Cell division and differentiation depend on massive and rapid organelle remodeling. The mitotic oscillator, centered on the cyclin-dependent kinase 1–anaphase-promoting complex/cyclosome (CDK1-APC/C) axis, spatiotemporally coordinates this reorganization in dividing cells. Here we discovered that nondividing cells could also implement this mitotic clocklike regulatory circuit to orchestrate subcellular reorganization associated with differentiation. These postmitotic progenitors fine-tuned mitotic oscillator activity to drive the orderly progression of centriole production, maturation, and motile ciliation while avoiding the mitosis commitment threshold. Insufficient CDK1 activity hindered differentiation, whereas excessive activity accelerated differentiation yet drove postmitotic progenitors into mitosis.
Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price movement prediction. We focus on a wrapper feature selection method using entropy, least-mean squares, and linear discriminant analysis. We also build a new quantitative feature based on adaptive logistic regression for online learning, which is constantly selected first among the majority of the proposed feature selection methods. This study examines the best combination of features using high frequency limit order book data from Nasdaq Nordic. Our results suggest that sorting methods and classifiers can be used in such a way that one can reach the best performance with a combination of only very few advanced hand-crafted features.
The cyanobacterial circadian clock oscillator can be reconstituted in a test tube from just three proteins--KaiA, KaiB, and KaiC--and adenosine triphosphate (ATP). Tseng et al. studied crystal and nuclear magnetic resonance structures of complexes of the oscillator proteins and their signaling output proteins and tested the in vivo effects of structure-based mutants. Large conformational changes in KaiB and ATP hydrolysis by KaiC are coordinated with binding to output protein, which couples signaling and the day-night transitions of the clock. Snijder et al. provide complementary analysis of the oscillator proteins by mass spectrometry and cryo–electron microscopy. Their results help to explain the structural basis for the dynamic assembly of the oscillator complexes.
A paramount contemporary scientific challenge is to understand and control networks. General studies of networks are essential to a variety of disciplines, including materials science, neuroscience, electrical engineering, and microbiology. To date, most studies are observational or "top-down," relying on phenomenological models of nodal behavior deduced from data extracted from observations of the entire network. On the other hand, oscillator synchronization provides a popular "bottom-up" experimental paradigm for studies of network behavior. Synchronization occurs when a large number of networked oscillators tend to phase-lock and reach global consensus, despite the presence of internal disorder (such as differences in oscillator frequencies).
A new mathematical tool has revealed the rhythm of electrical activity in the brain as it responds to external stimuli. While researchers know that this activity, known as neural oscillation, takes place in the hippocampus region in rats to encode information on the animal's position, much of its function in the human brain still remains a mystery. In a series of colourful diagrams, researchers have now revealed how these responses fluctuate, and they say the breakthrough could help improve neuroscientists' understanding of these processes. The researcher developed a mathematical tool based on the concept of a'nonlinear oscillator,' that could shed new light on the process .Linear oscillators respond to a stimulus by mirroring its rhythm – but, nonlinear oscillators vary in their responses Alonso used the Wilson-Cowan model, a widely-used model that describes average activity of populations of interconnected neurons. With this, the researcher was able to develop a mathematical tool based on the concept of a'nonlinear oscillator,' that could shed new light on the process.