Constructing an Open-Structured J-Type ZnIn2S4/In(OH)3 Heterojunction for Photocatalytical Hydrogen Generation.

Constructing heterojunctions to separate photogenerated carriers is an effective strategy for improving the efficiency of photocatalytic reactions. A J-type heterojunction is a recently reported efficient anisotropic heterojunction. Herein, taking anisotropic ZnIn2S4 (ZIS) nanosheets as an example of a type-II heterojunction, we report for the first time the concept of open and closed structures (O and C structure) of J-type heterojunctions. A simple ammonia-post-treatment method was employed to prepare the O- and C-structured J-type ZnIn2S4/In(OH)3 (ZIS/IOH) heterojunctions. The O-structured J-type ZIS/IOH (OJ-ZIS/IOH) heterojunction exhibits a high hydrogen production activity, reaching 400 µmol·h-1, 2.67 times higher than that of pristine ZIS. However, the activity of the C-structured heterojunction (CJ-ZIS/IOH) is close to that of pristine ZIS. The findings emphasize the importance of the cooperation of photogenerated carrier separation and transport in J-type heterojunctions, providing insights into developing efficient heterojunction photocatalysts.

Solution Processing Silicon Heterojunction Photocathode for Efficient and Stable Hydrogen Production.

Efficient and stable photocathodes are crucial for the development of photoelectrochemical (PEC) water-splitting devices. Silicon heterojunction (SHJ) solar cell is one of the most advanced photovoltaic cells. However, due to the instability of its outermost indium tin oxide (ITO) layers in the electrolyte, a protective layer needs to be introduced on its surface. Previously reported high-quality protective layers almost all involved the use of expensive thin film manufacturing techniques such as atomic layer deposition (ALD). In this work, for the first time, a new strategy is proposed of modifying SHJ-based photocathode with yttrium hydroxide (Y(OH)3) through two-step solution methods to simultaneously improve the stability and activity. The optimized SHJ photocathode exhibits a high applied bias photon-to-current efficiency (ABPE) of 8.4% under simulated 100 mW cm-2 (1 Sun) with an AM 1.5G filter in 0.5 m KOH. Furthermore, the obtained SHJ photocathode demonstrates excellent stability of at least 110 h at 0.3 V versus RHE. In this work, combining facile direct current magnetron sputtering with a solution treatment technique provides a novel design strategy, which lowers the threshold for preparing high-quality protective layer, and paves the way for developing economic, efficient, and stable SHJ-based PEC devices.

Precisely Assembling a CoO Cocatalyst onto Tb4O7/CN and Pt-Tb4O7/CN for Promoting Photocatalytic Overall Water Splitting.

Photocatalytic overall water splitting (POWS) is a promising approach for solar-to-hydrogen conversion. For achieving this target, it is urgent to develop efficient photocatalysts. Constructing a heterojunction and loading a cocatalyst are two effective strategies for enhancing POWS. However, how to achieve the cooperation of loading the cocatalyst site with the charge separation of a heterojunction remains a huge challenge. Herein, we present an ingenious method: precisely assembling a H2O2-producing cocatalyst CoO on Tb4O7/CN. Assembling CoO on CN of Tb4O7/CN improves the photoinduced electron-hole pair separation and promotes the POWS performance. Inversely, engineering CoO on Tb4O7 leads to production of Co, deactivating POWS performance with a H2-evolution rate 5.2 times lower than that of Tb4O7/CN. Furthermore, we precisely assemble CoO on the CN section of Pt-oriented Pt-Tb4O7/CN. The bioriented CoO and Pt cooperatively promote photogenerated carrier separation. Consequently, the prepared Pt-Tb4O7/CN-CoO exhibits spectacularly high POWS activity. The H2-evolution rate reaches 450 µmol h-1 g-1, which is about 9.4 times higher than that of the initial Tb4O7/CN. The apparent quantum yield (AQY) for H2 evolution at 420 nm reaches 14.1%, surpassing those of most reported CN-based photocatalysts. This work offers an approach to precisely load cocatalysts on heterojunctions. These findings provide insights for designing cocatalyst-decorated heterojunctions for POWS.

[3-Deazaadenosine (3-DAA) accelerates Japanese encephalitis virus replication and down-regulates levels of inflammatory factors in mouse and hamster cells].

Objective To investigate the effect of 3-deazaadenosine (3-DAA), an N6-methyladenosine (m6A) methylation modification inhibitor, on the replication of the Japanese encephalitis virus (JEV). Methods Neuro2a mouse neuroblastoma cells, N9 mouse microglial cells, and BHK baby hamster kidney cells were exposed to JEV and then treated with 3-DAA. JEV was also injected into the footpad of adult C57BL/6 mice, which were then administered 3-DAA intraperitoneally. Real-time quantitative PCR was utilized to measure mRNA expression levels of JEV, interleukin 1ß (IL-1ß), IL-6, tumor necrosis factor α (TNF-α), monocyte chemoattractant protein 1 (MCP-1), inducible nitric oxide synthase (iNOS), arginase 1 (Arg1), interferon (IFN)-α, IFN-ß, IFN-γ, and C-X-C motif chemokine ligand 10 (CXCL10) in the cells and mouse brain tissues. Western blot analysis was used to detect JEV protein expression in the cells and mouse brain tissues. Furthermore, the survival of the mice was monitored and pathological changes in mouse brains were observed via hematoxylin and eosin (HE) staining. Results 3-DAA had a dose-dependent effect on the replication of RNA and protein expression of JEV in both BHK, N9, Neuro 2α cells and mouse brain tissues, which resulted in rapid progression of JEV infection in mice and a decrease in their survival rate. Furthermore, 3-DAA suppressed the expression of inflammatory factors such as IL-6, TNF-α, CXCL10, IL-1ß and iNOS, thus weakening the immune response. Conclusion 3-DAA promotes JEV infection and hastens death of infected cells and mice, indicating that m6A modification may negatively regulate JEV replication.

Triplet-branch network with contrastive prior-knowledge embedding for disease grading.

Since different disease grades require different treatments from physicians, i.e., the low-grade patients may recover with follow-up observations whereas the high-grade may need immediate surgery, the accuracy of disease grading is pivotal in clinical practice. In this paper, we propose a Triplet-Branch Network with ContRastive priOr-knoWledge embeddiNg (TBN-CROWN) for the accurate disease grading, which enables physicians to accordingly take appropriate treatments. Specifically, our TBN-CROWN has three branches, which are implemented for representation learning, classifier learning and grade-related prior-knowledge learning, respectively. The former two branches deal with the issue of class-imbalanced training samples, while the latter one embeds the grade-related prior-knowledge via a novel auxiliary module, termed contrastive embedding module. The proposed auxiliary module takes the features embedded by different branches as input, and accordingly constructs positive and negative embeddings for the model to deploy grade-related prior-knowledge via contrastive learning. Extensive experiments on our private and two publicly available disease grading datasets show that our TBN-CROWN can effectively tackle the class-imbalance problem and yield a satisfactory grading accuracy for various diseases, such as fatigue fracture, ulcerative colitis, and diabetic retinopathy.

N6-methyladenosine modification positively regulate Japanese encephalitis virus replication.

N6-methyladenosine (m6A) is present in diverse viral RNA and plays important regulatory roles in virus replication and host antiviral innate immunity. However, the role of m6A in regulating JEV replication has not been investigated. Here, we show that the JEV genome contains m6A modification upon infection of mouse neuroblast cells (neuro2a). JEV infection results in a decrease in the expression of m6A writer METTL3 in mouse brain tissue. METTL3 knockdown by siRNA leads to a substantial decrease in JEV replication and the production of progeny viruses at 48 hpi. Mechanically, JEV triggered a considerable increase in the innate immune response of METTL3 knockdown neuro2a cells compared to the control cells. Our study has revealed the distinctive m6A signatures of both the virus and host in neuro2a cells infected with JEV, illustrating the positive role of m6A modification in JEV infection. Our study further enhances understanding of the role of m6A modification in Flaviviridae viruses.

Cross-Modal Vertical Federated Learning for MRI Reconstruction.

Federated learning enables multiple hospitals to cooperatively learn a shared model without privacy disclosure. Existing methods often take a common assumption that the data from different hospitals have the same modalities. However, such a setting is difficult to fully satisfy in practical applications, since the imaging guidelines may be different between hospitals, which makes the number of individuals with the same set of modalities limited. To this end, we formulate this practical-yet-challenging cross-modal vertical federated learning task, in which data from multiple hospitals have different modalities with a small amount of multi-modality data collected from the same individuals. To tackle such a situation, we develop a novel framework, namely Federated Consistent Regularization constrained Feature Disentanglement (Fed-CRFD), for boosting MRI reconstruction by effectively exploring the overlapping samples (i.e., same patients with different modalities at different hospitals) and solving the domain shift problem caused by different modalities. Particularly, our Fed-CRFD involves an intra-client feature disentangle scheme to decouple data into modality-invariant and modality-specific features, where the modality-invariant features are leveraged to mitigate the domain shift problem. In addition, a cross-client latent representation consistency constraint is proposed specifically for the overlapping samples to further align the modality-invariant features extracted from different modalities. Hence, our method can fully exploit the multi-source data from hospitals while alleviating the domain shift problem. Extensive experiments on two typical MRI datasets demonstrate that our network clearly outperforms state-of-the-art MRI reconstruction methods. The source code is available at https://github.com/IAMJackYan/FedCRFD.

Facilely and efficiently constructing anti-oil-fouling zwitterionic coatings on membranes for oil-in-water emulsion separation.

A facile and efficient strategy for constructing anti-oil-fouling zwitterionic coatings on membranes is developed. The resultant membrane exhibits excellent anti-oil-fouling ability even in a dry state, and has a high efficiency for emulsion separation with a high flux of 5800 L m-2 h-1 bar-1 and an oil rejection of up to 99.6%.

Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views.

BACKGROUND: View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice. PURPOSE: Existing approaches to its automation relied either on an additional volumetric image not typically acquired in clinic routine, or on laborious manual annotations of cardiac structural landmarks. This work presents a clinic-compatible, annotation-free system for automatic CMR view planning. METHODS: The system mines the spatial relationship-more specifically, locates the intersecting lines-between the target planes and source views, and trains U-Net-based deep networks to regress heatmaps defined by distances from the intersecting lines. On the one hand, the intersection lines are the prescription lines prescribed by the technologists at the time of image acquisition using cardiac landmarks, and retrospectively identified from the spatial relationship. On the other hand, as the spatial relationship is self-contained in properly stored data, for example, in the DICOM format, the need for additional manual annotation is eliminated. In addition, the interplay of the multiple target planes predicted in a source view is utilized in a stacked hourglass architecture consisting of repeated U-Net-style building blocks to gradually improve the regression. Then, a multiview planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers. For performance evaluation, the retrospectively identified planes prescribed by the technologists are used as the ground truth, and the plane angle differences and localization distances between the planes prescribed by our system and the ground truth are compared. RESULTS: The retrospective experiments include 181 clinical CMR exams, which are randomly split into training, validation, and test sets in the ratio of 64:16:20. Our system yields the mean angular difference and point-to-plane distance of 5.68 ∘ $^\circ$ and 3.12 mm, respectively, on the held-out test set. It not only achieves superior accuracy to existing approaches including conventional atlas-based and newer deep-learning-based in prescribing the four standard CMR planes but also demonstrates prescription of the first cardiac-anatomy-oriented plane(s) from the body-oriented scout. CONCLUSIONS: The proposed system demonstrates accurate automatic CMR view plane prescription based on deep learning on properly archived data, without the need for further manual annotation. This work opens a new direction for automatic view planning of anatomy-oriented medical imaging beyond CMR.

Amplified Targeted Drug Delivery Independent of Target Number through Alternative Administration of Two Matched Nanoparticles.

Targeting nanoparticles (NPs) based on the specific binding of ligands with molecular targets provides a promising tool for tissue-selective drug delivery. However, the number of molecular targets on the cell surface is limited, hindering the number of NPs that can bind and, thus, limiting the therapeutic outcome. Although several strategies have been developed to enhance drug delivery, such as enhancing drug loading and circulation time or increasing the enhanced permeability and retention effect of nanocarriers, none have resolved this issue. Herein, we designed a simple method for amplified and targeted drug delivery using two matched NPs. One NP was aptamer-functionalized to specifically bind to target cells, while the other was aptamer-complementary DNA-functionalized to specifically bind to aptamer-NPs. Alternate administration of the two matched NPs enables their continuous accumulation in the disease site despite their limited molecular targets. As a proof of concept, the method was tested in a breast cancer model and significantly enhanced chemotherapy of tumor cells in vitro and in vivo. The potential applications of this method in a brain injury model were also demonstrated. Overall, the study describes a method for amplified targeted drug delivery independent of the target number.

Water-based drilling fluids containing hydrophobic nanoparticles for minimizing shale hydration and formation damage.

Wellbore instability is always inevitable in shale formation due to hydration, swelling, and dispersion of clay, especially when using water-based drilling fluids (WBDFs). To mitigate the wellbore instability of shale formation and avoid correlative detriments such as formation damage, various nano-silica particles (nano-SiO2) were employed to plug the nano-sized pores, inhibit water invasion into shale, and prevent shale swelling. Firstly, the influence of various nano-SiO2 on rheological and filtration properties of a set WBDF was evaluated. Then, the linear swelling test, shale recovery test, zeta potential test, imbibition test, contact angle measurement, scanning electron microscopy (SEM) observation, and computed tomography (CT) analysis were conducted to assess the characteristic of nano-SiO2. Experimental results showed that solid phase nano-SiO2 could dramatically increase the viscosity and yield point while liquid phase nano-SiO2 only caused small fluctuations on these parameters. Besides, hydrophobic nano-SiO2 displayed better filtration performance than hydrophilic nano-SiO2. On the whole, the hydrophobic nano-SiO2 dispersion, called as nano-2, showed the best performance in WBDFs. Furthermore, nano-2 exhibited better inhibition than hydrophilic nano-SiO2, KCl, and polyether amine (PA). Mechanism analysis demonstrated that nano-2 could improve the hydrophobic degree of shale surface and prevent water from contacting with the shale. Meanwhile, nano-2 plugged the pores and throats in the shale. As a consequence, water in the drilling fluid was prevented from invading the shale, and the shale was inhibited. Nano-2 could form a thin barrier in the surface of shale, and mitigate the damage degree of shale cores after perforation operation. As a result, nano-2 displayed great potential to stabilize shale and protect formation in WBDFs.

Adversarial Medical Image with Hierarchical Feature Hiding.

Deep learning based methods for medical images can be easily compromised by adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has been discovered that conventional adversarial attacks like PGD which optimize the classification logits, are easy to distinguish in the feature space, resulting in accurate reactive defenses. To better understand this phenomenon and reassess the reliability of the reactive defenses for medical AEs, we thoroughly investigate the characteristic of conventional medical AEs. Specifically, we first theoretically prove that conventional adversarial attacks change the outputs by continuously optimizing vulnerable features in a fixed direction, thereby leading to outlier representations in the feature space. Then, a stress test is conducted to reveal the vulnerability of medical images, by comparing with natural images. Interestingly, this vulnerability is a double-edged sword, which can be exploited to hide AEs. We then propose a simple-yet-effective hierarchical feature constraint (HFC), a novel add-on to conventional white-box attacks, which assists to hide the adversarial feature in the target feature distribution. The proposed method is evaluated on three medical datasets, both 2D and 3D, with different modalities. The experimental results demonstrate the superiority of HFC, i.e., it bypasses an array of state-of-the-art adversarial medical AE detectors more efficiently than competing adaptive attacks1, which reveals the deficiencies of medical reactive defense and allows to develop more robust defenses in future.

A deep weakly semi-supervised framework for endoscopic lesion segmentation.

In the field of medical image analysis, accurate lesion segmentation is beneficial for the subsequent clinical diagnosis and treatment planning. Currently, various deep learning-based methods have been proposed to deal with the segmentation task. Albeit achieving some promising performances, the fully-supervised learning approaches require pixel-level annotations for model training, which is tedious and time-consuming for experienced radiologists to collect. In this paper, we propose a weakly semi-supervised segmentation framework, called Point Segmentation Transformer (Point SEGTR). Particularly, the framework utilizes a small amount of fully-supervised data with pixel-level segmentation masks and a large amount of weakly-supervised data with point-level annotations (i.e., annotating a point inside each object) for network training, which largely reduces the demand of pixel-level annotations significantly. To fully exploit the pixel-level and point-level annotations, we propose two regularization terms, i.e., multi-point consistency and symmetric consistency, to boost the quality of pseudo labels, which are then adopted to train a student model for inference. Extensive experiments are conducted on three endoscopy datasets with different lesion structures and several body sites (e.g., colorectal and nasopharynx). Comprehensive experimental results finely substantiate the effectiveness and the generality of our proposed method, as well as its potential to loosen the requirements of pixel-level annotations, which is valuable for clinical applications.

Effect of molten-salt modulation on the composition and structure of g-C3N4-based photocatalysts.

Graphitic carbon nitride (g-C3N4), as an attractive metal-free polymer photocatalyst, has attracted extensive attention in energy and environmental fields in recent years. The photoactivity of bulk g-C3N4 is moderate on account of solid-phase thermal-condensation synthesis. This leads to inadequate light absorption, limited surface area, and easy recombination of charge carriers. The composition and nanostructure of g-C3N4 have been studied extensively. Molten-salt modulation is fascinating because of its "green" credentials and the properties of liquid-phase reaction systems. The review focuses mainly on molten-salt modulation of the composition and structure of g-C3N4 based-photocatalysts. We focus on elemental doping, molecular doping, and defect engineering, as well as control of the crystal structure, multi-dimensional structure, hom/heterostructures for photocatalytic applications. This review provides new insights to develop g-C3N4-based photocatalysts with control of composition and structure by facile molten-salt modulation in energy-conversion and environmental fields.

Nuclei segmentation with point annotations from pathology images via self-supervised learning and co-training.

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. Codes are available at https://github.com/hust-linyi/SC-Net.

Synthesis of oriented J type ZnIn2S4@CdIn2S4 heterojunction by controllable cation exchange for enhancing photocatalytic hydrogen evolution.

Construction of semiconductor heterojunctions which promote the separation and transport of photogenerated carriers is an effective strategy for improving photocatalytic reaction efficiency. Based on the anisotropic electrical conductivity of layered ZnIn2S4 (ZIS) photocatalyst, an efficient heterojunction should be constructed along the layer plane of ZIS, that is, a J type heterojunction. However, achieving controllable synthesis of the oriented heterojunction of ZIS faces challenges. Herein, we develop a facile, cost-effective and spatially-selective cation exchange synthesis approach to construct J type ZnIn2S4@CdIn2S4 (J-ZIS@CIS) heterojunction using a flower-like hexagonal ZIS as the parent material. The developed synthesis approach can also control crystal structure of the heterojunction component CIS. This work presents a facile and controllable synthesis strategy to construct oriented anisotropic heterojunctions that are otherwise inaccessible. The as-prepared J-ZIS@CIS heterojunction displays a greatly enhanced photocatalytic hydrogen evolution activity with a rate of 183 µmol h-1, 2.77 times higher than that of pristine ZIS. Furthermore, the possible photocatalytic reaction mechanism is presented for the heterojunction.

Construction of π-conjugated crystalline carbon dots with carbon nitride nanofragments for efficient photocatalytic H2 evolution.

Crystalline carbon dots (CCDs) embedded in carbon nitride (CN) nanofragments (CCDs-CN) have been developed through facile molten salt treatment. Molten salt treatment not only reconstructs CN layered sheets to form nanofragments, but also promotes the crystallization of CDs-CN. The π-conjugated electric field between CCDs and CN accelerates charge carrier separation for efficient photocatalytic H2 evolution.

Studies on the Effect of Lipofectamine and Cell-Penetrating Peptide on the Properties of 10-23 DNAzyme.

Cationic polymeric materials and cell-penetrating peptides (CPPs) were often used as the delivery vectors in the evaluation of nucleic acid therapeutics. 10-23 DNAzyme is a kind of potential antisense therapeutics by catalytic cleavage of the disease-related RNAs. Here, lipofectamine 2000 and Tat peptide were evaluated for their effect on the catalytic activity of 10-23 DNAzyme, with the observed rate constant, thermal stability, CD spectra, and PAGE analysis, with a duplex DNA mimicking DNAzyme-substrate as a control. It was shown that the cationic carriers had a negative effect on the catalytic performance of the 10-23 DNAzyme. Significantly, the destabilizing effect of the cationic carriers on the duplex formation was noteworthy, as a duplex formation is an essential prerequisite in the silencing mechanisms of antisense and RNAi.

A dual functional polypeptide with antibacterial and anti-inflammatory properties for the treatment of periodontitis.

Periodontitis has been reported as the sixth most prevalent disease in human beings. This destructive disease is closely related to systemic diseases. Existing local drug delivery systems for periodontitis suffer from poor antibacterial effect and drug resistance. Inspired by the pathogenesis of periodontitis, we implemented a strategy to construct a dual functional polypeptide LL37-C15, which exhibited remarkable antibacterial effect against P. gingivalis and A. actinomycetemcomitans. In addition, LL37-C15 inhibits the release of pro-inflammatory cytokines by controlling the inflammatory pathway and reversing macrophage M1. Furthermore, the anti-inflammatory effect of LL37-C15 was also verified in vivo in a periodontitis rat model through the morphometry and histological observations of alveolar bone, hematoxylin-eosin, and Trap staining in gingival tissue. The results of molecular dynamics simulations showed that LL37-C15 could selectively destroy the bacterial cell membrane and protect the animal cell membrane in a self-destructive manner. The results showed that the polypeptide LL37-C15, as a novel promising therapeutic agent, exhibited a great potential for the periodontitis management. What's more, this dual functional polypeptide provides a promising strategy for building a multifunctional therapeutic platform against the inflammation and other diseases.