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Three-dimensional (3D) scene reconstruction for moving objects remains a challenging research topic. It is crucial to effectively capture feature representations from dynamic and complex scenarios. Consequently, this work introduces the integration of multi-scale attention and dilated convolution to create an enhanced deep-learning structure for this purpose. Therefore, this paper proposes a 3D reconstruction method for moving objects based on multi-scale attention and a dilated convolutional neural network (CNN). Specifically, a multi-scale attention algorithm framework that incorporates dilated CNNs is designed to extract multi-scale features of moving targets. The dilated CNN is incorporated to enhance the model's perception ability and receptive field while maintaining a lightweight structure. This integrated design aims to achieve automatic learning targeted at features and scene information at different scales. By increasing the effective range of information perception and further enhancing the quality of reconstruction results, a coordinate system is established for 3D scene reconstruction of moving targets. Finally, a comparative analysis of subjective vision, visualization, and reconstruction algorithms is conducted using real-world cases. The experimental results demonstrate that the proposed method exhibits significant advantages in the 3D scene reconstruction task of moving targets compared to traditional methods. [ABSTRACT FROM AUTHOR]

Glycyrrhiza uralensis is an important plant in desert ecology, where low rainfall and water scarcity limit its growth. In order to explore harvested rainwater and use for Glycyrrhiza uralensis growth and to reduce water scarcity in Northwest China's arid area, this study was conducted in April and July of 2023. Five types of slope micro rainwater collection measures, including horizontal terraces, square ridges, and contour ridges, were utilized to monitor rainfall and runoff. Glycyrrhiza uralensis seedlings were utilized as test subjects for supplementary irrigation in pots utilizing the natural drought method. The results show that supplemental irrigation boosts Glycyrrhiza uralensis root growth and water uptake capacity in short-term drought conditions. Irrigation with 42.97 mm enhanced Glycyrrhiza uralensis root length, belowground dry weight, and water use efficiency by 104.5%, 39.54%, and 4.18%. Supplemental irrigation under prolonged drought stress shifted Glycyrrhiza uralensis development from below- to aboveground, resulting in decreased activity of osmotic adjustment material activity in leaves. After 31 days of continuous drought following supplemental irrigation, plant height and aboveground fresh weight increased by 58.16% and 20.03%, respectively, whereas the superoxide dismutase activity was reduced by 63.16% in the 42.97 mm irrigated treatment. Furthermore, under short-term drought stress following supplemental irrigation, leaf water use efficiency was primarily influenced by osmoregulatory substances and plant growth characteristics. Under long-term drought stress, it was influenced by osmoregulatory substances and photosynthetic properties. This research is critical for preventing soil erosion and restoring grassland ecological health in the Ili River Valley. [ABSTRACT FROM AUTHOR]

The rapid expansion of radiological imaging data has placed a significant burden on radiologists, increasing the risk of diagnostic errors. Vision-language models offer a promising solution to alleviate this workload and improve diagnostic accuracy within the medical imaging domain. However, most current models rely solely on training data to activate general-purpose performance, which often results in inadequate understanding and generation of high-quality outputs in complex and specialized medical scenarios due to insufficient domain knowledge. To address this limitation, we propose a Vision-Language Model with Multi-Granular Knowledge Fusion (MGKF) that integrates diverse sources of knowledge to enhance performance across medical imaging tasks. Our model dynamically incorporates multi-granular knowledge, including medical entities, their definitions, and retrieved auxiliary knowledge. We improve the semantic alignment of visual and textual information through fine-tuning, introduce a pre-generation mechanism to incorporate this multi-granular knowledge, and ultimately enhance the model’s ability to apply medical knowledge during inference. Experimental results across multiple medical imaging tasks, including Medical Report Generation, Medical Image Captioning, and Medical Visual Question Answering, demonstrate the effectiveness of the proposed MGKF model. This work provides valuable insights into the integration of specialized knowledge in medical imaging and contributes to reducing diagnostic errors. [ABSTRACT FROM AUTHOR]

Urea supplements, such as humic acids, could enhance fertilizer nitrogen use effectiveness. Melting is superior to mixing for humate urea application; however, the effects of diverse humate ureas from various coal sources on soil N supply remain unclear. This study compared the properties of two humic acids from different coal sources (HA1, weathered coal; HA2, lignite coal), and their impact on soil mineral N supply and the nitrate–ammonium ratio under flooded and 60% water-filled pore space (WFPS) over a 14-day incubation. Humate ureas stimulated soil mineral N accumulation and balanced the soil nitrate–ammonium ratio at 1:1; however, no significant difference existed between the two humate ureas under 60% WFPS. Humate urea enhanced soil ammonium nitrogen (NH4+-N) retention and delayed nitrate nitrogen (NH4−-N) release, leading to soil mineral N retention, especially in lignite humic acid urea (H2AU) treatments from lignite under flooding. Structural equation modeling (SEM) and linear regression revealed that humic acids elevated soil redox potential (Eh) and electrical conductivity (EC), stimulating soil N mineralization and adjusting the optimal nitrate–ammonium ratio. Humate urea improved soil mineral N supply compared to traditional urea treatments, and humic acids from lignite were more beneficial for crop cultivation from a mineral soil N supply perspective. These findings enhance our understanding of humate urea benefits and aid in optimizing humic acids application for N management. [ABSTRACT FROM AUTHOR]

Landmark-based 3-hop cover labeling is a category of approaches for shortest distance/path queries on large-scale complex networks. It pre-computes an index offline to accelerate the online distance/path query. Most real-world graphs undergo rapid changes in topology, which makes index maintenance on dynamic graphs necessary. So far, the majority of index maintenance methods can handle only one edge update (either an addition or deletion) each time. To keep up with frequently changing graphs, we research the fully batch maintenance problem for the 3-hop cover labeling, and proposed the method called FulBM. FulBM is composed of two algorithms: InsBM and DelBM, which are designed to handle batch edge insertions and deletions, respectively. This separation is motivated by the insight that batch maintenance for edge insertions are much more time-efficient and the fact that most edge updates in the real world are incremental. Both InsBM and DelBM are equipped with well-designed pruning strategies to minimize the number of vertex accesses. We have conducted comprehensive experiments on both synthetic and real-world graphs to verify the efficiency of FulBM and its variants for weighted graphs. The results show that our methods achieve 5.5× to 228× speedup compared with the state-of-the-art method. [ABSTRACT FROM AUTHOR]

Universal protein coatings have recently gained wide interest in medical applications due to their biocompatibility and ease of fabrication. However, the challenge persists in protein activity preservation, significantly complicating the functional design of these coatings. Herein, an active dual‐protein surface engineering strategy assisted by a facile stepwise protein–protein interactions assembly (SPPIA) method for catheters to reduce clot formation and infection is proposed. This strategy is realized first by the partial oxidation of bovine serum albumin (BSA) and lysozyme (LZM) for creating stable nucleation platforms via hydrophobic interaction, followed by the assembly of nonoxidized BSA (pI, the isoelectric point, ≈4.7) and LZM (pI ≈11) through electrostatic interaction owing to their opposite charge under neutral conditions. The SPPIA method effectively preserves the conformation and functionality of both BSA and LZM, thus endowing the resultant coating with potent antithrombotic and bactericidal properties. Furthermore, the stable nucleation platform ensures the adhesion and durability of the coating, resisting thrombosis and bacterial proliferation even after 15 days of PBS immersion. Overall, the SPPIA approach not only provides a new strategy for the fabrication of active protein coatings but also shows promise for the surface engineering technology of catheters. [ABSTRACT FROM AUTHOR]

Objective: Cavernous sinus invasion (CSI) plays a pivotal role in determining management in pituitary adenomas. The study aimed to develop a Convolutional Neural Network (CNN) model to diagnose CSI in multiple centers. Methods: A total of 729 cases were retrospectively obtained in five medical centers with (n = 543) or without CSI (n = 186) from January 2011 to December 2021. The CNN model was trained using T1-enhanced MRI from two pituitary centers of excellence (n = 647). The other three municipal centers (n = 82) as the external testing set were imported to evaluate the model performance. The area-under-the-receiver-operating-characteristic-curve values (AUC-ROC) analyses were employed to evaluate predicted performance. Gradient-weighted class activation mapping (Grad-CAM) was used to determine models' regions of interest. Results: The CNN model achieved high diagnostic accuracy (0.89) in identifying CSI in the external testing set, with an AUC-ROC value of 0.92 (95% CI, 0.88–0.97), better than CSI clinical predictor of diameter (AUC-ROC: 0.75), length (AUC-ROC: 0.80), and the three kinds of dichotomizations of the Knosp grading system (AUC-ROC: 0.70–0.82). In cases with Knosp grade 3A (n = 24, CSI rate, 0.35), the accuracy the model accounted for 0.78, with sensitivity and specificity values of 0.72 and 0.78, respectively. According to the Grad-CAM results, the views of the model were confirmed around the sellar region with CSI. Conclusions: The deep learning model is capable of accurately identifying CSI and satisfactorily able to localize CSI in multicenters. [ABSTRACT FROM AUTHOR]

Hemostatic materials facilitate rapid hemostasis and significantly mitigate the potential of fatal hemorrhage in civilian and military traumas. However, most existing hemostatic materials are limited in material‐dependent forms and fail to integrate multifunctionality, thus constraining their versatility for differing settings and wound healing capacity. Herein, a facile, versatile armor strategy is proposed to endow various biomaterials with rapid hemostasis, infection prevention, and tissue healing capabilities. The armor is fabricated on the surface of substrates first through chemical cross‐linking of adhesive catechol (phenol) and collagen (polyamine) inspired by insect sclerotization, followed by zinc ions (Zn2+) chelation based on mussel‐inspired metal‐phenol coordination chemistry, referred to "metal‐phenol‐polyamine system". This armor facilitates clot formation by promoting platelet aggregation and activating both intrinsic and extrinsic coagulation pathways. Moreover, the integrated Zn2+ endows the armor with potent antibacterial properties against both Gram‐positive and Gram‐negative bacteria. Consequently, this strategy armors a hemostatic sponge that effectively controls bleeding in rabbit hemorrhage models and successfully facilitates the complete healing of epidermal traumas in rats within 14 days. This metal‐phenol‐polyamine system‐assisted armor provides a potential and universal strategy for efficient hemostasis and wound healing. [ABSTRACT FROM AUTHOR]

Cystic lesions are common lesions of the sellar region with various pathological types, including pituitary apoplexy, Rathke's cleft cyst, cystic craniopharyngioma, etc. Suggested surgical approaches are not unique when dealing with different cystic lesions. However, cystic lesions with different pathological types were hard to differentiate on MRI with the naked eye by doctors. This study aimed to distinguish different pathological types of cystic lesions in the sellar region using preoperative magnetic resonance imaging (MRI). Radiomics and deep learning approaches were used to extract features from gadolinium-enhanced MRIs of 399 patients enrolled at Peking Union Medical College Hospital over the past 15 years. Paired imaging differentiations were performed on four subtypes, including pituitary apoplexy, cystic pituitary adenoma (cysticA), Rathke's cleft cyst, and cystic craniopharyngioma. Results showed that the model achieved an average AUC value of 0.7685. The model based on a support vector machine could distinguish cystic craniopharyngioma from Rathke's cleft cyst with the highest AUC value of 0.8584. However, distinguishing cystic apoplexy from pituitary apoplexy was difficult and almost unclassifiable with any algorithms on any feature set, with the AUC value being only 0.6641. Finally, the proposed methods achieved an average Accuracy of 0.7532, which outperformed the traditional clinical knowledge-based method by about 8%. Therefore, in this study, we first fill the gap in the existing literature and provide a non-invasive method for accurately differentiating between these lesions, which could improve preoperative diagnosis accuracy and help to make surgery plans in clinical work. [ABSTRACT FROM AUTHOR]

Phosphorus (P) deficiencies are widespread in calcareous soils. The poor availability of nitrogen (N) and P in soils often restricts crop growth. However, the effects of P addition on plant growth and plant nutrient transport changes during the establishment of Leymus chinensis fields in Xinjiang are not clear. We investigated the responses of Leymus chinensis biomass and nutrient absorption and utilization to changes in soil N and P by adding P (0, 15.3, 30.6, and 45.9 kg P ha−1 year−1) with basally applied N fertilizer (150 kg N ha−1 year−1). The results showed that (a) Principal component analysis (PCA) of biomass, nutrient accumulation, soil available P, and soil available N during the different periods of Leymus chinensis growth showed that their cumulative contributions during the jointing and harvest periods reached 95.4% and 88%, respectively. (b) Phosphorus use efficiency (PUE) increased with the increase of P fertilizer gradient and then decreased and the maximum PUE was 13.14% under moderate P addition. The accumulation of biomass and nutrients in Leymus chinensis can be effectively improved by the addition of P fertilizer at 30.6 kg ha−1. Different P additions either moderately promoted or excessively inhibited Leymus chinensis growth and nutrient utilization. [ABSTRACT FROM AUTHOR]

Braided neurovascular flow diverters (FDs) have revolutionized the treatment of aneurysms. However, dual antiplatelet therapy is mandatory for patients with FDs, which increases the risk of adverse side effects like bleeding complications. Surface modification would be of critical relevance to improve the biocompatibility and therefore clinical performance of FDs. Herein, a titanium dioxide (TiO2) covalently immobilized citric acid (CA) nanohybrid coating is fabricated on nitinol (NiTi) braided FDs by liquid phase deposition and dip‐coating, to inhibit thrombosis and promote re‐endothelialization. The CA molecules are covalently bound onto the pre‐deposited TiO2 nanoparticulate coating. The coating has a unique homogenously nanostructured morphology as well as super‐hydrophilicity. Both in vitro and in vivo results verify that the coated samples inhibit platelets and fibrinogen adhesion, delay coagulation time, and concomitantly promote re‐endothelialization. Such appealing properties are ascribed to the nature of CA and TiO2 per se as well as the nanostructured morphology. The present strategy may not only provide a new avenue to surface‐modify braided neurovascular FDs but also shed light on advanced nanohybrid materials for biomedical applications among others. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Fibers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Isoprene is the most abundant non-methane biogenic volatile organic compound in the Earth's atmosphere and has the potential to influence photochemistry in the remote ocean–atmosphere. Marine isoprene emission estimates vary over multiple orders of magnitude using different methods, and the paucity of continuous in-situ measurements makes it challenging to distinguish their spatiotemporal variations. Here we present marine isoprene emission estimates inferred from Himawari-8 observations and model simulation covering the western Pacific Ocean and the eastern Indian Ocean. Although most isoprene emission hotspots were near coasts, we found an unexpected emission pool in the center of the equatorial Pacific Ocean with 18% higher emissions than those in the North and South Pacific Oceans. Remarkably, the isoprene emissions increased by 5.5 ± 0.1% per year in the center of the equatorial Pacific Ocean between August 2015 and December 2020, while no significant trend for emissions in other ocean regions. We investigated marine isoprene oxidation impacts based on satellite observations, and the results suggest NO2 may play a critical role during aerosol formation from isoprene in the remote ocean air. [ABSTRACT FROM AUTHOR]

Objectives: Convolutional neural network (CNN) is a deep-learning method for image classification and recognition based on a multi-layer NN. In this study, CNN was used to accurately assess cavernous sinus invasion (CSI) in pituitary adenoma (PA). Methods: A total of 371 patients with PA were enrolled in the retrospective study. The cohort was divided into the invasive (n = 102) and non-invasive groups (n = 269) based on surgically confirmed CSI. Images were selected on the T1-enhanced imaging on MR scans. The cohort underwent a fivefold division of randomized datasets for cross-validation. Then, a tenfold augmented dataset (horizontal flip and rotation) of the training set was enrolled in the pre-trained Resnet50 model for transfer learning. The testing set was imported into the trained model for evaluation. Gradient-weighted class activation mapping (Grad-CAM) was used to obtain the occlusion map. The diagnostic values were compared with different dichotomizations of the Knosp grading system (grades 0-1/2-4, 0-2/3a-4, and 0-3a/3b-4). Results: Based on Knosp grades, 20 cases of grade 0, 107 cases of grade 1, 82 cases of grade 2, 104 cases of grade 3a, 22 cases of grade 3b, and 36 cases of grade 4 were recorded. The CSI rates were 0%, 3.7%, 18.3%, 37.5%, 54.5%, and 88.9%. The predicted accuracies of the three dichotomies were 60%, 74%, and 81%. The area under the receiver operating characteristic (AUC-ROC) of Knosp grade for CSI prediction was 0.84; the cutoff was 2.5 with a Youden value of 0.62. The accuracies of the CNN model ranged from 0.80 to 0.96, with AUC-ROC values ranging from 0.89 to 0.98. The Grad-CAM saliency maps confirmed that the region of interest of the model was around the sellar region. Conclusions: We constructed a CNN model with a high proficiency at CSI diagnosis. A more accurate CSI identification was achieved with the constructed CNN than the Knosp grading system. [ABSTRACT FROM AUTHOR]

Biodegradable Zn‐based metals have recently gained great attention for orthopedic implant applications, due to their attractive attributes in biodegradability, mechanics, and bio‐functionalities. However, Zn‐based metals suffer from excessive Zn2+ release leading to bio‐incompatibility and insufficient osteogenic properties. Herein, bioactive molecules incorporated zinc phosphate (ZnP) of metal–organic/inorganic hybrid coatings are constructed on Zn surfaces by dip‐coating, targeting a decrease in Zn2+ release rate and an improvement in biocompatible and osteogenic properties. The bioactive molecules, cysteine, phenylalanine, and bovine serum albumin, are incorporated into inorganic ZnP using a metal–organic/inorganic coordination strategy. As a result, these hybrid coatings are compact, and present different micro/nanostructured surface morphology. The electrochemical and long‐term static immersion results show that the hybrid coatings decrease the corrosion rate of Zn, reduce the Zn2+ ion release rate, and promote hydroxyapatite deposition. In addition, they promote the proliferation and adhesion of preosteoblast MC3T3‐E1 and rat bone marrow mesenchymal stem cells, and up‐regulate the expression of osteogenesis‐related genes. Such appealing properties can be attributed to the characteristic micro/nanostructured surface morphologies and the presence of the bioactive components. These metal–organic/inorganic hybrid coatings provide a novel avenue to modify the surface of Zn‐based metals for orthopedic implant applications. [ABSTRACT FROM AUTHOR]

Purpose: Cushing's disease (CD) is a rare disease that contributes to 70–80% hypercortisolemia, which presents similarities and differences between pediatric and adult patients, and even between male and female patients. However, the comparative study of CD between different age groups and different genders is still insufficient. The aim of the study is to make a systematic comparison to reveal the gender differences in children and adult patients of CD, helping clinicians to provide optimal treatment for different groups of patients. Methods: We conducted a retrospective research consisting of 30 pediatric and 392 adult CD patients in a single center in Peking Union Medical College Hospital. All 422 patients showed symptoms related to hypercortisolism and received adenoma excision surgery in the department of neurosurgery between 2014 and 2020. Results: For the accuracy of diagnosis, the sensitivity of BIPSS at baseline in pediatric patients was lower than in adults (75 vs. 91%, P = 0.054) but increased greatly after desmopressin stimulation (94 vs. 95%). However, the accuracy of lateralization for BIPSS was not preferred for prediction. As for clinical manifestations, growth retardation, weight gain, hirsutism, and acne were more prevalent for children, while for adults, hypertension, osteopenia, glucometabolic disorder, easy bruising, hair loss, and weight loss were more frequently seen. As previously reported, we observed a significant difference between the male prevalence of pediatric and adult patients (50 vs. 17%, P < 0.001), which was possibly caused by the more severe and earlier onset of a series of symptoms. Gender-related comparison showed greater morbidity of nephrolithiasis, hypokalemia, hypertension, easy bruising, osteopenia, and striae for male patients, while irregular menses, hirsutism, and hair loss were more common for female patients. Further analysis showed that the secretory activity of the PA axis was higher for males, presenting as the more remarkable alteration of laboratory parameters and contributing to the more severe clinical manifestations. For patients treated with transsphenoidal pituitary surgery (TSS), the immediate prognosis could be predicted by operation history, invasiveness, Ki-67, and information provided by MRI, including tumor size and Knosp grading. However, we still lack methods to predict long-term prognosis. Conclusions: Our study is the first detailed and systematic comparison between pediatric and adult CD patients. Further exploration of the impact of CD on different genders reveals a more severe and probably an earlier-onset pattern of CD for male patients. [ABSTRACT FROM AUTHOR]

Background: No existing machine learning (ML)-based models use free text from electronic medical records (EMR) as input to predict immediate remission (IR) of Cushing's disease (CD) after transsphenoidal surgery. Purpose: The aim of the present study is to develop an ML-based model that uses EMR that include both structured features and free text as input to preoperatively predict IR after transsphenoidal surgery. Methods: A total of 419 patients with CD from Peking Union Medical College Hospital were enrolled between January 2014 and August 2020. The EMR of the patients were embedded and transformed into low-dimensional dense vectors that can be included in four ML-based models together with structured features. The area under the curve (AUC) of receiver operating characteristic curves was used to evaluate the performance of the models. Results: The overall remission rate of the 419 patients was 75.7%. From the results of logistic multivariate analysis, operation (p < 0.001), invasion of cavernous sinus from MRI (p = 0.046), and ACTH (p = 0.024) were strongly correlated with IR. The AUC values for the four ML-based models ranged from 0.686 to 0.793. The highest AUC value (0.793) was for logistic regression when 11 structured features and "individual conclusions of the case by doctor" were included. Conclusion: An ML-based model was developed using both structured and unstructured features (after being processed using a word embedding method) as input to preoperatively predict postoperative IR. [ABSTRACT FROM AUTHOR]

Objective: No accurate predictive models were identified for hormonal prognosis in non-functioning pituitary adenoma (NFPA). This study aimed to develop machine learning (ML) models to facilitate the prognostic assessment of pituitary hormonal outcomes after surgery. Methods: A total of 215 male patients with NFPA, who underwent surgery in four medical centers from 2015 to 2021, were retrospectively reviewed. The data were pooled after heterogeneity assessment, and they were randomly divided into training and testing sets (172:43). Six ML models and logistic regression models were developed using six anterior pituitary hormones. Results: Only thyroid-stimulating hormone (p < 0.001), follicle-stimulating hormone (p < 0.001), and prolactin (PRL; p < 0.001) decreased significantly following surgery, whereas growth hormone (GH) (p < 0.001) increased significantly. The postoperative GH (p = 0.07) levels were slightly higher in patients with gross total resection, but the PRL (p = 0.03) level was significantly lower than that in patients with subtotal resection. The optimal model achieved area-under-the-receiver-operating-characteristic-curve values of 0.82, 0.74, and 0.85 in predicting hormonal hypofunction, new deficiency, and hormonal recovery following surgery, respectively. According to feature importance analyses, the preoperative levels of the same type and other hormones were all important in predicting postoperative individual hormonal hypofunction. Conclusion: Fluctuation in anterior pituitary hormones varies with increases and decreases because of transsphenoidal surgery. The ML models could accurately predict postoperative pituitary outcomes based on preoperative anterior pituitary hormones in NFPA. [ABSTRACT FROM AUTHOR]

Context: The resection plan of pituitary adenoma (PA) needs preoperative observation of the sellar region. Radiomics prediction requires high-quality segmentations. Manual delineation is time-consuming and subject to rater variability.Objective: This work aims to create an automated segmentation method for the sellar region, several tools to extract invasiveness-related features, and evaluate their clinical usefulness by predicting the tumor consistency.Methods: Patients included were diagnosed with pituitary adenoma at Peking Union Medical College Hospital. A deep convolutional neural network, called gated-shaped U-net (GSU-Net), was created to automatically segment the sellar region into 8 classes. Five magnetic resonance imaging (MRI) features were extracted from the segmentation results, including tumor diameters, volume, optic chiasma height, Knosp grading system, and degree of internal carotid artery contact. The clinical usefulness of the proposed methods was evaluated by the diagnostic accuracy of the tumor consistency.Results: A total of 163 patients with confirmed pituitary adenoma were included as the first group and were randomly divided into a training data set and test data set (131 and 32 patients, respectively). Fifty patients with confirmed acromegaly were included as the second group. The Dice coefficient of pituitary adenoma in important image slices was 0.940. The proposed methods achieved accuracies of more than 80% for the prediction of 5 invasive-related MRI features. Methods derived from the automatic segmentation showed better performance than original methods and achieved areas under the curve of 0.840 and 0.920 for clinical models and radiomics models, respectively.Conclusion: The proposed methods could automatically segment the sellar region and extract features with high accuracy. The outstanding performance of the prediction of the tumor consistency indicates the methods' clinical usefulness for supporting neurosurgeons in judging patients' conditions, predicting prognosis, and other downstream tasks during the preoperative period. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (CT) is commonly used to obtain vivo images to characterize diseases but results in radiation exposure to patients. Low-dose CT (LDCT) provides CT images of clinical quality with reduced cumulative radiation dose. Iterative image reconstruction methods with effective regularization are used for LDCT but generally require more computing resources and induce higher computational load than the conventional filtered backprojection (FBP) methods. The high computational demand of the iterative reconstruction (IR) with notably increased reconstruction time precludes its routine clinical application. In this work, we focus on the FPGA acceleration of a compute-intensive full IR (full-IR) algorithm based on the Mumford–Shah regularization. At the algorithmic level, we propose a beam-based asynchronous update algorithm to reduce the computational cost and alleviate the conflicts. At the hardware-level, we first present pipeline-friendly optimization for the original algorithm to increase the computation throughput. We then apply the LDCT-specific tiling strategy to improve the data reuse rate. The experimental results show that our implementation takes 8.5 min to reconstruct a typical physical phantom with the image quality comparable with the vendor’s result. The FPGA implementation achieves 11.6 × throughput against the state-of-the-art GPU version. [ABSTRACT FROM AUTHOR]

Background: There are no established accurate models that use machine learning (ML) methods to preoperatively predict immediate remission after transsphenoidal surgery (TSS) in patients diagnosed with histology-positive Cushing's disease (CD). Purpose: Our current study aims to devise and assess an ML-based model to preoperatively predict immediate remission after TSS in patients with CD. Methods: A total of 1,045 participants with CD who received TSS at Peking Union Medical College Hospital in a 20-year period (between February 2000 and September 2019) were enrolled in the present study. In total nine ML classifiers were applied to construct models for the preoperative prediction of immediate remission with preoperative factors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the models. The performance of each ML-based model was evaluated in terms of AUC. Results: The overall immediate remission rate was 73.3% (766/1045). First operation (p<0.001), cavernous sinus invasion on preoperative MRI(p<0.001), tumour size (p<0.001), preoperative ACTH (p=0.008), and disease duration (p=0.010) were significantly related to immediate remission on logistic univariate analysis. The AUCs of the models ranged between 0.664 and 0.743. The highest AUC, i.e., the best performance, was 0.743, which was achieved by stacking ensemble method with four factors: first operation, cavernous sinus invasion on preoperative MRI, tumour size and preoperative ACTH. Conclusion: We developed a readily available ML-based model for the preoperative prediction of immediate remission in patients with CD. [ABSTRACT FROM AUTHOR]

Serial equivalency enables easier regression testing and customer support in production-grade parallel CAD tools. While existing parallel routing techniques have become sufficiently advanced to provide good speedup, support for serial equivalency still has been very limited or ignored because it was considered costly. In this paper, we present a serial-equivalent parallel router that not only provides significant speedup but also produces the same result as the serial router. This parallel router primarily leverages a dependency-aware scheduling algorithm to facilitate the serial equivalency. Moreover, regardless of how many processor cores are used, this scheduling algorithm also enables parallel router to have the same result as the serial router. In scheduling algorithm, according to the original net order of serial router, all of the nets are scheduled to a series of different stages. Specifically, the independent nets are scheduled to the same stage and they can be routed in parallel while the dependent nets are scheduled in different stages and they are processed in serial. Note that the parallel routing of independent nets can be explored in static and dynamic fashions, and the data synchronization between dependent stages is implemented in MPI-based message queue. Experimental evaluations using ten large designs from the academic VTR benchmark suite show that our parallel router can scale to 32 processor cores at least to provide an average $19.13 \times $ speedup compared to the state-of-the-art academic VPR router. And most importantly, our parallel router can maintain the serial equivalency which achieves the same results as the serial router. To the best of our knowledge, it is the first parallel router that provides significant speedup with a serial equivalency guarantee. [ABSTRACT FROM AUTHOR]

Abstract: Soil depth reflects the quantity and ecosystem service functions of soil resources. However, there is no universal standard to measure soil depth at present, and digital soil mapping approaches for predicting soil depth at the regional scale remain immature. Using observation of soil profile morphology, we compared the soil depth nomenclatures from the World Reference Base for Soil Resources, Chinese Soil Taxonomy, and Soil Taxonomy. For this study, shallow soils were defined as those with an effective soil depth < 100 cm. Based on legacy data and field soil survey, the spatial distribution of shallow soils in Xinjiang, China, and the main controlling environmental factors were explored. Results showed that shallow soils in Xinjiang are mainly distributed in high altitude regions such as the Tian Mountains. At the regional scale, significant correlations were observed between soil depth and climate factors, as well as between soil depth and vegetation fractional coverage. Contrary to previous conclusions at small spatial scales, terrain attributes could not explain soil depth variation at the regional scale. This study addressed knowledge gaps on soil depth prediction at regional scales while elucidating climate‐vegetation‐soil coevolution. [ABSTRACT FROM AUTHOR]

Heparin (Hep) or bivalirudin (BVLD) were immobilized in an organic phytic acid (PA) coating on Mg by an in situ chemical route. Such a drug-loaded PA coating was designed to enhance both corrosion control and biocompatibility. It was found that both Hep- and BVLD-loaded PA coatings exhibited a dual role in effectively controlling corrosion as well as providing a biofunctional effect. Experiments involving electrochemical corrosion and in vitro degradation by immersion revealed that PA&Hep- and PA&BVLD-coated Mg had the same effect or even slower corrosion/degradation in phosphate buffered saline compared to PA-coated Mg, and it degraded significantly slower than untreated Mg. Moreover, Hep- or BVLD-loaded PA coatings showed relatively good hemocompatibility, with a prolonged clotting time, inhibited platelets adhesion as well as reduced hemolysis compared to untreated Mg. In addition, both PA&Hep and PA&BVLD coatings promoted endothelial cells growth and restrained the proliferation of smooth muscle cells. In vivo assays indicated that PA&Hep-coated Mg exhibited a significant difference in mass loss compared to untreated Mg, as well as better histocompatibility than other samples. These results demonstrate that our coating strategy shows a great potential in surface modification of biodegradable Mg. Finally, the mechanism for the incorporation of the drugs into the PA coating is discussed from both theoretical and practical perspectives. [ABSTRACT FROM AUTHOR]

Ecological applications of remote-sensing techniques are generally limited to images after atmospheric correction, though other radiometric correction data are potentially valuable. In this article, six spectral vegetation indices (VIs) were derived from a SPOT 5 image at four radiometric correction levels: digital number (DN), at-sensor radiance (SR), top of atmosphere reflectance (TOA) and post-atmospheric correction reflectance (PAC). These VIs include the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), slope ratio of radiation curve (K), general radiance level (L), visible-infrared radiation balance (B) and band radiance variation (V). They were then related to the leaf area index (LAI), acquired from in situ measurement in Hetian town, Fujian Province, China. The VI–LAI correlation coefficients varied greatly across vegetation types, VIs as well as image radiometric correction levels, and were not surely increased by image radiometric corrections. Among all 330 VI–LAI models established, the R 2 of multi-variable models were generally higher than those of the single-variable ones. The independent variables of the best VI–LAI models contained all VIs from all radiometric correction levels, showing the potentials of multi-radiometric correction images in LAI estimating. The results indicated that the use of VIs from multiple radiometric correction images can better exploit the capabilities of remote-sensing information, thus improving the accuracy of LAI estimating. [ABSTRACT FROM AUTHOR]

Soil erosion by water under forest cover is a serious problem in southern China. A comparative study was carried out on the use of leaf area index (LAI) and vegetation fractional coverage (VFC) in quantifying soil loss under vegetation cover. Five types of vegetation with varied LAI and VFC under field conditions were exposed to two rainfall rates (40 mm h and 54 mm h) using a portable rainfall simulator. Runoff rate, sediment concentration and soil loss rate were measured at relatively runoff stable state. Significant negative exponential relationship (p < 0.05, R = 0.83) and linear relationship (p < 0.05, R = 0.84) were obtained between LAI and sediment concentration, while no significant relationship existed between VFC and sediment concentration. The mechanism by which vegetation canopy prevents soil loss was by reducing rainfall kinetic energy and sediment concentration. LAI could better quantify such a role than VFC. However, neither LAI nor VFC could explain runoff rate or soil loss rate. Caution must be taken when using LAI to quantify the role of certain vegetation in soil and water conservation. [ABSTRACT FROM AUTHOR]

Along with the global climate change, gully erosion, flood and drought jointly restrict the sustainable development of arid rangeland in Central Asia. Rainwater harvesting (RWH) system in gully is a flexible practice that alleviate complex environmental problems. In the Kulusitai watershed of Xinjiang, China, our study presented a decision-making system using GIS combined with multi-criteria analysis and a field survey to identify suitability of gully for RWH. The results showed that nearly 40% of rangeland belonged to high runoff potential area, and gullies as the runoff collection channel became the potential site of RWH. The selection of RWH systems depended on catchment environment and gully characteristics. Therefore, based on the unique natural conditions of Xinjiang and successful RWH cases in other regions, we discussed some suitable low-cost RWH techniques to restore degraded grassland and promote community development. Our study will provide some suggestions for ecological restoration and pasture management in arid regions of Central Asia. [ABSTRACT FROM AUTHOR]