Template Database Research Articles

Promoting Recovery in Long-Term Institutional Mental Health Care: An International Delphi Study

Service provision in psychiatric and social care is increasingly guided by recovery principles. However, little is known about the degree of consensus among stakeholders in diverse contexts on the components of care that most promote recovery. This study aimed to identify specific items of care that key stakeholders regard as most important in promoting recovery for people with longer-term mental health problems in institutional care, to measure consensus between and across stakeholder groups and countries, and to develop a conceptual framework of the most important domains of care. Ten European countries in various stages of deinstitutionalization participated in a series of conventional three-round iterative Delphi exercises. In each country individuals in four separate expert groups (service users, mental health professionals, caregivers, and advocates) identified components of care that they considered important to recovery and then rated their group's suggestions in terms of importance. Median and consensus ratings were measured. High-ranking items were grouped into domains. A total of 4,098 separate items of care were proposed by the 40 participating groups. Eleven broad domains of care important for recovery were identified: social policy and human rights, social inclusion, self-management and autonomy, therapeutic interventions, governance, staffing, staff attitudes, institutional environment, postdischarge care, caregivers, and physical health care. Consensus between groups and countries was generally high, but some modest differences in priorities were noted. The most consistently highly rated consensus domain was therapeutic interventions. Domains and components of care related to recovery principles were also viewed as important across stakeholder groups.

Considering scores between unrelated proteins in the search database improves profile comparison

BackgroundProfile-based comparison of multiple sequence alignments is a powerful methodology for the detection remote protein sequence similarity, which is essential for the inference and analysis of protein structure, function, and evolution. Accurate estimation of statistical significance of detected profile similarities is essential for further development of this methodology. Here we analyze a novel approach to estimate the statistical significance of profile similarity: the explicit consideration of background score distributions for each database template (subject).ResultsUsing a simple scheme to combine and analytically approximate query- and subject-based distributions, we show that (i) inclusion of background distributions for the subjects increases the quality of homology detection; (ii) this increase is higher when the distributions are based on the scores to all known non-homologs of the subject rather than a small calibration subset of the database representatives; and (iii) these all known non-homolog distributions of scores for the subject make the dominant contribution to the improved performance: adding the calibration distribution of the query has a negligible additional effect.ConclusionThe construction of distributions based on the complete sets of non-homologs for each subject is particularly relevant in the setting of structure prediction where the database consists of proteins with solved 3D structure (PDB, SCOP, CATH, etc.) and therefore structural relationships between proteins are known. These results point to a potential new direction in the development of more powerful methods for remote homology detection.

Use of Templates to Identify Source of Norovirus Outbreak

Use of Templates to Identify Source of Norovirus Outbreak

Empirical Capacity of a Recognition Channel for Single- and Multipose Object Recognition Under the Constraint of PCA Encoding

The ability of practical recognition systems to recognize a large number of objects is constrained by a variety of factors that include choice of a feature extraction technique, quality of images, complexity and variability of underlying objects and of collected data. Given a feature extraction technique generating templates of objects from data and a resolution of the original images, the remaining factors can be attributed to distortions due to a recognition channel. We define the recognition channel as the environment that transforms reference templates of objects in a database into templates submitted for recognition. If templates in an object database are generated to be statistically independent and the noise in a query template is statistically independent of templates in the database, then the abilities of the recognition channel to recognize a large number of object classes can be characterized by a number called recognition capacity. In this paper, we evaluate the empirical recognition capacity of PCA-based object recognition systems. The encoded data (templates) and the additive noise in query templates are modeled to be Gaussian distributed with zero mean and estimated variances. We analyze both the case of a single encoded image and the case of encoded correlated multiple images. For this case, we propose a model that is orientation and elevation angle (pose) dependent. The fit of proposed models is judged using statistical goodness of fit tests. We define recognition rate as the ratio R=log(M)/n, where M is the number of objects to recognize and n is the length of PCA templates. The empirical capacity of PCA-based recognition systems is numerically evaluated. The empirical random coding exponent is also numerically evaluated and plotted as a function of the recognition rate. With these results, given a value of the recognition capacity and the length of templates (assume large), we can predict the number of distinct object classes that can be stored in an object library and be identified with probability of error close to zero.

Persistence modeling of angularly dependent synthetic aperture radar imagery

An image persistence framework is developed to analyze azimuthally varying synthetic aperture radar (SAR) data. The model focuses on cases containing rich aspect data from a single depression angle. The goal is to replace the data's intrinsic viewing geometry dependencies with target-specific dependencies. Both direct mapping functions and cost functions are presented for data transformation. An intensity-only mapping function is realized to illustrate the persistence model in terms of a canonical example, visualization, and classification. The limitations of an intensity-only mapping function are also discussed. Because the new image-space output from the persistence model is closely tied to the radio frequency (RF) characteristics of two different targets and not to the collection geometry, it shows promise for integration into various automated target recognition (ATR) algorithms. Target classification potential was tested using the MSTAR database and simplified template matching schemes. The intensity-only transformation function permits a very low number of persistence images to represent the general RF target characteristics of an assortment of vehicles. The ability to represent target characteristics with relatively low resources could also be beneficial in ATR applications. Overall, the persistence framework shows strong potential as a new tool that can be used in the analysis of multiaspect SAR images.

An effective defect inspection system for polarized film images using image segmentation and template matching techniques

In this paper, we present an effective defect inspection system that identifies film defects and determines their types in order to produce polarized films for TFT-LCD (thin film transistor – liquid crystal display). The proposed system is designed and implemented to find defects from polarized film images using image segmentation techniques and to determine defect types through the image analysis of detected defects using template matching techniques. We extract features of the defects such as shape and texture, and compare them to the features of referential defect images stored in a template database. Experimental results using the proposed system show that it identifies defects of test images effectively (Recall = 1.00, Precision = 0.95) and efficiently (Average response time = 0.64 s), and also achieves a high correctness in determining the types (Recall = 0.95, Precision = 0.96) for five classes of defects. In addition the experiment shows that our system is fairly robust with respect to the rotational transformation, achieving the desirable property of the rotation invariance.

PROTCOM: searchable database of protein complexes enhanced with domain-domain structures

The database of protein complexes (PROTCOM) is a compilation of known 3D structures of protein–protein complexes enriched with artificially created domain–domain structures using the available entries in the Protein Data Bank. The domain–domain structures are generated by parsing single chain structures into loosely connected domains and are important features of the database. The database () could be used for benchmarking purposes of the docking and other algorithms for predicting 3D structures of protein–protein complexes. The database can be utilized as a template database in the homology or threading methods for modeling the 3D structures of unknown protein–protein complexes. PROTCOM provides the scientific community with an integrated set of tools for browsing, searching, visualizing and downloading a pool of protein complexes. The user is given the option to select a subset of entries using a combination of up to 10 different criteria. As on July 2006 the database contains 1770 entries, each of which consists of the known 3D structures and additional relevant information that can be displayed either in text-only or in visual mode.

Radar HRRP target recognition based on higher order spectra

Radar high-resolution range profile (HRRP) is very sensitive to time-shift and target-aspect variation; therefore, HRRP-based radar automatic target recognition (RATR) requires efficient time-shift invariant features and robust feature templates. Although higher order spectra are a set of well-known time-shift invariant features, direct use of them (except for power spectrum) is impractical due to their complexity. A method for calculating the Euclidean distance in higher order spectra feature space is proposed in this paper, which avoids calculating the higher order spectra, effectively reducing the computation complexity and storage requirement. Moreover, according to the widely used scattering center model, theoretical analysis and experimental results in this paper show that the feature vector extracted from the average profile in a small target-aspect sector has better generalization performance than the average feature vector in the same sector when both of them are used as feature templates in HRRP-based RATR. The proposed Euclidean distance calculation method and average profile-based template database are applied to two classification algorithms [the template matching method (TMM) and the radial basis function network (RBFN)] to evaluate the recognition performances of higher order spectra features. Experimental results for measured data show that the power spectrum has the best recognition performance among higher order spectra.

Comparing and combining a semantic tagger and a statistical tool for MWE extraction

Automatic extraction of multiword expressions (MWEs) presents a tough challenge for the NLP community and corpus linguistics. Indeed, although numerous knowledge-based symbolic approaches and statistically driven algorithms have been proposed, efficient MWE extraction still remains an unsolved issue. In this paper, we evaluate the Lancaster UCREL Semantic Analysis System (henceforth USAS (Rayson, P., Archer, D., Piao, S., McEnery, T., 2004. The UCREL semantic analysis system. In: Proceedings of the LREC-04 Workshop, Beyond Named Entity Recognition Semantic labelling for NLP tasks, Lisbon, Portugal. pp. 7–12)) for MWE extraction, and explore the possibility of improving USAS by incorporating a statistical algorithm. Developed at Lancaster University, the USAS system automatically annotates English corpora with semantic category information. Employing a large-scale semantically classified multi-word expression template database, the system is also capable of detecting many multiword expressions, as well as assigning semantic field information to the MWEs extracted. Whilst USAS therefore offers a unique tool for MWE extraction, allowing us to both extract and semantically classify MWEs, it can sometimes suffer from low recall. Consequently, we have been comparing USAS, which employs a symbolic approach, to a statistical tool, which is based on collocational information, in order to determine the pros and cons of these different tools, and more importantly, to examine the possibility of improving MWE extraction by combining them. As we report in this paper, we have found a highly complementary relation between the different tools: USAS missed many domain-specific MWEs (law/court terms in this case), and the statistical tool missed many commonly used MWEs that occur in low frequencies (lower than three in this case). Due to their complementary relation, we are proposing that MWE coverage can be significantly increased by combining a lexicon-based symbolic approach and a collocation-based statistical approach.

Have we seen all structures corresponding to short protein fragments in the Protein Data Bank? An update

Assembling short fragments from known structures has been a widely used approach to construct novel protein structures. To what extent there exist structurally similar fragments in the database of known structures for short fragments of a novel protein is a question that is fundamental to this approach. This work addresses that question for seven-, nine- and 15-residue fragments. For each fragment size, two databases, a query database and a template database of fragments from high-quality protein structures in SCOP20 and SCOP90, respectively, were constructed. For each fragment in the query database, the template database was scanned to find the lowest r.m.s.d. fragment among non-homologous structures. For seven-residue fragments, there is a 99% probability that there exists such a fragment within 0.7 A r.m.s.d. for each loop fragment. For nine-residue fragments there is a 96% probability of a fragment within 1 A r.m.s.d., while for 15-residue fragments there is a 91% probability of a fragment within 2 A r.m.s.d. These results, which update previous studies, show that there exists sufficient coverage to model even a novel fold using fragments from the Protein Data Bank, as the current database of known structures has increased enormously in the last few years. We have also explored the use of a grid search method for loop homology modeling and make some observations about the use of a grid search compared with a database search for the loop modeling problem.

A novel web application frame developed by MVC

The MVC (Model/View/Controller) design pattern was developed in Smalltalk-80 and widely used in software design. This paper introduces a novel Web application frame based on MVC. This frame separates the transaction logic from the presentation format. It also improves the system maintain-ability, scalability and performance by using the module data-base, template database, messaging object and buffer queue.

Speech synthesis employing prosody templates

Prosody templates, constructed during system design, store intonation (F0) and duration information based on syllabic stress patterns for the target word. The prosody templates are constructed so that words exhibiting the same stress pattern will be assigned the same prosody template. The prosody template information is preferably stored in a normalized form to reduce noise level in the statistical measures. The synthesizer uses a word dictionary that specifies the stress patterns associated with each stored word. These stress patterns are used to access the prosody template database. F0 and duration information is then extracted from the selected template, de-normalized and applied to the phonemic information to produce a natural human-sounding prosody in the synthesized output.

Planar shape recognition by shape morphing

A novel method based on shape morphing is proposed for 2D shape recognition. In this framework, the shape of objects is described by using their contour. Shape recognition involves a morph between the contours of the objects being compared. The morph is quantified by using a physics-based formulation. This quantification is used as a dissimilarity measure to find the reference shape most similar to the input. The dissimilarity measure is shown to have the properties of a metric as well as invariance to Euclidean transformations. The recognition paradigm is applicable to both convex and non-convex shapes. Moreover, the applicability of the method is not constrained to closed shapes. Based on the metric properties of the dissimilarity method, a search strategy is described that obviates an exhaustive search of the template database during recognition experiments. Experimental results on the recognition of various types of shapes are presented.

Method for automatic speech recognition in telephony

Disclosed is a method and system for utilizing supplemental data to enable a voice-driven telecommunication services system to build a speech recognition template data base. Such supplemental data includes textual name data associated with many or all of a telecommunication subscriber's incoming and outgoing calls. The textual name data, which typically represents the calling or called party's name, is used to generate a speech recognition template using text-to-speech methods. The number and the speech recognition template may then be recorded in a data base to facilitate voice-driven services in connection with that party. A method according to the present invention includes obtaining a first input, where the first input identifies an aspect of a telecommunication network user. The first input may, for example, be the telephone number of the telecommunication network user. Then, supplemental information that is related to the first input in a supplemental data base is identified and retrieved from the supplemental data base. The supplemental information may, for example, be the textual name data associated with the telecommunication network user. This supplemental information is then utilized to create a speech recognition template. The speech recognition template may thereafter be used to recognize a second input, said second input comprising a spoken utterance and containing information representative of the supplemental information.

An automated recording system for page oriented volume holographic memories

A system for the automatic acquisition and recording of images from video input scenes to a volume holographic optical memory has been designed and constructed. This memory is intended for use as a template database for reference images for an optical correlator. The system has been tested and commissioned; initial results indicate that system parameters such as hologram reconstruction quality are of a sufficiently high standard for the application.

A clinical research data base system for the Apple IIe/IIc

A data base program for some types of patient visits, especially medical procedures, is described. It would be primarily useful as a tool for clinical research, but its implementation would depend on its contributing to the routine documentation of patient visits (i.e. generate typed reports appropriate for charting). To achieve this, and to standardize the information to be collected, several concepts have been developed: a data base template; a branched data structure; bit-mapped, multi-option data fields; and flexible, efficient search capabilities. The program is presently configured for the Apple IIe/IIc and it is not a substitute for keeping patient records (although it may be helpful). It is presented as an approach to the “computerization” of medical information developed through a combined appreciation of basic computer capabilities, and an awareness of how some clinical information may be categorized.

OPTIMIZING STANDARDS FOR COLLECTING AGRICULTURAL DATA FOR ANALYTICAL PURPOSES

The article discusses the importance of using big data in agriculture. Technologies of "big" imply the processing of a huge amount of data of various structured and unstructured data, as well as the use of various tools, methods of their processing, which allowing to analyze information, special solutions for specific goals and objectives. Extracting data from such data sets and improving its intellectual analysis, open up the opportunity for agricultural producers to improve the quality of decisions made and timely, more efficient methods of farming. The developed database templates are presented, they which fully take into account the features of the collected and analyzed information in the interests of agricultural producers and industry experts. The task was set in the work to create a flexible structure that can be quickly supplemented with new relevant information. A test filling of the developed templates with primary information to check the performance of the created database structure was carried out. Often statistics is undertaken not for the purpose for obtaining objective information, but for some kind of "political" or market manipulation. Additionally, there is a problem of "discontinuity" of data collected during the period of another campaigns, which after a while they’re gone. Using standards to ensure interoperability between systems is a key requirement for effective information integration. Taking into account the experience of filling in the creating database as well as the tested possibilities of using, the requirements for the data collection standards of agricultural producers were formulated.