Metabolomics and network pharmacology reveal the mechanism of antithrombotic effect of Asperosaponin VI.

Dipsaci Radix may possess antithrombotic properties, and one of its primary active ingredients is Asperosaponin VI. However, the antithrombotic effects and pharmacological mechanisms of Asperosaponin VI remain unclear. An in vivo experimental study has demonstrated the antithrombotic activity of Asperosaponin VI. Asperosaponin VI also exhibits anticoagulant properties. Asperosaponin VI significantly hindered collagen adrenergic-induced acute pulmonary thrombosis in mice and enhanced their survival rate. This hinders the formation of acute pulmonary embolisms induced by adenosine diphosphate (ADP) and decreases recovery time. A comprehensive strategy that combines metabolomics, network pharmacology, molecular docking, and experimental validation has the potential to reveal the antithrombotic mechanisms of Asperosaponin VI. Metabolomic evidence suggests that Asperosaponin VI may influence platelet aggregation and the production of anti-inflammatory metabolites through the regulation of pathways such as phenylalanine and arachidonic acid metabolism, thereby inhibiting thrombosis. Network pharmacology identified the pharmacological targets of Asperosaponin VI and indicated that it treats thrombi by partially regulating the signaling pathways related to inflammation and platelet aggregation. Asperosaponin VI showed strong binding affinity for F2, PTPRC, JUN, STAT3, SRC, AKT1. The antiplatelet aggregation activity of Asperosaponin VI was validated based on the metabolomic and network pharmacology results. Asperosaponin VI inhibits platelet aggregation induced by ADP, AA, and collagen. Therefore, Asperosaponin VI exerts antithrombotic effects through antiplatelet aggregation. Therefore, Asperosaponin VI is a promising antithrombotic agent.

Temporal structured illumination and vision-transformer enables large field-of-view binary snapshot ptychography.

Ptychography, a widely used computational imaging method, generates images by processing coherent interference patterns scattered from an object of interest. In order to capture scenes with large field-of-view (FoV) and high spatial resolution simultaneously in a single shot, we propose a temporal-compressive structured-light Ptychography system. A novel three-step reconstruction algorithm composed of multi-frame spectra reconstruction, phase retrieval, and multi-frame image stitching is developed, where we employ the emerging Transformer-based network in the first step. Experimental results demonstrate that our system can expand the FoV by 20× without losing spatial resolution. Our results offer huge potential for enabling lensless imaging of molecules with large FoV as well as high spatial-temporal resolutions. We also notice that due to the loss of low-intensity information caused by the compressed sensing process, our method so far is only applicable to binary targets.

Pathological Priors Inspired Network for Vertebral Osteophytes Recognition.

Automatic vertebral osteophyte recognition in Digital Radiography is of great importance for the early prediction of degenerative disease but is still a challenge because of the tiny size and high inter-class similarity between normal and osteophyte vertebrae. Meanwhile, common sampling strategies applied in Convolution Neural Network could cause detailed context loss. All of these could lead to an incorrect positioning predicament. In this paper, based on important pathological priors, we define a set of potential lesions of each vertebra and propose a novel Pathological Priors Inspired Network (PPIN) to achieve accurate osteophyte recognition. PPIN comprises a backbone feature extractor integrating with a Wavelet Transform Sampling module for high-frequency detailed context extraction, a detection branch for locating all potential lesions and a classification branch for producing final osteophyte recognition. The Anatomical Map-guided Filter between two branches helps the network focus on the specific anatomical regions via the generated heatmaps of potential lesions in the detection branch to address the incorrect positioning problem. To reduce the inter-class similarity, a Bilateral Augmentation Module based on the graph relationship is proposed to imitate the clinical diagnosis process and to extract discriminative contextual information between adjacent vertebrae in the classification branch. Experiments on the two osteophytes-specific datasets collected from the public VinDr-Spine database show that the proposed PPIN achieves the best recognition performance among multitask frameworks and shows strong generalization. The results on a private dataset demonstrate the potential in clinical application. The Class Activation Maps also show the powerful localization capability of PPIN. The source codes are available in https://github.com/Phalo/PPIN.

Optical Microfiber Intelligent Sensor: Wearable Cardiorespiratory and Behavior Monitoring with a Flexible Wave-Shaped Polymer Optical Microfiber.

With the advantages of high flexibility, strong real-time monitoring capabilities, and convenience, wearable devices have shown increasingly powerful application potential in medical rehabilitation, health monitoring, the Internet of Things, and human-computer interaction. In this paper, we propose a novel and wearable optical microfiber intelligent sensor based on a wavy-shaped polymer optical microfiber (WPOMF) for cardiorespiratory and behavioral monitoring of humans. The optical fibers based on polymer materials are prepared into optical microfibers, fully using the advantages of the polymer material and optical microfibers. The prepared polymer optical microfiber is designed into a flexible wave-shaped structure, which enables the WPOMF sensor to have higher tensile properties and detection sensitivity. Cardiorespiratory and behavioral detection experiments based on the WPOMF sensor are successfully performed, which demonstrates the high sensitivity and stability potential of the WPOMF sensor when performing wearable tasks. Further, the success of the AI-assisted medical keyword pronunciation recognition experiment fully demonstrates the feasibility of integrating AI technology with the WPOMF sensor, which can effectively improve the intelligence of the sensor as a wearable device. As an optical microfiber intelligent sensor, the WPOMF sensor offers broad application prospects in disease monitoring, rehabilitation medicine, the Internet of Things, and other fields.

Multiphoton emission of single CdZnSe/ZnS quantum dots coupled with plasmonic Au nanoparticles.

The fluorescence blinking and low multiphoton emission of quantum dots (QDs) have limited their application in lasing, light-emitting diodes, and so on. Coupling of single QDs to plasmonic nanostructures is an effective approach to control the photon properties. Here plasmon-exciton systems including Au nanoparticles and CdZnSe/ZnS QDs were investigated at the single particle level. With the modulation of the local electromagnetic field, the fluorescence intensity of single QDs is increased, accompanied by a significant suppression in blinking behavior, and the lifetime is shortened from 15 ns to 2 ns. Moreover, the second-order photon intensity correlation at zero lag time g2(0) of coupled single QDs is larger than 0.5, indicating an increased probability of multiphoton emission. The enhancement factors of radiative and nonradiative decay rates of QDs coupled with Au nanoparticles are calculated. The sharply increased radiative decay rate can be comparable to the nonradiative Auger rate, leading to dominated multiple exciton radiative recombination with PL intensity enhancement, suppressed blinking, lifetime shortening, and multiphoton emission. The results of the exciton decay dynamics and emission properties of single QDs in this work are helpful in exploring the mechanism of plasmon-exciton interaction and optoelectronic application of single QDs.

Spatio-temporal layers based intra-operative stereo depth estimation network via hierarchical prediction and progressive training.

BACKGROUND AND OBJECTIVE: Safety of robotic surgery can be enhanced through augmented vision or artificial constraints to the robotl motion, and intra-operative depth estimation is the cornerstone of these applications because it provides precise position information of surgical scenes in 3D space. High-quality depth estimation of endoscopic scenes has been a valuable issue, and the development of deep learning provides more possibility and potential to address this issue. METHODS: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes. To this aim, a fully 3D encoder-decoder network integrating spatio-temporal layers is designed, and it adopts hierarchical prediction and progressive learning to enhance prediction accuracy and shorten training time. RESULTS: Our network gets the depth estimation accuracy of MAE 2.55±1.51 (mm) and RMSE 5.23±1.40 (mm) using 8 surgical videos with a resolution of 1280×1024, which performs better compared with six other state-of-the-art methods that were trained on the same data. CONCLUSIONS: Our network can implement a promising depth estimation performance in intra-operative scenes using stereo images, allowing the integration in robot-assisted surgery to enhance safety.

Plant hypersensitive induced reaction protein facilitates cell death induced by secreted xylanase associated with the pathogenicity of Sclerotinia sclerotiorum.

Necrotrophic fungal plant pathogens employ cell death-inducing proteins (CDIPs) to facilitate infection. However, the specific CDIPs and their mechanisms in pathogenic processes of Sclerotinia sclerotiorum, a necrotrophic pathogen that causes disease in many economically important crop species, have not yet been clearly defined. This study found that S. sclerotiorum secretes SsXyl2, a glycosyl hydrolase family 11 xylanase, at the late stage of hyphal infection. SsXyl2 targets the apoplast of host plants to induce cell death independent of xylanase activity. Targeted disruption of SsXyl2 leads to serious impairment of virulence, which can be recovered by a catalytically impaired SsXyl2 variant, thus supporting the critical role of cell death-inducing activity of SsXyl2 in establishing successful colonization of S. sclerotiorum. Remarkably, infection by S. sclerotiorum induces the accumulation of Nicotiana benthamiana hypersensitive-induced reaction protein 2 (NbHIR2). NbHIR2 interacts with SsXyl2 at the plasma membrane and promotes its localization to the cell membrane and cell death-inducing activity. Furthermore, gene-edited mutants of NbHIR2 displayed increased resistance to the wild-type strain of S. sclerotiorum, but not to the SsXyl2-deletion strain. Hence, SsXyl2 acts as a CDIP that manipulates host cell physiology by interacting with hypersensitive induced reaction protein to facilitate colonization by S. sclerotiorum. These findings provide valuable insights into the pathogenic mechanisms of CDIPs in necrotrophic pathogens and lead to a more promising approach for breeding resistant crops against S. sclerotiorum.

Node-downloadable frequency transfer system based on a mode-locked laser with over 100 km of fiber.

To meet the requirements of time-frequency networks and enable frequency downloadability for nodes along the link, we demonstrated the extraction of stable frequency signals at nodes using a mode-locked laser under the condition of 100 km laboratory fiber. The node consists of a simple structure that utilizes widely used optoelectronic devices and enables plug-and-play applications. In addition, the node can recover frequency signals with multiple frequencies, which are useful for scenarios that require different frequencies. Here, we experimentally demonstrated a short-term frequency instability of 2.83 × 10-13@1 s and a long-term frequency instability of 1.18 × 10-15@10,000 s at the node, which is similar to that at the remote site of the frequency transfer system. At the same time, frequency signals with different frequencies also achieved stable extraction with the same performance at the node. Our results can support the distributed application under large-scale time-frequency networks.

Hundred-femtosecond-level concise optical time delay measurement system based on linear optical sampling.

Fiber-delay measurement is one of the key fundamental technologies in numerous fields. Here, we propose and experimentally demonstrate a high-precision and concise optical time delay measurement system based on the technique of linear optical sampling, reaching the precision better than 100 fs under averaging. The use of only two optical frequency combs without locking the carrier-envelope-offset frequency greatly simplifies the structure of the time-delay measurement system. We also experimentally investigate the current limitations on the precision of the system. The timing jitter noises of two sources are mainly non-common mode and are both restricted to the frequency sources. Our results indicate that the proposed device can measure fiber length fluctuations below 10 µm, paving the way for further analyses of the external disturbances on the fiber link.

Data-Driven Safe Policy Optimization for Black-Box Dynamical Systems With Temporal Logic Specifications.

Learning-based policy optimization methods have shown great potential for building general-purpose control systems. However, existing methods still struggle to achieve complex task objectives while ensuring policy safety during learning and execution phases for black-box systems. To address these challenges, we develop data-driven safe policy optimization (D 2 SPO), a novel reinforcement learning (RL)-based policy improvement method that jointly learns a control barrier function (CBF) for system safety and a linear temporal logic (LTL) guided RL algorithm for complex task objectives. Unlike many existing works that assume known system dynamics, by carefully constructing the data sets and redesigning the loss functions of D 2 SPO, a provably safe CBF is learned for black-box dynamical systems, which continuously evolves for improved system safety as RL interacts with the environment. To deal with complex task objectives, we take advantage of the capability of LTL in representing the task progress and develop LTL-guided RL policy for efficient completion of various tasks with LTL objectives. Extensive numerical and experimental studies demonstrate that D 2 SPO outperforms most state-of-the-art (SOTA) baselines and can achieve over 95% safety rate and nearly 100% task completion rates. The experiment video is available at https://youtu.be/2RgaH-zcmkY.

Identification of Triazolo-quinazolinone Derivatives as Novel and Potent Chitinase OfChi-h Inhibitors Based on Structure-Based Virtual Screening.

Insect chitinase, OfChi-h, from Ostrinia furnacalis, is considered as a promising target for the development of green pesticides. On the basis of the crystal structure of OfChi-h, we successfully obtained a triazolo-quinazolinone scaffold as the novel class of OfChi-h inhibitor via a structure-based virtual screening approach. Rational compound screening enabled us to acquire a potent OfChi-h inhibitor TQ19 with a Ki value of 0.33 µM. Furthermore, the in vivo biological activity of target compounds was assayed. The results showed that compounds TQ8 and TQ19 could dramatically inhibit the growth and development of Ostrinia nubilalis larvae, and most of the compounds showed higher insecticidal activity than hexaflumuron. This present work reveals that triazolo-quinazolinone derivatives can serve as novel candidates for insect growth regulators.

Paraventricular Mast Cell-Derived Histamine Activates CRH Neurons to Mediate Adult Visceral Hypersensitivity Induced by Neonatal Maternal Separation.

Neonatal maternal separation (NMS) is an early-life stress (ELS) that can result in adult visceral hypersensitivity, which is usually manifested as chronic visceral pain. Although mast cells and corticotropin-releasing hormone (CRH) neurons are involved in stress response, whether there is an interaction between mast cells and CRH neurons in hypothalamic paraventricular nucleus (PVN) during the ELS-induced visceral hypersensitivity remains elusive. Herein, we established an NMS model by separating neonatal mice from their mothers, and observed that these mice presented visceral hypersensitivity in adulthood, as indicated by elevated abdominal withdrawal reflex and lowered visceral pain threshold. The NMS-induced adult visceral hypersensitivity was accompanied by activation of mast cells and CRH neurons in PVN. Also, NMS increased the histamine content (an inflammatory mediator mainly released by mast cells) and histamine H2 receptor (H2R) expression of CRH neurons in PVN. Remarkably, intra-PVN administration with mast cell stabilizer attenuated the NMS-induced CRH neuronal activation and adult visceral pain, while histamine administration showed the opposite effects. Moreover, intra-PVN injection with H2R antagonist alleviated the NMS-induced CRH neuronal activation, PKA and CREB phosphorylation, and importantly, adult visceral pain. Together, our findings revealed a role of an interaction between paraventricular mast cells and CRH neurons in NMS-induced adult visceral hypersensitivity, thereby providing a perspective for the management of visceral pain.

Deep learning-based detection and classification of lumbar disc herniation on magnetic resonance images.

Background: The severity assessment of lumbar disc herniation (LDH) on MR images is crucial for selecting suitable surgical candidates. However, the interpretation of MR images is time-consuming and requires repetitive work. This study aims to develop and evaluate a deep learning-based diagnostic model for automated LDH detection and classification on lumbar axial T2-weighted MR images. Methods: A total of 1115 patients were analyzed in this retrospective study; both a development dataset (1015 patients, 15 249 images) and an external test dataset (100 patients, 1273 images) were utilized. According to the Michigan State University (MSU) classification criterion, experts labeled all images with consensus, and the final labeled results were regarded as the reference standard. The automated diagnostic model comprised Faster R-CNN and ResNeXt101 as the detection and classification network, respectively. The deep learning-based diagnostic performance was evaluated by calculating mean intersection over union (IoU), accuracy, precision, sensitivity, specificity, F1 score, the area under the receiver operating characteristics curve (AUC), and intraclass correlation coefficient (ICC) with 95% confidence intervals (CIs). Results: High detection consistency was obtained in the internal test dataset (mean IoU = 0.82, precision = 98.4%, sensitivity = 99.4%) and external test dataset (mean IoU = 0.70, precision = 96.3%, sensitivity = 97.8%). Overall accuracy for LDH classification was 87.70% (95% CI: 86.59%-88.86%) and 74.23% (95% CI: 71.83%-76.75%) in the internal and external test datasets, respectively. For internal testing, the proposed model achieved a high agreement in classification (ICC = 0.87, 95% CI: 0.86-0.88, P < 0.001), which was higher than that of external testing (ICC = 0.79, 95% CI: 0.76-0.81, P < 0.001). The AUC for model classification was 0.965 (95% CI: 0.962-0.968) and 0.916 (95% CI: 0.908-0.925) in the internal and external test datasets, respectively. Conclusions: The automated diagnostic model achieved high performance in detecting and classifying LDH and exhibited considerable consistency with experts' classification.

Generation of flattop beams from a distorted optical field by the wavefront shaping technique.

Uniform laser beams with controllable patterns are crucial for various applications, including laser processing and inertial confinement fusion. While some methods have been proposed to generate flattop beams, they often require complex optical systems that can become ineffective because of the misalignment of the system or the imperfection of optical elements. To overcome these issues, we utilized feedback-based wavefront shaping (FWS) technology to generate flattop beams with desired patterns from a disordered light. To solve the multi-goal optimization problem, we propose some modifications based on the Non-dominated Sorting Genetic Algorithm II (NSGA2) and successfully generate focal beams with a uniform intensity distribution and controllable beam shape from the disordered light field.

Transcriptional plasticity of schizotrophic Sclerotinia sclerotiorum responds to symptomatic rapeseed and endophytic wheat hosts.

IMPORTANCE: The broad host range of fungi with differential fungal responses leads to either a pathogenic or an endophytic lifestyle in various host plants. Yet, the molecular basis of schizotrophic fungal responses to different plant hosts remains unexplored. Here, we observed a general increase in the gene expression of S. sclerotiorum associated with pathogenicity in symptomatic rapeseed, including small protein secretion, appressorial formation, and oxalic acid toxin production. Conversely, in wheat, many carbohydrate metabolism and transport-associated genes were induced, indicating a general increase in processes associated with carbohydrate acquisition. Appressorium is required for S. sclerotiorum during colonization in symptomatic hosts but not in endophytic wheat. These findings provide new clues for understanding schizotrophic fungi, fungal evolution, and the emergence pathways of new plant diseases.

Genomic prediction of pig growth traits based on machine learning.

This study aimed to assess and compare the performance of different machine learning models in predicting selected pig growth traits and genomic estimated breeding values (GEBV) using automated machine learning, with the goal of optimizing whole-genome evaluation methods in pig breeding. The research employed genomic information, pedigree matrices, fixed effects, and phenotype data from 9968 pigs across multiple companies to derive four optimal machine learning models: deep learning (DL), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB). Through 10-fold cross-validation, predictions were made for GEBV and phenotypes of pigs reaching weight milestones (100 kg and 115 kg) with adjustments for backfat and days to weight. The findings indicated that machine learning models exhibited higher accuracy in predicting GEBV compared to phenotypic traits. Notably, GBM demonstrated superior GEBV prediction accuracy, with values of 0.683, 0.710, 0.866, and 0.871 for B100, B115, D100, and D115, respectively, slightly outperforming other methods. In phenotype prediction, GBM emerged as the best-performing model for pigs with B100, B115, D100, and D115 traits, achieving prediction accuracies of 0.547, followed by DL at 0.547, and then XGB with accuracies of 0.672 and 0.670. In terms of model training time, RF required the most time, while GBM and DL fell in between, and XGB demonstrated the shortest training time. In summary, machine learning models obtained through automated techniques exhibited higher GEBV prediction accuracy compared to phenotypic traits. GBM emerged as the overall top performer in terms of prediction accuracy and training time efficiency, while XGB demonstrated the ability to train accurate prediction models within a short timeframe. RF, on the other hand, had longer training times and insufficient accuracy, rendering it unsuitable for predicting pig growth traits and GEBV.

Multi-nodes dissemination of stable radio frequency with 10-17 instability over 2000 km optical fiber.

To meet the demand of flexible access for high-precision synchronization frequency, we demonstrate multi-node stable radio frequency (RF) dissemination over a long-distance optical fiber. Stable radio frequency signals can be extracted at any node along the optical fiber, not just at the endpoint. The differential mixing structure (DMS) is employed to avoid the frequency harmonic leakage and enhance the precision. The phase-locked loop (PLL) provides frequency reference for the DMS while improving the signal to noise ratio (SNR) of dissemination signal. We measure the frequency instability of multi-node stable frequency dissemination system (MFDS) at different locations along the 2,000 km optical fiber. The measured short-term instability with average time of 1 s are 1.90 × 10-14 @ 500 km, 2.81 × 10-14 @ 1,000 km, 3.46 × 10-14 @ 1,500 km, and 3.84 × 10-14 @ 2,000 km respectively. The long-term instability with average time of 10,000 s are basically the same at any position of the optical fiber, which is about (6.24 ± 0.05) × 10-17. The resulting instability is sufficient for the propagation of precision active hydrogen masers.

A non-thermal modification method to promote the interaction of zein-alginate oligosaccharides composites for better encapsulation and stability-Cold plasma.

This current research explored the application of cold plasma (CP) treatment to modify zein-alginate oligosaccharide (zein-AOS) composites in an ethanol-water solution. Anti-solvent method was used to prepare zein-AOS nanoparticles (NPs), and the objective was to investigate the mechanism by which CP promotes interaction between protein and saccharides. Characterization results indicated that CP treatment improved hydrogen bonding and electrostatic interaction between zein and AOS. The CP zein-AOS NPs underwent dispersion and rearrangement, resulting in smaller aggregates with better dispersibility. Among the various induction conditions tested, the zein-AOS85 NPs (induced at 85 W for 2 min) exhibited superior performance as delivery wall materials, with smaller particle size (234.67 nm), larger specific surface area (9.443 m2/g), and higher surface charge (-35.43 mV). In addition, zein-AOS85 showed high stability when used as delivery wall material, providing more binding sites and self-assembly dynamics for nutrients. Curcumin was used as the nutrient model in this study, and CP was found to enhance hydrogen bonding, electrostatic interaction, and hydrophobic interaction between zein, AOS, and nutrients, resulting in increased encapsulation efficiency (EE) from 63.80 % to 85.17 %. The delivery system also exhibited good pH, ionic strength, storage, and dispersion stability.

Experimental upstream transmission of continuous variable quantum key distribution access network.

Continuous variable quantum key distribution that can be implemented using only low-cost and off-the-shelf components reveals great potential in practical large-scale realization. Access networks, as a modern network necessity, connect many end-users to the network backbone. In this work, we first demonstrate upstream transmission quantum access networks using continuous variable quantum key distribution. A two-end-user quantum access network is then experimentally realized. Through phase compensation, data synchronization, and other technical upgrades, we achieve a secret key rate of the total network of 390 kbits/s. In addition, we extend the case of a two-end-user quantum access network to the case of a multiplicity of users, and analyze the network capacity in the case of a multiplicity of users by measuring the additive excess noise from different time slots.

FRSR: Framework for real-time scene reconstruction in robot-assisted minimally invasive surgery.

3D reconstruction of the intra-operative scenes provides precise position information which is the foundation of various safety related applications in robot-assisted surgery, such as augmented reality. Herein, a framework integrated into a known surgical system is proposed to enhance the safety of robotic surgery. In this paper, we present a scene reconstruction framework to restore the 3D information of the surgical site in real time. In particular, a lightweight encoder-decoder network is designed to perform disparity estimation, which is the key component of the scene reconstruction framework. The stereo endoscope of da Vinci Research Kit (dVRK) is adopted to explore the feasibility of the proposed approach, and it provides the possibility for the migration to other Robot Operating System (ROS) based robot platforms due to the strong independence on hardware. The framework is evaluated using three different scenarios, including a public dataset (3018 pairs of endoscopic images), the scene from the dVRK endoscope in our lab as well as a self-made clinical dataset captured from an oncology hospital. Experimental results show that the proposed framework can reconstruct 3D surgical scenes in real time (25 FPS), and achieve high accuracy (2.69 ± 1.48 mm in MAE, 5.47 ± 1.34 mm in RMSE and 0.41 ± 0.23 in SRE, respectively). It demonstrates that our framework can reconstruct intra-operative scenes with high reliability of both accuracy and speed, and the validation of clinical data also shows its potential in surgery. This work enhances the state of art in 3D intra-operative scene reconstruction based on medical robot platforms. The clinical dataset has been released to promote the development of scene reconstruction in the medical image community.