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Frontal circuits play a critical role in motor, cognitive and affective processing, and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)functions remains largely elusive. We studied 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregated the frontal cortex into circuits that had become dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to frontal, ranging from interconnections with sensorimotor cortices in dystonia, the primary motor cortex in Tourettes syndrome, the supplementary motor area in Parkinsons disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairments in the human brain.

We calculate the tensor bispectrum mediated by an excited scalar field during inflation and find that the bispectrum peaks in the squeezed configuration, which is different from that of gravitational waves induced by enhanced curvature perturbations re-entering the horizon in the radiation-dominated era. Measuring the bispectrum provides a promising way to distinguish the stochastic gravitational-wave background generated during inflation from that generated after inflation., Comment: 6 pages, 4 figures

Establishing appropriate mathematical models for complex systems in natural phenomena not only helps deepen our understanding of nature but can also be used for state estimation and prediction. However, the extreme complexity of natural phenomena makes it extremely challenging to develop full-order models (FOMs) and apply them to studying many quantities of interest. In contrast, appropriate reduced-order models (ROMs) are favored due to their high computational efficiency and ability to describe the key dynamics and statistical characteristics of natural phenomena. Taking the viscous Burgers equation as an example, this paper constructs a Convolutional Autoencoder-Reservoir Computing-Normalizing Flow algorithm framework, where the Convolutional Autoencoder is used to construct latent space representations, and the Reservoir Computing-Normalizing Flow framework is used to characterize the evolution of latent state variables. In this way, a data-driven stochastic parameter reduced-order model is constructed to describe the complex system and its dynamic behavior.

Video Anomaly Detection (VAD) systems can autonomously monitor and identify disturbances, reducing the need for manual labor and associated costs. However, current VAD systems are often limited by their superficial semantic understanding of scenes and minimal user interaction. Additionally, the prevalent data scarcity in existing datasets restricts their applicability in open-world scenarios. In this paper, we introduce Hawk, a novel framework that leverages interactive large Visual Language Models (VLM) to interpret video anomalies precisely. Recognizing the difference in motion information between abnormal and normal videos, Hawk explicitly integrates motion modality to enhance anomaly identification. To reinforce motion attention, we construct an auxiliary consistency loss within the motion and video space, guiding the video branch to focus on the motion modality. Moreover, to improve the interpretation of motion-to-language, we establish a clear supervisory relationship between motion and its linguistic representation. Furthermore, we have annotated over 8,000 anomaly videos with language descriptions, enabling effective training across diverse open-world scenarios, and also created 8,000 question-answering pairs for users' open-world questions. The final results demonstrate that Hawk achieves SOTA performance, surpassing existing baselines in both video description generation and question-answering. Our codes/dataset/demo will be released at https://github.com/jqtangust/hawk.

There is a growing demand to deploy computation-intensive deep learning (DL) models on resource-constrained mobile devices for real-time intelligent applications. Equipped with a variety of processing units such as CPUs, GPUs, and NPUs, the mobile devices hold potential to accelerate DL inference via parallel execution across heterogeneous processors. Various efficient parallel methods have been explored to optimize computation distribution, achieve load balance, and minimize communication cost across processors. Yet their practical effectiveness in the dynamic and diverse real-world mobile environment is less explored. This paper presents a holistic empirical study to assess the capabilities and challenges associated with parallel DL inference on heterogeneous mobile processors. Through carefully designed experiments covering various DL models, mobile software/hardware environments, workload patterns, and resource availability, we identify limitations of existing techniques and highlight opportunities for cross-level optimization.

The advancement and development of human modeling have greatly benefited from principles used in robotics, for instance, multibody dynamics laid the foundations for physics engines of human movement simulation, and the robotics and control theory were used to contextualize human sensorimotor control. There are many common interests and interconnections between the fields of human modeling and robotics. In recent years, as robots have become safer and smarter, they actively participate in our lives and help us in various scenarios. Roboticists need tools and data from human modeling to build next-generation robots that better assist humans. In this survey, we focus on the connections between physical human-robot interaction and human modeling. On one hand, human neuromusculoskeletal and sensorimotor control models provide novel insights into the human response that robots can utilize to improve human performance. On the other hand, robots are becoming instrumental in quantifying the performance of the (neuro)musculoskeletal system. Thus, the combined use of human modeling and robotic methods in physical human-robot interaction can lead to both improved human understanding and functional assistance., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Human-Robot Interaction, Biomechatronics & Human-Machine Control

The heating mechanisms of solar white-light flares remain unclear. We present an X1.0 white-light flare on 2022 October 2 (SOL2022-10-02T20:25) observed by the Chinese \ha\ Solar Explorer (CHASE) that provides two-dimensional spectra in the visible light for the full solar disk with a seeing-free condition. The flare shows a prominent enhancement of $\sim$40\% in the photospheric \fe\ line at 6569.2 \AA, and the nearby continuum also exhibits a maximum enhancement of $\sim$40\%. For the continuum near the \fe\ line at 6173 \AA\ from the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO), it is enhanced up to $\sim$20\%. At the white-light kernels, the \fe\ line at 6569.2 \AA\ has a symmetric Gaussian profile that is still in absorption and the H$\alpha$ line at 6562.8 \AA\ displays a very broad emission profile with a central reversal plus a red or blue asymmetry. The white-light kernels are co-spatial with the microwave footpoint sources observed by the Expanded Owens Valley Solar Array (EOVSA) and the time profile of the white-light emission matches that of the hard X-ray emission above 30 keV from the Gamma-ray Burst Monitor (GBM) on Fermi. These facts indicate that the white-light emission is qualitatively related to a nonthermal electron beam. We also perform a radiative hydrodynamic simulation with the electron beam parameters constrained by the hard X-ray observations from Fermi/GBM. The result reveals that the white-light enhancement cannot be well explained by a pure electron-beam heating together with its induced radiative backwarming but may need additional heating sources such as \alfven\ waves., Comment: 15 pages, 5 figures, and 1 table

Time series classification faces two unavoidable problems. One is partial feature information and the other is poor label quality, which may affect model performance. To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework. There are three main contributions. First, we train the label correction model with a two-branch neural network in the outer loop. While in the model-agnostic inner loop, we use pre-existing classification models in a multi-task way and jointly update the meta-knowledge so as to help us achieve adaptive labeling on complex time series. Second, we devise new data visualization methods for both image patterns of the historical data and data in the prediction horizon. Finally, we test our method with various financial datasets, including XOM, S\&P500, and SZ50. Results show that our method is more effective and accurate than some existing label correction techniques.

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR (Findable, Accessible, Interoperabile, and Reusable) data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

To enhance robotic grasping capabilities, we are developing new contactless fingertip sensors to measure distance in close proximity and simultaneously detect the type of material and the interior structure. These sensors are referred to as pre-touch dual-modal and dual-mechanism (PDM$^2$) sensors, and they operate using both pulse-echo ultrasound (US) and optoacoustic (OA) modalities. We present the design of a PDM$^2$ sensor that utilizes a pulsed laser beam and a customized ultrasound transceiver with a wide acoustic bandwidth for ranging and sensing. Both US and OA signals are collected simultaneously, triggered by the same laser pulse. To validate our design, we have fabricated a prototype of the PDM$^2$ sensor and integrated it into an object scanning system. We have also developed algorithms to enable the sensor, including time-of-flight (ToF) auto estimation, ranging rectification, sensor and system calibration, distance ranging, material/structure detection, and object contour detection and reconstruction. The experimental results demonstrate that the new PDM$^2$ sensor and its algorithms effectively enable the object scanning system to achieve satisfactory ranging and contour reconstruction performances, along with satisfying material/structure detection capabilities. In conclusion, the PDM$^2$ sensor offers a practical and powerful solution to improve grasping of unknown objects with the robotic gripper by providing advanced perception capabilities.

Radar has stronger adaptability in adverse scenarios for autonomous driving environmental perception compared to widely adopted cameras and LiDARs. Compared with commonly used 3D radars, the latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception. However, due to the much higher noise than LiDAR, manufacturers choose different filtering strategies, resulting in an inverse ratio between noise level and point cloud density. There is still a lack of comparative analysis on which method is beneficial for deep learning-based perception algorithms in autonomous driving. One of the main reasons is that current datasets only adopt one type of 4D radar, making it difficult to compare different 4D radars in the same scene. Therefore, in this paper, we introduce a novel large-scale multi-modal dataset featuring, for the first time, two types of 4D radars captured simultaneously. This dataset enables further research into effective 4D radar perception algorithms.Our dataset consists of 151 consecutive series, most of which last 20 seconds and contain 10,007 meticulously synchronized and annotated frames. Moreover, our dataset captures a variety of challenging driving scenarios, including many road conditions, weather conditions, nighttime and daytime with different lighting intensities and periods. Our dataset annotates consecutive frames, which can be applied to 3D object detection and tracking, and also supports the study of multi-modal tasks. We experimentally validate our dataset, providing valuable results for studying different types of 4D radars. This dataset is released on https://github.com/adept-thu/Dual-Radar.

The emerging field of artificial intelligence of things (AIoT, AI+IoT) is driven by the widespread use of intelligent infrastructures and the impressive success of deep learning (DL). With the deployment of DL on various intelligent infrastructures featuring rich sensors and weak DL computing capabilities, a diverse range of AIoT applications has become possible. However, DL models are notoriously resource-intensive. Existing research strives to realize near-/realtime inference of AIoT live data and low-cost training using AIoT datasets on resource-scare infrastructures. Accordingly, the accuracy and responsiveness of DL models are bounded by resource availability. To this end, the algorithm-system co-design that jointly optimizes the resource-friendly DL models and model-adaptive system scheduling improves the runtime resource availability and thus pushes the performance boundary set by the standalone level. Unlike previous surveys on resource-friendly DL models or hand-crafted DL compilers/frameworks with partially fine-tuned components, this survey aims to provide a broader optimization space for more free resource-performance tradeoffs. The cross-level optimization landscape involves various granularity, including the DL model, computation graph, operator, memory schedule, and hardware instructor in both on-device and distributed paradigms. Furthermore, due to the dynamic nature of AIoT context, which includes heterogeneous hardware, agnostic sensing data, varying user-specified performance demands, and resource constraints, this survey explores the context-aware inter-/intra-device controllers for automatic cross-level adaptation. Additionally, we identify some potential directions for resource-efficient AIoT systems. By consolidating problems and techniques scattered over diverse levels, we aim to help readers understand their connections and stimulate further discussions.

Deep learning models usually require sufficient training data to achieve high accuracy, but obtaining labeled data can be time-consuming and labor-intensive. Here we introduce a template-based training method to train a 3D U-Net model from scratch using only one population-averaged brain MRI template and its associated segmentation label. The process incorporated visual perception augmentation to enhance the model's robustness in handling diverse image inputs and mitigating overfitting. Leveraging this approach, we trained 3D U-Net models for mouse, rat, marmoset, rhesus, and human brain MRI to achieve segmentation tasks such as skull-stripping, brain segmentation, and tissue probability mapping. This tool effectively addresses the limited availability of training data and holds significant potential for expanding deep learning applications in image analysis, providing researchers with a unified solution to train deep neural networks with only one image sample.

Background: Early recognition and prevention of in-hospital cardiac arrest (IHCA) have played an increasingly important role in the chain of survival. However, clinical tools for predicting IHCA are scarce, particularly in the emergency department (ED). We sought to estimate the incidence of ED-based IHCA and to develop and validate a novel triage tool, the Emergency Department In-hospital Cardiac Arrest Score (EDICAS), for predicting ED-based IHCA.Methods: In this retrospective cohort study we used electronic clinical warehouse data from a tertiary medical center with approximately 100,000 ED visits per year. We extracted data from 733,398 ED visits over a seven-year period. We selected one ED visit per person and excluded out-of-hospital cardiac arrest or children. Patient demographics and computerized triage information were included as potential predictors.Results: A total of 325,502 adult ED patients were included. Of these patients, 623 (0.2%) developed ED-based IHCA. The EDICAS, which includes age and arrival mode and categorizes vital signs with simple cut-offs, showed excellent discrimination (area under the receiver operating characteristic [AUROC] curve, 0.87) and maintained its discriminatory ability (AUROC, 0.86) in cross-validation. Previously developed early warning scores showed lower AUROC (0.77 for the Modified Early Warning Score and 0.83 for the National Early Warning Score) when applied to our ED population.Conclusion: In-hospital cardiac arrest in the ED is relatively uncommon. We developed and internally validated a novel tool for predicting imminent IHCA in the ED. Future studies are warranted to determine whether this tool could gain lead time to identify high-risk patients and potentially reduce ED-based IHCA.

Supplementary control (SC) technology is widely leveraged by power supply companies in active distribution networks (ADNs) to improve their stability and dynamic characteristics. Yet, the existed SCs are generally implemented from inside the converter controllers of distributed generators (DGs) or active loads, so there is a need to redesign the internal physical structure of the existing controller, resulting in the increasing work amount of assembling and workability. This paper studies the specific R & D process of a novel external coupling type SC (ECSC), which is based upon current source injection (CSI-ECSC) for improving the dynamic characteristics of ADN. The SC current signals are coupled to the current sampling loop from outside the converter controller. And the employment of the existing current sample makes it unnecessary to redesign the internal physical structure of the existing controller. As a result, the SC assembling is simplified and its workability is improved. In this paper, a detailed exemplary ADN with direct-drive permanent magnet synchronous generator (PMSG) is firstly set up in math for full eigenvalue analysis. Then, the CSI-ECSC is designed with its control loop, interface circuit, and parameter setting. Furthermore, by using PSCAD/EMTDC, groups of case studies are conducted in ADNs where photovoltaics (PVs) and energy storage (ES) are included. Finally, the real-time hardware-in-the-loop (HIL) testing validates the functionality of the realized CSI-ECSC in RTDS., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Intelligent Electrical Power Grids

Objectives: Although factors related to a return visit to the emergency department (ED) have been reported, only few studies have examined “high-risk” ED revisits with serious adverse outcomes. This study aimed to describe the incidence and trend of high-risk ED revisits in United States EDs and to investigate factors associated with these revisits.Methods: Data were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS), 2010–2018. Adult ED revisits within 72 hours of a previous discharge were identified using a mark on the Patient Record Form. High-risk revisits were defined as revisits with serious adverse outcomes, including intensive care unit admissions, emergency surgery, cardiac catheterization, or cardiopulmonary resuscitation (CPR) during the return visit. Analyses used descriptive statistics and multivariable logistic regression accounting for NHAMCS's complex survey design.Results: Over the 9-year study period, there were an estimated 37,700,000 revisits, and the proportion of revisits in the entire ED population decreased slightly from 5.1% in 2010 to 4.5% in 2018 (P for trend = 0.02). By contrast, there were an estimated 827,000 high-risk ED revisits, and the proportion of high-risk revisits in the entire ED population remained stable at approximately 0.1%. The mean age of these high-risk revisit patients was 57 years, and 43% were men. Approximately 6% of the patients were intubated, and 13% received CPR. Most of them were hospitalized, and 2% died in the ED. Multivariable analysis showed older age (65+ years), Hispanic ethnicity, daytime visits, and arrival by ambulance during the revisit were independent predictors of high-risk revisits.Conclusions: High-risk revisits accounted for a relatively small fraction (0.1%) of the ED visits. Over the time period of NHAMCS survey between 2010-2018, this fraction remained stable. We identified factors during the return visit that could be used to label high-risk revisits for tim

Technological advancement provides an increasing number and variety of solutions to interact with digital content. However, the complexity of the devices we use to interact with such content grows according to the users' needs as well as the complexity of the target interactions. This also includes all those tools designed to mediate touch interactions with virtual and/or remote environments, i.e., haptic interfaces and rendering techniques. We propose three hours of tutorials to discuss the technology, challenges, and perspective of haptic systems and rendering techniques for immersive human-computer interaction.

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

Recently, extracting data-driven governing laws of dynamical systems through deep learning frameworks has gained a lot of attention in various fields. Moreover, a growing amount of research work tends to transfer deterministic dynamical systems to stochastic dynamical systems, especially those driven by non-Gaussian multiplicative noise. However, lots of log-likelihood based algorithms that work well for Gaussian cases cannot be directly extended to non-Gaussian scenarios which could have high error and low convergence issues. In this work, we overcome some of these challenges and identify stochastic dynamical systems driven by $\alpha$-stable L\'evy noise from only random pairwise data. Our innovations include: (1) designing a deep learning approach to learn both drift and diffusion coefficients for L\'evy induced noise with $\alpha$ across all values, (2) learning complex multiplicative noise without restrictions on small noise intensity, (3) proposing an end-to-end complete framework for stochastic systems identification under a general input data assumption, that is, $\alpha$-stable random variable. Finally, numerical experiments and comparisons with the non-local Kramers-Moyal formulas with moment generating function confirm the effectiveness of our method., Comment: 26 pages,15 figures

The Si I 6560.58 \r{A} line in the H$\alpha$ blue wing is blended with a telluric absorption line from water vapor in ground-based observations. Recent observations with the space-based telescope CHASE provide a new window to study this line. We aim to study the Si I line statistically and to explore possible diagnostics. We select three scannings in the CHASE observations, and measure the equivalent width (EW) and the full width at half maximum (FWHM) for each pixel on the solar disk. We then calculate the theoretical EW and FWHM from the VALC model. An active region is also studied in particular for difference in the quiet Sun and the sunspots. The Si I line is formed at the bottom of the photosphere. The EW of this line increases from the disk center to $\mu$ = 0.2, and then decreases toward the solar limb, while the FWHM shows a monotonically increasing trend. Theoretically predicted EW agrees well with observations, while the predicted FWHM is far smaller due to the absence of unresolved turbulence in models. The macroturbulent velocity is estimated to be 2.80 km s$^{-1}$ at the disk center, and increases to 3.52 km s$^{-1}$ at $\mu$ = 0.2. We do not find any response to flare heating in current observations. Doppler shifts and line widths of the Si I 6560.58 \r{A} and Fe I 6569.21 \r{A} lines can be used to study the mass flows and turbulence of the different photospheric layers. The Si I line has good potentials to diagnose the dynamics and energy transport in the photosphere., Comment: 7 pages, 10 figures, 2 tables. Accepted for publication in A&A. CHASE data are available at https://ssdc.nju.edu.cn

The H{\alpha} line is an important optical line in solar observations containing the information from the photosphere to the chromosphere. To study the mechanisms of solar eruptions and the plasma dynamics in the lower atmosphere, the Chinese H{\alpha} Solar Explorer (CHASE) was launched into a Sun-synchronous orbit on October 14, 2021. The scientific payload of the CHASE satellite is the H{\alpha} Imaging Spectrograph (HIS). The CHASE/HIS acquires, for the first time, seeing-free H{\alpha} spectroscopic observations with high spectral and temporal resolutions. It consists of two observational modes. The raster scanning mode provides full-Sun or region-of-interest spectra at H{\alpha} (6559.7-6565.9 {\AA}) and Fe I (6567.8-6570.6 {\AA}) wavebands. The continuum imaging mode obtains full-Sun photospheric images at around 6689 {\AA}. In this paper, we present detailed calibration procedures for the CHASE/HIS science data, including the dark-field and flat-field correction, slit image curvature correction, wavelength and intensity calibration, and coordinate transformation. The higher-level data products can be directly used for scientific research., Comment: 9 pages, 7 figures

The Chinese H{\alpha} Solar Explorer (CHASE), dubbed "Xihe" - Goddess of the Sun, was launched on October 14, 2021 as the first solar space mission of China National Space Administration (CNSA). The CHASE mission is designed to test a newly developed satellite platform and to acquire the spectroscopic observations in the H{\alpha} waveband. The H{\alpha} Imaging Spectrograph (HIS) is the scientific payload of the CHASE satellite. It consists of two observational modes: raster scanning mode and continuum imaging mode. The raster scanning mode obtains full-Sun or region-of-interest spectral images from 6559.7 to 6565.9 {\AA} and from 6567.8 to 6570.6 {\AA} with 0.024 {\AA} pixel spectral resolution and 1 minute temporal resolution. The continuum imaging mode obtains photospheric images in continuum around 6689 {\AA} with the full width at half maximum of 13.4 {\AA}. The CHASE mission will advance our understanding of the dynamics of solar activity in the photosphere and chromosphere. In this paper, we present an overview of the CHASE mission including the scientific objectives, HIS instrument overview, data calibration flow, and first results of on-orbit observations., Comment: 9 pages, 6 figures

Character line drawing synthesis can be formulated as a special case of image-to-image translation problem that automatically manipulates the photo-to-line drawing style transformation. In this paper, we present the first generative adversarial network based end-to-end trainable translation architecture, dubbed P2LDGAN, for automatic generation of high-quality character drawings from input photos/images. The core component of our approach is the joint geometric-semantic driven generator, which uses our well-designed cross-scale dense skip connections framework to embed learned geometric and semantic information for generating delicate line drawings. In order to support the evaluation of our model, we release a new dataset including 1,532 well-matched pairs of freehand character line drawings as well as corresponding character images/photos, where these line drawings with diverse styles are manually drawn by skilled artists. Extensive experiments on our introduced dataset demonstrate the superior performance of our proposed models against the state-of-the-art approaches in terms of quantitative, qualitative and human evaluations. Our code, models and dataset will be available at Github., Comment: Published in ICMR '23: Proceedings of the 2023 ACM International Conference on Multimedia Retrieval, June 2023

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

Molecular reprogramming of stromal microarchitecture by tumour-derived extracellular vesicles (EVs) is proposed to favour pre-metastatic niche formation. We elucidated the role of extravesicular tissue inhibitor of matrix metalloproteinase-1 (TIMP1EV) in pro-invasive extracellular matrix (ECM) remodelling of the liver microenvironment to aid tumour progression in colorectal cancer (CRC). Immunohistochemistry analysis revealed a high expression of stromal TIMP1 in the invasion front that was associated with poor progression-free survival in patients with colorectal liver metastases. Molecular analysis identified TIMP1EV enrichment in CRC-EVs as a major factor in the induction of TIMP1 upregulation in recipient fibroblasts. Mechanistically, we proved that EV-mediated TIMP1 upregulation in recipient fibroblasts induced ECM remodelling. This effect was recapitulated by human serum-derived EVs providing strong evidence that CRC release active EVs into the blood circulation of patients for the horizontal transfer of malignant traits to recipient cells. Moreover, EV-associated TIMP1 binds to HSP90AA, a heat-shock protein, and the inhibition of HSP90AA on human-derived serum EVs attenuates TIMP1EV-mediated ECM remodelling, rendering EV-associated TIMP1 a potential therapeutic target. Eventually, in accordance with REMARK guidelines, we demonstrated in three independent cohorts that EV-bound TIMP1 is a robust circulating biomarker for a non-invasive, preoperative risk stratification in patients with colorectal liver metastases.

The rapidly growing global internet traffic requires ever faster data transfer within computer networks. Photonic integrated circuits can pave the way to accelerated signal transmission and increase bandwidth of optical interconnects in a compact way. One of the key building blocks for photonic integrated circuits is an on-chip nanolaser. In this dissertation, we explore ways to reduce resistance and the Joule heating of the nanolasers We experimentally demonstrated an electrically pumped nanolaser by incorporating a tunnel junction. The resistance is reduced, so as the threshold voltage and joule heating. Besides nanolasers, an optically controlled microwave switch is another essential component in photonic integrated circuits. We designed and demonstrated a Monolithic Optically Reconfigurable Integrated Microwave Switches (MORIMSs) as a more efficient and integrated photoconductive switch. The two devices we explore in this dissertation have the potential to be essential building blocks for photonic integrated circuits in the future.

Diffusion tensor imaging (DTI) metrics, such as the fractional anisotropy (FA) and estimates of diffusivity are sensitive to the microstructure of peripheral nerves and may be displayed as tractograms. However, the ideal conditions for tractography of the roots of the brachial plexus are unclear, which represents the rationale for this study. Ten healthy adults were scanned using a Siemens Prisma (3T) and single-shot echo-planar imaging (b-value 0/1000 s/mm2, 64 directions, 2.5 mm3 with 4 averages; repeated in opposing phase encoding directions). Susceptibility correction and tractography were performed in DSI Studio by two independent raters. The effect of FA thresholding at increments of 0.01 (from 0.04 to 0.10) were tested. The mean FA varied between subjects by 2% (95% CI 1%, 3%). FA thresholds of 0.04, 0.05 and 0.06 all propagated 96% of tracts representing the roots; thresholding at 0.07 yielded 4% fewer tracts (p = 0.2), 0.08 yielded 11% fewer tracts (p = 0.008), 0.09 yielded 15% fewer tracts (p = 0.001) and 0.1 yielded 20% fewer tracts (p < 0.001). There was < 0.1% inter-rater variability in the measured FA and 99% agreement for tractography (κ = 0.92, p < 0.001). The fractional anisotropy thresholds required to generate tractograms of the roots of the brachial plexus appears to be lower than those used in the brain. We provide estimates of the probability of generating true tracts for each spinal nerve root of the brachial plexus, at different fractional anisotropy thresholds.

Major depressive disorder (MDD) is associated with reductions in white matter microstructural integrity as measured by fractional anisotropy (FA), an index derived from diffusion tensor imaging (DTI). The neurotropic herpesvirus, human cytomegalovirus (HCMV), is a major cause of white matter pathology in immunosuppressed populations but its relationship with FA has never been tested in MDD despite the presence of inflammation and weakened antiviral immunity in a subset of depressed patients. We tested the relationship between FA and HCMV infection in two independent samples consisting of 176 individuals with MDD and 44 healthy controls (HC) (Discovery sample) and 88 participants with MDD and 48 HCs (Replication sample). Equal numbers of HCMV positive (HCMV+) and HCMV negative (HCMV-) groups within each sample were balanced on ten different clinical/demographic variables using propensity score matching. Anti-HCMV IgG antibodies were measured using a solid-phase ELISA. In the Discovery sample, significantly lower FA was observed in the right inferior fronto-occipital fasciculus (IFOF) in HCMV+ participants with MDD compared to HCMV- participants with MDD (cluster size 1316 mm3; pFWE < 0.05, d = -0.58). This association was confirmed in the replication sample by extracting the mean FA from this exact cluster and applying the identical statistical model (p < 0.05, d = -0.45). There was no significant effect of diagnosis or interaction between diagnosis and HCMV in either sample. The effect of chronic HCMV infection on white matter integrity may-in at-risk individuals-contribute to the psychopathology of depression. These findings may provide a novel target of intervention for a subgroup of patients with MDD.

Introduction: Emergency department (ED) revisits are traditionally used to measure potential lapses in emergency care. However, recent studies on in-hospital outcomes following ED revisits have begun to challenge this notion. We aimed to examine inpatient outcomes and resource use among patients who were hospitalized following a return visit to the ED using a national database. Methods: This was a retrospective cohort study using the National Health Insurance Research Database in Taiwan. One-third of ED visits from 2012–2013 were randomly selected and their subsequent hospitalizations included. We analyzed the inpatient outcomes (mortality and intensive care unit [ICU] admission) and resource use (length of stay [LOS] and costs). Comparisons were made between patients who were hospitalized after a return visit to the ED and those who were hospitalized during the index ED visit.  Results: Of the 3,019,416 index ED visits, 477,326 patients (16%) were directly admitted to the hospital. Among the 2,504,972 patients who were discharged during the index ED visit, 229,059 (9.1%) returned to the ED within three days. Of them, 37,118 (16%) were hospitalized. In multivariable analyses, the inpatient mortality rates and hospital LOS were similar between the two groups. Compared with the direct-admission group, the return-admission group had a lower ICU admission rate (adjusted odds ratio, 0.78; 95% confidence interval [CI], 0.72-0.84), and lower costs (adjusted difference, -5,198 New Taiwan dollars, 95% CI, -6,224 to -4,172).   Conclusion: Patients who were hospitalized after a return visit to the ED had a lower ICU admission rate and lower costs, compared to those who were directly admitted. Our findings suggest that ED revisits do not necessarily translate to poor initial care and that subsequent inpatient outcomes should also be considered for better assessment.

Available online 22 August 2021., White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

Isolation of anti-proliferative compounds from plants is always hindered by the complexi-ties of the plant’s nature and tedious processes. Clinacanthus nutans (Burm. f.) Lindau is a medicinal plant with reported anti-proliferative activities. Our study aimed to isolate potential anti-proliferative compounds present in C. nutans plant. To start with, for our study, we came up with a strategy by first profiling the volatile compounds present in the leaf, stem and root of C. nutans using GC-MS. Comparing the plant’s volatile profiles greatly narrowed down our target of study. We decided to start with the isolation and characterization of a pentacyclic terpenoid, i.e., lupeol from the roots of C. nutans, as this compound was found to present abundantly in the roots compared to the leaf or stem. We developed a simple maceration and re-crystallization method, without the necessity to go through the fractionation or column chromatography for the isolation of lupeol. Characterizations of the isolated compound identified the compound as lupeol. The anti-proliferative activity of the isolated lupeol was further investigated against the MCF-7 cell line, which showed comparable anti-proliferative activity with the authentic lupeol and camptothecin. Our strategy to profile every part of the plant first, followed by selection of the most suitable plant part and targeted compound proved useful for further isolation and characterization bioactive compound from C. nutans.

(1) Background: Growing evidence indicates that inflammation can induce neural circuit dysfunction and plays a vital role in the pathogenesis of major depressive disorder (MDD). Nevertheless, whether inflammation affects the integrity of white matter pathways is only beginning to be explored. (2) Methods: We computed quantitative anisotropy (QA) from diffusion magnetic resonance imaging as an index of white matter integrity and regressed QA on C-reactive protein (CRP), controlling for age, sex, and BMI, in 176 participants with MDD. (3) Results: The QA values of several white matter tracts were negatively correlated with CRP concentration (standardized beta coefficient = -0.22, 95%CI = -0.38--0.06, FDR < 0.05). These tracts included the bilateral cortico-striatal tracts, thalamic radiations, inferior longitudinal fasciculi, corpus callosum (the forceps minor portion and the tapetum portion), cingulum bundles, and the left superior longitudinal fasciculus III. Importantly, the association remained robust after regressing up to twelve potential confounders. The bilateral fornix and a small portion of the thalamic radiation showed a positive association with CRP levels, but these associations did not remain significant after adjusting for confounders. (4) Conclusions: Peripheral inflammation may contribute to the etiology of MDD by impacting the microstructural integrity of brain corticolimbic white matter pathways.

Clay brick is one of the major components of demolition waste, which is generally landfilled. Effective and new uses of recycled clay brick may provide sustainability benefits in terms of landfill reduction. Therefore, this research aims at applying Recycled fine clay brick aggregates (RFCBA) with sizes from 0.075 mm–4.75 mm to prepare Self-compacting concrete (SCC). The effects of RFCBA on fresh and hardened properties of SCC were investigated. Saturated surface dry RFCBA was used to replace Natural fine aggregate (NFA) with the percentage of 25%, 50%, 75% and 100%, respectively, in making the SCC mixes. Although experimental results showed that the flowability, passing ability, and segregation resistance of SCC containing RFCBA (RFCBA-SCC) decreased with the increasing RFCBA content, these properties still satisfy the criteria of SCC. The compressive strength, splitting strength, flexural strength, and elastic modulus of the RFCBA-SCC mixes decreased with an increase of RFCBA content. Due to their porous nature, recycled fine clay brick aggregates may also be a source of additional water for internal curing. The internal curing effect was confirmed by the mercury intrusion porosimetry, X-ray diffraction, and thermogravimetric analysis measurements. Moreover, a significant autogenous shrinkage reduction of SCC is achieved by using the RFCBA due to the release of additional water pre-stored in the RFCBA. Therefore, it can be concluded that the addition of RFCBA to SCC mixtures can provide additional practical benefits in the hardened state., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public, Materials and Environment

Self-healing soft electronic material composition is crucial to sustain the device long-term durability. The fabrication of self-healing soft electronics exposed to high moisture environment is a significant challenge that has yet to be fully achieved. This paper presents the novel concept of a water-assisted room-temperature autonomous self-healing mechanism based on synergistically dynamic covalent Schiff-based imine bonds with hydrogen bonds. The supramolecular water-assisted self-healing polymer (WASHP) films possess rapid self-healing kinetic behavior and high stretchability due to a reversible dissociation–association process. In comparison with the pristine room-temperature self-healing polymer, the WASHP demonstrates favorable mechanical performance at room temperature and a short self-healing time of 1 h; furthermore, it achieves a tensile strain of 9050%, self-healing efficiency of 95%, and toughness of 144.2 MJ m−3. As a proof of concept, a versatile WASHP-based light-emitting touch-responsive device (WASHP-LETD) and perovskite quantum dot (PeQD)-based white LED backlight are designed. The WASHP-LETD has favorable mechanical deformation performance under pressure, bending, and strain, whereas the WASHP-PeQDs exhibit outstanding long-term stability even over a period exceeding one year in a boiling water environment. This paper provides a mechanically robust approach for producing eco-friendly, economical, and waterproof e-skin device components., ChemE/Product and Process Engineering

Ergonomics of human workers is one of the key elements in design and evaluation of production processes. Human ergonomics have a major impact on productivity as well as chronic health risks incurred by inappropriate working postures and conditions. In this paper we propose a novel method for estimating and communicating the ergonomic work condition called Binary Work-Condition Map, which provides a visualized feedback about work conditions of different configurations of an arm. The map is of binary nature and is derived by imposing the desired thresholds on considered ergonomic and safety related criteria. Therefore, the suggested arm postures in the map guarantee that all considered criteria are satisfied. This eliminates the ambiguity compared to state-of-the-art maps that uses continuous scales derived from weighted sum of multiple ergonomics criteria. In addition, to combine the advantages of both the binary map and the continuous map, we additionally propose a Hybrid Work-Condition Map that rules out unsuitable workspace with the binary map approach and renders the suitable workspace with the continuous map approach. The proposed approach was tested in simulation for various tasks and conditions. In addition, we conducted subjective evaluation experiments to compare the proposed methods with the state-of-the art method regarding the usability. The results indicated that the binary map is simpler to use, while the hybrid map is a good tradeoff between the binary and the continuous map. In selecting the map, strong points of each map should be considered with respect to the requirements of a specific application and task., Human-Robot Interaction

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

Solving planning and scheduling problems for multiple tasks with highly coupled state and temporal constraints is notoriously challenging. An appealing approach to effectively decouple the problem is to judiciously order the events such that decisions can be made over sequences of tasks. As many problems encountered in practice are over-constrained, we must instead find relaxed solutions in which certain requirements are dropped. This motivates a formulation of optimality with respect to the costs of relaxing constraints and the problem of finding an optimal ordering under which this relaxing cost is minimum. In this paper, we present Generalized Conflict-directed Ordering (GCDO), a branch-and-bound ordering method that generates an optimal total order of events by leveraging the generalized conflicts of both inconsistency and suboptimality from sub-solvers for cost estimation and solution space pruning. Due to its ability to reason over generalized conflicts, GCDO is much more efficient in finding high-quality total orders than the previous conflict-directed approach CDITO. We demonstrate this by benchmarking on temporal network configuration problems, which involves managing networks over time and makes necessary tradeoffs between network flows against CDITO and Mixed Integer-Linear Programing (MILP). Our algorithm is able to solve two orders of magnitude more benchmark problems to optimality and twice the problems compared to CDITO and MILP within a runtime limit, respectively., Comment: Accepted at SOCS2021. 9 pages, 4 figures, 2 tables

Background: Fermented foods, with a history as long as the human civilization, form an indispensable constituent in our daily life. However, most fermented foods are produced by spontaneous fermentation, and the fermentation processes are still uncontrolled. To ensure consistent food quality, it is of paramount importance to understand and control the spontaneous food fermentations. Scope and approach: In spontaneous food fermentations, metabolic activity of a succession of complex microbiota results in desired flavour that is the key criterion to decide consumers’ preference. Therefore, flavour compound formation by microbial metabolism can be used as the control target in spontaneous food fermentations. However, relatively little is known about the complexity of the microbiota associated with the flavour compound formation. Therefore, in this review by using Chinese liquor as a model system, we present key biotechnological aspects of the microbiota crucial for flavour of fermented foods, including the driving forces for the microbiota succession, flavour compound formation, and the regulation of flavour compound formation. Key findings and conclusions: Core microbiota, associated with flavour compound formation, can be identified and eventually used to construct a synthetic (i.e. designed) microbiota. Meanwhile, key environmental factors, affecting the core microbiota, can also be identified and controlled to regulate the synthetic microbiota. Furthermore, modelling can be used to predict, optimize and control the flavour compound formation by the synthetic microbiota, so that such spontaneous food fermentations can become controllable, with the ultimate goal to monitor, control, and improve the quality, productivity and safety of fermented foods.

Three-dimensional organic–inorganic hybrid halide perovskites have been demonstrated as great materials for applications in optoelectronics and photonics. However, their inherent instabilities in the presence of moisture, light, and heat may hinder their commercialization. Alternatively, emerging two-dimensional (2D) organic–inorganic hybrid perovskites have recently attracted increasing attention owing to their great environmental stability and inherent natural quantum-well structure. In this work, we have synthesized a high-quality long-chain organic diammonium spacer assisted 2D hybrid perovskite FA-(N-MPDA)PbBr4 (FA = formamidinium and N-MPDA = N-methylpropane-1,3-diammonium) by the slow evaporation at constant temperature method. The millimeter-sized single-crystalline microrods demonstrate low threshold random lasing behavior at room temperature. The single-crystalline 2D hybrid perovskite random laser achieved a very narrow linewidth (∼0.1 nm) with a low threshold (∼0.5 μJ cm−2) and a high quality factor (∼5350). Furthermore, the 2D hybrid microrod laser shows stable lasing emission with no measurable degradation after at least 2 h under continuous illumination, which substantially proves the stability of 2D perovskites. Our results demonstrate the promise of 2D organic–inorganic microrod-shaped perovskites and provide an important step toward the realization of high-performance optoelectronic devices.

Purpose: Diffusion tensor magnetic resonance imaging (DTI) characterises tissue microstructure and provides proxy measures of myelination, axon diameter, fibre density and organisation. This may be valuable in the assessment of the roots of the brachial plexus in health and disease. Therefore, there is a need to define the normal DTI values. Methods: The literature was systematically searched for studies of asymptomatic adults who underwent DTI of the brachial plexus. Participant characteristics, scanning protocols, and measurements of the fractional anisotropy (FA) and mean diffusivity (MD) of each spinal root were extracted by two independent review authors. Generalised linear modelling was used to estimate the effect of experimental conditions on the FA and MD. Meta-analysis of root-level estimates was performed using Cohen’s method with random effects. Results: Nine articles, describing 316 adults (1:1 male:female) of mean age 35 years (SD 6) were included. Increments of ten diffusion sensitising gradient directions reduced the mean FA by 0.01 (95% CI 0.01, 0.03). Each year of life reduced the mean MD by 0.03 × 10–3 mm2/s (95% CI 0.01, 0.04). At 3-T, the pooled mean FA of the roots was 0.36 (95% CI 0.34, 0.38; I2 98%). The pooled mean MD of the roots was 1.51 × 10–3 mm2/s (95% CI 1.45, 1.56; I2 99%). Conclusions: The FA and MD of the roots of the brachial plexus vary according to experimental conditions and participant factors. We provide summary estimates of the normative values in different conditions which may be valuable to researchers and clinicians alike.

2D transition‐metal dichalcogenides have attracted significant interest in recent years due to their multiple degrees of freedom, allowing for tuning their physical properties via band engineering and dimensionality adjustment. The study of ternary 2D hafnium selenosulfide HfSSe (HSS) high‐quality single crystals grown with the chemical vapor transport (CVT) technique is reported. An as‐grown HSS single crystal exhibits excellent phototransistor performance from the visible to the near‐infrared with outstanding stability. A giant photoresponsivity (≈6.4 × 104 A W−1 at 488 nm) and high specific detectivity (≈1014 Jones) are exhibited by a device fabricated by exfoliating single‐crystal HSS of nano‐thickness on a rigid Si/SiO2 substrate. The application of HSS single crystal is extended to yield a sensible flexible photodetector of photoresponsivity up to ≈1.3 A W−1 at 980 nm. The photoresponsivity of CVT‐grown HSS single crystal is significantly larger than those fabricated with other existing Hf‐based chalcogenides. The results suggest that the layered multi‐elemental 2D chalcogenide single crystals hold great promise for future wearable electronics and integrated optoelectronic circuits.

Adsorption of per- and polyfluoroalkyl substances (PFAS) such as perfluorooctane sulfonate (PFOS), is a key issue in the environmental area now but not yet fully understood. As a monolayer adsorbent, phosphorene has attracted a body of research interests. Defects and strain are reported to be important for its electronic structure regulations. In this work, we use the density functional theory (DFT) calculations to explore the adsorption of PFOS on the pristine, the Stone-Wales defected (SW), the single vacancy defected (SV) and the double vacancy defected phosphorenes (DV), respectively. Moreover, the effects of the strain of phosphorene along both a- and b-directions (two directions of a monolayer) on the PFOS adsorption are systematically investigated by analyzing the adsorption energy (Eads), electron transferring and the partial density of states. Finally, the synergistic effects of SV defects and tensile strain of phosphorene towards the enhancement of PFOS adsorption is proposed.

In this paper, we propose a method for improving the human operator's arm posture during bilateral teleoperation. The method is based on a musculoskeletal model that considers human operator's arm dynamics and the feedback force from the haptic interface (master), which is used to control a robotic arm (slave) in a remote environment. We perform an online optimisation to find the optimal configuration that has the longest endurance time with respect to muscle fatigue. Next, a trajectory is generated on the haptic interface in order to guide the human arm into the optimal configuration. The teleoperation is temporarily suspended by decoupling the master from the slave robot when the haptic device is being reconfigured. Afterwards, the loop is coupled again and the slave robot is controlled from the position where it stopped after the haptic interface guided the operator's arm to the optimised configuration. The main advantage of the proposed method is that the human operator can perform the task with less effort, which increases the endurance time. To validate our approach, we performed proof-of-concept experiments on a teleoperation system composed of two Franka Emika robots, where one was serving as master and the other as slave., Accepted Author Manuscript, Human-Robot Interaction

Fibre-reinforced polymer composites are gaining increasing attention in various applications for constructing mechanical structures such as wind turbine blades. The interface between fibres and a polymer matrix should be optimally designed to promote the mechanical performance of the composites. Plasma treatment shows obvious advantages over conventional approaches, since it has the characteristic of environmental friendliness, low-cost, and easy operation. A plasma can be favourably generated at atmospheric pressure. One of the most commonly used atmospheric pressure plasmas is a dielectric barrier discharge (DBD). In the present work, an air-to-air DBD is introduced. The DBD was generated in a gas mixture of helium and fluorocarbon between a rod-shaped water-cooled powered electrode covered with alumina and a one-dimensionally movable ground aluminium plate. Polyethylene terephthalate films were used as model specimens, and attached on the aluminium plate for the surface modification. The results indicate that specimen surfaces can be oxidized or fluorinated, depending on the conditions, and that the gap between the electrodes and gas flowrates significantly affect the treatment effect.