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We previously reported that amino acids 20 to 50 of nuclear receptor interacting factor-3 mediates rapid apoptosis in breast cancer cell lines but not in cells derived from other tissues. We refer to this short region as death domain-1 (DD1). Small interfering RNA studies indicated that DD1-mediated apoptosis is caspase-2 dependent. In this study, we examined DD1-mediated apoptosis in more detail and generated stable caspase-2 knockdown breast cancer cells. These cells are resistant to DD1-mediated apoptosis. Time-lapse movies suggested that DD1-mediated apoptosis also leads to a “bystander effect.” We found that within 5 h of DD1 expression, breast cancer cells release a factor(s) into the medium that leads to apoptosis of naive breast cancer cells or DD1-resistant cells (e.g., HeLa). The DD1-expressing caspase-2 knockdown cells also release a factor(s) that kills other cells, indicating that this effect is not dependent on the apoptogenic process. The bystander effect seems dependent on the production of reactive oxygen species (ROS). These and other studies indicate that DD1 expression in breast cancer cells leads to at least two death signals: one involving the rapid production of ROS and/or other soluble factors that directly or indirectly leads to a bystander effect and a second caspase-2–dependent process that leads to apoptosis in cells in which DD1 is expressed. [Cancer Res 2007;67(4):1775–82]

Supplementary Movie 1 from The Nuclear Receptor Interacting Factor-3 Transcriptional Coregulator Mediates Rapid Apoptosis in Breast Cancer Cells through Direct and Bystander-Mediated Events

In this paper, we propose a Wasserstein metric- based random path planning algorithm. Wasserstein Safe RRT (W-Safe RRT) provides finite-sample probabilistic guarantees on the safety of a returned path in an uncertain obstacle environment. Vehicle and obstacle states are modeled as distributions based upon state and model observations. We define limits on distributional sampling error so the Wasserstein distance between a vehicle state distribution and obstacle distributions can be bounded. This enables the algorithm to return safe paths with a confidence bound through combining finite sampling error bounds with calculations of the Wasserstein distance between discrete distributions. W-Safe RRT is compared against a baseline minimum encompassing ball algorithm, which ensures balls that minimally encompass discrete state and obstacle distributions do not overlap. The improved performance is verified in a 3D environment using single, multi, and rotating non-convex obstacle cases, with and without forced obstacle error in adversarial directions, showing that W-Safe RRT can handle poorly modeled complex environments.

This work finds a lower bound on the average dwell-time (ADT) of switching signals such that a continuous-time, graph-based, switched system is globally asymptotically stable, input-to-state stable, or integral input-to-state stable. We first formulate the lower bound on the ADT as a nonconvex optimization problem with bilinear matrix inequality constraints. Because this formulation is independent of the choice of Lyapunov functions, its solution gives a less conservative lower bound than previous Lyapunov-function-based approaches. We then design a numerical iterative algorithm to solve the optimization based on sequential convex programming with a convex-concave decomposition of the constraints. We analyze the convergence properties of the proposed algorithm, establishing the monotonic evolution of the estimates of the average dwell-time lower bound. Finally, we demonstrate the benefits of the proposed approach in two examples and compare it against other baseline methods.

This paper presents one of the first real-life demonstrations of coordinated and distributed resource control for secondary frequency response in a power distribution grid. A series of tests involved up to 69 heterogeneous active distributed energy resources consisting of air handling units, unidirectional and bidirectional electric vehicle charging stations, a battery energy storage system, and 107 passive distributed energy resources consisting of building loads and solar photovoltaic systems. The distributed control setup consists of a set of Raspberry Pi end-points exchanging messages via an Ethernet switch. Actuation commands for the distributed energy resources are obtained by solving a power allocation problem at every regulation instant using distributed ratio-consensus, primal-dual, and Newton-like algorithms. The problem formulation minimizes the sum of distributed energy resource costs while tracking the aggregate setpoint provided by the system operator. We demonstrate accurate and fast real-time distributed computation of the optimization solution and effective tracking of the regulation signal over 40 min time horizons. An economic benefit analysis confirms eligibility to participate in an ancillary services market and demonstrates up to $\$ $ 53k of potential annual revenue for the selected population of distributed energy resources.

This paper considers a class of real-time stochastic optimization problems dependent on an unknown probability distribution. In the considered scenario, data is streaming frequently while trying to reach a decision. Thus, we aim to devise a procedure that incorporates samples (data) of the distribution sequentially and adjusts decisions accordingly. We approach this problem in a distributionally robust optimization framework and propose a novel Online Data Assimilation Algorithm (ONDA Algorithm) for this purpose. This algorithm guarantees out-of-sample performance of decisions with high probability, and gradually improves the quality of the decisions by incorporating the streaming data. We show that the ONDA Algorithm converges under a sufficiently slow data streaming rate, and provide a criteria for its termination after certain number of data have been collected. Simulations illustrate the results., IEEE Transactions on Automatic Control. A preliminary work appeared in 10.1109/CDC.2018.8619159 and arxiv:1803.07984

Pumpkin [Cucurbita moschata (Duchesne) Poir], one of the most important vegetable crops in the Caribbean, ranking second among all the vegetables produced in Puerto Rico, had a farm value of 4.1 million dollars for the 2009-2010 growing season. Drip irrigation is the method most commonly used to meet water requirements in many of the vegetables planted in Puerto Rico. However, more information is needed on water requirements and response to different applications. An experiment was established using Pan A evaporation readings to determine the appropriate microirrigation management practices to enhance pumpkin production. Pumpkin plants were treated with four microirrigation applications, consisting of four levels of evapotranspiration replenishment (ERT) treatments. The ERT levels used were 25, 50, 75 and 100%; levels were based on evaporation data recorded at two locations in southwestern Puerto Rico (Lajas and Juana Díaz). The Pan A evaporation method was utilized to estimate reference evapotranspiration (ET0) and pumpkin evapotranspiration (ETC). The ERT did not significantly and consistently affect total leaf area, biomass or yield at both locations, although occasionally significant interactions were found between ERT and leaf areas and biomass measured through time. On the basis of a two-year average, commercial yield in Juana Díaz tended to be higher than yield in Lajas by 1,594 kg/ha. Juana Díaz showed higher water use efficiency than Lajas. The ETc estimates were found to be lower in Lajas.

Ambiguity sets of probability distributions are used to hedge against uncertainty about the true probabilities of random quantities of interest (QoIs). When available, these ambiguity sets are constructed from both data (collected at the initial time and along the boundaries of the physical domain) and concentration-of-measure results on the Wasserstein metric. To propagate the ambiguity sets into the future, we use a physics-dependent equation governing the evolution of cumulative distribution functions (CDF) obtained through the method of distributions. This study focuses on the latter step by investigating the spatio-temporal evolution of data-driven ambiguity sets and their associated guarantees when the random QoIs they describe obey hyperbolic partial-differential equations with random inputs. For general nonlinear hyperbolic equations with smooth solutions, the CDF equation is used to propagate the upper and lower envelopes of pointwise ambiguity bands. For linear dynamics, the CDF equation allows us to construct an evolution equation for tighter ambiguity balls. We demonstrate that, in both cases, the ambiguity sets are guaranteed to contain the true (unknown) distributions within a prescribed confidence.

In this work, we develop a novel Human-Swarm Interaction (HSI) framework for formation control using the notion of an interpreter, enabling the user to control a robotic swarm's shape and formation using abstraction. The user conveys their intended commands by drawing shapes through arm gestures and motions which are recorded by an off-the-shelf wearable device. We propose a novel interpreter system, which acts as an intermediary between the user and the swarm to simplify the roles of both. The interpreter takes in high level input in the form of shapes drawn by the user, and translates it into swarm control commands by planning in the shape space using novel shape morphing dynamics (SMD), which is also used for user feedback. The proposed interpreter employs machine learning, estimation and optimal control techniques to translate the users intention into swarm control parameters. The dynamics of the swarm are realized by means of a novel decentralized formation controller based on distributed linear iterations and dynamic average consensus. Theoretical guarantees of convergence along with convergence rate of the proposed swarm controller are given. The resulting shape morphing dynamics are illustrated and discussed through simulations. The entire framework is demonstrated theoretically as well as experimentally in a 2D environment, with a human controlling a swarm of simulated robots in real time with the help of a Graphical User Environemnt (GUI).

Radical chemistry is an essential part of the organic chemistry curriculum. Free radical halogenation experiments provide an excellent opportunity to introduce important topics related to the outcome and mechanism of organic reactions. In this communication, a green photochemically activated benzylic bromination of p-toluic acid is described. In contrast with previous versions of this reaction, visible light provides the energy necessary to homolytically cleave the Br—Br bond and initiate the radical cascade. Thus, photochemical activation allows a safer, faster, and cheaper alternative to chemical free radical initiators. Furthermore, the reaction is conveniently performed in environmentally friendly acetonitrile, as a replacement for toxic and ozone-depleting chlorinated solvents, such as the usual carbon tetrachloride.

In this paper, we introduce a new notion of guaranteed privacy that requires that the change of the range of the corresponding inclusion function to the true function is small. In particular, leveraging mixed-monotone inclusion functions, we propose a privacy-preserving mechanism for nonconvex distributed optimization, which is based on deterministic, but unknown, affine perturbation of the local objective functions, which is stronger than probabilistic differential privacy. The design requires a robust optimization method to characterize the best accuracy that can be achieved by an optimal perturbation. Subsequently, this is used to guide the refinement of a guaranteed-private perturbation mechanism that can achieve a quantifiable accuracy via a theoretical upper bound that is shown to be independent of the chosen optimization algorithm.

50 p., [EN] The Porvenir serpentinites are an ~600-m-thick body of meta-peridotites exposed in SW Iberia (Variscan Orogen). The serpentinites occur as a horse within a Carboniferous, out-of-sequence thrust system (Espiel thrust). This thrust juxtaposes the serpentinites and peri-Gondwanan strata onto younger peri-Gondwanan strata, with the serpentinites occupying an intermediate position. Reconstruction of the pre-Espiel thrust structure results in a vertical juxtaposition of terranes: Cambrian strata below, Porvenir serpentinites in the middle, and the strata at the footwall to the Espiel thrust culminating the tectonic pile. The reconstructed tectonic pile accounts for yet another major thrusting event, since a section of upper mantle (Porvenir serpentinites) was sandwiched between two tectonic slices of continental crust (a suture zone sensu lato). The primary lower plate to the suture is now overlying the upper plate due to the Espiel thrust. Lochkovian strata in the upper plate and the Devonian, NE-verging folds in the lower plate suggest SW-directed accretion of the lower plate during the Devonian, i.e., Laurussia-directed underthrusting for the closure of a Devonian intra-Gondwana basin. Obduction of the Porvenir serpentinites was a two-step process: one connected to the development of a Devonian suture zone, and another related to out-of-sequence thrusting that cut the suture zone and brought upward a tectonic slice of upper mantle rocks hosted in that suture. The primary Laurussia-dipping geometry inferred for this partially obducted suture zone fits the geometry, kinematics, and timing of the Late Devonian suture zone exposed in NW Iberia and may represent the continuation of such suture into SW Iberia., Research funded by Spanish project CGL2016-76438-P (Ministerio de Economía, Industria y Competitividad).

This work proposes a novel risk-perception-aware (RPA) control design using non-rational perception of risks associated with uncertain dynamic spatial costs. We use Cumulative Prospect Theory (CPT) to model the risk perception of a decision maker (DM) and use it to construct perceived risk functions that transform the uncertain dynamic spatial cost to deterministic perceived risks of a DM. These risks are then used to build safety sets which can represent risk-averse to risk-insensitive perception. We define a notions of "inclusiveness" and "versatility" based on safety sets and use it to compare with other models such as Conditional value at Risk (CVaR) and Expected risk (ER). We theoretically prove that CPT is the most "inclusive" and "versatile" model of the lot in the context of risk-perception-aware controls. We further use the perceived risk function along with ideas from control barrier functions (CBF) to construct a class of perceived risk CBFs. For a class of truncated-Gaussian costs, we find sufficient geometric conditions for the validity of this class of CBFs, thus guaranteeing safety. Then, we generate perceived-safety-critical controls using a Quadratic program (QP) to guide an agent safely according to a given perceived risk model. We present simulations in a 2D environment to illustrate the performance of the proposed controller., 8 pages

This work proposes an algorithmic method to verify differential privacy for estimation mechanisms with performance guarantees. Differential privacy makes it hard to distinguish outputs of a mechanism produced by adjacent inputs. While obtaining theoretical conditions that guarantee differential privacy may be possible, evaluating these conditions in practice can be hard. This is especially true for estimation mechanisms that take values in continuous spaces, as this requires checking for an infinite set of inequalities. Instead, our verification approach consists of testing the differential privacy condition for a suitably chosen finite collection of events at the expense of some information loss. More precisely, our data-driven, test framework for continuous range mechanisms first finds a highly-likely, compact event set, as well as a partition of this event, and then evaluates differential privacy wrt this partition. This results into a type of differential privacy with high confidence, which we are able to quantify precisely. This approach is then used to evaluate the differential-privacy properties of the recently proposed $W_2$ Moving Horizon Estimator. We confirm its properties, while comparing its performance with alternative approaches in simulation., The paper is accepted by IEEE Control System Letter (L-CSS)

This work deals with the coordinated charging/discharging of a population of plug-in electric vehicles (PEVs) under energy efficiency, SOC, battery capacity, and power-line capacity constraints, to minimize energy cost. To address this, we introduce a framework in which the power grid is modeled as an undirected rooted tree, the root of this tree represents the generation/transmission side of the system and the leaves represent PEVs. Due to the several constraints, we are led to a non-convex optimization problem formulation subject to a complementarity constraint. We then show how a relaxed version of the problem can capture a wide set of optimal solutions for the original problem. After this, we propose a hierarchical algorithm for the computation of the PEVs’ charging/discharging profiles based on the relaxed problem formulation. The root generates a control signal based on the price per unit of power according to the demand for each time. Intermediate nodes represent congestible elements on the distribution side (e.g., transformers), which have a bound on the demand they can satisfy. In the proposed algorithm, intermediate nodes modify the control signal according to the difference between the demand they take care of, and its capacity upper bound. PEVs update their charging/discharging strategies according to this pricing signal. A proof of algorithm convergence to the optimizer of the problem is provided. Simulations demonstrate the algorithm performance and convergence rate.

This paper considers a class of stochastic source-seeking problems to drive a mobile robot to the minimizer of a source signal. Our approach is first analyzed in an obstacle-free scenario, where measurements of the signal at the robot location and information of a contact sensor are required. We extend our results to environments with obstacles under mild assumptions on the step size. Our approach builds on the simultaneous perturbation stochastic approximation idea to obtain information of the signal field. We prove the practical convergence of the algorithms to a ball whose size depends on the step size that contains the location of the source. The novelty relies in that we consider nondifferentiable convex functions, a fixed step size, and the environment may contain obstacles. Our proof methods employ nonsmooth Lyapunov function theory, tools from convex analysis, and stochastic difference inclusions. Finally, we illustrate the applicability of the proposed algorithms in a two-dimensional scenarios.

Optimal power flow (OPF) problems are non-convex and large-scale optimization problems with important applications in power networks. This paper proposes the scheduled-asynchronous algorithm to solve a distributed semidefinite programming (SDP) formulation of the OPF problem. In this formulation, every agent seeks to solve a local optimization with its own cost function, physical constraints on its nodal power injection, voltage, and power flow of the lines it is connected to, and decision constraints on variables shared with neighbors to ensure consistency of the obtained solution. In the scheduled-asynchronous algorithm, every pair of connected nodes in the electrical network update their local variables in an alternating fashion. This strategy is asynchronous, in the sense that no clock synchronization is required, and relies on an orientation of the electrical network that prescribes the precise ordering of node updates. We establish the asymptotic convergence properties to the primal-dual optimizer when the orientation is acyclic. Given the dependence of the convergence rate on the network orientation, we also develop a distributed graph coloring algorithm that finds an orientation with diameter at most five for electrical networks with geometric degree distribution. Simulations illustrate our results on various IEEE bus test cases.

We propose a novel Human-Swarm Interaction (HSI) framework which enables the user to control a swarm’s shape and formation. The user commands the swarm utilizing just arm gestures and motions which are recorded by an off-the-shelf wearable armband. We propose a novel interpreter system, which acts as an intermediary between the user and the swarm to simplify the user’s role in the interaction. The interpreter takes high level input drawn using gestures by the user, and translates it into low level swarm control commands. This interpreter employs machine learning, Kalman filtering and optimal control techniques to translate the user input into swarm control parameters. A notion of Human Interpretable dynamics is introduced, which is used by the interpreter for planning as well as to provide feedback to the user. The dynamics of the swarm are controlled using a novel decentralized formation controller based on distributed linear iterations and dynamic average consensus. The framework is demonstrated theoretically as well as experimentally in a 2D environment, with a human controlling a swarm of simulated robots in real time.

Completion axillary lymph node dissection (cALND) can currently be avoided in those patients with a low tumor load (LTL) and/or a low-risk profile that tested with positive sentinel lymph node biopsy (SLNB). Our objective is to identify prognostic factors that significantly influence axillary lymph node involvement to identify patients who could benefit from surgery without axillary lymphadenectomy.This is an observational retrospective study of consecutive patients diagnosed and operated of breast cancer between 2000 and 2014 at University Hospital La Ribera (UHR).The size of the sample was 1641 patients, from which 1174 underwent SLNB. In the multivariate analysis, we objectify a raise of risk of positive sentinel lymph node (SLN) up to 5.2% for every millimeter of increase. The risk of positive SLNB when showing lymphovascular invasion seems to be 2.80 times greater but becomes lower when SLN involvement appears in luminal A, luminal B and triple-negative types, regarding HER2. In case of triple negatives, the difference is statistically significant. 16.7% present affected additional lymph nodes. The proportion of patients with affected additional lymph nodes increase dramatically above OSNA values of 12,000 copies/μl of CK19 mRNA and it depends on tumor size and lymphovascular infiltration.Tumors smaller than 5 cm whose OSNA SLNB analysis is less than 12,000 copies/μl of CK19 mRNA have a low chance to develop additional affected lymph nodes, thus cALND can be avoided.

This paper studies network resilience against structured additive perturbations to its topology. We consider dynamic networks modeled as linear time-invariant systems subject to perturbations of bounded energy satisfying specific sparsity and entry-wise constraints. Given an energy level, the structured pseudospectral abscissa captures the worst-possible perturbation an adversary could employ to de-stabilize the network, and the structured stability radius is the maximum energy in the structured perturbation that the network can withstand without becoming unstable. Building on a novel characterization of the worst-case structured perturbation, we propose iterative algorithms that efficiently compute the structured pseudospectral abscissa and structured stability radius. We provide theoretical guarantees of the local convergence of the algorithms and illustrate their efficacy and accuracy on several network examples., Comment: 13 pages, 5 figures, 2 tables, intended for TCNS

It has been shown after several studies, that in view of the need to strengthen the deficient columns of reinforced concrete (RC), one of the most attractive and used techniques to increase strength and strain capacity, is the confinement of concrete whit FRP (fibre reinforced polymers). It is well known that the confinement of square or rectangular columns is less efficient than the confinement of circular columns. Most of the experimental studies focused on the behaviour of small-scale concrete circular specimens. However, in practice, the most typical findings are the square / rectangular columns of reinforced concrete (RC) on a large scale, so that there is still significant uncertainty in the models used to calculate the reinforcement with FRP and their applicability (especially in the case of columns with rectangular section). One of the key parameters in the prediction of the models is the ultimate strain of the FRP. Some models consider a strain efficiency factor of approximately 0.6, the value of which has been determined from tests on small-scale cylindrical specimens. This paper presents an analysis of the design formulations proposed in the main published international guidelines and compares them with the experimental results of larger square and rectangular section specimens (intermediate and full scale)., This work is part of research project BIA2016-80310-P funded by the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF) and Project PIE-202060E267 funded by the Spanish National Research Council. V. J. Castro acknowledges the financial support (FPI grant BES2017-080647) of the Spanish Ministry of Science and Innovation and the European Social Fund.

The cooperative localization problem consists of a group of networked agents aiming to find the true probability density function(pdf) of their states. Unlike existing algorithms such as Distributed Kalman filters or non-Bayesian social learning, our algorithm restricts each agent’s estimates to a local pdf on its own and its neighbors’ state variables. The agents update these pdfs via local observations of their neighbors and their shared messages. This partial state estimation problem is formulated as a distributed constrained optimization in the space of probability density functions. Consistent estimates across the agents are enforced with a constraint requiring equal estimated densities over common states in every communicating agent pair. Stochastic mirror descent steps are then computed to develop a novel cooperative estimation algorithm with geometric averaging over the common marginals to enforce the constraint. We specialize this algorithm to update rules with Gaussian observation models and density estimates. The Gaussian relative position observations are simulated and accuracy is compared to Belief propagation and full state consensus algorithms in varying graph topology.