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.

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.

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.

In situ reprogramming of cardiac fibroblasts into cardiomyocytes in mouse heart with chemicals.

Cardiomyocytes are terminal differentiated cells and have limited ability to proliferate or regenerate. Condition like myocardial infarction causes massive death of cardiomyocytes and is the leading cause of death. Previous studies have demonstrated that cardiac fibroblasts can be induced to transdifferentiate into cardiomyocytes in vitro and in vivo by forced expression of cardiac transcription factors and microRNAs. Our previous study have demonstrated that full chemical cocktails could also induce fibroblast to cardiomyocyte transdifferentiation both in vitro and in vivo. With the development of tissue clearing techniques, it is possible to visualize the reprogramming at the whole-organ level. In this study, we investigated the effect of the chemical cocktail CRFVPTM in inducing in situ fibroblast to cardiomyocyte transdifferentiation with two strains of genetic tracing mice, and the reprogramming was observed at whole-heart level with CUBIC tissue clearing technique and 3D imaging. In addition, single-cell RNA sequencing (scRNA-seq) confirmed the generation of cardiomyocytes from cardiac fibroblasts which carries the tracing marker. Our study confirms the use of small molecule cocktails in inducing in situ fibroblast to cardiomyocyte reprogramming at the whole-heart level and proof-of-conceptly providing a new source of naturally incorporated cardiomyocytes to help heart regeneration.

Surface functionalized nanomaterial systems for targeted therapy of endocrine related tumors: a review of recent advancements.

The application of multidisciplinary techniques in the management of endocrine-related cancers is crucial for harnessing the advantages of multiple disciplines and their coordinated efforts in eliminating tumors. Due to the malignant characteristics of cancer cells, they possess the capacity to develop resistance to traditional treatments such as chemotherapy and radiotherapy. Nevertheless, despite diligent endeavors to enhance the prediction of outcomes, the overall survival rate for individuals afflicted with endocrine-related malignancy remains quite miserable. Hence, it is imperative to investigate innovative therapy strategies. The latest advancements in therapeutic tactics have offered novel approaches for the therapy of various endocrine tumors. This paper examines the advancements in nano-drug delivery techniques and the utilization of nanomaterials for precise cancer cures through targeted therapy. This review provides a thorough analysis of the potential of combined drug delivery strategies in the treatment of thyroid cancer, adrenal gland tumors, and pancreatic cancer. The objective of this study is to gain a deeper understanding of current therapeutic approaches, stimulate the development of new drug DDS, and improve the effectiveness of treatment for patients with these diseases. The intracellular uptake of pharmaceuticals into cancer cells can be significantly improved through the implantation of synthetic or natural substances into nanoparticles, resulting in a substantial reduction in the development of endocrine malignancies.

CADS: A Self-supervised Learner via Cross-modal Alignment and Deep Self-distillation for CT Volume Segmentation.

Self-supervised learning (SSL) has long had great success in advancing the field of annotation-efficient learning. However, when applied to CT volume segmentation, most SSL methods suffer from two limitations, including rarely using the information acquired by different imaging modalities and providing supervision only to the bottleneck encoder layer. To address both limitations, we design a pretext task to align the information in each 3D CT volume and the corresponding 2D generated X-ray image and extend self-distillation to deep self-distillation. Thus, we propose a self-supervised learner based on Cross-modal Alignment and Deep Self-distillation (CADS) to improve the encoder's ability to characterize CT volumes. The cross-modal alignment is a more challenging pretext task that forces the encoder to learn better image representation ability. Deep self-distillation provides supervision to not only the bottleneck layer but also shallow layers, thus boosting the abilities of both. Comparative experiments show that, during pre-training, our CADS has lower computational complexity and GPU memory cost than competing SSL methods. Based on the pre-trained encoder, we construct PVT-UNet for 3D CT volume segmentation. Our results on seven downstream tasks indicate that PVT-UNet outperforms state-of-the-art SSL methods like MOCOv3 and DiRA, as well as prevalent medical image segmentation methods like nnUNet and CoTr. Code and pre-trained weight will be available at https://github.com/yeerwen/CADS.

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.

TriLA: Triple-Level Alignment based Unsupervised Domain Adaptation for Joint Segmentation of Optic Disc and Optic Cup.

Cross-domain joint segmentation of optic disc and optic cup on fundus images is essential, yet challenging, for effective glaucoma screening. Although many unsupervised domain adaptation (UDA) methods have been proposed, these methods can hardly achieve complete domain alignment, leading to suboptimal performance. In this paper, we propose a triple-level alignment (TriLA) model to address this issue by aligning the source and target domains at the input level, feature level, and output level simultaneously. At the input level, a learnable Fourier domain adaptation (LFDA) module is developed to learn the cut-off frequency adaptively for frequency-domain translation. At the feature level, we disentangle the style and content features and align them in the corresponding feature spaces using consistency constraints. At the output level, we design a segmentation consistency constraint to emphasize the segmentation consistency across domains. The proposed model is trained on the RIGA+ dataset and widely evaluated on six different UDA scenarios. Our comprehensive results not only demonstrate that the proposed TriLA substantially outperforms other state-of-the-art UDA methods in joint segmentation of optic disc and optic cup, but also suggest the effectiveness of the triple-level alignment strategy.

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.

Tyrosine Mutation in the Characteristic Motif of the Amorphous Region of Spidroin for Self-Assembly Capability Enhancement.

Spidroin, with robust mechanical performance and good biocompatibility, could fulfill broad applications in material science and biomedical fields. Development of miniature spidroin has made abundant fiber production economically feasible, but the mechanical properties of artificial silk still fall short of natural silk. The mechanism behind mechanical properties of spidroin usually focuses on ß-microcrystalline regions; the effect of amorphous regions was barely studied. In this study, residue tyrosines (Y) were designed to replace asparagine (N)/glutamic acid (Q) in the characteristic motifs (GGX)n in amorphous regions for performance enhancement of spidroin; the mutants presented lower free energy and significantly exhibited stronger van der Waals and electrostatic interactions, which might result from π-π stacking interactions between the phenyl rings in the side chain of tyrosine. Additionally, the soluble expressions of wild-type spidroin and mutant spidroin were achieved when heterologously expressed in E. coli, with yields of 560 mg/L (2REP), 590 mg/L (2REPM), 240 mg/L (4REP), and 280 mg/L (4REPM). Significantly, secondary structure analysis confirmed that the mutant spidroin more avidly forms more ß-sheets than the wild-type spidroin, and aggregation morphology suggested that mutant spidroin displayed better self-assembly capacity and was easier to form artificial spider silk fibers; in particular, self-assembled 4REPM nanofibrils had an average modulus of 11.2 ± 0.35 GPa, about 2 times higher than self-assembled B. mori silk nanofibrils and almost the same as that of native spider dragline silk fibers (10-15 GPa). Thus, we first demonstrated a new influence mechanism of the amorphous region's characteristic motif on the self-assembly and material properties of spidroin. Our study provides a reference for the design of high-performance material proteins and their heterologous preparation.

Iron-Catalyzed Photoredox Alcohol α-C-H Alkylation and Tandem Intramolecular Cyclization: Facile Access to Multisubstituted 2,3-Dihydrofurans and γ-Butyrolactones.

Multisubstituted furans occupy a pivotal position within the realms of synthetic chemistry and pharmacological science due to their distinctive chemical configurations and inherent properties. We herein introduce a tandem difunctionalization protocol of alcohols for the efficient synthesis of multisubstituted 2,3-dihydrofurans and γ-butyrolactones through the combination of photocatalysis and iron catalysis under mild conditions. Photoredox alcohol α-C(sp3)-H activation and Pinner-type intramolecular cyclization are two key processes. This method features significant convenience, economic benefits, and environmental friendliness.

A qualitative exploration of e-cigarette prevention advertisements' effectiveness among college students in China.

INTRODUCTION: The rapid growth of e-cigarette usage among youth and young people has emerged as a significant public health concern. It is imperative to initiate effective vaping prevention campaigns and undertake relevant research to address this pressing issue. This research seeks to identify effective video advertisements to deter young people from starting to use e-cigarettes. It aims to offer evidence-based insights and recommendations for creating communication materials and designing messages for youth e-cigarette prevention efforts. METHODS: College students aged 18-24 years (n=40) participated in focus groups within this qualitative study. After viewing four stimulus videos, participants discussed what they perceived as effective and ineffective video characteristics, as well as suggestions for future videos. RESULTS: Effective video characteristics included the use of real-life testimonials, displaying specific health hazards, revealing harmful chemical ingredients and the deceptive nature of flavors, and positively perceived effectiveness. Participants generally found that videos with strong visual impact and graphics were more engaging and that approaches using fear and emotion were more effective. Ineffective characteristics included complex and exaggerated information, lack of empathy and irrelevance, insufficiently specific information, extreme and death-themed content, industry messages, as well as preachy tones, animations, metaphors, dull formats, excessive length, and scenes of e-cigarette use. CONCLUSIONS: Developing anti-e-cigarette campaign materials for youth necessitates target audience-focused qualitative research. This helps in deeply exploring and identifying effective themes and messages, as well as video characteristics and details while avoiding ineffective or even misleading messages and themes from young people's perspectives outside the United States. Future development of e-cigarette prevention videos for Chinese college students may consider incorporating localized real-life testimonial cases to convey specific harms, including self-efficacy information, and utilizing fear and emotional appeals.

Towards safer robot-assisted surgery: A markerless augmented reality framework.

Robot-assisted surgery is rapidly developing in the medical field, and the integration of augmented reality shows the potential to improve the operation performance of surgeons by providing more visual information. In this paper, we proposed a markerless augmented reality framework to enhance safety by avoiding intra-operative bleeding, which is a high risk caused by collision between surgical instruments and delicate blood vessels (arteries or veins). Advanced stereo reconstruction and segmentation networks are compared to find the best combination to reconstruct the intra-operative blood vessel in 3D space for registration with the pre-operative model, and the minimum distance detection between the instruments and the blood vessel is implemented. A robot-assisted lymphadenectomy is emulated on the da Vinci Research Kit in a dry lab, and ten human subjects perform this operation to explore the usability of the proposed framework. The result shows that the augmented reality framework can help the users to avoid the dangerous collision between the instruments and the delicate blood vessel while not introducing an extra load. It provides a flexible framework that integrates augmented reality into the medical robotic platform to enhance safety during surgery.

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.

Monoallelic de novo AJAP1 loss-of-function variants disrupt trans-synaptic control of neurotransmitter release.

Adherens junction-associated protein 1 (AJAP1) has been implicated in brain diseases; however, a pathogenic mechanism has not been identified. AJAP1 is widely expressed in neurons and binds to γ-aminobutyric acid type B receptors (GBRs), which inhibit neurotransmitter release at most synapses in the brain. Here, we show that AJAP1 is selectively expressed in dendrites and trans-synaptically recruits GBRs to presynaptic sites of neurons expressing AJAP1. We have identified several monoallelic AJAP1 variants in individuals with epilepsy and/or neurodevelopmental disorders. Specifically, we show that the variant p.(W183C) lacks binding to GBRs, resulting in the inability to recruit them. Ultrastructural analysis revealed significantly decreased presynaptic GBR levels in Ajap1-/- and Ajap1W183C/+ mice. Consequently, these mice exhibited reduced GBR-mediated presynaptic inhibition at excitatory and inhibitory synapses, along with impaired synaptic plasticity. Our study reveals that AJAP1 enables the postsynaptic neuron to regulate the level of presynaptic GBR-mediated inhibition, supporting the clinical relevance of loss-of-function AJAP1 variants.

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.