developmental Screening Test (DDST) were retained in comparison to 69% (32/46) of the newly created items, many of these having poor reliability and goodness of fit. Gender had an effect on 23% (8/35) of the social items, which were removed. Items not attained by 6 years came entirely from the Denver II fine motor section (4/34). Overall, 110 of the 138 items (80%) were retained in the revised instrument with some items needing further modification.Conclusions:When creating developmental tools for a rural African setting, many items from Western tools can be adapted. The gross motor domain is more culturally adaptable, whereas social development is difficult to adapt and is culturally specific.

A review of the literature was conducted for articles published between 1975 and 2007 on the application of time delay as an instructional procedure to teach word and picture recognition to students with severe developmental disabilities in an effort to evaluate time delay as an evidence-based practice. A total of 30 experiments were analyzed using quality indicators for single-subject design research. In general, we found that time delay was an evidence-based practice for teaching picture and sight word recognition supported by standards for evidence-based practice proposed by Horner et al. (2005). We discuss lessons learned in summarizing a body of literature to define an evidence-based practice and suggestions for better defining the practice.

Background: Improving the detection of drug safety signals has led several pharmacovigilance regulatory agencies to incorporate automated quantitative methods into their spontaneous reporting management systems. The three largest worldwide pharmacovigilance databases are routinely screened by the lower bound of the 95% confidence interval of proportional reporting ratio (PRR 02.5 ), the 2.5% quantile of the Information Component (IC 02.5 ) or the 5% quantile of the Gamma Poisson Shrinker (GPS 05 ). More recently, Bayesian and non-Bayesian False Discovery Rate (FDR)-based methods were proposed that address the arbitrariness of thresholds and allow for a built-in estimate of the FDR. These methods were also shown through simulation studies to be interesting alternatives to the currently used methods. Objective: The objective of this work was twofold. Based on an extensive retrospective study, we compared PRR 02.5 , GPS 05 and IC 02.5 with two FDR-based methods derived from the Fisher’s exact test and the GPS model (GPS pH0 [posterior probability of the null hypothesis H 0 calculated from the Gamma Poisson Shrinker model]). Secondly, restricting the analysis to GPS pH0 , we aimed to evaluate the added value of using automated signal detection tools compared with ‘traditional’ methods, i.e. non-automated surveillance operated by pharmacovigilance experts. Methods: The analysis was performed sequentially, i.e. every month, and retrospectively on the whole French pharmacovigilance database over the period 1 January 1996–1 July 2002. Evaluation was based on a list of 243 reference signals (RSs) corresponding to investigations launched by the French Pharmacovigilance Technical Committee (PhVTC) during the same period. The comparison of detection methods was made on the basis of the number of RSs detected as well as the time to detection. Results: Results comparing the five automated quantitative methods were in favour of GPS pH0 in terms of both number of detections of true signals and time to detection. Additionally, based on an FDR threshold of 5%, GPS pH0 detected 87% of the RSs associated with more than three reports, anticipating the date of investigation by the PhVTC by 15.8 months on average. Conclusions: Our results show that as soon as there is reasonable support for the data, automated signal detection tools are powerful tools to explore large spontaneous reporting system databases and detect relevant signals quickly compared with traditional pharmacovigilance methods.

We propose a hierarchical (nested) variant of a recurrent random neural network (RNN) with reinforced learning, introduced by Gelenbe. Each neuron (committee) in a top-level RNN represents a different bottom-level RNN (or sub-committee). The bottom-level RNNs choose the best routing and the top-level RNN chooses the currently best bottom-level RNN. Each of the bottom RNNs is trained in a different way. When they differ in their choice of the best path, several cognitive packets are routed according to the different decisions. In that case, a respective ACK packet trains individual bottom RNNs and not all bottom RNNs at once. An example presents an optimisation of a real-time routing in a dense mesh network of wireless sensors relaying small metering messages between each other, until the messages reach a common gateway. The network is experiencing a periodic electromagnetic interference. The hierarchical variant causes a small increase in the number of smart packets but allows a considerably better routing quality.

Background Facial expressions are important in facilitating human communication and interactions. Also, they are used as an important tool in behavioural studies and in medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for non-invasive mood detection. The purpose of the present study was to develop an intelligent system for facial image based expression classification using committee neural networks. Methods Several facial parameters were extracted from a facial image and were used to train several generalized and specialized neural networks. Based on initial testing, the best performing generalized and specialized neural networks were recruited into decision making committees which formed an integrated committee neural network system. The integrated committee neural network system was then evaluated using data obtained from subjects not used in training or in initial testing. Results and conclusion The system correctly identified the correct facial expression in 255 of the 282 images (90.43% of the cases), from 62 subjects not used in training or in initial testing. Committee neural networks offer a potential tool for image based mood detection.

Here is this week’s news of grants or gifts to historically Black colleges and universities or for programs of particular interest to African Americans in higher education.

This paper investigates efferent and afferent conductions of the central nervous system by various evoked potentials in patients with adrenomyeloneuropathy (AMN). Ten pure AMN patients without cerebral involvement were studied. Motor evoked potentials (MEPs), somatosensory evoked potentials (SEPs), auditory brainstem response (ABR), and pattern reversal full-field visual evoked potentials (VEPs) were recorded. For MEP recording, single-pulse or double-pulse magnetic brainstem stimulation (BST) was also performed. Abnormal MEP was observed in all ten patients, abnormal SEP in all ten, abnormal ABR in nine, and abnormal VEP in only one. Brainstem latency was measured in three of the seven patients with central motor conduction time (CMCT) prolongation. The cortical–brainstem conduction time was severely prolonged along the normal or mildly delayed brainstem–cervical conduction time in those three patients. The pattern of normal VEP and abnormal MEP, SEP, ABR is a clinically useful electrophysiological feature for the diagnosis. BST techniques are helpful to detect, functionally, intracranial corticospinal tract involvement, probably demyelination, in pure AMN patients.