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RISS 인기검색어
- 검색키워드
- 저자 : Cava, R. J. (검색결과 3 건)
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The FBH family of bHLH transcription factors controls ACC synthase expression in sugarcane
Alessio, Valter Miotto,Cavaç,ana, Natale,Dantas, Luí,za Lane de Barros,Lee, Nayoung,Hotta, Carlos Takeshi,Imaizumi, Takato,Menossi, Marcelo Oxford University Press 2018 Journal of experimental botany Vol.69 No.10
<▼1><P>Identification of transcription factors that control the expression of the sugarcane <I>ACS</I> gene, which is likely involved in ethylene-controlled sucrose accumulation and the circadian regulation of ethylene biosynthesis.</P></▼1><▼2><P><B>Abstract</B></P><P>Ethylene is a phytohormone involved in the regulation of several aspects of plant development and in responses to biotic and abiotic stress. The effects of exogenous application of ethylene to sugarcane plants are well characterized as growth inhibition of immature internodes and stimulation of sucrose accumulation. However, the molecular network underlying the control of ethylene biosynthesis in sugarcane remains largely unknown. The chemical reaction catalyzed by 1-aminocyclopropane-1-carboxylic acid synthase (ACS) is an important rate-limiting step that regulates ethylene production in plants. In this work, using a yeast one-hybrid approach, we identified three basic helix-loop-helix (bHLH) transcription factors, homologs of Arabidopsis FBH (FLOWERING BHLH), that bind to the promoter of <I>ScACS2</I> (Sugarcane <I>ACS2</I>), a sugarcane type 3 ACS isozyme gene. Protein–protein interaction assays showed that sugarcane FBH1 (ScFBH1), ScFBH2, and ScFBH3 form homo- and heterodimers in the nucleus. Gene expression analysis revealed that <I>ScFBHs</I> and <I>ScACS2</I> transcripts are more abundant in maturing internodes during afternoon and night. In addition, Arabidopsis functional analysis demonstrated that FBH controls ethylene production by regulating transcript levels of <I>ACS7</I>, a homolog of <I>ScACS2</I>. These results indicate that ScFBHs transcriptionally regulate ethylene biosynthesis in maturing internodes of sugarcane.</P></▼2>
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A semi-supervised interpretable machine learning framework for sensor fault detection
Panagiotis Martakis,Artur Movsessian,Yves Reuland,Sai G.S. Pai,Said Quqa,David Garcıa Cava,Dmitri Tcherniak,Eleni Chatzi 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easyto-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.
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Transport and thermodynamic properties ofSr3Ru2O7near the quantum critical point
Zhou, Z. X.,McCall, S.,Alexander, C. S.,Crow, J. E.,Schlottmann, P.,Bianchi, A.,Capan, C.,Movshovich, R.,Kim, K. H.,Jaime, M.,Harrison, N.,Haas, M. K.,Cava, R. J.,Cao, G. American Physical Society 2004 Physical review. B, Condensed matter and materials Vol.69 No.14
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