Hasil untuk "Biology"

R. Ross

B. Mckibbin, Professor B Mckibbin

H. Akil, S. Watson, E. Young et al.

D. Simberloff, M. Soulé, B. Wilcox

D. Krause, M. Fackler, C. Civin et al.

T. McMahon

D. Vaux, A. Strasser

T. Caro, Gillian M. O'Doherty

H. Bayley, P. Cremer

P. Hedrick, S. Kalinowski

L. Ignarro

C. Dobson

E. Phizicky, A. Hopper

tRNA biology has come of age, revealing an unprecedented level of understanding and many unexpected discoveries along the way. This review highlights new findings on the diverse pathways of tRNA maturation, and on the formation and function of a number of modifications. Topics of special focus include the regulation of tRNA biosynthesis, quality control tRNA turnover mechanisms, widespread tRNA cleavage pathways activated in response to stress and other growth conditions, emerging evidence of signaling pathways involving tRNA and cleavage fragments, and the sophisticated intracellular tRNA trafficking that occurs during and after biosynthesis.

Hua Wei Zhang, Y. Song, R. Tan

P. Bassereau, P. Nassoy

D. Selkoe

O. Schwartz, M. Albert

D. Parrish, J. Fike

D. Culver, T. Pipan

Caves and other subterranean habitats with their often strange (even bizarre) inhabitants have long been objects of fascination, curiosity, and debate. The question of how such organisms have evolved, and the relative roles of natural selection and genetic drift, has engaged subterranean biologists for decades. Indeed, these studies continue to inform the general theory of adaptation and evolution. Subterranean ecosystems generally exhibit little or no primary productivity and, as extreme ecosystems, provide general insights into ecosystem function. The Biology of Caves and other Subterranean Habitats offers a concise but comprehensive introduction to cave ecology and evolution. Whilst there is an emphasis on biological processes occurring in these unique environments, conservation and management aspects are also considered. The monograph includes a global range of examples from more than 25 countries, and case studies from both caves and non-cave subterranean habitats; it also provides a clear explanation of specialized terms used by speleologists. This accessible text will appeal to researchers new to the field and to the many professional ecologists and conservation practitioners requiring a concise but authoritative overview. Its engaging style will also make it suitable for undergraduate and graduate students taking courses in cave and subterranean biology. Its more than 650 references, 150 of which are new since the first edition, provide many entry points to the research literature.

Hyuk Lee

As edge computing environments become increasingly dynamic, the need for efficient job scheduling and proactive fault prevention is becoming paramount. In such environments, minimizing machine downtime and maintaining productivity are critical challenges. In this paper, we propose an integrated approach to scheduling optimization that combines deep learning-based fault prediction with Satisfiability Modulo Theories (SMT)-based scheduling techniques. The proposed system predicts fault probabilities for machines in real time by leveraging operational state features such as temperature, vibration, tool wear, and operating hours. These fault predictions are then used as inputs to the SMT solver, which dynamically optimizes job scheduling. The system ensures task completion within deadlines while minimizing fault risks and optimizing resource utilization. To achieve this, the deep learning model continuously updates fault probabilities through a rolling prediction mechanism, allowing the scheduling system to proactively adapt to changing machine conditions. The SMT solver incorporates these predictions into its optimization process, ensuring that the schedule dynamically reflects the latest system state. The proposed method has been evaluated in simulated production line scenarios, demonstrating significant reductions in machine faults, improved scheduling efficiency, and enhanced overall system reliability. By integrating predictive maintenance with optimization techniques, this research contributes to the development of robust and adaptive scheduling systems for dynamic production environments.