Showing papers by "Eindhoven University of Technology published in 2020"

Defining trained immunity and its role in health and disease

Abstract: Immune memory is a defining feature of the acquired immune system, but activation of the innate immune system can also result in enhanced responsiveness to subsequent triggers. This process has been termed 'trained immunity', a de facto innate immune memory. Research in the past decade has pointed to the broad benefits of trained immunity for host defence but has also suggested potentially detrimental outcomes in immune-mediated and chronic inflammatory diseases. Here we define 'trained immunity' as a biological process and discuss the innate stimuli and the epigenetic and metabolic reprogramming events that shape the induction of trained immunity.

Variability in the analysis of a single neuroimaging dataset by many teams

Abstract: Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

Abstract: Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

The EPR effect and beyond: Strategies to improve tumor targeting and cancer nanomedicine treatment efficacy.

Abstract: Following its discovery more than 30 years ago, the enhanced permeability and retention (EPR) effect has become the guiding principle for cancer nanomedicine development. Over the years, the tumor-targeted drug delivery field has made significant progress, as evidenced by the approval of several nanomedicinal anticancer drugs. Recently, however, the existence and the extent of the EPR effect - particularly in patients - have become the focus of intense debate. This is partially due to the disbalance between the huge number of preclinical cancer nanomedicine papers and relatively small number of cancer nanomedicine drug products reaching the market. To move the field forward, we have to improve our understanding of the EPR effect, of its cancer type-specific pathophysiology, of nanomedicine interactions with the heterogeneous tumor microenvironment, of nanomedicine behavior in the body, and of translational aspects that specifically complicate nanomedicinal drug development. In this virtual special issue, 24 research articles and reviews discussing different aspects of the EPR effect and cancer nanomedicine are collected, together providing a comprehensive and complete overview of the current state-of-the-art and future directions in tumor-targeted drug delivery.

Continuous scanning for Bragg coherent X-ray imaging.

Abstract: We explore the use of continuous scanning during data acquisition for Bragg coherent diffraction imaging, i.e., where the sample is in continuous motion. The fidelity of continuous scanning Bragg coherent diffraction imaging is demonstrated on a single Pt nanoparticle in a flow reactor at $$400\,^\circ \hbox {C}$$ in an Ar-based gas flowed at 50 ml/min. We show a reduction of 30% in total scan time compared to conventional step-by-step scanning. The reconstructed Bragg electron density, phase, displacement and strain fields are in excellent agreement with the results obtained from conventional step-by-step scanning. Continuous scanning will allow to minimise sample instability under the beam and will become increasingly important at diffraction-limited storage ring light sources.

Service robot implementation: a theoretical framework and research agenda

Abstract: Service robots and artificial intelligence promise to increase productivity and reduce costs, prompting substantial growth in sales of service robots and research dedicated to understanding their i...

Super-resolution Ultrasound Imaging.

Abstract: The majority of exchanges of oxygen and nutrients are performed around vessels smaller than 100 μm, allowing cells to thrive everywhere in the body. Pathologies such as cancer, diabetes and arteriosclerosis can profoundly alter the microvasculature. Unfortunately, medical imaging modalities only provide indirect observation at this scale. Inspired by optical microscopy, ultrasound localization microscopy has bypassed the classic compromise between penetration and resolution in ultrasonic imaging. By localization of individual injected microbubbles and tracking of their displacement with a subwavelength resolution, vascular and velocity maps can be produced at the scale of the micrometer. Super-resolution ultrasound has also been performed through signal fluctuations with the same type of contrast agents, or through switching on and off nano-sized phase-change contrast agents. These techniques are now being applied pre-clinically and clinically for imaging of the microvasculature of the brain, kidney, skin, tumors and lymph nodes.

Flow Photochemistry: Shine Some Light on Those Tubes!

Abstract: Continuous-flow chemistry has recently attracted significant interest from chemists in both academia and industry working in different disciplines and from different backgrounds. Flow methods are now being used in reaction discovery/methodology, in scale-up and production, and for rapid screening and optimization. Photochemical processes are currently an important research field in the scientific community and the recent exploitation of flow methods for these methodologies has made clear the advantages of flow chemistry and its importance in modern chemistry and technology worldwide. This review highlights the most important features of continuous-flow technology applied to photochemical processes and provides a general perspective on this rapidly evolving research field.

Direct-bandgap emission from hexagonal Ge and SiGe alloys

Abstract: Silicon crystallized in the usual cubic (diamond) lattice structure has dominated the electronics industry for more than half a century. However, cubic silicon (Si), germanium (Ge) and SiGe alloys are all indirect-bandgap semiconductors that cannot emit light efficiently. The goal1 of achieving efficient light emission from group-IV materials in silicon technology has been elusive for decades2–6. Here we demonstrate efficient light emission from direct-bandgap hexagonal Ge and SiGe alloys. We measure a sub-nanosecond, temperature-insensitive radiative recombination lifetime and observe an emission yield similar to that of direct-bandgap group-III–V semiconductors. Moreover, we demonstrate that, by controlling the composition of the hexagonal SiGe alloy, the emission wavelength can be continuously tuned over a broad range, while preserving the direct bandgap. Our experimental findings are in excellent quantitative agreement with ab initio theory. Hexagonal SiGe embodies an ideal material system in which to combine electronic and optoelectronic functionalities on a single chip, opening the way towards integrated device concepts and information-processing technologies. A hexagonal (rather than cubic) alloy of silicon and germanium that has a direct (rather than indirect) bandgap emits light efficiently across a range of wavelengths, enabling electronic and optoelectronic functionalities to be combined on a single chip.

Organic Photodetectors and their Application in Large Area and Flexible Image Sensors: The Role of Dark Current

Abstract: Organic photodetectors (OPDs) have gained increasing interest as they offer cost-effective fabrication methods using low temperature processes, making them particularly attractive for large area image detectors on lightweight flexible plastic substrates. Moreover, their photophysical and optoelectronic properties can be tuned both at a material and device level. Visible-light OPDs are proposed for use in indirect-conversion X-ray detectors, fingerprint scanners, and intelligent surfaces for gesture recognition. Near-infrared OPDs find applications in biomedical imaging and optical communications. For most applications, minimizing the OPD dark current density (Jd) is crucial to improve important figures of merits such as the signal-to-noise ratio, the linear dynamic range, and the specific detectivity (D*). Here, a quantitative analysis of the intrinsic dark current processes shows that charge injection from the electrodes is the dominant contribution to Jd in OPDs. Jd reduction is typically addressed by fine-tuning the active layer energetics and stratification or by using charge blocking layers. Yet, most experimental Jd values are higher than the calculated intrinsic limit. Possible reasons for this deviation are discussed, including extrinsic defects in the photoactive layer and the presence of trap states. This provides the reader with guidelines to improve the OPD performances in view of imaging applications.

C(sp3)-H functionalizations of light hydrocarbons using decatungstate photocatalysis in flow.

Abstract: Direct activation of gaseous hydrocarbons remains a major challenge for the chemistry community Because of the intrinsic inertness of these compounds, harsh reaction conditions are typically required to enable C(sp3)–H bond cleavage, barring potential applications in synthetic organic chemistry Here, we report a general and mild strategy to activate C(sp3)–H bonds in methane, ethane, propane, and isobutane through hydrogen atom transfer using inexpensive decatungstate as photocatalyst at room temperature The corresponding carbon-centered radicals can be effectively trapped by a variety of Michael acceptors, leading to the corresponding hydroalkylated adducts in good isolated yields and high selectivity (38 examples)

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Abstract: With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.

From Li-Ion Batteries toward Na-Ion Chemistries: Challenges and Opportunities

Abstract: Among the existing energy storage technologies, lithium-ion batteries (LIBs) have unmatched energy density and versatility. From the time of their first commercialization in 1991, the growth in LIBs has been driven by portable devices. In recent years, however, large-scale electric vehicle and stationary applications have emerged. Because LIB raw material deposits are unevenly distributed and prone to price fluctuations, these large-scale applications have put unprecedented pressure on the LIB value chain, resulting in the need for alternative energy storage chemistries. The sodium-ion battery (SIB) chemistry is one of the most promising “beyond-lithium” energy storage technologies. Herein, the prospects and key challenges for the commercialization of SIBs are discussed. By comparing the technological evolutions of both LIBs and SIBs, key differences between the two battery chemistries are unraveled. Based on outstanding results in power, cyclability, and safety, the path toward SIB commercialization is seen imminent.

Investigating electrode flooding in a flowing electrolyte, gas‐fed carbon dioxide electrolyzer

Abstract: Managing the gas-liquid interface within gas-diffusion electrodes (GDEs) is key to maintaining high product selectivities in carbon dioxide electroreduction. By screening silver-catalyzed GDEs over a range of applied current densities, an inverse correlation was observed between carbon monoxide selectivity and the electrochemical double-layer capacitance, a proxy for wetted electrode area. Plotting current-dependent performance as a function of cumulative charge led to data collapse onto a single sigmoidal curve indicating that the passage of faradaic current accelerates flooding. It was hypothesized that high cathode alkalinity, driven by both initial electrolyte conditions and cathode half-reactions, promotes carbonate formation and precipitation which, in turn, facilitates electrolyte permeation. This mechanism was reinforced by the observations that post-test GDEs retain less hydrophobicity than pristine materials and that water-rinsing and drying electrodes temporarily recovers peak selectivity. This knowledge offers an opportunity to design electrodes with greater carbonation tolerance to improve device longevity.

Assessment on the Achievable Throughput of Multi-Band ITU-T G.652.D Fiber Transmission Systems

Abstract: Fiber-optic multi-band transmission (MBT) aims at exploiting the low-loss spectral windows of single-mode fibers (SMFs) for data transport, expanding by $\sim\!11\times$ the available bandwidth of C-band line systems and by $\sim\!5\times$ C+L-band line systems’. MBT offers a high potential for cost-efficient throughput upgrades of optical networks, even in absence of available dark-fibers, as it utilizes more efficiently the existing infrastructures. This represents the main advantage compared to approaches such as multi-mode/-core fibers or spatial division multiplexing. Furthermore, the industrial trend is clear: the first commercial C $+$ L-band systems are entering the market and research has moved toward the neighboring S-band. This article discusses the potential and challenges of MBT covering the ITU-T optical bands O $\rightarrow$ L. MBT performance is assessed by addressing the generalized SNR (GSNR) including both the linear and non-linear fiber propagation effects. Non-linear fiber propagation is taken into account by computing the generated non-linear interference by using the generalized Gaussian-noise (GGN) model, which takes into account the interaction of non-linear fiber propagation with stimulated Raman scattering (SRS), and in general considers wavelength-dependent fiber parameters. For linear effects, we hypothesize typical components’ figures and discussion on components’ limitations, such as transceivers,’ amplifiers’ and filters’ are not part of this work. We focus on assessing the transmission throughput that is realistic to achieve by using feasible multi-band components without specific optimizations and implementation discussion. So, results are meant to address the potential throughput scaling by turning-on excess fiber transmission bands. As transmission fiber, we focus exclusively on the ITU-T G.652.D, since it is the most widely deployed fiber type worldwide and the mostly suitable to multi-band transmission, thanks to its ultra-wide low-loss single-mode high-dispersion spectral region. Similar analyses could be carried out for other single-mode fiber types. We estimate a total single-fiber throughput of 450 Tb/s over a distance of 50 km and 220 Tb/s over regional distances of 600 km: $\sim\!10\times$ and 8× more than C-band transmission respectively and $\sim\!2.5\times$ more than full C+L.

Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist)

Abstract: Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Improving Inferences about Null Effects with Bayes Factors and Equivalence Tests

Abstract: Researchers often conclude an effect is absent when a null-hypothesis significance test yields a nonsignificant p value. However, it is neither logically nor statistically correct to conclude an effect is absent when a hypothesis test is not significant. We present two methods to evaluate the presence or absence of effects: Equivalence testing (based on frequentist statistics) and Bayes factors (based on Bayesian statistics). In four examples from the gerontology literature, we illustrate different ways to specify alternative models that can be used to reject the presence of a meaningful or predicted effect in hypothesis tests. We provide detailed explanations of how to calculate, report, and interpret Bayes factors and equivalence tests. We also discuss how to design informative studies that can provide support for a null model or for the absence of a meaningful effect. The conceptual differences between Bayes factors and equivalence tests are discussed, and we also note when and why they might lead to similar or different inferences in practice. It is important that researchers are able to falsify predictions or can quantify the support for predicted null effects. Bayes factors and equivalence tests provide useful statistical tools to improve inferences about null effects.

Deep Learning in Ultrasound Imaging

Abstract: In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g., sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage. As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for the color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have a considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.

A biohybrid synapse with neurotransmitter-mediated plasticity

Abstract: Brain-inspired computing paradigms have led to substantial advances in the automation of visual and linguistic tasks by emulating the distributed information processing of biological systems1. The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics2 and brain-machine interfaces3. While promising, these implementations rely on software to run ANN algorithms. Ultimately, it is desirable to build hardware ANNs4,5 that can both directly interface with living tissue and adapt based on biofeedback6,7. The first essential step towards biologically integrated neuromorphic systems is to achieve synaptic conditioning based on biochemical signalling activity. Here, we directly couple an organic neuromorphic device with dopaminergic cells to constitute a biohybrid synapse with neurotransmitter-mediated synaptic plasticity. By mimicking the dopamine recycling machinery of the synaptic cleft, we demonstrate both long-term conditioning and recovery of the synaptic weight, paving the way towards combining artificial neuromorphic systems with biological neural networks.

Prostate cancer reactivates developmental epigenomic programs during metastatic progression

Abstract: Epigenetic processes govern prostate cancer (PCa) biology, as evidenced by the dependency of PCa cells on the androgen receptor (AR), a prostate master transcription factor. We generated 268 epigenomic datasets spanning two state transitions—from normal prostate epithelium to localized PCa to metastases—in specimens derived from human tissue. We discovered that reprogrammed AR sites in metastatic PCa are not created de novo; rather, they are prepopulated by the transcription factors FOXA1 and HOXB13 in normal prostate epithelium. Reprogrammed regulatory elements commissioned in metastatic disease hijack latent developmental programs, accessing sites that are implicated in prostate organogenesis. Analysis of reactivated regulatory elements enabled the identification and functional validation of previously unknown metastasis-specific enhancers at HOXB13, FOXA1 and NKX3-1. Finally, we observed that prostate lineage-specific regulatory elements were strongly associated with PCa risk heritability and somatic mutation density. Examining prostate biology through an epigenomic lens is fundamental for understanding the mechanisms underlying tumor progression. Analyses of epigenomic datasets spanning transitions from normal prostate epithelium to localized prostate cancer to metastases show that latent developmental programs are reactivated in metastatic disease and that prostate lineage-specific regulatory elements are strongly enriched for prostate cancer risk heritability.

Bioinspired light-driven soft robots based on liquid crystal polymers

Abstract: Nature is a constant source of inspiration for materials scientists, fueling the dream of mimicking life-like motion and tasks in untethered, man-made devices. Liquid crystalline polymers (LCPs) programmed to undergo three-dimensional shape changes in response to light are promising materials for fulfilling this dream. The successful development of autonomous, highly controlled light-driven soft robots calls for an understanding of light-driven actuation, advancements in material function and performance, and progress in engineering principles for transforming actuation into life-like motions, from simple bending to walking, for example. This tutorial review includes an introduction to liquid crystal (LC)-based materials and highlights developments in light-responsive LC polymers, shape programmability and sustained motions to finally achieve bioinspired untethered soft robots able to perform locomotion and tasks.