A comparison of mycolic acid analysis for nontuberculous mycobacteria identification by thin-layer chromatography and molecular methods

The development of fast, inexpensive, and reliable tests to identify nontuberculous mycobacteria (NTM) is needed. Studies have indicated that the conventional identification procedures, including biochemical assays, are imprecise. This study evaluated a proposed alternative identification method in which 83 NTM isolates, previously identified by conventional biochemical testing and in-house M. avium IS1245-PCR amplification, were submitted to the following tests: thin-layer chromatography (TLC) of mycolic acids and PCR-restriction enzyme analysis of hsp65 (PRA). High-performance liquid chromatography (HPLC) analysis of mycolic acids and Southern blot analysis for M. avium IS1245 were performed on the strains that evidenced discrepancies on either of the above tests. Sixty-eight out of 83 (82%) isolates were concordantly identified by the presence of IS1245 and PRA and by TLC mycolic acid analysis. Discrepant results were found between the phenotypic and molecular tests in 12/83 (14.4%) isolates. Most of these strains were isolated from non-sterile body sites and were most probably colonizing in the host tissue. While TLC patterns suggested the presence of polymycobacterial infection in 3/83 (3.6%) cultures, this was the case in only one HPLC-tested culture and in none of those tested by PRA. The results of this study indicated that, as a phenotypic identification procedure, TLC mycolic acid determination could be considered a relatively simple and cost-effective method for routine screening of NTM isolates in mycobacteriology laboratory practice with a potential for use in developing countries. Further positive evidence was that this method demonstrated general agreement on MAC and M. simiae identification, including in the mixed cultures that predominated in the isolates of the disseminated infections in the AIDS patients under study. In view of the fact that the same treatment regimen is recommended for infections caused by these two species, TLC mycolic acid analysis may be a useful identification tool wherever molecular methods are unaffordable.

Measuring and analyzing color and texture information in anatomical leaf cross sections: An approach using computer vision to aid plant species identification

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Are Greenhouse Gas Signals of Northern Hemisphere winter extra-tropical cyclone activity dependent on the identification and tracking algorithm?

For Northern Hemisphere extra-tropical cyclone activity, the dependency of a potential anthropogenic climate change signal on the identification method applied is analysed. This study investigates the impact of the used algorithm on the changing signal, not the robustness of the climate change signal itself. Using one single transient AOGCM simulation as standard input for eleven state-of-the-art identification methods, the patterns of model simulated present day climatologies are found to be close to those computed from re-analysis, independent of the method applied. Although differences in the total number of cyclones identified exist, the climate change signals (IPCC SRES A1B) in the model run considered are largely similar between methods for all cyclones. Taking into account all tracks, decreasing numbers are found in the Mediterranean, the Arctic in the Barents and Greenland Seas, the mid-latitude Pacific and North America. Changing patterns are even more similar, if only the most severe systems are considered: the methods reveal a coherent statistically significant increase in frequency over the eastern North Atlantic and North Pacific. We found that the differences between the methods considered are largely due to the different role of weaker systems in the specific methods.

The Quiet Eye and Information Processing: Facilitation of Stimulus Identification and Response Selection

Background: A prerequisite for high performance in motor tasks is the acquisition of egocentric sensory information that must be translated into motor actions. A phenomenon that supports this process is the Quiet Eye (QE) defined as long final fixation before movement initiation. It is assumed that the QE facilitates information processing, particularly regarding movement parameterization. Aims: The question remains whether this facilitation also holds for the information-processing stage of response selection and – related to perception crucial – stage of stimulus identification. Method: In two experiments with sport science students, performance-enhancing effects of experimentally manipulated QE durations were tested as a function of target position predictability and target visibility, thereby selectively manipulating response selection and stimulus identification demands, respectively. Results: The results support the hypothesis of facilitated information processing through long QE durations since in both experiments performance-enhancing effects of long QE durations were found under increased processing demands only. In Experiment 1, QE duration affected performance only if the target position was not predictable and positional information had to be processed over the QE period. In Experiment 2, in a full vs. no target visibility comparison with saccades to the upcoming target position induced by flicker cues, the functionality of a long QE duration depended on the visual stimulus identification period as soon as the interval falls below a certain threshold. Conclusions: The results corroborate earlier findings that QE efficiency depends on demands put on the visuomotor system, thereby furthering the assumption that the phenomenon supports the processes of sensorimotor integration.

Computational Methods for Identification of Vibrating Structures

System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system" [1]. In the context of civil engineering, the system refers to a large scale structure such as a building, bridge, or an offshore structure, and identification mostly involves the determination of modal parameters (the natural frequencies, damping ratios, and mode shapes). This paper presents some modal identification results obtained using a state-of-the-art time domain system identification method (data-driven stochastic subspace algorithms [2]) applied to the output-only data measured in a steel arch bridge. First, a three dimensional finite element model was developed for the numerical analysis of the structure using ANSYS. Modal analysis was carried out and modal parameters were extracted in the frequency range of interest, 0-10 Hz. The results obtained from the finite element modal analysis were used to determine the location of the sensors. After that, ambient vibration tests were conducted during April 23-24, 2009. The response of the structure was measured using eight accelerometers. Two stations of three sensors were formed (triaxial stations). These sensors were held stationary for reference during the test. The two remaining sensors were placed at the different measurement points along the bridge deck, in which only vertical and transversal measurements were conducted (biaxial stations). Point estimate and interval estimate have been carried out in the state space model using these ambient vibration measurements. In the case of parametric models (like state space), the dynamic behaviour of a system is described using mathematical models. Then, mathematical relationships can be established between modal parameters and estimated point parameters (thus, it is common to use experimental modal analysis as a synonym for system identification). Stable modal parameters are found using a stabilization diagram. Furthermore, this paper proposes a method for assessing the precision of estimates of the parameters of state-space models (confidence interval). This approach employs the nonparametric bootstrap procedure [3] and is applied to subspace parameter estimation algorithm. Using bootstrap results, a plot similar to a stabilization diagram is developed. These graphics differentiate system modes from spurious noise modes for a given order system. Additionally, using the modal assurance criterion, the experimental modes obtained have been compared with those evaluated from a finite element analysis. A quite good agreement between numerical and experimental results is observed.

Richards growth model and viability indicators for populations subject to interventions

In this work we study the problem of modeling identification of a population employing a discrete dynamic model based on the Richards growth model. The population is subjected to interventions due to consumption, such as hunting or farming animals. The model identification allows us to estimate the probability or the average time for a population number to reach a certain level. The parameter inference for these models are obtained with the use of the likelihood profile technique as developed in this paper. The identification method here developed can be applied to evaluate the productivity of animal husbandry or to evaluate the risk of extinction of autochthon populations. It is applied to data of the Brazilian beef cattle herd population, and the the population number to reach a certain goal level is investigated.

UNIQUENESS AND NONUNIQUENESS RESULTS FOR A CERTAIN CLASS OF ALMOST PERIODIC DISTRIBUTIONS

We consider distributions u is an element of S'(R) of the form u(t) = Sigma(n is an element of N) a(n)e(i lambda nt), where (a(n))(n is an element of N) subset of C and Lambda = (lambda n)(n is an element of N) subset of R have the following properties: (a(n))(n is an element of N) is an element of s', that is, there is a q is an element of N such that (n(-q) a(n))(n is an element of N) is an element of l(1); for the real sequence., there are n(0) is an element of N, C > 0, and alpha > 0 such that n >= n(0) double right arrow vertical bar lambda(n)vertical bar >= Cn(alpha). Let I(epsilon) subset of R be an interval of length epsilon. We prove that for given Lambda, (1) if Lambda = O(n(alpha)) with alpha < 1, then there exists epsilon > 0 such that u vertical bar I(epsilon) = 0 double right arrow u 0; (2) if Lambda = O(n) is uniformly discrete, then there exists epsilon > 0 such that u vertical bar I(epsilon) = 0 double right arrow u 0; (3) if alpha > 1 and. is uniformly discrete, then for all epsilon > 0, u vertical bar I(epsilon) = 0 double right arrow u = 0. Since distributions of the above mentioned form are very common in engineering, as in the case of the modeling of ocean waves, signal processing, and vibrations of beams, plates, and shells, those uniqueness and nonuniqueness results have important consequences for identification problems in the applied sciences. We show an identification method and close this article with a simple example to show that the recovery of geometrical imperfections in a cylindrical shell is possible from a measurement of its dynamics.

Työterveys- ja työturvallisuusjohtamisjärjestelmän (OHSAS 18001) kehittäminen

Yritykset kiinnittävät yhä enemmän huomiota toimintaansa ja sen tasoon. Viime vuosien aikana on keskitytty laatu- ja ympäristöasioiden lisäksi myös yrityksen työturvallisuuteen. Tähän ovat vaikuttaneet muun muassa kansainvälisten markkinoiden vaatimukset ja asiakkaiden kiinnostus. Tämä työ on osa Wärtsilä Finland Oy:n työterveys- ja työturvallisuusjärjestelmän kehittämisprojektia. Työn tavoitteena oli tunnistaa työterveys- ja työturvallisuusjohtamisjärjestelmään liittyvät vaatimukset OHSAS 18001 -spesifikaation mukaisesti. Tarkoituksena oli myös osittain kehittää työterveys- ja työturvallisuusjärjestelmää. Organisaation päämääränä on päästä Nolla tapaturmaa -ohjelman tavoitteeseen. Tämä tapahtuu eri keinoja käyttäen, kuten täyttämällä OHSAS 18001 -vaatimukset. OHSAS -vaatimuksista keskeiseen asemaan tässä työssä nousivat yrityksen riskien arviointi sekä tapaturmien tutkinta ja tilastointi. Riskien arviointi tehtiin pilottitehtaassa OHSAS 18001 -koulutukseen liittyvänä harjoituksena. Saatuja tuloksia voidaan pitää kohtalaisina kun huomioidaan, että kyseessä oli ensimmäinen riskien arviointikerta. Osa riskeistä jäi kuitenkin luultavasti havaitsematta, joten kehitystyötä on jatkettava. Tarkistusta ja kehittämistä vaativia asioita löytyi myös tapaturmien tutkimisen ja tilastoinnin saralta. Työn tuloksista voidaan todeta, että yrityksellä on käytössä joitakin OH&S -menettelyjä, mutta kehitettäviä osa-alueita on havaittavissa. Tulevaisuudessa olisi tärkeää, että yritys kehittäisi käyttöönotettua riskien arviointimenetelmää tai etsisi muita mahdollisia tapoja arvioida riskejä. Tätä kautta yrityksen on mahdollista päästä ennaltaehkäisevään työskentelynäkökulmaan ja edetä kohti Nolla tapaturmaa -ohjelman tavoitetta.

Hb Köln [a2b298(FG5) val-met] identified by DNA analysis in a Brazilian family

Hb Köln was identified by DNA analysis in a Brazilian patient. A four-year old Brazilian female, with jaundice since birth, presented an abnormal band, between A2 and S, in hemoglobin electrophoresis on a cellulose acetate membrane, and a band with electrophoretic migration similar to Hb C on agar gel. Thermic instability and isopropanol precipitation tests were positive. Heinz bodies were observed in the patient’s peripheral blood. Sequencing of the three exons of the b globin gene detected a transition from G to A in the first position of codon 98. This alteration does not create or abolish any known restriction site. In this case, confirmation of the mutation was accomplished by allele-specific oligonucleotide hybridization, which is a simple and fast identification method when the clinical data and hematological and electrophoretic patterns are suggestive of Hb Köln.

Tuotteen identifiointi tuotantoyksikössä

Tämän kandidaatintyön tarkoituksena on käsitellä tuotteen identifiointia. Työssä määritellään mitä tuotteen identifiointi tarkoittaa käsitteenä ja mitä teknologiaa on identifioinnin takana. Työn tärkein anti on konkreettiseen kohdeyritykseen sovellettavien tuotteen identifiointimenetelmien esitteleminen, vertaileminen ja soveltuvimman menetelmän valitseminen. Työssä etsitään soveltuvinta tuotteen identifiointimenetelmää kohdeyrityksen tuotantolinjan vaatimin rajauksin. Tärkeimpiä rajauksia kohdeyrityksen tuotantoon etsittävään menetelmään ovat tuotteen identifioinnin sähköistäminen ja automatisointi. Näiden rajauksien puitteissa kolmeksi tutkittavaksi menetelmäksi on valikoitunut RFID-menetelmä, RuBee-menetelmä ja viivakoodimenetelmä. Näistä jokaisesta menetelmästä esitellään työssä niiden ominaisuudet, laitteistot ja yritysesimerkki, missä käytetään kyseistä menetelmää. Menetelmien ominaisuuksien avulla työssä vertaillaan niiden soveltuvuutta kohdeyrityksen tuotantolinjastoon sen vaatimuksineen. Työssä esiintyvien menetelmien ominaisuuksien vertailua suoritettiin tietyin painoarvoin. Identifiointimenetelmien ominaisuudet ovat hyvin erilaisia ja painoarvottamalla ominaisuuksia on saatu soveltuvin menetelmä kohdeyritykseen. Kohdeyritykseen soveltuvimmaksi menetelmäksi on valittu viivakoodimenetelmä sen ollessa kustannustehokkain kohdeyrityksen tuotannon rajauksien puitteissa.

Development of a Biometric Personal Authentication System Based on Fingerprint and Speech

Biometrics deals with the physiological and behavioral characteristics of an individual to establish identity. Fingerprint based authentication is the most advanced biometric authentication technology. The minutiae based fingerprint identification method offer reasonable identification rate. The feature minutiae map consists of about 70-100 minutia points and matching accuracy is dropping down while the size of database is growing up. Hence it is inevitable to make the size of the fingerprint feature code to be as smaller as possible so that identification may be much easier. In this research, a novel global singularity based fingerprint representation is proposed. Fingerprint baseline, which is the line between distal and intermediate phalangeal joint line in the fingerprint, is taken as the reference line. A polygon is formed with the singularities and the fingerprint baseline. The feature vectors are the polygonal angle, sides, area, type and the ridge counts in between the singularities. 100% recognition rate is achieved in this method. The method is compared with the conventional minutiae based recognition method in terms of computation time, receiver operator characteristics (ROC) and the feature vector length. Speech is a behavioural biometric modality and can be used for identification of a speaker. In this work, MFCC of text dependant speeches are computed and clustered using k-means algorithm. A backpropagation based Artificial Neural Network is trained to identify the clustered speech code. The performance of the neural network classifier is compared with the VQ based Euclidean minimum classifier. Biometric systems that use a single modality are usually affected by problems like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks. Multifinger feature level fusion based fingerprint recognition is developed and the performances are measured in terms of the ROC curve. Score level fusion of fingerprint and speech based recognition system is done and 100% accuracy is achieved for a considerable range of matching threshold

An objective climatology of the dynamical forcing of polar lows in the Nordic seas

A new objective climatology of polar lows in the Nordic (Norwegian and Barents) seas has been derived from a database of diagnostics of objectively identified cyclones spanning the period January 2000 to April 2004. There are two distinct parts to this study: the development of the objective climatology and a characterization of the dynamical forcing of the polar lows identified. Polar lows are an intense subset of polar mesocyclones. Polar mesocyclones are distinguished from other cyclones in the database as those that occur in cold air outbreaks over the open ocean. The difference between the wet-bulb potential temperature at 700 hPa and the sea surface temperature (SST) is found to be an effective discriminator between the atmospheric conditions associated with polar lows and other cyclones in the Nordic seas. A verification study shows that the objective identification method is reliable in the Nordic seas region. After demonstrating success at identifying polar lows using the above method, the dynamical forcing of the polar lows in the Nordic seas is characterized. Diagnostics of the ratio of mid-level vertical motion attributable to quasi-geostrophic forcing from upper and lower levels (U/L ratio) are used to determine the prevalence of a recently proposed category of extratropical cyclogenesis, type C, for which latent heat release is crucial to development. Thirty-one percent of the objectively identified polar low events (36 from 115) exceeded the U/L ratio of 4.0, previously identified as a threshold for type C cyclones. There is a contrast between polar lows to the north and south of the Nordic seas. In the southern Norwegian Sea, the population of polar low events is dominated by type C cyclones. These possess strong convection and weak low-level baroclinicity. Over the Barents and northern Norwegian seas, the well-known cyclogenesis types A and B dominate. These possess stronger low-level baroclinicity and weaker convection.

Modified radial basis function neural network using output transformation

A modified radial basis function (RBF) neural network and its identification algorithm based on observational data with heterogeneous noise are introduced. The transformed system output of Box-Cox is represented by the RBF neural network. To identify the model from observational data, the singular value decomposition of the full regression matrix consisting of basis functions formed by system input data is initially carried out and a new fast identification method is then developed using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator (MLE) for a model base spanned by the largest eigenvectors. Finally, the Box-Cox transformation-based RBF neural network, with good generalisation and sparsity, is identified based on the derived optimal Box-Cox transformation and an orthogonal forward regression algorithm using a pseudo-PRESS statistic to select a sparse RBF model with good generalisation. The proposed algorithm and its efficacy are demonstrated with numerical examples.

Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds

In this paper, data from spaceborne radar, lidar and infrared radiometers on the “A-Train” of satellites are combined in a variational algorithm to retrieve ice cloud properties. The method allows a seamless retrieval between regions where both radar and lidar are sensitive to the regions where one detects the cloud. We first implement a cloud phase identification method, including identification of supercooled water layers using the lidar signal and temperature to discriminate ice from liquid. We also include rigorous calculation of errors assigned in the variational scheme. We estimate the impact of the microphysical assumptions on the algorithm when radiances are not assimilated by evaluating the impact of the change in the area-diameter and the density-diameter relationships in the retrieval of cloud properties. We show that changes to these assumptions affect the radar-only and lidar-only retrieval more than the radar-lidar retrieval, although the lidar-only extinction retrieval is only weakly affected. We also show that making use of the molecular lidar signal beyond the cloud as a constraint on optical depth, when ice clouds are sufficiently thin to allow the lidar signal to penetrate them entirely, improves the retrieved extinction. When infrared radiances are available, they provide an extra constraint and allow the extinction-to-backscatter ratio to vary linearly with height instead of being constant, which improves the vertical distribution of retrieved cloud properties.

Rapid protein domain assignment from amino acid sequence using predicted secondary structure

The elucidation of the domain content of a given protein sequence in the absence of determined structure or significant sequence homology to known domains is an important problem in structural biology. Here we address how successfully the delineation of continuous domains can be accomplished in the absence of sequence homology using simple baseline methods, an existing prediction algorithm (Domain Guess by Size), and a newly developed method (DomSSEA). The study was undertaken with a view to measuring the usefulness of these prediction methods in terms of their application to fully automatic domain assignment. Thus, the sensitivity of each domain assignment method was measured by calculating the number of correctly assigned top scoring predictions. We have implemented a new continuous domain identification method using the alignment of predicted secondary structures of target sequences against observed secondary structures of chains with known domain boundaries as assigned by Class Architecture Topology Homology (CATH). Taking top predictions only, the success rate of the method in correctly assigning domain number to the representative chain set is 73.3%. The top prediction for domain number and location of domain boundaries was correct for 24% of the multidomain set (±20 residues). These results have been put into context in relation to the results obtained from the other prediction methods assessed