Search

Additive Manufacturing (AM) has revolutionized the production landscape by enabling on-demand customized manufacturing. However, the efficient management of dynamic AM orders poses significant challenges for production planning and scheduling. This paper addresses the dynamic scheduling problem considering batch processing, random order arrival and machine eligibility constraints, aiming to minimize total tardiness in a parallel non-identical AM machine environment. To tackle this problem, we propose the out-of-order enabled dueling deep Q network (O3-DDQN) approach. In the proposed approach, the problem is formulated as a Markov decision process (MDP). Three-dimensional features, encompassing dynamic orders, AM machines, and delays, are extracted using a ‘look around’ method to represent the production status at a rescheduling point. Additionally, five novel composite scheduling rules based on the out-of-order principle are introduced for selection when an AM machine completes processing or a new order arrives. Moreover, we design a reward function that is strongly correlated with the objective to evaluate the agent's chosen action. Experimental results demonstrate the superiority of the O3-DDQN approach over single scheduling rules, randomly selected rules, and the classic DQN method. The average improvement rate of performance reaches 13.09% compared to composite scheduling rules and random rules. Additionally, the O3-DDQN outperforms the classic DQN agent with a 6.54% improvement rate. The O3-DDQN algorithm improves scheduling in dynamic AM environments, enhancing productivity and on-time delivery. This research contributes to advancing AM production and offers insights into efficient resource allocation., QC 20240823

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Tasks are called self-suspending if they can yield their ready state (specifically, releasing the processor while having highest priority) despite being incomplete, for instance, to offload computation to an external device or when waiting on access rights for shared resources or data. This self-suspending behavior requires special treatment when applying analytical results to compute worst-case response time bounds. One typical treatment is modeling self-suspension as release jitter in a so-called jitter-based analysis. The state of the art, when considering task-level fixed-priority scheduling, individually quantifies the jitter term of each higher-priority task by its worst-case response time minus its worst-case execution time. This work tightens the jitter term by taking the execution behavior of the other higher-priority tasks into account. Our improved jitter-based analysis analytically dominates the previous jitter-based analysis. Moreover, an evaluation for synthetically generated sporadic tasks demonstrates that this jitter term results in tighter worst-case response time bounds for self-suspending tasks. We observe an improvement for up to 55.89 % of the tasksets compared to the previous jitter-based analysis.

Viruses target mitochondria to promote their replication, and infection-induced stress during the progression of infection leads to the regulation of antiviral defenses and mitochondrial metabolism which are opposed by counteracting viral factors. The precise structural and functional changes that underlie how mitochondria react to the infection remain largely unclear. Here we show extensive transcriptional remodeling of protein-encoding host genes involved in the respiratory chain, apoptosis, and structural organization of mitochondria as herpes simplex virus type 1 lytic infection proceeds from early to late stages of infection. High-resolution microscopy and interaction analyses unveiled infection-induced emergence of rough, thin, and elongated mitochondria relocalized to the perinuclear area, a significant increase in the number and clustering of endoplasmic reticulum-mitochondria contact sites, and thickening and shortening of mitochondrial cristae. Finally, metabolic analyses demonstrated that reactivation of ATP production is accompanied by increased mitochondrial Ca2+ content and proton leakage as the infection proceeds. Overall, the significant structural and functional changes in the mitochondria triggered by the viral invasion are tightly connected to the progression of the virus infection.

Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the application of graph neural networks (GNNs) to solve JSSPs, albeit lacking a systematic survey of the relevant literature. This paper aims to thoroughly review prevailing GNN methods for different types of JSSPs and the closely related flow-shop scheduling problems (FSPs), especially those leveraging deep reinforcement learning (DRL). We begin by presenting the graph representations of various JSSPs, followed by an introduction to the most commonly used GNN architectures. We then review current GNN-based methods for each problem type, highlighting key technical elements such as graph representations, GNN architectures, GNN tasks, and training algorithms. Finally, we summarize and analyze the advantages and limitations of GNNs in solving JSSPs and provide potential future research opportunities. We hope this survey can motivate and inspire innovative approaches for more powerful GNN-based approaches in tackling JSSPs and other scheduling problems.

The influence of cavitation on the wake behind a NACA 0009 hydrofoil with a truncated trailing edge has been numerically investigated using a homogeneous mixture model coupled with a controlled decay SST ¿-Re¿t turbulence model. Using optimal definitions of the inlet turbulent intensity and the empirical condensation and vaporization coefficients of the cavitation model, simulated results have shown a good agreement with experimental data. Notably, the numerical results exhibit negligible deviations of less than 0.7% in vortex shedding frequencies for different cavitation levels. If the size of the vortex cavitation grows, a substantial increase in both lift and drag coefficients on the hydrofoil is observed. Furthermore, the influence of cavitation on the trajectories of vortex centers and the morphology of the primary shedding vortices has been revealed using a vortex identification method. The findings highlight that the cavitation development enhances the advected velocity of the shedding vortices while decreasing the streamwise inter-vortex spacing. Consequently, both factors are found to contribute to the increase of the vortex-shedding frequency behind the NACA 0009 hydrofoil with the truncated trailing edge when cavitation appears and develops., Peer Reviewed, Postprint (published version)

Yang J, Li K, Chen J, Hu X, Wang H, Zhu X. Onco Targets Ther. 2020;13:1979–1991. We, the Editor and Publisher of the journal OncoTargets and Therapy are retracting the published article. Following publication of the article, the authors raised concerns about the duplication of images from Figure 5. Specifically, The images for Figure 5G, HepG2 and Huh 7, LINC00460+miR-342-3p have been duplicated. The corresponding author was cooperative and responded to our queries and explained how the images came to be duplicated but was unable to provide satisfactory original data for the study. As verifying the validity of published work is core to the integrity of the scholarly record, the Publisher and Editor requested to retract the article and the authors agreed with this decision. We have been informed in our decision-making by our editorial policies and COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

Shuang Du,1,* Ke Lin,2,* Jing Li,3 Xin Zhou,4 Chaolan Wang,2 Jun Liu,3 Na Li,2 Jian Chen2 1Department of Rehabilitation Medicine, The First People’s Hospital of Jin Tang County, Chengdu, Sichuan, 610400, People’s Republic of China; 2Traditional Chinese Medicine Department, The First People’s Hospital of Jintang County, Chengdu, Sichuan, 610400, People’s Republic of China; 3Department of Respiratory and Critical Care Medicine, The First People’s Hospital of Jintang County, Chengdu, Sichuan, 610400, People’s Republic of China; 4Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, 610041, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jian Chen, Traditional Chinese Medicine Department, The First People’s Hospital of Jintang County, Chengdu, Sichuan, 610400, People’s Republic of China, Tel +86-10-18908176456, Email chenjian792@163.comPurpose: COPD patients frequently have abnormal serum phosphorus levels. The objective of this study was to examine the correlation between serum phosphorus levels with hospital and 90-day mortality in critically ill patients with COPD.Patients and Methods: The MIMIC IV database was used for this retrospective cohort analysis. We extracted demographics, vital signs, laboratory tests, comorbidity, antibiotic usage, ventilation and scoring systems within the first 24 hours of ICU admission. Restricted cubic splines and multivariate cox regression analysis models were used to evaluate the connection between serum phosphorus with hospital and 90-day mortality. We assessed and classified various factors including gender, age, renal disease, severe liver disease, the utilization of antibiotics and congestive heart failure.Results: We included a total of 3611 patients with COPD, with a median age of 70.7 years. After adjusting for all other factors, we observed a significant positive association between serum phosphate levels with

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Background: Chemotaxis and trafficking of dendritic cells (DCs) induced by cytokine receptors are crucial steps in rheumatoid arthritis (RA) pathogenesis. C–C chemokine receptor type 5 (CCR5) plays a key role in DC movement and has been implicated in multitudinous inflammatory and immunology diseases. Thus, targeting CCR5 to suppress DC chemotaxis is considered as a potential strategy for the management of RA. Methods: Herein, we first synthesized a new hybrid named CT3-1 which based on artesunate and isatin. Besides, we studied the regulating effectiveness of CT3-1 on bone marrow-derived DCs (BMDCs) and on collagen-induced arthritis (CIA) through RNA-seq analysis, cell function experiments in vitro and mice model in vivo. Results: The results shown that CT3-1 mainly reduced CCR5 expression of immature BMDCs and importantly inhibited immature BMDC migration induced by CCR5 in vitro, with no or minor influence on other functions of DCs, such as phagocytosis and maturation. In the mouse model, CT3-1 relieved arthritis severity and inhibited CIA development. Furthermore, CT3-1 intervention decreased the expression of CCR5 in DCs and reduced the proportion of DCs in the peripheral blood of CIA mice. Conclusions: Our findings suggest that CCR5-induced chemotaxis and trafficking of immature DCs are important in RA. Targeting CCR5 and inhibiting immature DC chemotaxis may provide a novel choice for the treatment of RA and other similar autoimmune diseases. Moreover, we synthesized a new hybrid compound CT3-1 that could inhibit immature DC trafficking and effectively relieve RA by directly reducing the CCR5 expression of immature DCs. © 2024

Fatigue damage prognosis always requires a degradation model describing the damage evolution with time; thus, the prognostic performance highly depends on the selection of such a model. The best model should probably be case specific, calling for the fusion of multiple degradation models for a robust prognosis. In this context, this paper proposes a scheme of online fusing multiple models in a particle filter (PF)-based damage prognosis framework. First, each prognostic model has its process equation built through a physics-based or data-driven degradation model and has its measurement equation linking the damage state and the measurement. Second, each model is independently processed through one PF to provide one group of particles. Then, the particles from all models are adopted for remaining useful life prediction. Finally, the particles from each PF are fused with those from all the other PFs to improve their particle diversity, and consequently, to provide better estimation and prognostic performance. The feasibility and robustness of the proposed method are validated by an experimental study, where an aluminum lug structure subject to fatigue crack growth is monitored by a guided wave measurement system., Group Zarouchas

Since the avalanche phenomenon was first found in bulk materials, avalanche photodiodes (APDs) have been exclusively investigated. Among the many devices that have been developed, silicon APDs stand out because of their low cost, performance stability, and compatibility with CMOS. However, the increasing industrial needs pose challenges for the fabrication cycle time and fabrication cost. In this work, we proposed an improved fabrication process for ultra-deep mesa-structured silicon APDs for photodetection in the visible and near-infrared wavelengths with improved performance and reduced costs. The improved process reduced the complexity through significantly reduced photolithography steps, e.g., half of the steps of the existing process. Additionally, single ion implantation was performed under low energy (lower than 30 keV) to further reduce the fabrication costs. Based on the improved ultra-concise process, a deep-mesa silicon APD with a 140 V breakdown voltage was obtained. The device exhibited a low capacitance of 500 fF, the measured rise time was 2.7 ns, and the reverse bias voltage was 55 V. Moreover, a high responsivity of 103 A/W@870 nm at 120 V was achieved, as well as a low dark current of 1 nA at punch-through voltage and a maximum gain exceeding 1000.

Kai Guo,1,* Yong Wang,1,* Zi-Xuan Feng,1 Xiao-Ying Lin,1 Zhang-Rui Wu,1 Xin-Cao Zhong,1 Ze-Ming Zhuang,1 Tao Zhang,1 Jian Chen,2 Wei-Qiang Tan1 1Department of Plastic Surgery, Sir Run Run Shaw Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People’s Republic of China; 2Department of Ultrasonography, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jian Chen, Department of Ultrasonography, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang Province, People’s Republic of China, Email chenjianzuj4h@zju.edu.cn Wei-Qiang Tan, Department of Plastic Surgery, Sir Run Run Shaw Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People’s Republic of China, Email tanweixxxx@zju.edu.cnAbstract: The various tissue damages are a severe problem to human health. The limited human tissue regenerate ability requires suitable biomaterials to help damage tissue repair and regeneration. Therefore, many researchers devoted themselves to exploring biomaterials suitable for tissue repair and regeneration. Polydopamine (PDA) as a natural and multifunctional material which is inspired by mussel has been widely applied in different biomaterials. The excellent properties of PDA, such as strong adhesion, photothermal and high drug-loaded capacity, seem to be born for tissue repair and regeneration. Furthermore, PDA combined with different materials can exert unexpected effects. Thus, to inspire researchers, this review summarizes the recent and representative development of PDA biomaterials in tissue repair and regeneration. This article focuses on why apply PDA in these biomaterials and what PDA can do in different tissue injuries. Keywords: polydopamine, biomaterial, tissue damage, tissue regeneration

The atmosphere of Triton was probed directly by observing a ground-based stellar occultation on 6 October 2022. This rare event yielded 23 positive light curves collected from 13 separate observation stations contributing to our campaign. The significance of this event lies in its potential to directly validate the modest pressure fluctuation on Triton, a phenomenon not definitively verified by previous observations, including only five stellar occultations, and the Voyager 2 radio occultation in 1989. Using an approach consistent with a comparable study, we precisely determined a surface pressure of $14.07_{-0.13}^{+0.21}~\mathrm{\mu bar}$ in 2022. This new pressure rules out any significant monotonic variation in pressure between 2017 and 2022 through direct observations, as it is in alignment with the 2017 value. Additionally, both the pressures in 2017 and 2022 align with the 1989 value. This provides further support for the conclusion drawn from the previous volatile transport model simulation, which is consistent with the observed alignment between the pressures in 1989 and 2017; that is to say, the pressure fluctuation is modest. Moreover, this conclusion suggests the existence of a northern polar cap extended down to at least $45^\circ$N$-60^\circ$N and the presence of nitrogen between $30^\circ$S and $0^\circ$., Comment: Astronomy & Astrophysics, in press. 9 pages, 2 figures, 3 tables

Yayun Gan,1 Jian Chen,1 Lini Xian,2 Yuan Shi2 1Department of Ultrasound, The First Hospital of Kunming City, The Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, 650011, People’s Republic of China; 2Department of Ultrasound, Yanan Hospital of Kunming City, The Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, 650051, People’s Republic of ChinaCorrespondence: Jian Chen, The First Hospital of Kunming City, The Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, 650011, People’s Republic of China, Email chenjian6@kmmu.edu.cnPurpose: Emerging evidence suggests that although Horner’s syndrome manifests observable facial changes, it may not comprehensively evaluate the hemodynamic alterations associated with stellate ganglion block (SGB). This study endeavors to systematically evaluate the influence of SGB on the elasticity and flow velocity of the common carotid artery (CCA) and brachial artery utilizing ultrasound wave intensity analysis (usWIA). Particularly, it focuses on patients necessitating monitoring for its effects on specific organs or regions.Methods: Totally, we selected 33 patients, where only 31 patients (comprising 15 males and 16 females) were included between September 2020 to January 2022 after screening patients who require SGB treatment for painful disorders. The side on which the SGB was administered depended on the patient’s painful side, 13 cases underwent left stellate ganglion block (LSGB), and 18 cases underwent right stellate ganglion block (RSGB). Wave intensity (WI) data were collected by usWIA on the CCA and brachial artery before the administration of SGB and after the manifestation of Horner’s syndrome. We then compared the changes in these data pre- and post-SGB using SPSS 26.0.Results: The results showed an increase in arterial compliance (AC) of the CCA and brachial artery on the blocked side after SGB (P < 0.05). In contrast, pressure-strain

Constructing well-defined heterostructure interfaces in catalysts is an efficient strategy to break the so-called scaling relationships and to accelerate the reactions involving multiple intermediates. Here a cluster-cluster heterostructure catalyst composed of crystalline ruthenium cluster and amorphous chromium oxide cluster is designed to realize high-performance alkaline hydrogen electrocatalysis. The strongly coupled cluster-cluster heterostructure interface induces a unique interfacial interpenetration effect, which simultaneously optimizes the adsorption of intermediates on each cluster. The resulting catalyst exhibits impressive catalytic activities for the hydrogen oxidation reaction (exchange current density of 2.8 A mg-1Ru) and the hydrogen evolution reaction (mass activity of 23.0 A mg(-1)Ru at the overpotential of 100 mV) in alkaline media. The hydroxide exchange membrane fuel cell delivers a mass activity of 22.4 A mg(-1)Ru at 0.65 V and outstanding durability with no voltage loss over 105 h operation at 500 mA cm(-2). The present work demonstrates the superiority of cluster-cluster heterostructure interface towards the development of advanced catalysts.

We present and discuss the results of a qualitative analysis of visual representations from images. We labeled each image's essential stimuli, the removal of which would render a visualization uninterpretable. As a result, we derive a typology of 10 visualization types of defined groups. We describe the typology derivation process in which we engaged. The resulting typology and image analysis can serve a number of purposes: enabling researchers to study the evolution of the community and its research output over time, facilitating the categorization of visualization images for the purpose of research and teaching, allowing researchers and practitioners to identify visual design styles to further align the quantification of any visual information processor, be that a person or an algorithm observer, and it facilitates a discussion of standardization in visualization. In addition to the visualization typology from images, we provide a dataset of 6,833 tagged images and an online tool that can be used to explore and analyze the large set of labeled images. The tool and data set enable scholars to closely examine the diverse visual designs used and how they are published and communicated in our community. A pre-registration, a free copy of this paper, and all supplemental materials are available via osf.io/dxjwt., Comment: arXiv admin note: text overlap with arXiv:2209.07533

A novel near-field transmission framework is proposed for dynamic metasurface antenna (DMA)-enabled non-orthogonal multiple access (NOMA) networks. The base station (BS) exploits the hybrid beamforming to communicate with multiple near users (NUs) and far users (FUs) using the NOMA principle. Based on this framework, two novel beamforming schemes are proposed. 1) For the case of the grouped users distributed in the same direction, a beam-steering scheme is developed. The metric of beam pattern error (BPE) is introduced for the characterization of the gap between the hybrid beamformers and the desired ideal beamformers, where a two-layer algorithm is proposed to minimize BPE by optimizing hybrid beamformers. Then, the optimal power allocation strategy is obtained to maximize the sum achievable rate of the network. 2) For the case of users randomly distributed, a beam-splitting scheme is proposed, where two sub-beamformers are extracted from the single beamformer to serve different users in the same group. An alternating optimization (AO) algorithm is proposed for hybrid beamformer optimization, and the optimal power allocation is also derived. Numerical results validate that: 1) the proposed beamforming schemes exhibit superior performance compared with the existing imperfect-resolution-based beamforming scheme; 2) the communication rate of the proposed transmission framework is sensitive to the imperfect distance knowledge of NUs but not to that of FUs., Comment: 13 pages

Dilute magnetic semiconductors (DMSs) have attracted much attention because of their potential use in spintronic devices. Here, we demonstrate the observation of robust ferromagnetism in a solution-processable halide perovskite semiconductor with dilute magnetic ions. By codoping of magnetic (Fe2+) and aliovalent (Bi3+) metal ions into CH3NH3PbCl3 (MAPbCl(3)) perovskite, ferromagnetism with well-saturated magnetic hysteresis loops and a maximum coercivity field of 1280 Oe was observed below 12 K. The ferromagnetic resonance measurements revealed that the incorporation of aliovalent ions modulates the carrier concentration and plays an essential role in realizing the ferromagnetism in dilute magnetic halide perovskites. Magnetic ions are proposed to interact through itinerant charge carriers to achieve ferromagnetic coupling. Our work provides a new avenue for the development of solution-processable magnetic semiconductors.

In response to the ever-increasing global population and the growing demand for energy and food, human activities have exerted a substantial impact on the global nitrogen cycles. In this context, the electrocatalytic upgrading of nitrogenous wastes into high-value chemicals under ambient conditions, ideally powered by renewable electricity, emerges as a promising approach to concurrently manage nitrogen-containing wastes and facilitate sustainable production of valuable chemicals. This review presents the electrochemical “waste-to-valuables” concept by discussing its practicality in terms of waste removal efficiency, valuable production efficiency, downstream recovery of valuables, potential applications, and economic feasibility. Specifically, the electrocatalytic upgrading of nitrogenous wastes, i.e., nitric oxide and nitrate as representative air and aqueous pollutants, respectively, into high-value-added chemicals, i.e., ammonia via nitric oxide/nitrate reduction and urea/amide/amine via nitrogen-integrated carbon dioxide reduction is focused. Targeting nitrogenous waste exhausts/streams with low/high concentrations, reactor design and catalyst design principles are reviewed with representative examples. Finally, the major challenges and opportunities associated with the practical applications of the “waste-to-valuables” concept are discussed. © 2024 Elsevier Ltd

Bronchoscopy plays a significant role in the early diagnosis and treatment of lung diseases. This process demands physicians to maneuver the flexible endoscope for reaching distal lesions, particularly requiring substantial expertise when examining the airways of the upper lung lobe. With the development of artificial intelligence and robotics, reinforcement learning (RL) method has been applied to the manipulation of interventional surgical robots. However, unlike human physicians who utilize multimodal information, most of the current RL methods rely on a single modality, limiting their performance. In this paper, we propose BronchoCopilot, a multimodal RL agent designed to acquire manipulation skills for autonomous bronchoscopy. BronchoCopilot specifically integrates images from the bronchoscope camera and estimated robot poses, aiming for a higher success rate within challenging airway environment. We employ auxiliary reconstruction tasks to compress multimodal data and utilize attention mechanisms to achieve an efficient latent representation of this data, serving as input for the RL module. This framework adopts a stepwise training and fine-tuning approach to mitigate the challenges of training difficulty. Our evaluation in the realistic simulation environment reveals that BronchoCopilot, by effectively harnessing multimodal information, attains a success rate of approximately 90\% in fifth generation airways with consistent movements. Additionally, it demonstrates a robust capacity to adapt to diverse cases.

Real-time 6 DOF localization of bronchoscopes is crucial for enhancing intervention quality. However, current vision-based technologies struggle to balance between generalization to unseen data and computational speed. In this study, we propose a Depth-based Dual-Loop framework for real-time Visually Navigated Bronchoscopy (DD-VNB) that can generalize across patient cases without the need of re-training. The DD-VNB framework integrates two key modules: depth estimation and dual-loop localization. To address the domain gap among patients, we propose a knowledge-embedded depth estimation network that maps endoscope frames to depth, ensuring generalization by eliminating patient-specific textures. The network embeds view synthesis knowledge into a cycle adversarial architecture for scale-constrained monocular depth estimation. For real-time performance, our localization module embeds a fast ego-motion estimation network into the loop of depth registration. The ego-motion inference network estimates the pose change of the bronchoscope in high frequency while depth registration against the pre-operative 3D model provides absolute pose periodically. Specifically, the relative pose changes are fed into the registration process as the initial guess to boost its accuracy and speed. Experiments on phantom and in-vivo data from patients demonstrate the effectiveness of our framework: 1) monocular depth estimation outperforms SOTA, 2) localization achieves an accuracy of Absolute Tracking Error (ATE) of 4.7 $\pm$ 3.17 mm in phantom and 6.49 $\pm$ 3.88 mm in patient data, 3) with a frame-rate approaching video capture speed, 4) without the necessity of case-wise network retraining. The framework's superior speed and accuracy demonstrate its promising clinical potential for real-time bronchoscopic navigation.

This article demonstrates a new kind of programmable logic for the representation of an integer that can be used for the programmable Josephson voltage standard. It can enable the numbers of junctions in most bits to be variable integer values, which is different from normal binary logic or ternary logic. Consequently, missing junctions due to superconducting short circuits can be tolerated under this logic. This logic can also have nearly the same segmentation efficiency as ternary logic. The completeness of the sequences using this logic is proven by the recursive method in mathematics in this paper. After that, a new algorithm for the representation of integers is presented according to the proven process, and an analysis of the number of fault-tolerant junctions for each bit is provided. Although the first and second bits are not tolerant to missing junctions, bits beyond these can tolerate one to hundreds of missing junctions. Due to the non-fixed multiples between the bits of the sequence, this logic is called non-integer-multiple logic. Finally, the design and fabrication of a 3 V programmable Josephson junction array using this logic are described, and the measurements and analysis of the characteristic parameters are presented.

Localizing the bronchoscope in real time is essential for ensuring intervention quality. However, most existing methods struggle to balance between speed and generalization. To address these challenges, we present BronchoTrack, an innovative real-time framework for accurate branch-level localization, encompassing lumen detection, tracking, and airway association.To achieve real-time performance, we employ a benchmark lightweight detector for efficient lumen detection. We are the first to introduce multi-object tracking to bronchoscopic localization, mitigating temporal confusion in lumen identification caused by rapid bronchoscope movement and complex airway structures. To ensure generalization across patient cases, we propose a training-free detection-airway association method based on a semantic airway graph that encodes the hierarchy of bronchial tree structures.Experiments on nine patient datasets demonstrate BronchoTrack's localization accuracy of 85.64 \%, while accessing up to the 4th generation of airways.Furthermore, we tested BronchoTrack in an in-vivo animal study using a porcine model, where it successfully localized the bronchoscope into the 8th generation airway.Experimental evaluation underscores BronchoTrack's real-time performance in both satisfying accuracy and generalization, demonstrating its potential for clinical applications.

Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot's ability to adaptively track mobile objects and resist external interference. Experimental results underscore that the proposed control system enhances motion stability and precision. Phantom testing substantiates its considerable potential for deployment in neurosurgery., Comment: Accepted by IEEE International Conference on Robotics and Automation (ICRA) 2024, 7 pages, 9 figures

Controllable layout generation refers to the process of creating a plausible visual arrangement of elements within a graphic design (e.g., document and web designs) with constraints representing design intentions. Although recent diffusion-based models have achieved state-of-the-art FID scores, they tend to exhibit more pronounced misalignment compared to earlier transformer-based models. In this work, we propose the $\textbf{LA}$yout $\textbf{C}$onstraint diffusion mod$\textbf{E}$l (LACE), a unified model to handle a broad range of layout generation tasks, such as arranging elements with specified attributes and refining or completing a coarse layout design. The model is based on continuous diffusion models. Compared with existing methods that use discrete diffusion models, continuous state-space design can enable the incorporation of differentiable aesthetic constraint functions in training. For conditional generation, we introduce conditions via masked input. Extensive experiment results show that LACE produces high-quality layouts and outperforms existing state-of-the-art baselines., Comment: Accepted by ICLR 2024

Occasional deadline misses are acceptable for soft real-time systems. Quantifying probabilistic and deterministic characteristics of deadline misses is therefore essential to ensure that deadline misses indeed happen only occasionally. This is supported by recent research activities on probabilistic worst-case execution time, worst-case deadline failure probability, the maximum number of deadline misses, upper bounds on the deadline miss probability, and the deadline miss rate. This paper focuses on the deadline miss rate of a periodic soft real-time task in the long run. Our model assumes that this soft real-time task has an arbitrary relative deadline and that a job can still be executed after a deadline-miss until a dismiss point. This model generalizes the existing models that either dismiss a job immediately after its deadline miss or never dismiss a job. We provide mathematical notation on the convergence of the deadline miss rate in the long run and essential properties to calculate the deadline miss rate. Specifically, we use a Markov chain to model the execution behavior of a periodic soft real-time task. We present the required ergodicity property to ensure that the deadline miss rate in the long run is described by a stationary distribution.

Shapley value-based data valuation methods, originating from cooperative game theory, quantify the usefulness of each individual sample by considering its contribution to all possible training subsets. Despite their extensive applications, these methods encounter the challenge of value inflation - while samples with negative Shapley values are detrimental, some with positive values can also be harmful. This challenge prompts two fundamental questions: the suitability of zero as a threshold for distinguishing detrimental from beneficial samples and the determination of an appropriate threshold. To address these questions, we focus on KNN-Shapley and propose Calibrated KNN-Shapley (CKNN-Shapley), which calibrates zero as the threshold to distinguish detrimental samples from beneficial ones by mitigating the negative effects of small-sized training subsets. Through extensive experiments, we demonstrate the effectiveness of CKNN-Shapley in alleviating data valuation inflation, detecting detrimental samples, and assessing data quality. We also extend our approach beyond conventional classification settings, applying it to diverse and practical scenarios such as learning with mislabeled data, online learning with stream data, and active learning for label annotation.

Influence functions serve as crucial tools for assessing sample influence in model interpretation, subset training set selection, noisy label detection, and more. By employing the first-order Taylor extension, influence functions can estimate sample influence without the need for expensive model retraining. However, applying influence functions directly to deep models presents challenges, primarily due to the non-convex nature of the loss function and the large size of model parameters. This difficulty not only makes computing the inverse of the Hessian matrix costly but also renders it non-existent in some cases. Various approaches, including matrix decomposition, have been explored to expedite and approximate the inversion of the Hessian matrix, with the aim of making influence functions applicable to deep models. In this paper, we revisit a specific, albeit naive, yet effective approximation method known as TracIn. This method substitutes the inverse of the Hessian matrix with an identity matrix. We provide deeper insights into why this simple approximation method performs well. Furthermore, we extend its applications beyond measuring model utility to include considerations of fairness and robustness. Finally, we enhance TracIn through an ensemble strategy. To validate its effectiveness, we conduct experiments on synthetic data and extensive evaluations on noisy label detection, sample selection for large language model fine-tuning, and defense against adversarial attacks.

Accurate evaluation of financial question answering (QA) systems necessitates a comprehensive dataset encompassing diverse question types and contexts. However, current financial QA datasets lack scope diversity and question complexity. This work introduces FinTextQA, a novel dataset for long-form question answering (LFQA) in finance. FinTextQA comprises 1,262 high-quality, source-attributed QA pairs extracted and selected from finance textbooks and government agency websites.Moreover, we developed a Retrieval-Augmented Generation (RAG)-based LFQA system, comprising an embedder, retriever, reranker, and generator. A multi-faceted evaluation approach, including human ranking, automatic metrics, and GPT-4 scoring, was employed to benchmark the performance of different LFQA system configurations under heightened noisy conditions. The results indicate that: (1) Among all compared generators, Baichuan2-7B competes closely with GPT-3.5-turbo in accuracy score; (2) The most effective system configuration on our dataset involved setting the embedder, retriever, reranker, and generator as Ada2, Automated Merged Retrieval, Bge-Reranker-Base, and Baichuan2-7B, respectively; (3) models are less susceptible to noise after the length of contexts reaching a specific threshold.

Bringing high-level machine learning models to efficient and well-suited machine implementations often invokes a bunch of tools, e.g.~code generators, compilers, and optimizers. Along such tool chains, abstractions have to be applied. This leads to not optimally used CPU registers. This is a shortcoming, especially in resource constrained embedded setups. In this work, we present a code generation approach for decision tree ensembles, which produces machine assembly code within a single conversion step directly from the high-level model representation. Specifically, we develop various approaches to effectively allocate registers for the inference of decision tree ensembles. Extensive evaluations of the proposed method are conducted in comparison to the basic realization of C code from the high-level machine learning model and succeeding compilation. The results show that the performance of decision tree ensemble inference can be significantly improved (by up to $\approx1.6\times$), if the methods are applied carefully to the appropriate scenario.

Recent research in the field of multimodal machine translation (MMT) has indicated that the visual modality is either dispensable or offers only marginal advantages. However, most of these conclusions are drawn from the analysis of experimental results based on a limited set of bilingual sentence-image pairs, such as Multi30k. In these kinds of datasets, the content of one bilingual parallel sentence pair must be well represented by a manually annotated image, which is different from the real-world translation scenario. In this work, we adhere to the universal multimodal machine translation framework proposed by Tang et al. (2022). This approach allows us to delve into the impact of the visual modality on translation efficacy by leveraging real-world translation datasets. Through a comprehensive exploration via probing tasks, we find that the visual modality proves advantageous for the majority of authentic translation datasets. Notably, the translation performance primarily hinges on the alignment and coherence between textual and visual contents. Furthermore, our results suggest that visual information serves a supplementary role in multimodal translation and can be substituted., Comment: bucc 2024 accepted

We study a graph partition problem where we are given a directed acyclic graph (DAG) whose vertices and arcs can be respectively regarded as tasks and dependencies among tasks. The objective of the problem is to minimize the total energy consumed for completing these tasks by assigning the tasks to k heterogeneous machines. We first show that the problem is NP-hard. Then, we present polynomial-time algorithms for two special cases where there are only two machines and where the input DAG is a directed path. Finally, we study a natural variant where there are only two machines with one of them being capable of executing a limited number of tasks. We show that this special case remains computationally hard.

Element segmentation is a key step in nondestructive testing of Printed Circuit Boards (PCB) based on Computed Tomography (CT) technology. In recent years, the rapid development of self-supervised pretraining technology can obtain general image features without labeled samples, and then use a small amount of labeled samples to solve downstream tasks, which has a good potential in PCB element segmentation. At present, Masked Image Modeling (MIM) pretraining model has been initially applied in PCB CT image element segmentation. However, due to the small and regular size of PCB elements such as vias, wires, and pads, the global visual field has redundancy for a single element reconstruction, which may damage the performance of the model. Based on this issue, we propose an efficient pretraining model based on multi-scale local visual field feature reconstruction for PCB CT image element segmentation (EMLR-seg). In this model, the teacher-guided MIM pretraining model is introduced into PCB CT image element segmentation for the first time, and a multi-scale local visual field extraction (MVE) module is proposed to reduce redundancy by focusing on local visual fields. At the same time, a simple 4-Transformer-blocks decoder is used. Experiments show that EMLR-seg can achieve 88.6% mIoU on the PCB CT image dataset we proposed, which exceeds 1.2% by the baseline model, and the training time is reduced by 29.6 hours, a reduction of 17.4% under the same experimental condition, which reflects the advantage of EMLR-seg in terms of performance and efficiency.

Influence functions offer a robust framework for assessing the impact of each training data sample on model predictions, serving as a prominent tool in data-centric learning. Despite their widespread use in various tasks, the strong convexity assumption on the model and the computational cost associated with calculating the inverse of the Hessian matrix pose constraints, particularly when analyzing large deep models. This paper focuses on a classical data-centric scenario--trimming detrimental samples--and addresses both challenges within a unified framework. Specifically, we establish an equivalence transformation between identifying detrimental training samples via influence functions and outlier gradient detection. This transformation not only presents a straightforward and Hessian-free formulation but also provides profound insights into the role of the gradient in sample impact. Moreover, it relaxes the convexity assumption of influence functions, extending their applicability to non-convex deep models. Through systematic empirical evaluations, we first validate the correctness of our proposed outlier gradient analysis on synthetic datasets and then demonstrate its effectiveness in detecting mislabeled samples in vision models, selecting data samples for improving performance of transformer models for natural language processing, and identifying influential samples for fine-tuned Large Language Models.

This paper proposes an extended interconnection and damping assignment passivity-based control technique (IDA-PBC) to control the pressure dynamics in the fuel delivery subsystem (FDS) of proton exchange membrane fuel cells. The fuel cell stack is a distributed parameter model which can be modeled by partial differential equations PDEs). In this paper, the segmentation concept is used to approximate the PDEs model by ordinary differential equations (ODEs) model. Therefore, each segments are having multiple ODEs to obtain the lump-sum model of the segments. Subsequently, a generalized multi-input multi-output lumped parameters model is developed in port-Hamiltonian framework based on mass balance to minimize the modeling error. The modeling errors arises due to the difference between spatially distributed pressures in FDS segments, and also due to the difference between the actual stack pressure and the measured output pressure of the anode. The segments interconnection feasibilities are ensured by maintaining passivity of each segment. With consideration of re-circulation and bleeding of the anode in the modeling, an extended energy-shaping and output tracking IDA-PBC based state-feedback controller is proposed to control the spatially distributed pressure dynamics in the anode. Furthermore, a sliding mode observer of high order is designed to estimate the unmeasurable pressures in FDS with known disturbances. Performance recovery of output feedback control is accomplished with explicit stability analysis. The effectiveness of the proposed IDA-PBC approach is validated by the simulation results., Comment: 12 pages, 11 Figures

The protocol describes step-by-step sample preparation, data acquisition, and segmentation of cellular organelles with soft X-ray tomography. It is designed for microscopes built to perform full-rotation data acquisition on specimens in cylindrical sample holders, such as the XM-2 microscope at the Advanced Light Source, LBNL; however, it might be generalized for similar sample holder designs for both synchrotron and table-top microscopes. For complete details on the use and execution of this profile, please refer to Loconte et al. (2021).

Upon infection, viruses hijack the cell machinery and remodel host cell structures to utilize them for viral proliferation. Since viruses are about a thousand times smaller than their host cells, imaging virus-host interactions at high spatial resolution is like looking for a needle in a haystack. Scouting gross cellular changes with fluorescent microscopy is only possible for well-established viruses, where fluorescent tagging is developed. Soft X-ray tomography (SXT) offers 3D imaging of entire cells without the need for chemical fixation or labeling. Here, we use full-rotation SXT to visualize entire human B cells infected by the herpes simplex virus 1 (HSV-1). We have mapped the temporospatial remodeling of cells during the infection and observed changes in cellular structures, such as the presence of cytoplasmic stress granules and multivesicular structures, formation of nuclear virus-induced dense bodies, and aggregates of capsids. Our results demonstrate the power of SXT imaging for scouting virus-induced changes in infected cells and understanding the orchestration of virus-host remodeling quantitatively.

Chenxing Wei,1,&ast; Jian Chen,1,&ast; Tanveer Muhammad Anwar,2,&ast; Lingling Huang,1 Wenjie Yang,3 Xueyan Dong,1 Qiong Chen,1 Min Yue,2,4,5 Daojun Yu1,3 1Department of Medical Laboratory, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, People’s Republic of China; 2Department of Veterinary Medicine, Institute of Preventive Veterinary Sciences, Zhejiang University College of Animal Sciences, Hangzhou, 310058, People’s Republic of China; 3Department of Medical Laboratory, The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, People’s Republic of China; 4Hainan Institute, Zhejiang University, Sanya, 572025, People’s Republic of China; 5State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Min Yue; Daojun Yu, Email myue@zju.edu.cn; yudaojun98@zju.edu.cnPurpose: Acinetobacter baumannii (A. baumannii or AB) is one of the most opportunistic, nosocomial pathogens threatening public healthcare across countries. A. baumannii has become a primary growing concern due to its exceptional ability to acquire antimicrobial resistance (AMR) to multiple antimicrobial agents which is increasingly reported and more prevalent every year. Therefore, there is an urgent need to evaluate the AMR knowledge of A. baumannii for effective clinical treatment of nosocomial infections. This study aimed to investigate the clinical distribution AMR phenotypes and genotypes, and genomic characteristics of A. baumannii isolates recovered from hospitalized patients of different clinical departments of a sentinel hospital to improve clinical practices.Methods: A total of 123 c

Jing Li,1,&ast; Yuzhen Mo,2,&ast; Qingqing Wei,1 Jian Chen,3 Guozeng Xu1 1Department of Oncology, Liuzhou People’s Hospital of Guangxi Medical University, Liuzhou, Guangxi, People’s Republic of China; 2Department of Radiation Oncology, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, Guangdong, People’s Republic of China; 3Department of Medical Oncology, Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Guozeng Xu; Jian Chen, Email lzry19831002@yeah.net; chenjianyt@163.comBackground: Activated mast cells (AMCs) have been fully researched in inflammation and allergic reactions. However, the protumoral role of AMCs and their biomarker CD203c has not yet been investigated in colorectal cancer (CRC).Methods: We retrospectively collected 449 postoperative patients with stage II–III CRC at two different hospitals as the training (n=310) and validation (n=139) cohorts. These findings were further validated in the independent cohort (Integration of GSE39582 and GSE17536, n=489). The AMC density was assessed using CD203c staining or the CIBERSORT method. The main analysis was recurrence-free survival (RFS) and overall survival (OS).Results: As an independent factor, high AMC infiltration was associated with worse RFS/OS in the training (hazard ratio [HR]=3.437/3.014, all p< 0.001) and validation (HR=3.537/2.382, all p< 0.001) cohorts. We developed and validated an AMC-based nomogram for better stratification for postoperative recurrence in these two cohorts. The role of AMC density was further validated in the independent cohort. High AMC infiltration was associated with decreased RFS/OS after adjuvant chemotherapy (all p< 0.05). Approximately 74.2% of intramural CD203c+ AMCs expressed a high level of PD-L1. Multiple immunosuppressive pathways were enriched in high AMC infiltration tumors, including upregulation of the TN

Developing in silico models that accurately reflect a whole, functional cell is an ongoing challenge in biology. Current efforts bring together mathematical models, probabilistic models, visual representations, and data to create a multi-scale description of cellular processes. A realistic whole-cell model requires imaging data since it provides spatial constraints and other critical cellular characteristics that are still impossible to obtain by calculation alone. This review introduces Soft X-ray Tomography (SXT) as a powerful imaging technique to visualize and quantify the mesoscopic (~25 nm spatial scale) organelle landscape in whole cells. SXT generates three-dimensional reconstructions of cellular ultrastructure and provides a measured structural framework for whole-cell modeling. Combining SXT with data from disparate technologies at varying spatial resolutions provides further biochemical details and constraints for modeling cellular mechanisms. We conclude, based on the results discussed here, that SXT provides a foundational dataset for a broad spectrum of whole-cell modeling experiments.

Stochastic fractional differential systems are important and useful in the mathematics, physics, and engineering fields. However, the determination of their probabilistic responses is difficult due to their non-Markovian property. The recently developed globally-evolving-based generalized density evolution equation (GE-GDEE), which is a unified partial differential equation (PDE) governing the transient probability density function (PDF) of a generic path-continuous process, including non-Markovian ones, provides a feasible tool to solve this problem. In the paper, the GE-GDEE for multi-dimensional linear fractional differential systems subject to Gaussian white noise is established. In particular, it is proved that in the GE-GDEE corresponding to the state-quantities of interest, the intrinsic drift coefficient is a time-varying linear function, and can be analytically determined. In this sense, an alternative low-dimensional equivalent linear integer-order differential system with exact closed-form coefficients for the original high-dimensional linear fractional differential system can be constructed such that their transient PDFs are identical. Specifically, for a multi-dimensional linear fractional differential system, if only one or two quantities are of interest, GE-GDEE is only in one or two dimensions, and the surrogate system would be a one- or two-dimensional linear integer-order system. Several examples are studied to assess the merit of the proposed method. Though presently the closed-form intrinsic drift coefficient is only available for linear stochastic fractional differential systems, the findings in the present paper provide a remarkable demonstration on the existence and eligibility of GE-GDEE for the case that the original high-dimensional system itself is non-Markovian, and provide insights for the physical-mechanism-informed determination of intrinsic drift and diffusion coefficients of GE-GDEE of more generic complex nonlinear systems.

CDK4/6 are important regulators of cell cycle and their inhibitors have been approved as anti-cancer drugs. Here, we report a STING-dependent anti-tumor immune mechanism responsible for tumor suppression by CDK4/6 blockade. Clinical datasets show that in human tissues, CDK4 and CDK6 are over-expressed and their expressions are negatively correlated with patients overall survival and T cell infiltration. Deletion of Cdk4 or Cdk6 in tumor cells significantly reduce tumor growth. Mechanistically, we find that Cdk4 or Cdk6 deficiency contributes to an increased level of endogenous DNA damage, which triggers the cGAS-STING signaling pathway to activate type I interferon response. Knockout of Sting is sufficient to reverse and partially reverse the anti-tumor effect of Cdk4 and Cdk6 deficiency respectively. Therefore, our findings suggest that CDK4/6 inhibitors may enhance anti-tumor immunity through the STING-dependent type I interferon response.

Maximizing the production of heterologous biomolecules is a complex problem that can be addressed with a systems-level understanding of cellular metabolism and regulation. Specifically, growth-coupling approaches can increase product titers and yields and also enhance production rates. However, implementing these methods for non-canonical carbon streams is challenging due to gaps in metabolic models. Over four design-build-test-learn cycles, we rewire Pseudomonas putida KT2440 for growth-coupled production of indigoidine from para-coumarate. We explore 4,114 potential growth-coupling solutions and refine one design through laboratory evolution and ensemble data-driven methods. The final growth-coupled strain produces 7.3 g/L indigoidine at 77% maximum theoretical yield in para-coumarate minimal medium. The iterative use of growth-coupling designs and functional genomics with experimental validation was highly effective and agnostic to specific hosts, carbon streams, and final products and thus generalizable across many systems.

Judging the working status of a gearbox through the characteristics of vibration signals was an effective fault diagnosis method. In order to improve the efficiency and reliability of vibration detection, a multi node testing system based on a microcontroller was designed and validated. The system mainly consists of signal processing equipment, signal collector, upper computer, sensors, and experimental workbench. Under different types of gear faults, the vibration signals of ten sets of gearboxes were synchronously collected and processed. In order to ensure the resolution of frequency conversion and accurately locate frequency conversion, the low-pass filtered spectrum with a filtering frequency of 1000 was verified and used to calculate the amplitude of frequency conversion. The normalization of amplitude was more conducive to feature recognition, and the frequency amplitude was proportional to its corresponding energy. According to standard spectral data and spectral analysis, wear, pitting, fracture, and bonding faults in the gearbox were identified and classified. Through the verification of three order feature spectrum, it can be seen that the accuracy of fault prediction for gearbox through vibration signals is high, which can effectively reduce maintenance and repair costs.

Autoimmune diseases are characterized by the immune system's attack on one's own tissues which are highly diverse and diseases differ in severity, causing damage in virtually all human systems including connective tissue (e.g., rheumatoid arthritis), neurological system (e.g., multiple sclerosis) and digestive system (e.g., inflammatory bowel disease). Historically, treatments normally include pain-killing medication, anti-inflammatory drugs, corticosteroids, and immunosuppressant drugs. However, given the above characteristics, treatment of autoimmune diseases has always been a challenge. Artemisinin is a natural sesquiterpene lactone initially extracted and separated from Chinese medicine Artemisia annua L., which has a long history of curing malaria. Artemisinin's derivatives such as artesunate, dihydroartemisinin, artemether, artemisitene, and so forth, are a family of artemisinins with antimalarial activity. Over the past decades, accumulating evidence have indicated the promising therapeutic potential of artemisinins in autoimmune diseases. Herein, we systematically summarized the research regarding the immunoregulatory properties of artemisinins including artemisinin and its derivatives, discussing their potential therapeutic viability toward major autoimmune diseases and the underlying mechanisms. This review will provide new directions for basic research and clinical translational medicine of artemisinins.

Min Li,1,2,&ast; Shuling Wan,1,&ast; Nanbu Wang,3 Jiahao Chen,4 Jiangang Duan,5 Jian Chen,6 Xuxiang Zhang,7 Ran Meng,1,2 Xunming Ji2,6 1Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Beijing Institute for Brain Disorders, Capital Medical University, Beijing, People’s Republic of China; 3Department of Neurology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 4Department of Neurobiology, School of Basic Medical Sciences, Capital Medical University, Beijing, People’s Republic of China; 5Department of Emergency, Xuanwu Hospital, Capital Medical University, Beijing, People’s Republic of China; 6Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, People’s Republic of China; 7Department of Ophthalmology, Xuanwu Hospital, Capital Medical University, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Ran Meng, Department of Neurology, Xuanwu Hospital, Capital Medical University, 45 Changchun Road, Xicheng District, Beijing, 100053, People’s Republic of China, Tel +86-13693080599, Email ranmeng2011@126.com Xunming Ji, Beijing Institute for Brain Disorders, Capital Medical University, 10 You an Men Wai Road, Fengtai District, Beijing, 100069, People’s Republic of China, Tel +86-13911077166, Email jixm@ccmu.edu.cnIntroduction: Cerebral venous thrombosis (CVT) is a rare subtype of stroke. However, existing scales were insufficient to evaluate the overall severity of CVT. The aim of this study is to develop and validate a CVT severity scale.Methods: Items 1– 11 were directly derived from NIHSS. New items were generated from a literature review and focus group discussion. A total of 170 CVT patients were prospectively recruited from 26 top tertiary hospitals in China Mainland from January 2021 to May 2022 to validate the CVT severity scale. The C

The behavior of quark matter with both external electric field and chiral chemical potential is theoretically and experimentally interesting to consider. In this paper, the case of simultaneous presence of imaginary electric field and chiral chemical potential is investigated using the lattice QCD approach with $N_f=1+1$ dynamical staggered fermions. We find that overall both the imaginary electric field and the chiral chemical potential can exacerbate chiral symmetry breaking, which is consistent with theoretical predictions. However we also find a non-monotonic behavior of chiral condensation at specific electric field strengths and chiral chemical potentials. In addition to this, we find that the behavior of Polyakov loop in the complex plane is not significantly affected by chiral chemical potential in the region of the parameters consider in this paper., Comment: 24 pages, 13 figures

Background: Rheumatoid arthritis (RA) is an autoimmune disease known as a leading cause of disability with considerable mortality. Developing alternative drugs and targets for RA treatment is an urgent issue. Sesamol is a phenolic compound isolated from natural food sesame (Sesamum indicum L.) with various biological activities. Purpose: The current research intended to illuminate the bioactivity and mechanisms of sesamol in RA fibroblast-like synoviocytes (FLS), and aimed to estimate the potential clinical application value of sesamol in RA treatment. Methods: CCK-8, EdU, and flow cytometry assays, as well as transwell tests were applied to observe the effects of sesamol on the abnormal functions of RA-FLS. Moreover, synovial organoids and a collagen-induced arthritis (CIA) mouse model were constructed to further explore the therapeutic capacity of sesamol on RA. Furthermore, RNA sequencing combined with quantitative real-time PCR assay, Western blot as well as co-immunoprecipitation were employed to clarify the mechanism of sesamol in regulating RA progression. Results: Sesamol suppressed the proliferation through inhibiting DNA replication, triggering cell cycle arrest and apoptosis of RA-FLS. Besides, sesamol impaired RA-FLS migration and invasion. Interestingly, sesamol inhibited the growth of constructed synovial organoids and alleviated RA symptoms in CIA mice. Moreover, RNA sequencing further implicated p53 signaling as a downstream pathway of sesamol. Furthermore, sesamol was shown to decrease p53 ubiquitination and degradation, thereby activating p53 signaling. Finally, bioinformatics analyses also highlighted the importance of sesamol-regulated networks in the progression of RA. Conclusions: Our investigation demonstrated that sesamol served as a novel p53 stabilizer to attenuate the abnormal functions of RA-FLS via facilitating the activation of p53 signaling. Moreover, our study highlighted that sesamol might be an effective lead compound or candidate d