Custom-designed Algorithm Research Articles

Synchronous Measurements of Extracellular Action Potentials and Neurochemical Activity with Carbon Fiber Electrodes in Nonhuman Primates.

Measuring the dynamic relationship between neuromodulators, such as dopamine, and neuronal action potentials is imperative to understand how these fundamental modes of neural signaling interact to mediate behavior. We developed methods to measure concurrently dopamine and extracellular action potentials (i.e., spikes) in monkeys. Standard fast-scan cyclic voltammetric (FSCV) electrochemical (EChem) and electrophysiological (EPhys) recording systems are combined and used to collect spike and dopamine signals, respectively, from an array of carbon fiber (CF) sensors implanted in the monkey striatum. FSCV requires the application of small voltages at the implanted sensors to measure redox currents generated from target molecules, such as dopamine. These applied voltages create artifacts at neighboring EPhys measurement sensors which may lead to misclassification of these signals as physiological spikes. Therefore, simple automated temporal interpolation algorithms were designed to remove these artifacts and enable accurate spike extraction. We validated these methods using simulated artifacts and demonstrated an average spike recovery rate of 84.5%. We identified and discriminated cell type-specific units in the monkey striatum that were shown to correlate to specific behavioral task parameters related to reward size and eye movement direction. Synchronously recorded spike and dopamine signals displayed contrasting relations to the task variables, suggesting a complex relationship between these two modes of neural signaling. Future application of our methods will help advance our understanding of the interactions between neuromodulator signaling and neuronal activity, to elucidate more detailed mechanisms of neural circuitry and plasticity mediating behaviors in health and in disease.

Where is the center of resistance of a maxillary first molar? A 3-dimensional finite element analysis

<h3>Introduction</h3> The center of resistance (C_Res) is regarded as the fundamental reference point for predictable tooth movement. Accurate estimation can greatly enhance the efficiency of orthodontic tooth movement. Only a handful of studies have evaluated the C_Res of a maxillary first molar; however, most had a low sample size (in single digits), used idealized models, or involved 2-dimensional analysis. The objectives of this study were to: (1) determine the 3-dimensional (3D) location of the C_Res of maxillary first molars, (2) evaluate its variability in a large sample, and (3) investigate the effects of applying orthodontic load from 2 directions on the location of the C_Res. <h3>Methods</h3> Cone-beam computed tomography scans of 50 maxillary molars from 25 patients (mean age, 20.8 ± 8.7 years) were used. The cone-beam computed tomography volume images were manipulated to extract 3D biological structures via segmentation. The segmented structures were cleaned and converted into virtual mesh models made of tetrahedral triangles having a maximum edge length of 1 mm. The block, which included the molars and periodontal ligament, consisted of a mean of 7753 ± 2748 nodes and 38,355 ± 14,910 tetrahedral elements. Specialized software was used to preprocess the models to create an assembly and assign material properties, interaction conditions, boundary conditions, and load applications. Specific loads were applied, and custom-designed algorithms were used to analyze the stress and strain to locate the C_Res. The C_Res was measured in relation to the geometric center of the buccal surface of the molar and the trifurcation of the molar roots. <h3>Results</h3> The average location of the C_Res for the maxillary first molar was 4.94 ± 1.39 mm lingual, 2.54 ± 2.7 mm distal, and 7.86 ± 1.66 mm gingival relative to the geometric center of the buccal surface of the molar and 0.136 ± 1.51 mm lingual (<i>P</i> <0.01), 1.48 ± 2.26 mm distal (<i>P</i> <0.01), and 0.188 ± 1.75 mm gingival (<i>P</i> >0.01) relative to the trifurcation of the molar roots. In the anteroposterior (y-axis) and the vertical (z-axis) planes, the C_Res showed significant association with root divergence (<i>P</i> <0.01). <h3>Conclusions</h3> The C_Res of the maxillary first molar was located apical and distal to the trifurcation area. It showed significant variation in its location. The 3D location of and also varied with the force direction. In some samples, this deviation was large. For accurate and predictable movement, tooth-specific C_Res need to be calculated.

AutoSpec: Fast Automated Spectral Extraction Software for IFU Data Cubes

Abstract With the ever-growing popularity of integral field unit (IFU) spectroscopy, countless observations are being performed over multiple object systems such as blank fields and galaxy clusters. With this, an increasing amount of time is being spent extracting one-dimensional object spectra from large three-dimensional data cubes. However, a great deal of information available within these data cubes is overlooked in favor of photometrically based spatial information. Here we present a novel yet simple approach of optimal source identification utilizing the wealth of information available within an IFU data cube, rather than relying on ancillary imaging. Through the application of these techniques, we show that we are able to obtain object spectra comparable to deep photometry-weighted extractions without the need for ancillary imaging. Further, implementing our custom-designed algorithms can improve the signal-to-noise ratio of extracted spectra and successfully deblend sources from nearby contaminants. This will be a critical tool for future IFU observations of blank and deep fields, especially over large areas where automation is necessary. We implement these techniques in the Python-based spectral extraction software, AutoSpec, which is available via GitHub at https://github.com/a-griffiths/AutoSpec and Zenodo at https://doi.org/10.5281/zenodo.1305848.

Hierarchical Inspection System Using Visual and MFL Probe Robots

&lt;p&gt;Structural Health Monitoring (SHM) represents a critical appurtenance to modern engineering that amalgamates the skills and techniques from various disciplines of engineering and computational science. Modern civil architectures, involving high-rise buildings, complex structural designs, and innovative shapes, on one hand represent the urban development, but at the same time is a challenge from sustainability perspective. In order to ensure the tenability of such structures, advanced SHM procedures need to be developed. The presented work in this paper is an effort on these lines. The wear and tear in the buildings related to weather, as well as other natural disasters, needs to be monitored regularly and systematically in order to prevent any serious structural damage. In current SHM practices, human experts are deployed at various structurally critical places on these buildings to perform specific measurements and analyze them to decide on the structural health condition. This simple approach is becoming more and more complicated as well as perilous for the human personnel involved, due to the modern architecture that involves greater heights, and complex structures. The proposed system utilizes flying and crawling/roving robots for this purpose. The flying robots, first, scan the surface of the building to any height needed, and then the custom-designed algorithms analyze the images from these scans in order to discern the possible defects/anomalies in the structure. Using these defect pointers, the custom-designed rover robot on top of the structure lowers a robotic probe that scans only those areas for substantiating the anomalies and the degree of defects present.&lt;/p&gt;

Competitive online algorithms for resource allocation over the positive semidefinite cone

We consider a new and general online resource allocation problem, where the goal is to maximize a function of a positive semidefinite (PSD) matrix with a scalar budget constraint. The problem data arrives online, and the algorithm needs to make an irrevocable decision at each step. Of particular interest are classic experiment design problems in the online setting, with the algorithm deciding whether to allocate budget to each experiment as new experiments become available sequentially. We analyze two greedy primal-dual algorithms and provide bounds on their competitive ratios. Our analysis relies on a smooth surrogate of the objective function that needs to satisfy a new diminishing returns (PSD-DR) property (that its gradient is order-reversing with respect to the PSD cone). Using the representation for monotone maps on the PSD cone given by L\"owner's theorem, we obtain a convex parametrization of the family of functions satisfying PSD-DR. We then formulate a convex optimization problem to directly optimize our competitive ratio bound over this set. This design problem can be solved offline before the data start arriving. The online algorithm that uses the designed smoothing is tailored to the given cost function, and enjoys a competitive ratio at least as good as our optimized bound. We provide examples of computing the smooth surrogate for D-optimal and A-optimal experiment design, and demonstrate the performance of the custom-designed algorithm.

Simplifying the use of pharmacogenomics in clinical practice: Building the genomic prescribing system

BackgroundA barrier to the use of genomic information during prescribing is the limited number of software solutions that combine a user-friendly interface with complex medical data. We built and designed an online, secure, electronic custom interface termed the Genomic Prescribing System (GPS). MethodsActionable pharmacogenomic (PGx) information was reviewed, collected, and stored in the back-end of GPS to enable creation of customized drug- and variant-specific clinical decision support (CDS) summaries. The database architecture utilized the star schema to store information. Patient raw genomic data underwent transformation via custom-designed algorithms to enable gene and phenotype-level associations. Multiple external data sets (PubMed, The Systematized Nomenclature of Medicine (SNOMED), National Drug File – Reference Terminology (ND-FRT), and a publically-available PGx knowledgebase) were integrated to facilitate the delivery of patient, drug, disease, and genomic information. Institutional security infrastructure was leveraged to securely store patient genomic and clinical data on a HIPAA-compliant server farm. ResultsAs of May 17, 2016, the GPS back-end housed 257 CDS encompassing 112 genetic variants, 42 genes, and 46 PGx-actionable drugs. The GPS user interface presented patient-specific CDS alongside a recognizable traffic light symbol (green/yellow/red), denoting PGx risk for each genomic result. The number of traffic lights per visit increased with the corresponding increase in the number of available PGx-annotated drugs over time. An integrated drug and disease search functionality, links to primary literature sources, and potential alternative PGx drugs were indicated. The system, which was initially used as stand-alone CDS software within our clinical environment, was then integrated with the institutional electronic medical record for enhanced usability. There have been nearly 2000 logins in 43months since inception, with usage exceeding 56 logins per month and system up-times of 99.99%. For all patient-provider visits encompassing >3years of implementation, unique alert click-through rates corresponded to genomic risk: red lights clicked 100%, yellow lights 79%, green lights 43%. ConclusionsSuccessful deployment of GPS by combining complex data and recognizable iconography led to a tool that enabled point-of-care genomic delivery with high usability. Continued scalability and incorporation of additional clinical elements to be considered alongside PGx information could expand future impact.

Abstract LB-009: An automated approach to identifying disease-gene associations from the medical literature to inform gene panel design

Abstract Rational approaches to the design of gene panels for next-generation molecular diagnostic assays are made challenging by the amount and complexity of information that must be manually collected and assessed from the medical literature. We have developed a novel text-mining infrastructure that automatically identifies disease-gene-treatment associations from the titles and abstracts of more than 30M scientific publications in PubMed and disease-gene-variant associations from the full-text of 3.1M prioritized articles. For this work, curated lists of diseases and genes comprising 11.7K and 50.9K total entries, respectively, were used as initial search parameters to prioritize full-text content. Next, custom-designed algorithms were used to scan full-text articles using a comprehensive variant lists comprising 602M total entries for every possible cDNA or protein variant in all gene transcripts sorted by biological outcome as second-tier search parameters. In total, we identified 909K putative disease-gene associations (24 articles per association on average) and 543K total variants distributed across all genes. This information was organized according to the strength of the association based on the total number and quality of individual citations and the position of disease-gene and disease-gene-variant key terms within the text. We decided to demonstrate this approach in AML to create an NGS panel, where outcome is very poor and lists of well-known mutations and biomarkers for treatment are not well characterized outside of cytogenetics. In total, 11K unique variants in 151 genes were found to be associated with AML and ranked according to the number of citations for each. Each variant had been cited at least once from 3,865 individual scientific publications. These variants were further classified according to the journals in which they appeared and the resulting data was manually inspected for accuracy. Additionally, custom search algorithms identified recurrent gene amplifications, gene deletions and gene-fusions resulting from translocations and were also catalogued and incorporated in the final panel design. The final in silico panel design was compared to commercially-available panels and included several genes with a clear association to acute myeloid leukemia with potential clinical significance that are otherwise not present on any such panel. In conclusion, we have developed and tested a tool that rapidly and comprehensively interrogates, organizes and displays genomic targets for diseases and has promising applications for clinical panel construction with significantly reduced curation and research time. Citation Format: Mark Kiel, Matthew Schu, Steve Schwartz, Victor Weigman. An automated approach to identifying disease-gene associations from the medical literature to inform gene panel design [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-009. doi:10.1158/1538-7445.AM2017-LB-009

A comparison of kinematic algorithms to estimate gait events during overground running

The gait cycle is frequently divided into two distinct phases, stance and swing, which can be accurately determined from ground reaction force data. In the absence of such data, kinematic algorithms can be used to estimate footstrike and toe-off. The performance of previously published algorithms is not consistent between studies. Furthermore, previous algorithms have not been tested at higher running speeds nor used to estimate ground contact times. Therefore the purpose of this study was to both develop a new, custom-designed, event detection algorithm and compare its performance with four previously tested algorithms at higher running speeds. Kinematic and force data were collected on twenty runners during overground running at 5.6m/s. The five algorithms were then implemented and estimated times for footstrike, toe-off and contact time were compared to ground reaction force data. There were large differences in the performance of each algorithm. The custom-designed algorithm provided the most accurate estimation of footstrike (True Error 1.2±17.1ms) and contact time (True Error 3.5±18.2ms). Compared to the other tested algorithms, the custom-designed algorithm provided an accurate estimation of footstrike and toe-off across different footstrike patterns. The custom-designed algorithm provides a simple but effective method to accurately estimate footstrike, toe-off and contact time from kinematic data.

An automated integrated platform for rapid and sensitive multiplexed protein profiling using human saliva samples

During the last decade, saliva has emerged as a potentially ideal diagnostic biofluid for noninvasive testing. In this paper, we present an automated, integrated platform useable by minimally trained personnel in the field for the diagnosis of respiratory diseases using human saliva as a sample specimen. In this platform, a saliva sample is loaded onto a disposable microfluidic chip containing all the necessary reagents and components required for saliva analysis. The chip is then inserted into the automated analyzer, the SDReader, where multiple potential protein biomarkers for respiratory diseases are measured simultaneously using a microsphere-based array via fluorescence sandwich immunoassays. The results are read optically, and the images are analyzed by a custom-designed algorithm. The fully automated assay requires as little as 10 μL of saliva sample, and the results are reported in 70 min. The performance of the platform was characterized by testing protein standard solutions, and the results were comparable to those from the 3.5 h lab bench assay that we have previously reported. The device was also deployed in two clinical environments where 273 human saliva samples collected from different subjects were successfully tested, demonstrating the device's potential to assist clinicians with the diagnosis of respiratory diseases by providing timely protein biomarker profiling information. This platform, which combines noninvasive sample collection and fully automated analysis, can also be utilized in point-of-care diagnostics.

High-Content Analysis to Leverage a Robust Phenotypic Profiling Approach to Vascular Modulation

Phenotypic screening seeks to identify substances that modulate phenotypes in a desired manner with the aim of progressing first-in-class agents. Successful campaigns require physiological relevance, robust screening, and an ability to deconvolute perturbed pathways. High-content analysis (HCA) is increasingly used in cell biology and offers one approach to prosecution of phenotypic screens, but challenges exist in exploitation where data generated are high volume and complex. We combine development of an organotypic model with novel HCA tools to map phenotypic responses to pharmacological perturbations. We describe implementation for angiogenesis, a process that has long been a focus for therapeutic intervention but has lacked robust models that recapitulate more completely mechanisms involved. The study used human primary endothelial cells in co-culture with stromal fibroblasts to model multiple aspects of angiogenic signaling: cell interactions, proliferation, migration, and differentiation. Multiple quantitative descriptors were derived from automated microscopy using custom-designed algorithms. Data were extracted using a bespoke informatics platform that integrates processing, statistics, and feature display into a streamlined workflow for building and interrogating fingerprints. Ninety compounds were characterized, defining mode of action by phenotype. Our approach for assessing phenotypic outcomes in complex assay models is robust and capable of supporting a range of phenotypic screens at scale.

A novel visualization tool for art history and conservation: Automated colorization of black and white archival photographs of works of art

This paper describes the use of a customized algorithm for the colorization of historical black and white photographs documenting earlier states of paintings. This study specifically focuses on Pablo Picasso's mid-century Mediterranean masterpiece La Joie de Vivre, 1946 (Musée Picasso, Antibes, France). The custom-designed algorithm allows computer-controlled spreading of color information on a digital image of black and white historical photographs to obtain accurate color renditions. Expert observation of the present state of the painting, coupled with stratigraphic information from cross sections allows the attribution of color information to selected pixels in the digitized images. The algorithm uses the localized color information and the grayscale intensities of the black and white historical photographs to formulate a set of equations for the missing color values of the remaining pixels. The computational resolution of such equations allows an accurate colorization that preserves brushwork and shading. This new method is proposed as a valuable alternative to the use of commercial software to apply flat areas of color, which is currently the most common practice for colorization efforts in the conservation community. Availability of such colorized images enhances the art-historical understanding of the works and might lead to better-informed treatment.

Reconstruction of a persistent random walk from exit time distributions

In this paper, we study the inverse problem of reconstructing the spatially dependent transition rate F (x) of a one-dimensional Broadwell process from exit time distributions. In such a process, an advecting particle is assumed to undergo transitions between states with constant positive (+v) and negative (−v) velocities. The goal is to reconstruct the transition rate function F (x) from the exit time distributions out of a finite interval. Using the associated backward equation, we compute the distribution of exit times and its Laplace transform, given a fixed starting position and velocity. We propose two methods (called ‘t’ and ‘s’) for finding F (x). In both methods, we represent F (x) as a linear combination of polynomials and repeatedly solve the backward equation to minimize the difference between its solution and given first exit time data. In the t-method we work in the time domain, using exit times directly and leveraging a novel series solution for the exit time distribution. In the s-method, we work with the Laplace-transformed equation and Laplace-transformed exit times. Noisy data is generated using a custom-designed algorithm to simulate the trajectories of a Broadwell process. In most cases we can find 4 coefficients to within O(10−1) accuracy from O(104) exit times, with the t-method method slightly out-performing the s-method. We also explore the effectiveness of our algorithms for a fixed number of exit times under different advection speeds and find that optimal reconstruction occurs when v = O(1).

Automated Quantitative RNA in Situ Hybridization for Resolution of Equivocal and Heterogeneous ERBB2 (HER2) Status in Invasive Breast Carcinoma

Patient management based on HER2 status in breast carcinoma is an archetypical example of personalized medicine but remains hampered by equivocal testing and intratumoral heterogeneity. We developed a fully automated, quantitative, bright-field in situ hybridization technique (RNAscope), applied it to quantify single-cell HER2 mRNA levels in 132 invasive breast carcinomas, and compared the results with those by real-time quantitative PCR (qPCR) and Food and Drug Administration-approved methods, including fluorescence in situ hybridization (FISH), IHC, chromogenic in situ hybridization, and dual in situ hybridization. Both RNAscope and qPCR were 97.3% concordant with FISH in cases in which FISH results were unequivocal. RNAscope was superior to qPCR in cases with intratumoral heterogeneity or equivocal FISH results. This novel assay may enable ultimate HER2 status resolution as a reflex test for current testing algorithms. Quantitative in situ RNA measurement at the single-cell level may be broadly applicable in companion diagnostic applications.

Desktop Software for Patch-Clamp Raw Binary Data Conversion and Preprocessing

Since raw data recorded by patch-clamp systems are always stored in binary format, electrophysiologists may experience difficulties with patch clamp data preprocessing especially when they want to analyze by custom-designed algorithms. In this study, we present desktop software, called PCDReader, which could be an effective and convenient solution for patch clamp data preprocessing for daily laboratory use. We designed a novel class module, called clsPulseData, to directly read the raw data along with the parameters recorded from HEKA instruments without any other program support. By a graphical user interface, raw binary data files can be converted into several kinds of ASCII text files for further analysis, with several preprocessing options. And the parameters can also be viewed, modified and exported into ASCII files by a user-friendly Explorer style window. The real-time data loading technique and optimized memory management programming makes PCDReader a fast and efficient tool. The compiled software along with the source code of the clsPulseData class module is freely available to academic and nonprofit users.

Computational Image Analysis of Cellular Dynamics: A Case Study Based on Particle Tracking

INTRODUCTIONObtaining quantitative data from live cell images is the key to testing mechanistic hypotheses of molecular and cellular processes. The importance of using computer vision-based methods to accomplish this task is well recognized. However, in practice, investigators often encounter obstacles that render the application of computational image processing in cell biology far from routine. First, it is not always clear which measurements are necessary to characterize a molecular system and whether these measurements are sufficient to characterize the cellular process under investigation. Second, even when the requirements for measurements are well defined, it often is difficult to find a software tool to extract these data. It can be even more challenging to find software tools to answer specific questions raised by the hypotheses underlying the experiments. One solution is for investigators to develop their own software tools. This is feasible for some applications with the assistance of commercial and open-source software packages that support the assembly and integration of custom-designed algorithms, even for users with limited computational expertise. Another solution is for investigators to develop interdisciplinary collaborations with computer scientists. Such collaborations require close interaction between the computer scientists and experimental biologists to optimize the data acquisition and analytical procedures, which must be tightly coupled in any project applying computational analysis to biological image data. This article introduces the basic concepts that make the application of computational image processing to live cell image data successful.

Learning Algorithms for Keyphrase Extraction

Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variety of tasks for which keyphrases are useful, as we discuss in this paper. We approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. We evaluate the performance of nine different configurations of C4.5. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for automatically extracting keyphrases from text. The experimental results support the claim that a custom-designed algorithm (GenEx), incorporating specialized procedural domain knowledge, can generate better keyphrases than a general-purpose algorithm (C4.5). Subjective human evaluation of the keyphrases generated by GenEx suggests that about 80% of the keyphrases are acceptable to human readers. This level of performance should be satisfactory for a wide variety of applications.

Resolution enhancement by extrapolation of the optically measured spectrum of surface profiles

Surface features are measured by the analysis of the diffracted field intensity distribution generated by a scanning Gaussian beam. The measured data are shown to represent the amplitude of the Gabor expansion coefficients of the complex function embossed by the surface onto the reflected wave front. The surface is reconstructed by use of a custom-designed algorithm based on generalized projections, with a resolution exceeding the classical diffraction limit.