Automatic Extraction of Polar Adjectives for the Creation of Polarity Lexicons

Automatic creation of polarity lexicons is a crucial issue to be solved in order to reduce time andefforts in the first steps of Sentiment Analysis. In this paper we present a methodology based onlinguistic cues that allows us to automatically discover, extract and label subjective adjectivesthat should be collected in a domain-based polarity lexicon. For this purpose, we designed abootstrapping algorithm that, from a small set of seed polar adjectives, is capable to iterativelyidentify, extract and annotate positive and negative adjectives. Additionally, the methodautomatically creates lists of highly subjective elements that change their prior polarity evenwithin the same domain. The algorithm proposed reached a precision of 97.5% for positiveadjectives and 71.4% for negative ones in the semantic orientation identification task.

Smoked cannabis and doping control: looking for the wrong target analyte?

Introduction: Since 2004, cannabis is prohibited by the World Anti-Doping Agency (WADA) for all sports in competition. In the years since then, about half of all positive doping cases in Switzerland have been related to cannabis consumption. In most cases, the athletes plausibly claim to have consumed cannabis several days or even weeks before competition and only for recreational purposes not related to competition. In doping analysis, the target analyte in urine samples is 11-nor-delta-9-tetrahydrocannabinol- 9-carboxylic acid (THC-COOH), the reporting threshold for laboratories is 15 ng/mL. However, the wide detection window of this long-term THC metabolite in urine does not allow a conclusion concerning the time of consumption or the impact on the physical performance. Aim: The purpose of the present pharmacokinetic study on volunteers was to evaluate target analytes with shorter urinary excretion time. Subsequently, urines from athletes tested positive for cannabis should be reanalyzed including these analytes. Methods: In an one-session clinical trial (approved by IRB, Swissmedic, and Federal Office of Public Health), 12 healthy, male volunteers (age 26 ± 3 yrs, BMI 24 ± 2 kg/m2) with cannabis experience (> once/month) smoked a Cannabis cigarette standardized to 70 mg THC/cigarette (Bedrobinol® 7%, Dutch Office for Medicinal Cannabis) following a paced-puffing procedure. Plasma and urine was collected up to 8 h and 11 days, respectively. Total THC, 11-hydroxy-THC (THC-OH), and THC-COOH were determined after enzymatic hydrolyzation followed by SPE and GC/MS-SIM. The limit of quantitation (LOQ) for all analytes was 0.1 ng/mL. Visual analog scales (VAS) and vital functions were used for monitoring psychological and somatic side-effects at every timepoint of specimen collection (up to 480 min). Results: Eight puffs delivered a mean THC dose of 45 mg. Mean plasma levels of total THC, THC-OH and THC-COOH were measured in the range of 0.1-20.9, 0.1-1.8, and 1.8-7.5 ng/mL, respectively. Peak concentrations were observed at 5, 10, and 90 min. Mean urine levels were measured in the range of 0.1-0.7, 0.10-6.2, and 0.1-13.4 ng/mL, respectively. The detection windows were 2-8, 2-96, and 2-120 h. No or only mild effects were observed, such as dry mouth, sedation, and tachycardia. Besides high to very high THC-COOH levels (0-978 ng/mL), THC (0.1-24 ng/mL) and THC-OH (1-234 ng/mL) were found in 90 and 96% of the cannabis-positive urines from athletes. Conclusion: Instead of or in addition to THC-COOH, the pharmacologically active THC and THC-OH should be the target analytes for doping urine analysis. This would allow the estimation of more recent Cannabis consumption, probably influencing performance during competition. Keywords: cannabis, doping, clinical trial, plasma and urine levels, athlete's samples

Postoperative delirium. Part 2: detection, prevention and treatment.

To target pharmacological prevention, instruments giving an approximation of an individual patient's risk of developing postoperative delirium are available. In view of the variable clinical presentation, identifying patients in whom prophylaxis has failed (that is, who develop delirium) remains a challenge. Several bedside instruments are available for the routine ward and ICU setting. Several have been shown to have a high specificity and sensitivity when compared with the standard definitions according to DSM-IV-TR and ICD-10. The Confusion Assessment Method (CAM) and a version specifically developed for the intensive care setting (CAM-ICU) have emerged as a standard. However, alternatives allowing grading of the severity of delirium are also available. In many units, the approach to delirium follows a three-step strategy. Initially, non-pharmacological multicomponent strategies are used for primary prevention. As a second step, pharmacological prophylaxis may be added. Perioperative administration of haloperidol has been shown to reduce the severity, but not the incidence, of delirium. Perioperative administration of atypical antipsychotics has been shown to reduce the incidence of delirium in specific groups of patients. In patients with delirium, both symptomatic and causal treatment of delirium need to be considered. So far symptomatic treatment of delirium is primarily based on antipsychotics. Currently, cholinesterase inhibitors cannot be recommended and the data on dexmedetomidine are inconclusive. With the exception of alcohol-withdrawal delirium, there is no role for benzodiazepines in the treatment of delirium. It is unclear whether treating delirium prevents long-term sequelae.

Landmark detection for fusion of fundus and MRI toward a patient-specific multimodal eye model.

Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Improved HIV-1 RNA quantitation by COBAS AmpliPrep/COBAS TaqMan HIV-1 Test, v2.0 using a novel dual-target approach.

BACKGROUND: HIV-1 RNA viral load is a key parameter for reliable treatment monitoring of HIV-1 infection. Accurate HIV-1 RNA quantitation can be impaired by primer and probe sequence polymorphisms as a result of tremendous genetic diversity and ongoing evolution of HIV-1. A novel dual HIV-1 target amplification approach was realized in the quantitative COBAS AmpliPrep/COBAS TaqMan HIV-1 Test, v2.0 (HIV-1 TaqMan test v2.0) to cope with the high genetic diversity of the virus. OBJECTIVES AND STUDY DESIGN: The performance of the new assay was evaluated for sensitivity, dynamic range, precision, subtype inclusivity, diagnostic and analytical specificity, interfering substances, and correlation with the COBAS AmpliPrep/COBAS TaqMan HIV-1 (HIV-1 TaqMan test v1.0) predecessor test in patients specimens. RESULTS: The new assay demonstrated a sensitivity of 20 copies/mL, a linear measuring range of 20-10,000,000 copies/mL, with a lower limit of quantitation of 20 copies/mL. HIV-1 Group M subtypes and HIV-1 Group O were quantified within +/-0.3 log(10) of the assigned titers. Specificity was 100% in 660 tested specimens, no cross reactivity was found for 15 pathogens nor any interference for endogenous substances or 29 drugs. Good comparability with the predecessor assay was demonstrated in 82 positive patient samples. In selected clinical samples 35/66 specimens were found underquantitated in the predecessor assay; all were quantitated correctly in the new assay. CONCLUSIONS: The dual-target approach for the HIV-1 TaqMan test v2.0 enables superior HIV-1 Group M subtype coverage including HIV-1 Group O detection. Correct quantitation of specimens underquantitated in the HIV-1 TaqMan test v1.0 test was demonstrated.

Cross validation of experts versus registration methods for target localization in deep brain stimulation.

In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatent of Parkinson's disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different registration algorithms and to evaluate their ability to locate the STN automatically. Using this scheme we can evaluate the expert variability against the error of the algorithms and we demonstrate that automatic STN location is possible and as accurate as the methods currently used.

Correlation postprocessing-based method for the detection of de focused images

We present a method to detect patterns in defocused scenes by means of a joint transform correlator. We describe analytically the correlation plane, and we also introduce an original procedure to recognize the target by postprocessing the correlation plane. The performance of the methodology when the defocused images are corrupted by additive noise is also considered.

Performance of the 47-Kilodalton Membrane Protein versus DNA Polymerase I Genes for Detection of Treponema pallidum by PCR in Ulcers.

Treponema pallidum PCR (Tp-PCR) is a direct diagnostic method for primary and secondary syphilis, but there is no recommendation regarding the best choice of target gene. In this study, we sequentially tested 272 specimens from patients with sexually transmitted ulcers using Tp-PCR targeting the tpp47 and then polA genes. The two methods showed similar accuracies and an almost-perfect agreement.

A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder

Background Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.

Proof of concept for microarray-based detection of DNA-binding oncogenes in cell extracts.

The function of DNA-binding proteins is controlled not just by their abundance, but mainly at the level of their activity in terms of their interactions with DNA and protein targets. Moreover, the affinity of such transcription factors to their target sequences is often controlled by co-factors and/or modifications that are not easily assessed from biological samples. Here, we describe a scalable method for monitoring protein-DNA interactions on a microarray surface. This approach was designed to determine the DNA-binding activity of proteins in crude cell extracts, complementing conventional expression profiling arrays. Enzymatic labeling of DNA enables direct normalization of the protein binding to the microarray, allowing the estimation of relative binding affinities. Using DNA sequences covering a range of affinities, we show that the new microarray-based method yields binding strength estimates similar to low-throughput gel mobility-shift assays. The microarray is also of high sensitivity, as it allows the detection of a rare DNA-binding protein from breast cancer cells, the human tumor suppressor AP-2. This approach thus mediates precise and robust assessment of the activity of DNA-binding proteins and takes present DNA-binding assays to a high throughput level.

An Experimental Validation of a Statistical-Based Damage Detection Approach, January 2011

In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and predicted behavior of the bridge caused under a subset of ambient trucks. The predicted behavior is derived from a statistics-based model trained with field data from the undamaged bridge (not a finite element model). The differences between actual and predicted responses, called residuals, are then used to construct control charts, which compare undamaged and damaged structure data. Validation of the damage-detection approach was achieved by using sacrificial specimens that were mounted to the bridge and exposed to ambient traffic loads and which simulated actual damage-sensitive locations. Different damage types and levels were introduced to the sacrificial specimens to study the sensitivity and applicability. The damage-detection algorithm was able to identify damage, but it also had a high false-positive rate. An evaluation of the sub-components of the damage-detection methodology and methods was completed for the purpose of improving the approach. Several of the underlying assumptions within the algorithm were being violated, which was the source of the false-positives. Furthermore, the lack of an automatic evaluation process was thought to potentially be an impediment to widespread use. Recommendations for the improvement of the methodology were developed and preliminarily evaluated. These recommendations are believed to improve the efficacy of the damage-detection approach.

Seismic detection and characterization of landslides and other mass movements

Seismic methods used in the study of snow avalanches may be employed to detect and characterize landslides and other mass movements, using standard spectrogram/sonogram analysis. For snow avalanches, the spectrogram for a station that is approached by a sliding mass exhibits a triangular time/frequency signature due to an increase over time in the higher-frequency constituents. Recognition of this characteristic footprint in a spectrogram suggests a useful metric for identifying other mass-movement events such as landslides. The 1 June 2005 slide at Laguna Beach, California is examined using data obtained from the Caltech/USGS Regional Seismic Network. This event exhibits the same general spectrogram features observed in studies of Alpine snow avalanches. We propose that these features are due to the systematic relative increase in high-frequency energy transmitted to a seismometer in the path of a mass slide owing to a reduction of distance from the source signal. This phenomenon is related to the path of the waves whose high frequencies are less attenuated as they traverse shorter source-receiver paths. Entrainment of material in the course of the slide may also contribute to the triangular time/frequency signature as a consequence of the increase in the energy involved in the process; in this case the contribution would be a source effect. By applying this commonly observed characteristic to routine monitoring algorithms, along with custom adjustments for local site effects, we seek to contribute to the improvement in automatic detection and monitoring methods of landslides and other mass movements.

Development of a real-time PCR for the specific detection of Waddlia chondrophila in clinical samples.

Waddlia chondrophila is considered as an emerging human pathogen likely involved in miscarriage and lower respiratory tract infections. Given the low sensitivity of cell culture to recover such an obligate intracellular bacteria, molecular-based diagnostic approaches are warranted. We thus developed a real-time PCR that amplifies Waddlia chondrophila DNA. Specific primers and probe were selected to target the 16S rRNA gene. The PCR specifically amplified W. chondrophila but did not amplify other related-bacteria such as Parachlamydia acanthamoebae, Simkania negevensis and Chlamydia pneumoniae. The PCR exhibited a good intra-run and inter-run reproducibility and a sensitivity of less than ten copies of the positive control. This real-time PCR was then applied to 32 nasopharyngeal aspirates taken from children with bronchiolitis not due to respiratory syncytial virus (RSV). Three samples revealed to be Waddlia positive, suggesting a possible role of this Chlamydia-related bacteria in this setting.