Pre-defined Set Research Articles

Formal and informal prediction of recurrent stroke and myocardial infarction after stroke: a systematic review and evaluation of clinical prediction models in a new cohort

BackgroundThe objective of this study was to: (1) systematically review the reporting and methods used in the development of clinical prediction models for recurrent stroke or myocardial infarction (MI) after ischemic stroke; (2) to meta-analyze their external performance; and (3) to compare clinical prediction models to informal clinicians’ prediction in the Edinburgh Stroke Study (ESS).MethodsWe searched Medline, EMBASE, reference lists and forward citations of relevant articles from 1980 to 19 April 2013. We included articles which developed multivariable clinical prediction models for the prediction of recurrent stroke and/or MI following ischemic stroke. We extracted information to assess aspects of model development as well as metrics of performance to determine predictive ability. Model quality was assessed against a pre-defined set of criteria. We used random-effects meta-analysis to pool performance metrics.ResultsWe identified twelve model development studies and eleven evaluation studies. Investigators often did not report effective sample size, regression coefficients, handling of missing data; typically categorized continuous predictors; and used data dependent methods to build models. A meta-analysis of the area under the receiver operating characteristic curve (AUROCC) was possible for the Essen Stroke Risk Score (ESRS) and for the Stroke Prognosis Instrument II (SPI-II); the pooled AUROCCs were 0.60 (95% CI 0.59 to 0.62) and 0.62 (95% CI 0.60 to 0.64), respectively. An evaluation among minor stroke patients in the ESS demonstrated that clinicians discriminated poorly between those with and those without recurrent events and that this was similar to clinical prediction models.ConclusionsThe available models for recurrent stroke discriminate poorly between patients with and without a recurrent stroke or MI after stroke. Models had a similar discrimination to informal clinicians' predictions. Formal prediction may be improved by addressing commonly encountered methodological problems.

Static planning of the expansion of electrical energy transmission systems using particle swarm optimization

Static planning of the expansion of electrical energy transmission systems consists of determining, among a pre-defined set of candidate expansion circuits, which ones must be built to minimize the operational costs (deficit) and investment in the electrical system, supplying the predicted demand for a planning horizon. Considering this problem, the proposed methodology uses a Constructive Heuristic Algorithm (CHA) to identify the most relevant route from a set of candidate expansion routes to reduce the search space and, consequently, increase the efficiency of the bio-inspired particle swarm search process. Therefore, the proposed methodology is divided into two stages: (i) obtaining the reduced set of candidate expansion routes using the CHA to decrease the search space without losing relevant expansion paths; (ii) using particle swarm optimization and heuristic information from the first stage to find the minimum expansion cost of the transmission system. In both stages, the transmission system is represented by the linearized load flow model, where the expansion decisions are incorporated into the problem using the original equations of the DC model. The proposed methodology was applied to the Garver system and to two real equivalent systems for the South and Southeast of Brazil, where the efficiency of the proposed system can be verified.

Exome Sequencing Implicates an Increased Burden of Rare Potassium Channel Variants in the Risk of Drug-Induced Long QT Interval Syndrome

The aim of this study was to test the hypothesis that rare variants are associated with drug-induced long QT interval syndrome (diLQTS) and torsades de pointes. diLQTS is associated with the potentially fatal arrhythmia torsades de pointes. The contribution of rare genetic variants to the underlying genetic framework predisposing to diLQTS has not been systematically examined. We performed whole-exome sequencing on 65 diLQTS patients and 148 drug-exposed control subjects of European descent. We used rare variant analyses (variable threshold and sequence kernel association test) and gene-set analyses to identify genes enriched with rare amino acid coding (AAC) variants associated with diLQTS. Significant associations were reanalyzed by comparing diLQTS patients with 515 ethnically matched control subjects from the National Heart, Lung, and Blood Grand Opportunity Exome Sequencing Project. Rare variants in 7 genes were enriched in the diLQTS patients according to the sequence kernel association test or variable threshold compared with drug-exposed controls (p < 0.001). Of these, we replicated the diLQTS associations for KCNE1 and ACN9 using 515 Exome Sequencing Project control subjects (p < 0.05). A total of 37% of the diLQTS patients also had 1 or more rare AAC variants compared with 21% of control subjects (p = 0.009), in a pre-defined set of 7 congenital long QT interval syndrome (cLQTS) genes encoding potassium channels or channel modulators (KCNE1, KCNE2, KCNH2, KCNJ2, KCNJ5, KCNQ1, AKAP9). By combining whole-exome sequencing with aggregated rare variant analyses, we implicate rare variants in KCNE1 and ACN9 as risk factors for diLQTS. Moreover, diLQTS patients were more burdened by rare AAC variants in cLQTS genes encoding potassium channel modulators, supporting the idea that multiple rare variants, notably across cLQTS genes, predispose to diLQTS.

Transmission network expansion planning for the Colombian electrical system: Connecting the Ituango hydroelectric power plant

The hydroelectric power plant HidroItuango represents a major expansion for the Colombian electrical system (with a total capacity of 2400MW). This paper analyzes the possible interconnections and investments involved in connecting HidroItuango, in order to strengthen the Colombian national transmission system. A Mixed Binary Linear Programming (MBLP) model was used to solve the Multistage Transmission Network Expansion Planning (MTEP) problem of the Colombian electrical system, taking the N-1 safety criterion into account. The N-1 safety criterion indicates that the transmission system must be expanded so that the system will continue to operate properly if an outage in a system element (within a pre-defined set of contingencies) occurs. The use of a MBLP model guaranteed the convergence with existing classical optimization methods and the optimal solution for the MTEP using commercial solvers. Multiple scenarios for generation and demand were used to consider uncertainties within these parameters. The model was implemented using the algebraic modeling language AMPL and solved using the commercial solver CPLEX. The proposed model was then applied to the Colombian electrical system using the planning horizon of 2018–2025.

A DNA Methylation Network Interaction Measure, and Detection of Network Oncomarkers

Epigenetic processes–including DNA methylation–are increasingly seen as having a fundamental role in chronic diseases like cancer. DNA methylation patterns offer a route to develop prognostic measures based directly on DNA measurements, rather than less-stable RNA measurements. A novel DNA methylation-based measure of the co-ordinated interactive behaviour of genes is developed, in a network context. It is shown that this measure reflects well the co-regulatory behaviour linked to gene expression (at the mRNA level) over the same network interactions. This measure, defined for pairs of genes in a single patient/sample, associates with overall survival outcome independent of known prognostic clinical features, in several independent data sets relating to different cancer types. In total, more than half a billion CpGs in over 1600 samples, taken from nine different cancer entities, are analysed. It is found that groups of gene-pair interactions which associate significantly with survival identify statistically significant subnetwork modules. Many of these subnetwork modules are shown to be biologically relevant by strong correlation with pre-defined gene sets, such as immune function, wound healing, mitochondrial function and MAP-kinase signalling. In particular, the wound healing module corresponds to an increase in co-ordinated interactive behaviour between genes for worse prognosis, and the immune module corresponds to a decrease in co-ordinated interactive behaviour between genes for worse prognosis. This measure has great potential for defining DNA-based cancer biomarkers. Such biomarkers could naturally be developed further, by drawing on the rapidly expanding knowledge base of network science.

ICD programming

Appropriate ICD programming is the key to prevent inappropriate shock delivery, that is closely associated to a negative patients' outcome. Review of the literature on ICD therapy to generate ICD programmings that can be applied to the broad population of ICD and CRT-D carriers. Arrhythmia detection should occur with a detection time ranging 9″-12″ in the VF zone, and 15″-60″ in the VT zone. Discriminator should be applied at least up to 200 bpm. ATP therapy is applied to all VTs up to 250 bpm, with a success rate of 70%. Inappropriate shocks should occur in <3.6% of patients. Tailored ICD programming can be achieved following evidence from large ICD trials. Pre-defined settings that are saved on the programmer and that can be uploaded at device implantation help to ensure optimal programming and to avoid random errors.

Context-Aware Systems: A More Appropriate Response System to Hurricanes and Other Natural Disasters

A context-aware system may be defined as a system that can understand the context of a given situation and either share this context with other systems for their response or respond by itself. Context has many definitions such as additional information that further describes a given situation. However, the definition of context is very contentious – what is considered context in one system may be considered unimportant in another.In this paper, we focus on the ability of context-aware systems to respond to external events, in particular incoming hurricanes, in an appropriate, authorised and regular manner. Hurricanes are often problematic in that they may be difficult to predict, in terms of direction and intensity, and require immediate and appropriate responses. We look at how this system will respond to an authorised set of stimuli using a pre-defined sequence and set of behavioural rules. The response produced by the system will be appropriate depending on the context of the stimuli received.

Body Conducted Speech Enhancement by Equalization and Signal Fusion

This paper studies body-conducted speech for noise robust speech processing purposes. As body-conducted speech is typically limited in bandwidth, signal processing is required to obtain a signal that is both high in quality and low in noise. We propose an algorithm that first equalizes the body-conducted speech using filters obtained from a pre-defined filter set and subsequently fuses this equalized signal with a noisy conventional microphone signal using an optimal clean speech amplitude and phase estimator. We evaluated the proposed equalization and fusion technique using a combination of a conventional close-talk and a throat microphone. Subjective listening tests show that the proposed method successfully fuses the speech quality of the conventional signal and the noise robustness of the throat microphone signal. The listening tests also indicate that the inclusion of the body-conducted signal can improve single-channel speech enhancement methods, while a calculated set of objective signal quality measures confirm these observations.

Marmal-aid - a database for Infinium HumanMethylation450

BackgroundDNA methylation is indispensible for normal human genome function. Currently there is an increasingly large number of DNA methylomic data being released in the public domain allowing for an opportunity to investigate the relationships between the DNA methylome, genome function, and human phenotypes. The Illumina450K is one of the most popular platforms for assessing DNA methylation with over 10,000 samples available in the public domain. However, accessing all this data requires downloading each individual experiment and due to inconsistent annotation, accessing the right data can be a challenge.DescriptionHere we introduce ‘Marmal-aid’, the first standardised database for DNA methylation (freely available at http://marmal-aid.org). In Marmal-aid, the majority of publicly available Illumina HumanMethylation450 data is incorporated into a single repository allowing for re-processing of data including normalisation and imputation of missing values. The database is accessible in two ways: (1) Using an R package to allow for incorporation into existing analysis pipelines which can then be easily queried to gain insight into the functionality of certain CpG sites. This is aimed at a bioinformatician with experience in R. (2) Using a graphical interface allowing general biologists to query a pre-defined set of tissues (currently 15) providing a reference database of the methylation state in these tissues for the 450,000 CpG sites profiled by the Illumina HumanMethylation450.ConclusionMarmal-aid is the largest publicly available Illumina HumanMethylation450 methylation database combining Illumina HumanMethylation450 data from a number of sources into a single location with a single common annotation format. This allows for automated extraction using the R package and inclusion into existing analysis pipelines. Marmal-aid also provides a easy to use GUI to visualise methylation data in user defined genomic regions for various reference tissues.

ABACUS: an entropy-based cumulative bivariate statistic robust to rare variants and different direction of genotype effect

In the past years, both sequencing and microarray have been widely used to search for relations between genetic variations and predisposition to complex pathologies such as diabetes or neurological disorders. These studies, however, have been able to explain only a small fraction of disease heritability, possibly because complex pathologies cannot be referred to few dysfunctional genes, but are rather heterogeneous and multicausal, as a result of a combination of rare and common variants possibly impairing multiple regulatory pathways. Rare variants, though, are difficult to detect, especially when the effects of causal variants are in different directions, i.e. with protective and detrimental effects. Here, we propose ABACUS, an Algorithm based on a BivAriate CUmulative Statistic to identify single nucleotide polymorphisms (SNPs) significantly associated with a disease within predefined sets of SNPs such as pathways or genomic regions. ABACUS is robust to the concurrent presence of SNPs with protective and detrimental effects and of common and rare variants; moreover, it is powerful even when few SNPs in the SNP-set are associated with the phenotype. We assessed ABACUS performance on simulated and real data and compared it with three state-of-the-art methods. When ABACUS was applied to type 1 and 2 diabetes data, besides observing a wide overlap with already known associations, we found a number of biologically sound pathways, which might shed light on diabetes mechanism and etiology. ABACUS is available at http://www.dei.unipd.it/∼dicamill/pagine/Software.html.

A New Dispatching Model of Automated Lifting Vehicles in Automated Container Terminal with Limited Buffer Space

Automation of the processes at the quays of the world's large container ports is one of the answers to the required ever-increasing transshipment volumes within the same timeframe. For such purpose, using new generation of vehicles is unavoidable. One of the automatic vehicles that can be used in container terminals is Automated Lifting Vehicle (ALV). Integrated scheduling of handling equipments with quay cranes can increase the efficiency of automated transport systems in container. In this paper, an integrated scheduling of quay cranes and automated lifting vehicles with limited buffer space is formulated as a mixed integer linear programming model. This model minimizes the makespan of all the loading and unloading tasks for a pre-defined set of cranes in a scheduling problem.

Unsupervised language model adaptation for handwritten Chinese text recognition

This paper presents an effective approach for unsupervised language model adaptation (LMA) using multiple models in offline recognition of unconstrained handwritten Chinese texts. The domain of the document to recognize is variable and usually unknown a priori, so we use a two-pass recognition strategy with a pre-defined multi-domain language model set. We propose three methods to dynamically generate an adaptive language model to match the text output by first-pass recognition: model selection, model combination and model reconstruction. In model selection, we use the language model with minimum perplexity on the first-pass recognized text. By model combination, we learn the combination weights via minimizing the sum of squared error with both L2-norm and L1-norm regularization. For model reconstruction, we use a group of orthogonal bases to reconstruct a language model with the coefficients learned to match the document to recognize. Moreover, we reduce the storage size of multiple language models using two compression methods of split vector quantization (SVQ) and principal component analysis (PCA). Comprehensive experiments on two public Chinese handwriting databases CASIA-HWDB and HIT-MW show that the proposed unsupervised LMA approach improves the recognition performance impressively, particularly for ancient domain documents with the recognition accuracy improved by 7 percent. Meanwhile, the combination of the two compression methods largely reduces the storage size of language models with little loss of recognition accuracy.

Peer Review Chart Rounds as a Component of the Automated Workflow in a New Department

Peer-review chart rounds (PRCRs) are a crucial part of a radiation oncology department's Quality Assurance (QA) program. Ideally, all patients should be reviewed before starting treatment or at least before completing a significant portion of it. To increase likelihood of this, we decided to include PRCR as part of an automated workflow process. The radiation oncology department of Centre de Santé et de Services Sociaux de Laval opened its doors on February 21st 2012 as a fully electronic clinic as all medical, clinical and administrative activities are carried out through an Oncology Electronic Medical Record. There are 175 comprehensive and standardized care plans currently in use consisting of pre-defined order sets starting from decision to treat and extending beyond treatment completion. They are used in conjunction with more than 500 quality checklists to customize and automate the workflow. The automation reinforces the standardized care plan by specifying a sequence. When a radiation oncologist approves a dosimetry plan, a Planning Task is completed. This automatically pushes a PRCR task to the PRCR location in the sequence, which automatically adds the case to the list of patients up for review at the next PRCR. These bi-weekly meetings are interdisciplinary and attended by radiation oncologists, physicists, dosimetrists, therapists and nurses. The intent is to present all cases to insure that diagnosis, indication for treatment, contouring of target volumes and dosimetry are up to standards. The review outcome is documented. Since the opening, the PRCR data have been prospectively collected in customized assessments with discrete data. Between February 21, 2012 and December 31, 2012, 644 plans were reviewed in 82 sessions (630 patients treated): 86.8% of all plans underwent review before 25% of the prescribed dose was delivered and 64.3% actually had review prior to treatment start. When stratified for the aim of treatment, 98.8% of curative cases were presented before completing 25% of treatment (79.6% before treatment started) and 65.7% of palliative cases were presented before completing 25% of treatment (37.3% before treatment started). After peer review, 26 (4%) charts/plans needed follow-up. Of these, 6 (0.9%) plans required changes. These results suggest that including PRCR within an automated workflow process may help to improve the amount of patients peer reviewed within an appropriate time and thus the quality of care. The difference between the proportion of curative cases reviewed before starting treatment compared to palliative cases may be explained by the fact that a significant part of palliative cases were single dose treatments very often treated the same day as planning which made it impossible for them to be presented before starting treatment.

Adaptive Power Capture Control of Variable-Speed Wind Energy Conversion Systems With Guaranteed Transient and Steady-State Performance

This paper deals with the power capture control of variable-speed wind energy conversion systems. The control objective is to optimize the capture of wind energy by tracking the desired power output. Arbitrary steady-state performance is achieved in the sense that the tracking error is guaranteed to converge to any predefined small set. In addition, to maximize the wind energy capture, transient performance is enhanced such that the convergence rate can be larger than an arbitrary value, which further limits the maximum overshoot. First, an adaptive controller is designed for the case where known aerodynamic torque is assumed. Then, by utilizing an online approximator to estimate the uncertain aerodynamics, the need for the exact knowledge of the aerodynamic torque is waived to imitate the practical experience. With the aid of a novel output error transformation technique, both of the proposed controllers are capable of shaping the system performance arbitrarily on transient and steady-state stages. Meanwhile, it is also proved that all the signals in the closed-loop system are bounded via Lyapunov synthesis. Finally, the feasibility of the proposed controllers is demonstrated on an 1.5-MW three-blade wind turbine using the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code developed by the National Renewable Energy Laboratory.

String method for calculation of minimum free-energy paths in Cartesian space in freely-tumbling systems.

The string method is a molecular-simulation technique that aims to calculate the minimum free-energy path of a chemical reaction or conformational transition, in the space of a pre-defined set of reaction coordinates that is typically highly dimensional. Any descriptor may be used as a reaction coordinate, but arguably the Cartesian coordinates of the atoms involved are the most unprejudiced and intuitive choice. Cartesian coordinates, however, present a non-trivial problem, in that they are not invariant to rigid-body molecular rotations and translations, which ideally ought to be unrestricted in the simulations. To overcome this difficulty, we reformulate the framework of the string method to integrate an on-the-fly structural-alignment algorithm. This approach, referred to as SOMA (String method with Optimal Molecular Alignment), enables the use of Cartesian reaction coordinates in freely tumbling molecular systems. In addition, this scheme permits the dissection of the free-energy change along the most probable path into individual atomic contributions, thus revealing the dominant mechanism of the simulated process. This detailed analysis also provides a physically-meaningful criterion to coarse-grain the representation of the path. To demonstrate the accuracy of the method we analyze the isomerization of the alanine dipeptide in vacuum and the chair-to-inverted-chair transition of β-D mannose in explicit water. Notwithstanding the simplicity of these systems, the SOMA approach reveals novel insights into the atomic mechanism of these isomerizations. In both cases, we find that the dynamics and the energetics of these processes are controlled by interactions involving only a handful of atoms in each molecule. Consistent with this result, we show that a coarse-grained SOMA calculation defined in terms of these subsets of atoms yields nearidentical minimum free-energy paths and committor distributions to those obtained via a highly-dimensional string.

A Decision‐Theory Approach to Interpretable Set Analysis for High‐Dimensional Data

A key problem in high-dimensional significance analysis is to find pre-defined sets that show enrichment for a statistical signal of interest; the classic example is the enrichment of gene sets for differentially expressed genes. Here, we propose a new decision-theory approach to the analysis of gene sets which focuses on estimating the fraction of non-null variables in a set. We introduce the idea of "atoms," non-overlapping sets based on the original pre-defined set annotations. Our approach focuses on finding the union of atoms that minimizes a weighted average of the number of false discoveries and missed discoveries. We introduce a new false discovery rate for sets, called the atomic false discovery rate (afdr), and prove that the optimal estimator in our decision-theory framework is to threshold the afdr. These results provide a coherent and interpretable framework for the analysis of sets that addresses the key issues of overlapping annotations and difficulty in interpreting p values in both competitive and self-contained tests. We illustrate our method and compare it to a popular existing method using simulated examples, as well as gene-set and brain ROI data analyses.

A genomic random interval model for statistical analysis of genomic lesion data.

Tumors exhibit numerous genomic lesions such as copy number variations, structural variations and sequence variations. It is difficult to determine whether a specific constellation of lesions observed across a cohort of multiple tumors provides statistically significant evidence that the lesions target a set of genes that may be located across different chromosomes but yet are all involved in a single specific biological process or function. We introduce the genomic random interval (GRIN) statistical model and analysis method that evaluates the statistical significance of the abundance of genomic lesions that overlap a specific locus or a pre-defined set of biologically related loci. The GRIN model retains certain biologically important properties of genomic lesions that are ignored by other methods. In a simulation study and two example analyses of leukemia genomic lesion data, GRIN more effectively identified important loci as significant than did three methods based on a permutation-of-markers model. GRIN also identified biologically relevant pathways with a significant abundance of lesions in both examples. An R package will be freely available at CRAN and www.stjuderesearch.org/site/depts/biostats/software.

Blind Identification of Nonbinary LDPC Codes Using Average LLR of Syndrome a Posteriori Probability

Prevalent adaptive modulation and coding (AMC) techniques can facilitate the flexible strategies subject to the dynamic channel quality. It would be quite intriguing for one to build a blind encoder identification technique without spectrum-efficiency sacrifice for AMC transceivers. In this paper, we make the first-ever attempt to tackle the blind nonbinary low-density parity-check (LDPC) encoder identification given a predefined encoder candidate set over the Galois field GF(q) for q-ary quadrature amplitude modulation (q-QAM) signals. Our proposed method establishes the log-likelihood ratios (LLRs) of syndrome a posteriori probabilities (APPs), which specify the potential correctness of the underlying parity-check relations, and identifies the nonbinary LDPC encoder leading to the maximum average LLR over the candidate set. Monte Carlo simulation results verify the effectiveness of our proposed new scheme.

Investigation of the inter-observer variability effect on the prognostic value of somatosensory evoked potentials of the median nerve (SSEP) in cardiac arrest survivors using an SSEP classification

Background and purposeWe investigated the inter-observer variability in interpretation of median nerve SSEPs with regard to neurological prognosis in survivors of cardiac arrest (CA). MethodsFour experienced neurologists analyzed 163 median nerve SSEPs on the basis of a pre-defined classification of SSEPs into five patterns (A–E), with consideration of cortical potentials up to a latency of 150ms. Of these, 133 recordings were from CA survivors and 30 were from healthy volunteers. The experts were blinded to whether a SSEP finding was from a CA survivor or a healthy volunteer. They were also unaware of the neurological outcome for the resuscitated patients. Three categories were defined for decision making. These were “good neurological outcome” represented by patterns A–C, “poor neurological outcome” (patterns D and E), and “not evaluable”. Experts’ agreement was calculated using the kappa-coefficient. ResultsThe mean correct prediction by the experts was 81.8% (range 76.3–86.6%) in resuscitated patients with good neurological outcome. In those with poor neurological outcome, however, correct prediction was achieved in only 63% (60.5–66%). All SSEPs from healthy volunteers were classified as “good neurological outcome”. The kappa-coefficient (κ) for all decision-making classifications was 0.75; for patients with poor outcome it was 0.76 and for those with good outcome 0.88. The predictive value for poor neurological outcome of the SSEP pattern D achieved a specificity of 93.5% and that of E a specificity of 98.4%. ConclusionOur study demonstrates good inter-observer agreement in the interpretation of median nerve SSEPs in CA survivors on the basis of a pre-defined SSEP evaluation set. The strongest correlation with poor outcome was found for pattern E, bilateral absence of the N20 peak.

Analysis of discrete time schemes for milling forces control under fractional order holds

AbstractIn this paper, discrete time model reference control schemes for practical milling using different discretization of the continuous-time plant are presented. First, a basic controller scheme is addressed where a fractional order hold with pre-fixed value of the gain is used. Secondly, a multi-model scheme, which outputs different discretization in parallel with the continuous-time milling system transfer function under a fractional order hold (FROH) of correcting β ∈ [−1, 1], is dealt with. Then, an intelligent design framework is designed as a supervisory scheme with two hierarchical levels in order to find the most appropriate value for the gain β. For choosing the value of β, a tracking performance index is designed. It evaluates each pre-defined discretization of the continuous time milling transfer function and the scheme chooses the one with the smallest value of the index in order to generate the real control input to the plant. Two different methods of adjusting this value are presented and discussed. The first one selects a β-value among a fixed pre-defined set of possible values, while the second one the value of β is updated by adding or subtracting a small quantity.