Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution.

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

Thirst regulates motivated behavior through modulation of brainwide neural population dynamics.

Physiological needs produce motivational drives, such as thirst and hunger, that regulate behaviors essential to survival. Hypothalamic neurons sense these needs and must coordinate relevant brainwide neuronal activity to produce the appropriate behavior. We studied dynamics from ~24,000 neurons in 34 brain regions during thirst-motivated choice behavior in 21 mice as they consumed water and became sated. Water-predicting sensory cues elicited activity that rapidly spread throughout the brain of thirsty animals. These dynamics were gated by a brainwide mode of population activity that encoded motivational state. After satiation, focal optogenetic activation of hypothalamic thirst-sensing neurons returned global activity to the pre-satiation state. Thus, motivational states specify initial conditions that determine how a brainwide dynamical system transforms sensory input into behavioral output.

A variable stiffness gripper based on differential drive particle jamming.

Compared with rigid grippers, soft grippers show fantastic adaptability and flexibility in grasping irregularly shaped and fragile objects. However, the low stiffness of the soft actuator limits the scope of applications. Particle jamming has emerged as an important method to adjust the stiffness of soft grippers. This paper proposes a novel particle jamming mechanism based on the differential pressure drive. With the differential drive particle jamming mechanism, a soft actuator is designed, which is characterized by a dual-deformable chamber structure in which one chamber is filled with particles. The simultaneous inflation of the two chambers will result in the bending behavior without significant stiffening. However, if the air chamber is pressurized with a larger pressure, the differential pressure will cause the particles inside the particle chamber to jam each other, which increases the stiffness of the actuator significantly. Thus, the differential drive particle jamming mechanism can achieve the independent control of the stiffness and the bending angle. Both theoretical and experimental studies in this area have shown that the gripper based on the differential drive particle jamming mechanism can stiffen itself effectively, and achieve the independent control of the stiffness and the bending angle, which can be adopted in applications where both high stiffness and dexterity are required.

Semi-Global Output Consensus for Discrete-Time Switching Networked Systems Subject to Input Saturation and External Disturbances.

The semi-global output consensus problem for multiagent systems depicted by discrete-time dynamics subject to external disturbances and input saturation over switching networks is investigated in this paper. Assume that only a small part of subsystems have directly received the output of the exosystem. The distributed consensus algorithms are proposed by adopting the low-gain state feedback and the modified algebraic Riccati equation. Then, the outputs of all subsystems can reach synchronization asymptotically with those of the exosystem by using the proposed consensus protocols on some preconditions. Both the connected switching networks and the jointly connected switching networks are considered for the semi-global output consensus problem, respectively. Some numerical simulation results are shown to validate the theoretical analysis.

Quantized Consensus of Multi-Agent Networks With Sampled Data and Markovian Interaction Links.

This paper investigates the joint effect of quantization, sampled data, and general Markovian interaction links on consensus networks with a leader under directed graphs. The diversity of edges formed by all the followers and the leader is also considered. Each agent in the network possesses continuous-time general linear dynamics. Each agent's state is measured only at sampling time instants, which is encoded before transmission. Subsequently, the encoded state is transmitted through noiseless digital communication links with Markovian switching rates. For this problem, a sufficient condition is derived to guarantee the convergence of the encoded states, based on which a necessary and sufficient condition is obtained to achieve consensus tracking in the mean-square sense. In addition, two sufficient conditions on coupling gain, one of which is fully distributed, are provided by proposing an optimal linear quadratic regulator-based gain matrix to ensure consensus tracking and then, the analysis of consensus region is presented. Finally, a numerical example is presented for illustrating the effectiveness of the theoretical results.

Nonfragile State Estimation of Quantized Complex Networks With Switching Topologies.

This paper considers the nonfragile $H_\infty $ estimation problem for a class of complex networks with switching topologies and quantization effects. The network architecture is assumed to be dynamic and evolves with time according to a random process subject to a sojourn probability. The coupled signal is to be quantized before transmission due to power and bandwidth constraints, and the quantization errors are transformed into sector-bounded uncertainties. The concept of nonfragility is introduced by inserting randomly occurred uncertainties into the estimator parameters to cope with the unavoidable small gain variations emerging from the implementations of estimators. Both the quantizers and the estimators have several operation modes depending on the switching signal of the underlying network structure. A sufficient condition is provided via a linear matrix inequality approach to ensure the estimation error dynamic to be stochastically stable in the absence of external disturbances, and the $H_\infty $ performance with a prescribed index is also satisfied. Finally, a numerical example is presented to clarify the validity of the proposed method.

Observer-Based Robust Coordinated Control of Multiagent Systems With Input Saturation.

This paper addresses the robust semiglobal coordinated control of multiple-input multiple-output multiagent systems with input saturation together with dead zone and input additive disturbance. Observer-based coordinated control protocol is constructed, by combining the parameterized low-and-high-gain feedback technique and the high-gain observer design approach. It is shown that, under some mild assumptions on agents' intrinsic dynamics, the robust semiglobal consensus or robust semiglobal swarm can be approached for undirected connected multiagent systems. Then, specific guidelines on the selection of the low-gain parameter, the high-gain parameter, and the high-gain observer gain have been provided. At last, numerical simulations are presented to illustrate the theoretical results.

The competitive nature of signal transducer and activator of transcription complex formation drives phenotype switching of T cells.

Signal transducers and activators of transcription (STATs) are key molecular determinants of T-cell fate and effector function. Several inflammatory diseases are characterized by an altered balance of T-cell phenotypes and cytokine secretion. STATs, therefore, represent viable therapeutic targets in numerous pathologies. However, the underlying mechanisms by which the same STAT proteins regulate both the development of different T-cell phenotypes and their plasticity during changes in extracellular conditions remain unclear. In this study, we investigated the STAT-mediated regulation of T-cell phenotype formation and plasticity using mathematical modelling and experimental data for intracellular STAT signalling proteins. The close fit of our model predictions to the experimental data allows us to propose a potential mechanism for T-cell switching. According to this mechanism, T-cell phenotype switching is the result of the relative redistribution of STAT dimer complexes caused by the extracellular cytokine-dependent STAT competition effects. The developed model predicts that the balance between the intracellular STAT species defines the amount of the produced cytokines and thereby T-cell phenotypes. The model predictions are consistent with the experimentally observed interferon-γ to interleukin-10 switching that regulates human T helper type 1/type 1 regulatory T-cell responses. The proposed model is applicable to a number of STAT signalling circuits.

Reaching Non-Negative Edge Consensus of Networked Dynamical Systems.

In this paper, the problem of non-negative edge consensus of undirected networked linear time-invariant systems is addressed by associating each edge of the network with a state variable, for which a distributed algorithm is constructed. Sufficient conditions referring only to the number of edges are derived for non-negative edge consensus of the networked systems. Subsequently, the linear programming method and a low-gain feedback technique are introduced to simplify the design of the feedback gain matrix for achieving the non-negative edge consensus. It is found that the low-gain feedback technique has a good effect on the non-negative edge consensus of the networked systems subject to input saturation. Numerical simulations are presented to verify the effectiveness of the theoretical results.