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Brisingida Fisher 1928 is one of the seven currently recognised starfish orders, and one of the least known taxa as being exclusive deep-sea inhabitants. Modern deep-sea expeditions revealed their common occurrences in various deep-sea settings including seamounts, basins and hydrothermal vent peripheral, underlining the necessity of clarifying their global diversity and phylogeny. In this study, we present a comprehensive molecular phylogeny of Brisingida which encompasses the highest taxonomic diversity to date. DNA sequences (COI, 16S, 12S and 28S) were obtained from 225 specimens collected in the global ocean, identified as 58 species spanning 15 of the 17 extant genera. Phylogenetic relationship was inferred using both maximum likelihood and Bayesian inference methods, revealing polyphyletic families and genera and indicating nonnegligible bias in prior morphology-based systematics. Based on the new phylogeny, a novel classification of the order, consisting of 5 families and 17 genera, is proposed. Families Odinellidae, Brisingasteridae and Novodiniidae (sensu Clark and Mah, 2001) were resurrected to encompass the genera Odinella, Brisingaster and Novodinia. Brisingidae and Freyellidae were revised to include 11 and 3 genera, respectively. A new genus and species, two new subgenera and seven new combinations are described and a key to each genus and family is provided. Transformations of morphological traits were evaluated under the present phylogenetic hypothesis. A series of paedomorphic characters were found in many genera and species, which led to a high degree of homoplasy across phylogenetically distant groups. Our results provide new insights in the phylogeny and ontogeny of the order, and highlight the necessity to evaluate character convergence under sound phylogenetic hypothesis.

Transformer-based NLP models are powerful but have high computational costs that limit deployment. Finetuned encoder-decoder models are popular in specialized domains and can outperform larger more generalized decoder-only models, such as GPT-4. We introduce a new configuration for encoder-decoder models that improves efficiency on structured output and decomposable tasks where multiple outputs are required for a single shared input. Our method, prompt-in-decoder (PiD), encodes the input once and decodes the output in parallel, boosting both training and inference efficiency by avoiding duplicate input encoding and increasing the operational intensity (ratio of numbers of arithmetic operation to memory access) of decoding process by sharing the input key-value cache. We achieve computation reduction that roughly scales with the number of subtasks, gaining up to 4.6x speed-up over state-of-the-art models for dialogue state tracking, summarization, and question-answering tasks, with comparable or better performance., Comment: 14 pages, 4 figures. https://github.com/boru-roylu/encode-once-and-decode-in-parallel

Machine translation focuses mainly on high-resource languages (HRLs), while low-resource languages (LRLs) like Taiwanese Hokkien are relatively under-explored. The study aims to address this gap by developing a dual translation model between Taiwanese Hokkien and both Traditional Mandarin Chinese and English. We employ a pre-trained LLaMA 2-7B model specialized in Traditional Mandarin Chinese to leverage the orthographic similarities between Taiwanese Hokkien Han and Traditional Mandarin Chinese. Our comprehensive experiments involve translation tasks across various writing systems of Taiwanese Hokkien as well as between Taiwanese Hokkien and other HRLs. We find that the use of a limited monolingual corpus still further improves the model's Taiwanese Hokkien capabilities. We then utilize our translation model to standardize all Taiwanese Hokkien writing systems into Hokkien Han, resulting in further performance improvements. Additionally, we introduce an evaluation method incorporating back-translation and GPT-4 to ensure reliable translation quality assessment even for LRLs. The study contributes to narrowing the resource gap for Taiwanese Hokkien and empirically investigates the advantages and limitations of pre-training and fine-tuning based on LLaMA 2., Comment: Accepted by LREC-COLING 2024 as a long oral paper

Ensemble weather forecasting is essential for weather predictions and mitigating the impacts of extreme weather events. Constructing an ensemble prediction system (EPS) based on conventional numerical weather prediction (NWP) models is highly computationally expensive. Machine learning (ML) models have emerged as valuable tools for deterministic weather forecasts, providing forecasts with significantly reduced computational requirements and even surpassing the forecast performance of traditional NWP models. However, challenges arise when applying ML models to ensemble forecasting. Recent ML models, such as GenCast and SEEDS model, rely on the ERA5 Ensemble of Data Assimilations (EDA) or two operational NWP ensemble members for forecast generation. The spatial resolution of 1{\deg} or 2{\deg} in these models is often considered too coarse for many applications. To overcome these limitations, we introduce FuXi-ENS, an advanced ML model designed to deliver 6-hourly global ensemble weather forecasts up to 15 days. This model runs at a significantly improved spatial resolution of 0.25{\deg}, incorporating 5 upper-air atmospheric variables at 13 pressure levels, along with 13 surface variables. By leveraging the inherent probabilistic nature of Variational AutoEncoder (VAE), FuXi-ENS optimizes a loss function that combines the continuous ranked probability score (CRPS) and the KL divergence between the predicted and target distribution. This innovative approach represents an advancement over the traditional use of L1 loss combined with the KL loss in standard VAE models when VAE for ensemble weather forecasts. Evaluation results demonstrate that FuXi-ENS outperforms ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), a world leading NWP model, on 98.1% of 360 variable and forecast lead time combinations on CRPS.

The recent observation of the Hellings-Downs angular correlation by NANOGrav and other PTA experiments indicates the presence of stochastic gravitational wave background in the frequency $\sim 1-10$ nHz. In the clockwork axion model, the network of cosmic strings and domain walls forms after the spontaneous symmetry breaking and can survive until the QCD phase transition. The QCD axion potential induced by the QCD phase transition can serve as an energy bias, which leads to the annihilation of the domain walls. In Ref.~\cite{Chiang:2020aui}, we have shown that the GWs radiated during the annihilation of the domain walls formed at the Peccei-Quinn symmetry breaking scale $f\simeq 200$~TeV can give rise to the potential signal in the NANOGrav 12.5-year data. In this work, we show that both the amplitude and the spectrum shape of the GW signals from the domain walls annihilation at a scale $f=1.69_{-0.34}^{+0.36}\times 100$~TeV can well account for the NANOGrav 15-year results., Comment: 5 pages, 2 figures. arXiv admin note: substantial text overlap with arXiv:2012.14071

PURPOSE: RTOG 0617 was a phase III randomized trial for patients with unresectable stage IIIA/IIIB non-small cell lung cancer comparing standard-dose (60 Gy) versus high-dose (74 Gy) radiotherapy and chemotherapy, plus or minus cetuximab. Although the study was negative, based on prior evidence that patients with the KRAS-variant, an inherited germline mutation, benefit from cetuximab, we evaluated KRAS-variant patients in RTOG 0617. EXPERIMENTAL DESIGN: From RTOG 0617, 328 of 496 (66%) of patients were included in this analysis. For time-to-event outcomes, stratified log-rank tests and multivariable Cox regression models were used. For binary outcomes, Cochran-Mantel-Haenzel tests and multivariable logistic regression models were used. All statistical tests were two sided, and a P value <0.05 was considered significant. RESULTS: A total of 17.1% (56/328) of patients had the KRAS-variant, and overall survival rates were similar between KRAS-variant and non-variant patients. However, there was a time-dependent effect of cetuximab seen only in KRAS-variant patients-while the hazard of death was higher in cetuximab-treated patients within year 1 [HR = 3.37, 95% confidence interval (CI): 1.13-10.10, P = 0.030], death was lower from year 1 to 4 (HR = 0.33, 95% CI: 0.11-0.97, P = 0.043). In contrast, in non-variant patients, the addition of cetuximab significantly increased local failure (HR = 1.59, 95% CI: 1.11-2.28, P = 0.012). CONCLUSIONS/DISCUSSION: Although an overall survival advantage was not achieved in KRAS-variant patients, there is potential impact of cetuximab for this genetic subset of patients. In contrast, cetuximab seems to harm non-variant patients. These findings further support the importance of genetic patient selection in trials studying the addition of systemic agents to radiotherapy. SIGNIFICANCE: The KRAS-variant is the first functional, inherited miRNA-disrupting variant identified in cancer. Our findings support that cetuximab has a potentiall

The recent Pulsar Timing Arrays (PTAs) nano-Hz gravitational wave (GW) background signal can be naturally induced by the annihilation of domain walls (DWs) formed at a symmetry-breaking scale $f\simeq 200$~TeV in the clockwork axion framework. Based on our first successful and precise prediction, we for the first time suggest that the recent PTA observations strongly support the novel mechanism of the QCD instanton-induced DW annihilation in the clockwork axion framework. We also for the first time discover a novel correlation between dark matter (DM) relic abundance and nano-Hz GW background, which in turn indicates a natural connection between the axion decay constant and the symmetry-breaking scale in the clockwork framework. We find that the GW signal has a peak $h^2\Omega_{\rm GW}\simeq 10^{-6.6}-10^{-6.1}$ at about 50~nHz, which is definite and testable for future PTA data at frequencies $\gtrsim 25$~nHz and CMB-S4 experiment. We also propose various phenomena that may appear in PTAs and future GW interferometers., Comment: 5+8 pages, 3+3 figures, 3 tables, accepted for publication in PRD Lett

In natural language processing (NLP), code-mixing (CM) is a challenging task, especially when the mixed languages include dialects. In Southeast Asian countries such as Singapore, Indonesia, and Malaysia, Hokkien-Mandarin is the most widespread code-mixed language pair among Chinese immigrants, and it is also common in Taiwan. However, dialects such as Hokkien often have a scarcity of resources and the lack of an official writing system, limiting the development of dialect CM research. In this paper, we propose a method to construct a Hokkien-Mandarin CM dataset to mitigate the limitation, overcome the morphological issue under the Sino-Tibetan language family, and offer an efficient Hokkien word segmentation method through a linguistics-based toolkit. Furthermore, we use our proposed dataset and employ transfer learning to train the XLM (cross-lingual language model) for translation tasks. To fit the code-mixing scenario, we adapt XLM slightly. We found that by using linguistic knowledge, rules, and language tags, the model produces good results on CM data translation while maintaining monolingual translation quality., Comment: The paper was accepted by EMNLP 2022 findings

Skillful subseasonal forecasts beyond 2 weeks are crucial for a wide range of applications across various sectors of society. Recently, state-of-the-art machine learning based weather forecasting models have made significant advancements, outperforming the high-resolution forecast (HRES) from the European Centre for Medium-Range Weather Forecasts (ECMWF). However, the full potential of machine learning models in subseasonal forecasts has yet to be fully explored. In this study, we introduce FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning based subseasonal forecasting model that provides global daily mean forecasts up to 42 days, covering 5 upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S integrates an enhanced FuXi base model with a perturbation module for flow-dependent perturbations in hidden features, and incorporates Perlin noise to perturb initial conditions. The model is developed using 72 years of daily statistics from ECMWF ERA5 reanalysis data. When compared to the ECMWF Subseasonal-to-Seasonal (S2S) reforecasts, the FuXi-S2S forecasts demonstrate superior deterministic and ensemble forecasts for total precipitation (TP), outgoing longwave radiation (OLR), and geopotential at 500 hPa (Z500). Although it shows slightly inferior performance in predicting 2-meter temperature (T2M), it has clear advantages over land area. Regarding the extreme forecasts, FuXi-S2S outperforms ECMWF S2S globally for TP. Furthermore, FuXi-S2S forecasts surpass the ECMWF S2S reforecasts in predicting the Madden Julian Oscillation (MJO), a key source of subseasonal predictability. They extend the skillful prediction of MJO from 30 days to 36 days.

The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fact that most data is protected from public release for privacy reasons. Problem-solving human dialogues in real applications can be much more complex than existing Wizard-of-Oz collections, preventing successful domain transfer. To support information extraction (IE) for a private call center dataset, we introduce a human-in-the-loop dialogue generation framework capable of synthesizing realistic dialogues. In IE experiments with auto insurance call center dialogues, we observe 25\% relative improvement in $F_1$ after augmenting a small set of real human conversations with synthetic data. We release code and our synthetic dataset to illustrate the complexity of real-world call center conversations and encourage development of complex dialogue datasets that are more representative of natural data.

Although searches for dark matter have lasted for decades, no convincing signal has been found without ambiguity in underground detections, cosmic ray observations, and collider experiments. We show by example that gravitational wave (GW) observations can be a supplement to dark matter detections if the production of dark matter follows a strong first-order cosmological phase transition. We explore this possibility in a complex singlet extension of the standard model with CP symmetry. We demonstrate three benchmarks in which the GW signals from the first-order phase transition are loud enough for future space-based GW observations, for example, BBO, U-DECIGO, LISA, Taiji, and TianQin. While satisfying the constraints from the XENON1T experiment and the Fermi-LAT gamma-ray observations, the dark matter candidate with its mass around $\sim 1$~TeV in these scenarios has a correct relic abundance obtained by the Planck observations of the cosmic microwave background radiation., Comment: 11 pages, 2 figures, 2 tables

With theoretical constraints such as perturbative unitarity and vacuum stability conditions and updated experimental data of Higgs measurements and direct searches for exotic scalars at the LHC, we perform an updated scan of the allowed parameter space of the Georgi-Machacek (GM) model. With the refined global fit, we examine the allowed parameter space for inducing strong first-order electroweak phase transitions (EWPTs) and find only the one-step phase transition is phenomenologically viable. Based upon the result, we study the associated gravitational wave (GW) signals and find most of which can be detected by several proposed experiments. We also make predictions on processes that may serve as promising probes to the GM model in the near future at the LHC, including the di-Higgs productions and several exotic scalar production channels., Comment: 42 pages, 11 figures, 9 tables

Immune checkpoint inhibitors (ICIs) have led recent advances in the field of cancer immunotherapy improving overall survival in multiple malignancies with abysmal prognoses prior to their introduction. The remarkable efficacy of ICIs is however limited by their potential for systemic and organ specific immune-related adverse events (irAEs), most of which present with mild to moderate symptoms that can resolve spontaneously, with discontinuation of therapy or glucocorticoid therapy. Cardiac irAEs however are potentially fatal. The understanding of autoimmune cardiotoxicity remains limited due to its rareness. In this paper, we provide an updated review of the literature on the pathologic mechanisms, diagnosis, and management of autoimmune cardiotoxicity resulting from ICIs and their combinations and provide perspective on potential strategies and ongoing research developments to prevent and mitigate their occurrence.

It is widely believed that extensions of the minimal Higgs sector is one of the promising directions for resolving many puzzles beyond the Standard Model (SM). In this work, we study the unitarity bounds on the models by extending the two-Higgs-doublet model with an additional real or complex Higgs triplet scalar. By noting that the SM gauge symmetries $SU(2)_L\times U(1)_Y$ are recovered at high energies, we can classify the two-body scattering states by decomposing the direct product of two scalar multiplets into their direct sum of irreducible representations of electroweak gauge groups. In such state bases, the s-wave amplitudes of two-body scalar scatterings can be written in the form of block-diagonalized scattering matrices. Then the application of the perturbative unitarity conditions on the eigenvalues of scattering matrices leads to the analytic constraints on the model parameters. Finally, we numerically investigate the complex triplet scalar extension of the two-Higgs-doublet model, finding that the perturbative unitarity places useful stringent bounds on the model parameter space., Comment: 30 pages, 2 figures

Robotic peg-in-hole assembly remains a challenging task due to its high accuracy demand. Previous work tends to simplify the problem by restricting the degree of freedom of the end-effector, or limiting the distance between the target and the initial pose position, which prevents them from being deployed in real-world manufacturing. Thus, we present a Coarse-to-Fine Visual Servoing (CFVS) peg-in-hole method, achieving 6-DoF end-effector motion control based on 3D visual feedback. CFVS can handle arbitrary tilt angles and large initial alignment errors through a fast pose estimation before refinement. Furthermore, by introducing a confidence map to ignore the irrelevant contour of objects, CFVS is robust against noise and can deal with various targets beyond training data. Extensive experiments show CFVS outperforms state-of-the-art methods and obtains 100%, 91%, and 82% average success rates in 3-DoF, 4-DoF, and 6-DoF peg-in-hole, respectively., Comment: Accepted by ICRA 2023

In this paper, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots embedded with fiber Bragg grating (FBG) sensors. Developments of 3-D shape perception and control technologies are crucial for continuum robots to perform the tasks autonomously in surgical interventions. However, owing to the nonlinear properties of continuum robots, one main difficulty lies in the modeling of them, especially for soft robots with variable stiffness. To address this problem, we propose a versatile learning-based adaptive controller by leveraging FBG shape feedback that can online estimate the unknown model of continuum robot against unexpected disturbances and exhibit an adaptive behavior to the unmodeled system without priori data exploration. Based on a new composite adaptation algorithm, the asymptotic convergences of the closed-loop system with learning parameters have been proven by Lyapunov theory. To validate the proposed method, we present a comprehensive experimental study by using two continuum robots both integrated with multi-core FBGs, including a robotic-assisted colonoscope and multi-section extensible soft manipulators. The results demonstrate the feasibility, adaptability, and superiority of our controller in various unstructured environments as well as phantom experiments.

Spiking neural network is a kind of neuromorphic computing that is believed to improve the level of intelligence and provide advantages for quantum computing. In this work, we address this issue by designing an optical spiking neural network and find that it can be used to accelerate the speed of computation, especially on combinatorial optimization problems. Here the spiking neural network is constructed by the antisymmetrically coupled degenerate optical parametric oscillator pulses and dissipative pulses. A nonlinear transfer function is chosen to mitigate amplitude inhomogeneities and destabilize the resulting local minima according to the dynamical behavior of spiking neurons. It is numerically shown that the spiking neural network-coherent Ising machines have excellent performance on combinatorial optimization problems, which is expected to offer new applications for neural computing and optical computing., Comment: 10 pages, 5 figures, accepted by Quantum

Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. The video data streamed from the endoscope provides rich information to support context-awareness for next-generation intelligent surgical systems. To achieve accurate perception and automatic manipulation during the procedure, learning based technique is a promising way, which enables advanced image analysis and scene understanding in recent years. However, learning such models highly relies on large-scale, high-quality, and multi-task labelled data. This is currently a bottleneck for the topic, as available public dataset is still extremely limited in the field of CAI. In this paper, we present and release the first integrated dataset (named AutoLaparo) with multiple image-based perception tasks to facilitate learning-based automation in hysterectomy surgery. Our AutoLaparo dataset is developed based on full-length videos of entire hysterectomy procedures. Specifically, three different yet highly correlated tasks are formulated in the dataset, including surgical workflow recognition, laparoscope motion prediction, and instrument and key anatomy segmentation. In addition, we provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset. The dataset is available at https://autolaparo.github.io., Comment: Accepted at MICCAI 2022

Shape control of deformable objects is a challenging and important robotic problem. This paper proposes a model-free controller using novel 3D global deformation features based on modal analysis. Unlike most existing controllers using geometric features, our controller employs a physically-based deformation feature by decoupling 3D global deformation into low-frequency mode shapes. Although modal analysis is widely adopted in computer vision and simulation, it has not been used in robotic deformation control. We develop a new model-free framework for modal-based deformation control under robot manipulation. Physical interpretation of mode shapes enables us to formulate an analytical deformation Jacobian matrix mapping the robot manipulation onto changes of the modal features. In the Jacobian matrix, unknown geometry and physical properties of the object are treated as low-dimensional modal parameters which can be used to linearly parameterize the closed-loop system. Thus, an adaptive controller with proven stability can be designed to deform the object while online estimating the modal parameters. Simulations and experiments are conducted using linear, planar, and solid objects under different settings. The results not only confirm the superior performance of our controller but also demonstrate its advantages over the baseline method., Comment: Accepted by the IEEE Transactions on Robotics. The paper will appear in the IEEE Transactions on Robotics. IEEE copyright

Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dialogue structure, but it can be highly domain-dependent. This work introduces an unsupervised approach to learning hierarchical conversation structure, including turn and sub-dialogue segment labels, corresponding roughly to dialogue acts and sub-tasks, respectively. The decoded structure is shown to be useful in enhancing neural models of language for three conversation-level understanding tasks. Further, the learned finite-state sub-dialogue network is made interpretable through automatic summarization., Comment: In Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2022 Findings)

Automatic laparoscope motion control is fundamentally important for surgeons to efficiently perform operations. However, its traditional control methods based on tool tracking without considering information hidden in surgical scenes are not intelligent enough, while the latest supervised imitation learning (IL)-based methods require expensive sensor data and suffer from distribution mismatch issues caused by limited demonstrations. In this paper, we propose a novel Imitation Learning framework for Laparoscope Control (ILLC) with reinforcement learning (RL), which can efficiently learn the control policy from limited surgical video clips. Specially, we first extract surgical laparoscope trajectories from unlabeled videos as the demonstrations and reconstruct the corresponding surgical scenes. To fully learn from limited motion trajectory demonstrations, we propose Shape Preserving Trajectory Augmentation (SPTA) to augment these data, and build a simulation environment that supports parallel RGB-D rendering to reinforce the RL policy for interacting with the environment efficiently. With adversarial training for IL, we obtain the laparoscope control policy based on the generated rollouts and surgical demonstrations. Extensive experiments are conducted in unseen reconstructed surgical scenes, and our method outperforms the previous IL methods, which proves the feasibility of our unified learning-based framework for laparoscope control., Comment: 7 pages, 7 figures, 2022 IEEE International Conference on Robotics and Automation (ICRA)

Ping Zhang,1 Julie Marill,1 Audrey Darmon,1 Naeemunnisa Mohamed Anesary,1 Bo Lu,2 Sébastien Paris1 1Nanobiotix, Paris, France; 2Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, 19107, USACorrespondence: Sébastien ParisNanobiotix, 60 rue de Wattignies, Paris, 75012, FranceTel +33 1 75 44 72 33Fax +33 1 40 26 04 44Email sebastien.paris@nanobiotix.comPurpose: The side effects of radiotherapy induced on healthy tissue limit its use. To overcome this issue and fully exploit the potential of radiotherapy to treat cancers, the first-in-class radioenhancer NBTXR3 (functionalized hafnium oxide nanoparticles) has been designed to amplify the effects of radiotherapy.Patients and Methods: Thanks to its physical mode of action, NBTXR3 has the potential to be used to treat any type of solid tumor. Here we demonstrate that NBTXR3 can be used to treat a wide variety of solid cancers. For this, we evaluated different parameters on a large panel of human cancer models, such as nanoparticle endocytosis, in vitro cell death induction, dispersion, and retention of NBTXR3 in the tumor tissue and tumor growth control.Results: Whatever the model considered, we show that NBTXR3 was internalized by cancer cells and persisted within the tumors throughout radiotherapy treatment. NBTXR3 activated by radiotherapy was also more effective in destroying cancer cells and in controlling tumor growth than radiotherapy alone. Beyond the effects of NBTXR3 as single agent, we show that the antitumor efficacy of cisplatin-based chemoradiotherapy treatment was improved when combined with NBTXR3.Conclusion: These data support that NBTXR3 could be universally used to treat solid cancers when radiotherapy is indicated, opening promising new therapeutic perspectives of treatment for the benefit of many patients.Keywords: radiotherapy, radioenhancer, nanoparticle, NBTXR3, cancer

We present an investigation on the production of light dark gauge bosons by the nucleon bremsstrahlung processes in the core of neutron stars. The dark vector is assumed to be a $U(1)_{B-L}$ gauge boson with a mass much below keV. We calculate the emission rate of the dark vector produced by the nucleon bremsstrahlung in the degenerate nuclear matter. In addition, we take into account the photon-dark vector conversion for the photon luminosity observed at infinity. Combining with the observation of J1856 surface luminosity, we find that a recently discovered excess of J1856 hard x-ray emission in the 2-8 keV energy range by XMM-Newton and Chandra x-ray telescopes could be consistently explained by a dark vector with gauge coupling $e^{\prime}=5.56\times 10^{-15}$, mixing angle $\varepsilon=1.29\times 10^{-9}$, and mass $m_{\gamma^{\prime}}\lesssim 10^{-5}$ eV. We also show that the mixing angle $\varepsilon > 7.97 \times 10^{-9}$ for $m_{\gamma^{\prime}} \lesssim 3 \times 10^{-5}~\rm eV$ and the gauge coupling $e^{\prime} > 4.13 \times 10^{-13}$ for $m_{\gamma^{\prime}} \lesssim 1~\rm keV$ have been excluded at 95% confidence level by the J1856 surface luminosity observation. Our best-fit dark vector model satisfies the current limits on hard x-ray intensities from the Swift and INTEGRAL hard x-ray surveys. Future hard x-ray experiments such as NuSTAR may give a further test on our model., Comment: 43 pages, 13 figures, 2 tables, accepted for publication in PRD

Objective: The computation of anatomical information and laparoscope position is a fundamental block of surgical navigation in Minimally Invasive Surgery (MIS). Recovering a dense 3D structure of surgical scene using visual cues remains a challenge, and the online laparoscopic tracking primarily relies on external sensors, which increases system complexity. Methods: Here, we propose a learning-driven framework, in which an image-guided laparoscopic localization with 3D reconstructions of complex anatomical structures is obtained. To reconstruct the 3D structure of the whole surgical environment, we first fine-tune a learning-based stereoscopic depth perception method, which is robust to the texture-less and variant soft tissues, for depth estimation. Then, we develop a dense visual reconstruction algorithm to represent the scene by surfels, estimate the laparoscope poses and fuse the depth maps into a unified reference coordinate for tissue reconstruction. To estimate poses of new laparoscope views, we achieve a coarse-to-fine localization method, which incorporates our reconstructed 3D model. Results: We evaluate the reconstruction method and the localization module on three datasets, namely, the stereo correspondence and reconstruction of endoscopic data (SCARED), the ex-vivo phantom and tissue data collected with Universal Robot (UR) and Karl Storz Laparoscope, and the in-vivo DaVinci robotic surgery dataset, where the reconstructed 3D structures have rich details of surface texture with an accuracy error under 1.71 mm and the localization module can accurately track the laparoscope with only images as input. Conclusions: Experimental results demonstrate the superior performance of the proposed method in 3D anatomy reconstruction and laparoscopic localization. Significance: The proposed framework can be potentially extended to the current surgical navigation system.

A black hole X-ray binary produces hard X-ray radiation from its corona and disk when the accreting matter heats up. During an outburst, the disk and corona co-evolves with each other. However, such an evolution is still unclear in both its geometry and dynamics. Here we report the unusual decrease of the reflection fraction in MAXI J1820+070, which is the ratio of the coronal intensity illuminating the disk to the coronal intensity reaching the observer, as the corona is observed to contrast during the decay phase. We postulate a jet-like corona model, in which the corona can be understood as a standing shock where the material flowing through. In this dynamical scenario, the decrease of the reflection fraction is a signature of the corona's bulk velocity. Our findings suggest that as the corona is observed to get closer to the black hole, the coronal material might be outflowing faster., Comment: 11 pages, 5 figures; published in Nature Communications; An artist's impression of Figure 5 is updated here, and the animation is available at http://youbei.work/research

Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. An auxiliary loss captures the history of dialogue-document connections. We demonstrate the effectiveness of our model on two document-grounded conversational datasets and provide analyses showing generalization to unseen documents and long dialogue contexts., Comment: EMNLP 2021 camera-ready

In this work, we study the problem of how to leverage instructional videos to facilitate the understanding of human decision-making processes, focusing on training a model with the ability to plan a goal-directed procedure from real-world videos. Learning structured and plannable state and action spaces directly from unstructured videos is the key technical challenge of our task. There are two problems: first, the appearance gap between the training and validation datasets could be large for unstructured videos; second, these gaps lead to decision errors that compound over the steps. We address these limitations with Planning Transformer (PlaTe), which has the advantage of circumventing the compounding prediction errors that occur with single-step models during long model-based rollouts. Our method simultaneously learns the latent state and action information of assigned tasks and the representations of the decision-making process from human demonstrations. Experiments conducted on real-world instructional videos and an interactive environment show that our method can achieve a better performance in reaching the indicated goal than previous algorithms. We also validated the possibility of applying procedural tasks on a UR-5 platform. We make our code publicly available and support academic research purposes.

Autonomous surgical execution relieves tedious routines and surgeon's fatigue. Recent learning-based methods, especially reinforcement learning (RL) based methods, achieve promising performance for dexterous manipulation, which usually requires the simulation to collect data efficiently and reduce the hardware cost. The existing learning-based simulation platforms for medical robots suffer from limited scenarios and simplified physical interactions, which degrades the real-world performance of learned policies. In this work, we designed SurRoL, an RL-centered simulation platform for surgical robot learning compatible with the da Vinci Research Kit (dVRK). The designed SurRoL integrates a user-friendly RL library for algorithm development and a real-time physics engine, which is able to support more PSM/ECM scenarios and more realistic physical interactions. Ten learning-based surgical tasks are built in the platform, which are common in the real autonomous surgical execution. We evaluate SurRoL using RL algorithms in simulation, provide in-depth analysis, deploy the trained policies on the real dVRK, and show that our SurRoL achieves better transferability in the real world., Comment: 8 pages, 8 figures, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

A cable-driven soft-bodied robot with redundancy can conduct the trajectory tracking task and in the meanwhile fulfill some extra constraints, such as tracking through an end-effector in designated orientation, or get rid of the evitable manipulator-obstacle collision. Those constraints require rational planning of the robot motion. In this work, we derived the compressible curvature kinematics of a cable-driven soft robot which takes the compressible soft segment into account. The motion planning of the soft robot for a trajectory tracking task in constrained conditions, including fixed orientation end-effector and manipulator-obstacle collision avoidance, has been investigated. The inverse solution of cable actuation was formulated as a damped least-square optimization problem and iteratively computed off-line. The performance of trajectory tracking and the obedience to constraints were evaluated via the simulation we made open-source, as well as the prototype experiments. The method can be generalized to the similar multisegment cable-driven soft robotic systems by customizing the robot parameters for the prior motion planning of the manipulator., Comment: 8 pages, 9 figures

Surgical instrument segmentation in robot-assisted surgery (RAS) - especially that using learning-based models - relies on the assumption that training and testing videos are sampled from the same domain. However, it is impractical and expensive to collect and annotate sufficient data from every new domain. To greatly increase the label efficiency, we explore a new problem, i.e., adaptive instrument segmentation, which is to effectively adapt one source model to new robotic surgical videos from multiple target domains, only given the annotated instruments in the first frame. We propose MDAL, a meta-learning based dynamic online adaptive learning scheme with a two-stage framework to fast adapt the model parameters on the first frame and partial subsequent frames while predicting the results. MDAL learns the general knowledge of instruments and the fast adaptation ability through the video-specific meta-learning paradigm. The added gradient gate excludes the noisy supervision from pseudo masks for dynamic online adaptation on target videos. We demonstrate empirically that MDAL outperforms other state-of-the-art methods on two datasets (including a real-world RAS dataset). The promising performance on ex-vivo scenes also benefits the downstream tasks such as robot-assisted suturing and camera control., Comment: Accepted by ICRA 2021

LE is the low energy telescope of Insight-HXMT. It uses swept charge devices (SCDs) to detect soft X-ray photons. The time response of LE is caused by the structure of SCDs. With theoretical analysis and Monte Carlo simulations we discuss the influence of LE time response (LTR) on the timing analysis from three aspects: the power spectral density, the pulse profile and the time lag. After the LTR, the value of power spectral density monotonously decreases with the increasing frequency. The power spectral density of a sinusoidal signal reduces by a half at frequency 536 Hz. The corresponding frequency for QPO signals is 458 Hz. The Root mean square (RMS) of QPOs holds the similar behaviour. After the LTR, the centroid frequency and full width at half maxima (FWHM) of QPOs signals do not change. The LTR reduces the RMS of pulse profiles and shifts the pulse phase. In the time domain, the LTR only reduces the peak value of the crosscorrelation function while it does not change the peak position. Thus it will not affect the result of the time lag. When considering the time lag obtained from two instruments and one among them is LE, a 1.18 ms lag is expected caused by the LTR. The time lag calculated in the frequency domain is the same as that in the time domain., Comment: 11 pages, 9 figures

We propose that the nearly massless dark photons produced from the annihilation of keV dark fermions in the Galaxy can induce the excess of electron recoil events recently observed in the XENON1T experiment. The minimal model for this is the extension of a $U(1)_{X}$ gauge symmetry, under which the dark photon couples to both dark and visible matter currents. We find that the best-fit parameters of the dark sector are compatible with the most stringent constraints from stellar cooling. We also show that in the freeze-out scenario, the dark fermions can explain the anomaly while contributing $\gtrsim 1\%$ of the DM relic density., Comment: 5 pages, 3 figures. v2: minor changes to match the published version

We study the type-II first-order electroweak phase transition and dark matter (DM) phenomenology in both real and complex singlet extensions of SM. In the real singlet extension with a $\mathbb{Z}_2$ symmetry, we show that the parameter regions favored by the phase transition suffer from strong constraints from DM direct detection so that only a negligible fraction ($f_{X}\sim 10^{-4}-10^{-5}$) of DM composed of the real singlet scalar can survive the LUX and XENON1T constraints. In the complex singlet $S$ case, we impose a $CP$ symmetry $S\to S^{*}$ to the scalar potential. The real component of $S$ can mix with SM Higgs boson while the imaginary component becomes a DM candidate due to the protection of the $CP$ symmetry. By taking into account the current experimental constraints of invisible Higgs decays, Higgs signal strength measurements, and dark matter detections, we find that there exists a large parameter space for the type-II electroweak phase transition to occur while explaining all of the dark matter relic density. We identify a subset of parameter space that is promising for future experiments, including the di-Higgs and Higgs signal strength measurements at the HL-LHC and the dark matter direct detection in the XENONnT project., Comment: 30 pages, 11 figures, 3 tables, v2 matches published version in JCAP

We investigate the gravitational waves (GWs) produced from the Peccei-Quinn (PQ) phase transition associated with the clockwork axion. The PQ phase transition can be first-order when the dimension-6 operator is included into the scalar potential. The GWs from the PQ phase transition at scale in the range of $10^3-10^6$ GeV are detectable for the BBO and ALIA interferometers. The LISA and Taiji interferometers can probe the GWs from the PQ scale $f\lesssim 10^4$ GeV, while the GW signals from the scale $f\gtrsim 10^5$ GeV can be detected by the ground-based GW observatories ET and CE. We find that the parameter space $\kappa_m\sim 0.06-0.001$, $\kappa_l\sim 0.04-0.001$, and $\varepsilon\sim 0.1-0.01$ at the scale $f=10^5$ GeV and most of the parameter regions at the scale $f=10^6$ GeV have been excluded by the LIGO O2 run. The LIGO O3 and design phases can further probe the remaining parameter space. We show that the GWs from the annihilation of domain walls with a PQ scale $f\simeq 2\times 10^5$ GeV can induce the stochastic signals indicated by the 12.5-year observation of NANOGrav. The LIGO O3 run has the opportunity of detecting the GW signals from the first-order PQ phase transition around this scale., Comment: 29 pages, 10 figures, 1 table, comments are welcome

We present the observational results from a detailed timing analysis of the black hole candidate EXO 1846-031 during its outburst in 2019 with the observations of Insight-HXMT, NICER and MAXI. This outburst can be classfied roughly into four different states. Type-C quasi-periodic oscillations (QPOs) observed by NICER (about 0.1-6Hz) and Insight-HXMT (about 0.7-8Hz) are also reported in this work. Meanwhile, we study various physical quantities related to QPO frequency.The QPO rms-frequency relationship in three energy band 1-10 keV indicates that there is a turning pointing in frequency around 2 Hz,which is similar to that of GRS 1915+105. A possible hypothesis for the relationship above may be related to the inclination of the source, which may require a high inclination to explain it. The relationships between QPO frequency and QPO rms,hardness,total fractional rms and count rate have also been found in other transient sources, which can indicate that the origin of type-C QPOs is non-thermal.

Low-frequency quasi-periodic oscillations (LFQPOs) are commonly found in black hole X-ray binaries, and their origin is still under debate. The properties of LFQPOs at high energies (above 30 keV) are closely related to the nature of the accretion flow in the innermost regions, and thus play a crucial role in critically testing various theoretical models. The Hard X-ray Modulation Telescope (Insight-HXMT) is capable of detecting emissions above 30 keV, and is therefore an ideal instrument to do so. Here we report the discovery of LFQPOs above 200 keV in the new black hole MAXI J1820+070 in the X-ray hard state, which allows us to understand the behaviours of LFQPOs at hundreds of kiloelectronvolts. The phase lag of the LFQPO is constant around zero below 30 keV, and becomes a soft lag (that is, the high-energy photons arrive first) above 30 keV. The soft lag gradually increases with energy and reaches ~0.9s in the 150-200 keV band. The detection at energies above 200 keV, the large soft lag and the energy-related behaviors of the LFQPO pose a great challenge for most currently existing models, but suggest that the LFQPO probably originates from the precession of a small-scale jet., Comment: 36 pages, 12 figures, 3 tables; published in Nature Astronomy

Surgical knot tying is one of the most fundamental and important procedures in surgery, and a high-quality knot can significantly benefit the postoperative recovery of the patient. However, a longtime operation may easily cause fatigue to surgeons, especially during the tedious wound closure task. In this paper, we present a vision-based method to automate the suture thread grasping, which is a sub-task in surgical knot tying and an intermediate step between the stitching and looping manipulations. To achieve this goal, the acquisition of a suture's three-dimensional (3D) information is critical. Towards this objective, we adopt a transfer-learning strategy first to fine-tune a pre-trained model by learning the information from large legacy surgical data and images obtained by the on-site equipment. Thus, a robust suture segmentation can be achieved regardless of inherent environment noises. We further leverage a searching strategy with termination policies for a suture's sequence inference based on the analysis of multiple topologies. Exact results of the pixel-level sequence along a suture can be obtained, and they can be further applied for a 3D shape reconstruction using our optimized shortest path approach. The grasping point considering the suturing criterion can be ultimately acquired. Experiments regarding the suture 2D segmentation and ordering sequence inference under environmental noises were extensively evaluated. Results related to the automated grasping operation were demonstrated by simulations in V-REP and by robot experiments using Universal Robot (UR) together with the da Vinci Research Kit (dVRK) adopting our learning-driven framework.

Laparoscopic Field of View (FOV) control is one of the most fundamental and important components in Minimally Invasive Surgery (MIS), nevertheless, the traditional manual holding paradigm may easily bring fatigue to surgical assistants, and misunderstanding between surgeons also hinders assistants to provide a high-quality FOV. Targeting this problem, we here present a data-driven framework to realize an automated laparoscopic optimal FOV control. To achieve this goal, we offline learn a motion strategy of laparoscope relative to the surgeon's hand-held surgical tool from our in-house surgical videos, developing our control domain knowledge and an optimal view generator. To adjust the laparoscope online, we first adopt a learning-based method to segment the two-dimensional (2D) position of the surgical tool, and further leverage this outcome to obtain its scale-aware depth from dense depth estimation results calculated by our novel unsupervised RoboDepth model only with the monocular camera feedback, hence in return fusing the above real-time 3D position into our control loop. To eliminate the misorientation of FOV caused by Remote Center of Motion (RCM) constraints when moving the laparoscope, we propose a novel distortion constraint using an affine map to minimize the visual warping problem, and a null-space controller is also embedded into the framework to optimize all types of errors in a unified and decoupled manner. Experiments are conducted using Universal Robot (UR) and Karl Storz Laparoscope/Instruments, which prove the feasibility of our domain knowledge and learning enabled framework for automated camera control., Comment: 7 pages, 7 figures, 2021 IEEE International Conference on Robotics and Automation (ICRA)

Automatic surgical gesture recognition is fundamentally important to enable intelligent cognitive assistance in robotic surgery. With recent advancement in robot-assisted minimally invasive surgery, rich information including surgical videos and robotic kinematics can be recorded, which provide complementary knowledge for understanding surgical gestures. However, existing methods either solely adopt uni-modal data or directly concatenate multi-modal representations, which can not sufficiently exploit the informative correlations inherent in visual and kinematics data to boost gesture recognition accuracies. In this regard, we propose a novel online approach of multi-modal relational graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information through interactive message propagation in the latent feature space. In specific, we first extract embeddings from video and kinematics sequences with temporal convolutional networks and LSTM units. Next, we identify multi-relations in these multi-modal embeddings and leverage them through a hierarchical relational graph learning module. The effectiveness of our method is demonstrated with state-of-the-art results on the public JIGSAWS dataset, outperforming current uni-modal and multi-modal methods on both suturing and knot typing tasks. Furthermore, we validated our method on in-house visual-kinematics datasets collected with da Vinci Research Kit (dVRK) platforms in two centers, with consistent promising performance achieved., Comment: Accepted for ICRA 2021

The low energy (LE) X-ray telescope is one of the three main instruments of the Insight-Hard X-ray Modulation Telescope (Insight-HXMT). It is equipped with Swept Charge Device (SCD) sensor arrays with a total geometrical area of 384 cm2 and an energy band from 0.7 keV to 13 keV. In order to evaluate the particle induced X-ray background and the cosmic X-ray background simultaneously, LE adopts collimators to define four types of Field Of Views (FOVs). LE is constituted of three detector boxes (LEDs) and an electric control box (LEB) and achieves a good energy resolution of 140 eV at 5.9 keV, an excellent time resolution of 0.98 ms, as well as an extremely low pileup (<1% at 18000 cts/s). Detailed performance tests and calibration on the ground have been performed, including energy-channel relation, energy response, detection efficiency and time response., Comment: submitted to Science China Physics, Mechanics & Astronomy

The Hard X-ray Modulation Telescope (HXMT) named Insight is China's first X-ray astronomical satellite. The Low Energy X-ray Telescope (LE) is one of its main payloads onboard. The detectors of LE adopt swept charge device CCD236 with L-shaped transfer electrodes. Charges in detection area are read out continuously along specific paths, which leads to a time response distribution of photons readout time. We designed a long exposure readout mode to measure the time response distribution. In this mode, CCD236 firstly performs exposure without readout, then all charges generated in preceding exposure phase are read out completely. Through analysis of the photons readout time in this mode, we obtained the probability distribution of photons readout time.

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results., Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech. Astron. arXiv admin note: text overlap with arXiv:1910.04434

The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star., Comment: published

The interest in deep-sea mining increased along with the environmental concerns of these activities to the deep-sea fauna. The discovery of optimal biomarkers of deep-sea mining activities in deep-sea species is a crucial step toward the supply of important ecological information for environmental impact assessment. In this study, an in situ copper exposure experiment was performed on deep-sea scavenging amphipods. Abyssorchomene distinctus individuals were selected among all the exposed amphipods for molecular characterization. Copper concentration within the gut was assessed, followed by a tandem mass tag-based coupled with two-dimensional liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) applied to identify and quantify the protein expression changes after 48 h of exposure. 2937 proteins were identified and annotated, and 1918 proteins among all identified proteins were assigned by at least two nonambiguous peptides. The screening process was performed based on the differences in protein abundance and the specific correlation between the proteins and copper in previous studies. These differentially produced proteins include Na+/K+ ATPase, cuticle, chitinase, and proteins with unknown function. Their abundances showed correlation with copper and had high sensitivity to indicate the copper level, being here proposed as biomarker candidates for deep-sea mining activities in the future. This is a key step in the development of environmental impact assessment of deep-sea mining activities integrating ecotoxicological data. © Copyright © 2019 American Chemical Society.

We consider an extension of the Standard Model with a complex singlet scalar, where a global $U(1)$ symmetry is explicitly broken to $\mathbb{Z}_3$ symmetry. We study the two-step electroweak phase transition in the model and find that it can be of first-order if the heavy scalar mass falls in the range of $1-2$~TeV and the mixing angle $\left | \theta \right |\gtrsim 0.2$ ($11.5^{\circ}$). The Higgs signal strength measurements at the LHC, on the other hand, restrict the mixing angle $\left | \theta \right |\lesssim 0.4$ ($23^{\circ}$). Future colliders including high-luminosity LHC can probe the remaining parameter space of first-order phase transition in this scenario. After the $U(1)$ symmetry breaking, the pseudo-Goldstone boson becomes a dark matter candidate due to a hidden $\mathbb{Z}_2$ symmetry of the model. We find that the pseudo-Goldstone boson can make up a small fraction of the observed dark matter and escape from the constraints of current direct detection. We also show that the stochastic gravitational wave signals from the phase transition are potentially discoverable with future space-based interferometers., Comment: 50 pages, 17 figures, 2 tables, published in JHEP

We propose that the kinematical observations of dwarf galaxies can be used to constrain the primordial black hole's (PBH) abundance in dark matter since the presence of primordial black holes in star clusters will lead to the radial velocity dispersion of the system. For instance, using the velocity dispersion observations from Leo I we show that the primordial black hole fraction $f_{\rm PBH}\gtrsim 2.0\times(1~M_{\odot}/m_{\rm PBH})^2$ is ruled out at a 99.99\% confidence level. This method yields the most stringent limits on the PBH abundance at the mass scales $\sim (1-10^3)~M_{\odot}$ and tightly constrains the primordial origin of gravitational wave events observed by the LIGO experiments., Comment: 6 pages, 4 figures, and 1 table. Accepted by PRD. Comments are welcome

INTRODUCTION:Children with attention-deficit/hyperactivity disorder (ADHD) are at increased risk for smoking cigarettes, but there is little longitudinal research on the array of smoking characteristics known to be prognostic of long-term smoking outcomes into adulthood. These variables were studied into early adulthood in a multisite sample diagnosed with ADHD combined type at ages 7-9.9 and followed prospectively alongside an age- and sex-matched local normative comparison group (LNCG). METHODS:Cigarette smoking quantity, quit attempts, dependence, and other characteristics were assessed in the longitudinal Multimodal Treatment Study of Children with ADHD (MTA) eight times to a mean age of 24.9 years: ADHD n = 469; LNCG n = 240. RESULTS:In adulthood, the ADHD group had higher rates of daily cigarette smoking, one or more quit attempts, shorter time to first cigarette of the day, and more severe withdrawal than the LNCG. The ADHD group did not appear to have better smoking cessation rates despite a higher proportion quitting at least once. Smoking quantity and nicotine dependence did not differ between groups. The ADHD group reported younger daily smoking onset and faster progression from smoking initiation to daily smoking across assessments. Finally, ADHD symptom severity in later adolescence and adulthood was associated with higher risk for daily smoking across assessments in the ADHD sample. CONCLUSIONS:This study shows that ADHD-related smoking risk begins at a young age, progresses rapidly, and becomes resistant to cessation attempts by adulthood. Prevention efforts should acknowledge the speed of uptake; treatments should target the higher relapse risk in this vulnerable population. IMPLICATIONS:Although childhood ADHD predicts later smoking, longitudinal studies of this population have yet to fully characterize smoking behaviors into adulthood that are known to be prognostic of long-term smoking outcome. The current study demonstrates earlier and faster pro

Ischemia reperfusion injury is one of the major complications responsible for delayed graft function in kidney transplantation. Applications to reduce reperfusion injury are essential due to the widespread use of kidneys from deceased organ donors where the risk for delayed graft function is especially prominent. We have recently shown that coating of inflamed or damaged endothelial cells with a unique heparin conjugate reduces thrombosis and leukocyte recruitment. In this study we evaluated the binding capacity of the heparin conjugate to cultured human endothelial cells, to kidneys from brain-dead porcine donors, and to murine kidneys during static cold storage. The heparin conjugate was able to stably bind cultured endothelial cells with high avidity, and to the renal vasculature of explanted kidneys from pigs and mice. Treatment of murine kidneys prior to transplantation reduced platelet deposition and leukocyte infiltration 24 hours post-transplantation, and significantly improved graft function. The present study thus shows the benefits of enhanced protection of the renal vasculature during cold storage, whereby increasing the antithrombotic and anti-adhesive properties of the vascular endothelium yields improved renal function early after transplantation.