Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research.

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies. Furthermore, current approaches for the prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML platforms that can be used to comprehensively understand cardiovascular adaptations to pregnancy and discuss the interpretability of ML outcomes, the consequences of model bias, and the importance of ethical consideration in ML use.

Comparison of the Lund and Browder table to computed tomography scan three-dimensional surface area measurement for a pediatric cohort.

BACKGROUND: Treating burns effectively requires accurately assessing the percentage of the total body surface area (%TBSA) affected by burns. Current methods for estimating %TBSA, such as Lund and Browder (L&B) tables, rely on historic body statistics. An increasingly obese population has been blamed for increasing errors in %TBSA estimates. However, this assumption has not been experimentally validated. We hypothesized that errors in %TBSA estimates using L&B were due to differences in the physical proportions of today's children compared with children in the early 1940s when the chart was developed and that these differences would appear as body mass index (BMI)-associated systematic errors in the L&B values versus actual body surface areas. MATERIALS AND METHODS: We measured the TBSA of human pediatric cadavers using computed tomography scans. Subjects ranged from 9 mo to 15 y in age. We chose outliers of the BMI distribution (from the 31st percentile at the low through the 99th percentile at the high). We examined surface area proportions corresponding to L&B regions. RESULTS: Measured regional proportions based on computed tomography scans were in reasonable agreement with L&B, even with subjects in the tails of the BMI range. The largest deviation was 3.4%, significantly less than the error seen in real-world %TBSA estimates. CONCLUSIONS: While today's population is more obese than those studied by L&B, their body region proportions scale surprisingly well. The primary error in %TBSA estimation is not due to changing physical proportions of today's children and may instead lie in the application of the L&B table.

Facilitating surgeon understanding of complex anatomy using a three-dimensional printed model.

BACKGROUND: 3-dimensional prints (3DP) anecdotally facilitate surgeon understanding of anatomy and decision-making. However, the actual benefit to surgeons or patients has not been quantified. This study investigates how surgeon understanding of complex anatomy is altered by a 3DP compared to computed tomography (CT) scan or CT + digital reconstruction (CT + DR). MATERIALS AND METHODS: Key anatomic features were segmented from a CT-abdomen/pelvis of pygopagus twins to build a DR and printed in color on a 3D printer. Pediatric surgery trainees and attendings (n = 21) were tested regarding anatomy identification and their understanding of point-to-point distances, scale, and shape. RESULTS: There was no difference between media regarding point-to-point distances. The 3DP led to an increased number of correct answers for questions of scale and shape compared to CT (P < 0.05). CT + DR performance was intermediate but not statistically different from 3DP or CT. Identification of anatomy was inconsistent between media; however, answers were significantly closer to correct when using the 3DP. Participants completed the test faster with the 3DP (6.6 ± 0.5 min) (P < 0.05) than with CT (18.9 ± 2.5 min) or CT + 3DR (14.9 ± 1.5 min). CONCLUSIONS: Although point-to-point measurements were not different, 3DP increased the understanding of shape, scale, and anatomy. It enabled understanding significantly faster than other media. In difficult surgical cases with complex anatomy and a need for efficient multidisciplinary coordination, 3D printed models should be considered for surgical planning.

Valve Endothelial Cell-Derived Tgfß1 Signaling Promotes Nuclear Localization of Sox9 in Interstitial Cells Associated With Attenuated Calcification.

OBJECTIVE: Aortic valve disease, including calcification, affects >2% of the human population and is caused by complex interactions between multiple risk factors, including genetic mutations, the environment, and biomechanics. At present, there are no effective treatments other than surgery, and this is because of the limited understanding of the mechanisms that underlie the condition. Previous work has shown that valve interstitial cells within the aortic valve cusps differentiate toward an osteoblast-like cell and deposit bone-like matrix that leads to leaflet stiffening and calcific aortic valve stenosis. However, the mechanisms that promote pathological phenotypes in valve interstitial cells are unknown. APPROACH AND RESULTS: Using a combination of in vitro and in vivo tools with mouse, porcine, and human tissue, we show that in valve interstitial cells, reduced Sox9 expression and nuclear localization precedes the onset of calcification. In vitro, Sox9 nuclear export and calcific nodule formation is prevented by valve endothelial cells. However, in vivo, loss of Tgfß1 in the endothelium leads to reduced Sox9 expression and calcific aortic valve disease. CONCLUSIONS: Together, these findings suggest that reduced nuclear localization of Sox9 in valve interstitial cells is an early indicator of calcification, and therefore, pharmacological targeting to prevent nuclear export could serve as a novel therapeutic tool in the prevention of calcification and stenosis.

Addressing the unmet need for visualizing conditional random fields in biological data.

BACKGROUND: The biological world is replete with phenomena that appear to be ideally modeled and analyzed by one archetypal statistical framework - the Graphical Probabilistic Model (GPM). The structure of GPMs is a uniquely good match for biological problems that range from aligning sequences to modeling the genome-to-phenome relationship. The fundamental questions that GPMs address involve making decisions based on a complex web of interacting factors. Unfortunately, while GPMs ideally fit many questions in biology, they are not an easy solution to apply. Building a GPM is not a simple task for an end user. Moreover, applying GPMs is also impeded by the insidious fact that the "complex web of interacting factors" inherent to a problem might be easy to define and also intractable to compute upon. DISCUSSION: We propose that the visualization sciences can contribute to many domains of the bio-sciences, by developing tools to address archetypal representation and user interaction issues in GPMs, and in particular a variety of GPM called a Conditional Random Field(CRF). CRFs bring additional power, and additional complexity, because the CRF dependency network can be conditioned on the query data. CONCLUSIONS: In this manuscript we examine the shared features of several biological problems that are amenable to modeling with CRFs, highlight the challenges that existing visualization and visual analytics paradigms induce for these data, and document an experimental solution called StickWRLD which, while leaving room for improvement, has been successfully applied in several biological research projects. Software and tutorials are available at http://www.stickwrld.org/.

Structure and function of respiratory syncytial virus surface glycoproteins.

The two major glycoproteins on the surface of the respiratory syncytial virus (RSV) virion, the attachment glycoprotein (G) and the fusion glycoprotein (F), control the initial phases of infection. G targets the ciliated cells of the airways, and F causes the virion membrane to fuse with the target cell membrane. The F protein is the major target for antiviral drug development, and both G and F glycoproteins are the antigens targeted by neutralizing antibodies induced by infection. In this chapter, we review the structure and function of the RSV surface glycoproteins, including recent X-ray crystallographic data of the F glycoprotein in its pre- and postfusion conformations, and discuss how this information informs antigen selection and vaccine development.

Invasive or More Direct Measurements Can Provide an Objective Early-Stopping Ceiling for Training Deep Neural Networks on Non-invasive or Less-Direct Biomedical Data.

Early stopping is an extremely common tool to minimize overfitting, which would otherwise be a cause of poor generalization of the model to novel data. However, early stopping is a heuristic that, while effective, primarily relies on ad hoc parameters and metrics. Optimizing when to stop remains a challenge. In this paper, we suggest that for some biomedical applications, a natural dichotomy of invasive/non-invasive measurements, or more generally proximal vs distal measurements of a biological system can be exploited to provide objective advice on early stopping. We discuss the conditions where invasive measurements of a biological process should provide better predictions than non-invasive measurements, or at best offer parity. Hence, if data from an invasive measurement are available locally, or from the literature, that information can be leveraged to know with high certainty whether a model of non-invasive data is overfitted. We present paired invasive/non-invasive cardiac and coronary artery measurements from two mouse strains, one of which spontaneously develops type 2 diabetes, posed as a classification problem. Examination of the various stopping rules shows that generalization is reduced with more training epochs and commonly applied stopping rules give widely different generalization error estimates. The use of an empirically derived training ceiling is demonstrated to be helpful as added information to leverage early stopping in order to reduce overfitting.

An eQTL biological data visualization challenge and approaches from the visualization community.

In 2011, the IEEE VisWeek conferences inaugurated a symposium on Biological Data Visualization. Like other domain-oriented Vis symposia, this symposium's purpose was to explore the unique characteristics and requirements of visualization within the domain, and to enhance both the Visualization and Bio/Life-Sciences communities by pushing Biological data sets and domain understanding into the Visualization community, and well-informed Visualization solutions back to the Biological community. Amongst several other activities, the BioVis symposium created a data analysis and visualization contest. Unlike many contests in other venues, where the purpose is primarily to allow entrants to demonstrate tour-de-force programming skills on sample problems with known solutions, the BioVis contest was intended to whet the participants' appetites for a tremendously challenging biological domain, and simultaneously produce viable tools for a biological grand challenge domain with no extant solutions. For this purpose expression Quantitative Trait Locus (eQTL) data analysis was selected. In the BioVis 2011 contest, we provided contestants with a synthetic eQTL data set containing real biological variation, as well as a spiked-in gene expression interaction network influenced by single nucleotide polymorphism (SNP) DNA variation and a hypothetical disease model. Contestants were asked to elucidate the pattern of SNPs and interactions that predicted an individual's disease state. 9 teams competed in the contest using a mixture of methods, some analytical and others through visual exploratory methods. Independent panels of visualization and biological experts judged entries. Awards were given for each panel's favorite entry, and an overall best entry agreed upon by both panels. Three special mention awards were given for particularly innovative and useful aspects of those entries. And further recognition was given to entries that correctly answered a bonus question about how a proposed "gene therapy" change to a SNP might change an individual's disease status, which served as a calibration for each approaches' applicability to a typical domain question. In the future, BioVis will continue the data analysis and visualization contest, maintaining the philosophy of providing new challenging questions in open-ended and dramatically underserved Bio/Life Sciences domains.

The Importance of Weakly Co-Evolving Residue Networks in Proteins is Revealed by Visual Analytics.

Small changes in a protein's core packing produce changes in function, and even small changes in function bias species fitness and survival. Therefore individually deleterious mutations should be evolutionarily coupled with compensating mutations that recover fitness. Co-evolving pairs of mutations should be littered across evolutionary history. Despite longstanding intuition, the results of co-evolution analyses have largely disappointed expectations. Regardless of the statistics applied, only a small majority of the most strongly co-evolving residues are typically found to be in contact, and much of the "meaning" of observed co-evolution has been opaque. In a medium-sized protein of 300 amino acids, there are almost 20 million potentially-important interdependencies. It is impossible to understand this data in textual format without extreme summarization or truncation. And, due to summarization and truncation, it is impossible to identify most patterns in the data. We developed a visualization approach that eschews the common "look at a long list of statistics" approach and instead enables the user to literally look at all of the co-evolution statistics simultaneously. Users of our tool reported visually obvious "clouds" of co-evolution statistics forming distinct patterns in the data, and analysis demonstrated that these clouds had structural relevance. To determine whether this phenomenon generalized, we repeated this experiment in three proteins we had not previously studied. The results provide evidence about how structural constrains have impacted co-evolution, why previous "examine the most frequently co-evolving residues" approaches have had limited success, and additionally shed light on the biophysical importance of different types of co-evolution.

Chromatin structure in cancer.

In the past decade, we have seen the emergence of sequence-based methods to understand chromosome organization. With the confluence of in situ approaches to capture information on looping, topological domains, and larger chromatin compartments, understanding chromatin-driven disease is becoming feasible. Excitingly, recent advances in single molecule imaging with capacity to reconstruct "bulk-cell" features of chromosome conformation have revealed cell-to-cell chromatin structural variation. The fundamental question motivating our analysis of the literature is, can altered chromatin structure drive tumorigenesis? As our community learns more about rare disease, including low mutational frequency cancers, understanding "chromatin-driven" pathology will illuminate the regulatory structures of the genome. We describe recent insights into altered genome architecture in human cancer, highlighting multiple pathways toward disruptions of chromatin structure, including structural variation, noncoding mutations, metabolism, and de novo mutations to architectural regulators themselves. Our analysis of the literature reveals that deregulation of genome structure is characteristic in distinct classes of chromatin-driven tumors. As we begin to integrate the findings from single cell imaging studies and chromatin structural sequencing, we will be able to understand the diversity of cells within a common diagnosis, and begin to define structure-function relationships of the misfolded genome.

Improvement of automated analysis of coronary Doppler echocardiograms.

Coronary artery disease is the leading cause of heart disease, and while it can be assessed through transthoracic Doppler echocardiography (TTDE) by observing changes in coronary flow, manual analysis of TTDE is time consuming and subject to bias. In a previous study, a program was created to automatically analyze coronary flow patterns by parsing Doppler videos into a single continuous image, binarizing and separating the image into cardiac cycles, and extracting data values from each of these cycles. The program significantly reduced variability and time to complete TTDE analysis, but some obstacles such as interfering noise and varying video sizes left room to increase the program's accuracy. The goal of this current study was to refine the existing automation algorithm and heuristics by (1) moving the program to a Python environment, (2) increasing the program's ability to handle challenging cases and video variations, and (3) removing unrepresentative cardiac cycles from the final data set. With this improved analysis, examiners can use the automatic program to easily and accurately identify the early signs of serious heart diseases.

A 1% TBSA Chart Reduces Math Errors While Retaining Acceptable First-Estimate Accuracy.

Life-threatening and treatment-altering errors occur in estimates of the percentage of total body surface area burned (%TBSA burned) with unacceptable frequency. In response, numerous attempts have been made to improve the charts commonly used for %TBSA-burned estimation. Recent research shows that the largest errors in %TBSA-burned estimates probably come from sources other than inaccurate values in the charts. Here, we develop a taxonomy of the possible sources of error and their impact on %TBSA-burned estimates. Also, we observe that different caregivers have different estimation needs: First-responders require a rapid estimate with sufficient accuracy to enable them to begin care and determine patient transport options, while burn surgeons ordering skin grafts desire accuracy to the square centimeter, and can afford considerable time to attain that accuracy. These competing needs suggest that a one-tool-fits-all-caregivers approach is suboptimal. We therefore present a validated, simplified burn chart that minimizes one of the largest sources of random errors in %TBSA-burned estimates-simple calculation errors-while also being quick and requiring little training. NCHart-1 also enables simple consensus estimates, as well as separation of estimation subtasks across caregivers, leading to several potential improvements in mass casualty situations. Our results demonstrate that NCHart-1 possesses the accuracy necessary for first responders, while reliably producing results in less than 2 minutes. Of 76 healthcare professionals surveyed, a large majority indicated a preference for NCHart-1 over their previous methods for ease of both use and training. For clinical or commercial use of NCHart-1, please contact: tech.commercialization@nationwidechildrens.org.

Heme utilization by nontypeable Haemophilus influenzae is essential and dependent on Sap transporter function.

Bacterial strategies of innate immune evasion and essential metabolic functions are critical for commensal-host homeostasis. Previously, we showed that Sap translocator function is necessary for nontypeable Haemophilus influenzae (NTHI) behaviors that mediate diseases of the human airway. Antimicrobial peptide (AP) lethality is limited by binding mediated by the Sap complex. SapA shares homology with the dipeptide-binding protein (DppA) and the heme-binding lipoprotein (HbpA), both of which have previously been shown to bind the iron-containing compound heme, whose acquisition is essential for Haemophilus survival. Computational modeling revealed conserved SapA residues, similarly modeled to mediate heme binding in HbpA. Here, we directly demonstrate that SapA bound heme and was essential for heme utilization by iron-starved NTHI. Further, the Sap translocator permease mediated heme transport into the bacterial cytoplasm, thus defining a heretofore unknown mechanism of intracytoplasmic membrane heme transport in Haemophilus. Since we demonstrate multiple ligand specificity for the SapA-binding protein, we tested whether APs would compete with heme for SapA binding. We showed that human ß-defensins 2 and 3, human cathelicidin LL-37, human neutrophil protein 1, and melittin displaced heme bound to SapA, thus supporting a hierarchy wherein immune evasion supercedes even the needed iron acquisition functions of the Sap system.

FIND: a new software tool and development platform for enhanced multicolor flow analysis.

BACKGROUND: Flow Cytometry is a process by which cells, and other microscopic particles, can be identified, counted, and sorted mechanically through the use of hydrodynamic pressure and laser-activated fluorescence labeling. As immunostained cells pass individually through the flow chamber of the instrument, laser pulses cause fluorescence emissions that are recorded digitally for later analysis as multidimensional vectors. Current, widely adopted analysis software limits users to manual separation of events based on viewing two or three simultaneous dimensions. While this may be adequate for experiments using four or fewer colors, advances have lead to laser flow cytometers capable of recording 20 different colors simultaneously. In addition, mass-spectrometry based machines capable of recording at least 100 separate channels are being developed. Analysis of such high-dimensional data by visual exploration alone can be error-prone and susceptible to unnecessary bias. Fortunately, the field of Data Mining provides many tools for automated group classification of multi-dimensional data, and many algorithms have been adapted or created for flow cytometry. However, the majority of this research has not been made available to users through analysis software packages and, as such, are not in wide use. RESULTS: We have developed a new software application for analysis of multi-color flow cytometry data. The main goals of this effort were to provide a user-friendly tool for automated gating (classification) of multi-color data as well as a platform for development and dissemination of new analysis tools. With this software, users can easily load single or multiple data sets, perform automated event classification, and graphically compare results within and between experiments. We also make available a simple plugin system that enables researchers to implement and share their data analysis and classification/population discovery algorithms. CONCLUSIONS: The FIND (Flow Investigation using N-Dimensions) platform presented here provides a powerful, user-friendly environment for analysis of Flow Cytometry data as well as providing a common platform for implementation and distribution of new automated analysis techniques to users around the world.

Dramatically reduced precision in microarray analysis retains quantitative properties and provides additional benefits.

Microarray technology is a primary tool for elucidating the differences between similar populations of nucleic acid molecules. It is frequently used to detect mRNA population shifts that result from perturbations such as environmental changes, host-pathogen interactions, or the shift from therapeutic to toxic drug doses. Unfortunately, current microarray analysis methods provide only undirected discovery tools and cannot be directed to a particular hypothesis. To address this issue, we demonstrate that biologically relevant aspects of expression profiles may be captured by precision-reduced descriptions that are fully human readable, and that biologically relevant relationships may be captured by applying familiar pattern searching techniques to these precision-reduced descriptions. Even expression profiles that are reduced to only a single bit of precision, retain the surprising ability to reproduce the clustering results from the full 16-bit precision original data. We also illustrate that simple verbal descriptions ("my gene's expression went up briefly at timepoint 10") of expression profiles are quantitative entities fully compatible with clustering and searching within the precision reduced data.

Forming Big Datasets through Latent Class Concatenation of Imperfectly Matched Databases Features.

Informatics researchers often need to combine data from many different sources to increase statistical power and study subtle or complicated effects. Perfect overlap of measurements across academic studies is rare since virtually every dataset is collected for a unique purpose and without coordination across parties not-at-hand (i.e., informatics researchers in the future). Thus, incomplete concordance of measurements across datasets poses a major challenge for researchers seeking to combine public databases. In any given field, some measurements are fairly standard, but every organization collecting data makes unique decisions on instruments, protocols, and methods of processing the data. This typically denies literal concatenation of the raw data since constituent cohorts do not have the same measurements (i.e., columns of data). When measurements across datasets are similar prima facie, there is a desire to combine the data to increase power, but mixing non-identical measurements could greatly reduce the sensitivity of the downstream analysis. Here, we discuss a statistical method that is applicable when certain patterns of missing data are found; namely, it is possible to combine datasets that measure the same underlying constructs (or latent traits) when there is only partial overlap of measurements across the constituent datasets. Our method, ROSETTA empirically derives a set of common latent trait metrics for each related measurement domain using a novel variation of factor analysis to ensure equivalence across the constituent datasets. The advantage of combining datasets this way is the simplicity, statistical power, and modeling flexibility of a single joint analysis of all the data. Three simulation studies show the performance of ROSETTA on datasets with only partially overlapping measurements (i.e., systematically missing information), benchmarked to a condition of perfectly overlapped data (i.e., full information). The first study examined a range of correlations, while the second study was modeled after the observed correlations in a well-characterized clinical, behavioral cohort. Both studies consistently show significant correlations >0.94, often >0.96, indicating the robustness of the method and validating the general approach. The third study varied within and between domain correlations and compared ROSETTA to multiple imputation and meta-analysis as two commonly used methods that ostensibly solve the same data integration problem. We provide one alternative to meta-analysis and multiple imputation by developing a method that statistically equates similar but distinct manifest metrics into a set of empirically derived metrics that can be used for analysis across all datasets.

In Silico Modeling of Biofilm Formation by Nontypeable Haemophilus influenzae In Vivo.

Biofilms formed by nontypeable Haemophilus influenzae (NTHI) bacteria play an important role in multiple respiratory tract diseases. Visual inspection of the morphology of biofilms formed during chronic infections shows distinct differences from biofilms formed in vitro To better understand these differences, we analyzed images of NTHI biofilms formed in the middle ears of Chinchilla lanigera and developed an in silico agent-based model of the formation of NTHI biofilms in vivo We found that, as in vitro, NTHI bacteria are organized in self-similar patterns; however, the sizes of NTHI clusters in vivo are more than 10-fold smaller than their in vitro counterparts. The agent-based model reproduced these patterns and suggested that smaller clusters occur due to elimination of planktonic NTHI cells by the host responses. Estimation of model parameters by fitting simulation results to imaging data showed that the effects of several processes in the model change during the course of the infection.IMPORTANCE Multiple respiratory illnesses are associated with formation of biofilms within the human airway by NTHI. However, a substantial amount of our understanding of the mechanisms that underlie NTHI biofilm formation is obtained from in vitro studies. Our in silico model that describes biofilm formation by NTHI within the middle ears of Chinchilla lanigera will help isolate processes potentially responsible for the differences between the morphologies of biofilms formed in vivo versus those formed in vitro Thus, the in silico model can be used to glean mechanisms that underlie biofilm formation in vivo and connect those mechanisms to those obtained from in vitro experiments. The in silico model developed here can be extended to investigate potential roles of specific host responses (e.g., mucociliary clearance) on NTHI biofilm formation in vivo The developed computational tools can also be used to analyze and describe biofilm formation by other bacterial species in vivo.

MAVL/StickWRLD: analyzing structural constraints using interpositional dependencies in biomolecular sequence alignments.

The increasing availability of structurally aligned protein families has made it possible to use statistical methods to discover regions of interpositional dependencies of residue identity. Such dependencies amongst residues often have structural or functional implications, and their discovery can supply valuable constraints that assist in the refinement of measured, or predicted molecular structure assignments. Multiple Alignment Variation Linker (MAVL) and StickWRLD [W. Ray (2004) Nucleic Acids Res., 32, W59-W63] were developed to analyze and visualize nucleic acid and protein alignments, to discover and illuminate position/location relationships to the user. The original system analyzed users' data from a web-form submission and presented the user with a static VRML diagram describing their data. We are pleased to report that MAVL/StickWRLD has been completely redesigned and rewritten. MAVL/StickWRLD now functions as a platform-independent Java applet, with real-time dynamic controls that enable much more intuitive exploration and interaction with the data. The system has also been upgraded to enable visualization of a range of aggregate residue properties, and an extensive database of pre-computed StickWRLD diagrams based on PFAM families is now available directly from the interface. The Java StickWRLD applet is available via the WWW at http://www.microbial-pathogenesis.org/stickwrld/.

A member of the cathelicidin family of antimicrobial peptides is produced in the upper airway of the chinchilla and its mRNA expression is altered by common viral and bacterial co-pathogens of otitis media.

Cationic antimicrobial peptides (AMPs), a component of the innate immune system, play a major role in defense of mucosal surfaces against a wide spectrum of microorganisms such as viral and bacterial co-pathogens of the polymicrobial disease otitis media (OM). To further understand the role of AMPs in OM, we cloned a cDNA encoding a cathelicidin homolog (cCRAMP) from upper respiratory tract (URT) mucosae of the chinchilla, the predominant host used to model experimental OM. Recombinant cCRAMP exhibited alpha-helical secondary structure and killed the three main bacterial pathogens of OM. In situ hybridization showed cCRAMP mRNA production in epithelium of the chinchilla Eustachian tube and RT-PCR was used to amplify cCRAMP mRNA from several other tissues of the chinchilla URT. Quantitative RT-PCR analysis of chinchilla middle ear epithelial cells (CMEEs) incubated with either viral (influenza A virus, adenovirus, or RSV) or bacterial (nontypeable H. influenzae, M. catarrhalis, or S. pneumoniae) pathogens associated with OM demonstrated distinct microbe-specific patterns of altered expression. Collectively, these data showed that viruses and bacteria modulate AMP messages in the URT, which likely contributes to the disease course of OM.

MAVL/StickWRLD for protein: visualizing protein sequence families to detect non-consensus features.

A fundamental problem with applying Consensus, Weight-Matrix or hidden Markov models as search tools for biosequences is that there is no way to know, from the model, if the modeled sequences display any dependencies between positional identities. In some instances, these dependencies are crucial in correctly accepting or rejecting other sequences as members of the family. MAVL (multiple alignment variation linker) and StickWRLD provide a web-based method to visually survey the model-training sequences to discover and characterize possible dependencies. Initially introduced for nucleic acid sequences, with MAVL/StickWRLD, it is easy to distinguish typical DNA or RNA structural dependencies in input families, identify mixed populations of distinct subfamilies, or discover novel dependencies that result from binding interactions or other selective pressures [W. Ray (2004) Nucleic Acids Res., 32, W59-W63]. Since the announcement of MAVL/StickWRLD for nucleic acids, one of the most requested new features has been the extension of this visualization method to support protein alignments. We are pleased to report that this extension has been successful, that the basic visualization has been augmented in several ways to enhance protein viewing, and that the results with protein alignments are even more dramatic than with NA alignments. MAVL/StickWRLD can be accessed at http://www.microbial-pathogenesis.org/stickwrld/.