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.

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.

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.

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.

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.

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.

Composite Backstepping Consensus Algorithms of Leader-Follower Higher-Order Nonlinear Multiagent Systems Subject to Mismatched Disturbances.

This paper is devoted to solving the output consensus problem of leader-follower higher-order nonlinear multiagent systems subject to mismatched disturbances. The disturbances are allowed to be in higher-order forms. First, by constructing a generalized proportional-integral observer for each follower, estimates of the disturbances and their derivatives are obtained. At the same time, a distributed observer is also developed for the followers to estimate the leader state information. Second, based on the estimates of the disturbances and the leader state, together with the backstepping technique, a feedforward-feedback composite consensus control scheme is proposed. The designed distributed protocols guarantee asymptotic output consensus for the agents. Simulation results validate the effectiveness of the proposed composite control scheme.

Bioinspired Robotic Fingers Based on Pneumatic Actuator and 3D Printing of Smart Material.

In this article, we have proposed a novel robotic finger design principle aimed to address two challenges in soft pneumatic grippers-the controllability of the stiffness and the controllability of the bending position. The proposed finger design is composed of a 3D printed multimaterial substrate and a soft pneumatic actuator. The substrate has four polylactic acid (PLA) segments interlocked with three shape memory polymer (SMP) joints, inspired by bones and joints in human fingers. By controlling the thermal energy of an SMP joint, the stiffness of the joints is modulated due to the dramatic change in SMP elastic modulus around its glass transition temperature (Tg). When SMP joints are heated above Tg, they exhibit very small stiffness, allowing the finger to easily bend around the SMP joints if the attached soft actuator is actuated. When there is no force from the soft actuator, shape recovery stress in SMP contributes to the finger's shape restoration. Since each joint's rotation can be individually controlled, the position control of the finger is made possible. Experimental analysis has been conducted to show the finger's variable stiffness and the result is compared with the analytical values. It is found that the stiffness ratio can be 24.9 times for a joint at room temperature (20°C) and at an elevated temperature of 60°C when air pressure p of the soft actuator is turned off. Finally, a gripper composed of two fingers is fabricated for demonstration.

Thirst-associated preoptic neurons encode an aversive motivational drive.

Water deprivation produces a drive to seek and consume water. How neural activity creates this motivation remains poorly understood. We used activity-dependent genetic labeling to characterize neurons activated by water deprivation in the hypothalamic median preoptic nucleus (MnPO). Single-cell transcriptional profiling revealed that dehydration-activated MnPO neurons consist of a single excitatory cell type. After optogenetic activation of these neurons, mice drank water and performed an operant lever-pressing task for water reward with rates that scaled with stimulation frequency. This stimulation was aversive, and instrumentally pausing stimulation could reinforce lever-pressing. Activity of these neurons gradually decreased over the course of an operant session. Thus, the activity of dehydration-activated MnPO neurons establishes a scalable, persistent, and aversive internal state that dynamically controls thirst-motivated behavior.

The Discovery of Citral-Like Thiophenes in Fried Chicken.

The isomers of 3,7-dimethyl-2,6-octadienal, more commonly known together as citral, are two of the most notable natural compounds in the flavor and fragrance industry. However, both isomers are inherently unstable, limiting their potential use in various applications. To identify molecules in nature that can impart the fresh lemon character of citral while demonstrating stability under acidic and thermal conditions has been a major challenge and goal for the flavor and fragrance industry. In the study of fried chicken, several alkyl thiophenecarbaldehydes were identified by gas chromatography-mass spectrometry and gas chromatography-olfactometry that provided a similar citral-like aroma. The potential mechanism of formation in fried chicken is discussed. Furthermore, in order to explore the organoleptic properties of this structural backbone, a total of 35 thiophenecarbaldehyde derivatives were synthesized or purchased for evaluation by odor and taste. Certain organoleptic trends were observed as the length of the alkyl or alkenyl chain increased or when the chain was moved to different positions on the thiophene backbone. The 3-substituted alkyl thiophenecarbaldehydes, specifically 3-butyl-2-thiophenecarbaldehyde and 3-(3-methylbut-2-en-1-yl)-2-thiophenecarbaldehyde, exhibited strong citrus and citral-like notes. Several alkyl thiophenecarbaldehydes were tested in high acid stability trials (4 °C vs 38 °C) and outperformed citral both in terms of maintaining freshness over time and minimizing off-notes. Additional measurements were completed to calculate the odor thresholds for a select group of thiophenecarbaldehydes, which were found to be between 4.7-215.0 ng/L in air.

A novel body frame based approach to aerospacecraft attitude tracking.

In the common practice of designing an attitude tracker for an aerospacecraft, one transforms the Newton-Euler rotation equations to obtain the dynamic equations of some chosen inertial frame based attitude metrics, such as Euler angles and unit quaternions. A Lyapunov approach is then used to design a controller which ensures asymptotic convergence of the attitude to the desired orientation. Although this design methodology is pretty standard, it usually involves singularity-prone coordinate transformations which complicates the analysis process and controller design. A new, singularity free error feedback method is proposed in the paper to provide simple and intuitive stability analysis and controller synthesis. This new body frame based method utilizes the concept of Euleraxis and angles to generate the smallest error angles from a body frame perspective, without coordinate transformations. Global tracking convergence is illustrated with the use of a feedback linearizing PD tracker, a sliding mode controller, and a model reference adaptive controller. Experimental results are also obtained on a quadrotor platform with unknown system parameters and disturbances, using a boundary layer approximated sliding mode controller, a PIDD controller, and a unit sliding mode controller. Significant tracking quality is attained.

Global Representations of Goal-Directed Behavior in Distinct Cell Types of Mouse Neocortex.

The successful planning and execution of adaptive behaviors in mammals may require long-range coordination of neural networks throughout cerebral cortex. The neuronal implementation of signals that could orchestrate cortex-wide activity remains unclear. Here, we develop and apply methods for cortex-wide Ca2+ imaging in mice performing decision-making behavior and identify a global cortical representation of task engagement encoded in the activity dynamics of both single cells and superficial neuropil distributed across the majority of dorsal cortex. The activity of multiple molecularly defined cell types was found to reflect this representation with type-specific dynamics. Focal optogenetic inhibition tiled across cortex revealed a crucial role for frontal cortex in triggering this cortex-wide phenomenon; local inhibition of this region blocked both the cortex-wide response to task-initiating cues and the voluntary behavior. These findings reveal cell-type-specific processes in cortex for globally representing goal-directed behavior and identify a major cortical node that gates the global broadcast of task-related information.

Aperiodic Optimal Linear Estimation for Networked Systems With Communication Uncertainties.

The aperiodic optimal linear estimator design problem is investigated in this paper for networked systems with communication uncertainties, including delays and data losses, where the sampling and estimation are nonuniform and asynchronous. Based on the idea of measurement fusion, two approaches are proposed to design the aperiodic estimators, and it is shown that the estimator is equivalent to that designed by the measurement augmentation method in performance. Moreover, the estimation performance is improved by using a newly proposed measurement retransmission scheme as compared with the commonly used hold-input and zero-input schemes, by which the lost measurements are never used once they are lost.

Approximate-master-equation approach for the Kinouchi-Copelli neural model on networks.

In this work, we use the approximate-master-equation approach to study the dynamics of the Kinouchi-Copelli neural model on various networks. By categorizing each neuron in terms of its state and also the states of its neighbors, we are able to uncover how the coupled system evolves with respective to time by directly solving a set of ordinary differential equations. In particular, we can easily calculate the statistical properties of the time evolution of the network instantaneous response, the network response curve, the dynamic range, and the critical point in the framework of the approximate-master-equation approach. The possible usage of the proposed theoretical approach to other spreading phenomena is briefly discussed.

Observer-Based Consensus Tracking of Nonlinear Agents in Hybrid Varying Directed Topology.

The problem of leader-following consensus of nonlinear agents in hybrid varying directed topology is considering not only the agent but also that the directed edges can have a time-varying nonlinear dynamics with jump discontinuity, which contains the switching topology as its special case. This paper has the following contributions toward this problem. The leader-following consensus problem in hybrid varying directed topology is first addressed, and an online leader switching method is proposed, which reduces the dependence on some global conditions and the connectivity assumptions on the selection of leaders. Second, we generalize the Lipschitz condition and the combined condition of one-sided Lipschitz and quadratically inner-boundedness conditions to a new generalized linear incremental condition, which gives us a more generalized result in the Lyapunov proof and better performance in simulation. Third, an observer-based consensus protocol is constructed with two sufficient observability and controllability conditions and two optimal control design algorithms. Finally, an example of teleoperating multirobotic manipulator joint network is provided to illustrate the performance improvement by comparing with the existing results.