Replicating human expertise of mechanical ventilation waveform analysis in detecting patient-ventilator cycling asynchrony using machine learning.

PubMed

Gholami, Behnood; Phan, Timothy S; Haddad, Wassim M; Cason, Andrew; Mullis, Jerry; Price, Levi; Bailey, James M

2018-06-01

- Acute respiratory failure is one of the most common problems encountered in intensive care units (ICU) and mechanical ventilation is the mainstay of supportive therapy for such patients. A mismatch between ventilator delivery and patient demand is referred to as patient-ventilator asynchrony (PVA). An important hurdle in addressing PVA is the lack of a reliable framework for continuously and automatically monitoring the patient and detecting various types of PVA. - The problem of replicating human expertise of waveform analysis for detecting cycling asynchrony (i.e., delayed termination, premature termination, or none) was investigated in a pilot study involving 11 patients in the ICU under invasive mechanical ventilation. A machine learning framework is used to detect cycling asynchrony based on waveform analysis. - A panel of five experts with experience in PVA evaluated a total of 1377 breath cycles from 11 mechanically ventilated critical care patients. The majority vote was used to label each breath cycle according to cycling asynchrony type. The proposed framework accurately detected the presence or absence of cycling asynchrony with sensitivity (specificity) of 89% (99%), 94% (98%), and 97% (93%) for delayed termination, premature termination, and no cycling asynchrony, respectively. The system showed strong agreement with human experts as reflected by the kappa coefficients of 0.90, 0.91, and 0.90 for delayed termination, premature termination, and no cycling asynchrony, respectively. - The pilot study establishes the feasibility of using a machine learning framework to provide waveform analysis equivalent to an expert human. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multi-Target Single Cycle Instrument Placement

NASA Technical Reports Server (NTRS)

Pedersen, Liam; Smith, David E.; Deans, Matthew; Sargent, Randy; Kunz, Clay; Lees, David; Rajagopalan, Srikanth; Bualat, Maria

2005-01-01

This presentation is about the robotic exploration of Mars using multiple targets command cycle, safe instrument placements, safe operation, and K9 Rover which has a 6 wheel steer rocket-bogey chassis (FIDO, MER), 70% MER size, 1.2 GHz Pentium M laptop running Linux OS, Odometry and compass/inclinometer, CLARAty architecture, 5 DOF manipulator w/CHAMP microscopic camera, SciCams, NavCams and HazCams.

Improvement of the COP of the LiBr-Water Double-Effect Absorption Cycles

NASA Astrophysics Data System (ADS)

Shitara, Atsushi

Prevention of the global warming has called for a great necessity for energy saving. This applies to the improvement of the COP of absorption chiller-heaters. We started the development of the high efficiency gas-fired double-effect absorption chiller-heater using LiBr-H2O to achieve target performance in short or middle term. To maintain marketability, the volume of the high efficiency machine has been set below the equal to the conventional machine. The absorption cycle technology for improving the COP and the element technology for downsizing the machine is necessary in this development. In this study, the former is investigated. In this report, first of all the target performance has been set at cooling COP of 1.35(on HHV), which is 0.35 higher than the COP of 1.0 for conventional machines in the market. This COP of 1.35 is practically close to the maximum limit achievable by double-effect absorption chiller-heater. Next, the design condition of each element to achieve the target performance and the effect of each mean to improve the COP are investigated. Moreover, as a result of comparing the various flows(series, parallel, reverse)to which the each mean is applied, it has been found the optimum cycle is the parallel flow.

Target Detection over the Diurnal Cycle Using a Multispectral Infrared Sensor.

PubMed

Zhao, Huijie; Ji, Zheng; Li, Na; Gu, Jianrong; Li, Yansong

2016-12-29

When detecting a target over the diurnal cycle, a conventional infrared thermal sensor might lose the target due to the thermal crossover, which could happen at any time throughout the day when the infrared image contrast between target and background in a scene is indistinguishable due to the temperature variation. In this paper, the benefits of using a multispectral-based infrared sensor over the diurnal cycle have been shown. Firstly, a brief theoretical analysis on how the thermal crossover influences a conventional thermal sensor, within the conditions where the thermal crossover would happen and why the mid-infrared (3~5 μm) multispectral technology is effective, is presented. Furthermore, the effectiveness of this technology is also described and we describe how the prototype design and multispectral technology is employed to help solve the thermal crossover detection problem. Thirdly, several targets are set up outside and imaged in the field experiment over a 24-h period. The experimental results show that the multispectral infrared imaging system can enhance the contrast of the detected images and effectively solve the failure of the conventional infrared sensor during the diurnal cycle, which is of great significance for infrared surveillance applications.

Target Detection over the Diurnal Cycle Using a Multispectral Infrared Sensor

PubMed Central

Zhao, Huijie; Ji, Zheng; Li, Na; Gu, Jianrong; Li, Yansong

2016-01-01

When detecting a target over the diurnal cycle, a conventional infrared thermal sensor might lose the target due to the thermal crossover, which could happen at any time throughout the day when the infrared image contrast between target and background in a scene is indistinguishable due to the temperature variation. In this paper, the benefits of using a multispectral-based infrared sensor over the diurnal cycle have been shown. Firstly, a brief theoretical analysis on how the thermal crossover influences a conventional thermal sensor, within the conditions where the thermal crossover would happen and why the mid-infrared (3~5 μm) multispectral technology is effective, is presented. Furthermore, the effectiveness of this technology is also described and we describe how the prototype design and multispectral technology is employed to help solve the thermal crossover detection problem. Thirdly, several targets are set up outside and imaged in the field experiment over a 24-h period. The experimental results show that the multispectral infrared imaging system can enhance the contrast of the detected images and effectively solve the failure of the conventional infrared sensor during the diurnal cycle, which is of great significance for infrared surveillance applications. PMID:28036073

New support vector machine-based method for microRNA target prediction.

PubMed

Li, L; Gao, Q; Mao, X; Cao, Y

2014-06-09

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model. The model supplies information of two binding sites (primary and secondary) for a radial basis function kernel as a similarity measure for SVM features. The information is categorized based on structural, thermodynamic, and sequence conservation. Using high-confidence datasets selected from public miRNA target databases, we obtained a human miRNA target SVM classifier model with high performance and provided an efficient tool for human miRNA target gene identification. Experiments have shown that our method is a reliable tool for miRNA target-gene prediction, and a successful application of an SVM classifier. Compared with other methods, the method proposed here improves the sensitivity and accuracy of miRNA prediction. Its performance can be further improved by providing more training examples.

Cycle 24 HST+COS Target Acquisition Monitor Summary

NASA Astrophysics Data System (ADS)

Penton, Steven V.; White, James

2018-06-01

HST/COS calibration program 14847 (P14857) was designed to verify that all three COS Target Acquisition (TA) modes were performing nominally during Cycle 24. The program was designed not only to determine if any of the COS TA flight software (FSW) patchable constants need updating but also to determine the values of any required parameter updates. All TA modes were determined to be performing nominally during the Cycle 24 calendar period of October 1, 2016 - October 1, 2017. No COS SIAF, TA subarray, or FSW parameter updates were required as a result of this program.

Cogs in the endless machine: lakes, climate change and nutrient cycles: a review.

PubMed

Moss, Brian

2012-09-15

Lakes have, rather grandly, been described as sentinels, integrators and regulators of climate change (Williamson et al., Limnol. Oceanogr. 2009; 54: 2273-82). Lakes are also part of the continuum of the water cycle, cogs in a machine that processes water and elements dissolved and suspended in myriad forms. Assessing the changes in the functioning of the cogs and the machine with respect to these substances as climate changes is clearly important, but difficult. Many other human-induced influences, not least eutrophication, that impact on catchment areas and consequently on lakes, have generally complicated the recording of recent change in sediment records and modern sets of data. The least confounded evidence comes from remote lakes in mountain and polar regions and suggests effects of warming that include mobilisation of ions and increased amounts of phosphorus. A cottage industry has arisen in deduction and prediction of the future effects of climate change on lakes, but the results are very general and precision is marred not only by confounding influences but by the complexity of the lake system and the infinite variety of possible future scenarios. A common conclusion, however, is that warming will increase the intensity of symptoms of eutrophication. Direct experimentation, though expensive and still unusual and confined to shallow lake and wetland systems is perhaps the most reliable approach. Results suggest increased symptoms of eutrophication, and changes in ecosystem structure, but in some respects are different from those deduced from comparisons along latitudinal gradients or by inference from knowledge of lake behaviour. Experiments have shown marked increases in community respiration compared with gross photosynthesis in mesocosm systems and it may be that the most significant churnings of these cogs in the earth-air-water machine will be in their influence on the carbon cycle, with possibly large positive feedback effects on warming. Copyright �

Method and infrastructure for cycle-reproducible simulation on large scale digital circuits on a coordinated set of field-programmable gate arrays (FPGAs)

DOEpatents

Asaad, Sameh W; Bellofatto, Ralph E; Brezzo, Bernard; Haymes, Charles L; Kapur, Mohit; Parker, Benjamin D; Roewer, Thomas; Tierno, Jose A

2014-01-28

A plurality of target field programmable gate arrays are interconnected in accordance with a connection topology and map portions of a target system. A control module is coupled to the plurality of target field programmable gate arrays. A balanced clock distribution network is configured to distribute a reference clock signal, and a balanced reset distribution network is coupled to the control module and configured to distribute a reset signal to the plurality of target field programmable gate arrays. The control module and the balanced reset distribution network are cooperatively configured to initiate and control a simulation of the target system with the plurality of target field programmable gate arrays. A plurality of local clock control state machines reside in the target field programmable gate arrays. The local clock state machines are configured to generate a set of synchronized free-running and stoppable clocks to maintain cycle-accurate and cycle-reproducible execution of the simulation of the target system. A method is also provided.

Coordination of Myeloid Differentiation with Reduced Cell Cycle Progression by PU.1 Induction of MicroRNAs Targeting Cell Cycle Regulators and Lipid Anabolism.

PubMed

Solomon, Lauren A; Podder, Shreya; He, Jessica; Jackson-Chornenki, Nicholas L; Gibson, Kristen; Ziliotto, Rachel G; Rhee, Jess; DeKoter, Rodney P

2017-05-15

During macrophage development, myeloid progenitor cells undergo terminal differentiation coordinated with reduced cell cycle progression. Differentiation of macrophages from myeloid progenitors is accompanied by increased expression of the E26 transformation-specific transcription factor PU.1. Reduced PU.1 expression leads to increased proliferation and impaired differentiation of myeloid progenitor cells. It is not understood how PU.1 coordinates macrophage differentiation with reduced cell cycle progression. In this study, we utilized cultured PU.1-inducible myeloid cells to perform genome-wide chromatin immunoprecipitation sequencing (ChIP-seq) analysis coupled with gene expression analysis to determine targets of PU.1 that may be involved in regulating cell cycle progression. We found that genes encoding cell cycle regulators and enzymes involved in lipid anabolism were directly and inducibly bound by PU.1 although their steady-state mRNA transcript levels were reduced. Inhibition of lipid anabolism was sufficient to reduce cell cycle progression in these cells. Induction of PU.1 reduced expression of E2f1 , an important activator of genes involved in cell cycle and lipid anabolism, indirectly through microRNA 223. Next-generation sequencing identified microRNAs validated as targeting cell cycle and lipid anabolism for downregulation. These results suggest that PU.1 coordinates cell cycle progression with differentiation through induction of microRNAs targeting cell cycle regulators and lipid anabolism. Copyright © 2017 American Society for Microbiology.

A comparison of machine learning techniques for detection of drug target articles.

PubMed

Danger, Roxana; Segura-Bedmar, Isabel; Martínez, Paloma; Rosso, Paolo

2010-12-01

Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure. Copyright © 2010 Elsevier Inc. All rights reserved.

Machine compliance in compression tests

NASA Astrophysics Data System (ADS)

Sousa, Pedro; Ivens, Jan; Lomov, Stepan V.

2018-05-01

The compression behavior of a material cannot be accurately determined if the machine compliance is not accounted prior to the measurements. This work discusses the machine compliance during a compressibility test with fiberglass fabrics. The thickness variation was measured during loading and unloading cycles with a relaxation stage of 30 minutes between them. The measurements were performed using an indirect technique based on the comparison between the displacement at a free compression cycle and the displacement with a sample. Relating to the free test, it has been noticed the nonexistence of machine relaxation during relaxation stage. Considering relaxation or not, the characteristic curves for a free compression cycle can be overlapped precisely in the majority of the points. For the compression test with sample, it was noticed a non-physical decrease of about 30 µm during the relaxation stage, what can be explained by the greater fabric relaxation in relation to the machine relaxation. Beyond the technique normally used, another technique was used which allows a constant thickness during relaxation. Within this second method, machine displacement with sample is simply subtracted to the machine displacement without sample being imposed as constant. If imposed as a constant it will remain constant during relaxation stage and it will suddenly decrease after relaxation. If constantly calculated it will decrease gradually during relaxation stage. Independently of the technique used the final result will remain unchanged. The uncertainty introduced by this imprecision is about ±15 µm.

Washing machine usage in remote aboriginal communities.

PubMed

Lloyd, C R

1998-10-01

The use of washing machines was investigated in two remote Aboriginal communities in the Anangu Pitjantjatjara homelands. The aim was to look both at machine reliability and to investigate the health aspect of washing clothes. A total of 39 machines were inspected for wear and component reliability every three months over a one-year period. Of these, 10 machines were monitored in detail for water consumption, hours of use and cycles of operation. The machines monitored were Speed Queen model EA2011 (7 kg washing load) commercial units. The field survey results suggested a high rate of operation of the machines with an average of around 1,100 washing cycles per year (range 150 and 2,300 cycles per year). The results were compared with available figures for the average Australian household. A literature survey, to ascertain the health outcomes relating to washing clothes and bedding, confirmed that washing machines are efficient at removal of bacteria from clothes and bedding but suggested that recontamination of clothing after washing often negated the prior removal. High temperature washing (> 60 degrees C) appeared to be advantageous from a health perspective. With regards to larger organisms, while dust mites and body lice transmission between people would probably be decreased by washing clothes, scabies appeared to be mainly transmitted by body contact and thus transmission would be only marginally decreased by the use of washing machines.

The research and application of visual saliency and adaptive support vector machine in target tracking field.

PubMed

Chen, Yuantao; Xu, Weihong; Kuang, Fangjun; Gao, Shangbing

2013-01-01

The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking's accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper's algorithm has been based on the mixture saliency of image features. These features include color, brightness, and sport feature. The execution process used visual saliency features and those common characteristics have been expressed as the target's saliency. Numerous experiments demonstrate the effectiveness and timeliness of the proposed target tracking algorithm in video sequences where the target objects undergo large changes in pose, scale, and illumination.

Employing machine learning for reliable miRNA target identification in plants.

PubMed

Jha, Ashwani; Shankar, Ravi

2011-12-29

miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited development. Most of them have been centered around exact complementarity match. Very few of them considered other factors like multiple target sites and role of flanking regions. In the present work, a Support Vector Regression (SVR) approach has been implemented for plant miRNA target identification, utilizing position specific dinucleotide density variation information around the target sites, to yield highly reliable result. It has been named as p-TAREF (plant-Target Refiner). Performance comparison for p-TAREF was done with other prediction tools for plants with utmost rigor and where p-TAREF was found better performing in several aspects. Further, p-TAREF was run over the experimentally validated miRNA targets from species like Arabidopsis, Medicago, Rice and Tomato, and detected them accurately, suggesting gross usability of p-TAREF for plant species. Using p-TAREF, target identification was done for the complete Rice transcriptome, supported by expression and degradome based data. miR156 was found as an important component of the Rice regulatory system, where control of genes associated with growth and transcription looked predominant. The entire methodology has been implemented in a multi-threaded parallel architecture in Java, to enable fast processing for web-server version as well as standalone version. This also makes it to run even on a simple desktop computer in concurrent mode. It also provides a facility to gather experimental support for predictions made, through on the spot expression data analysis, in its web-server version. A machine learning multivariate feature tool has been implemented in parallel and

Energy efficient quantum machines

NASA Astrophysics Data System (ADS)

Abah, Obinna; Lutz, Eric

2017-05-01

We investigate the performance of a quantum thermal machine operating in finite time based on shortcut-to-adiabaticity techniques. We compute efficiency and power for a paradigmatic harmonic quantum Otto engine by taking the energetic cost of the shortcut driving explicitly into account. We demonstrate that shortcut-to-adiabaticity machines outperform conventional ones for fast cycles. We further derive generic upper bounds on both quantities, valid for any heat engine cycle, using the notion of quantum speed limit for driven systems. We establish that these quantum bounds are tighter than those stemming from the second law of thermodynamics.

Targeting methionine cycle as a potential therapeutic strategy for immune disorders.

PubMed

Li, Heng; Lu, Huimin; Tang, Wei; Zuo, Jianping

2017-08-23

Methionine cycle plays an essential role in regulating many cellular events, especially transmethylation reactions, incorporating the methyl donor S-adenosylmethionine (SAM). The transmethylations and substances involved in the cycle have shown complicated effects and mechanisms on immunocytes developments and activations, and exert crucial impacts on the pathological processes in immune disorders. Areas covered: Methionine cycle has been considered as an effective means of drug developments. This review discussed the role of methionine cycle in immune responses and summarized the potential therapeutic strategies based on the cycle, including SAM analogs, methyltransferase inhibitors, S-adenosylhomocysteine hydrolase (SAHH) inhibitors, adenosine receptors specific agonists or antagonists and homocysteine (Hcy)-lowering reagents, in treating human immunodeficiency virus (HIV) infections, systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), multiple sclerosis (MS), systemic sclerosis (SSc) and other immune disorders. Expert opinion: New targets and biomarkers grown out of methionine cycle have developed rapidly in the past decades. However, impacts of epigenetic regulations on immune disorders are unclear and whether the substances in methionine cycle can be clarified as biomarkers remains controversial. Therefore, further elucidation on the role of epigenetic regulations and substances in methionine cycle may contribute to exploring the cycle-derived biomarkers and drugs in immune disorders.

Nutrient cycling Microbial Ecosystems: Assembly, Function and Targeted Design

DTIC Science & Technology

2017-05-05

different chemical transformations, converting potentially harmful chemicals via a series of intermediates, to harmless waste products. This shuttling of...Report: Nutrient-cycling Microbial Ecosystems: Assembly, Function and Targeted Design The views, opinions and/or findings contained in this report...are those of the author(s) and should not contrued as an official Department of the Army position, policy or decision, unless so designated by other

Employing machine learning for reliable miRNA target identification in plants

PubMed Central

2011-01-01

Background miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited development. Most of them have been centered around exact complementarity match. Very few of them considered other factors like multiple target sites and role of flanking regions. Result In the present work, a Support Vector Regression (SVR) approach has been implemented for plant miRNA target identification, utilizing position specific dinucleotide density variation information around the target sites, to yield highly reliable result. It has been named as p-TAREF (plant-Target Refiner). Performance comparison for p-TAREF was done with other prediction tools for plants with utmost rigor and where p-TAREF was found better performing in several aspects. Further, p-TAREF was run over the experimentally validated miRNA targets from species like Arabidopsis, Medicago, Rice and Tomato, and detected them accurately, suggesting gross usability of p-TAREF for plant species. Using p-TAREF, target identification was done for the complete Rice transcriptome, supported by expression and degradome based data. miR156 was found as an important component of the Rice regulatory system, where control of genes associated with growth and transcription looked predominant. The entire methodology has been implemented in a multi-threaded parallel architecture in Java, to enable fast processing for web-server version as well as standalone version. This also makes it to run even on a simple desktop computer in concurrent mode. It also provides a facility to gather experimental support for predictions made, through on the spot expression data analysis, in its web-server version. Conclusion A machine learning multivariate feature tool has been

The emerging role and targetability of the TCA cycle in cancer metabolism.

PubMed

Anderson, Nicole M; Mucka, Patrick; Kern, Joseph G; Feng, Hui

2018-02-01

The tricarboxylic acid (TCA) cycle is a central route for oxidative phosphorylation in cells, and fulfills their bioenergetic, biosynthetic, and redox balance requirements. Despite early dogma that cancer cells bypass the TCA cycle and primarily utilize aerobic glycolysis, emerging evidence demonstrates that certain cancer cells, especially those with deregulated oncogene and tumor suppressor expression, rely heavily on the TCA cycle for energy production and macromolecule synthesis. As the field progresses, the importance of aberrant TCA cycle function in tumorigenesis and the potentials of applying small molecule inhibitors to perturb the enhanced cycle function for cancer treatment start to evolve. In this review, we summarize current knowledge about the fuels feeding the cycle, effects of oncogenes and tumor suppressors on fuel and cycle usage, common genetic alterations and deregulation of cycle enzymes, and potential therapeutic opportunities for targeting the TCA cycle in cancer cells. With the application of advanced technology and in vivo model organism studies, it is our hope that studies of this previously overlooked biochemical hub will provide fresh insights into cancer metabolism and tumorigenesis, subsequently revealing vulnerabilities for therapeutic interventions in various cancer types.

C1 Domain-Targeted Isophthalate Derivatives Induce Cell Elongation and Cell Cycle Arrest in HeLa Cells

PubMed Central

Talman, Virpi; Tuominen, Raimo K.; Gennäs, Gustav Boije af; Yli-Kauhaluoma, Jari; Ekokoski, Elina

2011-01-01

Diacylglycerol (DAG)-mediated signaling pathways, such as those mediated by protein kinase C (PKC), are central in regulating cell proliferation and apoptosis. DAG-responsive C1 domains are therefore considered attractive drug targets. Our group has designed a novel class of compounds targeted to the DAG binding site within the C1 domain of PKC. We have previously shown that these 5-(hydroxymethyl)isophthalates modulate PKC activation in living cells. In this study we investigated their effects on HeLa human cervical cancer cell viability and proliferation by using standard cytotoxicity tests and an automated imaging platform with machine vision technology. Cellular effects and their mechanisms were further characterized with the most potent compound, HMI-1a3. Isophthalate derivatives with high affinity to the PKC C1 domain exhibited antiproliferative and non-necrotic cytotoxic effects on HeLa cells. The anti-proliferative effect was irreversible and accompanied by cell elongation. HMI-1a3 induced down-regulation of retinoblastoma protein and cyclins A, B1, D1, and E. Effects of isophthalates on cell morphology, cell proliferation and expression of cell cycle-related proteins were different from those induced by phorbol 12-myristate-13-acetate (PMA) or bryostatin 1, but correlated closely to binding affinities. Therefore, the results strongly indicate that the effect is C1 domain-mediated. PMID:21629792

Pulsed, Hydraulic Coal-Mining Machine

NASA Technical Reports Server (NTRS)

Collins, Earl R., Jr.

1986-01-01

In proposed coal-cutting machine, piston forces water through nozzle, expelling pulsed jet that cuts into coal face. Spring-loaded piston reciprocates at end of travel to refill water chamber. Machine a onecylinder, two-cycle, internal-combustion engine, fueled by gasoline, diesel fuel, or hydrogen. Fuel converted more directly into mechanical energy of water jet.

Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

PubMed

Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

2017-10-01

Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Achieving Precision Death with Cell-Cycle Inhibitors that Target DNA Replication and Repair.

PubMed

Lin, Aimee Bence; McNeely, Samuel C; Beckmann, Richard P

2017-07-01

All cancers are characterized by defects in the systems that ensure strict control of the cell cycle in normal tissues. The consequent excess tissue growth can be countered by drugs that halt cell division, and, indeed, the majority of chemotherapeutics developed during the last century work by disrupting processes essential for the cell cycle, particularly DNA synthesis, DNA replication, and chromatid segregation. In certain contexts, the efficacy of these classes of drugs can be impressive, but because they indiscriminately block the cell cycle of all actively dividing cells, their side effects severely constrain the dose and duration with which they can be administered, allowing both normal and malignant cells to escape complete growth arrest. Recent progress in understanding how cancers lose control of the cell cycle, coupled with comprehensive genomic profiling of human tumor biopsies, has shown that many cancers have mutations affecting various regulators and checkpoints that impinge on the core cell-cycle machinery. These defects introduce unique vulnerabilities that can be exploited by a next generation of drugs that promise improved therapeutic windows in patients whose tumors bear particular genomic aberrations, permitting increased dose intensity and efficacy. These developments, coupled with the success of new drugs targeting cell-cycle regulators, have led to a resurgence of interest in cell-cycle inhibitors. This review in particular focuses on the newer strategies that may facilitate better therapeutic targeting of drugs that inhibit the various components that safeguard the fidelity of the fundamental processes of DNA replication and repair. Clin Cancer Res; 23(13); 3232-40. ©2017 AACR . ©2017 American Association for Cancer Research.

Using Support Vector Machine Ensembles for Target Audience Classification on Twitter

PubMed Central

Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

2015-01-01

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space. PMID:25874768

Using support vector machine ensembles for target audience classification on Twitter.

PubMed

Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

2015-01-01

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.

Evaluation of Community-Based Policy, Systems, and Environment Interventions Targeting the Vending Machines.

PubMed

Garcia, Kristen M; Garney, Whitney R; Primm, Kristin M; McLeroy, Kenneth R

The American Heart Association conducted policy, systems, and environmental (PSE) focused interventions to increase healthy vending in 8 communities. PSE interventions were assessed using the Nutrition Environment Measures Survey Vending Assessment to see changes in the food environment. Baseline and follow-up assessments were conducted with 3 settings and a total of 19 machines. PSE changes resulted in increased availability of healthy options and decreased unhealthy options. Implementation of PSE interventions targeting the food environment can be an effective method of providing increased access to healthy foods and beverages with the goal of increasing consumption to decrease chronic diseases.

Interplay between cancer cell cycle and metabolism: Challenges, targets and therapeutic opportunities.

PubMed

Roy, Debmalya; Sheng, Gao Ying; Herve, Semukunzi; Carvalho, Evandro; Mahanty, Arpan; Yuan, Shengtao; Sun, Li

2017-05-01

A growing interest has emerged in the field of studying the cross-talk between cancer cell cycle and metabolism. In this review, we aimed to present how metabolism and cell cycle are correlated and how cancer cells get energy to drive cell cycle. Cell proliferation and cell death largely depend on the metabolic activity of the cell. Cell cycle proteins, e.g. cyclin D, cyclin dependent kinase (CDK), some pro-apoptotic and anti-apoptotic proteins, and P53 have been shown to be regulated by metabolic crosstalk. Dysregulation of this cross-talk between metabolism and cell cycle leads to degenerative disorder(s) and cancer. It is not fully understood the actual reason of aberration between metabolism and cell cycle, but it is a hallmark of cancer research. Herein, we discussed the role of some regulatory molecules relative of cell cycle and metabolism and highlight how they control the function of each other. We also pointed out, current therapeutic opportunities and some additional crucial therapeutic targets on these fields that could be a breakthrough in cancer research. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

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.

Machine learning enhanced optical distance sensor

NASA Astrophysics Data System (ADS)

Amin, M. Junaid; Riza, N. A.

2018-01-01

Presented for the first time is a machine learning enhanced optical distance sensor. The distance sensor is based on our previously demonstrated distance measurement technique that uses an Electronically Controlled Variable Focus Lens (ECVFL) with a laser source to illuminate a target plane with a controlled optical beam spot. This spot with varying spot sizes is viewed by an off-axis camera and the spot size data is processed to compute the distance. In particular, proposed and demonstrated in this paper is the use of a regularized polynomial regression based supervised machine learning algorithm to enhance the accuracy of the operational sensor. The algorithm uses the acquired features and corresponding labels that are the actual target distance values to train a machine learning model. The optimized training model is trained over a 1000 mm (or 1 m) experimental target distance range. Using the machine learning algorithm produces a training set and testing set distance measurement errors of <0.8 mm and <2.2 mm, respectively. The test measurement error is at least a factor of 4 improvement over our prior sensor demonstration without the use of machine learning. Applications for the proposed sensor include industrial scenario distance sensing where target material specific training models can be generated to realize low <1% measurement error distance measurements.

Identification of novel plant peroxisomal targeting signals by a combination of machine learning methods and in vivo subcellular targeting analyses.

PubMed

Lingner, Thomas; Kataya, Amr R; Antonicelli, Gerardo E; Benichou, Aline; Nilssen, Kjersti; Chen, Xiong-Yan; Siemsen, Tanja; Morgenstern, Burkhard; Meinicke, Peter; Reumann, Sigrun

2011-04-01

In the postgenomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisome-targeted proteins in animals and fungi but are missing specifically for plants. For development of a predictor for plant proteins carrying peroxisome targeting signals type 1 (PTS1), we assembled more than 2500 homologous plant sequences, mainly from EST databases. We applied a discriminative machine learning approach to derive two different prediction methods, both of which showed high prediction accuracy and recognized specific targeting-enhancing patterns in the regions upstream of the PTS1 tripeptides. Upon application of these methods to the Arabidopsis thaliana genome, 392 gene models were predicted to be peroxisome targeted. These predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. The prediction methods were able to correctly infer novel PTS1 tripeptides, which even included novel residues. Twenty-three newly predicted PTS1 tripeptides were experimentally confirmed, and a high variability of the plant PTS1 motif was discovered. These prediction methods will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants.

Precision Robotic Assembly Machine

ScienceCinema

None

2017-12-09

The world's largest laser system is the National Ignition Facility (NIF), located at Lawrence Livermore National Laboratory. NIF's 192 laser beams are amplified to extremely high energy, and then focused onto a tiny target about the size of a BB, containing frozen hydrogen gas. The target must be perfectly machined to incredibly demanding specifications. The Laboratory's scientists and engineers have developed a device called the "Precision Robotic Assembly Machine" for this purpose. Its unique design won a prestigious R&D-100 award from R&D Magazine.

EDM machinability of SiCw/Al composites

NASA Technical Reports Server (NTRS)

Ramulu, M.; Taya, M.

1989-01-01

Machinability of high temperature composites was investigated. Target materials, 15 and 25 vol pct SiC whisker-2124 aluminum composites, were machined by electrodischarge sinker machining and diamond saw. The machined surfaces of these metal matrix composites were examined by SEM and profilometry to determine the surface finish. Microhardness measurements were also performed on the as-machined composites.

Multiple man-machine interfaces

NASA Technical Reports Server (NTRS)

Stanton, L.; Cook, C. W.

1981-01-01

The multiple man machine interfaces inherent in military pilot training, their social implications, and the issue of possible negative feedback were explored. Modern technology has produced machines which can see, hear, and touch with greater accuracy and precision than human beings. Consequently, the military pilot is more a systems manager, often doing battle against a target he never sees. It is concluded that unquantifiable human activity requires motivation that is not intrinsic in a machine.

Experimental investigation of time and repeated cycles in nucleate pool boiling of alumina/water nanofluid on polished and machined surfaces

NASA Astrophysics Data System (ADS)

Rajabzadeh Dareh, F.; Haghshenasfard, M.; Nasr Esfahany, M.; Salimi Jazi, H.

2018-06-01

Pool boiling heat transfer of pure water and nanofluids on a copper block has been studied experimentally. Nanofluids with various concentrations of 0.0025, 0.005 and 0.01 vol.% are employed and two simple surfaces (polished and machined copper surface) are used as the heating surfaces. The results indicated that the critical heat flux (CHF) in boiling of fluids on the polished surface is 7% higher than CHF on the machined surface. In the case of machined surface, the heat transfer coefficient (HTC) of 0.01 vol.% nanofluid is about 37% higher than HTC of base fluid, while in the polished surface the average HTC of 0.01% nanofluid is about 19% lower than HTC of the pure water. The results also showed that the boiling time and boiling cycles on the polished surface changes the heat transfer performance. By increasing the boiling time from 5 to 10 min, the roughness enhances about 150%, but by increasing the boiling time to 15 min, the roughness enhancement is only 8%.

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

PubMed Central

Liu, Yongxiang; Huo, Kai; Zhang, Zhongshuai

2018-01-01

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available. PMID:29320453

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.

PubMed

Zhao, Feixiang; Liu, Yongxiang; Huo, Kai; Zhang, Shuanghui; Zhang, Zhongshuai

2018-01-10

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

Self-Calibrating Surface Measuring Machine

NASA Astrophysics Data System (ADS)

Greenleaf, Allen H.

1983-04-01

A new kind of surface-measuring machine has been developed under government contract at Itek Optical Systems, a Division of Itek Corporation, to assist in the fabrication of large, highly aspheric optical elements. The machine uses four steerable distance-measuring interferometers at the corners of a tetrahedron to measure the positions of a retroreflective target placed at various locations against the surface being measured. Using four interferometers gives redundant information so that, from a set of measurement data, the dimensions of the machine as well as the coordinates of the measurement points can be determined. The machine is, therefore, self-calibrating and does not require a structure made to high accuracy. A wood-structured prototype of this machine was made whose key components are a simple form of air bearing steering mirror, a wide-angle cat's eye retroreflector used as the movable target, and tracking sensors and servos to provide automatic tracking of the cat's eye by the four laser beams. The data are taken and analyzed by computer. The output is given in terms of error relative to an equation of the desired surface. In tests of this machine, measurements of a 0.7 m diameter mirror blank have been made with an accuracy on the order of 0.2µm rms.

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

NASA Astrophysics Data System (ADS)

Huang, Bing; von Lilienfeld, O. Anatole

2016-10-01

The predictive accuracy of Machine Learning (ML) models of molecular properties depends on the choice of the molecular representation. Inspired by the postulates of quantum mechanics, we introduce a hierarchy of representations which meet uniqueness and target similarity criteria. To systematically control target similarity, we simply rely on interatomic many body expansions, as implemented in universal force-fields, including Bonding, Angular (BA), and higher order terms. Addition of higher order contributions systematically increases similarity to the true potential energy and predictive accuracy of the resulting ML models. We report numerical evidence for the performance of BAML models trained on molecular properties pre-calculated at electron-correlated and density functional theory level of theory for thousands of small organic molecules. Properties studied include enthalpies and free energies of atomization, heat capacity, zero-point vibrational energies, dipole-moment, polarizability, HOMO/LUMO energies and gap, ionization potential, electron affinity, and electronic excitations. After training, BAML predicts energies or electronic properties of out-of-sample molecules with unprecedented accuracy and speed.

Design features and results from fatigue reliability research machines.

NASA Technical Reports Server (NTRS)

Lalli, V. R.; Kececioglu, D.; Mcconnell, J. B.

1971-01-01

The design, fabrication, development, operation, calibration and results from reversed bending combined with steady torque fatigue research machines are presented. Fifteen-centimeter long, notched, SAE 4340 steel specimens are subjected to various combinations of these stresses and cycled to failure. Failure occurs when the crack in the notch passes through the specimen automatically shutting down the test machine. These cycles-to-failure data are statistically analyzed to develop a probabilistic S-N diagram. These diagrams have many uses; a rotating component design example given in the literature shows that minimum size and weight for a specified number of cycles and reliability can be calculated using these diagrams.

The effect of fresh gas flow rate and type of anesthesia machine on time to reach target sevoflurane concentration.

PubMed

Shin, Hye Won; Yu, Hae Na; Bae, Go Eun; Huh, Hyub; Park, Ji Yong; Kim, Ji Young

2017-01-19

Anesthesia machines have been developed by the application of new technology for rapid and easier control of anesthetic concentration. In this study, we used a test lung to investigate whether the time taken to reach the target sevoflurane concentration varies with the rate of fresh gas flow (FGF) and type of anesthesia machine (AM). We measured the times taken to reach the target sevoflurane concentration (2 minimum alveolar concentration = 4%) at variable rates of FGF (0.5, 1, or 3 L/min) and different types of AM (Primus ® , Perseus ® , and Zeus ® [Zeus ® -F; Zeus ® fresh gas mode, Zeus ® -A; Zeus ® auto-mode]). Concomitant ventilation was supplied using 100% O 2. The AMs were connected to a test lung. A sevoflurane vaporizer setting of 6% was used in Primus ® , Perseus ® , and Zeus ® -F; a target end-tidal setting of 4% was used in Zeus ® -A (from a vaporizer setting of 0%). The time taken to reach the target concentration was measured in every group. When the same AM was used (Primus ® , Perseus ® , or Zeus ® -F), the times to target concentration shortened as the FGF rate increased (P < 0.05). Conversely, when the same FGF rate was used, but with different AMs, the time to target concentration was shortest in Perseus ® , followed by Primus ® , and finally by Zeus ® -F (P < 0.05). With regards to both modes of Zeus ® , at FGF rates of 0.5 and 1 L/min, the time to target concentration was shorter in Zeus ® -A than in Zeus ® -F; however, the time was longer in Zeus ® -A than in Zeus ® -F at FGF rate of 3 L/min (P < 0.05). Shorter times taken to reach the target concentration were associated with high FGF rates, smaller internal volume of the AM, proximity of the fresh gas inlets to patients, absence of a decoupling system, and use of blower-driven ventilators in AM.

Laser Machining of Melt Infiltrated Ceramic Matrix Composite

NASA Technical Reports Server (NTRS)

Jarmon, D. C.; Ojard, G.; Brewer, D.

2012-01-01

As interest grows in considering the use of ceramic matrix composites for critical components, the effects of different machining techniques, and the resulting machined surfaces, on strength need to be understood. This work presents the characterization of a Melt Infiltrated SiC/SiC composite material system machined by different methods. While a range of machining approaches were initially considered, only diamond grinding and laser machining were investigated on a series of tensile coupons. The coupons were tested for residual tensile strength, after a stressed steam exposure cycle. The data clearly differentiated the laser machined coupons as having better capability for the samples tested. These results, along with micro-structural characterization, will be presented.

Applied physiology of cycling.

PubMed

Faria, I E

1984-01-01

Historically, the bicycle has evolved through the stages of a machine for efficient human transportation, a toy for children, a finely-tuned racing machine, and a tool for physical fitness development, maintenance and testing. Recently, major strides have been made in the aerodynamic design of the bicycle. These innovations have resulted in new land speed records for human powered machines. Performance in cycling is affected by a variety of factors, including aerobic and anaerobic capacity, muscular strength and endurance, and body composition. Bicycle races range from a 200m sprint to approximately 5000km. This vast range of competitive racing requires special attention to the principle of specificity of training. The physiological demands of cycling have been examined through the use of bicycle ergometers, rollers, cycling trainers, treadmill cycling, high speed photography, computer graphics, strain gauges, electromyography, wind tunnels, muscle biopsy, and body composition analysis. These techniques have been useful in providing definitive data for the development of a work/performance profile of the cyclist. Research evidence strongly suggests that when measuring the cyclist's aerobic or anaerobic capacity, a cycling protocol employing a high pedalling rpm should be used. The research bicycle should be modified to resemble a racing bicycle and the cyclist should wear cycling shoes. Prolonged cycling requires special nutritional considerations. Ingestion of carbohydrates, in solid form and carefully timed, influences performance. Caffeine appears to enhance lipid metabolism. Injuries, particularly knee problems which are prevalent among cyclists, may be avoided through the use of proper gearing and orthotics. Air pollution has been shown to impair physical performance. When pollution levels are high, training should be altered or curtailed. Effective training programmes simulate competitive conditions. Short and long interval training, blended with long

Cell cycle-regulated proteolysis of mitotic target proteins.

PubMed

Bastians, H; Topper, L M; Gorbsky, G L; Ruderman, J V

1999-11-01

The ubiquitin-dependent proteolysis of mitotic cyclin B, which is catalyzed by the anaphase-promoting complex/cyclosome (APC/C) and ubiquitin-conjugating enzyme H10 (UbcH10), begins around the time of the metaphase-anaphase transition and continues through G1 phase of the next cell cycle. We have used cell-free systems from mammalian somatic cells collected at different cell cycle stages (G0, G1, S, G2, and M) to investigate the regulated degradation of four targets of the mitotic destruction machinery: cyclins A and B, geminin H (an inhibitor of S phase identified in Xenopus), and Cut2p (an inhibitor of anaphase onset identified in fission yeast). All four are degraded by G1 extracts but not by extracts of S phase cells. Maintenance of destruction during G1 requires the activity of a PP2A-like phosphatase. Destruction of each target is dependent on the presence of an N-terminal destruction box motif, is accelerated by additional wild-type UbcH10 and is blocked by dominant negative UbcH10. Destruction of each is terminated by a dominant activity that appears in nuclei near the start of S phase. Previous work indicates that the APC/C-dependent destruction of anaphase inhibitors is activated after chromosome alignment at the metaphase plate. In support of this, we show that addition of dominant negative UbcH10 to G1 extracts blocks destruction of the yeast anaphase inhibitor Cut2p in vitro, and injection of dominant negative UbcH10 blocks anaphase onset in vivo. Finally, we report that injection of dominant negative Ubc3/Cdc34, whose role in G1-S control is well established and has been implicated in kinetochore function during mitosis in yeast, dramatically interferes with congression of chromosomes to the metaphase plate. These results demonstrate that the regulated ubiquitination and destruction of critical mitotic proteins is highly conserved from yeast to humans.

On-target diagnosing of few-cycle pulses by high-order-harmonic generation

NASA Astrophysics Data System (ADS)

Brambila, Danilo S.; Husakou, Anton; Ivanov, Misha; Zhavoronkov, Nickolai

2017-12-01

We propose an approach to determine the residual phase distortion directly in the interaction region of few-cycle laser radiation with a gaseous target. We describe how the spectra of the generated high harmonics measured as a function of externally introduced dispersion into the driving few-cycle laser pulse can be used to decode small amounts of second- and third-order spectral phase, including the sign. The diagnosis is based on the analysis of several key features in the high-harmonic spectrum: the depth of spectral modulation, the position of the cutoff, and the symmetry of the spectrum with respect to the introduced dispersion. The approach is applicable to pulses without carrier-envelope phase (CEP) stabilization. Surprisingly, we find that for nearly-single-cycle pulses with nonstabilized CEP, deep spectral modulations in the harmonic spectra emerge for positively rather than negatively chirped pulses, in contrast to the case of CEP-stabilized pulses.

Financial heat machine

NASA Astrophysics Data System (ADS)

Khrennikov, Andrei

2005-05-01

We consider dynamics of financial markets as dynamics of expectations and discuss such a dynamics from the point of view of phenomenological thermodynamics. We describe a financial Carnot cycle and the financial analog of a heat machine. We see, that while in physics a perpetuum mobile is absolutely impossible, in economics such mobile may exist under some conditions.

[MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

PubMed

Chen, Zhiru; Hong, Wenxue

2016-02-01

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

Senescence-associated microRNAs target cell cycle regulatory genes in normal human lung fibroblasts.

PubMed

Markopoulos, Georgios S; Roupakia, Eugenia; Tokamani, Maria; Vartholomatos, George; Tzavaras, Theodore; Hatziapostolou, Maria; Fackelmayer, Frank O; Sandaltzopoulos, Raphael; Polytarchou, Christos; Kolettas, Evangelos

2017-10-01

Senescence recapitulates the ageing process at the cell level. A senescent cell stops dividing and exits the cell cycle. MicroRNAs (miRNAs) acting as master regulators of transcription, have been implicated in senescence. In the current study we investigated and compared the expression of miRNAs in young versus senescent human fibroblasts (HDFs), and analysed the role of mRNAs expressed in replicative senescent HFL-1 HDFs. Cell cycle analysis confirmed that HDFs accumulated in G 1 /S cell cycle phase. Nanostring analysis of isolated miRNAs from young and senescent HFL-1 showed that a distinct set of 15 miRNAs were significantly up-regulated in senescent cells including hsa-let-7d-5p, hsa-let-7e-5p, hsa-miR-23a-3p, hsa-miR-34a-5p, hsa-miR-122-5p, hsa-miR-125a-3p, hsa-miR-125a-5p, hsa-miR-125b-5p, hsa-miR-181a-5p, hsa-miR-221-3p, hsa-miR-222-3p, hsa-miR-503-5p, hsa-miR-574-3p, hsa-miR-574-5p and hsa-miR-4454. Importantly, pathway analysis of miRNA target genes down-regulated during replicative senescence in a public RNA-seq data set revealed a significant high number of genes regulating cell cycle progression, both G 1 /S and G 2 /M cell cycle phase transitions and telomere maintenance. The reduced expression of selected miRNA targets, upon replicative and oxidative-stress induced senescence, such as the cell cycle effectors E2F1, CcnE, Cdc6, CcnB1 and Cdc25C was verified at the protein and/or RNA levels. Induction of G1/S cell cycle phase arrest and down-regulation of cell cycle effectors correlated with the up-regulation of miR-221 upon both replicative and oxidative stress-induced senescence. Transient expression of miR-221/222 in HDFs promoted the accumulation of HDFs in G1/S cell cycle phase. We propose that miRNAs up-regulated during replicative senescence may act in concert to induce cell cycle phase arrest and telomere erosion, establishing a senescent phenotype. Copyright © 2017 Elsevier Inc. All rights reserved.

Plant microRNA-Target Interaction Identification Model Based on the Integration of Prediction Tools and Support Vector Machine

PubMed Central

Meng, Jun; Shi, Lin; Luan, Yushi

2014-01-01

Background Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA). Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA–target interactions. Results Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA) feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species. Conclusions The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided. PMID:25051153

Experimental investigation of the tip based micro/nano machining

NASA Astrophysics Data System (ADS)

Guo, Z.; Tian, Y.; Liu, X.; Wang, F.; Zhou, C.; Zhang, D.

2017-12-01

Based on the self-developed three dimensional micro/nano machining system, the effects of machining parameters and sample material on micro/nano machining are investigated. The micro/nano machining system is mainly composed of the probe system and micro/nano positioning stage. The former is applied to control the normal load and the latter is utilized to realize high precision motion in the xy plane. A sample examination method is firstly introduced to estimate whether the sample is placed horizontally. The machining parameters include scratching direction, speed, cycles, normal load and feed. According to the experimental results, the scratching depth is significantly affected by the normal load in all four defined scratching directions but is rarely influenced by the scratching speed. The increase of scratching cycle number can increase the scratching depth as well as smooth the groove wall. In addition, the scratching tests of silicon and copper attest that the harder material is easier to be removed. In the scratching with different feed amount, the machining results indicate that the machined depth increases as the feed reduces. Further, a cubic polynomial is used to fit the experimental results to predict the scratching depth. With the selected machining parameters of scratching direction d3/d4, scratching speed 5 μm/s and feed 0.06 μm, some more micro structures including stair, sinusoidal groove, Chinese character '田', 'TJU' and Chinese panda have been fabricated on the silicon substrate.

Managing virtual machines with Vac and Vcycle

NASA Astrophysics Data System (ADS)

McNab, A.; Love, P.; MacMahon, E.

2015-12-01

We compare the Vac and Vcycle virtual machine lifecycle managers and our experiences in providing production job execution services for ATLAS, CMS, LHCb, and the GridPP VO at sites in the UK, France and at CERN. In both the Vac and Vcycle systems, the virtual machines are created outside of the experiment's job submission and pilot framework. In the case of Vac, a daemon runs on each physical host which manages a pool of virtual machines on that host, and a peer-to-peer UDP protocol is used to achieve the desired target shares between experiments across the site. In the case of Vcycle, a daemon manages a pool of virtual machines on an Infrastructure-as-a-Service cloud system such as OpenStack, and has within itself enough information to create the types of virtual machines to achieve the desired target shares. Both systems allow unused shares for one experiment to temporarily taken up by other experiements with work to be done. The virtual machine lifecycle is managed with a minimum of information, gathered from the virtual machine creation mechanism (such as libvirt or OpenStack) and using the proposed Machine/Job Features API from WLCG. We demonstrate that the same virtual machine designs can be used to run production jobs on Vac and Vcycle/OpenStack sites for ATLAS, CMS, LHCb, and GridPP, and that these technologies allow sites to be operated in a reliable and robust way.

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

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing.

PubMed

Oyetunde, Tolutola; Bao, Forrest Sheng; Chen, Jiung-Wen; Martin, Hector Garcia; Tang, Yinjie J

2018-05-03

Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O 2 ). This review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate "Learn and Design" for strain development. Copyright © 2018. Published by Elsevier Inc.

Towards a molecular logic machine

NASA Astrophysics Data System (ADS)

Remacle, F.; Levine, R. D.

2001-06-01

Finite state logic machines can be realized by pump-probe spectroscopic experiments on an isolated molecule. The most elaborate setup, a Turing machine, can be programmed to carry out a specific computation. We argue that a molecule can be similarly programmed, and provide examples using two photon spectroscopies. The states of the molecule serve as the possible states of the head of the Turing machine and the physics of the problem determines the possible instructions of the program. The tape is written in an alphabet that allows the listing of the different pump and probe signals that are applied in a given experiment. Different experiments using the same set of molecular levels correspond to different tapes that can be read and processed by the same head and program. The analogy to a Turing machine is not a mechanical one and is not completely molecular because the tape is not part of the molecular machine. We therefore also discuss molecular finite state machines, such as sequential devices, for which the tape is not part of the machine. Nonmolecular tapes allow for quite long input sequences with a rich alphabet (at the level of 7 bits) and laser pulse shaping experiments provide concrete examples. Single molecule spectroscopies show that a single molecule can be repeatedly cycled through a logical operation.

Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Vanderhoff, Alex

2013-07-15

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 6/1/13 to 6/30/13

Protein thin film machines

NASA Astrophysics Data System (ADS)

Federici, Stefania; Oliviero, Giulio; Hamad-Schifferli, Kimberly; Bergese, Paolo

2010-12-01

We report the first example of microcantilever beams that are reversibly driven by protein thin film machines fuelled by cycling the salt concentration of the surrounding solution. We also show that upon the same salinity stimulus the drive can be completely reversed in its direction by introducing a surface coating ligand. Experimental results are throughout discussed within a general yet simple thermodynamic model.

Express Control of the Mechanical Properties of High-Strength and Hard-to-Machine Materials at All Stages of the Technological Cycle of Producing Mechanical Engineering Products

NASA Astrophysics Data System (ADS)

Matyunin, V. M.; Marchenkov, A. Yu.; Demidov, A. N.; Karimbekov, M. A.

2017-12-01

It is shown that depth-sensing indentation can be used to perform express control of the mechanical properties of high-strength and hard-to-machine materials. This control can be performed at various stages of a technological cycle of processing materials and parts without preparing and testing tensile specimens, which will significantly reduce the consumption of materials, time, and labor.

Active vibration and balance system for closed cycle thermodynamic machines

NASA Technical Reports Server (NTRS)

Augenblick, John E. (Inventor); Peterson, Allen A. (Inventor); White, Maurice A. (Inventor); Qiu, Songgang (Inventor)

2004-01-01

An active balance system is provided for counterbalancing vibrations of an axially reciprocating machine. The balance system includes a support member, a flexure assembly, a counterbalance mass, and a linear motor or an actuator. The support member is configured for attachment to the machine. The flexure assembly includes at least one flat spring having connections along a central portion and an outer peripheral portion. One of the central portion and the outer peripheral portion is fixedly mounted to the support member. The counterbalance mass is fixedly carried by the flexure assembly along another of the central portion and the outer peripheral portion. The linear motor has one of a stator and a mover fixedly mounted to the support member and another of the stator and the mover fixedly mounted to the counterbalance mass. The linear motor is operative to axially reciprocate the counterbalance mass. A method is also provided.

Time-reversal imaging for classification of submerged elastic targets via Gibbs sampling and the Relevance Vector Machine.

PubMed

Dasgupta, Nilanjan; Carin, Lawrence

2005-04-01

Time-reversal imaging (TRI) is analogous to matched-field processing, although TRI is typically very wideband and is appropriate for subsequent target classification (in addition to localization). Time-reversal techniques, as applied to acoustic target classification, are highly sensitive to channel mismatch. Hence, it is crucial to estimate the channel parameters before time-reversal imaging is performed. The channel-parameter statistics are estimated here by applying a geoacoustic inversion technique based on Gibbs sampling. The maximum a posteriori (MAP) estimate of the channel parameters are then used to perform time-reversal imaging. Time-reversal implementation requires a fast forward model, implemented here by a normal-mode framework. In addition to imaging, extraction of features from the time-reversed images is explored, with these applied to subsequent target classification. The classification of time-reversed signatures is performed by the relevance vector machine (RVM). The efficacy of the technique is analyzed on simulated in-channel data generated by a free-field finite element method (FEM) code, in conjunction with a channel propagation model, wherein the final classification performance is demonstrated to be relatively insensitive to the associated channel parameters. The underlying theory of Gibbs sampling and TRI are presented along with the feature extraction and target classification via the RVM.

Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana.

PubMed

Van Leene, Jelle; Hollunder, Jens; Eeckhout, Dominique; Persiau, Geert; Van De Slijke, Eveline; Stals, Hilde; Van Isterdael, Gert; Verkest, Aurine; Neirynck, Sandy; Buffel, Yelle; De Bodt, Stefanie; Maere, Steven; Laukens, Kris; Pharazyn, Anne; Ferreira, Paulo C G; Eloy, Nubia; Renne, Charlotte; Meyer, Christian; Faure, Jean-Denis; Steinbrenner, Jens; Beynon, Jim; Larkin, John C; Van de Peer, Yves; Hilson, Pierre; Kuiper, Martin; De Veylder, Lieven; Van Onckelen, Harry; Inzé, Dirk; Witters, Erwin; De Jaeger, Geert

2010-08-10

Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)-cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK-cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants.

Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana

PubMed Central

Van Leene, Jelle; Hollunder, Jens; Eeckhout, Dominique; Persiau, Geert; Van De Slijke, Eveline; Stals, Hilde; Van Isterdael, Gert; Verkest, Aurine; Neirynck, Sandy; Buffel, Yelle; De Bodt, Stefanie; Maere, Steven; Laukens, Kris; Pharazyn, Anne; Ferreira, Paulo C G; Eloy, Nubia; Renne, Charlotte; Meyer, Christian; Faure, Jean-Denis; Steinbrenner, Jens; Beynon, Jim; Larkin, John C; Van de Peer, Yves; Hilson, Pierre; Kuiper, Martin; De Veylder, Lieven; Van Onckelen, Harry; Inzé, Dirk; Witters, Erwin; De Jaeger, Geert

2010-01-01

Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)–cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK–cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants. PMID:20706207

20130416_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Vanderhoff, Alex

2013-04-24

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 4/16/13.

Methods, systems and apparatus for controlling operation of two alternating current (AC) machines

DOEpatents

Gallegos-Lopez, Gabriel [Torrance, CA; Nagashima, James M [Cerritos, CA; Perisic, Milun [Torrance, CA; Hiti, Silva [Redondo Beach, CA

2012-06-05

A system is provided for controlling two alternating current (AC) machines via a five-phase PWM inverter module. The system comprises a first control loop, a second control loop, and a current command adjustment module. The current command adjustment module operates in conjunction with the first control loop and the second control loop to continuously adjust current command signals that control the first AC machine and the second AC machine such that they share the input voltage available to them without compromising the target mechanical output power of either machine. This way, even when the phase voltage available to either one of the machines decreases, that machine outputs its target mechanical output power.

Augmentation of machine structure to improve its diagnosability

NASA Technical Reports Server (NTRS)

Hsieh, L.

1973-01-01

Two methods of augmenting the structure of a sequential machine so that it is diagnosable are presented. The checkable (checking sequences) and repeated symbol distinguishing sequences (RDS) are discussed. It was found that as few as twice the number of outputs of the given machine is sufficient for constructing a state-output augmentation with RDS. Techniques for minimizing the number of states in resolving convergences and in resolving equivalent and nonreduced cycles are developed.

Minimal universal quantum heat machine.

PubMed

Gelbwaser-Klimovsky, D; Alicki, R; Kurizki, G

2013-01-01

In traditional thermodynamics the Carnot cycle yields the ideal performance bound of heat engines and refrigerators. We propose and analyze a minimal model of a heat machine that can play a similar role in quantum regimes. The minimal model consists of a single two-level system with periodically modulated energy splitting that is permanently, weakly, coupled to two spectrally separated heat baths at different temperatures. The equation of motion allows us to compute the stationary power and heat currents in the machine consistent with the second law of thermodynamics. This dual-purpose machine can act as either an engine or a refrigerator (heat pump) depending on the modulation rate. In both modes of operation, the maximal Carnot efficiency is reached at zero power. We study the conditions for finite-time optimal performance for several variants of the model. Possible realizations of the model are discussed.

The cell cycle in Alzheimer disease: a unique target for neuropharmacology.

PubMed

Webber, Kate M; Raina, Arun K; Marlatt, Michael W; Zhu, Xiongwei; Prat, María I; Morelli, Laura; Casadesus, Gemma; Perry, George; Smith, Mark A

2005-10-01

Several hypotheses have been proposed attempting to explain the pathogenesis of Alzheimer disease including, among others, theories involving amyloid deposition, tau phosphorylation, oxidative stress, metal ion dysregulation and inflammation. While there is strong evidence suggesting that each one of these proposed mechanisms contributes to disease pathogenesis, none of these mechanisms are able to account for all the physiological changes that occur during the course of the disease. For this reason, we and others have begun the search for a causative factor that predates known features found in Alzheimer disease, and that might therefore be a fundamental initiator of the pathophysiological cascade. We propose that the dysregulation of the cell cycle that occurs in neurons susceptible to degeneration in the hippocampus during Alzheimer disease is a potential causative factor that, together with oxidative stress, would initiate all known pathological events. Neuronal changes supporting alterations in cell cycle control in the etiology of Alzheimer disease include the ectopic expression of markers of the cell cycle, organelle kinesis and cytoskeletal alterations including tau phosphorylation. Such mitotic alterations are not only one of the earliest neuronal abnormalities in the disease, but as discussed herein, are also intimately linked to all of the other pathological hallmarks of Alzheimer disease including tau protein, amyloid beta protein precursor and oxidative stress, and even risk factors such as mutations in the presenilin genes. Therefore, therapeutic interventions targeted toward ameliorating mitotic changes would be predicted to have a profound and positive impact on Alzheimer disease progression.

20140430_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-05-05

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 April to 30 April 2014.

Green Machine Florida Canyon Hourly Data 20130731

DOE Data Explorer

Vanderhoff, Alex

2013-08-30

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 7/1/13 to 7/31/13.

Integrative Analysis Reveals an Outcome-associated and Targetable Pattern of p53 and Cell Cycle Deregulation in Diffuse Large B-cell Lymphoma

PubMed Central

Monti, Stefano; Chapuy, Bjoern; Takeyama, Kunihiko; Rodig, Scott J; Hao, Yangsheng; Yeda, Kelly T.; Inguilizian, Haig; Mermel, Craig; Curie, Treeve; Dogan, Ahmed; Kutok, Jeffery L; Beroukim, Rameen; Neuberg, Donna; Habermann, Thomas; Getz, Gad; Kung, Andrew L; Golub, Todd R; Shipp, Margaret A

2013-01-01

Summary Diffuse large B-cell lymphoma (DLBCL) is a clinically and biologically heterogeneous disease with a high proliferation rate. By integrating copy number data with transcriptional profiles and performing pathway analysis in primary DLBCLs, we identified a comprehensive set of copy number alterations (CNAs) that decreased p53 activity and perturbed cell cycle regulation. Primary tumors either had multiple complementary alterations of p53 and cell cycle components or largely lacked these lesions. DLBCLs with p53 and cell cycle pathway CNAs had decreased abundance of p53 target transcripts and increased expression of E2F target genes and the Ki67 proliferation marker. CNAs of the CDKN2A-TP53-RB-E2F axis provide a structural basis for increased proliferation in DLBCL, predict outcome with current therapy and suggest targeted treatment approaches. PMID:22975378

Programmable phase plate for tool modification in laser machining applications

DOEpatents

Thompson Jr., Charles A.; Kartz, Michael W.; Brase, James M.; Pennington, Deanna; Perry, Michael D.

2004-04-06

A system for laser machining includes a laser source for propagating a laser beam toward a target location, and a spatial light modulator having individual controllable elements capable of modifying a phase profile of the laser beam to produce a corresponding irradiance pattern on the target location. The system also includes a controller operably connected to the spatial light modulator for controlling the individual controllable elements. By controlling the individual controllable elements, the phase profile of the laser beam may be modified into a desired phase profile so as to produce a corresponding desired irradiance pattern on the target location capable of performing a machining operation on the target location.

A control technology evaluation of state-of-the-art, perchloroethylene dry-cleaning machines.

PubMed

Earnest, G Scott

2002-05-01

NIOSH researchers evaluated the ability of fifth-generation dry-cleaning machines to control occupational exposure to perchloroethylene (PERC). Use of these machines is mandated in some countries; however, less than 1 percent of all U.S. shops have them. A study was conducted at a U.S. dry-cleaning shop where two fifth-generation machines were used. Both machines had a refrigerated condenser as a primary control and a carbon adsorber as a secondary control to recover PERC vapors during the dry cycle. These machines were designed to lower the PERC concentration in the cylinder at the end of the dry cycle to below 290 ppm. A single-beam infrared photometer continuously monitors the PERC concentration in the machine cylinder, and a door interlock prevents opening until the concentration is below 290 ppm. Personal breathing zone air samples were measured for the machine operator and presser. The operator had time-weighted average (TWA) PERC exposures that were less than 2 ppm. Highest exposures occurred during loading and unloading the machine and when performing routine machine maintenance. All presser samples were below the limit of detection. Real-time video exposure monitoring showed that the operator had peak exposures near 160 ppm during loading and unloading the machine (below the OSHA maximum of 300 ppm). This exposure (160 ppm) is an order of magnitude lower than exposures with more traditional machines that are widely used in the United States. The evaluated machines were very effective at reducing TWA PERC exposures as well as peak exposures that occur during machine loading and unloading. State-of-the-art dry-cleaning machines equipped with refrigerated condensers, carbon adsorbers, drum monitors, and door interlocks can provide substantially better protection than more traditional machines that are widely used in the United States.

Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries.

PubMed

Shi, Z; Ma, X H; Qin, C; Jia, J; Jiang, Y Y; Tan, C Y; Chen, Y Z

2012-02-01

Selective multi-target serotonin reuptake inhibitors enhance antidepressant efficacy. Their discovery can be facilitated by multiple methods, including in silico ones. In this study, we developed and tested an in silico method, combinatorial support vector machines (COMBI-SVMs), for virtual screening (VS) multi-target serotonin reuptake inhibitors of seven target pairs (serotonin transporter paired with noradrenaline transporter, H(3) receptor, 5-HT(1A) receptor, 5-HT(1B) receptor, 5-HT(2C) receptor, melanocortin 4 receptor and neurokinin 1 receptor respectively) from large compound libraries. COMBI-SVMs trained with 917-1951 individual target inhibitors correctly identified 22-83.3% (majority >31.1%) of the 6-216 dual inhibitors collected from literature as independent testing sets. COMBI-SVMs showed moderate to good target selectivity in misclassifying as dual inhibitors 2.2-29.8% (majority <15.4%) of the individual target inhibitors of the same target pair and 0.58-7.1% of the other 6 targets outside the target pair. COMBI-SVMs showed low dual inhibitor false hit rates (0.006-0.056%, 0.042-0.21%, 0.2-4%) in screening 17 million PubChem compounds, 168,000 MDDR compounds, and 7-8181 MDDR compounds similar to the dual inhibitors. Compared with similarity searching, k-NN and PNN methods, COMBI-SVM produced comparable dual inhibitor yields, similar target selectivity, and lower false hit rate in screening 168,000 MDDR compounds. The annotated classes of many COMBI-SVMs identified MDDR virtual hits correlate with the reported effects of their predicted targets. COMBI-SVM is potentially useful for searching selective multi-target agents without explicit knowledge of these agents. Copyright © 2011 Elsevier Inc. All rights reserved.

Smarter Instruments, Smarter Archives: Machine Learning for Tactical Science

NASA Astrophysics Data System (ADS)

Thompson, D. R.; Kiran, R.; Allwood, A.; Altinok, A.; Estlin, T.; Flannery, D.

2014-12-01

There has been a growing interest by Earth and Planetary Sciences in machine learning, visualization and cyberinfrastructure to interpret ever-increasing volumes of instrument data. Such tools are commonly used to analyze archival datasets, but they can also play a valuable real-time role during missions. Here we discuss ways that machine learning can benefit tactical science decisions during Earth and Planetary Exploration. Machine learning's potential begins at the instrument itself. Smart instruments endowed with pattern recognition can immediately recognize science features of interest. This allows robotic explorers to optimize their limited communications bandwidth, triaging science products and prioritizing the most relevant data. Smart instruments can also target their data collection on the fly, using principles of experimental design to reduce redundancy and generally improve sampling efficiency for time-limited operations. Moreover, smart instruments can respond immediately to transient or unexpected phenomena. Examples include detections of cometary plumes, terrestrial floods, or volcanism. We show recent examples of smart instruments from 2014 tests including: aircraft and spacecraft remote sensing instruments that recognize cloud contamination, field tests of a "smart camera" for robotic surface geology, and adaptive data collection by X-Ray fluorescence spectrometers. Machine learning can also assist human operators when tactical decision making is required. Terrestrial scenarios include airborne remote sensing, where the decision to re-fly a transect must be made immediately. Planetary scenarios include deep space encounters or planetary surface exploration, where the number of command cycles is limited and operators make rapid daily decisions about where next to collect measurements. Visualization and modeling can reveal trends, clusters, and outliers in new data. This can help operators recognize instrument artifacts or spot anomalies in real time

Measurement-induced operation of two-ion quantum heat machines

NASA Astrophysics Data System (ADS)

Chand, Suman; Biswas, Asoka

2017-03-01

We show how one can implement a quantum heat machine by using two interacting trapped ions, in presence of a thermal bath. The electronic states of the ions act like a working substance, while the vibrational mode is modelled as the cold bath. The heat exchange with the cold bath is mimicked by the projective measurement of the electronic states. We show how such measurement in a suitable basis can lead to either a quantum heat engine or a refrigerator, which undergoes a quantum Otto cycle. The local magnetic field is adiabatically changed during the heat cycle. The performance of the heat machine depends upon the interaction strength between the ions, the magnetic fields, and the measurement cost. In our model, the coupling to the hot and the cold baths is never switched off in an alternative fashion during the heat cycle, unlike other existing proposals of quantum heat engines. This makes our proposal experimentally realizable using current tapped-ion technology.

Measurement-induced operation of two-ion quantum heat machines.

PubMed

Chand, Suman; Biswas, Asoka

2017-03-01

We show how one can implement a quantum heat machine by using two interacting trapped ions, in presence of a thermal bath. The electronic states of the ions act like a working substance, while the vibrational mode is modelled as the cold bath. The heat exchange with the cold bath is mimicked by the projective measurement of the electronic states. We show how such measurement in a suitable basis can lead to either a quantum heat engine or a refrigerator, which undergoes a quantum Otto cycle. The local magnetic field is adiabatically changed during the heat cycle. The performance of the heat machine depends upon the interaction strength between the ions, the magnetic fields, and the measurement cost. In our model, the coupling to the hot and the cold baths is never switched off in an alternative fashion during the heat cycle, unlike other existing proposals of quantum heat engines. This makes our proposal experimentally realizable using current tapped-ion technology.

A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design

PubMed Central

Shanechi, Maryam M.; Williams, Ziv M.; Wornell, Gregory W.; Hu, Rollin C.; Powers, Marissa; Brown, Emery N.

2013-01-01

Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system. PMID:23593130

Revisit of Machine Learning Supported Biological and Biomedical Studies.

PubMed

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Extreme ultraviolet lithography machine

DOEpatents

Tichenor, Daniel A.; Kubiak, Glenn D.; Haney, Steven J.; Sweeney, Donald W.

2000-01-01

An extreme ultraviolet lithography (EUVL) machine or system for producing integrated circuit (IC) components, such as transistors, formed on a substrate. The EUVL machine utilizes a laser plasma point source directed via an optical arrangement onto a mask or reticle which is reflected by a multiple mirror system onto the substrate or target. The EUVL machine operates in the 10-14 nm wavelength soft x-ray photon. Basically the EUV machine includes an evacuated source chamber, an evacuated main or project chamber interconnected by a transport tube arrangement, wherein a laser beam is directed into a plasma generator which produces an illumination beam which is directed by optics from the source chamber through the connecting tube, into the projection chamber, and onto the reticle or mask, from which a patterned beam is reflected by optics in a projection optics (PO) box mounted in the main or projection chamber onto the substrate. In one embodiment of a EUVL machine, nine optical components are utilized, with four of the optical components located in the PO box. The main or projection chamber includes vibration isolators for the PO box and a vibration isolator mounting for the substrate, with the main or projection chamber being mounted on a support structure and being isolated.

20130501-20130531_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Vanderhoff, Alex

2013-06-18

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from May 2013

Ada Compiler Validation Summary Report: Certificate Number 880318W1. 09042, International Business Machines Corporation, IBM Development System for the Ada Language, Version 2.1.0, IBM 4381 under MVS/XA, Host and Target

DTIC Science & Technology

1988-03-28

International Business Machines Corporation IBM Development System for the Ada Language, Version 2.1.0 IBM 4381 under MVS/XA, host and target Completion...Joint Program Office, AJPO 20. ABSTRACT (Continue on reverse side if necessary and identify by block number) International Business Machines Corporation...in the compiler listed in this declaration. I declare that International Business Machines Corporation is the owner of record of the object code of

Ada Compiler Validation Summary Report: Certificate Number: 880318W1. 09041, International Business Machines Corporation, IBM Development System for the Ada Language, Version 2.1.0, IBM 4381 under VM/HPO, Host and Target

DTIC Science & Technology

1988-03-28

International Business Machines Corporation IBM Development System for the Ada Language, Version 2.1.0 IBM 4381 under VM/HPO, host and target DTIC...necessary and identify by block number) International Business Machines Corporation, IBM Development System for the Ada Language, Version 2.1.0, IBM...in the compiler listed in this declaration. I declare that International Business Machines Corporation is the owner of record of the object code of the

Improvement of automatic fish feeder machine design

NASA Astrophysics Data System (ADS)

Chui Wei, How; Salleh, S. M.; Ezree, Abdullah Mohd; Zaman, I.; Hatta, M. H.; Zain, B. A. Md; Mahzan, S.; Rahman, M. N. A.; Mahmud, W. A. W.

2017-10-01

Nation Plan of action for management of fishing is target to achieve an efficient, equitable and transparent management of fishing capacity in marine capture fisheries by 2018. However, several factors influence the fishery production and efficiency of marine system such as automatic fish feeder machine could be taken in consideration. Two latest fish feeder machines have been chosen as the reference for this study. Based on the observation, it has found that the both machine was made with heavy structure, low water and temperature resistance materials. This research’s objective is to develop the automatic feeder machine to increase the efficiency of fish feeding. The experiment has conducted to testing the new design of machine. The new machine with maximum storage of 5 kg and functioning with two DC motors. This machine able to distribute 500 grams of pellets within 90 seconds and longest distance of 4.7 meter. The higher speed could reduce time needed and increase the distance as well. The minimum speed range for both motor is 110 and 120 with same full speed range of 255.

Neuromorphic Optical Signal Processing and Image Understanding for Automated Target Recognition

DTIC Science & Technology

1989-12-01

34 Stochastic Learning Machine " Neuromorphic Target Identification * Cognitive Networks 3. Conclusions ..... ................ .. 12 4. Publications...16 5. References ...... ................... . 17 6. Appendices ....... .................. 18 I. Optoelectronic Neural Networks and...Learning Machines. II. Stochastic Optical Learning Machine. III. Learning Network for Extrapolation AccesFon For and Radar Target Identification

Cell-cycle research with synchronous cultures: an evaluation

NASA Technical Reports Server (NTRS)

Helmstetter, C. E.; Thornton, M.; Grover, N. B.

2001-01-01

The baby-machine system, which produces new-born Escherichia coli cells from cultures immobilized on a membrane, was developed many years ago in an attempt to attain optimal synchrony with minimal disturbance of steady-state growth. In the present article, we put forward a model to describe the behaviour of cells produced by this method, and provide quantitative evaluation of the parameters involved, at each of four different growth rates. Considering the high level of selection achievable with this technique and the natural dispersion in interdivision times, we believe that the output of the baby machine is probably close to optimal in terms of both quality and persistence of synchrony. We show that considerable information on events in the cell cycle can be obtained from populations with age distributions very much broader than those achieved with the baby machine and differing only modestly from steady state. The data presented here, together with the long and fruitful history of findings employing the baby-machine technique, suggest that minimisation of stress on cells is the single most important factor for successful cell-cycle analysis.

Testing of the Support Vector Machine for Binary-Class Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew

2011-01-01

The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SMV as a method for classification. From trial to trial, SVM produces consistent results

Identification of tissue-specific targeting peptide

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Lee, Nam Kyung; Kang, Sang-Kee; Choi, Seung-Hoon; Kim, Daejin; Park, Kisoo; Choi, Kihang; Choi, Yun-Jaie; Jung, Dong Hyun

2012-11-01

Using phage display technique, we identified tissue-targeting peptide sets that recognize specific tissues (bone-marrow dendritic cell, kidney, liver, lung, spleen and visceral adipose tissue). In order to rapidly evaluate tissue-specific targeting peptides, we performed machine learning studies for predicting the tissue-specific targeting activity of peptides on the basis of peptide sequence information using four machine learning models and isolated the groups of peptides capable of mediating selective targeting to specific tissues. As a representative liver-specific targeting sequence, the peptide "DKNLQLH" was selected by the sequence similarity analysis. This peptide has a high degree of homology with protein ligands which can interact with corresponding membrane counterparts. We anticipate that our models will be applicable to the prediction of tissue-specific targeting peptides which can recognize the endothelial markers of target tissues.

Chatter active control in a lathe machine using magnetostrictive actuator

NASA Astrophysics Data System (ADS)

Nosouhi, R.; Behbahani, S.

2011-01-01

This paper analyzes the chatter phenomena in lathe machines. Chatter is one of the main causes of inaccuracy, reduction of life cycle of the machine and tool wear in machine tools. This phenomenon limits the depth of cut as a function of the cutting speed, which consequently reduces the material removal rate and machining efficiency. Chatter control is therefore important since it increases the stability region in machining and increases the critical depth of cut in machining case. To control the chatter in lathe machines, a magnetostrictive actuator is used. The materials with magnetostriction properties are kind of smart materials of which their length changes as a result of applying an exterior magnetic field, which make them suitable for control applications. It is assumed that the actuator applies the proper force exactly at the point where the machining force is applied on the tool. In this paper the chatter stability lobes is excelled as a result of applying a PID controller on the magnetostrictive actuator equipped-tool in turning.

20131201-1231_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-01-08

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Dec to 31 Dec 2013.

20131101-1130_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2013-12-02

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Nov to 30 Nov 2013.

20131001-1031_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2013-11-05

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 1 Oct 2013 to 31 Oct 2013.

20140201-0228_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-03-03

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Feb to 28 Feb 2014.

20130801-0831_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Vanderhoff, Alex

2013-09-10

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 8/1/13 to 8/31/13.

20140101-0131_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-02-03

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Jan to 31 Jan 2014.

20140301-0331_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-04-07

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Mar to 31 Mar 2014.

20140501-0531_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-06-02

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 May to 31 May 2014.

20140601-0630_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-06-30

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 June to 30 June 2014.

20140701-0731_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-07-31

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 July to 31 July 2014.

20130901-0930_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2013-10-25

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 1 September 2013 to 30 September 2013.

Can we bet on negative emissions to achieve the 2°C target even under strong carbon cycle feedbacks?

NASA Astrophysics Data System (ADS)

Tanaka, K.; Yamagata, Y.; Yokohata, T.; Emori, S.; Hanaoka, T.

2015-12-01

Negative emission technologies such as Bioenergy with Carbon dioxide Capture and Storage (BioCCS) play an ever more crucial role in meeting the 2°C stabilization target. However, such technologies are currently at their infancy and their future penetrations may fall short of the scale required to stabilize the warming. Furthermore, the overshoot in the mid-century prior to a full realization of negative emissions would give rise to a risk because such a temporal but excessive warming above 2°C might amplify itself by strengthening climate-carbon cycle feedbacks. It has not been extensively assessed yet how carbon cycle feedbacks might play out during the overshoot in the context of negative emissions. This study explores how 2°C stabilization pathways, in particular those which undergo overshoot, can be influenced by carbon cycle feedbacks and asks their climatic and economic consequences. We compute 2°C stabilization emissions scenarios under a cost-effectiveness principle, in which the total abatement costs are minimized such that the global warming is capped at 2°C. We employ a reduced-complexity model, the Aggregated Carbon Cycle, Atmospheric Chemistry, and Climate model (ACC2), which comprises a box model of the global carbon cycle, simple parameterizations of the atmospheric chemistry, and a land-ocean energy balance model. The total abatement costs are estimated from the marginal abatement cost functions for CO2, CH4, N2O, and BC.Our preliminary results show that, if carbon cycle feedbacks turn out to be stronger than what is known today, it would incur substantial abatement costs to keep up with the 2°C stabilization goal. Our results also suggest that it would be less expensive in the long run to plan for a 2°C stabilization pathway by considering strong carbon cycle feedbacks because it would cost more if we correct the emission pathway in the mid-century to adjust for unexpectedly large carbon cycle feedbacks during overshoot. Furthermore, our

Andrographolide Induces Cell Cycle Arrest and Apoptosis of Chondrosarcoma by Targeting TCF-1/SOX9 Axis.

PubMed

Zhang, Huan-Tian; Yang, Jie; Liang, Gui-Hong; Gao, Xue-Juan; Sang, Yuan; Gui, Tao; Liang, Zu-Jian; Tam, Man-Seng; Zha, Zhen-Gang

2017-12-01

Chondrosarcoma is the second most malignant bone tumor with poor prognosis and limited treatment options. Thus, development of more effective treatments has become urgent. Recently, natural compounds derived from medicinal plants have emerged as promising therapeutic options via targeting multiple key cellular molecules. Andrographolide (Andro) is such a compound, which has previously been shown to induce cell cycle arrest and apoptosis in several human cancers. However, the molecular mechanism through which Andro exerts its anti-cancer effect on chondrosarcoma remains to be elucidated. In the present study, we showed that Andro-induced G2/M cell cycle arrest of chondrosarcoma by fine-tuning the expressions of several cell cycle regulators such as p21, p27, and Cyclins, and that prolonged treatment of cells with Andro caused pronounced cell apoptosis. Remarkably, we found that SOX9 was highly expressed in poor-differentiated chondrosarcoma, and that knockdown of SOX9 suppressed chondrosarcoma cell growth. Further, our results showed that Andro dose-dependently down-regulated SOX9 expression in chondrosarcoma cells. Concomitantly, an inhibition of T cell factor 1 (TCF-1) mRNA expression and an enhancement of TCF-1 protein degradation by Andro were observed. In contrast, the expression and subcellular localization of β-catenin were not altered upon the treatment of Andro, suggesting that β-catenin might not function as the primary target of Andro. Additionally, we provided evidence that there was a mutual regulation between TCF-1 and SOX9 in chondrosarcoma cells. In conclusion, these results highlight the potential therapeutic effects of Andro in treatment of chondrosarcoma via targeting the TCF-1/SOX9 axis. J. Cell. Biochem. 118: 4575-4586, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Automated Discovery of Machine-Specific Code Improvements

DTIC Science & Technology

1984-12-01

operation of the source language. Additional analysis may reveal special features of the target architecture that may be exploited to generate efficient...Additional analysis may reveal special features of the target architecture that may be exploited to generate efficient code. Such analysis is optional...incorporate knowledge of the source language, but do not refer to features of the target machine. These early phases are sometimes referred to as the

Alumina additions may improve the damage tolerance of soft machined zirconia-based ceramics.

PubMed

Oilo, Marit; Tvinnereim, Helene M; Gjerdet, Nils Roar

2011-01-01

The aim of this study was to evaluate the damage tolerance of different zirconia-based materials. Bars of one hard machined and one soft machined dental zirconia and an experimental 95% zirconia 5% alumina ceramic were subjected to 100,000 stress cycles (n = 10), indented to provoke cracks on the tensile stress side (n = 10), and left untreated as controls (n = 10). The experimental material demonstrated a higher relative damage tolerance, with a 40% reduction compared to 68% for the hard machined zirconia and 84% for the soft machined zirconia.

Silver nanoclusters-assisted ion-exchange reaction with CdTe quantum dots for photoelectrochemical detection of adenosine by target-triggering multiple-cycle amplification strategy.

PubMed

Zhao, Yang; Tan, Lu; Gao, Xiaoshan; Jie, Guifen; Huang, Tingyu

2018-07-01

Herein, we successfully devised a novel photoelectrochemical (PEC) platform for ultrasensitive detection of adenosine by target-triggering cascade multiple cycle amplification based on the silver nanoparticles-assisted ion-exchange reaction with CdTe quantum dots (QDs). In the presence of target adenosine, DNA s1 is released from the aptamer and then hybridizes with hairpin DNA (HP1), which could initiate the cycling cleavage process under the reaction of nicking endonuclease. Then the product (DNA b) of cycle I could act as the "DNA trigger" of cycle II to further generate a large number of DNA s1, which again go back to cycle I, thus a cascade multiple DNA cycle amplification was carried out to produce abundant DNA c. These DNA c fragments with the cytosine (C)-rich loop were captured by magnetic beads, and numerous silver nanoclusters (Ag NCs) were synthesized by AgNO 3 and sodium borohydride. The dissolved AgNCs released numerous silver ions which could induce ion exchange reaction with the CdTe QDs, thus resulting in greatly amplified change of photocurrent for target detection. The detection linear range for adenosine was 1.0 fM ~10 nM with the detection limit of 0.5 fM. The present PEC strategy combining cascade multiple DNA cycle amplification and AgNCs-induced ion-exchange reaction with QDs provides new insight into rapid, and ultrasensitive PEC detection of different biomolecules, which showed great potential for detecting trace amounts in bioanalysis and clinical biomedicine. Copyright © 2018 Elsevier B.V. All rights reserved.

CAK-Cyclin-dependent Activating Kinase: a key kinase in cell cycle control and a target for drugs?

PubMed

Lolli, Graziano; Johnson, Louise N

2005-04-01

The Cyclin-dependent kinase (CDK) Activating Kinase (CAK) is responsible for the activating phosphorylation of CDK1, CDK2, CDK4 and CDK6 and regulation of the cell cycle. The kinase is composed of three subunits: CDK7, Cyclin H and MAT1 (ménage a trois). Together with six other subunits, CAK is also part of the general transcription factor TFIIH where it is involved in promoter clearance and progression of transcription from the preinitiation to the initiation stage. CAK is required for cell cycle progression, which suggests that CDK7 could be a target for cancer therapy. However its role in transcription and its ubiquitous presence raise sensible concerns about possible toxicity of its inhibitors. The recently determined structure of CDK7 allows the design of inhibitors with differential specificity for the different CDKs. We review the role of CAK in different biological processes and evaluate the biological evidence for CDK7 as a possible pharmacological target.

Superconducting Coil Winding Machine Control System

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

Nogiec, J. M.; Kotelnikov, S.; Makulski, A.

The Spirex coil winding machine is used at Fermilab to build coils for superconducting magnets. Recently this ma-chine was equipped with a new control system, which al-lows operation from both a computer and a portable remote control unit. This control system is distributed between three layers, implemented on a PC, real-time target, and FPGA, providing respectively HMI, operational logic and direct controls. The system controls motion of all mechan-ical components and regulates the cable tension. Safety is ensured by a failsafe, redundant system.

Anaphase-promoting complex/cyclosome protein Cdc27 is a target for curcumin-induced cell cycle arrest and apoptosis.

PubMed

Lee, Seung Joon; Langhans, Sigrid A

2012-01-26

Curcumin (diferuloylmethane), the yellow pigment in the Asian spice turmeric, is a hydrophobic polyphenol from the rhizome of Curcuma longa. Because of its chemopreventive and chemotherapeutic potential with no discernable side effects, it has become one of the major natural agents being developed for cancer therapy. Accumulating evidence suggests that curcumin induces cell death through activation of apoptotic pathways and inhibition of cell growth and proliferation. The mitotic checkpoint, or spindle assembly checkpoint (SAC), is the major cell cycle control mechanism to delay the onset of anaphase during mitosis. One of the key regulators of the SAC is the anaphase promoting complex/cyclosome (APC/C) which ubiquitinates cyclin B and securin and targets them for proteolysis. Because APC/C not only ensures cell cycle arrest upon spindle disruption but also promotes cell death in response to prolonged mitotic arrest, it has become an attractive drug target in cancer therapy. Cell cycle profiles were determined in control and curcumin-treated medulloblastoma and various other cancer cell lines. Pull-down assays were used to confirm curcumin binding. APC/C activity was determined using an in vitro APC activity assay. We identified Cdc27/APC3, a component of the APC/C, as a novel molecular target of curcumin and showed that curcumin binds to and crosslinks Cdc27 to affect APC/C function. We further provide evidence that curcumin preferably induces apoptosis in cells expressing phosphorylated Cdc27 usually found in highly proliferating cells. We report that curcumin directly targets the SAC to induce apoptosis preferably in cells with high levels of phosphorylated Cdc27. Our studies provide a possible molecular mechanism why curcumin induces apoptosis preferentially in cancer cells and suggest that phosphorylation of Cdc27 could be used as a biomarker to predict the therapeutic response of cancer cells to curcumin.

Anaphase-promoting complex/cyclosome protein Cdc27 is a target for curcumin-induced cell cycle arrest and apoptosis

PubMed Central

2012-01-01

Background Curcumin (diferuloylmethane), the yellow pigment in the Asian spice turmeric, is a hydrophobic polyphenol from the rhizome of Curcuma longa. Because of its chemopreventive and chemotherapeutic potential with no discernable side effects, it has become one of the major natural agents being developed for cancer therapy. Accumulating evidence suggests that curcumin induces cell death through activation of apoptotic pathways and inhibition of cell growth and proliferation. The mitotic checkpoint, or spindle assembly checkpoint (SAC), is the major cell cycle control mechanism to delay the onset of anaphase during mitosis. One of the key regulators of the SAC is the anaphase promoting complex/cyclosome (APC/C) which ubiquitinates cyclin B and securin and targets them for proteolysis. Because APC/C not only ensures cell cycle arrest upon spindle disruption but also promotes cell death in response to prolonged mitotic arrest, it has become an attractive drug target in cancer therapy. Methods Cell cycle profiles were determined in control and curcumin-treated medulloblastoma and various other cancer cell lines. Pull-down assays were used to confirm curcumin binding. APC/C activity was determined using an in vitro APC activity assay. Results We identified Cdc27/APC3, a component of the APC/C, as a novel molecular target of curcumin and showed that curcumin binds to and crosslinks Cdc27 to affect APC/C function. We further provide evidence that curcumin preferably induces apoptosis in cells expressing phosphorylated Cdc27 usually found in highly proliferating cells. Conclusions We report that curcumin directly targets the SAC to induce apoptosis preferably in cells with high levels of phosphorylated Cdc27. Our studies provide a possible molecular mechanism why curcumin induces apoptosis preferentially in cancer cells and suggest that phosphorylation of Cdc27 could be used as a biomarker to predict the therapeutic response of cancer cells to curcumin. PMID:22280307

SU-C-BRA-05: Delineating High-Dose Clinical Target Volumes for Head and Neck Tumors Using Machine Learning Algorithms

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

Cardenas, C; The University of Texas Graduate School of Biomedical Sciences, Houston, TX; Wong, A

Purpose: To develop and test population-based machine learning algorithms for delineating high-dose clinical target volumes (CTVs) in H&N tumors. Automating and standardizing the contouring of CTVs can reduce both physician contouring time and inter-physician variability, which is one of the largest sources of uncertainty in H&N radiotherapy. Methods: Twenty-five node-negative patients treated with definitive radiotherapy were selected (6 right base of tongue, 11 left and 9 right tonsil). All patients had GTV and CTVs manually contoured by an experienced radiation oncologist prior to treatment. This contouring process, which is driven by anatomical, pathological, and patient specific information, typically results inmore » non-uniform margin expansions about the GTV. Therefore, we tested two methods to delineate high-dose CTV given a manually-contoured GTV: (1) regression-support vector machines(SVM) and (2) classification-SVM. These models were trained and tested on each patient group using leave-one-out cross-validation. The volume difference(VD) and Dice similarity coefficient(DSC) between the manual and auto-contoured CTV were calculated to evaluate the results. Distances from GTV-to-CTV were computed about each patient’s GTV and these distances, in addition to distances from GTV to surrounding anatomy in the expansion direction, were utilized in the regression-SVM method. The classification-SVM method used categorical voxel-information (GTV, selected anatomical structures, else) from a 3×3×3cm3 ROI centered about the voxel to classify voxels as CTV. Results: Volumes for the auto-contoured CTVs ranged from 17.1 to 149.1cc and 17.4 to 151.9cc; the average(range) VD between manual and auto-contoured CTV were 0.93 (0.48–1.59) and 1.16(0.48–1.97); while average(range) DSC values were 0.75(0.59–0.88) and 0.74(0.59–0.81) for the regression-SVM and classification-SVM methods, respectively. Conclusion: We developed two novel machine learning methods to

DEVELOPMENTAL NEUROTOXICITY OF ORGANOPHOSPHATES TARGETS CELL CYCLE AND APOPTOSIS, REVEALED BY TRANSCRIPTIONAL PROFILES IN VIVO AND IN VITRO

PubMed Central

Slotkin, Theodore A.; Seidler, Frederic J.

2012-01-01

Developmental organophosphate exposure reduces the numbers of neural cells, contributing to neurobehavioral deficits. We administered chlorpyrifos or diazinon to newborn rats on postnatal days 1–4, in doses straddling the threshold for barely-detectable cholinesterase, and evaluated gene expression in the cell cycle and apoptosis pathways on postnatal day 5. Both organophosphates evoked transcriptional changes in 20–25% of the genes in each category; chlorpyrifos and diazinon targeted the same genes, with similar magnitudes of change, as evidenced by high concordance. Furthermore, the same effects were obtained with doses above or below the threshold for cholinesterase inhibition, indicating a mechanism unrelated to anticholinesterase actions. We then evaluated the effects of chlorpyrifos in undifferentiated and differentiating PC12 cells and found even greater targeting of cell cycle and apoptosis genes, affecting up to 40% of all genes in the pathways. Notably, the genes affected in undifferentiated cells were not concordant with those in differentiating cells, pointing to dissimilar outcomes dependent on developmental stage. The in vitro model successfully identified 60–70% of the genes affected by chlorpyrifos in vivo, indicating that the effects are exerted directly on developing neural cells. Our results show that organophosphates target the genes regulating the cell cycle and apoptosis in the developing brain and in neuronotypic cells in culture, with the pattern of vulnerability dependent on the specific stage of development. Equally important, these effects do not reflect actions on cholinesterase and operate at exposures below the threshold for any detectable inhibition of this enzyme. PMID:22222554

Developmental neurotoxicity of organophosphates targets cell cycle and apoptosis, revealed by transcriptional profiles in vivo and in vitro.

PubMed

Slotkin, Theodore A; Seidler, Frederic J

2012-03-01

Developmental organophosphate exposure reduces the numbers of neural cells, contributing to neurobehavioral deficits. We administered chlorpyrifos or diazinon to newborn rats on postnatal days 1-4, in doses straddling the threshold for barely-detectable cholinesterase inhibition, and evaluated gene expression in the cell cycle and apoptosis pathways on postnatal day 5. Both organophosphates evoked transcriptional changes in 20-25% of the genes in each category; chlorpyrifos and diazinon targeted the same genes, with similar magnitudes of change, as evidenced by high concordance. Furthermore, the same effects were obtained with doses above or below the threshold for cholinesterase inhibition, indicating a mechanism unrelated to anticholinesterase actions. We then evaluated the effects of chlorpyrifos in undifferentiated and differentiating PC12 cells and found even greater targeting of cell cycle and apoptosis genes, affecting up to 40% of all genes in the pathways. Notably, the genes affected in undifferentiated cells were not concordant with those in differentiating cells, pointing to dissimilar outcomes dependent on developmental stage. The in vitro model successfully identified 60-70% of the genes affected by chlorpyrifos in vivo, indicating that the effects are exerted directly on developing neural cells. Our results show that organophosphates target the genes regulating the cell cycle and apoptosis in the developing brain and in neuronotypic cells in culture, with the pattern of vulnerability dependent on the specific stage of development. Equally important, these effects do not reflect actions on cholinesterase and operate at exposures below the threshold for any detectable inhibition of this enzyme. Copyright © 2011 Elsevier Inc. All rights reserved.

Study on effect of tool electrodes on surface finish during electrical discharge machining of Nitinol

NASA Astrophysics Data System (ADS)

Sahu, Anshuman Kumar; Chatterjee, Suman; Nayak, Praveen Kumar; Sankar Mahapatra, Siba

2018-03-01

Electrical discharge machining (EDM) is a non-traditional machining process which is widely used in machining of difficult-to-machine materials. EDM process can produce complex and intrinsic shaped component made of difficult-to-machine materials, largely applied in aerospace, biomedical, die and mold making industries. To meet the required applications, the EDMed components need to possess high accuracy and excellent surface finish. In this work, EDM process is performed using Nitinol as work piece material and AlSiMg prepared by selective laser sintering (SLS) as tool electrode along with conventional copper and graphite electrodes. The SLS is a rapid prototyping (RP) method to produce complex metallic parts by additive manufacturing (AM) process. Experiments have been carried out varying different process parameters like open circuit voltage (V), discharge current (Ip), duty cycle (τ), pulse-on-time (Ton) and tool material. The surface roughness parameter like average roughness (Ra), maximum height of the profile (Rt) and average height of the profile (Rz) are measured using surface roughness measuring instrument (Talysurf). To reduce the number of experiments, design of experiment (DOE) approach like Taguchi’s L27 orthogonal array has been chosen. The surface properties of the EDM specimen are optimized by desirability function approach and the best parametric setting is reported for the EDM process. Type of tool happens to be the most significant parameter followed by interaction of tool type and duty cycle, duty cycle, discharge current and voltage. Better surface finish of EDMed specimen can be obtained with low value of voltage (V), discharge current (Ip), duty cycle (τ) and pulse on time (Ton) along with the use of AlSiMg RP electrode.

Work stress of women in sewing machine operation.

PubMed

Nag, A; Desai, H; Nag, P K

1992-06-01

The study examined the work stresses of 107 women who were engaged in sewing machine operation in small garment manufacturing units. Of the three types of sewing machines (motor-operated, full and half shuttle foot-operated), 74% of the machines were foot-operated, where throttle action of the lower limb is required to move the shuttle of the machine. The motor-operated machines were faster than the foot-operated machines. The short cycle sewing work involves repetitive action of hand and feet. The women had to maintain a constant seated position on a stool without backrest and the body inclined forward. Long-term sewing work had a cumulative load on the musculo-skeletal structures, including the vertebral column and reflected in the form of high prevalence of discomfort and pain in different body parts. About 68% of the women complained of back pain, among whom 35% reported a persistent low back pain. Common sewing work accident is piercing of the needle through the fingers, particularly the right forefingers. Unsatisfactory man-machine incompatibility, work posture and fatigue, improper coordination of eye, leg and hand are the major problems of the operators. The design mis-match of the work place may be significantly improved by taking women's anthropometric dimensions in modifying the workplace, i.e. the seat surface, seat height, work height, backrest, etc.

MicroRNA let-7c Inhibits Cell Proliferation and Induces Cell Cycle Arrest by Targeting CDC25A in Human Hepatocellular Carcinoma

PubMed Central

Zhu, Xiuming; Wu, Lingjiao; Yao, Jian; Jiang, Han; Wang, Qiangfeng; Yang, Zhijian; Wu, Fusheng

2015-01-01

Down-regulation of the microRNA let-7c plays an important role in the pathogenesis of human hepatocellular carcinoma (HCC). The aim of the present study was to determine whether the cell cycle regulator CDC25A is involved in the antitumor effect of let-7c in HCC. The expression levels of let-7c in HCC cell lines were examined by quantitative real-time PCR, and a let-7c agomir was transfected into HCC cells to overexpress let-7c. The effects of let-7c on HCC proliferation, apoptosis and cell cycle were analyzed. The in vivo tumor-inhibitory efficacy of let-7c was evaluated in a xenograft mouse model of HCC. Luciferase reporter assays and western blotting were conducted to identify the targets of let-7c and to determine the effects of let-7c on CDC25A, CyclinD1, CDK6, pRb and E2F2 expression. The results showed that the expression levels of let-7c were significantly decreased in HCC cell lines. Overexpression of let-7c repressed cell growth, induced cell apoptosis, led to G1 cell cycle arrest in vitro, and suppressed tumor growth in a HepG2 xenograft model in vivo. The luciferase reporter assay showed that CDC25A was a direct target of let-7c, and that let-7c inhibited the expression of CDC25A protein by directly targeting its 3ʹ UTR. Restoration of CDC25A induced a let-7c-mediated G1-to-S phase transition. Western blot analysis demonstrated that overexpression of let-7c decreased CyclinD1, CDK6, pRb and E2F2 protein levels. In conclusion, this study indicates that let-7c suppresses HCC progression, possibly by directly targeting the cell cycle regulator CDC25A and indirectly affecting its downstream target molecules. Let-7c may therefore be an effective therapeutic target for HCC. PMID:25909324

Organic rankine cycle waste heat applications

DOEpatents

Brasz, Joost J.; Biederman, Bruce P.

2007-02-13

A machine designed as a centrifugal compressor is applied as an organic rankine cycle turbine by operating the machine in reverse. In order to accommodate the higher pressures when operating as a turbine, a suitable refrigerant is chosen such that the pressures and temperatures are maintained within established limits. Such an adaptation of existing, relatively inexpensive equipment to an application that may be otherwise uneconomical, allows for the convenient and economical use of energy that would be otherwise lost by waste heat to the atmosphere.

The paradigm compiler: Mapping a functional language for the connection machine

NASA Technical Reports Server (NTRS)

Dennis, Jack B.

1989-01-01

The Paradigm Compiler implements a new approach to compiling programs written in high level languages for execution on highly parallel computers. The general approach is to identify the principal data structures constructed by the program and to map these structures onto the processing elements of the target machine. The mapping is chosen to maximize performance as determined through compile time global analysis of the source program. The source language is Sisal, a functional language designed for scientific computations, and the target language is Paris, the published low level interface to the Connection Machine. The data structures considered are multidimensional arrays whose dimensions are known at compile time. Computations that build such arrays usually offer opportunities for highly parallel execution; they are data parallel. The Connection Machine is an attractive target for these computations, and the parallel for construct of the Sisal language is a convenient high level notation for data parallel algorithms. The principles and organization of the Paradigm Compiler are discussed.

Adapting human-machine interfaces to user performance.

PubMed

Danziger, Zachary; Fishbach, Alon; Mussa-Ivaldi, Ferdinando A

2008-01-01

The goal of this study was to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user of a human-machine interface and the controlled device. In this experiment, subjects' high-dimensional finger motions remotely controlled the joint angles of a simulated planar 2-link arm, which was used to hit targets on a computer screen. Subjects were required to move the cursor at the endpoint of the simulated arm.

Parallel machine architecture and compiler design facilities

NASA Technical Reports Server (NTRS)

Kuck, David J.; Yew, Pen-Chung; Padua, David; Sameh, Ahmed; Veidenbaum, Alex

1990-01-01

The objective is to provide an integrated simulation environment for studying and evaluating various issues in designing parallel systems, including machine architectures, parallelizing compiler techniques, and parallel algorithms. The status of Delta project (which objective is to provide a facility to allow rapid prototyping of parallelized compilers that can target toward different machine architectures) is summarized. Included are the surveys of the program manipulation tools developed, the environmental software supporting Delta, and the compiler research projects in which Delta has played a role.

Behavioral Modeling for Mental Health using Machine Learning Algorithms.

PubMed

Srividya, M; Mohanavalli, S; Bhalaji, N

2018-04-03

Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.

Towards a generalized energy prediction model for machine tools

PubMed Central

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H.; Dornfeld, David A.; Helu, Moneer; Rachuri, Sudarsan

2017-01-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process. PMID:28652687

Towards a generalized energy prediction model for machine tools.

PubMed

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

2017-04-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Cycle accurate and cycle reproducible memory for an FPGA based hardware accelerator

DOEpatents

Asaad, Sameh W.; Kapur, Mohit

2016-03-15

A method, system and computer program product are disclosed for using a Field Programmable Gate Array (FPGA) to simulate operations of a device under test (DUT). The DUT includes a device memory having a number of input ports, and the FPGA is associated with a target memory having a second number of input ports, the second number being less than the first number. In one embodiment, a given set of inputs is applied to the device memory at a frequency Fd and in a defined cycle of time, and the given set of inputs is applied to the target memory at a frequency Ft. Ft is greater than Fd and cycle accuracy is maintained between the device memory and the target memory. In an embodiment, a cycle accurate model of the DUT memory is created by separating the DUT memory interface protocol from the target memory storage array.

The application of machine learning techniques in the clinical drug therapy.

PubMed

Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

2018-05-25

The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Ideal thermodynamic processes of oscillatory-flow regenerative engines will go to ideal stirling cycle?

NASA Astrophysics Data System (ADS)

Luo, Ercang

2012-06-01

This paper analyzes the thermodynamic cycle of oscillating-flow regenerative machines. Unlike the classical analysis of thermodynamic textbooks, the assumptions for pistons' movement limitations are not needed and only ideal flowing and heat transfer should be maintained in our present analysis. Under such simple assumptions, the meso-scale thermodynamic cycles of each gas parcel in typical locations of a regenerator are analyzed. It is observed that the gas parcels in the regenerator undergo Lorentz cycle in different temperature levels, whereas the locus of all gas parcels inside the regenerator is the Ericson-like thermodynamic cycle. Based on this new finding, the author argued that ideal oscillating-flow machines without heat transfer and flowing losses is not the Stirling cycle. However, this new thermodynamic cycle can still achieve the same efficiency of the Carnot heat engine and can be considered a new reversible thermodynamic cycle under two constant-temperature heat sinks.

Carnot's cycle for small systems: Irreversibility and cost of operations

NASA Astrophysics Data System (ADS)

Sekimoto, Ken; Takagi, Fumiko; Hondou, Tsuyoshi

2000-12-01

In the thermodynamic limit, the existence of a maximal efficiency of energy conversion attainable by a Carnot cycle consisting of quasistatic isothermal and adiabatic processes precludes the existence of a perpetual machine of the second kind, whose cycles yield positive work in an isothermal environment. We employ the recently developed framework of the energetics of stochastic processes (called ``stochastic energetics'') to reanalyze the Carnot cycle in detail, taking account of fluctuations, without taking the thermodynamic limit. We find that in this nonmacroscopic situation both processes of connection to and disconnection from heat baths and adiabatic processes that cause distortion of the energy distribution are sources of inevitable irreversibility within the cycle. Also, the so-called null-recurrence property of the cumulative efficiency of energy conversion over many cycles and the irreversible property of isolated, purely mechanical processes under external ``macroscopic'' operations are discussed in relation to the impossibility of a perpetual machine, or Maxwell's demon. This analysis may serve as the basis for the design and analysis of mesoscopic energy converters in the near future.

Process for laser machining and surface treatment

DOEpatents

Neil, George R.; Shinn, Michelle D.

2004-10-26

An improved method and apparatus increasing the accuracy and reducing the time required to machine materials, surface treat materials, and allow better control of defects such as particulates in pulsed laser deposition. The speed and quality of machining is improved by combining an ultrashort pulsed laser at high average power with a continuous wave laser. The ultrashort pulsed laser provides an initial ultrashort pulse, on the order of several hundred femtoseconds, to stimulate an electron avalanche in the target material. Coincident with the ultrashort pulse or shortly after it, a pulse from a continuous wave laser is applied to the target. The micromachining method and apparatus creates an initial ultrashort laser pulse to ignite the ablation followed by a longer laser pulse to sustain and enlarge on the ablation effect launched in the initial pulse. The pulse pairs are repeated at a high pulse repetition frequency and as often as desired to produce the desired micromachining effect. The micromachining method enables a lower threshold for ablation, provides more deterministic damage, minimizes the heat affected zone, minimizes cracking or melting, and reduces the time involved to create the desired machining effect.

Ada Compiler Validation Summary Report: Certificate Number: 880318W1. 09043 International Business Machines Corporation IBM Development System for the Ada Language, Version 2.1.0 IBM 4381 under VM/HPO, Host IBM 4381 under MVS/XA, Target

DTIC Science & Technology

1988-03-28

International Business Machines Corporation IBM Development System for the Ada Language, Version 2.1.0 IBM 4381 under VM/HPO, host IBM 4381 under MVS/XA, target...Program Office, AJPO 20. ABSTRACT (Continue on reverse side if necessary and identify by block number) International Business Machines Corporation, IBM...Standard ANSI/MIL-STD-1815A in the compiler listed in this declaration. I declare that International Business Machines Corporation is the owner of record

Biomimetic machine vision system.

PubMed

Harman, William M; Barrett, Steven F; Wright, Cameron H G; Wilcox, Michael

2005-01-01

Real-time application of digital imaging for use in machine vision systems has proven to be prohibitive when used within control systems that employ low-power single processors without compromising the scope of vision or resolution of captured images. Development of a real-time machine analog vision system is the focus of research taking place at the University of Wyoming. This new vision system is based upon the biological vision system of the common house fly. Development of a single sensor is accomplished, representing a single facet of the fly's eye. This new sensor is then incorporated into an array of sensors capable of detecting objects and tracking motion in 2-D space. This system "preprocesses" incoming image data resulting in minimal data processing to determine the location of a target object. Due to the nature of the sensors in the array, hyperacuity is achieved thereby eliminating resolutions issues found in digital vision systems. In this paper, we will discuss the biological traits of the fly eye and the specific traits that led to the development of this machine vision system. We will also discuss the process of developing an analog based sensor that mimics the characteristics of interest in the biological vision system. This paper will conclude with a discussion of how an array of these sensors can be applied toward solving real-world machine vision issues.

Targeting hardwoods

Treesearch

Douglass F. Jacobs

2011-01-01

Increasing demand for hardwood seedlings has prompted research to identify target seedling characteristics that promote hardwood plantation establishment. Operational establishment of hardwood plantations has typically emphasized seed collection from non-improved genetic sources, bareroot nursery seedling production, and spring planting using machine planters. The...

Predicting ground contact events for a continuum of gait types: An application of targeted machine learning using principal component analysis.

PubMed

Osis, Sean T; Hettinga, Blayne A; Ferber, Reed

2016-05-01

An ongoing challenge in the application of gait analysis to clinical settings is the standardized detection of temporal events, with unobtrusive and cost-effective equipment, for a wide range of gait types. The purpose of the current study was to investigate a targeted machine learning approach for the prediction of timing for foot strike (or initial contact) and toe-off, using only kinematics for walking, forefoot running, and heel-toe running. Data were categorized by gait type and split into a training set (∼30%) and a validation set (∼70%). A principal component analysis was performed, and separate linear models were trained and validated for foot strike and toe-off, using ground reaction force data as a gold-standard for event timing. Results indicate the model predicted both foot strike and toe-off timing to within 20ms of the gold-standard for more than 95% of cases in walking and running gaits. The machine learning approach continues to provide robust timing predictions for clinical use, and may offer a flexible methodology to handle new events and gait types. Copyright © 2016 Elsevier B.V. All rights reserved.

Insect-machine interface based neurocybernetics.

PubMed

Bozkurt, Alper; Gilmour, Robert F; Sinha, Ayesa; Stern, David; Lal, Amit

2009-06-01

We present details of a novel bioelectric interface formed by placing microfabricated probes into insect during metamorphic growth cycles. The inserted microprobes emerge with the insect where the development of tissue around the electronics during the pupal development allows mechanically stable and electrically reliable structures coupled to the insect. Remarkably, the insects do not react adversely or otherwise to the inserted electronics in the pupae stage, as is true when the electrodes are inserted in adult stages. We report on the electrical and mechanical characteristics of this novel bioelectronic interface, which we believe would be adopted by many investigators trying to investigate biological behavior in insects with negligible or minimal traumatic effect encountered when probes are inserted in adult stages. This novel insect-machine interface also allows for hybrid insect-machine platforms for further studies. As an application, we demonstrate our first results toward navigation of flight in moths. When instrumented with equipment to gather information for environmental sensing, such insects potentially can assist man to monitor the ecosystems that we share with them for sustainability. The simplicity of the optimized surgical procedure we invented allows for batch insertions to the insect for automatic and mass production of such hybrid insect-machine platforms. Therefore, our bioelectronic interface and hybrid insect-machine platform enables multidisciplinary scientific and engineering studies not only to investigate the details of insect behavioral physiology but also to control it.

Target-triggering multiple-cycle signal amplification strategy for ultrasensitive detection of DNA based on QCM and SPR.

PubMed

Song, Weiling; Yin, Wenshuo; Sun, Wenbo; Guo, Xiaoyan; He, Peng; Yang, Xiaoyan; Zhang, Xiaoru

2018-04-24

Detection of ultralow concentrations of nucleic acid sequences is a central challenge in the early diagnosis of genetic diseases. Herein, we developed a target-triggering cascade multiple cycle amplification for ultrasensitive DNA detection using quartz crystal microbalance (QCM) and surface plasmon resonance (SPR). It was based on the exonuclease Ⅲ (Exo Ⅲ)-assisted signal amplification and the hybridization chain reaction (HCR). The streptavidin-coated Au-NPs (Au-NPs-SA) were assembled on the HCR products as recognition element. Upon sensing of target DNA, the duplex DNA probe triggered the Exo Ⅲ cleavage process, accompanied by generating a new secondary target DNA and releasing target DNA. The released target DNA and the secondary target DNA were recycled. Simultaneously, numerous single strands were liberated and acted as the trigger of HCR to generate further signal amplification, resulting in the immobilization of abundant Au-NPs-SA on the gold substrate. The QCM sensor results were found to be comparable to that achieved using a SPR sensor platform. This method exhibited a high sensitivity toward target DNA with a detection limit of 0.70 fM. The high sensitivity and specificity make this method a great potential for detecting DNA with trace amounts in bioanalysis and clinical biomedicine. Copyright © 2018 Elsevier Inc. All rights reserved.

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...

Experimental research of kinetic and dynamic characteristics of temperature movements of machines

NASA Astrophysics Data System (ADS)

Parfenov, I. V.; Polyakov, A. N.

2018-03-01

Nowadays, the urgency of informational support of machines at different stages of their life cycle is increasing in the form of various experimental characteristics that determine the criteria for working capacity. The effectiveness of forming the base of experimental characteristics of machines is related directly to the duration of their field tests. In this research, the authors consider a new technique that allows reducing the duration of full-scale testing of machines by 30%. To this end, three new indicator coefficients were calculated in real time to determine the moments corresponding to the characteristic points. In the work, new terms for thermal characteristics of machine tools are introduced: kinetic and dynamic characteristics of the temperature movements of the machine. This allow taking into account not only the experimental values for the temperature displacements of the elements of the carrier system of the machine, but also their derivatives up to the third order, inclusively. The work is based on experimental data obtained in the course of full-scale thermal tests of a drilling-milling and boring CNC machine.

The Cell Cycle Regulator CCDC6 Is a Key Target of RNA-Binding Protein EWS

PubMed Central

Duggimpudi, Sujitha; Larsson, Erik; Nabhani, Schafiq; Borkhardt, Arndt; Hoell, Jessica I

2015-01-01

Genetic translocation of EWSR1 to ETS transcription factor coding region is considered as primary cause for Ewing sarcoma. Previous studies focused on the biology of chimeric transcription factors formed due to this translocation. However, the physiological consequences of heterozygous EWSR1 loss in these tumors have largely remained elusive. Previously, we have identified various mRNAs bound to EWS using PAR-CLIP. In this study, we demonstrate CCDC6, a known cell cycle regulator protein, as a novel target regulated by EWS. siRNA mediated down regulation of EWS caused an elevated apoptosis in cells in a CCDC6-dependant manner. This effect was rescued upon re-expression of CCDC6. This study provides evidence for a novel functional link through which wild-type EWS operates in a target-dependant manner in Ewing sarcoma. PMID:25751255

Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics.

PubMed

Ding, Michael Q; Chen, Lujia; Cooper, Gregory F; Young, Jonathan D; Lu, Xinghua

2018-02-01

Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome-scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines. The methodology introduced here can accurately predict the efficacy of drugs, regardless of whether they are molecularly targeted or nonspecific chemotherapy drugs. This approach, on a per-drug basis, can identify sensitive cancer cells with an average sensitivity of 0.82 and specificity of 0.82; on a per-cell line basis, it can identify effective drugs with an average sensitivity of 0.80 and specificity of 0.82. This report describes a data-driven precision medicine approach that is not only generalizable but also optimizes therapeutic efficacy. The framework detailed herein, when successfully translated to clinical environments, could significantly broaden the scope of precision oncology beyond targeted therapies, benefiting an expanded proportion of cancer patients. Mol Cancer Res; 16(2); 269-78. ©2017 AACR . ©2017 American Association for Cancer Research.

Identification of Cell Cycle-Regulated Genes by Convolutional Neural Network.

PubMed

Liu, Chenglin; Cui, Peng; Huang, Tao

2017-01-01

The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big problems in cell cycle-regulated gene detection. Here, a computational framework called DLGene was proposed for cell cycle-regulated gene detection. It is based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution. First, the expression data was transformed to categorical state data to denote the changing state of gene expression, and four different expression patterns were revealed for the reported cell cycle-regulated genes. Then, DLGene was applied to discriminate the non-cell cycle gene and the four subtypes of cell cycle genes. Its performances were compared with six traditional machine learning methods. At last, the biological functions of representative cell cycle genes for each subtype are analyzed. Our method showed better and more balanced performance of sensitivity and specificity comparing to other machine learning algorithms. The cell cycle genes had very different expression pattern with non-cell cycle genes and among the cell-cycle genes, there were four subtypes. Our method not only detects the cell cycle genes, but also describes its expression pattern, such as when its highest expression level is reached and how it changes with time. For each type, we analyzed the biological functions of the representative genes and such results provided novel insight to the cell cycle mechanisms. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Media-Augmented Exercise Machines

NASA Astrophysics Data System (ADS)

Krueger, T.

2002-01-01

Cardio-vascular exercise has been used to mitigate the muscle and cardiac atrophy associated with adaptation to micro-gravity environments. Several hours per day may be required. In confined spaces and long duration missions this kind of exercise is inevitably repetitive and rapidly becomes uninteresting. At the same time, there are pressures to accomplish as much as possible given the cost- per-hour for humans occupying orbiting or interplanetary. Media augmentation provides a the means to overlap activities in time by supplementing the exercise with social, recreational, training or collaborative activities and thereby reducing time pressures. In addition, the machine functions as an interface to a wide range of digital environments allowing for spatial variety in an otherwise confined environment. We hypothesize that the adoption of media augmented exercise machines will have a positive effect on psycho-social well-being on long duration missions. By organizing and supplementing exercise machines, data acquisition hardware, computers and displays into an interacting system this proposal increases functionality with limited additional mass. This paper reviews preliminary work on a project to augment exercise equipment in a manner that addresses these issues and at the same time opens possibilities for additional benefits. A testbed augmented exercise machine uses a specialty built cycle trainer as both input to a virtual environment and as an output device from it using spatialized sound, and visual displays, vibration transducers and variable resistance. The resulting interactivity increases a sense of engagement in the exercise, provides a rich experience of the digital environments. Activities in the virtual environment and accompanying physiological and psychological indicators may be correlated to track and evaluate the health of the crew.

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy

PubMed Central

Mani, Subramani; Chen, Yukun; Li, Xia; Arlinghaus, Lori; Chakravarthy, A Bapsi; Abramson, Vandana; Bhave, Sandeep R; Levy, Mia A; Xu, Hua; Yankeelov, Thomas E

2013-01-01

Objective To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). Materials and methods Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building. Results The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82. Discussion With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem. Conclusions Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC. PMID:23616206

A Cooperative Approach to Virtual Machine Based Fault Injection

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

Naughton III, Thomas J; Engelmann, Christian; Vallee, Geoffroy R

Resilience investigations often employ fault injection (FI) tools to study the effects of simulated errors on a target system. It is important to keep the target system under test (SUT) isolated from the controlling environment in order to maintain control of the experiement. Virtual machines (VMs) have been used to aid these investigations due to the strong isolation properties of system-level virtualization. A key challenge in fault injection tools is to gain proper insight and context about the SUT. In VM-based FI tools, this challenge of target con- text is increased due to the separation between host and guest (VM).more » We discuss an approach to VM-based FI that leverages virtual machine introspection (VMI) methods to gain insight into the target s context running within the VM. The key to this environment is the ability to provide basic information to the FI system that can be used to create a map of the target environment. We describe a proof- of-concept implementation and a demonstration of its use to introduce simulated soft errors into an iterative solver benchmark running in user-space of a guest VM.« less

Variability in the skin exposure of machine operators exposed to cutting fluids.

PubMed

Wassenius, O; Järvholm, B; Engström, T; Lillienberg, L; Meding, B

1998-04-01

This study describes a new technique for measuring skin exposure to cutting fluids and evaluates the variability of skin exposure among machine operators performing cyclic (repetitive) work. The technique is based on video recording and subsequent analysis of the video tape by means of computer-synchronized video equipment. The time intervals at which the machine operator's hand was exposed to fluid were registered, and the total wet time of the skin was calculated by assuming different evaporation times for the fluid. The exposure of 12 operators with different work methods was analyzed in 6 different workshops, which included a range of machine types, from highly automated metal cutting machines (ie, actual cutting and chip removal machines) requiring operator supervision to conventional metal cutting machines, where the operator was required to maneuver the machine and manually exchange products. The relative wet time varied between 0% and 100%. A significant association between short cycle time and high relative wet time was noted. However, there was no relationship between the degree of automatization of the metal cutting machines and wet time. The study shows that skin exposure to cutting fluids can vary considerably between machine operators involved in manufacturing processes using different types of metal cutting machines. The machine type was not associated with dermal wetness. The technique appears to give objective information about dermal wetness.

Target attribute-based false alarm rejection in small infrared target detection

NASA Astrophysics Data System (ADS)

Kim, Sungho

2012-11-01

Infrared search and track is an important research area in military applications. Although there are a lot of works on small infrared target detection methods, we cannot apply them in real field due to high false alarm rate caused by clutters. This paper presents a novel target attribute extraction and machine learning-based target discrimination method. Eight kinds of target features are extracted and analyzed statistically. Learning-based classifiers such as SVM and Adaboost are developed and compared with conventional classifiers for real infrared images. In addition, the generalization capability is also inspected for various infrared clutters.

[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.

Targeting EphA2 impairs cell cycle progression and growth of basal-like/triple-negative breast cancers.

PubMed

Song, W; Hwang, Y; Youngblood, V M; Cook, R S; Balko, J M; Chen, J; Brantley-Sieders, D M

2017-10-05

Basal-like/triple-negative breast cancers (TNBCs) are among the most aggressive forms of breast cancer, and disproportionally affects young premenopausal women and women of African descent. Patients with TNBC suffer a poor prognosis due in part to a lack of molecularly targeted therapies, which represents a critical barrier for effective treatment. Here, we identify EphA2 receptor tyrosine kinase as a clinically relevant target for TNBC. EphA2 expression is enriched in the basal-like molecular subtype in human breast cancers. Loss of EphA2 function in both human and genetically engineered mouse models of TNBC reduced tumor growth in culture and in vivo. Mechanistically, targeting EphA2 impaired cell cycle progression through S-phase via downregulation of c-Myc and stabilization of the cyclin-dependent kinase inhibitor p27/KIP1. A small molecule kinase inhibitor of EphA2 effectively suppressed tumor cell growth in vivo, including TNBC patient-derived xenografts. Thus, our data identify EphA2 as a novel molecular target for TNBC.

Targeting EphA2 impairs cell cycle progression and growth of basal-like/triple-negative breast cancers

PubMed Central

Song, W; Hwang, Y; Youngblood, V M; Cook, R S; Balko, J M; Chen, J; Brantley-Sieders, D M

2017-01-01

Basal-like/triple-negative breast cancers (TNBCs) are among the most aggressive forms of breast cancer, and disproportionally affects young premenopausal women and women of African descent. Patients with TNBC suffer a poor prognosis due in part to a lack of molecularly targeted therapies, which represents a critical barrier for effective treatment. Here, we identify EphA2 receptor tyrosine kinase as a clinically relevant target for TNBC. EphA2 expression is enriched in the basal-like molecular subtype in human breast cancers. Loss of EphA2 function in both human and genetically engineered mouse models of TNBC reduced tumor growth in culture and in vivo. Mechanistically, targeting EphA2 impaired cell cycle progression through S-phase via downregulation of c-Myc and stabilization of the cyclin-dependent kinase inhibitor p27/KIP1. A small molecule kinase inhibitor of EphA2 effectively suppressed tumor cell growth in vivo, including TNBC patient-derived xenografts. Thus, our data identify EphA2 as a novel molecular target for TNBC. PMID:28581527

Space Shuttle ET Friction Stir Weld Machines

NASA Technical Reports Server (NTRS)

Thompson, Jack M.

2003-01-01

NASA and Lockheed-Martin approached the FSW machine vendor community with a specification for longitudinal barrel production FSW weld machines and a shorter travel process development machine in June of 2000. This specification was based on three years of FSW process development on the Space Shuttle External Tank alloys, AL2 195-T8M4 and AL22 19-T87. The primary motivations for changing the ET longitudinal welds from the existing variable polarity Plasma Arc plasma weld process included: (1) Significantly reduced weld defect rates and related reduction in cycle time and uncertainty; (2) Many fewer process variables to control (5 vs. 17); (3) Fewer manufacturing steps; (4) Lower residual stresses and distortion; (5) Improved weld strengths, particularly at cryogenic temperatures; (6) Fewer hazards to production personnel. General Tool was the successful bidder. The equipment is at this writing installed and welding flight hardware. This paper is a means of sharing with the rest of the FSW community the unique features developed to assure NASA/L-M of successful production welds.

Homopolar machine for reversible energy storage and transfer systems

DOEpatents

Stillwagon, Roy E.

1978-01-01

A homopolar machine designed to operate as a generator and motor in reversibly storing and transferring energy between the machine and a magnetic load coil for a thermo-nuclear reactor. The machine rotor comprises hollow thin-walled cylinders or sleeves which form the basis of the system by utilizing substantially all of the rotor mass as a conductor thus making it possible to transfer substantially all the rotor kinetic energy electrically to the load coil in a highly economical and efficient manner. The rotor is divided into multiple separate cylinders or sleeves of modular design, connected in series and arranged to rotate in opposite directions but maintain the supply of current in a single direction to the machine terminals. A stator concentrically disposed around the sleeves consists of a hollow cylinder having a number of excitation coils each located radially outward from the ends of adjacent sleeves. Current collected at an end of each sleeve by sleeve slip rings and brushes is transferred through terminals to the magnetic load coil. Thereafter, electrical energy returned from the coil then flows through the machine which causes the sleeves to motor up to the desired speed in preparation for repetition of the cycle. To eliminate drag on the rotor between current pulses, the brush rigging is designed to lift brushes from all slip rings in the machine.

Homopolar machine for reversible energy storage and transfer systems

DOEpatents

Stillwagon, Roy E.

1981-01-01

A homopolar machine designed to operate as a generator and motor in reversibly storing and transferring energy between the machine and a magnetic load coil for a thermo-nuclear reactor. The machine rotor comprises hollow thin-walled cylinders or sleeves which form the basis of the system by utilizing substantially all of the rotor mass as a conductor thus making it possible to transfer substantially all the rotor kinetic energy electrically to the load coil in a highly economical and efficient manner. The rotor is divided into multiple separate cylinders or sleeves of modular design, connected in series and arranged to rotate in opposite directions but maintain the supply of current in a single direction to the machine terminals. A stator concentrically disposed around the sleeves consists of a hollow cylinder having a number of excitation coils each located radially outward from the ends of adjacent sleeves. Current collected at an end of each sleeve by sleeve slip rings and brushes is transferred through terminals to the magnetic load coil. Thereafter, electrical energy returned from the coil then flows through the machine which causes the sleeves to motor up to the desired speed in preparation for repetition of the cycle. To eliminate drag on the rotor between current pulses, the brush rigging is designed to lift brushes from all slip rings in the machine.

Relativistic Velocity Addition Law from Machine Gun Analogy

NASA Astrophysics Data System (ADS)

Rothenstein, Bernhard; Popescu, Stefan

2009-01-01

Many derivations of the relativistic addition law of parallel velocities without use of the Lorentz transformations (LT) are known.1-5 Some of them are based on thought experiments that require knowledge of the time dilation and the length contraction effects.1,4,5 Other derivations involve the Doppler effect in the optic domain considered from three inertial reference frames in relative motion.6 A few derivations simply involve only the principle of constancy of the light velocity.2 Such derivations are interesting for the teaching of special relativity theory since the relativistic addition of velocities leads directly to the LT.7 The derivation we propose is based on a machine gun-target analogy8 of the acoustic Doppler effect, considered from the rest frame of the machine gun and from the rest frame of the target.

Trends of Occupational Fatalities Involving Machines, United States, 1992–2010

PubMed Central

Marsh, Suzanne M.; Fosbroke, David E.

2016-01-01

Background This paper describes trends of occupational machine-related fatalities from 1992–2010. We examine temporal patterns by worker demographics, machine types (e.g., stationary, mobile), and industries. Methods We analyzed fatalities from Census of Fatal Occupational Injuries data provided by the Bureau of Labor Statistics to the National Institute for Occupational Safety and Health. We used injury source to identify machine-related incidents and Poisson regression to assess trends over the 19-year period. Results There was an average annual decrease of 2.8% in overall machine-related fatality rates from 1992 through 2010. Mobile machine-related fatality rates decreased an average of 2.6% annually and stationary machine-related rates decreased an average of 3.5% annually. Groups that continued to be at high risk included older workers; self-employed; and workers in agriculture/forestry/fishing, construction, and mining. Conclusion Addressing dangers posed by tractors, excavators, and other mobile machines needs to continue. High-risk worker groups should receive targeted information on machine safety. PMID:26358658

TargetM6A: Identifying N6-Methyladenosine Sites From RNA Sequences via Position-Specific Nucleotide Propensities and a Support Vector Machine.

PubMed

Li, Guang-Qing; Liu, Zi; Shen, Hong-Bin; Yu, Dong-Jun

2016-10-01

As one of the most ubiquitous post-transcriptional modifications of RNA, N 6 -methyladenosine ( [Formula: see text]) plays an essential role in many vital biological processes. The identification of [Formula: see text] sites in RNAs is significantly important for both basic biomedical research and practical drug development. In this study, we designed a computational-based method, called TargetM6A, to rapidly and accurately target [Formula: see text] sites solely from the primary RNA sequences. Two new features, i.e., position-specific nucleotide/dinucleotide propensities (PSNP/PSDP), are introduced and combined with the traditional nucleotide composition (NC) feature to formulate RNA sequences. The extracted features are further optimized to obtain a much more compact and discriminative feature subset by applying an incremental feature selection (IFS) procedure. Based on the optimized feature subset, we trained TargetM6A on the training dataset with a support vector machine (SVM) as the prediction engine. We compared the proposed TargetM6A method with existing methods for predicting [Formula: see text] sites by performing stringent jackknife tests and independent validation tests on benchmark datasets. The experimental results show that the proposed TargetM6A method outperformed the existing methods for predicting [Formula: see text] sites and remarkably improved the prediction performances, with MCC = 0.526 and AUC = 0.818. We also provided a user-friendly web server for TargetM6A, which is publicly accessible for academic use at http://csbio.njust.edu.cn/bioinf/TargetM6A.

Machinability of CAD-CAM materials.

PubMed

Chavali, Ramakiran; Nejat, Amir H; Lawson, Nathaniel C

2017-08-01

Although new materials are available for computer-aided design and computer-aided manufacturing (CAD-CAM) fabrication, limited information is available regarding their machinability. The depth of penetration of a milling tool into a material during a timed milling cycle may indicate its machinability. The purpose of this in vitro study was to compare the tool penetration rate for 2 polymer-containing CAD-CAM materials (Lava Ultimate and Enamic) and 2 ceramic-based CAD-CAM materials (e.max CAD and Celtra Duo). The materials were sectioned into 4-mm-thick specimens (n=5/material) and polished with 320-grit SiC paper. Each specimen was loaded into a custom milling apparatus. The apparatus pushed the specimens against a milling tool (E4D Tapered 2016000) rotating at 40 000 RPM with a constant force of 0.98 N. After a 6-minute timed milling cycle, the length of each milling cut was measured with image analysis software under a digital light microscope. Representative specimens and milling tools were examined with scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy. The penetration rate of Lava Ultimate (3.21 ±0.46 mm/min) and Enamic (2.53 ±0.57 mm/min) was significantly greater than that of e.max CAD (1.12 ±0.32 mm/min) or Celtra Duo (0.80 ±0.21 mm/min) materials. SEM observations showed little tool damage, regardless of material type. Residual material was found on the tools used with polymer-containing materials, and wear of the embedding medium was seen on the tools used with the ceramic-based materials. Edge chipping was noted on cuts made in the ceramic-based materials. Lava Ultimate and Enamic have greater machinability and less edge chipping than e.max CAD and Celtra Duo. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

An Analysis of EMG Electrode Configuration for Targeted Muscle Reinnervation Based Neural Machine Interface

PubMed Central

Huang, He; Zhou, Ping; Li, Guanglin; Kuiken, Todd A.

2015-01-01

Targeted muscle reinnervation (TMR) is a novel neural machine interface for improved myoelectric prosthesis control. Previous high-density (HD) surface electromyography (EMG) studies have indicated that tremendous neural control information can be extracted from the reinnervated muscles by EMG pattern recognition (PR). However, using a large number of EMG electrodes hinders clinical application of the TMR technique. This study investigated a reduced number of electrodes and the placement required to extract sufficient neural control information for accurate identification of user movement intents. An electrode selection algorithm was applied to the HD EMG recordings from each of 4 TMR amputee subjects. The results show that when using only 12 selected bipolar electrodes the average accuracy over subjects for classifying 16 movement intents was 93.0(±3.3)%, just 1.2% lower than when using the entire HD electrode complement. The locations of selected electrodes were consistent with the anatomical reinnervation sites. Additionally, a practical protocol for clinical electrode placement was developed, which does not rely on complex HD EMG experiment and analysis while maintaining a classification accuracy of 88.7±4.5%. These outcomes provide important guidelines for practical electrode placement that can promote future clinical application of TMR and EMG PR in the control of multifunctional prostheses. PMID:18303804

Engineered Surface Properties of Porous Tungsten from Cryogenic Machining

NASA Astrophysics Data System (ADS)

Schoop, Julius Malte

Porous tungsten is used to manufacture dispenser cathodes due to it refractory properties. Surface porosity is critical to functional performance of dispenser cathodes because it allows for an impregnated ceramic compound to migrate to the emitting surface, lowering its work function. Likewise, surface roughness is important because it is necessary to ensure uniform wetting of the molten impregnate during high temperature service. Current industry practice to achieve surface roughness and surface porosity requirements involves the use of a plastic infiltrant during machining. After machining, the infiltrant is baked and the cathode pellet is impregnated. In this context, cryogenic machining is investigated as a substitutionary process for the current plastic infiltration process. Along with significant reductions in cycle time and resource use, surface quality of cryogenically machined un-infiltrated (as-sintered) porous tungsten has been shown to significantly outperform dry machining. The present study is focused on examining the relationship between machining parameters and cooling condition on the as-machined surface integrity of porous tungsten. The effects of cryogenic pre-cooling, rake angle, cutting speed, depth of cut and feed are all taken into consideration with respect to machining-induced surface morphology. Cermet and Polycrystalline diamond (PCD) cutting tools are used to develop high performance cryogenic machining of porous tungsten. Dry and pre-heated machining were investigated as a means to allow for ductile mode machining, yet severe tool-wear and undesirable smearing limited the feasibility of these approaches. By using modified PCD cutting tools, high speed machining of porous tungsten at cutting speeds up to 400 m/min is achieved for the first time. Beyond a critical speed, brittle fracture and built-up edge are eliminated as the result of a brittle to ductile transition. A model of critical chip thickness ( hc ) effects based on cutting

SU-E-T-113: Dose Distribution Using Respiratory Signals and Machine Parameters During Treatment

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

Imae, T; Haga, A; Saotome, N

Purpose: Volumetric modulated arc therapy (VMAT) is a rotational intensity-modulated radiotherapy (IMRT) technique capable of acquiring projection images during treatment. Treatment plans for lung tumors using stereotactic body radiotherapy (SBRT) are calculated with planning computed tomography (CT) images only exhale phase. Purpose of this study is to evaluate dose distribution by reconstructing from only the data such as respiratory signals and machine parameters acquired during treatment. Methods: Phantom and three patients with lung tumor underwent CT scans for treatment planning. They were treated by VMAT while acquiring projection images to derive their respiratory signals and machine parameters including positions ofmore » multi leaf collimators, dose rates and integrated monitor units. The respiratory signals were divided into 4 and 10 phases and machine parameters were correlated with the divided respiratory signals based on the gantry angle. Dose distributions of each respiratory phase were calculated from plans which were reconstructed from the respiratory signals and the machine parameters during treatment. The doses at isocenter, maximum point and the centroid of target were evaluated. Results and Discussion: Dose distributions during treatment were calculated using the machine parameters and the respiratory signals detected from projection images. Maximum dose difference between plan and in treatment distribution was −1.8±0.4% at centroid of target and dose differences of evaluated points between 4 and 10 phases were no significant. Conclusion: The present method successfully evaluated dose distribution using respiratory signals and machine parameters during treatment. This method is feasible to verify the actual dose for moving target.« less

TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples.

PubMed

Bandyopadhyay, Sanghamitra; Mitra, Ramkrishna

2009-10-15

Prediction of microRNA (miRNA) target mRNAs using machine learning approaches is an important area of research. However, most of the methods suffer from either high false positive or false negative rates. One reason for this is the marked deficiency of negative examples or miRNA non-target pairs. Systematic identification of non-target mRNAs is still not addressed properly, and therefore, current machine learning approaches are compelled to rely on artificially generated negative examples for training. In this article, we have identified approximately 300 tissue-specific negative examples using a novel approach that involves expression profiling of both miRNAs and mRNAs, miRNA-mRNA structural interactions and seed-site conservation. The newly generated negative examples are validated with pSILAC dataset, which elucidate the fact that the identified non-targets are indeed non-targets.These high-throughput tissue-specific negative examples and a set of experimentally verified positive examples are then used to build a system called TargetMiner, a support vector machine (SVM)-based classifier. In addition to assessing the prediction accuracy on cross-validation experiments, TargetMiner has been validated with a completely independent experimental test dataset. Our method outperforms 10 existing target prediction algorithms and provides a good balance between sensitivity and specificity that is not reflected in the existing methods. We achieve a significantly higher sensitivity and specificity of 69% and 67.8% based on a pool of 90 feature set and 76.5% and 66.1% using a set of 30 selected feature set on the completely independent test dataset. In order to establish the effectiveness of the systematically generated negative examples, the SVM is trained using a different set of negative data generated using the method in Yousef et al. A significantly higher false positive rate (70.6%) is observed when tested on the independent set, while all other factors are kept the

Neon turbo-Brayton cycle refrigerator for HTS power machines

NASA Astrophysics Data System (ADS)

Hirai, Hirokazu; Hirokawa, M.; Yoshida, Shigeru; Nara, N.; Ozaki, S.; Hayashi, H.; Okamoto, H.; Shiohara, Y.

2012-06-01

We developed a prototype turbo-Brayton refrigerator whose working fluid is neon gas. The refrigerator is designed for a HTS (High Temperature Superconducting) power transformer and its cooling power is more than 2 kW at 65 K. The refrigerator has a turboexpander and a turbo-compressor, which utilize magnetic bearings. These rotational machines have no rubbing parts and no oil-components. Those make a long maintenance interval of the refrigerator. The refrigerator is very compact because our newly developed turbo-compressor is volumetrically smaller than a displacement type compressor in same operating specification. Another feature of the refrigerator is a wide range operation capability for various heat-loads. Cooling power is controlled by the input-power of the turbo-compressor instead of the conventional method of using an electric heater. The rotational speed of the compressor motor is adjusted by an inverter. This system is expected to be more efficient. We show design details, specification and cooling test results of the new refrigerator in this paper.

Intense X-ray machine for penetrating radiography

NASA Astrophysics Data System (ADS)

Lucht, Roy A.; Eckhouse, Shimon

Penetrating radiography has been used for many years in the nuclear weapons research programs. Infrequently penetrating radiography has been used in conventional weapons research programs. For example the Los Alamos PHERMEX machine was used to view uranium rods penetrating steel for the GAU-8 program, and the Ector machine was used to see low density regions in forming metal jets. The armor/anti-armor program at Los Alamos has created a need for an intense flash X-ray machine that can be dedicated to conventional weapons research. The Balanced Technology Initiative, through DARPA, has funded the design and construction of such a machine at Los Alamos. It will be an 8- to 10-MeV diode machine capable of delivering a dose of 500 R at 1 m with a spot size of less than 5 mm. The machine used an 87.5-stage low inductance Marx generator that charges up a 7.4-(Omega), 32-ns water line. The water line is discharged through a self-breakdown oil switch into a 12.4-(Omega) water line that rings up the voltage into the high impendance X-ray diode. A long (233-cm) vacuum drift tube is used to separate the large diameter oil-insulated diode region from the X-ray source area that may be exposed to high overpressures by the explosive experiments. The electron beam is selffocused at the target area using a low pressure background gas.

Machine vision for various manipulation tasks

NASA Astrophysics Data System (ADS)

Domae, Yukiyasu

2017-03-01

Bin-picking, re-grasping, pick-and-place, kitting, etc. There are many manipulation tasks in the fields of automation of factory, warehouse and so on. The main problem of the automation is that the target objects (items/parts) have various shapes, weights and surface materials. In my talk, I will show latest machine vision systems and algorithms against the problem.

S-phase duration is the main target of cell cycle regulation in neural progenitors of developing ferret neocortex.

PubMed

Turrero García, Miguel; Chang, YoonJeung; Arai, Yoko; Huttner, Wieland B

2016-02-15

The evolutionary expansion of the neocortex primarily reflects increases in abundance and proliferative capacity of cortical progenitors and in the length of the neurogenic period during development. Cell cycle parameters of neocortical progenitors are an important determinant of cortical development. The ferret (Mustela putorius furo), a gyrencephalic mammal, has gained increasing importance as a model for studying corticogenesis. Here, we have studied the abundance, proliferation, and cell cycle parameters of different neural progenitor types, defined by their differential expression of the transcription factors Pax6 and Tbr2, in the various germinal zones of developing ferret neocortex. We focused our analyses on postnatal day 1, a late stage of cortical neurogenesis when upper-layer neurons are produced. Based on cumulative 5-ethynyl-2'-deoxyuridine (EdU) labeling as well as Ki67 and proliferating cell nuclear antigen (PCNA) immunofluorescence, we determined the duration of the various cell cycle phases of the different neocortical progenitor subpopulations. Ferret neocortical progenitors were found to exhibit longer cell cycles than those of rodents and little variation in the duration of G1 among distinct progenitor types, also in contrast to rodents. Remarkably, the main difference in cell cycle parameters among the various progenitor types was the duration of S-phase, which became shorter as progenitors progressively changed transcription factor expression from patterns characteristic of self-renewal to those of neuron production. Hence, S-phase duration emerges as major target of cell cycle regulation in cortical progenitors of this gyrencephalic mammal. © 2015 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

Stirling Refrigerator

NASA Astrophysics Data System (ADS)

Kagawa, Noboru

A Stirling cooler (refrigerator) was proposed in 1862 and the first Stirling cooler was put on market in 1955. Since then, many Stirling coolers have been developed and marketed as cryocoolers. Recently, Stirling cycle machines for heating and cooling at near-ambient temperatures between 173 and 400K, are recognized as promising candidates for alternative system which are more compatible with people and the Earth. The ideal cycles of Stirling cycle machine offer the highest thermal efficiencies and the working fluids do not cause serious environmental problems of ozone depletion and global warming. In this review, the basic thermodynamics of Stirling cycle are briefly described to quantify the attractive cycle performance. The fundamentals to realize actual Stirling coolers and heat pumps are introduced in detail. The current status of the Stirling cycle machine technologies is reviewed. Some machines have almost achieved the target performance. Also, duplex-Stirling-cycle and Vuilleumier-cycle machines and their performance are introduced.

AC Loss Analysis of MgB2-Based Fully Superconducting Machines

NASA Astrophysics Data System (ADS)

Feddersen, M.; Haran, K. S.; Berg, F.

2017-12-01

Superconducting electric machines have shown potential for significant increase in power density, making them attractive for size and weight sensitive applications such as offshore wind generation, marine propulsion, and hybrid-electric aircraft propulsion. Superconductors exhibit no loss under dc conditions, though ac current and field produce considerable losses due to hysteresis, eddy currents, and coupling mechanisms. For this reason, many present machines are designed to be partially superconducting, meaning that the dc field components are superconducting while the ac armature coils are conventional conductors. Fully superconducting designs can provide increases in power density with significantly higher armature current; however, a good estimate of ac losses is required to determine the feasibility under the machines intended operating conditions. This paper aims to characterize the expected losses in a fully superconducting machine targeted towards aircraft, based on an actively-shielded, partially superconducting machine from prior work. Various factors are examined such as magnet strength, operating frequency, and machine load to produce a model for the loss in the superconducting components of the machine. This model is then used to optimize the design of the machine for minimal ac loss while maximizing power density. Important observations from the study are discussed.

A Topical Mitochondria-Targeted Redox Cycling Nitroxide Mitigates Oxidative Stress Induced Skin Damage

PubMed Central

Brand, Rhonda M.; Epperly, Michael W.; Stottlemyer, J. Mark; Skoda, Erin M.; Gao, Xiang; Li, Song; Huq, Saiful; Wipf, Peter; Kagan, Valerian E.; Greenberger, Joel S.; Falo, Louis D.

2017-01-01

Skin is the largest human organ and provides a first line of defense that includes physical, chemical, and immune mechanisms to combat environmental stress. Radiation is a prevalent environmental stressor. Radiation induced skin damage ranges from photoaging and cutaneous carcinogenesis from UV exposure, to treatment-limiting radiation dermatitis associated with radiotherapy, to cutaneous radiation syndrome, a frequently fatal consequence of exposures from nuclear accidents. The major mechanism of skin injury common to these exposures is radiation induced oxidative stress. Efforts to prevent or mitigate radiation damage have included development of antioxidants capable of reducing reactive oxygen species (ROS). Mitochondria are particularly susceptible to oxidative stress, and mitochondrial dependent apoptosis plays a major role in radiation induced tissue damage. We reasoned that targeting a redox cycling nitroxide to mitochondria could prevent ROS accumulation, limiting downstream oxidative damage and preserving mitochondrial function. Here we show that in both mouse and human skin, topical application of a mitochondrial targeted antioxidant prevents and mitigates radiation induced skin damage characterized by clinical dermatitis, loss of barrier function, inflammation, and fibrosis. Further, damage mitigation is associated with reduced apoptosis, preservation of the skin’s antioxidant capacity, and reduction of irreversible DNA and protein oxidation associated with oxidative stress. PMID:27794421

Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4.

PubMed

Xing, Jing; Lu, Wenchao; Liu, Rongfeng; Wang, Yulan; Xie, Yiqian; Zhang, Hao; Shi, Zhe; Jiang, Hao; Liu, Yu-Chih; Chen, Kaixian; Jiang, Hualiang; Luo, Cheng; Zheng, Mingyue

2017-07-24

Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential antiresistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4. In addition to positive experimental data, such as X-ray structures of BRD4-ligand complexes and BRD4 inhibitory potencies, negative data such as false positives (FPs) identified from our earlier ligand screening results were incorporated into our knowledge base. We used the resulting data to train a machine-learning model named BRD4LGR to predict the BRD4i-likeness of a compound. BRD4LGR achieved a 20-30% higher AUC-ROC than that of Glide using the same test set. When conducting in vitro experiments against a library of previously untested, commercially available organic compounds, the second round of VS using BRD4LGR generated 15 new BRD4is. Moreover, inverting the machine-learning model provided easy access to structure-activity relationship (SAR) interpretation for hit-to-lead optimization.

Molecular machines open cell membranes

NASA Astrophysics Data System (ADS)

García-López, Víctor; Chen, Fang; Nilewski, Lizanne G.; Duret, Guillaume; Aliyan, Amir; Kolomeisky, Anatoly B.; Robinson, Jacob T.; Wang, Gufeng; Pal, Robert; Tour, James M.

2017-08-01

Beyond the more common chemical delivery strategies, several physical techniques are used to open the lipid bilayers of cellular membranes. These include using electric and magnetic fields, temperature, ultrasound or light to introduce compounds into cells, to release molecular species from cells or to selectively induce programmed cell death (apoptosis) or uncontrolled cell death (necrosis). More recently, molecular motors and switches that can change their conformation in a controlled manner in response to external stimuli have been used to produce mechanical actions on tissue for biomedical applications. Here we show that molecular machines can drill through cellular bilayers using their molecular-scale actuation, specifically nanomechanical action. Upon physical adsorption of the molecular motors onto lipid bilayers and subsequent activation of the motors using ultraviolet light, holes are drilled in the cell membranes. We designed molecular motors and complementary experimental protocols that use nanomechanical action to induce the diffusion of chemical species out of synthetic vesicles, to enhance the diffusion of traceable molecular machines into and within live cells, to induce necrosis and to introduce chemical species into live cells. We also show that, by using molecular machines that bear short peptide addends, nanomechanical action can selectively target specific cell-surface recognition sites. Beyond the in vitro applications demonstrated here, we expect that molecular machines could also be used in vivo, especially as their design progresses to allow two-photon, near-infrared and radio-frequency activation.

Molecular machines open cell membranes.

PubMed

García-López, Víctor; Chen, Fang; Nilewski, Lizanne G; Duret, Guillaume; Aliyan, Amir; Kolomeisky, Anatoly B; Robinson, Jacob T; Wang, Gufeng; Pal, Robert; Tour, James M

2017-08-30

Beyond the more common chemical delivery strategies, several physical techniques are used to open the lipid bilayers of cellular membranes. These include using electric and magnetic fields, temperature, ultrasound or light to introduce compounds into cells, to release molecular species from cells or to selectively induce programmed cell death (apoptosis) or uncontrolled cell death (necrosis). More recently, molecular motors and switches that can change their conformation in a controlled manner in response to external stimuli have been used to produce mechanical actions on tissue for biomedical applications. Here we show that molecular machines can drill through cellular bilayers using their molecular-scale actuation, specifically nanomechanical action. Upon physical adsorption of the molecular motors onto lipid bilayers and subsequent activation of the motors using ultraviolet light, holes are drilled in the cell membranes. We designed molecular motors and complementary experimental protocols that use nanomechanical action to induce the diffusion of chemical species out of synthetic vesicles, to enhance the diffusion of traceable molecular machines into and within live cells, to induce necrosis and to introduce chemical species into live cells. We also show that, by using molecular machines that bear short peptide addends, nanomechanical action can selectively target specific cell-surface recognition sites. Beyond the in vitro applications demonstrated here, we expect that molecular machines could also be used in vivo, especially as their design progresses to allow two-photon, near-infrared and radio-frequency activation.

Towards Polarised Antiprotons: Machine Developments for Spin-Filtering Studies

NASA Astrophysics Data System (ADS)

Lenisa, Paolo

2016-02-01

We address the commissioning of the experimental equipment and the machine studies required for the first spin-filtering experiment with protons at the COSY ring in Jülich (Germany) at a beam kinetic energy of 49.3 MeV. The implementation of a low-beta insertion made it possible to achieve beam lifetimes of 8000 s in the presence of a dense polarized hydrogen storage cell target. The developed techniques can be directly applied to antiproton machines and allow for the determination of the spin-dependent pbar-p cross sections via spin-filtering.

Real Time Intelligent Target Detection and Analysis with Machine Vision

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Padgett, Curtis; Brown, Kenneth

2000-01-01

We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

S‐phase duration is the main target of cell cycle regulation in neural progenitors of developing ferret neocortex

PubMed Central

Turrero García, Miguel; Chang, YoonJeung; Arai, Yoko

2016-01-01

ABSTRACT The evolutionary expansion of the neocortex primarily reflects increases in abundance and proliferative capacity of cortical progenitors and in the length of the neurogenic period during development. Cell cycle parameters of neocortical progenitors are an important determinant of cortical development. The ferret (Mustela putorius furo), a gyrencephalic mammal, has gained increasing importance as a model for studying corticogenesis. Here, we have studied the abundance, proliferation, and cell cycle parameters of different neural progenitor types, defined by their differential expression of the transcription factors Pax6 and Tbr2, in the various germinal zones of developing ferret neocortex. We focused our analyses on postnatal day 1, a late stage of cortical neurogenesis when upper‐layer neurons are produced. Based on cumulative 5‐ethynyl‐2′‐deoxyuridine (EdU) labeling as well as Ki67 and proliferating cell nuclear antigen (PCNA) immunofluorescence, we determined the duration of the various cell cycle phases of the different neocortical progenitor subpopulations. Ferret neocortical progenitors were found to exhibit longer cell cycles than those of rodents and little variation in the duration of G1 among distinct progenitor types, also in contrast to rodents. Remarkably, the main difference in cell cycle parameters among the various progenitor types was the duration of S‐phase, which became shorter as progenitors progressively changed transcription factor expression from patterns characteristic of self‐renewal to those of neuron production. Hence, S‐phase duration emerges as major target of cell cycle regulation in cortical progenitors of this gyrencephalic mammal. J. Comp. Neurol. 524:456–470, 2016. © 2015 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc. PMID:25963823

Internal cycle modeling and environmental assessment of multiple cycle consumer products

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

Tsiliyannis, C.A., E-mail: anion@otenet.gr

2012-01-15

Highlights: Black-Right-Pointing-Pointer Dynamic flow models are presented for remanufactured, reused or recycled products. Black-Right-Pointing-Pointer Early loss and stochastic return are included for fast and slow cycling products. Black-Right-Pointing-Pointer The reuse-to-input flow ratio (Internal Cycle Factor, ICF) is determined. Black-Right-Pointing-Pointer The cycle rate, which is increasing with the ICF, monitors eco-performance. Black-Right-Pointing-Pointer Early internal cycle losses diminish the ICF, the cycle rate and performance. - Abstract: Dynamic annual flow models incorporating consumer discard and usage loss and featuring deterministic and stochastic end-of-cycle (EOC) return by the consumer are developed for reused or remanufactured products (multiple cycle products, MCPs), including fast andmore » slow cycling, short and long-lived products. It is shown that internal flows (reuse and overall consumption) increase proportionally to the dimensionless internal cycle factor (ICF) which is related to environmental impact reduction factors. The combined reuse/recycle (or cycle) rate is shown capable for shortcut, albeit effective, monitoring of environmental performance in terms of waste production, virgin material extraction and manufacturing impacts of all MCPs, a task, which physical variables (lifetime, cycling frequency, mean or total number of return trips) and conventional rates, via which environmental policy has been officially implemented (e.g. recycling rate) cannot accomplish. The cycle rate is shown to be an increasing (hyperbolic) function of ICF. The impact of the stochastic EOC return characteristics on total reuse and consumption flows, as well as on eco-performance, is assessed: symmetric EOC return has a small, positive effect on performance compared to deterministic, while early shifted EOC return is more beneficial. In order to be efficient, environmental policy should set higher minimum reuse targets for higher trippage MCPs

MicroRNA-139-5p acts as a tumor suppressor by targeting ELTD1 and regulating cell cycle in glioblastoma multiforme

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

Dai, Shouping; Wang, Xianjun; Li, Xiao

MicroRNA-139-5p was identified to be significantly down-regulated in glioblastoma multiform (GBM) by miRNA array. In this report we aimed to clarify its biological function, molecular mechanisms and direct target gene in GBM. Twelve patients with GBM were analyzed for the expression of miR-139-5p by quantitative RT-PCR. miR-139-5p overexpression was established by transfecting miR-139-5p-mimic into U87MG and T98G cells, and its effects on cell proliferation were studied using MTT assay and colony formation assays. We concluded that ectopic expression of miR-139-5p in GBM cell lines significantly suppressed cell proliferation and inducing apoptosis. Bioinformatics coupled with luciferase and western blot assays alsomore » revealed that miR-139-5p suppresses glioma cell proliferation by targeting ELTD1 and regulating cell cycle. - Highlights: • miR-139-5p is downregulated in GBM. • miR-139-5p regulates cell proliferation through inducing apoptosis. • miR-139-5p regulates glioblastoma tumorigenesis by targeting 3′UTR of ELTD1. • miR-139-5p is involved in cell cycle regulation.« less

Avian leukosis virus subgroup J promotes cell proliferation and cell cycle progression through miR-221 by targeting CDKN1B.

PubMed

Ren, Chaoqi; Yu, Mengmeng; Zhang, Yao; Fan, Minghui; Chang, Fangfang; Xing, Lixiao; Liu, Yongzhen; Wang, Yongqiang; Qi, Xiaole; Liu, Changjun; Zhang, Yanping; Cui, Hongyu; Li, Kai; Gao, Li; Pan, Qing; Wang, Xiaomei; Gao, Yulong

2018-06-01

Avian leukosis virus subgroup J (ALV-J), a highly oncogenic retrovirus, causes leukemia-like proliferative diseases in chickens. microRNAs post-transcriptionally suppress targets and are involved in the development of various tumors. We previously showed that miR-221 is upregulated in ALV-J-induced tumors. In this study, we analyzed the possible function of miR-221 in ALV-J tumorigenesis. The target validation system showed that CDKN1B is a target of miR-221 and is downregulated in ALV-J infection. As CDKN1B arrests the cell cycle and regulates its progression, we analyzed the proliferation of ALV-J-infected DF-1 cells. ALV-J-infection-induced DF1 cell derepression of G1/S transition and overproliferation required high miR-221 expression followed by CDKN1B downregulation. Cell cycle pathway analysis showed that ALV-J infection induced DF-1 cell overproliferation via the CDKN1B-CDK2/CDK6 pathway. Thus, miR-221 may play an important role in ALV-J-induced aggressive growth of DF-1 cells; these findings have expanded our insights into the mechanism underlying ALV-J infection and tumorigenesis. Copyright © 2018 Elsevier Inc. All rights reserved.

High purity tungsten targets

NASA Technical Reports Server (NTRS)

1975-01-01

High purity tungsten, which is used for targets in X-ray tubes was considered for space processing. The demand for X-ray tubes was calculated using the growth rates for dental and medical X-ray machines. It is concluded that the cost benefits are uncertain.

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

Significant improvements of electrical discharge machining performance by step-by-step updated adaptive control laws

NASA Astrophysics Data System (ADS)

Zhou, Ming; Wu, Jianyang; Xu, Xiaoyi; Mu, Xin; Dou, Yunping

2018-02-01

In order to obtain improved electrical discharge machining (EDM) performance, we have dedicated more than a decade to correcting one essential EDM defect, the weak stability of the machining, by developing adaptive control systems. The instabilities of machining are mainly caused by complicated disturbances in discharging. To counteract the effects from the disturbances on machining, we theoretically developed three control laws from minimum variance (MV) control law to minimum variance and pole placements coupled (MVPPC) control law and then to a two-step-ahead prediction (TP) control law. Based on real-time estimation of EDM process model parameters and measured ratio of arcing pulses which is also called gap state, electrode discharging cycle was directly and adaptively tuned so that a stable machining could be achieved. To this end, we not only theoretically provide three proved control laws for a developed EDM adaptive control system, but also practically proved the TP control law to be the best in dealing with machining instability and machining efficiency though the MVPPC control law provided much better EDM performance than the MV control law. It was also shown that the TP control law also provided a burn free machining.

Design Study for a Free-piston Vuilleumier Cycle Heat Pump

NASA Astrophysics Data System (ADS)

Matsue, Junji; Hoshino, Norimasa; Ikumi, Yonezou; Shirai, Hiroyuki

Conceptual design for a free-piston Vuilleumier cycle heat pump machine was proposed. The machine was designed based upon the numerical results of a dynamic analysis method. The method included the effect of self excitation vibration with dissipation caused by the flow friction of an oscillating working gas flow and solid friction of seals. It was found that the design values of reciprocating masses and spring constants proposed in published papers related to this study were suitable for practical use. The fundamental effects of heat exchanger elements on dynamic behaviors of the machine were clarified. It has been pointed out that some improvements were required for thermodynamic analysis of heat exchangers and working spaces.

A fuel-limited isothermal DNA machine for the sensitive detection of cellular deoxyribonucleoside triphosphates.

PubMed

Dong, Jiantong; Wu, Tongbo; Xiao, Yu; Xu, Lei; Fang, Simin; Zhao, Meiping

2016-09-29

A fuel-limited isothermal DNA machine has been built for the sensitive fluorescence detection of cellular deoxyribonucleoside triphosphates (dNTPs) at the fmol level, which greatly reduces the required sample cell number. Upon the input of the limiting target dNTP, the machine runs automatically at 37 °C without the need for higher temperature.

DUBbing Cancer: Deubiquitylating Enzymes Involved in Epigenetics, DNA Damage and the Cell Cycle As Therapeutic Targets.

PubMed

Pinto-Fernandez, Adan; Kessler, Benedikt M

2016-01-01

Controlling cell proliferation is one of the hallmarks of cancer. A number of critical checkpoints ascertain progression through the different stages of the cell cycle, which can be aborted when perturbed, for instance by errors in DNA replication and repair. These molecular checkpoints are regulated by a number of proteins that need to be present at the right time and quantity. The ubiquitin system has emerged as a central player controlling the fate and function of such molecules such as cyclins, oncogenes and components of the DNA repair machinery. In particular, proteases that cleave ubiquitin chains, referred to as deubiquitylating enzymes (DUBs), have attracted recent attention due to their accessibility to modulation by small molecules. In this review, we describe recent evidence of the critical role of DUBs in aspects of cell cycle checkpoint control, associated DNA repair mechanisms and regulation of transcription, representing pathways altered in cancer. Therefore, DUBs involved in these processes emerge as potentially critical targets for the treatment of not only hematological, but potentially also solid tumors.

DUBbing Cancer: Deubiquitylating Enzymes Involved in Epigenetics, DNA Damage and the Cell Cycle As Therapeutic Targets

PubMed Central

Pinto-Fernandez, Adan; Kessler, Benedikt M.

2016-01-01

Controlling cell proliferation is one of the hallmarks of cancer. A number of critical checkpoints ascertain progression through the different stages of the cell cycle, which can be aborted when perturbed, for instance by errors in DNA replication and repair. These molecular checkpoints are regulated by a number of proteins that need to be present at the right time and quantity. The ubiquitin system has emerged as a central player controlling the fate and function of such molecules such as cyclins, oncogenes and components of the DNA repair machinery. In particular, proteases that cleave ubiquitin chains, referred to as deubiquitylating enzymes (DUBs), have attracted recent attention due to their accessibility to modulation by small molecules. In this review, we describe recent evidence of the critical role of DUBs in aspects of cell cycle checkpoint control, associated DNA repair mechanisms and regulation of transcription, representing pathways altered in cancer. Therefore, DUBs involved in these processes emerge as potentially critical targets for the treatment of not only hematological, but potentially also solid tumors. PMID:27516771

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.

Retrieval of target structure information from laser-induced photoelectrons by few-cycle bicircular laser fields

NASA Astrophysics Data System (ADS)

Hoang, Van-Hung; Le, Van-Hoang; Lin, C. D.; Le, Anh-Thu

2017-03-01

By analyzing theoretical results from a numerical solution of the time-dependent Schrödinger equation for atoms in few-cycle bicircular laser pulses, we show that high-energy photoelectron momentum spectra can be used to extract accurate elastic scattering differential cross sections of the target ion with free electrons. We find that the retrieval range for a scattering angle with bicircular pulses is wider than with linearly polarized pulses, although the retrieval method has to be modified to account for different returning directions of the electron in the continuum. This result can be used to extend the range of applicability of ultrafast imaging techniques such as laser-induced electron diffraction and for the accurate characterization of laser pulses.

Molecular Machine Powered Surface Programmatic Chain Reaction for Highly Sensitive Electrochemical Detection of Protein.

PubMed

Zhu, Jing; Gan, Haiying; Wu, Jie; Ju, Huangxian

2018-04-17

A bipedal molecular machine powered surface programmatic chain reaction was designed for electrochemical signal amplification and highly sensitive electrochemical detection of protein. The bipedal molecular machine was built through aptamer-target specific recognition for the binding of one target protein with two DNA probes, which hybridized with surface-tethered hairpin DNA 1 (H1) via proximity effect to expose the prelocked toehold domain of H1 for the hybridization of ferrocene-labeled hairpin DNA 2 (H2-Fc). The toehold-mediated strand displacement reaction brought the electrochemical signal molecule Fc close to the electrode and meanwhile released the bipedal molecular machine to traverse the sensing surface by the surface programmatic chain reaction. Eventually, a large number of duplex structures of H1-H2 with ferrocene groups facing to the electrode were formed on the sensor surface to generate an amplified electrochemical signal. Using thrombin as a model target, this method showed a linear detection range from 2 pM to 20 nM with a detection limit of 0.76 pM. The proposed detection strategy was enzyme-free and allowed highly sensitive and selective detection of a variety of protein targets by using corresponding DNA-based affinity probes, showing potential application in bioanalysis.

A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications.

PubMed

Johnson, Corinne; Price, Gareth; Khalifa, Jonathan; Faivre-Finn, Corinne; Dekker, Andre; Moore, Christopher; van Herk, Marcel

2018-02-01

The gross tumour volume (GTV) is predictive of clinical outcome and consequently features in many machine-learned models. 4D-planning, however, has prompted substitution of the GTV with the internal gross target volume (iGTV). We present and validate a method to synthesise GTV data from the iGTV, allowing the combination of 3D and 4D planned patient cohorts for modelling. Expert delineations in 40 non-small cell lung cancer patients were used to develop linear fit and erosion methods to synthesise the GTV volume and shape. Quality was assessed using Dice Similarity Coefficients (DSC) and closest point measurements; by calculating dosimetric features; and by assessing the quality of random forest models built on patient populations with and without synthetic GTVs. Volume estimates were within the magnitudes of inter-observer delineation variability. Shape comparisons produced mean DSCs of 0.8817 and 0.8584 for upper and lower lobe cases, respectively. A model trained on combined true and synthetic data performed significantly better than models trained on GTV alone, or combined GTV and iGTV data. Accurate synthesis of GTV size from the iGTV permits the combination of lung cancer patient cohorts, facilitating machine learning applications in thoracic radiotherapy. Copyright © 2017 Elsevier B.V. All rights reserved.

Novel target fabrication using 3D printing developed at University of Michigan

DOE PAGES

Klein, Sallee R.; Deininger, Michael; Gillespie, Robb S.; ...

2016-05-24

The University of Michigan has been fabricating targets for high-energy-density experiments for the past decade. We utilize the technique of machined acrylic bodies and mating components acting as constraints to build repeatable targets. Combining 3D printing with traditional machining, we are able to take advantage of the very best part of both aspects of manufacturing. Furthermore, we present several recent campaigns to act as showcase and introduction of our techniques and our experience with 3D printing, effecting how we utilize 3D printing in our target builds.

Novel target fabrication using 3D printing developed at University of Michigan

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

Klein, Sallee R.; Deininger, Michael; Gillespie, Robb S.

The University of Michigan has been fabricating targets for high-energy-density experiments for the past decade. We utilize the technique of machined acrylic bodies and mating components acting as constraints to build repeatable targets. Combining 3D printing with traditional machining, we are able to take advantage of the very best part of both aspects of manufacturing. Furthermore, we present several recent campaigns to act as showcase and introduction of our techniques and our experience with 3D printing, effecting how we utilize 3D printing in our target builds.

Targeting of the Fun30 nucleosome remodeller by the Dpb11 scaffold facilitates cell cycle-regulated DNA end resection

PubMed Central

Bantele, Susanne CS; Ferreira, Pedro; Gritenaite, Dalia; Boos, Dominik; Pfander, Boris

2017-01-01

DNA double strand breaks (DSBs) can be repaired by either recombination-based or direct ligation-based mechanisms. Pathway choice is made at the level of DNA end resection, a nucleolytic processing step, which primes DSBs for repair by recombination. Resection is thus under cell cycle control, but additionally regulated by chromatin and nucleosome remodellers. Here, we show that both layers of control converge in the regulation of resection by the evolutionarily conserved Fun30/SMARCAD1 remodeller. Budding yeast Fun30 and human SMARCAD1 are cell cycle-regulated by interaction with the DSB-localized scaffold protein Dpb11/TOPBP1, respectively. In yeast, this protein assembly additionally comprises the 9-1-1 damage sensor, is involved in localizing Fun30 to damaged chromatin, and thus is required for efficient long-range resection of DSBs. Notably, artificial targeting of Fun30 to DSBs is sufficient to bypass the cell cycle regulation of long-range resection, indicating that chromatin remodelling during resection is underlying DSB repair pathway choice. DOI: http://dx.doi.org/10.7554/eLife.21687.001 PMID:28063255

Design of a line-VISAR interferometer system for the Sandia Z Machine

NASA Astrophysics Data System (ADS)

Galbraith, J.; Austin, K.; Baker, J.; Bettencourt, R.; Bliss, E.; Celeste, J.; Clancy, T.; Cohen, S.; Crosley, M.; Datte, P.; Fratanduono, D.; Frieders, G.; Hammer, J.; Jackson, J.; Johnson, D.; Jones, M.; Koen, D.; Lusk, J.; Martinez, A.; Massey, W.; McCarville, T.; McLean, H.; Raman, K.; Rodriguez, S.; Spencer, D.; Springer, P.; Wong, J.

2017-08-01

A joint team comprised of Lawrence Livermore National Laboratory (LLNL) and Sandia National Laboratory (SNL) personnel is designing a line-VISAR (Velocity Interferometer System for Any Reflector) for the Sandia Z Machine, Z Line-VISAR. The diagnostic utilizes interferometry to assess current delivery as a function of radius during a magnetically-driven implosion. The Z Line-VISAR system is comprised of the following: a two-leg line-VISAR interferometer, an eight-channel Gated Optical Imager (GOI), and a fifty-meter transport beampath to/from the target of interest. The Z Machine presents unique optomechanical design challenges. The machine utilizes magnetically driven pulsed power to drive a target to elevated temperatures and pressures useful for high energy density science. Shock accelerations exceeding 30g and a strong electromagnetic pulse (EMP) are generated during the shot event as the machine discharges currents of over 25 million amps. Sensitive optical components must be protected from shock loading, and electrical equipment must be adequately shielded from the EMP. The optical design must accommodate temperature and humidity fluctuations in the facility as well as airborne hydrocarbons from the pulsed power components. We will describe the engineering design and concept of operations of the Z Line-VISAR system. Focus will be on optomechanical design.

A comparison of the fracture resistance of three machinable ceramics after thermal and mechanical fatigue.

PubMed

Yang, Rui; Arola, Dwayne; Han, Zhihui; Zhang, Xiuyin

2014-10-01

Mechanical and thermal fatigue may affect ceramic restorations in the oral environment. The purpose of this study was to determine the influence of thermal and mechanical cycling on the fracture load and fracture patterns of 3 machinable ceramics. Seventy-two human third molar teeth were prepared for bonding ceramic specimens of Sirona CEREC Blocs, IPS e.maxCAD, or inCoris ZI meso blocks. The 24 specimens of each ceramic were divided into 4 groups (n=6), which underwent no preloading (control), thermocycling (5°C-55°C, 2000 cycles), mechanical cycling (10(5) cycles, 100 N), and thermocycling (5°C-55°C, 2000 cycles) plus mechanical cycling (10(5) cycles, 100 N). The specimens were subsequently loaded to failure, and both stereomicroscopy and scanning electron microscopy were used to investigate the fracture patterns. The data were analyzed with 2-way ANOVA and the Fisher exact probability test (α=.05). Mechanical and thermal cycling had a significant influence on the critical load to failure of the 3 ceramics. No significant difference was found between mechanical cycling for 10(5) times and thermocycling for 2000 times within the same ceramic. The specimens of inCoris ZI experienced significantly higher fracture loads for all the groups. The fracture patterns of the 3 machinable ceramics showed that failure mainly occurred at the cement-dentin interface. The effects of combined thermal and mechanical cycling on the fracture load of ceramics were more significant than any individual mode of cyclic fatigue. Overall, the inCoris ZI resisted thermal and mechanical fatigue better than the Sirona CEREC and IPS e.maxCAD. Copyright © 2014 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

The seasonal-cycle climate model

NASA Technical Reports Server (NTRS)

Marx, L.; Randall, D. A.

1981-01-01

The seasonal cycle run which will become the control run for the comparison with runs utilizing codes and parameterizations developed by outside investigators is discussed. The climate model currently exists in two parallel versions: one running on the Amdahl and the other running on the CYBER 203. These two versions are as nearly identical as machine capability and the requirement for high speed performance will allow. Developmental changes are made on the Amdahl/CMS version for ease of testing and rapidity of turnaround. The changes are subsequently incorporated into the CYBER 203 version using vectorization techniques where speed improvement can be realized. The 400 day seasonal cycle run serves as a control run for both medium and long range climate forecasts alsensitivity studies.

Targeted Metagenomic Survey of the Fe-Cycling Microbial Community at Chocolate Pots Hot Springs, Yellowstone National Park

NASA Astrophysics Data System (ADS)

Fortney, N. W.; He, S.; Kulkarni, A.; Friedrich, M. W.; Boyd, E. S.; Roden, E. E.

2016-12-01

Chocolate Pots hot springs (CP) is a circumneutral pH, Fe-rich geothermal feature located in Yellowstone National Park. Fe-based metabolic processes are deeply rooted in the tree of life and studying environments like CP are important for us to study to gain insight into ancient Earth ecosystems. Recently identified features on Mars are indicative of near-surface hydrothermal environments and studies of modern Earth systems like CP allow us a glimpse into how life may have potentially arisen on other rocky worlds. Previous enrichment culture studies of the microbial community present at CP identified close relatives of dissimilatory Fe-reducing bacteria (DIRB), including Geobacter metallireducens and Melioribacter roseus. However, the question still remains as to the composition and activity of the microbial community in situ. Here we used 13C stable isotope probing to gain an understanding of the Fe cycling microbial community at CP. Fe-Si oxide sediments collected from near the hot spring vent were incubated under in situ conditions and amended with 13C-acetate or -bicarbonate to target DIRB and Fe-oxidizing bacteria, respectively. 16S rRNA gene amplicon libraries along with shotgun metagenomic libraries were obtained from both sets of incubations. Differential read coverage mapping of metagenomic reads identified a set of taxonomic bins that showed a response to the incubation treatments. We searched the Fe-reducing incubation bins for homologues of genes involved in known extracellular electron transfer (EET) systems such as Pcc and MtrAB, as well as putative porins proximal to multiheme cytochrome c genes. We also searched bins from the Fe-oxidizing incubations for these EET systems in addition to homologues of the outer membrane cytochrome c Cyc2. The Fe-oxidizing bins were also examined for genes encoding RuBisCo to identify potential chemolithoautotrophs. Our targeted metagenomic analysis will identify which organisms are likely to be part of an active Fe

Is whole-culture synchronization biology's 'perpetual-motion machine'?

PubMed

Cooper, Stephen

2004-06-01

Whole-culture or batch synchronization cannot, in theory, produce a synchronized culture because it violates a fundamental law that proposes that no batch treatment can alter the cell-age order of a culture. In analogy with the history of perpetual-motion machines, it is suggested that the study of these whole-culture 'synchronization' methods might lead to an understanding of general biological principles even though these methods cannot be used to study the normal cell cycle.

Internal cycle modeling and environmental assessment of multiple cycle consumer products.

PubMed

Tsiliyannis, C A

2012-01-01

Dynamic annual flow models incorporating consumer discard and usage loss and featuring deterministic and stochastic end-of-cycle (EOC) return by the consumer are developed for reused or remanufactured products (multiple cycle products, MCPs), including fast and slow cycling, short and long-lived products. It is shown that internal flows (reuse and overall consumption) increase proportionally to the dimensionless internal cycle factor (ICF) which is related to environmental impact reduction factors. The combined reuse/recycle (or cycle) rate is shown capable for shortcut, albeit effective, monitoring of environmental performance in terms of waste production, virgin material extraction and manufacturing impacts of all MCPs, a task, which physical variables (lifetime, cycling frequency, mean or total number of return trips) and conventional rates, via which environmental policy has been officially implemented (e.g. recycling rate) cannot accomplish. The cycle rate is shown to be an increasing (hyperbolic) function of ICF. The impact of the stochastic EOC return characteristics on total reuse and consumption flows, as well as on eco-performance, is assessed: symmetric EOC return has a small, positive effect on performance compared to deterministic, while early shifted EOC return is more beneficial. In order to be efficient, environmental policy should set higher minimum reuse targets for higher trippage MCPs. The results may serve for monitoring, flow accounting and comparative eco-assessment of MCPs. They may be useful in identifying reachable and efficient reuse/recycle targets for consumer products and in planning return via appropriate labelling and digital coding for enhancing environmental performance, while satisfying consumer demand. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

Targeted Exome Sequencing of Krebs Cycle Genes Reveals Candidate Cancer-Predisposing Mutations in Pheochromocytomas and Paragangliomas.

PubMed

Remacha, Laura; Comino-Méndez, Iñaki; Richter, Susan; Contreras, Laura; Currás-Freixes, María; Pita, Guillermo; Letón, Rocío; Galarreta, Antonio; Torres-Pérez, Rafael; Honrado, Emiliano; Jiménez, Scherezade; Maestre, Lorena; Moran, Sebastian; Esteller, Manel; Satrústegui, Jorgina; Eisenhofer, Graeme; Robledo, Mercedes; Cascón, Alberto

2017-10-15

Purpose: Mutations in Krebs cycle genes are frequently found in patients with pheochromocytomas/paragangliomas. Disruption of SDH, FH or MDH2 enzymatic activities lead to accumulation of specific metabolites, which give rise to epigenetic changes in the genome that cause a characteristic hypermethylated phenotype. Tumors showing this phenotype, but no alterations in the known predisposing genes, could harbor mutations in other Krebs cycle genes. Experimental Design: We used downregulation and methylation of RBP1, as a marker of a hypermethylation phenotype, to select eleven pheochromocytomas and paragangliomas for targeted exome sequencing of a panel of Krebs cycle-related genes. Methylation profiling, metabolite assessment and additional analyses were also performed in selected cases. Results: One of the 11 tumors was found to carry a known cancer-predisposing somatic mutation in IDH1 A variant in GOT2 , c.357A>T, found in a patient with multiple tumors, was associated with higher tumor mRNA and protein expression levels, increased GOT2 enzymatic activity in lymphoblastic cells, and altered metabolite ratios both in tumors and in GOT2 knockdown HeLa cells transfected with the variant. Array methylation-based analysis uncovered a somatic epigenetic mutation in SDHC in a patient with multiple pheochromocytomas and a gastrointestinal stromal tumor. Finally, a truncating germline IDH3B mutation was found in a patient with a single paraganglioma showing an altered α-ketoglutarate/isocitrate ratio. Conclusions: This study further attests to the relevance of the Krebs cycle in the development of PCC and PGL, and points to a potential role of other metabolic enzymes involved in metabolite exchange between mitochondria and cytosol. Clin Cancer Res; 23(20); 6315-24. ©2017 AACR . ©2017 American Association for Cancer Research.

Machine Learning of Fault Friction

NASA Astrophysics Data System (ADS)

Johnson, P. A.; Rouet-Leduc, B.; Hulbert, C.; Marone, C.; Guyer, R. A.

2017-12-01

We are applying machine learning (ML) techniques to continuous acoustic emission (AE) data from laboratory earthquake experiments. Our goal is to apply explicit ML methods to this acoustic datathe AE in order to infer frictional properties of a laboratory fault. The experiment is a double direct shear apparatus comprised of fault blocks surrounding fault gouge comprised of glass beads or quartz powder. Fault characteristics are recorded, including shear stress, applied load (bulk friction = shear stress/normal load) and shear velocity. The raw acoustic signal is continuously recorded. We rely on explicit decision tree approaches (Random Forest and Gradient Boosted Trees) that allow us to identify important features linked to the fault friction. A training procedure that employs both the AE and the recorded shear stress from the experiment is first conducted. Then, testing takes place on data the algorithm has never seen before, using only the continuous AE signal. We find that these methods provide rich information regarding frictional processes during slip (Rouet-Leduc et al., 2017a; Hulbert et al., 2017). In addition, similar machine learning approaches predict failure times, as well as slip magnitudes in some cases. We find that these methods work for both stick slip and slow slip experiments, for periodic slip and for aperiodic slip. We also derive a fundamental relationship between the AE and the friction describing the frictional behavior of any earthquake slip cycle in a given experiment (Rouet-Leduc et al., 2017b). Our goal is to ultimately scale these approaches to Earth geophysical data to probe fault friction. References Rouet-Leduc, B., C. Hulbert, N. Lubbers, K. Barros, C. Humphreys and P. A. Johnson, Machine learning predicts laboratory earthquakes, in review (2017). https://arxiv.org/abs/1702.05774Rouet-LeDuc, B. et al., Friction Laws Derived From the Acoustic Emissions of a Laboratory Fault by Machine Learning (2017), AGU Fall Meeting Session S025

Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction.

PubMed

Zhang, Jie; Xiao, Wendong; Zhang, Sen; Huang, Shoudong

2017-04-17

Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach.

Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction

PubMed Central

Zhang, Jie; Xiao, Wendong; Zhang, Sen; Huang, Shoudong

2017-01-01

Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach. PMID:28420187

A novel spiroindoline targets cell cycle and migration via modulation of microtubule cytoskeleton.

PubMed

Kumar, Naveen; Hati, Santanu; Munshi, Parthapratim; Sen, Subhabrata; Sehrawat, Seema; Singh, Shailja

2017-05-01

Natural product-inspired libraries of molecules with diverse architectures have evolved as one of the most useful tools for discovering lead molecules for drug discovery. In comparison to conventional combinatorial libraries, these molecules have been inferred to perform better in phenotypic screening against complicated targets. Diversity-oriented synthesis (DOS) is a forward directional strategy to access such multifaceted library of molecules. From a successful DOS campaign of a natural product-inspired library, recently a small molecule with spiroindoline motif was identified as a potent anti-breast cancer compound. Herein we report the subcellular studies performed for this molecule on breast cancer cells. Our investigation revealed that it repositions microtubule cytoskeleton and displaces AKAP9 located at the microtubule organization centre. DNA ladder assay and cell cycle experiments further established the molecule as an apoptotic agent. This work further substantiated the amalgamation of DOS-phenotypic screening-sub-cellular studies as a consolidated blueprint for the discovery of potential pharmaceutical drug candidates.

A comparative study of machine learning models for ethnicity classification

NASA Astrophysics Data System (ADS)

Trivedi, Advait; Bessie Amali, D. Geraldine

2017-11-01

This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

A target-triggered strand displacement reaction cycle: the design and application in adenosine triphosphate sensing.

PubMed

Cheng, Sheng; Zheng, Bin; Wang, Mozhen; Lam, Michael Hon-Wah; Ge, Xuewu

2014-02-01

A strand displacement reaction (SDR) system that runs solely on oligonucleotides has been developed for the amplification detection of adenosine triphosphate (ATP). It involves a target-induced SDR and an entropy-driven catalytic cycle of two SDRs with five oligonucleotides, denoted as substrate, fuel, catalyst, C-1, and C-2. Catalyst, released from the ATP aptamer-catalyst duplex by ATP molecule, catalyzes the SDRs to finally form the substrate-fuel duplex. All of the intermediates in the catalytic SDR processes have been identified by polyacrylamide gel electrophoresis (PAGE) analysis. The introduction of ATP into the SDR system will induce the ATP aptamer to form G-quadruplex conformation so as to release catalyst and trigger the SDR cycle. When the substrate and C-2 oligonucleotides were labeled with a carboxyfluorescein (FAM) fluorophore and a 4-([4-(dimethylamino)phenyl]azo)benzoic acid (DABCYL) quencher, this SDR catalytic system exhibited a "turn-on" response for ATP. The condition for detecting ATP, such as Mg²⁺ concentration, has been optimized to afford a detection limit of 20 nM. This work provides an enzyme-free biosensing strategy and has potential application in aptamer-based biosensing. Copyright © 2013 Elsevier Inc. All rights reserved.

Closed Brayton Cycle (CBC) Power Generation from an Electric Systems Perspective

NASA Astrophysics Data System (ADS)

Halsey, David G.; Fox, David A.

2006-01-01

Several forms of closed cycle heat engines exist to produce electrical energy suitable for space exploration or planetary surface applications. These engines include Stirling and Closed Brayton Cycle (CBC). Of these two, CBC has often been cited as providing the best balance of mass and efficiency for deep space or planetary power systems. Combined with an alternator on the same shaft, the hermetically sealed system provides the potential for long life and reliable operation. There is also a list of choices for the type of alternator. Choices include wound rotor machines, induction machines, switched reluctance machines, and permanent magnet generators (PMGs). In trades involving size, mass and efficiency the PMG is a favorable solution. This paper will discuss the consequences of using a CBC-PMG source for an electrical power system, and the system parameters that must be defined and controlled to provide a stable, useful power source. Considerations of voltage, frequency (including DC), and power quality will be discussed. Load interactions and constraints for various power types will also be addressed. Control of the CBC-PMG system during steady state operation and startup is also a factor.s

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

Toward polarized antiprotons: Machine development for spin-filtering experiments

NASA Astrophysics Data System (ADS)

Weidemann, C.; Rathmann, F.; Stein, H. J.; Lorentz, B.; Bagdasarian, Z.; Barion, L.; Barsov, S.; Bechstedt, U.; Bertelli, S.; Chiladze, D.; Ciullo, G.; Contalbrigo, M.; Dymov, S.; Engels, R.; Gaisser, M.; Gebel, R.; Goslawski, P.; Grigoriev, K.; Guidoboni, G.; Kacharava, A.; Kamerdzhiev, V.; Khoukaz, A.; Kulikov, A.; Lehrach, A.; Lenisa, P.; Lomidze, N.; Macharashvili, G.; Maier, R.; Martin, S.; Mchedlishvili, D.; Meyer, H. O.; Merzliakov, S.; Mielke, M.; Mikirtychiants, M.; Mikirtychiants, S.; Nass, A.; Nikolaev, N. N.; Oellers, D.; Papenbrock, M.; Pesce, A.; Prasuhn, D.; Retzlaff, M.; Schleichert, R.; Schröer, D.; Seyfarth, H.; Soltner, H.; Statera, M.; Steffens, E.; Stockhorst, H.; Ströher, H.; Tabidze, M.; Tagliente, G.; Engblom, P. Thörngren; Trusov, S.; Valdau, Yu.; Vasiliev, A.; Wüstner, P.

2015-02-01

The paper describes the commissioning of the experimental equipment and the machine studies required for the first spin-filtering experiment with protons at a beam kinetic energy of 49.3 MeV in COSY. The implementation of a low-β insertion made it possible to achieve beam lifetimes of τb=8000 s in the presence of a dense polarized hydrogen storage-cell target of areal density dt=(5.5 ±0.2 )×1 013 atoms /cm2 . The developed techniques can be directly applied to antiproton machines and allow the determination of the spin-dependent p ¯p cross sections via spin filtering.

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.

Methods, systems and apparatus for adjusting duty cycle of pulse width modulated (PWM) waveforms

DOEpatents

Gallegos-Lopez, Gabriel; Kinoshita, Michael H; Ransom, Ray M; Perisic, Milun

2013-05-21

Embodiments of the present invention relate to methods, systems and apparatus for controlling operation of a multi-phase machine in a vector controlled motor drive system when the multi-phase machine operates in an overmodulation region. The disclosed embodiments provide a mechanism for adjusting a duty cycle of PWM waveforms so that the correct phase voltage command signals are applied at the angle transitions. This can reduce variations/errors in the phase voltage command signals applied to the multi-phase machine so that phase current may be properly regulated thus reducing current/torque oscillation, which can in turn improve machine efficiency and performance, as well as utilization of the DC voltage source.

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.

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).

Current Status of an Organic Rankine Cycle Engine Development Program

NASA Technical Reports Server (NTRS)

Barber, R. E.

1984-01-01

The steps taken to achieve improved bearing life in the organic Rankine cycle (ORC) engine being developed for use on solar parabolic dishes are presented. A summary of test results is given. Dynamic tests on the machine shaft and rotors of the ORC engine are also discussed.

Targeting Unknowns Just Underfoot: Microbial Ecology and Community Genomics of C Cycling in Soil Informed and Enabled with DNA-SIP

NASA Astrophysics Data System (ADS)

Pepe-Ranney, C. P.; Campbell, A.; Buckley, D. H.

2015-12-01

Microorganisms drive biogeochemical cycles and because soil is a large global carbon (C) reservoir (soil contains more C than plants and the atmosphere combined), soil microorganisms are important players in the global C-cycle. Frustratingly, however, many soil microorganisms resist cultivation and soil communities are astoundingly complex. This makes soil microbiology difficult to study and without a solid understanding of soil microbial ecology, models of soil C feedbacks to climate change are under-informed. Stable isotope probing (SIP) is a useful approach for establishing identity-function connections in microbial communities but has been challenging to employ in soil due to the inadequate resolution of microbial community fingerprinting techniques. High throughput DNA sequencing improves SIP resolving power transforming it into a powerful tool for studying the soil C cycle. We conducted a DNA-SIP experiment to track flow of xylose-C, a labile component of plant biomass, and cellulose-C, the most abundant global biopolymer, through a soil microbial community. We could track 13C into microbial DNA even when added 13C amounted to less than 5% of native C and found Spartobacteria, Chloroflexi, and Planctomycetes taxa were among those that assimilated 13C cellulose. These lineages are cosmopolitan in soil but little is known of their ecophysiology. By profiling SSU rRNA genes across entire DNA-SIP density gradients, we assessed relative DNA atom % 13C per taxon in 13C treatments and found cellulose degraders exhibited signal consistent with a specialist lifestyle with respect to C preference. Further, DNA-SIP enriches DNA of targeted microorganisms (Verrucomicrobia cellulose degraders were enriched by nearly two orders of magnitude) and this enriched DNA can serve as template for community genomics. We produced draft genomes from soil cellulose degraders including microorganisms belonging to Verrucomicrobia, Chloroflexi, and Planctomycetes from SIP enriched DNA

Pricing and availability intervention in vending machines at four bus garages.

PubMed

French, Simone A; Hannan, Peter J; Harnack, Lisa J; Mitchell, Nathan R; Toomey, Traci L; Gerlach, Anne

2010-01-01

To evaluate the effects of lowering prices and increasing availability on sales of healthy foods and beverages from 33 vending machines in 4 bus garages as part of a multicomponent worksite obesity prevention intervention. Availability of healthy items was increased to 50% and prices were lowered at least 10% in the vending machines in two metropolitan bus garages for an 18-month period. Two control garages offered vending choices at usual availability and prices. Sales data were collected monthly from each of the vending machines at the four garages. Increases in availability to 50% and price reductions of an average of 31% resulted in 10% to 42% higher sales of the healthy items. Employees were mostly price responsive for snack purchases. Greater availability and lower prices on targeted food and beverage items from vending machines was associated with greater purchases of these items over an 18-month period. Efforts to promote healthful food purchases in worksite settings should incorporate these two strategies.

Learning algorithms for human-machine interfaces.

PubMed

Danziger, Zachary; Fishbach, Alon; Mussa-Ivaldi, Ferdinando A

2009-05-01

The goal of this study is to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user and the controlled device. To evaluate these algorithms, we have developed a simple experimental framework. Subjects wear an instrumented data glove that records finger motions. The high-dimensional glove signals remotely control the joint angles of a simulated planar two-link arm on a computer screen, which is used to acquire targets. A machine learning algorithm was applied to adaptively change the transformation between finger motion and the simulated robot arm. This algorithm was either LMS gradient descent or the Moore-Penrose (MP) pseudoinverse transformation. Both algorithms modified the glove-to-joint angle map so as to reduce the endpoint errors measured in past performance. The MP group performed worse than the control group (subjects not exposed to any machine learning), while the LMS group outperformed the control subjects. However, the LMS subjects failed to achieve better generalization than the control subjects, and after extensive training converged to the same level of performance as the control subjects. These results highlight the limitations of coadaptive learning using only endpoint error reduction.

Learning Algorithms for Human–Machine Interfaces

PubMed Central

Fishbach, Alon; Mussa-Ivaldi, Ferdinando A.

2012-01-01

The goal of this study is to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user and the controlled device. To evaluate these algorithms, we have developed a simple experimental framework. Subjects wear an instrumented data glove that records finger motions. The high-dimensional glove signals remotely control the joint angles of a simulated planar two-link arm on a computer screen, which is used to acquire targets. A machine learning algorithm was applied to adaptively change the transformation between finger motion and the simulated robot arm. This algorithm was either LMS gradient descent or the Moore–Penrose (MP) pseudoinverse transformation. Both algorithms modified the glove-to-joint angle map so as to reduce the endpoint errors measured in past performance. The MP group performed worse than the control group (subjects not exposed to any machine learning), while the LMS group outperformed the control subjects. However, the LMS subjects failed to achieve better generalization than the control subjects, and after extensive training converged to the same level of performance as the control subjects. These results highlight the limitations of coadaptive learning using only endpoint error reduction. PMID:19203886

Cell division cycle 45 promotes papillary thyroid cancer progression via regulating cell cycle.

PubMed

Sun, Jing; Shi, Run; Zhao, Sha; Li, Xiaona; Lu, Shan; Bu, Hemei; Ma, Xianghua

2017-05-01

Cell division cycle 45 was reported to be overexpressed in some cancer-derived cell lines and was predicted to be a candidate oncogene in cervical cancer. However, the clinical and biological significance of cell division cycle 45 in papillary thyroid cancer has never been investigated. We determined the expression level and clinical significance of cell division cycle 45 using The Cancer Genome Atlas, quantitative real-time polymerase chain reaction, and immunohistochemistry. A great upregulation of cell division cycle 45 was observed in papillary thyroid cancer tissues compared with adjacent normal tissues. Furthermore, overexpression of cell division cycle 45 positively correlates with more advanced clinical characteristics. Silence of cell division cycle 45 suppressed proliferation of papillary thyroid cancer cells via G1-phase arrest and inducing apoptosis. The oncogenic activity of cell division cycle 45 was also confirmed in vivo. In conclusion, cell division cycle 45 may serve as a novel biomarker and a potential therapeutic target for papillary thyroid cancer.

Camouflage target reconnaissance based on hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Hua, Wenshen; Guo, Tong; Liu, Xun

2015-08-01

Efficient camouflaged target reconnaissance technology makes great influence on modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. Hyperspectral target detection and classification technology are utilized to achieve single class and multi-class camouflaged targets reconnaissance respectively. Constrained energy minimization (CEM), a widely used algorithm in hyperspectral target detection, is employed to achieve one class camouflage target reconnaissance. Then, support vector machine (SVM), a classification method, is proposed to achieve multi-class camouflage target reconnaissance. Experiments have been conducted to demonstrate the efficiency of the proposed method.

Bean Soup Translation: Flexible, Linguistically-Motivated Syntax for Machine Translation

ERIC Educational Resources Information Center

Mehay, Dennis Nolan

2012-01-01

Machine translation (MT) systems attempt to translate texts from one language into another by translating words from a "source language" and rearranging them into fluent utterances in a "target language." When the two languages organize concepts in very different ways, knowledge of their general sentence structure, or…

Machine processing of ERTS and ground truth data

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator); Peacock, K.

1973-01-01

The author has identified the following significant results. Results achieved by ERTS-Atmospheric Experiment PR303, whose objective is to establish a radiometric calibration technique, are reported. This technique, which determines and removes solar and atmospheric parameters that degrade the radiometric fidelity of ERTS-1 data, transforms the ERTS-1 sensor radiance measurements to absolute target reflectance signatures. A radiant power measuring instrument and its use in determining atmospheric parameters needed for ground truth are discussed. The procedures used and results achieved in machine processing ERTS-1 computer -compatible tapes and atmospheric parameters to obtain target reflectance are reviewed.

Analysis of Availability of Longwall-Shearer Based On Its Working Cycle

NASA Astrophysics Data System (ADS)

Brodny, Jaroslaw; Tutak, Magdalena

2017-12-01

Effective use of any type of devices, particularly machines has very significant meaning for mining enterprises. High costs of their purchase and tenancy cause that these enterprises tend to the best use of own technical potential. However, characteristics of mining production causes that this process not always proceeds without interferences. Practical experiences show that determination of objective measure of utilization of machine in mining company is not simple. In the paper methodology allowing to solve this problem is presented. Longwall-shearer, as the most important machine between longwall mechanical complex. Also it was assumed that the most significant meaning for determination of effectiveness of longwall-shearer has its availability, i.e. its effective time of work related to standard time. Such an approach is conforming to OEE model. However, specification of mining branch causes that determined availability do not give actual state of longwall-shearer’s operation. Therefore, this availability was related to the operation cycle of longwall-shearer. In presented example a longwall-shearer works in unidirectional cycle of mining. It causes that in one direction longwall-shearer mines, moving with operating velocity, and in other direction it does not mine and moves with manoeuvre velocity. Such defined working cycle became a base for determinate availability of longwall-shearer. Using indications of industrial automatic system for each of working shift there were determined number of cycles of longwall-shearer and availability of each one. Accepted of such way of determination of availability of longwall-shearer enabled to perform accurate analysis of losses of its availability. These losses result from non-planned shutdowns of longwall-shearer. Thanks to performed analysis based on the operating cycle of longwall-shearer time of its standstill for particular phase of cycle were determined. Presented methodology of determination of longwall

Cell Cycle Inhibition To Treat Sleeping Sickness.

PubMed

Epting, Conrad L; Emmer, Brian T; Du, Nga Y; Taylor, Joann M; Makanji, Ming Y; Olson, Cheryl L; Engman, David M

2017-09-19

African trypanosomiasis is caused by infection with the protozoan parasite Trypanosoma brucei During infection, this pathogen divides rapidly to high density in the bloodstream of its mammalian host in a manner similar to that of leukemia. Like all eukaryotes, T. brucei has a cell cycle involving the de novo synthesis of DNA regulated by ribonucleotide reductase (RNR), which catalyzes the conversion of ribonucleotides into their deoxy form. As an essential enzyme for the cell cycle, RNR is a common target for cancer chemotherapy. We hypothesized that inhibition of RNR by genetic or pharmacological means would impair parasite growth in vitro and prolong the survival of infected animals. Our results demonstrate that RNR inhibition is highly effective in suppressing parasite growth both in vitro and in vivo These results support drug discovery efforts targeting the cell cycle, not only for African trypanosomiasis but possibly also for other infections by eukaryotic pathogens. IMPORTANCE The development of drugs to treat infections with eukaryotic pathogens is challenging because many key virulence factors have closely related homologues in humans. Drug toxicity greatly limits these development efforts. For pathogens that replicate at a high rate, especially in the blood, an alternative approach is to target the cell cycle directly, much as is done to treat some hematologic malignancies. The results presented here indicate that targeting the cell cycle via inhibition of ribonucleotide reductase is effective at killing trypanosomes and prolonging the survival of infected animals. Copyright © 2017 Epting et al.

Object recognition of ladar with support vector machine

NASA Astrophysics Data System (ADS)

Sun, Jian-Feng; Li, Qi; Wang, Qi

2005-01-01

Intensity, range and Doppler images can be obtained by using laser radar. Laser radar can detect much more object information than other detecting sensor, such as passive infrared imaging and synthetic aperture radar (SAR), so it is well suited as the sensor of object recognition. Traditional method of laser radar object recognition is extracting target features, which can be influenced by noise. In this paper, a laser radar recognition method-Support Vector Machine is introduced. Support Vector Machine (SVM) is a new hotspot of recognition research after neural network. It has well performance on digital written and face recognition. Two series experiments about SVM designed for preprocessing and non-preprocessing samples are performed by real laser radar images, and the experiments results are compared.

Brainstorming: weighted voting prediction of inhibitors for protein targets.

PubMed

Plewczynski, Dariusz

2011-09-01

The "Brainstorming" approach presented in this paper is a weighted voting method that can improve the quality of predictions generated by several machine learning (ML) methods. First, an ensemble of heterogeneous ML algorithms is trained on available experimental data, then all solutions are gathered and a consensus is built between them. The final prediction is performed using a voting procedure, whereby the vote of each method is weighted according to a quality coefficient calculated using multivariable linear regression (MLR). The MLR optimization procedure is very fast, therefore no additional computational cost is introduced by using this jury approach. Here, brainstorming is applied to selecting actives from large collections of compounds relating to five diverse biological targets of medicinal interest, namely HIV-reverse transcriptase, cyclooxygenase-2, dihydrofolate reductase, estrogen receptor, and thrombin. The MDL Drug Data Report (MDDR) database was used for selecting known inhibitors for these protein targets, and experimental data was then used to train a set of machine learning methods. The benchmark dataset (available at http://bio.icm.edu.pl/∼darman/chemoinfo/benchmark.tar.gz ) can be used for further testing of various clustering and machine learning methods when predicting the biological activity of compounds. Depending on the protein target, the overall recall value is raised by at least 20% in comparison to any single machine learning method (including ensemble methods like random forest) and unweighted simple majority voting procedures.

Quantitative regulation of histone variant H2A.Z during cell cycle by ubiquitin proteasome system and SUMO-targeted ubiquitin ligases.

PubMed

Takahashi, Daisuke; Orihara, Yuki; Kitagawa, Saho; Kusakabe, Masayuki; Shintani, Takahiro; Oma, Yukako; Harata, Masahiko

2017-08-01

Quantitative control of histones and histone variants during cell cycle is relevant to their epigenetic functions. We found that the level of yeast histone variant H2A.Z in the G2/M-phase is actively kept low by the ubiquitin proteasome system and SUMO-targeted ubiquitin ligases. Overexpression of H2A.Z induced defects in mitotic progression, suggesting functional importance of this quantitative control.

A Machine LearningFramework to Forecast Wave Conditions

NASA Astrophysics Data System (ADS)

Zhang, Y.; James, S. C.; O'Donncha, F.

2017-12-01

Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in

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

Synaptic Vesicle-Recycling Machinery Components as Potential Therapeutic Targets

PubMed Central

Li, Ying C.

2017-01-01

Presynaptic nerve terminals are highly specialized vesicle-trafficking machines. Neurotransmitter release from these terminals is sustained by constant local recycling of synaptic vesicles independent from the neuronal cell body. This independence places significant constraints on maintenance of synaptic protein complexes and scaffolds. Key events during the synaptic vesicle cycle—such as exocytosis and endocytosis—require formation and disassembly of protein complexes. This extremely dynamic environment poses unique challenges for proteostasis at synaptic terminals. Therefore, it is not surprising that subtle alterations in synaptic vesicle cycle-associated proteins directly or indirectly contribute to pathophysiology seen in several neurologic and psychiatric diseases. In contrast to the increasing number of examples in which presynaptic dysfunction causes neurologic symptoms or cognitive deficits associated with multiple brain disorders, synaptic vesicle-recycling machinery remains an underexplored drug target. In addition, irrespective of the involvement of presynaptic function in the disease process, presynaptic machinery may also prove to be a viable therapeutic target because subtle alterations in the neurotransmitter release may counter disease mechanisms, correct, or compensate for synaptic communication deficits without the need to interfere with postsynaptic receptor signaling. In this article, we will overview critical properties of presynaptic release machinery to help elucidate novel presynaptic avenues for the development of therapeutic strategies against neurologic and neuropsychiatric disorders. PMID:28265000

MLACP: machine-learning-based prediction of anticancer peptides

PubMed Central

Manavalan, Balachandran; Basith, Shaherin; Shin, Tae Hwan; Choi, Sun; Kim, Myeong Ok; Lee, Gwang

2017-01-01

Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html. PMID:29100375

Micro Fluidic Channel Machining on Fused Silica Glass Using Powder Blasting

PubMed Central

Jang, Ho-Su; Cho, Myeong-Woo; Park, Dong-Sam

2008-01-01

In this study, micro fluid channels are machined on fused silica glass via powder blasting, a mechanical etching process, and the machining characteristics of the channels are experimentally evaluated. In the process, material removal is performed by the collision of micro abrasives injected by highly compressed air on to the target surface. This approach can be characterized as an integration of brittle mode machining based on micro crack propagation. Fused silica glass, a high purity synthetic amorphous silicon dioxide, is selected as a workpiece material. It has a very low thermal expansion coefficient and excellent optical qualities and exceptional transmittance over a wide spectral range, especially in the ultraviolet range. The powder blasting process parameters affecting the machined results are injection pressure, abrasive particle size and density, stand-off distance, number of nozzle scanning, and shape/size of the required patterns. In this study, the influence of the number of nozzle scanning, abrasive particle size, and pattern size on the formation of micro channels is investigated. Machined shapes and surface roughness are measured using a 3-dimensional vision profiler and the results are discussed. PMID:27879730

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.

PubMed

Sutphin, George L; Mahoney, J Matthew; Sheppard, Keith; Walton, David O; Korstanje, Ron

2016-11-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

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…

A new class of high-G and long-duration shock testing machines

NASA Astrophysics Data System (ADS)

Rastegar, Jahangir

2018-03-01

Currently available methods and systems for testing components for survival and performance under shock loading suffer from several shortcomings for use to simulate high-G acceleration events with relatively long duration. Such events include most munitions firing and target impact, vehicular accidents, drops from relatively high heights, air drops, impact between machine components, and other similar events. In this paper, a new class of shock testing machines are presented that can be used to subject components to be tested to high-G acceleration pulses of prescribed amplitudes and relatively long durations. The machines provide for highly repeatable testing of components. The components are mounted on an open platform for ease of instrumentation and video recording of their dynamic behavior during shock loading tests.

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

Microcompartments and Protein Machines in Prokaryotes

PubMed Central

Saier, Milton H.

2013-01-01

The prokaryotic cell was once thought of as a “bag of enzymes” with little or no intracellular compartmentalization. In this view, most reactions essential for life occurred as a consequence of random molecular collisions involving substrates, cofactors and cytoplasmic enzymes. Our current conception of a prokaryote is far from this view. We now consider a bacterium or an archaeon as a highly structured, non-random collection of functional membrane-embedded and proteinaceous molecular machines, each of which serves a specialized function. In this article we shall present an overview of such microcompartments including (i) the bacterial cytoskeleton and the apparati allowing DNA segregation during cells division, (ii) energy transduction apparati involving light-driven proton pumping and ion gradient-driven ATP synthesis, (iii) prokaryotic motility and taxis machines that mediate cell movements in response to gradients of chemicals and physical forces, (iv) machines of protein folding, secretion and degradation, (v) metabolasomes carrying out specific chemical reactions, (vi) 24 hour clocks allowing bacteria to coordinate their metabolic activities with the daily solar cycle and (vii) proteinaceous membrane compartmentalized structures such as sulfur granules and gas vacuoles. Membrane-bounded prokaryotic organelles were considered in a recent JMMB written symposium concerned with membraneous compartmentalization in bacteria [Saier and Bogdanov, 2013]. By contrast, in this symposium, we focus on proteinaceous microcompartments. These two symposia, taken together, provide the interested reader with an objective view of the remarkable complexity of what was once thought of as a simple non-compartmentalized cell. PMID:23920489

Microcompartments and protein machines in prokaryotes.

PubMed

Saier, Milton H

2013-01-01

The prokaryotic cell was once thought of as a 'bag of enzymes' with little or no intracellular compartmentalization. In this view, most reactions essential for life occurred as a consequence of random molecular collisions involving substrates, cofactors and cytoplasmic enzymes. Our current conception of a prokaryote is far from this view. We now consider a bacterium or an archaeon as a highly structured, nonrandom collection of functional membrane-embedded and proteinaceous molecular machines, each of which serves a specialized function. In this article we shall present an overview of such microcompartments including (1) the bacterial cytoskeleton and the apparati allowing DNA segregation during cell division; (2) energy transduction apparati involving light-driven proton pumping and ion gradient-driven ATP synthesis; (3) prokaryotic motility and taxis machines that mediate cell movements in response to gradients of chemicals and physical forces; (4) machines of protein folding, secretion and degradation; (5) metabolosomes carrying out specific chemical reactions; (6) 24-hour clocks allowing bacteria to coordinate their metabolic activities with the daily solar cycle, and (7) proteinaceous membrane compartmentalized structures such as sulfur granules and gas vacuoles. Membrane-bound prokaryotic organelles were considered in a recent Journal of Molecular Microbiology and Biotechnology written symposium concerned with membranous compartmentalization in bacteria [J Mol Microbiol Biotechnol 2013;23:1-192]. By contrast, in this symposium, we focus on proteinaceous microcompartments. These two symposia, taken together, provide the interested reader with an objective view of the remarkable complexity of what was once thought of as a simple noncompartmentalized cell. Copyright © 2013 S. Karger AG, Basel.

Life cycle costing as a decision making tool for technology acquisition in radio-diagnosis

PubMed Central

Chakravarty, Abhijit; Debnath, Jyotindu

2014-01-01

Background Life cycle costing analysis is an emerging conceptual tool to validate capital investment in healthcare. Methods A preliminary study was done to analyze the long-term cost impact of acquiring a new 3 T MRI system when compared to technological upgradation of the existing 1.5 T MRI system with a view to evolve a decision matrix for correct investment planning and technology management. Operating costing method was utilized to estimate cost per unit MRI scan, costing inputs were considered for the existing 1.5 T and the proposed 3 T machine. Cost for each expected year in the life span of both 1.5 T and 3 T MRI scan options were then discounted to its Net Present Value. Net Present Value thus calculated for both the alternative options of 1.5 T and 3 T MRI machine was charted along with various intangible but critical Figures of Merit (FOM) to create a decision matrix for capital investment planning. Result Considering all fixed and variable costs contributing towards assumed operation, unit cost per MRI procedure was found to be Rs. 4244.58 for the 1.5 T upgrade and Rs. 6059.37 for the new 3 T MRI machine. Life Cycle Cost Analysis of the proposed 1.5 T upgrade and new 3 T machine showed a Net Present Value of Rs. 42,148,587.80 and Rs. 27,587,842.38 respectively. Conclusion The utility of life cycle costing as a strategic decision making tool towards evaluating alternative options for capital investment planning in health care environment is reiterated. PMID:25609862

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

An easy-to-use calculating machine to simulate steady state and non-steady-state preparative separations by multiple dual mode counter-current chromatography with semi-continuous loading of feed mixtures.

PubMed

Kostanyan, Artak E; Shishilov, Oleg N

2018-06-01

Multiple dual mode counter-current chromatography (MDM CCC) separation processes with semi-continuous large sample loading consist of a succession of two counter-current steps: with "x" phase (first step) and "y" phase (second step) flow periods. A feed mixture dissolved in the "x" phase is continuously loaded into a CCC machine at the beginning of the first step of each cycle over a constant time with the volumetric rate equal to the flow rate of the pure "x" phase. An easy-to-use calculating machine is developed to simulate the chromatograms and the amounts of solutes eluted with the phases at each cycle for steady-state (the duration of the flow periods of the phases is kept constant for all the cycles) and non-steady-state (with variable duration of alternating phase elution steps) separations. Using the calculating machine, the separation of mixtures containing up to five components can be simulated and designed. Examples of the application of the calculating machine for the simulation of MDM CCC processes are discussed. Copyright © 2018 Elsevier B.V. All rights reserved.

Internal adaptation, marginal accuracy and microleakage of a pressable versus a machinable ceramic laminate veneers.

PubMed

Aboushelib, Moustafa Nabil; Elmahy, Waleed AbdelMeguid; Ghazy, Mohammed Hamed

2012-08-01

The aim of this study was to evaluate the internal adaptation and marginal properties of ceramic laminate veneers fabricated using pressable and machinable CAD/CAM techniques. 40 ceramic laminate veneers were fabricated by either milling ceramic blocks using a CAD/CAM system (group 1 n=20) or press-on veneering using lost wax technique (group 2 n=20). The veneers were acid etched using hydrofluoric acid, silanated, and cemented on their corresponding prepared teeth. All specimens were stored under water (37 °C) for 60 days, then received thermocycling (15,000 cycles between 5 and 55 °C and dwell time of 90 s) followed by cyclic loading (100,000 cycles between 50 and 100 N) before immersion in basic fuchsine dye for 24 h. Half of the specimens in each group were sectioned in labio-lingual direction and the rest were horizontally sectioned using precision cutting machine (n=10). Dye penetration, internal cement film thickness, and vertical and horizontal marginal gaps at the incisal and cervical regions were measured (α=0.05). Pressable ceramic veneers demonstrated significantly lower (F=8.916, P<0.005) vertical and horizontal marginal gaps at the cervical and incisal margins and lower cement film thickness (F=50.921, P<0.001) compared to machinable ceramic veneers. The inferior marginal properties of machinable ceramic veneers were associated with significantly higher microleakage values. Pressable ceramic laminate veneers produced higher marginal adaptation, homogenous and thinner cement film thickness, and improved resistance to microleakage compared to machinable ceramic veneers. The manufacturing process influences internal and marginal fit of ceramic veneers. Therefore, dentist and laboratory technicians should choose a manufacturing process with careful consideration. Copyright © 2012 Elsevier Ltd. All rights reserved.

Microstructural and hardness changes in aluminum alloy Al-7075: Correlating machining and equal channel angular pressing

NASA Astrophysics Data System (ADS)

Imbrogno, Stano; Segebade, Eric; Fellmeth, Andreas; Gerstenmeyer, Michael; Zanger, Frederik; Schulze, Volker; Umbrello, Domenico

2017-10-01

Recently, the study and understanding of surface integrity of various materials after machining is becoming one of the interpretative keys to quantify a product's quality and life cycle performance. The possibility to provide fundamental details about the mechanical response and the behavior of the affected material layers caused by thermo-mechanical loads resulting from machining operations can help the designer to produce parts with superior quality. The aim of this work is to study the experimental outcomes obtained from orthogonal cutting tests and a Severe Plastic Deformation (SPD) process known as Equal Channel Angular Pressing (ECAP) in order to find possible links regarding induced microstructural and hardness changes between machined surface layer and SPD-bulk material for Al-7075. This scientific investigation aims to establish the basis for an innovative method to study and quantify metallurgical phenomena that occur beneath the machined surface of bulk material.

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.

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.

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

PubMed

Howard, Rebecca; Rattray, Magnus; Prosperi, Mattia; Custovic, Adnan

2015-07-01

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as 'asthma endotypes'. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies.

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.

Pricing and Availability Intervention in Vending Machines at Four Bus Garages

PubMed Central

Hannan, Peter J; Harnack, Lisa J; Mitchell, Nathan R; Toomey, Traci L; Gerlach, Anne

2009-01-01

Objective To evaluate the effects of lowering prices and increasing availability on sales of healthy foods and beverages from 33 vending machines in four bus garages as part of a multi-component worksite obesity prevention intervention. Methods Availability of healthy items was increased to 50% and prices were lowered at least 10% in the vending machines in two metropolitan bus garages for an 18-month period. Two control garages offered vending choices at usual availability and prices. Sales data were collected monthly from each of the vending machines at the four garages. Results Increases in availability to 50% and price reductions of an average of 31% resulted in 10-42% higher sales of the healthy items. Employees were most price-responsive for snack purchases. Conclusions Greater availability and lower prices on targeted food and beverage items from vending machines was associated with greater purchases of these items over an eighteen-month period. Efforts to promote healthful food purchases in worksite settings should incorporate these two strategies. PMID:20061884

Relative optical navigation around small bodies via Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Law, Andrew M.

To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure Optimization.

PubMed

Podlewska, Sabina; Czarnecki, Wojciech M; Kafel, Rafał; Bojarski, Andrzej J

2017-02-27

The growing computational abilities of various tools that are applied in the broadly understood field of computer-aided drug design have led to the extreme popularity of virtual screening in the search for new biologically active compounds. Most often, the source of such molecules consists of commercially available compound databases, but they can also be searched for within the libraries of structures generated in silico from existing ligands. Various computational combinatorial approaches are based solely on the chemical structure of compounds, using different types of substitutions for new molecules formation. In this study, the starting point for combinatorial library generation was the fingerprint referring to the optimal substructural composition in terms of the activity toward a considered target, which was obtained using a machine learning-based optimization procedure. The systematic enumeration of all possible connections between preferred substructures resulted in the formation of target-focused libraries of new potential ligands. The compounds were initially assessed by machine learning methods using a hashed fingerprint to represent molecules; the distribution of their physicochemical properties was also investigated, as well as their synthetic accessibility. The examination of various fingerprints and machine learning algorithms indicated that the Klekota-Roth fingerprint and support vector machine were an optimal combination for such experiments. This study was performed for 8 protein targets, and the obtained compound sets and their characterization are publically available at http://skandal.if-pan.krakow.pl/comb_lib/ .

Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I.

PubMed

Ekins, Sean; Godbole, Adwait Anand; Kéri, György; Orfi, Lászlo; Pato, János; Bhat, Rajeshwari Subray; Verma, Rinkee; Bradley, Erin K; Nagaraja, Valakunja

2017-03-01

There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target in this regard. However, it suffers from a shortage of known inhibitors. We have previously used computational approaches such as homology modeling and docking to propose 38 FDA approved drugs for testing and identified several active molecules. To follow on from this, we now describe the in vitro testing of a library of 639 compounds. These data were used to create machine learning models for Mttopo I which were further validated. The combined Mttopo I Bayesian model had a 5 fold cross validation receiver operator characteristic of 0.74 and sensitivity, specificity and concordance values above 0.76 and was used to select commercially available compounds for testing in vitro. The recently described crystal structure of Mttopo I was also compared with the previously described homology model and then used to dock the Mttopo I actives norclomipramine and imipramine. In summary, we describe our efforts to identify small molecule inhibitors of Mttopo I using a combination of machine learning modeling and docking studies in conjunction with screening of the selected molecules for enzyme inhibition. We demonstrate the experimental inhibition of Mttopo I by small molecule inhibitors and show that the enzyme can be readily targeted for lead molecule development. Copyright © 2017 Elsevier Ltd. All rights reserved.

Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.

PubMed

Torkzaban, Bahareh; Kayvanjoo, Amir Hossein; Ardalan, Arman; Mousavi, Soraya; Mariotti, Roberto; Baldoni, Luciana; Ebrahimie, Esmaeil; Ebrahimi, Mansour; Hosseini-Mazinani, Mehdi

2015-01-01

Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two '4-targeted' and '16-targeted' experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.

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.

MiR-188 Inhibits Glioma Cell Proliferation and Cell Cycle Progression through Targeting ß-catenin.

PubMed

Li, Nan; Shi, Hangyu; Zhang, Lu; Li, Xu; Gao, Lu; Zhang, Gang; Shi, Yongqiang; Guo, Shiwen

2017-12-21

MicroRNAs (miRNAs) play important roles in several human cancers. Although miR188 has been suggested to function as a tumor repressor in cancers, its precise role in glioma and the molecular mechanism remain unknown. In the present study, we investigated the effect of miR-188 on glioma and explored its relevant mechanisms. We found that the expression of miR-188 is dramatically downregulated in glioma tissues and cell lines. Subsequent investigation revealed that miR-188 expression was inversely correlated with ß-catenin expression in glioma tissue samples. Using a luciferase reporter assay, ß-catenin was determined to be a direct target of miR-188. Overexpression of miR-188 reduced ß-catenin expression at both the mRNA and protein levels, and inhibition of miR-188 increased ß-catenin expression. Moreover, we found that overexpression of miR-188 suppressed glioma cell proliferation and cell cycle G1-S transition, whereas inhibition of miR-188 promoted glioma cell proliferation. Importantly, silencing ß-catenin recapitulated the cellular and molecular effects seen upon miR-188 overexpression, which included inhibiting glioma cell proliferation and G1-S transition. Taken together, our results demonstrated that miR188 inhibits glioma cell proliferation by targeting ß-catenin, representing an effective therapeutic strategy for glioma.

High-pressure microscopy for tracking dynamic properties of molecular machines.

PubMed

Nishiyama, Masayoshi

2017-12-01

High-pressure microscopy is one of the powerful techniques to visualize the effects of hydrostatic pressures on research targets. It could be used for monitoring the pressure-induced changes in the structure and function of molecular machines in vitro and in vivo. This review focuses on the dynamic properties of the assemblies and machines, analyzed by means of high-pressure microscopy measurement. We developed a high-pressure microscope that is optimized both for the best image formation and for the stability to hydrostatic pressure up to 150 MPa. Application of pressure could change polymerization and depolymerization processes of the microtubule cytoskeleton, suggesting a modulation of the intermolecular interaction between tubulin molecules. A novel motility assay demonstrated that high hydrostatic pressure induces counterclockwise (CCW) to clockwise (CW) reversals of the Escherichia coli flagellar motor. The present techniques could be extended to study how molecular machines in complicated systems respond to mechanical stimuli. Copyright © 2017 Elsevier B.V. All rights reserved.

Efficient full-chip SRAF placement using machine learning for best accuracy and improved consistency

NASA Astrophysics Data System (ADS)

Wang, Shibing; Baron, Stanislas; Kachwala, Nishrin; Kallingal, Chidam; Sun, Dezheng; Shu, Vincent; Fong, Weichun; Li, Zero; Elsaid, Ahmad; Gao, Jin-Wei; Su, Jing; Ser, Jung-Hoon; Zhang, Quan; Chen, Been-Der; Howell, Rafael; Hsu, Stephen; Luo, Larry; Zou, Yi; Zhang, Gary; Lu, Yen-Wen; Cao, Yu

2018-03-01

Various computational approaches from rule-based to model-based methods exist to place Sub-Resolution Assist Features (SRAF) in order to increase process window for lithography. Each method has its advantages and drawbacks, and typically requires the user to make a trade-off between time of development, accuracy, consistency and cycle time. Rule-based methods, used since the 90 nm node, require long development time and struggle to achieve good process window performance for complex patterns. Heuristically driven, their development is often iterative and involves significant engineering time from multiple disciplines (Litho, OPC and DTCO). Model-based approaches have been widely adopted since the 20 nm node. While the development of model-driven placement methods is relatively straightforward, they often become computationally expensive when high accuracy is required. Furthermore these methods tend to yield less consistent SRAFs due to the nature of the approach: they rely on a model which is sensitive to the pattern placement on the native simulation grid, and can be impacted by such related grid dependency effects. Those undesirable effects tend to become stronger when more iterations or complexity are needed in the algorithm to achieve required accuracy. ASML Brion has developed a new SRAF placement technique on the Tachyon platform that is assisted by machine learning and significantly improves the accuracy of full chip SRAF placement while keeping consistency and runtime under control. A Deep Convolutional Neural Network (DCNN) is trained using the target wafer layout and corresponding Continuous Transmission Mask (CTM) images. These CTM images have been fully optimized using the Tachyon inverse mask optimization engine. The neural network generated SRAF guidance map is then used to place SRAF on full-chip. This is different from our existing full-chip MB-SRAF approach which utilizes a SRAF guidance map (SGM) of mask sensitivity to improve the contrast of

PMLB: a large benchmark suite for machine learning evaluation and comparison.

PubMed

Olson, Randal S; La Cava, William; Orzechowski, Patryk; Urbanowicz, Ryan J; Moore, Jason H

2017-01-01

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered. This work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

PubMed

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning

PubMed Central

Sutphin, George L.; Mahoney, J. Matthew; Sheppard, Keith; Walton, David O.; Korstanje, Ron

2016-01-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/. PMID:27812085

Business Case Analysis for Replacing the Mazak 30Y Mill-Turn Machine in SM-39. Summary

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

Booth, Steven Richard; Dinehart, Timothy Grant; Benson, Faith Ann

2015-03-19

Business case studies are being looked at to support procurement of new machines and capital equipment in the SM-39 and TA-03-0102 machine shops. The first effort conducted economic analysis of replacing the Mazak 30Y Mill-Turn Machine located in SM-39. To determine the value of switching machinery, a baseline scenario was compared with a future scenario where new machinery was purchased and installed. The conditions under the two scenarios were defined via interviews with subject matter experts in terms of one-time and periodic costs. The results of the analysis were compiled in a life-cycle cost/benefit table. The costs of procuring, installing,more » and maintaining a new machine were balanced against the costs avoided by replacing older machinery. Productivity savings were included as a measure to show the costs avoided by being able to produce parts at a quicker and more efficient pace.« less

The Universal Anaesthesia Machine (UAM): assessment of a new anaesthesia workstation built with global health in mind.

PubMed

de Beer, D A H; Nesbitt, F D; Bell, G T; Rapuleng, A

2017-04-01

The Universal Anaesthesia Machine has been developed as a complete anaesthesia workstation for use in low- and middle-income countries, where the provision of safe general anaesthesia is often compromised by unreliable supply of electricity and anaesthetic gases. We performed a functional and clinical assessment of this anaesthetic machine, with particular reference to novel features and functioning in the intended environment. The Universal Anaesthesia Machine was found to be reliable, safe and consistent across a range of tests during targeted functional testing. © 2016 The Association of Anaesthetists of Great Britain and Ireland.

Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

PubMed

Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

2009-05-01

Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.

Coupling of kinesin ATP turnover to translocation and microtubule regulation: one engine, many machines.

PubMed

Friel, Claire T; Howard, Jonathon

2012-12-01

The cycle of ATP turnover is integral to the action of motor proteins. Here we discuss how variation in this cycle leads to variation of function observed amongst members of the kinesin superfamily of microtubule associated motor proteins. Variation in the ATP turnover cycle among superfamily members can tune the characteristic kinesin motor to one of the range of microtubule-based functions performed by kinesins. The speed at which ATP is hydrolysed affects the speed of translocation. The ratio of rate constants of ATP turnover in relation to association and dissociation from the microtubule influence the processivity of translocation. Variation in the rate-limiting step of the cycle can reverse the way in which the motor domain interacts with the microtubule producing non-motile kinesins. Because the ATP turnover cycle is not fully understood for the majority of kinesins, much work remains to show how the kinesin engine functions in such a wide variety of molecular machines.

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.

The Slow Cycling Phenotype: A Growing Problem for Treatment Resistance in Melanoma.

PubMed

Ahn, Antonio; Chatterjee, Aniruddha; Eccles, Michael R

2017-06-01

Treatment resistance in metastatic melanoma is a longstanding issue. Current targeted therapy regimes in melanoma largely target the proliferating cancer population, leaving slow-cycling cancer cells undamaged. Consequently, slow-cycling cells are enriched upon drug therapy and can remain in the body for years until acquiring proliferative potential that triggers cancer relapse. Here we overview the molecular mechanisms of slow-cycling cells that underlie treatment resistance in melanoma. Three main areas of molecular reprogramming are discussed that mediate slow cycling and treatment resistance. First, a low microphthalmia-associated transcription factor (MITF) dedifferentiated state activates various signaling pathways. This includes WNT5A, EGFR, as well as other signaling activators, such as AXL and NF-κB. Second, the chromatin-remodeling factor Jumonji/ARID domain-containing protein 1B (JARID1B, KDM5B ) orchestrates and maintains slow cycling and treatment resistance in a small subpopulation of melanoma cells. Finally, a shift in metabolic state toward oxidative phosphorylation has been demonstrated to regulate treatment resistance in slow-cycling cells. Elucidation of the underlying processes of slow cycling and its utilization by melanoma cells may reveal new vulnerable characteristics as therapeutic targets. Moreover, combining current therapies with targeting slow-cycling subpopulations of melanoma cells may allow for more durable and greater treatment responses. Mol Cancer Ther; 16(6); 1002-9. ©2017 AACR . ©2017 American Association for Cancer Research.

Progress toward a unified kJ-machine CANDY

NASA Astrophysics Data System (ADS)

Kitagawa, Yoneyoshi; Mori, Yoshitaka; Komeda, Osamu; Hanayama, Ryohei; Ishii, Katsuhiro; Okihara, Shinichiro; Fujita, Kazuhisa; Nakayama, Suisei; Sekine, Takashi; Sato, Nakahiro; Kurita, Takashi; Kawashima, Toshiyuki; Watari, Takeshi; Kan, Hirofumi; Nakamura, Naoki; Kondo, Takuya; Fujine, Manabu; Azuma, Hirozumi; Motohiro, Tomoyoshi; Hioki, Tatsumi; Kakeno, Mitsutaka; Nishimura, Yasuhiko; Sunahara, Atsushi; Sentoku, Yasuhiko; Miura, Eisuke; Arikawa, Yasunobu; Nagai, Takahiro; Abe, Yuki; Ozaki, Satoshi; Noda, Akira

2016-03-01

To construct a unified experimental machine CANDY using a kJ DPSSL driver in the fast-ignition scheme, the Laser for Fast Ignition Experiment (LFEX) at Osaka is used, showing that the laser-driven ions heat the preimploded core of a deuterated polystyrene (CD) shell target from 0.8 keV to 2 keV, resulting in 5 x 108 DD neutrons best ever obtained in the scheme. 4-J/10-Hz DPSSL laser HAMA is for the first time applied to the CD shell implosion- core heating experiments in the fast ignition scheme to yield neutrons and also to a continuous target injection, which yields neutrons of 3 x 105 n/4πsr n/shot.

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…

Rho-associated Kinase Connects a Cell Cycle-controlling Anchorage Signal to the Mammalian Target of Rapamycin Pathway*

PubMed Central

Park, Jung-ha; Arakawa-Takeuchi, Shiho; Jinno, Shigeki; Okayama, Hiroto

2011-01-01

When deprived of anchorage to the extracellular matrix, fibroblasts arrest in G1 phase at least in part due to inactivation of G1 cyclin-dependent kinases. Despite great effort, how anchorage signals control the G1-S transition of fibroblasts remains highly elusive. We recently found that the mammalian target of rapamycin (mTOR) cascade might convey an anchorage signal that regulates S phase entry. Here, we show that Rho-associated kinase connects this signal to the TSC1/TSC2-RHEB-mTOR pathway. Expression of a constitutively active form of ROCK1 suppressed all of the anchorage deprivation effects suppressible by tsc2 mutation in rat embryonic fibroblasts. TSC2 contains one evolutionarily conserved ROCK target-like sequence, and an alanine substitution for Thr1203 in this sequence severely impaired the ability of ROCK1 to counteract the anchorage loss-imposed down-regulation of both G1 cell cycle factors and mTORC1 activity. Moreover, TSC2 Thr1203 underwent ROCK-dependent phosphorylation in vivo and could be phosphorylated by bacterially expressed active ROCK1 in vitro, providing biochemical evidence for a direct physical interaction between ROCK and TSC2. PMID:21561859

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

Estimate for interstage water injection in air compressor incorporated into gas-turbine cycles and combined power plants cycles

NASA Astrophysics Data System (ADS)

Kler, A. M.; Zakharov, Yu. B.; Potanina, Yu. M.

2017-05-01

The objects of study are the gas turbine (GT) plant and combined cycle power plant (CCPP) with opportunity for injection between the stages of air compressor. The objective of this paper is technical and economy optimization calculations for these classes of plants with water interstage injection. The integrated development environment "System of machine building program" was a tool for creating the mathematic models for these classes of power plants. Optimization calculations with the criterion of minimum for specific capital investment as a function of the unit efficiency have been carried out. For a gas-turbine plant, the economic gain from water injection exists for entire range of power efficiency. For the combined cycle plant, the economic benefit was observed only for a certain range of plant's power efficiency.

Thermal Management and Reliability of Automotive Power Electronics and Electric Machines

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

Narumanchi, Sreekant V; Bennion, Kevin S; Cousineau, Justine E

Low-cost, high-performance thermal management technologies are helping meet aggressive power density, specific power, cost, and reliability targets for power electronics and electric machines. The National Renewable Energy Laboratory is working closely with numerous industry and research partners to help influence development of components that meet aggressive performance and cost targets through development and characterization of cooling technologies, and thermal characterization and improvements of passive stack materials and interfaces. Thermomechanical reliability and lifetime estimation models are important enablers for industry in cost-and time-effective design.

Travel with CPAP machines: how frequent and what are the problems?

PubMed

Bodington, Richard; Johnson, Owen; Carveth-Johnson, Pippa; Faruqi, Shoaib

2018-01-01

Obstructive sleep apnoea syndrome is a common condition for which continuous positive airways pressure (CPAP) is the standard treatment. The condition affects a population of which a substantial proportion will be travelling. We use a questionnaire survey of CPAP users to gain understanding regarding the behaviours, attitudes and problems surrounding travel with CPAP machines during travel and while abroad. All CPAP patients on our database at a UK district general hospital reviewed over a period of 4 years were sent a postal questionnaire. A response rate of 53% was achieved giving data on 588 trips. In the last 2 years, 63.7% of respondents had travelled; reasons for not travelling were CPAP related in only five cases. Travellers took their CPAP machines on 81% of trips. A similar proportion of patients took their CPAP machines regardless of the mode of travel, destination or length of holiday. Problems with checking in the CPAP machine were encountered in 4% of trips, all as part of air travel. Just over a third of patients faced problems either with the power cord, adapter or transport of the CPAP machine. Of those taking overnight flights, half did not sleep and none used their CPAP machines in flight. CPAP usage while away did not differ to usage at home. This is the first report to describe in some detail CPAP machine use and associated problems in travel and while away. The data may aid the targeting of brief interventions in CPAP clinics as well as helping to standardize the process of check-in in order to help travellers with CPAP machines. © International Society of Travel Medicine, 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare.

PubMed

Mozaffari-Kermani, Mehran; Sur-Kolay, Susmita; Raghunathan, Anand; Jha, Niraj K

2015-11-01

Machine learning is being used in a wide range of application domains to discover patterns in large datasets. Increasingly, the results of machine learning drive critical decisions in applications related to healthcare and biomedicine. Such health-related applications are often sensitive, and thus, any security breach would be catastrophic. Naturally, the integrity of the results computed by machine learning is of great importance. Recent research has shown that some machine-learning algorithms can be compromised by augmenting their training datasets with malicious data, leading to a new class of attacks called poisoning attacks. Hindrance of a diagnosis may have life-threatening consequences and could cause distrust. On the other hand, not only may a false diagnosis prompt users to distrust the machine-learning algorithm and even abandon the entire system but also such a false positive classification may cause patient distress. In this paper, we present a systematic, algorithm-independent approach for mounting poisoning attacks across a wide range of machine-learning algorithms and healthcare datasets. The proposed attack procedure generates input data, which, when added to the training set, can either cause the results of machine learning to have targeted errors (e.g., increase the likelihood of classification into a specific class), or simply introduce arbitrary errors (incorrect classification). These attacks may be applied to both fixed and evolving datasets. They can be applied even when only statistics of the training dataset are available or, in some cases, even without access to the training dataset, although at a lower efficacy. We establish the effectiveness of the proposed attacks using a suite of six machine-learning algorithms and five healthcare datasets. Finally, we present countermeasures against the proposed generic attacks that are based on tracking and detecting deviations in various accuracy metrics, and benchmark their effectiveness.

Machine-Learning Algorithms to Code Public Health Spending Accounts

PubMed Central

Leider, Jonathon P.; Resnick, Beth A.; Alfonso, Y. Natalia; Bishai, David

2017-01-01

Objectives: Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. Methods: We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Results: Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Conclusions: Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence

Machine-Learning Algorithms to Code Public Health Spending Accounts.

PubMed

Brady, Eoghan S; Leider, Jonathon P; Resnick, Beth A; Alfonso, Y Natalia; Bishai, David

Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.

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

Impact of machining on the flexural fatigue strength of glass and polycrystalline CAD/CAM ceramics.

PubMed

Fraga, Sara; Amaral, Marina; Bottino, Marco Antônio; Valandro, Luiz Felipe; Kleverlaan, Cornelis Johannes; May, Liliana Gressler

2017-11-01

To assess the effect of machining on the flexural fatigue strength and on the surface roughness of different computer-aided design, computer-aided manufacturing (CAD/CAM) ceramics by comparing machined and polished after machining specimens. Disc-shaped specimens of yttria-stabilized polycrystalline tetragonal zirconia (Y-TZP), leucite-, and lithium disilicate-based glass ceramics were prepared by CAD/CAM machining, and divided into two groups: machining (M) and machining followed by polishing (MP). The surface roughness was measured and the flexural fatigue strength was evaluated by the step-test method (n=20). The initial load and the load increment for each ceramic material were based on a monotonic test (n=5). A maximum of 10,000 cycles was applied in each load step, at 1.4Hz. Weibull probability statistics was used for the analysis of the flexural fatigue strength, and Mann-Whitney test (α=5%) to compare roughness between the M and MP conditions. Machining resulted in lower values of characteristic flexural fatigue strength than machining followed by polishing. The greatest reduction in flexural fatigue strength from MP to M was observed for Y-TZP (40%; M=536.48MPa; MP=894.50MPa), followed by lithium disilicate (33%; M=187.71MPa; MP=278.93MPa) and leucite (29%; M=72.61MPa; MP=102.55MPa). Significantly higher values of roughness (Ra) were observed for M compared to MP (leucite: M=1.59μm and MP=0.08μm; lithium disilicate: M=1.84μm and MP=0.13μm; Y-TZP: M=1.79μm and MP=0.18μm). Machining negatively affected the flexural fatigue strength of CAD/CAM ceramics, indicating that machining of partially or fully sintered ceramics is deleterious to fatigue strength. Copyright © 2017 The Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Defect detection and classification of machined surfaces under multiple illuminant directions

NASA Astrophysics Data System (ADS)

Liao, Yi; Weng, Xin; Swonger, C. W.; Ni, Jun

2010-08-01

Continuous improvement of product quality is crucial to the successful and competitive automotive manufacturing industry in the 21st century. The presence of surface porosity located on flat machined surfaces such as cylinder heads/blocks and transmission cases may allow leaks of coolant, oil, or combustion gas between critical mating surfaces, thus causing damage to the engine or transmission. Therefore 100% inline inspection plays an important role for improving product quality. Although the techniques of image processing and machine vision have been applied to machined surface inspection and well improved in the past 20 years, in today's automotive industry, surface porosity inspection is still done by skilled humans, which is costly, tedious, time consuming and not capable of reliably detecting small defects. In our study, an automated defect detection and classification system for flat machined surfaces has been designed and constructed. In this paper, the importance of the illuminant direction in a machine vision system was first emphasized and then the surface defect inspection system under multiple directional illuminations was designed and constructed. After that, image processing algorithms were developed to realize 5 types of 2D or 3D surface defects (pore, 2D blemish, residue dirt, scratch, and gouge) detection and classification. The steps of image processing include: (1) image acquisition and contrast enhancement (2) defect segmentation and feature extraction (3) defect classification. An artificial machined surface and an actual automotive part: cylinder head surface were tested and, as a result, microscopic surface defects can be accurately detected and assigned to a surface defect class. The cycle time of this system can be sufficiently fast that implementation of 100% inline inspection is feasible. The field of view of this system is 150mm×225mm and the surfaces larger than the field of view can be stitched together in software.

English to Sanskrit Machine Translation Using Transfer Based approach

NASA Astrophysics Data System (ADS)

Pathak, Ganesh R.; Godse, Sachin P.

2010-11-01

Translation is one of the needs of global society for communicating thoughts and ideas of one country with other country. Translation is the process of interpretation of text meaning and subsequent production of equivalent text, also called as communicating same meaning (message) in another language. In this paper we gave detail information on how to convert source language text in to target language text using Transfer Based Approach for machine translation. Here we implemented English to Sanskrit machine translator using transfer based approach. English is global language used for business and communication but large amount of population in India is not using and understand the English. Sanskrit is ancient language of India most of the languages in India are derived from Sanskrit. Sanskrit can be act as an intermediate language for multilingual translation.

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.

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)…

Process Development and Micro-Machining of MARBLE Foam-Cored Rexolite Hemi-Shell Ablator Capsules

DOE PAGES

Randolph, Randall Blaine; Oertel, John A.; Schmidt, Derek William; ...

2016-06-30

For this study, machined CH hemi-shell ablator capsules have been successfully produced by the MST-7 Target Fabrication Team at Los Alamos National Laboratory. Process development and micro-machining techniques have been developed to produce capsules for both the Omega and National Ignition Facility (NIF) campaigns. These capsules are gas filled up to 10 atm and consist of a machined plastic hemi-shell outer layer that accommodates various specially engineered low-density polystyrene foam cores. Machining and assembly of the two-part, step-jointed plastic hemi-shell outer layer required development of new techniques, processes, and tooling while still meeting very aggressive shot schedules for both campaigns.more » Finally, problems encountered and process improvements will be discussed that describe this very unique, complex capsule design approach through the first Omega proof-of-concept version to the larger NIF version.« less

Optimization of Support Vector Machine (SVM) for Object Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

Template For Aiming An X-Ray Machine

NASA Technical Reports Server (NTRS)

Morphet, W. J.

1994-01-01

Relatively inexpensive template helps in aligning x-ray machine with phenolic ring to be inspected for flaws. Phenolic ring in original application part of rocket nozzle. Concept also applicable to x-ray inspection of other rings. Template contains alignment holes for adjusting orientation, plus target spot for adjusting lateral position, of laser spotting beam. (Laser spotting beam coincides with the x-ray beam, turned on later, after alignment completed.) Use of template decreases positioning time and error, providing consistent sensitivity for detection of flaws.

Calibrated thermal microscopy of the tool-chip interface in machining

NASA Astrophysics Data System (ADS)

Yoon, Howard W.; Davies, Matthew A.; Burns, Timothy J.; Kennedy, M. D.

2000-03-01

A critical parameter in predicting tool wear during machining and in accurate computer simulations of machining is the spatially-resolved temperature at the tool-chip interface. We describe the development and the calibration of a nearly diffraction-limited thermal-imaging microscope to measure the spatially-resolved temperatures during the machining of an AISI 1045 steel with a tungsten-carbide tool bit. The microscope has a target area of 0.5 mm X 0.5 mm square region with a < 5 micrometers spatial resolution and is based on a commercial InSb 128 X 128 focal plane array with an all reflective microscope objective. The minimum frame image acquisition time is < 1 ms. The microscope is calibrated using a standard blackbody source from the radiance temperature calibration laboratory at the National Institute of Standards and Technology, and the emissivity of the machined material is deduced from the infrared reflectivity measurements. The steady-state thermal images from the machining of 1045 steel are compared to previous determinations of tool temperatures from micro-hardness measurements and are found to be in agreement with those studies. The measured average chip temperatures are also in agreement with the temperature rise estimated from energy balance considerations. From these calculations and the agreement between the experimental and the calculated determinations of the emissivity of the 1045 steel, the standard uncertainty of the temperature measurements is estimated to be about 45 degree(s)C at 900 degree(s)C.

Influence of micromachined targets on laser accelerated proton beam profiles

NASA Astrophysics Data System (ADS)

Dalui, Malay; Permogorov, Alexander; Pahl, Hannes; Persson, Anders; Wahlström, Claes-Göran

2018-03-01

High intensity laser-driven proton acceleration from micromachined targets is studied experimentally in the target-normal-sheath-acceleration regime. Conical pits are created on the front surface of flat aluminium foils of initial thickness 12.5 and 3 μm using series of low energy pulses (0.5-2.5 μJ). Proton acceleration from such micromachined targets is compared with flat foils of equivalent thickness at a laser intensity of 7 × 1019 W cm-2. The maximum proton energy obtained from targets machined from 12.5 μm thick foils is found to be slightly lower than that of flat foils of equivalent remaining thickness, and the angular divergence of the proton beam is observed to increase as the depth of the pit approaches the foil thickness. Targets machined from 3 μm thick foils, on the other hand, show evidence of increasing the maximum proton energy when the depths of the structures are small. Furthermore, shallow pits on 3 μm thick foils are found to be efficient in reducing the proton beam divergence by a factor of up to three compared to that obtained from flat foils, while maintaining the maximum proton energy.

Electrical test prediction using hybrid metrology and machine learning

NASA Astrophysics Data System (ADS)

Breton, Mary; Chao, Robin; Muthinti, Gangadhara Raja; de la Peña, Abraham A.; Simon, Jacques; Cepler, Aron J.; Sendelbach, Matthew; Gaudiello, John; Emans, Susan; Shifrin, Michael; Etzioni, Yoav; Urenski, Ronen; Lee, Wei Ti

2017-03-01

Electrical test measurement in the back-end of line (BEOL) is crucial for wafer and die sorting as well as comparing intended process splits. Any in-line, nondestructive technique in the process flow to accurately predict these measurements can significantly improve mean-time-to-detect (MTTD) of defects and improve cycle times for yield and process learning. Measuring after BEOL metallization is commonly done for process control and learning, particularly with scatterometry (also called OCD (Optical Critical Dimension)), which can solve for multiple profile parameters such as metal line height or sidewall angle and does so within patterned regions. This gives scatterometry an advantage over inline microscopy-based techniques, which provide top-down information, since such techniques can be insensitive to sidewall variations hidden under the metal fill of the trench. But when faced with correlation to electrical test measurements that are specific to the BEOL processing, both techniques face the additional challenge of sampling. Microscopy-based techniques are sampling-limited by their small probe size, while scatterometry is traditionally limited (for microprocessors) to scribe targets that mimic device ground rules but are not necessarily designed to be electrically testable. A solution to this sampling challenge lies in a fast reference-based machine learning capability that allows for OCD measurement directly of the electrically-testable structures, even when they are not OCD-compatible. By incorporating such direct OCD measurements, correlation to, and therefore prediction of, resistance of BEOL electrical test structures is significantly improved. Improvements in prediction capability for multiple types of in-die electrically-testable device structures is demonstrated. To further improve the quality of the prediction of the electrical resistance measurements, hybrid metrology using the OCD measurements as well as X-ray metrology (XRF) is used. Hybrid metrology

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)

Kv2.2: A Novel Molecular Target to Study the Role of Basal Forebrain GABAergic Neurons in the Sleep-Wake Cycle

PubMed Central

Hermanstyne, Tracey O.; Subedi, Kalpana; Le, Wei Wei; Hoffman, Gloria E.; Meredith, Andrea L.; Mong, Jessica A.; Misonou, Hiroaki

2013-01-01

Study Objectives: The basal forebrain (BF) has been implicated as an important brain region that regulates the sleep-wake cycle of animals. Gamma-aminobutyric acidergic (GABAergic) neurons are the most predominant neuronal population within this region. However, due to the lack of specific molecular tools, the roles of the BF GABAergic neurons have not been fully elucidated. Previously, we have found high expression levels of the Kv2.2 voltage-gated potassium channel on approximately 60% of GABAergic neurons in the magnocellular preoptic area and horizontal limb of the diagonal band of Broca of the BF and therefore proposed it as a potential molecular target to study this neuronal population. In this study, we sought to determine the functional roles of the Kv2.2-expressing neurons in the regulation of the sleep-wake cycle. Design: Sleep analysis between two genotypes and within each genotype before and after sleep deprivation. Setting: Animal sleep research laboratory. Participants: Adult mice. Wild-type and Kv2.2 knockout mice with C57/BL6 background. Interventions: EEG/EMG recordings from the basal state and after sleep-deprivation which was induced by mild aggitation for 6 h. Results: Immunostaining of a marker of neuronal activity indicates that these Kv2.2-expressing neurons appear to be preferentially active during the wake state. Therefore, we tested whether Kv2.2-expressing neurons in the BF are involved in arousal using Kv2.2-deficient mice. BF GABAergic neurons exhibited augmented expression of c-Fos. These knockout mice exhibited longer consolidated wake bouts than wild-type littermates, and that phenotype was further exacerbated by sleep deprivation. Moreover, in-depth analyses of their cortical electroencephalogram revealed a significant decrease in the delta-frequency activity during the nonrapid eye movement sleep state. Conclusions: These results revealed the significance of Kv2.2-expressing neurons in the regulation of the sleep-wake cycle

Preliminary Investigation on Life Cycle Inventory of Powder Bed Fusion of Stainless Steel

NASA Astrophysics Data System (ADS)

Nyamekye, Patricia; Piili, Heidi; Leino, Maija; Salminen, Antti

Manufacturing of work pieces from stainless steel with laser additive manufacturing, known also as laser sintering or 3D printing may increase energy and material efficiency. The use of powder bed fusion offers advantages to make parts for dynamic applications of light weight and near-net-shape products. Due to these advantages among others, PBF may also reduce emissions and operational cost in various applications. However, there are only few life cycle assessment studies examining this subject despite its prospect to business opportunity. The application of Life Cycle Inventory (LCI) in Powder Bed Fusion (PBF) provides a distinct evaluation of material and energy consumption. LCI offers a possibility to improve knowledge of process efficiency. This study investigates effect of process sustainability in terms of raw material, energy and time consumption with PBF and CNC machining. The results of the experimental study indicated lower energy efficiency in the production process with PBF. This study revealed that specific energy consumption in PBF decreased when several components are built simultaneously than if they would be built individually. This is due to fact that energy consumption per part is lower. On the contrary, amount of energy needed to machine on part in case of CNC machining is lower when parts are done separately.

Laser-fusion targets for reactors

DOEpatents

Nuckolls, John H.; Thiessen, Albert R.

1987-01-01

A laser target comprising a thermonuclear fuel capsule composed of a centrally located quantity of fuel surrounded by at least one or more layers or shells of material for forming an atmosphere around the capsule by a low energy laser prepulse. The fuel may be formed as a solid core or hollow shell, and, under certain applications, a pusher-layer or shell is located intermediate the fuel and the atmosphere forming material. The fuel is ignited by symmetrical implosion via energy produced by a laser, or other energy sources such as an electron beam machine or ion beam machine, whereby thermonuclear burn of the fuel capsule creates energy for applications such as generation of electricity via a laser fusion reactor.

Methods, systems and apparatus for controlling operation of two alternating current (AC) machines

DOEpatents

Gallegos-Lopez, Gabriel [Torrance, CA; Nagashima, James M [Cerritos, CA; Perisic, Milun [Torrance, CA; Hiti, Silva [Redondo Beach, CA

2012-02-14

A system is provided for controlling two AC machines. The system comprises a DC input voltage source that provides a DC input voltage, a voltage boost command control module (VBCCM), a five-phase PWM inverter module coupled to the two AC machines, and a boost converter coupled to the inverter module and the DC input voltage source. The boost converter is designed to supply a new DC input voltage to the inverter module having a value that is greater than or equal to a value of the DC input voltage. The VBCCM generates a boost command signal (BCS) based on modulation indexes from the two AC machines. The BCS controls the boost converter such that the boost converter generates the new DC input voltage in response to the BCS. When the two AC machines require additional voltage that exceeds the DC input voltage required to meet a combined target mechanical power required by the two AC machines, the BCS controls the boost converter to drive the new DC input voltage generated by the boost converter to a value greater than the DC input voltage.

High duty cycle to low duty cycle: echolocation behaviour of the hipposiderid bat Coelops frithii.

PubMed

Ho, Ying-Yi; Fang, Yin-Ping; Chou, Cheng-Han; Cheng, Hsi-Chi; Chang, Hsueh-Wen

2013-01-01

Laryngeally echolocating bats avoid self-deafening (forward masking) by separating pulse and echo either in time using low duty cycle (LDC) echolocation, or in frequency using high duty cycle (HDC) echolocation. HDC echolocators are specialized to detect fluttering targets in cluttered environments. HDC echolocation is found only in the families Rhinolophidae and Hipposideridae in the Old World and in the New World mormoopid, Pteronotus parnellii. Here we report that the hipposiderid Coelops frithii, ostensibly an HDC bat, consistently uses an LDC echolocation strategy whether roosting, flying, or approaching a fluttering target rotating at 50 to 80 Hz. We recorded the echolocation calls of free-flying C. frithii in the field in various situations, including presenting bats with a mechanical fluttering target. The echolocation calls of C. frithii consisted of an initial narrowband component (0.5±0.3 ms, 90.6±2.0 kHz) followed immediately by a frequency modulated (FM) sweep (194 to 113 kHz). This species emitted echolocation calls at duty cycles averaging 7.7±2.8% (n = 87 sequences). Coelops frithii approached fluttering targets more frequently than did LDC bats (C.frithii, approach frequency  = 40.4%, n = 80; Myotis spp., approach frequency  = 0%, n = 13), and at the same frequency as sympatrically feeding HDC species (Hipposideros armiger, approach rate  = 53.3%, n = 15; Rhinolophus monoceros, approach rate  = 56.7%, n = 97). We propose that the LDC echolocation strategy used by C. frithii is derived from HDC ancestors, that this species adjusts the harmonic contents of its echolocation calls, and that it may use both the narrowband component and the FM sweep of echolocations calls to detect fluttering targets.

High Duty Cycle to Low Duty Cycle: Echolocation Behaviour of the Hipposiderid Bat Coelops frithii

PubMed Central

Ho, Ying-Yi; Fang, Yin-Ping; Chou, Cheng-Han; Cheng, Hsi-Chi; Chang, Hsueh-Wen

2013-01-01

Laryngeally echolocating bats avoid self-deafening (forward masking) by separating pulse and echo either in time using low duty cycle (LDC) echolocation, or in frequency using high duty cycle (HDC) echolocation. HDC echolocators are specialized to detect fluttering targets in cluttered environments. HDC echolocation is found only in the families Rhinolophidae and Hipposideridae in the Old World and in the New World mormoopid, Pteronotus parnellii. Here we report that the hipposiderid Coelops frithii, ostensibly an HDC bat, consistently uses an LDC echolocation strategy whether roosting, flying, or approaching a fluttering target rotating at 50 to 80 Hz. We recorded the echolocation calls of free-flying C. frithii in the field in various situations, including presenting bats with a mechanical fluttering target. The echolocation calls of C. frithii consisted of an initial narrowband component (0.5±0.3 ms, 90.6±2.0 kHz) followed immediately by a frequency modulated (FM) sweep (194 to 113 kHz). This species emitted echolocation calls at duty cycles averaging 7.7±2.8% (n = 87 sequences). Coelops frithii approached fluttering targets more frequently than did LDC bats (C.frithii, approach frequency  = 40.4%, n = 80; Myotis spp., approach frequency  = 0%, n = 13), and at the same frequency as sympatrically feeding HDC species (Hipposideros armiger, approach rate  = 53.3%, n = 15; Rhinolophus monoceros, approach rate  = 56.7%, n = 97). We propose that the LDC echolocation strategy used by C. frithii is derived from HDC ancestors, that this species adjusts the harmonic contents of its echolocation calls, and that it may use both the narrowband component and the FM sweep of echolocations calls to detect fluttering targets. PMID:23717396

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

Using machine learning algorithms to guide rehabilitation planning for home care clients.

PubMed

Zhu, Mu; Zhang, Zhanyang; Hirdes, John P; Stolee, Paul

2007-12-20

Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients. This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

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...

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.

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.

Machine Learning in Computer-Aided Synthesis Planning.

PubMed

Coley, Connor W; Green, William H; Jensen, Klavs F

2018-05-15

Computer-aided synthesis planning (CASP) is focused on the goal of accelerating the process by which chemists decide how to synthesize small molecule compounds. The ideal CASP program would take a molecular structure as input and output a sorted list of detailed reaction schemes that each connect that target to purchasable starting materials via a series of chemically feasible reaction steps. Early work in this field relied on expert-crafted reaction rules and heuristics to describe possible retrosynthetic disconnections and selectivity rules but suffered from incompleteness, infeasible suggestions, and human bias. With the relatively recent availability of large reaction corpora (such as the United States Patent and Trademark Office (USPTO), Reaxys, and SciFinder databases), consisting of millions of tabulated reaction examples, it is now possible to construct and validate purely data-driven approaches to synthesis planning. As a result, synthesis planning has been opened to machine learning techniques, and the field is advancing rapidly. In this Account, we focus on two critical aspects of CASP and recent machine learning approaches to both challenges. First, we discuss the problem of retrosynthetic planning, which requires a recommender system to propose synthetic disconnections starting from a target molecule. We describe how the search strategy, necessary to overcome the exponential growth of the search space with increasing number of reaction steps, can be assisted through a learned synthetic complexity metric. We also describe how the recursive expansion can be performed by a straightforward nearest neighbor model that makes clever use of reaction data to generate high quality retrosynthetic disconnections. Second, we discuss the problem of anticipating the products of chemical reactions, which can be used to validate proposed reactions in a computer-generated synthesis plan (i.e., reduce false positives) to increase the likelihood of experimental success

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.

Target-specific support vector machine scoring in structure-based virtual screening: computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferation.

PubMed

Li, Liwei; Khanna, May; Jo, Inha; Wang, Fang; Ashpole, Nicole M; Hudmon, Andy; Meroueh, Samy O

2011-04-25

We assess the performance of our previously reported structure-based support vector machine target-specific scoring function across 41 targets, 40 among them from the Directory of Useful Decoys (DUD). The area under the curve of receiver operating characteristic plots (ROC-AUC) revealed that scoring with SVM-SP resulted in consistently better enrichment over all target families, outperforming Glide and other scoring functions, most notably among kinases. In addition, SVM-SP performance showed little variation among protein classes, exhibited excellent performance in a test case using a homology model, and in some cases showed high enrichment even with few structures used to train a model. We put SVM-SP to the test by virtual screening 1125 compounds against two kinases, EGFR and CaMKII. Among the top 25 EGFR compounds, three compounds (1-3) inhibited kinase activity in vitro with IC₅₀ of 58, 2, and 10 μM. In cell cultures, compounds 1-3 inhibited nonsmall cell lung carcinoma (H1299) cancer cell proliferation with similar IC₅₀ values for compound 3. For CaMKII, one compound inhibited kinase activity in a dose-dependent manner among 20 tested with an IC₅₀ of 48 μM. These results are encouraging given that our in-house library consists of compounds that emerged from virtual screening of other targets with pockets that are different from typical ATP binding sites found in kinases. In light of the importance of kinases in chemical biology, these findings could have implications in future efforts to identify chemical probes of kinases within the human kinome.

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.

Hydraulic Hybrid and Conventional Parcel Delivery Vehicles' Measured Laboratory Fuel Economy on Targeted Drive Cycles

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

Lammert, M. P.; Burton, J.; Sindler, P.

2014-10-01

This research project compares laboratory-measured fuel economy of a medium-duty diesel powered hydraulic hybrid vehicle drivetrain to both a conventional diesel drivetrain and a conventional gasoline drivetrain in a typical commercial parcel delivery application. Vehicles in this study included a model year 2012 Freightliner P100H hybrid compared to a 2012 conventional gasoline P100 and a 2012 conventional diesel parcel delivery van of similar specifications. Drive cycle analysis of 484 days of hybrid parcel delivery van commercial operation from multiple vehicles was used to select three standard laboratory drive cycles as well as to create a custom representative cycle. These fourmore » cycles encompass and bracket the range of real world in-use data observed in Baltimore United Parcel Service operations. The NY Composite cycle, the City Suburban Heavy Vehicle Cycle cycle, and the California Air Resources Board Heavy Heavy-Duty Diesel Truck (HHDDT) cycle as well as a custom Baltimore parcel delivery cycle were tested at the National Renewable Energy Laboratory's Renewable Fuels and Lubricants Laboratory. Fuel consumption was measured and analyzed for all three vehicles. Vehicle laboratory results are compared on the basis of fuel economy. The hydraulic hybrid parcel delivery van demonstrated 19%-52% better fuel economy than the conventional diesel parcel delivery van and 30%-56% better fuel economy than the conventional gasoline parcel delivery van on cycles other than the highway-oriented HHDDT cycle.« less

Perception of mind and dehumanization: Human, animal, or machine?

PubMed

Morera, María D; Quiles, María N; Correa, Ana D; Delgado, Naira; Leyens, Jacques-Philippe

2016-08-02

Dehumanization is reached through several approaches, including the attribute-based model of mind perception and the metaphor-based model of dehumanization. We performed two studies to find different (de)humanized images for three targets: Professional people, Evil people, and Lowest of the low. In Study 1, we examined dimensions of mind, expecting the last two categories to be dehumanized through denial of agency (Lowest of the low) or experience (Evil people), compared with humanized targets (Professional people). Study 2 aimed to distinguish these targets using metaphors. We predicted that Evil and Lowest of the low targets would suffer mechanistic and animalistic dehumanization, respectively; our predictions were confirmed, but the metaphor-based model nuanced these results: animalistic and mechanistic dehumanization were shown as overlapping rather than independent. Evil persons were perceived as "killing machines" and "predators." Finally, Lowest of the low were not animalized but considered human beings. We discuss possible interpretations. © 2016 International Union of Psychological Science.

Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning

NASA Astrophysics Data System (ADS)

Mølgaard, Lasse L.; Buus, Ole T.; Larsen, Jan; Babamoradi, Hamid; Thygesen, Ida L.; Laustsen, Milan; Munk, Jens Kristian; Dossi, Eleftheria; O'Keeffe, Caroline; Lässig, Lina; Tatlow, Sol; Sandström, Lars; Jakobsen, Mogens H.

2017-05-01

We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fully- integrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios. To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions. The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios. The technology under development in CRIM-TRACK has the potential as an effective tool to control trafficking of illegal drugs, explosive detection, or in other law enforcement applications.

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

Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

PubMed

Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

2014-08-01

Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

PubMed Central

Su, Yi; Routhu, Sudhamayee; Moon, Kee S.; Lee, Sung Q.; Youm, WooSub; Ozturk, Yusuf

2016-01-01

All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time. PMID:27669264

e-Learning Application for Machine Maintenance Process using Iterative Method in XYZ Company

NASA Astrophysics Data System (ADS)

Nurunisa, Suaidah; Kurniawati, Amelia; Pramuditya Soesanto, Rayinda; Yunan Kurnia Septo Hediyanto, Umar

2016-02-01

XYZ Company is a company based on manufacturing part for airplane, one of the machine that is categorized as key facility in the company is Millac 5H6P. As a key facility, the machines should be assured to work well and in peak condition, therefore, maintenance process is needed periodically. From the data gathering, it is known that there are lack of competency from the maintenance staff to maintain different type of machine which is not assigned by the supervisor, this indicate that knowledge which possessed by maintenance staff are uneven. The purpose of this research is to create knowledge-based e-learning application as a realization from externalization process in knowledge transfer process to maintain the machine. The application feature are adjusted for maintenance purpose using e-learning framework for maintenance process, the content of the application support multimedia for learning purpose. QFD is used in this research to understand the needs from user. The application is built using moodle with iterative method for software development cycle and UML Diagram. The result from this research is e-learning application as sharing knowledge media for maintenance staff in the company. From the test, it is known that the application make maintenance staff easy to understand the competencies.

Model-Driven Engineering of Machine Executable Code

NASA Astrophysics Data System (ADS)

Eichberg, Michael; Monperrus, Martin; Kloppenburg, Sven; Mezini, Mira

Implementing static analyses of machine-level executable code is labor intensive and complex. We show how to leverage model-driven engineering to facilitate the design and implementation of programs doing static analyses. Further, we report on important lessons learned on the benefits and drawbacks while using the following technologies: using the Scala programming language as target of code generation, using XML-Schema to express a metamodel, and using XSLT to implement (a) transformations and (b) a lint like tool. Finally, we report on the use of Prolog for writing model transformations.

Emissions-critical charge cooling using an organic rankine cycle

DOEpatents

Ernst, Timothy C.; Nelson, Christopher R.

2014-07-15

The disclosure provides a system including a Rankine power cycle cooling subsystem providing emissions-critical charge cooling of an input charge flow. The system includes a boiler fluidly coupled to the input charge flow, an energy conversion device fluidly coupled to the boiler, a condenser fluidly coupled to the energy conversion device, a pump fluidly coupled to the condenser and the boiler, an adjuster that adjusts at least one parameter of the Rankine power cycle subsystem to change a temperature of the input charge exiting the boiler, and a sensor adapted to sense a temperature characteristic of the vaporized input charge. The system includes a controller that can determine a target temperature of the input charge sufficient to meet or exceed predetermined target emissions and cause the adjuster to adjust at least one parameter of the Rankine power cycle to achieve the predetermined target emissions.

Investigation of Low-Cycle Bending Fatigue of AISI 9310 Steel Spur Gears

NASA Technical Reports Server (NTRS)

Handschuh, Robert F.; Krantz, Timothy L.; Lerch, Bradley A.; Burke, Christopher S.

2007-01-01

An investigation of the low-cycle bending fatigue of spur gears made from AISI 9310 gear steel was completed. Tests were conducted using the single-tooth bending method to achieve crack initiation and propagation. Tests were conducted on spur gears in a fatigue test machine using a dedicated gear test fixture. Test loads were applied at the highest point of single tooth contact. Gear bending stresses for a given testing load were calculated using a linear-elastic finite element model. Test data were accumulated from 1/4 cycle to several thousand cycles depending on the test stress level. The relationship of stress and cycles for crack initiation was found to be semi-logarithmic. The relationship of stress and cycles for crack propagation was found to be linear. For the range of loads investigated, the crack propagation phase is related to the level of load being applied. Very high loads have comparable crack initiation and propagation times whereas lower loads can have a much smaller number of cycles for crack propagation cycles as compared to crack initiation.

Investigation of Low-Cycle Bending Fatigue of AISI 9310 Steel Spur Gears

NASA Technical Reports Server (NTRS)

Handschuh, Robert F.; Krantz, Timothy L.; Lerch, Bradley A.; Burke, Christopher S.

2007-01-01

An investigation of the low-cycle bending fatigue of spur gears made from AISI 9310 gear steel was completed. Tests were conducted using the single-tooth bending method to achieve crack initiation and propagation. Tests were conducted on spur gears in a fatigue test machine using a dedicated gear test fixture. Test loads were applied at the highest point of single tooth contact. Gear bending stresses for a given testing load were calculated using a linear-elastic finite element model. Test data were accumulated from 1/4 cycle to several thousand cycles depending on the test stress level. The relationship of stress and cycles for crack initiation was found to be semilogarithmic. The relationship of stress and cycles for crack propagation was found to be linear. For the range of loads investigated, the crack propagation phase is related to the level of load being applied. Very high loads have comparable crack initiation and propagation times whereas lower loads can have a much smaller number of cycles for crack propagation cycles as compared to crack initiation.

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.

Transient Characteristics of Free Piston Vuilleurnier Cycle Heat Pumps

NASA Astrophysics Data System (ADS)

Matsue, Junji; Fujimoto, Norioki; Shirai, Hiroyuki

A dynamic analysis of a free piston Vuilleumier cycle heat pump was performed using a time-stepping integration method to investigate transient characteristics under power controlling. The nonlinear relationship between displacement and force for pistons was taken into account for the motion of reciprocating components. The force for pistons is mainly caused by the pressure change of working gas varying with piston displacements; moreover nonlinear viscous dissipative force due to the oscillating flow of working gas in heat exchangers and discontinuous damping force caused by solid friction at piston seals and rod seals are included. The displacements of pistons and pressure changes in the Vuilleumier cycle heat pump were integrated by an ideal isothermal thermodynamic relationship. It was assumed that the flow friction was proportional to the kinematic pressure of working gas, and that the solid friction at the seals was due to the functions of the working gas pressure and the tension of seal springs. In order to investigate the transient characteristics of a proposed free piston Vuilleumier cycle heat pump machine when hot-side working gas temperatures and alternate force were changed, some calculations were performed and discussed. These calculation results make clear transient characteristics at starting and power controlling. It was further found that only a small amount of starter power is required in particular conditions. During controlling, the machine becomes unstable when there is ar elatively large reduction in cooling or heating power. Therefore, an auxiliary device is additionally needed to obtain stable operation, such as al inear motor.

Application of grey-fuzzy approach in parametric optimization of EDM process in machining of MDN 300 steel

NASA Astrophysics Data System (ADS)

Protim Das, Partha; Gupta, P.; Das, S.; Pradhan, B. B.; Chakraborty, S.

2018-01-01

Maraging steel (MDN 300) find its application in many industries as it exhibits high hardness which are very difficult to machine material. Electro discharge machining (EDM) is an extensively popular machining process which can be used in machining of such materials. Optimization of response parameters are essential for effective machining of these materials. Past researchers have already used Taguchi for obtaining the optimal responses of EDM process for this material with responses such as material removal rate (MRR), tool wear rate (TWR), relative wear ratio (RWR), and surface roughness (SR) considering discharge current, pulse on time, pulse off time, arc gap, and duty cycle as process parameters. In this paper, grey relation analysis (GRA) with fuzzy logic is applied to this multi objective optimization problem to check the responses by an implementation of the derived parametric setting. It was found that the parametric setting derived by the proposed method results in better a response than those reported by the past researchers. Obtained results are also verified using the technique for order of preference by similarity to ideal solution (TOPSIS). The predicted result also shows that there is a significant improvement in comparison to the results of past researchers.

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.

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

DOE PAGES

Faulon, Jean-Loup; Misra, Milind; Martin, Shawn; ...

2007-11-23

Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. Additionally, there is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein–chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformaticsmore » representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Lastly, such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.« less

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.

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.

Clinical implementation of target tracking by breathing synchronized delivery

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

Tewatia, Dinesh; Zhang Tiezhi; Tome, Wolfgang

2006-11-15

Target-tracking techniques can be categorized based on the mechanism of the feedback loop. In real time tracking, breathing-delivery phase correlation is provided to the treatment delivery hardware. Clinical implementation of target tracking in real time requires major hardware modifications. In breathing synchronized delivery (BSD), the patient is guided to breathe in accordance with target motion derived from four-dimensional computed tomography (4D-CT). Violations of mechanical limitations of hardware are to be avoided at the treatment planning stage. Hardware modifications are not required. In this article, using sliding window IMRT delivery as an example, we have described step-by-step the implementation of targetmore » tracking by the BSD technique: (1) A breathing guide is developed from patient's normal breathing pattern. The patient tries to reproduce this guiding cycle by following the display in the goggles; (2) 4D-CT scans are acquired at all the phases of the breathing cycle; (3) The average tumor trajectory is obtained by deformable image registration of 4D-CT datasets and is smoothed by Fourier filtering; (4) Conventional IMRT planning is performed using the images at reference phase (full exhalation phase) and a leaf sequence based on optimized fluence map is generated; (5) Assuming the patient breathes with a reproducible breathing pattern and the machine maintains a constant dose rate, the treatment process is correlated with the breathing phase; (6) The instantaneous average tumor displacement is overlaid on the dMLC position at corresponding phase; and (7) DMLC leaf speed and acceleration are evaluated to ensure treatment delivery. A custom-built mobile phantom driven by a computer-controlled stepper motor was used in the dosimetry verification. A stepper motor was programmed such that the phantom moved according to the linear component of tumor motion used in BSD treatment planning. A conventional plan was delivered on the phantom with and

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.

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

Testing Saliency Parameters for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Pandya, Sagar

2012-01-01

A bottom-up visual attention model (the saliency model) is tested to enhance the performance of Automated Target Recognition (ATR). JPL has developed an ATR system that identifies regions of interest (ROI) using a trained OT-MACH filter, and then classifies potential targets as true- or false-positives using machine-learning techniques. In this project, saliency is used as a pre-processing step to reduce the space for performing OT-MACH filtering. Saliency parameters, such as output level and orientation weight, are tuned to detect known target features. Preliminary results are promising and future work entails a rigrous and parameter-based search to gain maximum insight about this method.

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

A hip joint simulator study using simplified loading and motion cycles generating physiological wear paths and rates.

PubMed

Barbour, P S; Stone, M H; Fisher, J

1999-01-01

In some designs of hip joint simulator the cost of building a highly complex machine has been offset with the requirement for a large number of test stations. The application of the wear results generated by these machines depends on their ability to reproduce physiological wear rates and processes. In this study a hip joint simulator has been shown to reproduce physiological wear using only one load vector and two degrees of motion with simplified input cycles. The actual path of points on the femoral head relative to the acetabular cup were calculated and compared for physiological and simplified input cycles. The in vitro wear rates were found to be highly dependent on the shape of these paths and similarities could be drawn between the shape of the physiological paths and the simplified elliptical paths.

An automatic taxonomy of galaxy morphology using unsupervised machine learning

NASA Astrophysics Data System (ADS)

Hocking, Alex; Geach, James E.; Sun, Yi; Davey, Neil

2018-01-01

We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy we use no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. We demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS 0416.1-2403), we show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an 'early' or 'late' type galaxy is. We then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of approximately 60 000 classifications. We show how the automatic classification groups galaxies of similar morphological (and photometric) type and make the classifications public via a catalogue, a visual catalogue and galaxy similarity search. We compare the CANDELS machine-based classifications to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping between Galaxy Zoo and our hierarchical labelling, we demonstrate a good level of concordance between human and machine classifications. Finally, we show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging.

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;…

Review of smoothing methods for enhancement of noisy data from heavy-duty LHD mining machines

NASA Astrophysics Data System (ADS)

Wodecki, Jacek; Michalak, Anna; Stefaniak, Paweł

2018-01-01

Appropriate analysis of data measured on heavy-duty mining machines is essential for processes monitoring, management and optimization. Some particular classes of machines, for example LHD (load-haul-dump) machines, hauling trucks, drilling/bolting machines etc. are characterized with cyclicity of operations. In those cases, identification of cycles and their segments or in other words - simply data segmentation is a key to evaluate their performance, which may be very useful from the management point of view, for example leading to introducing optimization to the process. However, in many cases such raw signals are contaminated with various artifacts, and in general are expected to be very noisy, which makes the segmentation task very difficult or even impossible. To deal with that problem, there is a need for efficient smoothing methods that will allow to retain informative trends in the signals while disregarding noises and other undesired non-deterministic components. In this paper authors present a review of various approaches to diagnostic data smoothing. Described methods can be used in a fast and efficient way, effectively cleaning the signals while preserving informative deterministic behaviour, that is a crucial to precise segmentation and other approaches to industrial data analysis.

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.

Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches

NASA Astrophysics Data System (ADS)

Klump, J. F.; Fouedjio, F.

2017-12-01

Geostatistical methods such as kriging with external drift as well as machine learning techniques such as quantile regression forest have been intensively used for modelling spatial data. In addition to providing predictions for target variables, both approaches are able to deliver a quantification of the uncertainty associated with the prediction at a target location. Geostatistical approaches are, by essence, adequate for providing such prediction uncertainties and their behaviour is well understood. However, they often require significant data pre-processing and rely on assumptions that are rarely met in practice. Machine learning algorithms such as random forest regression, on the other hand, require less data pre-processing and are non-parametric. This makes the application of machine learning algorithms to geostatistical problems an attractive proposition. The objective of this study is to compare kriging with external drift and quantile regression forest with respect to their ability to deliver reliable prediction uncertainties of spatial data. In our comparison we use both simulated and real world datasets. Apart from classical performance indicators, comparisons make use of accuracy plots, probability interval width plots, and the visual examinations of the uncertainty maps provided by the two approaches. By comparing random forest regression to kriging we found that both methods produced comparable maps of estimated values for our variables of interest. However, the measure of uncertainty provided by random forest seems to be quite different to the measure of uncertainty provided by kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. These preliminary results raise questions about assessing the risks associated with decisions based on the predictions from geostatistical and machine learning algorithms in a spatial context, e.g. mineral exploration.

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

Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase.

PubMed

de Ávila, Maurício Boff; de Azevedo, Walter Filgueira

2018-04-20

In this study, we describe the development of new machine learning models to predict inhibition of the enzyme 3-dehydroquinate dehydratase (DHQD). This enzyme is the third step of the shikimate pathway and is responsible for the synthesis of chorismate, which is a natural precursor of aromatic amino acids. The enzymes of shikimate pathway are absent in humans, which make them protein targets for the design of antimicrobial drugs. We focus our study on the crystallographic structures of DHQD in complex with competitive inhibitors, for which experimental inhibition constant data is available. Application of supervised machine learning techniques was able to elaborate a robust DHQD-targeted model to predict binding affinity. Combination of high-resolution crystallographic structures and binding information indicates that the prevalence of intermolecular electrostatic interactions between DHQD and competitive inhibitors is of pivotal importance for the binding affinity against this enzyme. The present findings can be used to speed up virtual screening studies focused on the DHQD structure. © 2018 John Wiley & Sons A/S.

Combined Auditory and Vibrotactile Feedback for Human-Machine-Interface Control.

PubMed

Thorp, Elias B; Larson, Eric; Stepp, Cara E

2014-01-01

The purpose of this study was to determine the effect of the addition of binary vibrotactile stimulation to continuous auditory feedback (vowel synthesis) for human-machine interface (HMI) control. Sixteen healthy participants controlled facial surface electromyography to achieve 2-D targets (vowels). Eight participants used only real-time auditory feedback to locate targets whereas the other eight participants were additionally alerted to having achieved targets with confirmatory vibrotactile stimulation at the index finger. All participants trained using their assigned feedback modality (auditory alone or combined auditory and vibrotactile) over three sessions on three days and completed a fourth session on the third day using novel targets to assess generalization. Analyses of variance performed on the 1) percentage of targets reached and 2) percentage of trial time at the target revealed a main effect for feedback modality: participants using combined auditory and vibrotactile feedback performed significantly better than those using auditory feedback alone. No effect was found for session or the interaction of feedback modality and session, indicating a successful generalization to novel targets but lack of improvement over training sessions. Future research is necessary to determine the cognitive cost associated with combined auditory and vibrotactile feedback during HMI control.

Stride-Cycle Influences on Goal-Directed Head Movements Made During Walking

NASA Technical Reports Server (NTRS)

Peters, Brian T.; vanEmmerik, Richard E. A.; Bloomberg, Jacob J.

2006-01-01

Horizontal head movements were studied in six subjects as they made rapid horizontal gaze adjustments while walking. The aim of the present research was to determine if gait-cycle events alter the head movement response to a visual target acquisition task. Gaze shifts of approximately 40deg were elicited by a step change in the position of a visual target from a central location to a second location in the left or right horizontal periphery. The timing of the target position change was constrained to occur at 25,50,75 and 100% of the stride cycle. The trials were randomly presented as the subjects walked on a treadmill at their preferred speed (range: 1.25 to 1.48 m/s, mean: 1.39 +/- 0.09 m/s ) . Analyses focused on the movement onset latencies of the head and eyes and on the peak velocity and saccade amplitude of the head movement response. A comparison of the group means indicated that the head movement onset lagged the eye onset (262 ms versus 252 ms). The head and eye movement onset latencies were not affected by either the direction of the target change nor the point in the gait cycle during which the target relocation occurred. However, the presence of an interaction between the gait cycle events and the direction of the visual target shift indicates that the peak head saccade velocity and head saccade amplitude are affected by the natural head oscillations that occur while walking.

Deep kernel learning method for SAR image target recognition

NASA Astrophysics Data System (ADS)

Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

2017-10-01

With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

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.

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

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.

Image Reconstruction is a New Frontier of Machine Learning.

PubMed

Wang, Ge; Ye, Jong Chu; Mueller, Klaus; Fessler, Jeffrey A

2018-06-01

Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of "Machine learning for image reconstruction." This special issue is a sister issue of the special issue published in May 2016 of this journal with the theme "Deep learning in medical imaging" [item 2) in the Appendix]. While the previous special issue targeted medical image processing/analysis, this special issue focuses on data-driven tomographic reconstruction. These two special issues are highly complementary, since image reconstruction and image analysis are two of the main pillars for medical imaging. Together we cover the whole workflow of medical imaging: from tomographic raw data/features to reconstructed images and then extracted diagnostic features/readings.

Sensitivity of Support Vector Machine Predictions of Passive Microwave Brightness Temperature Over Snow-covered Terrain in High Mountain Asia

NASA Astrophysics Data System (ADS)

Ahmad, J. A.; Forman, B. A.

2017-12-01

High Mountain Asia (HMA) serves as a water supply source for over 1.3 billion people, primarily in south-east Asia. Most of this water originates as snow (or ice) that melts during the summer months and contributes to the run-off downstream. In spite of its critical role, there is still considerable uncertainty regarding the total amount of snow in HMA and its spatial and temporal variation. In this study, the NASA Land Information Systems (LIS) is used to model the hydrologic cycle over the Indus basin. In addition, the ability of support vector machines (SVM), a machine learning technique, to predict passive microwave brightness temperatures at a specific frequency and polarization as a function of LIS-derived land surface model output is explored in a sensitivity analysis. Multi-frequency, multi-polarization passive microwave brightness temperatures as measured by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) over the Indus basin are used as training targets during the SVM training process. Normalized sensitivity coefficients (NSC) are then computed to assess the sensitivity of a well-trained SVM to each LIS-derived state variable. Preliminary results conform with the known first-order physics. For example, input states directly linked to physical temperature like snow temperature, air temperature, and vegetation temperature have positive NSC's whereas input states that increase volume scattering such as snow water equivalent or snow density yield negative NSC's. Air temperature exhibits the largest sensitivity coefficients due to its inherent, high-frequency variability. Adherence of this machine learning algorithm to the first-order physics bodes well for its potential use in LIS as the observation operator within a radiance data assimilation system aimed at improving regional- and continental-scale snow estimates.

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.

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.

PubMed

Randhawa, Vinay; Kumar Singh, Anil; Acharya, Vishal

2015-12-01

Systems-biology inspired identification of drug targets and machine learning-based screening of small molecules which modulate their activity have the potential to revolutionize modern drug discovery by complementing conventional methods. To utilize the effectiveness of such pipelines, we first analyzed the dysregulated gene pairs between control and tumor samples and then implemented an ensemble-based feature selection approach to prioritize targets in oral squamous cell carcinoma (OSCC) for therapeutic exploration. Based on the structural information of known inhibitors of CXCR4-one of the best targets identified in this study-a feature selection was implemented for the identification of optimal structural features (molecular descriptor) based on which a classification model was generated. Furthermore, the CXCR4-centered descriptor-based classification model was finally utilized to screen a repository of plant derived small-molecules to obtain potential inhibitors. The application of our methodology may assist effective selection of the best targets which may have previously been overlooked, that in turn will lead to the development of new oral cancer medications. The small molecules identified in this study can be ideal candidates for trials as potential novel anti-oral cancer agents. Importantly, distinct steps of this whole study may provide reference for the analysis of other complex human diseases.

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,…

Shot-by-shot Spectrum Model for Rod-pinch, Pulsed Radiography Machines

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

Wood, William Monford

A simplified model of bremsstrahlung production is developed for determining the x-ray spectrum output of a rod-pinch radiography machine, on a shot-by-shot basis, using the measured voltage, V(t), and current, I(t). The motivation for this model is the need for an agile means of providing shot-by-shot spectrum prediction, from a laptop or desktop computer, for quantitative radiographic analysis. Simplifying assumptions are discussed, and the model is applied to the Cygnus rod-pinch machine. Output is compared to wedge transmission data for a series of radiographs from shots with identical target objects. Resulting model enables variation of parameters in real time, thusmore » allowing for rapid optimization of the model across many shots. “Goodness of fit” is compared with output from LSP Particle-In-Cell code, as well as the Monte Carlo Neutron Propagation with Xrays (“MCNPX”) model codes, and is shown to provide an excellent predictive representation of the spectral output of the Cygnus machine. In conclusion, improvements to the model, specifically for application to other geometries, are discussed.« less

Shot-by-shot Spectrum Model for Rod-pinch, Pulsed Radiography Machines

DOE PAGES

Wood, William Monford

2018-02-07

A simplified model of bremsstrahlung production is developed for determining the x-ray spectrum output of a rod-pinch radiography machine, on a shot-by-shot basis, using the measured voltage, V(t), and current, I(t). The motivation for this model is the need for an agile means of providing shot-by-shot spectrum prediction, from a laptop or desktop computer, for quantitative radiographic analysis. Simplifying assumptions are discussed, and the model is applied to the Cygnus rod-pinch machine. Output is compared to wedge transmission data for a series of radiographs from shots with identical target objects. Resulting model enables variation of parameters in real time, thusmore » allowing for rapid optimization of the model across many shots. “Goodness of fit” is compared with output from LSP Particle-In-Cell code, as well as the Monte Carlo Neutron Propagation with Xrays (“MCNPX”) model codes, and is shown to provide an excellent predictive representation of the spectral output of the Cygnus machine. In conclusion, improvements to the model, specifically for application to other geometries, are discussed.« less

Shot-by-shot spectrum model for rod-pinch, pulsed radiography machines

NASA Astrophysics Data System (ADS)

Wood, Wm M.

2018-02-01

A simplified model of bremsstrahlung production is developed for determining the x-ray spectrum output of a rod-pinch radiography machine, on a shot-by-shot basis, using the measured voltage, V(t), and current, I(t). The motivation for this model is the need for an agile means of providing shot-by-shot spectrum prediction, from a laptop or desktop computer, for quantitative radiographic analysis. Simplifying assumptions are discussed, and the model is applied to the Cygnus rod-pinch machine. Output is compared to wedge transmission data for a series of radiographs from shots with identical target objects. Resulting model enables variation of parameters in real time, thus allowing for rapid optimization of the model across many shots. "Goodness of fit" is compared with output from LSP Particle-In-Cell code, as well as the Monte Carlo Neutron Propagation with Xrays ("MCNPX") model codes, and is shown to provide an excellent predictive representation of the spectral output of the Cygnus machine. Improvements to the model, specifically for application to other geometries, are discussed.

Drosophila CLOCK target gene characterization: implications for circadian tissue-specific gene expression

PubMed Central

Abruzzi, Katharine Compton; Rodriguez, Joseph; Menet, Jerome S.; Desrochers, Jennifer; Zadina, Abigail; Luo, Weifei; Tkachev, Sasha; Rosbash, Michael

2011-01-01

CLOCK (CLK) is a master transcriptional regulator of the circadian clock in Drosophila. To identify CLK direct target genes and address circadian transcriptional regulation in Drosophila, we performed chromatin immunoprecipitation (ChIP) tiling array assays (ChIP–chip) with a number of circadian proteins. CLK binding cycles on at least 800 sites with maximal binding in the early night. The CLK partner protein CYCLE (CYC) is on most of these sites. The CLK/CYC heterodimer is joined 4–6 h later by the transcriptional repressor PERIOD (PER), indicating that the majority of CLK targets are regulated similarly to core circadian genes. About 30% of target genes also show cycling RNA polymerase II (Pol II) binding. Many of these generate cycling RNAs despite not being documented in prior RNA cycling studies. This is due in part to different RNA isoforms and to fly head tissue heterogeneity. CLK has specific targets in different tissues, implying that important CLK partner proteins and/or mechanisms contribute to gene-specific and tissue-specific regulation. PMID:22085964

Collaborative filtering on a family of biological targets.

PubMed

Erhan, Dumitru; L'heureux, Pierre-Jean; Yue, Shi Yi; Bengio, Yoshua

2006-01-01

Building a QSAR model of a new biological target for which few screening data are available is a statistical challenge. However, the new target may be part of a bigger family, for which we have more screening data. Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves the generalization performance of an algorithm by using information from related tasks as an inductive bias. We use collaborative filtering techniques for building predictive models that link multiple targets to multiple examples. The more commonalities between the targets, the better the multi-target model that can be built. We show an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for collaborative filtering. We show their performance on compound prioritization for an HTS campaign and the underlying shared representation between targets. JRank outperformed the neural network both in the single- and multi-target models.

Autonomous proximity operations using machine vision for trajectory control and pose estimation

NASA Technical Reports Server (NTRS)

Cleghorn, Timothy F.; Sternberg, Stanley R.

1991-01-01

A machine vision algorithm was developed which permits guidance control to be maintained during autonomous proximity operations. At present this algorithm exists as a simulation, running upon an 80386 based personal computer, using a ModelMATE CAD package to render the target vehicle. However, the algorithm is sufficiently simple, so that following off-line training on a known target vehicle, it should run in real time with existing vision hardware. The basis of the algorithm is a sequence of single camera images of the target vehicle, upon which radial transforms were performed. Selected points of the resulting radial signatures are fed through a decision tree, to determine whether the signature matches that of the known reference signatures for a particular view of the target. Based upon recognized scenes, the position of the maneuvering vehicle with respect to the target vehicles can be calculated, and adjustments made in the former's trajectory. In addition, the pose and spin rates of the target satellite can be estimated using this method.

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

Cycle 24 COS FUV Internal/External Wavelength Scale Monitor

NASA Astrophysics Data System (ADS)

Fischer, William J.

2018-02-01

We report on the monitoring of the COS FUV wavelength scale zero-points during Cycle 24 in program 14855. Select cenwaves were monitored for all FUV gratings at Lifetime Position 3. The target and cenwaves have remained the same since Cycle 21, with a change only to the target acquisition sequence. All measured offsets are within the error goals, although the G140L cenwaves show offsets at the short-wavelength end of segment A that are approaching the tolerance. This behavior will be closely monitored in subsequent iterations of the program.

[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.

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.

The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning.

PubMed

Decherchi, Sergio; Berteotti, Anna; Bottegoni, Giovanni; Rocchia, Walter; Cavalli, Andrea

2015-01-27

The study of biomolecular interactions between a drug and its biological target is of paramount importance for the design of novel bioactive compounds. In this paper, we report on the use of molecular dynamics (MD) simulations and machine learning to study the binding mechanism of a transition state analogue (DADMe-immucillin-H) to the purine nucleoside phosphorylase (PNP) enzyme. Microsecond-long MD simulations allow us to observe several binding events, following different dynamical routes and reaching diverse binding configurations. These simulations are used to estimate kinetic and thermodynamic quantities, such as kon and binding free energy, obtaining a good agreement with available experimental data. In addition, we advance a hypothesis for the slow-onset inhibition mechanism of DADMe-immucillin-H against PNP. Combining extensive MD simulations with machine learning algorithms could therefore be a fruitful approach for capturing key aspects of drug-target recognition and binding.

Development of a wearable measurement and control unit for personal customizing machine-supported exercise.

PubMed

Wang, Zhihui; Tamura, Naoki; Kiryu, Tohru

2005-01-01

Wearable technology has been used in various health-related fields to develop advanced monitoring solutions. However, the monitoring function alone cannot meet all the requirements of personal customizing machine-supported exercise that have biosignal-based controls. In this paper, we propose a new wearable unit design equipped with measurement and control functions to support the personal customization process. The wearable unit can measure the heart rate and electromyogram signals during exercise and output workload control commands to the exercise machines. We then applied a prototype of the wearable unit to an Internet-based cycle ergometer system. The wearable unit was examined using twelve young people to check its feasibility. The results verified that the unit could successfully adapt to the control of the workload and was effective for continuously supporting gradual changes in physical activities.

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

Experimental investigation of the ecological hybrid refrigeration cycle

NASA Astrophysics Data System (ADS)

Cyklis, Piotr; Kantor, Ryszard; Ryncarz, Tomasz; Górski, Bogusław; Duda, Roman

2014-09-01

The requirements for environmentally friendly refrigerants promote application of CO2 and water as working fluids. However there are two problems related to that, namely high temperature limit for CO2 in condenser due to the low critical temperature, and low temperature limit for water being the result of high triple point temperature. This can be avoided by application of the hybrid adsorption-compression system, where water is the working fluid in the adsorption high temperature cycle used to cool down the CO2 compression cycle condenser. The adsorption process is powered with a low temperature renewable heat source as solar collectors or other waste heat source. The refrigeration system integrating adsorption and compression system has been designed and constructed in the Laboratory of Thermodynamics and Thermal Machine Measurements of Cracow University of Technology. The heat source for adsorption system consists of 16 tube tulbular collectors. The CO2 compression low temperature cycle is based on two parallel compressors with frequency inverter. Energy efficiency and TEWI of this hybrid system is quite promising in comparison with the compression only systems.

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.

Markerless gating for lung cancer radiotherapy based on machine learning techniques

NASA Astrophysics Data System (ADS)

Lin, Tong; Li, Ruijiang; Tang, Xiaoli; Dy, Jennifer G.; Jiang, Steve B.

2009-03-01

In lung cancer radiotherapy, radiation to a mobile target can be delivered by respiratory gating, for which we need to know whether the target is inside or outside a predefined gating window at any time point during the treatment. This can be achieved by tracking one or more fiducial markers implanted inside or near the target, either fluoroscopically or electromagnetically. However, the clinical implementation of marker tracking is limited for lung cancer radiotherapy mainly due to the risk of pneumothorax. Therefore, gating without implanted fiducial markers is a promising clinical direction. We have developed several template-matching methods for fluoroscopic marker-less gating. Recently, we have modeled the gating problem as a binary pattern classification problem, in which principal component analysis (PCA) and support vector machine (SVM) are combined to perform the classification task. Following the same framework, we investigated different combinations of dimensionality reduction techniques (PCA and four nonlinear manifold learning methods) and two machine learning classification methods (artificial neural networks—ANN and SVM). Performance was evaluated on ten fluoroscopic image sequences of nine lung cancer patients. We found that among all combinations of dimensionality reduction techniques and classification methods, PCA combined with either ANN or SVM achieved a better performance than the other nonlinear manifold learning methods. ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods. Furthermore, the running time for both ANN and SVM with PCA is within tolerance for real-time applications. Overall, ANN combined with PCA is a better candidate than other combinations we investigated in this work for real-time gated radiotherapy.

Interpreting linear support vector machine models with heat map molecule coloring

PubMed Central

2011-01-01

Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor. PMID:21439031

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…

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.

Application of Machine Learning to Proteomics Data: Classification and Biomarker Identification in Postgenomics Biology

PubMed Central

Swan, Anna Louise; Mobasheri, Ali; Allaway, David; Liddell, Susan

2013-01-01

Abstract Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes. PMID:24116388

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.

Scanning Electron Microscopy Analysis of the Adaptation of Single-Unit Screw-Retained Computer-Aided Design/Computer-Aided Manufacture Abutments After Mechanical Cycling.

PubMed

Markarian, Roberto Adrian; Galles, Deborah Pedroso; Gomes França, Fabiana Mantovani

To measure the microgap between dental implants and custom abutments fabricated using different computer-aided design/computer-aided manufacture (CAD/CAM) methods before and after mechanical cycling. CAD software (Dental System, 3Shape) was used to design a custom abutment for a single-unit, screw-retained crown compatible with a 4.1-mm external hexagon dental implant. The resulting stereolithography file was sent for manufacturing using four CAD/CAM methods (n = 40): milling and sintering of zirconium dioxide (ZO group), cobalt-chromium (Co-Cr) sintered via selective laser melting (SLM group), fully sintered machined Co-Cr alloy (MM group), and machined and sintered agglutinated Co-Cr alloy powder (AM group). Prefabricated titanium abutments (TI group) were used as controls. Each abutment was placed on a dental implant measuring 4.1× 11 mm (SA411, SIN) inserted into an aluminum block. Measurements were taken using scanning electron microscopy (SEM) (×4,000) on four regions of the implant-abutment interface (IAI) and at a relative distance of 90 degrees from each other. The specimens were mechanically aged (1 million cycles, 2 Hz, 100 N, 37°C) and the IAI width was measured again using the same approach. Data were analyzed using two-way analysis of variance, followed by the Tukey test. After mechanical cycling, the best adaptation results were obtained from the TI (2.29 ± 1.13 μm), AM (3.58 ± 1.80 μm), and MM (1.89 ± 0.98 μm) groups. A significantly worse adaptation outcome was observed for the SLM (18.40 ± 20.78 μm) and ZO (10.42 ± 0.80 μm) groups. Mechanical cycling had a marked effect only on the AM specimens, which significantly increased the microgap at the IAI. Custom abutments fabricated using fully sintered machined Co-Cr alloy and machined and sintered agglutinated Co-Cr alloy powder demonstrated the best adaptation results at the IAI, similar to those obtained with commercial prefabricated titanium abutments after mechanical cycling. The

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

A Photometric Machine-Learning Method to Infer Stellar Metallicity

NASA Technical Reports Server (NTRS)

Miller, Adam A.

2015-01-01

Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' < or = 18 mag), with 4500 K < or = Teff < or = 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of approx.0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

The Hooey Machine.

ERIC Educational Resources Information Center

Scarnati, James T.; Tice, Craig J.

1992-01-01

Describes how students can make and use Hooey Machines to learn how mechanical energy can be transferred from one object to another within a system. The Hooey Machine is made using a pencil, eight thumbtacks, one pushpin, tape, scissors, graph paper, and a plastic lid. (PR)

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

PubMed Central

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-01-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. PMID:29652405

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

NASA Astrophysics Data System (ADS)

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-03-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified data sets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.

Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.

PubMed

Zamora-Martinez, Francisco; Castro-Bleda, Maria Jose

2018-02-22

Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

Dynamism in a Semiconductor Industrial Machine Allocation Problem using a Hybrid of the Bio-inspired and Musical-Harmony Approach

NASA Astrophysics Data System (ADS)

Kalsom Yusof, Umi; Nor Akmal Khalid, Mohd

2015-05-01

Semiconductor industries need to constantly adjust to the rapid pace of change in the market. Most manufactured products usually have a very short life cycle. These scenarios imply the need to improve the efficiency of capacity planning, an important aspect of the machine allocation plan known for its complexity. Various studies have been performed to balance productivity and flexibility in the flexible manufacturing system (FMS). Many approaches have been developed by the researchers to determine the suitable balance between exploration (global improvement) and exploitation (local improvement). However, not much work has been focused on the domain of machine allocation problem that considers the effects of machine breakdowns. This paper develops a model to minimize the effect of machine breakdowns, thus increasing the productivity. The objectives are to minimize system unbalance and makespan as well as increase throughput while satisfying the technological constraints such as machine time availability. To examine the effectiveness of the proposed model, results for throughput, system unbalance and makespan on real industrial datasets were performed with applications of intelligence techniques, that is, a hybrid of genetic algorithm and harmony search. The result aims to obtain a feasible solution to the domain problem.

Effects of target typicality on categorical search.

PubMed

Maxfield, Justin T; Stalder, Westri D; Zelinsky, Gregory J

2014-10-01

The role of target typicality in a categorical visual search task was investigated by cueing observers with a target name, followed by a five-item target present/absent search array in which the target images were rated in a pretest to be high, medium, or low in typicality with respect to the basic-level target cue. Contrary to previous work, we found that search guidance was better for high-typicality targets compared to low-typicality targets, as measured by both the proportion of immediate target fixations and the time to fixate the target. Consistent with previous work, we also found an effect of typicality on target verification times, the time between target fixation and the search judgment; as target typicality decreased, verification times increased. To model these typicality effects, we trained Support Vector Machine (SVM) classifiers on the target categories, and tested these on the corresponding specific targets used in the search task. This analysis revealed significant differences in classifier confidence between the high-, medium-, and low-typicality groups, paralleling the behavioral results. Collectively, these findings suggest that target typicality broadly affects both search guidance and verification, and that differences in typicality can be predicted by distance from an SVM classification boundary. © 2014 ARVO.

Effect of the Machined Surfaces of AISI 4337 Steel to Cutting Conditions on Dry Machining Lathe

NASA Astrophysics Data System (ADS)

Rahim, Robbi; Napid, Suhardi; Hasibuan, Abdurrozzaq; Rahmah Sibuea, Siti; Yusmartato, Y.

2018-04-01

The objective of the research is to obtain a cutting condition which has a good chance of realizing dry machining concept on AISI 4337 steel material by studying surface roughness, microstructure and hardness of machining surface. The data generated from the experiment were then processed and analyzed using the standard Taguchi method L9 (34) orthogonal array. Testing of dry and wet machining used surface test and micro hardness test for each of 27 test specimens. The machining results of the experiments showed that average surface roughness (Raavg) was obtained at optimum cutting conditions when VB 0.1 μm, 0.3 μm and 0.6 μm respectively 1.467 μm, 2.133 μm and 2,800 μm fo r dry machining while which was carried out by wet machining the results obtained were 1,833 μm, 2,667 μm and 3,000 μm. It can be concluded that dry machining provides better surface quality of machinery results than wet machining. Therefore, dry machining is a good choice that may be realized in the manufacturing and automotive industries.

Evaluation of machinability and flexural strength of a novel dental machinable glass-ceramic.

PubMed

Qin, Feng; Zheng, Shucan; Luo, Zufeng; Li, Yong; Guo, Ling; Zhao, Yunfeng; Fu, Qiang

2009-10-01

To evaluate the machinability and flexural strength of a novel dental machinable glass-ceramic (named PMC), and to compare the machinability property with that of Vita Mark II and human enamel. The raw batch materials were selected and mixed. Four groups of novel glass-ceramics were formed at different nucleation temperatures, and were assigned to Group 1, Group 2, Group 3 and Group 4. The machinability of the four groups of novel glass-ceramics, Vita Mark II ceramic and freshly extracted human premolars were compared by means of drilling depth measurement. A three-point bending test was used to measure the flexural strength of the novel glass-ceramics. The crystalline phases of the group with the best machinability were identified by X-ray diffraction. In terms of the drilling depth, Group 2 of the novel glass-ceramics proves to have the largest drilling depth. There was no statistical difference among Group 1, Group 4 and the natural teeth. The drilling depth of Vita MK II was statistically less than that of Group 1, Group 4 and the natural teeth. Group 3 had the least drilling depth. In respect of the flexural strength, Group 2 exhibited the maximum flexural strength; Group 1 was statistically weaker than Group 2; there was no statistical difference between Group 3 and Group 4, and they were the weakest materials. XRD of Group 2 ceramic showed that a new type of dental machinable glass-ceramic containing calcium-mica had been developed by the present study and was named PMC. PMC is promising for application as a dental machinable ceramic due to its good machinability and relatively high strength.

Electrical machines with superconducting windings. Part 3: Homopolar dc machines

NASA Astrophysics Data System (ADS)

Kullman, D.; Henninger, P.

1981-01-01

The losses in rotating liquid metal contacts and the problems in including liquid metals were theoretically and experimentally studied. These machines are shown realiable. For electric ship propulsion, they are a more efficient method of power transmission than mechanical gearboxes. However, weight reduction as compared to mechanical gearboxes can hardly be achieved with machines fully shielded by magnetic iron.

Malone-brayton cycle engine/heat pump

NASA Astrophysics Data System (ADS)

Gilmour, Thomas A.

1994-07-01

A machine, such as a heat pump, and having an all liquid heat exchange fluid, operates over a more nearly ideal thermodynamic cycle by adjustment of the proportionality of the volumetric capacities of a compressor and an expander to approximate the proportionality of the densities of the liquid heat exchange fluid at the chosen working pressures. Preferred forms of a unit including both the compressor and the expander on a common shaft employs difference in axial lengths of rotary pumps of the gear or vane type to achieve the adjustment of volumetric capacity. Adjustment of the heat pump system for differing heat sink conditions preferably employs variable compression ratio pumps.

In silico re-identification of properties of drug target proteins.

PubMed

Kim, Baeksoo; Jo, Jihoon; Han, Jonghyun; Park, Chungoo; Lee, Hyunju

2017-05-31

Computational approaches in the identification of drug targets are expected to reduce time and effort in drug development. Advances in genomics and proteomics provide the opportunity to uncover properties of druggable genomes. Although several studies have been conducted for distinguishing drug targets from non-drug targets, they mainly focus on the sequences and functional roles of proteins. Many other properties of proteins have not been fully investigated. Using the DrugBank (version 3.0) database containing nearly 6,816 drug entries including 760 FDA-approved drugs and 1822 of their targets and human UniProt/Swiss-Prot databases, we defined 1578 non-redundant drug target and 17,575 non-drug target proteins. To select these non-redundant protein datasets, we built four datasets (A, B, C, and D) by considering clustering of paralogous proteins. We first reassessed the widely used properties of drug target proteins. We confirmed and extended that drug target proteins (1) are likely to have more hydrophobic, less polar, less PEST sequences, and more signal peptide sequences higher and (2) are more involved in enzyme catalysis, oxidation and reduction in cellular respiration, and operational genes. In this study, we proposed new properties (essentiality, expression pattern, PTMs, and solvent accessibility) for effectively identifying drug target proteins. We found that (1) drug targetability and protein essentiality are decoupled, (2) druggability of proteins has high expression level and tissue specificity, and (3) functional post-translational modification residues are enriched in drug target proteins. In addition, to predict the drug targetability of proteins, we exploited two machine learning methods (Support Vector Machine and Random Forest). When we predicted drug targets by combining previously known protein properties and proposed new properties, an F-score of 0.8307 was obtained. When the newly proposed properties are integrated, the prediction performance

The method of micro-motion cycle feature extraction based on confidence coefficient evaluation criteria

NASA Astrophysics Data System (ADS)

Tang, Chuanzi; Ren, Hongmei; Bo, Li; Jing, Huang

2017-11-01

In radar target recognition, the micro motion characteristics of target is one of the characteristics that researchers pay attention to at home and abroad, in which the characteristics of target precession cycle is one of the important characteristics of target movement characteristics. Periodic feature extraction methods have been studied for years, the complex shape of the target and the scattering center stack lead to random fluctuations of the RCS. These random fluctuations also exist certain periodicity, which has a great influence on the target recognition result. In order to solve the problem, this paper proposes a extraction method of micro-motion cycle feature based on confidence coefficient evaluation criteria.

Dual linear structured support vector machine tracking method via scale correlation filter

NASA Astrophysics Data System (ADS)

Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

2018-01-01

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

Machine-learning-assisted materials discovery using failed experiments

NASA Astrophysics Data System (ADS)

Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F.; Falk, Casey; Wenny, Malia B.; Mollo, Aurelio; Zeller, Matthias; Friedler, Sorelle A.; Schrier, Joshua; Norquist, Alexander J.

2016-05-01

Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on ‘dark’ reactions—failed or unsuccessful hydrothermal syntheses—collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted

Triangular Quantum Loop Topography for Machine Learning

NASA Astrophysics Data System (ADS)

Zhang, Yi; Kim, Eun-Ah

Despite rapidly growing interest in harnessing machine learning in the study of quantum many-body systems there has been little success in training neural networks to identify topological phases. The key challenge is in efficiently extracting essential information from the many-body Hamiltonian or wave function and turning the information into an image that can be fed into a neural network. When targeting topological phases, this task becomes particularly challenging as topological phases are defined in terms of non-local properties. Here we introduce triangular quantum loop (TQL) topography: a procedure of constructing a multi-dimensional image from the ''sample'' Hamiltonian or wave function using two-point functions that form triangles. Feeding the TQL topography to a fully-connected neural network with a single hidden layer, we demonstrate that the architecture can be effectively trained to distinguish Chern insulator and fractional Chern insulator from trivial insulators with high fidelity. Given the versatility of the TQL topography procedure that can handle different lattice geometries, disorder, interaction and even degeneracy our work paves the route towards powerful applications of machine learning in the study of topological quantum matters.

Targeting Quiescence in Prostate Cancer

DTIC Science & Technology

2017-10-01

CRISPR /Cas9 to generate cell lines where the reporters are integrated endogenously into 5 essential cell cycle genes to avoid epigenetic silencing. In...Developed and began an improved CRISPR /Cas9-based strategy to target reporters to endogenous gene loci in PC3 and C4-2B cells to prevent silencing...serum. An improved CRISPR /Cas9-based strategy to avoid cell cycle reporter silencing and incorporate a constitutive nuclear marker As described

Flexible Elements in the Mechanisms of Weaving Machines

NASA Astrophysics Data System (ADS)

Žák, J.

Weaving machines use several mechanisms to produce a fabric; their relative (mutual) position is exactly defined at any point of working cycle and must be maintained as accurately as possible. From that, it results some requirements on their design, such as stiffness of the joint frame, synchronization of their drives, accuracy and stiffness of particular links of those mechanisms and minimization of the clearances between them. In this paper, we have attempted to outline the possibility of replacing the binary links by using the flexible mechanism elements. In this step, we always removed one rotary constraint at least which is necessary when using a binary link, i.e., a rod, pitman or connecting rod. In practice, it means reducing the number of bearings which have a limited service life, require maintenance and when using them we cannot avoid the formation of clearances. In the case of a slay of the CAMEL weaving machine, it was furthermore possible to use the deformation energy to a relief of the drive, its better regulation and an overall reduction of energy consumption. Although this procedure is not subject to the use of special materials, there can be advantageously used fiber composites whose certain features make the design of such mechanisms easy to a great extent.

Oil-Free Rotor Support Technologies for Long Life, Closed Cycle Brayton Turbines

NASA Technical Reports Server (NTRS)

Lucero, John M.; DellaCorte, Christopher

2004-01-01

The goal of this study is to provide technological support to ensure successful life and operation of a 50-300 kW dynamic power conversion system specifically with response to the rotor support system. By utilizing technical expertise in tribology, bearings, rotordynamic, solid lubricant coatings and extensive test facilities, valuable input for mission success is provided. A discussion of the history of closed cycle Brayton turboalternators (TA) will be included. This includes the 2 kW Mini-Brayton Rotating Unit (Mini-BRU), the 10kW Brayton Rotating Unit (BRU) and the 125 kW turboalternator-compressor (TAC) designed in mid 1970's. Also included is the development of air-cycle machines and terrestrial oil-free gas turbine power systems in the form of microturbines, specifically Capstone microturbines. A short discussion of the self-acting compliant surface hydrodynamic fluid film bearings, or foil bearings, will follow, including a short history of the load capacity advances, the NASA coatings advancements as well as design model advances. Successes in terrestrial based machines will be noted and NASA tribology and bearing research test facilities will be described. Finally, implementation of a four step integration process will be included in the discussion.

Bearingless AC Homopolar Machine Design and Control for Distributed Flywheel Energy Storage

NASA Astrophysics Data System (ADS)

Severson, Eric Loren

The increasing ownership of electric vehicles, in-home solar and wind generation, and wider penetration of renewable energies onto the power grid has created a need for grid-based energy storage to provide energy-neutral services. These services include frequency regulation, which requires short response-times, high power ramping capabilities, and several charge cycles over the course of one day; and diurnal load-/generation-following services to offset the inherent mismatch between renewable generation and the power grid's load profile, which requires low self-discharge so that a reasonable efficiency is obtained over a 24 hour storage interval. To realize the maximum benefits of energy storage, the technology should be modular and have minimum geographic constraints, so that it is easily scalable according to local demands. Furthermore, the technology must be economically viable to participate in the energy markets. There is currently no storage technology that is able to simultaneously meet all of these needs. This dissertation focuses on developing a new energy storage device based on flywheel technology to meet these needs. It is shown that the bearingless ac homopolar machine can be used to overcome key obstacles in flywheel technology, namely: unacceptable self-discharge and overall system cost and complexity. Bearingless machines combine the functionality of a magnetic bearing and a motor/generator into a single electromechanical device. Design of these machines is particularly challenging due to cross-coupling effects and trade-offs between motor and magnetic bearing capabilities. The bearingless ac homopolar machine adds to these design challenges due to its 3D flux paths requiring computationally expensive 3D finite element analysis. At the time this dissertation was started, bearingless ac homopolar machines were a highly immature technology. This dissertation advances the state-of-the-art of these machines through research contributions in the areas of

Design Control Systems of Human Machine Interface in the NTVS-2894 Seat Grinder Machine to Increase the Productivity

NASA Astrophysics Data System (ADS)

Ardi, S.; Ardyansyah, D.

2018-02-01

In the Manufacturing of automotive spare parts, increased sales of vehicles is resulted in increased demand for production of engine valve of the customer. To meet customer demand, we carry out improvement and overhaul of the NTVS-2894 seat grinder machine on a machining line. NTVS-2894 seat grinder machine has been decreased machine productivity, the amount of trouble, and the amount of downtime. To overcome these problems on overhaul the NTVS-2984 seat grinder machine include mechanical and programs, is to do the design and manufacture of HMI (Human Machine Interface) GP-4501T program. Because of the time prior to the overhaul, NTVS-2894 seat grinder machine does not have a backup HMI (Human Machine Interface) program. The goal of the design and manufacture in this program is to improve the achievement of production, and allows an operator to operate beside it easier to troubleshoot the NTVS-2894 seat grinder machine thereby reducing downtime on the NTVS-2894 seat grinder machine. The results after the design are HMI program successfully made it back, machine productivity increased by 34.8%, the amount of trouble, and downtime decreased 40% decrease from 3,160 minutes to 1,700 minutes. The implication of our design, it could facilitate the operator in operating machine and the technician easer to maintain and do the troubleshooting the machine problems.

Focus on flaviviruses: current and future drug targets.

PubMed

Geiss, Brian J; Stahla, Hillary; Hannah, Amanda M; Gari, Amanda M; Keenan, Susan M

2009-05-01

Infection by mosquito-borne flaviviruses (family Flaviviridae) is increasing in prevalence worldwide. The vast global, social and economic impact due to the morbidity and mortality associated with the diseases caused by these viruses necessitates therapeutic intervention. There is currently no effective clinical treatment for any flaviviral infection. Therefore, there is a great need for the identification of novel inhibitors to target the virus life cycle. In this article, we discuss structural and nonstructural viral proteins that are the focus of current target validation and drug discovery efforts. Both inhibition of essential enzymatic activities and disruption of necessary protein–protein interactions are considered. In addition, we address promising new targets for future research. As our molecular and biochemical understanding of the flavivirus life cycle increases, the number of targets for antiviral therapeutic discovery grows and the possibility for novel drug discovery continues to strengthen.

Linear and angular retroreflecting interferometric alignment target

DOEpatents

Maxey, L. Curtis

2001-01-01

The present invention provides a method and apparatus for measuring both the linear displacement and angular displacement of an object using a linear interferometer system and an optical target comprising a lens, a reflective surface and a retroreflector. The lens, reflecting surface and retroreflector are specifically aligned and fixed in optical connection with one another, creating a single optical target which moves as a unit that provides multi-axis displacement information for the object with which it is associated. This displacement information is useful in many applications including machine tool control systems and laser tracker systems, among others.

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

Machine learning for autonomous crystal structure identification.

PubMed

Reinhart, Wesley F; Long, Andrew W; Howard, Michael P; Ferguson, Andrew L; Panagiotopoulos, Athanassios Z

2017-07-21

We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.

MicroRNA-424/503 cluster members regulate bovine granulosa cell proliferation and cell cycle progression by targeting SMAD7 gene through activin signalling pathway.

PubMed

Pande, Hari Om; Tesfaye, Dawit; Hoelker, Michael; Gebremedhn, Samuel; Held, Eva; Neuhoff, Christiane; Tholen, Ernst; Schellander, Karl; Wondim, Dessie Salilew

2018-05-01

The granulosa cells are indispensable for follicular development and its function is orchestrated by several genes, which in turn posttranscriptionally regulated by microRNAs (miRNA). In our previous study, the miRRNA-424/503 cluster was found to be highly abundant in bovine granulosa cells (bGCs) of preovulatory dominant follicle compared to subordinate counterpart at day 19 of the bovine estrous cycle. Other study also indicated the involvement of miR-424/503 cluster in tumour cell resistance to apoptosis suggesting this miRNA cluster may involve in cell survival. However, the role of miR-424/503 cluster in granulosa cell function remains elusive Therefore, this study aimed to investigate the role of miRNA-424/503 cluster in bGCs function using microRNA gain- and loss-of-function approaches. The role of miR-424/503 cluster members in granulosa cell function was investigated by overexpressing or inhibiting its activity in vitro cultured granulosa cells using miR-424/503 mimic or inhibitor, respectively. Luciferase reporter assay showed that SMAD7 and ACVR2A are the direct targets of the miRNA-424/503 cluster members. In line with this, overexpression of miRNA-424/503 cluster members using its mimic and inhibition of its activity by its inhibitor reduced and increased, respectively the expression of SMAD7 and ACVR2A. Furthermore, flow cytometric analysis indicated that overexpression of miRNA-424/503 cluster members enhanced bGCs proliferation by promoting G1- to S- phase cell cycle transition. Modulation of miRNA-424/503 cluster members tended to increase phosphorylation of SMAD2/3 in the Activin signalling pathway. Moreover, sequence specific knockdown of SMAD7, the target gene of miRNA-424/503 cluster members, using small interfering RNA also revealed similar phenotypic and molecular alterations observed when miRNA-424/503 cluster members were overexpressed. Similarly, to get more insight about the role of miRNA-424/503 cluster members in activin signalling

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

PubMed

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

Weldability, machinability and surfacing of commercial duplex stainless steel AISI2205 for marine applications - A recent review.

PubMed

Vinoth Jebaraj, A; Ajaykumar, L; Deepak, C R; Aditya, K V V

2017-05-01

In the present review, attempts have been made to analyze the metallurgical, mechanical, and corrosion properties of commercial marine alloy duplex stainless steel AISI 2205 with special reference to its weldability, machinability, and surfacing. In the first part, effects of various fusion and solid-state welding processes on joining DSS 2205 with similar and dissimilar metals are addressed. Microstructural changes during the weld cooling cycle such as austenite reformation, partitioning of alloying elements, HAZ transformations, and the intermetallic precipitations are analyzed and compared with the different welding techniques. In the second part, machinability of DSS 2205 is compared with the commercial ASS grades in order to justify the quality of machining. In the third part, the importance of surface quality in a marine exposure is emphasized and the enhancement of surface properties through peening techniques is highlighted. The research gaps and inferences highlighted in this review will be more useful for the fabrications involved in the marine applications.

The histone acetyltransferase component TRRAP is targeted for destruction during the cell cycle.

PubMed

Ichim, G; Mola, M; Finkbeiner, M G; Cros, M-P; Herceg, Z; Hernandez-Vargas, H

2014-01-09

Chromosomes are dynamic structures that must be reversibly condensed and unfolded to accommodate mitotic division and chromosome segregation. Histone modifications are involved in the striking chromatin reconfiguration taking place during mitosis. However, the mechanisms that regulate activity and function of histone-modifying factors as cells enter and exit mitosis are poorly understood. Here, we show that the anaphase-promoting complex or cyclosome (APC/C) is involved in the mitotic turnover of TRRAP (TRansformation/tRanscription domain-Associated Protein), a common component of histone acetyltransferase (HAT) complexes, and that the pre-mitotic degradation of TRRAP is mediated by the APC/C ubiquitin ligase activators Cdc20 and Cdh1. Ectopic expression of both Cdh1 and Cdc20 reduced the levels of coexpressed TRRAP protein and induced its ubiquitination. TRRAP overexpression or stabilization induces multiple mitotic defects, including lagging chromosomes, chromosome bridges and multipolar spindles. In addition, lack of sister chromatid cohesion and impaired chromosome condensation were found after TRRAP overexpression or stabilization. By using a truncated form of TRRAP, we show that mitotic delay is associated with a global histone H4 hyperacetylation induced by TRRAP overexpression. These results demonstrate that the chromatin modifier TRRAP is targeted for destruction in a cell cycle-dependent fashion. They also suggest that degradation of TRRAP by the APC/C is necessary for a proper condensation of chromatin and proper chromosome segregation. Chromatin compaction mediated by histone modifiers may represent a fundamental arm for APC/C orchestration of the mitotic machinery.

High duty cycle echolocation and prey detection by bats.

PubMed

Lazure, Louis; Fenton, M Brock

2011-04-01

There are two very different approaches to laryngeal echolocation in bats. Although most bats separate pulse and echo in time by signalling at low duty cycles (LDCs), almost 20% of species produce calls at high duty cycles (HDCs) and separate pulse and echo in frequency. HDC echolocators are sensitive to Doppler shifts. HDC echolocation is well suited to detecting fluttering targets such as flying insects against a cluttered background. We used two complementary experiments to evaluate the relative effectiveness of LDC and HDC echolocation for detecting fluttering prey. We measured echoes from fluttering targets by broadcasting artificial bat calls, and found that echo amplitude was greatest for sounds similar to those used in HDC echolocation. We also collected field recordings of syntopic LDC and HDC bats approaching an insect-like fluttering target and found that HDC bats approached the target more often (18.6% of passes) than LDC bats (1.2% of passes). Our results suggest that some echolocation call characteristics, particularly duty cycle and pulse duration, translate into improved ability to detect fluttering targets in clutter, and that HDC echolocation confers a superior ability to detect fluttering prey in the forest understory compared with LDC echolocation. The prevalence of moths in the diets of HDC bats, which is often used as support for the allotonic frequency hypothesis, can therefore be partly explained by the better flutter detection ability of HDC bats.

Machine vision for digital microfluidics

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun; Lee, Jeong-Bong

2010-01-01

Machine vision is widely used in an industrial environment today. It can perform various tasks, such as inspecting and controlling production processes, that may require humanlike intelligence. The importance of imaging technology for biological research or medical diagnosis is greater than ever. For example, fluorescent reporter imaging enables scientists to study the dynamics of gene networks with high spatial and temporal resolution. Such high-throughput imaging is increasingly demanding the use of machine vision for real-time analysis and control. Digital microfluidics is a relatively new technology with expectations of becoming a true lab-on-a-chip platform. Utilizing digital microfluidics, only small amounts of biological samples are required and the experimental procedures can be automatically controlled. There is a strong need for the development of a digital microfluidics system integrated with machine vision for innovative biological research today. In this paper, we show how machine vision can be applied to digital microfluidics by demonstrating two applications: machine vision-based measurement of the kinetics of biomolecular interactions and machine vision-based droplet motion control. It is expected that digital microfluidics-based machine vision system will add intelligence and automation to high-throughput biological imaging in the future.

Monel Machining

NASA Technical Reports Server (NTRS)

1983-01-01

Castle Industries, Inc. is a small machine shop manufacturing replacement plumbing repair parts, such as faucet, tub and ballcock seats. Therese Castley, president of Castle decided to introduce Monel because it offered a chance to improve competitiveness and expand the product line. Before expanding, Castley sought NERAC assistance on Monel technology. NERAC (New England Research Application Center) provided an information package which proved very helpful. The NASA database was included in NERAC's search and yielded a wealth of information on machining Monel.

Transfer rates of enteric microorganisms in recycled water during machine clothes washing.

PubMed

O'Toole, Joanne; Sinclair, Martha; Leder, Karin

2009-03-01

Approximately 15% of overall Australian household water usage is in the laundry; hence, a significant reduction in household drinking water demand could be achieved if potable-quality water used for clothes washing is replaced with recycled water. To investigate the microbiological safety of using recycled water in washing machines, bacteriophages MS-2 and PRD-1, Escherichia coli, and Cryptosporidium parvum oocysts were used in a series of experiments to investigate the transfer efficiency of enteric microorganisms from washing machine water to objects including hands, environmental surfaces, air, and fabric swatches. By determining the transference efficiency, it is possible to estimate the numbers of microorganisms that the user will be exposed to if recycled water with various levels of residual microorganisms is used in washing machines. Results, expressed as transfer rates to a given surface area per object, showed that the mean transfer efficiency of E. coli, bacteriophages MS-2 and PRD-1, and C. parvum oocysts from seeded water to fabric swatches ranged from 0.001% to 0.090%. Greatest exposure to microorganisms occurred through direct contact of hands with seeded water and via hand contact with contaminated fabric swatches. No microorganisms were detected in the air samples during the washing machine spin cycle, and transfer rates of bacteriophages from water to environmental surfaces were 100-fold less than from water directly to hands. Findings from this study provide relevant information that can be used to refine regulations governing recycled water and to allay public concerns about the use of recycled water.

Target-induced formation of gold amalgamation on DNA-based sensing platform for electrochemical monitoring of mercury ion coupling with cycling signal amplification strategy.

PubMed

Chen, Jinfeng; Tang, Juan; Zhou, Jun; Zhang, Lan; Chen, Guonan; Tang, Dianping

2014-01-31

Heavy metal ion pollution poses severe risks in human health and environmental pollutant, because of the likelihood of bioaccumulation and toxicity. Driven by the requirement to monitor trace-level mercury ion (Hg(2+)), herein we construct a new DNA-based sensor for sensitive electrochemical monitoring of Hg(2+) by coupling target-induced formation of gold amalgamation on DNA-based sensing platform with gold amalgamation-catalyzed cycling signal amplification strategy. The sensor was simply prepared by covalent conjugation of aminated poly-T(25) oligonucleotide onto the glassy carbon electrode by typical carbodiimide coupling. Upon introduction of target analyte, Hg(2+) ion was intercalated into the DNA polyion complex membrane based on T-Hg(2+)-T coordination chemistry. The chelated Hg(2+) ion could induce the formation of gold amalgamation, which could catalyze the p-nitrophenol with the aid of NaBH4 and Ru(NH3)6(3+) for cycling signal amplification. Experimental results indicated that the electronic signal of our system increased with the increasing Hg(2+) level in the sample, and has a detection limit of 0.02nM with a dynamic range of up to 1000nM Hg(2+). The strategy afforded exquisite selectivity for Hg(2+) against other environmentally related metal ions. In addition, the methodology was evaluated for the analysis of Hg(2+) in spiked tap-water samples, and the recovery was 87.9-113.8%. Copyright © 2013 Elsevier B.V. All rights reserved.

Gloved Human-Machine Interface

NASA Technical Reports Server (NTRS)

Adams, Richard (Inventor); Hannaford, Blake (Inventor); Olowin, Aaron (Inventor)

2015-01-01

Certain exemplary embodiments can provide a system, machine, device, manufacture, circuit, composition of matter, and/or user interface adapted for and/or resulting from, and/or a method and/or machine-readable medium comprising machine-implementable instructions for, activities that can comprise and/or relate to: tracking movement of a gloved hand of a human; interpreting a gloved finger movement of the human; and/or in response to interpreting the gloved finger movement, providing feedback to the human.

Vapor Compression Cycle Design Program (CYCLE_D)

National Institute of Standards and Technology Data Gateway

SRD 49 NIST Vapor Compression Cycle Design Program (CYCLE_D) (PC database for purchase)   The CYCLE_D database package simulates the vapor compression refrigeration cycles. It is fully compatible with REFPROP 9.0 and covers the 62 single-compound refrigerants . Fluids can be used in mixtures comprising up to five components.

Friction Laws Derived From the Acoustic Emissions of a Laboratory Fault by Machine Learning

NASA Astrophysics Data System (ADS)

Rouet-Leduc, B.; Hulbert, C.; Ren, C. X.; Bolton, D. C.; Marone, C.; Johnson, P. A.

2017-12-01

Fault friction controls nearly all aspects of fault rupture, yet it is only possible to measure in the laboratory. Here we describe laboratory experiments where acoustic emissions are recorded from the fault. We find that by applying a machine learning approach known as "extreme gradient boosting trees" to the continuous acoustical signal, the fault friction can be directly inferred, showing that instantaneous characteristics of the acoustic signal are a fingerprint of the frictional state. This machine learning-based inference leads to a simple law that links the acoustic signal to the friction state, and holds for every stress cycle the laboratory fault goes through. The approach does not use any other measured parameter than instantaneous statistics of the acoustic signal. This finding may have importance for inferring frictional characteristics from seismic waves in Earth where fault friction cannot be measured.

THE TEACHING MACHINE.

ERIC Educational Resources Information Center

KLEIN, CHARLES; WAYNE, ELLIS

THE ROLE OF THE TEACHING MACHINE IS COMPARED WITH THE ROLE OF THE PROGRAMED TEXTBOOK. THE TEACHING MACHINE IS USED FOR INDIVIDUAL INSTRUCTION, CONTAINS AND PRESENTS PROGRAM CONTENT IN STEPS, PROVIDES A MEANS WHEREBY THE STUDENT MAY RESPOND TO THE PROGRAM, PROVIDES THE STUDENT WITH IMMEDIATE INFORMATION OF SOME KIND CONCERNING HIS RESPONSE THAT CAN…

Machine Translation Project

NASA Technical Reports Server (NTRS)

Bajis, Katie

1993-01-01

The characteristics and capabilities of existing machine translation systems were examined and procurement recommendations were developed. Four systems, SYSTRAN, GLOBALINK, PC TRANSLATOR, and STYLUS, were determined to meet the NASA requirements for a machine translation system. Initially, four language pairs were selected for implementation. These are Russian-English, French-English, German-English, and Japanese-English.

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.

PubMed

Ferreira da Costa, Joana; Silva, David; Caamaño, Olga; Brea, José M; Loza, Maria Isabel; Munteanu, Cristian R; Pazos, Alejandro; García-Mera, Xerardo; González-Díaz, Humbert

2018-06-25

Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound

OptiCentric lathe centering machine

NASA Astrophysics Data System (ADS)

Buß, C.; Heinisch, J.

2013-09-01

High precision optics depend on precisely aligned lenses. The shift and tilt of individual lenses as well as the air gap between elements require accuracies in the single micron regime. These accuracies are hard to meet with traditional assembly methods. Instead, lathe centering can be used to machine the mount with respect to the optical axis. Using a diamond turning process, all relevant errors of single mounted lenses can be corrected in one post-machining step. Building on the OptiCentric® and OptiSurf® measurement systems, Trioptics has developed their first lathe centering machines. The machine and specific design elements of the setup will be shown. For example, the machine can be used to turn optics for i-line steppers with highest precision.

Machinability of Stellite 6 hardfacing

NASA Astrophysics Data System (ADS)

Benghersallah, M.; Boulanouar, L.; Le Coz, G.; Devillez, A.; Dudzinski, D.

2010-06-01

This paper reports some experimental findings concerning the machinability at high cutting speed of nickel-base weld-deposited hardfacings for the manufacture of hot tooling. The forging work involves extreme impacts, forces, stresses and temperatures. Thus, mould dies must be extremely resistant. The aim of the project is to create a rapid prototyping process answering to forging conditions integrating a Stellite 6 hardfacing deposed PTA process. This study talks about the dry machining of the hardfacing, using a two tips machining tool and a high speed milling machine equipped by a power consumption recorder Wattpilote. The aim is to show the machinability of the hardfacing, measuring the power and the tip wear by optical microscope and white light interferometer, using different strategies and cutting conditions.

A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis

PubMed Central

Ni, Ying; Aghamirzaie, Delasa; Elmarakeby, Haitham; Collakova, Eva; Li, Song; Grene, Ruth; Heath, Lenwood S.

2016-01-01

Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date. Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developing Arabidopsis thaliana embryos. We developed the Beacon GRN inference tool to predict GRNs occurring during seed development in Arabidopsis based on a support vector machine (SVM) model. We developed both global and local inference models and compared their performance, demonstrating that local models are generally superior for our application. Using both the expression levels of the genes expressed in developing embryos and prior known regulatory relationships, GRNs were predicted for specific embryonic developmental stages. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. Potential direct targets were identified based on a match between the promoter regions of these inferred targets and the cis elements recognized by specific regulators. Our analysis also provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression. The Beacon GRN inference tool provides a valuable model system for context-specific GRN inference and is freely available at https://github.com/BeaconProjectAtVirginiaTech/beacon_network_inference.git. PMID:28066488

34 CFR 395.32 - Collection and distribution of vending machine income from vending machines on Federal property.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 34 Education 2 2011-07-01 2010-07-01 true Collection and distribution of vending machine income from vending machines on Federal property. 395.32 Section 395.32 Education Regulations of the Offices... Management § 395.32 Collection and distribution of vending machine income from vending machines on Federal...