Noninvasive Brain Stimulation With High-Frequency and Low-Intensity Repetitive Transcranial Magnetic Stimulation Treatment for Posttraumatic Stress Disorder

Objective: We aimed to investigate the efficacy of 20 Hz repetitive transcranial magnetic stimulation (rTMS) of either right or left dorsolateral prefrontal cortex (DLPFC) as compared to sham rTMS for the relief of posttraumatic stress disorder (PTSD)-associated symptoms. Method: In this double-blind, placebo-controlled phase II trial conducted between October 2005 and July 2008, 30 patients with DSM-IV-diagnosed PTSD were randomly assigned to receive 1 of the following treatments: active 20 Hz rTMS of the right DLPFC, active 20 Hz rTMS of the left DLPFC, or sham rTMS. Treatments were administered in 10 daily sessions over 2 weeks. A blinded rater assessed severity of core PTSD symptoms, depression, and anxiety before, during, and after completion of the treatment protocol. In addition, a battery of neuropsychological tests was measured before and after treatment. Results: Results show that both active conditions-20 Hz rTMS of left and right DLPFC induced a significant decrease in PTSD symptoms as indexed by the PTSD Checklist and Treatment Outcome PTSD Scale; however, right rTMS induced a larger effect as compared to left rTMS. In addition, there was a significant improvement of mood after left rTMS and a significant reduction of anxiety following right rTMS. Improvements in PTSD symptoms were long lasting; effects were still significant at the 3-month follow-up. Finally, neuropsychological evaluation showed that active 20 Hz rTMS is not associated with cognitive worsening and is safe for use in patients with PTSD. Conclusions: These results support the notion that modulation of prefrontal cortex can alleviate the core symptoms of PTSD and suggest that high-frequency rTMS of right DLPFC might be the optimal treatment strategy. J an Psychiatry 2010;71(8):992-999 (C) Copyright 2009 Physicians Postgraduate Press, Inc.

Bilevel vs ICU Ventilators Providing Noninvasive Ventilation: Effect of System Leaks A COPD Lung Model Comparison

Background: Noninvasive positive-pressure ventilation (NPPV) modes are currently available on bilevel and ICU ventilators. However, little data comparing the performance of the NPPV modes on these ventilators are available. Methods: In an experimental bench study, the ability of nine ICU ventilators to function in the presence of leaks was compared with a bilevel ventilator using the IngMar ASL5000 lung simulator (IngMar Medical; Pittsburgh, PA) set at a compliance of 60 mL/cm H(2)O, an inspiratory resistance of 10 cm H(2)O/L/s, an expiratory resistance of 20 cm H(2)O/L/s, and a respiratory rate of 15 breaths/min. All of the ventilators were set at 12 cm H(2)O pressure support and 5 cm H(2)O positive end-expiratory pressure. The data were collected at baseline and at three customized leaks. Main results: At baseline, all of the ventilators were able to deliver adequate tidal volumes, to maintain airway pressure, and to synchronize with the simulator, without missed efforts or auto-triggering. As the leak was increased, all of the ventilators (except the Vision [Respironics; Murrysville, PA] and Servo I [Maquet; Solna, Sweden]) needed adjustment of sensitivity or cycling criteria to maintain adequate ventilation, and some transitioned to backup ventilation. Significant differences in triggering and cycling were observed between the Servo I and the Vision ventilators. Conclusions: The Vision and Servo I were the only ventilators that required no adjustments as they adapted to increasing leaks. There were differences in performance between these two ventilators, although the clinical significance of these differences is unclear. Clinicians should be aware that in the presence of leaks, most ICU ventilators require adjustments to maintain an adequate tidal volume. (CHEST 2009; 136:448-456)

Comparison of three systems of classification in predicting the outcome of diabetic foot ulcers in a Brazilian population

Objective: The aim was to compare there ulcer classification systems as predictors of the outcome of diabetic foot ulcers; the Wagner, the University of Texas (UT) and the size (area, depth), sepsis, arteriopathy, denervation system (S(AD)SAD) systems in specialist clinic in Brazil. Methods: Ulcer area, depth, appearance, infection and associated ischaemia and neuropathy were recorded in a consecutive series of 94 subjects. A novel score, the S(AD)SAD score, was derived from the sum of individual items of the S(AD)SAD system, and was evaluated. Follow-up was for at least 6 months. The primary outcome measure was the incidence of healing. Results: Mean age was 57.6 years; 57 (60.6%) were made. Forty-eight ulcers (51.1%) healed without surgery; 11 (12.2%) subjects underwent minor amputation. Significant differences in terms of healing were observed for depth (P = 0.002), infection (P = 0.006) and denervation (P = 0.002) using the S(AD)SAD system, for UT grade (P = 0.002) and stage (P = 0.032) and for Wagner grades (P = 0.002). Ulcers with an S(AD)SAD score of <= 9 (total possible 15) were 7.6 times more likely to heal than scores >= 10 (P < 0.001). Conclusions: All three systems predicted ulcer outcome. The S(AD)SAD score of ulcer severity could represent a useful addition to routine clinical practice. The association between outcome and ulcer depth confirms earlier reports. The association with infection was stronger than that reported from the centres in Europe or North America. The very strong association with neuropathy has only previously been observed in Tanzania. Studies designed to compare the outcome in different countries should adopt systems of classification, which are valid for the populations studied.

Adjuncts to Physical Training of Patients With Severe COPD: Oxygen or Noninvasive Ventilation?

BACKGROUND: Previous studies have shown positive effects from noninvasive ventilation (NIV) or supplemental oxygen on exercise capacity in patients with COPD. However, the best adjunct for promoting physiologic adaptations to physical training in patients with severe COPD remains to be investigated. METHODS: Twenty-eight patients (mean +/- SD age 68 +/- 7 y) with stable COPD (FEV(1) 34 +/- 9% of predicted) undergoing an exercise training program were randomized to either NIV (n = 14) or supplemental oxygen (n = 14) during group training to maintain peripheral oxygen saturation (S(pO2)) >= 90%. Physical training consisted of treadmill walking (at 70% of maximal speed) 3 times a week, for 6 weeks. Patients were assessed at baseline and after 6 weeks. Assessments included physiological adaptations during incremental exercise testing (ratio of lactate concentration to walk speed, oxygen uptake [(V) over dot(O2)], and dyspnea), exercise tolerance during 6-min walk test, leg fatigue, maximum inspiratory pressure, and health-related quality of life. RESULTS: Two patients in each group dropped out due to COPD exacerbations and lack of exercise program adherence, and 24 completed the training program. Both groups improved 6-min walk distance, symptoms, and health-related quality of life. However, there were significant differences between the NIV and supplemental-oxygen groups in lactate/speed ratio (33% vs -4%), maximum inspiratory pressure (80% vs 23%), 6-min walk distance (122 m vs 47 m), and leg fatigue (25% vs 11%). In addition, changes in S(pO2)/speed, (V) over dot(O2), and dyspnea were greater with NIV than with supplemental-oxygen. CONCLUSIONS: NIV alone is better than supplemental oxygen alone in promoting beneficial physiologic adaptations to physical exercise in patients with severe COPD.

Meningiomas and schwannomas: Molecular subgroup classification found by expression arrays

Microarray gene expression profiling is a high-throughput system used to identify differentially expressed genes and regulation patterns, and to discover new tumor markers. As the molecular pathogenesis of meningiomas and schwannomas, characterized by NF2 gene alterations, remains unclear and suitable molecular targets need to be identified, we used low density cDNA microarrays to establish expression patterns of 96 cancer-related genes on 23 schwannomas, 42 meningiomas and 3 normal cerebral meninges. We also performed a mutational analysis of the NF2 gene (PCR, dHPLC, Sequencing and MLPA), a search for 22q LOH and an analysis of gene silencing by promoter hypermethylation (MS-MLPA). Results showed a high frequency of NF2 gene mutations (40%), increased 22q LOH as aggressiveness increased, frequent losses and gains by MLPA in benign meningiomas, and gene expression silencing by hypermethylation. Array analysis showed decreased expression of 7 genes in meningiomas. Unsupervised analyses identified 2 molecular subgroups for both meningiomas and schwannomas showing 38 and 20 differentially expressed genes, respectively, and 19 genes differentially expressed between the two tumor types. These findings provide a molecular subgroup classification for meningiomas and schwannomas with possible implications for clinical practice.

NONINVASIVE MONITORING OF ANDROGENS IN MALE AMAZONIAN MANATEE (TRICHECHUS INUNGUIS): BIOLOGIC VALIDATION

The Amazonian manatee (Trichechus inunguis) is endemic in the Amazonian basin and is the only exclusively fresh water sirenian. Historically hunted on a large scale, this species is now considered endangered, and Studies on the reproductive physiology are critical for the improvement of reproductive management of captive and wild Populations of manatees. The aim of this Study was to verify the viability of androgen measurement in saliva, lacrimal, urine, and fecal samples of the Amazonian manatee by conducting a hormone challenge. Two adult male manatees (A-1 and A-2) were Submitted to an experimentation protocol of 12 day (D1 to D10). On D0, the animals received an intramuscular injection of gonadotropin-releasing hormone (GnRH)-analogue. Salivary, lacrimal, urinary, and fecal samples were collected daily (between 0800 hours and 0900 hours) and frozen at -20 degrees C until assayed. Fecal samples were lyophilized, extracted with 80% methanol, and diluted in buffer before the radioimmunoassay (RIA). Urine samples underwent acid hydrolysis and were diluted in depleted bovine serum. Salivary and lacrimal samples were assayed without the extraction step. Hormonal assays were conducted with a commercial testosterone RIA kit. An androgen peak (>median + 2 interquartile range [IQR]) was observed in all matrices of both animals, although it was less prominent in the lacrimal samples of A-2. However, the fecal androgen peak (A-1 peak = 293.78 ng/g dry feces, median [IQR] = 143.58 [32.38] ng/g dry feces; A-2 peak = 686.72 ng/g dry feces, median [IQR] = 243.82 [193.16] ng/g dry feces) occurred later than urinary (A-1 peak = 648.16 ng/mg creatinine [Cr], median [IQR] = 23.88 [30.44] ng/mg Cr; A-2 peak = 370.44 ng/mg Cr, median [IQR] = 113.87 [117.73] ng/mg Cr) and salivary (A-1 peak = 678.89 pg/ml, median [IQR] = 103.69 [119.86] pg/ml; A-2 peak = 733.71 pg/ml, median [IQR] = 262.92 [211.44] pg/ml) androgen peaks. These intervals appear to be correlated with the long digesta passage time in this species. The salivary and urinary peaks were closely associated. These results demonstrate that androgen concentrations in saliva, urine, or feces samples reflect reliably physiologic events and are a powerful tool for noninvasive reproductive monitoring of Amazonian manatees.

Phylogenetic relationships and infrageneric classification of Epidendrum subgenus Amphiglottium (Laeliinae, Orchidaceae)

Epidendrum L. is the largest genus of Orchidaceae in the Neotropical region; it has an impressive morphological diversification, which imposes difficulties in delimitation of both infrageneric and interspecific boundaries. In this study, we review infrageneric boundaries within the subgenus Amphiglottium and try to contribute to the understanding of morphological diversification and taxa delimitation within this group. We tested the monophyly of the subgenus Amphiglottium sect. Amphiglottium, expanding previous phylogenetic investigations and reevaluated previous infrageneric classifications proposed. Sequence data from the trnL-trnF region were analyzed with both parsimony and maximum likelihood criteria. AFLP markers were also obtained and analyzed with phylogenetic and principal coordinate analyses. Additionally, we obtained chromosome numbers for representative species within the group. The results strengthen the monophyly of the subgenus Amphiglottium but do not support the current classification system proposed by previous authors. Only section Tuberculata comprises a well-supported monophyletic group, with sections Carinata and Integra not supported. Instead of morphology, biogeographical and ecological patterns are reflected in the phylogenetic signal in this group. This study also confirms the large variability of chromosome numbers for the subgenus Amphiglottium (numbers ranging from 2n = 24 to 2n = 240), suggesting that polyploidy and hybridization are probably important mechanisms of speciation within the group.

A Survey on Graphical Methods for Classification Predictive Performance Evaluation

Predictive performance evaluation is a fundamental issue in design, development, and deployment of classification systems. As predictive performance evaluation is a multidimensional problem, single scalar summaries such as error rate, although quite convenient due to its simplicity, can seldom evaluate all the aspects that a complete and reliable evaluation must consider. Due to this, various graphical performance evaluation methods are increasingly drawing the attention of machine learning, data mining, and pattern recognition communities. The main advantage of these types of methods resides in their ability to depict the trade-offs between evaluation aspects in a multidimensional space rather than reducing these aspects to an arbitrarily chosen (and often biased) single scalar measure. Furthermore, to appropriately select a suitable graphical method for a given task, it is crucial to identify its strengths and weaknesses. This paper surveys various graphical methods often used for predictive performance evaluation. By presenting these methods in the same framework, we hope this paper may shed some light on deciding which methods are more suitable to use in different situations.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

On the influence of imputation in classification: practical issues

The substitution of missing values, also called imputation, is an important data preparation task for many domains. Ideally, the substitution of missing values should not insert biases into the dataset. This aspect has been usually assessed by some measures of the prediction capability of imputation methods. Such measures assume the simulation of missing entries for some attributes whose values are actually known. These artificially missing values are imputed and then compared with the original values. Although this evaluation is useful, it does not allow the influence of imputed values in the ultimate modelling task (e.g. in classification) to be inferred. We argue that imputation cannot be properly evaluated apart from the modelling task. Thus, alternative approaches are needed. This article elaborates on the influence of imputed values in classification. In particular, a practical procedure for estimating the inserted bias is described. As an additional contribution, we have used such a procedure to empirically illustrate the performance of three imputation methods (majority, naive Bayes and Bayesian networks) in three datasets. Three classifiers (decision tree, naive Bayes and nearest neighbours) have been used as modelling tools in our experiments. The achieved results illustrate a variety of situations that can take place in the data preparation practice.

Poly-bagging predictors for classification modelling for credit scoring

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement. (C) 2011 Elsevier Ltd. All rights reserved.

Classification of quantum relativistic orientable objects

Extending our previous work `Fields on the Poincare group and quantum description of orientable objects` (Gitman and Shelepin 2009 Eur. Phys. J. C 61 111-39), we consider here a classification of orientable relativistic quantum objects in 3 + 1 dimensions. In such a classification, one uses a maximal set of ten commuting operators (generators of left and right transformations) in the space of functions on the Poincare group. In addition to the usual six quantum numbers related to external symmetries (given by left generators), there appear additional quantum numbers related to internal symmetries (given by right generators). Spectra of internal and external symmetry operators are interrelated, which, however, does not contradict the Coleman-Mandula no-go theorem. We believe that the proposed approach can be useful for the description of elementary spinning particles considered as orientable objects. In particular, it gives a group-theoretical interpretation of some facts of the existing phenomenological classification of spinning particles.

Texture analysis and classification using deterministic tourist walk

In this paper, we present a study on a deterministic partially self-avoiding walk (tourist walk), which provides a novel method for texture feature extraction. The method is able to explore an image on all scales simultaneously. Experiments were conducted using different dynamics concerning the tourist walk. A new strategy, based on histograms. to extract information from its joint probability distribution is presented. The promising results are discussed and compared to the best-known methods for texture description reported in the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.

Objective Detection and Delineation of Oral Neoplasia Using Autofluorescence Imaging

Although the oral cavity is easily accessible to inspection, patients with oral cancer most often present at a late stage, leading to high morbidity and mortality. Autofluorescence imaging has emerged as a promising technology to aid clinicians in screening for oral neoplasia and as an aid to resection, but current approaches rely on subjective interpretation. We present a new method to objectively delineate neoplastic oral mucosa using autofluorescence imaging. Autofluorescence images were obtained from 56 patients with oral lesions and 11 normal volunteers. From these images, 276 measurements from 159 unique regions of interest (ROI) sites corresponding to normal and confirmed neoplastic areas were identified. Data from ROIs in the first 46 subjects were used to develop a simple classification algorithm based on the ratio of red-to-green fluorescence; performance of this algorithm was then validated using data from the ROIs in the last 21 subjects. This algorithm was applied to patient images to create visual disease probability maps across the field of view. Histologic sections of resected tissue were used to validate the disease probability maps. The best discrimination between neoplastic and nonneoplastic areas was obtained at 405 nm excitation; normal tissue could be discriminated from dysplasia and invasive cancer with a 95.9% sensitivity and 96.2% specificity in the training set, and with a 100% sensitivity and 91.4% specificity in the validation set. Disease probability maps qualitatively agreed with both clinical impression and histology. Autofluorescence imaging coupled with objective image analysis provided a sensitive and noninvasive tool for the detection of oral neoplasia.