The failure of routine rapid HIV testing: a case study of improving low sensitivity in the field

<p>Abstract</p> <p>Background</p> <p>The rapid HIV antibody test is the diagnostic tool of choice in low and middle-income countries. Previous evidence suggests that rapid HIV diagnostic tests may underperform in the field, failing to detect a substantial number of infections. A research study inadvertently discovered that a clinic rapid HIV testing process was failing to detect cases of established (high antibody titer) infection, exhibiting an estimated 68.7% sensitivity (95% CI [41.3%-89.0%]) over the course of the first three weeks of observation. The setting is a public service clinic that provides STI diagnosis and treatment in an impoverished, peri-urban community outside of Cape Town, South Africa.</p> <p>Methods</p> <p>The researchers and local health administrators collaborated to investigate the cause of the poor test performance and make necessary corrections. The clinic changed the brand of rapid test being used and later introduced quality improvement measures. Observations were made of the clinic staff as they administered rapid HIV tests to real patients. Estimated testing sensitivity was calculated as the number of rapid HIV test positive individuals detected by the clinic divided by this number plus the number of PCR positive, highly reactive 3^rdgeneration ELISA patients identified among those who were rapid test negative at the clinic.</p> <p>Results</p> <p>In the period of five months after the clinic made the switch of rapid HIV tests, estimated sensitivity improved to 93.5% (95% CI [86.5%-97.6%]), during which time observations of counselors administering tests at the clinic found poor adherence to the recommended testing protocol. Quality improvement measures were implemented and estimated sensitivity rose to 95.1% (95% CI [83.5%-99.4%]) during the final two months of full observation.</p> <p>Conclusions</p> <p>Poor testing procedure in the field can lead to exceedingly low levels of rapid HIV test sensitivity, making it imperative that stringent quality control measures are implemented where they do not already exist. Certain brands of rapid-testing kits may perform better than others when faced with sub-optimal use.</p

Evidence for the different physiological significance of the 6- and 2-minute walk tests in multiple sclerosis

<p>Abstract</p> <p>Background</p> <p>Researchers have recently advocated for the 2-minute walk (2MW) as an alternative for the 6-minute walk (6MW) to assess long distance ambulation in persons with multiple sclerosis (MS). This recommendation has not been based on physiological considerations such as the rate of oxygen consumption (V·O₂) over the 6MW range.</p> <p>Objective</p> <p>This study examined the pattern of change in V·O₂over the range of the 6MW in a large sample of persons with MS who varied as a function of disability status.</p> <p>Method</p> <p>Ninety-five persons with clinically-definite MS underwent a neurological examination for generating an Expanded Disability Status Scale (EDSS) score, and then completion of the 6MW protocol while wearing a portable metabolic unit and an accelerometer.</p> <p>Results</p> <p>There was a time main effect on V·O₂during the 6MW (<it>p </it>= .0001) such that V·O₂increased significantly every 30 seconds over the first 3 minutes of the 6MW, and then remained stable over the second 3 minutes of the 6MW. This occurred despite no change in cadence across the 6MW (<it>p </it>= .84).</p> <p>Conclusions</p> <p>The pattern of change in V·O₂indicates that there are different metabolic systems providing energy for ambulation during the 6MW in MS subjects and steady state aerobic metabolism is reached during the last 3 minutes of the 6MW. By extension, the first 3 minutes would represent a test of mixed aerobic and anaerobic work, whereas the second 3 minutes would represent a test of aerobic work during walking.</p

An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis σ66 promoters

<p>Abstract</p> <p>Background</p> <p>Promoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from <it>Escherichia coli</it>. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between <it>Escherichia coli </it>and <it>Chlamydia trachomatis </it>are large enough to recommend an organism-specific modeling effort.</p> <p>Results</p> <p>Here we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model <it>Chlamydia trachomatis </it>σ⁶⁶promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for <it>Chlamydia trachomatis </it>RNA polymerase σ⁶⁶/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability.</p> <p>Conclusion</p> <p>This strategy and resulting model support the conjecture that DNA biophysical properties, along with RNA polymerase σ-factor/DNA binding collaboratively, contribute to a sequence's ability to promote transcription. This work provides a baseline model that can evolve as new <it>Chlamydia trachomatis </it>σ⁶⁶promoters are identified with assistance from the provided genome-wide predictions. The proposed methodology is ideal for organisms with few identified promoters and relatively small genomes.</p

Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties

BACKGROUND: The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties. RESULTS: To determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features. CONCLUSION: The SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function

Quantification and analysis of icebergs in a tidewater glacier fjord using an object-based approach

Tidewater glaciers are glaciers that terminate in, and calve icebergs into, the ocean. In addition to the influence that tidewater glaciers have on physical and chemical oceanography, floating icebergs serve as habitat for marine animals such as harbor seals (Phoca vitulina richardii). The availability and spatial distribution of glacier ice in the fjords is likely a key environmental variable that influences the abundance and distribution of selected marine mammals; however, the amount of ice and the fine-scale characteristics of ice in fjords have not been systematically quantified. Given the predicted changes in glacier habitat, there is a need for the development of methods that could be broadly applied to quantify changes in available ice habitat in tidewater glacier fjords. We present a case study to describe a novel method that uses object-based image analysis (OBIA) to classify floating glacier ice in a tidewater glacier fjord from high-resolution aerial digital imagery. Our objectives were to (i) develop workflows and rule sets to classify high spatial resolution airborne imagery of floating glacier ice; (ii) quantify the amount and fine-scale characteristics of floating glacier ice; (iii) and develop processes for automating the object-based analysis of floating glacier ice for large number of images from a representative survey day during June 2007 in Johns Hopkins Inlet (JHI), a tidewater glacier fjord in Glacier Bay National Park, southeastern Alaska. On 18 June 2007, JHI was comprised of brash ice ([Formula: see text] = 45.2%, SD = 41.5%), water ([Formula: see text] = 52.7%, SD = 42.3%), and icebergs ([Formula: see text] = 2.1%, SD = 1.4%). Average iceberg size per scene was 5.7 m2 (SD = 2.6 m2). We estimate the total area (± uncertainty) of iceberg habitat in the fjord to be 455,400 ± 123,000 m2. The method works well for classifying icebergs across scenes (classification accuracy of 75.6%); the largest classification errors occur in areas with densely-packed ice, low contrast between neighboring ice cover, or dark or sediment-covered ice, where icebergs may be misclassified as brash ice about 20% of the time. OBIA is a powerful image classification tool, and the method we present could be adapted and applied to other ice habitats, such as sea ice, to assess changes in ice characteristics and availability

Neural expression and post-transcriptional dosage compensation of the steroid metabolic enzyme 17β-HSD type 4

<p>Abstract</p> <p>Background</p> <p>Steroids affect many tissues, including the brain. In the zebra finch, the estrogenic steroid estradiol (E₂) is especially effective at promoting growth of the neural circuit specialized for song. In this species, only the males sing and they have a much larger and more interconnected song circuit than females. Thus, it was surprising that the gene for 17β-hydroxysteroid dehydrogenase type 4 (HSD17B4), an enzyme that converts E₂to a less potent estrogen, had been mapped to the Z sex chromosome. As a consequence, it was likely that HSD17B4 was differentially expressed in males (ZZ) and females (ZW) because dosage compensation of Z chromosome genes is incomplete in birds. If a higher abundance of HSD17B4 mRNA in males than females was translated into functional enzyme in the brain, then contrary to expectation, males could produce less E₂in their brains than females.</p> <p>Results</p> <p>Here, we used molecular and biochemical techniques to confirm the HSD17B4 Z chromosome location in the zebra finch and to determine that HSD17B4 mRNA and activity were detectable in the early developing and adult brain. As expected, HSD17B4 mRNA expression levels were higher in males compared to females. This provides further evidence of the incomplete Z chromosome inactivation mechanisms in birds. We detected HSD17B4 mRNA in regions that suggested a role for this enzyme in the early organization and adult function of song nuclei. We did not, however, detect significant sex differences in HSD17B4 activity levels in the adult brain.</p> <p>Conclusions</p> <p>Our results demonstrate that the HSD17B4 gene is expressed and active in the zebra finch brain as an E₂metabolizing enzyme, but that dosage compensation of this Z-linked gene may occur via post-transcriptional mechanisms.</p

Associating Facial Expressions and Upper-Body Gestures with Learning Tasks for Enhancing Intelligent Tutoring Systems

Learning involves a substantial amount of cognitive, social and emotional states. Therefore, recognizing and understanding these states in the context of learning is key in designing informed interventions and addressing the needs of the individual student to provide personalized education. In this paper, we explore the automatic detection of learner’s nonverbal behaviors involving hand-over-face gestures, head and eye movements and emotions via facial expressions during learning. The proposed computer vision-based behavior monitoring method uses a low-cost webcam and can easily be integrated with modern tutoring technologies. We investigate these behaviors in-depth over time in a classroom session of 40 minutes involving reading and problem-solving exercises. The exercises in the sessions are divided into three categories: an easy, medium and difficult topic within the context of undergraduate computer science. We found that there is a significant increase in head and eye movements as time progresses, as well as with the increase of difficulty level. We demonstrated that there is a considerable occurrence of hand-over-face gestures (on average 21.35%) during the 40 minutes session and is unexplored in the education domain. We propose a novel deep learning approach for automatic detection of hand-over-face gestures in images with a classification accuracy of 86.87%. There is a prominent increase in hand-over-face gestures when the difficulty level of the given exercise increases. The hand-over-face gestures occur more frequently during problem-solving (easy 23.79%, medium 19.84% and difficult 30.46%) exercises in comparison to reading (easy 16.20%, medium 20.06% and difficult 20.18%)

The integrated analysis of metabolic and protein interaction networks reveals novel molecular organizing principles

Background: The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency. Results: Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems. Conclusion: Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. Thus, our results shed further light on the unifying principles shaping the evolution of both the functional (metabolic) as well as the physical interaction network