Brightening triplet excitons enable high-performance white-light emission in organic small molecules via integrating n-π*/π-π* transitions.

Luminescent materials that simultaneously embody bright singlet and triplet excitons hold great potential in optoelectronics, signage, and information encryption. However, achieving high-performance white-light emission is severely hampered by their inherent unbalanced contribution of fluorescence and phosphorescence. Herein, we address this challenge by pressure treatment engineering via the hydrogen bonding cooperativity effect to realize the mixture of n-π*/π-π* transitions, where the triplet state emission was boosted from 7% to 40% in isophthalic acid (IPA). A superior white-light emission based on hybrid fluorescence and phosphorescence was harvested in pressure-treated IPA, and the photoluminescence quantum yield was increased to 75% from the initial 19% (blue-light emission). In-situ high-pressure IR spectra, X-ray diffraction, and neutron diffraction reveal continuous strengthening of the hydrogen bonds with the increase of pressure. Furthermore, this enhanced hydrogen bond is retained down to the ambient conditions after pressure treatment, awarding the targeted IPA efficient intersystem crossing for balanced singlet/triplet excitons population and resulting in efficient white-light emission. This work not only proposes a route for brightening triplet states in organic small molecules, but also regulates the ratio of singlet and triplet excitons to construct high-performance white-light emission.

Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study.

OBJECTIVES: Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers. METHODS: This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct the predictive models (LUNAI-uCT model and LUNAI-eCT model), respectively. After the feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. SHapley Additive exPlanations analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping to generate saliency heatmaps. RESULTS: The training and validation datasets included 113 patients from Center A at the 8:2 ratio, and the test dataset included 112 patients (Center B n=73, Center C n=20, Center D n=19). In the test dataset, the LUNAI-uCT, LUNAI-eCT, and LUNAI-fCT models achieved AUCs of 0.762 (95% CI 0.654 to 0.791), 0.797 (95% CI 0.724 to 0.844), and 0.866 (95% CI 0.821 to 0.883), respectively. CONCLUSIONS: By extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.

NUAK1-Mediated Phosphorylation of NADK Mitigates ROS Accumulation to Promote Osimertinib Resistance in Non-Small Cell Lung Carcinoma.

Osimertinib, a third-generation epidermal growth factor receptor tyrosine kinase inhibitor, is approved as a first-line therapy in advanced non-small cell lung carcinoma (NSCLC) patients with EGFR-activating mutations or the T790M resistance mutation. However, the efficacy of osimertinib is limited due to acquired resistance, highlighting the need to elucidate resistance mechanisms to facilitate the development of improved treatment strategies. Here, we screened for significantly upregulated genes encoding protein kinases in osimertinib-resistant NSCLC cells and identified NUAK1 as a pivotal regulator of osimertinib resistance. NUAK1 was highly expressed in osimertinib-resistant NSCLC and promoted the emergence of osimertinib resistance. Genetic or pharmacological blockade of NUAK1 restored the sensitivity of resistant NSCLC cells to osimertinib in vitro and in vivo. Mechanistically, NUAK1 directly interacted with and phosphorylated NADK at serine 64 (S64), which mitigated osimertinib-induced accumulation of reactive oxygen species (ROS) and contributed to the acquisition of osimertinib resistance in NSCLC. Furthermore, virtual drug screening identified T21195 as an inhibitor of NADK-S64 phosphorylation, and T21195 synergized with osimertinib to reverse acquired resistance by inducing ROS accumulation. Collectively, these findings highlight the role of the NUAK1-NADK axis in governing osimertinib resistance in NSCLC and indicate the potential of targeting this axis as a strategy for circumventing resistance.

Microalgae-based biofertilizer improves fruit yield and controls greenhouse gas emissions in a hawthorn orchard.

Raising attentions have focused on how to alleviate greenhouse gas (GHG) emissions from orchard system while simultaneously increase fruit production. Microalgae-based biofertilizer represents a promising resource for improving soil fertility and higher productivity. However, the effects of microalgae application more especially live microalgae on GHG emissions are understudied. In this study, fruit yield and quality, GHG emissions, as well as soil organic carbon and nitrogen fractions were examined in a hawthorn orchard, under the effects of live microalgae-based biofertilizer applied at three doses and two modes. Compared with conventional fertilization, microalgae improved hawthorn yield by 15.7%-29.6% with a maximal increment at medium dose by root application, and significantly increased soluble and reducing sugars contents at high dose. While microalgae did not increase GHG emissions except for nitrous oxide at high dose by root application, instead it significantly increased methane uptake by 1.5-2.3 times in root application. In addition, microalgae showed an increasing trend in soil organic carbon content, and significantly increased the contents of soil dissolved organic carbon and microbial biomass carbon, as well as soil ammonium nitrogen and dissolved organic nitrogen at medium dose with root application. Overall, the results indicated that the live microalgae could be used as a green biofertilizer for improving fruit yield without increasing GHG emissions intensity and the comprehensive greenhouse effect, in particular at medium dose with root application. We presume that if lowering chemical fertilizer rates, application of the live microalgae-based biofertilizer may help to reduce nitrous oxide emissions without compromising fruit yield and quality.

Automatic localization of anatomical landmarks in head cine fluoroscopy images via deep learning.

BACKGROUND: Fluoroscopy guided interventions (FGIs) pose a risk of prolonged radiation exposure; personalized patient dosimetry is necessary to improve patient safety during these procedures. However, current FGIs systems do not capture the precise exposure regions of the patient, making it challenging to perform patient-procedure-specific dosimetry. Thus, there is a pressing need to develop approaches to extract and use this information to enable personalized radiation dosimetry for interventional procedures. PURPOSE: To propose a deep learning (DL) approach for the automatic localization of 3D anatomical landmarks on randomly collimated and magnified 2D head fluoroscopy images. MATERIALS AND METHODS: The model was developed with datasets comprising 800 000 pseudo 2D synthetic images (mixture of vessel-enhanced and non-enhancement), each with 55 annotated anatomical landmarks (two are landmarks for eye lenses), generated from 135 retrospectively collected head computed tomography (CT) volumetric data. Before training, dynamic random cropping was performed to mimic the varied field-size collimation in FGI procedures. Gaussian-distributed additive noise was applied to each individual image to enhance the robustness of the DL model in handling image degradation that may occur during clinical image acquisition in a clinical environment. The model was trained with 629 370 synthetic images for approximately 275 000 iterations and evaluated against a synthetic image test set and a clinical fluoroscopy test set. RESULTS: The model shows good performance in estimating in- and out-of-image landmark positions and shows feasibility to instantiate the skull shape. The model successfully detected 96.4% and 92.5% 2D and 3D landmarks, respectively, within a 10 mm error on synthetic test images. It demonstrated an average of 3.6 ± 2.3 mm mean radial error and successfully detected 96.8% 2D landmarks within 10 mm error on clinical fluoroscopy images. CONCLUSION: Our deep-learning model successfully localizes anatomical landmarks and estimates the gross shape of skull structures from collimated 2D projection views. This method may help identify the exposure region required for patient-specific organ dosimetry in FGIs procedures.

A new dataset for video-based cow behavior recognition.

A new video based multi behavior dataset for cows, CBVD-5, is introduced in this paper. The dataset includes five cow behaviors: standing, lying down, foraging,rumination and drinking. The dataset comprises 107 cows from the entire barn, maintaining an 80% stocking density. Monitoring occurred over 96 h for these 20-month-old cows, considering varying light conditions and nighttime data to ensure standardization and inclusivity.The dataset consists of ranch monitoring footage collected by seven cameras, including 687 video segment samples and 206,100 image samples, covering five daily behaviors of cows. The data collection process entailed the deployment of cameras, hard drives, software, and servers for storage. Data annotation was conducted using the VIA web tool, leveraging the video expertise of pertinent professionals. The annotation coordinates and category labels of each individual cow in the image, as well as the generated configuration file, are also saved in the dataset. With this dataset,we propose a slowfast cow multi behavior recognition model based on video sequences as the baseline evaluation model. The experimental results show that the model can effectively learn corresponding category labels from the behavior type data of the dataset, with an error rate of 21.28% on the test set. In addition to cow behavior recognition, the dataset can also be used for cow target detection, and so on.The CBVD-5 dataset significantly influences dairy cow behavior recognition, advancing research, enriching data resources, standardizing datasets, enhancing dairy cow health and welfare monitoring, and fostering agricultural intelligence development. Additionally, it serves educational and training needs, supporting research and practical applications in related fields. The dataset will be made freely available to researchers world-wide.

Evaluation of the Alveolar Crest and Cemento-Enamel Junction in Periodontitis Using Object Detection on Periapical Radiographs.

The severity of periodontitis can be analyzed by calculating the loss of alveolar crest (ALC) level and the level of bone loss between the tooth's bone and the cemento-enamel junction (CEJ). However, dentists need to manually mark symptoms on periapical radiographs (PAs) to assess bone loss, a process that is both time-consuming and prone to errors. This study proposes the following new method that contributes to the evaluation of disease and reduces errors. Firstly, innovative periodontitis image enhancement methods are employed to improve PA image quality. Subsequently, single teeth can be accurately extracted from PA images by object detection with a maximum accuracy of 97.01%. An instance segmentation developed in this study accurately extracts regions of interest, enabling the generation of masks for tooth bone and tooth crown with accuracies of 93.48% and 96.95%. Finally, a novel detection algorithm is proposed to automatically mark the CEJ and ALC of symptomatic teeth, facilitating faster accurate assessment of bone loss severity by dentists. The PA image database used in this study, with the IRB number 02002030B0 provided by Chang Gung Medical Center, Taiwan, significantly reduces the time required for dental diagnosis and enhances healthcare quality through the techniques developed in this research.

Bacterial profile, and independent predictors for healthcare-associated pneumonia persistently caused by multidrug-resistant Gram-negative bacteria for patients with the preceding multidrug-resistant Gram-negative pneumonia in Taiwan.

OBJECTIVES: To understand the microbial profile and investigate the independent predictors for healthcare-associated pneumonia (HCAP) pertinaciously caused by isolates of multidrug-resistant (MDR) Gram-negative bacteria (GNB). METHODS: Multicenter ICU patients who received appropriate antibiotic treatments for preceding pneumonia due to MDR GNB isolates and subsequently developed HCAP caused by either MDR GNB (n = 126) or non-MDR GNB (n = 40) isolates in Taiwan between 2018 and 2023 were enrolled. Between the groups of patients with HCAP due to MDR GNB and non-MDR GNB, the proportions of the following variables, including demographic characteristics, important co-morbidities, nursing home residence, physiological severity, intervals between two hospitalizations, steroid use, the tracheostomy tube use alone, ventilator support, and the predominant GNB species involving HCAP, were analyzed using the chi-square test. Logistic regression was employed to explore the independent predictors for HCAP persistently caused by MDR GNB in the aforementioned variables with a P-value of <0.15 in the univariate analysis. RESULTS: MDR-Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii complex were the three predominant species causing HCAP. Chronic structural lung disorders, diabetes mellitus, intervals of ≤30 days between two hospitalizations, use of the tracheostomy tube alone, and prior pneumonia caused by MDR A. baumannii complex were shown to independently predict the HCAP tenaciously caused by MDR GNB. Conversely, the preceding pneumonia caused by MDR P. aeruginosa was a negative predictor. CONCLUSION: Identifying predictors for HCAP persistently caused by MDR GNB is crucial for prescribing appropriate antibiotics.

Classification of optic neuritis in neuromyelitis optica spectrum disorders (NMOSD) on MRI using CNN with transfer learning and manipulation of pre-processing on augmentation.

Neuromyelitis optica spectrum disorder (NMOSD), also known as Devic disease, is an autoimmune central nervous system disorder in humans that commonly causes inflammatory demyelination in the optic nerves and spinal cord. Inflammation in the optic nerves is termed optic neuritis (ON). ON is a common clinical presentation; however, it is not necessarily present in all NMOSD patients. ON in NMOSD can be relapsing and result in severe vision loss. To the best of our knowledge, no study utilises deep learning to classify ON changes on MRI among patients with NMOSD. Therefore, this study aims to deploy eight state-of-the-art CNN models (Inception-v3, Inception-ResNet-v2, ResNet-101, Xception, ShuffleNet, DenseNet-201, MobileNet-v2, and EfficientNet-B0) with transfer learning to classify NMOSD patients with and without chronic ON using optic nerve magnetic resonance imaging. This study also investigated the effects of data augmentation before and after dataset splitting on cropped and whole images. Both quantitative and qualitative assessments (with Grad-Cam) were used to evaluate the performances of the CNN models. The Inception-v3 was identified as the best CNN model for classifying ON among NMOSD patients, with accuracy of 99.5%, sensitivity of 98.9%, specificity of 93.0%, precision of 100%, NPV of 99.0%, and F1-score of 99.4%. This study also demonstrated that the application of augmentation after dataset splitting could avoid information leaking into the testing datasets, hence producing more realistic and reliable results.

Auxiliary Diagnosis of Dental Calculus Based on Deep Learning and Image Enhancement by Bitewing Radiographs.

In the field of dentistry, the presence of dental calculus is a commonly encountered issue. If not addressed promptly, it has the potential to lead to gum inflammation and eventual tooth loss. Bitewing (BW) images play a crucial role by providing a comprehensive visual representation of the tooth structure, allowing dentists to examine hard-to-reach areas with precision during clinical assessments. This visual aid significantly aids in the early detection of calculus, facilitating timely interventions and improving overall outcomes for patients. This study introduces a system designed for the detection of dental calculus in BW images, leveraging the power of YOLOv8 to identify individual teeth accurately. This system boasts an impressive precision rate of 97.48%, a recall (sensitivity) of 96.81%, and a specificity rate of 98.25%. Furthermore, this study introduces a novel approach to enhancing interdental edges through an advanced image-enhancement algorithm. This algorithm combines the use of a median filter and bilateral filter to refine the accuracy of convolutional neural networks in classifying dental calculus. Before image enhancement, the accuracy achieved using GoogLeNet stands at 75.00%, which significantly improves to 96.11% post-enhancement. These results hold the potential for streamlining dental consultations, enhancing the overall efficiency of dental services.

[Research progress on biological characteristics and propagation technology of Atractylodes lancea].

Atractylodes lancea is a perennial herb of the Asteraceae family and is one of the well-known traditional Chinese medicine(TCM). Several studies have documented polyene alkyne and sesquiterpenoid compounds as the main bioactive compounds of A. lancea, especially atractylodin, atractylon, ß-eudesmol, and hinesol in its rhizomes, which possess anti-virus, anti-inflammation, hypoglycemic, anti-hypoxia, liver protection, and diuresis activities. In parallel with the recent advancements in biotechnology, important achievements have been made in the study of biological characteristics and propagation technology of A. lancea. This study reviews the research progress on morphological features, cytogenetics, ecological planting, effective ingredients, and tissue culture techniques of A. lancea from the biology perspective, so as to provide a theoretical basis for reasonable development of A. lancea resources.

Innovations in detecting skull fractures: A review of computer-aided techniques in CT imaging.

BACKGROUND/INTRODUCTION: Traumatic brain injury (TBI) remains a leading cause of disability and mortality, with skull fractures being a frequent and serious consequence. Accurate and rapid diagnosis of these fractures is crucial, yet current manual methods via cranial CT scans are time-consuming and prone to error. METHODS: This review paper focuses on the evolution of computer-aided diagnosis (CAD) systems for detecting skull fractures in TBI patients. It critically assesses advancements from feature-based algorithms to modern machine learning and deep learning techniques. We examine current approaches to data acquisition, the use of public datasets, algorithmic strategies, and performance metrics RESULTS: The review highlights the potential of CAD systems to provide quick and reliable diagnostics, particularly outside regular clinical hours and in under-resourced settings. Our discussion encapsulates the challenges inherent in automated skull fracture assessment and suggests directions for future research to enhance diagnostic accuracy and patient care. CONCLUSION: With CAD systems, we stand on the cusp of significantly improving TBI management, underscoring the need for continued innovation in this field.

Implementation of Sound Direction Detection and Mixed Source Separation in Embedded Systems.

In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people's activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2. For mixed source separation, in the first experiment, the average signal-to-interference ratio (SIR) at 1 m and 2 m distances was 16.72 and 15.76, respectively. In the second experiment, when evaluated using speech recognition, the algorithm improved speech recognition accuracy to 95%.

CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer.

Objectives: To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image. Methods: This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Results: In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686. Conclusion: The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

N-Containing Porous Carbon-Based MnO Composites as Anode with High Capacity and Stability for Lithium-Ion Batteries.

MnO has attracted much attention as the anode for Li-ion batteries (LIBs) owing to its high specific capacity. However, the low conductivity limited its large application. An effective solution to solve this problem is carbon coating. Biomass carbon materials have aroused much interest for being low-cost and rich in functional groups and hetero atoms. This work designs porous N-containing MnO composites based on the chemical-activated tremella using a self-templated method. The tremella, after activation, could offer more active sites for carbon to coordinate with the Mn ions. And the as-prepared composites could also inherit the special porous nanostructures of the tremella, which is beneficial for Li+ transfer. Moreover, the pyrrolic/pyridinic N from the tremella can further improve the conductivity and the electrolyte wettability of the composites. Finally, the composites show a high reversible specific capacity of 1000 mAh g-1 with 98% capacity retention after 200 cycles at 100 mA g-1. They also displayed excellent long-cycle performance with 99% capacity retention (relative to the capacity second cycle) after long 1000 cycles under high current density, which is higher than in most reported transition metal oxide anodes. Above all, this study put forward an efficient and convenient strategy based on the low-cost biomass to construct N-containing porous composite anodes with a fast Li+ diffusion rate, high electronic conductivity, and outstanding structure stability.

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System.

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle's onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle's onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.

Allochthonous Trichoderma Isolates Boost Atractylodes lancea Herb Quality at the Cost of Rhizome Growth.

Atractylodes lancea is a perennial herb whose rhizome (AR) is a valuable traditional Chinese medicine with immense market demand. The cultivation of Atractylodes lancea faces outbreaks of root rot and deterioration in herb quality due to complex causes. Here, we investigated the effects of Trichoderma spp., well-known biocontrol agents and plant-growth-promoters, on ARs. We isolated Trichoderma strains from healthy ARs collected in different habitats and selected three T. harzianum strains (Th2, Th3 and Th4) with the strongest antagonizing effects on root rot pathogens (Fusarium spp.). We inoculated geo-authentic A. lancea plantlets with Th2, Th3 and Th4 and measured the biomass and quality of 70-day-old ARs. Th2 and Th3 promoted root rot resistance of A. lancea. Th2, Th3 and Th4 all boosted AR quality: the concentration of the four major medicinal compounds in ARs (atractylon, atractylodin, hinesol and ß-eudesmol) each increased 1.6- to 18.2-fold. Meanwhile, however, the yield of ARs decreased by 0.58- to 0.27-fold. Overall, Th3 dramatically increased the quality of ARs at a relatively low cost, namely lower yield, showing great potential for practical application. Our results showed selectivity between A. lancea and allochthonous Trichoderma isolates, indicating the importance of selecting specific microbial patches for herb cultivation.

Exploring the causal association between genetically determined circulating metabolome and hemorrhagic stroke.

Background: Hemorrhagic stroke (HS), a leading cause of death and disability worldwide, has not been clarified in terms of the underlying biomolecular mechanisms of its development. Circulating metabolites have been closely associated with HS in recent years. Therefore, we explored the causal association between circulating metabolomes and HS using Mendelian randomization (MR) analysis and identified the molecular mechanisms of effects. Methods: We assessed the causal relationship between circulating serum metabolites (CSMs) and HS using a bidirectional two-sample MR method supplemented with five ways: weighted median, MR Egger, simple mode, weighted mode, and MR-PRESSO. The Cochran Q-test, MR-Egger intercept test, and MR-PRESSO served for the sensitivity analyses. The Steiger test and reverse MR were used to estimate reverse causality. Metabolic pathway analyses were performed using MetaboAnalyst 5.0, and genetic effects were assessed by linkage disequilibrium score regression. Significant metabolites were further synthesized using meta-analysis, and we used multivariate MR to correct for common confounders. Results: We finally recognized four metabolites, biliverdin (OR 0.62, 95% CI 0.40-0.96, PMVMR = 0.030), linoleate (18. 2n6) (OR 0.20, 95% CI 0.08-0.54, PMVMR = 0.001),1-eicosadienoylglycerophosphocholine* (OR 2.21, 95% CI 1.02-4.76, PMVMR = 0.044),7-alpha-hydroxy-3 -oxo-4-cholestenoate (7-Hoca) (OR 0.27, 95% CI 0.09-0.77, PMVMR = 0.015) with significant causal relation to HS. Conclusion: We demonstrated significant causal associations between circulating serum metabolites and hemorrhagic stroke. Monitoring, diagnosis, and treatment of hemorrhagic stroke by serum metabolites might be a valuable approach.

Harvesting Multicolor Photoluminescence in Nonaromatic Interpenetrated Metal-Organic Framework Nanocrystals via Pressure-Modulated Carbonyls Aggregation.

Interpenetrated metal-organic frameworks (MOFs) with nonaromatic ligands provide a unique platform for adsorption, catalysis, and sensing applications. However, nonemission and the lack of optical property tailoring make it challenging to fabricate smart responsive devices with nonaromatic interpenetrated MOFs based on ligand-centered emission. In this paper, the pressure-induced aggregation effect is introduced in nonaromatic interpenetrated Zn4O(ADC)4(Et3N)6 (IRMOF-0) nanocrystals (NCs), where carbonyl groups aggregation results in O─O distances smaller than the sum of the van der Waals radii (3.04 Å), triggering the photoluminescence turn-on behavior. It is noteworthy that the IRMOF-0 NCs display an ultrabroad emission tunability of 130 nm from deep blue (440 nm) to yellow (570 nm) upon release to ambient conditions at different pressures. The eventual retention of through-space n-π* interactions in different degrees via pressure treatment is primarily responsible for achieving a controllable multicolor emission behavior in initially nonemissive IRMOF-0 NCs. The fabricated multicolor phosphor-converted light-emitting diodes based on the pressure-treated IRMOF-0 NCs exhibit excellent thermal, chromaticity, and fatigue stability. The proposed strategy not only imparts new vitality to nonaromatic interpenetrated MOFs but also offers new perspectives for advancements in the field of multicolor displays and daylight illumination.

Comparison of commercial atlas-based automatic segmentation software for prostate radiotherapy treatment planning.

This study aims to assess the accuracy of automatic atlas-based contours for various key anatomical structures in prostate radiotherapy treatment planning. The evaluated structures include the bladder, rectum, prostate, seminal vesicles, femoral heads and penile bulb. CT images from 20 patients who underwent intensity-modulated radiotherapy were randomly chosen to create an atlas library. Atlas contours of the seven anatomical structures were generated using four software packages: ABAS, Eclipse, MIM, and RayStation. These contours were then compared to manual delineations performed by oncologists, which served as the ground truth. Evaluation metrics such as dice similarity coefficient (DSC), mean distance to agreement (MDA), and volume ratio (VR) were calculated to assess the accuracy of the contours. Additionally, the time taken by each software to generate the atlas contour was recorded. The mean DSC values for the bladder exhibited strong agreement (>0.8) with manual delineations for all software except for Eclipse and RayStation. Similarly, the femoral heads showed significant similarity between the atlas contours and ground truth across all software, with mean DSC values exceeding 0.9 and MDA values close to zero. On the other hand, the penile bulb displayed only moderate agreement with the ground truth, with mean DSC values ranging from 0.5 to 0.7 for all software. A similar trend was observed in the prostate atlas contours, except for MIM, which achieved a mean DSC of over 0.8. For the rectum, both ABAS and MIM atlases demonstrated strong agreement with the ground truth, resulting in mean DSC values of more than 0.8. Overall, MIM and ABAS outperformed Eclipse and RayStation in both DSC and MDA. These results indicate that the atlas-based segmentation employed in this study produces acceptable contours for the anatomical structures of interest in prostate radiotherapy treatment planning.