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Learning systems represent a particularly important class of practical data-driven systems that adapt to their environment based on the environment's response to the system's action. Despite the success of learning-based methods, finding suitable control frameworks for learning systems when there is uncertainty in the assumptions related to the system dynamics is still an open problem. They propose a novel constrained ILC design and further develop a decentralized implementation of the resulting ILC algorithm using the alternating direction method of multipliers, allowing the design to scale up to handle large-scale and varying system dynamics. [Extracted from the article]

This paper studies the output tracking control problems for multiple-input, multiple-output (MIMO) locally Lipschitz nonlinear (LLNL) systems subject to iterative operation and uncertain, iteration-varying external disturbances and initial conditions. Under the assumption of a linear, P-type iterative learning control (ILC) update law, a double-dynamics analysis (DDA) approach is proposed to show the convergence of the ILC process in the presence of the locally Lipschitz nonlinearities and iteration-varying uncertainties. The DDA approach results in a contraction mapping-based convergence condition that guarantees both: 1) the boundedness of all system trajectories and 2) the robust convergence of the output tracking error. Further, a basic system relative degree condition is given that provides a necessary and sufficient (NAS) guarantee of the convergence of the ILC process. As a corollary, it is noted that in the absence of iteration-varying uncertainties, the results likewise provide an NAS convergence guarantee for MIMO LLNL systems. The simulations are presented to illustrate the ideas. [ABSTRACT FROM AUTHOR]

This paper studies the robust convergence properties of iterative learning control (ILC) for single-input, single-output (SISO), nonrepetitive systems subject to iteration-dependent uncertainties that arise in not only initial states and external disturbances but also plant models. Given an extended relative degree condition, it is possible to propose necessary and sufficient (NAS) conditions for robust ILC convergence. The tracking error bound is shown to depend continuously on the bounds of the iteration-dependent uncertainties. When the iteration-dependent uncertainties are bounded, NAS conditions exist to guarantee bounded system trajectories and output tracking error. If the iteration-dependent uncertainties converge, then NAS conditions ensure bounded system trajectories and zero output tracking error. The results are also extended to a class of affine nonlinear systems satisfying a Lipschitz condition. Simulation tests on a representative batch process demonstrate the validity of the obtained robust ILC convergence results. [ABSTRACT FROM AUTHOR]

This paper studies a class of robust cooperative learning control problems for directed networks of agents (a) with nonidentical nonlinear dynamics that do not satisfy a global Lipschitz condition and (b) in the presence of switching topologies, initial state shifts and external disturbances. All uncertainties are not only time-varying but also iteration-varying. It is shown that the relative formation of nonlinear agents achieved via cooperative learning can be guaranteed to converge to the desired formation exponentially fast as the number of iterations increases. A necessary and sufficient condition for exponential convergence of the cooperative learning process is that at each time step, the network topology graph of nonlinear agents can be rendered quasi-strongly connected through switching along the iteration axis. Simulation tests illustrate the effectiveness of our proposed cooperative learning results in refining arbitrary high precision relative formation of nonlinear agents. [ABSTRACT FROM AUTHOR]

This paper addresses the consensus control problem in multi‐agent systems under the influence of diffusively coupled disturbances. Two different types of systems are considered regarding the homogeneity of the disturbance coupling parameters. The main goal is to derive conditions that guarantee the prescribed performance of the designed control system on disturbance attenuation. To estimate the disturbance attenuation performance of the controller, the ℋ∞$$ {\mathscr{H}}_{\infty } $$ norm of the transfer function from the redefined disturbance vector to the disagreement vector is measured. Then the sufficient conditions for the system to achieve consensus within the desired performance are derived in terms of linear matrix inequalities (LMI). Numerical examples of both one‐ and multi‐dimensional consensus control systems are provided to check the validity of the derived LMI feasibility test along with the illustration of consensus behavior in time domain. [ABSTRACT FROM AUTHOR]

Motivated by the prototypical problem of a marching band, this paper studies a class of multi-agent formation problems characterized by two features: (i) the agents attempt to complete a finite duration, coordinated formation task with high precision by repeating the task and (ii) the feedback mechanism by which the agents control their motions is based on relative differences between nearest neighbors, but the underlying graph topology can vary both during a repetition and from one repetition to the next. Adopting the framework of iterative learning control leads to the notion of multi-agent networks with switching topologies along two directions: a finite time axis and an infinite iteration axis. For such systems, we present distributed algorithms using nearest neighbor information whose exponential convergence can be demonstrated. It is shown that as the number of repetition increases, the relative formation between agents approaches the desired formation exponentially fast if and only if at each time step, the union of the interaction graphs has a spanning tree frequently enough along the iteration axis. That is, the agents can “learn to cooperate.” The remarkable point of this result is that it is not necessary to have a spanning tree at any specific time step or iteration in order for the system to converge. Two examples are given to illustrate the ideas, including a general example, where through iteration the agents can form a desired formation, and a special case of it, where an additional agent specifies a reference to regulate the formation shape simultaneously. [ABSTRACT FROM AUTHOR]

Background and purpose Neurocognitive decline in brain tumor patients treated with radiotherapy (RT) may be linked to cortical atrophy. We developed models to determine radiation treatment-planning objectives for cortex, which were tested on a sample population to identify the dosimetric cost of cortical sparing. Material and methods The relationship between the probability of cortical atrophy in fifteen high-grade glioma patients at 1-year post-RT and radiation dose was fit using logistic mixed effects modeling. Cortical sparing was implemented using two strategies: region-specific sparing using model parameters, and non-specific sparing of all normal brain tissue. Results A dose threshold of 28.6 Gy was found to result in a 20% probability of severe atrophy. Average cortical sparing at 30 Gy was greater for region-specific dose avoidance (4.6%) compared to non-specific (3.6%). Cortical sparing resulted in an increase in heterogeneity index of the planning target volume (PTV) with an average increase of 1.9% (region-specific) and 0.9% (non-specific). Conclusions We found RT doses above 28.6 Gy resulted in a greater than 20% probability of cortical atrophy. Cortical sparing can be achieved using region-specific or non-specific dose avoidance strategies at the cost of an increase in the dose heterogeneity of the PTV. [ABSTRACT FROM AUTHOR]

Purpose: To demonstrate knowledge-based 3D dose prediction for external beam radiotherapy. Methods: Using previously treated plans as training data, an artificial neural network (ANN) was trained to predict a dose matrix based on patient-specific geometric and planning parameters, such as the closest distance (r) to planning target volume (PTV) and organ-at-risks (OARs). Twenty-three prostate and 43 stereotactic radiosurgery/radiotherapy (SRS/SRT) cases with at least one nearby OAR were studied. All were planned with volumetric-modulated arc therapy to prescription doses of 81 Gy for prostate and 12-30 Gy for SRS. Using these clinically approved plans, ANNs were trained to predict dose matrix and the predictive accuracy was evaluated using the dose difference between the clinical plan and prediction, δD = Dclin-Dpred. The mean (〈δDr〉), standard deviation (σδDr), and their interquartile range (IQR) for the training plans were evaluated at a 2-3 mm interval from the PTV boundary (rPTV) to assess prediction bias and precision. Initially, unfiltered models which were trained using all plans in the cohorts were created for each treatment site. The models predict approximately the average quality of OAR sparing. Emphasizing a subset of plans that exhibited superior to the average OAR sparing during training, refined models were created to predict high-quality rectum sparing for prostate and brainstem sparing for SRS. Using the refined model, potentially suboptimal plans were identified where the model predicted further sparing of the OARs was achievable. Replans were performed to test if the OAR sparing could be improved as predicted by the model. Results: The refined models demonstrated highly accurate dose distribution prediction. For prostate cases, the average prediction bias for all voxels irrespective of organ delineation ranged from -1% to 0% with maximum IQR of 3% over rPTV ∈ [-6,30] mm. The average prediction error was less than 10% for the same rPTV range. For SRS cases, the average prediction bias ranged from -0.7% to 1.5% with maximum IQR of 5% over rPTV ∈ [-4,32] mm. The average prediction error was less than 8%. Four potentially suboptimal plans were identified for each site and subsequent replanning demonstrated improved sparing of rectum and brainstem. Conclusions: The study demonstrates highly accurate knowledge-based 3D dose predictions for radiotherapy plans. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this study was to quantify the frequency and clinical severity of quality deficiencies in intensity modulated radiation therapy (IMRT) planning in the Radiation Therapy Oncology Group 0126 protocol. Methods and Materials A total of 219 IMRT patients from the high-dose arm (79.2 Gy) of RTOG 0126 were analyzed. To quantify plan quality, we used established knowledge-based methods for patient-specific dose-volume histogram (DVH) prediction of organs at risk and a Lyman-Kutcher-Burman (LKB) model for grade ≥2 rectal complications to convert DVHs into normal tissue complication probabilities (NTCPs). The LKB model was validated by fitting dose-response parameters relative to observed toxicities. The 90th percentile (22 of 219) of plans with the lowest excess risk (difference between clinical and model-predicted NTCP) were used to create a model for the presumed best practices in the protocol (pDVH 0126,top10% ). Applying the resultant model to the entire sample enabled comparisons between DVHs that patients could have received to DVHs they actually received. Excess risk quantified the clinical impact of suboptimal planning. Accuracy of pDVH predictions was validated by replanning 30 of 219 patients (13.7%), including equal numbers of presumed “high-quality,” “low-quality,” and randomly sampled plans. NTCP-predicted toxicities were compared to adverse events on protocol. Results Existing models showed that bladder-sparing variations were less prevalent than rectum quality variations and that increased rectal sparing was not correlated with target metrics (dose received by 98% and 2% of the PTV, respectively). Observed toxicities were consistent with current LKB parameters. Converting DVH and pDVH 0126,top10% to rectal NTCPs, we observed 94 of 219 patients (42.9%) with ≥5% excess risk, 20 of 219 patients (9.1%) with ≥10% excess risk, and 2 of 219 patients (0.9%) with ≥15% excess risk. Replanning demonstrated the predicted NTCP reductions while maintaining the volume of the PTV receiving prescription dose. An equivalent sample of high-quality plans showed fewer toxicities than low-quality plans, 6 of 73 versus 10 of 73 respectively, although these differences were not significant ( P =.21) due to insufficient statistical power in this retrospective study. Conclusions Plan quality deficiencies in RTOG 0126 exposed patients to substantial excess risk for rectal complications. [ABSTRACT FROM AUTHOR]

This Vision 20/20 paper considers what computational advances are likely to be implemented in clinical radiation oncology in the coming years and how the adoption of these changes might alter the practice of radiotherapy. Four main areas of likely advancement are explored: cloud computing, aggregate data analyses, parallel computation, and automation. As these developments promise both new opportunities and new risks to clinicians and patients alike, the potential benefits are weighed against the hazards associated with each advance, with special considerations regarding patient safety under new computational platforms and methodologies. While the concerns of patient safety are legitimate, the authors contend that progress toward next-generation clinical informatics systems will bring about extremely valuable developments in quality improvement initiatives, clinical efficiency, outcomes analyses, data sharing, and adaptive radiotherapy. [ABSTRACT FROM AUTHOR]

Purpose: In our clinic, physicists spend from 15 to 60 min to verify the physical and dosimetric integrity of radiotherapy plans before presentation to radiation oncology physicians for approval. The purpose of this study was to design and implement a framework to automate as many elements of this quality control (QC) step as possible. Methods: A comprehensive computer application was developed to carry out a majority of these verification tasks in the Philips PINNACLE treatment planning system (TPS). This QC tool functions based on both PINNACLE scripting elements and PERL sub-routines. The core of this technique is the method of dynamic scripting, which involves a PERL programming module that is flexible and powerful for treatment plan data handling. Run-time plan data are collected, saved into temporary files, and analyzed against standard values and predefined logical rules. The results were summarized in a hypertext markup language (HTML) report that is displayed to the user. Results: This tool has been in clinical use for over a year. The occurrence frequency of technical problems, which would cause delays and suboptimal plans, has been reduced since clinical implementation. Conclusions: In addition to drastically reducing the set of human-driven logical comparisons, this QC tool also accomplished some tasks that are otherwise either quite laborious or impractical for humans to verify, e.g., identifying conflicts amongst IMRT optimization objectives. [ABSTRACT FROM AUTHOR]

Purpose: To incorporate a quality control tool, according to previous planning experience and patient-specific anatomic information, into the intensity-modulated radiotherapy (IMRT) plan generation process and to determine whether the tool improved treatment plan quality. Methods and Materials: A retrospective study of 42 IMRT plans demonstrated a correlation between the fraction of organs at risk (OARs) overlapping the planning target volume and the mean dose. This yielded a model, predicted dose = prescription dose (0.2 + 0.8 [1 − exp(−3 overlapping planning target volume/volume of OAR)]), that predicted the achievable mean doses according to the planning target volume overlap/volume of OAR and the prescription dose. The model was incorporated into the planning process by way of a user-executable script that reported the predicted dose for any OAR. The script was introduced to clinicians engaged in IMRT planning and deployed thereafter. The script’s effect was evaluated by tracking δ = (mean dose-predicted dose)/predicted dose, the fraction by which the mean dose exceeded the model. Results: All OARs under investigation (rectum and bladder in prostate cancer; parotid glands, esophagus, and larynx in head-and-neck cancer) exhibited both smaller δ and reduced variability after script implementation. These effects were substantial for the parotid glands, for which the previous δ = 0.28 ± 0.24 was reduced to δ = 0.13 ± 0.10. The clinical relevance was most evident in the subset of cases in which the parotid glands were potentially salvageable (predicted dose <30 Gy). Before script implementation, an average of 30.1 Gy was delivered to the salvageable cases, with an average predicted dose of 20.3 Gy. After implementation, an average of 18.7 Gy was delivered to salvageable cases, with an average predicted dose of 17.2 Gy. In the prostate cases, the rectum model excess was reduced from δ = 0.28 ± 0.20 to δ = 0.07 ± 0.15. On surveying dosimetrists at the end of the study, most reported that the script both improved their IMRT planning (8 of 10) and increased their efficiency (6 of 10). Conclusions: This tool proved successful in increasing normal tissue sparing and reducing interclinician variability, providing effective quality control of the IMRT plan development process. [Copyright &y& Elsevier]

Purpose: To investigate a protocol which efficiently localizes TomoTherapy patients with a scout imaging (topogram) mode that can be used with or instead of 3D megavoltage computed tomography (MVCT) imaging. Methods: The process presented here is twofold: (a) The acquisition of the topogram using the TomoTherapy MV imaging system and (b) the generation of a digitally reconstructed topogram (DRT) derived from a standard kV CT simulation data set. The unique geometric characteristics of the current TomoTherapy imaging system were explored both theoretically and by acquiring topograms of anthropomorphic phantoms and comparing these images to DRT images. The performance of the MV topogram imaging system in terms of image quality, dose incurred to the patient, and acquisition time was investigated using ionization chamber and radiographic film measurements. Results: The time required to acquire a clinically usable topogram, limited by the maximum couch speed of 4.0 cm s-1, was 12.5 s for a 50 cm long field. The patient dose was less than 1% of that delivered by a helical MVCT scan. Further refinements within the current TomoTherapy system, most notably decreasing the imaging beam repetition rate during MV topogram acquisition, would further reduce the topogram dose to less than 25 μGy per scan without compromising image quality. Conclusions: Topogram localization on TomoTherapy is a fast and low-dose alternative to 3D MVCT localization. A protocol designed that exclusively utilized MV topograms would result in a 30-fold reduction in imaging time and a 100-fold reduction in dose from localization scans using the current TomoTherapy workflow. [ABSTRACT FROM AUTHOR]

CC chemokine receptor 5 (CCR5) is the receptor for several inflammatory chemokines and is a coreceptor for HIV-1, Posttranslational sulfation of tyrosines in the N-terminal regions of chemokine receptors has been shown to be important in the binding affinity for chemokine ligands. In addition, sulfation of CCR5 is crucial for mediating interactions with HIV-1 envelope protein gp120. The major sulfation pathway for peptides derived from the N-terminal domains of CCR5 and CCR8 and variations of the peptides were determined by in vitro enzymatic sulfation by tyrosylprotein sulfotranferase-2 (TPST-2), subsequent separation of products by RP-HPLC, and mass spectrometry analysis. It was found that the patterns of sulfation and the rates of sulfation for CCR5 and CCR8 depend on the number of amino acids N-terminal of Tyr-3. Results herein address previous seeminglycontradictory studies and delineate the temporal sulfation of N-terminal chemokine receptor peptides. [ABSTRACT FROM AUTHOR]

Current research on micromechanical resonators strives for quantum-limited detection of the motion of macroscopic objects. Prerequisite to this goal is the observation of measurement backaction consistent with quantum metrology limits. However, thermal noise currently dominates measurements and precludes ground-state preparation of the resonator. Here, we establish the collective motion of an ultracold atomic gas confined tightly within a Fabry–Perot optical cavity as a system for investigating the quantum mechanics of macroscopic bodies. The cavity-mode structure selects a particular collective vibrational motion that is measured by the cavity’s optical properties, actuated by the cavity optical field and subject to backaction by the quantum force fluctuations of this field. Experimentally, we quantify such fluctuations by measuring the cavity-light-induced heating of the intracavity atomic ensemble. These measurements represent the first observation of backaction on a macroscopic mechanical resonator at the standard quantum limit. [ABSTRACT FROM AUTHOR]

In this paper we study ℓth-order (ℓ ⩾ 3) consensus algorithms, which generalize the existing first-order and second-order consensus algorithms in the literature. We show necessary and sufficient conditions under which each information variable and its higher-order derivatives converge to common values. We also present the idea of higher-order consensus with a leader and introduce the concept of an th-order model-reference consensus problem, where each information variable and its high-order derivatives not only reach consensus, but also converge to the solution of a prescribed dynamic model. The effectiveness of these algorithms is demonstrated through simulations and a multivehicle cooperative control application, which mimics a flocking behavior in birds. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a stability analysis of the iterative learning control (ILC) problem for discrete-time systems when the plant Markov parameters are subject to interval uncertainty. Using the so-called super-vector approach to ILC, vertex impulse response matrices are employed to develop sufficient conditions for both asymptotic stability and monotonic convergence of the ILC process. It is shown that the stability of such interval ILC systems can be determined by checking the stability of the system using only the vertex points of the interval Markov parameters. [Copyright &y& Elsevier]

Abstract: In iterative learning control schemes for linear discrete time systems, conditions to guarantee the monotonic convergence of the tracking error norms are derived. By using the Markov parameters, it is shown in the time-domain that there exists a non-increasing function such that when the properly chosen constant learning gain is multiplied by this function, the convergence of the tracking error norms is monotonic, without resort to high-gain feedback. [Copyright &y& Elsevier]

A new tuning method for proportional-integral-derivative (PID) controller design is proposed for a class of unknown, stable, and minimum phase plants. We are able to design a PID controller to ensure that the phase Bode plot is flat, i.e., the phase derivative w.r.t. the frequency is zero, at a given frequency called the "tangent frequency" so that the closed-loop system is robust to gain variations and the step responses exhibit an iso-damping property. At the "tangent frequency," the Nyquist curve tangentially touches the sensitivity circle. Several relay feedback tests are used to identify the plant gain and phase at the tangent frequency in an iterative way. The identified plant gain and phase at the desired tangent frequency are used to estimate the derivatives of amplitude and phase of the plant with respect to frequency at the same frequency point by Bode's integral relationship. Then, these derivatives are used to design a PID controller for slope adjustment of the Nyquist plot to achieve the robustness of the system to gain variations. No plant model is assumed during the PID controller design. Only several relay tests are needed. Simulation examples illustrate the effectiveness and the simplicity of the proposed method for robust PID controller design with an iso-damping property. [ABSTRACT FROM AUTHOR]

We have developed an improved scheme for loading atoms into a magneto-optical trap (MOT) from a directed rubidium alkali metal dispenser in <10-10 Torr ultrahigh vacuum conditions. A current-driven dispenser was surrounded with a cold absorbing “shroud” held at <=0 °C, pumping rubidium atoms not directed into the MOT. This nearly eliminates background atoms and reduces the detrimental rise in pressure normally associated with these devices. The system can be well-described as a current-controlled, rapidly switched, two-temperature thermal beam, and was used to load a MOT with 3×108 atoms. [ABSTRACT FROM AUTHOR]

This paper presents a frequency-domain analysis and design approach for a learning feed forward controller (LFFC) using a dilated B-spline network. The LFFC acts as an add-on element to the existing feedback controller (FBC). The LFFC signal is updated iteratively based on the FBC signal of the previous iteration as the task repeats. Similar to proportional-integral- derivative controller tuning, there are only two parameters to adjust: The B-spline support width and the learning gain. The effect of dilation in the B-spline network is discussed. Detailed design formulae are given based on a stability analysis. As an illustration, simulation results on the path tracking control of a wheeled mobile robot are presented. [ABSTRACT FROM AUTHOR]

Examines the biology and enzymology of protein tyrosine O-sulfation. Substrate specificity of tyrosylprotein and sulfotransferases; Two types of tyrosylprotein sulfotransferase; Regulation of tyrosine O-sulfation; Role of tyrosine O-sulfation in protein function.

The omni-directional inspection system (ODIS) is a small, man-portable mobile robotic system that can be used for autonomous or semi-autonomous inspection under vehicles in a parking area. Customers for such a system include military police (MP) and other law enforcement and security entities. The robot features three “smart wheels” in which both the speed and direction of the wheel can be independently controlled and a vehicle electronic capability that includes multiple processors and a sensor array with a laser, sonar and IR sensors, and a video camera. ODIS employs a novel parameterized command language for intelligent behavior generation. A key feature of the ODIS control system is the use of an object recognition system that fits models to sensor data. These models are then used as input parameters to the motion and behavior control commands. [Copyright &y& Elsevier]

By using Lambert function, the analytical stability bound is obtained in this paper for delayed second-order systems with repeatable poles. An example is presented to illustrate the obtained analytical result. [Copyright &y& Elsevier]

Presents two discretization methods for fractional-order differentiators and integrators. Overview of fractional calculus; Recursive formula for Tustin discretization of the fractional-order derivative; Illustrative examples of the two schemes presented.

Editorial. Discusses issues pertaining to the use of iterative learning control (ILC) in dealing with difficulties associated with performance design of control systems. Background on the ILC concept; Formulation of the ILC problem.

In this paper we use the formalism of iterative learning control (ILC) to solve the repetitive control problem of forcing a system to track a prescribed periodic reference signal. Although the systems we consider operate continuously in time, rather than with trials that have distinct starting and ending times, we use the ILC approach by defining a 'trial' in terms of completion of a single 'period' of the output trajectory, where a period is an interval from the start of the trial until the system returns to its initial state. The ILC scheme we develop does not use the standard assumption of uniform trial length. In the final result the periodic motion is achieved by 'repetition' of the learned ILC input signal for a single period. Analysis of the convergence of the algorithm uses an intermediate convergence result for the typical ILC problem. This intermediate result is based on a multi-loop control interpretation of the signal flow in ILC. The idea is demonstrated on an example and it is noted that it may be possible to generalize the ideas to broader classes of systems and ILC algorithms. [ABSTRACT FROM AUTHOR]

Presents an experimental procedure for developing two immunoglobulin G monoclonal antibodies that appear to recognize protein-dependent epitopes on human P-selectin glycoprotein ligand 1 (PSGL-1). PSGL-1 as high affinity ligand for P-selectin on myeloid cells; How neutrophils roll on P-selectin at physiological shear stresses.

Discusses the structure and function of P-selectin glycoprotein ligand-1. Expression of the selectin family of adhesion molecules on activated endothelial cells, activated platelets and peripheral blood leukocytes; Interaction with P-, L- and E-selectin; Mediation function in cell adhesion.

Consistency of nomenclature within radiation oncology is increasingly important as big data efforts and data sharing become more feasible. Automation of radiation oncology workflows depends on standardized contour nomenclature that enables toxicity and outcomes research, while also reducing medical errors and facilitating quality improvement activities. Recommendations for standardized nomenclature have been published in the American Association of Physicists in Medicine (AAPM) report from Task Group 263 (TG-263). Transitioning to TG-263 requires creation and management of structure template libraries and retraining of staff, which can be a considerable burden on clinical resources. Our aim is to develop a program that allows users to create TG-263–compliant structure templates in English, Spanish, or French to facilitate data sharing. Fifty-three premade structure templates were arranged by treated organ based on an American Society for Radiation Oncology (ASTRO) consensus paper. Templates were further customized with common target structures, relevant organs at risk (OARs) (eg, spleen for anatomically relevant sites such as the gastroesophageal junction or stomach), subsite- specific templates (eg, partial breast, whole breast, intact prostate, postoperative prostate, etc) and brachytherapy templates. An informal consensus on OAR and target coloration was also achieved, although color selections are fully customizable within the program. The resulting program is usable on any Windows system and generates template files in practice-specific Digital Imaging and Communications In Medicine (DICOM) or XML formats, extracting standardized structure nomenclature from an online database maintained by members of the TG-263U1, which ensures continuous access to up-to-date templates. We have developed a tool to easily create and name DICOM radiation therapy (DICOM-RT) structures sets that are TG-263–compliant for all planning systems using the DICOM standard. The program and source code are publicly available via GitHub to encourage feedback from community users for improvement and guide further development. [ABSTRACT FROM AUTHOR]

The article provides information on the significance of protein tyrosine sulfation to the plants and animals' posttranslational modification. It mentions that the posttranslational modification focuses on animals and plants' protein and was first found in bovine fibrinopeptide B. It highlights several experimentations gor gene modification. Furthermore, the success of its research are also highlighted.

Objective. To lay the foundation for automated knowledge-based brachytherapy treatment planning using 3D dose estimations, we describe an optimization framework to convert brachytherapy dose distributions directly into dwell times (DTs). Approach. A dose rate kernel d ̇ (r , θ , φ) was produced by exporting 3D dose for one dwell position from the treatment planning system and normalizing by DT. By translating and rotating this kernel to each dwell position, scaling by DT and summing over all dwell positions, dose was computed (D calc). We used a Python-coded COBYLA optimizer to iteratively determine the DTs that minimize the mean squared error between D calc and reference dose D ref, computed using voxels with D ref 80%–120% of prescription. As validation of the optimization, we showed that the optimizer replicates clinical plans when D ref = clinical dose in 40 patients treated with tandem-and-ovoid (T&O) or tandem-and-ring (T&R) and 0–3 needles. Then we demonstrated automated planning in 10 T&O using D ref = dose predicted from a convolutional neural network developed in past work. Validation and automated plans were compared to clinical plans using mean absolute differences ( MAD = 1 N ∑ n = 1 N abs x n − x n ′ ) over all voxels (xn = Dose, N = #voxels) and DTs (xn = DT, N = #dwell positions), mean differences (MD) in organ D 2cc and high-risk CTV D90 over all patients (where positive indicates higher clinical dose), and mean Dice similarity coefficients (DSC) for 100% isodose contours. Main results. Validation plans agreed well with clinical plans (MADdose = 1.1%, MADDT = 4 s or 0.8% of total plan time, D 2cc MD = −0.2% to 0.2% and D90 MD = −0.6%, DSC = 0.99). For automated plans, MADdose = 6.5% and MADDT = 10.3 s (2.1%). The slightly higher clinical metrics in automated plans (D 2cc MD = −3.8% to 1.3% and D90 MD = −5.1%) were due to higher neural network dose predictions. The overall shape of the automated dose distributions were similar to clinical doses (DSC = 0.91). Significance. Automated planning with 3D dose predictions could provide significant time savings and standardize treatment planning across practitioners, regardless of experience. [ABSTRACT FROM AUTHOR]

Purpose: Programmed death-1 immune checkpoint blockade improves survival of patients with recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), but the benefits of addition to (chemo)radiation for newly diagnosed patients with HNSCC remain unknown.Methods and Materials: We evaluated the safety of nivolumab concomitant with 70 Gy intensity modulated radiation therapy and weekly cisplatin (arm 1), every 3-week cisplatin (arm 2), cetuximab (arm 3), or alone for platinum-ineligible patients (arm 4) in newly diagnosed intermediate- or high-risk locoregionally advanced HNSCC. Patients received nivolumab from 2 weeks prior to radiation therapy until 3 months post-radiation therapy. The primary endpoint was dose-limiting toxicity (DLT). If ≤2 of the first 8 evaluable patients experienced a DLT, an arm was considered safe. Secondary endpoints included toxicity and feasibility of adjuvant nivolumab to 1 year, defined as all 7 additional doses received by ≥4 of the first 8 evaluable patients across arms.Results: Of 39 patients (10 in arms 1, 3, 4 and 9 in arm 2), 72% had T3-4 tumors, 85% had N2-3 nodal disease, and 67% had >10 pack-years of smoking. There were no DLTs in arms 1 and 2, 1 in arm 3 (mucositis), and 2 in arm 4 (lipase elevation and mucositis in 1 and fatigue in another). The most common grade ≥3 nivolumab-related adverse events were lipase increase, mucositis, diarrhea, lymphopenia, hyponatremia, leukopenia, fatigue, and serum amylase increase. Adjuvant nivolumab was feasible as defined in the protocol.Conclusions: Concomitant nivolumab with the 4 tested regimens was safe for patients with intermediate- and high-risk HNSCC, and subsequent adjuvant nivolumab was feasible as defined (NCT02764593). [ABSTRACT FROM AUTHOR]

Our purpose was to investigate the effect of physicist-patient consults on patient anxiety and patient satisfaction with a randomized prospective phase III clinical trial. Sixty-six patients were randomly assigned to the physics direct patient care (PDPC) arm or the control arm of the trial. Patients assigned to the PDPC arm received 2 physicist-patient consults to educate them on the technical aspects of their radiation therapy, while patients assigned to the control arm received the standard of care (ie, standard radiation therapy workflow without any additional physicist-patient consults). Questionnaires were administered to all patients at 4 time points (after enrollment, after the simulation, after the first treatment, and after the last treatment) to assess anxiety and satisfaction. The decrease in anxiety for the PDPC arm, compared with the control arm, was statistically significant at the first treatment (P =.027) time point. The increase in technical satisfaction for the PDPC arm, compared with the control arm, was statistically significant at the simulation (P =.005), first treatment (P <.001), and last treatment (P =.002) time points. The increase in overall satisfaction for the PDPC arm, compared with the control arm, was statistically significant at the first treatment (P =.014) and last treatment (P =.001) time points. Physicist-patient consults improved the patient experience by decreasing anxiety and increasing satisfaction. Future work is needed to modify current radiation oncology workflows and medical physics responsibilities to allow all patients to benefit from this advancement in patient care. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for coordination and control of multiple photovoltaic (PV) generators for distributed power generation in a microgrid. Strategies are provided for coordination of active power generation and control of active power flows among distributed energy resources, while achieving supply–demand balance. Distributed approaches for optimal dispatch and control of active power flows are considered for active power support in the continuous-time domain. The effectiveness of the proposed approaches is investigated through an illustrative example. [ABSTRACT FROM AUTHOR]

The purpose of this work was to develop a knowledge-based dose prediction system using a convolution neural network (CNN) for cervical brachytherapy treatments with a tandem-and-ovoid applicator. A 3D U-NET CNN was utilized to make voxel-wise dose predictions based on organ-at-risk (OAR), high-risk clinical target volume (HRCTV), and possible source location geometry. The model comprised 395 previously treated cases: training (273), validation (61), test (61). To assess voxel prediction accuracy, we evaluated dose differences in all cohorts across the dose range of 20−130% of prescription, mean (SD) and standard deviation (σ) , as well as isodose dice similarity coefficients for clinical and/or predicted dose distributions. We examined discrete Dose-Volume Histogram (DVH) metrics utilized for brachytherapy plan quality assessment (HRCTV D90%; bladder, rectum, and sigmoid D2cc) with Δ D x = D x , a c t u a l − D x , p r e d i c t e d mean, standard deviation, and Pearson correlation coefficient further quantifying model performance. Ranges of voxel-wise dose difference accuracy ( δ D ¯ ± σ) for 20−130% dose interval in training (test) sets ranged from [-0.5% ± 2.0% to +2.0% ± 14.0%] ([-0.1% ± 4.0% to +4.0% ± 26.0%]) in all voxels, [-1.7% ± 5.1% to -3.5% ± 12.8%] ([-2.9% ± 4.8% to -2.6% ± 18.9%]) in HRCTV, [-0.02% ± 2.40% to +3.2% ± 12.0%] ([-2.5% ± 3.6% to +0.8% ± 12.7%]) in bladder, [-0.7% ± 2.4% to +15.5% ± 11.0%] ([-0.9% ± 3.2% to +27.8% ± 11.6%]) in rectum, and [-0.7% ± 2.3% to +10.7% ± 15.0%] ([-0.4% ± 3.0% to +18.4% ± 11.4%]) in sigmoid. Isodose dice similarity coefficients ranged from [0.96,0.91] for training and [0.94,0.87] for test cohorts. Relative DVH metric prediction in the training (test) set were HRCTV Δ D ¯ 90 ± σ Δ D = -0.19 ± 0.55Gy (-0.09 ± 0.67 Gy), bladder Δ D ¯ 2 c c ± σ Δ D = -0.06 ± 0.54Gy (-0.17 ± 0.67 Gy), rectum Δ D ¯ 2 c c ± σ Δ D = -0.03 ± 0.36Gy (-0.04 ± 0.46 Gy), and sigmoid Δ D ¯ 2 c c ± σ Δ D = -0.01 ± 0.34Gy (0.00 ± 0.44 Gy). A 3D knowledge-based dose predictions provide voxel-level and DVH metric estimates that could be used for treatment plan quality control and data-driven plan guidance. [ABSTRACT FROM AUTHOR]

We study output consensus in a network of interconnected non-identical positive real systems cascaded with a single integrator. Assuming undirected, diffusive interconnections, sufficient conditions are provided to ensure output consensus for two cases: (i) the individual systems are weakly strictly positive real systems cascaded with a single integrator, and (ii) the individual systems are positive real systems cascaded with a single integrator. We illustrate our results with the example of a load frequency control network of synchronous generators. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Currently, there is a lack of patient-specific tools to guide brachytherapy planning and applicator choice for cervical cancer. The purpose of this study is to evaluate the accuracy of organ-at-risk (OAR) dose predictions using knowledge-based intracavitary models, and the use of these models and clinical data to determine the dosimetric differences of tandem-and-ring (T&R) and tandem-and-ovoids (T&O) applicators. Knowledge-based models, which predict organ D 2cc , were trained on 77/75 cases and validated on 32/38 for T&R/T&O applicators. Model performance was quantified using ΔD 2cc =D 2cc,actual –D 2cc,predicted , with standard deviation (σ(ΔD 2cc)) representing precision. Model-predicted applicator dose differences were determined by applying T&O models to T&R cases, and vice versa, and compared to clinically-achieved D 2cc differences. Applicator differences were assessed using a Student's t -test (p < 0.05 significant). Validation T&O/T&R model precision was 0.65/0.55 Gy, 0.55/0.38 Gy, and 0.43/0.60 Gy for bladder, rectum and sigmoid, respectively, and similar to training. When applying T&O/T&R models to T&R/T&O cases, bladder, rectum and sigmoid D 2cc values in EQD2 were on average 5.69/2.62 Gy, 7.31/6.15 Gy and 3.65/0.69 Gy lower for T&R, with similar HRCTV volume and coverage. Clinical data also showed lower T&R OAR doses, with mean EQD2 D 2cc deviations of 0.61 Gy, 7.96 Gy (p < 0.01) and 5.86 Gy (p < 0.01) for bladder, rectum and sigmoid. Accurate knowledge-based dose prediction models were developed for two common intracavitary applicators. These models could be beneficial for standardizing and improving the quality of brachytherapy plans. Both models and clinical data suggest that significant OAR sparing can be achieved with T&R over T&O applicators, particularly for the rectum. [ABSTRACT FROM AUTHOR]

The use of interstitial needles, combined with intracavitary applicators, enables customized dose distributions and is beneficial for complex cases, but increases procedure time. Overall, applicator selection is not standardized and depends on physician expertise and preference. The purpose of this study is to determine whether dose prediction models can guide needle supplementation decision-making for cervical cancer. Intracavitary knowledge-based models for organ-at-risk (OAR) dose estimation were trained and validated for tandem-and-ring/ovoids (T&R/T&O) implants. Models were applied to hybrid cases with 1–3 implanted needles to predict OAR dose without needles. As a reference, 70/67 hybrid T&R/T&O cases were replanned without needles, following a standardized procedure guided by dose predictions. If a replanned dose exceeded the dose objective, the case was categorized as requiring needles. Receiver operating characteristic (ROC) curves of needle classification accuracy were generated. Optimal classification thresholds were determined from the Youden Index. Needle supplementation reduced dose to OARs. However, 67%/39% of replans for T&R/T&O met all dose constraints without needles. The ROC for T&R/T&O models had an area-under-curve of 0.89/0.86, proving high classification accuracy. The optimal threshold of 99%/101% of the dose limit for T&R/T&O resulted in classification sensitivity and specificity of 78%/86% and 85%/78%. Needle supplementation reduced OAR dose for most cases but was not always required to meet standard dose objectives, particularly for T&R cases. Our knowledge-based dose prediction model accurately identified cases that could have met constraints without needle supplementation, suggesting that such models may be beneficial for applicator selection. [ABSTRACT FROM AUTHOR]

Purpose: Our purpose was to investigate the effect of automated knowledge-based planning (KBP) on real-world clinical workflow efficiency, assess whether manual refinement of KBP plans improves plan quality across multiple disease sites, and develop a data-driven method to periodically improve KBP automated planning routines.Methods and Materials: Using clinical knowledge-based automated planning routines for prostate, prostatic fossa, head and neck, and hypofractionated lung disease sites in a commercial KBP solution, workflow efficiency was compared in terms of planning time in a pre-KBP (n = 145 plans) and post-KBP (n = 503) patient cohort. Post-KBP, planning was initialized with KBP (KBP-only) and subsequently manually refined (KBP +human). Differences in planning time were tested for significance using a 2-tailed Mann-Whitney U test (P < .05, null hypothesis: planning time unchanged). Post-refinement plan quality was assessed using site-specific dosimetric parameters of the original KBP-only plan versus KBP +human; 2-tailed paired t test quantified statistical significance (Bonferroni-corrected P < .05, null hypothesis: no dosimetric difference after refinement). If KBP +human significantly improved plans across the cohort, optimization objectives were changed to create an updated KBP routine (KBP'). Patients were replanned with KBP' and plan quality was compared with KBP +human as described previously.Results: KBP significantly reduced planning time in all disease sites: prostate (median: 7.6 hrs → 2.1 hrs; P < .001), prostatic fossa (11.1 hrs → 3.7 hrs; P = .001), lung (9.9 hrs → 2.0 hrs; P < .001), and head and neck (12.9 hrs → 3.5 hrs; P <.001). In prostate, prostatic fossa, and lung disease sites, organ-at-risk dose changes in KBP +human versus KBP-only were minimal (<1% prescription dose). In head and neck, KBP +human did achieve clinically relevant dose reductions in some parameters. The head and neck routine was updated (KBP'HN) to incorporate dose improvements from manual refinement. The only significant dosimetric differences to KBP +human after replanning with KBP'HN were in favor of the new routine.Conclusions: KBP increased clinical efficiency by significantly reducing planning time. On average, human refinement offered minimal dose improvements over KBP-only plans. In the single disease site where KBP +human was superior to KBP-only, differences were eliminated by adjusting optimization parameters in a revised KBP routine. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present two iterative learning control (ILC) frameworks for tracking problems with specified data points that are desired points at certain time instants. To design ILC systems for such problems, unlike traditional ILC approaches, we first develop an algorithm in which not only the control signal but also the reference trajectory is updated at each trial. We investigate the relationship between the reference trajectory and ILC tracking control as it relates to the rate of convergence. Second, a new ILC scheme is proposed to produce output curves that pass close to the desired points. Here, the control signals are generated by solving an optimal ILC problem with respect to the desired sampling points. One of the key advantages of the proposed approaches is a significant reduction of the computational cost. [Copyright &y& Elsevier]

Purpose: To advance fair and consistent comparisons of dose prediction methods for knowledge‐based planning (KBP) in radiation therapy research. Methods: We hosted OpenKBP, a 2020 AAPM Grand Challenge, and challenged participants to develop the best method for predicting the dose of contoured computed tomography (CT) images. The models were evaluated according to two separate scores: (a) dose score, which evaluates the full three‐dimensional (3D) dose distributions, and (b) dose‐volume histogram (DVH) score, which evaluates a set DVH metrics. We used these scores to quantify the quality of the models based on their out‐of‐sample predictions. To develop and test their models, participants were given the data of 340 patients who were treated for head‐and‐neck cancer with radiation therapy. The data were partitioned into training (n=200), validation (n=40), and testing (n=100) datasets. All participants performed training and validation with the corresponding datasets during the first (validation) phase of the Challenge. In the second (testing) phase, the participants used their model on the testing data to quantify the out‐of‐sample performance, which was hidden from participants and used to determine the final competition ranking. Participants also responded to a survey to summarize their models. Results: The Challenge attracted 195 participants from 28 countries, and 73 of those participants formed 44 teams in the validation phase, which received a total of 1750 submissions. The testing phase garnered submissions from 28 of those teams, which represents 28 unique prediction methods. On average, over the course of the validation phase, participants improved the dose and DVH scores of their models by a factor of 2.7 and 5.7, respectively. In the testing phase one model achieved the best dose score (2.429) and DVH score (1.478), which were both significantly better than the dose score (2.564) and the DVH score (1.529) that was achieved by the runner‐up models. Lastly, many of the top performing teams reported that they used generalizable techniques (e.g., ensembles) to achieve higher performance than their competition. Conclusion: OpenKBP is the first competition for knowledge‐based planning research. The Challenge helped launch the first platform that enables researchers to compare KBP prediction methods fairly and consistently using a large open‐source dataset and standardized metrics. OpenKBP has also democratized KBP research by making it accessible to everyone, which should help accelerate the progress of KBP research. The OpenKBP datasets are available publicly to help benchmark future KBP research. [ABSTRACT FROM AUTHOR]