A segmentation network for farmland ridge based on encoder-decoder architecture in combined with strip pooling module and ASPP.

In order to effectively support wheat breeding, farmland ridge segmentation can be used to visualize the size and spacing of a wheat field. At the same time, accurate ridge information collecting can deliver useful data support for farmland management. However, in the farming ridge segmentation scenarios based on remote sensing photos, the commonly used semantic segmentation methods tend to overlook the ridge edges and ridge strip features, which impair the segmentation effect.

An Introduction and Overview of RON Receptor Tyrosine Kinase Signaling.

RON is a receptor tyrosine kinase (RTK) of the MET receptor family that is canonically involved in mediating growth and inflammatory signaling. RON is expressed at low levels in a variety of tissues, but its overexpression and activation have been associated with malignancies in multiple tissue types and worse patient outcomes. RON and its ligand HGFL demonstrate cross-talk with other growth receptors and, consequentially, positions RON at the intersection of numerous tumorigenic signaling programs.

Uncovering the biological basis of control energy: Structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy.

Network control theory is increasingly used to profile the brain's energy landscape via simulations of neural dynamics. This approach estimates the control energy required to simulate the activation of brain circuits based on structural connectome measured using diffusion magnetic resonance imaging, thereby quantifying those circuits' energetic efficiency. The biological basis of control energy, however, remains unknown, hampering its further application.

Path-dependent dynamics induced by rewiring networks of inertial oscillators.

In networks of coupled oscillators, it is of interest to understand how interaction topology affects synchronization. Many studies have gained key insights into this question by studying the classic Kuramoto oscillator model on static networks. However, new questions arise when the network structure is time varying or when the oscillator system is multistable, the latter of which can occur when an inertial term is added to the Kuramoto model.

Learning continuous chaotic attractors with a reservoir computer.

Neural systems are well known for their ability to learn and store information as memories. Even more impressive is their ability to abstract these memories to create complex internal representations, enabling advanced functions such as the spatial manipulation of mental representations. While recurrent neural networks (RNNs) are capable of representing complex information, the exact mechanisms of how dynamical neural systems perform abstraction are still not well-understood, thereby hindering the development of more advanced functions.

Pharmacological Inhibition of CDK8 in Triple-Negative Breast Cancer Cell Line MDA-MB-468 Increases E2F1 Protein, Induces Phosphorylation of STAT3 and Apoptosis.

Cyclin-dependent kinase 8 (CDK8) has been identified as a colon cancer oncogene. Since this initial observation, CDK8 has been implicated as a potential driver of other cancers including acute myelogenous leukemia (AML) and some breast cancers. Here, we observed different biological responses to CDK8 inhibition among colon cancer cell lines and the triple-negative breast cancer (TNBC) cell line MDA-MB-468.

Invertible generalized synchronization: A putative mechanism for implicit learning in neural systems.

Regardless of the marked differences between biological and artificial neural systems, one fundamental similarity is that they are essentially dynamical systems that can learn to imitate other dynamical systems whose governing equations are unknown. The brain is able to learn the dynamic nature of the physical world via experience; analogously, artificial neural systems such as reservoir computing networks (RCNs) can learn the long-term behavior of complex dynamical systems from data. Recent work has shown that the mechanism of such learning in RCNs is invertible generalized synchronization (IGS).

Optimization of energy state transition trajectory supports the development of executive function during youth.

Executive function develops during adolescence, yet it remains unknown how structural brain networks mature to facilitate activation of the fronto-parietal system, which is critical for executive function. In a sample of 946 human youths (ages 8-23y) who completed diffusion imaging, we capitalized upon recent advances in linear dynamical network control theory to calculate the energetic cost necessary to activate the fronto-parietal system through the control of multiple brain regions given existing structural network topology. We found that the energy required to activate the fronto-parietal system declined with development, and the pattern of regional energetic cost predicts unseen individuals' brain maturity.

Supervised chaotic source separation by a tank of water.

Whether listening to overlapping conversations in a crowded room or recording the simultaneous electrical activity of millions of neurons, the natural world abounds with sparse measurements of complex overlapping signals that arise from dynamical processes. While tools that separate mixed signals into linear sources have proven necessary and useful, the underlying equational forms of most natural signals are unknown and nonlinear. Hence, there is a need for a framework that is general enough to extract sources without knowledge of their generating equations and flexible enough to accommodate nonlinear, even chaotic, sources.

Attractor reconstruction by machine learning.

A machine-learning approach called "reservoir computing" has been used successfully for short-term prediction and attractor reconstruction of chaotic dynamical systems from time series data. We present a theoretical framework that describes conditions under which reservoir computing can create an empirical model capable of skillful short-term forecasts and accurate long-term ergodic behavior. We illustrate this theory through numerical experiments.

Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach.

We demonstrate the effectiveness of using machine learning for model-free prediction of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension purely from observations of the system's past evolution. We present a parallel scheme with an example implementation based on the reservoir computing paradigm and demonstrate the scalability of our scheme using the Kuramoto-Sivashinsky equation as an example of a spatiotemporally chaotic system.

Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data.

We use recent advances in the machine learning area known as "reservoir computing" to formulate a method for model-free estimation from data of the Lyapunov exponents of a chaotic process. The technique uses a limited time series of measurements as input to a high-dimensional dynamical system called a "reservoir." After the reservoir's response to the data is recorded, linear regression is used to learn a large set of parameters, called the "output weights.

Reservoir observers: Model-free inference of unmeasured variables in chaotic systems.

Deducing the state of a dynamical system as a function of time from a limited number of concurrent system state measurements is an important problem of great practical utility. A scheme that accomplishes this is called an "observer." We consider the case in which a model of the system is unavailable or insufficiently accurate, but "training" time series data of the desired state variables are available for a short period of time, and a limited number of other system variables are continually measured.

Inhibitory neurons promote robust critical firing dynamics in networks of integrate-and-fire neurons.

We study the firing dynamics of a discrete-state and discrete-time version of an integrate-and-fire neuronal network model with both excitatory and inhibitory neurons. When the integer-valued state of a neuron exceeds a threshold value, the neuron fires, sends out state-changing signals to its connected neurons, and returns to the resting state. In this model, a continuous phase transition from non-ceaseless firing to ceaseless firing is observed.

Resynchronization of circadian oscillators and the east-west asymmetry of jet-lag.

Cells in the brain's Suprachiasmatic Nucleus (SCN) are known to regulate circadian rhythms in mammals. We model synchronization of SCN cells using the forced Kuramoto model, which consists of a large population of coupled phase oscillators (modeling individual SCN cells) with heterogeneous intrinsic frequencies and external periodic forcing. Here, the periodic forcing models diurnally varying external inputs such as sunrise, sunset, and alarm clocks.

Apparent topologically forbidden interchange of energy surfaces under slow variation of a Hamiltonian.

In this paper we consider the motion of point particles in a particular type of one-degree-of-freedom, slowly changing, temporally periodic Hamiltonian. Through most of the time cycle, the particles conserve their action, but when a separatrix is approached and crossed, the conservation of action breaks down, as shown in previous theoretical studies. These crossings have the effect that the numerical solution shows an apparent contradiction.

Structural and functional analysis of the β-barrel domain of BamA from Escherichia coli.

In gram-negative bacteria, the assembly of outer membrane proteins (OMPs) requires a β-barrel assembly machinery (BAM) complex, of which BamA is an essential and evolutionarily conserved component. To elucidate the mechanism of BamA-mediated OMP biogenesis, we determined the crystal structure of the C-terminal transmembrane domain of BamA from Escherichia coli (EcBamA) at 2.6 Å resolution.

Base-induced mechanistic variation in palladium-catalyzed carbonylation of aryl iodides.

A mechanism, which is distinct from the traditional one when sodium alkoxide was used instead of tertiary amines, was proposed for the alkoxycarbonylation of aryl iodides. The catalytic cycle was composed of oxidative addition, subsequent ArPdOR formation, CO insertion to Pd-OR, and final reductive elimination of ArPdCOOR. The kinetic simultaneity of the formation of deiodinated side product from the aryl iodide and aldehyde from corresponding alcohol provided strong evidence for the existence of ArPdOR species.