DOVE: Doodled vessel enhancement for photoacoustic angiography super resolution.

Deep-learning-based super-resolution photoacoustic angiography (PAA) has emerged as a valuable tool for enhancing the resolution of blood vessel images and aiding in disease diagnosis. However, due to the scarcity of training samples, PAA super-resolution models do not generalize well, especially in the challenging in-vivo imaging of organs with deep tissue penetration. Furthermore, prolonged exposure to high laser intensity during the image acquisition process can lead to tissue damage and secondary infections. To address these challenges, we propose an approach doodled vessel enhancement (DOVE) that utilizes hand-drawn doodles to train a PAA super-resolution model. With a training dataset consisting of only 32 real PAA images, we construct a diffusion model that interprets hand-drawn doodles as low-resolution images. DOVE enables us to generate a large number of realistic PAA images, achieving a 49.375% fool rate, even among experts in photoacoustic imaging. Subsequently, we employ these generated images to train a self-similarity-based model for super-resolution. During cross-domain tests, our method, trained solely on generated images, achieves a structural similarity value of 0.8591, surpassing the scores of all other models trained with real high-resolution images. DOVE successfully overcomes the limitation of insufficient training samples and unlocks the clinic application potential of super-resolution-based biomedical imaging.

Transferability-Guided Cross-Domain Cross-Task Transfer Learning.

We propose two novel transferability metrics fast optimal transport-based conditional entropy (F-OTCE) and joint correspondence OTCE (JC-OTCE) to evaluate how much the source model (task) can benefit the learning of the target task and to learn more generalizable representations for cross-domain cross-task transfer learning. Unlike the original OTCE metric that requires evaluating the empirical transferability on auxiliary tasks, our metrics are auxiliary-free such that they can be computed much more efficiently. Specifically, F-OTCE estimates transferability by first solving an optimal transport (OT) problem between source and target distributions and then uses the optimal coupling to compute the negative conditional entropy (NCE) between the source and target labels. It can also serve as an objective function to enhance downstream transfer learning tasks including model finetuning and domain generalization (DG). Meanwhile, JC-OTCE improves the transferability accuracy of F-OTCE by including label distances in the OT problem, though it incurs additional computation costs. Extensive experiments demonstrate that F-OTCE and JC-OTCE outperform state-of-the-art auxiliary-free metrics by 21.1% and 25.8% , respectively, in correlation coefficient with the ground-truth transfer accuracy. By eliminating the training cost of auxiliary tasks, the two metrics reduce the total computation time of the previous method from 43 min to 9.32 and 10.78 s, respectively, for a pair of tasks. When applied in the model finetuning and DG tasks, F-OTCE shows significant improvements in the transfer accuracy in few-shot classification experiments, with up to 4.41% and 2.34% accuracy gains, respectively.

UCL-Dehaze: Toward Real-World Image Dehazing via Unsupervised Contrastive Learning.

While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain shift problem. From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and clean images, thus alleviating the domain shift problem and enhancing the network's generalization ability in real-world scenarios. We propose an effective unsupervised contrastive learning paradigm for image dehazing, dubbed UCL-Dehaze. Unpaired real-world clean and hazy images are easily captured, and will serve as the important positive and negative samples respectively when training our UCL-Dehaze network. To train the network more effectively, we formulate a new self-contrastive perceptual loss function, which encourages the restored images to approach the positive samples and keep away from the negative samples in the embedding space. Besides the overall network architecture of UCL-Dehaze, adversarial training is utilized to align the distributions between the positive samples and the dehazed images. Compared with recent image dehazing works, UCL-Dehaze does not require paired data during training and utilizes unpaired positive/negative data to better enhance the dehazing performance. We conduct comprehensive experiments to evaluate our UCL-Dehaze and demonstrate its superiority over the state-of-the-arts, even only 1,800 unpaired real-world images are used to train our network. Source code is publicly available at https://github.com/yz-wang/UCL-Dehaze.

The proteolytic activity in inflammatory bowel disease: insight from gut microbiota.

Inflammatory bowel disease (IBD) is a chronic, recurrent inflammatory disease caused by the destruction of the intestinal mucosal epithelium that affects a growing number of people worldwide. Although the etiology of IBD is complex and still elucidated, the role of dysbiosis and dysregulated proteolysis is well recognized. Various studies observed altered composition and diversity of gut microbiota, as well as increased proteolytic activity (PA) in serum, plasma, colonic mucosa, and fecal supernatant of IBD compared to healthy individuals. The imbalance of intestinal microecology and intestinal protein hydrolysis were gradually considered to be closely related to IBD. Notably, the pivotal role of intestinal microbiota in maintaining proteolytic balance received increasing attention. In summary, we have speculated a mesmerizing story, regarding the hidden role of PA and microbiota-derived PA hidden in IBD. Most importantly, we provided the diagnosis and therapeutic targets for IBD as well as the formulation of new treatment strategies for other digestive diseases and protease-related diseases.

Study on mechanical properties and acoustic emission characteristics of deep diorite under uniaxial compression.

The research object is diorite in the Lingbei TBM section of the Hanjiang-To-Weihe River Qinling tunnel, with a buried depth of over 1 km. Using MTS-2000 microcomputer-controlled electro-hydraulic servo universal testing machine and DS5-16b acoustic emission (AE) monitoring system, uniaxial compression and acoustic emission monitoring tests were carried out on rock samples, to study the uniaxial compression mechanical properties and acoustic emission characteristics of the deep diorite. The results of the study indicate that: (1) During uniaxial compression, diorite undergoes four stages: initial compaction, elasticity, yield and failure, in which the curve of the initial compaction stage is significantly smoother. The uniaxial compressive strength is 41.95 MPã102.42 MPa, with an average of 74.07 Mpa; The axial peak strain ranges from 1 % to 1.4 %, and the failure mode belongs to brittle ductile splitting failure. (2) The cumulative ringing count and energy showed a very slow increase trend during the calm period; After entering a surge period (with the appearance of Kaiser points), both show a significant transition state; During the slow increase period, the overall growth rate of the two slowed down and remained almost silent. (3) On the basis of the analysis of RA-AF values during the deformation and rupture process of diorite, it can be seen that the damage type of diorite is tensile damage by the significant low RA value and high AF value characteristics, which coincides with the actual damage fracture characteristics of the rocks in the sample. (4) During the compaction stage, there are few acoustic emission location points, which correspond to low energy and are mainly distributed at the higher and lower ends of the sample; After entering the elasticity stage, the number of positioning points significantly increases and gradually expands towards the middle; Near Kaiser point, the number of location points and corresponding energy are both in a sharp increase state, and this trend is in good agreement with the changes in the ringing count-time and energy-time curves. (5) The damage time mainly starts at the end of the calm period, and the pattern of change in the damage curve coincides with the localization point and energy evolution. The results of the research can be used as a referential basis for the development of the excavation, protection and other construction plans for the Lingbei TBM section of the Hanjiang-To-Weihe River Qinling tunnel or similar surrounding rock tunnels, as well as for further conducting triaxial unloading tests on diorite.

Layered hybrid PAM-DMT for IM/DD OWC systems.

Traditional pulse-amplitude-modulated discrete multitone modulation (PAM-DMT) suffers from poor overall performance of spectral and power efficiencies in optical wireless communication (OWC) systems. We propose layered hybrid PAM-DMT (LHPAM-DMT) to utilize more subcarriers to improve the performance. The real part of frequency domain signal is divided into several layers and symmetry biases are added in time domain to generate real-valued and nonnegative signals for intensity modulation with direct detection (IM/DD) OWC systems. By appropriately designing the orthogonality between the signals in lower layers and signals & added biases in higher layers, we further propose an iterative receiver to recover the transmitted information. Theoretical derivation proves that LHPAM-DMT has higher spectral efficiency than PAM-DMT and lower complexity than layered PAM-DMT. Numerical results suggest that LHPAM-DMT is more power efficient than PAM-DMT as well as direct-current (DC) biased optical OFDM (DCO-OFDM), one of the most popular schemes.

Synthesis and Antioxidant Properties of Psoralen Derivatives.

Five psoralen derivatives were synthesized and the structures of them were characterized by 1 H-NMR, 13 C-NMR, and IR. The antioxidant properties of the compounds were tested by inhibiting the free radical-initiated DNA oxidation and scavenging the radical reaction. The results showed that the effective stoichiometric factors (n) of the compounds V and IV could reach 2.00 and 2.11 in the system of inhibiting the DNA oxidation reaction initiated by 2,2'-Azobis(2-methylpropionamidine) dihydrochloride (AAPH). In the inhibition of ⋅OH-oxidation of the DNA system, compounds I~V showed antioxidant properties. The thiobarbituric acid absorbance (TBARS) percentages of compounds IV and V were 76.19 % and 78.84 %. Compounds I~V could also inhibit Cu2+ /GSH-oxidation of DNA, and all compounds exhibited good antioxidant properties except compound II (94.00 %). All the five compounds were able to trap diammonium 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonate) salt radical (ABTS+ ⋅), 2,2-diphenyl-1-picrylhydrazyl radical (DPPH⋅) and 2,6-di-tert-butyl-alpha-(3,5-di-tert-butyl-4-oxo-2,5-cyclohexadien-p-tolylox radical (galvinoxyl⋅). The ability of compounds I~V to scavenge those free radicals can be measured by the k values. The k values ranged from 0.07 to 0.82 in scavenging ABTS+ ⋅, galvinoxyl, and DPPH radicals, respectively.

Steganography for medical record image.

Medical record images in EHR system are users' privacy and an asset, and there is an urgent need to protect this data. Image steganography can offer a potential solution. A steganographic model for medical record images is therefore developed based on StegaStamp. In contrast to natural images, medical record images are document images, which can be very vulnerable to image cropping attacks. Therefore, we use text region segmentation and watermark region localization to combat the image cropping attack. The distortion network has been designed to take into account the distortion that can occur during the transmission of medical record images, making the model robust against communication induced distortions. In addition, based on StegaStamp, we innovatively introduced FISM as part of the loss function to reduce the ripple texture in the steganographic image. The experimental results show that the designed distortion network and the FISM loss function term can be well suited for the steganographic task of medical record images from the perspective of decoding accuracy and image quality.

[Delayed Blood Transfusion Adverse Reactions Caused by Hypothermia Cardiopulmonary Bypass and Its Coping Strategies].

OBJECTIVE: To investigate a case of delayed hemolytic transfusion reaction (DHTR), and find the reason and coping strategies to prevent similar incidents in the future. METHODS: Relative antibody identification was carried out, suitable erythrocytes was screened, and relevant indicators after transfusion were evaluated. Medical record and laboratory report of the patient were reviewed, the cause was analyzed, and corresponding treatment was carried out. RESULTS: The patient suffered from DHTR caused by IgM anti-M antibody and low body temperature cardiopulmonary bypass. After anti-hemolytic treatment, the patient was gradually improved, the bilirubin gradually decreased, and finally cured and discharged. CONCLUSION: When the transfusion department finds any antibodies which have activity at room temperature but no respond at 37 ℃, they should communicate with clinical medical staff in time. Warm transfusion should be used during blood transfusion to prevent transfusion-related hemolytic reaction.

Bioequivalence Study of Miglitol Orally Disintegrating Tablets in Healthy Chinese Volunteers Under Fasting Condition Based on Pharmacodynamic and Pharmacokinetic Parameters.

To investigate the bioequivalence of miglitol orally disintegrating tablets in healthy Chinese volunteers based on pharmacodynamic (PD) and pharmacokinetic (PK) parameters. Additionally, the safety profile was estimated. Two randomized, open-label, single-dose, crossover trials were conducted under fasting conditions. In the PD trial (CTR20191811), 45 healthy volunteers were randomly divided into 3 groups in a 1:1:1 ratio and administered sucrose alone or coadministered with 50 mg of miglitol orally disintegrating tablet test or reference formulation/sucrose. In the PK trial (CTR20191696), 24 healthy volunteers were randomized (1:1) to receive the test or reference formulation (50 mg). Blood samples were collected at 15 and 17 sampling points per cycle in the PD and PK trials, respectively. Plasma miglitol and serum glucose concentrations were analyzed using a validated liquid chromatography-tandem mass spectrometry method. Serum insulin concentrations were measured using electrochemiluminescent immunoassay. Statistical analyses for the PD and PK parameters were subsequently performed. The volunteers' physical indicators were monitored and documented during the entire study to estimate drug safety. The PD and PK parameters of the two formulations were similar. The main PD and PK end points were both within the prespecified range of 80%-125%. The incidences of treatment-emergent adverse events (TEAEs) and drug-related TEAEs were similar between the test and reference formulation groups, and no serious TEAEs or deaths occurred during the 2 trials. These 2 formulations were demonstrated to be bioequivalent and well tolerated in healthy Chinese volunteers under fasting condition.

Blind Image Quality Assessment for Pathological Microscopic Image Under Screen and Immersion Scenarios.

The high-quality pathological microscopic images are essential for physicians or pathologists to make a correct diagnosis. Image quality assessment (IQA) can quantify the visual distortion degree of images and guide the imaging system to improve image quality, thus raising the quality of pathological microscopic images. Current IQA methods are not ideal for pathological microscopy images due to their specificity. In this paper, we present deep learning-based blind image quality assessment model with saliency block and patch block for pathological microscopic images. The saliency block and patch block can handle the local and global distortions, respectively. To better capture the area of interest of pathologists when viewing pathological images, the saliency block is fine-tuned by eye movement data of pathologists. The patch block can capture lots of global information strongly related to image quality via the interaction between different image patches from different positions. The performance of the developed model is validated by the home-made Pathological Microscopic Image Quality Database under Screen and Immersion Scenarios (PMIQD-SIS) and cross-validated by the five public datasets. The results of ablation experiments demonstrate the contribution of the added blocks. The dataset and the corresponding code are publicly available at: https://github.com/mikugyf/PMIQD-SIS.

LineDL: Processing Images Line-by-Line With Deep Learning.

Although deep learning-based (DL-based) image processing algorithms have achieved superior performance, they are still difficult to apply on mobile devices (e.g., smartphones and cameras) due to the following reasons: 1) the high memory demand and 2) large model size. To adapt DL-based methods to mobile devices, motivated by the characteristics of image signal processors (ISPs), we propose a novel algorithm named LineDL. In LineDL, the default mode of the whole-image processing is reformulated as a line-by-line mode, eliminating the need to store large amounts of intermediate data for the whole image. An information transmission module (ITM) is designed to extract and convey the interline correlation and integrate the interline features. Furthermore, we develop a model compression method to reduce the model size while maintaining competitive performance; that is, knowledge is redefined, and compression is performed in two directions. We evaluate LineDL on general image processing tasks, including denoising and superresolution. The extensive experimental results demonstrate that LineDL achieves image quality comparable to that of state-of-the-art (SOTA) DL-based algorithms with a much smaller memory demand and competitive model size.

[Carbon Sequestration Characteristics Under Natural Vegetation Restoration in Ziwuling Area of the Loess Plateau].

The aim of this study was to analyze the carbon sink effect under natural vegetation restoration and the influence of changes in vegetation community characteristics on ecosystem carbon density in ecologically fragile areas of the Loess Plateau. In this study, the changes in carbon sequestration of a vegetation-soil system under eight successional stages[slope cropland, abandoned cropland for 10 years, abandoned cropland for 20 years, Sophora davidii (Franch.) Skeels., Betula platyphylla Suk., Pinus tabulaeformis Carr., Quercus wutaishanic Mary+P. tabulaeformis Carr mixed forests, and Q. wutaishanic Mary] in Ziwuling area over 150 restoration periods were investigated using the common method of spatial and temporal substitution. This study also discussed the relationship between changes in vegetation community characteristics and vegetation-soil system carbon density. The results showed that the community coverage of the investigated vegetation fluctuated from 85% in the slope cropland stage to 100% in the arbor stage. The number of species, Margalef index, Shannon-Wiener index, Pielou index, and Simpson index initially increased rapidly, then declined slowly until becoming stable, and reached a peak in the middle of the succession (B. platyphylla Suk.). The biomass and carbon density of vegetation components (above-ground biomass, below-ground roots, and litter) increased exponentially during the succession, i.e., increased slowly before B. platyphylla Suk. but increased significantly in B. platyphylla Suk. and P. tabulaeformis Carr.(P<0.05). The biomass and carbon density reached the maximum values of 27858.08 g·m-2 and 13232.51 g·m-2, respectively, in Q. wutaishanic Mary+P. tabulaeformis Carr mixed forests and tended to be stable in the late succession stage. Soil organic carbon density showed a power function relationship with vegetation restoration, with the greatest increase in the stages of abandoned cropland for 10 years and B. platyphylla, but no significant changes in the subsequent stages (P>0.05). In the early succession stage, the carbon density of the farmland ecosystem was the lowest (4395.70 g·m-2), whereas the other seven stages increased by 55.54%, 40.37%, 69.96%, 202.48%, 326.35%, 357.43%, and 351.07%, respectively, compared with the farmland ecosystem. Community coverage, Margalef index, Shannon-Wiener index, above-ground biomass, root biomass, and litter biomass were significantly positively correlated with vegetation-soil system carbon density (P<0.05). The carbon sink effect of long-term natural restoration in Ziwuling Region was significant, and the carbon density of the vegetation-soil system under interspecific competition tended to be stable in the late succession stage. Dynamic changes in the vegetation community structure and plant diversity during the succession process increased vegetation carbon density and soil carbon density. This study helps to clarify the carbon sink effect of natural vegetation restoration in ecologically fragile areas of the Loess Plateau and provides a theoretical basis for promoting natural forest conservation and achieving carbon neutrality.

Dual Vision Transformer.

Recent advances have presented several strategies to mitigate the computations of self-attention mechanism with high-resolution inputs. Many of these works consider decomposing the global self-attention procedure over image patches into regional and local feature extraction procedures that each incurs a smaller computational complexity. Despite good efficiency, these approaches seldom explore the holistic interactions among all patches, and are thus difficult to fully capture the global semantics. In this paper, we propose a novel Transformer architecture that elegantly exploits the global semantics for self-attention learning, namely Dual Vision Transformer (Dual-ViT). The new architecture incorporates a critical semantic pathway that can more efficiently compress token vectors into global semantics with reduced order of complexity. Such compressed global semantics then serve as useful prior information in learning finer local pixel level details, through another constructed pixel pathway. The semantic pathway and pixel pathway are integrated together and are jointly trained, spreading the enhanced self-attention information in parallel through both of the pathways. Dual-ViT is henceforth able to capitalize on global semantics to boost self-attention learning without compromising much computational complexity. We empirically demonstrate that Dual-ViT provides superior accuracy than SOTA Transformer architectures with comparable training complexity.

Volatile Organic Compounds of Bacillus pumilus Strain S1-10 Exhibit Fumigant Activity Against Meloidogyne incognita.

Root-knot nematodes (RKNs) are highly specialized parasites that cause significant yield losses worldwide. In this study, we isolated Bacillus pumilus strain S1-10 from the rhizosphere soil of Zingiber officinale Rosc. plants and evaluated its fumigant activity against Meloidogyne incognita. S1-10 exhibited a strong repellent effect on second-stage juveniles (J2s) of M. incognita, and in vitro assays indicated that S1-10 volatile organic compounds (VOCs) suppressed J2 activity and egg hatching. Under greenhouse conditions, 71 and 79% reductions of nematodes and eggs were detected on plants treated with S-10 VOCs compared with controls. Ten VOCs were identified through gas chromatography and mass spectrometry (GC-MS), of which 2-(methylamino)-ethanol (2-ME) had strong fumigant activity against J2s of M. incognita, with an LC50 value of 1.5 mM at 12 h. These results indicate that S1-10 represents a potential novel biocontrol agent for RKNs.

[Spatial Distribution Characteristics and Influencing Factors of Soil Organic Carbon Density in Yellow River Basin Based on MGWR Model].

As the largest terrestrial carbon pool, the spatial distribution characteristics and influencing factors of soil organic carbon have important implications for global carbon cycle processes. Soil organic carbon density (SOCD) and influencing factors were predicted in the Yellow River basin using a mixed geographically weighted regression (MGWR) model based on soil organic carbon density data and environmental factors. The results showed that:① the SOCD ranged from 0-14.82 kg·m-2 and 0-32.39 kg·m-2 for the soil depths of 0-20 cm and 0-100 cm, with mean values of 3.48 kg·m-2 and 8.07 kg·m-2 and reserves of 2.76 Pg and 6.48 Pg, respectively. The high SOCD value areas were mainly located in the southern part of the Qinghai-Tibet Plateau and Loess Plateau, and the low value areas were located in the eastern part of the upper Yellow River and the inland flow area. ②Among the ecosystem types, the SOCD of soil depth in 0-20 cm was in the descending order of:forest>water body and wetland>other>grassland>farmland>settlement>desert, with mean values of 4.52, 4.31, 3.84, 3.73, 2.89, 2.78, and 2.22 kg·m-2, respectively, and the SOCD of the 0-100 cm soil depth was in the descending order of:water bodies and wetlands>forest>other>grassland>farmland>settlement>desert, with mean values of 9.58, 9.58, 8.85, 8.66, 7.07, 6.81, and 5.29 kg·m-2, respectively. The SOCR in descending order was:grassland>farmland>forest>desert>water bodies and wetlands>settlement>others, with 1.40, 0.60, 0.47, 0.11, 0.07, 0.06, and 0.05 Pg at a soil depth of 0-20 cm and 3.31, 1.49, 0.99, 0.26, 0.17, 0.14, and 0.12 Pg at a soil depth of 0-100 cm, respectively. ③ The main factors affecting the SOCD distribution were intercept, profile curvature, NDVI, and precipitation; in addition, curvature and silt also had important effects on the deep SOCD distribution in the Yellow River basin. Among the ecosystem types, precipitation and NDVI were the main factors affecting the SOCD distribution. The intercept also had important effects on the SOCD distribution in the all ecosystems except forests, whereas curvature and silt only had important effects on deserts and other ecosystems. These results revealed the spatial distribution of SOCD, influencing factors, and SOCR in the Yellow River basin and can provide a scientific basis for carbon balance, soil quality evaluation, and ecological management restoration and consolidation in the region.

Expanding the use of salivary cortisol as a non-invasive outpatient test in the dynamic evaluation of suspected adrenal insufficiency.

Background: Adrenal insufficiency (AI) is potentially life-threatening, and accurate diagnosis is crucial. The first-line diagnostic test, the adrenocorticotrophic hormone (ACTH) stimulation test, measures serum total cortisol. However, this is affected in states of altered albumin or cortisol-binding globulin levels, limiting reliability. Salivary cortisol reflects free bioactive cortisol levels and is a promising alternative. However, few studies are available, and heterogenous methodologies limit applicability. Methods: This study prospectively recruited 42 outpatients undergoing evaluation for AI, excluding participants with altered cortisol-binding states. Serum (immunoassay) and salivary (liquid chromatography tandem mass spectrometry) cortisol levels were sampled at baseline, 30 min, and 60 min following 250 µg synacthen administration. AI was defined as a peak serum cortisol level <500 nmol/L in accordance with guidelines. Results: The study recruited 21 (50%) participants with AI and 21 without AI. There were no significant differences in baseline characteristics, blood pressure, or sodium levels between groups. Following synacthen stimulation, serum and salivary cortisol levels showed good correlation at all timepoints (R2 = 0.74, P < 0.001), at peak levels (R2 = 0.72, P < 0.001), and at 60 min (R2 = 0.72, P < 0.001). A salivary cortisol cut-off of 16.0 nmol/L had a sensitivity of 90.5% and a specificity of 76.2% for the diagnosis of AI. Conclusion: This study demonstrates a good correlation between serum and salivary cortisol levels during the 250 µg synacthen test. A peak salivary cortisol cut-off of 16.0 nmol/L can be used for the diagnosis of AI. It is a less invasive alternative to evaluate patients with suspected AI. Its potential utility in the diagnosis of AI in patients with altered cortisol-binding states should be further studied.

Angiopoietin-like protein 8 deficiency attenuates thoracic aortic aneurysm/dissection development in ß-aminopropionitrile monofumarate-induced model mice.

Thoracic aortic aneurysm/dissection (TAAD) is a life-threatening cardiovascular disorder. Endoplasmic reticulum stress (ERS) and vascular smooth muscle cell (VSMC) apoptosis are involved in TAAD progression. The Protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK) pathway is associated with VSMC apoptosis. Serum Angiopoietin-Like Protein 8 (ANGPTL8) levels are associated with aortic diameter and rupture rate of TAAD. However, a direct role of ANGPTL8 in TAAD has not been determined. ß-Aminopropionitrile monofumarate (BAPN) was used to induce TAAD in C57BL/6 mice. ANGPTL8 knockout mice were used to detect the effects of ANGPTL8 on TAAD development. ANGPTL8knockdown in vitro was used to analyze the role of ANGPTL8 in VSMCs and ERS. In addition, over-expression of ANGPTL8 in VSMCs and a PERK inhibitor were used to assess the effect of ANGPTL8 on the PERK pathway. ANGPTL8 levels were increased in the aortic wall and VSMCs of BAPN-induced TAAD mice. Compared with BAPN-treated wild-type mice, ANGPTL8 knockout significantly reduced the rupture rate of TAAD to 0 %. In addition, the protein levels of proinflammatory cytokines and matrix metalloproteinase 9 (MMP9) and ERS proteins were decreased in the aorta wall. Angptl8 shRNA decreased MMP9 and ERS protein levels in VSMCs in vitro. Overexpression of ANGPTL8 significantly increased the levels of ERS proteins and MMPs, while a PERK inhibitor significantly decreased the effects of ANGPTL8 in VSMCs. ANGPTL8 contributed to TAAD development by inducing ERS activation and degradation of extracellular matrix in the aorta wall. Inhibition of ANGPTL8 may therefore represent a new strategy for TAAD therapy.

Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds.

Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional geometric tasks such as surface consolidation and reconstruction, but also facilitates cutting-edge learning-based techniques for shape analysis and generation. In this paper, we propose a normal refinement network, called Refine-Net, to predict accurate normals for noisy point clouds. Traditional normal estimation wisdom heavily depends on priors such as surface shapes or noise distributions, while learning-based solutions settle for single types of hand-crafted features. Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations. To this end, several feature modules are developed and incorporated into Refine-Net by a novel connection module. Besides the overall network architecture of Refine-Net, we propose a new multi-scale fitting patch selection scheme for the initial normal estimation, by absorbing geometry domain knowledge. Also, Refine-Net is a generic normal estimation framework: 1) point normals obtained from other methods can be further refined, and 2) any feature module related to the surface geometric structures can be potentially integrated into the framework. Qualitative and quantitative evaluations demonstrate the clear superiority of Refine-Net over the state-of-the-arts on both synthetic and real-scanned datasets.