The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform
D. D. Mulcahy - One of the best experts on this subject based on the ideXlab platform.
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Astronomy & Astrophysics - The LOFAR Multifrequency Snapshot Sky Survey (MSSS) - I. Survey description and first results
Astronomy & Astrophysics, 2015Co-Authors: George Heald, Roberto Pizzo, E. Orru, Rene P. Breton, D. Carbone, Chiara Ferrari, Martin J. Hardcastle, W. Jurusik, Giulia Macario, D. D. MulcahyAbstract:We present the Multifrequency Snapshot Sky Survey (MSSS), the first northern-sky LOFAR imaging survey. In this introductory paper, we first describe in detail the motivation and design of the survey. Compared to previous radio surveys, MSSS is exceptional due to its intrinsic multifrequency nature providing information about the spectral properties of the detected sources over more than two octaves (from 30 to 160 MHz). The broadband frequency coverage, together with the fast survey speed generated by LOFAR's multibeaming capabilities, make MSSS the first survey of the sort anticipated to be carried out with the forthcoming Square Kilometre Array (SKA). Two of the sixteen frequency bands included in the survey were chosen to exactly overlap the frequency coverage of large-area Very Large Array (VLA) and Giant Metrewave Radio Telescope (GMRT) surveys at 74 MHz and 151 MHz respectively. The survey performance is illustrated within the "MSSS Verification Field" (MVF), a region of 100 square degrees centered at J2000 (RA,Dec)=(15h,69deg). The MSSS results from the MVF are compared with previous radio survey catalogs. We assess the flux and astrometric uncertainties in the catalog, as well as the completeness and reliability considering our source finding strategy. We determine the 90% completeness levels within the MVF to be 100 mJy at 135 MHz with 108" resolution, and 550 mJy at 50 MHz with 166" resolution. Images and catalogs for the full survey, expected to contain 150,000-200,000 sources, will be released to a Public Web Server. We outline the plans for the ongoing production of the final survey products, and the ultimate Public release of images and source catalogs.
Frederic T Chong - One of the best experts on this subject based on the ideXlab platform.
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temporal search detecting hidden malware timebombs with virtual machines
Architectural Support for Programming Languages and Operating Systems, 2006Co-Authors: Jedidiah R Crandall, Gary Wassermann, Daniela Oliveira, Zhendong Su, Felix S Wu, Frederic T ChongAbstract:Worms, viruses, and other malware can be ticking bombs counting down to a specific time, when they might, for example, delete files or download new instructions from a Public Web Server. We propose a novel virtual-machine-based analysis technique to automatically discover the timetable of a piece of malware, or when events will be triggered, so that other types of analysis can discern what those events are. This information can be invaluable for responding to rapid malware, and automating its discovery can provide more accurate information with less delay than careful human analysis.Developing an automated system that produces the timetable of a piece of malware is a challenging research problem. In this paper, we describe our implementation of a key component of such a system: the discovery of timers without making assumptions about the integrity of the infected system's kernel. Our technique runs a virtual machine at slightly different rates of perceived time (time as seen by the virtual machine), and identifies time counters by correlating memory write frequency to timer interrupt frequency.We also analyze real malware to assess the feasibility of using full-system, machine-level symbolic execution on these timers to discover predicates. Because of the intricacies of the Gregorian calendar (leap years, different number of days in each month, etc.) these predicates will not be direct expressions on the timer but instead an annotated trace; so we formalize the calculation of a timetable as a weakest precondition calculation. Our analysis of six real worms sheds light on two challenges for future work: 1) time-dependent malware behavior often does not follow a linear timetable; and 2) that an attacker with knowledge of the analysis technique can evade analysis. Our current results are promising in that with simple symbolic execution we are able to discover predicates on the day of the month for four real worms. Then through more traditional manual analysis we conclude that a more control-flow-sensitive symbolic execution implementation would discover all predicates for the malware we analyzed.
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Temporal search
ACM SIGARCH Computer Architecture News, 2006Co-Authors: Jedidiah R Crandall, Gary Wassermann, Daniela A. S. De Oliveira, Frederic T ChongAbstract:Worms, viruses, and other malware can be ticking bombs counting down to a specific time, when they might, for example, delete files or download new instructions from a Public Web Server. We propose a novel virtual-machine-based analysis technique to automatically discover the timetable of a piece of malware, or when events will be triggered, so that other types of analysis can discern what those events are. This information can be invaluable for responding to rapid malware, and automating its discovery can provide more accurate information with less delay than careful human analysis.Developing an automated system that produces the timetable of a piece of malware is a challenging research problem. In this paper, we describe our implementation of a key component of such a system: the discovery of timers without making assumptions about the integrity of the infected system's kernel. Our technique runs a virtual machine at slightly different rates of perceived time (time as seen by the virtual machine), and identifies time counters by correlating memory write frequency to timer interrupt frequency.We also analyze real malware to assess the feasibility of using full-system, machine-level symbolic execution on these timers to discover predicates. Because of the intricacies of the Gregorian calendar (leap years, different number of days in each month, etc.) these predicates will not be direct expressions on the timer but instead an annotated trace; so we formalize the calculation of a timetable as a weakest precondition calculation. Our analysis of six real worms sheds light on two challenges for future work: 1) time-dependent malware behavior often does not follow a linear timetable; and 2) that an attacker with knowledge of the analysis technique can evade analysis. Our current results are promising in that with simple symbolic execution we are able to discover predicates on the day of the month for four real worms. Then through more traditional manual analysis we conclude that a more control-flow-sensitive symbolic execution implementation would discover all predicates for the malware we analyzed.
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ASPLOS - Temporal search: detecting hidden malware timebombs with virtual machines
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems - ASPLOS-XII, 2006Co-Authors: Jedidiah R Crandall, Gary Wassermann, Daniela Oliveira, Frederic T ChongAbstract:Worms, viruses, and other malware can be ticking bombs counting down to a specific time, when they might, for example, delete files or download new instructions from a Public Web Server. We propose a novel virtual-machine-based analysis technique to automatically discover the timetable of a piece of malware, or when events will be triggered, so that other types of analysis can discern what those events are. This information can be invaluable for responding to rapid malware, and automating its discovery can provide more accurate information with less delay than careful human analysis.Developing an automated system that produces the timetable of a piece of malware is a challenging research problem. In this paper, we describe our implementation of a key component of such a system: the discovery of timers without making assumptions about the integrity of the infected system's kernel. Our technique runs a virtual machine at slightly different rates of perceived time (time as seen by the virtual machine), and identifies time counters by correlating memory write frequency to timer interrupt frequency.We also analyze real malware to assess the feasibility of using full-system, machine-level symbolic execution on these timers to discover predicates. Because of the intricacies of the Gregorian calendar (leap years, different number of days in each month, etc.) these predicates will not be direct expressions on the timer but instead an annotated trace; so we formalize the calculation of a timetable as a weakest precondition calculation. Our analysis of six real worms sheds light on two challenges for future work: 1) time-dependent malware behavior often does not follow a linear timetable; and 2) that an attacker with knowledge of the analysis technique can evade analysis. Our current results are promising in that with simple symbolic execution we are able to discover predicates on the day of the month for four real worms. Then through more traditional manual analysis we conclude that a more control-flow-sensitive symbolic execution implementation would discover all predicates for the malware we analyzed.
Cristian R. Munteanu - One of the best experts on this subject based on the ideXlab platform.
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Web Server and R Library for the Calculation of Markov Chains Molecular Descriptors
Proceedings, 2020Co-Authors: Paula Carracedo-reboredo, Cristian R. Munteanu, Humbert González-díaz, Carlos Fernandez-lozanoAbstract:Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. The software to perform the calculation is not always available for general users. In this work, we developed the first library in R for the calculation of MCDs and we also report the first Public Web Server for the calculation of MCDs online that include the calculation of a new class of MCDs called Markov Singular values. We also report the first Cheminformatics study of the biological activity of 5644 compounds against colorectal cancer.
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NL MIND-BEST: a Web Server for ligands & proteins discovery; theoretic-experimental study of proteins of and new compounds active against
Journal of Theoretical Biology, 2011Co-Authors: Humberto González-díaz, Francisco Prado-prado, Eduardo Sobarzo-sánchez, Mohamed Haddad, Séverine Maurel Chevalley, Alexis Valentin, Joëlle Quetin-leclercq, María A. Dea-ayuela, María Teresa Gomez-muños, Cristian R. MunteanuAbstract:There are many protein ligands and/or drugs described with very different affinity to a large number of target proteins or receptors. In this work, we selected Ligands or Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein target and/or have not been implemented in the form of Public Web-Server freely accessible online to the scientific community. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 20:20-15-1:1. This MLP classifies correctly 611 out of 678 DTPs (Sensitivity = 90.12%) and 3083 out of 3408 nDTPs (Specificity = 90.46%), corresponding to training Accuracy = 90.41%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 310 out of 338 DTPs (Sensitivity = 91.72%) and 1527 out of 1674 nDTP (Specificity = 91.22%) in validation series, corresponding to total Accuracy = 91.30% for validation series (Predictability). This model favorably compares with other ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. We implemented the present model at Web portal Bio-AIMS in the form of an online Server called: on-inear ested rug-ank xploration & creening ool (); which is located at URL: http://miaja.tic.udc.es/Bio-AIMS/NL-MIND-BEST.php.This online tool is based on PHP/HTML/Python and MARCH-INSIDE routines. Finally, we illustrated two practical uses of this Server with two different experiments. In experiment 1, we report by first time Quantum QSAR study, synthesis, characterization, and experimental assay of antiplasmodial and cytotoxic activities of oxoisoaporphine alkaloids derivatives as well as NL MIND-BEST prediction of potential target proteins. In experiment 2, we report sampling, parasite culture, sample preparation, 2-DE, MALDI-TOF and -TOF/TOF MS, MASCOT search, MM/MD 3D structure modeling, and prediction for different peptides a new protein of the found in the proteome of the human parasite ; which is promising for anti-parasite drug targets discovery.
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mind best Web Server for drugs and target discovery design synthesis and assay of mao b inhibitors and theoretical experimental study of g3pdh protein from trichomonas gallinae
Journal of Proteome Research, 2011Co-Authors: Humberto Gonzalezdiaz, Cristian R. Munteanu, Francisco J Pradoprado, Xerardo Garciamera, Nerea Alonso, Paula Abeijon, Olga Caamano, Matilde Yanez, Alejandro Pazos, Maria Auxiliadora DeaayuelaAbstract:Many drugs with very different affinity to a large number of receptors are described. Thus, in this work, we selected drug−target pairs (DTPs/nDTPs) of drugs with high affinity/nonaffinity for different targets. Quantitative structure−activity relationship (QSAR) models become a very useful tool in this context because they substantially reduce time and resource-consuming experiments. Unfortunately, most QSAR models predict activity against only one protein target and/or they have not been implemented on a Public Web Server yet, freely available online to the scientific community. To solve this problem, we developed a multitarget QSAR (mt-QSAR) classifier combining the MARCH-INSIDE software for the calculation of the structural parameters of drug and target with the linear discriminant analysis (LDA) method in order to seek the best model. The accuracy of the best LDA model was 94.4% (3,859/4,086 cases) for training and 94.9% (1,909/2,012 cases) for the external validation series. In addition, we implemen...
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NL MIND-BEST: A Web Server for ligands and proteins discovery—Theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum
Journal of theoretical biology, 2011Co-Authors: Humberto González-díaz, Francisco Prado-prado, Eduardo Sobarzo-sánchez, Mohamed Haddad, Alexis Valentin, Joëlle Quetin-leclercq, María A. Dea-ayuela, Séverine Chevalley, María Teresa Gomez-muños, Cristian R. MunteanuAbstract:There are many protein ligands and/or drugs described with very different affinity to a large number of target proteins or receptors. In this work, we selected Ligands or Drug-target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately most QSAR models predict activity against only one protein target and/or have not been implemented in the form of Public Web Server freely accessible online to the scientific community. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 20:20-15-1:1. This MLP classifies correctly 611 out of 678 DTPs (sensitivity=90.12%) and 3083 out of 3408 nDTPs (specificity=90.46%), corresponding to training accuracy=90.41%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 310 out of 338 DTPs (sensitivity=91.72%) and 1527 out of 1674 nDTP (specificity=91.22%) in validation series, corresponding to total accuracy=91.30% for validation series (predictability). This model favorably compares with other ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. We implemented the present model at Web portal Bio-AIMS in the form of an online Server called: Non-Linear MARCH-INSIDE Nested Drug-Bank Exploration & Screening Tool (NL MIND-BEST), which is located at URL: http://miaja.tic.udc.es/Bio-AIMS/NL-MIND-BEST.php. This online tool is based on PHP/HTML/Python and MARCH-INSIDE routines. Finally we illustrated two practical uses of this Server with two different experiments. In experiment 1, we report by first time Quantum QSAR study, synthesis, characterization, and experimental assay of antiplasmodial and cytotoxic activities of oxoisoaporphine alkaloids derivatives as well as NL MIND-BEST prediction of potential target proteins. In experiment 2, we report sampling, parasite culture, sample preparation, 2-DE, MALDI-TOF, and -TOF/TOF MS, MASCOT search, MM/MD 3D structure modeling, and NL MIND-BEST prediction for different peptides a new protein of the found in the proteome of the human parasite Giardia lamblia, which is promising for anti-parasite drug-targets discovery.
George Heald - One of the best experts on this subject based on the ideXlab platform.
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Astronomy & Astrophysics - The LOFAR Multifrequency Snapshot Sky Survey (MSSS) - I. Survey description and first results
Astronomy & Astrophysics, 2015Co-Authors: George Heald, Roberto Pizzo, E. Orru, Rene P. Breton, D. Carbone, Chiara Ferrari, Martin J. Hardcastle, W. Jurusik, Giulia Macario, D. D. MulcahyAbstract:We present the Multifrequency Snapshot Sky Survey (MSSS), the first northern-sky LOFAR imaging survey. In this introductory paper, we first describe in detail the motivation and design of the survey. Compared to previous radio surveys, MSSS is exceptional due to its intrinsic multifrequency nature providing information about the spectral properties of the detected sources over more than two octaves (from 30 to 160 MHz). The broadband frequency coverage, together with the fast survey speed generated by LOFAR's multibeaming capabilities, make MSSS the first survey of the sort anticipated to be carried out with the forthcoming Square Kilometre Array (SKA). Two of the sixteen frequency bands included in the survey were chosen to exactly overlap the frequency coverage of large-area Very Large Array (VLA) and Giant Metrewave Radio Telescope (GMRT) surveys at 74 MHz and 151 MHz respectively. The survey performance is illustrated within the "MSSS Verification Field" (MVF), a region of 100 square degrees centered at J2000 (RA,Dec)=(15h,69deg). The MSSS results from the MVF are compared with previous radio survey catalogs. We assess the flux and astrometric uncertainties in the catalog, as well as the completeness and reliability considering our source finding strategy. We determine the 90% completeness levels within the MVF to be 100 mJy at 135 MHz with 108" resolution, and 550 mJy at 50 MHz with 166" resolution. Images and catalogs for the full survey, expected to contain 150,000-200,000 sources, will be released to a Public Web Server. We outline the plans for the ongoing production of the final survey products, and the ultimate Public release of images and source catalogs.
Jedidiah R Crandall - One of the best experts on this subject based on the ideXlab platform.
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temporal search detecting hidden malware timebombs with virtual machines
Architectural Support for Programming Languages and Operating Systems, 2006Co-Authors: Jedidiah R Crandall, Gary Wassermann, Daniela Oliveira, Zhendong Su, Felix S Wu, Frederic T ChongAbstract:Worms, viruses, and other malware can be ticking bombs counting down to a specific time, when they might, for example, delete files or download new instructions from a Public Web Server. We propose a novel virtual-machine-based analysis technique to automatically discover the timetable of a piece of malware, or when events will be triggered, so that other types of analysis can discern what those events are. This information can be invaluable for responding to rapid malware, and automating its discovery can provide more accurate information with less delay than careful human analysis.Developing an automated system that produces the timetable of a piece of malware is a challenging research problem. In this paper, we describe our implementation of a key component of such a system: the discovery of timers without making assumptions about the integrity of the infected system's kernel. Our technique runs a virtual machine at slightly different rates of perceived time (time as seen by the virtual machine), and identifies time counters by correlating memory write frequency to timer interrupt frequency.We also analyze real malware to assess the feasibility of using full-system, machine-level symbolic execution on these timers to discover predicates. Because of the intricacies of the Gregorian calendar (leap years, different number of days in each month, etc.) these predicates will not be direct expressions on the timer but instead an annotated trace; so we formalize the calculation of a timetable as a weakest precondition calculation. Our analysis of six real worms sheds light on two challenges for future work: 1) time-dependent malware behavior often does not follow a linear timetable; and 2) that an attacker with knowledge of the analysis technique can evade analysis. Our current results are promising in that with simple symbolic execution we are able to discover predicates on the day of the month for four real worms. Then through more traditional manual analysis we conclude that a more control-flow-sensitive symbolic execution implementation would discover all predicates for the malware we analyzed.
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Temporal search
ACM SIGARCH Computer Architecture News, 2006Co-Authors: Jedidiah R Crandall, Gary Wassermann, Daniela A. S. De Oliveira, Frederic T ChongAbstract:Worms, viruses, and other malware can be ticking bombs counting down to a specific time, when they might, for example, delete files or download new instructions from a Public Web Server. We propose a novel virtual-machine-based analysis technique to automatically discover the timetable of a piece of malware, or when events will be triggered, so that other types of analysis can discern what those events are. This information can be invaluable for responding to rapid malware, and automating its discovery can provide more accurate information with less delay than careful human analysis.Developing an automated system that produces the timetable of a piece of malware is a challenging research problem. In this paper, we describe our implementation of a key component of such a system: the discovery of timers without making assumptions about the integrity of the infected system's kernel. Our technique runs a virtual machine at slightly different rates of perceived time (time as seen by the virtual machine), and identifies time counters by correlating memory write frequency to timer interrupt frequency.We also analyze real malware to assess the feasibility of using full-system, machine-level symbolic execution on these timers to discover predicates. Because of the intricacies of the Gregorian calendar (leap years, different number of days in each month, etc.) these predicates will not be direct expressions on the timer but instead an annotated trace; so we formalize the calculation of a timetable as a weakest precondition calculation. Our analysis of six real worms sheds light on two challenges for future work: 1) time-dependent malware behavior often does not follow a linear timetable; and 2) that an attacker with knowledge of the analysis technique can evade analysis. Our current results are promising in that with simple symbolic execution we are able to discover predicates on the day of the month for four real worms. Then through more traditional manual analysis we conclude that a more control-flow-sensitive symbolic execution implementation would discover all predicates for the malware we analyzed.
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ASPLOS - Temporal search: detecting hidden malware timebombs with virtual machines
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems - ASPLOS-XII, 2006Co-Authors: Jedidiah R Crandall, Gary Wassermann, Daniela Oliveira, Frederic T ChongAbstract:Worms, viruses, and other malware can be ticking bombs counting down to a specific time, when they might, for example, delete files or download new instructions from a Public Web Server. We propose a novel virtual-machine-based analysis technique to automatically discover the timetable of a piece of malware, or when events will be triggered, so that other types of analysis can discern what those events are. This information can be invaluable for responding to rapid malware, and automating its discovery can provide more accurate information with less delay than careful human analysis.Developing an automated system that produces the timetable of a piece of malware is a challenging research problem. In this paper, we describe our implementation of a key component of such a system: the discovery of timers without making assumptions about the integrity of the infected system's kernel. Our technique runs a virtual machine at slightly different rates of perceived time (time as seen by the virtual machine), and identifies time counters by correlating memory write frequency to timer interrupt frequency.We also analyze real malware to assess the feasibility of using full-system, machine-level symbolic execution on these timers to discover predicates. Because of the intricacies of the Gregorian calendar (leap years, different number of days in each month, etc.) these predicates will not be direct expressions on the timer but instead an annotated trace; so we formalize the calculation of a timetable as a weakest precondition calculation. Our analysis of six real worms sheds light on two challenges for future work: 1) time-dependent malware behavior often does not follow a linear timetable; and 2) that an attacker with knowledge of the analysis technique can evade analysis. Our current results are promising in that with simple symbolic execution we are able to discover predicates on the day of the month for four real worms. Then through more traditional manual analysis we conclude that a more control-flow-sensitive symbolic execution implementation would discover all predicates for the malware we analyzed.
