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BackgroundInflammation plays an essential role in the pathogenesis of hypertension. A novel inflammatory biomarker systemic inflammatory response index (SIRI) is related with all-cause and cardiovascular (CVD) mortality, while the role of SIRI in hypertension patients is unclear.MethodsA total of 21,506 participants with hypertension were recruited in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. SIRI was calculated as the neutrophil count * monocyte count/lymphocyte count. Hypertension was defined according to the examination of blood pressure, prescription, and self-reported physician diagnosis. Survival status was followed through 31 December 2019. The non-linear relationship was assessed using restricted cubic spline analysis. The association of all-cause mortality with SIRI was evaluated using the Kaplan–Meier curve and the weighted Cox regression analysis. The predictive abilities were assessed with Receiver operating curve.ResultsDuring 189,063 person-years of follow-up, 5,680 (26.41%) death events were documented, including 1,967 (9.15%) CVD related deaths. A J-shaped association was observed between SIRI and all-cause and CVD mortality. The Kaplan–Meier curve indicated the all-cause and CVD mortality risks were higher in high SIRI quartiles compared with lower SIRI quartiles. After adjusting for all covariates, the SIRI was positively associated with the all-mortality risk with HR = 1.19 (1.15, 1.22), and CVD mortality with HR = 1.19 (1.15, 1.24). The result was robust in subgroup analysis and sensitivity analysis.ConclusionElevated SIRI level is associated with increased all-cause and CVD mortality among patients with hypertension. SIRI is considered as a potential inflammatory biomarker in the clinical practice. Further large-scale cohort studies are required to confirm our findings.

Magnetic Resonance Spectroscopic Imaging (MRSI) is an essential tool for quantifying metabolites in the body, but the low spatial resolution limits its clinical applications. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but the super-resolved images are often blurry compared to the experimentally-acquired high-resolution images. Attempts have been made with the generative adversarial networks to improve the image visual quality. In this work, we consider another type of generative model, the flow-based model, of which the training is more stable and interpretable compared to the adversarial networks. Specifically, we propose a flow-based enhancer network to improve the visual quality of super-resolution MRSI. Different from previous flow-based models, our enhancer network incorporates anatomical information from additional image modalities (MRI) and uses a learnable base distribution. In addition, we impose a guide loss and a data-consistency loss to encourage the network to generate images with high visual quality while maintaining high fidelity. Experiments on a 1H-MRSI dataset acquired from 25 high-grade glioma patients indicate that our enhancer network outperforms the adversarial networks and the baseline flow-based methods. Our method also allows visual quality adjustment and uncertainty estimation., Accepted by DGM4MICCAI 2022

Smart local energy system (SLES) can support tailored regional solutions through the orchestration of cyber physical architectures, coordinating distributed technologies, with operational and forecasting models across all energy actors. Unprecedented access to new information, data streams and remotely accessible control can substantially benefit the multi-objective optimisation of multiple performance metrics. Given the expansion of this internet of things (IoT) and cyber-physical system (CPS), it is important to not only design effective detection and management of potential cybersecurity issues, but also to address the challenges in having affective and adaptive governance—built on standards to ensure the security of the IoT to minimise risks and harms to all users. This study conducts an extensive and critical investigation into the existing standards and identifies areas to focus on as to support the expansive adoption of cyber physical networks. Although existing standards and protocols are highly fragmented, our findings suggest that many of them can meet the requirements of the applications and infrastructures of SLES. Additionally, many standards have been introduced to protect information security and personal privacy due to their increasing importance. The research also suggests that the industry needs to produce more affordable and cyber-secured devices and services. For the government and regulators, relevant guidelines on the minimum function and security requirements for applications should be provided. Additionally, compliance testing and certifications should be in place and carried out by an independent third party to ensure the components of SLES ecosystem with a satisfied security level by design.

Magnetic Resonance Spectroscopic Imaging (MRSI) is a valuable tool for studying metabolic activities in the human body, but the current applications are limited to low spatial resolutions. The existing deep learning-based MRSI super-resolution methods require training a separate network for each upscaling factor, which is time-consuming and memory inefficient. We tackle this multi-scale super-resolution problem using a Filter Scaling strategy that modulates the convolution filters based on the upscaling factor, such that a single network can be used for various upscaling factors. Observing that each metabolite has distinct spatial characteristics, we also modulate the network based on the specific metabolite. Furthermore, our network is conditioned on the weight of adversarial loss so that the perceptual sharpness of the super-resolved metabolic maps can be adjusted within a single network. We incorporate these network conditionings using a novel Multi-Conditional Module. The experiments were carried out on a 1H-MRSI dataset from 15 high-grade glioma patients. Results indicate that the proposed network achieves the best performance among several multi-scale super-resolution methods and can provide super-resolved metabolic maps with adjustable sharpness., Accepted by MICCAI 2022

Transformers have made remarkable progress towards modeling long-range dependencies within the medical image analysis domain. However, current transformer-based models suffer from several disadvantages: (1) existing methods fail to capture the important features of the images due to the naive tokenization scheme; (2) the models suffer from information loss because they only consider single-scale feature representations; and (3) the segmentation label maps generated by the models are not accurate enough without considering rich semantic contexts and anatomical textures. In this work, we present CASTformer, a novel type of adversarial transformers, for 2D medical image segmentation. First, we take advantage of the pyramid structure to construct multi-scale representations and handle multi-scale variations. We then design a novel class-aware transformer module to better learn the discriminative regions of objects with semantic structures. Lastly, we utilize an adversarial training strategy that boosts segmentation accuracy and correspondingly allows a transformer-based discriminator to capture high-level semantically correlated contents and low-level anatomical features. Our experiments demonstrate that CASTformer dramatically outperforms previous state-of-the-art transformer-based approaches on three benchmarks, obtaining 2.54%-5.88% absolute improvements in Dice over previous models. Further qualitative experiments provide a more detailed picture of the model's inner workings, shed light on the challenges in improved transparency, and demonstrate that transfer learning can greatly improve performance and reduce the size of medical image datasets in training, making CASTformer a strong starting point for downstream medical image analysis tasks.

Adding renewable energy sources and storage units to an electric grid has led to a change in the way energy is generated and billed. This shift cannot be managed without a unified view of energy systems and their components. This unified view is captured within the idea of a Smart Local Energy System (SLES). Currently, various isolated control and market elements are proposed to resolve network constraints, demand side response and utility optimisation. They rely on topology estimations, forecasting and fault detection methods to complete their tasks. This disjointed design has led to most systems being capable of fulfilling only a single role or being resistant to change and extensions in functionality. By allocating roles, functional responsibilities and technical requirements to bounded systems a more unified view of energy systems can be achieved. This is made possible by representing an energy system as a distributed peer-to-peer (P2P) environment where each individual demand energy resource (DER) on the consumer's side of the meter is responsible for their portion of the network and can facilitate trade with numerous entities including the grid. Advances in control engineering, markets and services such as forecasting, topology identification and cyber-security can enable such trading and communication to be done securely and robustly. To enable this advantage however, we need to redefine how we view the design of the sub-systems and interconnections within smart local energy systems (SLES). In this paper we describe a way in which whole system design could be achieved by integrating control, markets and analytics into each system. We propose the use of physical, control, market and service layers to create system of systems representation., 14 Pages White paper

High-quality MRI reconstruction plays a critical role in clinical applications. Deep learning-based methods have achieved promising results on MRI reconstruction. However, most state-of-the-art methods were designed to optimize the evaluation metrics commonly used for natural images, such as PSNR and SSIM, whereas the visual quality is not primarily pursued. Compared to the fully-sampled images, the reconstructed images are often blurry, where high-frequency features might not be sharp enough for confident clinical diagnosis. To this end, we propose an invertible sharpening network (InvSharpNet) to improve the visual quality of MRI reconstructions. During training, unlike the traditional methods that learn to map the input data to the ground truth, InvSharpNet adapts a backward training strategy that learns a blurring transform from the ground truth (fully-sampled image) to the input data (blurry reconstruction). During inference, the learned blurring transform can be inverted to a sharpening transform leveraging the network's invertibility. The experiments on various MRI datasets demonstrate that InvSharpNet can improve reconstruction sharpness with few artifacts. The results were also evaluated by radiologists, indicating better visual quality and diagnostic confidence of our proposed method., Comment: Accepted by MICCAI 2022

We present Neural Contact Fields, a method that brings together neural fields and tactile sensing to address the problem of tracking extrinsic contact between object and environment. Knowing where the external contact occurs is a first step towards methods that can actively control it in facilitating downstream manipulation tasks. Prior work for localizing environmental contacts typically assume a contact type (e.g. point or line), does not capture contact/no-contact transitions, and only works with basic geometric-shaped objects. Neural Contact Fields are the first method that can track arbitrary multi-modal extrinsic contacts without making any assumptions about the contact type. Our key insight is to estimate the probability of contact for any 3D point in the latent space of object shapes, given vision-based tactile inputs that sense the local motion resulting from the external contact. In experiments, we find that Neural Contact Fields are able to localize multiple contact patches without making any assumptions about the geometry of the contact, and capture contact/no-contact transitions for known categories of objects with unseen shapes in unseen environment configurations. In addition to Neural Contact Fields, we also release our YCB-Extrinsic-Contact dataset of simulated extrinsic contact interactions to enable further research in this area. Project page: https://github.com/carolinahiguera/NCF, Comment: 2023 International Conference on Robotics and Automation (ICRA)

Wanfu Men,1 Peiyao Zhu,1 Siyuan Dong,1 Wenke Liu,1 Kun Zhou,1 Yu Bai,1 Xiangli Liu,1 Shulei Gong,1 Can Yang Zhang,2 Shuguang Zhang1 1Department of Thoracic Surgery, The First Affiliated Hospital of China Medical University, Shenyang 110001, People’s Republic of China; 2Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, WA 99210, USACorrespondence: Shuguang ZhangDepartment of Thoracic Surgery, The First Affiliated Hospital of China Medical University, No. 155 North Nanjing Street, Heping District, Shenyang 110001, Liaoning Province, People’s Republic of ChinaTel +86 13909886618Email shgzhang@cmu.edu.cnCan Yang ZhangSingapore-MIT Alliance for Research and Technology, 1 Create Way, #03-12/13/14 Enterprise Wing, Singapore 138602, SingaporeTel +65 9499 0710Email canyang.zhang@smart.mit.eduBackground: The development of biocompatible nanocarriers that can efficiently encapsulate and deliver anticancer drug to the tumor site and provide controlled release of cargos in response to the specific cues for cancer therapy is of great significance.Methods: In this work, dual pH/redox-responsive fabrication of hybrid lipid-polymer nanoparticles (LPNPs) self-assembled from amphiphilic polymer poly(ethylene glycol) methyl ether-grafted disulfide-poly(β-amino esters) (PBAE-ss-mPEG) and PEGylated lipid were prepared and used as drug delivery carriers. The optimization of PEGylated lipid modification was confirmed by analysis of particle size, polydispersity index (PDI), cellular uptake, serum stability, and drug loading capacity. The pKb value of LPNPs was determined as 6.55, indicating the pH-sensitivity. The critical micelle concentration (CMC) values and zeta-potential of LPNPs at different pH values were investigated to confirm its pH-sensitivity. The morphology of LPNPs before and after incubation with reducing agent was imaged to study the redox-responsibility.Results: The in vitro results showed that the drug had controlled release from LPNPs triggered by low pH and high concentration of reducing agent. Furthermore, the cytotoxicity of LPNPs was very low, and the doxorubicin (DOX)-loaded LPNPs could efficiently induce the death of tumor cells in comparison to free DOX.Conclusion: All results demonstrated that the fabricated LPNPs could be potential anticancer drug delivery carriers with a pH/redox-triggered drug release profile, and PEGylated lipid modification might be a useful method to fabricate the drug delivery platform.Keywords: pH-sensitive, redox-sensitive, lipid-polymer, hybrid, drug delivery, anticancer, stimuli-responsiveness

Smart local energy system (SLES) is considered as a promising pathway facilitating a more effective and localised operation, benefited from the complex information and communication technology (ICT) infrastructures and Internet of things (IoT) technologies. As a part of the critical infrastructure, it is important to not only put effective detection and management to tackle potential cybersecurity issues, but also require considerable numbers of standards to ensure the security of the internet of things system to minimise the risks. This study aims to review the existing standards, investigate how the compatibility with SLES development, and identify the area to focus on in the future. Although existing standards and protocols are highly fragmented, our findings suggest that many of them can meet the requirements of the applications and infrastructures of SLES. Additionally, many standards have been introduced to protect information security and personal privacy due to their increasing importance. The research also suggests that the industry needs to produce more affordable and cyber-secured devices and services. For the government and regulators, relevant guidelines on the minimum function and security requirements for applications should be provided. Additionally, compliance testing and certifications should be in place and carried out by an independent third party to ensure the components of SLES ecosystem with a satisfied security level by design.

This work presents a new version of the tactile-sensing finger GelSlim 3.0, which integrates the ability to sense high-resolution shape, force, and slip in a compact form factor for use with small parallel jaw grippers in cluttered bin-picking scenarios. The novel design incorporates the capability to use real-time analytic methods to measure shape, estimate the contact 3D force distribution, and detect incipient slip. To achieve a compact integration, we optimize the optical path from illumination source to camera and other geometric variables in a optical simulation environment. In particular, we optimize the illumination sources and a light shaping lens around the constraints imposed by the photometric stereo algorithm used for depth reconstruction. The optimized optical configuration is integrated into a finger design composed of robust and easily replaceable snap-to-fit fingetip module that allow for ease of manufacture, assembly, use, and repair. To stimulate future research in tactile-sensing and provide the robotics community access to reliable and easily-reproducible tactile finger with a diversity of sensing modalities, we open-source the design and software at https://github.com/mcubelab/gelslim.

Discharge in liquid has been studied in many forms and water is the mostly used liquid in those researches. In this paper, alcohol aqueous solution is used to form stable discharging. A kilo-Hz alternating current high voltage power source is used to generate plasma under the surface of liquid and the power frequency set is the range of 1~20 kilo-Hz. The voltage-current curve, optical emission spectrum and many other evidences show that the discharging in liquid is intermittent with high optic effect.

In this paper, we propose an approach to detect incipient slip, i.e. predict slip, by using a high-resolution vision-based tactile sensor, GelSlim. The sensor dynamically captures the tactile imprints of the contact object and their changes with a soft gel pad. The method assumes the object is mostly rigid and treats the motion of object's imprint on sensor surface as a 2D rigid-body motion. We use the deviation of the true motion field from that of a 2D planar rigid transformation as a measure of slip. The output is a dense slip field which we use to detect when small areas of the contact patch start to slip (incipient slip). The method can detect both translational and rotational incipient slip without any prior knowledge of the object at 24 Hz. We test the method on 10 objects 240 times and achieve 86.25% detection accuracy. We further show how the slip feedback can be used to monitor the gripping force to avoid slip with a closed-loop bottle-cap screwing and unscrewing experiment with incipient slip detection feedback. The method was demonstrated to be useful for the robot to apply proper gripping force and stop screwing at the right point before breaking objects. The method can be applied to many manipulation tasks in both structured and unstructured environments.

Taking Lake Taihu which was the third largest shallow lake in China as an example,we researched the demarcation of buffer zone on the basis of information collection,topographic survey and field investigation.On domestic and international literatures about buffer zones of lakes,we preliminarily defined approximately 2 km wide as the width of Lake Taihu buffer zone.Considering the principles of land markers(villages,roads,mountains)and regional differences of regulations and planning,we defined the upper and lower boundaries of the buffer zone.The lower boundary was at the lake shoreline or levees around the lake.The upper boundary was at approximately 2km from the lower boundary.According to land use types,there were three typical types of villages,roads and mountains in Lake Taihu buffer zone.The upper boundary at village area was divided by the administrative villages about 2.0 km from the lakeshore,and was divided by roads when the roads are paralleled with the lake shores.At mountain area,the upper boundary was divided by mountain ridges.On the 1∶5000 topographic maps,we calculated 382.75 km in total length and 452.31 km2 in the total area of Lake Taihu buffer zone by means of Auto CAD software.

Motivation: Estimation of bacterial community composition from a high-throughput sequenced sample is an important task in metagenomics applications. Since the sample sequence data typically harbors reads of variable lengths and different levels of biological and technical noise, accurate statistical analysis of such data is challenging. Currently popular estimation methods are typically very time consuming in a desktop computing environment. Results: Using sparsity enforcing methods from the general sparse signal processing field (such as compressed sensing), we derive a solution to the community composition estimation problem by a simultaneous assignment of all sample reads to a pre-processed reference database. A general statistical model based on kernel density estimation techniques is introduced for the assignment task and the model solution is obtained using convex optimization tools. Further, we design a greedy algorithm solution for a fast solution. Our approach offers a reasonably fast community composition estimation method which is shown to be more robust to input data variation than a recently introduced related method. Availability: A platform-independent Matlab implementation of the method is freely available at http://www.ee.kth.se/ctsoftware; source code that does not require access to Matlab is currently being tested and will be made available later through the above website., Comment: 10 pages