Focusing on RISC assembly in mammalian cells.

PubMed

Hong, Junmei; Wei, Na; Chalk, Alistair; Wang, Jue; Song, Yutong; Yi, Fan; Qiao, Ren-Ping; Sonnhammer, Erik L L; Wahlestedt, Claes; Liang, Zicai; Du, Quan

2008-04-11

RISC (RNA-induced silencing complex) is a central protein complex in RNAi, into which a siRNA strand is assembled to become effective in gene silencing. By using an in vitro RNAi reaction based on Drosophila embryo extract, an asymmetric model was recently proposed for RISC assembly of siRNA strands, suggesting that the strand that is more loosely paired at its 5' end is selectively assembled into RISC and results in target gene silencing. However, in the present study, we were unable to establish such a correlation in cell-based RNAi assays, as well as in large-scale RNAi data analyses. This suggests that the thermodynamic stability of siRNA is not a major determinant of gene silencing in mammalian cells. Further studies on fork siRNAs showed that mismatch at the 5' end of the siRNA sense strand decreased RISC assembly of the antisense strand, but surprisingly did not increase RISC assembly of the sense strand. More interestingly, measurements of melting temperature showed that the terminal stability of fork siRNAs correlated with the positions of the mismatches, but not gene silencing efficacy. In summary, our data demonstrate that there is no definite correlation between siRNA stability and gene silencing in mammalian cells, which suggests that instead of thermodynamic stability, other features of the siRNA duplex contribute to RISC assembly in RNAi.

RISC Processors and High Performance Computing

NASA Technical Reports Server (NTRS)

Bailey, David H.; Saini, Subhash; Craw, James M. (Technical Monitor)

1995-01-01

This tutorial will discuss the top five RISC microprocessors and the parallel systems in which they are used. It will provide a unique cross-machine comparison not available elsewhere. The effective performance of these processors will be compared by citing standard benchmarks in the context of real applications. The latest NAS Parallel Benchmarks, both absolute performance and performance per dollar, will be listed. The next generation of the NPB will be described. The tutorial will conclude with a discussion of future directions in the field. Technology Transfer Considerations: All of these computer systems are commercially available internationally. Information about these processors is available in the public domain, mostly from the vendors themselves. The NAS Parallel Benchmarks and their results have been previously approved numerous times for public release, beginning back in 1991.

Asymmetry of intronic pre-miRNA structures in functional RISC assembly

PubMed Central

Lin, Shi-Lung; Chang, Donald; Ying, Shao-Yao

2006-01-01

The two oligonucleotide strands of a siRNA duplex are functionally asymmetric in assembling the RNAi effector, RNA-induced gene silencing complex (RISC). Based on this asymmetric RISC assembly model in vitro, formation of a microRNA (miRNA) and complementary miRNA (miRNA*) duplex was proposed to be an essential step for the assembly of miRNA-associated RISC (miRISC). We observed here that a strong structural bias exists in the selection of a mature miRNA strand for RISC assembly in zebrafish using an intronic miRNA-like vector to target EGFP mRNA for regulation. The position of the stemloop in a precursor miRNA (pre-miRNA) was involved in the determination of miRNA–miRNA* asymmetry of the pre-miRNA stemarm, leading to different miRNA maturation during miRISC assembly. These findings suggest that the miRISC assembly is likely different from the RISC assembly model of siRNA in zebrafish, providing the first in vivo evidence for asymmetric miRISC assembly. PMID:16005165

[Options for a future risc structure compensation in Germany].

PubMed

Greiner, W

2006-07-01

AIM OF THE ARTICLE: The risc structure compensation scheme within the German compulsory health insurance system is intended to enforce the principle of solidarity all over the statutory health insurance and not only within the different sickness funds. Differences in the contribution rates should not reflect different risc profiles, but the differences of the efficiency in social care. The criticism against the current adjustment system in Germany is multifarious and points e. g. on the missing orientation to morbidity. This article follows the question, whether this criticism is valid. The variables and methods, which are currently used to calculate the risc structure adjustment are discussed and compared to an alternative proposal for the future form of the risc structure adjustment, which includes both a higher orientation to riscs and incentives for social health insurance funds to decline the costs for the social care system on long-term. Currently, for the calculation of the risc structure adjustment the following variables are used: age, sex, income, number of family members who are exempted from contributions and persons who get occupational disability pension, and number of insured persons who are registered to an accredited Disease-Management-Program (DMP). Especially the last variable includes a high control effort, because the higher co-payments of the adjustment system are aligned to the voluntariness of participation and active collaboration of the patients in DMP. The argument, a further development to a morbidity-oriented risc structure adjustment leads to less cost management of the sickness funds is not totally correct, because not actual, but standardised costs are the basis for compensation. On the other hand the morbidity determined cost components should not totally be adjusted, as a proper distribution of savings to the risc structure adjustment and the single funds would still be an incentive for cost management and prevention. An ongoing

RISC-interacting clearing 3'- 5' exoribonucleases (RICEs) degrade uridylated cleavage fragments to maintain functional RISC in Arabidopsis thaliana.

PubMed

Zhang, Zhonghui; Hu, Fuqu; Sung, Min Woo; Shu, Chang; Castillo-González, Claudia; Koiwa, Hisashi; Tang, Guiliang; Dickman, Martin; Li, Pingwei; Zhang, Xiuren

2017-05-02

RNA-induced silencing complex (RISC) is composed of miRNAs and AGO proteins. AGOs use miRNAs as guides to slice target mRNAs to produce truncated 5' and 3' RNA fragments. The 5' cleaved RNA fragments are marked with uridylation for degradation. Here, we identified novel cofactors of Arabidopsis AGOs, named RICE1 and RICE2. RICE proteins specifically degraded single-strand (ss) RNAs in vitro; but neither miRNAs nor miRNA*s in vivo. RICE1 exhibited a DnaQ-like exonuclease fold and formed a homohexamer with the active sites located at the interfaces between RICE1 subunits. Notably, ectopic expression of catalytically-inactive RICE1 not only significantly reduced miRNA levels; but also increased 5' cleavage RISC fragments with extended uridine tails. We conclude that RICEs act to degrade uridylated 5' products of AGO cleavage to maintain functional RISC. Our study also suggests a possible link between decay of cleaved target mRNAs and miRNA stability in RISC.

RISC assembly: Coordination between small RNAs and Argonaute proteins.

PubMed

Kobayashi, Hotaka; Tomari, Yukihide

2016-01-01

Non-coding RNAs generally form ribonucleoprotein (RNP) complexes with their partner proteins to exert their functions. Small RNAs, including microRNAs, small interfering RNAs, and PIWI-interacting RNAs, assemble with Argonaute (Ago) family proteins into the effector complex called RNA-induced silencing complex (RISC), which mediates sequence-specific target gene silencing. RISC assembly is not a simple binding between a small RNA and Ago; rather, it follows an ordered multi-step pathway that requires specific accessory factors. Some steps of RISC assembly and RISC-mediated gene silencing are dependent on or facilitated by particular intracellular platforms, suggesting their spatial regulation. In this review, we summarize the currently known mechanisms for RISC assembly of each small RNA class and propose a revised model for the role of the chaperone machinery in the duplex-initiated RISC assembly pathway. This article is part of a Special Issue entitled: Clues to long noncoding RNA taxonomy1, edited by Dr. Tetsuro Hirose and Dr. Shinichi Nakagawa. Copyright © 2015 Elsevier B.V. All rights reserved.

Slicing-independent RISC activation requires the argonaute PAZ domain.

PubMed

Gu, Shuo; Jin, Lan; Huang, Yong; Zhang, Feijie; Kay, Mark A

2012-08-21

Small RNAs regulate genetic networks through a ribonucleoprotein complex called the RNA-induced silencing complex (RISC), which, in mammals, contains at its center one of four Argonaute proteins (Ago1-Ago4). A key regulatory event in the RNA interference (RNAi) and microRNA (miRNA) pathways is Ago loading, wherein double-stranded small-RNA duplexes are incorporated into RISC (pre-RISC) and then become single-stranded (mature RISC), a process that is not well understood. The Agos contain an evolutionarily conserved PAZ (Piwi/Argonaute/Zwille) domain whose primary function is to bind the 3' end of small RNAs. We created multiple PAZ-domain-disrupted mutant Ago proteins and studied their biochemical properties and biological functionality in cells. We found that the PAZ domain is dispensable for Ago loading of slicing-competent RISC. In contrast, in the absence of slicer activity or slicer-substrate duplex RNAs, PAZ-disrupted Agos bound duplex small interfering RNAs, but were unable to unwind or eject the passenger strand and form functional RISC complexes. We have discovered that the highly conserved PAZ domain plays an important role in RISC activation, providing new mechanistic insights into how miRNAs regulate genes, as well as new insights for future design of miRNA- and RNAi-based therapeutics. Copyright © 2012 Elsevier Ltd. All rights reserved.

3' fragment of miR173-programmed RISC-cleaved RNA is protected from degradation in a complex with RISC and SGS3.

PubMed

Yoshikawa, Manabu; Iki, Taichiro; Tsutsui, Yasuhiro; Miyashita, Kyoko; Poethig, R Scott; Habu, Yoshiki; Ishikawa, Masayuki

2013-03-05

trans-acting small interfering RNAs (tasiRNAs) are plant-specific endogenous siRNAs produced via a unique pathway whose first step is the microRNA (miRNA)-programmed RNA-induced silencing complex (RISC)-mediated cleavage of tasiRNA gene (TAS) transcripts. One of the products is subsequently transformed into tasiRNAs by a pathway that requires several factors including SUPPRESSOR OF GENE SILENCING3 (SGS3) and RNA-DEPENDENT RNA POLYMERASE6. Here, using in vitro assembled ARGONAUTE (AGO)1-RISCs, we show that SGS3 is recruited onto RISCs only when they bind target RNA. Following cleavage by miRNA173 (miR173)-programmed RISC, SGS3 was found in complexes containing cleaved TAS2 RNA and RISC. The 3' cleavage fragment (the source of tasiRNAs) was protected from degradation in this complex. Depletion of SGS3 did not affect TAS2 RNA cleavage by miR173-programmed RISC, but did affect the stability of the 3' cleavage fragment. When the 3' nucleotide of 22-nt miR173 was deleted or the corresponding nucleotide in TAS2 RNA was mutated, the complex was not observed and the 3' cleavage fragment was degraded. Importantly, these changes in miR173 or TAS2 RNA are known to lead to a loss of tasiRNA production in vivo. These results suggest that (i) SGS3 associates with AGO1-RISC via the double-stranded RNA formed by the 3'-terminal nucleotides of 22-nt miR173 and corresponding target RNA, which probably protrudes from the AGO1-RISC molecular surface, (ii) SGS3 protects the 3' cleavage fragment of TAS2 RNA from degradation, and (iii) the observed SGS3-dependent stabilization of the 3' fragment of TAS2 RNA is key to tasiRNA production.

Single-Molecule Analysis for RISC Assembly and Target Cleavage.

PubMed

Sasaki, Hiroshi M; Tadakuma, Hisashi; Tomari, Yukihide

2018-01-01

RNA-induced silencing complex (RISC) is a small RNA-protein complex that mediates silencing of complementary target RNAs. Biochemistry has been successfully used to characterize the molecular mechanism of RISC assembly and function for nearly two decades. However, further dissection of intermediate states during the reactions has been warranted to fill in the gaps in our understanding of RNA silencing mechanisms. Single-molecule analysis with total internal reflection fluorescence (TIRF) microscopy is a powerful imaging-based approach to interrogate complex formation and dynamics at the individual molecule level with high sensitivity. Combining this technique with our recently established in vitro reconstitution system of fly Ago2-RISC, we have developed a single-molecule observation system for RISC assembly. In this chapter, we summarize the detailed protocol for single-molecule analysis of chaperone-assisted assembly of fly Ago2-RISC as well as its target cleavage reaction.

In vitro reconstitution of chaperone-mediated human RISC assembly.

PubMed

Naruse, Ken; Matsuura-Suzuki, Eriko; Watanabe, Mariko; Iwasaki, Shintaro; Tomari, Yukihide

2018-01-01

To silence target mRNAs, small RNAs and Argonaute (Ago) proteins need to be assembled into RNA-induced silencing complexes (RISCs). Although the assembly of Drosophila melanogaster RISC was recently reconstituted by Ago2, the Dicer-2/R2D2 heterodimer, and five chaperone proteins, the absence of a reconstitution system for mammalian RISC assembly has posed analytical challenges. Here we describe reconstitution of human RISC assembly using Ago2 and five recombinant chaperone proteins: Hsp90β, Hsc70, Hop, Dnaja2, and p23. Our data show that ATP hydrolysis by both Hsp90β and Hsc70 is required for RISC assembly of small RNA duplexes but not for that of single-stranded RNAs. The reconstitution system lays the groundwork for further studies of small RNA-mediated gene silencing in mammals. © 2018 Naruse et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

RNA helicase A is not required for RISC activity.

PubMed

Liang, Xue-Hai; Crooke, Stanley T

2013-10-01

It has been shown that siRNAs can compete with each other or with endogenous miRNAs for RISC components. This competition may complicate the interpretations of phenotypes observed through siRNA-mediated knockdown of genes, especially those genes implicated in the RISC pathway. In this study, we re-examined the function of RNA helicase A (RHA), which has been previously proposed to function in RISC loading based on siRNA-mediated knockdown studies. Here we show that reduced RISC activity or loading of siRNAs was observed only in cells depleted of RHA using siRNA, but not using RNaseH-dependent antisense oligonucleotides (ASOs), suggesting that the impaired RISC function stems from the competition between pre-existing and newly transfected siRNAs, but not from reduction of the RHA protein. This view is further supported by the findings that cells depleted of a control protein, NCL1, using siRNA, but not ASO, exhibited similar defects on the loading and activity of a subsequently transfected siRNA. Transfection of RHA or NCL1 siRNAs, but not ASOs, reduced the levels of endogenous miRNAs, suggesting a competition mechanism. As a positive control, we showed that reduction of MOV10 by either siRNA or ASO decreased siRNA activity, confirming its role in RISC function. Together, our results indicate that RHA is not required for RISC activity or loading, and suggest that proper controls are required when using siRNAs to functionalize genes to avoid competition effects. © 2013. Published by Elsevier B.V. All rights reserved.

3′ fragment of miR173-programmed RISC-cleaved RNA is protected from degradation in a complex with RISC and SGS3

PubMed Central

Yoshikawa, Manabu; Iki, Taichiro; Tsutsui, Yasuhiro; Miyashita, Kyoko; Poethig, R. Scott; Habu, Yoshiki; Ishikawa, Masayuki

2013-01-01

trans-acting small interfering RNAs (tasiRNAs) are plant-specific endogenous siRNAs produced via a unique pathway whose first step is the microRNA (miRNA)-programmed RNA-induced silencing complex (RISC)–mediated cleavage of tasiRNA gene (TAS) transcripts. One of the products is subsequently transformed into tasiRNAs by a pathway that requires several factors including SUPPRESSOR OF GENE SILENCING3 (SGS3) and RNA-DEPENDENT RNA POLYMERASE6. Here, using in vitro assembled ARGONAUTE (AGO)1–RISCs, we show that SGS3 is recruited onto RISCs only when they bind target RNA. Following cleavage by miRNA173 (miR173)-programmed RISC, SGS3 was found in complexes containing cleaved TAS2 RNA and RISC. The 3′ cleavage fragment (the source of tasiRNAs) was protected from degradation in this complex. Depletion of SGS3 did not affect TAS2 RNA cleavage by miR173-programmed RISC, but did affect the stability of the 3′ cleavage fragment. When the 3′ nucleotide of 22-nt miR173 was deleted or the corresponding nucleotide in TAS2 RNA was mutated, the complex was not observed and the 3′ cleavage fragment was degraded. Importantly, these changes in miR173 or TAS2 RNA are known to lead to a loss of tasiRNA production in vivo. These results suggest that (i) SGS3 associates with AGO1–RISC via the double-stranded RNA formed by the 3′-terminal nucleotides of 22-nt miR173 and corresponding target RNA, which probably protrudes from the AGO1–RISC molecular surface, (ii) SGS3 protects the 3′ cleavage fragment of TAS2 RNA from degradation, and (iii) the observed SGS3-dependent stabilization of the 3′ fragment of TAS2 RNA is key to tasiRNA production. PMID:23417299

RISC-interacting clearing 3’- 5’ exoribonucleases (RICEs) degrade uridylated cleavage fragments to maintain functional RISC in Arabidopsis thaliana

PubMed Central

Zhang, Zhonghui; Hu, Fuqu; Sung, Min Woo; Shu, Chang; Castillo-González, Claudia; Koiwa, Hisashi; Tang, Guiliang; Dickman, Martin; Li, Pingwei; Zhang, Xiuren

2017-01-01

RNA-induced silencing complex (RISC) is composed of miRNAs and AGO proteins. AGOs use miRNAs as guides to slice target mRNAs to produce truncated 5' and 3' RNA fragments. The 5' cleaved RNA fragments are marked with uridylation for degradation. Here, we identified novel cofactors of Arabidopsis AGOs, named RICE1 and RICE2. RICE proteins specifically degraded single-strand (ss) RNAs in vitro; but neither miRNAs nor miRNA*s in vivo. RICE1 exhibited a DnaQ-like exonuclease fold and formed a homohexamer with the active sites located at the interfaces between RICE1 subunits. Notably, ectopic expression of catalytically-inactive RICE1 not only significantly reduced miRNA levels; but also increased 5' cleavage RISC fragments with extended uridine tails. We conclude that RICEs act to degrade uridylated 5’ products of AGO cleavage to maintain functional RISC. Our study also suggests a possible link between decay of cleaved target mRNAs and miRNA stability in RISC. DOI: http://dx.doi.org/10.7554/eLife.24466.001 PMID:28463111

An antiviral RISC isolated from Tobacco rattle virus-infected plants

PubMed Central

Ciomperlik, Jessica J.; Omarov, Rustem T.; Scholthof, Herman B.

2011-01-01

The RNAi model predicts that during antiviral defense a RNA-induced silencing complex (RISC) is programmed with viral short-interfering RNAs (siRNAs) to target the cognate viral RNA for degradation. We show that infection of Nicotiana benthamiana with Tobacco rattle virus (TRV) activates an antiviral nuclease that specifically cleaves TRV RNA in vitro. In agreement with known RISC properties, the nuclease activity was inhibited by NaCl and EDTA and stimulated by divalent metal cations; a novel property was its preferential targeting of elongated RNA molecules. Intriguingly, the specificity of the TRV RISC could be re-programmed by exogenous addition of RNA (containing siRNAs) from plants infected with an unrelated virus, resulting in a newly acquired ability of RISC to target this heterologous genome in vitro. Evidently the virus-specific nuclease complex from N. benthamiana represents a genuine RISC that functions as a readily employable and reprogrammable antiviral defense unit. PMID:21272908

An antiviral RISC isolated from Tobacco rattle virus-infected plants.

PubMed

Ciomperlik, Jessica J; Omarov, Rustem T; Scholthof, Herman B

2011-03-30

The RNAi model predicts that during antiviral defense a RNA-induced silencing complex (RISC) is programmed with viral short-interfering RNAs (siRNAs) to target the cognate viral RNA for degradation. We show that infection of Nicotiana benthamiana with Tobacco rattle virus (TRV) activates an antiviral nuclease that specifically cleaves TRV RNA in vitro. In agreement with known RISC properties, the nuclease activity was inhibited by NaCl and EDTA and stimulated by divalent metal cations; a novel property was its preferential targeting of elongated RNA molecules. Intriguingly, the specificity of the TRV RISC could be reprogrammed by exogenous addition of RNA (containing siRNAs) from plants infected with an unrelated virus, resulting in a newly acquired ability of RISC to target this heterologous genome in vitro. Evidently the virus-specific nuclease complex from N. benthamiana represents a genuine RISC that functions as a readily employable and reprogrammable antiviral defense unit. Copyright © 2011 Elsevier Inc. All rights reserved.

A Real-Time Image Acquisition And Processing System For A RISC-Based Microcomputer

NASA Astrophysics Data System (ADS)

Luckman, Adrian J.; Allinson, Nigel M.

1989-03-01

A low cost image acquisition and processing system has been developed for the Acorn Archimedes microcomputer. Using a Reduced Instruction Set Computer (RISC) architecture, the ARM (Acorn Risc Machine) processor provides instruction speeds suitable for image processing applications. The associated improvement in data transfer rate has allowed real-time video image acquisition without the need for frame-store memory external to the microcomputer. The system is comprised of real-time video digitising hardware which interfaces directly to the Archimedes memory, and software to provide an integrated image acquisition and processing environment. The hardware can digitise a video signal at up to 640 samples per video line with programmable parameters such as sampling rate and gain. Software support includes a work environment for image capture and processing with pixel, neighbourhood and global operators. A friendly user interface is provided with the help of the Archimedes Operating System WIMP (Windows, Icons, Mouse and Pointer) Manager. Windows provide a convenient way of handling images on the screen and program control is directed mostly by pop-up menus.

AGO/RISC-mediated antiviral RNA silencing in a plant in vitro system.

PubMed

Schuck, Jana; Gursinsky, Torsten; Pantaleo, Vitantonio; Burgyán, Jozsef; Behrens, Sven-Erik

2013-05-01

AGO/RISC-mediated antiviral RNA silencing, an important component of the plant's immune response against RNA virus infections, was recapitulated in vitro. Cytoplasmic extracts of tobacco protoplasts were applied that supported Tombusvirus RNA replication, as well as the formation of RNA-induced silencing complexes (RISC) that could be functionally reconstituted with various plant ARGONAUTE (AGO) proteins. For example, when RISC containing AGO1, 2, 3 or 5 were programmed with exogenous siRNAs that specifically targeted the viral RNA, endonucleolytic cleavages occurred and viral replication was inhibited. Antiviral RNA silencing was disabled by the viral silencing suppressor p19 when this was present early during RISC formation. Notably, with replicating viral RNA, only (+)RNA molecules were accessible to RISC, whereas (-)RNA replication intermediates were not. The vulnerability of viral RNAs to RISC activity also depended on the RNA structure of the target sequence. This was most evident when we characterized viral siRNAs (vsiRNAs) that were particularly effective in silencing with AGO1- or AGO2/RISC. These vsiRNAs targeted similar sites, suggesting that accessible parts of the viral (+)RNA may be collectively attacked by different AGO/RISC. The in vitro system was, hence, established as a valuable tool to define and characterize individual molecular determinants of antiviral RNA silencing.

Structural determinants of miRNAs for RISC loading and slicer-independent unwinding.

PubMed

Kawamata, Tomoko; Seitz, Hervé; Tomari, Yukihide

2009-09-01

MicroRNAs (miRNAs) regulate expression of their target mRNAs through the RNA-induced silencing complex (RISC), which contains an Argonaute (Ago) family protein as a core component. In Drosophila melanogaster, miRNAs are generally sorted into Ago1-containing RISC (Ago1-RISC). We established a native gel system that can biochemically dissect the Ago1-RISC assembly pathway. We found that miRNA-miRNA* duplexes are loaded into Ago1 as double-stranded RNAs in an ATP-dependent fashion. In contrast, unexpectedly, unwinding of miRNA-miRNA* duplexes is a passive process that does not require ATP or slicer activity of Ago1. Central mismatches direct miRNA-miRNA* duplexes into pre-Ago1-RISC, whereas mismatches in the seed or guide strand positions 12-15 promote conversion of pre-Ago1-RISC into mature Ago1-RISC. Our findings show that unwinding of miRNAs is a precise mirror-image process of target recognition, and both processes reflect the unique geometry of RNAs in Ago proteins.

Cyclophilin 40 facilitates HSP90-mediated RISC assembly in plants.

PubMed

Iki, Taichiro; Yoshikawa, Manabu; Meshi, Tetsuo; Ishikawa, Masayuki

2012-01-18

Posttranscriptional gene silencing is mediated by RNA-induced silencing complexes (RISCs) that contain AGO proteins and single-stranded small RNAs. The assembly of plant AGO1-containing RISCs depends on the molecular chaperone HSP90. Here, we demonstrate that cyclophilin 40 (CYP40), protein phosphatase 5 (PP5), and several other proteins with the tetratricopeptide repeat (TPR) domain associates with AGO1 in an HSP90-dependent manner in extracts of evacuolated tobacco protoplasts (BYL). Intriguingly, CYP40, but not the other TPR proteins, could form a complex with small RNA duplex-bound AGO1. Moreover, CYP40 that was synthesized by in-vitro translation using BYL uniquely facilitated binding of small RNA duplexes to AGO1, and as a result, increased the amount of mature RISCs that could cleave target RNAs. CYP40 was not contained in mature RISCs, indicating that the association is transient. Addition of PP5 or cyclophilin-binding drug cyclosporine A prevented the association of endogenous CYP40 with HSP90-AGO1 complex and inhibited RISC assembly. These results suggest that a complex of AGO1, HSP90, CYP40, and a small RNA duplex is a key intermediate of RISC assembly in plants.

Structure of C3PO and Mechanism of Human RISC Activation

PubMed Central

Ye, Xuecheng; Huang, Nian; Liu, Ying; Paroo, Zain; Huerta, Carlos; Li, Peng; Chen, She; Liu, Qinghua; Zhang, Hong

2011-01-01

Assembly of the RNA-induced silencing complex (RISC) consists of loading duplex (guide/passenger) siRNA onto and removing the passenger strand from Argonaute (Ago2). Ago2 contributes critically to RISC activation by nicking the passenger strand. Here, we reconstituted duplex siRNA-initiated RISC activity using recombinant human (h)Ago2 and C3PO, indicating a critical role for C3PO in hAgo2-RISC activation. Consistently, genetic depletion of C3PO compromised RNA silencing in mammalian cells. We determined the crystal structure of hC3PO, which reveals an asymmetric octamer barrel consisting of six Translin and two TRAX subunits. This asymmetric assembly is critical for the function of C3PO as a novel endonuclease that cleaves RNA at the interior surface. The current work supports a Dicer-independent mechanism for human RISC activation: 1) Ago2 directly binds duplex siRNA and nicks the passenger strand; 2) C3PO activates RISC by degrading Ago2-nicked passenger strand. PMID:21552258

AGO/RISC-mediated antiviral RNA silencing in a plant in vitro system

PubMed Central

Schuck, Jana; Gursinsky, Torsten; Pantaleo, Vitantonio; Burgyán, Jozsef; Behrens, Sven-Erik

2013-01-01

AGO/RISC-mediated antiviral RNA silencing, an important component of the plant’s immune response against RNA virus infections, was recapitulated in vitro. Cytoplasmic extracts of tobacco protoplasts were applied that supported Tombusvirus RNA replication, as well as the formation of RNA-induced silencing complexes (RISC) that could be functionally reconstituted with various plant ARGONAUTE (AGO) proteins. For example, when RISC containing AGO1, 2, 3 or 5 were programmed with exogenous siRNAs that specifically targeted the viral RNA, endonucleolytic cleavages occurred and viral replication was inhibited. Antiviral RNA silencing was disabled by the viral silencing suppressor p19 when this was present early during RISC formation. Notably, with replicating viral RNA, only (+)RNA molecules were accessible to RISC, whereas (−)RNA replication intermediates were not. The vulnerability of viral RNAs to RISC activity also depended on the RNA structure of the target sequence. This was most evident when we characterized viral siRNAs (vsiRNAs) that were particularly effective in silencing with AGO1- or AGO2/RISC. These vsiRNAs targeted similar sites, suggesting that accessible parts of the viral (+)RNA may be collectively attacked by different AGO/RISC. The in vitro system was, hence, established as a valuable tool to define and characterize individual molecular determinants of antiviral RNA silencing. PMID:23535144

RISC RNA sequencing for context-specific identification of in vivo miR targets

PubMed Central

Matkovich, Scot J; Van Booven, Derek J; Eschenbacher, William H; Dorn, Gerald W

2010-01-01

Rationale MicroRNAs (miRs) are expanding our understanding of cardiac disease and have the potential to transform cardiovascular therapeutics. One miR can target hundreds of individual mRNAs, but existing methodologies are not sufficient to accurately and comprehensively identify these mRNA targets in vivo. Objective To develop methods permitting identification of in vivo miR targets in an unbiased manner, using massively parallel sequencing of mouse cardiac transcriptomes in combination with sequencing of mRNA associated with mouse cardiac RNA-induced silencing complexes (RISCs). Methods and Results We optimized techniques for expression profiling small amounts of RNA without introducing amplification bias, and applied this to anti-Argonaute 2 immunoprecipitated RISCs (RISC-Seq) from mouse hearts. By comparing RNA-sequencing results of cardiac RISC and transcriptome from the same individual hearts, we defined 1,645 mRNAs consistently targeted to mouse cardiac RISCs. We employed this approach in hearts overexpressing miRs from Myh6 promoter-driven precursors (programmed RISC-Seq) to identify 209 in vivo targets of miR-133a and 81 in vivo targets of miR-499. Consistent with the fact that miR-133a and miR-499 have widely differing ‘seed’ sequences and belong to different miR families, only 6 targets were common to miR-133a- and miR-499-programmed hearts. Conclusions RISC-sequencing is a highly sensitive method for general RISC profiling and individual miR target identification in biological context, and is applicable to any tissue and any disease state. Summary MicroRNAs (miRs) are key regulators of mRNA translation in health and disease. While bioinformatic predictions suggest that a single miR may target hundreds of mRNAs, the number of experimentally verified targets of miRs is low. To enable comprehensive, unbiased examination of miR targets, we have performed deep RNA sequencing of cardiac transcriptomes in parallel with cardiac RNA-induced silencing complex

Using Machine Learning to Advance Personality Assessment and Theory.

PubMed

Bleidorn, Wiebke; Hopwood, Christopher James

2018-05-01

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.

The Radiation, Interplanetary Shocks, and Coronal Sources (RISCS) Toolset

NASA Technical Reports Server (NTRS)

Zank, G. P.; Spann, James F.

2014-01-01

The goal of this project is to serve the needs of space system designers and operators by developing an interplanetary radiation environment model within 10 AU:Radiation, Interplanetary Shocks, and Coronal Sources (RISCS) toolset: (1) The RISCS toolset will provide specific reference environments for space system designers and nowcasting and forecasting capabilities for space system operators; (2) We envision the RISCS toolset providing the spatial and temporal radiation environment external to the Earth's (and other planets') magnetosphere, as well as possessing the modularity to integrate separate applications (apps) that can map to specific magnetosphere locations and/or perform the subsequent radiation transport and dosimetry for a specific target.

Multilayer checkpoints for microRNA authenticity during RISC assembly.

PubMed

Kawamata, Tomoko; Yoda, Mayuko; Tomari, Yukihide

2011-09-01

MicroRNAs (miRNAs) function through the RNA-induced silencing complex (RISC), which contains an Argonaute (Ago) protein at the core. RISC assembly follows a two-step pathway: miRNA/miRNA* duplex loading into Ago, and separation of the two strands within Ago. Here we show that the 5' phosphate of the miRNA strand is essential for duplex loading into Ago, whereas the preferred 5' nucleotide of the miRNA strand and the base-pairing status in the seed region and the middle of the 3' region function as additive anchors to Ago. Consequently, the miRNA authenticity is inspected at multiple steps during RISC assembly.

Gallium-arsenide process evaluation based on a RISC microprocessor example

NASA Astrophysics Data System (ADS)

Brown, Richard B.; Upton, Michael; Chandna, Ajay; Huff, Thomas R.; Mudge, Trevor N.; Oettel, Richard E.

1993-10-01

This work evaluates the features of a gallium-arsenide E/D MESFET process in which a 32-b RISC microprocessor was implemented. The design methodology and architecture of this prototype CPU are described. The performance sensitivity of the microprocessor and other large circuit blocks to different process parameters is analyzed, and recommendations for future process features, circuit approaches, and layout styles are made. These recommendations are reflected in the design of a second microprocessor using a more advanced process that achieves much higher density and performance.

A Comparative Study : Microprogrammed Vs Risc Architectures For Symbolic Processing

NASA Astrophysics Data System (ADS)

Heudin, J. C.; Metivier, C.; Demigny, D.; Maurin, T.; Zavidovique, B.; Devos, F.

1987-05-01

It is oftenclaimed that conventional computers are not well suited for human-like tasks : Vision (Image Processing), Intelligence (Symbolic Processing) ... In the particular case of Artificial Intelligence, dynamic type-checking is one example of basic task that must be improved. The solution implemented in most Lisp work-stations consists in a microprogrammed architecture with a tagged memory. Another way to gain efficiency is to design a well suited instruction set for symbolic processing, which reduces the semantic gap between the high level language and the machine code. In this framework, the RISC concept provides a convenient approach to study new architectures for symbolic processing. This paper compares both approaches and describes our projectof designing a compact symbolic processor for Artificial Intelligence applications.

Multilayer checkpoints for microRNA authenticity during RISC assembly

PubMed Central

Kawamata, Tomoko; Yoda, Mayuko; Tomari, Yukihide

2011-01-01

MicroRNAs (miRNAs) function through the RNA-induced silencing complex (RISC), which contains an Argonaute (Ago) protein at the core. RISC assembly follows a two-step pathway: miRNA/miRNA* duplex loading into Ago, and separation of the two strands within Ago. Here we show that the 5′ phosphate of the miRNA strand is essential for duplex loading into Ago, whereas the preferred 5′ nucleotide of the miRNA strand and the base-pairing status in the seed region and the middle of the 3′ region function as additive anchors to Ago. Consequently, the miRNA authenticity is inspected at multiple steps during RISC assembly. PMID:21738221

An in vitro reprogrammable antiviral RISC with size-preferential ribonuclease activity.

PubMed

Omarov, Rustem T; Ciomperlik, Jessica; Scholthof, Herman B

2016-03-01

Infection of Nicotiana benthamiana plants with Tomato bushy stunt virus (TBSV) mutants compromised for silencing suppression induces formation of an antiviral RISC (vRISC) that can be isolated using chromatography procedures. The isolated vRISC sequence-specifically degrades TBSV RNA in vitro, its activity can be down-regulated by removing siRNAs, and re-stimulated by exogenous supply of siRNAs. vRISC is most effective at hydrolyzing the ~4.8kb genomic RNA, but less so for a ~2.2kb TBSV subgenomic mRNA (sgRNA1), while the 3' co-terminal sgRNA2 of ~0.9kb appears insensitive to vRISC cleavage. Moreover, experiments with in vitro generated 5' co-terminal viral transcripts show that RNAs of ~2.7kb are efficiently cleaved while those of ~1.1kb or shorter are unaffected. The isolated antiviral ribonuclease complex fails to degrade ~0.4kb defective interfering RNAs (DIs) in vitro, agreeing with findings that in plants DIs are not targeted by silencing. Copyright © 2016. Published by Elsevier Inc.

INS3D - NUMERICAL SOLUTION OF THE INCOMPRESSIBLE NAVIER-STOKES EQUATIONS IN THREE-DIMENSIONAL GENERALIZED CURVILINEAR COORDINATES (DEC RISC ULTRIX VERSION)

NASA Technical Reports Server (NTRS)

Biyabani, S. R.

1994-01-01

-field boundaries. Three machine versions of INS3D are available. INS3D for the CRAY is written in CRAY FORTRAN for execution on a CRAY X-MP under COS, INS3D for the IBM is written in FORTRAN 77 for execution on an IBM 3090 under the VM or MVS operating system, and INS3D for DEC RISC-based systems is written in RISC FORTRAN for execution on a DEC workstation running RISC ULTRIX 3.1 or later. The CRAY version has a central memory requirement of 730279 words. The central memory requirement for the IBM is 150Mb. The memory requirement for the DEC RISC ULTRIX version is 3Mb of main memory. INS3D was developed in 1987. The port to the IBM was done in 1990. The port to the DECstation 3100 was done in 1991. CRAY is a registered trademark of Cray Research Inc. IBM is a registered trademark of International Business Machines. DEC, DECstation, and ULTRIX are trademarks of the Digital Equipment Corporation.

Differential RISC association of endogenous human microRNAs predicts their inhibitory potential

PubMed Central

Flores, Omar; Kennedy, Edward M.; Skalsky, Rebecca L.; Cullen, Bryan R.

2014-01-01

It has previously been assumed that the generally high stability of microRNAs (miRNAs) reflects their tight association with Argonaute (Ago) proteins, essential components of the RNA-induced silencing complex (RISC). However, recent data have suggested that the majority of mature miRNAs are not, in fact, Ago associated. Here, we demonstrate that endogenous human miRNAs vary widely, by >100-fold, in their level of RISC association and show that the level of Ago binding is a better indicator of inhibitory potential than is the total level of miRNA expression. While miRNAs of closely similar sequence showed comparable levels of RISC association in the same cell line, these varied between different cell types. Moreover, the level of RISC association could be modulated by overexpression of complementary target mRNAs. Together, these data indicate that the level of RISC association of a given endogenous miRNA is regulated by the available RNA targetome and predicts miRNA function. PMID:24464996

Differential RISC association of endogenous human microRNAs predicts their inhibitory potential.

PubMed

Flores, Omar; Kennedy, Edward M; Skalsky, Rebecca L; Cullen, Bryan R

2014-04-01

It has previously been assumed that the generally high stability of microRNAs (miRNAs) reflects their tight association with Argonaute (Ago) proteins, essential components of the RNA-induced silencing complex (RISC). However, recent data have suggested that the majority of mature miRNAs are not, in fact, Ago associated. Here, we demonstrate that endogenous human miRNAs vary widely, by >100-fold, in their level of RISC association and show that the level of Ago binding is a better indicator of inhibitory potential than is the total level of miRNA expression. While miRNAs of closely similar sequence showed comparable levels of RISC association in the same cell line, these varied between different cell types. Moreover, the level of RISC association could be modulated by overexpression of complementary target mRNAs. Together, these data indicate that the level of RISC association of a given endogenous miRNA is regulated by the available RNA targetome and predicts miRNA function.

RISC RNA sequencing for context-specific identification of in vivo microRNA targets.

PubMed

Matkovich, Scot J; Van Booven, Derek J; Eschenbacher, William H; Dorn, Gerald W

2011-01-07

MicroRNAs (miRs) are expanding our understanding of cardiac disease and have the potential to transform cardiovascular therapeutics. One miR can target hundreds of individual mRNAs, but existing methodologies are not sufficient to accurately and comprehensively identify these mRNA targets in vivo. To develop methods permitting identification of in vivo miR targets in an unbiased manner, using massively parallel sequencing of mouse cardiac transcriptomes in combination with sequencing of mRNA associated with mouse cardiac RNA-induced silencing complexes (RISCs). We optimized techniques for expression profiling small amounts of RNA without introducing amplification bias and applied this to anti-Argonaute 2 immunoprecipitated RISCs (RISC-Seq) from mouse hearts. By comparing RNA-sequencing results of cardiac RISC and transcriptome from the same individual hearts, we defined 1645 mRNAs consistently targeted to mouse cardiac RISCs. We used this approach in hearts overexpressing miRs from Myh6 promoter-driven precursors (programmed RISC-Seq) to identify 209 in vivo targets of miR-133a and 81 in vivo targets of miR-499. Consistent with the fact that miR-133a and miR-499 have widely differing "seed" sequences and belong to different miR families, only 6 targets were common to miR-133a- and miR-499-programmed hearts. RISC-sequencing is a highly sensitive method for general RISC profiling and individual miR target identification in biological context and is applicable to any tissue and any disease state.

Autoantigen La promotes efficient RNAi, antiviral response, and transposon silencing by facilitating multiple-turnover RISC catalysis

PubMed Central

Liu, Ying; Tan, Huiling; Tian, Hui; Liang, Chunyang; Chen, She; Liu, Qinghua

2011-01-01

SUMMARY The effector of RNA interference (RNAi) is the RNA-induced silencing complex (RISC). C3PO promotes the activation of RISC by degrading Argonaute2 (Ago2)-nicked passenger strand of duplex siRNA. Active RISC is a multiple-turnover enzyme that uses the guide strand of siRNA to direct Ago2-mediated sequence-specific cleavage of complementary mRNA. How this effector step of RNAi is regulated is currently unknown. Here, we used human Ago2 minimal RISC system to purify Sjögren’s syndrome antigen B (SSB)/autoantigen La as an activator of the RISC-mediated mRNA cleavage activity. Our reconstitution studies showed that La could promote multiple-turnover RISC catalysis by facilitating the release of cleaved mRNA from RISC. Moreover, we demonstrated that La was required for efficient RNAi, antiviral defense, and transposon silencing in vivo. Taken together, the findings of C3PO and La reveal a general concept that regulatory factors are required to remove Ago2-cleaved products to assemble or restore active RISC. PMID:22055194

C3PO, an endoribonuclease that promotes RNAi by facilitating RISC activation.

PubMed

Liu, Ying; Ye, Xuecheng; Jiang, Feng; Liang, Chunyang; Chen, Dongmei; Peng, Junmin; Kinch, Lisa N; Grishin, Nick V; Liu, Qinghua

2009-08-07

The catalytic engine of RNA interference (RNAi) is the RNA-induced silencing complex (RISC), wherein the endoribonuclease Argonaute and single-stranded small interfering RNA (siRNA) direct target mRNA cleavage. We reconstituted long double-stranded RNA- and duplex siRNA-initiated RISC activities with the use of recombinant Drosophila Dicer-2, R2D2, and Ago2 proteins. We used this core reconstitution system to purify an RNAi regulator that we term C3PO (component 3 promoter of RISC), a complex of Translin and Trax. C3PO is a Mg2+-dependent endoribonuclease that promotes RISC activation by removing siRNA passenger strand cleavage products. These studies establish an in vitro RNAi reconstitution system and identify C3PO as a key activator of the core RNAi machinery.

Autoantigen La promotes efficient RNAi, antiviral response, and transposon silencing by facilitating multiple-turnover RISC catalysis.

PubMed

Liu, Ying; Tan, Huiling; Tian, Hui; Liang, Chunyang; Chen, She; Liu, Qinghua

2011-11-04

The effector of RNA interference (RNAi) is the RNA-induced silencing complex (RISC). C3PO promotes the activation of RISC by degrading the Argonaute2 (Ago2)-nicked passenger strand of duplex siRNA. Active RISC is a multiple-turnover enzyme that uses the guide strand of siRNA to direct the Ago2-mediated sequence-specific cleavage of complementary mRNA. How this effector step of RNAi is regulated is currently unknown. Here, we used the human Ago2 minimal RISC system to purify Sjögren's syndrome antigen B (SSB)/autoantigen La as an activator of the RISC-mediated mRNA cleavage activity. Our reconstitution studies showed that La could promote multiple-turnover RISC catalysis by facilitating the release of cleaved mRNA from RISC. Moreover, we demonstrated that La was required for efficient RNAi, antiviral defense, and transposon silencing in vivo. Taken together, the findings of C3PO and La reveal a general concept that regulatory factors are required to remove Ago2-cleaved products to assemble or restore active RISC. Copyright © 2011 Elsevier Inc. All rights reserved.

Precision machining of advanced materials with waterjets

NASA Astrophysics Data System (ADS)

Liu, H. T.

2017-01-01

Recent advances in abrasive waterjet technology have elevated to the state that it often competes on equal footing with lasers and EDM for precision machining. Under the support of a National Science Foundation SBIR Phase II grant, OMAX has developed and commercialized micro abrasive water technology that is incorporated into a MicroMAX® JetMa- chining® Center. Waterjet technology, combined both abrasive waterjet and micro abrasive waterjet technology, is capable of machining most materials from macro to micro scales for a wide range of part size and thickness. Waterjet technology has technological and manufacturing merits that cannot be matched by most existing tools. As a cold cutting tool that creates no heat-affected zone, for example, waterjet cuts much faster than wire EDM and laser when measures to minimize a heat-affected zone are taken into account. In addition, waterjet is material independent; it cuts materials that cannot be cut or are difficult to cut otherwise. The versatility of waterjet has also demonstrated machining simulated nanomaterials with large gradients of material properties from metal, nonmetal, to anything in between. This paper presents waterjet-machined samples made of a wide range of advanced materials from macro to micro scales.

A scoring system for ascertainment of incident stroke; the Risk Index Score (RISc).

PubMed

Kass-Hout, T A; Moyé, L A; Smith, M A; Morgenstern, L B

2006-01-01

The main objective of this study was to develop and validate a computer-based statistical algorithm that could be translated into a simple scoring system in order to ascertain incident stroke cases using hospital admission medical records data. The Risk Index Score (RISc) algorithm was developed using data collected prospectively by the Brain Attack Surveillance in Corpus Christi (BASIC) project, 2000. The validity of RISc was evaluated by estimating the concordance of scoring system stroke ascertainment to stroke ascertainment by physician and/or abstractor review of hospital admission records. RISc was developed on 1718 randomly selected patients (training set) and then statistically validated on an independent sample of 858 patients (validation set). A multivariable logistic model was used to develop RISc and subsequently evaluated by goodness-of-fit and receiver operating characteristic (ROC) analyses. The higher the value of RISc, the higher the patient's risk of potential stroke. The study showed RISc was well calibrated and discriminated those who had potential stroke from those that did not on initial screening. In this study we developed and validated a rapid, easy, efficient, and accurate method to ascertain incident stroke cases from routine hospital admission records for epidemiologic investigations. Validation of this scoring system was achieved statistically; however, clinical validation in a community hospital setting is warranted.

Argonaute pull-down and RISC analysis using 2'-O-methylated oligonucleotides affinity matrices.

PubMed

Jannot, Guillaume; Vasquez-Rifo, Alejandro; Simard, Martin J

2011-01-01

During the last decade, several novel small non-coding RNA pathways have been unveiled, which reach out to many biological processes. Common to all these pathways is the binding of a small RNA molecule to a protein member of the Argonaute family, which forms a minimal core complex called the RNA-induced silencing complex or RISC. The RISC targets mRNAs in a sequence-specific manner, either to induce mRNA cleavage through the intrinsic activity of the Argonaute protein or to abrogate protein synthesis by a mechanism that is still under investigation. We describe here, in details, a method for the affinity chromatography of the let-7 RISC starting from extracts of the nematode Caenorhabditis elegans. Our method exploits the sequence specificity of the RISC and makes use of biotinylated and 2'-O-methylated oligonucleotides to trap and pull-down small RNAs and their associated proteins. Importantly, this technique may easily be adapted to target other small RNAs expressed in different cell types or model organisms. This method provides a useful strategy to identify the proteins associated with the RISC, and hence gain insight in the functions of small RNAs.

Industrial Inspection with Open Eyes: Advance with Machine Vision Technology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Zheng; Ukida, H.; Niel, Kurt

Machine vision systems have evolved significantly with the technology advances to tackle the challenges from modern manufacturing industry. A wide range of industrial inspection applications for quality control are benefiting from visual information captured by different types of cameras variously configured in a machine vision system. This chapter screens the state of the art in machine vision technologies in the light of hardware, software tools, and major algorithm advances for industrial inspection. The inspection beyond visual spectrum offers a significant complementary to the visual inspection. The combination with multiple technologies makes it possible for the inspection to achieve a bettermore » performance and efficiency in varied applications. The diversity of the applications demonstrates the great potential of machine vision systems for industry.« less

Slicer-independent mechanism drives small-RNA strand separation during human RISC assembly

PubMed Central

Park, June Hyun; Shin, Chanseok

2015-01-01

Small RNA silencing is mediated by the effector RNA-induced silencing complex (RISC) that consists of an Argonaute protein (AGOs 1–4 in humans). A fundamental step during RISC assembly involves the separation of two strands of a small RNA duplex, whereby only the guide strand is retained to form the mature RISC, a process not well understood. Despite the widely accepted view that ‘slicer-dependent unwinding’ via passenger-strand cleavage is a prerequisite for the assembly of a highly complementary siRNA into the AGO2-RISC, here we show by careful re-examination that ‘slicer-independent unwinding’ plays a more significant role in human RISC maturation than previously appreciated, not only for a miRNA duplex, but, unexpectedly, for a highly complementary siRNA as well. We discovered that ‘slicer-dependency’ for the unwinding was affected primarily by certain parameters such as temperature and Mg2+. We further validate these observations in non-slicer AGOs (1, 3 and 4) that can be programmed with siRNAs at the physiological temperature of humans, suggesting that slicer-independent mechanism is likely a common feature of human AGOs. Our results now clearly explain why both miRNA and siRNA are found in all four human AGOs, which is in striking contrast to the strict small-RNA sorting system in Drosophila. PMID:26384428

Anatomy of RISC: how do small RNAs and chaperones activate Argonaute proteins?

PubMed Central

2016-01-01

RNA silencing is a eukaryote‐specific phenomenon in which microRNAs and small interfering RNAs degrade messenger RNAs containing a complementary sequence. To this end, these small RNAs need to be loaded onto an Argonaute protein (AGO protein) to form the effector complex referred to as RNA‐induced silencing complex (RISC). RISC assembly undergoes multiple and sequential steps with the aid of Hsc70/Hsp90 chaperone machinery. The molecular mechanisms for this assembly process remain unclear, despite their significance for the development of gene silencing techniques and RNA interference‐based therapeutics. This review dissects the currently available structures of AGO proteins and proposes models and hypotheses for RISC assembly, covering the conformation of unloaded AGO proteins, the chaperone‐assisted duplex loading, and the slicer‐dependent and slicer‐independent duplex separation. The differences in the properties of RISC between prokaryotes and eukaryotes will also be clarified. WIREs RNA 2016, 7:637–660. doi: 10.1002/wrna.1356 For further resources related to this article, please visit the WIREs website. PMID:27184117

Anatomy of RISC: how do small RNAs and chaperones activate Argonaute proteins?

PubMed

Nakanishi, Kotaro

2016-09-01

RNA silencing is a eukaryote-specific phenomenon in which microRNAs and small interfering RNAs degrade messenger RNAs containing a complementary sequence. To this end, these small RNAs need to be loaded onto an Argonaute protein (AGO protein) to form the effector complex referred to as RNA-induced silencing complex (RISC). RISC assembly undergoes multiple and sequential steps with the aid of Hsc70/Hsp90 chaperone machinery. The molecular mechanisms for this assembly process remain unclear, despite their significance for the development of gene silencing techniques and RNA interference-based therapeutics. This review dissects the currently available structures of AGO proteins and proposes models and hypotheses for RISC assembly, covering the conformation of unloaded AGO proteins, the chaperone-assisted duplex loading, and the slicer-dependent and slicer-independent duplex separation. The differences in the properties of RISC between prokaryotes and eukaryotes will also be clarified. WIREs RNA 2016, 7:637-660. doi: 10.1002/wrna.1356 For further resources related to this article, please visit the WIREs website. © 2016 The Authors. WIREs RNA published by Wiley Periodicals, Inc.

Perspex machine: V. Compilation of C programs

NASA Astrophysics Data System (ADS)

Spanner, Matthew P.; Anderson, James A. D. W.

2006-01-01

The perspex machine arose from the unification of the Turing machine with projective geometry. The original, constructive proof used four special, perspective transformations to implement the Turing machine in projective geometry. These four transformations are now generalised and applied in a compiler, implemented in Pop11, that converts a subset of the C programming language into perspexes. This is interesting both from a geometrical and a computational point of view. Geometrically, it is interesting that program source can be converted automatically to a sequence of perspective transformations and conditional jumps, though we find that the product of homogeneous transformations with normalisation can be non-associative. Computationally, it is interesting that program source can be compiled for a Reduced Instruction Set Computer (RISC), the perspex machine, that is a Single Instruction, Zero Exception (SIZE) computer.

Slicer-independent mechanism drives small-RNA strand separation during human RISC assembly.

PubMed

Park, June Hyun; Shin, Chanseok

2015-10-30

Small RNA silencing is mediated by the effector RNA-induced silencing complex (RISC) that consists of an Argonaute protein (AGOs 1-4 in humans). A fundamental step during RISC assembly involves the separation of two strands of a small RNA duplex, whereby only the guide strand is retained to form the mature RISC, a process not well understood. Despite the widely accepted view that 'slicer-dependent unwinding' via passenger-strand cleavage is a prerequisite for the assembly of a highly complementary siRNA into the AGO2-RISC, here we show by careful re-examination that 'slicer-independent unwinding' plays a more significant role in human RISC maturation than previously appreciated, not only for a miRNA duplex, but, unexpectedly, for a highly complementary siRNA as well. We discovered that 'slicer-dependency' for the unwinding was affected primarily by certain parameters such as temperature and Mg(2+). We further validate these observations in non-slicer AGOs (1, 3 and 4) that can be programmed with siRNAs at the physiological temperature of humans, suggesting that slicer-independent mechanism is likely a common feature of human AGOs. Our results now clearly explain why both miRNA and siRNA are found in all four human AGOs, which is in striking contrast to the strict small-RNA sorting system in Drosophila. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

RISC-Target Interaction: Cleavage and Translational Suppression

PubMed Central

van den Berg, Arjen; Mols, Johann; Han, Jiahuai

2008-01-01

Summary Small RNA molecules have been known and utilized to suppress gene expression for more than a decade. The discovery that these small RNA molecules are endogenously expressed in many organisms and have a critical role in controlling gene expression have led to the arising of a whole new field of research. Termed small interfering RNA (siRNA) or microRNA (miRNA) these ~22 nt RNA molecules have the capability to suppress gene expression through various mechanisms once they are incorporated in the multi-protein RNA-Induced Silencing Complex (RISC) and interact with their target mRNA. This review introduces siRNAs and microRNAs in a historical perspective and focuses on the key molecules in RISC, structural properties and mechanisms underlying the process of small RNA regulated post-transcriptional suppression of gene expression. PMID:18692607

A Fault-tolerant RISC Microprocessor for Spacecraft Applications

NASA Technical Reports Server (NTRS)

Timoc, Constantin; Benz, Harry

1990-01-01

Viewgraphs on a fault-tolerant RISC microprocessor for spacecraft applications are presented. Topics covered include: reduced instruction set computer; fault tolerant registers; fault tolerant ALU; and double rail CMOS logic.

Efficient Ada multitasking on a RISC register window architecture

NASA Technical Reports Server (NTRS)

Kearns, J. P.; Quammen, D.

1987-01-01

This work addresses the problem of reducing context switch overhead on a processor which supports a large register file - a register file much like that which is part of the Berkeley RISC processors and several other emerging architectures (which are not necessarily reduced instruction set machines in the purest sense). Such a reduction in overhead is particularly desirable in a real-time embedded application, in which task-to-task context switch overhead may result in failure to meet crucial deadlines. A storage management technique by which a context switch may be implemented as cheaply as a procedure call is presented. The essence of this technique is the avoidance of the save/restore of registers on the context switch. This is achieved through analysis of the static source text of an Ada tasking program. Information gained during that analysis directs the optimized storage management strategy for that program at run time. A formal verification of the technique in terms of an operational control model and an evaluation of the technique's performance via simulations driven by synthetic Ada program traces are presented.

A Big RISC

DTIC Science & Technology

1983-07-18

architecture . Design , performance, and cost of BRISC is presented. Performance is shown to be better than high end mainframes such as the IBM 3081 and Amdahl 470V/8 on integer benchmarks written in C, Pascal and LISP. The cost, conservatively estimated to be $132,400 is about the same as a high end minicomputer such as the VAX-11/780. BRISC has a CPU cycle time of 46 ns, providing a RISC I instruction execution rate of greater than 15 MIPs. BRISC is designed with a Structured Computer Aided Logic Design System (SCALD) by Valid Logic Systems. An evaluation of the utility of

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Viral Protein Inhibits RISC Activity by Argonaute Binding through Conserved WG/GW Motifs

PubMed Central

García-Chapa, Meritxell; López-Moya, Juan José; Burgyán, József

2010-01-01

RNA silencing is an evolutionarily conserved sequence-specific gene-inactivation system that also functions as an antiviral mechanism in higher plants and insects. To overcome antiviral RNA silencing, viruses express silencing-suppressor proteins. These viral proteins can target one or more key points in the silencing machinery. Here we show that in Sweet potato mild mottle virus (SPMMV, type member of the Ipomovirus genus, family Potyviridae), the role of silencing suppressor is played by the P1 protein (the largest serine protease among all known potyvirids) despite the presence in its genome of an HC-Pro protein, which, in potyviruses, acts as the suppressor. Using in vivo studies we have demonstrated that SPMMV P1 inhibits si/miRNA-programmed RISC activity. Inhibition of RISC activity occurs by binding P1 to mature high molecular weight RISC, as we have shown by immunoprecipitation. Our results revealed that P1 targets Argonaute1 (AGO1), the catalytic unit of RISC, and that suppressor/binding activities are localized at the N-terminal half of P1. In this region three WG/GW motifs were found resembling the AGO-binding linear peptide motif conserved in metazoans and plants. Site-directed mutagenesis proved that these three motifs are absolutely required for both binding and suppression of AGO1 function. In contrast to other viral silencing suppressors analyzed so far P1 inhibits both existing and de novo formed AGO1 containing RISC complexes. Thus P1 represents a novel RNA silencing suppressor mechanism. The discovery of the molecular bases of P1 mediated silencing suppression may help to get better insight into the function and assembly of the poorly explored multiprotein containing RISC. PMID:20657820

Machine Shop Suggested Job and Task Sheets. Part II. 21 Advanced Jobs.

ERIC Educational Resources Information Center

Texas A and M Univ., College Station. Vocational Instructional Services.

This volume consists of advanced job and task sheets adaptable for use in the regular vocational industrial education programs for the training of machinists and machine shop operators. Twenty-one advanced machine shop job sheets are included. Some or all of this material is provided for each job: an introductory sheet with aim, checking…

Psychometric Evaluation of the Connor-Davidson Resilience Scale (CD-RISC) Using Iranian Students

ERIC Educational Resources Information Center

Khoshouei, Mahdieh Sadat

2009-01-01

The purpose of this study was to evaluate the psychometric properties of the Persian version of the Connor-Davidson Resilience Scale (CD-RISC). The CD-RISC was completed by a sample of 323 Isfahan university students (168 females, 155 males) aged 19-34 years. A maximum likelihood method with an oblique solution resulted in four factors…

A viral suppressor of RNA silencing inhibits ARGONAUTE 1 function by precluding target RNA binding to pre-assembled RISC

PubMed Central

Kenesi, Erzsébet; Lózsa, Rita

2017-01-01

Abstract In most eukaryotes, RNA silencing is an adaptive immune system regulating key biological processes including antiviral defense. To evade this response, viruses of plants, worms and insects have evolved viral suppressors of RNA silencing proteins (VSRs). Various VSRs, such as P1 from Sweet potato mild mottle virus (SPMMV), inhibit the activity of RNA-induced silencing complexes (RISCs) including an ARGONAUTE (AGO) protein loaded with a small RNA. However, the specific mechanisms explaining this class of inhibition are unknown. Here, we show that SPMMV P1 interacts with AGO1 and AGO2 from Arabidopsis thaliana, but solely interferes with AGO1 function. Moreover, a mutational analysis of a newly identified zinc finger domain in P1 revealed that this domain could represent an effector domain as it is required for P1 suppressor activity but not for AGO1 binding. Finally, a comparative analysis of the target RNA binding capacity of AGO1 in the presence of wild-type or suppressor-defective P1 forms revealed that P1 blocks target RNA binding to AGO1. Our results describe the negative regulation of RISC, the small RNA containing molecular machine. PMID:28499009

The co-chaperones Fkbp4/5 control Argonaute2 expression and facilitate RISC assembly.

PubMed

Martinez, Natalia J; Chang, Hao-Ming; Borrajo, Jacob de Riba; Gregory, Richard I

2013-11-01

Argonaute2 (Ago2) protein and associated microRNAs (miRNAs) or small interfering RNAs (siRNAs) form the RNA-induced silencing complex (RISC) for target messenger RNA cleavage and post-transcriptional gene silencing. Although Ago2 is essential for RISC activity, the mechanism of RISC assembly is not well understood, and factors controlling Ago2 protein expression are largely unknown. A role for the Hsc70/Hsp90 chaperone complex in loading small RNA duplexes into the RISC has been demonstrated in cell extracts, and unloaded Ago2 is unstable and degraded by the lysosome in mammalian cells. Here we identify the co-chaperones Fkbp4 and Fkbp5 as Ago2-associated proteins in mouse embryonic stem cells. Pharmacological inhibition of this interaction using FK506 or siRNA-mediated Fkbp4/5 depletion leads to decreased Ago2 protein levels. We find FK506 treatment inhibits, whereas Fkbp4/5 overexpression promotes, miRNA-mediated stabilization of Ago2 expression. Simultaneous treatment with a lysosome inhibitor revealed the accumulation of unloaded Ago2 complexes in FK506-treated cells. We find that, consistent with unloaded miRNAs being unstable, FK506 treatment also affects miRNA abundance, particularly nascent miRNAs. Our results support a role for Fkbp4/5 in RISC assembly.

Using a Cray Y-MP as an array processor for a RISC Workstation

NASA Technical Reports Server (NTRS)

Lamaster, Hugh; Rogallo, Sarah J.

1992-01-01

As microprocessors increase in power, the economics of centralized computing has changed dramatically. At the beginning of the 1980's, mainframes and super computers were often considered to be cost-effective machines for scalar computing. Today, microprocessor-based RISC (reduced-instruction-set computer) systems have displaced many uses of mainframes and supercomputers. Supercomputers are still cost competitive when processing jobs that require both large memory size and high memory bandwidth. One such application is array processing. Certain numerical operations are appropriate to use in a Remote Procedure Call (RPC)-based environment. Matrix multiplication is an example of an operation that can have a sufficient number of arithmetic operations to amortize the cost of an RPC call. An experiment which demonstrates that matrix multiplication can be executed remotely on a large system to speed the execution over that experienced on a workstation is described.

Man-machine interface requirements - advanced technology

NASA Technical Reports Server (NTRS)

Remington, R. W.; Wiener, E. L.

1984-01-01

Research issues and areas are identified where increased understanding of the human operator and the interaction between the operator and the avionics could lead to improvements in the performance of current and proposed helicopters. Both current and advanced helicopter systems and avionics are considered. Areas critical to man-machine interface requirements include: (1) artificial intelligence; (2) visual displays; (3) voice technology; (4) cockpit integration; and (5) pilot work loads and performance.

TAF11 assembles RISC loading complex to enhance RNAi efficiency

PubMed Central

Liang, Chunyang; Wang, Yibing; Murota, Yukiko; Liu, Xiang; Smith, Dean; Siomi, Mikiko C.; Liu, Qinghua

2015-01-01

SUMMARY Assembly of the RNA-induced silencing complex (RISC) requires formation of the RISC loading complex (RLC), which contains Dicer-2(Dcr-2)-R2D2 complex and recruits duplex siRNA to Ago2 in Drosophila melanogaster. However, the precise composition and action mechanism of Drosophila RLC remain unclear. Here, we identified the missing factor of RLC as TATA-binding protein associated factor 11 (TAF11) by genetic screen. Although an annotated nuclear transcription factor, we found that TAF11 also associated with Dcr-2/R2D2 and localized to cytoplasmic D2 bodies. Consistent with defective RLC assembly in taf11−/− ovary extract, we reconstituted the RLC in vitro using recombinant Dcr-2-R2D2 complex, TAF11, and duplex siRNA. Furthermore, we showed that TAF11 tetramer facilitates Dcr-2-R2D2 tetramerization to enhance siRNA binding and RISC loading activities. Together, our genetic and biochemical studies define the molecular nature of Drosophila RLC and elucidate a novel cytoplasmic function of TAF11 in organizing RLC assembly to enhance RNAi efficiency. PMID:26257286

A viral suppressor of RNA silencing inhibits ARGONAUTE 1 function by precluding target RNA binding to pre-assembled RISC.

PubMed

Kenesi, Erzsébet; Carbonell, Alberto; Lózsa, Rita; Vértessy, Beáta; Lakatos, Lóránt

2017-07-27

In most eukaryotes, RNA silencing is an adaptive immune system regulating key biological processes including antiviral defense. To evade this response, viruses of plants, worms and insects have evolved viral suppressors of RNA silencing proteins (VSRs). Various VSRs, such as P1 from Sweet potato mild mottle virus (SPMMV), inhibit the activity of RNA-induced silencing complexes (RISCs) including an ARGONAUTE (AGO) protein loaded with a small RNA. However, the specific mechanisms explaining this class of inhibition are unknown. Here, we show that SPMMV P1 interacts with AGO1 and AGO2 from Arabidopsis thaliana, but solely interferes with AGO1 function. Moreover, a mutational analysis of a newly identified zinc finger domain in P1 revealed that this domain could represent an effector domain as it is required for P1 suppressor activity but not for AGO1 binding. Finally, a comparative analysis of the target RNA binding capacity of AGO1 in the presence of wild-type or suppressor-defective P1 forms revealed that P1 blocks target RNA binding to AGO1. Our results describe the negative regulation of RISC, the small RNA containing molecular machine. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

The Radiation, Interplanetary Shocks, and Coronal Sources (RISCS) Toolset

NASA Technical Reports Server (NTRS)

Zank, G. P.; Spann, J.

2014-01-01

We outline a plan to develop a physics based predictive toolset RISCS to describe the interplanetary energetic particle and radiation environment throughout the inner heliosphere, including at the Earth. To forecast and "nowcast" the radiation environment requires the fusing of three components: 1) the ability to provide probabilities for incipient solar activity; 2) the use of these probabilities and daily coronal and solar wind observations to model the 3D spatial and temporal heliosphere, including magnetic field structure and transients, within 10 AU; and 3) the ability to model the acceleration and transport of energetic particles based on current and anticipated coronal and heliospheric conditions. We describe how to address 1) - 3) based on our existing, well developed, and validated codes and models. The goal of RISCS toolset is to provide an operational forecast and "nowcast" capability that will a) predict solar energetic particle (SEP) intensities; b) spectra for protons and heavy ions; c) predict maximum energies and their duration; d) SEP composition; e) cosmic ray intensities, and f) plasma parameters, including shock arrival times, strength and obliquity at any given heliospheric location and time. The toolset would have a 72 hour predicative capability, with associated probabilistic bounds, that would be updated hourly thereafter to improve the predicted event(s) and reduce the associated probability bounds. The RISCS toolset would be highly adaptable and portable, capable of running on a variety of platforms to accommodate various operational needs and requirements.

TAF11 Assembles the RISC Loading Complex to Enhance RNAi Efficiency.

PubMed

Liang, Chunyang; Wang, Yibing; Murota, Yukiko; Liu, Xiang; Smith, Dean; Siomi, Mikiko C; Liu, Qinghua

2015-09-03

Assembly of the RNA-induced silencing complex (RISC) requires formation of the RISC loading complex (RLC), which contains the Dicer-2 (Dcr-2)-R2D2 complex and recruits duplex siRNA to Ago2 in Drosophila melanogaster. However, the precise composition and action mechanism of Drosophila RLC remain unclear. Here we identified the missing factor of RLC as TATA-binding protein-associated factor 11 (TAF11) by genetic screen. Although it is an annotated nuclear transcription factor, we found that TAF11 also associated with Dcr-2/R2D2 and localized to cytoplasmic D2 bodies. Consistent with defective RLC assembly in taf11(-/-) ovary extract, we reconstituted the RLC in vitro using the recombinant Dcr-2-R2D2 complex, TAF11, and duplex siRNA. Furthermore, we showed that TAF11 tetramer facilitates Dcr-2-R2D2 tetramerization to enhance siRNA binding and RISC loading activities. Together, our genetic and biochemical studies define the molecular nature of the Drosophila RLC and elucidate a cytoplasmic function of TAF11 in organizing RLC assembly to enhance RNAi efficiency. Copyright © 2015 Elsevier Inc. All rights reserved.

Optimization of Particle-in-Cell Codes on RISC Processors

NASA Technical Reports Server (NTRS)

Decyk, Viktor K.; Karmesin, Steve Roy; Boer, Aeint de; Liewer, Paulette C.

1996-01-01

General strategies are developed to optimize particle-cell-codes written in Fortran for RISC processors which are commonly used on massively parallel computers. These strategies include data reorganization to improve cache utilization and code reorganization to improve efficiency of arithmetic pipelines.

Spanish validation of the 10-item Connor-Davidson Resilience Scale (CD-RISC 10) with non-professional caregivers.

PubMed

Blanco, Vanessa; Guisande, María Adelina; Sánchez, María Teresa; Otero, Patricia; Vázquez, Fernando L

2017-11-08

Despite the importance of resilience in populations under stress, and the fact that the 10-item version Connor-Davidson Resilience Scale (CD-RISC 10) is the shortest instrument for reliable and valid evaluation of resilience, there are no data on their psychometric properties in non-professional caregivers. The aim of this study was to analyze the psychometric properties and factorial structure of the spanish version of the CD-RISC 10 in non-professional caregivers. Independently trained assessors evaluated resilience, self-esteem, social support, emotional distress and depression in a sample of 294 caregivers (89.8% women, mean age 55.3 years). The internal consistency of CD-RISC 10 was α = .86. A single factor was found that accounted for 44.7% of the total variance. Confirmatory factor analysis corroborated this unifactorial model. The CD-RISC 10 was significantly correlated with the self-esteem (r = .416, p < .001) and social support (r = .228, p < .001) scales, and the emotional distress scale (r = -.311, p < .001), though this was an inverse relationship. A score ≤ 23 was a suitable cut-off point for discriminating caregivers with depression (sensitivity = 70.0%, specificity = 68.2%). The CD-RISC 10 is a reliable and valid instrument to evaluate resilience in the caregiver population.

Dysregulated RNA-Induced Silencing Complex (RISC) Assembly within CNS Corresponds with Abnormal miRNA Expression during Autoimmune Demyelination.

PubMed

Lewkowicz, Przemysław; Cwiklińska, Hanna; Mycko, Marcin P; Cichalewska, Maria; Domowicz, Małgorzata; Lewkowicz, Natalia; Jurewicz, Anna; Selmaj, Krzysztof W

2015-05-13

MicroRNAs (miRNAs) associate with Argonaute (Ago), GW182, and FXR1 proteins to form RNA-induced silencing complexes (RISCs). RISCs represent a critical checkpoint in the regulation and bioavailability of miRNAs. Recent studies have revealed dysregulation of miRNAs in multiple sclerosis (MS) and its animal model, experimental autoimmune encephalomyelitis (EAE); however, the function of RISCs in EAE and MS is largely unknown. Here, we examined the expression of Ago, GW182, and FXR1 in CNS tissue, oligodendrocytes (OLs), brain-infiltrating T lymphocytes, and CD3(+)splenocytes (SCs) of EAE mic, and found that global RISC protein levels were significantly dysregulated. Specifically, Ago2 and FXR1 levels were decreased in OLs and brain-infiltrating T cells in EAE mice. Accordingly, assembly of Ago2/GW182/FXR1 complexes in EAE brain tissues was disrupted, as confirmed by immunoprecipitation experiments. In parallel with alterations in RISC complex content in OLs, we found downregulation of miRNAs essential for differentiation and survival of OLs and myelin synthesis. In brain-infiltrating T lymphocytes, aberrant RISC formation contributed to miRNA-dependent proinflammatory helper T-cell polarization. In CD3(+) SCs, we found increased expression of both Ago2 and FXR1 in EAE compared with nonimmunized mice. Therefore, our results demonstrate a gradient in expression of miRNA between primary activated T cells in the periphery and polarized CNS-infiltrating T cells. These results suggest that, in polarized autoreactive effector T cells, miRNA synthesis is inhibited in response to dysregulated RISC assembly, allowing these cells to maintain a highly specific proinflammatory program. Therefore, our findings may provide a mechanism that leads to miRNA dysregulation in EAE/MS. Copyright © 2015 the authors 0270-6474/15/357521-17$15.00/0.

Design of RISC Processor Using VHDL and Cadence

NASA Astrophysics Data System (ADS)

Moslehpour, Saeid; Puliroju, Chandrasekhar; Abu-Aisheh, Akram

The project deals about development of a basic RISC processor. The processor is designed with basic architecture consisting of internal modules like clock generator, memory, program counter, instruction register, accumulator, arithmetic and logic unit and decoder. This processor is mainly used for simple general purpose like arithmetic operations and which can be further developed for general purpose processor by increasing the size of the instruction register. The processor is designed in VHDL by using Xilinx 8.1i version. The present project also serves as an application of the knowledge gained from past studies of the PSPICE program. The study will show how PSPICE can be used to simplify massive complex circuits designed in VHDL Synthesis. The purpose of the project is to explore the designed RISC model piece by piece, examine and understand the Input/ Output pins, and to show how the VHDL synthesis code can be converted to a simplified PSPICE model. The project will also serve as a collection of various research materials about the pieces of the circuit.

The Resilient Schools Consortium (RiSC): Linking Climate Literacy, Resilience Thinking and Service Learning

NASA Astrophysics Data System (ADS)

Branco, B. F.; Fano, E.; Adams, J.; Shon, L.; Zimmermann, A.; Sioux, H.; Gillis, A.

2017-12-01

Public schools and youth voices are largely absent from climate resilience planning and projects in New York City. Additionally, research shows that U.S. science teachers' understanding of climate science is lacking, hence there is not only an urgent need to train and support teachers on both the science and pedagogy of climate change, but to link climate literacy, resilience thinking and service learning in K-12 education. However, research on participation of students and teachers in authentic, civic-oriented experiences points to increased engagement and learning outcomes in science. The Resilient Schools Consortium (RiSC) Project will address all these needs through an afterschool program in six coastal Brooklyn schools that engages teachers and urban youth (grades 6-12), in school and community climate resilience assessment and project design. The RiSC climate curriculum, co-designed by New York City school teachers with Brooklyn College, the National Wildlife Federation, New York Sea Grant and the Science and Resilience Institute at Jamaica Bay, will begin by helping students to understand the difference between climate and weather. The curriculum makes extensive use of existing resources such as NOAA's Digital Coast and the Coastal Resilience Mapping Portal. Through a series of four modules over two school years, the six RiSC teams will; 1. explore and understand the human-induced drivers of climate change and, particularly, the significant climate and extreme weather related risks to their schools and surrounding communities; 2. complete a climate vulnerability assessment within the school and the community that is aligned to OneNYC - the city's resilience planning document; 3. design and execute a school-based resilience project; and 4. propose resilience guidelines for NYC Department of Education schools. At the end of each school year, the six RiSC teams will convene a RiSC summit with city officials and resilience practitioners to share ideas and

Advanced Aircraft Interfaces: The Machine Side of the Man-Machine Interface (Les Interfaces sur les Avions de Pointe: L’Aspect Machine de l’Interface Homme-Machine)

DTIC Science & Technology

1992-10-01

Manager , Advanced Transport Operating Systems Program Office Langley Research Center Mail Stop 265 Hampton, VA 23665-5225 United States Programme Committee...J.H.Lind, and C.G.Burge Advanced Cockpit - Mission and Image Management 4 by J. Struck Aircrew Acceptance of Automation in the Cockpit 5 by M. Hicks and I...DESIGN CONCEPTS AND TOOLS A Systems Approach to the Advanced Aircraft Man-Machine Interface 23 by F. Armogida Management of Avionics Data in the Cockpit

Advanced warfighter machine interface (Invited Paper)

NASA Astrophysics Data System (ADS)

Franks, Erin

2005-05-01

Future military crewmen may have more individual and shared tasks to complete throughout a mission as a result of smaller crew sizes and an increased number of technology interactions. To maintain reasonable workload levels, the Warfighter Machine Interface (WMI) must provide information in a consistent, logical manner, tailored to the environment in which the soldier will be completing their mission. This paper addresses design criteria for creating an advanced, multi-modal warfighter machine interface for on-the-move mounted operations. The Vetronics Technology Integration (VTI) WMI currently provides capabilities such as mission planning and rehearsal, voice and data communications, and manned/unmanned vehicle payload and mobility control. A history of the crewstation and more importantly, the WMI software will be provided with an overview of requirements and criteria used for completing the design. Multiple phases of field and laboratory testing provide the opportunity to evaluate the design and hardware in stationary and motion environments. Lessons learned related to system usability and user performance are presented with mitigation strategies to be tested in the future.

Schizotypal Estimators in Adolescence: The Concurrent Validity of the RISC.

ERIC Educational Resources Information Center

Rust, John; Chiu, Herbert

1988-01-01

Administered Minnesota Counseling Inventory and Rust Inventory of Schizotypal Cognition (RISC) to 174 adolescents in Hong Kong. Results showed that negative schizophrenic symptoms of social dysfunction and emotional instability as measured by Minnesota Counseling Inventory were positively and significantly correlated with positive schizotypal…

siRNAs targeted to certain polyadenylation sites promote specific, RISC-independent degradation of messenger RNAs.

PubMed

Vickers, Timothy A; Crooke, Stanley T

2012-07-01

While most siRNAs induce sequence-specific target mRNA cleavage and degradation in a process mediated by Ago2/RNA-induced silencing complex (RISC), certain siRNAs have also been demonstrated to direct target RNA reduction through deadenylation and subsequent degradation of target transcripts in a process which involves Ago1/RISC and P-bodies. In the current study, we present data suggesting that a third class of siRNA exist, which are capable of promoting target RNA reduction that is independent of both Ago and RISC. These siRNAs bind the target messenger RNA at the polyA signal and are capable of redirecting a small amount of polyadenylation to downstream polyA sites when present, however, the majority of the activity appears to be due to inhibition of polyadenylation or deadenylation of the transcript, followed by exosomal degradation of the immature mRNA.

Hsc70/Hsp90 chaperone machinery mediates ATP-dependent RISC loading of small RNA duplexes.

PubMed

Iwasaki, Shintaro; Kobayashi, Maki; Yoda, Mayuko; Sakaguchi, Yuriko; Katsuma, Susumu; Suzuki, Tsutomu; Tomari, Yukihide

2010-07-30

Small silencing RNAs--small interfering RNAs (siRNAs) or microRNAs (miRNAs)--direct posttranscriptional gene silencing of their mRNA targets as guides for the RNA-induced silencing complex (RISC). Both siRNAs and miRNAs are born double stranded. Surprisingly, loading these small RNA duplexes into Argonaute proteins, the core components of RISC, requires ATP, whereas separating the two small RNA strands within Argonaute does not. Here we show that the Hsc70/Hsp90 chaperone machinery is required to load small RNA duplexes into Argonaute proteins, but not for subsequent strand separation or target cleavage. We envision that the chaperone machinery uses ATP and mediates a conformational opening of Ago proteins so that they can receive bulky small RNA duplexes. Our data suggest that the chaperone machinery may serve as the driving force for the RISC assembly pathway. Copyright 2010 Elsevier Inc. All rights reserved.

Security Aspects of Smart Cards vs. Embedded Security in Machine-to-Machine (M2M) Advanced Mobile Network Applications

NASA Astrophysics Data System (ADS)

Meyerstein, Mike; Cha, Inhyok; Shah, Yogendra

The Third Generation Partnership Project (3GPP) standardisation group currently discusses advanced applications of mobile networks such as Machine-to-Machine (M2M) communication. Several security issues arise in these contexts which warrant a fresh look at mobile networks’ security foundations, resting on smart cards. This paper contributes a security/efficiency analysis to this discussion and highlights the role of trusted platform technology to approach these issues.

Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma.

PubMed

Zhang, Bin; He, Xin; Ouyang, Fusheng; Gu, Dongsheng; Dong, Yuhao; Zhang, Lu; Mo, Xiaokai; Huang, Wenhui; Tian, Jie; Zhang, Shuixing

2017-09-10

We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were extracted from MRI images for each patient. Six feature selection methods and nine classification methods were evaluated in terms of their performance. We applied the 10-fold cross-validation as the criterion for feature selection and classification. We repeated each combination for 50 times to obtain the mean area under the curve (AUC) and test error. We observed that the combination methods Random Forest (RF) + RF (AUC, 0.8464 ± 0.0069; test error, 0.3135 ± 0.0088) had the highest prognostic performance, followed by RF + Adaptive Boosting (AdaBoost) (AUC, 0.8204 ± 0.0095; test error, 0.3384 ± 0.0097), and Sure Independence Screening (SIS) + Linear Support Vector Machines (LSVM) (AUC, 0.7883 ± 0.0096; test error, 0.3985 ± 0.0100). Our radiomics study identified optimal machine-learning methods for the radiomics-based prediction of local failure and distant failure in advanced NPC, which could enhance the applications of radiomics in precision oncology and clinical practice. Copyright © 2017 Elsevier B.V. All rights reserved.

Decay of mRNAs targeted by RISC requires XRN1, the Ski complex, and the exosome

PubMed Central

ORBAN, TAMAS I.; IZAURRALDE, ELISA

2005-01-01

RNA interference (RNAi) is a conserved RNA silencing pathway that leads to sequence-specific mRNA decay in response to the presence of double-stranded RNA (dsRNA). Long dsRNA molecules are first processed by Dicer into 21–22-nucleotide small interfering RNAs (siRNAs). The siRNAs are incorporated into a multimeric RNA-induced silencing complex (RISC) that cleaves mRNAs at a site determined by complementarity with the siRNAs. Following this initial endonucleolytic cleavage, the mRNA is degraded by a mechanism that is not completely understood. We investigated the decay pathway of mRNAs targeted by RISC in Drosophila cells. We show that 5′ mRNA fragments generated by RISC cleavage are rapidly degraded from their 3′ ends by the exosome, whereas the 3′ fragments are degraded from their 5′ ends by XRN1. Exosome-mediated decay of the 5′ fragments requires the Drosophila homologs of yeast Ski2p, Ski3p, and Ski8p, suggesting that their role as regulators of exosome activity is conserved. Our findings indicate that mRNAs targeted by siRNAs are degraded from the ends generated by RISC cleavage, without undergoing decapping or deadenylation. PMID:15703439

Fluorescence correlation spectroscopy and fluorescence cross-correlation spectroscopy reveal the cytoplasmic origination of loaded nuclear RISC in vivo in human cells

PubMed Central

Mütze, Jörg; Staroske, Wolfgang; Weinmann, Lasse; Höck, Julia; Crell, Karin; Meister, Gunter; Schwille, Petra

2008-01-01

Studies of RNA interference (RNAi) provide evidence that in addition to the well-characterized cytoplasmic mechanisms, nuclear mechanisms also exist. The mechanism by which the nuclear RNA-induced silencing complex (RISC) is formed in mammalian cells, as well as the relationship between the RNA silencing pathways in nuclear and cytoplasmic compartments is still unknown. Here we show by applying fluorescence correlation and cross-correlation spectroscopy (FCS/FCCS) in vivo that two distinct RISC exist: a large ∼3 MDa complex in the cytoplasm and a 20-fold smaller complex of ∼158 kDa in the nucleus. We further show that nuclear RISC, consisting only of Ago2 and a short RNA, is loaded in the cytoplasm and imported into the nucleus. The loaded RISC accumulates in the nucleus depending on the presence of a target, based on an miRNA-like interaction with impaired cleavage of the cognate RNA. Together, these results suggest a new RISC shuttling mechanism between nucleus and cytoplasm ensuring concomitant gene regulation by small RNAs in both compartments. PMID:18842624

Fluorescence correlation spectroscopy and fluorescence cross-correlation spectroscopy reveal the cytoplasmic origination of loaded nuclear RISC in vivo in human cells.

PubMed

Ohrt, Thomas; Mütze, Jörg; Staroske, Wolfgang; Weinmann, Lasse; Höck, Julia; Crell, Karin; Meister, Gunter; Schwille, Petra

2008-11-01

Studies of RNA interference (RNAi) provide evidence that in addition to the well-characterized cytoplasmic mechanisms, nuclear mechanisms also exist. The mechanism by which the nuclear RNA-induced silencing complex (RISC) is formed in mammalian cells, as well as the relationship between the RNA silencing pathways in nuclear and cytoplasmic compartments is still unknown. Here we show by applying fluorescence correlation and cross-correlation spectroscopy (FCS/FCCS) in vivo that two distinct RISC exist: a large approximately 3 MDa complex in the cytoplasm and a 20-fold smaller complex of approximately 158 kDa in the nucleus. We further show that nuclear RISC, consisting only of Ago2 and a short RNA, is loaded in the cytoplasm and imported into the nucleus. The loaded RISC accumulates in the nucleus depending on the presence of a target, based on an miRNA-like interaction with impaired cleavage of the cognate RNA. Together, these results suggest a new RISC shuttling mechanism between nucleus and cytoplasm ensuring concomitant gene regulation by small RNAs in both compartments.

Ada Compiler Validation Summary Report. Certificate Number: 900726W1. 11017, Verdix Corporation VADS IBM RISC System/6000, AIX 3.1, VAda-110-7171, Version 6.0 IBM RISC System/6000 Model 530 = IBM RISC System/6000 Model 530

DTIC Science & Technology

1991-01-22

Customer Agreement Number: 90-05-29- VRX See Section 3.1 for any additional information about the testing environment. As a result of this validation...22 January 1991 90-05-29- VRX Ada COMPILER VALIDATION SUMMARY REPORT: Certificate Number: 900726W1.11017 Verdix Corporation VADS IBM RISC System/6000

120-MHz BiCMOS superscalar RISC processor

NASA Astrophysics Data System (ADS)

Tanaka, Shigeya; Hotta, Takashi; Murabayashi, Fumio; Yamada, Hiromichi; Yoshida, Shoji; Shimamura, Kotaro; Katsura, Koyo; Bandoh, Tadaaki; Ikeda, Koichi; Matsubara, Kenji

1994-04-01

A superscalar RISC processor contains 2.8 million transistors in a die size of 16.2 mm x 16.5 mm, and utilizes 3.3 V/0.5 micron BiCMOS technology. In order to take advantage of superscalar performance without incurring penalties from a slower clock or a longer pipeline, a tag bit is implemented in the instruction cache to indicate dependency between two instructions. A performance gain of up to 37% is obtained with only a 3.5% area overhead from our superscalar design.

Seminar for High School Students “Practice on Manufacturing Technology by Advanced Machine Tools”

NASA Astrophysics Data System (ADS)

Marui, Etsuo; Yamawaki, Masao; Taga, Yuken; Omoto, Ken'ichi; Miyaji, Reiji; Ogura, Takahiro; Tsubata, Yoko; Sakai, Toshimasa

The seminar ‘Practice on Manufacturing Technology by Advanced Machine Tools’ for high school students was held at the supporting center for technology education of Gifu University, under the sponsorship of the Japan Society of Mechanical Engineers. This seminar was held, hoping that many students become interested in manufacturing through the experience of the seminar. Operating CNC milling machine and CNC wire-cut electric discharge machine, they made original nameplates. Participants made the program to control CNC machine tools themselves. In this report, some valuable results obtained through such experience are explained.

Slicer function of Drosophila Argonautes and its involvement in RISC formation

PubMed Central

Miyoshi, Keita; Tsukumo, Hiroko; Nagami, Tomoko; Siomi, Haruhiko; Siomi, Mikiko C.

2005-01-01

Argonaute proteins play important yet distinct roles in RNA silencing. Human Argonaute2 (hAgo2) was shown to be responsible for target RNA cleavage (“Slicer”) activity in RNA interference (RNAi), whereas other Argonaute subfamily members do not exhibit the Slicer activity in humans. In Drosophila, AGO2 was shown to possess the Slicer activity. Here we show that AGO1, another member of the Drosophila Argonaute subfamily, immunopurified from Schneider2 (S2) cells associates with microRNA (miRNA) and cleaves target RNA completely complementary to the miRNA. Slicer activity is reconstituted with recombinant full-length AGO1. Thus, in Drosophila, unlike in humans, both AGO1 and AGO2 have Slicer functions. Further, reconstitution of Slicer activity with recombinant PIWI domains of AGO1 and AGO2 demonstrates that other regions in the Argonautes are not strictly necessary for small interfering RNA (siRNA)-binding and cleavage activities. It has been shown that in circumstances with AGO2-lacking, the siRNA duplex is not unwound and consequently an RNA-induced silencing complex (RISC) is not formed. We show that upon addition of an siRNA duplex in S2 lysate, the passenger strand is cleaved in an AGO2-dependent manner, and nuclease-resistant modification of the passenger strand impairs RISC formation. These findings give rise to a new model in which AGO2 is directly involved in RISC formation as “Slicer” of the passenger strand of the siRNA duplex. PMID:16287716

RNA-induced silencing complex (RISC) Proteins PACT, TRBP, and Dicer are SRA binding nuclear receptor coregulators

PubMed Central

Redfern, Andrew D.; Colley, Shane M.; Beveridge, Dianne J.; Ikeda, Naoya; Epis, Michael R.; Li, Xia; Foulds, Charles E.; Stuart, Lisa M.; Barker, Andrew; Russell, Victoria J.; Ramsay, Kerry; Kobelke, Simon J.; Li, Xiaotao; Hatchell, Esme C.; Payne, Christine; Giles, Keith M.; Messineo, Adriana; Gatignol, Anne; Lanz, Rainer B.; O’Malley, Bert W.; Leedman, Peter J.

2013-01-01

The cytoplasmic RNA-induced silencing complex (RISC) contains dsRNA binding proteins, including protein kinase RNA activator (PACT), transactivation response RNA binding protein (TRBP), and Dicer, that process pre-microRNAs into mature microRNAs (miRNAs) that target specific mRNA species for regulation. There is increasing evidence for important functional interactions between the miRNA and nuclear receptor (NR) signaling networks, with recent data showing that estrogen, acting through the estrogen receptor, can modulate initial aspects of nuclear miRNA processing. Here, we show that the cytoplasmic RISC proteins PACT, TRBP, and Dicer are steroid receptor RNA activator (SRA) binding NR coregulators that target steroid-responsive promoters and regulate NR activity and downstream gene expression. Furthermore, each of the RISC proteins, together with Argonaute 2, associates with SRA and specific pre-microRNAs in both the nucleus and cytoplasm, providing evidence for links between NR-mediated transcription and some of the factors involved in miRNA processing. PMID:23550157

RNA-induced silencing complex (RISC) Proteins PACT, TRBP, and Dicer are SRA binding nuclear receptor coregulators.

PubMed

Redfern, Andrew D; Colley, Shane M; Beveridge, Dianne J; Ikeda, Naoya; Epis, Michael R; Li, Xia; Foulds, Charles E; Stuart, Lisa M; Barker, Andrew; Russell, Victoria J; Ramsay, Kerry; Kobelke, Simon J; Li, Xiaotao; Hatchell, Esme C; Payne, Christine; Giles, Keith M; Messineo, Adriana; Gatignol, Anne; Lanz, Rainer B; O'Malley, Bert W; Leedman, Peter J

2013-04-16

The cytoplasmic RNA-induced silencing complex (RISC) contains dsRNA binding proteins, including protein kinase RNA activator (PACT), transactivation response RNA binding protein (TRBP), and Dicer, that process pre-microRNAs into mature microRNAs (miRNAs) that target specific mRNA species for regulation. There is increasing evidence for important functional interactions between the miRNA and nuclear receptor (NR) signaling networks, with recent data showing that estrogen, acting through the estrogen receptor, can modulate initial aspects of nuclear miRNA processing. Here, we show that the cytoplasmic RISC proteins PACT, TRBP, and Dicer are steroid receptor RNA activator (SRA) binding NR coregulators that target steroid-responsive promoters and regulate NR activity and downstream gene expression. Furthermore, each of the RISC proteins, together with Argonaute 2, associates with SRA and specific pre-microRNAs in both the nucleus and cytoplasm, providing evidence for links between NR-mediated transcription and some of the factors involved in miRNA processing.

Structural insights into RNA processing by the human RISC-loading complex.

PubMed

Wang, Hong-Wei; Noland, Cameron; Siridechadilok, Bunpote; Taylor, David W; Ma, Enbo; Felderer, Karin; Doudna, Jennifer A; Nogales, Eva

2009-11-01

Targeted gene silencing by RNA interference (RNAi) requires loading of a short guide RNA (small interfering RNA (siRNA) or microRNA (miRNA)) onto an Argonaute protein to form the functional center of an RNA-induced silencing complex (RISC). In humans, Argonaute2 (AGO2) assembles with the guide RNA-generating enzyme Dicer and the RNA-binding protein TRBP to form a RISC-loading complex (RLC), which is necessary for efficient transfer of nascent siRNAs and miRNAs from Dicer to AGO2. Here, using single-particle EM analysis, we show that human Dicer has an L-shaped structure. The RLC Dicer's N-terminal DExH/D domain, located in a short 'base branch', interacts with TRBP, whereas its C-terminal catalytic domains in the main body are proximal to AGO2. A model generated by docking the available atomic structures of Dicer and Argonaute homologs into the RLC reconstruction suggests a mechanism for siRNA transfer from Dicer to AGO2.

Validity and reliability of the Spanish version of the 10-item CD-RISC in patients with fibromyalgia

PubMed Central

2014-01-01

Background No resilience scale has been validated in Spanish patients with fibromyalgia. The aim of this study was to evaluate the validity and reliability of the 10-item CD-RISC in a sample of Spanish patients with fibromyalgia. Methods Design: Observational prospective multicenter study. Sample: Patients with diagnoses of fibromyalgia recruited from primary care settings (N = 208). Instruments: In addition to sociodemographic data, the following questionnaires were administered: Pain Visual Analogue Scale (PVAS), the 10-item Connor-Davidson Resilience scale (10-item CD-RISC), the Fibromyalgia Impact Questionnaire (FIQ), the Hospital Anxiety and Depression Scale (HADS), the Pain Catastrophizing Scale (PCS), the Chronic Pain Acceptance Questionnaire (CPAQ), and the Mindful Attention Awareness Scale (MAAS). Results Regarding construct validity, the factor solution in the Principal Component Analysis (PCA) was considered adequate, so the KMO test had a value of 0.91, and the Barlett’s test of sphericity was significant (χ2 = 852.8; gl = 45; p < 0.001). Only one factor showed an eigenvalue greater than 1, and it explained 50.4% of the variance. PCA and Confirmatory Factor Analysis (CFA) results did not show significant differences between groups. The 10-item CD-RISC scale demonstrated good internal consistency (Cronbach’s alpha = 0.88) and test-retest reliability (r = 0.89 for a six-week interval). The 10-item CD-RISC score was significantly correlated with all of the other psychometric instruments in the expected direction, except for the PVAS (−0.115; p = 0.113). Conclusions Our study confirms that the Spanish version of the 10-item CD-RISC shows, in patients with fibromyalgia, acceptable psychometric properties, with a high level of reliability and validity. PMID:24484847

Machine Tool Advanced Skills Technology Program (MAST). Overview and Methodology.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

The Machine Tool Advanced Skills Technology Program (MAST) is a geographical partnership of six of the nation's best two-year colleges located in the six states that have about one-third of the density of metals-related industries in the United States. The purpose of the MAST grant is to develop and implement a national training model to overcome…

Single-molecule fluorescence measurements reveal the reaction mechanisms of the core RISC, composed of human Argonaute 2 and a guide RNA.

PubMed

Jo, Myung Hyun; Song, Ji-Joon; Hohng, Sungchul

2015-12-01

In eukaryotes, small RNAs play important roles in both gene regulation and resistance to viral infection. Argonaute proteins have been identified as a key component of the effector complexes of various RNA-silencing pathways, but the mechanistic roles of Argonaute proteins in these pathways are not clearly understood. To address this question, we performed single-molecule fluorescence experiments using an RNA-induced silencing complex (core-RISC) composed of a small RNA and human Argonaute 2. We found that target binding of core-RISC starts at the seed region of the guide RNA. After target binding, four distinct reactions followed: target cleavage, transient binding, stable binding, and Argonaute unloading. Target cleavage required extensive sequence complementarity and accelerated core-RISC dissociation for recycling. In contrast, the stable binding of core-RISC to target RNAs required seed-match only, suggesting a potential explanation for the seed-match rule of microRNA (miRNA) target selection.

Development of a CPM Machine for Injured Fingers.

PubMed

Fu, Yili; Zhang, Fuxiang; Ma, Xin; Meng, Qinggang

2005-01-01

Human fingers are easy to be injured. A CPM machine is a mechanism based on the rehabilitation theory of continuous passive motion (CPM). To develop a CPM machine for the clinic application in the rehabilitation of injured fingers is a significant task. Therefore, based on the theories of evidence based medicine (EBM) and CPM, we've developed a set of biomimetic mechanism after modeling the motions of fingers and analyzing its kinematics and dynamics analysis. We also design an embedded operating system based on ARM (a kind of 32-bit RISC microprocessor). The equipment can achieve the precise control of moving scope of fingers, finger's force and speed. It can serves as a rational checking method and a way of assessment for functional rehabilitation of human hands. Now, the first prototype has been finished and will start the clinical testing in Harbin Medical University shortly.

Arabidopsis ARGONAUTE7 selects miR390 through multiple checkpoints during RISC assembly

PubMed Central

Endo, Yayoi; Iwakawa, Hiro-oki; Tomari, Yukihide

2013-01-01

Plant ARGONAUTE7 (AGO7) assembles RNA-induced silencing complex (RISC) specifically with miR390 and regulates the auxin-signalling pathway via production of TAS3 trans-acting siRNAs (tasiRNAs). However, how AGO7 discerns miR390 among other miRNAs remains unclear. Here, we show that the 5′ adenosine of miR390 and the central region of miR390/miR390* duplex are critical for the specific interaction with AGO7. Furthermore, despite the existence of mismatches in the seed and central regions of the duplex, cleavage of the miR390* strand is required for maturation of AGO7–RISC. These findings suggest that AGO7 uses multiple checkpoints to select miR390, thereby circumventing promiscuous tasiRNA production. PMID:23732541

Arabidopsis ARGONAUTE7 selects miR390 through multiple checkpoints during RISC assembly.

PubMed

Endo, Yayoi; Iwakawa, Hiro-oki; Tomari, Yukihide

2013-07-01

Plant ARGONAUTE7 (AGO7) assembles RNA-induced silencing complex (RISC) specifically with miR390 and regulates the auxin-signalling pathway via production of TAS3 trans-acting siRNAs (tasiRNAs). However, how AGO7 discerns miR390 among other miRNAs remains unclear. Here, we show that the 5' adenosine of miR390 and the central region of miR390/miR390* duplex are critical for the specific interaction with AGO7. Furthermore, despite the existence of mismatches in the seed and central regions of the duplex, cleavage of the miR390* strand is required for maturation of AGO7-RISC. These findings suggest that AGO7 uses multiple checkpoints to select miR390, thereby circumventing promiscuous tasiRNA production.

RISC-mediated control of selected chromatin regulators stabilizes ground state pluripotency of mouse embryonic stem cells.

PubMed

Pandolfini, Luca; Luzi, Ettore; Bressan, Dario; Ucciferri, Nadia; Bertacchi, Michele; Brandi, Rossella; Rocchiccioli, Silvia; D'Onofrio, Mara; Cremisi, Federico

2016-05-06

Embryonic stem cells are intrinsically unstable and differentiate spontaneously if they are not shielded from external stimuli. Although the nature of such instability is still controversial, growing evidence suggests that protein translation control may play a crucial role. We performed an integrated analysis of RNA and proteins at the transition between naïve embryonic stem cells and cells primed to differentiate. During this transition, mRNAs coding for chromatin regulators are specifically released from translational inhibition mediated by RNA-induced silencing complex (RISC). This suggests that, prior to differentiation, the propensity of embryonic stem cells to change their epigenetic status is hampered by RNA interference. The expression of these chromatin regulators is reinstated following acute inactivation of RISC and it correlates with loss of stemness markers and activation of early cell differentiation markers in treated embryonic stem cells. We propose that RISC-mediated inhibition of specific sets of chromatin regulators is a primary mechanism for preserving embryonic stem cell pluripotency while inhibiting the onset of embryonic developmental programs.

Development of a data acquisition system using a RISC/UNIX TM workstation

NASA Astrophysics Data System (ADS)

Takeuchi, Y.; Tanimori, T.; Yasu, Y.

1993-05-01

We have developed a compact data acquisition system on RISC/UNIX workstations. A SUN TM SPARCstation TM IPC was used, in which an extension bus "SBus TM" was linked to a VMEbus. The transfer rate achieved was better than 7 Mbyte/s between the VMEbus and the SUN. A device driver for CAMAC was developed in order to realize an interruptive feature in UNIX. In addition, list processing has been incorporated in order to keep the high priority of the data handling process in UNIX. The successful developments of both device driver and list processing have made it possible to realize the good real-time feature on the RISC/UNIX system. Based on this architecture, a portable and versatile data taking system has been developed, which consists of a graphical user interface, I/O handler, user analysis process, process manager and a CAMAC device driver.

ARC2D - EFFICIENT SOLUTION METHODS FOR THE NAVIER-STOKES EQUATIONS (DEC RISC ULTRIX VERSION)

NASA Technical Reports Server (NTRS)

Biyabani, S. R.

1994-01-01

computers to emulate Cray system time calls, which should be easy to modify for other machines as well. The standard distribution media for this version is a 9-track 1600 BPI ASCII Card Image format magnetic tape. The Cray version was developed in 1987. The IBM ES/3090 version is an IBM port of the Cray version. It is written in IBM VS FORTRAN and has the capability of executing in both vector and parallel modes on the MVS/XA operating system and in vector mode on the VM/XA operating system. Various options of the IBM VS FORTRAN compiler provide new features for the ES/3090 version, including 64-bit arithmetic and up to 2 GB of virtual addressability. The IBM ES/3090 version is available only as a 9-track, 1600 BPI IBM IEBCOPY format magnetic tape. The IBM ES/3090 version was developed in 1989. The DEC RISC ULTRIX version is a DEC port of the Cray version. It is written in FORTRAN 77 for RISC-based Digital Equipment platforms. The memory requirement is approximately 7Mb of main memory. It is available in UNIX tar format on TK50 tape cartridge. The port to DEC RISC ULTRIX was done in 1990. COS and UNICOS are trademarks and Cray is a registered trademark of Cray Research, Inc. IBM, ES/3090, VS FORTRAN, MVS/XA, and VM/XA are registered trademarks of International Business Machines. DEC and ULTRIX are registered trademarks of Digital Equipment Corporation.

Multi-bits error detection and fast recovery in RISC cores

NASA Astrophysics Data System (ADS)

Jing, Wang; Xing, Yang; Yuanfu, Zhao; Weigong, Zhang; Jiao, Shen; Keni, Qiu

2015-11-01

The particles-induced soft errors are a major threat to the reliability of microprocessors. Even worse, multi-bits upsets (MBUs) are ever-increased due to the rapidly shrinking feature size of the IC on a chip. Several architecture-level mechanisms have been proposed to protect microprocessors from soft errors, such as dual and triple modular redundancies (DMR and TMR). However, most of them are inefficient to combat the growing multi-bits errors or cannot well balance the critical paths delay, area and power penalty. This paper proposes a novel architecture, self-recovery dual-pipeline (SRDP), to effectively provide soft error detection and recovery with low cost for general RISC structures. We focus on the following three aspects. First, an advanced DMR pipeline is devised to detect soft error, especially MBU. Second, SEU/MBU errors can be located by enhancing self-checking logic into pipelines stage registers. Third, a recovery scheme is proposed with a recovery cost of 1 or 5 clock cycles. Our evaluation of a prototype implementation exhibits that the SRDP can successfully detect particle-induced soft errors up to 100% and recovery is nearly 95%, the other 5% will inter a specific trap.

Polerovirus protein P0 prevents the assembly of small RNA-containing RISC complexes and leads to degradation of ARGONAUTE1.

PubMed

Csorba, Tibor; Lózsa, Rita; Hutvágner, György; Burgyán, József

2010-05-01

RNA silencing plays an important role in plants in defence against viruses. To overcome this defence, plant viruses encode suppressors of RNA silencing. The most common mode of silencing suppression is sequestration of double-stranded RNAs involved in the antiviral silencing pathways. Viral suppressors can also overcome silencing responses through protein-protein interaction. The poleroviral P0 silencing suppressor protein targets ARGONAUTE (AGO) proteins for degradation. AGO proteins are the core component of the RNA-induced silencing complex (RISC). We found that P0 does not interfere with the slicer activity of pre-programmed siRNA/miRNA containing AGO1, but prevents de novo formation of siRNA/miRNA containing AGO1. We show that the AGO1 protein is part of a high-molecular-weight complex, suggesting the existence of a multi-protein RISC in plants. We propose that P0 prevents RISC assembly by interacting with one of its protein components, thus inhibiting formation of siRNA/miRNA-RISC, and ultimately leading to AGO1 degradation. Our findings also suggest that siRNAs enhance the stability of co-expressed AGO1 in both the presence and absence of P0.

Advancement in Productivity of Arabic into English Machine Translation Systems from 2008 to 2013

ERIC Educational Resources Information Center

Abu-Al-Sha'r, Awatif M.; AbuSeileek, Ali F.

2013-01-01

This paper attempts to compare between the advancements in the productivity of Arabic into English Machine Translation Systems between two years, 2008 and 2013. It also aims to evaluate the progress achieved by various systems of Arabic into English electronic translation between the two years. For tracing such advancement, a comparative analysis…

Advances in Machine Technology.

PubMed

Clark, William R; Villa, Gianluca; Neri, Mauro; Ronco, Claudio

2018-01-01

Continuous renal replacement therapy (CRRT) machines have evolved into devices specifically designed for critically ill over the past 40 years. In this chapter, a brief history of this evolution is first provided, with emphasis on the manner in which changes have been made to address the specific needs of the critically ill patient with acute kidney injury. Subsequently, specific examples of technology developments for CRRT machines are discussed, including the user interface, pumps, pressure monitoring, safety features, and anticoagulation capabilities. © 2018 S. Karger AG, Basel.

TEG-1 CD2BP2 controls miRNA levels by regulating miRISC stability in C. elegans and human cells

PubMed Central

Wang, Chris; Gupta, Pratyush; Fressigne, Lucile; Bossé, Gabriel D.; Wang, Xin; Simard, Martin J.

2017-01-01

Abstract MiRNAs post-transcriptionally regulate gene expression by recruiting the miRNA-induced silencing complex (miRISC) to target mRNAs. However, the mechanisms by which miRISC components are maintained at appropriate levels for proper function are largely unknown. Here, we demonstrate that Caenorhabditis elegans TEG-1 regulates the stability of two miRISC effectors, VIG-1 and ALG-1, which in turn affects the abundance of miRNAs in various families. We demonstrate that TEG-1 physically interacts with VIG-1, and complexes with mature let-7 miRNA. Also, loss of teg-1 in vivo phenocopies heterochronic defects observed in let-7 mutants, suggesting the association of TEG-1 with miRISC is necessary for let-7 to function properly during development. Loss of TEG-1 function also affects the abundance and function of other microRNAs, suggesting that TEG-1's role is not specific to let-7. We further demonstrate that the human orthologs of TEG-1, VIG-1 and ALG-1 (CD2BP2, SERBP1/PAI-RBP1 and AGO2) are found in a complex in HeLa cells, and knockdown of CD2BP2 results in reduced miRNA levels; therefore, TEG-1's role in affecting miRNA levels and function is likely conserved. Together, these data demonstrate that TEG-1 CD2BP2 stabilizes miRISC and mature miRNAs, maintaining them at levels necessary to properly regulate target gene expression. PMID:28180320

The RISC component VIG is a target for dsRNA-independent protein kinase activity in Drosophila S2 cells.

PubMed

Ivanov, Konstantin I; Tselykh, Timofey V; Heino, Tapio I; Mäkinen, Kristiina

2005-07-27

RNA interference (RNAi) is mediated by a multicomponent RNA-induced silencing complex (RISC). Here we examine the phosphorylation state of three Drosophila RISC-associated proteins, VIG, R2D2 and a truncated form of Argonaute2 devoid of the nonconserved N-terminal glutamine-rich domain. We show that of the three studied proteins, only VIG is phosphorylated in cultured Drosophila cells. We also demonstrate that the phosphorylation state of VIG remains unchanged after cell transfection with exogenous dsRNA. A sequence similarity search revealed that VIG shares significant similarity with the human phosphoprotein Ki-1/57, a known in vivo substrate for protein kinase C (PKC). In vitro kinase assays followed by tryptic phosphopeptide mapping showed that PKC could efficiently phosphorylate VIG on multiple sites, suggesting PKC as a candidate kinase for VIG phosphorylation in vivo. Taken together, our results identify the RISC component VIG as a novel kinase substrate in cultured Drosophila cells and suggest a possible involvement of PKC in its phosphorylation.

The RNA-induced silencing complex: a versatile gene-silencing machine.

PubMed

Pratt, Ashley J; MacRae, Ian J

2009-07-03

RNA interference is a powerful mechanism of gene silencing that underlies many aspects of eukaryotic biology. On the molecular level, RNA interference is mediated by a family of ribonucleoprotein complexes called RNA-induced silencing complexes (RISCs), which can be programmed to target virtually any nucleic acid sequence for silencing. The ability of RISC to locate target RNAs has been co-opted by evolution many times to generate a broad spectrum of gene-silencing pathways. Here, we review the fundamental biochemical and biophysical properties of RISC that facilitate gene targeting and describe the various mechanisms of gene silencing known to exploit RISC activity.

Development and Transition of the Radiation, Interplanetary Shocks, and Coronal Sources (RISCS) Toolset

NASA Technical Reports Server (NTRS)

Spann, James F.; Zank, G.

2014-01-01

We outline a plan to develop and transition a physics based predictive toolset called The Radiation, Interplanetary Shocks, and Coronal Sources (RISCS) to describe the interplanetary energetic particle and radiation environment throughout the inner heliosphere, including at the Earth. To forecast and "nowcast" the radiation environment requires the fusing of three components: 1) the ability to provide probabilities for incipient solar activity; 2) the use of these probabilities and daily coronal and solar wind observations to model the 3D spatial and temporal heliosphere, including magnetic field structure and transients, within 10 Astronomical Units; and 3) the ability to model the acceleration and transport of energetic particles based on current and anticipated coronal and heliospheric conditions. We describe how to address 1) - 3) based on our existing, well developed, and validated codes and models. The goal of RISCS toolset is to provide an operational forecast and "nowcast" capability that will a) predict solar energetic particle (SEP) intensities; b) spectra for protons and heavy ions; c) predict maximum energies and their duration; d) SEP composition; e) cosmic ray intensities, and f) plasma parameters, including shock arrival times, strength and obliquity at any given heliospheric location and time. The toolset would have a 72 hour predicative capability, with associated probabilistic bounds, that would be updated hourly thereafter to improve the predicted event(s) and reduce the associated probability bounds. The RISCS toolset would be highly adaptable and portable, capable of running on a variety of platforms to accommodate various operational needs and requirements. The described transition plan is based on a well established approach developed in the Earth Science discipline that ensures that the customer has a tool that meets their needs

Cadherin complexes recruit mRNAs and RISC to regulate epithelial cell signaling

PubMed Central

Lin, Wan-Hsin; Lu, Ruifeng; Feathers, Ryan W.; Asmann, Yan W.; Thompson, E. Aubrey

2017-01-01

Cumulative evidence demonstrates that most RNAs exhibit specific subcellular distribution. However, the mechanisms regulating this phenomenon and its functional consequences are still under investigation. Here, we reveal that cadherin complexes at the apical zonula adherens (ZA) of epithelial adherens junctions recruit the core components of the RNA-induced silencing complex (RISC) Ago2, GW182, and PABPC1, as well as a set of 522 messenger RNAs (mRNAs) and 28 mature microRNAs (miRNAs or miRs), via PLEKHA7. Top canonical pathways represented by these mRNAs include Wnt/β-catenin, TGF-β, and stem cell signaling. We specifically demonstrate the presence and silencing of MYC, JUN, and SOX2 mRNAs by miR-24 and miR-200c at the ZA. PLEKHA7 knockdown dissociates RISC from the ZA, decreases loading of the ZA-associated mRNAs and miRNAs to Ago2, and results in a corresponding increase of MYC, JUN, and SOX2 protein expression. The present work reveals a mechanism that directly links junction integrity to the silencing of a set of mRNAs that critically affect epithelial homeostasis. PMID:28877994

Cadherin complexes recruit mRNAs and RISC to regulate epithelial cell signaling.

PubMed

Kourtidis, Antonis; Necela, Brian; Lin, Wan-Hsin; Lu, Ruifeng; Feathers, Ryan W; Asmann, Yan W; Thompson, E Aubrey; Anastasiadis, Panos Z

2017-10-02

Cumulative evidence demonstrates that most RNAs exhibit specific subcellular distribution. However, the mechanisms regulating this phenomenon and its functional consequences are still under investigation. Here, we reveal that cadherin complexes at the apical zonula adherens (ZA) of epithelial adherens junctions recruit the core components of the RNA-induced silencing complex (RISC) Ago2, GW182, and PABPC1, as well as a set of 522 messenger RNAs (mRNAs) and 28 mature microRNAs (miRNAs or miRs), via PLEKHA7. Top canonical pathways represented by these mRNAs include Wnt/β-catenin, TGF-β, and stem cell signaling. We specifically demonstrate the presence and silencing of MYC, JUN, and SOX2 mRNAs by miR-24 and miR-200c at the ZA. PLEKHA7 knockdown dissociates RISC from the ZA, decreases loading of the ZA-associated mRNAs and miRNAs to Ago2, and results in a corresponding increase of MYC, JUN, and SOX2 protein expression. The present work reveals a mechanism that directly links junction integrity to the silencing of a set of mRNAs that critically affect epithelial homeostasis. © 2017 Kourtidis et al.

Depletion of key protein components of the RISC pathway impairs pre-ribosomal RNA processing.

PubMed

Liang, Xue-Hai; Crooke, Stanley T

2011-06-01

Little is known about whether components of the RNA-induced silencing complex (RISC) mediate the biogenesis of RNAs other than miRNA. Here, we show that depletion of key proteins of the RISC pathway by antisense oligonucleotides significantly impairs pre-rRNA processing in human cells. In cells depleted of Drosha or Dicer, different precursors to 5.8S rRNA strongly accumulated, without affecting normal endonucleolytic cleavages. Moderate yet distinct processing defects were also observed in Ago2-depleted cells. Physical links between pre-rRNA and these proteins were identified by co-immunoprecipitation analyses. Interestingly, simultaneous depletion of Dicer and Drosha led to a different processing defect, causing slower production of 28S rRNA and its precursor. Both Dicer and Ago2 were detected in the nuclear fraction, and reduction of Dicer altered the structure of the nucleolus, where pre-rRNA processing occurs. Together, these results suggest that Drosha and Dicer are implicated in rRNA biogenesis.

An Advanced Commanding and Telemetry System

NASA Astrophysics Data System (ADS)

Hill, Maxwell G. G.

The Loral Instrumentation System 500 configured as an Advanced Commanding and Telemetry System (ACTS) supports the acquisition of multiple telemetry downlink streams, and simultaneously supports multiple uplink command streams for today's satellite vehicles. By using industry and federal standards, the system is able to support, without relying on a host computer, a true distributed dataflow architecture that is complemented by state-of-the-art RISC-based workstations and file servers.

Statistical Use of Argonaute Expression and RISC Assembly in microRNA Target Identification

PubMed Central

Stanhope, Stephen A.; Sengupta, Srikumar; den Boon, Johan; Ahlquist, Paul; Newton, Michael A.

2009-01-01

MicroRNAs (miRNAs) posttranscriptionally regulate targeted messenger RNAs (mRNAs) by inducing cleavage or otherwise repressing their translation. We address the problem of detecting m/miRNA targeting relationships in homo sapiens from microarray data by developing statistical models that are motivated by the biological mechanisms used by miRNAs. The focus of our modeling is the construction, activity, and mediation of RNA-induced silencing complexes (RISCs) competent for targeted mRNA cleavage. We demonstrate that regression models accommodating RISC abundance and controlling for other mediating factors fit the expression profiles of known target pairs substantially better than models based on m/miRNA expressions alone, and lead to verifications of computational target pair predictions that are more sensitive than those based on marginal expression levels. Because our models are fully independent of exogenous results from sequence-based computational methods, they are appropriate for use as either a primary or secondary source of information regarding m/miRNA target pair relationships, especially in conjunction with high-throughput expression studies. PMID:19779550

Statistical use of argonaute expression and RISC assembly in microRNA target identification.

PubMed

Stanhope, Stephen A; Sengupta, Srikumar; den Boon, Johan; Ahlquist, Paul; Newton, Michael A

2009-09-01

MicroRNAs (miRNAs) posttranscriptionally regulate targeted messenger RNAs (mRNAs) by inducing cleavage or otherwise repressing their translation. We address the problem of detecting m/miRNA targeting relationships in homo sapiens from microarray data by developing statistical models that are motivated by the biological mechanisms used by miRNAs. The focus of our modeling is the construction, activity, and mediation of RNA-induced silencing complexes (RISCs) competent for targeted mRNA cleavage. We demonstrate that regression models accommodating RISC abundance and controlling for other mediating factors fit the expression profiles of known target pairs substantially better than models based on m/miRNA expressions alone, and lead to verifications of computational target pair predictions that are more sensitive than those based on marginal expression levels. Because our models are fully independent of exogenous results from sequence-based computational methods, they are appropriate for use as either a primary or secondary source of information regarding m/miRNA target pair relationships, especially in conjunction with high-throughput expression studies.

Advanced Machine Learning Emulators of Radiative Transfer Models

NASA Astrophysics Data System (ADS)

Camps-Valls, G.; Verrelst, J.; Martino, L.; Vicent, J.

2017-12-01

Physically-based model inversion methodologies are based on physical laws and established cause-effect relationships. A plethora of remote sensing applications rely on the physical inversion of a Radiative Transfer Model (RTM), which lead to physically meaningful bio-geo-physical parameter estimates. The process is however computationally expensive, needs expert knowledge for both the selection of the RTM, its parametrization and the the look-up table generation, as well as its inversion. Mimicking complex codes with statistical nonlinear machine learning algorithms has become the natural alternative very recently. Emulators are statistical constructs able to approximate the RTM, although at a fraction of the computational cost, providing an estimation of uncertainty, and estimations of the gradient or finite integral forms. We review the field and recent advances of emulation of RTMs with machine learning models. We posit Gaussian processes (GPs) as the proper framework to tackle the problem. Furthermore, we introduce an automatic methodology to construct emulators for costly RTMs. The Automatic Gaussian Process Emulator (AGAPE) methodology combines the interpolation capabilities of GPs with the accurate design of an acquisition function that favours sampling in low density regions and flatness of the interpolation function. We illustrate the good capabilities of our emulators in toy examples, leaf and canopy levels PROSPECT and PROSAIL RTMs, and for the construction of an optimal look-up-table for atmospheric correction based on MODTRAN5.

Sensing miRNA: Signal Amplification by Cognate RISC for Intracellular Detection of miRNA in Live Cells.

PubMed

Kavishwar, Amol; Medarova, Zdravka

2016-01-01

The ability to detect miRNA expression in live cells would leave these cells available for further manipulation or culture. Here, we describe the design of a miRNA sensor oligonucleotide whose sequence mimics the target mRNA. The sensor has a fluorescent label on one end of the oligo and a quencher on the other. When inside the cell, the sensor is recognized by its cognate miRNA-RISC and gets cleaved, setting the fluorophore free from its quencher. This results in fluorescence "turn on." Since cleavage by the RISC complex is an enzymatic process, the described approach has a very high level of sensitivity (nM). The rate of nonspecific cleavage of the sensor is very slow permitting the collection of meaningful signal over a long period of time.

Man-machine cooperation in advanced teleoperation

NASA Technical Reports Server (NTRS)

Fiorini, Paolo; Das, Hari; Lee, Sukhan

1993-01-01

Teleoperation experiments at JPL have shown that advanced features in a telerobotic system are a necessary condition for good results, but that they are not sufficient to assure consistently good performance by the operators. Two or three operators are normally used during training and experiments to maintain the desired performance. An alternative to this multi-operator control station is a man-machine interface embedding computer programs that can perform some of the operator's functions. In this paper we present our first experiments with these concepts, in which we focused on the areas of real-time task monitoring and interactive path planning. In the first case, when performing a known task, the operator has an automatic aid for setting control parameters and camera views. In the second case, an interactive path planner will rank different path alternatives so that the operator will make the correct control decision. The monitoring function has been implemented with a neural network doing the real-time task segmentation. The interactive path planner was implemented for redundant manipulators to specify arm configurations across the desired path and satisfy geometric, task, and performance constraints.

Validation and application of the Chinese version of the 10-item Connor-Davidson Resilience Scale (CD-RISC-10) among parents of children with cancer diagnosis.

PubMed

Ye, Zeng Jie; Qiu, Hong Zhong; Li, Peng Fei; Chen, Peng; Liang, Mu Zi; Liu, Mei Ling; Yu, Yuan Liang; Wang, Shu Ni; Quan, Xiao Ming

2017-04-01

Parents of children diagnosed with cancer often experience considerable emotional distress for their children with negative emotions, such as disbelief, depression, anxiety, hope and shock. Resilience is defined as the psychological characteristics that promote positive adaptation in the face of stress and adversity, which has been demonstrated to relate to positive coping and less psychological distress. Thus, a quick screening tool to evaluate the levels of resilience of parents with cancer-diagnosed children is urgently required. The Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to evaluate the CD-RISC-10 using a sample size of 500 parents. Velicer's Minimum Average Partial (MAP) Test and a parallel analysis were also supplemented to confirm the EFA-derived structure of the scale. The participants were given the 10-item Kessler Psychological Distress Scale (K-10), Perceived Social Support Scale (PSSS) and Medical Coping Modes Questionnaire (MCMQ) to test the associates with CD-RISC-10 and obtain the cut-off of the scale. The Chinese version of CD-RISC-10 has good psychometric properties and retains its single dimension in the original English version, which can explain 49.602% of the total variance. The CFA demonstrates the fit indices of a one-order model: Chi-Square = 39.987, CMIN/DF = 1.333, P < 0.001, TLI = 0.914, CFI = 0.981, GFI = 0.962, NFI = 0.926, IFI = 0.979, RFI = 0.889, RMR = 0.042, and RMSEA = 0.041. The CD-RISC presents statistical associations with other scales, and the cut-off is 25.5. The Chinese version of the CD-RISC-10, which is reliable, valid and easy to use, is suitable for clinical settings. The CD-RISC-10 enables a quick understanding of the level of resilience of the parents when their children undergo treatment, which can be the most important indicator to their psychological health. Copyright © 2017 Elsevier Ltd. All rights reserved.

Introducing RISC: A New Video Inventory for Testing Social Perception.

PubMed

Rothermich, Kathrin; Pell, Marc D

2015-01-01

Indirect forms of speech, such as sarcasm, jocularity (joking), and 'white lies' told to spare another's feelings, occur frequently in daily life and are a problem for many clinical populations. During social interactions, information about the literal or nonliteral meaning of a speaker unfolds simultaneously in several communication channels (e.g., linguistic, facial, vocal, and body cues); however, to date many studies have employed uni-modal stimuli, for example focusing only on the visual modality, limiting the generalizability of these results to everyday communication. Much of this research also neglects key factors for interpreting speaker intentions, such as verbal context and the relationship of social partners. Relational Inference in Social Communication (RISC) is a newly developed (English-language) database composed of short video vignettes depicting sincere, jocular, sarcastic, and white lie social exchanges between two people. Stimuli carefully manipulated the social relationship between communication partners (e.g., boss/employee, couple) and the availability of contextual cues (e.g. preceding conversations, physical objects) while controlling for major differences in the linguistic content of matched items. Here, we present initial perceptual validation data (N = 31) on a corpus of 920 items. Overall accuracy for identifying speaker intentions was above 80% correct and our results show that both relationship type and verbal context influence the categorization of literal and nonliteral interactions, underscoring the importance of these factors in research on speaker intentions. We believe that RISC will prove highly constructive as a tool in future research on social cognition, inter-personal communication, and the interpretation of speaker intentions in both healthy adults and clinical populations.

Viral RNAi suppressor reversibly binds siRNA to outcompete Dicer and RISC via multiple-turnover

PubMed Central

Rawlings, Renata A.; Krishnan, Vishalakshi; Walter, Nils G.

2011-01-01

RNA interference (RNAi) is a conserved gene regulatory mechanism employed by most eukaryotes as a key component of their innate immune response against viruses and retrotransposons. During viral infection, the RNase III-type endonuclease Dicer cleaves viral double-stranded RNA into small interfering RNAs (siRNAs), 21–24 nucleotides in length, and helps load them into the RNA-induced silencing complex (RISC) to guide cleavage of complementary viral RNA. As a countermeasure, many viruses have evolved viral RNA silencing suppressor (RSS) proteins that tightly, and presumably quantitatively, bind siRNAs to thwart RNAi-mediated degradation. Viral RSS proteins also act across kingdoms as potential immunosuppressors in gene therapeutic applications. Here we report fluorescence quenching and electrophoretic mobility shift assays that probe siRNA binding by the dimeric RSS p19 from Carnation Italian Ringspot Virus (CIRV), as well as by human Dicer and RISC assembly complexes. We find that the siRNA:p19 interaction is readily reversible, characterized by rapid binding ((1.69 ± 0.07)×108 M−1s−1) and marked dissociation (koff = 0.062 ± 0.002 s−1). We also observe that p19 efficiently competes with recombinant Dicer and inhibits formation of RISC-related assembly complexes found in human cell extract. Computational modeling based on these results provides evidence for the transient formation of a ternary complex between siRNA, human Dicer, and p19. An expanded model of RNA silencing indicates that multiple-turnover by reversible binding of siRNAs potentiates the efficiency of the suppressor protein. Our predictive model is expected to be applicable to the dosing of p19 as a silencing suppressor in viral gene therapy. PMID:21354178

Viral RNAi suppressor reversibly binds siRNA to outcompete Dicer and RISC via multiple turnover.

PubMed

Rawlings, Renata A; Krishnan, Vishalakshi; Walter, Nils G

2011-04-29

RNA interference is a conserved gene regulatory mechanism employed by most eukaryotes as a key component of their innate immune response to viruses and retrotransposons. During viral infection, the RNase-III-type endonuclease Dicer cleaves viral double-stranded RNA into small interfering RNAs (siRNAs) 21-24 nucleotides in length and helps load them into the RNA-induced silencing complex (RISC) to guide the cleavage of complementary viral RNA. As a countermeasure, many viruses have evolved viral RNA silencing suppressors (RSS) that tightly, and presumably quantitatively, bind siRNAs to thwart RNA-interference-mediated degradation. Viral RSS proteins also act across kingdoms as potential immunosuppressors in gene therapeutic applications. Here we report fluorescence quenching and electrophoretic mobility shift assays that probe siRNA binding by the dimeric RSS p19 from Carnation Italian Ringspot Virus, as well as by human Dicer and RISC assembly complexes. We find that the siRNA:p19 interaction is readily reversible, characterized by rapid binding [(1.69 ± 0.07) × 10(8) M(-)(1) s(-1)] and marked dissociation (k(off)=0.062 ± 0.002 s(-1)). We also observe that p19 efficiently competes with recombinant Dicer and inhibits the formation of RISC-related assembly complexes found in human cell extract. Computational modeling based on these results provides evidence for the transient formation of a ternary complex between siRNA, human Dicer, and p19. An expanded model of RNA silencing indicates that multiple turnover by reversible binding of siRNAs potentiates the efficiency of the suppressor protein. Our predictive model is expected to be applicable to the dosing of p19 as a silencing suppressor in viral gene therapy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Microbial Disruption of Autophagy Alters Expression of the RISC Component AGO2, a Critical Regulator of the miRNA Silencing Pathway.

PubMed

Sibony, Michal; Abdullah, Majd; Greenfield, Laura; Raju, Deepa; Wu, Ted; Rodrigues, David M; Galindo-Mata, Esther; Mascarenhas, Heidi; Philpott, Dana J; Silverberg, Mark S; Jones, Nicola L

2015-12-01

Autophagy is implicated in Crohn's disease (CD) pathogenesis. Recent evidence suggests autophagy regulates the microRNA (miRNA)-induced silencing complex (miRISC). Therefore, autophagy may play a novel role in CD by regulating expression of miRISC, thereby altering miRNA silencing. As microbes associated with CD can alter autophagy, we hypothesized that microbial disruption of autophagy affects the critical miRISC component AGO2. AGO2 expression was assessed in epithelial and immune cells, and intestinal organoids with disrupted autophagy. Microarray technology was used to determine the expression of downstream miRNAs in cells with defective autophagy. Increased AGO2 was detected in autophagy-deficient ATG5-/- and ATG16-/- mouse embryonic fibroblast cells (MEFs) in comparison with wild-type MEFs. Chemical agents and VacA toxin, which disrupt autophagy, increased AGO2 expression in MEFs, epithelial cells lines, and human monocytes, respectively. Increased AGO2 was also detected in ATG7-/- intestinal organoids, in comparison with wild-type organoids. Five miRNAs were differentially expressed in autophagy-deficient MEFs. Pathway enrichment analysis of the differentially expressed miRNAs implicated signaling pathways previously associated with CD. Taken together, our results suggest that autophagy is involved in the regulation of the critical miRISC component AGO2 in epithelial and immune cells and primary intestinal epithelial cells. We propose a mechanism by which autophagy alters miRNA expression, which likely impacts the regulation of CD-associated pathways. Furthermore, as enteric microbial products can manipulate autophagy and AGO2, our findings suggest a novel mechanism by which enteric microbes could influence miRNA to promote disease.

Introducing RISC: A New Video Inventory for Testing Social Perception

PubMed Central

Rothermich, Kathrin; Pell, Marc D.

2015-01-01

Indirect forms of speech, such as sarcasm, jocularity (joking), and ‘white lies’ told to spare another’s feelings, occur frequently in daily life and are a problem for many clinical populations. During social interactions, information about the literal or nonliteral meaning of a speaker unfolds simultaneously in several communication channels (e.g., linguistic, facial, vocal, and body cues); however, to date many studies have employed uni-modal stimuli, for example focusing only on the visual modality, limiting the generalizability of these results to everyday communication. Much of this research also neglects key factors for interpreting speaker intentions, such as verbal context and the relationship of social partners. Relational Inference in Social Communication (RISC) is a newly developed (English-language) database composed of short video vignettes depicting sincere, jocular, sarcastic, and white lie social exchanges between two people. Stimuli carefully manipulated the social relationship between communication partners (e.g., boss/employee, couple) and the availability of contextual cues (e.g. preceding conversations, physical objects) while controlling for major differences in the linguistic content of matched items. Here, we present initial perceptual validation data (N = 31) on a corpus of 920 items. Overall accuracy for identifying speaker intentions was above 80 % correct and our results show that both relationship type and verbal context influence the categorization of literal and nonliteral interactions, underscoring the importance of these factors in research on speaker intentions. We believe that RISC will prove highly constructive as a tool in future research on social cognition, inter-personal communication, and the interpretation of speaker intentions in both healthy adults and clinical populations. PMID:26226009

Recent advances in technologies required for a "Salad Machine".

PubMed

Kliss, M; Heyenga, A G; Hoehn, A; Stodieck, L S

2000-01-01

Future long duration, manned space flight missions will require life support systems that minimize resupply requirements and ultimately approach self-sufficiency in space. Bioregenerative life support systems are a promising approach, but they are far from mature. Early in the development of the NASA Controlled Ecological Life Support System Program, the idea of onboard cultivation of salad-type vegetables for crew consumption was proposed as a first step away from the total reliance on resupply for food in space. Since that time, significant advances in space-based plant growth hardware have occurred, and considerable flight experience has been gained. This paper revisits the "Salad Machine" concept and describes recent developments in subsystem technologies for both plant root and shoot environments that are directly relevant to the development of such a facility.

Recent Advances in Technologies Required for a ``Salad Machine''

NASA Astrophysics Data System (ADS)

Kliss, M.; Heyenga, A. G.; Hoehn, A.; Stodieck, L. S.

Future long duration, manned space flight missions will require life support systems that minimize resupply requirements and ultimately approach self-sufficiency in space. Bioregenerative life support systems are a promising approach, but they are far from mature. Early in the development of the NASA Controlled Ecological Life Support System Program, the idea of onboard cultivation of salad-type vegetables for crew consumption was proposed as a first step away from the total reliance on resupply for food in space. Since that time, significant advances in space-based plant growth hardware have occurred, and considerable flight experience has been gained. This paper revisits the ``Salad Machine'' concept and describes recent developments in subsystem technologies for both plant root and shoot environments that are directly relevant to the development of such a facility

A novel rotometer based on a RISC microcontroller.

PubMed

Heredia-López, F J; Bata-García, J L; Alvarez-Cervera, F J; Góngora-Alfaro, J L

2002-08-01

A new, low-cost rotometer, based on a reduced instruction set computer (RISC) microcontroller, is presented. Like earlier devices, it counts the number and direction of full turns for predetermined time periods during the evaluation of turning behavior induced by drug administration in rats. The present stand-alone system includes a nonvolatile memory for long-term data storage and a serial port for data transmission. It also contains a display for monitoring the experiments and has battery backup to avoid interruptions owing to power failures. A high correlation was found (r > .988, p < 2 x 10(-14)) between the counts of the rotometer and those of two trained observers. The system reflects quantitative differences in turning behavior owing to pharmacological manipulations. It provides the most common counting parameters and is inexpensive, flexible, highly reliable, and completely portable (weight including batteries, 159 g).

Functional analysis of neuronal microRNAs in Caenorhabditis elegans dauer formation by combinational genetics and Neuronal miRISC immunoprecipitation.

PubMed

Than, Minh T; Kudlow, Brian A; Han, Min

2013-06-01

Identifying the physiological functions of microRNAs (miRNAs) is often challenging because miRNAs commonly impact gene expression under specific physiological conditions through complex miRNA::mRNA interaction networks and in coordination with other means of gene regulation, such as transcriptional regulation and protein degradation. Such complexity creates difficulties in dissecting miRNA functions through traditional genetic methods using individual miRNA mutations. To investigate the physiological functions of miRNAs in neurons, we combined a genetic "enhancer" approach complemented by biochemical analysis of neuronal miRNA-induced silencing complexes (miRISCs) in C. elegans. Total miRNA function can be compromised by mutating one of the two GW182 proteins (AIN-1), an important component of miRISC. We found that combining an ain-1 mutation with a mutation in unc-3, a neuronal transcription factor, resulted in an inappropriate entrance into the stress-induced, alternative larval stage known as dauer, indicating a role of miRNAs in preventing aberrant dauer formation. Analysis of this genetic interaction suggests that neuronal miRNAs perform such a role partly by regulating endogenous cyclic guanosine monophosphate (cGMP) signaling, potentially influencing two other dauer-regulating pathways. Through tissue-specific immunoprecipitations of miRISC, we identified miRNAs and their likely target mRNAs within neuronal tissue. We verified the biological relevance of several of these miRNAs and found that many miRNAs likely regulate dauer formation through multiple dauer-related targets. Further analysis of target mRNAs suggests potential miRNA involvement in various neuronal processes, but the importance of these miRNA::mRNA interactions remains unclear. Finally, we found that neuronal genes may be more highly regulated by miRNAs than intestinal genes. Overall, our study identifies miRNAs and their targets, and a physiological function of these miRNAs in neurons. It

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 13: Laser Machining, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 3: Machining, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Reliability and validity of the Khmer version of the 10-item Connor-Davidson Resilience Scale (Kh-CD-RISC10) in Cambodian adolescents.

PubMed

Duong, Chanmettachampavieng; Hurst, Cameron P

2016-06-08

Resilience has been characterized as a defensive factor against the refinement of mental health problems. This study adapted the Connor-Davidson Resilience Scale (Kh-CD-RISC10) for use in Khmer adolescents and subsequently investigates its psychometric properties. Using stratified random sampling, this cross-sectional study sampled Cambodian adolescents from high schools selected randomly within three provinces (Phnom Penh, Battambang and Mondulkiri)-location (rural, urban) combinations. Parallel analysis was used to identify the number of component(s), and the structure of the single factor was subsequently explored using principal axis factoring. A confirmatory factor analysis was then performed to establish the fit of the Kh-CD-RISC10 to another sample. To assess convergent validity, the factor scores of the Khmer version of Connor-Davidson Resilience Scale were categorized into three levels, and then the general negative affectivity (GNA) and physiological hyperarousal (PH) scales (derived from the DASS 21) were compared among the three resilience groups. Of the 798 participants who responded (responded rate = 82.26 %), 440 (41.23 %) were female and the age ranged from 14 to 24 years old (mean = 17.36, SD = 1.325). The internal consistency of the Khmer 10-item CD-RISC was also shown to be high in Cambodian adolescents (Cronbach's alpha = 0. 82). Confirmatory factor analysis revealed the single factor model fit data adequately (χ(2) = 100.103, df = 35, p < 0.001, CFI = 0.9484, RMSEA = 0.0384). We found that there were significant differences in both General Negative affectivity and Physiological Hyperarousal among the three resilience groups (FGNA = 12. 84, df = 2, p < 0.001; FPH = 13. 01, df = 2, p < 0.001). The results from the present study indicate that the Khmer version of CD-RISC shows good psychometric properties in Cambodian adolescents. Our result confirms that a single dimension underlay the 10 items on the CD-RISC

Context Switching with Multiple Register Windows: A RISC Performance Study

NASA Technical Reports Server (NTRS)

Konsek, Marion B.; Reed, Daniel A.; Watcharawittayakul, Wittaya

1987-01-01

Although previous studies have shown that a large file of overlapping register windows can greatly reduce procedure call/return overhead, the effects of register windows in a multiprogramming environment are poorly understood. This paper investigates the performance of multiprogrammed, reduced instruction set computers (RISCs) as a function of window management strategy. Using an analytic model that reflects context switch and procedure call overheads, we analyze the performance of simple, linearly self-recursive programs. For more complex programs, we present the results of a simulation study. These studies show that a simple strategy that saves all windows prior to a context switch, but restores only a single window following a context switch, performs near optimally.

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 11: Computer-Aided Manufacturing & Advanced CNC, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

The RISC-V Instruction Set Manual. Volume 1: User-Level ISA, Version 2.0

DTIC Science & Technology

2014-05-06

x86 architecture (or for that matter, almost every other architecture) is not well supported in the mobile space, though both Intel and ARM are...certain domains, and has to be built for others. • Commercial ISAs come and go. Previous research infrastructures have been built around commercial ISAs...more effort than we had planned at the outset. We have now invested con- siderable effort in building up the RISC-V ISA infrastructure , including

RNAi and retroviruses: are they in RISC?

PubMed

Vasselon, Thierry; Bouttier, Manuella; Saumet, Anne; Lecellier, Charles-Henri

2013-02-01

RNA interference (RNAi) is a potent cellular system against viruses in various organisms. Although common traits are observed in plants, insects, and nematodes, the situation observed in mammals appears more complex. In mammalian somatic cells, RNAi is implicated in endonucleolytic cleavage mediated by artificially delivered small interfering RNAs (siRNAs) as well as in translation repression mediated by microRNAs (miRNAs). Because siRNAs and miRNAs recognize viral mRNAs, RNAi inherently limits virus production and participates in antiviral defense. However, several observations made in the cases of hepatitis C virus and retroviruses (including the human immunodeficiency virus and the primate foamy virus) bring evidence that this relationship is much more complex and that certain components of the RNAi effector complex [called the RNA-induced silencing complex (RISC)], such as AGO2, are also required for viral replication. Here, we summarize recent discoveries that have revealed this dual implication in virus biology. We further discuss their potential implications for the functions of RNAi-related proteins, with special emphasis on retrotransposition and genome stability.

Quantum machine learning.

PubMed

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-13

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Quantum machine learning

NASA Astrophysics Data System (ADS)

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-01

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 1: Executive Summary, of a 15-Volume Set of Skills Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

The Machine Tool Advanced Skills Technology (MAST) consortium was formed to address the shortage of skilled workers for the machine tools and metals-related industries. Featuring six of the nation's leading advanced technology centers, the MAST consortium developed, tested, and disseminated industry-specific skill standards and model curricula for…

CESAR research in intelligent machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weisbin, C.R.

1986-01-01

The Center for Engineering Systems Advanced Research (CESAR) was established in 1983 as a national center for multidisciplinary, long-range research and development in machine intelligence and advanced control theory for energy-related applications. Intelligent machines of interest here are artificially created operational systems that are capable of autonomous decision making and action. The initial emphasis for research is remote operations, with specific application to dexterous manipulation in unstructured dangerous environments where explosives, toxic chemicals, or radioactivity may be present, or in other environments with significant risk such as coal mining or oceanographic missions. Potential benefits include reduced risk to man inmore » hazardous situations, machine replication of scarce expertise, minimization of human error due to fear or fatigue, and enhanced capability using high resolution sensors and powerful computers. A CESAR goal is to explore the interface between the advanced teleoperation capability of today, and the autonomous machines of the future.« less

Analysis of tasks for dynamic man/machine load balancing in advanced helicopters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jorgensen, C.C.

1987-10-01

This report considers task allocation requirements imposed by advanced helicopter designs incorporating mixes of human pilots and intelligent machines. Specifically, it develops an analogy between load balancing using distributed non-homogeneous multiprocessors and human team functions. A taxonomy is presented which can be used to identify task combinations likely to cause overload for dynamic scheduling and process allocation mechanisms. Designer criteria are given for function decomposition, separation of control from data, and communication handling for dynamic tasks. Possible effects of n-p complete scheduling problems are noted and a class of combinatorial optimization methods are examined.

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

NASA Astrophysics Data System (ADS)

Kesava Reddy, Chirra; Manzoor Hussain, M.; Satyanarayana, S.; Krishna, M. V. S. Murali

2018-04-01

Emerging technology needs advanced machined parts with high strength and temperature resistance, high fatigue life at low production cost with good surface quality to fit into various industrial applications. Electro discharge machine is one of the extensively used machines to manufacture advanced machined parts which cannot be machined by other traditional machine with high precision and accuracy. Machining of DIN 17350-1.2080 (High Carbon High Chromium steel), using electro discharge machining has been discussed in this paper. In the present investigation an effort is made to use permanent magnet at various positions near the spark zone to improve surface quality of the machined surface. Taguchi methodology is used to obtain optimal choice for each machining parameter such as peak current, pulse duration, gap voltage and Servo reference voltage etc. Process parameters have significant influence on machining characteristics and surface finish. Improvement in surface finish is observed when process parameters are set at optimum condition under the influence of magnetic field at various positions.

Abrasives and Grinding Machines; Machine Shop Work--Advanced: 9557.02.

ERIC Educational Resources Information Center

Dade County Public Schools, Miami, FL.

The course outline has been prepared as a guide to assist the instructor in systematically planning and presenting a variety of meaningful lessons to facilitate the necessary training for the machine shop student. The material contained in the outline is designed to enable the student to learn the manipulative skills and related knowledge…

Steric restrictions of RISC in RNA interference identified with size-expanded RNA nucleobases.

PubMed

Hernández, Armando R; Peterson, Larryn W; Kool, Eric T

2012-08-17

Understanding the interactions between small interfering RNAs (siRNAs) and the RNA-induced silencing complex (RISC), the key protein complex of RNA interference (RNAi), is of great importance to the development of siRNAs with improved biological and potentially therapeutic function. Although various chemically modified siRNAs have been reported, relatively few studies with modified nucleobases exist. Here we describe the synthesis and hybridization properties of siRNAs bearing size-expanded RNA (xRNA) nucleobases and their use as a novel and systematic set of steric probes in RNAi. xRNA nucleobases are expanded by 2.4 Å using benzo-homologation and retain canonical Watson-Crick base-pairing groups. Our data show that the modified siRNA duplexes display small changes in melting temperature (+1.4 to -5.0 °C); substitutions near the center are somewhat destabilizing to the RNA duplex, while substitutions near the ends are stabilizing. RNAi studies in a dual-reporter luciferase assay in HeLa cells revealed that xRNA nucleobases in the antisense strand reduce activity at some central positions near the seed region but are generally well tolerated near the ends. Most importantly, we observed that xRNA substitutions near the 3'-end increased activity over that of wild-type siRNAs. The data are analyzed in terms of site-dependent steric effects in RISC. Circular dichroism experiments show that single xRNA substitutions do not significantly distort the native A-form helical structure of the siRNA duplex, and serum stability studies demonstrated that xRNA substitutions protect siRNAs against nuclease degradation.

Wig1 prevents cellular senescence by regulating p21 mRNA decay through control of RISC recruitment

PubMed Central

Kim, Bong Cho; Lee, Hyung Chul; Lee, Je-Jung; Choi, Chang-Min; Kim, Dong-Kwan; Lee, Jae Cheol; Ko, Young-Gyu; Lee, Jae-Seon

2012-01-01

Premature senescence, a key strategy used to suppress carcinogenesis, can be driven by p53/p21 proteins in response to various stresses. Here, we demonstrate that Wig1 plays a critical role in this process through regulation of p21 mRNA stability. Wig1 controls the association of Argonaute2 (Ago2), a central component of the RNA-induced silencing complex (RISC), with target p21 mRNA via binding of the stem-loop structure near the microRNA (miRNA) target site. Depletion of Wig1 prohibited miRNA-mediated p21 mRNA decay and resulted in premature senescence. Wig1 plays an essential role in cell proliferation, as demonstrated in tumour xenografts in mice, and Wig1 and p21 mRNA levels are inversely correlated in human normal and cancer tissues. Together, our data indicate a novel role of Wig1 in RISC target accessibility, which is a key step in RNA-mediated gene silencing. In addition, these findings indicate that fine-tuning of p21 levels by Wig1 is essential for the prevention of cellular senescence. PMID:23085987

Wig1 prevents cellular senescence by regulating p21 mRNA decay through control of RISC recruitment.

PubMed

Kim, Bong Cho; Lee, Hyung Chul; Lee, Je-Jung; Choi, Chang-Min; Kim, Dong-Kwan; Lee, Jae Cheol; Ko, Young-Gyu; Lee, Jae-Seon

2012-11-14

Premature senescence, a key strategy used to suppress carcinogenesis, can be driven by p53/p21 proteins in response to various stresses. Here, we demonstrate that Wig1 plays a critical role in this process through regulation of p21 mRNA stability. Wig1 controls the association of Argonaute2 (Ago2), a central component of the RNA-induced silencing complex (RISC), with target p21 mRNA via binding of the stem-loop structure near the microRNA (miRNA) target site. Depletion of Wig1 prohibited miRNA-mediated p21 mRNA decay and resulted in premature senescence. Wig1 plays an essential role in cell proliferation, as demonstrated in tumour xenografts in mice, and Wig1 and p21 mRNA levels are inversely correlated in human normal and cancer tissues. Together, our data indicate a novel role of Wig1 in RISC target accessibility, which is a key step in RNA-mediated gene silencing. In addition, these findings indicate that fine-tuning of p21 levels by Wig1 is essential for the prevention of cellular senescence.

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

PubMed

Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

2017-06-28

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

Network Modeling and Energy-Efficiency Optimization for Advanced Machine-to-Machine Sensor Networks

PubMed Central

Jung, Sungmo; Kim, Jong Hyun; Kim, Seoksoo

2012-01-01

Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods. PMID:23202190

Laser assisted machining: a state of art review

NASA Astrophysics Data System (ADS)

Punugupati, Gurabvaiah; Kandi, Kishore Kumar; Bose, P. S. C.; Rao, C. S. P.

2016-09-01

Difficult-to-cut materials have increasing demand in aerospace and automobile industries due to their high yield stress, high strength to weight ratio, high toughness, high wear resistance, high creep, high corrosion resistivity, ability to retain high strength at high temperature, etc. The machinability of these advanced materials, using conventional methods of machining is typical due to the high temperature and pressure at the cutting zone and tool and properties such as low thermal conductivity, high cutting forces and cutting temperatures makes the materials difficult to machine. Laser assisted machining (LAM) is a new and innovative technique for machining the difficult-to-cut materials. This paper deals with a review on the advances in lasers, tools and the mechanism of machining using LAM and their effects.

Steric Restrictions of RISC in RNA Interference Identified with Size-Expanded RNA Nucleobases

PubMed Central

Hernández, Armando R.; Peterson, Larryn W.; Kool, Eric T.

2012-01-01

Understanding the interactions between small interfering RNAs (siRNAs) and the RNA-induced silencing complex (RISC) – the key protein complex of RNA interference (RNAi) – is of great importance to the development of siRNAs with improved biological, and potentially therapeutic, function. Although various chemically modified siRNAs have been reported, relatively few studies with modified nucleobases exist. Here we describe the synthesis and hybridization properties of siRNAs bearing size-expanded RNA (xRNA) nucleobases, and their use as a novel and systematic set of steric probes in RNAi. xRNA nucleobases are expanded by 2.4 Å using benzo-homologation and retain canonical Watson-Crick base-pairing groups. Our data show that the modified siRNA duplexes display small changes in melting temperature (+1.4 to −5.0 °C); substitutions near the center are somewhat destabilizing to the RNA duplex, while substitutions near the ends are stabilizing. RNAi studies in a dual-reporter luciferase assay in HeLa cells revealed that xRNA nucleobases in the antisense strand reduce activity at some central positions near the seed region, but are generally well tolerated near the ends. Most importantly, we observed that xRNA substitutions near the 3′-end increased activity over wild-type siRNAs. The data are analyzed in terms of site-dependent steric effects in RISC. Circular dichroism experiments show that single xRNA substitutions do not significantly distort the native A-form helical structure of the siRNA duplex, and serum stability studies demonstrated that xRNA substitutions protect siRNAs against nuclease degradation. PMID:22646660

A GaAs vector processor based on parallel RISC microprocessors

NASA Astrophysics Data System (ADS)

Misko, Tim A.; Rasset, Terry L.

A vector processor architecture based on the development of a 32-bit microprocessor using gallium arsenide (GaAs) technology has been developed. The McDonnell Douglas vector processor (MVP) will be fabricated completely from GaAs digital integrated circuits. The MVP architecture includes a vector memory of 1 megabyte, a parallel bus architecture with eight processing elements connected in parallel, and a control processor. The processing elements consist of a reduced instruction set CPU (RISC) with four floating-point coprocessor units and necessary memory interface functions. This architecture has been simulated for several benchmark programs including complex fast Fourier transform (FFT), complex inner product, trigonometric functions, and sort-merge routine. The results of this study indicate that the MVP can process a 1024-point complex FFT at a speed of 112 microsec (389 megaflops) while consuming approximately 618 W of power in a volume of approximately 0.1 ft-cubed.

A Study on the Effectiveness of Lockup-Free Caches for a Reduced Instruction Set Computer (RISC) Processor

DTIC Science & Technology

1992-09-01

to acquire or develop effective simulation tools to observe the behavior of a RISC implementation as it executes different types of programs . We choose...Performance Computer performance is measured by the amount of the time required to execute a program . Performance encompasses two types of time, elapsed time...and CPU time. Elapsed time is the time required to execute a program from start to finish. It includes latency of input/output activities such as

Using Machine Learning for Advanced Anomaly Detection and Classification

NASA Astrophysics Data System (ADS)

Lane, B.; Poole, M.; Camp, M.; Murray-Krezan, J.

2016-09-01

Machine Learning (ML) techniques have successfully been used in a wide variety of applications to automatically detect and potentially classify changes in activity, or a series of activities by utilizing large amounts data, sometimes even seemingly-unrelated data. The amount of data being collected, processed, and stored in the Space Situational Awareness (SSA) domain has grown at an exponential rate and is now better suited for ML. This paper describes development of advanced algorithms to deliver significant improvements in characterization of deep space objects and indication and warning (I&W) using a global network of telescopes that are collecting photometric data on a multitude of space-based objects. The Phase II Air Force Research Laboratory (AFRL) Small Business Innovative Research (SBIR) project Autonomous Characterization Algorithms for Change Detection and Characterization (ACDC), contracted to ExoAnalytic Solutions Inc. is providing the ability to detect and identify photometric signature changes due to potential space object changes (e.g. stability, tumble rate, aspect ratio), and correlate observed changes to potential behavioral changes using a variety of techniques, including supervised learning. Furthermore, these algorithms run in real-time on data being collected and processed by the ExoAnalytic Space Operations Center (EspOC), providing timely alerts and warnings while dynamically creating collection requirements to the EspOC for the algorithms that generate higher fidelity I&W. This paper will discuss the recently implemented ACDC algorithms, including the general design approach and results to date. The usage of supervised algorithms, such as Support Vector Machines, Neural Networks, k-Nearest Neighbors, etc., and unsupervised algorithms, for example k-means, Principle Component Analysis, Hierarchical Clustering, etc., and the implementations of these algorithms is explored. Results of applying these algorithms to EspOC data both in an off

Safety Features in Anaesthesia Machine

PubMed Central

Subrahmanyam, M; Mohan, S

2013-01-01

Anaesthesia is one of the few sub-specialties of medicine, which has quickly adapted technology to improve patient safety. This application of technology can be seen in patient monitoring, advances in anaesthesia machines, intubating devices, ultrasound for visualisation of nerves and vessels, etc., Anaesthesia machines have come a long way in the last 100 years, the improvements being driven both by patient safety as well as functionality and economy of use. Incorporation of safety features in anaesthesia machines and ensuring that a proper check of the machine is done before use on a patient ensures patient safety. This review will trace all the present safety features in the machine and their evolution. PMID:24249880

A fluidics comparison of Alcon Infiniti, Bausch & Lomb Stellaris, and Advanced Medical Optics Signature phacoemulsification machines.

PubMed

Georgescu, Dan; Kuo, Annie F; Kinard, Krista I; Olson, Randall J

2008-06-01

To compare three phacoemulsification machines for measurement accuracy and postocclusion surge (POS) in human cadaver eyes. In vitro comparisons of machine accuracy and POS. Tip vacuum and flow were compared with machine indicated vacuum and flow. All machines were placed in two human cadaver eyes and POS was determined. Vacuum (% of actual) was 101.9% +/- 1.7% for Infiniti (Alcon, Fort Worth, Texas, USA), 93.2% +/- 3.9% for Stellaris (Bausch & Lomb, Rochester, New York, USA), and 107.8% +/- 4.6% for Signature (Advanced Medical Optics, Santa, Ana, California, USA; P < .0001). At 60 ml/minute flow, actual flow and unoccluded flow vacuum (UFV) was 55.8 +/- 0.4 ml/minute and 197.7 +/- 0.7 mm Hg for Infiniti, 53.5 +/- 0.0 ml/minute and 179.8 +/- 0.9 mm Hg for Stellaris, and 58.5 +/- 0.0 ml/minute and 115.1 +/- 2.3 mm Hg for Signature (P < .0001). POS in an 32-year-old eye was 0.33 +/- 0.05 mm for Infiniti, 0.16 +/- 0.06 mm for Stellaris, and 0.13 +/- 0.04 mm for Signature at 550 mm Hg, 60 cm bottle height, 45 ml/minute flow with 19-gauge tips (P < .0001 for Infiniti vs Stellaris and Signature). POS in an 81-year-old eye was 1.51 +/- 0.22 mm for Infiniti, 0.83 +/- 0.06 mm for Stellaris, 0.67 +/- 0.01 mm for Signature at 400 mm Hg vacuum, 70 cm bottle height, 40 ml/minute flow with 19-gauge tips (P < .0001). Machine-indicated accuracy, POS, and UFV were statistically significantly different. Signature had the lowest POS and vacuum to maintain flow. Regarding POS, Stellaris was close to Signature; regarding vacuum to maintain flow, Infiniti and Stellaris were similar. Minimizing POS and vacuum to maintain flow potentially are important in avoiding ocular damage and surgical complications.

Virtual Machine Language 2.1

NASA Technical Reports Server (NTRS)

Riedel, Joseph E.; Grasso, Christopher A.

2012-01-01

VML (Virtual Machine Language) is an advanced computing environment that allows spacecraft to operate using mechanisms ranging from simple, time-oriented sequencing to advanced, multicomponent reactive systems. VML has developed in four evolutionary stages. VML 0 is a core execution capability providing multi-threaded command execution, integer data types, and rudimentary branching. VML 1 added named parameterized procedures, extensive polymorphism, data typing, branching, looping issuance of commands using run-time parameters, and named global variables. VML 2 added for loops, data verification, telemetry reaction, and an open flight adaptation architecture. VML 2.1 contains major advances in control flow capabilities for executable state machines. On the resource requirements front, VML 2.1 features a reduced memory footprint in order to fit more capability into modestly sized flight processors, and endian-neutral data access for compatibility with Intel little-endian processors. Sequence packaging has been improved with object-oriented programming constructs and the use of implicit (rather than explicit) time tags on statements. Sequence event detection has been significantly enhanced with multi-variable waiting, which allows a sequence to detect and react to conditions defined by complex expressions with multiple global variables. This multi-variable waiting serves as the basis for implementing parallel rule checking, which in turn, makes possible executable state machines. The new state machine feature in VML 2.1 allows the creation of sophisticated autonomous reactive systems without the need to develop expensive flight software. Users specify named states and transitions, along with the truth conditions required, before taking transitions. Transitions with the same signal name allow separate state machines to coordinate actions: the conditions distributed across all state machines necessary to arm a particular signal are evaluated, and once found true, that

Analysis in Motion Initiative – Human Machine Intelligence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blaha, Leslie

As computers and machines become more pervasive in our everyday lives, we are looking for ways for humans and machines to work more intelligently together. How can we help machines understand their users so the team can do smarter things together? The Analysis in Motion Initiative is advancing the science of human machine intelligence — creating human-machine teams that work better together to make correct, useful, and timely interpretations of data.

High pressure water jet mining machine

DOEpatents

Barker, Clark R.

1981-05-05

A high pressure water jet mining machine for the longwall mining of coal is described. The machine is generally in the shape of a plowshare and is advanced in the direction in which the coal is cut. The machine has mounted thereon a plurality of nozzle modules each containing a high pressure water jet nozzle disposed to oscillate in a particular plane. The nozzle modules are oriented to cut in vertical and horizontal planes on the leading edge of the machine and the coal so cut is cleaved off by the wedge-shaped body.

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 15: Administrative Information, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This volume developed by the Machine Tool Advanced Skill Technology (MAST) program contains key administrative documents and provides additional sources for machine tool and precision manufacturing information and important points of contact in the industry. The document contains the following sections: a foreword; grant award letter; timeline for…

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 6: Welding, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 12: Instrumentation, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 5: Mold Making, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational speciality areas within the U.S. machine tool and metals-related…

Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools.

PubMed

Price, Jeffrey H; Goodacre, Angela; Hahn, Klaus; Hodgson, Louis; Hunter, Edward A; Krajewski, Stanislaw; Murphy, Robert F; Rabinovich, Andrew; Reed, John C; Heynen, Susanne

2002-01-01

Cellular behavior is complex. Successfully understanding systems at ever-increasing complexity is fundamental to advances in modern science and unraveling the functional details of cellular behavior is no exception. We present a collection of prospectives to provide a glimpse of the techniques that will aid in collecting, managing and utilizing information on complex cellular processes via molecular imaging tools. These include: 1) visualizing intracellular protein activity with fluorescent markers, 2) high throughput (and automated) imaging of multilabeled cells in statistically significant numbers, and 3) machine intelligence to analyze subcellular image localization and pattern. Although not addressed here, the importance of combining cell-image-based information with detailed molecular structure and ligand-receptor binding models cannot be overlooked. Advanced molecular imaging techniques have the potential to impact cellular diagnostics for cancer screening, clinical correlations of tissue molecular patterns for cancer biology, and cellular molecular interactions for accelerating drug discovery. The goal of finally understanding all cellular components and behaviors will be achieved by advances in both instrumentation engineering (software and hardware) and molecular biochemistry. Copyright 2002 Wiley-Liss, Inc.

Overview of the Machine-Tool Task Force

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, G.P.

1981-06-08

The Machine Tool Task Force, (MTTF) surveyed the state of the art of machine tool technology for material removal for two and one-half years. This overview gives a brief summary of the approach, specific subjects covered, principal conclusions and some of the key recommendations aimed at improving the technology and advancing the productivity of machine tools. The Task Force consisted of 123 experts from the US and other countries. Their findings are documented in a five-volume report, Technology of Machine Tools.

Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

NASA Astrophysics Data System (ADS)

Mohan Reddy, M.; Gorin, Alexander; Abou-El-Hossein, K. A.

2011-02-01

Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

Machining and characterization of self-reinforced polymers

NASA Astrophysics Data System (ADS)

Deepa, A.; Padmanabhan, K.; Kuppan, P.

2017-11-01

This Paper focuses on obtaining the mechanical properties and the effect of the different machining techniques on self-reinforced composites sample and to derive the best machining method with remarkable properties. Each sample was tested by the Tensile and Flexural tests, fabricated using hot compaction test and those loads were calculated. These composites are machined using conventional methods because of lack of advanced machinery in most of the industries. The advanced non-conventional methods like Abrasive water jet machining were used. These machining techniques are used to get the better output for the composite materials with good mechanical properties compared to conventional methods. But the use of non-conventional methods causes the changes in the work piece, tool properties and more economical compared to the conventional methods. Finding out the best method ideal for the designing of these Self Reinforced Composites with and without defects and the use of Scanning Electron Microscope (SEM) analysis for the comparing the microstructure of the PP and PE samples concludes our process.

Effect of focusing flow on stationary spot machining properties in elastic emission machining

PubMed Central

2013-01-01

Ultraprecise optical elements are applied in advanced optical apparatus. Elastic emission machining (EEM) is one of the ultraprecision machining methods used to fabricate shapes with 0.1-nm accuracy. In this study, we proposed and experimentally tested the control of the shape of a stationary spot profile by introducing a focusing-flow state between the nozzle outlet and the workpiece surface in EEM. The simulation results indicate that the focusing-flow nozzle sharpens the distribution of the velocity on the workpiece surface. The results of machining experiments verified those of the simulation. The obtained stationary spot conditions will be useful for surface processing with a high spatial resolution. PMID:23680043

The Effect of Heat Treatment on Residual Stress and Machining Distortions in Advanced Nickel Base Disk Alloys

NASA Technical Reports Server (NTRS)

Gayda, John

2001-01-01

This paper describes an extension of NASA's AST and IDPAT Programs which sought to predict the effect of stabilization heat treatments on residual stress and subsequent machining distortions in the advanced disk alloy, ME-209. Simple "pancake" forgings of ME-209 were produced and given four heat treats: 2075F(SUBSOLVUS)/OIL QUENCH/NO AGE; 2075F/OIL QUENCH/1400F@8HR;2075F/OIL QUENCH/1550F@3HR/l400F@8HR; and 2160F(SUPERSOLVUS)/OIL QUENCH/1550F@3HR/ 1400F@8HR. The forgings were then measured to obtain surface profiles in the heat treated condition. A simple machining plan consisting of face cuts from the top surface followed by measurements of the surface profile opposite the cut were made. This data provided warpage maps which were compared with analytical results. The analysis followed the IDPAT methodology and utilized a 2-D axisymmetric, viscoplastic FEA code. The analytical results accurately tracked the experimental data for each of the four heat treatments. The 1550F stabilization heat treatment was found to significantly reduce residual stresses and subsequent machining distortions for fine grain (subsolvus) ME209, while coarse grain (supersolvus) ME209 would require additional time or higher stabilization temperatures to attain the same degree of stress relief.

Microprocessor design for GaAs technology

NASA Astrophysics Data System (ADS)

Milutinovic, Veljko M.

Recent advances in the design of GaAs microprocessor chips are examined in chapters contributed by leading experts; the work is intended as reading material for a graduate engineering course or as a practical R&D reference. Topics addressed include the methodology used for the architecture, organization, and design of GaAs processors; GaAs device physics and circuit design; design concepts for microprocessor-based GaAs systems; a 32-bit GaAs microprocessor; a 32-bit processor implemented in GaAs JFET; and a direct coupled-FET-logic E/D-MESFET experimental RISC machine. Drawings, micrographs, and extensive circuit diagrams are provided.

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 9: Tool and Die, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 8: Sheet Metal & Composites, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 4: Manufacturing Engineering Technology, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 14: Automated Equipment Technician (CIM), of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 10: Computer-Aided Drafting & Design, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 7: Industrial Maintenance Technology, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Technology Roadmap Instrumentation, Control, and Human-Machine Interface to Support DOE Advanced Nuclear Energy Programs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Donald D Dudenhoeffer; Burce P Hallbert

Instrumentation, Controls, and Human-Machine Interface (ICHMI) technologies are essential to ensuring delivery and effective operation of optimized advanced Generation IV (Gen IV) nuclear energy systems. In 1996, the Watts Bar I nuclear power plant in Tennessee was the last U.S. nuclear power plant to go on line. It was, in fact, built based on pre-1990 technology. Since this last U.S. nuclear power plant was designed, there have been major advances in the field of ICHMI systems. Computer technology employed in other industries has advanced dramatically, and computing systems are now replaced every few years as they become functionally obsolete. Functionalmore » obsolescence occurs when newer, more functional technology replaces or supersedes an existing technology, even though an existing technology may well be in working order.Although ICHMI architectures are comprised of much of the same technology, they have not been updated nearly as often in the nuclear power industry. For example, some newer Personal Digital Assistants (PDAs) or handheld computers may, in fact, have more functionality than the 1996 computer control system at the Watts Bar I plant. This illustrates the need to transition and upgrade current nuclear power plant ICHMI technologies.« less

Advances in three-dimensional field analysis and evaluation of performance parameters of electrical machines

NASA Astrophysics Data System (ADS)

Sivasubramaniam, Kiruba

This thesis makes advances in three dimensional finite element analysis of electrical machines and the quantification of their parameters and performance. The principal objectives of the thesis are: (1)the development of a stable and accurate method of nonlinear three-dimensional field computation and application to electrical machinery and devices; and (2)improvement in the accuracy of determination of performance parameters, particularly forces and torque computed from finite elements. Contributions are made in two general areas: a more efficient formulation for three dimensional finite element analysis which saves time and improves accuracy, and new post-processing techniques to calculate flux density values from a given finite element solution. A novel three-dimensional magnetostatic solution based on a modified scalar potential method is implemented. This method has significant advantages over the traditional total scalar, reduced scalar or vector potential methods. The new method is applied to a 3D geometry of an iron core inductor and a permanent magnet motor. The results obtained are compared with those obtained from traditional methods, in terms of accuracy and speed of computation. A technique which has been observed to improve force computation in two dimensional analysis using a local solution of Laplace's equation in the airgap of machines is investigated and a similar method is implemented in the three dimensional analysis of electromagnetic devices. A new integral formulation to improve force calculation from a smoother flux-density profile is also explored and implemented. Comparisons are made and conclusions drawn as to how much improvement is obtained and at what cost. This thesis also demonstrates the use of finite element analysis to analyze torque ripples due to rotor eccentricity in permanent magnet BLDC motors. A new method for analyzing torque harmonics based on data obtained from a time stepping finite element analysis of the machine is

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 2: Career Development, General Education and Remediation, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Probabilistic machine learning and artificial intelligence.

PubMed

Ghahramani, Zoubin

2015-05-28

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Probabilistic machine learning and artificial intelligence

NASA Astrophysics Data System (ADS)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

TAE+ 5.2 - TRANSPORTABLE APPLICATIONS ENVIRONMENT PLUS, VERSION 5.2 (DEC RISC ULTRIX VERSION)

NASA Technical Reports Server (NTRS)

TAE SUPPORT OFFICE

1994-01-01

programs to display and control the user interfaces. Since the WPTs access the workbench-generated resource files during each execution, details such as color, font, location, and object type remain independent from the application code, allowing changes to the user interface without recompiling and relinking. In addition to WPTs, TAE Plus can control interaction of objects from the interpreted TAE Command Language. TCL provides a means for the more experienced developer to quickly prototype an application's use of TAE Plus interaction objects and add programming logic without the overhead of compiling or linking. TAE Plus requires MIT's X Window System and the Open Software Foundation's Motif. The HP 9000 Series 700/800 version of TAE 5.2 requires Version 11 Release 5 of the X Window System. All other machine versions of TAE 5.2 require Version 11, Release 4 of the X Window System. The Workbench and WPTs are written in C++ and the remaining code is written in C. TAE Plus is available by license for an unlimited time period. The licensed program product includes the TAE Plus source code and one set of supporting documentation. Additional documentation may be purchased separately at the price indicated below. The amount of disk space required to load the TAE Plus tar format tape is between 35Mb and 67Mb depending on the machine version. The recommended minimum memory is 12Mb. Each TAE Plus platform delivery tape includes pre-built libraries and executable binary code for that particular machine, as well as source code, so users do not have to do an installation. Users wishing to recompile the source will need both a C compiler and either GNU's C++ Version 1.39 or later, or a C++ compiler based on AT&T 2.0 cfront. TAE Plus was developed in 1989 and version 5.2 was released in 1993. TAE Plus 5.2 is available on media suitable for five different machine platforms: (1) IBM RS/6000 series workstations running AIX (.25 inch tape cartridge in UNIX tar format), (2) DEC RISC

Conceptual Study of Permanent Magnet Machine Ship Propulsion Systems

DTIC Science & Technology

1977-12-01

cycloconverter subsystem is designed using advanced thyristors and can be either water or air cooled. The machine-cycloconverter, many-phase or parallel...Turnb, Phase, Poles, Air Gap ................................. 3-9 3-5 Machine Characteristics Versus Number of Poles (large machine, 40 000 hp). Poles...cylindrical permanent magnet generator forces the power conditioner to provide for both frequency change and voltage control. The complexity of this dual

Machine intelligence and robotics: Report of the NASA study group

NASA Technical Reports Server (NTRS)

1980-01-01

Opportunities for the application of machine intelligence and robotics in NASA missions and systems were identified. The benefits of successful adoption of machine intelligence and robotics techniques were estimated and forecasts were prepared to show their growth potential. Program options for research, advanced development, and implementation of machine intelligence and robot technology for use in program planning are presented.

IGF2BP3 modulates the interaction of invasion-associated transcripts with RISC

PubMed Central

Ennajdaoui, Hanane; Howard, Jonathan M.; Sterne-Weiler, Timothy; Jahanbani, Fereshteh; Coyne, Doyle J.; Uren, Philip J.; Dargyte, Marija; Katzman, Sol; Draper, Jolene M.; Wallace, Andrew; Cazarez, Oscar; Burns, Suzanne C.; Qiao, Mei; Hinck, Lindsay; Smith, Andrew D.; Toloue, Masoud M.; Blencowe, Benjamin J.; Penalva, Luiz O.F.; Sanford, Jeremy R.

2016-01-01

Summary Insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) expression correlates with malignancy. But its role(s) in pathogenesis remain enigmatic. Here, we interrogated the IGF2BP3-RNA interaction network in pancreatic ductal adenocarcinoma (PDAC) cells. Using a combination of genome-wide approaches we identify 164 direct mRNA targets of IGF2BP3. These transcripts encode proteins enriched for functions such as cell migration, proliferation and adhesion. Loss of IGF2BP3 reduced PDAC cell invasiveness and remodeled focal adhesion junctions. Individual-nucleotide resolution crosslinking immunoprecipitation (iCLIP) revealed significant overlap of IGF2BP3 and miRNA binding sites. IGF2BP3 promotes association of the RNA induced silencing complex (RISC) with specific transcripts. Our results show that IGF2BP3 influences a malignancy-associated RNA regulon by modulating miRNA-mRNA interactions. PMID:27210763

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

PubMed

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

Trends and developments in industrial machine vision: 2013

NASA Astrophysics Data System (ADS)

Niel, Kurt; Heinzl, Christoph

2014-03-01

When following current advancements and implementations in the field of machine vision there seems to be no borders for future developments: Calculating power constantly increases, and new ideas are spreading and previously challenging approaches are introduced in to mass market. Within the past decades these advances have had dramatic impacts on our lives. Consumer electronics, e.g. computers or telephones, which once occupied large volumes, now fit in the palm of a hand. To note just a few examples e.g. face recognition was adopted by the consumer market, 3D capturing became cheap, due to the huge community SW-coding got easier using sophisticated development platforms. However, still there is a remaining gap between consumer and industrial applications. While the first ones have to be entertaining, the second have to be reliable. Recent studies (e.g. VDMA [1], Germany) show a moderately increasing market for machine vision in industry. Asking industry regarding their needs the main challenges for industrial machine vision are simple usage and reliability for the process, quick support, full automation, self/easy adjustment at changing process parameters, "forget it in the line". Furthermore a big challenge is to support quality control: Nowadays the operator has to accurately define the tested features for checking the probes. There is an upcoming development also to let automated machine vision applications find out essential parameters in a more abstract level (top down). In this work we focus on three current and future topics for industrial machine vision: Metrology supporting automation, quality control (inline/atline/offline) as well as visualization and analysis of datasets with steadily growing sizes. Finally the general trend of the pixel orientated towards object orientated evaluation is addressed. We do not directly address the field of robotics taking advances from machine vision. This is actually a fast changing area which is worth an own

Distribution of man-machine controls in space teleoperation

NASA Technical Reports Server (NTRS)

Bejczy, A. K.

1982-01-01

The distribution of control between man and machine is dependent on the tasks, available technology, human performance characteristics and control goals. This dependency has very specific projections on systems designed for teleoperation in space. This paper gives a brief outline of the space-related issues and presents the results of advanced teleoperator research and development at the Jet Propulsion Laboratory (JPL). The research and development work includes smart sensors, flexible computer controls and intelligent man-machine interface devices in the area of visual displays and kinesthetic man-machine coupling in remote control of manipulators. Some of the development results have been tested at the Johnson Space Center (JSC) using the simulated full-scale Shuttle Remote Manipulator System (RMS). The research and development work for advanced space teleoperation is far from complete and poses many interdisciplinary challenges.

Interferometric correction system for a numerically controlled machine

DOEpatents

Burleson, Robert R.

1978-01-01

An interferometric correction system for a numerically controlled machine is provided to improve the positioning accuracy of a machine tool, for example, for a high-precision numerically controlled machine. A laser interferometer feedback system is used to monitor the positioning of the machine tool which is being moved by command pulses to a positioning system to position the tool. The correction system compares the commanded position as indicated by a command pulse train applied to the positioning system with the actual position of the tool as monitored by the laser interferometer. If the tool position lags the commanded position by a preselected error, additional pulses are added to the pulse train applied to the positioning system to advance the tool closer to the commanded position, thereby reducing the lag error. If the actual tool position is leading in comparison to the commanded position, pulses are deleted from the pulse train where the advance error exceeds the preselected error magnitude to correct the position error of the tool relative to the commanded position.

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

PubMed

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

History of wood machining

Treesearch

Peter Koch

1967-01-01

The history of wood machining is closely tied to advanced in metallurgy and power sources. It has been strongly and continuously shaped by prevailing economic forces and the rise and decline of other contemporary industries. This paper sketches a few of the highlights, with emphasis on developments in North America.

Electrochemical advanced oxidation processes as decentralized water treatment technologies to remediate domestic washing machine effluents.

PubMed

Dos Santos, Alexsandro Jhones; Costa, Emily Cintia Tossi de Araújo; da Silva, Djalma Ribeiro; Garcia-Segura, Sergi; Martínez-Huitle, Carlos Alberto

2018-03-01

Water scarcity is one of the major concerns worldwide. In order to secure this appreciated natural resource, management and development of water treatment technologies are mandatory. One feasible alternative is the consideration of water recycling/reuse at the household scale. Here, the treatment of actual washing machine effluent by electrochemical advanced oxidation processes was considered. Electrochemical oxidation and electro-Fenton technologies can be applied as decentralized small-scale water treatment devices. Therefore, efficient decolorization and total organic abatement have been followed. The results demonstrate the promising performance of solar photoelectro-Fenton process, where complete color and organic removal was attained after 240 min of treatment under optimum conditions by applying a current density of 66.6 mA cm -2 . Thus, electrochemical technologies emerge as promising water-sustainable approaches.

SKI2 mediates degradation of RISC 5'-cleavage fragments and prevents secondary siRNA production from miRNA targets in Arabidopsis.

PubMed

Branscheid, Anja; Marchais, Antonin; Schott, Gregory; Lange, Heike; Gagliardi, Dominique; Andersen, Stig Uggerhøj; Voinnet, Olivier; Brodersen, Peter

2015-12-15

Small regulatory RNAs are fundamental in eukaryotic and prokaryotic gene regulation. In plants, an important element of post-transcriptional control is effected by 20-24 nt microRNAs (miRNAs) and short interfering RNAs (siRNAs) bound to the ARGONAUTE1 (AGO1) protein in an RNA induced silencing complex (RISC). AGO1 may cleave target mRNAs with small RNA complementarity, but the fate of the resulting cleavage fragments remains incompletely understood. Here, we show that SKI2, SKI3 and SKI8, subunits of a cytoplasmic cofactor of the RNA exosome, are required for degradation of RISC 5', but not 3'-cleavage fragments in Arabidopsis. In the absence of SKI2 activity, many miRNA targets produce siRNAs via the RNA-dependent RNA polymerase 6 (RDR6) pathway. These siRNAs are low-abundant, and map close to the cleavage site. In most cases, siRNAs were produced 5' to the cleavage site, but several examples of 3'-spreading were also identified. These observations suggest that siRNAs do not simply derive from RDR6 action on stable 5'-cleavage fragments and hence that SKI2 has a direct role in limiting secondary siRNA production in addition to its function in mediating degradation of 5'-cleavage fragments. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

IGF2BP3 Modulates the Interaction of Invasion-Associated Transcripts with RISC.

PubMed

Ennajdaoui, Hanane; Howard, Jonathan M; Sterne-Weiler, Timothy; Jahanbani, Fereshteh; Coyne, Doyle J; Uren, Philip J; Dargyte, Marija; Katzman, Sol; Draper, Jolene M; Wallace, Andrew; Cazarez, Oscar; Burns, Suzanne C; Qiao, Mei; Hinck, Lindsay; Smith, Andrew D; Toloue, Masoud M; Blencowe, Benjamin J; Penalva, Luiz O F; Sanford, Jeremy R

2016-05-31

Insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) expression correlates with malignancy, but its role(s) in pathogenesis remains enigmatic. We interrogated the IGF2BP3-RNA interaction network in pancreatic ductal adenocarcinoma (PDAC) cells. Using a combination of genome-wide approaches, we have identified 164 direct mRNA targets of IGF2BP3. These transcripts encode proteins enriched for functions such as cell migration, proliferation, and adhesion. Loss of IGF2BP3 reduced PDAC cell invasiveness and remodeled focal adhesion junctions. Individual nucleotide resolution crosslinking immunoprecipitation (iCLIP) revealed significant overlap of IGF2BP3 and microRNA (miRNA) binding sites. IGF2BP3 promotes association of the RNA-induced silencing complex (RISC) with specific transcripts. Our results show that IGF2BP3 influences a malignancy-associated RNA regulon by modulating miRNA-mRNA interactions. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Advances in molecular dynamics simulation of ultra-precision machining of hard and brittle materials

NASA Astrophysics Data System (ADS)

Guo, Xiaoguang; Li, Qiang; Liu, Tao; Kang, Renke; Jin, Zhuji; Guo, Dongming

2017-03-01

Hard and brittle materials, such as silicon, SiC, and optical glasses, are widely used in aerospace, military, integrated circuit, and other fields because of their excellent physical and chemical properties. However, these materials display poor machinability because of their hard and brittle properties. Damages such as surface micro-crack and subsurface damage often occur during machining of hard and brittle materials. Ultra-precision machining is widely used in processing hard and brittle materials to obtain nanoscale machining quality. However, the theoretical mechanism underlying this method remains unclear. This paper provides a review of present research on the molecular dynamics simulation of ultra-precision machining of hard and brittle materials. The future trends in this field are also discussed.

Distributed state machine supervision for long-baseline gravitational-wave detectors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rollins, Jameson Graef, E-mail: jameson.rollins@ligo.org

The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of two identical yet independent, widely separated, long-baseline gravitational-wave detectors. Each Advanced LIGO detector consists of complex optical-mechanical systems isolated from the ground by multiple layers of active seismic isolation, all controlled by hundreds of fast, digital, feedback control systems. This article describes a novel state machine-based automation platform developed to handle the automation and supervisory control challenges of these detectors. The platform, called Guardian, consists of distributed, independent, state machine automaton nodes organized hierarchically for full detector control. User code is written in standard Python and the platform is designed to facilitatemore » the fast-paced development process associated with commissioning the complicated Advanced LIGO instruments. While developed specifically for the Advanced LIGO detectors, Guardian is a generic state machine automation platform that is useful for experimental control at all levels, from simple table-top setups to large-scale multi-million dollar facilities.« less

Contemporary machine learning: techniques for practitioners in the physical sciences

NASA Astrophysics Data System (ADS)

Spears, Brian

2017-10-01

Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Advanced methods in NDE using machine learning approaches

NASA Astrophysics Data System (ADS)

Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

2018-04-01

Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability

Advances in Pancreatic CT Imaging.

PubMed

Almeida, Renata R; Lo, Grace C; Patino, Manuel; Bizzo, Bernardo; Canellas, Rodrigo; Sahani, Dushyant V

2018-07-01

The purpose of this article is to discuss the advances in CT acquisition and image postprocessing as they apply to imaging the pancreas and to conceptualize the role of radiogenomics and machine learning in pancreatic imaging. CT is the preferred imaging modality for assessment of pancreatic diseases. Recent advances in CT (dual-energy CT, CT perfusion, CT volumetry, and radiogenomics) and emerging computational algorithms (machine learning) have the potential to further increase the value of CT in pancreatic imaging.

2014 Bio-Acoustics Data Challenge for the International Community on Machine Learning and Bioacoustics

DTIC Science & Technology

2014-09-30

This ONR grant promotes the development and application of advanced machine learning techniques for detection and classification of marine mammal...sounds. The objective is to engage a broad community of data scientists in the development and application of advanced machine learning techniques for detection and classification of marine mammal sounds.

Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines.

PubMed

Abuassba, Adnan O M; Zhang, Dezheng; Luo, Xiong; Shaheryar, Ahmad; Ali, Hazrat

2017-01-01

Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L 2 -norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets.

Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines

PubMed Central

Abuassba, Adnan O. M.; Ali, Hazrat

2017-01-01

Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets. PMID:28546808

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

PubMed

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-03-01

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Cardiac imaging: working towards fully-automated machine analysis & interpretation

PubMed Central

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-01-01

Introduction Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation. PMID:28277804

Tool path strategy and cutting process monitoring in intelligent machining

NASA Astrophysics Data System (ADS)

Chen, Ming; Wang, Chengdong; An, Qinglong; Ming, Weiwei

2018-06-01

Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

PubMed

Billings, John M; Eder, Maxwell; Flood, William C; Dhami, Devendra Singh; Natarajan, Sriraam; Whitlow, Christopher T

2017-11-01

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances. Copyright © 2017 Elsevier Inc. All rights reserved.

Turning assistive machines into assistive robots

NASA Astrophysics Data System (ADS)

Argall, Brenna D.

2015-01-01

For decades, the potential for automation in particular, in the form of smart wheelchairs to aid those with motor, or cognitive, impairments has been recognized. It is a paradox that often the more severe a person's motor impairment, the more challenging it is for them to operate the very assistive machines which might enhance their quality of life. A primary aim of my lab is to address this confound by incorporating robotics autonomy and intelligence into assistive machines turning the machine into a kind of robot, and offloading some of the control burden from the user. Robots already synthetically sense, act in and reason about the world, and these technologies can be leveraged to help bridge the gap left by sensory, motor or cognitive impairments in the users of assistive machines. This paper overviews some of the ongoing projects in my lab, which strives to advance human ability through robotics autonomy.

SKI2 mediates degradation of RISC 5′-cleavage fragments and prevents secondary siRNA production from miRNA targets in Arabidopsis

PubMed Central

Branscheid, Anja; Marchais, Antonin; Schott, Gregory; Lange, Heike; Gagliardi, Dominique; Andersen, Stig Uggerhøj; Voinnet, Olivier; Brodersen, Peter

2015-01-01

Small regulatory RNAs are fundamental in eukaryotic and prokaryotic gene regulation. In plants, an important element of post-transcriptional control is effected by 20–24 nt microRNAs (miRNAs) and short interfering RNAs (siRNAs) bound to the ARGONAUTE1 (AGO1) protein in an RNA induced silencing complex (RISC). AGO1 may cleave target mRNAs with small RNA complementarity, but the fate of the resulting cleavage fragments remains incompletely understood. Here, we show that SKI2, SKI3 and SKI8, subunits of a cytoplasmic cofactor of the RNA exosome, are required for degradation of RISC 5′, but not 3′-cleavage fragments in Arabidopsis. In the absence of SKI2 activity, many miRNA targets produce siRNAs via the RNA-dependent RNA polymerase 6 (RDR6) pathway. These siRNAs are low-abundant, and map close to the cleavage site. In most cases, siRNAs were produced 5′ to the cleavage site, but several examples of 3′-spreading were also identified. These observations suggest that siRNAs do not simply derive from RDR6 action on stable 5′-cleavage fragments and hence that SKI2 has a direct role in limiting secondary siRNA production in addition to its function in mediating degradation of 5′-cleavage fragments. PMID:26464441

Use of Advanced Machine-Learning Techniques for Non-Invasive Monitoring of Hemorrhage

DTIC Science & Technology

2010-04-01

that state-of-the-art machine learning techniques when integrated with novel non-invasive monitoring technologies could detect subtle, physiological...decompensation. Continuous, non-invasively measured hemodynamic signals (e.g., ECG, blood pressures, stroke volume) were used for the development of machine ... learning algorithms. Accuracy estimates were obtained by building models using 27 subjects and testing on the 28th. This process was repeated 28 times

Machine learning: Trends, perspectives, and prospects.

PubMed

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid

PubMed Central

Li, Yuancheng; Jing, Sitong

2018-01-01

Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can’t satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy. PMID:29485990

Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid.

PubMed

Li, Yuancheng; Qiu, Rixuan; Jing, Sitong

2018-01-01

Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

Software/hardware distributed processing network supporting the Ada environment

NASA Astrophysics Data System (ADS)

Wood, Richard J.; Pryk, Zen

1993-09-01

A high-performance, fault-tolerant, distributed network has been developed, tested, and demonstrated. The network is based on the MIPS Computer Systems, Inc. R3000 Risc for processing, VHSIC ASICs for high speed, reliable, inter-node communications and compatible commercial memory and I/O boards. The network is an evolution of the Advanced Onboard Signal Processor (AOSP) architecture. It supports Ada application software with an Ada- implemented operating system. A six-node implementation (capable of expansion up to 256 nodes) of the RISC multiprocessor architecture provides 120 MIPS of scalar throughput, 96 Mbytes of RAM and 24 Mbytes of non-volatile memory. The network provides for all ground processing applications, has merit for space-qualified RISC-based network, and interfaces to advanced Computer Aided Software Engineering (CASE) tools for application software development.

Motion Simulation Analysis of Rail Weld CNC Fine Milling Machine

NASA Astrophysics Data System (ADS)

Mao, Huajie; Shu, Min; Li, Chao; Zhang, Baojun

CNC fine milling machine is a new advanced equipment of rail weld precision machining with high precision, high efficiency, low environmental pollution and other technical advantages. The motion performance of this machine directly affects its machining accuracy and stability, which makes it an important consideration for its design. Based on the design drawings, this article completed 3D modeling of 60mm/kg rail weld CNC fine milling machine by using Solidworks. After that, the geometry was imported into Adams to finish the motion simulation analysis. The displacement, velocity, angular velocity and some other kinematical parameters curves of the main components were obtained in the post-processing and these are the scientific basis for the design and development for this machine.

Air Bearings Machined On Ultra Precision, Hydrostatic CNC-Lathe

NASA Astrophysics Data System (ADS)

Knol, Pierre H.; Szepesi, Denis; Deurwaarder, Jan M.

1987-01-01

Micromachining of precision elements requires an adequate machine concept to meet the high demand of surface finish, dimensional and shape accuracy. The Hembrug ultra precision lathes have been exclusively designed with hydrostatic principles for main spindle and guideways. This concept is to be explained with some major advantages of hydrostatics compared with aerostatics at universal micromachining applications. Hembrug has originally developed the conventional Mikroturn ultra precision facing lathes, for diamond turning of computer memory discs. This first generation of machines was followed by the advanced computer numerically controlled types for machining of complex precision workpieces. One of these parts, an aerostatic bearing component has been succesfully machined on the Super-Mikroturn CNC. A case study of airbearing machining confirms the statement that a good result of the micromachining does not depend on machine performance alone, but also on the technology applied.

Combinatorial delivery of small interfering RNAs reduces RNAi efficacy by selective incorporation into RISC

PubMed Central

Castanotto, Daniela; Sakurai, Kumi; Lingeman, Robert; Li, Haitang; Shively, Louise; Aagaard, Lars; Soifer, Harris; Gatignol, Anne; Riggs, Arthur; Rossi, John J.

2007-01-01

Despite the great potential of RNAi, ectopic expression of shRNA or siRNAs holds the inherent risk of competition for critical RNAi components, thus altering the regulatory functions of some cellular microRNAs. In addition, specific siRNA sequences can potentially hinder incorporation of other siRNAs when used in a combinatorial approach. We show that both synthetic siRNAs and expressed shRNAs compete against each other and with the endogenous microRNAs for transport and for incorporation into the RNA induced silencing complex (RISC). The same siRNA sequences do not display competition when expressed from a microRNA backbone. We also show that TAR RNA binding protein (TRBP) is one of the sensors for selection and incorporation of the guide sequence of interfering RNAs. These findings reveal that combinatorial siRNA approaches can be problematic and have important implications for the methodology of expression and use of therapeutic interfering RNAs. PMID:17660190

Capturing microRNA targets using an RNA-induced silencing complex (RISC)-trap approach

PubMed Central

Cambronne, Xiaolu A.; Shen, Rongkun; Auer, Paul L.; Goodman, Richard H.

2012-01-01

Identifying targets is critical for understanding the biological effects of microRNA (miRNA) expression. The challenge lies in characterizing the cohort of targets for a specific miRNA, especially when targets are being actively down-regulated in miRNA– RNA-induced silencing complex (RISC)–messengerRNA (mRNA) complexes. We have developed a robust and versatile strategy called RISCtrap to stabilize and purify targets from this transient interaction. Its utility was demonstrated by determining specific high-confidence target datasets for miR-124, miR-132, and miR-181 that contained known and previously unknown transcripts. Two previously unknown miR-132 targets identified with RISCtrap, adaptor protein CT10 regulator of kinase 1 (CRK1) and tight junction-associated protein 1 (TJAP1), were shown to be endogenously regulated by miR-132 in adult mouse forebrain. The datasets, moreover, differed in the number of targets and in the types and frequency of microRNA recognition element (MRE) motifs, thus revealing a previously underappreciated level of specificity in the target sets regulated by individual miRNAs. PMID:23184980

Capturing microRNA targets using an RNA-induced silencing complex (RISC)-trap approach.

PubMed

Cambronne, Xiaolu A; Shen, Rongkun; Auer, Paul L; Goodman, Richard H

2012-12-11

Identifying targets is critical for understanding the biological effects of microRNA (miRNA) expression. The challenge lies in characterizing the cohort of targets for a specific miRNA, especially when targets are being actively down-regulated in miRNA- RNA-induced silencing complex (RISC)-messengerRNA (mRNA) complexes. We have developed a robust and versatile strategy called RISCtrap to stabilize and purify targets from this transient interaction. Its utility was demonstrated by determining specific high-confidence target datasets for miR-124, miR-132, and miR-181 that contained known and previously unknown transcripts. Two previously unknown miR-132 targets identified with RISCtrap, adaptor protein CT10 regulator of kinase 1 (CRK1) and tight junction-associated protein 1 (TJAP1), were shown to be endogenously regulated by miR-132 in adult mouse forebrain. The datasets, moreover, differed in the number of targets and in the types and frequency of microRNA recognition element (MRE) motifs, thus revealing a previously underappreciated level of specificity in the target sets regulated by individual miRNAs.

Informatics and machine learning to define the phenotype.

PubMed

Basile, Anna Okula; Ritchie, Marylyn DeRiggi

2018-03-01

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

Advances in 3-dimensional braiding

NASA Technical Reports Server (NTRS)

Thaxton, Cirrelia; Reid, Rona; El-Shiekh, Aly

1992-01-01

This paper encompasses an overview of the history of 3-D braiding and an in-depth survey of the most recent, technological advances in machine design and implementation. Its purpose is to review the major efforts of university and industry research and development into the successful machining of this textile process.

Advanced data management system architectures testbed

NASA Technical Reports Server (NTRS)

Grant, Terry

1990-01-01

The objective of the Architecture and Tools Testbed is to provide a working, experimental focus to the evolving automation applications for the Space Station Freedom data management system. Emphasis is on defining and refining real-world applications including the following: the validation of user needs; understanding system requirements and capabilities; and extending capabilities. The approach is to provide an open, distributed system of high performance workstations representing both the standard data processors and networks and advanced RISC-based processors and multiprocessor systems. The system provides a base from which to develop and evaluate new performance and risk management concepts and for sharing the results. Participants are given a common view of requirements and capability via: remote login to the testbed; standard, natural user interfaces to simulations and emulations; special attention to user manuals for all software tools; and E-mail communication. The testbed elements which instantiate the approach are briefly described including the workstations, the software simulation and monitoring tools, and performance and fault tolerance experiments.

Creating Situational Awareness in Spacecraft Operations with the Machine Learning Approach

NASA Astrophysics Data System (ADS)

Li, Z.

2016-09-01

This paper presents a machine learning approach for the situational awareness capability in spacecraft operations. There are two types of time dependent data patterns for spacecraft datasets: the absolute time pattern (ATP) and the relative time pattern (RTP). The machine learning captures the data patterns of the satellite datasets through the data training during the normal operations, which is represented by its time dependent trend. The data monitoring compares the values of the incoming data with the predictions of machine learning algorithm, which can detect any meaningful changes to a dataset above the noise level. If the difference between the value of incoming telemetry and the machine learning prediction are larger than the threshold defined by the standard deviation of datasets, it could indicate the potential anomaly that may need special attention. The application of the machine-learning approach to the Advanced Himawari Imager (AHI) on Japanese Himawari spacecraft series is presented, which has the same configuration as the Advanced Baseline Imager (ABI) on Geostationary Environment Operational Satellite (GOES) R series. The time dependent trends generated by the data-training algorithm are in excellent agreement with the datasets. The standard deviation in the time dependent trend provides a metric for measuring the data quality, which is particularly useful in evaluating the detector quality for both AHI and ABI with multiple detectors in each channel. The machine-learning approach creates the situational awareness capability, and enables engineers to handle the huge data volume that would have been impossible with the existing approach, and it leads to significant advances to more dynamic, proactive, and autonomous spacecraft operations.

Evidence and mechanism of efficient thermally activated delayed fluorescence promoted by delocalized excited states

PubMed Central

Hosokai, Takuya; Matsuzaki, Hiroyuki; Nakanotani, Hajime; Tokumaru, Katsumi; Tsutsui, Tetsuo; Furube, Akihiro; Nasu, Keirou; Nomura, Hiroko; Yahiro, Masayuki; Adachi, Chihaya

2017-01-01

The design of organic compounds with nearly no gap between the first excited singlet (S1) and triplet (T1) states has been demonstrated to result in an efficient spin-flip transition from the T1 to S1 state, that is, reverse intersystem crossing (RISC), and facilitate light emission as thermally activated delayed fluorescence (TADF). However, many TADF molecules have shown that a relatively appreciable energy difference between the S1 and T1 states (~0.2 eV) could also result in a high RISC rate. We revealed from a comprehensive study of optical properties of TADF molecules that the formation of delocalized states is the key to efficient RISC and identified a chemical template for these materials. In addition, simple structural confinement further enhances RISC by suppressing structural relaxation in the triplet states. Our findings aid in designing advanced organic molecules with a high rate of RISC and, thus, achieving the maximum theoretical electroluminescence efficiency in organic light-emitting diodes. PMID:28508081

Advanced Resistive Exercise Device

NASA Technical Reports Server (NTRS)

Raboin, Jasen; Niebuhr, Jason; Cruz, Santana; Lamoreaux, chris

2007-01-01

The advanced resistive exercise device (ARED), now at the prototype stage of development, is a versatile machine that can be used to perform different customized exercises for which, heretofore, it has been necessary to use different machines. Conceived as a means of helping astronauts and others to maintain muscle and bone strength and endurance in low-gravity environments, the ARED could also prove advantageous in terrestrial settings (e.g., health clubs and military training facilities) in which many users are exercising simultaneously and there is heavy demand for use of exercise machines.

Computation of emotions in man and machines.

PubMed

Robinson, Peter; el Kaliouby, Rana

2009-12-12

The importance of emotional expression as part of human communication has been understood since Aristotle, and the subject has been explored scientifically since Charles Darwin and others in the nineteenth century. Advances in computer technology now allow machines to recognize and express emotions, paving the way for improved human-computer and human-human communications. Recent advances in psychology have greatly improved our understanding of the role of affect in communication, perception, decision-making, attention and memory. At the same time, advances in technology mean that it is becoming possible for machines to sense, analyse and express emotions. We can now consider how these advances relate to each other and how they can be brought together to influence future research in perception, attention, learning, memory, communication, decision-making and other applications. The computation of emotions includes both recognition and synthesis, using channels such as facial expressions, non-verbal aspects of speech, posture, gestures, physiology, brain imaging and general behaviour. The combination of new results in psychology with new techniques of computation is leading to new technologies with applications in commerce, education, entertainment, security, therapy and everyday life. However, there are important issues of privacy and personal expression that must also be considered.

Computation of emotions in man and machines

PubMed Central

Robinson, Peter; el Kaliouby, Rana

2009-01-01

The importance of emotional expression as part of human communication has been understood since Aristotle, and the subject has been explored scientifically since Charles Darwin and others in the nineteenth century. Advances in computer technology now allow machines to recognize and express emotions, paving the way for improved human–computer and human–human communications. Recent advances in psychology have greatly improved our understanding of the role of affect in communication, perception, decision-making, attention and memory. At the same time, advances in technology mean that it is becoming possible for machines to sense, analyse and express emotions. We can now consider how these advances relate to each other and how they can be brought together to influence future research in perception, attention, learning, memory, communication, decision-making and other applications. The computation of emotions includes both recognition and synthesis, using channels such as facial expressions, non-verbal aspects of speech, posture, gestures, physiology, brain imaging and general behaviour. The combination of new results in psychology with new techniques of computation is leading to new technologies with applications in commerce, education, entertainment, security, therapy and everyday life. However, there are important issues of privacy and personal expression that must also be considered. PMID:19884138

Ex-vivo machine perfusion for kidney preservation.

PubMed

Hamar, Matyas; Selzner, Markus

2018-06-01

Machine perfusion is a novel strategy to decrease preservation injury, improve graft assessment, and increase organ acceptance for transplantation. This review summarizes the current advances in ex-vivo machine-based kidney preservation technologies over the last year. Ex-vivo perfusion technologies, such as hypothermic and normothermic machine perfusion and controlled oxygenated rewarming, have gained high interest in the field of organ preservation. Keeping kidney grafts functionally and metabolically active during the preservation period offers a unique chance for viability assessment, reconditioning, and organ repair. Normothermic ex-vivo kidney perfusion has been recently translated into clinical practice. Preclinical results suggest that prolonged warm perfusion appears superior than a brief end-ischemic reconditioning in terms of renal function and injury. An established standardized protocol for continuous warm perfusion is still not available for human grafts. Ex-vivo machine perfusion represents a superior organ preservation method over static cold storage. There is still an urgent need for the optimization of the perfusion fluid and machine technology and to identify the optimal indication in kidney transplantation. Recent research is focusing on graft assessment and therapeutic strategies.

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science.

PubMed

Zevin, M; Coughlin, S; Bahaadini, S; Besler, E; Rohani, N; Allen, S; Cabero, M; Crowston, K; Katsaggelos, A K; Larson, S L; Lee, T K; Lintott, C; Littenberg, T B; Lundgren, A; Østerlund, C; Smith, J R; Trouille, L; Kalogera, V

2017-01-01

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches , which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual

Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

NASA Technical Reports Server (NTRS)

Zevin, M.; Coughlin, S.; Bahaadini, S.; Besler, E.; Rohani, N.; Allen, S.; Cabero, M.; Crowston, K.; Katsaggelos, A. K.; Littenberg, T. B.

2017-01-01

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science

NASA Astrophysics Data System (ADS)

Zevin, M.; Coughlin, S.; Bahaadini, S.; Besler, E.; Rohani, N.; Allen, S.; Cabero, M.; Crowston, K.; Katsaggelos, A. K.; Larson, S. L.; Lee, T. K.; Lintott, C.; Littenberg, T. B.; Lundgren, A.; Østerlund, C.; Smith, J. R.; Trouille, L.; Kalogera, V.

2017-03-01

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science

PubMed Central

Zevin, M; Coughlin, S; Bahaadini, S; Besler, E; Rohani, N; Allen, S; Cabero, M; Crowston, K; Katsaggelos, A K; Larson, S L; Lee, T K; Lintott, C; Littenberg, T B; Lundgren, A; Østerlund, C; Smith, J R; Trouille, L; Kalogera, V

2018-01-01

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual

Man-machine interface for the control of a lunar transport machine

NASA Technical Reports Server (NTRS)

Ashley, Richard; Bacon, Loring; Carlton, Scott Tim; May, Mark; Moore, Jimmy; Peek, Dennis

1987-01-01

A proposed first generation human interface control panel is described which will be used to control SKITTER, a three-legged lunar walking machine. Under development at Georgia Tech, SKITTER will be a multi-purpose, un-manned vehicle capable of preparing a site for the proposed lunar base in advance of the arrival of men. This walking machine will be able to accept modular special purpose tools, such as a crane, a core sampling drill, and a digging device, among others. The project was concerned with the design of a human interface which could be used, from earth, to control the movements of SKITTER on the lunar surface. Preliminary inquiries were also made into necessary modifications required to adapt the panel to both a shirt-sleeve lunar environment and to a mobile unit which could be used by a man in a space suit at a lunar work site.

A programmable controller based on CAN field bus embedded microprocessor and FPGA

NASA Astrophysics Data System (ADS)

Cai, Qizhong; Guo, Yifeng; Chen, Wenhei; Wang, Mingtao

2008-10-01

One kind of new programmable controller(PLC) is introduced in this paper. The advanced embedded microprocessor and Field-Programmable Gate Array (FPGA) device are applied in the PLC system. The PLC system structure was presented in this paper. It includes 32 bits Advanced RISC Machines (ARM) embedded microprocessor as control core, FPGA as control arithmetic coprocessor and CAN bus as data communication criteria protocol connected the host controller and its various extension modules. It is detailed given that the circuits and working principle, IiO interface circuit between ARM and FPGA and interface circuit between ARM and FPGA coprocessor. Furthermore the interface circuit diagrams between various modules are written. In addition, it is introduced that ladder chart program how to control the transfer info of control arithmetic part in FPGA coprocessor. The PLC, through nearly two months of operation to meet the design of the basic requirements.

An overview of rotating machine systems with high-temperature bulk superconductors

NASA Astrophysics Data System (ADS)

Zhou, Difan; Izumi, Mitsuru; Miki, Motohiro; Felder, Brice; Ida, Tetsuya; Kitano, Masahiro

2012-10-01

The paper contains a review of recent advancements in rotating machines with bulk high-temperature superconductors (HTS). The high critical current density of bulk HTS enables us to design rotating machines with a compact configuration in a practical scheme. The development of an axial-gap-type trapped flux synchronous rotating machine together with the systematic research works at the Tokyo University of Marine Science and Technology since 2001 are briefly introduced. Developments in bulk HTS rotating machines in other research groups are also summarized. The key issues of bulk HTS machines, including material progress of bulk HTS, in situ magnetization, and cooling together with AC loss at low-temperature operation are discussed.

Electric machine differential for vehicle traction control and stability control

NASA Astrophysics Data System (ADS)

Kuruppu, Sandun Shivantha

Evolving requirements in energy efficiency and tightening regulations for reliable electric drivetrains drive the advancement of the hybrid electric (HEV) and full electric vehicle (EV) technology. Different configurations of EV and HEV architectures are evaluated for their performance. The future technology is trending towards utilizing distinctive properties in electric machines to not only to improve efficiency but also to realize advanced road adhesion controls and vehicle stability controls. Electric machine differential (EMD) is such a concept under current investigation for applications in the near future. Reliability of a power train is critical. Therefore, sophisticated fault detection schemes are essential in guaranteeing reliable operation of a complex system such as an EMD. The research presented here emphasize on implementation of a 4kW electric machine differential, a novel single open phase fault diagnostic scheme, an implementation of a real time slip optimization algorithm and an electric machine differential based yaw stability improvement study. The proposed d-q current signature based SPO fault diagnostic algorithm detects the fault within one electrical cycle. The EMD based extremum seeking slip optimization algorithm reduces stopping distance by 30% compared to hydraulic braking based ABS.

Aeromechanics and man-machine integration technology opportunities for rotorcraft of the 1990s and beyond

NASA Technical Reports Server (NTRS)

Kerr, Andrew W.

1989-01-01

Programs related to rotorcraft aeromechanics and man-machine integration are discussed which will support advanced army rotorcraft design. In aeromechanics, recent advances in computational fluid dynamics will be used to characterize the complex unsteady flowfields of rotorcraft, and a second-generation comprehensive helicopter analysis system will be used along with models of aerodynamics, engines, and control systems to study the structural dynamics of rotor/body configurations. The man-machine integration program includes the development of advanced cockpit design technology and the evaluation of cockpit and mission equipment concepts in a real-time full-combat environment.

Machine learning for science: state of the art and future prospects.

PubMed

Mjolsness, E; DeCoste, D

2001-09-14

Recent advances in machine learning methods, along with successful applications across a wide variety of fields such as planetary science and bioinformatics, promise powerful new tools for practicing scientists. This viewpoint highlights some useful characteristics of modern machine learning methods and their relevance to scientific applications. We conclude with some speculations on near-term progress and promising directions.

Applications of Machine Learning for Radiation Therapy.

PubMed

Arimura, Hidetaka; Nakamoto, Takahiro

2016-01-01

Radiation therapy has been highly advanced as image guided radiation therapy (IGRT) by making advantage of image engineering technologies. Recently, novel frameworks based on image engineering technologies as well as machine learning technologies have been studied for sophisticating the radiation therapy. In this review paper, the author introduces several researches of applications of machine learning for radiation therapy. For examples, a method to determine the threshold values for standardized uptake value (SUV) for estimation of gross tumor volume (GTV) in positron emission tomography (PET) images, an approach to estimate the multileaf collimator (MLC) position errors between treatment plans and radiation delivery time, and prediction frameworks for esophageal stenosis and radiation pneumonitis risk after radiation therapy are described. Finally, the author introduces seven issues that one should consider when applying machine learning models to radiation therapy.

Fluid machines: Expanding the limits, past and future

NASA Technical Reports Server (NTRS)

Hartmann, M. J.; Sandercock, D. M.

1985-01-01

During the 40 yr period from 1940 to 1980, the capabilities and operating limits of fluid machines were greatly extended. This was due to a research program, carried out to meet the needs of aerospace programs. Some of the events are reviewed. Overall advancements of all machinery components are discussed followed by a detailed examination of technology advancements in axial compressors and pumps. Future technology needs are suggested.

Research on numerical control system based on S3C2410 and MCX314AL

NASA Astrophysics Data System (ADS)

Ren, Qiang; Jiang, Tingbiao

2008-10-01

With the rapid development of micro-computer technology, embedded system, CNC technology and integrated circuits, numerical control system with powerful functions can be realized by several high-speed CPU chips and RISC (Reduced Instruction Set Computing) chips which have small size and strong stability. In addition, the real-time operating system also makes the attainment of embedded system possible. Developing the NC system based on embedded technology can overcome some shortcomings of common PC-based CNC system, such as the waste of resources, low control precision, low frequency and low integration. This paper discusses a hardware platform of ENC (Embedded Numerical Control) system based on embedded processor chip ARM (Advanced RISC Machines)-S3C2410 and DSP (Digital Signal Processor)-MCX314AL and introduces the process of developing ENC system software. Finally write the MCX314AL's driver under the embedded Linux operating system. The embedded Linux operating system can deal with multitask well moreover satisfy the real-time and reliability of movement control. NC system has the advantages of best using resources and compact system with embedded technology. It provides a wealth of functions and superior performance with a lower cost. It can be sure that ENC is the direction of the future development.

Precision Machining Technology. Technical Committee Report.

ERIC Educational Resources Information Center

Idaho State Dept. of Education, Boise. Div. of Vocational Education.

This Technical Committee Report prepared by industry representatives in Idaho lists the skills currently necessary for an employee in that state to obtain a job in precision machining technology, retain a job once hired, and advance in that occupational field. (Task lists are grouped according to duty areas generally used in industry settings, and…

Proposal for continued research in intelligent machines at the Center for Engineering Systems Advanced Research (CESAR) for FY 1988 to FY 1991

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weisbin, C.R.

1987-03-01

This document reviews research accomplishments achieved by the staff of the Center for Engineering Systems Advanced Research (CESAR) during the fiscal years 1984 through 1987. The manuscript also describes future CESAR objectives for the 1988-1991 planning horizon, and beyond. As much as possible, the basic research goals are derived from perceived Department of Energy (DOE) needs for increased safety, productivity, and competitiveness in the United States energy producing and consuming facilities. Research areas covered include the HERMIES-II Robot, autonomous robot navigation, hypercube computers, machine vision, and manipulators.

Validation of a model to investigate the effects of modifying cardiovascular disease (CVD) risk factors on the burden of CVD: the rotterdam ischemic heart disease and stroke computer simulation (RISC) model.

PubMed

van Kempen, Bob J H; Ferket, Bart S; Hofman, Albert; Steyerberg, Ewout W; Colkesen, Ersen B; Boekholdt, S Matthijs; Wareham, Nicholas J; Khaw, Kay-Tee; Hunink, M G Myriam

2012-12-06

We developed a Monte Carlo Markov model designed to investigate the effects of modifying cardiovascular disease (CVD) risk factors on the burden of CVD. Internal, predictive, and external validity of the model have not yet been established. The Rotterdam Ischemic Heart Disease and Stroke Computer Simulation (RISC) model was developed using data covering 5 years of follow-up from the Rotterdam Study. To prove 1) internal and 2) predictive validity, the incidences of coronary heart disease (CHD), stroke, CVD death, and non-CVD death simulated by the model over a 13-year period were compared with those recorded for 3,478 participants in the Rotterdam Study with at least 13 years of follow-up. 3) External validity was verified using 10 years of follow-up data from the European Prospective Investigation of Cancer (EPIC)-Norfolk study of 25,492 participants, for whom CVD and non-CVD mortality was compared. At year 5, the observed incidences (with simulated incidences in brackets) of CHD, stroke, and CVD and non-CVD mortality for the 3,478 Rotterdam Study participants were 5.30% (4.68%), 3.60% (3.23%), 4.70% (4.80%), and 7.50% (7.96%), respectively. At year 13, these percentages were 10.60% (10.91%), 9.90% (9.13%), 14.20% (15.12%), and 24.30% (23.42%). After recalibrating the model for the EPIC-Norfolk population, the 10-year observed (simulated) incidences of CVD and non-CVD mortality were 3.70% (4.95%) and 6.50% (6.29%). All observed incidences fell well within the 95% credibility intervals of the simulated incidences. We have confirmed the internal, predictive, and external validity of the RISC model. These findings provide a basis for analyzing the effects of modifying cardiovascular disease risk factors on the burden of CVD with the RISC model.

MLBCD: a machine learning tool for big clinical data.

PubMed

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

Human evolution in the age of the intelligent machine

NASA Technical Reports Server (NTRS)

Mclaughlin, W. I.

1983-01-01

A systems analysis of the future evolution of man can be conducted by analyzing the biological material of the galaxy into three subsystems: man, intelligent machines, and intelligent extraterrestrial organisms. A binomial interpretation is applied to this system wherein each of the subsystems is assigned a designation of success or failure. For man the two alternatives are, respectively, 'decline' or 'flourish', for machine they are 'become intelligent' or 'stay dumb', while for extraterrestrial intelligence the dichotomy is that of 'existence' or 'nonexistence'. The choices for each of three subsystems yield a total of eight possible states for the system. The relative lack of integration between brain components makes man a weak evolutionary contestant compared to machines. It is judged that machines should become dominant on earth within 100 years, probably by means of continuing development of existing man-machine systems. Advanced forms of extraterrestrial intelligence may exist but are too difficult to observe. The prospects for communication with extraterrestrial intelligence are reviewed.

Human evolution in the age of the intelligent machine

NASA Astrophysics Data System (ADS)

McLaughlin, W. I.

A systems analysis of the future evolution of man can be conducted by analyzing the biological material of the galaxy into three subsystems: man, intelligent machines, and intelligent extraterrestrial organisms. A binomial interpretation is applied to this system wherein each of the subsystems is assigned a designation of success or failure. For man the two alternatives are, respectively, 'decline' or 'flourish', for machine they are 'become intelligent' or 'stay dumb', while for extraterrestrial intelligence the dichotomy is that of 'existence' or 'nonexistence'. The choices for each of three subsystems yield a total of eight possible states for the system. The relative lack of integration between brain components makes man a weak evolutionary contestant compared to machines. It is judged that machines should become dominant on earth within 100 years, probably by means of continuing development of existing man-machine systems. Advanced forms of extraterrestrial intelligence may exist but are too difficult to observe. The prospects for communication with extraterrestrial intelligence are reviewed.

Machine learning for Big Data analytics in plants.

PubMed

Ma, Chuang; Zhang, Hao Helen; Wang, Xiangfeng

2014-12-01

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences. Copyright © 2014 Elsevier Ltd. All rights reserved.

Hands-free human-machine interaction with voice

NASA Astrophysics Data System (ADS)

Juang, B. H.

2004-05-01

Voice is natural communication interface between a human and a machine. The machine, when placed in today's communication networks, may be configured to provide automation to save substantial operating cost, as demonstrated in AT&T's VRCP (Voice Recognition Call Processing), or to facilitate intelligent services, such as virtual personal assistants, to enhance individual productivity. These intelligent services often need to be accessible anytime, anywhere (e.g., in cars when the user is in a hands-busy-eyes-busy situation or during meetings where constantly talking to a microphone is either undersirable or impossible), and thus call for advanced signal processing and automatic speech recognition techniques which support what we call ``hands-free'' human-machine communication. These techniques entail a broad spectrum of technical ideas, ranging from use of directional microphones and acoustic echo cancellatiion to robust speech recognition. In this talk, we highlight a number of key techniques that were developed for hands-free human-machine communication in the mid-1990s after Bell Labs became a unit of Lucent Technologies. A video clip will be played to demonstrate the accomplishement.

Assessing Advanced Technology in CENATE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tallent, Nathan R.; Barker, Kevin J.; Gioiosa, Roberto

PNNL's Center for Advanced Technology Evaluation (CENATE) is a new U.S. Department of Energy center whose mission is to assess and facilitate access to emerging computing technology. CENATE is assessing a range of advanced technologies, from evolutionary to disruptive. Technologies of interest include the processor socket (homogeneous and accelerated systems), memories (dynamic, static, memory cubes), motherboards, networks (network interface cards and switches), and input/output and storage devices. CENATE is developing a multi-perspective evaluation process based on integrating advanced system instrumentation, performance measurements, and modeling and simulation. We show evaluations of two emerging network technologies: silicon photonics interconnects and the Datamore » Vortex network. CENATE's evaluation also addresses the question of which machine is best for a given workload under certain constraints. We show a performance-power tradeoff analysis of a well-known machine learning application on two systems.« less

The laser micro-machining system for diamond anvil cell experiments and general precision machining applications at the High Pressure Collaborative Access Team

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hrubiak, Rostislav; Sinogeikin, Stanislav; Rod, Eric

We have designed and constructed a new system for micro-machining parts and sample assemblies used for diamond anvil cells and general user operations at the High Pressure Collaborative Access Team, sector 16 of the Advanced Photon Source. The new micro-machining system uses a pulsed laser of 400 ps pulse duration, ablating various materials without thermal melting, thus leaving a clean edge. With optics designed for a tight focus, the system can machine holes any size larger than 3 μm in diameter. Unlike a standard electrical discharge machining drill, the new laser system allows micro-machining of non-conductive materials such as: amorphousmore » boron and silicon carbide gaskets, diamond, oxides, and other materials including organic materials such as polyimide films (i.e., Kapton). An important feature of the new system is the use of gas-tight or gas-flow environmental chambers which allow the laser micro-machining to be done in a controlled (e.g., inert gas) atmosphere to prevent oxidation and other chemical reactions in air sensitive materials. The gas-tight workpiece enclosure is also useful for machining materials with known health risks (e.g., beryllium). Specialized control software with a graphical interface enables micro-machining of custom 2D and 3D shapes. The laser-machining system was designed in a Class 1 laser enclosure, i.e., it includes laser safety interlocks and computer controls and allows for routine operation. Though initially designed mainly for machining of the diamond anvil cell gaskets, the laser-machining system has since found many other micro-machining applications, several of which are presented here.« less

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

PubMed Central

Zhao, Changbo; Li, Guo-Zheng; Wang, Chengjun; Niu, Jinling

2015-01-01

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification. PMID:26246834

Expression levels of the microRNA maturing microprocessor complex component DGCR8 and the RNA-induced silencing complex (RISC) components argonaute-1, argonaute-2, PACT, TARBP1, and TARBP2 in epithelial skin cancer.

PubMed

Sand, Michael; Skrygan, Marina; Georgas, Dimitrios; Arenz, Christoph; Gambichler, Thilo; Sand, Daniel; Altmeyer, Peter; Bechara, Falk G

2012-11-01

The microprocessor complex mediates intranuclear biogenesis of precursor microRNAs from the primary microRNA transcript. Extranuclear, mature microRNAs are incorporated into the RNA-induced silencing complex (RISC) before interaction with complementary target mRNA leads to transcriptional repression or cleavage. In this study, we investigated the expression profiles of the microprocessor complex subunit DiGeorge syndrome critical region gene 8 (DGCR8) and the RISC components argonaute-1 (AGO1), argonaute-2 (AGO2), as well as double-stranded RNA-binding proteins PACT, TARBP1, and TARBP2 in epithelial skin cancer and its premalignant stage. Patients with premalignant actinic keratoses (AK, n = 6), basal cell carcinomas (BCC, n = 15), and squamous cell carcinomas (SCC, n = 7) were included in the study. Punch biopsies were harvested from the center of the tumors (lesional), from healthy skin sites (intraindividual controls), and from healthy skin sites in a healthy control group (n = 16; interindividual control). The DGCR8, AGO1, AGO2, PACT, TARBP1, and TARBP2 mRNA expression levels were detected by quantitative real-time reverse transcriptase polymerase chain reaction. The DGCR8, AGO1, AGO2, PACT, and TARBP1 expression levels were significantly higher in the AK, BCC, and SCC groups than the healthy controls (P < 0.05). There was no significant difference in the TARBP2 expression levels between groups (P > 0.05). This study indicates that major components of the miRNA pathway, such as the microprocessor complex and RISC, are dysregulated in epithelial skin cancer. Copyright © 2011 Wiley Periodicals, Inc.

ADVANCED MANUFACTURING TEAM

NASA Image and Video Library

2016-03-17

KEN COOPER, TEAM LEAD OF MSFC’S ADVANCED MANUFACTURING TEAM, WITH NICKEL ALLOY 718 PARTS FABRICATED USING THE M1 SELECTIVE LASER MELTING SYSTEM. THE M1 MACHINE IS DEDICATED TO BUILDING QUALIFICATION SAMPLES AND HARDWARE DEMONSTRATORS FOR THE RS25 ENGINE PROJECT.

Machine rates for selected forest harvesting machines

Treesearch

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

Automated planning for intelligent machines in energy-related applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weisbin, C.R.; de Saussure, G.; Barhen, J.

1984-01-01

This paper discusses the current activities of the Center for Engineering Systems Advanced Research (CESAR) program related to plan generation and execution by an intelligent machine. The system architecture for the CESAR mobile robot (named HERMIES-1) is described. The minimal cut-set approach is developed to reduce the tree search time of conventional backward chaining planning techniques. Finally, a real-time concept of an Intelligent Machine Operating System is presented in which planning and reasoning is embedded in a system for resource allocation and process management.

Parameter optimization of electrochemical machining process using black hole algorithm

NASA Astrophysics Data System (ADS)

Singh, Dinesh; Shukla, Rajkamal

2017-12-01

Advanced machining processes are significant as higher accuracy in machined component is required in the manufacturing industries. Parameter optimization of machining processes gives optimum control to achieve the desired goals. In this paper, electrochemical machining (ECM) process is considered to evaluate the performance of the considered process using black hole algorithm (BHA). BHA considers the fundamental idea of a black hole theory and it has less operating parameters to tune. The two performance parameters, material removal rate (MRR) and overcut (OC) are considered separately to get optimum machining parameter settings using BHA. The variations of process parameters with respect to the performance parameters are reported for better and effective understanding of the considered process using single objective at a time. The results obtained using BHA are found better while compared with results of other metaheuristic algorithms, such as, genetic algorithm (GA), artificial bee colony (ABC) and bio-geography based optimization (BBO) attempted by previous researchers.

An embedded EEG analyzing system based on muC/os-II.

PubMed

Liu, Boqiang; Zhang, Yanyan; Liu, Zhongguo; Yin, Cong

2007-01-01

An EEG analyzing system based on Advanced RISC Machines (ARM) and muC/os-II real time operating system is discussed in this paper. The detailed system design including the producing of event signals and the synchronization between event signals and EEG signals is described. The details of data acquisition, data preprocessing, data transmitting through USB and system configurations are also contained in the system design. In this paper the design of high capability amplifier and the software of embedded subsystem are discussed. Also the design of realizing multi-task system in muC/os-II, the definition of communicating protocols between PC and the equipment and the detail configurations of USB are given out. The final test shows that the filter behaviors of this equipment are feasible.

Single instruction computer architecture and its application in image processing

NASA Astrophysics Data System (ADS)

Laplante, Phillip A.

1992-03-01

A single processing computer system using only half-adder circuits is described. In addition, it is shown that only a single hard-wired instruction is needed in the control unit to obtain a complete instruction set for this general purpose computer. Such a system has several advantages. First it is intrinsically a RISC machine--in fact the 'ultimate RISC' machine. Second, because only a single type of logic element is employed the entire computer system can be easily realized on a single, highly integrated chip. Finally, due to the homogeneous nature of the computer's logic elements, the computer has possible implementations as an optical or chemical machine. This in turn suggests possible paradigms for neural computing and artificial intelligence. After showing how we can implement a full-adder, min, max and other operations using the half-adder, we use an array of such full-adders to implement the dilation operation for two black and white images. Next we implement the erosion operation of two black and white images using a relative complement function and the properties of erosion and dilation. This approach was inspired by papers by van der Poel in which a single instruction is used to furnish a complete set of general purpose instructions and by Bohm- Jacopini where it is shown that any problem can be solved using a Turing machine with one entry and one exit.

Machine learning for micro-tomography

NASA Astrophysics Data System (ADS)

Parkinson, Dilworth Y.; Pelt, Daniël. M.; Perciano, Talita; Ushizima, Daniela; Krishnan, Harinarayan; Barnard, Harold S.; MacDowell, Alastair A.; Sethian, James

2017-09-01

Machine learning has revolutionized a number of fields, but many micro-tomography users have never used it for their work. The micro-tomography beamline at the Advanced Light Source (ALS), in collaboration with the Center for Applied Mathematics for Energy Research Applications (CAMERA) at Lawrence Berkeley National Laboratory, has now deployed a series of tools to automate data processing for ALS users using machine learning. This includes new reconstruction algorithms, feature extraction tools, and image classification and recommen- dation systems for scientific image. Some of these tools are either in automated pipelines that operate on data as it is collected or as stand-alone software. Others are deployed on computing resources at Berkeley Lab-from workstations to supercomputers-and made accessible to users through either scripting or easy-to-use graphical interfaces. This paper presents a progress report on this work.

Diverse applications of advanced man-telerobot interfaces

NASA Technical Reports Server (NTRS)

Mcaffee, Douglas A.

1991-01-01

Advancements in man-machine interfaces and control technologies used in space telerobotics and teleoperators have potential application wherever human operators need to manipulate multi-dimensional spatial relationships. Bilateral six degree-of-freedom position and force cues exchanged between the user and a complex system can broaden and improve the effectiveness of several diverse man-machine interfaces.

Machine learning and computer vision approaches for phenotypic profiling

PubMed Central

Morris, Quaid

2017-01-01

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. PMID:27940887

Machine learning and computer vision approaches for phenotypic profiling.

PubMed

Grys, Ben T; Lo, Dara S; Sahin, Nil; Kraus, Oren Z; Morris, Quaid; Boone, Charles; Andrews, Brenda J

2017-01-02

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. © 2017 Grys et al.

[A new machinability test machine and the machinability of composite resins for core built-up].

PubMed

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

Simulation-driven machine learning: Bearing fault classification

NASA Astrophysics Data System (ADS)

Sobie, Cameron; Freitas, Carina; Nicolai, Mike

2018-01-01

Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

Virtual C Machine and Integrated Development Environment for ATMS Controllers.

DOT National Transportation Integrated Search

2000-04-01

The overall objective of this project is to develop a prototype virtual machine that fits on current Advanced Traffic Management Systems (ATMS) controllers and provides functionality for complex traffic operations.;Prepared in cooperation with Utah S...

Synaptotagmin 11 interacts with components of the RNA-induced silencing complex RISC in clonal pancreatic β-cells.

PubMed

Milochau, Alexandra; Lagrée, Valérie; Benassy, Marie-Noëlle; Chaignepain, Stéphane; Papin, Julien; Garcia-Arcos, Itsaso; Lajoix, Anne; Monterrat, Carole; Coudert, Laetitia; Schmitter, Jean-Marie; Ochoa, Begoña; Lang, Jochen

2014-06-27

Synaptotagmins are two C2 domain-containing transmembrane proteins. The function of calcium-sensitive members in the regulation of post-Golgi traffic has been well established whereas little is known about the calcium-insensitive isoforms constituting half of the protein family. Novel binding partners of synaptotagmin 11 were identified in β-cells. A number of them had been assigned previously to ER/Golgi derived-vesicles or linked to RNA synthesis, translation and processing. Whereas the C2A domain interacted with the Q-SNARE Vti1a, the C2B domain of syt11 interacted with the SND1, Ago2 and FMRP, components of the RNA-induced silencing complex (RISC). Binding to SND was direct via its N-terminal tandem repeats. Our data indicate that syt11 may provide a link between gene regulation by microRNAs and membrane traffic. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

DARPA Robotics Challenge (DRC) Using Human-Machine Teamwork to Perform Disaster Response with a Humanoid Robot

DTIC Science & Technology

2017-02-01

DARPA ROBOTICS CHALLENGE (DRC) USING HUMAN-MACHINE TEAMWORK TO PERFORM DISASTER RESPONSE WITH A HUMANOID ROBOT FLORIDA INSTITUTE FOR HUMAN AND...AND SUBTITLE DARPA ROBOTICS CHALLENGE (DRC) USING HUMAN-MACHINE TEAMWORK TO PERFORM DISASTER RESPONSE WITH A HUMANOID ROBOT 5a. CONTRACT NUMBER...Human and Machine Cognition (IHMC) from 2012-2016 through three phases of the Defense Advanced Research Projects Agency (DARPA) Robotics Challenge

The Body-Machine Interface: A new perspective on an old theme

PubMed Central

Casadio, Maura; Ranganathan, Rajiv; Mussa-Ivaldi, Ferdinando A.

2012-01-01

Body-machine interfaces establish a way to interact with a variety of devices, allowing their users to extend the limits of their performance. Recent advances in this field, ranging from computer-interfaces to bionic limbs, have had important consequences for people with movement disorders. In this article, we provide an overview of the basic concepts underlying the body-machine interface with special emphasis on their use for rehabilitation and for operating assistive devices. We outline the steps involved in building such an interface and we highlight the critical role of body-machine interfaces in addressing theoretical issues in motor control as well as their utility in movement rehabilitation. PMID:23237465

Machine Translation: The Alternative for the 21st Century?

ERIC Educational Resources Information Center

Cribb, V. Michael

2000-01-01

Outlines a scenario for the future of Teaching English as a Second or Other Languages that has seldom, if ever been considered in academic discussion: that advances in and availability of quality machine translation could mitigate the need for English language learning. (Author/VWL)

Machine Learning in Medicine.

PubMed

Deo, Rahul C

2015-11-17

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. © 2015 American Heart Association, Inc.

Machine Learning in Medicine

PubMed Central

Deo, Rahul C.

2015-01-01

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games – tasks which would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in healthcare. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades – and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. PMID:26572668

Context in Models of Human-Machine Systems

NASA Technical Reports Server (NTRS)

Callantine, Todd J.; Null, Cynthia H. (Technical Monitor)

1998-01-01

All human-machine systems models represent context. This paper proposes a theory of context through which models may be usefully related and integrated for design. The paper presents examples of context representation in various models, describes an application to developing models for the Crew Activity Tracking System (CATS), and advances context as a foundation for integrated design of complex dynamic systems.

Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review.

PubMed

Foulquier, Nathan; Redou, Pascal; Le Gal, Christophe; Rouvière, Bénédicte; Pers, Jacques-Olivier; Saraux, Alain

2018-05-17

Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

Machine Learning, deep learning and optimization in computer vision

NASA Astrophysics Data System (ADS)

Canu, Stéphane

2017-03-01

As quoted in the Large Scale Computer Vision Systems NIPS workshop, computer vision is a mature field with a long tradition of research, but recent advances in machine learning, deep learning, representation learning and optimization have provided models with new capabilities to better understand visual content. The presentation will go through these new developments in machine learning covering basic motivations, ideas, models and optimization in deep learning for computer vision, identifying challenges and opportunities. It will focus on issues related with large scale learning that is: high dimensional features, large variety of visual classes, and large number of examples.

Machine learning for epigenetics and future medical applications

PubMed Central

Holder, Lawrence B.; Haque, M. Muksitul; Skinner, Michael K.

2017-01-01

ABSTRACT Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review. PMID:28524769

Machine learning for epigenetics and future medical applications.

PubMed

Holder, Lawrence B; Haque, M Muksitul; Skinner, Michael K

2017-07-03

Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review.

Classifying Black Hole States with Machine Learning

NASA Astrophysics Data System (ADS)

Huppenkothen, Daniela

2018-01-01

Galactic black hole binaries are known to go through different states with apparent signatures in both X-ray light curves and spectra, leading to important implications for accretion physics as well as our knowledge of General Relativity. Existing frameworks of classification are usually based on human interpretation of low-dimensional representations of the data, and generally only apply to fairly small data sets. Machine learning, in contrast, allows for rapid classification of large, high-dimensional data sets. In this talk, I will report on advances made in classification of states observed in Black Hole X-ray Binaries, focusing on the two sources GRS 1915+105 and Cygnus X-1, and show both the successes and limitations of using machine learning to derive physical constraints on these systems.

Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

ERIC Educational Resources Information Center

Crossley, Scott A.

2013-01-01

This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

Hybrid test on building structures using electrodynamic fatigue test machine

NASA Astrophysics Data System (ADS)

Xu, Zhao-Dong; Wang, Kai-Yang; Guo, Ying-Qing; Wu, Min-Dong; Xu, Meng

2017-01-01

Hybrid simulation is an advanced structural dynamic experimental method that combines experimental physical models with analytical numerical models. It has increasingly been recognised as a powerful methodology to evaluate structural nonlinear components and systems under realistic operating conditions. One of the barriers for this advanced testing is the lack of flexible software for hybrid simulation using heterogeneous experimental equipment. In this study, an electrodynamic fatigue test machine is made and a MATLAB program is developed for hybrid simulation. Compared with the servo-hydraulic system, electrodynamic fatigue test machine has the advantages of small volume, easy operation and fast response. A hybrid simulation is conducted to verify the flexibility and capability of the whole system whose experimental substructure is one spring brace and numerical substructure is a two-storey steel frame structure. Experimental and numerical results show the feasibility and applicability of the whole system.

Recent developments in machine learning applications in landslide susceptibility mapping

NASA Astrophysics Data System (ADS)

Lun, Na Kai; Liew, Mohd Shahir; Matori, Abdul Nasir; Zawawi, Noor Amila Wan Abdullah

2017-11-01

While the prediction of spatial distribution of potential landslide occurrences is a primary interest in landslide hazard mitigation, it remains a challenging task. To overcome the scarceness of complete, sufficiently detailed geomorphological attributes and environmental conditions, various machine-learning techniques are increasingly applied to effectively map landslide susceptibility for large regions. Nevertheless, limited review papers are devoted to this field, particularly on the various domain specific applications of machine learning techniques. Available literature often report relatively good predictive performance, however, papers discussing the limitations of each approaches are quite uncommon. The foremost aim of this paper is to narrow these gaps in literature and to review up-to-date machine learning and ensemble learning techniques applied in landslide susceptibility mapping. It provides new readers an introductory understanding on the subject matter and researchers a contemporary review of machine learning advancements alongside the future direction of these techniques in the landslide mitigation field.

Machine Translation-Assisted Language Learning: Writing for Beginners

ERIC Educational Resources Information Center

Garcia, Ignacio; Pena, Maria Isabel

2011-01-01

The few studies that deal with machine translation (MT) as a language learning tool focus on its use by advanced learners, never by beginners. Yet, freely available MT engines (i.e. Google Translate) and MT-related web initiatives (i.e. Gabble-on.com) position themselves to cater precisely to the needs of learners with a limited command of a…

What is consciousness, and could machines have it?

PubMed

Dehaene, Stanislas; Lau, Hakwan; Kouider, Sid

2017-10-27

The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word "consciousness" conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures. Copyright © 2017, American Association for the Advancement of Science.

Automation Applications in an Advanced Air Traffic Management System : Volume 3. Methodology for Man-Machine Task Allocation

DOT National Transportation Integrated Search

1974-08-01

Volume 3 describes the methodology for man-machine task allocation. It contains a description of man and machine performance capabilities and an explanation of the methodology employed to allocate tasks to human or automated resources. It also presen...

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation.

PubMed

Segreto, Tiziana; Caggiano, Alessandra; Karam, Sara; Teti, Roberto

2017-12-12

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

PubMed Central

Segreto, Tiziana; Karam, Sara; Teti, Roberto

2017-01-01

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions. PMID:29231864

ATCA for Machines-- Advanced Telecommunications Computing Architecture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larsen, R.S.; /SLAC

2008-04-22

The Advanced Telecommunications Computing Architecture is a new industry open standard for electronics instrument modules and shelves being evaluated for the International Linear Collider (ILC). It is the first industrial standard designed for High Availability (HA). ILC availability simulations have shown clearly that the capabilities of ATCA are needed in order to achieve acceptable integrated luminosity. The ATCA architecture looks attractive for beam instruments and detector applications as well. This paper provides an overview of ongoing R&D including application of HA principles to power electronics systems.

Identification of Synchronous Machine Stability - Parameters: AN On-Line Time-Domain Approach.

NASA Astrophysics Data System (ADS)

Le, Loc Xuan

1987-09-01

A time-domain modeling approach is described which enables the stability-study parameters of the synchronous machine to be determined directly from input-output data measured at the terminals of the machine operating under normal conditions. The transient responses due to system perturbations are used to identify the parameters of the equivalent circuit models. The described models are verified by comparing their responses with the machine responses generated from the transient stability models of a small three-generator multi-bus power system and of a single -machine infinite-bus power network. The least-squares method is used for the solution of the model parameters. As a precaution against ill-conditioned problems, the singular value decomposition (SVD) is employed for its inherent numerical stability. In order to identify the equivalent-circuit parameters uniquely, the solution of a linear optimization problem with non-linear constraints is required. Here, the SVD appears to offer a simple solution to this otherwise difficult problem. Furthermore, the SVD yields solutions with small bias and, therefore, physically meaningful parameters even in the presence of noise in the data. The question concerning the need for a more advanced model of the synchronous machine which describes subtransient and even sub-subtransient behavior is dealt with sensibly by the concept of condition number. The concept provides a quantitative measure for determining whether such an advanced model is indeed necessary. Finally, the recursive SVD algorithm is described for real-time parameter identification and tracking of slowly time-variant parameters. The algorithm is applied to identify the dynamic equivalent power system model.

Ethical, environmental and social issues for machine vision in manufacturing industry

NASA Astrophysics Data System (ADS)

Batchelor, Bruce G.; Whelan, Paul F.

1995-10-01

Some of the ethical, environmental and social issues relating to the design and use of machine vision systems in manufacturing industry are highlighted. The authors' aim is to emphasize some of the more important issues, and raise general awareness of the need to consider the potential advantages and hazards of machine vision technology. However, in a short article like this, it is impossible to cover the subject comprehensively. This paper should therefore be seen as a discussion document, which it is hoped will provoke more detailed consideration of these very important issues. It follows from an article presented at last year's workshop. Five major topics are discussed: (1) The impact of machine vision systems on the environment; (2) The implications of machine vision for product and factory safety, the health and well-being of employees; (3) The importance of intellectual integrity in a field requiring a careful balance of advanced ideas and technologies; (4) Commercial and managerial integrity; and (5) The impact of machine visions technology on employment prospects, particularly for people with low skill levels.

Binary pressure-sensitive paint measurements using miniaturised, colour, machine vision cameras

NASA Astrophysics Data System (ADS)

Quinn, Mark Kenneth

2018-05-01

Recent advances in machine vision technology and capability have led to machine vision cameras becoming applicable for scientific imaging. This study aims to demonstrate the applicability of machine vision colour cameras for the measurement of dual-component pressure-sensitive paint (PSP). The presence of a second luminophore component in the PSP mixture significantly reduces its inherent temperature sensitivity, increasing its applicability at low speeds. All of the devices tested are smaller than the cooled CCD cameras traditionally used and most are of significantly lower cost, thereby increasing the accessibility of such technology and techniques. Comparisons between three machine vision cameras, a three CCD camera, and a commercially available specialist PSP camera are made on a range of parameters, and a detailed PSP calibration is conducted in a static calibration chamber. The findings demonstrate that colour machine vision cameras can be used for quantitative, dual-component, pressure measurements. These results give rise to the possibility of performing on-board dual-component PSP measurements in wind tunnels or on real flight/road vehicles.

Micro Machining Enhances Precision Fabrication

NASA Technical Reports Server (NTRS)

2007-01-01

Advanced thermal systems developed for the Space Station Freedom project are now in use on the International Space Station. These thermal systems employ evaporative ammonia as their coolant, and though they employ the same series of chemical reactions as terrestrial refrigerators, the space-bound coolers are significantly smaller. Two Small Business Innovation Research (SBIR) contracts between Creare Inc. of Hanover, NH and Johnson Space Center developed an ammonia evaporator for thermal management systems aboard Freedom. The principal investigator for Creare Inc., formed Mikros Technologies Inc. to commercialize the work. Mikros Technologies then developed an advanced form of micro-electrical discharge machining (micro-EDM) to make tiny holes in the ammonia evaporator. Mikros Technologies has had great success applying this method to the fabrication of micro-nozzle array systems for industrial ink jet printing systems. The company is currently the world leader in fabrication of stainless steel micro-nozzles for this market, and in 2001 the company was awarded two SBIR research contracts from Goddard Space Flight Center to advance micro-fabrication and high-performance thermal management technologies.

Gradient boosting machine for modeling the energy consumption of commercial buildings

DOE PAGES

Touzani, Samir; Granderson, Jessica; Fernandes, Samuel

2017-11-26

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradientmore » boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.« less

Gradient boosting machine for modeling the energy consumption of commercial buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Touzani, Samir; Granderson, Jessica; Fernandes, Samuel

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradientmore » boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.« less

Steady State Advanced Tokamak (SSAT): The mission and the machine

NASA Astrophysics Data System (ADS)

Thomassen, K.; Goldston, R.; Nevins, B.; Neilson, H.; Shannon, T.; Montgomery, B.

1992-03-01

Extending the tokamak concept to the steady state regime and pursuing advances in tokamak physics are important and complementary steps for the magnetic fusion energy program. The required transition away from inductive current drive will provide exciting opportunities for advances in tokamak physics, as well as important impetus to drive advances in fusion technology. Recognizing this, the Fusion Policy Advisory Committee and the U.S. National Energy Strategy identified the development of steady state tokamak physics and technology, and improvements in the tokamak concept, as vital elements in the magnetic fusion energy development plan. Both called for the construction of a steady state tokamak facility to address these plan elements. Advances in physics that produce better confinement and higher pressure limits are required for a similar unit size reactor. Regimes with largely self-driven plasma current are required to permit a steady-state tokamak reactor with acceptable recirculating power. Reliable techniques of disruption control will be needed to achieve the availability goals of an economic reactor. Thus the central role of this new tokamak facility is to point the way to a more attractive demonstration reactor (DEMO) than the present data base would support. To meet the challenges, we propose a new 'Steady State Advanced Tokamak' (SSAT) facility that would develop and demonstrate optimized steady state tokamak operating mode. While other tokamaks in the world program employ superconducting toroidal field coils, SSAT would be the first major tokamak to operate with a fully superconducting coil set in the elongated, divertor geometry planned for ITER and DEMO.

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

PubMed Central

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao; Ramsey, Joseph D.; Glymour, Clark; Noecker, Robert J.; Ishikawa, Hiroshi; Schuman, Joel S.

2007-01-01

Purpose Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. Methods Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] ≥ −6 dB) and 20 had advanced glaucoma (MD < −6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. Results The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). Conclusions Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality. PMID:16249492

INS3D - NUMERICAL SOLUTION OF THE INCOMPRESSIBLE NAVIER-STOKES EQUATIONS IN THREE-DIMENSIONAL GENERALIZED CURVILINEAR COORDINATES (CRAY VERSION)

NASA Technical Reports Server (NTRS)

Rogers, S. E.

1994-01-01

-field boundaries. Three machine versions of INS3D are available. INS3D for the CRAY is written in CRAY FORTRAN for execution on a CRAY X-MP under COS, INS3D for the IBM is written in FORTRAN 77 for execution on an IBM 3090 under the VM or MVS operating system, and INS3D for DEC RISC-based systems is written in RISC FORTRAN for execution on a DEC workstation running RISC ULTRIX 3.1 or later. The CRAY version has a central memory requirement of 730279 words. The central memory requirement for the IBM is 150Mb. The memory requirement for the DEC RISC ULTRIX version is 3Mb of main memory. INS3D was developed in 1987. The port to the IBM was done in 1990. The port to the DECstation 3100 was done in 1991. CRAY is a registered trademark of Cray Research Inc. IBM is a registered trademark of International Business Machines. DEC, DECstation, and ULTRIX are trademarks of the Digital Equipment Corporation.

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1989-01-01

In the present technological society, there is a major need to build machines that would execute intelligent tasks operating in uncertain environments with minimum interaction with a human operator. Although some designers have built smart robots, utilizing heuristic ideas, there is no systematic approach to design such machines in an engineering manner. Recently, cross-disciplinary research from the fields of computers, systems AI and information theory has served to set the foundations of the emerging area of the design of intelligent machines. Since 1977 Saridis has been developing an approach, defined as Hierarchical Intelligent Control, designed to organize, coordinate and execute anthropomorphic tasks by a machine with minimum interaction with a human operator. This approach utilizes analytical (probabilistic) models to describe and control the various functions of the intelligent machine structured by the intuitively defined principle of Increasing Precision with Decreasing Intelligence (IPDI) (Saridis 1979). This principle, even though resembles the managerial structure of organizational systems (Levis 1988), has been derived on an analytic basis by Saridis (1988). The purpose is to derive analytically a Boltzmann machine suitable for optimal connection of nodes in a neural net (Fahlman, Hinton, Sejnowski, 1985). Then this machine will serve to search for the optimal design of the organization level of an intelligent machine. In order to accomplish this, some mathematical theory of the intelligent machines will be first outlined. Then some definitions of the variables associated with the principle, like machine intelligence, machine knowledge, and precision will be made (Saridis, Valavanis 1988). Then a procedure to establish the Boltzmann machine on an analytic basis will be presented and illustrated by an example in designing the organization level of an Intelligent Machine. A new search technique, the Modified Genetic Algorithm, is presented and proved

AAA+ Machines of Protein Destruction in Mycobacteria.

PubMed

Alhuwaider, Adnan Ali H; Dougan, David A

2017-01-01

The bacterial cytosol is a complex mixture of macromolecules (proteins, DNA, and RNA), which collectively are responsible for an enormous array of cellular tasks. Proteins are central to most, if not all, of these tasks and as such their maintenance (commonly referred to as protein homeostasis or proteostasis) is vital for cell survival during normal and stressful conditions. The two key aspects of protein homeostasis are, (i) the correct folding and assembly of proteins (coupled with their delivery to the correct cellular location) and (ii) the timely removal of unwanted or damaged proteins from the cell, which are performed by molecular chaperones and proteases, respectively. A major class of proteins that contribute to both of these tasks are the AAA+ (ATPases associated with a variety of cellular activities) protein superfamily. Although much is known about the structure of these machines and how they function in the model Gram-negative bacterium Escherichia coli , we are only just beginning to discover the molecular details of these machines and how they function in mycobacteria. Here we review the different AAA+ machines, that contribute to proteostasis in mycobacteria. Primarily we will focus on the recent advances in the structure and function of AAA+ proteases, the substrates they recognize and the cellular pathways they control. Finally, we will discuss the recent developments related to these machines as novel drug targets.

Building machines that learn and think like people.

PubMed

Lake, Brenden M; Ullman, Tomer D; Tenenbaum, Joshua B; Gershman, Samuel J

2017-01-01

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

A Review on Parametric Analysis of Magnetic Abrasive Machining Process

NASA Astrophysics Data System (ADS)

Khattri, Krishna; Choudhary, Gulshan; Bhuyan, B. K.; Selokar, Ashish

2018-03-01

The magnetic abrasive machining (MAM) process is a highly developed unconventional machining process. It is frequently used in manufacturing industries for nanometer range surface finishing of workpiece with the help of Magnetic abrasive particles (MAPs) and magnetic force applied in the machining zone. It is precise and faster than conventional methods and able to produce defect free finished components. This paper provides a comprehensive review on the recent advancement of MAM process carried out by different researcher till date. The effect of different input parameters such as rotational speed of electromagnet, voltage, magnetic flux density, abrasive particles size and working gap on the performances of Material Removal Rate (MRR) and surface roughness (Ra) have been discussed. On the basis of review, it is observed that the rotational speed of electromagnet, voltage and mesh size of abrasive particles have significant impact on MAM process.

Classifying smoking urges via machine learning.

PubMed

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-12-01

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

Classifying smoking urges via machine learning

PubMed Central

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-01-01

Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms’ performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions

Liquid lens: advances in adaptive optics

NASA Astrophysics Data System (ADS)

Casey, Shawn Patrick

2010-12-01

'Liquid lens' technologies promise significant advancements in machine vision and optical communications systems. Adaptations for machine vision, human vision correction, and optical communications are used to exemplify the versatile nature of this technology. Utilization of liquid lens elements allows the cost effective implementation of optical velocity measurement. The project consists of a custom image processor, camera, and interface. The images are passed into customized pattern recognition and optical character recognition algorithms. A single camera would be used for both speed detection and object recognition.

Human Cognitive Enhancement Ethical Implications for Airman-Machine Teaming

DTIC Science & Technology

2017-04-06

34 Psychological Constructs versus Neural Mechanisms: Different Perspectives for Advanced Research of Cognitive Processes and Development of Neuroadaptive...AIR WAR COLLEGE AIR UNIVERSITY HUMAN COGNITIVE ENHANCEMENT ETHICAL IMPLICATIONS FOR AIRMAN-MACHINE TEAMING by William M. Curlin...increasingly challenging adversarial threats. It is hypothesized that by the year 2030, human system operators will be “ cognitively challenged” to keep pace

Machine Learning Technologies and Their Applications for Science and Engineering Domains Workshop -- Summary Report

NASA Technical Reports Server (NTRS)

Ambur, Manjula; Schwartz, Katherine G.; Mavris, Dimitri N.

2016-01-01

The fields of machine learning and big data analytics have made significant advances in recent years, which has created an environment where cross-fertilization of methods and collaborations can achieve previously unattainable outcomes. The Comprehensive Digital Transformation (CDT) Machine Learning and Big Data Analytics team planned a workshop at NASA Langley in August 2016 to unite leading experts the field of machine learning and NASA scientists and engineers. The primary goal for this workshop was to assess the state-of-the-art in this field, introduce these leading experts to the aerospace and science subject matter experts, and develop opportunities for collaboration. The workshop was held over a three day-period with lectures from 15 leading experts followed by significant interactive discussions. This report provides an overview of the 15 invited lectures and a summary of the key discussion topics that arose during both formal and informal discussion sections. Four key workshop themes were identified after the closure of the workshop and are also highlighted in the report. Furthermore, several workshop attendees provided their feedback on how they are already utilizing machine learning algorithms to advance their research, new methods they learned about during the workshop, and collaboration opportunities they identified during the workshop.

Data Mining and Machine Learning in Astronomy

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Brunner, Robert J.

We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

Recent CESAR (Center for Engineering Systems Advanced Research) research activities in sensor based reasoning for autonomous machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pin, F.G.; de Saussure, G.; Spelt, P.F.

1988-01-01

This paper describes recent research activities at the Center for Engineering Systems Advanced Research (CESAR) in the area of sensor based reasoning, with emphasis being given to their application and implementation on our HERMIES-IIB autonomous mobile vehicle. These activities, including navigation and exploration in a-priori unknown and dynamic environments, goal recognition, vision-guided manipulation and sensor-driven machine learning, are discussed within the framework of a scenario in which an autonomous robot is asked to navigate through an unknown dynamic environment, explore, find and dock at the panel, read and understand the status of the panel's meters and dials, learn the functioningmore » of a process control panel, and successfully manipulate the control devices of the panel to solve a maintenance emergency problems. A demonstration of the successful implementation of the algorithms on our HERMIES-IIB autonomous robot for resolution of this scenario is presented. Conclusions are drawn concerning the applicability of the methodologies to more general classes of problems and implications for future work on sensor-driven reasoning for autonomous robots are discussed. 8 refs., 3 figs.« less

A Developmental Approach to Machine Learning?

PubMed Central

Smith, Linda B.; Slone, Lauren K.

2017-01-01

Visual learning depends on both the algorithms and the training material. This essay considers the natural statistics of infant- and toddler-egocentric vision. These natural training sets for human visual object recognition are very different from the training data fed into machine vision systems. Rather than equal experiences with all kinds of things, toddlers experience extremely skewed distributions with many repeated occurrences of a very few things. And though highly variable when considered as a whole, individual views of things are experienced in a specific order – with slow, smooth visual changes moment-to-moment, and developmentally ordered transitions in scene content. We propose that the skewed, ordered, biased visual experiences of infants and toddlers are the training data that allow human learners to develop a way to recognize everything, both the pervasively present entities and the rarely encountered ones. The joint consideration of real-world statistics for learning by researchers of human and machine learning seems likely to bring advances in both disciplines. PMID:29259573

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

PubMed Central

HUANG, SHUJUN; CAI, NIANGUANG; PACHECO, PEDRO PENZUTI; NARANDES, SHAVIRA; WANG, YANG; XU, WAYNE

2017-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. PMID:29275361

Proteus: a reconfigurable computational network for computer vision

NASA Astrophysics Data System (ADS)

Haralick, Robert M.; Somani, Arun K.; Wittenbrink, Craig M.; Johnson, Robert; Cooper, Kenneth; Shapiro, Linda G.; Phillips, Ihsin T.; Hwang, Jenq N.; Cheung, William; Yao, Yung H.; Chen, Chung-Ho; Yang, Larry; Daugherty, Brian; Lorbeski, Bob; Loving, Kent; Miller, Tom; Parkins, Larye; Soos, Steven L.

1992-04-01

The Proteus architecture is a highly parallel MIMD, multiple instruction, multiple-data machine, optimized for large granularity tasks such as machine vision and image processing The system can achieve 20 Giga-flops (80 Giga-flops peak). It accepts data via multiple serial links at a rate of up to 640 megabytes/second. The system employs a hierarchical reconfigurable interconnection network with the highest level being a circuit switched Enhanced Hypercube serial interconnection network for internal data transfers. The system is designed to use 256 to 1,024 RISC processors. The processors use one megabyte external Read/Write Allocating Caches for reduced multiprocessor contention. The system detects, locates, and replaces faulty subsystems using redundant hardware to facilitate fault tolerance. The parallelism is directly controllable through an advanced software system for partitioning, scheduling, and development. System software includes a translator for the INSIGHT language, a parallel debugger, low and high level simulators, and a message passing system for all control needs. Image processing application software includes a variety of point operators neighborhood, operators, convolution, and the mathematical morphology operations of binary and gray scale dilation, erosion, opening, and closing.

Machine learning of network metrics in ATLAS Distributed Data Management

NASA Astrophysics Data System (ADS)

Lassnig, Mario; Toler, Wesley; Vamosi, Ralf; Bogado, Joaquin; ATLAS Collaboration

2017-10-01

The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for networkaware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

A review of supervised machine learning applied to ageing research.

PubMed

Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

2017-04-01

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

16. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

16. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific Railroad Carlin Shops, view to south (90mm lens). Note the large segmental-arched doorway to move locomotives in and out of Machine Shop. - Southern Pacific Railroad, Carlin Shops, Roundhouse Machine Shop Extension, Foot of Sixth Street, Carlin, Elko County, NV

Smart Cutting Tools and Smart Machining: Development Approaches, and Their Implementation and Application Perspectives

NASA Astrophysics Data System (ADS)

Cheng, Kai; Niu, Zhi-Chao; Wang, Robin C.; Rakowski, Richard; Bateman, Richard

2017-09-01

Smart machining has tremendous potential and is becoming one of new generation high value precision manufacturing technologies in line with the advance of Industry 4.0 concepts. This paper presents some innovative design concepts and, in particular, the development of four types of smart cutting tools, including a force-based smart cutting tool, a temperature-based internally-cooled cutting tool, a fast tool servo (FTS) and smart collets for ultraprecision and micro manufacturing purposes. Implementation and application perspectives of these smart cutting tools are explored and discussed particularly for smart machining against a number of industrial application requirements. They are contamination-free machining, machining of tool-wear-prone Si-based infra-red devices and medical applications, high speed micro milling and micro drilling, etc. Furthermore, implementation techniques are presented focusing on: (a) plug-and-produce design principle and the associated smart control algorithms, (b) piezoelectric film and surface acoustic wave transducers to measure cutting forces in process, (c) critical cutting temperature control in real-time machining, (d) in-process calibration through machining trials, (e) FE-based design and analysis of smart cutting tools, and (f) application exemplars on adaptive smart machining.

Machine characterization based on an abstract high-level language machine

NASA Technical Reports Server (NTRS)

Saavedra-Barrera, Rafael H.; Smith, Alan Jay; Miya, Eugene

1989-01-01

Measurements are presented for a large number of machines ranging from small workstations to supercomputers. The authors combine these measurements into groups of parameters which relate to specific aspects of the machine implementation, and use these groups to provide overall machine characterizations. The authors also define the concept of pershapes, which represent the level of performance of a machine for different types of computation. A metric based on pershapes is introduced that provides a quantitative way of measuring how similar two machines are in terms of their performance distributions. The metric is related to the extent to which pairs of machines have varying relative performance levels depending on which benchmark is used.

Finding New Perovskite Halides via Machine learning

NASA Astrophysics Data System (ADS)

Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho; Lookman, Turab

2016-04-01

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach towards rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 181 experimentally known ABX3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. The trained and validated models then predict, with a high degree of confidence, several novel ABX3 compositions with perovskite crystal structure.

Finding new perovskite halides via machine learning

DOE PAGES

Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho; ...

2016-04-26

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vectormore » machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.« less

Finding new perovskite halides via machine learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vectormore » machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.« less

Detection of the Argonaute Protein Ago2 and microRNAs in the RNA Induced Silencing Complex (RISC) Using a Monoclonal Antibody

PubMed Central

Ikeda, Keigo; Satoh, Minoru; Pauley, Kaleb M.; Fritzler, Marvin J.; Reeves, Westley H.; Chan, Edward K.L.

2007-01-01

MicroRNAs (miRNAs) are short RNA molecules responsible for post-transcriptional gene silencing by the degradation or translational inhibition of their target messenger RNAs (mRNAs). This process of gene silencing, known as RNA interference (RNAi), is mediated by highly conserved Argonaute (Ago) proteins which are the key components of the RNA induced silencing complex (RISC). In humans, Ago2 is responsible for the endonuclease cleavage of targeted mRNA and it interacts with the mRNA-binding protein GW182, which is a marker for cytoplasmic foci referred to as GW bodies (GWBs). We demonstrated that the anti-Ago2 monoclonal antibody 4F9 recognized GWBs in a cell cycle dependent manner and was capable of capturing miRNAs associated with Ago2. Since Ago2 protein is the effector protein of RNAi, anti-Ago2 monoclonal antibody may be useful in capturing functional miRNAs. PMID:17054975

Detection of the argonaute protein Ago2 and microRNAs in the RNA induced silencing complex (RISC) using a monoclonal antibody.

PubMed

Ikeda, Keigo; Satoh, Minoru; Pauley, Kaleb M; Fritzler, Marvin J; Reeves, Westley H; Chan, Edward K L

2006-12-20

MicroRNAs (miRNAs) are short RNA molecules responsible for post-transcriptional gene silencing by the degradation or translational inhibition of their target messenger RNAs (mRNAs). This process of gene silencing, known as RNA interference (RNAi), is mediated by highly conserved Argonaute (Ago) proteins which are the key components of the RNA induced silencing complex (RISC). In humans, Ago2 is responsible for the endonuclease cleavage of targeted mRNA and it interacts with the mRNA-binding protein GW182, which is a marker for cytoplasmic foci referred to as GW bodies (GWBs). We demonstrated that the anti-Ago2 monoclonal antibody 4F9 recognized GWBs in a cell cycle dependent manner and was capable of capturing miRNAs associated with Ago2. Since Ago2 protein is the effector protein of RNAi, anti-Ago2 monoclonal antibody may be useful in capturing functional miRNAs.

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

PubMed

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

Physarum machines: encapsulating reaction-diffusion to compute spanning tree

NASA Astrophysics Data System (ADS)

Adamatzky, Andrew

2007-12-01

The Physarum machine is a biological computing device, which employs plasmodium of Physarum polycephalum as an unconventional computing substrate. A reaction-diffusion computer is a chemical computing device that computes by propagating diffusive or excitation wave fronts. Reaction-diffusion computers, despite being computationally universal machines, are unable to construct certain classes of proximity graphs without the assistance of an external computing device. I demonstrate that the problem can be solved if the reaction-diffusion system is enclosed in a membrane with few ‘growth points’, sites guiding the pattern propagation. Experimental approximation of spanning trees by P. polycephalum slime mold demonstrates the feasibility of the approach. Findings provided advance theory of reaction-diffusion computation by enriching it with ideas of slime mold computation.

Health Informatics via Machine Learning for the Clinical Management of Patients.

PubMed

Clifton, D A; Niehaus, K E; Charlton, P; Colopy, G W

2015-08-13

To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice. We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case. Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups. Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

Humanizing machines: Anthropomorphization of slot machines increases gambling.

PubMed

Riva, Paolo; Sacchi, Simona; Brambilla, Marco

2015-12-01

Do people gamble more on slot machines if they think that they are playing against humanlike minds rather than mathematical algorithms? Research has shown that people have a strong cognitive tendency to imbue humanlike mental states to nonhuman entities (i.e., anthropomorphism). The present research tested whether anthropomorphizing slot machines would increase gambling. Four studies manipulated slot machine anthropomorphization and found that exposing people to an anthropomorphized description of a slot machine increased gambling behavior and reduced gambling outcomes. Such findings emerged using tasks that focused on gambling behavior (Studies 1 to 3) as well as in experimental paradigms that included gambling outcomes (Studies 2 to 4). We found that gambling outcomes decrease because participants primed with the anthropomorphic slot machine gambled more (Study 4). Furthermore, we found that high-arousal positive emotions (e.g., feeling excited) played a role in the effect of anthropomorphism on gambling behavior (Studies 3 and 4). Our research indicates that the psychological process of gambling-machine anthropomorphism can be advantageous for the gaming industry; however, this may come at great expense for gamblers' (and their families') economic resources and psychological well-being. (c) 2015 APA, all rights reserved).

An implementation of support vector machine on sentiment classification of movie reviews

NASA Astrophysics Data System (ADS)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

Current Developments in Machine Learning Techniques in Biological Data Mining.

PubMed

Dumancas, Gerard G; Adrianto, Indra; Bello, Ghalib; Dozmorov, Mikhail

2017-01-01

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

Scalable Machine Learning for Massive Astronomical Datasets

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Gray, A.

2014-04-01

We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors. This is likely of particular interest to the radio astronomy community given, for example, that survey projects contain groups dedicated to this topic. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex

Scalable Machine Learning for Massive Astronomical Datasets

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Astronomy Data Centre, Canadian

2014-01-01

We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors, and the local outlier factor. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

ATST telescope mount: telescope of machine tool

NASA Astrophysics Data System (ADS)

Jeffers, Paul; Stolz, Günter; Bonomi, Giovanni; Dreyer, Oliver; Kärcher, Hans

2012-09-01

The Advanced Technology Solar Telescope (ATST) will be the largest solar telescope in the world, and will be able to provide the sharpest views ever taken of the solar surface. The telescope has a 4m aperture primary mirror, however due to the off axis nature of the optical layout, the telescope mount has proportions similar to an 8 meter class telescope. The technology normally used in this class of telescope is well understood in the telescope community and has been successfully implemented in numerous projects. The world of large machine tools has developed in a separate realm with similar levels of performance requirement but different boundary conditions. In addition the competitive nature of private industry has encouraged development and usage of more cost effective solutions both in initial capital cost and thru-life operating cost. Telescope mounts move relatively slowly with requirements for high stability under external environmental influences such as wind buffeting. Large machine tools operate under high speed requirements coupled with high application of force through the machine but with little or no external environmental influences. The benefits of these parallel development paths and the ATST system requirements are being combined in the ATST Telescope Mount Assembly (TMA). The process of balancing the system requirements with new technologies is based on the experience of the ATST project team, Ingersoll Machine Tools who are the main contractor for the TMA and MT Mechatronics who are their design subcontractors. This paper highlights a number of these proven technologies from the commercially driven machine tool world that are being introduced to the TMA design. Also the challenges of integrating and ensuring that the differences in application requirements are accounted for in the design are discussed.

Pulse electrochemical meso/micro/nano ultraprecision machining technology.

PubMed

Lee, Jeong Min; Kim, Young Bin; Park, Jeong Woo

2013-11-01

This study demonstrated meso/micro/nano-ultraprecision machining through electrochemical reactions using intermittent DC pulses. The experiment focused on two machining methods: (1) pulse electrochemical polishing (PECP) of stainless steel, and (2) pulse electrochemical nano-patterning (PECNP) on a silicon (Si) surface, using atomic force microscopy (AFM) for fabrication. The dissolution reaction at the stainless steel surface following PECP produced a very clean, smooth workpiece. The advantages of the PECP process included improvements in corrosion resistance, deburring of the sample surface, and removal of hydrogen from the stainless steel surface as verified by time-of-flight secondary-ion mass spectrometry (TOF-SIMS). In PECNP, the electrochemical reaction generated within water molecules produced nanoscale oxide textures on a Si surface. Scanning probe microscopy (SPM) was used to evaluate nanoscale-pattern processing on a Si wafer surface produced by AFM-PECNP For both processes using pulse electrochemical reactions, three-dimensional (3-D) measurements and AFM were used to investigate the changes on the machined surfaces. Preliminary results indicated the potential for advancing surface polishing techniques and localized micro/nano-texturing technology using PECP and PECNP processes.

Electric vehicle traction motors - The development of an advanced motor concept

NASA Technical Reports Server (NTRS)

Campbell, P.

1980-01-01

An axial-field permanent magnet traction motor is described, similar to several advanced motors that are being developed in the United States. This type of machine has several advantages over conventional dc motors, particularly in the electric vehicle application. The rapidly changing cost of magnetic materials, particularly cobalt, makes it important to study the utilization of permanent magnet materials in such machines. The impact of different magnets on machine design is evaluated, and the advantages of using iron powder composites in the armature are assessed.

14. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

14. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific Railroad Carlin Shops, view to north (90mm lens). - Southern Pacific Railroad, Carlin Shops, Roundhouse Machine Shop Extension, Foot of Sixth Street, Carlin, Elko County, NV

Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease.

PubMed

Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

2016-09-01

The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer's disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.

Advanced Containment System

DOEpatents

Kostelnik, Kevin M.; Kawamura, Hideki; Richardson, John G.; Noda, Masaru

2004-10-12

An advanced containment system for containing buried waste and associated leachate. A trench is dug on either side of the zone of interest containing the buried waste so as to accommodate a micro tunnel boring machine. A series of small diameter tunnels are serially excavated underneath the buried waste. The tunnels are excavated by the micro tunnel boring machine at a consistent depth and are substantially parallel to each other. As tunneling progresses, steel casing sections are connected end to end in the excavated portion of the tunnel so that a steel tube is formed. Each casing section has complementary interlocking structure running its length that interlocks with complementary interlocking structure on the adjacent casing section. Thus, once the first tube is emplaced, placement of subsequent tubes is facilitated by the complementary interlocking structure on the adjacent, previously placed, casing sections.

Advanced Containment System

DOEpatents

Kostelnik, Kevin M.; Kawamura, Hideki; Richardson, John G.; Noda, Masaru

2005-05-24

An advanced containment system for containing buried waste and associated leachate. A trench is dug on either side of the zone of interest containing the buried waste so as to accommodate a micro tunnel boring machine. A series of small diameter tunnels are serially excavated underneath the buried waste. The tunnels are excavated by the micro tunnel boring machine at a consistent depth and are substantially parallel to each other. As tunneling progresses, steel casing sections are connected end to end in the excavated portion of the tunnel so that a steel tube is formed. Each casing section has complementary interlocking structure running its length that interlocks with complementary interlocking structure on the adjacent casing section. Thus, once the first tube is emplaced, placement of subsequent tubes is facilitated by the complementary interlocking structure on the adjacent, previously placed, casing sections.

Crystalline molecular machines: Encoding supramolecular dynamics into molecular structure

PubMed Central

Garcia-Garibay, Miguel A.

2005-01-01

Crystalline molecular machines represent an exciting new branch of crystal engineering and materials science with important implications to nanotechnology. Crystalline molecular machines are crystals built with molecules that are structurally programmed to respond collectively to mechanic, electric, magnetic, or photonic stimuli to fulfill specific functions. One of the main challenges in their construction derives from the picometric precision required for their mechanic operation within the close-packed, self-assembled environment of crystalline solids. In this article, we outline some of the general guidelines for their design and apply them for the construction of molecular crystals with units intended to emulate macroscopic gyroscopes and compasses. Recent advances in the preparation, crystallization, and dynamic characterization of these interesting systems offer a foothold to the possibilities and help highlight some avenues for future experimentation. PMID:16046543

Machine Shop Lathes.

ERIC Educational Resources Information Center

Dunn, James

This guide, the second in a series of five machine shop curriculum manuals, was designed for use in machine shop courses in Oklahoma. The purpose of the manual is to equip students with basic knowledge and skills that will enable them to enter the machine trade at the machine-operator level. The curriculum is designed so that it can be used in…

Advanced technologies for Mission Control Centers

NASA Technical Reports Server (NTRS)

Dalton, John T.; Hughes, Peter M.

1991-01-01

Advance technologies for Mission Control Centers are presented in the form of the viewgraphs. The following subject areas are covered: technology needs; current technology efforts at GSFC (human-machine interface development, object oriented software development, expert systems, knowledge-based software engineering environments, and high performance VLSI telemetry systems); and test beds.

Three-dimensionally printed biological machines powered by skeletal muscle.

PubMed

Cvetkovic, Caroline; Raman, Ritu; Chan, Vincent; Williams, Brian J; Tolish, Madeline; Bajaj, Piyush; Sakar, Mahmut Selman; Asada, H Harry; Saif, M Taher A; Bashir, Rashid

2014-07-15

Combining biological components, such as cells and tissues, with soft robotics can enable the fabrication of biological machines with the ability to sense, process signals, and produce force. An intuitive demonstration of a biological machine is one that can produce motion in response to controllable external signaling. Whereas cardiac cell-driven biological actuators have been demonstrated, the requirements of these machines to respond to stimuli and exhibit controlled movement merit the use of skeletal muscle, the primary generator of actuation in animals, as a contractile power source. Here, we report the development of 3D printed hydrogel "bio-bots" with an asymmetric physical design and powered by the actuation of an engineered mammalian skeletal muscle strip to result in net locomotion of the bio-bot. Geometric design and material properties of the hydrogel bio-bots were optimized using stereolithographic 3D printing, and the effect of collagen I and fibrin extracellular matrix proteins and insulin-like growth factor 1 on the force production of engineered skeletal muscle was characterized. Electrical stimulation triggered contraction of cells in the muscle strip and net locomotion of the bio-bot with a maximum velocity of ∼ 156 μm s(-1), which is over 1.5 body lengths per min. Modeling and simulation were used to understand both the effect of different design parameters on the bio-bot and the mechanism of motion. This demonstration advances the goal of realizing forward-engineered integrated cellular machines and systems, which can have a myriad array of applications in drug screening, programmable tissue engineering, drug delivery, and biomimetic machine design.

Fully automated measuring setup for tactile coordinate measuring machine for three dimensional measurement of freeform eyeglass frames

NASA Astrophysics Data System (ADS)

Rückwardt, M.; Göpfert, A.; Correns, M.; Schellhorn, M.; Linß, G.

2010-07-01

Coordinate measuring machines are high precession all-rounder in three dimensional measuring. Therefore the versatility of parameters and expandability of additionally hardware is very comprehensive. Consequently you need much expert knowledge of the user and mostly a lot of advanced information about the measuring object. In this paper a coordinate measuring machine and a specialized measuring machine are compared at the example of the measuring of eyeglass frames. For this case of three dimensional measuring challenges the main focus is divided into metrological and economical aspects. At first there is shown a fully automated method for tactile measuring of this abstract form. At second there is shown a comparison of the metrological characteristics of a coordinate measuring machine and a tracer for eyeglass frames. The result is in favour to the coordinate measuring machine. It was not surprising in these aspects. At last there is shown a comparison of the machine in front of the economical aspects.

Machine tool locator

DOEpatents

Hanlon, John A.; Gill, Timothy J.

2001-01-01

Machine tools can be accurately measured and positioned on manufacturing machines within very small tolerances by use of an autocollimator on a 3-axis mount on a manufacturing machine and positioned so as to focus on a reference tooling ball or a machine tool, a digital camera connected to the viewing end of the autocollimator, and a marker and measure generator for receiving digital images from the camera, then displaying or measuring distances between the projection reticle and the reference reticle on the monitoring screen, and relating the distances to the actual position of the autocollimator relative to the reference tooling ball. The images and measurements are used to set the position of the machine tool and to measure the size and shape of the machine tool tip, and examine cutting edge wear. patent

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

PubMed

Huang, Shujun; Cai, Nianguang; Pacheco, Pedro Penzuti; Narrandes, Shavira; Wang, Yang; Xu, Wayne

2018-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Machine learning and predictive data analytics enabling metrology and process control in IC fabrication

NASA Astrophysics Data System (ADS)

Rana, Narender; Zhang, Yunlin; Wall, Donald; Dirahoui, Bachir; Bailey, Todd C.

2015-03-01

Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.

Nanomedicine: Tiny Particles and Machines Give Huge Gains

PubMed Central

Tong, Sheng; Fine, Eli J.; Lin, Yanni; Cradick, Thomas J.; Bao, Gang

2014-01-01

Nanomedicine is an emerging field that integrates nanotechnology, biomolecular engineering, life sciences and medicine; it is expected to produce major breakthroughs in medical diagnostics and therapeutics. Nano-scale structures and devices are compatible in size with proteins and nucleic acids in living cells. Therefore, the design, characterization and application of nano-scale probes, carriers and machines may provide unprecedented opportunities for achieving a better control of biological processes, and drastic improvements in disease detection, therapy, and prevention. Recent advances in nanomedicine include the development of nanoparticle-based probes for molecular imaging, nano-carriers for drug/gene delivery, multi-functional nanoparticles for theranostics, and molecular machines for biological and medical studies. This article provides an overview of the nanomedicine field, with an emphasis on nanoparticles for imaging and therapy, as well as engineered nucleases for genome editing. The challenges in translating nanomedicine approaches to clinical applications are discussed. PMID:24297494

Machinability of nickel based alloys using electrical discharge machining process

NASA Astrophysics Data System (ADS)

Khan, M. Adam; Gokul, A. K.; Bharani Dharan, M. P.; Jeevakarthikeyan, R. V. S.; Uthayakumar, M.; Thirumalai Kumaran, S.; Duraiselvam, M.

2018-04-01

The high temperature materials such as nickel based alloys and austenitic steel are frequently used for manufacturing critical aero engine turbine components. Literature on conventional and unconventional machining of steel materials is abundant over the past three decades. However the machining studies on superalloy is still a challenging task due to its inherent property and quality. Thus this material is difficult to be cut in conventional processes. Study on unconventional machining process for nickel alloys is focused in this proposed research. Inconel718 and Monel 400 are the two different candidate materials used for electrical discharge machining (EDM) process. Investigation is to prepare a blind hole using copper electrode of 6mm diameter. Electrical parameters are varied to produce plasma spark for diffusion process and machining time is made constant to calculate the experimental results of both the material. Influence of process parameters on tool wear mechanism and material removal are considered from the proposed experimental design. While machining the tool has prone to discharge more materials due to production of high energy plasma spark and eddy current effect. The surface morphology of the machined surface were observed with high resolution FE SEM. Fused electrode found to be a spherical structure over the machined surface as clumps. Surface roughness were also measured with surface profile using profilometer. It is confirmed that there is no deviation and precise roundness of drilling is maintained.

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

NASA Astrophysics Data System (ADS)

Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.

2018-03-01

The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.

Ultra precision machining

NASA Astrophysics Data System (ADS)

Debra, Daniel B.; Hesselink, Lambertus; Binford, Thomas

1990-05-01

There are a number of fields that require or can use to advantage very high precision in machining. For example, further development of high energy lasers and x ray astronomy depend critically on the manufacture of light weight reflecting metal optical components. To fabricate these optical components with machine tools they will be made of metal with mirror quality surface finish. By mirror quality surface finish, it is meant that the dimensions tolerances on the order of 0.02 microns and surface roughness of 0.07. These accuracy targets fall in the category of ultra precision machining. They cannot be achieved by a simple extension of conventional machining processes and techniques. They require single crystal diamond tools, special attention to vibration isolation, special isolation of machine metrology, and on line correction of imperfection in the motion of the machine carriages on their way.

How DARHT Works - the World's Most Powerful X-ray Machine

ScienceCinema

None

2018-06-01

The Dual Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory is an essential scientific tool that supports Stockpile Stewardship at the Laboratory. The World's most powerful x-ray machine, it's used to take high-speed images of mock nuclear devices - data that is used to confirm and modify advanced computer codes in assuring the safety, security, and effectiveness of the U.S. nuclear deterrent.

Stirling machine operating experience

NASA Technical Reports Server (NTRS)

Ross, Brad; Dudenhoefer, James E.

1991-01-01

Numerous Stirling machines have been built and operated, but the operating experience of these machines is not well known. It is important to examine this operating experience in detail, because it largely substantiates the claim that Stirling machines are capable of reliable and lengthy lives. The amount of data that exists is impressive, considering that many of the machines that have been built are developmental machines intended to show proof of concept, and were not expected to operate for any lengthy period of time. Some Stirling machines (typically free-piston machines) achieve long life through non-contact bearings, while other Stirling machines (typically kinematic) have achieved long operating lives through regular seal and bearing replacements. In addition to engine and system testing, life testing of critical components is also considered.

Machine intelligence and robotics: Report of the NASA study group. Executive summary

NASA Technical Reports Server (NTRS)

1979-01-01

A brief overview of applications of machine intelligence and robotics in the space program is given. These space exploration robots, global service robots to collect data for public service use on soil conditions, sea states, global crop conditions, weather, geology, disasters, etc., from Earth orbit, space industrialization and processing technologies, and construction of large structures in space. Program options for research, advanced development, and implementation of machine intelligence and robot technology for use in program planning are discussed. A vigorous and long-range program to incorporate and keep pace with state of the art developments in computer technology, both in spaceborne and ground-based computer systems is recommended.

Automated Software Acceleration in Programmable Logic for an Efficient NFFT Algorithm Implementation: A Case Study.

PubMed

Rodríguez, Manuel; Magdaleno, Eduardo; Pérez, Fernando; García, Cristhian

2017-03-28

Non-equispaced Fast Fourier transform (NFFT) is a very important algorithm in several technological and scientific areas such as synthetic aperture radar, computational photography, medical imaging, telecommunications, seismic analysis and so on. However, its computation complexity is high. In this paper, we describe an efficient NFFT implementation with a hardware coprocessor using an All-Programmable System-on-Chip (APSoC). This is a hybrid device that employs an Advanced RISC Machine (ARM) as Processing System with Programmable Logic for high-performance digital signal processing through parallelism and pipeline techniques. The algorithm has been coded in C language with pragma directives to optimize the architecture of the system. We have used the very novel Software Develop System-on-Chip (SDSoC) evelopment tool that simplifies the interface and partitioning between hardware and software. This provides shorter development cycles and iterative improvements by exploring several architectures of the global system. The computational results shows that hardware acceleration significantly outperformed the software based implementation.

Automated Software Acceleration in Programmable Logic for an Efficient NFFT Algorithm Implementation: A Case Study

PubMed Central

Rodríguez, Manuel; Magdaleno, Eduardo; Pérez, Fernando; García, Cristhian

2017-01-01

Non-equispaced Fast Fourier transform (NFFT) is a very important algorithm in several technological and scientific areas such as synthetic aperture radar, computational photography, medical imaging, telecommunications, seismic analysis and so on. However, its computation complexity is high. In this paper, we describe an efficient NFFT implementation with a hardware coprocessor using an All-Programmable System-on-Chip (APSoC). This is a hybrid device that employs an Advanced RISC Machine (ARM) as Processing System with Programmable Logic for high-performance digital signal processing through parallelism and pipeline techniques. The algorithm has been coded in C language with pragma directives to optimize the architecture of the system. We have used the very novel Software Develop System-on-Chip (SDSoC) evelopment tool that simplifies the interface and partitioning between hardware and software. This provides shorter development cycles and iterative improvements by exploring several architectures of the global system. The computational results shows that hardware acceleration significantly outperformed the software based implementation. PMID:28350358

Space Weather in the Machine Learning Era: A Multidisciplinary Approach

NASA Astrophysics Data System (ADS)

Camporeale, E.; Wing, S.; Johnson, J.; Jackman, C. M.; McGranaghan, R.

2018-01-01

The workshop entitled Space Weather: A Multidisciplinary Approach took place at the Lorentz Center, University of Leiden, Netherlands, on 25-29 September 2017. The aim of this workshop was to bring together members of the Space Weather, Mathematics, Statistics, and Computer Science communities to address the use of advanced techniques such as Machine Learning, Information Theory, and Deep Learning, to better understand the Sun-Earth system and to improve space weather forecasting. Although individual efforts have been made toward this goal, the community consensus is that establishing interdisciplinary collaborations is the most promising strategy for fully utilizing the potential of these advanced techniques in solving Space Weather-related problems.

Prediction based proactive thermal virtual machine scheduling in green clouds.

PubMed

Kinger, Supriya; Kumar, Rajesh; Sharma, Anju

2014-01-01

Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated.

Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds

PubMed Central

Kinger, Supriya; Kumar, Rajesh; Sharma, Anju

2014-01-01

Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated. PMID:24737962

Machinability of titanium metal matrix composites (Ti-MMCs)

NASA Astrophysics Data System (ADS)

Aramesh, Maryam

Titanium metal matrix composites (Ti-MMCs), as a new generation of materials, have various potential applications in aerospace and automotive industries. The presence of ceramic particles enhances the physical and mechanical properties of the alloy matrix. However, the hard and abrasive nature of these particles causes various issues in the field of their machinability. Severe tool wear and short tool life are the most important drawbacks of machining this class of materials. There is very limited work in the literature regarding the machinability of this class of materials especially in the area of tool life estimation and tool wear. By far, polycrystalline diamond (PCD) tools appear to be the best choice for machining MMCs from researchers' point of view. However, due to their high cost, economical alternatives are sought. Cubic boron nitride (CBN) inserts, as the second hardest available tools, show superior characteristics such as great wear resistance, high hardness at elevated temperatures, a low coefficient of friction and a high melting point. Yet, so far CBN tools have not been studied during machining of Ti-MMCs. In this study, a comprehensive study has been performed to explore the tool wear mechanisms of CBN inserts during turning of Ti-MMCs. The unique morphology of the worn faces of the tools was investigated for the first time, which led to new insights in the identification of chemical wear mechanisms during machining of Ti-MMCs. Utilizing the full tool life capacity of cutting tools is also very crucial, due to the considerable costs associated with suboptimal replacement of tools. This strongly motivates development of a reliable model for tool life estimation under any cutting conditions. In this study, a novel model based on the survival analysis methodology is developed to estimate the progressive states of tool wear under any cutting conditions during machining of Ti-MMCs. This statistical model takes into account the machining time in

National Machine Guarding Program: Part 1. Machine safeguarding practices in small metal fabrication businesses.

PubMed

Parker, David L; Yamin, Samuel C; Brosseau, Lisa M; Xi, Min; Gordon, Robert; Most, Ivan G; Stanley, Rodney

2015-11-01

Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine-related hazards in 221 business. Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc.

An open-source solution for advanced imaging flow cytometry data analysis using machine learning.

PubMed

Hennig, Holger; Rees, Paul; Blasi, Thomas; Kamentsky, Lee; Hung, Jane; Dao, David; Carpenter, Anne E; Filby, Andrew

2017-01-01

Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using "user-friendly" platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery

Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.

PubMed

Vallmuur, Kirsten; Marucci-Wellman, Helen R; Taylor, Jennifer A; Lehto, Mark; Corns, Helen L; Smith, Gordon S

2016-04-01

Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database. The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of 'big injury narrative data' opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Toward more versatile and intuitive cortical brain-machine interfaces.

PubMed

Andersen, Richard A; Kellis, Spencer; Klaes, Christian; Aflalo, Tyson

2014-09-22

Brain-machine interfaces have great potential for the development of neuroprosthetic applications to assist patients suffering from brain injury or neurodegenerative disease. One type of brain-machine interface is a cortical motor prosthetic, which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms. These new advances promise to greatly accelerate the applicability and ease of operation of motor prosthetics. Copyright © 2014 Elsevier Ltd. All rights reserved.

Impact of Advance Rate on Entrapment Risk of a Double-Shielded TBM in Squeezing Ground

NASA Astrophysics Data System (ADS)

Hasanpour, Rohola; Rostami, Jamal; Barla, Giovanni

2015-05-01

Shielded tunnel boring machines (TBMs) can get stuck in squeezing ground due to excessive tunnel convergence under high in situ stress. This typically coincides with extended machine stoppages, when the ground has sufficient time to undergo substantial displacements. Excessive convergence of the ground beyond the designated overboring means ground pressure against the shield and high shield frictional resistance that, in some cases, cannot be overcome by the TBM thrust system. This leads to machine entrapment in the ground, which causes significant delays and requires labor-intensive and risky operations of manual excavation to release the machine. To evaluate the impact of the time factor on the possibility of machine entrapment, a comprehensive 3D finite difference simulation of a double-shielded TBM in squeezing ground was performed. The modeling allowed for observation of the impact of the tunnel advance rate on the possibility of machine entrapment in squeezing ground. For this purpose, the model included rock mass properties related to creep in severe squeezing conditions. This paper offers an overview of the modeling results for a given set of rock mass and TBM parameters, as well as lining characteristics, including the magnitude of displacement and contact forces on shields and ground pressure on segmental lining versus time for different advance rates.

Protein function in precision medicine: deep understanding with machine learning.

PubMed

Rost, Burkhard; Radivojac, Predrag; Bromberg, Yana

2016-08-01

Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both. © 2016 Federation of European Biochemical Societies.

How do environmental and behavioral factors impact ultraviolet radiation effects on health: the RISC-UV Project

NASA Astrophysics Data System (ADS)

Correa, M. P.; Godin-Beekmann, S.; Haeffelin, M.; Saiag, P.; Mahe, E.; Brogniez, C.; Dupont, J. C.; Pazmiño, A.; Auriol, F.; Bonnel, B.

2009-04-01

Introduction: RISC-UV is a research project on "Impact of climate change on ultraviolet radiation and risks for health", a research project in which physicists, meteorologists and physicians work together to assess the relative role played by environmental and behavioral factors in the UV-related diseases as skin cancer and vitamin D deficiency. Environmental factors are related to the role played by the alteration in intensity of UV radiation at the Earth's surface resulting from variation in several factors affected by climate change and human activities: stratospheric ozone, cloud cover, aerosols and the reflectivity of the surface. On the other hand, behavioral factors are related to the sun over/underexposure and the correct use of sun-protection (hats, caps, sunglasses, sunscreen lotion, etc.). RISC-UV is organized around three main areas: 1) Organization of a workshop, scheduled for January 2009, which aims to describe the state of the art in the subject within each community and define the requirements of pathologists for epidemiological studies; 2) A pilot study intended to evaluate the consistency between UV measurements delivered simultaneously by satellite-based instruments, ground instruments, radiometers and individual dosimeters. This study is based on measurements campaigns and an analysis of the long-term consistency of data series relating to UV radiation and associated parameters; and 3) Analysis of the weights of medical, behavioral and environmental parameters involved in skin carcinogenesis. A detailed description of these areas can be found in http://www.gisclimat.fr/Doc/GB/D_projects/RISC-UV_GB.html. This presentation focuses on the first results of the UV experimental measurements performed between September 8th and October 8th 2008 in Palaiseau, France (48.7˚ N; 2.2˚ E; 170m - Haeffelin et al., 2005). A second campaign is foreseen for the spring of 2009. The purpose of these campaigns is to obtain, analyze and quantitatively link the

Recent advances in the development and transfer of machine vision technologies for space

NASA Technical Reports Server (NTRS)

Defigueiredo, Rui J. P.; Pendleton, Thomas

1991-01-01

Recent work concerned with real-time machine vision is briefly reviewed. This work includes methodologies and techniques for optimal illumination, shape-from-shading of general (non-Lambertian) 3D surfaces, laser vision devices and technology, high level vision, sensor fusion, real-time computing, artificial neural network design and use, and motion estimation. Two new methods that are currently being developed for object recognition in clutter and for 3D attitude tracking based on line correspondence are discussed.

Identification of Tool Wear when Machining of Austenitic Steels and Titatium by Miniature Machining

NASA Astrophysics Data System (ADS)

Pilc, Jozef; Kameník, Roman; Varga, Daniel; Martinček, Juraj; Sadilek, Marek

2016-12-01

Application of miniature machining is currently rapidly increasing mainly in biomedical industry and machining of hard-to-machine materials. Machinability of materials with increased level of toughness depends on factors that are important in the final state of surface integrity. Because of this, it is necessary to achieve high precision (varying in microns) in miniature machining. If we want to guarantee machining high precision, it is necessary to analyse tool wear intensity in direct interaction with given machined materials. During long-term cutting process, different cutting wedge deformations occur, leading in most cases to a rapid wear and destruction of the cutting wedge. This article deal with experimental monitoring of tool wear intensity during miniature machining.

Advances in natural language processing.

PubMed

Hirschberg, Julia; Manning, Christopher D

2015-07-17

Natural language processing employs computational techniques for the purpose of learning, understanding, and producing human language content. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, speech recognition, and speech synthesis. Today's researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services. We describe successes and challenges in this rapidly advancing area. Copyright © 2015, American Association for the Advancement of Science.

Technology: The Culture of Machine Living. Program in American History and Civilization.

ERIC Educational Resources Information Center

Tufts Univ., Medford, MA. Lincoln Filene Center for Citizenship and Public Affairs.

The readings in this narrative unit are concerned with the machine's role today and its future use in shaping man's environment. The general teaching objectives are to enable the student: 1) to adapt to a society directed toward total living, rather than one in which he earns a living; 2) to understand that uncontrolled technological advance may…

Your Sewing Machine.

ERIC Educational Resources Information Center

Peacock, Marion E.

The programed instruction manual is designed to aid the student in learning the parts, uses, and operation of the sewing machine. Drawings of sewing machine parts are presented, and space is provided for the student's written responses. Following an introductory section identifying sewing machine parts, the manual deals with each part and its…

Machine Learning and Data Mining Methods in Diabetes Research.

PubMed

Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

2017-01-01

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

PubMed

Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

2017-01-01

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Exposure to nanoscale particles and fibers during machining of hybrid advanced composites containing carbon nanotubes

NASA Astrophysics Data System (ADS)

Bello, Dhimiter; Wardle, Brian L.; Yamamoto, Namiko; Guzman deVilloria, Roberto; Garcia, Enrique J.; Hart, Anastasios J.; Ahn, Kwangseog; Ellenbecker, Michael J.; Hallock, Marilyn

2009-01-01

This study investigated airborne exposures to nanoscale particles and fibers generated during dry and wet abrasive machining of two three-phase advanced composite systems containing carbon nanotubes (CNTs), micron-diameter continuous fibers (carbon or alumina), and thermoset polymer matrices. Exposures were evaluated with a suite of complementary instruments, including real-time particle number concentration and size distribution (0.005-20 μm), electron microscopy, and integrated sampling for fibers and respirable particulate at the source and breathing zone of the operator. Wet cutting, the usual procedure for such composites, did not produce exposures significantly different than background whereas dry cutting, without any emissions controls, provided a worst-case exposure and this article focuses here. Overall particle release levels, peaks in the size distribution of the particles, and surface area of released particles (including size distribution) were not significantly different for composites with and without CNTs. The majority of released particle surface area originated from the respirable (1-10 μm) fraction, whereas the nano fraction contributed 10% of the surface area. CNTs, either individual or in bundles, were not observed in extensive electron microscopy of collected samples. The mean number concentration of peaks for dry cutting was composite dependent and varied over an order of magnitude with highest values for thicker laminates at the source being >1 × 106 particles cm-3. Concentration of respirable fibers for dry cutting at the source ranged from 2 to 4 fibers cm-3 depending on the composite type. Further investigation is required and underway to determine the effects of various exposure determinants, such as specimen and tool geometry, on particle release and effectiveness of controls.

Machine Learning

DTIC Science & Technology

1990-04-01

DTIC i.LE COPY RADC-TR-90-25 Final Technical Report April 1990 MACHINE LEARNING The MITRE Corporation Melissa P. Chase Cs) CTIC ’- CT E 71 IN 2 11990...S. FUNDING NUMBERS MACHINE LEARNING C - F19628-89-C-0001 PE - 62702F PR - MOlE S. AUTHO(S) TA - 79 Melissa P. Chase WUT - 80 S. PERFORMING...341.280.5500 pm I " Aw Sig rill Ia 2110-01 SECTION 1 INTRODUCTION 1.1 BACKGROUND Research in machine learning has taken two directions in the problem of

Running accuracy analysis of a 3-RRR parallel kinematic machine considering the deformations of the links

NASA Astrophysics Data System (ADS)

Wang, Liping; Jiang, Yao; Li, Tiemin

2014-09-01

Parallel kinematic machines have drawn considerable attention and have been widely used in some special fields. However, high precision is still one of the challenges when they are used for advanced machine tools. One of the main reasons is that the kinematic chains of parallel kinematic machines are composed of elongated links that can easily suffer deformations, especially at high speeds and under heavy loads. A 3-RRR parallel kinematic machine is taken as a study object for investigating its accuracy with the consideration of the deformations of its links during the motion process. Based on the dynamic model constructed by the Newton-Euler method, all the inertia loads and constraint forces of the links are computed and their deformations are derived. Then the kinematic errors of the machine are derived with the consideration of the deformations of the links. Through further derivation, the accuracy of the machine is given in a simple explicit expression, which will be helpful to increase the calculating speed. The accuracy of this machine when following a selected circle path is simulated. The influences of magnitude of the maximum acceleration and external loads on the running accuracy of the machine are investigated. The results show that the external loads will deteriorate the accuracy of the machine tremendously when their direction coincides with the direction of the worst stiffness of the machine. The proposed method provides a solution for predicting the running accuracy of the parallel kinematic machines and can also be used in their design optimization as well as selection of suitable running parameters.

Innovative grinding wheel design for cost-effective machining of advanced ceramics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Licht, R.H.; Kuo, P.; Liu, S.

2000-05-01

This Final Report covers the Phase II Innovative Grinding Wheel (IGW) program in which Norton Company successfully developed a novel grinding wheel for cost-effective cylindrical grinding of advanced ceramics. In 1995, Norton Company successfully completed the 16-month Phase I technical effort to define requirements, design, develop, and evaluate a next-generation grinding wheel for cost-effective cylindrical grinding of advanced ceramics using small prototype wheels. The Phase II program was initiated to scale-up the new superabrasive wheel specification to larger diameters, 305-mm to 406-mm, required for most production grinding of cylindrical ceramic parts, and to perform in-house and independent validation grinding tests.

National machine guarding program: Part 1. Machine safeguarding practices in small metal fabrication businesses

PubMed Central

Yamin, Samuel C.; Brosseau, Lisa M.; Xi, Min; Gordon, Robert; Most, Ivan G.; Stanley, Rodney

2015-01-01

Background Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. Methods The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine‐related hazards in 221 business. Results Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. Conclusions The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. Am. J. Ind. Med. 58:1174–1183, 2015. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc. PMID:26332060

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

PubMed

Thabtah, Fadi

2018-02-13

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, and artistic capabilities, in such cases posing a challenging task for scientists to provide answers. In the last few years, ASD has been investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning to improve diagnostic timing, precision, and quality. Machine learning is a multidisciplinary research topic that employs intelligent techniques to discover useful concealed patterns, which are utilized in prediction to improve decision making. Machine learning techniques such as support vector machines, decision trees, logistic regressions, and others, have been applied to datasets related to autism in order to construct predictive models. These models claim to enhance the ability of clinicians to provide robust diagnoses and prognoses of ASD. However, studies concerning the use of machine learning in ASD diagnosis and treatment suffer from conceptual, implementation, and data issues such as the way diagnostic codes are used, the type of feature selection employed, the evaluation measures chosen, and class imbalances in data among others. A more serious claim in recent studies is the development of a new method for ASD diagnoses based on machine learning. This article critically analyses these recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data. Future studies concerning machine learning in autism research are greatly benefitted by such proposals.

Applying machine learning to identify autistic adults using imitation: An exploratory study.

PubMed

Li, Baihua; Sharma, Arjun; Meng, James; Purushwalkam, Senthil; Gowen, Emma

2017-01-01

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1990-01-01

A three-tier structure consisting of organization, coordination, and execution levels forms the architecture of an intelligent machine using the principle of increasing precision with decreasing intelligence from a hierarchically intelligent control. This system has been formulated as a probabilistic model, where uncertainty and imprecision can be expressed in terms of entropies. The optimal strategy for decision planning and task execution can be found by minimizing the total entropy in the system. The focus is on the design of the organization level as a Boltzmann machine. Since this level is responsible for planning the actions of the machine, the Boltzmann machine is reformulated to use entropy as the cost function to be minimized. Simulated annealing, expanding subinterval random search, and the genetic algorithm are presented as search techniques to efficiently find the desired action sequence and illustrated with numerical examples.

Recent manufacturing advances for spiral bevel gears

NASA Technical Reports Server (NTRS)

Handschuh, Robert F.; Bill, Robert C.

1991-01-01

The U.S. Army Aviation Systems Command (AVSCOM), through the Propulsion Directorate at NASA Lewis Research Center, has recently sponsored projects to advance the manufacturing process for spiral bevel gears. This type of gear is a critical component in rotary-wing propulsion systems. Two successfully completed contracted projects are described. The first project addresses the automated inspection of spiral bevel gears through the use of coordinate measuring machines. The second project entails the computer-numerical-control (CNC) conversion of a spiral bevel gear grinding machine that is used for all aerospace spiral bevel gears. The results of these projects are described with regard to the savings effected in manufacturing time.

Recent manufacturing advances for spiral bevel gears

NASA Technical Reports Server (NTRS)

Handschuh, Robert F.; Bill, Robert C.

1991-01-01

The U.S. Army Aviation Systems Command (AVSCOM), through the Propulsion Directorate at NASA LRC, has recently sponsored projects to advance the manufacturing process for spiral bevel gears. This type of gear is a critical component in rotary-wing propulsion systems. Two successfully completed contracted projects are described. The first project addresses the automated inspection of spiral bevel gears through the use of coordinate measuring machines. The second project entails the computer-numerical-control (CNC) conversion of a spiral bevel gear grinding machine that is used for all aerospace spiral bevel gears. The results of these projects are described with regard to the savings effected in manufacturing time.

Standardized Curriculum for Machine Tool Operation/Machine Shop.

ERIC Educational Resources Information Center

Mississippi State Dept. of Education, Jackson. Office of Vocational, Technical and Adult Education.

Standardized vocational education course titles and core contents for two courses in Mississippi are provided: machine tool operation/machine shop I and II. The first course contains the following units: (1) orientation; (2) shop safety; (3) shop math; (4) measuring tools and instruments; (5) hand and bench tools; (6) blueprint reading; (7)…

A Unified Approach to the Synthesis of Fully Testable Sequential Machines

DTIC Science & Technology

1989-10-01

N A Unified Approach to the Synthesis of Fully Testable Sequential Machines Srinivas Devadas and Kurt Keutzer Abstract • In this paper we attempt to...research was supported in part by the Defense Advanced Research Projects Agency under contract N00014-87-K-0825. Author Information Devadas : Department...Fully Testable Sequential Maine(S P Sritiivas Devadas Departinent of Electrical Engineerinig anid Comivi Sciec Massachusetts Institute of Technology

Machine Phase Fullerene Nanotechnology: 1996

NASA Technical Reports Server (NTRS)

Globus, Al; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

NASA has used exotic materials for spacecraft and experimental aircraft to good effect for many decades. In spite of many advances, transportation to space still costs about $10,000 per pound. Drexler has proposed a hypothetical nanotechnology based on diamond and investigated the properties of such molecular systems. These studies and others suggest enormous potential for aerospace systems. Unfortunately, methods to realize diamonoid nanotechnology are at best highly speculative. Recent computational efforts at NASA Ames Research Center and computation and experiment elsewhere suggest that a nanotechnology of machine phase functionalized fullerenes may be synthetically relatively accessible and of great aerospace interest. Machine phase materials are (hypothetical) materials consisting entirely or in large part of microscopic machines. In a sense, most living matter fits this definition. To begin investigation of fullerene nanotechnology, we used molecular dynamics to study the properties of carbon nanotube based gears and gear/shaft configurations. Experiments on C60 and quantum calculations suggest that benzyne may react with carbon nanotubes to form gear teeth. Han has computationally demonstrated that molecular gears fashioned from (14,0) single-walled carbon nanotubes and benzyne teeth should operate well at 50-100 gigahertz. Results suggest that rotation can be converted to rotating or linear motion, and linear motion may be converted into rotation. Preliminary results suggest that these mechanical systems can be cooled by a helium atmosphere. Furthermore, Deepak has successfully simulated using helical electric fields generated by a laser to power fullerene gears once a positive and negative charge have been added to form a dipole. Even with mechanical motion, cooling, and power; creating a viable nanotechnology requires support structures, computer control, a system architecture, a variety of components, and some approach to manufacture. Additional

Color line scan camera technology and machine vision: requirements to consider

NASA Astrophysics Data System (ADS)

Paernaenen, Pekka H. T.

1997-08-01

Color machine vision has shown a dynamic uptrend in use within the past few years as the introduction of new cameras and scanner technologies itself underscores. In the future, the movement from monochrome imaging to color will hasten, as machine vision system users demand more knowledge about their product stream. As color has come to the machine vision, certain requirements for the equipment used to digitize color images are needed. Color machine vision needs not only a good color separation but also a high dynamic range and a good linear response from the camera used. Good dynamic range and linear response is necessary for color machine vision. The importance of these features becomes even more important when the image is converted to another color space. There is always lost some information when converting integer data to another form. Traditionally the color image processing has been much slower technique than the gray level image processing due to the three times greater data amount per image. The same has applied for the three times more memory needed. The advancements in computers, memory and processing units has made it possible to handle even large color images today cost efficiently. In some cases he image analysis in color images can in fact even be easier and faster than with a similar gray level image because of more information per pixel. Color machine vision sets new requirements for lighting, too. High intensity and white color light is required in order to acquire good images for further image processing or analysis. New development in lighting technology is bringing eventually solutions for color imaging.

Machine Learning.

ERIC Educational Resources Information Center

Kirrane, Diane E.

1990-01-01

As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

Machine tool task force

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, G.P.

1980-10-22

The Machine Tool Task Force (MTTF) is a multidisciplined team of international experts, whose mission was to investigate the state of the art of machine tool technology, to identify promising future directions of that technology for both the US government and private industry, and to disseminate the findings of its research in a conference and through the publication of a final report. MTTF was a two and one-half year effort that involved the participation of 122 experts in the specialized technologies of machine tools and in the management of machine tool operations. The scope of the MTTF was limited tomore » cutting-type or material-removal-type machine tools, because they represent the major share and value of all machine tools now installed or being built. The activities of the MTTF and the technical, commercial and economic signifiance of recommended activities for improving machine tool technology are discussed. (LCL)« less

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2011 CFR

2011-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2012 CFR

2012-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2013 CFR

2013-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2014 CFR

2014-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2010 CFR

2010-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

Rapid and specific purification of Argonaute-small RNA complexes from crude cell lysates

PubMed Central

Flores-Jasso, C. Fabián; Salomon, William E.; Zamore, Phillip D.

2013-01-01

Small interfering RNAs (siRNAs) direct Argonaute proteins, the core components of the RNA-induced silencing complex (RISC), to cleave complementary target RNAs. Here, we describe a method to purify active RISC containing a single, unique small RNA guide sequence. We begin by capturing RISC using a complementary 2′-O-methyl oligonucleotide tethered to beads. Unlike other methods that capture RISC but do not allow its recovery, our strategy purifies active, soluble RISC in good yield. The method takes advantage of the finding that RISC partially paired to a target through its siRNA guide dissociates more than 300 times faster than a fully paired siRNA in RISC. We use this strategy to purify fly Ago1- and Ago2-RISC, as well as mouse AGO2-RISC. The method can discriminate among RISCs programmed with different guide strands, making it possible to deplete and recover specific RISC populations. Endogenous microRNA:Argonaute complexes can also be purified from cell lysates. Our method scales readily and takes less than a day to complete. PMID:23249751

Rapid and specific purification of Argonaute-small RNA complexes from crude cell lysates.

PubMed

Flores-Jasso, C Fabián; Salomon, William E; Zamore, Phillip D

2013-02-01

Small interfering RNAs (siRNAs) direct Argonaute proteins, the core components of the RNA-induced silencing complex (RISC), to cleave complementary target RNAs. Here, we describe a method to purify active RISC containing a single, unique small RNA guide sequence. We begin by capturing RISC using a complementary 2'-O-methyl oligonucleotide tethered to beads. Unlike other methods that capture RISC but do not allow its recovery, our strategy purifies active, soluble RISC in good yield. The method takes advantage of the finding that RISC partially paired to a target through its siRNA guide dissociates more than 300 times faster than a fully paired siRNA in RISC. We use this strategy to purify fly Ago1- and Ago2-RISC, as well as mouse AGO2-RISC. The method can discriminate among RISCs programmed with different guide strands, making it possible to deplete and recover specific RISC populations. Endogenous microRNA:Argonaute complexes can also be purified from cell lysates. Our method scales readily and takes less than a day to complete.

Perspectives for the high field approach in fusion research and advances within the Ignitor Program

NASA Astrophysics Data System (ADS)

Coppi, B.; Airoldi, A.; Albanese, R.; Ambrosino, G.; Belforte, G.; Boggio-Sella, E.; Cardinali, A.; Cenacchi, G.; Conti, F.; Costa, E.; D'Amico, A.; Detragiache, P.; De Tommasi, G.; DeVellis, A.; Faelli, G.; Ferraris, P.; Frattolillo, A.; Giammanco, F.; Grasso, G.; Lazzaretti, M.; Mantovani, S.; Merriman, L.; Migliori, S.; Napoli, R.; Perona, A.; Pierattini, S.; Pironti, A.; Ramogida, G.; Rubinacci, G.; Sassi, M.; Sestero, A.; Spillantini, S.; Tavani, M.; Tumino, A.; Villone, F.; Zucchi, L.

2015-05-01

The Ignitor Program maintains the objective of approaching D-T ignition conditions by incorporating systematical advances made with relevant high field magnet technology and with experiments on high density well confined plasmas in the present machine design. An additional objective is that of charting the development of the high field line of experiments that goes from the Alcator machine to the ignitor device. The rationale for this class of experiments, aimed at producing poloidal fields with the highest possible values (compatible with proven safety factors of known plasma instabilities) is given. On the basis of the favourable properties of high density plasmas produced systematically by this line of machines, the envisioned future for the line, based on novel high field superconducting magnets, includes the possibility of investigating more advanced fusion burn conditions than those of the D-T plasmas for which Ignitor is designed. Considering that a detailed machine design has been carried out (Coppi et al 2013 Nucl. Fusion 53 104013), the advances made in different areas of the physics and technology that are relevant to the Ignitor project are reported. These are included within the following sections of the present paper: main components issues, assembly and welding procedures; robotics criteria; non-linear feedback control; simulations with three-dimensional structures and disruption studies; ICRH and dedicated diagnostics systems; anomalous transport processes including self-organization for fusion burning regimes and the zero-dimensional model; tridimensional structures of the thermonuclear instability and control provisions; superconducting components of the present machine; envisioned experiments with high field superconducting magnets.

Cooperating reduction machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kluge, W.E.

1983-11-01

This paper presents a concept and a system architecture for the concurrent execution of program expressions of a concrete reduction language based on lamda-expressions. If formulated appropriately, these expressions are well-suited for concurrent execution, following a demand-driven model of computation. In particular, recursive program expressions with nonlinear expansion may, at run time, recursively be partitioned into a hierarchy of independent subexpressions which can be reduced by a corresponding hierarchy of virtual reduction machines. This hierarchy unfolds and collapses dynamically, with virtual machines recursively assuming the role of masters that create and eventually terminate, or synchronize with, slaves. The paper alsomore » proposes a nonhierarchically organized system of reduction machines, each featuring a stack architecture, that effectively supports the allocation of virtual machines to the real machines of the system in compliance with their hierarchical order of creation and termination. 25 references.« less

Compensation strategy for machining optical freeform surfaces by the combined on- and off-machine measurement.

PubMed

Zhang, Xiaodong; Zeng, Zhen; Liu, Xianlei; Fang, Fengzhou

2015-09-21

Freeform surface is promising to be the next generation optics, however it needs high form accuracy for excellent performance. The closed-loop of fabrication-measurement-compensation is necessary for the improvement of the form accuracy. It is difficult to do an off-machine measurement during the freeform machining because the remounting inaccuracy can result in significant form deviations. On the other side, on-machine measurement may hides the systematic errors of the machine because the measuring device is placed in situ on the machine. This study proposes a new compensation strategy based on the combination of on-machine and off-machine measurement. The freeform surface is measured in off-machine mode with nanometric accuracy, and the on-machine probe achieves accurate relative position between the workpiece and machine after remounting. The compensation cutting path is generated according to the calculated relative position and shape errors to avoid employing extra manual adjustment or highly accurate reference-feature fixture. Experimental results verified the effectiveness of the proposed method.

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

PubMed Central

2011-01-01

Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

PubMed

Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

2011-07-28

Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models

Fault Tolerant State Machines

NASA Technical Reports Server (NTRS)

Burke, Gary R.; Taft, Stephanie

2004-01-01

State machines are commonly used to control sequential logic in FPGAs and ASKS. An errant state machine can cause considerable damage to the device it is controlling. For example in space applications, the FPGA might be controlling Pyros, which when fired at the wrong time will cause a mission failure. Even a well designed state machine can be subject to random errors us a result of SEUs from the radiation environment in space. There are various ways to encode the states of a state machine, and the type of encoding makes a large difference in the susceptibility of the state machine to radiation. In this paper we compare 4 methods of state machine encoding and find which method gives the best fault tolerance, as well as determining the resources needed for each method.

UFMulti: A new parallel processing software system for HEP

NASA Astrophysics Data System (ADS)

Avery, Paul; White, Andrew

1989-12-01

UFMulti is a multiprocessing software package designed for general purpose high energy physics applications, including physics and detector simulation, data reduction and DST physics analysis. The system is particularly well suited for installations where several workstation or computers are connected through a local area network (LAN). The initial configuration of the software is currently running on VAX/VMS machines with a planned extension to ULTRIX, using the new RISC CPUs from Digital, in the near future.

Scheduling of hybrid types of machines with two-machine flowshop as the first type and a single machine as the second type

NASA Astrophysics Data System (ADS)

Hsiao, Ming-Chih; Su, Ling-Huey

2018-02-01

This research addresses the problem of scheduling hybrid machine types, in which one type is a two-machine flowshop and another type is a single machine. A job is either processed on the two-machine flowshop or on the single machine. The objective is to determine a production schedule for all jobs so as to minimize the makespan. The problem is NP-hard since the two parallel machines problem was proved to be NP-hard. Simulated annealing algorithms are developed to solve the problem optimally. A mixed integer programming (MIP) is developed and used to evaluate the performance for two SAs. Computational experiments demonstrate the efficiency of the simulated annealing algorithms, the quality of the simulated annealing algorithms will also be reported.

14. Machine in north 1922 section of Building 59. Machine ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

14. Machine in north 1922 section of Building 59. Machine is 24' Jointer made by Oliver Machinery Co. Camera pointed E. - Puget Sound Naval Shipyard, Pattern Shop, Farragut Avenue, Bremerton, Kitsap County, WA

A Multiprocessor SoC Architecture with Efficient Communication Infrastructure and Advanced Compiler Support for Easy Application Development

NASA Astrophysics Data System (ADS)

Urfianto, Mohammad Zalfany; Isshiki, Tsuyoshi; Khan, Arif Ullah; Li, Dongju; Kunieda, Hiroaki

This paper presentss a Multiprocessor System-on-Chips (MPSoC) architecture used as an execution platform for the new C-language based MPSoC design framework we are currently developing. The MPSoC architecture is based on an existing SoC platform with a commercial RISC core acting as the host CPU. We extend the existing SoC with a multiprocessor-array block that is used as the main engine to run parallel applications modeled in our design framework. Utilizing several optimizations provided by our compiler, an efficient inter-communication between processing elements with minimum overhead is implemented. A host-interface is designed to integrate the existing RISC core to the multiprocessor-array. The experimental results show that an efficacious integration is achieved, proving that the designed communication module can be used to efficiently incorporate off-the-shelf processors as a processing element for MPSoC architectures designed using our framework.

Design and implementation of a system for laser assisted milling of advanced materials

NASA Astrophysics Data System (ADS)

Wu, Xuefeng; Feng, Gaocheng; Liu, Xianli

2016-09-01

Laser assisted machining is an effective method to machine advanced materials with the added benefits of longer tool life and increased material removal rates. While extensive studies have investigated the machining properties for laser assisted milling(LAML), few attempts have been made to extend LAML to machining parts with complex geometric features. A methodology for continuous path machining for LAML is developed by integration of a rotary and movable table into an ordinary milling machine with a laser beam system. The machining strategy and processing path are investigated to determine alignment of the machining path with the laser spot. In order to keep the material removal temperatures above the softening temperature of silicon nitride, the transformation is coordinated and the temperature interpolated, establishing a transient thermal model. The temperatures of the laser center and cutting zone are also carefully controlled to achieve optimal machining results and avoid thermal damage. These experiments indicate that the system results in no surface damage as well as good surface roughness, validating the application of this machining strategy and thermal model in the development of a new LAML system for continuous path processing of silicon nitride. The proposed approach can be easily applied in LAML system to achieve continuous processing and improve efficiency in laser assisted machining.

15. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

15. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific Railroad Carlin Shops, view to northeast (90mm lens). The arched cutouts in the bottom chords of the roof trusses were necessary to provide clearance for the smokestacks of steam locomotives, and also mark the location of the former inspection pit in the floor (now filled in and covered by a new concrete floor). - Southern Pacific Railroad, Carlin Shops, Roundhouse Machine Shop Extension, Foot of Sixth Street, Carlin, Elko County, NV

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

PubMed

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

Method of Optimizing the Construction of Machining, Assembly and Control Devices

NASA Astrophysics Data System (ADS)

Iordache, D. M.; Costea, A.; Niţu, E. L.; Rizea, A. D.; Babă, A.

2017-10-01

Industry dynamics, driven by economic and social requirements, must generate more interest in technological optimization, capable of ensuring a steady development of advanced technical means to equip machining processes. For these reasons, the development of tools, devices, work equipment and control, as well as the modernization of machine tools, is the certain solution to modernize production systems that require considerable time and effort. This type of approach is also related to our theoretical, experimental and industrial applications of recent years, presented in this paper, which have as main objectives the elaboration and use of mathematical models, new calculation methods, optimization algorithms, new processing and control methods, as well as some structures for the construction and configuration of technological equipment with a high level of performance and substantially reduced costs..

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.

PubMed

Janssen, Ronald J; Mourão-Miranda, Janaina; Schnack, Hugo G

2018-04-22

Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of the disease. Combined with the usually scarcer longitudinal data and the variability in the definition of outcomes/transition, this makes prognostic predictions a challenging endeavor. Employing neuroimaging data in this endeavor introduces the additional hurdle of high dimensionality. Machine-learning techniques are especially suited to tackle this challenging problem. This review starts with a brief introduction to machine learning in the context of its application to clinical neuroimaging data. We highlight a few issues that are especially relevant for prediction of outcome and transition using neuroimaging. We then review the literature that discusses the application of machine learning for this purpose. Critical examination of the studies and their results with respect to the relevant issues revealed the following: 1) there is growing evidence for the prognostic capability of machine-learning-based models using neuroimaging; and 2) reported accuracies may be too optimistic owing to small sample sizes and the lack of independent test samples. Finally, we discuss options to improve the reliability of (prognostic) prediction models. These include new methodologies and multimodal modeling. Paramount, however, is our conclusion that future work will need to provide properly (cross-)validated accuracy estimates of models trained on sufficiently large datasets. Nevertheless, with the technological advances enabling acquisition of large databases of patients and healthy subjects, machine learning represents a powerful tool in the search for psychiatric biomarkers. Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Materials and optimized designs for human-machine interfaces via epidermal electronics.

PubMed

Jeong, Jae-Woong; Yeo, Woon-Hong; Akhtar, Aadeel; Norton, James J S; Kwack, Young-Jin; Li, Shuo; Jung, Sung-Young; Su, Yewang; Lee, Woosik; Xia, Jing; Cheng, Huanyu; Huang, Yonggang; Choi, Woon-Seop; Bretl, Timothy; Rogers, John A

2013-12-17

Thin, soft, and elastic electronics with physical properties well matched to the epidermis can be conformally and robustly integrated with the skin. Materials and optimized designs for such devices are presented for surface electromyography (sEMG). The findings enable sEMG from wide ranging areas of the body. The measurements have quality sufficient for advanced forms of human-machine interface. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Rationale and design of a multidisciplinary national real-world registry on carotid stenting: the Italian Registry for Carotid Stenting (RISC).

PubMed

Biasi, Giorgio M; Deleo, Gaetano; Froio, Alberto; Cremonesi, Alberto; Inglese, Luigi; Lavitrano, Marialuisa; Setacci, Carlo

2006-04-01

The Registro Italiano per lo Stenting Carotideo (RISC, Italian Registry for Carotid Stenting) has been organized by Italian specialists from different disciplines directly involved in the prevention of stroke due to carotid plaques through stenting of carotid lesions. The Registry has been endorsed by the national societies of 4 different specialties: vascular surgery, interventional cardiology, radiology, and neuroradiology. Each society contributed in the planning stage. The basis for the registry is to collect data on carotid stenting procedures performed by different specialists with different techniques in a "real-world" setting without the limitations of a randomized clinical trial. The Registry was funded to enroll at least 1200 patients over a minimum period of 36 months. The results will be analyzed using the intention-to-treat principle and are anticipated in late 2006. Primary endpoints of the registry are the 30-day combined death and stroke rate and the occurrence of restenosis and ipsilateral neurological deficit at 12 and 24 months. Considerable attention has been paid to the registry's quality control program to ensure scientific validation. An online database facilitates the collection of data with speed and accuracy.

Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

PubMed

Eskofier, Bjoern M; Lee, Sunghoon I; Daneault, Jean-Francois; Golabchi, Fatemeh N; Ferreira-Carvalho, Gabriela; Vergara-Diaz, Gloria; Sapienza, Stefano; Costante, Gianluca; Klucken, Jochen; Kautz, Thomas; Bonato, Paolo

2016-08-01

The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamann, Hendrik F.

The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.

Diamond machine tool face lapping machine

DOEpatents

Yetter, H.H.

1985-05-06

An apparatus for shaping, sharpening and polishing diamond-tipped single-point machine tools. The isolation of a rotating grinding wheel from its driving apparatus using an air bearing and causing the tool to be shaped, polished or sharpened to be moved across the surface of the grinding wheel so that it does not remain at one radius for more than a single rotation of the grinding wheel has been found to readily result in machine tools of a quality which can only be obtained by the most tedious and costly processing procedures, and previously unattainable by simple lapping techniques.

Quantification of uncertainty in machining operations for on-machine acceptance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Claudet, Andre A.; Tran, Hy D.; Su, Jiann-Chemg

2008-09-01

Manufactured parts are designed with acceptance tolerances, i.e. deviations from ideal design conditions, due to unavoidable errors in the manufacturing process. It is necessary to measure and evaluate the manufactured part, compared to the nominal design, to determine whether the part meets design specifications. The scope of this research project is dimensional acceptance of machined parts; specifically, parts machined using numerically controlled (NC, or also CNC for Computer Numerically Controlled) machines. In the design/build/accept cycle, the designer will specify both a nominal value, and an acceptable tolerance. As part of the typical design/build/accept business practice, it is required to verifymore » that the part did meet acceptable values prior to acceptance. Manufacturing cost must include not only raw materials and added labor, but also the cost of ensuring conformance to specifications. Ensuring conformance is a substantial portion of the cost of manufacturing. In this project, the costs of measurements were approximately 50% of the cost of the machined part. In production, cost of measurement would be smaller, but still a substantial proportion of manufacturing cost. The results of this research project will point to a science-based approach to reducing the cost of ensuring conformance to specifications. The approach that we take is to determine, a priori, how well a CNC machine can manufacture a particular geometry from stock. Based on the knowledge of the manufacturing process, we are then able to decide features which need further measurements from features which can be accepted 'as is' from the CNC. By calibration of the machine tool, and establishing a machining accuracy ratio, we can validate the ability of CNC to fabricate to a particular level of tolerance. This will eliminate the costs of checking for conformance for relatively large tolerances.« less

Hydraulic Fatigue-Testing Machine

NASA Technical Reports Server (NTRS)

Hodo, James D.; Moore, Dennis R.; Morris, Thomas F.; Tiller, Newton G.

1987-01-01

Fatigue-testing machine applies fluctuating tension to number of specimens at same time. When sample breaks, machine continues to test remaining specimens. Series of tensile tests needed to determine fatigue properties of materials performed more rapidly than in conventional fatigue-testing machine.

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

PubMed Central

Zhang, Jing-Kui; Yan, Weizhong; Cui, De-Mi

2016-01-01

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures. PMID:27023563

Probability machines: consistent probability estimation using nonparametric learning machines.

PubMed

Malley, J D; Kruppa, J; Dasgupta, A; Malley, K G; Ziegler, A

2012-01-01

Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

Risk of Type 2 Diabetes Mellitus Development in the Native Population of Low- and High-Altitude Regions of Kyrgyzstan: Finnish Diabetes Risc Score Questionnaire Results.

PubMed

Moldobaeva, Marina S; Vinogradova, Anastasiya V; Esenamanova, Marina K

2017-12-01

Moldobaeva, Marina S., Anastasiya V. Vinogradova, and Marina K. Esenamanova. Risk of type 2 diabetes mellitus development in the native population of low- and high-altitude regions of Kyrgyzstan: Finnish Diabetes Risc Score questionnaire results. High Alt Med Biol. 18:428-435, 2017. The number of patients with diabetes is steadily growing, but likely only half of all cases are ever identified. The Kyrgyz, native inhabitants of Central Asia, live in the mountainous area and have a particular lifestyle and nutrition. However, the risk of type 2 diabetes mellitus (T2DM) in our population is not well defined. Therefore, we aimed at determining the risk of T2DM development in the Kyrgyz population residing in low- and high-altitude (HAlt) regions by using the Finnish Diabetes Risc Score (FINDRISC) questionnaire. We included in the study 3190 randomly selected participants, including 1780 low-altitude (LAlt) residents (Chu region, 500-1200 m) and 1410 HAlt residents (Naryn region, 2000-4500 m), among whom there were 1207 men and 1983 women. Assessment of T2DM development was conducted by using the FINDRISC questionnaire and risk stratification was performed by region of residency, gender, and age. An irregular intake of vegetables and fruits, increased waist circumference (WC), and increased body mass index (BMI) were identified as leading risk factors of T2DM development in native residents of Chu and Naryn regions of Kyrgyzstan. The 10-year risk stratification of T2DM development revealed the absence of a very high-risk group; high-risk status was more frequently identified among residents of the LAlt Chu district (4.7% of women and 2.1% of men), as compared with the HAlt population (1.9% of women and 1% of men) (p = 0.0018 for women and p = 0.09 for men). In the Kyrgyz population, a 10-year high risk of T2DM development is greater among residents of LAlts as compared with HAlts, irrespective of gender. No very high-risk group was detected in residents of

Advanced Metalworking Solutions for Naval Systems that go in Harm’s Way

DTIC Science & Technology

2009-01-01

friction stir welding (FSW) and advanced machining and casting techniques to produce a prototype Automated weld seam facing on DDG 1000 ships will...transportable friction stir welding (FSW) machine. FSW is a solid state joining technology that offers benefits over traditional welding for several...addition, by locating FSW operation at the construction yard, the aluminum panels that will be friction stir - welded are built to the size needed instead

Investigations on high speed machining of EN-353 steel alloy under different machining environments

NASA Astrophysics Data System (ADS)

Venkata Vishnu, A.; Jamaleswara Kumar, P.

2018-03-01

The addition of Nano Particles into conventional cutting fluids enhances its cooling capabilities; in the present paper an attempt is made by adding nano sized particles into conventional cutting fluids. Taguchi Robust Design Methodology is employed in order to study the performance characteristics of different turning parameters i.e. cutting speed, feed rate, depth of cut and type of tool under different machining environments i.e. dry machining, machining with lubricant - SAE 40 and machining with mixture of nano sized particles of Boric acid and base fluid SAE 40. A series of turning operations were performed using L27 (3)13 orthogonal array, considering high cutting speeds and the other machining parameters to measure hardness. The results are compared among the different machining environments, and it is concluded that there is considerable improvement in the machining performance using lubricant SAE 40 and mixture of SAE 40 + boric acid compared with dry machining. The ANOVA suggests that the selected parameters and the interactions are significant and cutting speed has most significant effect on hardness.

A hybrid flowshop scheduling model considering dedicated machines and lot-splitting for the solar cell industry

NASA Astrophysics Data System (ADS)

Wang, Li-Chih; Chen, Yin-Yann; Chen, Tzu-Li; Cheng, Chen-Yang; Chang, Chin-Wei

2014-10-01

This paper studies a solar cell industry scheduling problem, which is similar to traditional hybrid flowshop scheduling (HFS). In a typical HFS problem, the allocation of machine resources for each order should be scheduled in advance. However, the challenge in solar cell manufacturing is the number of machines that can be adjusted dynamically to complete the job. An optimal production scheduling model is developed to explore these issues, considering the practical characteristics, such as hybrid flowshop, parallel machine system, dedicated machines, sequence independent job setup times and sequence dependent job setup times. The objective of this model is to minimise the makespan and to decide the processing sequence of the orders/lots in each stage, lot-splitting decisions for the orders and the number of machines used to satisfy the demands in each stage. From the experimental results, lot-splitting has significant effect on shortening the makespan, and the improvement effect is influenced by the processing time and the setup time of orders. Therefore, the threshold point to improve the makespan can be identified. In addition, the model also indicates that more lot-splitting approaches, that is, the flexibility of allocating orders/lots to machines is larger, will result in a better scheduling performance.

Electric machine

DOEpatents

El-Refaie, Ayman Mohamed Fawzi [Niskayuna, NY; Reddy, Patel Bhageerath [Madison, WI

2012-07-17

An interior permanent magnet electric machine is disclosed. The interior permanent magnet electric machine comprises a rotor comprising a plurality of radially placed magnets each having a proximal end and a distal end, wherein each magnet comprises a plurality of magnetic segments and at least one magnetic segment towards the distal end comprises a high resistivity magnetic material.

ERECTING/MACHINE SHOP, CRANE ACCESS GANGWAY BETWEEN ERECTING (L) AND MACHINE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

ERECTING/MACHINE SHOP, CRANE ACCESS GANGWAY BETWEEN ERECTING (L) AND MACHINE (R) SHOPS, LOOKING NORTH. - Southern Pacific, Sacramento Shops, Erecting Shop, 111 I Street, Sacramento, Sacramento County, CA

[Components and assembly of RNA-induced silencing complex].

PubMed

Song, Xue-Mei; Yan, Fei; Du, Li-Xin

2006-06-01

Degradation of homologous RNA in RNA interference is carried out by functional RNA-induced silencing complex (RISC). RISC contains Dicer, Argonaute proein, siRNA and other components. Researching structures and functions of these components is primary important for understanding assembly and functional mechanism of RISC, as well as the whole RNAi pathway. Recent research works showed that Dicer, containing RNaseIII domain, is responsible for production of siRNA at the beginning of RNAi, and guarantees the stability of RISC intermediate in assembly process. As the core component of RISC, Argonaute protein functions as slicer to cleave target RNA and offers the binding site of siRNA in RISC assembly, which are depended on PIWI domain and PAZ domain separately. Although there is only one strand of siRNA that is the guider of RISC, the double stranded structural character of siRNA is determinant of RNAi. Except those, there are still other components with unknown functions in RISC. The knowledge about RISC components and assembly now, is basis of a presumed RISC assembly model.

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

Quadrilateral Micro-Hole Array Machining on Invar Thin Film: Wet Etching and Electrochemical Fusion Machining

PubMed Central

Choi, Woong-Kirl; Kim, Seong-Hyun; Choi, Seung-Geon; Lee, Eun-Sang

2018-01-01

Ultra-precision products which contain a micro-hole array have recently shown remarkable demand growth in many fields, especially in the semiconductor and display industries. Photoresist etching and electrochemical machining are widely known as precision methods for machining micro-holes with no residual stress and lower surface roughness on the fabricated products. The Invar shadow masks used for organic light-emitting diodes (OLEDs) contain numerous micro-holes and are currently machined by a photoresist etching method. However, this method has several problems, such as uncontrollable hole machining accuracy, non-etched areas, and overcutting. To solve these problems, a machining method that combines photoresist etching and electrochemical machining can be applied. In this study, negative photoresist with a quadrilateral hole array pattern was dry coated onto 30-µm-thick Invar thin film, and then exposure and development were carried out. After that, photoresist single-side wet etching and a fusion method of wet etching-electrochemical machining were used to machine micro-holes on the Invar. The hole machining geometry, surface quality, and overcutting characteristics of the methods were studied. Wet etching and electrochemical fusion machining can improve the accuracy and surface quality. The overcutting phenomenon can also be controlled by the fusion machining. Experimental results show that the proposed method is promising for the fabrication of Invar film shadow masks. PMID:29351235

Apprentice Machine Theory Outline.

ERIC Educational Resources Information Center

Connecticut State Dept. of Education, Hartford. Div. of Vocational-Technical Schools.

This volume contains outlines for 16 courses in machine theory that are designed for machine tool apprentices. Addressed in the individual course outlines are the following topics: basic concepts; lathes; milling machines; drills, saws, and shapers; heat treatment and metallurgy; grinders; quality control; hydraulics and pneumatics;…

Technology of machine tools. Volume 4. Machine tool controls

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 3. Machine tool mechanics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tlusty, J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 5. Machine tool accuracy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hocken, R.J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Machine Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networksmore » and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.« less

Watson and Siri: The Rise of the BI Smart Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Troy Hiltbrand

Over the past few years, the industry has seen significant evolution in the area of human computer interaction. The era of the smart machines is upon us, with automation taking on a more advanced role than ever before, permeating into areas that have traditionally only been fulfilled by human beings. This movement has the potential of fundamentally altering the way that business intelligence is executed across the industry and the role that business intelligence has in all aspects of decision making.

A machine learning approach for viral genome classification.

PubMed

Remita, Mohamed Amine; Halioui, Ahmed; Malick Diouara, Abou Abdallah; Daigle, Bruno; Kiani, Golrokh; Diallo, Abdoulaye Baniré

2017-04-11

Advances in cloning and sequencing technology are yielding a massive number of viral genomes. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristics and disease mechanisms. Existing classification methods are often designed for specific well-studied family of viruses. Thus, the viral comparative genomic studies could benefit from more generic, fast and accurate tools for classifying and typing newly sequenced strains of diverse virus families. Here, we introduce a virus classification platform, CASTOR, based on machine learning methods. CASTOR is inspired by a well-known technique in molecular biology: restriction fragment length polymorphism (RFLP). It simulates, in silico, the restriction digestion of genomic material by different enzymes into fragments. It uses two metrics to construct feature vectors for machine learning algorithms in the classification step. We benchmark CASTOR for the classification of distinct datasets of human papillomaviruses (HPV), hepatitis B viruses (HBV) and human immunodeficiency viruses type 1 (HIV-1). Results reveal true positive rates of 99%, 99% and 98% for HPV Alpha species, HBV genotyping and HIV-1 M subtyping, respectively. Furthermore, CASTOR shows a competitive performance compared to well-known HIV-1 specific classifiers (REGA and COMET) on whole genomes and pol fragments. The performance of CASTOR, its genericity and robustness could permit to perform novel and accurate large scale virus studies. The CASTOR web platform provides an open access, collaborative and reproducible machine learning classifiers. CASTOR can be accessed at http://castor.bioinfo.uqam.ca .

X-ray power and yield measurements at the refurbished Z machine

DOE PAGES

Jones, M. C.; Ampleford, D. J.; Cuneo, M. E.; ...

2014-08-04

Advancements have been made in the diagnostic techniques to measure accurately the total radiated x-ray yield and power from z-pinch loads at the Z Machine with high accuracy. The Z-accelerator is capable of outputting 2MJ and 330 TW of x-ray yield and power, and accurately measuring these quantities is imperative. We will describe work over the past several years which include the development of new diagnostics, improvements to existing diagnostics, and implementation of automated data analysis routines. A set of experiments were conducted on the Z machine where the load and machine configuration were held constant. During this shot series,more » it was observed that total z-pinch x-ray emission power determined from the two common techniques for inferring the x-ray power, Kimfol filtered x-ray diode diagnostic and the Total Power and Energy diagnostic gave 450 TW and 327 TW respectively. Our analysis shows the latter to be the more accurate interpretation. More broadly, the comparison demonstrates the necessity to consider spectral response and field of view when inferring xray powers from z-pinch sources.« less

Machine vision systems using machine learning for industrial product inspection

NASA Astrophysics Data System (ADS)

Lu, Yi; Chen, Tie Q.; Chen, Jie; Zhang, Jian; Tisler, Anthony

2002-02-01

Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.

Machine learning approaches to analysing textual injury surveillance data: a systematic review.

PubMed

Vallmuur, Kirsten

2015-06-01

To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Systematic review. The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality

Walking Machine Control Programming

DTIC Science & Technology

1983-08-31

configuration is useful for two reasons: first, the machine won’t fit through the garage door unless it is in the tuck position, and second, a principal way...machine out of its garage . ’We call the garage a "laboratory" even though the shorter term is more apt.- We regularly run the machine in the parking...comes down from a high push-up. The natural position for the feet as the machine comes out of the garage is the "tuck" in which each knee is bent in as

Tradeoffs in the design of a system for high level language interpretation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Osorio, F.C.C.; Patt, Y.N.

The problem of designing a system for high-level language interpretation (HLLI) is considered. First, a model of the design process is presented where several styles of design, e.g. turing machine interpretation, CISC architecture interpretation and RISC architecture interpretation are treated uniformly. Second, the most significant characteristics of HLLI are analysed in the context of different design styles, and some guidelines are presented on how to identify the most suitable design style for a given high-level language problem. 12 references.

Machine learning and radiology.

PubMed

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

Vane Pump Casing Machining of Dumpling Machine Based on CAD/CAM

NASA Astrophysics Data System (ADS)

Huang, Yusen; Li, Shilong; Li, Chengcheng; Yang, Zhen

Automatic dumpling forming machine is also called dumpling machine, which makes dumplings through mechanical motions. This paper adopts the stuffing delivery mechanism featuring the improved and specially-designed vane pump casing, which can contribute to the formation of dumplings. Its 3D modeling in Pro/E software, machining process planning, milling path optimization, simulation based on UG and compiling post program were introduced and verified. The results indicated that adoption of CAD/CAM offers firms the potential to pursue new innovative strategies.

A Simple Universal Turing Machine for the Game of Life Turing Machine

NASA Astrophysics Data System (ADS)

Rendell, Paul

In this chapter we present a simple universal Turing machine which is small enough to fit into the design limits of the Turing machine build in Conway's Game of Life by the author. That limit is 8 symbols and 16 states. By way of comparison we also describe one of the smallest known universal Turing machines due to Rogozhin which has 6 symbols and 4 states.

The Knife Machine. Module 15.

ERIC Educational Resources Information Center

South Carolina State Dept. of Education, Columbia. Office of Vocational Education.

This module on the knife machine, one in a series dealing with industrial sewing machines, their attachments, and operation, covers one topic: performing special operations on the knife machine (a single needle or multi-needle machine which sews and cuts at the same time). These components are provided: an introduction, directions, an objective,…

LTE-advanced random access mechanism for M2M communication: A review

NASA Astrophysics Data System (ADS)

Mustafa, Rashid; Sarowa, Sandeep; Jaglan, Reena Rathee; Khan, Mohammad Junaid; Agrawal, Sunil

2016-03-01

Machine Type Communications (MTC) enables one or more self-sufficient machines to communicate directly with one another without human interference. MTC applications include smart grid, security, e-Health and intelligent automation system. To support huge numbers of MTC devices, one of the challenging issues is to provide a competent way for numerous access in the network and to minimize network overload. In this article, the different control mechanisms for overload random access are reviewed to avoid congestion caused by random access channel (RACH) of MTC devices. However, past and present wireless technologies have been engineered for Human-to-Human (H2H) communications, in particular, for transmission of voice. Consequently the Long Term Evolution (LTE) -Advanced is expected to play a central role in communicating Machine to Machine (M2M) and are very optimistic about H2H communications. Distinct and unique characteristics of M2M communications create new challenges from those in H2H communications. In this article, we investigate the impact of massive M2M terminals attempting random access to LTE-Advanced all at once. We discuss and review the solutions to alleviate the overload problem by Third Generation Partnership Project (3GPP). As a result, we evaluate and compare these solutions that can effectively eliminate the congestion on the random access channel for M2M communications without affecting H2H communications.

Ion beam figuring of highly steep mirrors with a 5-axis hybrid machine tool

NASA Astrophysics Data System (ADS)

Yin, Xiaolin; Tang, Wa; Hu, Haixiang; Zeng, Xuefeng; Wang, Dekang; Xue, Donglin; Zhang, Feng; Deng, Weijie; Zhang, Xuejun

2018-02-01

Ion beam figuring (IBF) is an advanced and deterministic method for optical mirror surface processing. The removal function of IBF varies with the different incident angles of ion beam. Therefore, for the curved surface especially the highly steep one, the Ion Beam Source (IBS) should be equipped with 5-axis machining capability to remove the material along the normal direction of the mirror surface, so as to ensure the stability of the removal function. Based on the 3-RPS parallel mechanism and two dimensional displacement platform, a new type of 5-axis hybrid machine tool for IBF is presented. With the hybrid machine tool, the figuring process of a highly steep fused silica spherical mirror is introduced. The R/# of the mirror is 0.96 and the aperture is 104mm. The figuring result shows that, PV value of the mirror surface error is converged from 121.1nm to32.3nm, and RMS value 23.6nm to 3.4nm.

Big Data and machine learning in radiation oncology: State of the art and future prospects.

PubMed

Bibault, Jean-Emmanuel; Giraud, Philippe; Burgun, Anita

2016-11-01

Precision medicine relies on an increasing amount of heterogeneous data. Advances in radiation oncology, through the use of CT Scan, dosimetry and imaging performed before each fraction, have generated a considerable flow of data that needs to be integrated. In the same time, Electronic Health Records now provide phenotypic profiles of large cohorts of patients that could be correlated to this information. In this review, we describe methods that could be used to create integrative predictive models in radiation oncology. Potential uses of machine learning methods such as support vector machine, artificial neural networks, and deep learning are also discussed. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Technologies for developing an advanced intelligent ATM with self-defence capabilities

NASA Astrophysics Data System (ADS)

Sako, Hiroshi

2010-01-01

We have developed several technologies for protecting automated teller machines. These technologies are based mainly on pattern recognition and are used to implement various self-defence functions. They include (i) banknote recognition and information retrieval for preventing machines from accepting counterfeit and damaged banknotes and for retrieving information about detected counterfeits from a relational database, (ii) form processing and character recognition for preventing machines from accepting remittance forms without due dates and/or insufficient payment, (iii) person identification to prevent machines from transacting with non-customers, and (iv) object recognition to guard machines against foreign objects such as spy cams that might be surreptitiously attached to them and to protect users against someone attempting to peek at their user information such as their personal identification number. The person identification technology has been implemented in most ATMs in Japan, and field tests have demonstrated that the banknote recognition technology can recognise more then 200 types of banknote from 30 different countries. We are developing an "advanced intelligent ATM" that incorporates all of these technologies.

Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms.

PubMed

Fergus, Paul; Hussain, Abir; Al-Jumeily, Dhiya; Huang, De-Shuang; Bouguila, Nizar

2017-07-06

Visual inspection of cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤ 7.20-acidosis, n = 18; pH > 7.20 and pH < 7.25-foetal deterioration, n = 4; or clinical decision without evidence of pathological outcome measures, n = 24). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. The findings show that deep learning classification achieves sensitivity = 94%, specificity = 91%, Area under the curve = 99%, F-score = 100%, and mean square error = 1%. The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies.

Machine learning research 1989-90

NASA Technical Reports Server (NTRS)

Porter, Bruce W.; Souther, Arthur

1990-01-01

Multifunctional knowledge bases offer a significant advance in artificial intelligence because they can support numerous expert tasks within a domain. As a result they amortize the costs of building a knowledge base over multiple expert systems and they reduce the brittleness of each system. Due to the inevitable size and complexity of multifunctional knowledge bases, their construction and maintenance require knowledge engineering and acquisition tools that can automatically identify interactions between new and existing knowledge. Furthermore, their use requires software for accessing those portions of the knowledge base that coherently answer questions. Considerable progress was made in developing software for building and accessing multifunctional knowledge bases. A language was developed for representing knowledge, along with software tools for editing and displaying knowledge, a machine learning program for integrating new information into existing knowledge, and a question answering system for accessing the knowledge base.

Investigation of Machine-ability of Inconel 800 in EDM with Coated Electrode

NASA Astrophysics Data System (ADS)

Karunakaran, K.; Chandrasekaran, M.

2017-03-01

The Inconel 800 is a high temperature application alloy which is classified as a nickel based super alloy. It has wide scope in aerospace engineering, gas Turbine etc. The machine-ability studies were found limited on this material. Hence This research focuses on machine-ability studies on EDM of Inconel 800 with Silver Coated Electrolyte Copper Electrode. The purpose of coating on electrode is to reduce tool wear. The factors pulse on Time, Pulse off Time and Peck Current were considered to observe the responses of surface roughness, material removal rate, tool wear rate. Taguchi Full Factorial Design is employed for Design the experiment. Some specific findings were reported and the percentage of contribution of each parameter was furnished

Advanced human machine interaction for an image interpretation workstation

NASA Astrophysics Data System (ADS)

Maier, S.; Martin, M.; van de Camp, F.; Peinsipp-Byma, E.; Beyerer, J.

2016-05-01

In recent years, many new interaction technologies have been developed that enhance the usability of computer systems and allow for novel types of interaction. The areas of application for these technologies have mostly been in gaming and entertainment. However, in professional environments, there are especially demanding tasks that would greatly benefit from improved human machine interfaces as well as an overall improved user experience. We, therefore, envisioned and built an image-interpretation-workstation of the future, a multi-monitor workplace comprised of four screens. Each screen is dedicated to a complex software product such as a geo-information system to provide geographic context, an image annotation tool, software to generate standardized reports and a tool to aid in the identification of objects. Using self-developed systems for hand tracking, pointing gestures and head pose estimation in addition to touchscreens, face identification, and speech recognition systems we created a novel approach to this complex task. For example, head pose information is used to save the position of the mouse cursor on the currently focused screen and to restore it as soon as the same screen is focused again while hand gestures allow for intuitive manipulation of 3d objects in mid-air. While the primary focus is on the task of image interpretation, all of the technologies involved provide generic ways of efficiently interacting with a multi-screen setup and could be utilized in other fields as well. In preliminary experiments, we received promising feedback from users in the military and started to tailor the functionality to their needs

Modeling of Residual Stress and Machining Distortion in Aerospace Components (PREPRINT)

DTIC Science & Technology

2010-03-01

John Gayda, “The Effect of Heat Treatment on Residual Stress and Machining Distortions in Advanced Nickel Base Disk Alloys,” NASA/TM-2001-210717. 2...Wei-Tsu Wu, Guoji Li, Juipeng Tang, Shesh Srivatsa, Ravi Shankar, Ron Wallis, Padu Ramasundaram and John Gayda, “A process modeling system for heat...Materials Processing Technology 98 (2000) 189-195. 6. M.A. Rist, S. Tin, B.A. Roder, J.A. James, and M.R. Daymond , “Residual Stresses in a

The circular form of the linear superconducting machine for marine propulsion

NASA Astrophysics Data System (ADS)

Rakels, J. H.; Mahtani, J. L.; Rhodes, R. G.

1981-01-01

The superconducting linear synchronous machine (LSM) is an efficient method of propulsion of advanced ground transport systems and can also be used in marine engineering for the propulsion of large commercial vessels, tankers, and military ships. It provides high torque at low shaft speeds and ease of reversibility; a circular LSM design is proposed as a drive motor. The equipment is compared with the superconducting homopolar motors, showing flexibility in design, built in redundancy features, and reliability.

Techniques and applications for binaural sound manipulation in human-machine interfaces

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Wenzel, Elizabeth M.

1990-01-01

The implementation of binaural sound to speech and auditory sound cues (auditory icons) is addressed from both an applications and technical standpoint. Techniques overviewed include processing by means of filtering with head-related transfer functions. Application to advanced cockpit human interface systems is discussed, although the techniques are extendable to any human-machine interface. Research issues pertaining to three-dimensional sound displays under investigation at the Aerospace Human Factors Division at NASA Ames Research Center are described.

Techniques and applications for binaural sound manipulation in human-machine interfaces

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Wenzel, Elizabeth M.

1992-01-01

The implementation of binaural sound to speech and auditory sound cues (auditory icons) is addressed from both an applications and technical standpoint. Techniques overviewed include processing by means of filtering with head-related transfer functions. Application to advanced cockpit human interface systems is discussed, although the techniques are extendable to any human-machine interface. Research issues pertaining to three-dimensional sound displays under investigation at the Aerospace Human Factors Division at NASA Ames Research Center are described.

[Comparison of machinability of two types of dental machinable ceramic].

PubMed

Fu, Qiang; Zhao, Yunfeng; Li, Yong; Fan, Xinping; Li, Yan; Lin, Xuefeng

2002-11-01

In terms of the problems of now available dental machinable ceramics, a new type of calcium-mica glass-ceramic, PMC-I ceramic, was developed, and its machinability was compared with that of Vita MKII quantitatively. Moreover, the relationship between the strength and the machinability of PMC-I ceramic was studied. Samples of PMC-I ceramic were divided into four groups according to their nucleation procedures. 600-seconds drilling tests were conducted with high-speed steel tools (Phi = 2.3 mm) to measure the drilling depths of Vita MKII ceramic and PMC-I ceramic, while constant drilling speed of 600 rpm and constant axial load of 39.2 N were used. And the 3-point bending strength of the four groups of PMC-I ceramic were recorded. Drilling depth of Vita MKII was 0.71 mm, while the depths of the four groups of PMC-I ceramic were 0.88 mm, 1.40 mm, 0.40 mm and 0.90 mm, respectively. Group B of PMC-I ceramic showed the largest depth of 1.40 mm and was statistically different from other groups and Vita MKII. And the strength of the four groups of PMC-I ceramic were 137.7, 210.2, 118.0 and 106.0 MPa, respectively. The machinability of the new developed dental machinable ceramic of PMC-I could meet the need of the clinic.

Recent Advances in Conotoxin Classification by Using Machine Learning Methods.

PubMed

Dao, Fu-Ying; Yang, Hui; Su, Zhen-Dong; Yang, Wuritu; Wu, Yun; Hui, Ding; Chen, Wei; Tang, Hua; Lin, Hao

2017-06-25

Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

Automatic soldering machine

NASA Technical Reports Server (NTRS)

Stein, J. A.

1974-01-01

Fully-automatic tube-joint soldering machine can be used to make leakproof joints in aluminum tubes of 3/16 to 2 in. in diameter. Machine consists of temperature-control unit, heater transformer and heater head, vibrator, and associated circuitry controls, and indicators.

Polishing of silicon based advanced ceramics

NASA Astrophysics Data System (ADS)

Klocke, Fritz; Dambon, Olaf; Zunke, Richard; Waechter, D.

2009-05-01

Silicon based advanced ceramics show advantages in comparison to other materials due to their extreme hardness, wear and creep resistance, low density and low coefficient of thermal expansion. As a matter of course, machining requires high efforts. In order to reach demanded low roughness for optical or tribological applications a defect free surface is indispensable. In this paper, polishing of silicon nitride and silicon carbide is investigated. The objective is to elaborate scientific understanding of the process interactions. Based on this knowledge, the optimization of removal rate, surface quality and form accuracy can be realized. For this purpose, fundamental investigations of polishing silicon based ceramics are undertaken and evaluated. Former scientific publications discuss removal mechanisms and wear behavior, but the scientific insight is mainly based on investigations in grinding and lapping. The removal mechanisms in polishing are not fully understood due to complexity of interactions. The role of, e.g., process parameters, slurry and abrasives, and their influence on the output parameters is still uncertain. Extensive technological investigations demonstrate the influence of the polishing system and the machining parameters on the stability and the reproducibility. It is shown that the interactions between the advanced ceramics and the polishing systems is of great relevance. Depending on the kind of slurry and polishing agent the material removal mechanisms differ. The observed effects can be explained by dominating mechanical or chemo-mechanical removal mechanisms. Therefore, hypotheses to state adequate explanations are presented and validated by advanced metrology devices, such as SEM, AFM and TEM.

The Security of Machine Learning

DTIC Science & Technology

2008-04-24

Machine learning has become a fundamental tool for computer security, since it can rapidly evolve to changing and complex situations. That...adaptability is also a vulnerability: attackers can exploit machine learning systems. We present a taxonomy identifying and analyzing attacks against machine ...We use our framework to survey and analyze the literature of attacks against machine learning systems. We also illustrate our taxonomy by showing

Findings from the National Machine Guarding Program–A Small Business Intervention: Machine Safety

PubMed Central

Yamin, Samuel C.; Xi, Min; Brosseau, Lisa M.; Gordon, Robert; Most, Ivan G.; Stanley, Rodney

2016-01-01

Objectives The purpose of this nationwide intervention was to improve machine safety in small metal fabrication businesses (3 – 150 employees). The failure to implement machine safety programs related to guarding and lockout/tagout (LOTO) are frequent causes of OSHA citations and may result in serious traumatic injury. Methods Insurance safety consultants conducted a standardized evaluation of machine guarding, safety programs, and LOTO. Businesses received a baseline evaluation, two intervention visits and a twelve-month follow-up evaluation. Results The intervention was completed by 160 businesses. Adding a safety committee was associated with a 10-percentage point increase in business-level machine scores (p< 0.0001) and a 33-percentage point increase in LOTO program scores (p <0.0001). Conclusions Insurance safety consultants proved effective at disseminating a machine safety and LOTO intervention via management-employee safety committees. PMID:26716850

Findings From the National Machine Guarding Program-A Small Business Intervention: Machine Safety.

PubMed

Parker, David L; Yamin, Samuel C; Xi, Min; Brosseau, Lisa M; Gordon, Robert; Most, Ivan G; Stanley, Rodney

2016-09-01

The purpose of this nationwide intervention was to improve machine safety in small metal fabrication businesses (3 to 150 employees). The failure to implement machine safety programs related to guarding and lockout/tagout (LOTO) are frequent causes of Occupational Safety and Health Administration (OSHA) citations and may result in serious traumatic injury. Insurance safety consultants conducted a standardized evaluation of machine guarding, safety programs, and LOTO. Businesses received a baseline evaluation, two intervention visits, and a 12-month follow-up evaluation. The intervention was completed by 160 businesses. Adding a safety committee was associated with a 10% point increase in business-level machine scores (P < 0.0001) and a 33% point increase in LOTO program scores (P < 0.0001). Insurance safety consultants proved effective at disseminating a machine safety and LOTO intervention via management-employee safety committees.

Analysis and design of asymmetrical reluctance machine

NASA Astrophysics Data System (ADS)

Harianto, Cahya A.

Over the past few decades the induction machine has been chosen for many applications due to its structural simplicity and low manufacturing cost. However, modest torque density and control challenges have motivated researchers to find alternative machines. The permanent magnet synchronous machine has been viewed as one of the alternatives because it features higher torque density for a given loss than the induction machine. However, the assembly and permanent magnet material cost, along with safety under fault conditions, have been concerns for this class of machine. An alternative machine type, namely the asymmetrical reluctance machine, is proposed in this work. Since the proposed machine is of the reluctance machine type, it possesses desirable feature, such as near absence of rotor losses, low assembly cost, low no-load rotational losses, modest torque ripple, and rather benign fault conditions. Through theoretical analysis performed herein, it is shown that this machine has a higher torque density for a given loss than typical reluctance machines, although not as high as the permanent magnet machines. Thus, the asymmetrical reluctance machine is a viable and advantageous machine alternative where the use of permanent magnet machines are undesirable.

Mississippi Curriculum Framework for Machine Tool Operation/Machine Shop (Program CIP: 48.0503--Machine Shop Assistant). Secondary Programs.

ERIC Educational Resources Information Center

Mississippi Research and Curriculum Unit for Vocational and Technical Education, State College.

This document, which reflects Mississippi's statutory requirement that instructional programs be based on core curricula and performance-based assessment, contains outlines of the instructional units required in local instructional management plans and daily lesson plans for machine tool operation/machine shop I and II. Presented first are a…

Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

PubMed

Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

2018-04-06

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

Machine Tool Software

NASA Technical Reports Server (NTRS)

1988-01-01

A NASA-developed software package has played a part in technical education of students who major in Mechanical Engineering Technology at William Rainey Harper College. Professor Hack has been using (APT) Automatically Programmed Tool Software since 1969 in his CAD/CAM Computer Aided Design and Manufacturing curriculum. Professor Hack teaches the use of APT programming languages for control of metal cutting machines. Machine tool instructions are geometry definitions written in APT Language to constitute a "part program." The part program is processed by the machine tool. CAD/CAM students go from writing a program to cutting steel in the course of a semester.

Nanocomposites for Machining Tools

PubMed Central

Loginov, Pavel; Mishnaevsky, Leon; Levashov, Evgeny

2017-01-01

Machining tools are used in many areas of production. To a considerable extent, the performance characteristics of the tools determine the quality and cost of obtained products. The main materials used for producing machining tools are steel, cemented carbides, ceramics and superhard materials. A promising way to improve the performance characteristics of these materials is to design new nanocomposites based on them. The application of micromechanical modeling during the elaboration of composite materials for machining tools can reduce the financial and time costs for development of new tools, with enhanced performance. This article reviews the main groups of nanocomposites for machining tools and their performance. PMID:29027926

Progress in machine consciousness.

PubMed

Gamez, David

2008-09-01

This paper is a review of the work that has been carried out on machine consciousness. A clear overview of this diverse field is achieved by breaking machine consciousness down into four different areas, which are used to understand its aims, discuss its relationship with other subjects and outline the work that has been carried out so far. The criticisms that have been made against machine consciousness are also covered, along with its potential benefits, and the work that has been done on analysing systems for signs of consciousness. Some of the social and ethical issues raised by machine consciousness are examined at the end of the paper.

Ceramic applications in the advanced Stirling automotive engine

NASA Technical Reports Server (NTRS)

Tomazic, W. A.; Cairelli, J. E.

1977-01-01

The ideal cycle, its application to a practical machine, and the specific advantages of high efficiency, low emissions, multi-fuel capability, and low noise of the stirling engine are discussed. Certain portions of the Stirling engine must operate continuously at high temperature. Ceramics offer the potential of cost reduction and efficiency improvement for advanced engine applications. Potential applications for ceramics in Stirling engines, and some of the special problems pertinent to using ceramics in the Stirling engine are described. The research and technology program in ceramics which is planned to support the development of advanced Stirling engines is outlined.

Toward Intelligent Machine Learning Algorithms

DTIC Science & Technology

1988-05-01

Machine learning is recognized as a tool for improving the performance of many kinds of systems, yet most machine learning systems themselves are not...directed systems, and with the addition of a knowledge store for organizing and maintaining knowledge to assist learning, a learning machine learning (L...ML) algorithm is possible. The necessary components of L-ML systems are presented along with several case descriptions of existing machine learning systems

Design of barrier bucket kicker control system

NASA Astrophysics Data System (ADS)

Ni, Fa-Fu; Wang, Yan-Yu; Yin, Jun; Zhou, De-Tai; Shen, Guo-Dong; Zheng, Yang-De.; Zhang, Jian-Chuan; Yin, Jia; Bai, Xiao; Ma, Xiao-Li

2018-05-01

The Heavy-Ion Research Facility in Lanzhou (HIRFL) contains two synchrotrons: the main cooler storage ring (CSRm) and the experimental cooler storage ring (CSRe). Beams are extracted from CSRm, and injected into CSRe. To apply the Barrier Bucket (BB) method on the CSRe beam accumulation, a new BB technology based kicker control system was designed and implemented. The controller of the system is implemented using an Advanced Reduced Instruction Set Computer (RISC) Machine (ARM) chip and a field-programmable gate array (FPGA) chip. Within the architecture, ARM is responsible for data presetting and floating number arithmetic processing. The FPGA computes the RF phase point of the two rings and offers more accurate control of the time delay. An online preliminary experiment on HIRFL was also designed to verify the functionalities of the control system. The result shows that the reference trigger point of two different sinusoidal RF signals for an arbitrary phase point was acquired with a matched phase error below 1° (approximately 2.1 ns), and the step delay time better than 2 ns were realized.

Design of quantum efficiency measurement system for variable doping GaAs photocathode

NASA Astrophysics Data System (ADS)

Chen, Liang; Yang, Kai; Liu, HongLin; Chang, Benkang

2008-03-01

To achieve high quantum efficiency and good stability has been a main direction to develop GaAs photocathode recently. Through early research, we proved that variable doping structure is executable and practical, and has great potential. In order to optimize variable doping GaAs photocathode preparation techniques and study the variable doping theory deeply, a real-time quantum efficiency measurement system for GaAs Photocathode has been designed. The system uses FPGA (Field-programmable gate array) device, and high speed A/D converter to design a high signal noise ratio and high speed data acquisition card. ARM (Advanced RISC Machines) core processor s3c2410 and real-time embedded system are used to obtain and show measurement results. The measurement precision of photocurrent could reach 1nA, and measurement range of spectral response curve is within 400~1000nm. GaAs photocathode preparation process can be real-time monitored by using this system. This system could easily be added other functions to show the physic variation of photocathode during the preparation process more roundly in the future.

New machine-learning algorithms for prediction of Parkinson's disease

NASA Astrophysics Data System (ADS)

Mandal, Indrajit; Sairam, N.

2014-03-01

This article presents an enhanced prediction accuracy of diagnosis of Parkinson's disease (PD) to prevent the delay and misdiagnosis of patients using the proposed robust inference system. New machine-learning methods are proposed and performance comparisons are based on specificity, sensitivity, accuracy and other measurable parameters. The robust methods of treating Parkinson's disease (PD) includes sparse multinomial logistic regression, rotation forest ensemble with support vector machines and principal components analysis, artificial neural networks, boosting methods. A new ensemble method comprising of the Bayesian network optimised by Tabu search algorithm as classifier and Haar wavelets as projection filter is used for relevant feature selection and ranking. The highest accuracy obtained by linear logistic regression and sparse multinomial logistic regression is 100% and sensitivity, specificity of 0.983 and 0.996, respectively. All the experiments are conducted over 95% and 99% confidence levels and establish the results with corrected t-tests. This work shows a high degree of advancement in software reliability and quality of the computer-aided diagnosis system and experimentally shows best results with supportive statistical inference.

ABB's advanced steam turbine program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chellini, R.

Demand for industrial steam turbines for combined-cycle applications and cogeneration plants has influenced turbine manufacturers to standardize their machines to reduce delivery time and cost. ABB, also a supplier of turnkey plants, manufactures steam turbines in Finspong, Sweden, at the former ASEA Stal facilities and in Nuernberg, Germany, at the former AEG facilities. The companies have joined forces, setting up the advanced Steam Turbine Program (ATP) that, once completed, will cover a power range from two to 100 MW. The company decided to use two criteria as a starting point, the high efficiency design of the Swedish turbines and themore » high reliability of the German machines. Thus, the main task was combining the two designs in standard machines that could be assembled quickly into predefined packages to meet specific needs of combined-cycle and cogeneration plants specified by customers. In carrying out this project, emphasis was put on cost reduction as one of the main goals. The first results of the ATP program, presented by ABB Turbinen Nuernberg, is the range of 2-30 MW turbines covered by two frame sizes comprising standard components supporting the thermodynamic module. An important feature is the standardization of the speed reduction gearbox.« less

The Hooey Machine.