Anticoagulation Control in Warfarin-Treated Patients Undergoing Cardioversion of Atrial Fibrillation (from the Edoxaban Versus Enoxaparin-Warfarin in Patients Undergoing Cardioversion of Atrial Fibrillation Trial).

In the Edoxaban Versus Enoxaparin-Warfarin in Patients Undergoing Cardioversion of Atrial Fibrillation (ENSURE-AF) study (NCT 02072434), edoxaban was compared with enoxaparin-warfarin in 2,199 patients undergoing electrical cardioversion of nonvalvular atrial fibrillation (AF). In this multicenter prospective randomized open blinded end-point trial, we analyzed patients randomized to enoxaparin-warfarin. We determined time to achieve therapeutic range (TtTR); time in therapeutic range (TiTR); their clinical determinants; relation to sex, age, medical history, treatment, tobacco use, race risk (SAMe-TT2R2) score; and impact on primary end points (composite of stroke, systemic embolic event[SEE], myocardial infarction [MI], and cardiovascular death [CVD] and composite of major + clinically relevant nonmajor bleeding). Among 1,104 patients randomized to enoxaparin-warfarin, 27% were naïve to oral anticoagulants. Mean age was 64.2 ± 11 years and mean congestive heart failure, hypertension, age ≥75 (doubled), diabetes mellitus, prior stroke or transient ischemic attack (doubled), vascular disease, age 65-74, female (CHA2DS2-VASc) score was 2.6. Mean TtTR was 7.7 days (median 7 days) and mean TiTR after reaching an international normalized ratio of 2.0 to 3.0 was 71%. In 695 patients who had an INR 2. On multivariate regression, an independent predictor of extended TtTR was creatinine clearance (p = 0.02). TtTR was marginally related to stroke/SEE/MI/CVD (p = 0.06; odds ratio  0.23, 95% confidence interval 0.02 to 1.17) but not to any bleeding. Independent predictors of TiTR were previous vitamin K antagonist experience (p65, concomitant drugs or alcohol (HAS-BLED) score (p = 0.02). TiTR was related to any bleeding (p = 0.02; odds ratio  0.39, 95% confidence interval 0.16 to 0.88), but not stroke/SEE/MI/CVD. In this cohort of warfarin users with a high TiTR no difference was seen between TtTR and TiTR in relation to SAMe-TT2R2 score. In conclusion, even in this short-term study, TiTR was significantly related to bleeding events

Hidden Markov Model Variants and their Application

Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way. This approach is based on mutual information, probabilistic measures, hidden Markov models, and other purely statistical inputs. This approach also provides a uniquely common ground for comparative prokaryotic genomics. The approach is an on-going effort by its nature, as a multi-pass learning process, where each round is more informed than the last, and thereby allows a shift to the more powerful methods available for supervised learning at each iteration. It is envisaged that this "bootstrap" learning process will also be useful as a knowledge discovery tool. For such an ab initio prokaryotic gene-finder to work, however, it needs a mechanism to identify critical motif structure, such as those around the start of coding or start of transcription (and then, hopefully more). For eukaryotes, even with better start-of-coding identification, parsing of eukaryotic coding regions by the HMM is still limited by the HMM's single gene assumption, as evidenced by the poor performance in alternatively spliced regions. To address these complications an approach is described to expand the states in a eukaryotic gene-predictor HMM, to operate with two layers of DNA parsing. This extension from the single layer gene prediction parse is indicated after preliminary analysis of the C. elegans alt-splice statistics. State profiles have made use of a novel hash-interpolating MM (hIMM) method. A new implementation for an HMM-with-Duration is also described, with far-reaching application to gene-structure identification and analysis of channel current blockade data

Nanopore Detector based analysis of single-molecule conformational kinetics and binding interactions

BACKGROUND: A Nanopore Detector provides a means to transduce single molecule events into observable channel current changes. Nanopore-based detection can report directly, or indirectly, on single molecule kinetics. The nanopore-based detector can directly measure molecular characteristics in terms of the blockade properties of individual molecules – this is possible due to the kinetic information that is embedded in the blockade measurements, where the adsorption-desorption history of the molecule to the surrounding channel, and the configurational changes in the molecule itself, imprint on the ionic flow through the channel. This rich source of information offers prospects for DNA sequencing and single nucleotide polymorphism (SNP) analysis. A nanopore-based detector can also measure molecular characteristics indirectly, by using a reporter molecule that binds to certain molecules, with subsequent distinctive blockade by the bound-molecule complex. RESULTS: It is hypothesized that reaction histories of individual molecules can be observed on model DNA/DNA, DNA/Protein, and Protein/Protein systems. Preliminary results are all consistent with this hypothesis. Nanopore detection capabilities are also described for highly discriminatory biosensing, binding strength characterization, and rapid immunological screening. CONCLUSION: In essence, the heart of chemistry is now accessible to a new, single-molecule, observation method that can track both external molecular binding states, and internal conformation states

A novel, fast, HMM-with-Duration implementation – for application with a new, pattern recognition informed, nanopore detector

<p>Abstract</p> <p>Background</p> <p>Hidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) is an HMM that can also exactly model the hidden-label length (recurrence) distributions – while the regular HMM will impose a best-fit geometric distribution in its modeling/representation.</p> <p>Results</p> <p>A Novel, Fast, HMM-with-Duration (HMMwD) Implementation is presented, and experimental results are shown that demonstrate its performance on two-state synthetic data designed to model Nanopore Detector Data. The HMMwD experimental results are compared to (i) the ideal model and to (ii) the conventional HMM. Its accuracy is clearly an improvement over the standard HMM, and matches that of the ideal solution in many cases where the standard HMM does not. Computationally, the new HMMwD has all the speed advantages of the conventional (simpler) HMM implementation. In preliminary work shown here, HMM feature extraction is then used to establish the first pattern recognition-informed (PRI) sampling control of a Nanopore Detector Device (on a "live" data-stream).</p> <p>Conclusion</p> <p>The improved accuracy of the new HMMwD implementation, at the same order of computational cost as the standard HMM, is an important augmentation for applications in gene structure identification and channel current analysis, especially PRI sampling control, for example, where speed is essential. The PRI experiment was designed to inherit the high accuracy of the well characterized and distinctive blockades of the DNA hairpin molecules used as controls (or blockade "test-probes"). For this test set, the accuracy inherited is 99.9%.</p

Support Vector Machine Implementations for Classification & Clustering

BACKGROUND: We describe Support Vector Machine (SVM) applications to classification and clustering of channel current data. SVMs are variational-calculus based methods that are constrained to have structural risk minimization (SRM), i.e., they provide noise tolerant solutions for pattern recognition. The SVM approach encapsulates a significant amount of model-fitting information in the choice of its kernel. In work thus far, novel, information-theoretic, kernels have been successfully employed for notably better performance over standard kernels. Currently there are two approaches for implementing multiclass SVMs. One is called external multi-class that arranges several binary classifiers as a decision tree such that they perform a single-class decision making function, with each leaf corresponding to a unique class. The second approach, namely internal-multiclass, involves solving a single optimization problem corresponding to the entire data set (with multiple hyperplanes). RESULTS: Each SVM approach encapsulates a significant amount of model-fitting information in its choice of kernel. In work thus far, novel, information-theoretic, kernels were successfully employed for notably better performance over standard kernels. Two SVM approaches to multiclass discrimination are described: (1) internal multiclass (with a single optimization), and (2) external multiclass (using an optimized decision tree). We describe benefits of the internal-SVM approach, along with further refinements to the internal-multiclass SVM algorithms that offer significant improvement in training time without sacrificing accuracy. In situations where the data isn't clearly separable, making for poor discrimination, signal clustering is used to provide robust and useful information – to this end, novel, SVM-based clustering methods are also described. As with the classification, there are Internal and External SVM Clustering algorithms, both of which are briefly described

Migration, health knowledge and teenage fertility: evidence from Mexico

Migration may affect fertility and child health care of those remaining in the country of origin. Mexican data show that having at least one household member who migrated to the United States decreases the occurrence of pregnancy among teenagers by 0.339 probability points. This finding can be partially explained by the fact that teenagers in migrant households have a higher knowledge of contraceptive methods and likely practice active birth control. I use potential migration, measured as historic migration rates interacted with the proportion of adult males in the household, as an instrument to account for the endogeneity of migrant status.Financial support from the Spanish MEC (Ref. ECO2014-58434-P) is gratefully acknowledged

Urticaria and angioedema

Urticaria (hives) is a common disorder that often presents with angioedema (swelling that occurs beneath the skin). It is generally classified as acute, chronic or physical. Second-generation, non-sedating H1-receptor antihistamines represent the mainstay of therapy for both acute and chronic urticaria. Angioedema can occur in the absence of urticaria, with angiotensin-converting enzyme (ACE) inhibitor-induced angioedema and idiopathic angioedema being the more common causes. Rarer causes are hereditary angioedema (HAE) or acquired angioedema (AAE). Although the angioedema associated with these disorders is often self-limited, laryngeal involvement can lead to fatal asphyxiation in some cases. The management of HAE and AAE involves both prophylactic strategies to prevent attacks of angioedema (i.e., trigger avoidance, attenuated androgens, tranexamic acid, and plasma-derived C1 inhibitor replacement therapy) as well as pharmacological interventions for the treatment of acute attacks (i.e., C1 inhibitor replacement therapy, ecallantide and icatibant). In this article, the authors review the causes, diagnosis and management of urticaria (with or without angioedema) as well as the work-up and management of isolated angioedema, which vary considerably from that of angioedema that occurs in the presence of urticaria