Bayesian tests for random mating in polyploids.

Hardy-Weinberg proportions (HWP) are often explored to evaluate the assumption of random mating. However, in autopolyploids, organisms with more than two sets of homologous chromosomes, HWP and random mating are different hypotheses that require different statistical testing approaches. Currently, the only available methods to test for random mating in autopolyploids (i) heavily rely on asymptotic approximations and (ii) assume genotypes are known, ignoring genotype uncertainty. Furthermore, these approaches are all frequentist, and so do not carry the benefits of Bayesian analysis, including ease of interpretability, incorporation of prior information, and consistency under the null. Here, we present Bayesian approaches to test for random mating, bringing the benefits of Bayesian analysis to this problem. Our Bayesian methods also (i) do not rely on asymptotic approximations, being appropriate for small sample sizes, and (ii) optionally account for genotype uncertainty via genotype likelihoods. We validate our methods in simulations and demonstrate on two real datasets how testing for random mating is more useful for detecting genotyping errors than testing for HWP (in a natural population) and testing for Mendelian segregation (in an experimental S1 population). Our methods are implemented in Version 2.0.2 of the hwep R package on the Comprehensive R Archive Network https://cran.r-project.org/package=hwep.

High-dose polyethylene glycol-3350 and gatorade solutions for patients with previous inadequate bowel preparations for colonoscopy are safe and effective.

BACKGROUND: Inadequate bowel preparation for colonoscopy remains an issue resulting in lower adenoma detection rates and increased cost. We assessed the efficacy, safety and tolerability of high-dose bowel preparations in subjects who previously had an inadequate colonoscopy preparation. METHODS: We performed a multi-step prospective trial of high-dose bowel preparations with subjects assigned to the dose higher than their previous inadequate preparation. Step 1: 1.5 times the standard-dose of polyethylene glycol 3350 (PEG, 459 g) and Gatorade; and Step 2: 2.0 times the standard-dose of PEG (612 g) and Gatorade, both were given as extended split-dose preparations. 69 outpatients consumed their preparation before a morning colonoscopy. The primary endpoint was colon cleanliness assessed by the Chicago bowel preparation scale (BPS). Safety was assessed by comparing a baseline basic metabolic panel (BMP) to a post-cleansing BMP. Patients with no history of inadequate colon cleansing who consumed standard doses of PEG (306 g to 357 g) and Gatorade were used as a comparison group. Tolerability of the bowel preparation was assessed using a subject-questionnaire. RESULTS: When compared to controls consuming standard-dose bowel preparations, subjects consuming high-dose preparations had no statistically significant difference in colon cleanliness as measured by the modified or total Chicago BPS scores or differences in tolerability. Baseline and post-cleaning BMPs were not significantly different other than the BUN falling (p < 0.0001) after the preparation. CONCLUSIONS: The multi-step high-dose bowel cleansing protocol proved highly efficacious, safe and well tolerated in subjects who previously had an inadequate colonoscopy preparation. TRIAL REGISTRATION: ClinicalTrials.gov NCT02661750.

Development of a Novel Circulating Autoantibody Biomarker Panel for the Identification of Patients with 'Actionable' Pulmonary Nodules.

Due to poor compliance and uptake of LDCT screening among high-risk populations, lung cancer is often diagnosed in advanced stages where treatment is rarely curative. Based upon the American College of Radiology's Lung Imaging and Reporting Data System (Lung-RADS) 80-90% of patients screened will have clinically "non-actionable" nodules (Lung-RADS 1 or 2), and those harboring larger, clinically "actionable" nodules (Lung-RADS 3 or 4) have a significantly greater risk of lung cancer. The development of a companion diagnostic method capable of identifying patients likely to have a clinically actionable nodule identified during LDCT is anticipated to improve accessibility and uptake of the paradigm and improve early detection rates. Using protein microarrays, we identified 501 circulating targets with differential immunoreactivities against cohorts characterized as possessing either actionable (n = 42) or non-actionable (n = 20) solid pulmonary nodules, per Lung-RADS guidelines. Quantitative assays were assembled on the Luminex platform for the 26 most promising targets. These assays were used to measure serum autoantibody levels in 841 patients, consisting of benign (BN; n = 101), early-stage non-small cell lung cancer (NSCLC; n = 245), other early-stage malignancies within the lung (n = 29), and individuals meeting United States Preventative Screening Task Force (USPSTF) screening inclusion criteria with both actionable (n = 87) and non-actionable radiologic findings (n = 379). These 841 patients were randomly split into three cohorts: Training, Validation 1, and Validation 2. Of the 26 candidate biomarkers tested, 17 differentiated patients with actionable nodules from those with non-actionable nodules. A random forest model consisting of six autoantibody (Annexin 2, DCD, MID1IP1, PNMA1, TAF10, ZNF696) biomarkers was developed to optimize our classification performance; it possessed a positive predictive value (PPV) of 61.4%/61.0% and negative predictive value (NPV) of 95.7%/83.9% against Validation cohorts 1 and 2, respectively. This panel may improve patient selection methods for lung cancer screening, serving to greatly reduce the futile screening rate while also improving accessibility to the paradigm for underserved populations.

Communicating diagnoses to individuals with a first episode psychosis: A qualitative study of individuals perspectives.

Background: Receiving the label of a psychotic disorder influences self-perception and may result in negative outcomes such as self-stigma and decreased self-esteem. The way the diagnosis is communicated to individuals may affect these outcomes. Aims: This study aims to explore the experiences and needs of individuals after a first episode of psychosis with regard to the way in which information about diagnosis, treatment options and prognosis is communicated with them. Design and methods: A descriptive interpretative phenomenological approach was used. Fifteen individuals who experienced a first episode of psychosis participated in individual semi-structured open-ended interviews on their experiences and needs regarding the process of providing information about diagnosis, treatment options and prognosis. Inductive thematic analysis was used to analyze the interviews. Results: Four recurring themes where identified (1) timing (when); (2) content (what); and (3) the way information is provided (how). Individuals also reported that the provided information could elicit an emotional reaction, for which they would require specific attention, therefore the fourth theme is (4) reactions and feelings. Conclusion: This study provides new insights into the experiences and specific information needed by individuals with a first episode of psychosis. Results suggest that individuals have different needs regarding the type of (what), how and when to receive information about diagnosis and treatment options. This requires a tailor-made process of communicating diagnosis. A guideline on when, how and what to inform, as well as providing personalized written information regarding the diagnosis and treatment options, is recommended.

Double reduction estimation and equilibrium tests in natural autopolyploid populations.

Many bioinformatics pipelines include tests for equilibrium. Tests for diploids are well studied and widely available, but extending these approaches to autopolyploids is hampered by the presence of double reduction, the comigration of sister chromatid segments into the same gamete during meiosis. Though a hindrance for equilibrium tests, double reduction rates are quantities of interest in their own right, as they provide insights about the meiotic behavior of autopolyploid organisms. Here, we develop procedures to (i) test for equilibrium while accounting for double reduction, and (ii) estimate the double reduction rate given equilibrium. To do so, we take two approaches: a likelihood approach, and a novel U-statistic minimization approach that we show generalizes the classical equilibrium χ2 test in diploids. For small sample sizes and uncertain genotypes, we further develop a bootstrap procedure based on our U-statistic to test for equilibrium. We validate our methods on both simulated and real data.

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19.

Multiplex technologies for interrogating multiple biomarkers in concert have existed for several decades; however, methods to evaluate multiple epitopes on the same analyte remain limited. This report describes the development and optimization of a multiplexed immunobead assay for serological testing of common immunoglobulin isotypes (e.g., IgA, IgM, and IgG) associated with an immune response to SARS-CoV-2 infection or vaccination. Assays were accomplished using a flow-based, multiplex fluorescent reader with dual-channel capability. Optimizations focused on analyte capture time, detection antibody concentration, and detection antibody incubation time. Analytical assay performance characteristics (e.g., assay range (including lower and upper limits of quantitation); and intra- and inter-assay precision) were established for either IgG/IgM or IgA/IgM serotype combination in tandem using the 'dual channel' mode. Analyte capture times of 30 min for IgG, 60 min for IgM, and 120 min for IgA were suitable for most applications, providing a balance of assay performance and throughput. Optimal detection antibody incubations at 4 µg/mL for 30 min was observed and are recommended for general applications, given the overall excellent precision (percent coefficient of variance (%CV) ≤ 20%) and sensitivity values observed. The dynamic range for the IgG isotype spanned several orders of magnitude for each assay (Spike S1, Nucleocapsid, and Membrane glycoproteins), which supports robust titer evaluations at a 1:500 dilution factor for clinical applications. Finally, the optimized protocol was applied to monitoring Spike S1 seroconversion for subjects (n = 4) that completed a SARS-CoV-2 vaccine regimen. Within this cohort, Spike S1 IgG levels were observed to reach maximum titers at 14 days following second dose administration, at a much higher (~40-fold) signal intensity than either IgM or IgA isotypes. Interestingly, we observed highly variable Spike S1 IgG titer decay rates that were largely subject-dependent were observed, which will be the topic of future studies.

The role of IGF-pathway biomarkers in determining risks, screening, and prognosis in lung cancer.

BACKGROUND: Detection rates of early-stage lung cancer are traditionally low, which contributes to inconsistent treatment responses and high rates of annual cancer deaths. Currently, low-dose computed tomography (LDCT) screening produces a high false discovery rate. This limitation has prompted research to identify biomarkers to more clearly define eligible patients for LDCT screening, differentiate indeterminate pulmonary nodules, and select individualized cancer therapy. Biomarkers within the Insulin-like Growth Factor (IGF) family have come to the forefront of this research. Main Body: Multiple biomarkers within the IGF family have been investigated, most notably IGF-I and IGF binding protein 3. However, newer studies seek to expand this search to other molecules within the IGF axis. Certain studies have demonstrated these biomarkers are useful when used in combination with lung cancer screening, but other findings were not as conclusive, possibly owing to measurement bias and non-standardized assay techniques. Research also has suggested IGF biomarkers may be beneficial in the prognostication and subsequent treatment via systemic therapy. Despite these advances, additional knowledge of complex regulatory mechanisms inherent to this system are necessary to more fully harness the potential clinical utility for diagnostic and therapeutic purposes. CONCLUSIONS: The IGF system likely plays a role in multiple phases of lung cancer; however, there is a surplus of conflicting data, especially prior to development of the disease and during early stages of detection. IGF biomarkers may be valuable in the screening, prognosis, and treatment of lung cancer, though their exact application requires further study.

One-Shot 3D-Printed Multimaterial Soft Robotic Jamming Grippers.

Soft gripping provides the potential for high performance in challenging tasks through morphological computing; however, design explorations are limited by a combination of a difficulty in generating useful models and use of laborious fabrication techniques. We focus on a class of grippers based on granular jamming that are particularly difficult to model and introduce a "one shot" technique that exploits multimaterial three-dimensional (3D) printing to create entire grippers, including membrane and grains, in a single print run. This technique fully supports the de facto physical generate-and-test methodology used for this class of grippers, as entire design iterations can be fitted onto a single print bed and fabricated from Computer-Aided Design (CAD) files in a matter of hours. Initial results demonstrate the approach by rapidly prototyping in materio solutions for two challenging problems in unconventional design spaces; a twisting gripper that uses programmed deformations to reliably pick a coin, and a multifunctional legged robot paw that offers the ability for compliant locomotion over rough terrains, as well as being able to pick objects in cluttered natural environments. The technique also allows us to easily characterize the design space of multimaterial printed jamming grippers and provide some useful design rules. The simplicity of our technique encourages and facilitates creativity and innovation. As such, we see our approach as an enabling tool to make informed principled forays into unconventional design spaces and support the creation of a new breed of novel soft actuators.

Scalable bias-corrected linkage disequilibrium estimation under genotype uncertainty.

Linkage disequilibrium (LD) estimates are often calculated genome-wide for use in many tasks, such as SNP pruning and LD decay estimation. However, in the presence of genotype uncertainty, naive approaches to calculating LD have extreme attenuation biases, incorrectly suggesting that SNPs are less dependent than in reality. These biases are particularly strong in polyploid organisms, which often exhibit greater levels of genotype uncertainty than diploids. A principled approach using maximum likelihood estimation with genotype likelihoods can reduce this bias, but is prohibitively slow for genome-wide applications. Here, we present scalable moment-based adjustments to LD estimates based on the marginal posterior distributions of the genotypes. We demonstrate, on both simulated and real data, that these moment-based estimators are as accurate as maximum likelihood estimators, but are almost as fast as naive approaches based only on posterior mean genotypes. This opens up bias-corrected LD estimation to genome-wide applications. In addition, we provide standard errors for these moment-based estimators. All methods discussed in this manuscript are implemented in the ldsep package, available on the Comprehensive R Archive Network ( https://cran.r-project.org/package=ldsep ).

Pairwise linkage disequilibrium estimation for polyploids.

Many tasks in statistical genetics involve pairwise estimation of linkage disequilibrium (LD). The study of LD in diploids is mature. However, in polyploids, the field lacks a comprehensive characterization of LD. Polyploids also exhibit greater levels of genotype uncertainty than diploids, yet no methods currently exist to estimate LD in polyploids in the presence of such genotype uncertainty. Furthermore, most LD estimation methods do not quantify the level of uncertainty in their LD estimates. Our study contains three major contributions. (i) We characterize haplotypic and composite measures of LD in polyploids. These composite measures of LD turn out to be functions of common statistical measures of association. (ii) We derive procedures to estimate haplotypic and composite LD in polyploids in the presence of genotype uncertainty. We do this by estimating LD directly from genotype likelihoods, which may be obtained from many genotyping platforms. (iii) We derive standard errors of all LD estimators that we discuss. We validate our methods on both real and simulated data. Our methods are implemented in the R package ldsep, available on the Comprehensive R Archive Network https://cran.r-project.org/package=ldsep.

Elevated inflammatory gene expression in intervertebral disc tissues in mice with ADAM8 inactivated.

We found ADAM8 enzymatic activity elevated in degenerative human intervertebral disc (IVD). Here, we examined the discs in ADAM8-inactivation mice that carry a mutation preventing self-activation of the enzyme. Surprisingly, elevated gene expression for inflammatory markers (Cxcl1, IL6) was observed in injured discs of ADAM8 mutant mice, along with elevated expression of type 2 collagen gene (Col2a1), compared with wild type controls. Injured annulus fibrosus of mutant and wild type mice contained a higher proportion of large collagen fibers compared with intact discs, as documented by microscopic examination under circular polarized light. In the intact IVDs, Adam8EQ mouse AF contained lower proportion of yellow (intermediate) fiber than WT mice. This suggests that ADAM8 may regulate inflammation and collagen fiber assembly. The seemingly contradictory findings of elevated inflammatory markers in mutant mice and excessive ADAM8 activity in human degenerative discs suggest that ADAM8 may interact with other enzymatic and pro-inflammatory processes needed for tissue maintenance and repair. As a future therapeutic intervention to retard intervertebral disc degeneration, partial inhibition of ADAM8 proteolysis may be more desirable than complete inactivation of this enzyme.

UNIFYING AND GENERALIZING METHODS FOR REMOVING UNWANTED VARIATION BASED ON NEGATIVE CONTROLS.

Unwanted variation, including hidden confounding, is a well-known problem in many fields, but particularly in large-scale gene expression studies. Recent proposals to use control genes, genes assumed to be unassociated with the covariates of interest, have led to new methods to deal with this problem. Several versions of these removing unwanted variation (RUV) methods have been proposed, including RUV1, RUV2, RUV4, RUVinv, RUVrinv, and RUVfun. Here, we introduce a general framework, RUV*, that both unites and generalizes these approaches. This unifying framework helps clarify the connections between existing methods. In particular, we provide conditions under which RUV2 and RUV4 are equivalent. The RUV* framework preserves an advantage of the RUV approaches, namely, their modularity, which facilitates the development of novel methods based on existing matrix imputation algorithms. We illustrate this by implementing RUVB, a version of RUV* based on Bayesian factor analysis. In realistic simulations based on real data, we found RUVB to be competitive with existing methods in terms of both power and calibration. However, providing a consistently reliable calibration among the data sets remains challenging.

Data-based RNA-seq simulations by binomial thinning.

BACKGROUND: With the explosion in the number of methods designed to analyze bulk and single-cell RNA-seq data, there is a growing need for approaches that assess and compare these methods. The usual technique is to compare methods on data simulated according to some theoretical model. However, as real data often exhibit violations from theoretical models, this can result in unsubstantiated claims of a method's performance. RESULTS: Rather than generate data from a theoretical model, in this paper we develop methods to add signal to real RNA-seq datasets. Since the resulting simulated data are not generated from an unrealistic theoretical model, they exhibit realistic (annoying) attributes of real data. This lets RNA-seq methods developers assess their procedures in non-ideal (model-violating) scenarios. Our procedures may be applied to both single-cell and bulk RNA-seq. We show that our simulation method results in more realistic datasets and can alter the conclusions of a differential expression analysis study. We also demonstrate our approach by comparing various factor analysis techniques on RNA-seq datasets. CONCLUSIONS: Using data simulated from a theoretical model can substantially impact the results of a study. We developed more realistic simulation techniques for RNA-seq data. Our tools are available in the seqgendiff R package on the Comprehensive R Archive Network: https://cran.r-project.org/package=seqgendiff.

Priors for genotyping polyploids.

MOTIVATION: Empirical Bayes techniques to genotype polyploid organisms usually either (i) assume technical artifacts are known a priori or (ii) estimate technical artifacts simultaneously with the prior genotype distribution. Case (i) is unappealing as it places the onus on the researcher to estimate these artifacts, or to ensure that there are no systematic biases in the data. However, as we demonstrate with a few empirical examples, case (ii) makes choosing the class of prior genotype distributions extremely important. Choosing a class is either too flexible or too restrictive results in poor genotyping performance. RESULTS: We propose two classes of prior genotype distributions that are of intermediate levels of flexibility: the class of proportional normal distributions and the class of unimodal distributions. We provide a complete characterization of and optimization details for the class of unimodal distributions. We demonstrate, using both simulated and real data that using these classes results in superior genotyping performance. AVAILABILITY AND IMPLEMENTATION: Genotyping methods that use these priors are implemented in the updog R package available on the Comprehensive R Archive Network: https://cran.r-project.org/package=updog. All code needed to reproduce the results of this article is available on GitHub: https://github.com/dcgerard/reproduce_prior_sims. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Empirical Bayes shrinkage and false discovery rate estimation, allowing for unwanted variation.

We combine two important ideas in the analysis of large-scale genomics experiments (e.g. experiments that aim to identify genes that are differentially expressed between two conditions). The first is use of Empirical Bayes (EB) methods to handle the large number of potentially-sparse effects, and estimate false discovery rates and related quantities. The second is use of factor analysis methods to deal with sources of unwanted variation such as batch effects and unmeasured confounders. We describe a simple modular fitting procedure that combines key ideas from both these lines of research. This yields new, powerful EB methods for analyzing genomics experiments that account for both sparse effects and unwanted variation. In realistic simulations, these new methods provide significant gains in power and calibration over competing methods. In real data analysis, we find that different methods, while often conceptually similar, can vary widely in their assessments of statistical significance. This highlights the need for care in both choice of methods and interpretation of results.

Evolutionary conservation and structural localizations suggest a physical trace of metabolism's progressive geochronological emergence.

We studied multiple sequence alignment (MSA) consensus amino acid distributional patterns in 2844 amino acid sequences of the eight enzymes of the Kreb's oxidative tricarboxylic acid pathway (oTCA) in Archaea, Bacteria and Eukarya and 5545 sequences of 33 bacteria as geochronologically separated enzymes with MSA consensus site modal identities. The 33 bacteria were 20 presumptive examples of early-oldest (Hadean-Archaean) ('Epoch I') or 13 late-newest (contemporary) ('Epoch III') appearing enzymes on Earth. The enzyme's MSA consensus sites were identified by their modal identity, % Occupancy in one of nine-graded evolutionary-conservation zones (CZs) and the Euclidean distance (Å) from each of their consensus MSA Cɑs to the same atom (Anchor-atom) in their reported functional center. These MSA consensus sites are tetrad-data points called recovered-amino acids (RAA). Across Domains, the % Occupancies of the eight-dominant RAAs of the Kreb's cycle and the 33 bacteria were found to be similarly ranked. Compared to Trifonov's 'putative ranked temporal order of the appearance of amino acids on Earth' (TOAE), the greatest statistical concordance with tetrad-RAAs across Domains were those characterized as within the most-evolutionary conserved conservation zone (CZ9), typically nearest (Å) their enzyme's catalytic/active center. The geochronologically characterized early-oldest Hadean-Archaean Bacteria 'Epoch I' enzymes, compared to late-newest Bacteria enzymes, had greater average numbers of amino acid residues/sequence and a statistically significant larger variability in their RAA compositional-Å3-volumes. The late-newest 'Epoch III' enzymes had statistically significant lower volumetric values, specifically, their native Å3-volume, void-volume and volume change on unfolding. Our enzyme data suggest a geochronological trace of 'metabolism's progressive emergence'.Communicated by Ramaswamy H. Sarma.

Genotyping Polyploids from Messy Sequencing Data.

Detecting and quantifying the differences in individual genomes (i.e., genotyping), plays a fundamental role in most modern bioinformatics pipelines. Many scientists now use reduced representation next-generation sequencing (NGS) approaches for genotyping. Genotyping diploid individuals using NGS is a well-studied field, and similar methods for polyploid individuals are just emerging. However, there are many aspects of NGS data, particularly in polyploids, that remain unexplored by most methods. Our contributions in this paper are fourfold: (i) We draw attention to, and then model, common aspects of NGS data: sequencing error, allelic bias, overdispersion, and outlying observations. (ii) Many datasets feature related individuals, and so we use the structure of Mendelian segregation to build an empirical Bayes approach for genotyping polyploid individuals. (iii) We develop novel models to account for preferential pairing of chromosomes, and harness these for genotyping. (iv) We derive oracle genotyping error rates that may be used for read depth suggestions. We assess the accuracy of our method in simulations, and apply it to a dataset of hexaploid sweet potato (Ipomoea batatas). An R package implementing our method is available at https://cran.r-project.org/package=updog.

Graphene nanoparticles as osteoinductive and osteoconductive platform for stem cell and bone regeneration.

The potential of graphene-based nanoparticles (GNPs) has recently gained significant attention in biomedicine, especially in tissue engineering. In this study, we investigated the osteoinductive and osteoconductive effects of low oxygen content graphene (LOG) nanoparticles on adult mesenchymal stem cells (MSCs) in vitro and in vivo. We showed that adult goat MSCs were viable in the presence of 0.1 mg/mL LOG and retained their stem cell properties. A 3D scaffold made from agarose was used to encapsulate MSCs and LOG nanoparticles. Scanning electron microscopy demonstrated the cell morphology and adherence of MSCs to LOG in the 3D form. The LOG and MSCs in the 3D scaffold were xenogenically implanted into a rat unicortical tibial bone defect. The combination of MSCs and LOG nanoparticles resulted in improved active bone formation and increased mineralization. These results strengthen the applicability of LOG nanoparticles as an adjunct treatment for bone tissue engineering.

Aeromedical Evacuation Using Extra Corporeal Life Support After Resuscitated Cardiac Arrest.

BACKGROUND: Extra corporeal life support (ECLS) is presently first line therapy for refractory cardiogenic shock. Mobile circulatory support teams implant ECLS or extra corporeal membrane oxygenation (ECMO) in patients in the hospital without circulatory support. These patients are then transported to specialized centers. Here we report a case of sending a mobile circulatory support team abroad, followed by air ambulance evacuation, which, to our knowledge, has never been used as part of medical assistance abroad. CASE REPORT: In June, during a holiday in Turkey, a 56-yr-old woman complained about chest pain. She had a cardiac arrest and was resuscitated with no no-flow time in the local hospital. ECG showed ST segment elevation. Medic'Air International medical assistance (Paris, France) contacted the hospital, which was not equipped with coronarography or cardiac ultrasound and the local treating doctor refused transfer of the patient to another facility. A medical team completed by a cardiothoracic surgeon and a perfusionist went from Paris to the patient's bedside by air ambulance. They implemented the ECLS and successfully repatriated the patient to her home country (Belgium). The patient's condition improved, she neurologically improved, and returned home on the 14th day. DISCUSSION: Possible indications for ECLS repatriations firstly take into account recognized ECLS indications and case-by-case discussions on the evaluation of inadequacy of the health facilities and risk-benefit balance. In international medical assistance, this case's description is an example of repatriation for patients who previously could not be transferred due to high risk of such intervention.Gerard D, Raffin H, Lebreton G. Aeromedical evacuation using extra corporeal life support after resuscitated cardiac arrest. Aerosp Med Hum Perform. 2017; 88(4):431-433.