인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Plants must cope with ever-changing temperature conditions in their environment. In many plant species, suboptimal high and low temperatures can induce adaptive mechanisms that allow optimal performance. Thermomorphogenesis is the acclimation to high ambient temperature, whereas cold acclimation refers to the acquisition of cold tolerance following a period of low temperatures. The molecular mechanisms underlying thermomorphogenesis and cold acclimation are increasingly well understood but neither signalling components that have an apparent role in acclimation to both cold and warmth, nor factors determining dose-responsiveness, are currently well defined. This can be explained in part by practical limitations, as applying temperature gradients requires the use of multiple growth conditions simultaneously, usually unavailable in research laboratories. Here we demonstrate that commercially available thermal gradient tables can be used to grow and assess plants over a defined and adjustable steep temperature gradient within one experiment. We describe technical and thermodynamic aspects and provide considerations for plant growth and treatment. We show that plants display the expected morphological, physiological, developmental and molecular responses that are typically associated with high temperature and cold acclimation. This includes temperature dose-response effects on seed germination, hypocotyl elongation, leaf development, hyponasty, rosette growth, temperature marker gene expression, stomatal conductance, chlorophyll content, ion leakage and hydrogen peroxide levels. In conclusion, thermal gradient table systems enable standardized and predictable environments to study plant responses to varying temperature regimes and can be swiftly implemented in research on temperature signalling and response.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
