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Exhaustive sampling of the fragment space associated to a molecule leading to the generation of conserved fragments

CHEMICAL BIOLOGY & DRUG DESIGN 2018年第3期91卷 655-667页

作者： Heikamp, Kathrin Zuccotto, Fabio Kiczun, Michael Ray, Peter Gilbert, Ian H. Univ Dundee Drug Discovery Unit Div Biol Chem & Drug Discovery Sch Life Sci Dundee Scotland

The first step in hit optimization is the identification of the pharmacophore, which is normally achieved by deconstruction of the hit molecule to generate deletion analogues. In silico fragmentation approaches often focus on the generation of small fragments that do not describe properly the fragment space associated to the deletion analogues. We present significant modifications to the molecular fragmentation programme molBLOCKS, which allows the exhaustive sampling of the fragment space associated with a molecule to generate all possible molecular fragments. This generates larger fragments, by combining the smallest fragments. Additionally, it has been modified to deal with the problem of changing pharmacophoric properties through fragmentation, by highlighting bond cuts. The modified molBLOCKS programme was used on a set of drug compounds, where it generated more unique fragments than standard fragmentation approaches by increasing the number of fragments derived per compound. This fragment set was found to be more diverse than those generated by standard fragmentation programmes and was relevant to drug discovery as it contains the key fragments representing the pharmacophoric elements associated with ligand recognition. The use of dummy atoms to highlight bond cuts further increases the information content of fragments by visualizing their previous bonding pattern.

关键词： algorithm development chemical space analysis fragment based drug discovery molecular fragmentation

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Challenges developing evidence-based algorithms for the trauma reception and resuscitation project

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INTERNATIONAL EMERGENCY NURSING 2013年第2期21卷 129-135页

作者： Lee, Geraldine A. Murray, Angela Bushnell, Rosie Niggemeyer, Louise E. La Trobe Univ Div Nursing & Midwifery Melbourne Vic Australia Alfred Hlth Alfred Hosp Emergency & Trauma Ctr Trauma Recept & Resuscitat Project Prahran Vic 3181 Australia Alfred Hosp Melbourne Vic 3181 Australia Alfred Hosp Dept Trauma Surg Prahran Vic 3181 Australia

A project based at the Alfred Emergency and Trauma Centre in Melbourne, Australia aimed to standardise trauma resuscitation, documentation and interventions by developing best practice algorithms. The primary study objective was to demonstrate a reduction in management errors using a real-time computer based algorithm (the study group) compared to the control group in an open randomised controlled interventional study. A baseline control group was also used for comparison with usual (current) practice. In order to examine the existing evidence and algorithms in trauma care, nine teams of emergency nurses and doctors were formed. Specific literature searches performed by each team revealed a paucity of evidence supporting clinical practice in the trauma setting for procedures. Subsequently, the multidisciplinary teams worked together and developed algorithms based on best practice. The process revealed three main areas of challenges in the development of algorithms: (i) clinical, (ii) research and (iii) nursing challenges. The completion of the project demonstrated benefits in the real-time computer based algorithm with a reduction in the error rate per patient from the baseline control group to the intervention study group (2.30 vs. 2.13, p = 0.04) and error-free resuscitations increasing from 16% to 21.8% (p = .049). This project supported the implementation of a real-time computer based algorithm system with improved protocol compliance and reduced errors and morbidity. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： algorithm development Evidence-based practice Trauma resuscitation

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To the Problem of Adaptive Guaranteed Estimation

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IFAC-PapersOnLine 2018年第32期51卷 717-722页

作者： Khadanovich, Dina V. Shiryaev, Vladimir I. Chelyabinsk Russia Chelyabinsk Russia

The article deals with linear dynamical system state estimation problem under uncertainty when disturbance and measurement error statistics are unknown but the sets of their possible values are available. The approach to adaptive algorithm development of guaranteed estimation is proposed. The approach is based on the processing of innovation sequence values in the Kalman filter under conditions of a small number of available measurements. The Kalman filter implementation is performed for measurement data preprocessing the result of which is the mathematical model development and refining the estimates of unknown measurement errors. © 2018

关键词： Kalman filters Adaptive algorithms Adaptive filtering Adaptive filters Dynamical systems Error statistics Estimation Forecasting Linear control systems Mathematical models Measurement errors Uncertainty analysis algorithm development Estimation algorithm Estimation problem Guaranteed estimation Innovation sequence Linear dynamical systems Measurement data Measurement Noise

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Special Section Guest Editorial: Representation Learning and Big Data Analytics for Remote Sensing

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JOURNAL OF APPLIED REMOTE SENSING 2020年第3期14卷

作者： Liu, Weifeng Zhou, Yicong Panetta, Karen Agaian, Sos China Univ Petr East China Coll Control Sci & Engn Qingdao Shandong Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA CUNY Coll Staten Isl New York NY 10021 USA

This guest editorial introduces the Special Section on Representation Learning and Big Data Analytics for Remote Sensing.

关键词： Remote sensing Analytics algorithm development Image classification Image segmentation Hyperspectral imaging Dimension reduction Earth observing sensors Feature extraction Neural networks

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Adaptive motion-compensated video coding scheme towards content-based bit rate allocation

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JOURNAL OF ELECTRONIC IMAGING 2000年第4期9卷 521-533页

作者： Fan, JP Yau, DKY Aref, WG Rezgui, A Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

An adaptive motion-compensated video coding scheme, that is based on structural video component segmentation and coding complexity analysis, is proposed in this paper. The bits are allocated more efficiently among different frame types and variant video components. A novel scene cut detection algorithm is proposed for partitioning the input video sequences into a set of shots and each shot may be encoded as one or multiple GOPs according to its length. Moreover, the positions of the reference frames (I and P frames) in a video shot are adapted to improve the temporal predictability among frames and provide high coding efficiency, thus high picture quality with the same bit rate. More bits are allocated for these reference frames for providing high quality of the reconstructed pictures. The residue frames in a video shot are encoded as the bidirectional interpolation frames (B frames) and can be also quantized more coarsely because they have high temporal predictability and are not used as references. The bits, that have been allocated for the three different frame types (I, P, B frames), can be further distributed more efficiently among variant video components to avoid the coding artifacts. Experimental results show that this proposed adaptive video coding scheme is more efficient than the traditional fixed GOP coding algorithms and may be an efficient development of the present adaptive coding techniques. (C) 2000 SPIE and IS&T. [S1017-9909(00)00504-3].

关键词： Video Video coding Cameras Image segmentation algorithm development Quantization Detection and tracking algorithms Visual system Computer programming Signal to noise ratio

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A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2019年 74卷 269-280页

作者： Chakraborty, T. Lee, X. Yale Univ Sch Forestry & Environm Studies New Haven CT 06520 USA

We develop a new algorithm, the simplified urban-extent (SUE) algorithm, to estimate the surface urban heat island (UHI) intensity at a global scale. We implement the SUE algorithm on the Google Earth Engine platform using Moderate Resolution Imaging Spectroradiometer (MODIS) images to calculate the UHI intensity for over 9500 urban clusters using over 15 years of data, making this one of the most comprehensive characterizations of the surface UHI to date. The results from this algorithm are validated against previous multi-city studies to demonstrate the suitability of the method. The dataset created is then filtered for elevation differentials and percentage of urban area and used to estimate the diurnal, monthly, and long-term variability in the surface UHI in different climate zones. The global mean surface UHI intensity is 0.85 degrees C during daytime and 0.55 degrees C at night. Cities in arid climate show distinct diurnal and seasonal patterns, with higher surface UHI during nighttime (compared to daytime) and two peaks throughout the year. The diurnal variability in surface UHI is highest for equatorial climate zone (0.88 degrees C) and lowest for arid zone (0.53 degrees C). The seasonality is highest in the snow climate zone and lowest for equatorial climate zone. While investigating the change in the surface UHI over a decade and a half, we find a consistent increase in the daytime surface UHI in the urban clusters of the warm temperate climate zone (0.04 degrees C/decade) and snow climate zone (0.05 degrees C/decade). Only arid climate zones show a statistically significant increase in the nighttime surface UHI intensity (0.03 degrees C/decade). Globally, the change is mainly seen during the daytime (0.03 degrees C/decade). Finally, the importance of vegetation differential between urban and rural areas on the spatiotemporal variability is examined. Vegetation has a strong control on the seasonal variability of the surface UHI and may also partly control

关键词： Urban heat island Urban climate Global study Remote sensing MODIS algorithm development

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An efficient repeating signal detector to investigate earthquake swarms

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JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2016年第8期121卷 5880-5897页

作者： Skoumal, Robert J. Brudzinski, Michael R. Currie, Brian S. Miami Univ Dept Geol & Environm Earth Sci Oxford OH 45056 USA

Repetitive earthquake swarms have been recognized as key signatures in fluid injection induced seismicity, precursors to volcanic eruptions, and slow slip events preceding megathrust earthquakes. We investigate earthquake swarms by developing a Repeating Signal Detector (RSD), a computationally efficient algorithm utilizing agglomerative clustering to identify similar waveforms buried in years of seismic recordings using a single seismometer. Instead of relying on existing earthquake catalogs of larger earthquakes, RSD identifies characteristic repetitive waveforms by rapidly identifying signals of interest above a low signal-to-noise ratio and then grouping based on spectral and time domain characteristics, resulting in dramatically shorter processing time than more exhaustive autocorrelation approaches. We investigate seismicity in four regions using RSD: (1) volcanic seismicity at Mammoth Mountain, California, (2) subduction-related seismicity in Oaxaca, Mexico, (3) induced seismicity in Central Alberta, Canada, and (4) induced seismicity in Harrison County, Ohio. In each case, RSD detects a similar or larger number of earthquakes than existing catalogs created using more time intensive methods. In Harrison County, RSD identifies 18 seismic sequences that correlate temporally and spatially to separate hydraulic fracturing operations, 15 of which were previously unreported. RSD utilizes a single seismometer for earthquake detection which enables seismicity to be quickly identified in poorly instrumented regions at the expense of relying on another method to locate the new detections. Due to the smaller computation overhead and success at distances up to similar to 50km, RSD is well suited for real-time detection of low-magnitude earthquake swarms with permanent regional networks.

关键词： earthquake swarms earthquake detection algorithm development induced seismicity

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Special Section Guest Editorial: Progress in Snow Remote Sensing

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JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷

作者： Xie, Hongjie Huang, Chunlin Liang, Tiangang Univ Texas San Antonio Lab Remote Sensing & Geoinformat San Antonio TX 78249 USA Chinese Acad Sci Cold & Arid Regions Environm & Engn Res Inst Lanzhou 730000 Peoples R China Lanzhou Univ State Key Lab Grassland Agroecosyst Lanzhou 730020 Peoples R China

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Satellite remote sensing of harmful algal blooms: A new multi-algorithm method for detecting the Florida Red Tide (Karenia brevis)

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HARMFUL ALGAE 2010年第5期9卷 440-448页

作者： Carvalho, Gustavo A. Minnett, Peter J. Fleming, Lora E. Banzon, Viva F. Baringer, Warner Univ Miami Rosenstiel Sch Marine & Atmospher Sci Div Meteorol & Phys Oceanog Miami FL 33149 USA Univ Miami Rosenstiel Sch Marine & Atmospher Sci NSF NIEHS Oceans & Human Hlth Ctr Miami FL 33149 USA Univ Miami Miller Sch Med Dept Epidemiol & Publ Hlth Miami FL 33149 USA NOAA NESDIS Remote Sensing & Applicat Div Natl Climat Data Ctr Asheville NC USA

In a continuing effort to develop suitable methods for the surveillance of harmful algal blooms (HABs) of Karenia brevis using satellite radiometers, a new multi-algorithm method was developed to explore whether improvements in the remote-sensing detection of the Florida Red Tide was possible. A Hybrid Scheme was introduced that sequentially applies the optimized versions of two pre-existing satellite-based algorithms: an Empirical Approach (using water-leaving radiance as a function of chlorophyll concentration) and a Bio-optical Technique (using particulate backscatter along with chlorophyll concentration). The long-term evaluation of the new multi-algorithm method was performed using a multi-year MODIS dataset (2002-2006;during the boreal Summer-Fall periods - July-December) along the Central West Florida Shelf between 25.75 degrees N and 28.25 degrees N. algorithm validation was done with in situ measurements of the abundances of K. brevis: cell counts >= 1.5 x 10(4) cells I-1 defined a detectable HAB. Encouraging statistical results were derived when either or both algorithms correctly flagged known samples. The majority of the valid match-ups were correctly identified (similar to 80% of both HABs and non-blooming conditions) and few false negatives or false positives were produced (similar to 20% of each). Additionally, most of the HAB-positive identifications in the satellite data were indeed HAB samples (positive predictive value: similar to 70%) and those classified as HAB-negative were almost all non-bloom cases (negative predictive value: similar to 86%). These results demonstrate an excellent detection capability, on average similar to 10% more accurate than the individual algorithms used separately. Thus, the new Hybrid Scheme could become a powerful tool for environmental monitoring of K. brevis blooms, with valuable consequences including leading to the more rapid and efficient use of ships to make in situ measurements of HABs. (C) 2010 Elsevier B.V.

关键词： algorithm development Central West Florida Shelf (Gulf of Mexico) Detection Florida Red Tide (Karenia brevis) Harmful algal bloom (HAB) Hybrid Scheme Ocean color (MODIS-Aqua) Satellite remote sensing

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Process and alarm data integration under a two-stage Bayesian framework for fault diagnostics

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IFAC-PapersOnLine 2018年第24期51卷 1220-1226页

作者： Stief, Anna Ottewill, James R. Tan, Ruomu Cao, Yi ABB Corporate Research Center ul. Starowislna 13a Krakow31-038 Poland School of Water Energy and Environment Cranfield University CranfieldUK MK43 0AL United Kingdom College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China

Process and alarm data are usually available from industrial processes. It is common practice to use process data for process monitoring and diagnostics. In contrast, alarm data is typically used to determine the instantaneous health state of the process, often as part of a protection system. Alarm data is also used in alarm management systems and for alarm flood detection. Despite the fact that both data types perform similar, although not identical, functions in process monitoring, they are rarely used in combination. One of the main reasons for this is that the fusion between alarm and process data is not trivial: process data is sampled continually and is numerical, while alarm data is binary and appears at discrete times. The two data sources can contain complimentary information regarding the health state of the process, therefore their fusion is a promising direction for fault diagnostics algorithm development. A two-stage Bayesian framework is proposed to fuse alarm and process data on the decision level for fault diagnostics targeting industrial processes. Instead of the raw process data, the principal components of the process variables are used as the inputs of a naïve Bayes classifier. This step reduces the correlation between the process variables and reduces the dimension of the data. The alarm history is transformed into binary alarm features and input to a second, separate naïve Bayes classifier. The second stage of the method fuses the local classification results of the alarm and process data and provides the final classification result. The results show that the overall performance of the method fusing alarm and process data is superior when compared to the results of a similar single stage method using either alarm or process data. © 2018

关键词： Alarm systems Data fusion Data integration Fault detection Information management Plasma diagnostics Process control Process monitoring Alarms algorithm development Bayes method Classification results In process monitoring Industrial processs Principal Components Process monitoring and diagnostic

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