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Data informativity is a crucial property to ensure the consistency of the prediction error estimate. This property has thus been extensively studied in the open-loop and in the closed-loop cases, but has only been briefly touched upon in the dynamic network case. In this paper, we consider the prediction error identification of the modules in a row of a dynamic network using the full input approach. Our main contribution is to propose a number of easily verifiable data informativity conditions for this identification problem. Among these conditions, we distinguish a sufficient data informativity condition that can be verified based on the topology of the network and a necessary and sufficient data informativity condition that can be verified via a rank condition on a matrix of coefficients that are related to a full-order model structure of the network. These data informativity conditions allow to determine different situations (i.e., different excitation patterns) leading to data informativity. In order to be able to distinguish between these different situations, we also propose an optimal experiment design problem that allows to determine the excitation pattern yielding a certain pre-specified accuracy with the least excitation power., QC 20230317

The Checklist of the Vascular Plants of the Republic of Guinea (CVPRG) is a specimen-based, expert-validated knowledge product, which provides a concise synthesis and overview of current knowledge on 3901 vascular plant species documented from Guinea (Conakry), West Africa, including their accepted names and synonyms, as well as their distribution and status within Guinea (indigenous or introduced, endemic or not). The CVPRG is generated automatically from the Guinea Collections Database and the Guinea Names Backbone Database, both developed and maintained at the Royal Botanic Gardens, Kew, in collaboration with the staff of the National Herbarium of Guinea. A total of 3505 indigenous vascular plant species are reported of which 3328 are flowering plants (angiosperms); this represents a 26% increase in known indigenous angiosperms since the last floristic overview. Intended as a reference for scientists documenting the diversity and distribution of the Guinea flora, the CVPRG will also inform those seeking to safeguard the rich plant diversity of Guinea and the societal, ecological and economic benefits accruing from these biological resources.

The Checklist of the Vascular Plants of the Republic of Guinea (CVPRG) is a specimen-based, expert-validated knowledge product, which provides a concise synthesis and overview of current knowledge on 3901 vascular plant species documented from Guinea (Conakry), West Africa, including their accepted names and synonyms, as well as their distribution and status within Guinea (indigenous or introduced, endemic or not). The CVPRG is generated automatically from the Guinea Collections Database and the Guinea Names Backbone Database, both developed and maintained at the Royal Botanic Gardens, Kew, in collaboration with the staff of the National Herbarium of Guinea. A total of 3505 indigenous vascular plant species are reported of which 3328 are flowering plants (angiosperms); this represents a 26% increase in known indigenous angiosperms since the last floristic overview. Intended as a reference for scientists documenting the diversity and distribution of the Guinea flora, the CVPRG will also inform those seeking to safeguard the rich plant diversity of Guinea and the societal, ecological and economic benefits accruing from these biological resources.

Control in a classical transfer function or state-space setting typically views a controller as a signal processor: sensor outputs are mapped to actuator inputs. In behavioral system theory, control is simply viewed as interconnection; the interconnection of a plant with a controller. In this paper we consider the problem of control of interconnected systems in a behavioral setting. The behavioral setting is especially fit for modelling interconnected systems, because it allows for the interconnection of subsystems without imposing inputs and outputs. We introduce a so-called canonical distributed controller that implements a given interconnected behavior that is desired, provided that necessary and sufficient conditions hold true. The controller design can be performed in a decentralized manner, in the sense that a local controller only depends on the local system behavior. Regularity of interconnections is an important property in behavioral control that yields feedback interconnections. We provide conditions under which the interconnection of this distributed controller with the plant is regular. Furthermore, we show that the interconnections of subsystems of the canonical distributed controller are regular if and only if the interconnections of the plant and desired behavior are regular.

In order to identify a system (module) embedded in a dynamic network, one has to formulate a multiple-input estimation problem that necessitates certain nodes to be measured and included as predictor inputs. However, some of these nodes may not be measurable in many practical cases due to sensor selection and placement issues. This may result in biased estimates of the target module. Furthermore, the identification problem associated with the multiple-input structure may require determining a large number of parameters that are not of particular interest to the experimenter, with increased computational complexity in large-sized networks. In this paper, we tackle these problems by using a data augmentation strategy that allows us to reconstruct the missing node measurements and increase the accuracy of the estimated target module. To this end, we develop a system identification method using regularized kernel-based methods coupled with approximate inference methods. Keeping a parametric model for the module of interest, we model the other modules as Gaussian Processes (GP) with a kernel given by the so-called stable spline kernel. An Empirical Bayes (EB) approach is used to estimate the parameters of the target module. The related optimization problem is solved using an Expectation-Maximization (EM) method, where we employ a Markov-chain Monte Carlo (MCMC) technique to reconstruct the unknown missing node information and the network dynamics. Numerical simulations on dynamic network examples illustrate the potentials of the developed method., Comment: 17 pages, 7 Figures. Draft paper submitted to Automatica

Urban streams can provide amenities to people living in cities, but those benefits are reduced when streams become degraded, potentially even causing harm (disease, toxic compounds, etc.). Governments and institutions invest resources to improve the values and services provided by urban streams; however, the conception, development, and implementation of such projects may not include meaningful involvement of community members and other stakeholders. Consequently, project objectives may be misaligned with community desires and needs, and projects may fail to achieve their goals. In February 2020, the 5th Symposium on Urbanization and Stream Ecology, an interdisciplinary meeting held every 3 to 5 y, met in Austin, Texas, USA, to explore new approaches to urban stream projects, including ways to maximize the full range of potential benefits by better integrating community members into project identification and decision making. The symposium included in-depth discussion about 4 nearby field case studies, participation of multidisciplinary urban stream experts from 5 continents, and input from the Austin community. Institutional barriers to community inclusion were identified and analyzed using real-world examples, both from the case studies and from the literature, which clarified disparities in power, equity, and values. Outcomes of the symposium have been aggregated into a vision that challenges the present institutional approach to urban stream management and a set of strategies to systematically address these barriers to improve restoration solutions. Integrating community members and other stakeholders throughout the urban restoration process, and a transparent decision-making process to resolve divergent objectives, can help identify appropriate goals for realizing both the ecological and social benefits of stream restoration.

Modeling and control of dynamical systems rely on measured data, which contains information about the system. Finite data measurements typically lead to a set of system models that are unfalsified, i.e., that explain the data. The problem of data-informativity for stabilization or control with quadratic performance is concerned with the existence of a controller that stabilizes all unfalsified systems or achieves a desired quadratic performance. Recent results in the literature provide informativity conditions for control based on input-state data and ellipsoidal noise bounds, such as energy or magnitude bounds. In this paper, we consider informativity of input-state data for control where noise bounds are defined through the cross-covariance of the noise with respect to an instrumental variable; bounds that were introduced originally as a noise characterization in parameter bounding identification. The considered cross-covariance bounds are defined by a finite number of hyperplanes, which induce a (possibly unbounded) polyhedral set of unfalsified systems. We provide informativity conditions for input-state data with polyhedral cross-covariance bounds for stabilization and $\mathcal{H}_2$/$\mathcal{H}_\infty$ control through vertex/half-space representations of the polyhedral set of unfalsified systems.

Fusions of NTRK (neurotrophic tyrosine receptor kinase) genes with 5′ partner genes can result in the expression of chimeric proteins that drive oncogenesis through ligand-independent kinase activation. Despite variable frequencies of NTRK fusions in different tumor types, the fact that they are common to a wide range of cancers raises the possibility of developing tumor-agnostic treatments specifically targeting NTRK fusion products, irrespective of tumor type. The first-generation Trk (tropomyosin receptor kinase) inhibitor, larotrectinib, was the first tumor-agnostic treatment of NTRK fusion-positive cancers in adults and children, to be approved in the European Union. This consensus, developed by a Belgian multidisciplinary expert panel, aims to highlight the unmet medical need associated to NTRK fusion-driven cancer treatment and, based on current knowledge of NTRK fusions and larotrectinib treatment outcome and safety, provide comprehensive guidance to oncologists regarding NTRK fusion-driven cancer diagnostics and the best use of larotrectinib in real-world clinical settings., SCOPUS: re.j, info:eu-repo/semantics/published

Purpose: FGFR genomic alterations (amplification, mutations, and/or fusions) occur in ∼8% of gliomas, particularly FGFR1 and FGFR3. We conducted a multicenter open-label, single-arm, phase II study of a selective FGFR1-3 inhibitor, infigratinib (BGJ398), in patients with FGFR-altered recurrent gliomas. Patients and methods: Adults with recurrent/progressive gliomas harboring FGFR alterations received oral infigratinib 125 mg on days 1 to 21 of 28-day cycles. The primary endpoint was investigator-assessed 6-month progression-free survival (PFS) rate by Response Assessment in Neuro-Oncology criteria. Comprehensive genomic profiling was performed on available pretreatment archival tissue to explore additional molecular correlations with efficacy. Results: Among 26 patients, the 6-month PFS rate was 16.0% [95% confidence interval (CI), 5.0-32.5], median PFS was 1.7 months (95% CI, 1.1-2.8), and objective response rate was 3.8%. However, 4 patients had durable disease control lasting longer than 1 year. Among these, 3 had tumors harboring activating point mutations at analogous positions of FGFR1 (K656E; n = 2) or FGFR3 (K650E; n = 1) in pretreatment tissue; an FGFR3-TACC3 fusion was detected in the other. Hyperphosphatemia was the most frequently reported treatment-related adverse event (all-grade, 76.9%; grade 3, 3.8%) and is a known on-target toxicity of FGFR inhibitors. Conclusions: FGFR inhibitor monotherapy with infigratinib had limited efficacy in a population of patients with recurrent gliomas and different FGFR genetic alterations, but durable disease control lasting more than 1 year was observed in patients with tumors harboring FGFR1 or FGFR3 point mutations or FGFR3-TACC3 fusions. A follow-up study with refined biomarker inclusion criteria and centralized FGFR testing is warranted.

BACKGROUND: In the EORTC 1410/INTELLANCE 2 randomised, phase II study (NCT02343406), with the antibody-drug conjugate depatuxizumab mafodotin (Depatux-M, ABT-414) in patients with recurrent EGFR-amplified glioblastoma, the primary end-point (overall survival) was not met, and the drug had ocular dose-limiting toxicity. This study reports results from the prespecified health-related quality of life (HRQoL) and neurological deterioration-free survival (NDFS) exploratory analysis. PATIENTS AND METHODS: Patients (n = 260) were randomised 1:1:1 to receive either Depatux-M 1.25 mg/kg or 1.0 mg/kg intravenously every 2 weeks with oral temozolomide (TMZ) 150 mg/m2, Depatux-M alone, or TMZ or oral lomustine (CCNU) 110 mg/m2 (TMZ/CCNU). HRQoL outcomes were recorded using the EORTC core Quality of Life QLQ-C30, and brain cancer-specific QLQ-BN20 questionnaires. Questionnaires were completed at baseline, weeks 8 and 16, and month 6, and changes from baseline to each time point were calculated. NDFS was defined as time to first deterioration in World Health Organisation performance status. RESULTS: Compliance with HRQoL was 88.1% at baseline and decreased to 37.9% at month 6. Differences from baseline between Depatux-M arms and TMZ/CCNU in global health/QoL status throughout treatment did not reach clinical relevance (≥10 points). Self-reported visual disorders deteriorated to a clinically relevant extent with Depatux-M arms versus TMZ/CCNU at all timepoints (mean differences range: 24.6-35.1 points). Changes from baseline for other HRQoL scales and NDFS were generally similar between treatment arms. CONCLUSIONS: Depatux-M had no impact on HRQoL and NDFS in patients with EGFR-amplified recurrent glioblastoma, except for more visual disorders, an expected side-effect of the study drug. CLINICAL TRIAL REGISTRATION: NCT02343406.

In this paper we develop new data informativity based controller synthesis methods that extend existing frameworks in two relevant directions: a more general noise characterization in terms of cross-covariance bounds and informativity conditions for control based on input-output data. Previous works have derived necessary and sufficient informativity conditions for noisy input-state data with quadratic noise bounds via an S-procedure. Although these bounds do not capture cross-covariance bounds in general, we show that the S-procedure is still applicable for obtaining non-conservative conditions on the data. Informativity-conditions for stability, $\mathcal{H}_\infty$ and $\mathcal{H}_2$ control are developed, which are sufficient for input-output data and also necessary for input-state data. Simulation experiments illustrate that cross-covariance bounds can be less conservative for informativity, compared to norm bounds typically employed in the literature., Comment: In the previous version of this arXiv article and the corresponding L-CSS article, DOI: 10.1109/LCSYS.2021.3139526, there was a typographical error in equation (21) and (25). The equations contain $P_-$, which should be $Y_-$. This typo is corrected in this version

Identification methods for dynamic networks typically require prior knowledge of the network and disturbance topology, and often rely on solving poorly scalable non-convex optimization problems. While methods for estimating network topology are available in the literature, less attention has been paid to estimating the disturbance topology, i.e., the (spatial) noise correlation structure and the noise rank in a filtered white noise representation of the disturbance signal. In this work we present an identification method for dynamic networks, in which an estimation of the disturbance topology precedes the identification of the full dynamic network with known network topology. To this end we extend the multi-step Sequential Linear Regression and Weighted Null Space Fitting methods to deal with reduced rank noise, and use these methods to estimate the disturbance topology and the network dynamics in the full measurement situation. As a result, we provide a multi-step least squares algorithm with parallel computation capabilities and that rely only on explicit analytical solutions, thereby avoiding the usual non-convex optimizations involved. Consequently we consistently estimate dynamic networks of Box Jenkins model structure, while keeping the computational burden low. We provide a consistency proof that includes path-based data informativity conditions for allocation of excitation signals in the experimental design. Numerical simulations performed on a dynamic network with reduced rank noise clearly illustrate the potential of this method., Comment: 17 pages, 4 figures, accepted and published in Automatica Volume 141, July 2022

Physical dynamic networks most commonly consist of interconnections of physical components that can be described by diffusive couplings. These diffusive couplings imply that the cause-effect relationships in the interconnections are symmetric and therefore physical dynamic networks can be represented by undirected graphs. This paper shows how prediction error identification methods developed for linear time-invariant systems in polynomial form can be configured to consistently identify the parameters and the interconnection structure of diffusively coupled networks. Further, a multi-step least squares convex optimization algorithm is developed to solve the nonconvex optimization problem that results from the identification method.

In classical approaches of dynamic network identification, in order to identify a system (module) embedded in a dynamic network, one has to formulate a Multi-input-Single-output (MISO) identification problem that requires identification of a parametric model for all the modules constituting the MISO setup including (possibly) the noise model, and determine their model order. This requirement leads to model order selection steps for modules that are of no interest to the experimenter which increases the computational complexity for large-sized networks. Also, identification using a parametric noise model (like BJ method) can suffer from local minima, however neglecting the noise model has its impact on the variance of the estimates. In this paper, we provide a two-step identification approach to avoid these problems. The first step involves performing a non-parametric indirect approach for a MISO identification problem to get the non-parametric frequency response function (FRF) estimates and its variance as a function of frequency. In the second step, the estimated non-parametric FRF of the target module is smoothed using a parametric frequency domain estimator with the estimated variance from the previous step as the non-parametric noise model. The developed approach is practical with weak assumptions on noise, uses the available toolbox, requires a parametric model only for the target module of interest, and uses a non-parametric noise model to reduce the variance of the estimates. Numerical simulations illustrate the potentials of the introduced method in comparison with the classical identification methods., Comment: 8 pages, 5 figures, Submitted for publication in Automatica, 22 May 2021

The increase in available data and complexity of dynamical systems has sparked the research on data-based system performance analysis and controller design. Recent approaches can guarantee performance and robust controller synthesis based on noisy input-state data of a single dynamical system. In this paper, we extend a recent data-based approach for guaranteed performance analysis to distributed analysis of interconnected linear systems. We present a new set of sufficient LMI conditions based on noisy input-state data that guarantees $\mathcal{H}_\infty$ performance and have a structure that lends itself well to distributed controller synthesis from data. Sufficient LMI conditions based on noisy data are provided for the existence of a dynamic distributed controller that achieves $\mathcal{H}_\infty$ performance. The presented approach enables scalable analysis and control of large-scale interconnected systems from noisy input-state data sets.

BACKGROUND In the EORTC 1410/INTELLANCE 2 randomised, phase II study (NCT02343406), with the antibody-drug conjugate depatuxizumab mafodotin (Depatux-M, ABT-414) in patients with recurrent EGFR-amplified glioblastoma, the primary end-point (overall survival) was not met, and the drug had ocular dose-limiting toxicity. This study reports results from the prespecified health-related quality of life (HRQoL) and neurological deterioration-free survival (NDFS) exploratory analysis. PATIENTS AND METHODS Patients (n = 260) were randomised 1:1:1 to receive either Depatux-M 1.25 mg/kg or 1.0 mg/kg intravenously every 2 weeks with oral temozolomide (TMZ) 150 mg/m$^{2}$, Depatux-M alone, or TMZ or oral lomustine (CCNU) 110 mg/m$^{2}$ (TMZ/CCNU). HRQoL outcomes were recorded using the EORTC core Quality of Life QLQ-C30, and brain cancer-specific QLQ-BN20 questionnaires. Questionnaires were completed at baseline, weeks 8 and 16, and month 6, and changes from baseline to each time point were calculated. NDFS was defined as time to first deterioration in World Health Organisation performance status. RESULTS Compliance with HRQoL was 88.1% at baseline and decreased to 37.9% at month 6. Differences from baseline between Depatux-M arms and TMZ/CCNU in global health/QoL status throughout treatment did not reach clinical relevance (≥10 points). Self-reported visual disorders deteriorated to a clinically relevant extent with Depatux-M arms versus TMZ/CCNU at all timepoints (mean differences range: 24.6-35.1 points). Changes from baseline for other HRQoL scales and NDFS were generally similar between treatment arms. CONCLUSIONS Depatux-M had no impact on HRQoL and NDFS in patients with EGFR-amplified recurrent glioblastoma, except for more visual disorders, an expected side-effect of the study drug. CLINICAL TRIAL REGISTRATION NCT02343406.

Identifiability conditions for single or multiple modules in a dynamic network specify under which conditions the considered modules can be uniquely recovered from the second-order statistical properties of the measured signals. Conditions for generic identifiability of multiple modules, i.e. a subnetwork, are developed for the situation that all node signals are measured and excitation of the network is provided by both measured excitation signals and unmeasured disturbance inputs. Additionally, the network model set is allowed to contain non-parametrized modules that are fixed, and e.g. reflect modules of which the dynamics are known to the user. The conditions take the form of path-based conditions on the graph of the network model set. Based on these conditions, synthesis results are formulated for allocating external excitation signals to achieve generic identifiability of particular subnetworks. If there are a sufficient number of measured external excitation signals, the formulated results give rise to a generalized indirect type of identification algorithm that requires only the measurement of a subset of the node signals in the network.

In order to identify one system (module) in an interconnected dynamic network, one typically has to solve a Multi-Input-Single-Output (MISO) identification problem that requires identification of all modules in the MISO setup. For application of a parametric identification method this would require estimating a large number of parameters, as well as an appropriate model order selection step for a possibly large scale MISO problem, thereby increasing the computational complexity of the identification algorithm to levels that are beyond feasibility. An alternative identification approach is presented employing regularized kernel-based methods. Keeping a parametric model for the module of interest, we model the impulse response of the remaining modules in the MISO structure as zero mean Gaussian processes (GP) with a covariance matrix (kernel) given by the first-order stable spline kernel, accounting for the noise model affecting the output of the target module and also for possible instability of systems in the MISO setup. Using an Empirical Bayes (EB) approach the target module parameters are estimated through an Expectation-Maximization (EM) algorithm with a substantially reduced computational complexity, while avoiding extensive model structure selection. Numerical simulations illustrate the potentials of the introduced method in comparison with the state-of-the-art techniques for local module identification., Comment: 15 pages, 7 figures, Submitted for publication in Automatica, 12 May 2020. Final version of paper submitted on 06 January 2021 (To appear in Automatica)

The current limitation in the synthesis of distributed $\mathscr{H}_2$ controllers for linear interconnected systems is scalability due to non-convex or unstructured synthesis conditions. In this paper we develop convex and structured conditions for the existence of a distributed $\mathscr{H}_2$ controller for discrete-time interconnected systems with an interconnection structure that corresponds to an arbitrary graph. Neutral interconnections and a storage function with a block-diagonal structure are utilized to attain coupling conditions that are of a considerably lower computational complexity compared to the corresponding centralized $\mathscr{H}_2$ controller synthesis problem. Additionally, the developed conditions are adapted for the corresponding decentralized $\mathscr{H}_2$ controller synthesis problem with fixed supply functions for the interconnections. The effectiveness and scalability of the developed distributed $\mathscr{H}_2$ controller synthesis method is demonstrated for small- to large-scale oscillator networks on a cycle graph., Comment: Changes in this version include: overview of dissipativity-based results for interconnected systems is removed, result on the existence of a decentralized controller is included and a simulation example for the illustration of scalability replaces the previous simulation example

Identifiability of a single module in a network of transfer functions is determined by whether a particular transfer function in the network can be uniquely distinguished within a network model set, on the basis of data. Whereas previous research has focused on the situations that all network signals are either excited or measured, we develop generalized analysis results for the situation of partial measurement and partial excitation. As identifiability conditions typically require a sufficient number of external excitation signals, this work introduces a novel network model structure such that excitation from unmeasured noise signals is included, which leads to less conservative identifiability conditions than relying on measured excitation signals only. More importantly, graphical conditions are developed to verify global and generic identifiability of a single module based on the topology of the dynamic network. Depending on whether the input or the output of the module can be measured, we present four identifiability conditions which cover all possible situations in single module identification. These conditions further lead to synthesis approaches for allocating excitation signals and selecting measured signals, to warrant single module identifiability. In addition, if the identifiability conditions are satisfied, indirect identification methods are developed to provide a consistent estimate of the module. All the obtained results are also extended to identifiability of multiple modules in the network.

In this paper, the problem of synthesizing a distributed controller from data is considered, with the objective to optimize a model-reference control criterion. We establish an explicit ideal distributed controller that solves the model-reference control problem for a structured reference model. On the basis of input-output data collected from the interconnected system, a virtual experiment setup is constructed which leads to a network identification problem. We formulate a prediction-error identification criterion that has the same global optimum as the model-reference criterion, when the controller class contains the ideal distributed controller. The developed distributed controller synthesis method is illustrated on an academic example network of nine subsystems and the influence of the controller interconnection structure on the achieved closed-loop performance is analyzed.

Several studies claim that listening to Mozart music affects cognition and can be used to treat neurological conditions like epilepsy. Research into this Mozart effect has not addressed how dynamic interactions between brain networks, i.e. effective connectivity, are affected. The Granger-causality analysis is often used to infer effective connectivity. First, we investigate if a new method, Bayesian topology identification, can be used as an alternative. Both methods are evaluated on simulation data, where the Bayesian method outperforms the Granger-causality analysis in the inference of connectivity graphs of dynamic networks, especially for short data lengths. In the second part, the Bayesian method is extended to enable the inference of changes in effective connectivity between groups of subjects. Next, we apply both methods to fMRI scans of 16 healthy subjects, who were scanned before and after exposure to Mozart's sonata K448 at least 2 hours a day for 7 days. Here, we investigate if the effective connectivity of the subjects significantly changed after listening to Mozart music. The Bayesian method detected changes in effective connectivity between networks related to cognitive processing and control: First, in the connection from the central executive to the superior sensori-motor network. Second, in the connection from the posterior default mode to the fronto-parietal right network. Finally, in the connection from the anterior default mode to the dorsal attention network, but only in a subgroup of subjects with a longer listening duration. Only in this last connection an effect was found by the Granger-causality analysis.

In abstractions of linear dynamic networks, selected node signals are removed from the network, while keeping the remaining node signals invariant. The topology and link dynamics, or modules, of an abstracted network will generally be changed compared to the original network. Abstractions of dynamic networks can be used to select an appropriate set of node signals that are to be measured, on the basis of which a particular local module can be estimated. A method is introduced for network abstraction that generalizes previously introduced algorithms, as e.g. immersion and the method of indirect inputs. For this abstraction method it is shown under which conditions on the selected signals a particular module will remain invariant. This leads to sets of conditions on selected measured node variables that allow identification of the target module. (C) 2020 Elsevier Ltd. All rights reserved., Funding Agencies|European Research Council (ERC), Advanced Research Grant SYSDYNET, under the European Unions Horizon 2020 research and innovation programme [694504]; Vinnova Industry Excellence Center LINK-SIC, Sweden [2007-02224]

The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module dynamics, under the assumption of having disturbances that are uncorrelated over the different nodes. The conditions typically reflect the selection of a set of node signals that are taken as predictor inputs in a MISO identification setup. In this paper an extension is made to arrive at an identification setup for the situation that process noises on the different node signals can be correlated with each other. In this situation the local module may need to be embedded in a MIMO identification setup for arriving at a consistent estimate with maximum likelihood properties. This requires the proper treatment of confounding variables. The result is a set of algorithms that, based on the given network topology and disturbance correlation structure, selects an appropriate set of node signals as predictor inputs and outputs in a MISO or MIMO identification setup. Three algorithms are presented that differ in their approach of selecting measured node signals. Either a maximum or a minimum number of measured node signals can be considered, as well as a preselected set of measured nodes., Comment: Submitted for publication in IEEE Transactions on Automatic Control, 2 August 2019. arXiv admin note: text overlap with arXiv:1809.07502. Final version of paper scheduled to appear in IEEE Transactions on Automatic Control, Vol. 66, No. 11, November 2021

In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper, we explore a Bayesian model selection method for identifying the connectivity of networks of transfer functions, without the need to estimate the dynamics. The algorithm employs a Bayesian measure and a forward-backward search algorithm. To obtain the Bayesian measure, the impulse responses of network modules are modeled as Gaussian processes and the hyperparameters are estimated by marginal likelihood maximization using the expectation-maximization algorithm. Numerical results demonstrate the effectiveness of this method.

In abstractions of linear dynamic networks, selected node signals are removed from the network, while keeping the remaining node signals invariant. The topology and link dynamics, or modules, of an abstracted network will generally be changed compared to the original network. Abstractions of dynamic networks can be used to select an appropriate set of node signals that are to be measured, on the basis of which a particular local module can be estimated. A method is introduced for network abstraction that generalizes previously introduced algorithms, as e.g. immersion and the method of indirect inputs. For this abstraction method it is shown under which conditions on the selected signals a particular module will remain invariant. This leads to sets of conditions on selected measured node variables that allow identification of the target module., Comment: 17 pages, 7 figures. Paper to appear in Automatica, Vol. 117, July 2020

The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module dynamics, typically under the assumption of having disturbances that are uncorrelated over the different nodes. The conditions typically reflect the selection of a set of node signals that are taken as predictor inputs in a MISO identification setup. In this paper an extension is made to arrive at an identification setup for the situation that process noises on the different node signals can be correlated with each other. In this situation the local module may need to be embedded in a MIMO identification setup for arriving at a consistent estimate with maximum likelihood properties. This requires the proper treatment of confounding variables. The result is an algorithm that, based on the given network topology and disturbance correlation structure, selects an appropriate set of node signals as predictor inputs and outputs in a MISO or MIMO identification setup. As a first step in the analysis, we restrict attention to the (slightly conservative) situation where the selected output node signals are predicted based on all of their in-neighbor node signals in the network., Comment: Extended version of paper submitted to the 58th IEEE Conf. Decision and Control, Nice, 2019

The problem of identifying single modules in multiple-input-single-output (MISO) systems is considered. A novel approach to distributed identification of MISO finite impulse response systems is presented. The distributed identification is discerned by the local estimation of local parameters, which correspond to a module in the MISO system. The local estimators are derived from the standard recursive least squares estimator and require limited information exchange. By Lyapunov's second method, sufficient conditions are derived for asymptotic convergence of the estimators to the true parameters in the absence of disturbances, which lead to asymptotic unbiasedness in the presence of additive output disturbances.

Recent developments in science and engineering have motivated control systems to be considered as interconnected and networked systems. From a system identification point of view, modelling of a local module in such a structured system is a relevant and interesting problem. This work focuses on the quality, in terms of variance, of an estimate of a local module. We analyse which predictor input signals are relevant and contribute to variance reduction, while still guaranteeing the consistency of the estimate. For a targeted local module, a comparison of its estimate variance is made between a full-MISO approach and an immersed network setting, where a reduced number of inputs is used, while still guaranteeing consistency. A case study of a four-node network is considered and it is shown that a smaller set of predictor inputs can, under some conditions, result in a smaller variance compared to the full-MISO approach., Comment: 6 pages, 6 figures

A recent development in data-driven modelling addresses the problem of identifying dynamic models of interconnected systems, represented as linear dynamic networks. For these networks the notion network identifiability has been introduced recently, which reflects the property that different network models can be distinguished from each other. Network identifiability is extended to cover the uniqueness of a single module in the network model. Conditions for single module identifiability are derived and formulated in terms of path-based topological properties of the network models., Comment: 6 pages, 2 figures, submitted to Control Systems Letters (L-CSS) and the 57th IEEE Conference on Decision and Control (CDC)

Background: To investigate the activity and safety of afatinib in the preoperative treatment of squamous cell carcinoma of the head and neck (SCCHN). Patients and methods: This study was an open-label, randomized, multicenter, phase II window of opportunity trial. Treatment-naïve SCCHN patients selected for primary curative surgery were randomized (5: 1 ratio) to receive afatinib during 14 days (day -15 until day -1) before surgery (day 0) or no treatment. Tumor biopsies, 2-[fluorine-18]-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET), and magnetic resonance imaging (MRI) were carried out at diagnosis and just before surgery. The primary end point was metabolic FDG-PET response (according to EORTC guidelines). Other end points included response assessment based on the Response Evaluation Criteria In Solid Tumors (RECIST) v1.1, dynamic contrast-enhanced (DCE)-MRI, diffusion weighted (DW)-MRI, safety, and translational research (TR). Results: Thirty patients were randomized: 25 to afatinib and 5 to control arm. Of the 23 eligible patients randomized to afatinib, 16 (70%; 95% CI: 47% to 87%) patients had a partial metabolic FDG-PET response (PMR). Five patients (22%; 95% CI: 8% to 44%) showed a partial response by RECISTv1.1. Responses assessed via DCE-MRI and DWI-MRI did not show a strong association with PMR or RECIST. One patient discontinued afatinib after 11 days for grade 3 diarrhea with subsequent renal failure and 24 days delay in surgery. No grade 4 toxicities or surgical comorbidities related to afatinib were reported. TR results indicated that PMR was more frequent in the tumors with high Cluster3-hypoxia score expression and with TP53 wild type. Conclusion: Afatinib given for 2 weeks to newly diagnosed SCCHN patients induces a high rate of FDG-PET partial metabolic response and partial response according to RECISTv1.1. Afatinib can be safely administered before surgery. Although exploratory, the hypoxic gene signature needs further investiga, SCOPUS: ar.j, info:eu-repo/semantics/published

Clinical trials of new cancer drugs should ideally include measurements of parameters such as molecular target expression, pharmacokinetic (PK) behavior, and pharmacodynamic (PD) endpoints that can be linked to measures of clinical effect. Appropriate PK/PD biomarkers facilitate proof-of-concept demonstrations for target modulation; enhance the rational selection of an optimal drug dose and schedule; aid decision-making, such as whether to continue or close a drug development project; and may explain or predict clinical outcomes. In addition, measurement of PK/PD biomarkers can minimize uncertainty associated with predicting drug safety and efficacy, reduce the high levels of drug attrition during development, accelerate drug approval, and decrease the overall costs of drug development. However, there are many challenges in the development and implementation of biomarkers that probably explain their disappointingly low implementation in phase I trials. The Pharmacodynamic/Pharmacokinetic Technologies Advisory committee of Cancer Research UK has found that submissions for phase I trials of new cancer drugs in the United Kingdom often lack detailed information about PK and/or PD endpoints, which leads to suboptimal information being obtained in those trials or to delays in starting the trials while PK/PD methods are developed and validated. Minimally invasive PK/PD technologies have logistic and ethical advantages over more invasive technologies. Here we review these technologies, emphasizing magnetic resonance spectroscopy and positron emission tomography, which provide detailed functional and metabolic information. Assays that measure effects of drugs on important biologic pathways and processes are likely to be more cost-effective than those that measure specific molecular targets. Development, validation, and implementation of minimally invasive PK/PD methods are encouraged

Dynamic networks are interconnected dynamic systems with measured node signals and dynamic modules reflecting the links between the nodes. We address the problem of \red{identifying a dynamic network with known topology, on the basis of measured signals}, for the situation of additive process noise on the node signals that is spatially correlated and that is allowed to have a spectral density that is singular. A prediction error approach is followed in which all node signals in the network are jointly predicted. The resulting joint-direct identification method, generalizes the classical direct method for closed-loop identification to handle situations of mutually correlated noise on inputs and outputs. When applied to general dynamic networks with rank-reduced noise, it appears that the natural identification criterion becomes a weighted LS criterion that is subject to a constraint. This constrained criterion is shown to lead to maximum likelihood estimates of the dynamic network and therefore to minimum variance properties, reaching the Cramer-Rao lower bound in the case of Gaussian noise., Comment: 17 pages, 5 figures, revision submitted for publication in Automatica, 4 April 2018

System identification is a common tool for estimating (linear) plant models as a basis for model-based predictive control and optimization. The current challenges in process industry, however, ask for data-driven modelling techniques that go beyond the single unit/plant models. While optimization and control problems become more and more structured in the form of decentralized and/or distributed solutions, the related modelling problems will need to address structured and interconnected systems. An introduction will be given to the current state of the art and related developments in the identification of linear dynamic networks. Starting from classical prediction error methods for open-loop and closed-loop systems, several consequences for the handling of network situations will be presented and new research questions will be highlighted.

Background: Our aim was to test the safety of cetuximab added to chemoradiation with either cisplatin or carboplatin after prior induction chemotherapy. Methods: Patients with stage III/IV unresectable, squamous cell carcinoma of the head and neck received up to four cycles of TPF-E (cisplatin and docetaxel 75 mg/m2 on day 1 followed by 5-FU 750 mg/m2/day as a continuous infusion on days 1-5 plus cetuximab at a loading dose of 400 mg/m2 followed by a weekly dose of 250 mg/m2), with prophylactic antibiotics but no growth factors. Patients not progressing after four cycles of TPF-E were randomly assigned to radiotherapy (70 Gy over 7 weeks in 2 Gy fractions) and weekly cetuximab with either weekly cisplatin 40 mg/m2 or carboplatin, AUC of 1.5 mg/ml/min. Primary endpoint was feasibility. Results: Forty-seven patients were recruited. One patient did not start TPF (hypersensitivity reaction during the cetuximab loading dose). Induction TPF-E was discontinued in 12 patients due to toxicity (6 patients), medical decision (2), death (1), patient refusal (1), protocol violation (1), co-morbidity (1). Three further patients were not randomized [progressive disease (1), protocol violation (1), toxicity and co-morbidity (1)]. Of particular interest are three patients who suffered from bowel perforation, one patient who died as results of pneumonia and septic shock, and a second patient who was found dead at home 12 days after starting TPF-E (cause of death unknown). Weekly cisplatin and carboplatin was stopped early in seven and four patients, respectively. Radiotherapy was stopped in two patients with cisplatin and interrupted in one patient with cisplatin and four patients with carboplatin. Conclusions: The addition of cetuximab to full dose TPF induction chemotherapy led to unacceptable complications and premature closing of the study. Only 34 out of 46 patients completed four cycles of TPF-E and only 30 started biochemoradiation., SCOPUS: ar.j, info:eu-repo/semantics/published

Model-based operation support technology such as Model Predictive Control (MPC) is a proven and accepted technology for multivariable and constrained large scale control problems in process industry. Despite the growing number of successful implementations, the low level of operational efficiency of MPC is an existing problem, specifically the lack of advanced maintenance technology. To this end, within the EU FP 7 program, a project (Autoprofit (1)) has been executed to advance the level of autonomy and automated maintenance of MPC technology. Taking linear model-based technology as a starting point, in the project a philosophy has been developed for autonomous performance monitoring, diagnosis, experiment design, model adaptation and controller re-tuning, that is driven by economic criteria in each step, working towards an operation support system in which effective maintenance and adaptation of MPC controllers becomes feasible. In this development, challenging research questions have been addressed in the areas of on-line performance monitoring and diagnosis, least costly experiment design, automated adaptation of models, and auto-tuning, and new fundamental techniques have been developed. Although a full fledge and industrially proven (semi-)automated system is not yet realised, parts of the on-line system have been implemented and validated on real life cases provided by the industrial partners, showing that the formulated objectives are within reach., QC 20170126

Dynamic networks are structured interconnections of dynamical systems (modules) driven by external excitation and disturbance signals. In order to identify their dynamical properties and/or their topology consistently from measured data, we need to make sure that the network model set is identifiable. We introduce the notion of network identifiability, as a property of a parameterized model set, that ensures that different network models can be distinguished from each other when performing identification on the basis of measured data. Different from the classical notion of (parameter) identifiability, we focus on the distinction between network models in terms of their transfer functions. For a given structured model set with a pre-chosen topology, identifiability typically requires conditions on the presence and location of excitation signals, and on presence, location and correlation of disturbance signals. Because in a dynamic network, disturbances cannot always be considered to be of full-rank, the reduced-rank situation is also covered, meaning that the number of driving white noise processes can be strictly less than the number of disturbance variables. This includes the situation of having noise-free nodes., Comment: Provisionally accepted for publication in Automatica

This work develops a measurement-driven and model-based formal verification approach, applicable to systems with partly unknown dynamics. We provide a principled method, grounded on reachability analysis and on Bayesian inference, to compute the confidence that a physical system driven by external inputs and accessed under noisy measurements, verifies a temporal logic property. A case study is discussed, where we investigate the bounded- and unbounded-time safety of a partly unknown linear time invariant system.

Current state-of-the-art correct-by-design controllers are designed for full-state measurable systems. This work first extends the applicability of correct-by-design controllers to partially observable LTI systems. Leveraging 2nd order bounds we give a design method that has a quantifiable robustness to probabilistic disturbances on state transitions and on output measurements. In a case study from smart buildings we evaluate the new output-based correct-by-design controller on a physical system with limited sensor information.

Bevacizumab (BEV) has demonstrated anti-tumor activity in patients with recurrent glioblastoma (rGB). Given the unmet need for active therapeutic options in rGB patients, a medical need program was initiated by the Belgian competent authorities. Between November 2010 and February 2013, a total of 313 patients with rGB initiated treatment with BEV administered at a dose of 10 mg/kg every 2 weeks. All patients had failed prior treatment with at least radiation therapy and temozolomide and the majority of patients (70 %) were treated with corticosteroids at baseline. Patients received a median of 6 BEV administrations (range 1–53). Overall, BEV was well tolerated. During BEV treatment the WHO-Performance Score (WHO-PS) improved in 59 patients (19 %) and stabilized for at least 6 weeks in an additional 139 (44 %) patients. Corticosteroid treatment could be stopped in 16 % or reduced in dose in 32 % of patients. The best objective tumor response rate using RANO criteria (investigator’s assessment) was 3.5 % CR, 22 % PR, 38 % SD and 37 % PD. The median and 6-month PFS were 13 weeks (95 % CI 12.7–14) and 27.3 % (95 % CI 22.3–32.5), median and 6-month OS rates were 26 weeks (23–29) and 52 % (46.4–58.6), respectively. WHO-PS (0–1 vs. 2–3) and baseline steroid use were significantly correlated with PFS and OS. Our observations support the use of BEV as a monotherapy for patients with rGB who have no alternative treatment options. Optimal benefit from BEV treatment is likely to be obtained when treatment is initiated before the performance status deteriorates to two or less., SCOPUS: ar.j, info:eu-repo/semantics/published

Combined radiochemotherapy treatment modalities are in use for many indications and there-fore of high interest. Even though a combined modality in clinical use is often driven by prag-matic aspects, mechanistic preclinical-based concepts of interaction are of importance in order to translate and implement an optimal combination and scheduling of two modalities into the clinics. The use of microtubule stabilizing agents is a promising strategy for anti-cancer therapy as a part of combined treatment modality with ionizing radiation. Traditionally, microtubule targeting agents are classified as cytotoxic chemotherapeutics and are mostly used in a maxi-mally tolerated dose regimen. Apart from direct cytotoxicity and similar to mechanisms of mo-lecular targeting agents, microtubule stabilizing agents interfere with multiple cellular process-es, which can be exploited as part of combined treatment modalities. Recent preclinical investi-gations on the combination of ionizing radiation and microtubule stabilizing agents reveal new mechanistic interactions on the cellular and tumor level and elucidate the supra-additive tumor response observed particularly in vivo. The major focus on the mechanism of interaction was primarily based on radiosensitization due to cell cycle arrest in the most radiosensitive G2/M-phase of the cell cycle. However, other mechanisms of interaction such as reoxygenation and direct as well as indirect endothelial damage have also been identified. In this review we sum-marize and allocate additive and synergistic effects induced by the combined treatment of clini-cally relevant microtubule stabilizing agents and ionizing radiation along a described radiobio-logical framework encompassing distinct mechanisms relevant for exploiting the combination of drugs and ionizing radiation.

Hypothesis testing is a classical methodology of making decisions using experimental data. In hypothesis testing one seeks to discover evidence that either accepts or rejects a given null hypothesis H0. The alternative hypothesis H1 is the hypothesis that is accepted when H0 is rejected. In hypothesis testing, the probability of deciding H1 when in fact H0 is true is known as the false alarm rate, whereas the probability of deciding H1when in fact H1is true is known as the detection rate (or power) of the test. It is not possible to optimize both rates simultaneously. In this paper, we consider the problem of determining the data to be used for hypothesis testing that maximize the detection rate for a given false alarm rate. We consider in particular a hypothesis test which is relevant in functional magnetic resonance imaging (fMRI)., QC 20120410

In order to accurately identify Linear Parameter-Varying (LPV) systems, order selection of LPV linear regression models has prime importance. Existing identification approaches in this context suffer from the drawback that a set of functional dependencies needs to be chosen a priori for the parametrization of the model coefficients. However in a black-box setting, it has not been possible so far to decide which functions from a given set are required for the parametrization and which are not. To provide a practical solution, a nonnegative garrote approach is applied. It is shown that using only a measured data record of the plant, both the order selection and the selection of structural coefficient dependence can be solved by the proposed method.

In order to accurately identify Linear Parameter-Varying (LPV) systems, order selection of LPV linear regression models has prime importance. Existing identification approaches in this context suffer from the drawback that a set of functional dependencies needs to be chosen a priori for the parametrization of the model coefficients. However in a black-box setting, it has not been possible so far to decide which functions from a given set are required for the parametrization and which are not. To provide a practical solution, a nonnegative garrote approach is applied. It is shown that using only a measured data record of the plant, both the order selection and the selection of structural coefficient dependence can be solved by the proposed method.

In order to accurately identify Linear Parameter-Varying (LPV) systems, order selection of LPV linear regression models has prime importance. Existing identification approaches in this context suffer from the drawback that a set of functional dependencies needs to be chosen a priori for the parametrization of the model coefficients. However in a black-box setting, it has not been possible so far to decide which functions from a given set are required for the parametrization and which are not. To provide a practical solution, a nonnegative garrote approach is applied. It is shown that using only a measured data record of the plant, both the order selection and the selection of structural coefficient dependence can be solved by the proposed method.

In order to accurately identify Linear Parameter-Varying (LPV) systems, order selection of LPV linear regression models has prime importance. Existing identification approaches in this context suffer from the drawback that a set of functional dependencies needs to be chosen a priori for the parametrization of the model coefficients. However in a black-box setting, it has not been possible so far to decide which functions from a given set are required for the parametrization and which are not. To provide a practical solution, a nonnegative garrote approach is applied. It is shown that using only a measured data record of the plant, both the order selection and the selection of structural coefficient dependence can be solved by the proposed method.