Influence of Pregnancy on Whole-Transcriptome Sequencing in the Mammary Gland of Kazakh Mares.

Kazakh mares have drawn significant attention for their outstanding lactation traits. Lactation, a complex physiological activity, is modulated by multiple factors. This study utilized high-throughput sequencing to conduct whole-transcriptome sequencing analysis on the mammary gland tissue of eight Kazakh mares, of which four were pregnant and four were non-pregnant, to systematically reveal the molecular regulatory mechanisms.

Differential analysis of testicular LncRNA in Kazakh horses of different ages.

The reproductive performance of horses is closely associated with testicular development, which involves a complex network of gene regulation. The molecular mechanisms underlying the reproductive traits of Kazakh horses are yet to be fully understood. In this study, transcriptomic analysis was performed on testicular tissues from Kazakh horses at different developmental stages using whole-transcriptome sequencing, aiming to investigate differential gene expression and regulatory mechanisms during testicular development.

Challenges and resistance mechanisms to EGFR targeted therapies in head and neck cancers and breast cancer: Insights into RTK dependent and independent mechanisms.

Epidermal Growth Factor Receptor (EGFR) targeted therapies have yielded variable results in clinical trials for breast and head and neck cancers, despite EGFR overexpression in these malignancies. Primary resistance to these therapies is common, with secondary resistance often arising due to the overexpression of other receptor tyrosine kinases (RTKs) and increased downstream signaling from these RTKs. Additionally, non-RTK-driven mechanisms also contribute to anti-EGFR therapy resistance.

[A simulation study of nerve fiber activation in the lumbar segment under kilohertz-frequency transcutaneously spinal cord stimulation].

Clinical trials have demonstrated that kilohertz-frequency transcutaneous spinal cord stimulation (TSCS) can be used to facilitate the recovery of sensory-motor function for patients with spinal cord injury, whereas the neural mechanism of TSCS is still undetermined so that the choice of stimulation parameters is largely dependent on the clinical experience. In this paper, a finite element model of transcutaneous spinal cord stimulation was used to calculate the electric field distribution of human spinal cord segments T to L , whereas the activation thresholds of spinal fibers were determined by using a double-cable neuron model. Then the variation of activation thresholds was obtained by varying the carrier waveform, the interphase delay, the modulating frequency, and the modulating pulse width.

RON Receptor Signaling and the Tumor Microenvironment.

The immune microenvironment plays a critical role in tumor growth and development. Immune activation within the tumor microenvironment is dynamic and can be modulated by tumor intrinsic and extrinsic signaling. The RON receptor tyrosine kinase is canonically associated with growth signaling and wound healing, and this receptor is frequently overexpressed in a variety of cancers.

Hierarchy of Chaotic Dynamics in Random Modular Networks.

We introduce a model of randomly connected neural populations and study its dynamics by means of the dynamical mean-field theory and simulations. Our analysis uncovers a rich phase diagram, featuring high- and low-dimensional chaotic phases, separated by a crossover region characterized by low values of the maximal Lyapunov exponent and participation ratio dimension, but with high values of the Lyapunov dimension that change significantly across the region. Counterintuitively, chaos can be attenuated by either adding noise to strongly modular connectivity or by introducing modularity into random connectivity.

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.

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).

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.

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.