Pre-specified Patterns Research Articles

Patterns of Left Ventricular Remodelling in Children and Young Patients with Hypertrophic Cardiomyopathy.

Introduction: The aim of this study was to evaluate the age at onset, clinical course, and patterns of left ventricular (LV) remodelling during follow-up in children and young patients with hypertrophic cardiomyopathy (HCM). Methods: We included consecutive patients with sarcomeric or non-syndromic HCM below 18 years old. Three pre-specified patterns of LV remodelling were assessed: maximal LV wall thickness (MLVWT) thickening; MLVWT thinning with preserved LV ejection fraction; and MLVWT thinning with progressive reduction in LV ejection fraction (hypokinetic end-stage evolution). Results: Fifty-three patients with sarcomeric/non-syndromic HCM (mean age 9.4 ± 5.5 years, 68% male) fulfilled the inclusion criteria. In total, 32 patients (60%) showed LV remodelling: 3 patients (6%) exhibited MLVWT thinning; 16 patients (30%) showed MLVWT thickening; and 13 patients (24%) progressed to hypokinetic end-stage HCM. Twenty-one patients (40%) had no LV remodelling during follow-up. In multivariate analysis, MLVWT was a predictor of the hypokinetic end-stage remodelling pattern during follow-up (OR 1.17 [95%CI 1.01-1.36] per 1 mm increase, p-value 0.043), regardless of sarcomeric variants and New York Heart Association class. Two patients with sarcomeric HCM, showing a pattern of MLVWT regression during childhood, experienced progression during adolescence. Conclusions: Different patterns of LV remodelling were observed in a cohort of children with sarcomeric/non-syndromic HCM. Interestingly, a pattern of progressive MLVWT thinning during childhood, with new progression of MLVWT during adolescence, was noted. A better understanding of the remodelling mechanisms in children with sarcomeric HCM may be relevant to defining the timing and possible efficacy of new targeted therapies in the preclinical stage of the disease.

Forced signal and predictability in a prototype climate model: Implications for fingerprinting based detection in the presence of multidecadal natural variability.

Advanced numerical models used for climate prediction are known to exhibit biases in their simulated climate response to variable concentrations of the atmospheric greenhouse gases and aerosols that force a non-uniform, in space and time, secular global warming. We argue here that these biases can be particularly pronounced due to misrepresentation, in these models, of the multidecadal internal climate variability characterized by large-scale, hemispheric-to-global patterns. This point is illustrated through the development and analysis of a prototype climate model comprised of two damped linear oscillators, which mimic interannual and multidecadal internal climate dynamics and are set into motion via a combination of stochastic driving, representing weather noise, and deterministic external forcing inducing a secular climate change. The model time series are paired with pre-specified patterns in the physical space and form, conceptually, a spatially extended time series of the zonal-mean near-surface temperature, which is further contaminated by a spatiotemporal noise simulating the rest of climate variability. The choices of patterns and model parameters were informed by observations and climate-model simulations of the 20th century near-surface air temperature. Our main finding is that the intensity and spatial patterns of the internal multidecadal variability associated with the slow-oscillator model component greatly affect (i) the ability of modern pattern-recognition/fingerprinting methods to isolate the forced response of the climate system in the 20th century ensemble simulations and (ii) climate-system predictability, especially decadal predictability, as well as the estimates of this predictability using climate models in which the internal multidecadal variability is underestimated or otherwise misrepresented.

Frontal electroencephalogram reveals emergence-like brain activity occurring during transition periods in cardiac surgery

BackgroundCardiac surgery has one of the highest incidences of intraoperative awareness. The periods of initiation and discontinuation of cardiopulmonary bypass could be high-risk periods. Certain frontal EEG patterns might plausibly occur with unintended intraoperative awareness. This study sought to quantify the incidence of these pre-specified patterns during cardiac surgery. MethodsTwo-channel bihemispheric frontal EEG was recorded in 1072 patients undergoing cardiac surgery as part of a prospective observational study. Spectrograms were created, and mean theta (4–7 Hz) power and peak alpha (7–17 Hz) frequency were measured in patients under general anaesthesia with isoflurane. Emergence-like EEG activity in the spectrogram during surgery was classified as an alpha peak frequency increase by 2 Hz or more, and a theta power decrease by 5 dB or more in comparison with the median pre-bypass values. ResultsData from 1002 patients were available for analysis. Fifty-five of those patients (5.5%) showed emergence-like EEG activity at least once during surgery with a median duration of 13.2 min. These patients were younger (median age, 59 vs 67 yr; P<0.001) and the median end-tidal isoflurane concentration before cardiopulmonary bypass was higher (0.82 vs 0.75 minimum alveolar concentration [MAC]; P=0.013). There was no significant difference between those with or without emergence-like EEG activity in sex, lowest core temperature, or duration of surgery. Forty-six of these EEG changes (84%) occurred within a 1 h time window centred on separation from cardiopulmonary bypass. ConclusionThe findings of this study suggest that approximately one in 20 patients undergoing cardiac surgery with a volatile anaesthetic agent have a sustained EEG pattern while surgery is ongoing that is often seen with emergence from general anaesthesia. Monitoring the frontal EEG during cardiopulmonary bypass may identify these events and potentially reduce the incidence of unintended awareness. Clinical trial registrationNCT 02976584.

(Re-)Imag(in)ing Price Trends

We reconsider the idea of trend-based predictability using methods that flexibly learn price patterns that are most predictive of future returns, rather than testing hypothesized or pre-specified patterns (e.g., momentum and reversal). Our raw predictor data are images—stock-level price charts—from which we elicit the price patterns that best predict returns using machine learning image analysis methods. The predictive patterns we identify are largely distinct from trend signals commonly analyzed in the literature, give more accurate return predictions, translate into more profitable investment strategies, and are robust to a battery of specification variations. They also appear context-independent: Predictive patterns estimated at short time scales (e.g., daily data) give similarly strong predictions when applied at longer time scales (e.g., monthly), and patterns learned from US stocks predict equally well in international markets.

Similarity search for numerous patterns over multiple time series streams under dynamic time warping which supports data normalization

A huge challenge in nowadays' data mining is similarity search in streaming time series under Dynamic Time Warping (DTW). In the similarity search, data normalization is a must to obtain accurate results. However, data normalization on the fly and the DTW calculation cost a great deal of computational time and memory space. In the paper, we present two methods, SUCR-DTW and ESUCR-DTW, which conduct similarity search for numerous prespecified patterns over multiple time-series streams under DTW supporting data normalization. These two methods utilize a combination of techniques to mitigate the aforementioned costs. The efficient methods inherit the cascading lower bounds introduced in UCR-DTW, a state-of-the-art method of similarity search in the static time series, to admissibly prune off unpromising subsequences. To be adaptive in the streaming setting, SUCR-DTW performs incremental updates on the envelopes of new-coming time-series subsequences and incremental data normalization on time-series data. However, like UCR-DTW, SUCR-DTW retrieves only similar subsequences that have the same length as the patterns. ESUCR-DTW, an extension of SUCR-DTW, can find similar subsequences whose lengths are different from those of the patterns. Furthermore, our proposed methods exploit multi-threading to have a fast response to high-speed time-series streams. The experimental results show that SUCR-DTW obtains the same precision as UCR-DTW and has lower wall clock time. Besides, the experimental results of SUCR-DTW and ESUCR-DTW reveal that the extended method has higher accuracy in spite of longer wall clock time. Also, the paper evaluates the influence of incremental z-score normalization and incremental min---max normalization on the obtained results.

Synthesis of Three-Dimensional Models from Drawings Based on Spectral Graph Theory

Task of automatic reconstruction of three-dimensional objects by drawing views presented. The algorithm based on a boundary representation of three-dimensional models. The algorithm consists of the following steps: automatic separation of the drawing per the views, determination of three-dimensional coordinates of vertices, definition and marking of wire model primitives, reconstruction of model faces and model elements. The fundamental concept of the algorithm is to find the structural elements of three-dimensional model with usage of pre-specified patterns. The templates are described by means of matrices. Matching algorithm uses spectral graph theory. Reconstruction results are presented.

Three-dimensional Reconstruction from Projections based on Incidence Matrices of Patterns

Task of automatic reconstruction of three-dimensional objects by drawing views presented. The algorithm based on a boundary representation of three-dimensional models. The algorithm consists of the following steps: automatic separation of the drawing per the views, determination of three-dimensional coordinates of vertices, definition and marking of wire model primitives, reconstruction of model faces and model elements. The fundamental concept of the algorithm is to find the structural elements of three-dimensional model with usage of pre-specified patterns. The templates are described by means of matrices. Matching algorithm uses invariants: the number of vertices, type of edges.

Interactive authoring of simulation-ready plants

Physically based simulation can produce quality motion of plants, but requires an authoring stage to convert plant "polygon soup" triangle meshes to a format suitable for physically based simulation. We give a system that can author complex simulation-ready plants in a manner of minutes. Our system decomposes the plant geometry, establishes a hierarchy, builds and connects simulation meshes, and detects instances. It scales to anatomically realistic geometry of adult plants, is robust to non-manifold input geometry, gaps between branches or leaves, free-flying leaves not connected to any branch, spurious geometry, and plant self-collisions in the input configuration. We demonstrate the results using a FEM model reduction simulator that can compute large-deformation dynamics of complex plants at interactive rates, subject to user forces, gravity or randomized wind. We also provide plant fracture (with pre-specified patterns), inverse kinematics to easily pose plants, as well as interactive design of plant material properties. We authored and simulated over 100 plants from diverse climates and geographic regions, including broadleaf (deciduous) trees and conifers, bushes and flowers. Our largest simulations involve anatomically realistic adult trees with hundreds of branches and over 100,000 leaves.

Using genetic algorithms to create meaningful poetic text

This article presents a series of experiments in automatically generating poetic texts. We confined our attention to the generation of texts which are syntactically well-formed, meet certain pre-specified patterns of metre and broadly convey some given meaning. Such aspects can be formally defined, thus avoiding the complications of imagery and interpretation that are central to assessing more free forms of verse. Our implemented system, McGONAGALL, applies the genetic algorithm to construct such texts. It uses a sophisticated linguistic formalism to represent its genomic information, from which can be computed the phenotypic information of both semantic representations and patterns of stress. The conducted experiments broadly indicated that relatively meaningful text could be produced if the constraints on metre were relaxed, and precise metric text was possible with loose semantic constraints, but it was difficult to produce text which was both semantically coherent and of high quality metrically.

The Effectiveness of Receipt-Based Attacks on ThreeBallot

The ThreeBallot voting system is an end-to-end voter-verifiable voting system. Each voter fills out three ballots according to a few simple rules and takes a copy of one of them home as a receipt for verification purposes. All ballots are posted on a public bulletin board so that any voter may verify the result. In this paper, we provide the first steps toward investigating the effectiveness of attacks using the voter's receipt and the bulletin board, using a theoretical rather than simulation-based approach. Focusing on two-candidate races, we determine thresholds for when a voter's vote can be reconstructed from their receipt, and when a coercer can effectively verify if a voter followed instructions by looking for prespecified patterns on the bulletin board. Combining these two results allows us to determine safe ballot sizes that resist known attacks. We also generalize a previous observation that an individual receipt can leak information about a voter's choices.

Nonlinear least-square solution to flat-top pattern synthesis using arbitrary linear array

This paper presents a new approach for synthesizing flat-top patterns, based on the least-square error criterion. The cost function is formulated according to the amplitude approximation error without phase constraint. The optimal array weight is obtained by using the Levenberg–Marquardt nonlinear optimization algorithm. Simulations are performed to compare the proposed approach with the Woodward–Lawson method and two recent methods using minimax and adaptive array techniques, respectively. The results indicate that the approach is effective in sidelobe control and synthesizing prespecified patterns for arbitrary linear arrays.

ACT-PRO action protocol analyzer: a tool for analyzing discrete action protocols.

This article presents a top-down approach for analyzing sequential events in behavioral data. Analysis of behavioral sequential data often entails identifying patterns specified by the researchers. Algorithms were developed and applied to analyze a kind of behavioral data, called discrete action protocol data. Discrete action protocols consist of discrete user actions, such as mouse clicks and keypresses. Unfortunately, the process of analyzing the huge volume of actions (typically, > 10(5)) is very labor intensive. To facilitate this process, we developed an action protocol analyzer (ACT-PRO) that provides two levels of pattern matching. Level one uses formal grammars to identify sequential patterns. Level two matches these patterns to a hierarchical structure. ACT-PRO can be used to determine how well data fit the patterns specified by an experimenter. Complementarily, it can be used to focus an experimenter's attention on data that do not fit the prespecified patterns.

Adaptive elevator group control with cameras

A new elevator group control system is proposed. The system learns dynamic traffic flows by analyzing passenger traffic without prespecified patterns. A genetic algorithm continuously generates dispatch functions according to changes in passenger traffic. By considering the status inside elevators, the directions of passenger movement, and the number of waiting passengers, the system occasionally allocates multiple elevators for a single hall call, which assists in reducing passengers' waiting time. Experimental results showed up to 25% improvement over a system without the above features.

Basic components and patterns of acute ischemia recovery assessed from continuous ST monitoring in acute myocardial infarction treated by thrombolytic therapy

Continuous ST monitoring of the lead showing the highest ST elevation on the admission 12-lead electrocardiogram was performed in patients with acute myocardial infarction of 6 hours or less enrolled in the OSIRIS and GUSTO trials. ST elevation measured at j + 50 ms was averaged from all normal beats every 20 seconds. ST trends were visually analyzed by two observers blinded from the thrombolytic treatment, its onset, and coronary angiograms performed 21 hours (median) after thrombolysis. Three basic and consecutive components were considered for analysis: the initial amplitude of ST elevation (A1), the maximal amplitude recovery (REC), and the minimal ST amplitude (A2). Prespecified patterns (PAT) were considered: PAT 1 integrated permanent A1 elevation followed by REC, PAT 2 intermittent A1 elevation, and REC. Prespecified pattern 3 was considered in absence of REC. Twenty-four-hour trends were recorded in 347 patients and judged adequate in 306 (88%) followed by angiography in 268 (77%). This group was not clinically different from the 79 patients without ST/angiography. Prespecified pattern 1 was identified in 81%, PAT 2 in 8%, and PAT 3 in 11% of the patients. The positive predictive value of PAT 1 + 2 for coronary patency was 94%, the negative predictive value 72%, sensitivity 96%, and specificity 60%. A salient feature was the occurrence of ST overshoot defined by a ≥1 mm increase above A1 within the first minutes of REC. Overshoot occurred in 35% of PAT 1 and predicted subsequent patency in all but two patients. Online display of the ST trend at the bedside contributes substantially to the immediate evaluation of thrombolytic efficacy. Finally, left ventricular ejection fraction correlated best with A2, which may significantly contribute to assessing the efficacy of myocardial reperfusion.

The first level trigger of the OBELIX spectrometer

Abstract The first level trigger of the OBELIX (PS 201) experiment is described. Based on the time of flight system (TOF) of the detector and including modules designed for the purpose, we have built and operated a system capable of selecting simple prespecified hit patterns within 200 ns.