Thales (Netherlands)

Abstract “…. For the truth of the conclusions of physical science, observation is the supreme Court of Appeal….” (Sir Arthur Eddington, The Philosophy of Physical Science.) In this paper we shall review the theoretical and experimental results obtained on simple magnetic model systems. We shall consider the Heisenberg, XY and Ising type of interaction (ferro and antiferromagnetic), on magnetic lattices of dimensionality 1, 2 and 3. Particular attention will be paid to the approximation of these model systems in real crystals, viz. how they can be realized or be expected to exist in nature. A large number of magnetic compounds which, according to the available experimental information, meet the requirements set by one or the other of the various models are considered and their properties discussed. Many examples will be given that demonstrate to what extent experiments on simple magnetic systems support theoretical descriptions of magnetic ordering phenomena and contribute to their understanding. It will also be indicated in which direction there is a need and/or a possibility for future work.

Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.

SUMMARY Structural Funds are the most intensively used policy instrument by the European Union to promote economic growth in its member states and to speed up the process of convergence. This paper empirically explores the effectiveness of European Structural Funds by means of a panel data analysis for 13 countries in the European Union. We show that – on average – Structural Funds are ineffective. For countries with a ‘proper’ institutional framework, however, Structural Funds are effective. The latter result is obtained for a wide range of conditioning variables, such as openness, institutional quality, corruption and indicators for good governance. It is robust to a wide range of robustness tests.

Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology.Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.

Background: The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed. Results: We released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available. Conclusions: A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use.

Abstract This paper is meant to be a report on the experimental work on dilute Pd-based alloys with Co, Fe and Mn. These alloys exhibit the phenomenon of giant moments. The importance of measurements on paramagnetic alloys is emphasized. From these measurements the conclusion can be drawn that Co and Fe dissolved in Pd does not behave like a normal paramagnet, i.e. according to a Brillouin function. This result makes it possible to explain the existing discrepancy in the interpretations of magnetic measurements on one hand and of specific-heat experiments on the other. The main conclusions of this paper are: The giant moment should be accounted for by ‘normal’ values of the magnetic quantum number (3/2 for Co, 2 for Fe and 5/2 for Mn) and a large value of geff. Paramagnetic alloys of Mn in Pd behave according to Brillouin functions, but alloys of Co or Fe in Pd do not. Hence, a number of interpretations of magnetic measurements should be considered as incorrect. The localized model for ferromagnetism can well account for the magnetic ordering of dilute Pd-based alloys (certainly if c < 1 at.%). A straightforward generalization of the Weiss molecular-field model may be applied. The transition temperature of Pd-Mn alloys is not proportional to the concentration, but after scaling the behaviour is similar to what has been found for Pd-Co and Pd-Fe alloys. The concentration dependence can be explained from a calculation of the strength of the interaction between two impurity atoms as a function of the distance. Comparison between alloys with equal concentrations shows that the magnetic ordering in Pd-Mn is not at all exceptional, but analogous to that in Pd-Co and in Pd-Fe. It should be mentioned, however, that Pd-Mn at c > 3 at.% is a so-called spin glass. Addition of Ag or Rh to Pd alloys with Co, Fe and Mn has important influences on their properties. Unfortunately these effects are not completely understood.

The popularity of small unmanned aerial vehicles (UAVs) is increasing. Therefore, the importance of security systems able to detect and classify them is increasing as well. In this paper, we propose a new approach for UAVs classification using continuous wave radar or high pulse repetition frequency (PRF) pulse radars. We consider all steps of processing required to make a decision out of the raw radar data. Before the classification, the micro-Doppler signature is filtered and aligned to compensate the Doppler shift caused by the target's body motion. Then, classification features are extracted from the micro-Doppler signature in order to represent information about class at a lower dimension space. Eigenpairs extracted from the correlation matrix of the signature are used as informative features for classification. The proposed approach is verified on real radar measurements collected with X-band radar. Planes, quadrocopter, helicopters, and stationary rotors as well as birds are considered for classification. Moreover, a possibility of distinguishing different number of rotors is considered. The obtained results show the effectiveness of the proposed approach. It provides the capability of correct classification with a probability of around 92%.

The design and realization of an online learning motion controller for a linear motor is presented, and its usefulness is evaluated. The controller consists of two components: (1) a model-based feedback component, and (2) a learning feedforward component. The feedback component is designed on the basis of a simple second-order linear model, which is known to have structural errors. In the design, an emphasis is placed on robustness. The learning feedforward component is a neural-network-based controller, comprised of a one-hidden-layer structure with second-order B-spline basis functions. Simulations and experimental evaluations show that, with little effort, a high-performance motion system can be obtained with this approach.

PURPOSE: Serum PSA (sPSA) testing has led to the identification of patients with indolent prostate cancer, and inevitably overtreatment has become a concern. Progensa PCA3 urine testing was shown to improve the diagnosis of prostate cancer, but its diagnostic value for aggressive prostate cancer is limited. Therefore, urinary biomarkers that can be used for prediction of Gleason score ≥7 prostate cancer in biopsies are urgently needed. EXPERIMENTAL DESIGN: Using gene expression profiling data, 39 prostate cancer biomarkers were identified. After quantitative PCR analysis on tissue specimens and urinary sediments, eight promising biomarkers for the urinary detection of prostate cancer were selected (ONECUT2, HOXC4, HOXC6, DLX1, TDRD1, NKAIN1, MS4A8B, PPFIA2). The hypothesis that biomarker combinations improve the diagnostic value for aggressive prostate cancer was tested on 358 urinary sediments of an intention-to-treat cohort. RESULTS: A urinary three-gene panel (HOXC6, TDRD1, and DLX1) had higher accuracy [area under the curve (AUC), 0.77; 95% confidence interval (CI), 0.71-0.83] to predict Gleason score ≥7 prostate cancer in biopsies compared with Progensa PCA3 (AUC, 0.68; 95% CI, 0.62-0.75) or sPSA (AUC, 0.72; 95% CI, 0.65-0.78). Combining the three-gene panel with sPSA further improved the predictive accuracy (AUC, 0.81; 95% CI, 0.75-0.86). The accuracy of the three-gene predictive model was maintained in subgroups with low sPSA concentrations. CONCLUSIONS: The urinary three-gene panel (HOXC6, TDRD1, and DLX1) represents a promising tool to identify patients with aggressive prostate cancer, also in those with low sPSA values. The combination of the urinary three-gene panel with sPSA bears great potential for the early diagnosis of patients with clinically significant prostate cancer.

A new method for multiple model particle filtering for Markovian switching systems is presented. This new method is a combination of the interacting multiple model (IMM) filter and a (regularised) particle filter. The mixing and interaction is similar to that in a conventional IMM filter. However, in every mode a regularised particle filter is running. The regularised particle filter probability density is a mixture of Gaussian probability densities. The proposed method is able to deal with nonlinearities and non-Gaussian noise. Furthermore, the new method keeps a fixed number of particles in each mode, and therefore it does not suffer from the potential drawbacks of existing multiple model particle filters for Markovian switching systems.

The paper deals with a radar ‘track before detect’ application in a multitarget setting. ‘Track before detect’ is a method to track weak objects on the basis of raw radar measurements, e.g. the reflected power of the target plus noise. In classical target tracking, the tracking process is performed on the basis of pre-processed measurements that are constructed from the original measurement data every time step. In this way no integration over time takes place and information is lost. The authors give details of a modelling setup and a particle filter based algorithm to deal with a ‘multiple target track before detect’ situation. Using simulations it is shown that with this method, it is possible to track multiple, closely spaced, weak targets.

A channel model for the residential power circuit used as a carrier for telecommunications signals is presented. This model is based on the results of an extensive measurement campaign executed in the city of Amsterdam, The Netherlands. As a basis for the channel model, the time-variant linear filter model is used. Channel noise is shown to be a summation of four noise types. Figures on channel-input impedance as well as signal attenuation are given. Finally, the phase shift introduced by the channel is considered.

The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.

We develop feedback control strategies for a chaotic dynamic system such as the Duffing equation. Our controllers are of the so-called Lyapunov-type and are inspired by robot manipulator feedback controls. The different controllers we propose include observer-based controllers that can cope with parametric uncertainties of the original system. Some simulation examples support the developed methods.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

OBJECTIVE: The endothelium is normally subjected to mechanical deformation resulting from shear stress and from strain associated with stretch of the vessel wall. These stimuli are detected by a mechanosensor that initiates a variety of signaling systems responsible for triggering the functional responses. The identity of the mechanosensor has not been established. This article discusses the different mechanisms of mechanosensing that have been proposed and reviews the literature with respect to signaling systems that are activated in response to stress and strain in endothelium. DATA SOURCES: Published literature related to mechanotransduction, signal transduction pathways initiated by strain in endothelium, and pathophysiologic effects of abnormal shear forces in diseases. DATA EXTRACTION AND SYNTHESIS: Proposed mechanisms of mechanosensing include stretch-sensitive ion channels, protein kinases associated with the cytoskeleton, integrin-cytoskeletal interactions, cytoskeletal-nuclear interactions, and oxidase systems capable of generating reactive oxygen species. However, the molecular identity of the mechanosensor is not known, nor is it clear whether multiple sensing mechanisms exist. CONCLUSIONS: Many responses are initiated in cells subjected to mechanical deformation, including alterations in ion channel conductance, activation of signal transduction pathways, and altered expression of specific genes. Future progress in this field will require a critical distinction between cell systems that become activated during mechanical strain and the identity of the cellular mechanosensor that triggers subsequent responses.

The radar micro-Doppler signature of a target is determined by parts of the target moving or rotating in addition to the main body motion. The relative motion of parts is characteristic for different classes of targets, e.g. the flapping motion of a bird's wings vs. the spinning of propeller blades. In the present study, the micro-Doppler signature is exploited to discriminate birds and small unmanned aerial vehicles (UAVs). Emphasis is on micro-Doppler features that can be extracted from spectrograms and cepstrograms, enabling the human eye or indeed automatic classification algorithms to make a quick distinction between man-made objects and bio-life. In addition, in case of man-made objects, it is desired to further characterize the type of mini-UAV to aid the threat assessment. Also this characterization is done on the basis of micro-Doppler features.

Intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs) are non-invasive neoplasms that are often observed in association with invasive pancreatic cancers, but their origins and evolutionary relationships are poorly understood. In this study, we analyze 148 samples from IPMNs, MCNs, and small associated invasive carcinomas from 18 patients using whole exome or targeted sequencing. Using evolutionary analyses, we establish that both IPMNs and MCNs are direct precursors to pancreatic cancer. Mutations in SMAD4 and TGFBR2 are frequently restricted to invasive carcinoma, while RNF43 alterations are largely in non-invasive lesions. Genomic analyses suggest an average window of over three years between the development of high-grade dysplasia and pancreatic cancer. Taken together, these data establish non-invasive IPMNs and MCNs as origins of invasive pancreatic cancer, identifying potential drivers of invasion, highlighting the complex clonal dynamics prior to malignant transformation, and providing opportunities for early detection and intervention.

A reflected power canceller (RPC) using modern p-i-n diode technology which enables a frequency modulated continuous wave (FMCW) radar to operate using a single antenna for transmission and reception is described. Results are presented which demonstrate that such a canceller solves the problems for many CW-type radars over large RF bandwidths (i.e. >2 GHz at X-band). The RPC has been successfully installed into the Bofors/Signal PILOT FMCW tactical navigation radar. Results from sea trials are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

BACKGROUND: The benefits of digital pathology for workflow improvement and thereby cost savings in pathology, at least partly outweighing investment costs, are being increasingly recognised. Successful implementations in a variety of scenarios have started to demonstrate the cost benefits of digital pathology for both research and routine diagnosis, contributing to a sound business case encouraging further adoption. To further support new adopters, there is still a need for detailed assessment of the impact that this technology has on the relevant pathology workflows, with an emphasis on time-saving. AIMS: To assess the impact of digital pathology adoption on logistic laboratory tasks (i.e. not including pathologists' time for diagnosis-making) in the Laboratorium Pathologie Oost Nederland, a large regional pathology laboratory in The Netherlands. METHODS AND RESULTS: To quantify the benefits of digitisation, we analysed the differences between the traditional analogue and new digital workflows, carried out detailed measurements of all relevant steps in key analogue and digital processes, and compared the time spent. We modelled and assessed the logistic savings in five workflows: (i) routine diagnosis; (ii) multidisciplinary meeting; (iii) external revision requests; (iv) extra stainings; and (v) external consultation. On average, >19 working hours were saved on a typical day by working digitally, with the highest savings in routine diagnosis and multidisciplinary meeting workflows. CONCLUSIONS: By working digitally, a significant amount of time could be saved in a large regional pathology laboratory with a typical case mix. We also present the data in each workflow per task and concrete logistic steps to allow extrapolation to the context and case mix of other laboratories.

Static, or electronic, energy meters are replacing the conventional electromechanical meters. Consumers are sometimes complaining about higher energy readings and billing after the change to a static meter, but there is not a clear common or root cause at present. Electromagnetic interference has been observed between active infeed converters as used in photo-voltaic systems and static meters. Reducing the interference levels eliminated inaccurate reading in static meters. Several field investigations failed to identify a clear root cause of inaccurate readings of static energy meters. Experiments were performed in a controlled lab environment. Three-phase meters showed large deviations, even when supplied with an ideal sinusoidal voltage from a fourquadrant power amplifier. Large variations could be observed when non-linear, fast switching, loads were connected. A deviation of +276 % was measured with one static energy meter, +265% with a second and -46% with a third static energy meter. After dismantling it was revealed that the meters with the positive deviation used a Rogowski coil current sensor. The meter with a Hall effect-based current sensor gave the -46% deviation. The fourth meter, with a current transformer, resulted in -10% in one experiment and +8% in another experiment, where the deviations are with respect to a conventional electromechanical meter. Measurements were repeated with more meters and supplied from standard, low internal impedance, mains supply in the laboratory. Deviations of +475%, +566%, +569%, +581%, +582% and -31% and -32% were registered, with again the positive deviation for Rogowski coil current sensors and negative deviations for the Hall sensors.