基于区域相似性测量的CT／PET图像配准

图像配准在临床诊断中有重要意义,针对这一问题已经提出了许多方法。本文以区域相似性匹配测度,运用改进的分割方法,结合Powell寻优算法实现了CT／PET多模医学图像配准。实验结果表明,该算法易于实现,配准速度快、精度高,鲁棒性较好。     

Registration Error and Intensity Similarity Based Label Fusion for Segmentation

Background

Label fusion is a core step of Multi-Atlas Segmentation (MAS), which has a decisive effect on segmentation results. Although existed strategies using image intensity or image shape to fuse labels have got acceptable results, there is still necessity for further performance improvement. Here, we propose a new label fusion strategy, which considers the joint information of intensity and registration quality.

Fuzzy Kernel Clustering of RNA Secondary Structure Ensemble Using a Novel Similarity Metric

Abstract

Measuring the (dis)similarity between RNA secondary structures is critical for the study of RNA secondary structures and has implications to RNA functional characterization. Although a number of methods have been developed for comparing RNA structural similarities, their applications have been limited by the complexity of the required computation. In this paper, we present a novel method for comparing the similarity of RNA secondary structures generated from the same RNA sequence, i.e., a secondary structure ensemble, using a matrix representation of the RNA structures. Relevant features of the RNA secondary structures can be easily extracted through singular value decomposition (SVD) of the representing matrices. We have mapped the feature vectors of the singular values to a kernel space, where (dis)similarities among the mapped feature vectors become more evident, making clustering of RNA secondary structures easier to handle. The pair-wise comparison of RNA structures is achieved through computing the distance between the singular value vectors in the kernel space. We have applied a fuzzy kernel clustering method, using this similarity metric, to cluster the RNA secondary structure ensembles. Our application results suggest that our fuzzy kernel clustering method is highly promising for classifications of RNA structure ensembles, because of its low computational complexity and high clustering accuracy.     

Personalized Mortality Prediction Driven by Electronic Medical Data and a Patient Similarity Metric

BackgroundClinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made.ConclusionsThe present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our novel medical data analytics contributes to meaningful use of EMR data.     

Compression-based classification of biological sequences and structures via the Universal Similarity Metric: experimental assessment

Background  

Similarity of sequences is a key mathematical notion for Classification and Phylogenetic studies in Biology. It is currently primarily handled using alignments. However, the alignment methods seem inadequate for post-genomic studies since they do not scale well with data set size and they seem to be confined only to genomic and proteomic sequences. Therefore, alignment-free similarity measures are actively pursued. Among those, USM (Universal Similarity Metric) has gained prominence. It is based on the deep theory of Kolmogorov Complexity and universality is its most novel striking feature. Since it can only be approximated via data compression, USM is a methodology rather than a formula quantifying the similarity of two strings. Three approximations of USM are available, namely UCD (Universal Compression Dissimilarity), NCD (Normalized Compression Dissimilarity) and CD (Compression Dissimilarity). Their applicability and robustness is tested on various data sets yielding a first massive quantitative estimate that the USM methodology and its approximations are of value. Despite the rich theory developed around USM, its experimental assessment has limitations: only a few data compressors have been tested in conjunction with USM and mostly at a qualitative level, no comparison among UCD, NCD and CD is available and no comparison of USM with existing methods, both based on alignments and not, seems to be available.     

Discovery of Novel Liver-Stage Antimalarials through Quantum Similarity

Without quantum theory any understanding of molecular interactions is incomplete. In principal, chemistry, and even biology, can be fully derived from non-relativistic quantum mechanics. In practice, conventional quantum chemical calculations are computationally too intensive and time consuming to be useful for drug discovery on more than a limited basis. A previously described, original, quantum-based computational process for drug discovery and design bridges this gap between theory and practice, and allows the application of quantum methods to large-scale in silico identification of active compounds. Here, we show the results of this quantum-similarity approach applied to the discovery of novel liver-stage antimalarials. Testing of only five of the model-predicted compounds in vitro and in vivo hepatic stage drug inhibition assays with P. berghei identified four novel chemical structures representing three separate quantum classes of liver-stage antimalarials. All four compounds inhibited liver-stage Plasmodium as a single oral dose in the quantitative PCR mouse liver-stage sporozoites-challenge model. One of the newly identified compounds, cethromycin [ABT-773], a macrolide-quinoline hybrid, is a drug with an extensive (over 5,000 people) safety profile warranting its exploitation as a new weapon for the current effort of malaria eradication. The results of our molecular modeling exceed current state-of-the-art computational methods. Drug discovery through quantum similarity is data-driven, agnostic to any particular target or disease process that can evaluate multiple phenotypic, target-specific, or co-crystal structural data. This allows the incorporation of additional pharmacological requirements, as well as rapid exploration of novel chemical spaces for therapeutic applications.     

自适应指数加权互信息配准医学图像的测度及相应算法

为配准医学图像,本文提出了一种新的自适应指数加权的互信息(Adaptive Exponential Weighted Mutual Informa- tion,AEWMI)测度,分析表明:通过对互信息(Mutual Information,MI)测度进行指数加权可以提高测度曲线的峰值尖锐性和平滑性;而指数的权值则可以通过评估待配准图像的质量和分辨率大小来自适应确定。仿真实验结果在验证分析结果的同时也表明,基于本文AEWMI测度的配准方案,对图像噪声、分辨率差异等有较高的鲁棒性,且可有效地提高配准的成功率。     

Accelerating all-atom protein folding simulations through reduced dihedral barriers

Protein folding requires extensive changes of backbone and sidechain dihedral angles, whose energy barriers constitute obstacles for the folding kinetics. Folding of small proteins is furthermore thought to be path-independent. Here, we propose that time-consuming all-atom protein folding simulations may be accelerated through a reduction of the dihedral barriers of the force field. In order to investigate this hypothesis, we performed various folding simulations of two small proteins. We report an acceleration towards smaller root-mean-square deviations from the native protein structure using our proposed method.     

Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU

This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the expectation maximization (EM) algorithm, to find the optimal parameters given a set of observations and the underlying probability model. As the EM algorithm is computationally demanding and the analysis faces possible combinatorial explosion of models to be tested, we propose a parallel computing scheme using the GPU to achieve a fast estimation of DCM for ERP. The computation of DCM for ERP is dynamically partitioned and distributed to threads for parallel processing, according to the DCM model complexity and the hardware constraints. The performance efficiency of this hardware-dependent thread arrangement strategy was evaluated using the synthetic data. The experimental data were used to validate the accuracy of the proposed computing scheme and quantify the time saving in practice. The simulation results show that the proposed scheme can accelerate the computation by a factor of 155 for the parallel part. For experimental data, the speedup factor is about 7 per model on average, depending on the model complexity and the data. This GPU-based implementation of DCM for ERP gives qualitatively the same results as the original MATLAB implementation does at the group level analysis. In conclusion, we believe that the proposed GPU-based implementation is very useful for users as a fast screen tool to select the most likely model and may provide implementation guidance for possible future clinical applications such as online diagnosis.     

Parallel Computation of the Matrix of the Chemical Distances on the Connection Machine

Abstract

We report here the parallel implementation of an original algorithm allowing a fast calculation of the distance matrix D of a graph representing a given chemical structure (molecule, polymer, crystal, etc.). Our algorithm fits perfectly in the SIMD parallel architecture of the Connection Machines CM-200 as we shall show. After discussing the performances of the parallel evaluation of D, we will end with a relevant application concerning C₆₀ and C₇₀ fullerenes. The present study applies to a generic globally connected graph without any restriction on the local connectivity of each graph's vertex.     

Accelerating the development of a sustainable bioenergy portfolio through stable isotopes

Bioenergy could help limit global warming to 2°C above pre-industrial levels while supplying almost a fourth of the world's renewable energy needs by 2050. However, the deployment of bioenergy raises concerns that adoption at meaningful scales may lead to unintended negative environmental consequences. Meanwhile, the full consolidation of a bioenergy industry is currently challenged by a sufficient, resilient, and resource-efficient biomass supply and an effective conversion process. Here, we provide a comprehensive analysis of how stable isotope approaches have accelerated the development of a robust bioeconomy by advancing knowledge about environmental sustainability, feedstock development, and biological conversion. We show that advances in stable isotope research have generated crucial information to (1) gain mechanistic insight into the potential of bioenergy crops to mitigate climate change as well as their impact on water and nutrient cycling; (2) develop high-yielding, resilient feedstocks that produce high-value bioproducts in planta; and (3) engineer microbes to enhance feedstock conversion to bioenergy products. Further, we highlight knowledge gaps that could benefit from future research facilitated by stable isotope approaches. We conclude that advances in mechanistic knowledge and innovations within the field of stable isotopes in cross-disciplinary research actions will greatly contribute to breaking down the barriers to establishing a robust bioeconomy.     

Accelerating the Gillespie Exact Stochastic Simulation Algorithm Using Hybrid Parallel Execution on Graphics Processing Units

The Gillespie Stochastic Simulation Algorithm (GSSA) and its variants are cornerstone techniques to simulate reaction kinetics in situations where the concentration of the reactant is too low to allow deterministic techniques such as differential equations. The inherent limitations of the GSSA include the time required for executing a single run and the need for multiple runs for parameter sweep exercises due to the stochastic nature of the simulation. Even very efficient variants of GSSA are prohibitively expensive to compute and perform parameter sweeps. Here we present a novel variant of the exact GSSA that is amenable to acceleration by using graphics processing units (GPUs). We parallelize the execution of a single realization across threads in a warp (fine-grained parallelism). A warp is a collection of threads that are executed synchronously on a single multi-processor. Warps executing in parallel on different multi-processors (coarse-grained parallelism) simultaneously generate multiple trajectories. Novel data-structures and algorithms reduce memory traffic, which is the bottleneck in computing the GSSA. Our benchmarks show an 8×−120× performance gain over various state-of-the-art serial algorithms when simulating different types of models.     

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex.     

Accelerating adoption of genetically modified crops in Africa through a trade liability regime

Given the apparently unbridgeable divide that has developed between the 25 odd countries that grow and trade GM crops and the evolving EU regulatory hurdles, it may be time to consider alternative strategies for realizing a global market for agricultural products. Africa is one area of the world where the battle over GM agriculture is being played out, yet it is the continent where GM could have the greatest positive impact. Numerous African nations, given their long‐standing trade connections to European nations, fear that allowing the commercialization of GM crops could lead to comingling of GM and conventional products and, hence, the loss of export opportunities to the EU. These are legitimate concerns. One potential solution that warrants serious consideration would be to establish a pool of funds that could be accessed by African agricultural commodity exporters in instances where exports to Europe are rejected. A production levy could be imposed in leading industrial adopting nations (i.e., Australia, Canada and the United States). The revenue raised would provide an endowment fund that could be used to offset the costs arising from import refusals. African‐sourced shipments rejected by the EU will most certainly have alternate markets, but could receive a reduced price or incur higher costs associated with serving alternate markets. The intent of the fund would be to compensate for the real difference between the net returns contracted with European importers and the final market price received. This article examines the feasibility of establishing such a fund and discusses the funding options.     

The Potential of Accelerating Early Detection of Autism through Content Analysis of YouTube Videos

Autism is on the rise, with 1 in 88 children receiving a diagnosis in the United States, yet the process for diagnosis remains cumbersome and time consuming. Research has shown that home videos of children can help increase the accuracy of diagnosis. However the use of videos in the diagnostic process is uncommon. In the present study, we assessed the feasibility of applying a gold-standard diagnostic instrument to brief and unstructured home videos and tested whether video analysis can enable more rapid detection of the core features of autism outside of clinical environments. We collected 100 public videos from YouTube of children ages 1–15 with either a self-reported diagnosis of an ASD (N = 45) or not (N = 55). Four non-clinical raters independently scored all videos using one of the most widely adopted tools for behavioral diagnosis of autism, the Autism Diagnostic Observation Schedule-Generic (ADOS). The classification accuracy was 96.8%, with 94.1% sensitivity and 100% specificity, the inter-rater correlation for the behavioral domains on the ADOS was 0.88, and the diagnoses matched a trained clinician in all but 3 of 22 randomly selected video cases. Despite the diversity of videos and non-clinical raters, our results indicate that it is possible to achieve high classification accuracy, sensitivity, and specificity as well as clinically acceptable inter-rater reliability with nonclinical personnel. Our results also demonstrate the potential for video-based detection of autism in short, unstructured home videos and further suggests that at least a percentage of the effort associated with detection and monitoring of autism may be mobilized and moved outside of traditional clinical environments.     

Accelerating sample preparation through enzyme‐assisted microfiltration of Salmonella in chicken extract

Microfiltration of chicken extracts has the potential to significantly decrease the time required to detect Salmonella, as long as the extract can be efficiently filtered and the pathogenic microorganisms kept in a viable state during this process. We present conditions that enable microfiltration by adding endopeptidase from Bacillus amyloliquefaciens to chicken extracts or chicken rinse, prior to microfiltration with fluid flow on both retentate and permeate sides of 0.2 μm cutoff polysulfone and polyethersulfone hollow fiber membranes. After treatment with this protease, the distribution of micron, submicron, and nanometer particles in chicken extracts changes so that the size of the remaining particles corresponds to 0.4–1 μm. Together with alteration of dissolved proteins, this change helps to explain how membrane fouling might be minimized because the potential foulants are significantly smaller or larger than the membrane pore size. At the same time, we found that the presence of protein protects Salmonella from protease action, thus maintaining cell viability. Concentration and recovery of 1–10 CFU Salmonella/mL from 400 mL chicken rinse is possible in less than 4 h, with the microfiltration step requiring less than 25 min at fluxes of 0.028–0.32 mL/cm²  min. The entire procedure—from sample processing to detection by polymerase chain reaction—is completed in 8 h. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1551–1562, 2015     

Intrinsic Precision of Aminoacyl-tRNA Synthesis Enhanced through Parallel Systems of Ligands

MOST aminoacyl-tRNAs possessed by an organism must contain amino-acids matched to their correct anticodon so that the meaning of structural genetic information will be preserved. It is only recently, however, that we have begun to understand the underlying molecular mechanisms with regard to isoleucyl-tRNA of Escherichia coli B. Isoleucyl-tRNA synthetase, which is representative of many others in size and other properties¹, is quite selective among tRNAs, in that it binds strongly only to the cognate tRNA^Ile species². However, a weaker, but still significant affinity for non-cognate tRNAs from E. coli can be detected³. In addition, non-cognates are isoleucylated (ref. 3 and unpublished work), albeit at a maximum rate considerably slower than tRNA^Ile. Two of these reactions, the binding and isoleucylation of tRNA^phe (E. coli)³ and of tRNA_f^Met (E. coli), have been studied in detail. The generality of this phenomenon could prove important. First, the tRNA concentrations in E. coli are high (they can be no less than about 0.5 × 10^?5 M^4,5 for individual species) compared with those usual in vitro and thus even weak binding could be significant. Second, many incorrect interactions are possible. Assuming that there are about sixty molecular species of tRNA and twenty species of aminoacyl-tRNA synthetase, there are 1,200 possible interacting pairs and only about sixty of these (or 5%) are cognates. Since it is presumably desirable that misacylated tRNAs be held to a very small fraction of the total, misacylation could be significant, even if it is always a slow reaction. I have, as of writing, examined five species of purified tRNA, of which the two already mentioned give misacylations which are sufficiently facile to be easily studied under usual in vitro conditions. The other three are much less easily isoleucylated, but also give indications of reaction (my unpublished data). Thus, this limited survey emphasizes that these reactions may be common and that rejection of non-cognate tRNAs by the aminoacyl-tRNA synthetase may not be the only mechanism by which the correctness of the aminoacyl-tRNAs is assured. In fact, I have already reported^3,6 that Ile-tRNA^phe, synthesized by isoleucyl-tRNA synthetase, is rapidly destroyed by phenylal-anyl-tRNA synthetase and have suggested⁶ that the aminoacyl-tRNA synthetases may have a function in addition to synthesis of aminoacyl-tRNAs; that of destruction of misacylated cognate tRNAs.