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Regulating interlayer distance is a crucial factor in the development of two-dimensional (2D) nanomaterials. A 2D metal-free photocatalyst, such as graphitic carbon nitride (g-C3N4), exhibits morphology- and microstructure-dependent photocatalytic activity. Herein, we report a straightforward and facile route for the preparation of unique lamellar g-C3N4, by co-firing melamine and ammonium chloride via microwave-assisted heating. Through the decomposition of NH4Cl, the evaporation of NH3 gas can effectively overcome van der Waals forces, expanding the interlayer distance of g-C3N4, thereby creating a lamellar structure consisting of nanosheets. Compared with bulk g-C3N4, the NH3-derived lamellar g-C3N4 exhibits a larger specific surface area and enhanced optical absorption capability, which increase photocatalytic hydrogen production because of the highly active structure, excellent utilization efficiency of photon energy, and low recombination efficiency of photogenerated charge carriers. This study provides a simple strategy for the regulation of the g-C3N4 microstructure toward highly efficient photocatalytic applications.

Multi-modal Magnetic Resonance Imaging (MRI) can provide complementary information for automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While missing modality data is common in clinical practice and it can result in the collapse of most previous methods relying on complete modality data. Current state-of-the-art approaches cope with the situations of missing modalities by fusing multi-modal images and features to learn shared representations of tumor regions, which often ignore explicitly capturing the correlations among modalities and tumor regions. Inspired by the fact that modality information plays distinct roles to segment different tumor regions, we aim to explicitly exploit the correlations among various modality-specific information and tumor-specific knowledge for segmentation. To this end, we propose a Dual Disentanglement Network (D

The partial admission axial turbines operate with the extreme pressure, temperature, and rotational speed for underwater applications. The thermal stress and mechanical stress need to be carefully examined to avoid the operation failure. In this paper, a two-way weakly fluid-thermal analysis is performed to attain the rotor disk temperature distribution. The fluid simulation is performed by solving the three-dimensional Reynolds averaged Navier-Stokes equations coupled with k-omega SST turbulence model, while the thermal analysis is conducted using the finite element method. The mixing plane method is applied when transferring the temperature due to the partial admission configuration. Once the steady state temperature distribution is achieved, the pressure distribution and centrifugal forces are also applied as the aerodynamic and mechanical loads. The corresponding stress and deformation are subsequently obtained. The results show that the maximum stress at the operating point exceeds the allowable material stress, indicating a proper cooling strategy is needed for future investigations.

Domain Adaptive Object Detection (DAOD) models a joint distribution of images and labels from an annotated source domain and learns a domain-invariant transformation to estimate the target labels with the given target domain images. Existing methods assume that the source domain labels are completely clean, yet large-scale datasets often contain error-prone annotations due to instance ambiguity, which may lead to a biased source distribution and severely degrade the performance of the domain adaptive detector de facto. In this paper, we represent the first effort to formulate noisy DAOD and propose a Noise Latent Transferability Exploration (NLTE) framework to address this issue. It is featured with 1) Potential Instance Mining (PIM), which leverages eligible proposals to recapture the miss-annotated instances from the background; 2) Morphable Graph Relation Module (MGRM), which models the adaptation feasibility and transition probability of noisy samples with relation matrices; 3) Entropy-Aware Gradient Reconcilement (EAGR), which incorporates the semantic information into the discrimination process and enforces the gradients provided by noisy and clean samples to be consistent towards learning domain-invariant representations. A thorough evaluation on benchmark DAOD datasets with noisy source annotations validates the effectiveness of NLTE. In particular, NLTE improves the mAP by 8.4\% under 60\% corrupted annotations and even approaches the ideal upper bound of training on a clean source dataset., CVPR-2022 Version

The present study aims to reveal the mechanism by which miR-430s regulate steroidogenesis in larval rice field eel Monopterus albus. To this end, M. albus embryos were respectively microinjected with miRNA-overexpressing mimics (agomir430a, agomir430b, and agomir430c) or miRNA-knockdown inhibitors (antagomir430a, antagomir430b, and antagomir430c). Transcriptome profiling of the larvae indicated that a total of more than 149 differentially expressed genes (DEGs) were identified among the eight treatments. Specifically, DEGs related to steroidogenesis, the GnRH signaling pathway, the erbB signaling pathway, the Wnt signaling pathway, and other pathways were characterized in the transcriptome. We found that steroidogenesis-related genes (hydroxysteroid 17-beta dehydrogenase 3 (17β-hsdb3), hydroxysteroid 17-beta dehydrogenase 7 (17β-hsdb7), hydroxysteroid 17-beta dehydrogenase 12 (17β-hsdb12), and cytochrome P450 family 19 subfamily a (cyp19a1b)) were significantly downregulated in miR-430 knockdown groups. The differential expressions of miR-430 in three gonads indicated different roles of three miR-430 (a, b, and c) isoforms in regulating steroidogenesis and sex differentiation. Mutation of the miR-430 sites reversed the downregulation of cytochrome P450 family 17 (cyp17), cyp19a1b, and forkhead box L2 (foxl2) reporter activities by miR-430, indicating that miR-430 directly interacted with cyp17, cyp19a1b, and foxl2 genes to inhibit their expressions. Combining these findings, we concluded that miR-430 regulated the steroidogenesis and the biosynthesis of steroid hormones by targeting cyp19a1b in larval M. albus. Our results provide a novel insight into steroidogenesis at the early stage of fish at the molecular level.

Because of the special imaging environment, sonar images have some problems, such as gray distortion, blurred edge, various shapes, and missing dataset. To solve the missing of underwater sonar images dataset, a dataset of underwater sonar images dataset is established, including synthetic sonar dataset and real sonar dataset. According to the characteristics of sonar images, a new segmentation algorithm based on Deep CNN for Image Denoising (DnCNN) for image denoising is proposed, which integrates the Receptive Field Block and Attention Search Function. Experimental results show that the proposed method can effectively improve the accuracy of segmentation and retain more details of sonar images. At the same time, compared with other common segmentation methods, the proposed method also has significant advantages in network performance.

It is well known that traditional trajectory tracking model-based control methods of underactuated ship are not able to adapt to complex and dynamic ocean environment. In this paper, taking into consideration of dynamic parameter uncertainty of the underlying underactuated ship model and unknown time-varying environmental disturbances, an S-plane-based controller with self-tuning parameters is proposed. The main contributions of this work are: (1) The Extended State Observer (ESO) and proportional control of PID are introduced into the S-plane control strategy to enhance robustness of the controller and fast convergence of tracking errors. (2) A self-tuning strategy for adaptively tuning the controller parameters is proposed. (3) Leveraging on the proposed self-tuning strategy, a new controller based on the Q-learning algorithm is designed so that the parameters of the controller can be adjusted independently. Simulation results show that the proposed control method is superior to the state-of-the-arts methods in terms of speed and accuracy of trajectory tracking.

Objective To compare the reliability of different methods for measuring fat content of pancreas by MR modified Dixon(mDixon) Sequence and accurately evaluate pancreatic fat in as simple a way as possible. Methods This is a retrospective study, 64 patients were included in this study who underwent abdominal MR scan that contained the mDixon sequence from June 2019 to May 2020(Included 7 patients with type 2 diabetes and 4 patients with impaired glucose tolerance (IGT), they were admitted to hospital through the obesity clinic set up by endocrine department, all of them were initially diagnosed and untreated). All of the 64 patients were scanned in 3.0T MR (Philips Ingenia II) due to their condition, 10–34 slice pancreas images were obtained, which were different from each other. Three different methods of measurement were employed by two observers using Philips Intellispace Portal software: (1) All images (whole-pancreas) measurement, the whole-pancreatic fat fraction (wPFF) was calculated by software. (2) Interval slices measurement, that is half-pancreatic slices fat fraction (hPFF) measured in the same way, fat fraction obtained by the interlayer assay was calculated. (3) As usual, the fat content of pancreatic head, body and tail fat was measured respectively, and in order to improve credibility, we also measured head、 body and tail in every layer, and its average value was taken. The elapsed time of the above different measurement methods was recorded. Intra-group correlation coefficient (ICC) was used to analyze the consistency of the measured data within and between observers. T-tests and Friedman tests were applied to compare the difference of measured values among groups. Results No matter in normal person or diabetic or IGT, hPFF has shown good stability (ICChPFF = 0.988), and there was no significant difference compared with wPFF. But the average fat percentage composition of head, body and tail were significantly different from wPFF and hPFF (P < 0.01). At the same time, compared with normal person, pancreatic fat content in IGT and diabetic patients showed progressive significance(P Conclusion The distribution of pancreatic fat is not uniform, the method of measuring half pancreas by interlayer data collection can reflect the fat content of the entire pancreas, this suggests that measuring 50% of the pancreas is sufficient, this method effectively saves time and effort without affecting the results, which may have a better clinical application prospect.

Accurate automated brain tumor segmentation with 3D Magnetic Resonance Image (MRIs) liberates doctors from tedious annotation work and further monitors and provides prompt treatment of the disease. Many recent Deep Convolutional Neural Networks (DCNN) achieve tremendous success on medical image analysis, especially tumor segmentation, while they usually use static networks without considering the inherent diversity of multi-modal inputs. In this paper, we introduce a dynamic convolutional module into brain tumor segmentation and help to learn input-adaptive parameters for specific multi-modal images. To the best of our knowledge, this is the first work to adopt dynamic convolutional networks to segment brain tumor with 3D MRI data. In addition, we employ multiple branches to learn low-level features from multi-modal inputs in an end-to-end fashion. We further investigate boundary information and propose a boundary-aware module to enforce our model to pay more attention to important pixels. Experimental results on the testing dataset and cross-validation dataset split from the training dataset of BraTS 2020 Challenge demonstrate that our proposed framework obtains competitive Dice scores compared with state-of-the-art approaches.

Agomelatine (AGO) is a new type of antidepressant with demonstrated antidepressant effects and a unique modulating circadian rhythm action. However, AGO has hepatotoxicity, which limits its clinical application. In order to develop new drugs that cause less liver injury than AGO, a series of derivatives were synthesized; compound GW117 was screened from derivatives due to its high receptor affinity. This study will investigate its sub-acute oral toxicity profile in rats in a sex-dependent manner. GW117 and AGO was administrated by gavage (200, 400, or 800 mg/kg/day) for 28 days. Hematological, biochemical tests, organ weights, histopathological examinations were carried out, the results showed that AGO and GW117 had adverse effects on platelet, liver and kidney, and had sex-differences in some indicators. Hematological tests showed that AGO and GW117 reduced the platelet count in male animals but had no effect in females. AGO increased plasma alanine aminotransferase (ALT) and total bilirubin in male animals, and GW117 had no effect on these two indicators. For females, AGO moderately elevated ALT, alkaline phosphatase (ALP), and total bilirubin, while GW117 only elevated ALP slightly. Two drugs could increase liver weight and coefficient, and cause liver pathological injury, including hepatic sinusoidal dilatation, hepatocyte fatty deposition and dotted cell necrosis in two genders. AGO caused mild to moderate hepatocyte and hepatobiliary injury in both genders, while only a mild hepatobiliary injury was caused by GW117 in females. Renal function tests showed that both drugs can increase blood urea nitrogen levels in males, while AGO, but not GW117, can slightly increase blood creatinine and urea nitrogen in females. The kidney weight and coefficient could be significantly increased by two drugs in males, and by AGO medium and GW117 high and low doses in females. The kidney pathological damage was mainly characterized by tubule dilatation, a thinning of the renal cortex. Kidney damage caused by GW117 was less than that of AGO, and there was no sex-difference. In summary, GW117 can cause mild liver and kidney damage in both genders, as well as mild platelets reduction in males, while degree of damage is less severe than AGO. Therefore, as an excellent derivative, GW117 deserves further development as an antidepressant.

With the accelerated development of the equipment under test towards informatization and systemization, higher requirements are put forward for the construction of the test capacity of the equipment under test. It is a good way to maximize the value of test data as a core resource through building a test data distributing and sharing platform. A test data distributing and sharing method based on the blackboard model is proposed in this paper. Then a data sharing model is established including data management service center, data demanders, and operating mechanism to meet the needs of remote storage and global sharing of test data.

Due to the rapid advances in railway industry, the rail surface defect detection task which inspects whether the rail is defective has become an increasingly critical issue. Detecting rails by an automatic and swift approach instead of present manual inspections enables the work more efficient and safe currently. In this paper, we propose a novel two-stage pipeline method for rail defect detection by localizing and classifying rail images. Specifically, in the first stage, we get the cropped images which focus on the rail part instead of the whole-original image by integrating traditional image processing methods. In the second stage, we put the cropped images into a fine-tuned convolution neural network (CNN) and extract part-level features for rail images classification. Especially, in the rail image defect detection scenario, we should take recall into account to some extent, so we propose a novel loss function to leverage both of them in the second stage. The results show that the proposed method has strong robustness and achieves practical performance in defect detection precision.

Transporting drugs through the lymphatic system has attracted increasing attention. Lipid-based formulations have been proved to be an effective way to improve systemic bioavailability of highly lipophilic drugs by increasing intestinal lymphatic transport.The formulation of polymer micelle was developed for probucol to improve its intestinal lymphatic transport.Methoxy-polyethylenelglycol-distearyl phosphatidyl-ethanolamine (mPEG-DSPE) polymer was chosen to develop the micelles for probucol. The physicochemical properties were characterized. Caco-2 cell model, unconscious and conscious lymph duct cannulated rat models were established for in vitro and in vivo evaluation of lymphatic transport.In vitro evaluation in the Caco-2 cell model showed that the micellar formulation could significantly increase the uptake and transport of probucol. The study in unconscious and conscious lymph duct cannulated rat models further verified the significant enhancement of lymphatic transport of probucol by mPEG-DSPE micelles.These results suggested that mPEG-DSPE micellar formulation could provide a useful alternative approach for improving the lymphatic transport of hydrophobic compounds.

A practical and highly efficient method for the protection of amines to their corresponding N-Boc derivatives is reported. In the absence of any solvent and catalyst, the present strategy works well for a series of electron-deficient and electron-rich aromatic amines as well as some sterically hindered substrates.

In this paper we solve the problem of secure communication in multicast graphs, which has been open for over a decade. At Eurocrypt '98, Franklin and Wright initiated the study of secure communication against a Byzantine adversary on multicast channels in a neighbor network setting. Their model requires node-disjoint and neighbor-disjoint paths between a sender and a receiver. This requirement is too strong and hence not necessary in the general multicast graph setting. The research to find the lower and upper bounds on network connectivity for secure communication in multicast graphs has been carried out ever since. However, up until this day, there is no tight bound found for any level of security. We study this problem from a new direction, i.e., we find the necessary and sufficient conditions (tight lower and upper bounds) for secure communication in the general adversary model with adversary structures, and then apply the results to the threshold model. Our solution uses an extended characterization of the multicast graphs, which is based on our observation on the eavesdropping and separating activities of the Byzantine adversary.

In the context of secure point-to-point message transmission in networks with minimal connectivity, previous studies showed that feedbacks from the receiver to the sender can be used to reduce the requirements of network connectivity. We observe that the way how feedbacks were used in previous work does not guarantee perfect privacy to the transmitted message, when the adversary performs a Guessing Attack. In this paper, we shall describe our new Guessing Attack to some existing protocols (in fact, we are the first to point out a flaw in the protocols of Desmedt-Wang's Eurocrypt'02 paper and of Patra-Shankar-Choudhary-Srinathan-Rangan's CANS'07 paper), and propose a scheme defending against a general adversary structure. In addition, we also show how to achieve almost perfectly secure message transmission with feedbacks when perfect reliability or perfect privacy is not strictly required.