Dimensionality Reduction of High Dimensional Time Series based on Artificial Neural Network

Molecular dynamics is a molecular simulation method which relies on Newtonian mechanics to simulate the motion of molecular system. In this method, some differential equations are integrated, and the results of integration are further processed to obtain the trajectory or momentum evolution process of some particles controlled by dynamic equations, and the technology of extracting the equilibrium state, motion process or related properties of classical particle system can be used. Through molecular dynamics simulation, we can obtain a series of properties of the system, which are widely used in experimental verification, theoretical derivation and other scenarios. Because it can obtain the dynamic state of macromolecules to make up for the limitations of these properties, it is widely used in the study of transmembrane proteins, polypeptide chains and other systems in life sciences. Through the kinetic path reduction of these systems, we can intuitively understand the characteristics of molecular folding, molecular motion and specific binding, which can play a very important role in the study of proteins and peptides. However, due to the characteristics of high-dimensional time series obtained by molecular dynamics simulation, it is difficult for us to pay attention to the collective state or characteristic process of the whole system in a non-equilibrium state or slow process. This is due to the difficulty in data processing and the difficulty in obtaining its characteristic function. This makes it very difficult to study the dynamic process of the whole system, especially the dynamic process at the intermediate non-equilibrium moment. It is difficult to solve this kind of problem by conventional methods, and only a few special simple systems can be solved by experience. Therefore, it is of great significance to find a method to obtain the characteristic function of the system through the trajectory obtained by molecular dynamics, and then reduce the molecular dynamics path. In order to solve this scientific problem, researchers focus on machine learning. In this study, machine learning method will be used to solve the overall non-equilibrium state of the system or the collective state of the slow process in molecular dynamics simulation. Firstly, we use this method to solve a simple one-dimensional four well model. By this method, we obtain a series of characteristic functions describing the motion process of the model. By sorting the eigenvalue contributions, we obtain some main characteristic functions describing the system. It includes the motion description of Markov smooth transition state and the motion description of four potential wells. At the same time, we use the traditional transition probability matrix to calculate. The difference between the characteristic function obtained by machine learning and the traditional method is very small, but the calculation method is simpler and more universal. After that, we apply the method to the actual scene. By solving the molecular dynamics simulation of alanine dipeptide structure in polymer protein molecule, the characteristic function of dihedral angle folding of alanine dipeptide structure was preliminarily calculated. The results were consistent with the traditional method.

Modeling Neuronal Systems as an Open Quantum System

We propose a physical model for neurons to describe how neurons interact with one another through the surrounding materials of neuronal cell bodies. We model the neuronal cell surroundings, include the dendrites, the axons and the synapses, as well as the surrounding glial cells, as a continuous distribution of oscillating modes inspired from the electric circuital picture of neuronal action potential. By analyzing the dynamics of this neuronal model by using the master equation approach of open quantum systems, we investigated the collective behavior of neurons. After applying stimulations to the neuronal system, the neuron collective state is activated and shows the action potential behavior. We find that this model can generate random neuron–neuron interactions and is appropriate for describing the process of information transmission in the neuronal system, which may pave a potential route toward understanding the dynamics of nervous system.

Spin Transport in a Quantum Hall Insulator

A novel experimental optical method, based on photoluminescence and photo-induced resonant reflection techniques, is used to investigate the spin transport over long distances in a new, recently discovered collective state—magnetofermionic condensate. The given Bose–Einstein condensate exists in a purely fermionic system (ν = 2 quantum Hall insulator) due to the presence of a non-equilibrium ensemble of spin-triplet magnetoexcitons—composite bosons. It is found that the condensate can spread over macroscopically long distances of approximately 200 μm. The propagation velocity of long-lived spin excitations is measured to be 25 m/s.

Revealing cell-fate bifurcations from transcriptomic trajectories of hematopoiesis

Inspired by Waddington's illustration of an epigenetic landscape, cell-fate transitions have been envisioned as bifurcating dynamical systems, wherein the dynamics of an exogenous signal couples to a cell's enormously complex signaling and transcriptional machinery, eliciting a qualitative transition in the collective state of a cell -- its fate. Single-cell RNA sequencing (scRNA-seq) measures the distributions of possible transcriptional states in large populations of differentiating cells, making it possible to interrogate cell fate-transitions at whole-genome scales with molecular-scale precision. However, it remains unclear how to bridge the disparate scales of the dynamics of whole transcriptomes to the molecules that define the collective fate-transitions in Waddington's geometric vision. We bridge these scales by showing that bifurcations in transcriptional states can be analytically pinpointed and their genetic bases revealed, directly from data. We demonstrate the power of our conceptual framework and analytical scheme in the context of a recent scRNA-seq based investigation of a classic case-study of sequential fate decisions -- the transition of hematopoietic stem cells to neutrophils. Our work provides a rigorous and model-independent mathematical framework for detecting and categorizing transitions in cell-fate directly from sequencing data, aiding in gene network inference and determination of the salient properties of cellular differentiation, such as when cell fate transitions are reversible and how these transitions generate a diversity of cell types.

Changes in geometrical aspects of a simple model of cilia synchronization control the dynamical state, a possible mechanism for switching of swimming gaits in microswimmers

Active oscillators, with purely hydrodynamic coupling, are useful simple models to understand various aspects of motile cilia synchronization. Motile cilia are used by microorganisms to swim and to control the flow fields in their surroundings; the patterns observed in cilia carpets can be remarkably complex, and can be changed over time by the organism. It is often not known to what extent the coupling between cilia is due to just hydrodynamic forces, and neither is it known if it is biological or physical triggers that can change the dynamical collective state. Here we treat this question from a very simplified point of view. We describe three possible mechanisms that enable a switch in the dynamical state, in a simple scenario of a chain of oscillators. We find that shape-change provides the most consistent strategy to control collective dynamics, but also imposing small changes in frequency produces some unique stable states. Demonstrating these effects in the abstract minimal model proves that these could be possible explanations for gait switching seen in ciliated micro organisms like Paramecium and others. Microorganisms with many cilia could in principle be taking advantage of hydrodynamic coupling, to switch their swimming gait through either a shape change that manifests in decreased coupling between groups of cilia, or alterations to the beat style of a small subset of the cilia.

Quantum Metrology Based on Nonclassical Atomic States

Quantum measurements with atoms have been at the forefront of quantum technology and provide crucial information for a better understanding of quantum physics ever since their conception over a century ago. The universality of the quantized energy states of atoms makes the collective state of an atomic ensemble an outstanding platform for quantum-enhanced metrology. We introduce basic concepts regarding the metrological gain acquired from using nonclassical quantum states via multiparticle entanglement. Current challenges and future prospects for further enhancement of the measurement sensitivity through the use of nonclassical atomic states are discussed with reference to the shot noise and Heisenberg limits.

Morphodynamic signatures of MDA-MB-231 single cells and cell doublets undergoing invasion in confined microenvironments

Cancer cell metastasis is a major factor in cancer-related mortality. During the process of metastasis, cancer cells exhibit migratory phenotypes and invade through pores in the dense extracellular matrix. However, the characterization of morphological and subcellular features of cells in similar migratory phenotypes and the effects of geometric confinement on cell morphodynamics are not well understood. Here, we investigate the phenotypes of highly aggressive MDA-MB-231 cells in single cell and cell doublet (an initial and simplified collective state) forms in confined microenvironments. We group phenotypically similar single cells and cell doublets and characterize related morphological and subcellular features. We further detect two distinct migratory phenotypes, fluctuating and non-fluctuating, within the fast migrating single cell group. In addition, we demonstrate an increase in the number of protrusions formed at the leading edge of cells after invasion through geometric confinement. Finally, we track the short and long term effects of varied degrees of confinement on protrusion formation. Overall, our findings elucidate the underlying morphological and subcellular features associated with different single cell and cell doublet phenotypes and the impact of invasion through confined geometry on cell behavior.

Using Adaptive Psychophysics to Identify the Neural Network Reset Time in Sub-second Interval Timing

State dependent network models of sub-second interval timing propose that duration is encoded in states of neuronal populations that need to reset prior to a novel timing operation in order to maintain optimal timing performance. Previous research has shown that the approximate boundary of this reset interval can be inferred by varying the interstimulus interval between two to-be-timed intervals. However, the estimated boundary of this reset interval is broad (250-500ms) and remains underspecified with implications for the characteristics of state dependent network dynamics subserving interval timing. Here we probed the interval specificity of this reset boundary by manipulating the interstimulus interval between standard and comparison intervals in two sub-second auditory duration discrimination tasks (100 and 200ms) and a control (pitch) discrimination task using adaptive psychophysics. We found that discrimination thresholds improved with the introduction of a 333ms interstimulus interval relative to a 250ms interstimulus interval in both duration discrimination tasks, but not in the control task. This effect corroborates previous findings of a breakpoint in the discrimination performance for sub-second stimulus interval pairs as a function of an incremental interstimulus delay but more precisely localizes the minimal interstimulus delay range. These results suggest that state dependent networks subserving sub-second timing require approximately 250-333ms for the network to reset in order to maintain optimal interval timing.New & NoteworthyThe state-dependent-network model considers interval timing as an intrinsic ability of neuronal populations to track the temporal evolution of their collective state. However, the time-dependent nature of neuronal properties imposes constraints on a maximum encodable interval and on the processing of intervals that are presented before the network resets to its baseline state. Investigating temporal discrimination thresholds as a function of variable inter-stimulus-intervals, we showed that the network reset time is between 250 and 333ms.

Pastoralism at Scale on the Kazakh Rangelands: From Clans to Workers to Ranchers

Eurasia contains the world's largest contiguous rangelands, grazed for millennia by mobile pastoralists' livestock. This paper reviews evidence from one Eurasian country, Kazakhstan, on how nomadic pastoralism developed from some 5,000 years ago to the present. We consider a timespan covering pre-industrial, socialist and capitalist periods, during which pastoral social formations were organized in terms of kinship, collective state farms, and private farms and ranches. The aim is to understand how events over the last 100 years have led to the sequential dissolution and re-formation of the social units necessary to manage livestock across a wide expanse of spatially heterogenous and seasonally variable rangeland ecosystems. It is argued that the social scale of extensive livestock management must be tailored to the geographical scale of biotic and abiotic conditions. The paper starts by pointing out the long duration of mobile pastoralism in the Kazakh rangelands and provides an overview of how events from the late 17th C onwards unraveled the relationships between Kazakh nomads' socio-economic units of livestock management and the rangeland environment. At present, mobile animal husbandry is not feasible for the majority of Kazakh livestock owners, who operate solely within small family units without state support. These reformulated post-Soviet livestock grazing patterns are still undergoing rapid change, influencing the composition of rangeland vegetation, wildlife biodiversity, and rates of carbon sequestration. By concentrating capital and landed resources, a minority of large-scale pastoralists have been able to re-extensify by combining mobility with selective intensification, including an increased reliance on cultivated feed. Current state and international efforts are leaving out the majority of small-scale livestock owners and their livestock who are unable to either intensify or extensify at sufficient scale, increasing environmental damage, and social inequality.