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The preparation of subnanoporous covalent-organic-framework (COF) membranes with high performance for ion/molecule sieving still remains a great challenge. In addition to the difficulties in fabricating large-area COF membranes, the main reason is that the pore size of 2D COFs is much larger than that of most gas molecules and/or ions. It is urgently required to further narrow their pore sizes to meet different separation demands. Herein, we report a simple and scalable way to grow large-area, pliable, free-standing COF membranes via a one-step route at organic–organic interface. The pore sizes of the membranes can be adjusted from >1 nm to sub-nm scale by changing the stacking mode of COF layers from AA to AB stacking. The obtained AB stacking COF membrane composed of highly-ordered nanoflakes is demonstrated to have narrow aperture (∼0.6 nm), uniform pore distribution and shows good potential in organic solvent nanofiltration, water treatment and gas separation. Fabrication of large scale and defect free covalent organic framework (COF) membranes with pores small enough for gas sieving remains challenging. Here, the authors report a scalable fabrication method to grow large area defect free COF membranes and to tune the pore size in the sub-nm region by adjusting the stacking modes of the COF layers.

1 mM of MCC950 targets the ROS-NEK7-NLRP3 axis to ameliorate T2DM in rats and exhibits peak efficacy in improving retinopathy. It has been found that the specific inhibitor MCC950 can alleviate diabetic retinopathy by inhibiting NLRP3 inflammasome, but its concentration-dependent efficacy on retinal pathology needs to be explored. The aim of this study was to quantify the effects of intravitreal injection of graded concentrations of MCC950 (0.01, 0.1, 1,10 mM) on retinal structure and NLRP3 inflammasome signalling in type 2 diabetic male rats, and to reveal that 1 mM MCC950 may exert optimal retinoprotective effects by down-regulating the NEK7-NLRP3 pathway. Type 2 diabetic male rats induced by streptozotocin were administered intravitreal injections of MCC950 at varying concentrations (0.01, 0.1, 1, 10 mM). Quantitative assessments revealed that a concentration of 1 mM MCC950 markedly improved retinal histopathological alterations ( p  < 0.05) and modulated retinal apoptosis and oxidative stress to a considerable degree ( p  < 0.05). On a mechanistic level, MCC950 suppressed NLRP3 inflammasome activation by disrupting the interaction between NEK7 and NLRP3 (manifested by the down-regulation of pathway-associated protein expression, p <0.05) and a strong positive correlation between NEK7 and NLRP3 protein expression ( r  = 0.62, p  = 0.19); inhibited the activation of the NLRP3 inflammasome (manifested by reduced levels of Cleaved Caspase-1, IL-1β, and IL-18, p  < 0.001). There was a positive correlation between the intensity of ROS fluorescence and the fluorescence expression of NEK7 ( r  = 0.8857, p  < 0.05), with MCC950 treatment significantly lowering retinal ROS levels at the 1 mM concentration. In conclusion, MCC950 inhibits ROS-mediated NEK7 upregulation, NLRP3 activation, and attenuates pathological damage, oxidative stress, retinal inflammation, and apoptosis in type 2 diabetic retina via ROS-NEK7-NLRP3 pathway.

In order to mitigate the influence of turbulence on pulse amplitude modulation–pulse position modulation (PAM-PPM) optical signals, which represents a promising avenue for future high-speed free-space optical (FSO) communication, this paper proposes a novel blind equalization scheme based on a modified third-order moment algorithm (MTOMA). The MTOMA is more robust to noise compared with the current fourth-order moment algorithms, such as the constant modulus algorithm (CMA) and the modified constant modulus algorithm (MCMA). Moreover, it will not increase the implementation complexity compared with the CMA and MCMA. The simulation results show that the MTOMA effectively reduces the distortion of PAM-PPM optical signals in atmospheric turbulence channels with a pointing error. Under different turbulence conditions, the MTOMA has a faster convergence rate than the CMA and MCMA. For example, when the signal-to-noise ratio (SNR) is 15 dB, the MTOMA requires about 530 iterations to reach convergence in moderate turbulence, which is about 230 and 170 fewer iterations than required by the CMA and MCMA, respectively; in addition, the differences in the number of iterations required by the MTOMA and those required by the CMA and MCMA, respectively, are 140 and 100 in weak turbulence and 150 and 90 in strong turbulence. Moreover, when the algorithms converge, the bit error rate (BER) performance of the PAM-PPM signals with MTOMA is also superior to that with CMA and MCMA. For example, when SNR = 20 dB, the BER performance of the PAM-PPM signals with MTOMA improves by 6.5 dB and 1.7 dB, respectively, compared to that with CMA and MCMA in moderate turbulence; this value improves by 4.3 dB and 1.4 dB in weak turbulence and 4.8 dB and 1.5 dB in strong turbulence. In addition, when the MTOMA reaches convergence, the decision-directed least mean square (DDLMS) algorithm can continue to be utilized to further improve the BER performance of PAM-PPM optical signals.

Background Although traditional diagnostic techniques of infection are mature and price favorable at present, most of them are time-consuming and with a low positivity. Metagenomic next⁃generation sequencing (mNGS) was studied widely because of identification and typing of all pathogens not rely on culture and retrieving all DNA without bias. Based on this background, we aim to detect the difference between mNGS and traditional culture method, and to explore the relationship between mNGS results and the severity, prognosis of infectious patients. Methods 109 adult patients were enrolled in our study in Shanghai Tenth People’s Hospital from October 2018 to December 2019. The diagnostic results, negative predictive values, positive predictive values, false positive rate, false negative rate, pathogen and sample types were analyzed by using both traditional culture and mNGS methods. Then, the samples and clinical information of 93 patients in the infected group (ID) were collected. According to whether mNGS detected pathogens, the patients in ID group were divided into the positive group of 67 cases and the negative group of 26 cases. Peripheral blood leukocytes, C-reactive protein (CRP), procalcitonin (PCT) and neutrophil counts were measured, and the concentrations of IL-2, IL-4, IL-6, TNF-α, IL-17A, IL-10 and INF-γ in the serum were determined by ELISA. The correlation between the positive detection of pathogens by mNGS and the severity of illness, hospitalization days, and mortality were analyzed. Results 109 samples were assigned into infected group (ID, 92/109, 84.4%), non-infected group (NID, 16/109, 14.7%), and unknown group (1/109, 0.9%). Blood was the most abundant type of samples with 37 cases, followed by bronchoalveolar lavage fluid in 36 cases, tissue, sputum, pleural effusion, cerebrospinal fluid (CSF), pus, bone marrow and nasal swab. In the ID group, the majority of patients were diagnosed with lower respiratory system infections (73/109, 67%), followed by bloodstream infections, pleural effusion and central nervous system infections. The sensitivity of mNGS was significantly higher than that of culture method (67.4% vs 23.6%; P  < 0.001), especially in sample types of bronchoalveolar lavage fluid ( P  = 0.002), blood ( P <  0.001) and sputum ( P  = 0.037), while the specificity of mNGS was not significantly different from culture method (68.8% vs 81.3%; P  = 0.41). The number of hospitals stays and 28-day-motality in the positive mNGS group were significantly higher than those in the negative group, and the difference was statistically significant ( P  < 0.05). Age was significant in multivariate logistic analyses of positive results of mNGS. Conclusions The study found that mNGS had a higher sensitivity than the traditional method, especially in blood, bronchoalveolar lavage fluid and sputum samples. And positive mNGS group had a higher hospital stay, 28-day-mortality, which means the positive of pathogen nucleic acid sequences detection may be a potential high-risk factor for poor prognosis of adult patients and has significant clinical value. MNGS should be used more in early pathogen diagnosis in the future.

Periodontitis, a chronic inflammatory disease caused by bacteria, is characterized by localized reactive oxygen species (ROS) accumulation, leading to an inflammatory response, which in turn leads to the destruction of periodontal supporting tissues. Therefore, antibacterial, scavenging ROS, reducing the inflammatory response, regulating periodontal microenvironment, and alleviating alveolar bone resorption are effective methods to treat periodontitis. In this study, we developed a ROS-responsive injectable hydrogel by modifying hyaluronic acid with 3-amino phenylboronic acid (PBA) and reacting it with poly(vinyl alcohol) (PVA) to form a borate bond. In addition, the ROS-responsive hydrogel encapsulated the antibacterial agent minocycline hydrochloride (MH) and Fe-Quercetin anti-inflammatory nanoparticles (Fe-Que NPs) for on-demand drug release in response to the periodontitis microenvironment. This hydrogel (HP-PVA@MH/Fe-Que) exhibited highly effective antibacterial properties. Moreover, by modulating the Nrf2/NF-κB pathway, it effectively eliminated ROS and promoted macrophage polarization to the M2 phenotype, reducing inflammation and enhancing the osteogenic differentiation potential of human periodontal ligament stem cells (hPDLSCs) in the periodontal microenvironment. Animal studies showed that HP-PVA@MH/Fe-Que significantly reduced alveolar bone loss and enhanced osteogenic factor expression by killing bacteria and inhibiting inflammation. Thus, HP-PVA@MH/Fe-Que hydrogel had efficient antibacterial, ROS-scavenging, anti-inflammatory, and alveolar bone resorption-alleviation abilities, showing excellent application potential for periodontitis healing. Graphical abstract

Determination on underlying mechanisms of crack initiation is of vital importance to understand the failure processes of geomaterials in practical engineering. In this study, uniaxial compression experiments of granitic samples containing a single pre-existing flaw were conducted and the failure processes were recorded by using the high-speed camera. To quantitatively determine the crack initiation mechanism, a novel method was first proposed based on digital image correlation (DIC) analysis and then its validity was confirmed. By utilizing this method, three types of cracks with different initiation mechanisms were identified and the effect of flaw inclination angle on crack initiation mechanisms was discussed from the viewpoint of theoretical analysis. With the increase of inclination angles, wing cracks change from mixed mode I/II cracks to mode I cracks, while anti-wing cracks have no evident changes and are dominated by mode II cracks. Under compressive pressure, the upper and bottom surfaces of pre-existing flaw deform to each other and the distributions of full-field tangential stress around flaw are different, which might induce the variation of crack initiation mechanisms with regard to the inclination angle.

Although efforts and progress have been made in crop classification using optical remote sensing images, it is still necessary to make full use of the high spatial, temporal, and spectral resolutions of remote sensing images. However, with the increasing volume of remote sensing data, a key emerging issue in the field of crop classification is how to find useful information from massive data to balance classification accuracy and processing time. To address this challenge, we developed a novel crop classification method, combining optimal feature selection (OFSM) with hybrid convolutional neural network-random forest (CNN-RF) networks for multi-temporal optical remote sensing images. This research used 234 features including spectral, segmentation, color, and texture features from three scenes of Sentinel-2 images to identify crop types in the Jilin province of northeast China. To effectively extract the effective features of remote sensing data with lower time requirements, the use of OFSM was proposed with the results compared with two traditional feature selection methods (TFSM): random forest feature importance selection (RF-FI) and random forest recursive feature elimination (RF-RFE). Although the time required for OFSM was 26.05 s, which was between RF-FI with 1.97 s and RF-RFE with 132.54 s, OFSM outperformed RF-FI and RF-RFE in terms of the overall accuracy (OA) of crop classification by 4% and 0.3%, respectively. On the basis of obtaining effective feature information, to further improve the accuracy of crop classification we designed two hybrid CNN-RF networks to leverage the advantages of one-dimensional convolution (Conv1D) and Visual Geometry Group (VGG) with random forest (RF), respectively. Based on the selected optimal features using OFSM, four networks were tested for comparison: Conv1D-RF, VGG-RF, Conv1D, and VGG. Conv1D-RF achieved the highest OA at 94.27% as compared with VGG-RF (93.23%), Conv1D (92.59%), and VGG (91.89%), indicating that the Conv1D-RF method with optimal feature input provides an effective and efficient method of time series representation for multi-temporal crop-type classification.

This paper developed a deep learning (DL) model for forecasting tropical cyclone (TC) intensity in the Northwest Pacific. A dataset containing 20 533 synchronized and collocated samples was assembled, which included ERA5 reanalysis data as well as satellite infrared (IR) imagery, covering the period from 1979 to 2021. The u -, v - and w -components of wind, sea surface temperature, IR satellite imagery, and historical TC information were selected as the model inputs. Then, a TC-intensity-forecast-fusion (TCIF-fusion) model was developed, in which two special branches were designed to learn multi-factor information to forecast 24 h TC intensity. Finally, heatmaps capturing the model’s insights are generated and applied to the original input data, creating an enhanced input set that results in more accurate forecasting. Employing this refined input, the heatmaps (model knowledge) were used to guide TCIF-fusion model modeling, and the model-knowledge-guided TCIF-fusion model achieved a 24 h forecast error of 3.56 m s −1 for Northwest Pacific TCs spanning 2020–2021. The results show that the performance of our method is significantly better than the official subjective prediction and advanced DL methods in forecasting TC intensity by 4% to 22%. Additionally, compared to operational approaches, model-guided knowledge methods can better forecast the intensity of landfalling TCs.

Originally designed for medical segmentation, the U-Net model excels in ocean remote sensing for segmentation, forecasting, and image enhancement. We propose enhancements like attention mechanisms, knowledge-data integration, and diffusion models to improve small target detection, ocean phenomena forecasting, and image super-resolution, expanding U-Net’s application and support in oceanographic research.

The El Niño–Southern Oscillation (ENSO) predictability has notably declined since the 1998–2013 global warming hiatus, challenging conventional sea surface temperature (SST)‐based forecast models. We identify sea surface salinity (SSS) as a critical yet underappreciated driver for restoring ENSO forecast skill post‐hiatus. Through an interpretable deep learning framework (STPNet), we show that incorporating SSS sustains forecast skill above 0.8 beyond 20‐month leads, outperforming SST‐only predictions after 2014. Attribution analysis reveals that Indo‐Pacific SSS anomalies promote interbasin heat redistribution and preserve ocean memory critical for ENSO evolution. The Indonesian Throughflow functions as a salinity‐sensitive conduit, where salinity‐mediated geostrophic transport compensates for thermally induced weakening, sustaining Indo‐Pacific connectivity. These findings reveal a new thermohaline pathway influencing ENSO forecasts and show that explainable AI can boost long‐lead forecast skill while uncovering key ocean–climate interactions, offering a salinity‐informed strategy to improve future predictions under climate change.