Enhancing CAR-T cell therapy against solid tumor by drug-free triboelectric immunotherapy.

Chimeric antigen receptor (CAR) T cell therapy is a highly effective immunotherapy for hematological tumors, but its efficacy against most solid tumors remains challenging. Herein, a novel synergistic combination therapy of drug-free triboelectric immunotherapy and CAR-T cell therapy against solid tumor was proposed. A triboelectric nanogenerator (TENG) that can generate pulsed direct-current by coupling triboelectrification effect and electrostatic breakdown effect was fabricated. The TENG can generate up to 30 pulse direct-current peaks with peak current output ≈35 µA in a single sliding to power the triboelectric immunotherapy. The pulsed direct-current stimulation induced immunogenic cell death of tumor cells (survival rate of 35.9 %), which promoted dendritic cells maturation, accelerated the process of antigen presentation to CAR-T cells and enhanced the systemic adaptive immune response. Furthermore, triboelectric immunotherapy promoted M1-like macrophage polarization, reduced regulatory T cells differentiation and reprogrammed the tumor immunosuppressive microenvironment, which ultimately enhanced the efficacy of CAR-T cells to eradicate nearly 60 % of NALM6 solid tumor mass. Notably, considering that triboelectric immunotherapy is a safe and effective drug-free antitumor strategy, the combined therapy did not increase the burden of double-medication on patients.

GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model.

Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across species. Recent breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this challenge. In this study, we built an extensive dataset of over 120 million human and mouse single-cell transcriptomes. After data preprocessing, we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model, named GeneCompass. During pre-training, GeneCompass effectively integrated four types of prior biological knowledge to enhance our understanding of gene regulatory mechanisms in a self-supervised manner. By fine-tuning for multiple downstream tasks, GeneCompass outperformed state-of-the-art models in diverse applications for a single species and unlocked new realms of cross-species biological investigations. We also employed GeneCompass to search for key factors associated with cell fate transition and showed that the predicted candidate genes could successfully induce the differentiation of human embryonic stem cells into the gonadal fate. Overall, GeneCompass demonstrates the advantages of using artificial intelligence technology to decipher universal gene regulatory mechanisms and shows tremendous potential for accelerating the discovery of critical cell fate regulators and candidate drug targets.

The association between physical activity and positive affect in adolescents: the chain mediating role of psychological resilience and regulatory emotional self-efficacy.

Previous studies have indicated the positive impact of physical activity on positive affect in adolescents. However, the psychological mechanism is still under-explored. The current study aims to investigate the mediating role of psychological resilience and regulatory emotional self-efficacy on the relationship between physical activity and positive affect. This cross-sectional study recruited 580 adolescents (280 females, 12 to 16 years old). All participants completed the measures of the Physical Activity Questionnaire for Adolescents (PAQ-A), the Positive and Negative Affect Scale (PANAS), the Connor-Davidson Resilience Scale, and the Regulatory Emotional Self-Efficacy Questionnaire. Results showed that the association between physical activity and positive affect was significant in adolescents. Psychological resilience played a mediating role between physical activity and positive affect. The chain mediation effect of psychological resilience and regulatory emotional self-efficacy on the relationship between physical activity and positive affect was significant. These results indicated that physical activity could positively impact adolescents' positive affect through psychological resilience and regulatory emotional self-efficacy. Our findings highlighted the importance of physical activity on positive affect on adolescents, which would benefit the mental health of this population.

LiveLattice: Real-time visualisation of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), move the sample substrate at an oblique angle relative to the detection objective's optical axis. Data from such tilted-sample-scan LSFMs require subsequent deskewing and rotation for proper visualisation and analysis. Such data preprocessing operations currently demand substantial memory allocation and pose significant computational challenges for large 4D dataset. The consequence is prolonged data preprocessing time compared to data acquisition time, which limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a memory-efficient transformation algorithm for deskewing and rotating the raw dataset, significantly reducing memory usage and the run time by more than 10-fold for large image stacks. Benchmarked against the conventional method and existing software, our approach demonstrates linear runtime compared to the cubic and quadratic runtime of the other approaches. Preprocessing a raw 3D volume of 2 GB (512 × 1536 × 600 pixels) can be accomplished in 3 s using a GPU with 24 GB of memory on a single workstation. Applied to 4D LLSM datasets of human hepatocytes, lung organoid tissue and brain organoid tissue, our method provided rapid and accurate preprocessing within seconds. Importantly, such preprocessing speeds now allow visualisation of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data preprocessing, visualisation, and analysis on standard workstations, thereby revolutionising biological imaging applications for LLSM and similar microscopes.

Dynamic handedness inversion of self-organized helical superstructures enabled by novel thermally stable light-driven chiral hydrazone switches.

Chiral hydrazone photoswitch features are its high thermal stability and negative photochromy, making it desirable in the fabrication of thermally stable optical device. However, chiral hydrazones capable of reversibly inversing chirality is scarcely reported. Herein, a series of new chiral hydrazone switches, HI-1, HI-2 and HI-3, were designed and synthesized. Due to the photoinduced configuration changes, the newly synthesized hydrazone photoswitch presents a surprising chirality inversion upon light stimulation. Photoisomerization of light-driven hydrazone switch molecules was investigated by nuclear magnetic resonance (NMR) spectra and Raman spectroscopy. The effect of the intramolecular hydrogen bond on photoresponsiveness was analyzed. By incorporating the photoswitch into a liquid crystal (LC) host, light-driven cholesteric liquid crystals (CLCs) with handedness invertibility, a feasible photonic bandgap tunability, and superior thermal stability were achieved. In addition, according to the optical-driven thermal stability of the hydrazone switches, the fine regulation of light-driven CLC materials with multistage photo stationary states was realized, and the application of CLC materials in erasable and rewritable display panels was also demonstrated.

Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning.

Prostate cancer (PCa) was the most frequently diagnosed cancer among American men in 2023 [1]. The histological grading of biopsies is essential for diagnosis, and various deep learning-based solutions have been developed to assist with this task. Existing deep learning frameworks are typically applied to individual 2D cross-sections sliced from 3D biopsy tissue specimens. This process impedes the analysis of complex tissue structures such as glands, which can vary depending on the tissue slice examined. We propose a novel digital pathology data source called a "volumetric core," obtained via the extraction and co-alignment of serially sectioned tissue sections using a novel morphology-preserving alignment framework. We trained an attention-based multiple-instance learning (ABMIL) framework on deep features extracted from volumetric patches to automatically classify the Gleason Grade Group (GGG). To handle volumetric patches, we used a modified video transformer with a deep feature extractor pretrained using self-supervised learning. We ran our morphology preserving alignment framework to construct 10,210 volumetric cores, leaving out 30% for pretraining. The rest of the dataset was used to train ABMIL, which resulted in a 0.958 macro-average AUC, 0.671 F1 score, 0.661 precision, and 0.695 recall averaged across all five GGG significantly outperforming the 2D baselines.

Built-in Electric Field Within CoSe2-FeSe2 Heterostructure for Enhanced Sulfur Reduction Reaction in Li-S Batteries.

The conversion of Li2S4 to Li2S is the most important and slowest rate-limiting step in the complex sulfur reduction reaction (SRR) for Li-S batteries, the adjustment of which can effectively inhibit the notorious "shuttle effect". Herein, a CoSe2-FeSe2 heterostructure embedded in 3D N-doped nanocage as a modified layer on commercial separator is designed (CoSe2-FeSe2@NC//PP). The CoSe2-FeSe2 heterostructure forms a built-in electric field at the two-phase interface, which leads to the optimized adsorption force on polysulfides and the accelerated reaction kinetics for Li2S4-Li2S evolution. Density functional theory (DFT) calculations and experimental results combine to show that the liquid-solid reaction (Li2S4-Li2S2/Li2S) is significantly enhanced in terms of thermodynamics and electrodynamics. Consequently, the batteries assembled with CoSe2-FeSe2@NC//PP delivered an excellent rate capability (606 mAh g-1 under 8.0 C) and a long cycling lifespan (only 0.056% at 1.0 C after 1000 cycles). In addition, the cells can provide high initial capacity of 887 mAh g-1 at sulfur loading of 5.8 mg cm-2 and 0.1 C. This work would provide valuable insights into binary metal selenide heterostructures for liquid-solid conversion in Li-S batteries.

SENGraph: A Self-Learning Evolutionary and Node-Aware Graph Network for Soft Sensing in Industrial Processes.

The last decade has witnessed the growing prevalence of deep models on soft sensing in industrial processes. However, most of the existing soft sensing models are developed to learn from regular data in the Euclidean space, ignoring the complex coupling relations among process variables. On the other hand, graph networks are gaining attraction in handling non-Euclidean relations in industrial data. However, the existing graph networks on soft sensing models still suffer from two major issues: 1) how to capture the intervariable structural relations and intravariable temporal dependencies from dynamic and strongly coupled industrial data and 2) how to learn from nodes with distinctive importance for the soft sensing task. To address these problems, we propose a self-learning evolutionary and node-aware graph network (SENGraph) for industrial soft sensing. We first develop a self-learning graph generation (SLG) module to combine the coarse-and fine-grained graphs to capture the global trend and local dynamics from process data. Then, we build a self-evolutionary graph module (EGM) to obtain diversified node features from the entire graph using mutation and crossover strategies. Finally, we design a node-aware module (NAM) to highlight the informative nodes and suppress the less significant ones to further improve the discriminative ability of the downstream soft sensing. Extensive experimental results and analysis on four real-world industrial datasets demonstrate that our proposed SENGraph model outperforms the existing state-of-the-art (SOTA) soft sensing methods.

Accurate segmentation of intracellular organelle networks using low-level features and topological self-similarity.

MOTIVATION: Intracellular organelle networks (IONs) such as the endoplasmic reticulum (ER) network and the mitochondrial (MITO) network serve crucial physiological functions. The morphology of these networks plays a critical role in mediating their functions. Accurate image segmentation is required for analyzing the morphology and topology of these networks for applications such as molecular mechanism analysis and drug target screening. So far, however, progress has been hindered by their structural complexity and density. RESULTS: In this study, we first establish a rigorous performance baseline for accurate segmentation of these organelle networks from fluorescence microscopy images by optimizing a baseline U-Net model. We then develop the multi-resolution encoder (MRE) and the hierarchical fusion loss (Lhf) based on two inductive components, namely low-level features and topological self-similarity, to assist the model in better adapting to the task of segmenting IONs. Empowered by MRE and Lhf, both U-Net and Pyramid Vision Transformer (PVT) outperform competing state-of-the-art models such as U-Net++, HR-Net, nnU-Net, and TransUNet on custom datasets of the ER network and the MITO network, as well as on public datasets of another biological network, the retinal blood vessel network. In addition, integrating MRE and Lhf with models such as HR-Net and TransUNet also enhances their segmentation performance. These experimental results confirm the generalization capability and potential of our approach. Furthermore, accurate segmentation of the ER network enables analysis that provides novel insights into its dynamic morphological and topological properties. AVAILABILITY AND IMPLEMENTATION: Code and data are openly accessible at https://github.com/cbmi-group/MRE.

Delirium Mediated the Association Between a History of Falls and Short-Term Mortality Risk in Critically Ill Ischemic Stroke Patients.

Patients with ischemic stroke have an increased propensity to fall, resulting in significant physical and psychological distress. This study examined the association between falls in the 3 months prior to intensive care unit (ICU) admission and mortality within 28 days among 2950 adult ICU patients diagnosed with ischemic stroke from 2008 to 2019, focusing on the potential mediating role of delirium. The primary outcomes were short-term mortality (28, 60, and 90 days) and the risk of delirium. Each patient was followed for at least 1 year. Delirium was primarily assessed using the Confusion Assessment Method for the ICU and by reviewing nursing notes. Group differences between patients with and without a history of falls were compared using the Wilcoxon rank-sum test or the chi-squared test. Cox proportional risk or logistic regression models were used to explore the association between fall history and outcomes, and causal mediation analysis was performed. Results showed that patients with a recent fall history had a significantly increased risk of 28-day (hazard ratio [HR]: 1.62, 95% confidence interval [CI]: 1.35-1.94), 60-day (HR: 1.67, 95% CI: 1.42-1.98), and 90-day mortality (HR: 1.66, 95% CI: 1.41-1.95), as well as an increased risk of delirium (odds ratio: 2.00, 95% CI: 1.66-2.42). Delirium significantly mediated the association between fall history and 28-day mortality (total effect: HR: 1.77, 95% CI: 1.45-2.16; natural indirect effect: HR: 1.12, 95% CI: 1.05-1.21; proportion mediated: 24.6%). These findings suggest that ischemic stroke patients with a recent fall have an increased risk of short-term mortality, partly mediated by delirium. Strategies aimed at preventing delirium may potentially improve prognosis in this patient population.

Eurasian otters prefer to prey on religious released non-native fish on the Qinghai-Tibetan Plateau.

Religious wildlife release is prevalent worldwide, especially in Asia countries. It is one of the anthropogenic pathways to cause biological invasions. Religious fish release is common on the Qinghai-Tibetan Plateau, yet few studies have assessed the influences of religious fish release on local species. In Yushu, a city on the Qinghai-Tibetan Plateau, we interviewed local people, conducted fish trap surveys in local rivers, and examined the diet of Eurasian otters Lutra lutra using a fecal DNA metabarcoding approach. We found that fish release started at least in 1980-1990s in Yushu. Tibetan residents released fish in large amounts and released fish were usually exotic commercial fish purchased from market. Despite such long-term and intensive fish release activities, released fish were few in local rivers. On the other hand, Eurasian otters mainly prey on fish and released fish accounted for ~20% of relative read abundance of prey DNA in otters' diet, indicating their high preference on released fish. Our study suggested that religious fish release may provide additional food resources for otters, whereas otters, as a top predator in local rivers, may deplete non-native fish once they were released and, therefore, reduce the probability of colonization of released fish, although further studies are required to assess otters' impact. Our study revealed otters' diet in Yushu, providing basic information for local otter management and conservation. Furthermore, it represents a case showing that native predators prey on religious released animals, implying a probable direction for controlling invasive species through native predator conservation.

Rapid iPSC inclusionopathy models shed light on formation, consequence, and molecular subtype of α-synuclein inclusions.

The heterogeneity of protein-rich inclusions and its significance in neurodegeneration is poorly understood. Standard patient-derived iPSC models develop inclusions neither reproducibly nor in a reasonable time frame. Here, we developed screenable iPSC "inclusionopathy" models utilizing piggyBac or targeted transgenes to rapidly induce CNS cells that express aggregation-prone proteins at brain-like levels. Inclusions and their effects on cell survival were trackable at single-inclusion resolution. Exemplar cortical neuron α-synuclein inclusionopathy models were engineered through transgenic expression of α-synuclein mutant forms or exogenous seeding with fibrils. We identified multiple inclusion classes, including neuroprotective p62-positive inclusions versus dynamic and neurotoxic lipid-rich inclusions, both identified in patient brains. Fusion events between these inclusion subtypes altered neuronal survival. Proteome-scale α-synuclein genetic- and physical-interaction screens pinpointed candidate RNA-processing and actin-cytoskeleton-modulator proteins like RhoA whose sequestration into inclusions could enhance toxicity. These tractable CNS models should prove useful in functional genomic analysis and drug development for proteinopathies.

p­Phenylenediamine antioxidants in PM2.5: New markers for traffic in positive matrix factorization source apportionment.

The extensive utilization of rubber-related products can lead to a substantial release of p-phenylenediamine (PPD) antioxidants into the environment. In recent years, studies mainly focus on the pollution characteristics and health risks of PM2.5-bound PPDs. This study presents long-time scale data of PPDs and N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPD-Q) in PM2.5 and proposes the innovative use of PPDs as new markers for vehicular emissions in the Positive Matrix Factorization (PMF) source apportionment. The results indicate that PPDs and 6PPD-Q were detectable in 100 % of the winter PM2.5 samples, and the concentration ranges of PPDs and 6PPD-Q are 15.6-2.92 × 103 pg·m-3 and 3.90-27.4 pg·m-3, respectively, in which 6PPD and DNPD are the main compounds. Moreover, a competitive formation mechanism between sulfate, nitrate, ammonium (SNA) and 6PPD-Q was observed. The source apportionment results show that the incorporation of PPDs in PMF reduced the contribution of traffic source to PM2.5 from 13.5 % to 9.5 %. In the traffic source factor profiles, the load of IPPD, CPPD, DPPD, DNPD and 6PPD reaches 91.8 %, 91.6 %, 92.9 %, 80.6 % and 87.2 %, respectively. It`s amazing that traditional markers of traffic source, which often overlap with coal burning and industrial sources, over-estimated the contribution of vehicles by one third or more. The discovery of PPDs as specific markers for vehicular emissions holds significant utility, particularly considering the growing proportion of new energy vehicles in the future. The results may prove more accurate policy implications for pollution control. SYNOPSIS: PPDs are excellent indicators of vehicle emissions, and PMF without PPDs over-estimated the contribution of traffic source to PM2.5.

Subminimum Inhibitory Concentrations Tetracycline Antibiotics Induce Biofilm Formation in Minocycline-Resistant Klebsiella pneumonia by Affecting Bacterial Physical and Chemical Properties and Associated Genes Expression.

Biofilm formation of Klebsiella pneumoniae can protect bacteria from antibiotics and is difficult to eradicate. Thus, the influence of subinhibitory concentrations of antibiotics on bacteria is becoming increasingly important. Our study showed that subminimum inhibitory concentrations (sub-MICs) of tetracycline antibiotics can increase biofilm formation in minocycline-resistant Klebsiella pneumoniae clinical strains. However, in the bacterial adhesion and invasion experiments, the adhesion and invasion ability decreased and the survival rate of Galleria mellonella increased. Under sub-MICs of tetracycline antibiotics treatment, abnormal stretching of bacteria was observed by scanning electron microscopy. Treatment with sub-MICs of tetracyclines leads to increased surface hydrophobicity and eDNA content and decreased outer membrane permeability. The expression levels of the fimA, luxS, qseB, and qseC genes decreased, the expression level of mrkA increased, and the expression level of acrA was inconsistent under different tetracycline antibiotics treatments. Together, our results suggested that the increase in Klebsiella pneumoniae biofilm formation caused by sub-MICs of tetracycline antibiotics may occur by affecting bacterial physical and chemical properties and associated genes expression.

Microfiber/Nanofiber/Attapulgite Multilayer Separator with a Pore-Size Gradient for High-Performance and Safe Lithium-Ion Batteries.

Lithium-ion batteries (LIBs) have an extremely diverse application nowadays as an environmentally friendly and renewable new energy storage technology. The porous structure of the separator, one essential component of LIBs, provides an ion transport channel for the migration of ions and directly affects the overall performance of the battery. In this work, we fabricated a composite separator (GOP-PH-ATP) via simply laminating an electrospun polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP) nanofibrous membrane coated with attapulgite (ATP) nanoparticles onto a PP nonwoven microfibrous fabric, which exhibits a unique porous structure with a pore-size gradient along the thickness direction that ranges from tens of microns to hundreds of nanometers. As a result, besides the enhanced thermal stability given by the chosen materials, the GOP-PH-ATP separator was endowed with a superhigh porosity of ~95%, strong affinity with electrolyte, and great electrolyte uptake of ~760%, thus effectively enabling an ionic conductivity of 2.38 mS cm-1 and a lithium-ion transference number of 0.62. Furthermore, the cell with the GOP-PH-ATP separator shows an excellent cycling performance with a capacity retention of 91.2% after 150 cycles at 1 C, suggesting that the composite separator with a pore-size gradient structure has great potential to be applied in LIBs.

Role of Emerin in regulating fibroblast differentiation and migration at the substrate of stiffness coupled topology.

In hypertrophic scars, the differentiation and migration of fibroblasts are influenced by the extracellular matrix microenvironment, which includes factors such as stiffness, restraint, and tensile force. These mechanical stresses incite alterations in cell behavior, accompanied by cytoskeletal protein reorganization. However, the role of nucleo-skeletal proteins in this context remains underexplored. In this study, we use a polyacrylamide hydrogel (PAA) to simulate the mechanical stress experienced by cells in scar tissue and investigate the impact of Emerin on cell behavior. We utilize atomic force microscopy (AFM) and RNA interference technology to analyze cell differentiation, migration, and stiffness. Our findings reveal that rigid substrates and cellular restriction elevate Emerin expression and diminish differentiation. Conversely, reducing Emerin expression leads to attenuated cell differentiation, where stiffness and constraining factors exert no notable influence. Furthermore, a softening of cells and an enhanced migration rate are also markedly observed. These observations indicate that variations in nuclear skeletal proteins, prompted by diverse matrix microenvironments, play a pivotal role in the pathogenesis of hypertrophic scars (HSs). This research offers novel insights and a reference point for understanding scar fibrosis formation mechanisms and preventing fibrosis.

A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Following endovascular thrombectomy in patients with large-vessel occlusion stroke, successful recanalization from 1 attempt, known as the first-pass effect, has correlated favorably with long-term outcomes. Pretreatment imaging may contain information that can be used to predict the first-pass effect. Recently, applications of machine learning models have shown promising results in predicting recanalization outcomes, albeit requiring manual segmentation. In this study, we sought to construct completely automated methods using deep learning to predict the first-pass effect from pretreatment CT and MR imaging. MATERIALS AND METHODS: Our models were developed and evaluated using a cohort of 326 patients who underwent endovascular thrombectomy at UCLA Ronald Reagan Medical Center from 2014 to 2021. We designed a hybrid transformer model with nonlocal and cross-attention modules to predict the first-pass effect on MR imaging and CT series. RESULTS: The proposed method achieved a mean 0.8506 (SD, 0.0712) for cross-validation receiver operating characteristic area under the curve (ROC-AUC) on MR imaging and 0.8719 (SD, 0.0831) for cross-validation ROC-AUC on CT. When evaluated on the prospective test sets, our proposed model achieved a mean ROC-AUC of 0.7967 (SD, 0.0335) with a mean sensitivity of 0.7286 (SD, 0.1849) and specificity of 0.8462 (SD, 0.1216) for MR imaging and a mean ROC-AUC of 0.8051 (SD, 0.0377) with a mean sensitivity of 0.8615 (SD, 0.1131) and specificity 0.7500 (SD, 0.1054) for CT, respectively, representing the first classification of the first-pass effect from MR imaging alone and the first automated first-pass effect classification method in CT. CONCLUSIONS: Results illustrate that both nonperfusion MR imaging and CT from admission contain signals that can predict a successful first-pass effect following endovascular thrombectomy using our deep learning methods without requiring time-intensive manual segmentation.

LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), rely on a tilting of the sample plane with respect to the light sheet of 30-45 degrees to ease sample preparation. Data from such tilted-sample-plane LSFMs require subsequent deskewing and rotation for proper visualization and analysis. Such transformations currently demand substantial memory allocation. This poses computational challenges, especially with large datasets. The consequence is long processing times compared to data acquisition times, which currently limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a novel GPU-accelerated memory-efficient algorithm that integrates deskewing and rotation into a single transformation, significantly reducing memory requirements and reducing the preprocessing run time by at least 10-fold for large image stacks. Benchmarked against conventional methods and existing software, our approach demonstrates linear scalability. Processing large 3D stacks of up to 15 GB is now possible within one minute using a single GPU with 24 GB of memory. Applied to 4D LLSM datasets of human hepatocytes, human lung organoid tissue, and human brain organoid tissue, our method outperforms alternatives, providing rapid, accurate preprocessing within seconds. Importantly, such processing speeds now allow visualization of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data processing, visualization, and analysis on standard workstations, thereby revolutionizing biological imaging applications for LLSM, SPIM and similar light microscopes.

Plasmonic Bound States in the Continuum Metasurface-Semiconductor-Metal Architecture Enables Efficient Hot-Electron-Based Photodetector.

Plasmonic hot-electron-based photodetectors (HEB-PDs) have received widespread attention for their ability to realize effective carrier collection under sub-bandgap illumination. However, due to the low hot electron emission probability, most of the existing HEB-PDs exhibit poor responsivity, which significantly restricts their practical applications. Here, by employing the binary-pore anodic alumina oxide template technique, we proposed a compact plasmonic bound state in continuum metasurface-semiconductor-metal-based (BIC M-S-M) HEB-PD. The symmetry-protected BIC can manipulate a strong gap surface plasmon in the stacked M-S-M structure, which effectively enhances light-matter interactions and improves the photoresponse of the integrated device. Notably, the optimal M-S-M HEB-PD with near-unit absorption (∼90%) around 800 nm delivers a responsivity of 5.18 A/W and an IPCE of 824.23% under 780 nm normal incidence (1 V external bias). Moreover, the ultrathin feature of BIC M-S-M (∼150 nm) on the flexible substrate demonstrates excellent stability under a wide range of illumination angles from -40° to 40° and at the curvature surface from 0.05 to 0.13 mm-1. The proposed plasmonic BIC strategy is very promising for many other hot-electron-related fields, such as photocatalysis, biosensing, imaging, and so on.

Spray vaccination with a Newcastle disease virus (NDV)-vectored infectious laryngotracheitis (ILT) vaccine protects commercial chickens from ILT in the presence of maternally-derived antibodies.

Infectious laryngotracheitis (ILT) poses a significant threat to the poultry industry, and vaccines play an important role in protection. However, due to the increasing scale of poultry production, there is an urgent need to develop vaccines that are suitable for convenient immunization methods such as spraying. Previous studies have shown that Newcastle disease virus (NDV)-ILT vaccines administered via intranasal and intraocular routes to commercial chickens carrying maternally-derived antibodies (MDAs) are still protective against ILT. In this study, a recombinant NDV (rNDV) was generated to express infectious laryngotracheitis virus (ILTV) glycoprotein B (gB), named rLS-gB, based on a full-length cDNA clone of the LaSota strain. The protective effect of different doses of rLS-gB administered by spray vaccination to commercial chickens at 1 d of age (doa) was evaluated. The chickens were exposed to 160-µm aerosol particles for 10 min for spray vaccination, and no adverse reactions were observed after vaccination. Despite the presence of anti-NDV MDAs and anti-ILTV MDAs in chickens, the ILTV- and NDV-specific antibody titres were significantly greater in the vaccinated groups than in the unvaccinated group. After challenge with a virulent ILTV strain, no clinical signs were observed in the 107 EID50/ml group compared to the other groups. Furthermore, vaccination with 107 EID50/ml rLS-gB significantly reduced the ILTV viral load and ameliorated gross and microscopic lesions in the trachea of chickens. Overall, these results suggested that rLS-gB is a safe and efficient candidate spray vaccine for ILT and is especially suitable for scaled chicken farms.