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Abstract Xanthomonas oryzae pv. oryzae (Xoo) causes bacterial blight in rice. Polyhydroxyalkanoates (PHAs) consitute a diverse group of biopolyesters synthesized by bacteria under nutrient-limited conditions. The phaC gene is important for PHA polymerization. We investigated the effects of phaC gene mutagensis in Xoo strain PXO99A. The phaC gene knock-out mutant exhibited reduced swarming ability relative to that of the wild-type. Under conditions where glucose was the sole sugar source, extracellular polysaccharide (EPS) production by ΔphaC declined by 44.8%. ΔphaC showed weak hypersensitive response (HR) induction in the leaves of non-host Nicotiana tabacum, concomitant with downregulation of hpa1 gene expression. When inoculated in rice leaves by the leaf-clipping method, ΔphaC displayed reduced virulence in terms of lesion length compared with the wild-type strain. The complemented strain showed no significant difference from the wild-type strain, suggesting that the deletion of phaC in Xoo induces significant alterations in various physiological and biological processes. These include bacterial swarming ability, EPS production, transcription of hrp genes, and glucose metabolism. These changes are intricately linked to the energy utilization and virulence of Xoo during plant infection. These findings revealed involvement of phaC in Xoo is in the maintaining carbon metabolism by functioning in the PHA metabolic pathway.

Abstract Backgroud Type II congenital pulmonary airway malformation (CPAM) is a rare pulmonary microcystic developmental malformation. Surgical excision is the primary treatment for CPAM, although maternal steroids and betamethasone have proven effective in reducing microcystic CPAM. Disturbed intercellular communication may contribute to the development of CPAM. This study aims to investigate the expression profile and analyze intercellular communication networks to identify genes potentially associated with type II CPAM pathogenesis and therapeutic targets. Methods RNA sequencing (RNA-seq) was performed on samples extracted from both the cystic area and the adjacent normal tissue post-surgery in CPAM patients. Iterative weighted gene correlation network analysis (iWGCNA) was used to identify genes specifically expressed in type II CPAM. Single-cell RNA-seq (scRNA-seq) was integrated to unveil the heterogeneity in cell populations and analyze the communication and interaction within epithelial cell sub-populations. Results A total of 2,618 differentially expressed genes were identified, primarily enriched in cilium-related biological process and inflammatory response process. Key genes such as EDN1, GPR17, FPR2, and CHRM1, involved in the G protein-coupled receptor (GPCR) signaling pathway and playing roles in cell differentiation, apoptosis, calcium homeostasis, and the immune response, were highlighted based on the protein-protein interaction network. Type II CPAM-associated modules, including ciliary function-related genes, were identified using iWGCNA. By integrating scRNA-seq data, AGR3 (related to calcium homeostasis) and SLC11A1 (immune related) were identified as the only two differently expressed genes in epithelial cells of CPAM. Cell communication analysis revealed that alveolar type 1 (AT1) and alveolar type 2 (AT2) cells were the predominant communication cells for outgoing and incoming signals in epithelial cells. The ligands and receptors between epithelial cell subtypes included COLLAGEN genes enriched in PI3K-AKT singaling and involved in epithelial to mesenchymal transition. Conclusions In summary, by integrating bulk RNA-seq data of type II CPAM with scRNA-seq data, the gene expression profile and critical signaling pathways such as GPCR signaling and PI3K-AKT signaling pathways were revealed. Abnormally expressed genes in these pathways may disrupt epithelial-mesenchymal transition and contribute to the development of CPAM. Given the effectiveness of prenatal treatments of microcystic CPAM using maternal steroids and maternal betamethasone administration, targeting the genes and signaling pathways involved in the development of CPAM presents a promising therapeutic strategy.

Abstract Thymic stromal lymphopoietin (TSLP) is a key player in atopic diseases, which has sparked great interest in therapeutically targeting TSLP. Yet, no small-molecule TSLP inhibitors exist due to the challenges of disrupting the protein–protein interaction between TSLP and its receptor. Here, we report the development of small-molecule TSLP receptor inhibitors using virtual screening and docking of >1,000,000 compounds followed by iterative chemical synthesis. BP79 emerged as our lead compound that effectively abrogates TSLP-triggered cytokines at low micromolar concentrations. For in-depth analysis, we developed a human atopic disease drug discovery platform using multi-organ chips. Here, topical application of BP79 onto atopic skin models that were co-cultivated with lung models and Th2 cells effectively suppressed immune cell infiltration and IL-13, IL-4, TSLP, and periostin secretion, while upregulating skin barrier proteins. RNA-Seq analysis corroborate these findings and indicate protective downstream effects on the lungs. To the best of our knowledge, this represents the first report of a potent putative small molecule TSLPR inhibitor which has the potential to expand the therapeutic and preventive options in atopic diseases.

Objective This study was performed to evaluate the efficacy of robot-assisted thoracoscopic surgery (RATS) in the treatment of pulmonary sequestration (PS) in children.Methods All video-assisted thoracoscopic surgery (VATS) and RAST performed on patients with PS at a single center from May 2019 to July 2023 were identified. The χ2 and Wilcoxon tests were used to compare the perioperative outcomes between VATS and RATS groups.Results Ninety-three patients underwent RATS while 77 patients underwent VATS. In both two groups, one patient converted to thoracotomy and no surgical mortality case. The median operation time was longer for the RATS group compared with the VATS group (75 min vs. 60 min, p

Abstract Emergency braking can generate forward displacement that may influence the effectiveness of protection in collisions, especially for passengers. The development of automated vehicles has enabled the diversification and rationalization of sitting positions, including reclined seating. However, the passenger response in pre-crash scenarios in reclined seating differs from that in standard seating, which poses different requirements for biofidelic human body models (HBMs) to evaluate passenger injuries in collisions. This study conducted emergency braking trials in vehicles at an initial velocity of 80 km/h. Five volunteers were exposed to approximately 1 g manual emergency braking (MEB), and the muscle responses at the front passenger seat with backrest angles of 25°, 45°, and 65° were recorded. The electromyography obtained from 14 muscles of the neck, torso, and lower extremity were normalized using maximum voluntary contractions (MVCs). In the quiet sitting phase, the activity levels were low (

Abstract Bivalent histone modifications, including functionally opposite H3K4me3 and H3K27me3 marks simultaneously on the same nucleosome, control various cellular processes by fine-tuning the gene expression in eukaryotes. However, the role of bivalent histone modifications in fungal virulence remains elusive. By mapping the genome-wide landscape of H3K4me3 and H3K27me3 dynamic modifications in Fusarium graminearum (Fg) during invasion, we identify the infection-related bivalent chromatin-marked genes (BCGs). BCG1 gene, which encodes a secreted Fusarium-specific xylanase containing a G/Q-rich motif, displays the highest increase of bivalent modification during Fg infection. We report that the G/Q-rich motif of BCG1 is a stimulator of its xylanase activity and is essential for the full virulence of Fg. Intriguingly, this G/Q-rich motif is recognized by pattern-recognition receptors to trigger plant immunity. We discover that Fg employs H3K4me3 modification to induce BCG1 expression required for host cell wall degradation. After breaching the cell wall barrier, this active chromatin state is reset to bivalency by co-modifying with H3K27me3, which enables epigenetic silencing of BCG1 to escape from host immune surveillance. Collectively, our study highlights how fungal pathogens deploy bivalent epigenetic modification to achieve temporally-coordinated activation and suppression of a critical fungal gene, thereby facilitating successful infection and host immune evasion.

In Bandpass Sampling Fourier Transform Spectroscopy, a comprehensive method for evaluating signal-to-noise ratio (SNR) has not yet been established. This paper employs an energy conservation approach to analyze the relationship of SNR between interferogram and spectra in Bandpass Sampling Time-Modulated Fourier Transform Spectroscopy (BPS-FTS). It systematically presents models for the average SNR of the system interferogram and the average SNR of reconstructed spectra under different parameters. These models are compared with SNR models in traditional Fourier transform spectroscopy (FTS) and are mutually validated through theoretical modeling and simulation analysis. It provides a theoretical basis and simulation verification for the design and implementation of the system.

Abstract Objective Several studies have illustrated that elevated RC levels are related to a heightened risk of acute ischemic stroke (AIS). Our research aimed to explore the correlation between RC levels and poor prognosis after a 90‐day interval in AIS patients. Methods A total of 287 individuals were enrolled in the study, the primary outcome was defined as poor prognosis. RC was derived by the exclusion of low‐density lipoprotein cholesterol (LDL‐C) and high‐density lipoprotein cholesterol (HDL‐C) from total cholesterol (TC). Results Following the screening process, 253 AIS patients were included in the study, presenting a median age of 66[57, 75] years. Upon stratifying RC levels into quartiles, those in the top quartile faced a greater likelihood of diabetes diagnosis (42.86%, p = .014) and experienced a higher rate of unfavorable outcomes after 90 days (36.51%, p = .001). After accounting for confounding factors, the correlation between the fourth quartile of RC levels and the amplified likelihood of poor prognosis remained significant (odds ratio (OR) 8.471, 95% confidence interval (CI) (1.841, 38.985); p = .006). Analysis of subgroups unveiled a notable correlation between higher RC levels and poor 90‐day prognosis, particularly in individuals with elevated NIHSS scores (p = .044). A progressively increasing 90‐day risk of poor prognosis after an RC greater than 0.38 mmol/L was visualized by restricted cubic spline plots (p‐overall = .011). Conclusions Including RC as a contributing element may refine the prediction of poor 90‐day prognosis for AIS patients. Integrating RC with traditional risk factors can potentially enhance the predictive value for cerebrovascular disease.

The persistent increase in forest pest outbreaks requires timely detection methods to monitor the disaster precisely. However, early detection is challenging due to insufficient temporal observation and subtle tree changes. This article proposed a novel framework that collaborates multi-source remote sensing data and uses a change detection algorithm to archive early detection of infestation caused by Dendrolimus tabulaeformis Tsai et Liu (D. tabulaeformis) attacks. First, all available Sentinel-2 images with less than 20% cloud cover were utilized. During periods with long intervals (>16 days) between Sentinel-2 images, Landsat-8 images with less than 20% cloud cover were downscaled to a spatial resolution of 10 m using a deep learning algorithm to meet the requirement for a high temporal frequency of clear observations. Second, the spectral index differences between healthy and infested trees were examined to address the challenge of detecting subtle changes in pest attacks. The Enhanced Vegetation Index (EVI) was selected for early defoliation detection. On this basis, the EWMACD (Exponentially Weighted Moving Average Change Detection) algorithm, which is sensitive to subtle changes, was enhanced to improve the capability of detecting early D. tabulaeformis attacks. The assessment showed that the overall accuracy of the change detection (F1 score) reached 0.86 during the early stage and 0.88 during the late stage. The temporal accuracy (Precision) was 84.1% during the early stage. The accuracy significantly improved compared to using a single remote sensing data source. This study presents a new framework capable of monitoring early forest defoliation caused by D. tabulaeformis attacks and offering opportunities for predicting future outbreaks and implementing preventive measures.

Abstract Dietary methionine interventions are beneficial to apoptosis-inducing chemotherapy and radiotherapy for cancer, while their effects on ferroptosis-targeting therapy and immunotherapy are unknown. Here we show the length of time methionine deprivation affects tumoral ferroptosis differently. Prolonged methionine deprivation prevents glutathione (GSH) depletion from exceeding the death threshold by blocking cation transport regulator homolog 1 (CHAC1) protein synthesis. Whereas, short-term methionine starvation accelerates ferroptosis by stimulating CHAC1 transcription. In vivo, dietary methionine with intermittent but not sustained deprivation augments tumoral ferroptosis. Intermittent methionine deprivation also sensitizes tumor cells against CD8+ T cell-mediated cytotoxicity and synergize checkpoint blockade therapy by CHAC1 upregulation. Clinically, tumor CHAC1 correlates with clinical benefits and improved survival in cancer patients treated with checkpoint blockades. Lastly, the triple combination of methionine intermittent deprivation, system xc - inhibitor and PD-1 blockade shows superior antitumor efficacy. Thus, intermittent methionine deprivation is a promising regimen to target ferroptosis and augment cancer immunotherapy.

Objective To compare the application value of the likelihood ratio （LR） method and identity by state （IBS） method in the identification involving half sibling relationships， and to provide a reference for the setting of relevant standards for identification of half sibling relationship. Methods （1） Based on the same genetic marker combinations， the reliability of computer simulation method was verified by comparing the distributions of cumulated identity by state score （CIBS） and combined full sibling index in actual cases with the distributions in simulated cases. （2） In different numbers of three genetic marker combinations, the simulation of full sibling, half sibling and unrelated individual pairs, each 1 million pairs, was obtained; the CIBS, as well as the corresponding types of cumulative LR parameters， were calculated. （3） The application value of LR method was compared with that of IBS method， by comparing the best system efficiency provided by LR method and IBS method when genetic markers in different amounts and of different types and accuracy were applied to distinguish the above three relational individual pairs. （4） According to the existing simulation data， the minimum number of genetic markers required to distinguish half siblings from the other two relationships using different types of genetic markers was estimated by curve fitting. Results （1） After the rank sum test， under the premise that the real relationship and the genetic marker combination tested were the same， there was no significant difference between the simulation method and the results obtained in the actual case. （2） In most cases， under the same conditions， the system effectiveness obtained by LR method was greater than that by IBS method. （3） According to the existing data， the number of genetic markers required for full-half siblings and half sibling identification could be obtained by curve fitting when the system effectiveness reached 0.95 or 0.99. Conclusion When distinguishing half sibling from full sibling pairs or unrelated pairs， it is recommended to give preference to the LR method， and estimate the required number of markers according to the identification types and the population data， to ensure the identification effect.

Abstract Background Congenital pulmonary airway malformation (CPAM) is the most frequent pulmonary developmental malformation and the pathophysiology remains poorly understood. This study aimed to identify the characteristic gene expression patterns and the marker genes essential to CPAM. Methods Tissues from the cystic area displaying CPAM and the area of normal appearance were obtained during surgery. Bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) were performed for integrating analysis. Iterative weighted gene correlation network analysis (iWGCNA) was used to identify specifically expressed genes to CPAM. Results In total, 2074 genes were significantly differentially expressed between the CPAM and control areas. Of these differentially expressed genes (DEGs), 1675 genes were up-regulated and 399 genes were down-regulated. Gene ontology analysis revealed these DEGs were specifically enriched in ciliated epithelium and involved in immune response. We also identified several CPAM-related modules by iWGCNA, among them, P15_I4_M3 module was the most influential module for distinguishing CPAMs from controls. By combining the analysis of the expression dataset from RNA-seq and scRNA-seq, SPOCK2, STX11, and ZNF331 were highlighted in CPAM. Conclusions Through our analysis of expression datasets from both scRNA-seq and bulk RNA-seq of tissues obtained from patients with CPAM, we identified the characteristic gene expression patterns associated with the condition. Our findings suggest that SPOCK2 could be a potential biomarker gene for the diagnosis and therapeutic target in the development of CPAM, whereas STX11 and ZNF331 might serve as prognostic markers for this condition. Further investigations with larger samples and function studies are necessary to confirm the involvement of these genes in CPAM.

Multi-frame super-resolution (MFSR) leverages complementary information between image sequences of the same scene to increase the resolution of the reconstructed image. As a branch of MFSR, burst super-resolution aims to restore image details by leveraging the complementary information between noisy sequences. In this paper, we propose an efficient burst-enhanced super-resolution network (BESR). Specifically, we introduce Geformer, a gate-enhanced transformer, and construct an enhanced CNN-Transformer block (ECTB) by combining convolutions to enhance local perception. ECTB efficiently aggregates intra-frame context and inter-frame correlation information, yielding an enhanced feature representation. Additionally, we leverage reference features to facilitate inter-frame communication, enhancing spatiotemporal coherence among multiple frames. To address the critical processes of inter-frame alignment and feature fusion, we propose optimized pyramid alignment (OPA) and hybrid feature fusion (HFF) modules to capture and utilize complementary information between multiple frames to recover more high-frequency details. Extensive experiments demonstrate that, compared to state-of-the-art methods, BESR achieves higher efficiency and competitively superior reconstruction results. On the synthetic dataset and real-world dataset of BurstSR, our BESR achieves PSNR values of 42.79 dB and 48.86 dB, respectively, outperforming other MFSR models significantly.

Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial–temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial–temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model.

Transformer models have great potential in the field of remote sensing super-resolution (SR) due to their excellent self-attention mechanisms. However, transformer models are prone to overfitting because of their large number of parameters, especially with the typically small remote sensing datasets. Additionally, the reliance of transformer-based SR models on convolution-based upsampling often leads to mismatched semantic information. To tackle these challenges, we propose an efficient super-resolution hybrid network (EHNet) based on the encoder composed of our designed lightweight convolution module and the decoder composed of an improved swin transformer. The encoder, featuring our novel Lightweight Feature Extraction Block (LFEB), employs a more efficient convolution method than depthwise separable convolution based on depthwise convolution. Our LFEB also integrates a Cross Stage Partial structure for enhanced feature extraction. In terms of the decoder, based on the swin transformer, we innovatively propose a sequence-based upsample block (SUB) for the first time, which directly uses the sequence of tokens in the transformer to focus on semantic information through the MLP layer, which enhances the feature expression ability of the model and improves the reconstruction accuracy. Experiments show that EHNet’s PSNR on UCMerced and AID datasets obtains a SOTA performance of 28.02 and 29.44, respectively, and is also visually better than other existing methods. Its 2.64 M parameters effectively balance model efficiency and computational demands.

Abstract Objective Previous studies have found the potential role of gout or hyperuricemia in subsequent development of Alzheimer's disease (AD) but reported inconsistent results. We conducted the current meta‐analysis to evaluate whether an association exists between gout/ hyperuricemia and AD. Methods We systematically searched PubMed and EMBASE for the published cohort studies that measured the risk of AD in subject with gout/ hyperuricemia up to May 20, 2023. Data extraction was employed by two authors independently. Rev Man 5.3 and Stata 15.0 software were used to calculate the relative ratio (RR) or hazard ratio (HR) for including studies. Subgroup analysis was performed to assess the sources of heterogeneity. A random‐effects model was adopted when heterogeneity was present. The funnel plot, Begg's test, and and Egger's test were used to assess publication bias. Results After rigorous screening, seven eligible studies were included in the final analyses. Pooled results indicated that gout or hyperuricemia decreases the risk of AD (RR: 0.69, 95% CI: 0.64∼0.72), with a high heterogeneity of 93%. Subgroup analyses showed that regional distribution was the source of heterogeneity. Egger's and Begg's tests as well as visual inspection of funnel plot suggested no publication bias in the studies. Conclusion The findings suggested that gout or hyperuricemia might have a protective effect against AD. This negative correlation should be verified by more cohort studies due to the existence of substantial heterogeneity.

For traditional Fourier transform (FTS), its integral sampling usually meets the Spectral Modulation Transfer Function (SMTF) criterion. However, for bandpass-sampling Fourier transform spectroscopy (BPS-FTS), based on our analysis, the integral sampling condition derived from the Spectral Modulation Transfer Function (SMTF) is excessively stringent. In other words, the interval of the integral sampling time that fulfills the tolerance requirements for the reconstructed spectrum is very narrow. There are numerous integration sampling time intervals outside this range that still meet the tolerance requirements for the reconstructed spectrum. In this paper, through theoretical modeling, we propose a method based on average |SMTF| as the selection criterion for the integration sampling time. Through simulation analysis, it is evident that the intervals and range of the integral sampling time obtained via this method are more accurate, ensuring the tolerance requirements of the reconstructed spectrum. Under these intervals, when conducting integral sampling on the interferogram, the spectral deviation of the reconstructed spectrum is minimal, and the Spectral Correlation Mapper (SCM) is nearly equal to one. This indicates that compared with the SMTF criterion in traditional FTS, this method is more suitable for the characteristics of BPS-FTS. The analysis in this paper can provide theoretical and simulation support for the implementation of BPS-FTS.

An efficient carbon dioxide reduction reaction (CO2RR), which reduces CO2 to low-carbon fuels and high-value chemicals, is a promising approach for realizing the goal of carbon neutrality, for which effective but low-cost catalysts are critically important. Recently, many inorganic perovskite-based materials with tunable chemical compositions have been applied in the electrochemical CO2RR, which exhibited advanced catalytic performance. Therefore, a timely review of this progress, which has not been reported to date, is imperative. Herein, the physicochemical characteristics, fabrication methods and applications of inorganic perovskites and their derivatives in electrochemical CO2RR are systematically reviewed, with emphasis on the structural evolution and product selectivity of these electrocatalysts. What is more, the current challenges and future directions of perovskite-based materials regarding efficient CO2RR are proposed, to shed light on the further development of this prospective research area.

In the field of edge computing, quantizing convolutional neural networks (CNNs) using extremely low bit widths can significantly alleviate the associated storage and computational burdens in embedded hardware, thereby improving computational efficiency. However, such quantization also presents a challenge related to substantial decreases in detection accuracy. This paper proposes an innovative method, called Adaptive Global Power-of-Two Ternary Quantization Based on Unfixed Boundary Thresholds (APTQ). APTQ achieves adaptive quantization by quantizing each filter into two binary subfilters represented as power-of-two values, thereby addressing the accuracy degradation caused by a lack of expression ability of low-bit-width weight values and the contradiction between fixed quantization boundaries and the uneven actual weight distribution. It effectively reduces the accuracy loss while at the same time presenting strong hardware-friendly characteristics because of the power-of-two quantization. This paper extends the APTQ algorithm to propose the APQ quantization algorithm, which can adapt to arbitrary quantization bit widths. Furthermore, this paper designs dedicated edge deployment convolutional computation modules for the obtained quantized models. Through quantization comparison experiments with multiple commonly used CNN models utilized on the CIFAR10, CIFAR100, and Mini-ImageNet data sets, it is verified that the APTQ and APQ algorithms possess better accuracy performance than most state-of-the-art quantization algorithms and can achieve results with very low accuracy loss in certain CNNs (e.g., the accuracy loss of the APTQ ternary ResNet-56 model on CIFAR10 is 0.13%). The dedicated convolutional computation modules enable the corresponding quantized models to occupy fewer on-chip hardware resources in edge chips, thereby effectively improving computational efficiency. This adaptive CNN quantization method, combined with the power-of-two quantization results, strikes a balance between the quantization accuracy performance and deployment efficiency in embedded hardware. As such, valuable insights for the industrial edge computing domain can be gained.

PurposeTo investigate the feasibility and safety of non-intubated general anesthesia with spontaneous breathing combined with paravertebral nerve blocks (PVNB) in young children undergoing video-assisted thoracic surgery (VATS) and to determine its significance for rapid recovery after pediatric thoracic surgery.MethodsThe data of 46 children aged 6–36 months with an American Society of Anesthesiologists status of I–II who underwent elective VATS under general anesthesia were retrospectively analyzed. Of these patients, 25 underwent non-intubated general anesthesia with spontaneous breathing combined with PVNB (non-intubation group), and 21 received conventional intubated general anesthesia combined with local infiltration anesthesia (intubation group). The following perioperative parameters were compared between the two groups: heart rate (HR), mean arterial pressure, saturation of pulse oximetry (SpO2), partial pressure end-tidal carbon dioxide, time from the completion of the operation to extubation or removing laryngeal masks, time to first feeding after the operation, length of postoperative in-hospital stay, incidence of postoperative complications, and hospitalization expenses.ResultsThe operations were completed successfully in both groups. When the non-intubation group was compared with the intubation group, the minimal SpO2 level during the surgery was higher (93% vs. 88%, P

Emerging as a new class of advanced functional materials with hierarchical architectures and redox characters, organic–inorganic hybrid materials (OIHs) have been well developed and widely applied in various energy conversion reactions recently. In this review, we focus on the applications and structure–performance relationship of OIHs for electrochemical water oxidation. The general principles of water oxidation will be presented first, followed by the progresses on the applications of OIHs that are classified as metal organic frameworks (MOFs) and their derivates, covalent organic framework (COF)-based hybrids and other OIHs. The roles of organic counterparts on catalytic active centers will be fully discussed and highlighted with typical examples. Finally, the challenges and perspectives assessing this promising hybrid material as an electrocatalyst will be provided.

Background: The anterior nucleus of thalamus (ANT) has been suggested as an extended hippocampal system. The circuit of ANT and hippocampus has been widely demonstrated to be associated with memory function. Both lesions to each region and disrupting inter-regional information flow can induce working memory impairment. However, the role of this circuit in working memory precision remains unknown. Objective: To test the role of the hippocampal-anterior thalamic pathway in working memory precision, we delivered intracranially electrical stimulation to the ANT. We hypothesize that ANT stimulation can improve working memory precision. Methods: Presurgical epilepsy patients with depth electrodes in ANT and hippocampus were recruited to perform a color-recall working memory task. Participants were instructed to point out the color they were supposed to recall by clicking a point on the color wheel, while the intracranial EEG data were synchronously recorded. For randomly selected half trials, a bipolar electrical stimulation was delivered to the ANT electrodes. Results: We found that compared to non-stimulation trials, working memory precision judgements were significantly improved for stimulation trials. ANT electrical stimulation significantly increased spectral power of gamma (30–100 Hz) oscillations and decreased interictal epileptiform discharges (IED) in the hippocampus. Moreover, the increased gamma power during the pre-stimulus and retrieval period predicted the improvement of working memory precision judgements. Conclusion: ANT electrical stimulation can improve working memory precision judgements and modulate hippocampal gamma activity, providing direct evidence on the role of the human hippocampal-anterior thalamic axis in working memory precision.

BackgroundWhite matter hyperintensities (WMHs) are a subtype of cerebral small vessel disease and can be divided into periventricular WMHs (pvWMHs) and deep WMHs (dWMHs). pvWMHs and dWMHs were proved to be determined by different etiologies. This study aimed to develop a 2D Cascade U-net (Cascade U) for the segmentation and differentiation of pvWMHs and dWMHs on 2D T2-FLAIR images.MethodsA total of 253 subjects were recruited in the present study. All subjects underwent 2D T2-FLAIR scan on a 3.0 Tesla MR scanner. Both contours of pvWMHs and dWMHs were manually delineated by the observers and considered as the gold standard. Fazekas scale was used to evaluate the burdens of pvWMHs and dWMHs, respectively. Cascade U consisted of a segmentation U-net and a differentiation U-net and was trained with a combined loss function. The performance of Cascade U was compared with two other U-net models (Pipeline U and Separate U). Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), precision, and recall were used to evaluate the performances of all models. The linear correlations between WMHs volume (WMHV) measured by all models and the gold standard were also conducted.ResultsCompared with other models, Cascade U exhibited a better performance on WMHs segmentation and pvWMHs identification. Cascade U achieved DSC values of 0.605 ± 0.135, 0.517 ± 0.263, and 0.510 ± 0.241 and MCC values of 0.617 ± 0.122, 0.526 ± 0.263, and 0.522 ± 0.243 on the segmentation of total WMHs, pvWMHs, and dWMHs, respectively. Cascade U exhibited strong correlations with the gold standard on measuring WMHV (R2 = 0.954, p < 0.001), pvWMHV (R2 = 0.933, p < 0.001), and dWMHV (R2 = 0.918, p < 0.001). A significant correlation was found on lesion volume between Cascade U and gold standard (r > 0.510, p < 0.001).ConclusionCascade U showed competitive results in segmentation and differentiation of pvWMHs and dWMHs on 2D T2-FLAIR images, indicating potential feasibility in precisely evaluating the burdens of WMHs.

Tellurite [Te(IV)] is a high-toxicity metalloid. In this study, a fungus with high Te(IV) resistance was isolated. Strain AB1 could efficiently reduce highly toxic Te(IV) to less toxic Te(0). The reduced products formed rod-shaped biogenetic Te(0) nanoparticles (Bio-TeNPs) intracellularly. Further TEM-element mapping, FTIR, and XPS analysis showed that the extracted Bio-TeNPs ranged from 100 to 500 nm and consisted of Te(0), proteins, lipids, aromatic compounds, and carbohydrates. Moreover, Bio-TeNPs exhibited excellent antibacterial ability against Shigella dysenteriae, Escherichia coli, Enterobacter sakazakii, and Salmonella typhimurium according to inhibition zone tests. Further growth and live/dead staining experiments showed that E. coli and S. typhimurium were significantly inhibited by Bio-TeNPs, and cells were broken or shriveled after treatment with Bio-TeNPs based on SEM observation. Additionally, the antioxidant and cytotoxicity tests showed that the Bio-TeNPs exhibited excellent antioxidant capacity with no cytotoxicity. All these results suggested that strain AB1 showed great potential in bioremediation and Bio-TeNPs were excellent antibacterial nanomaterials with no cytotoxicity.

Deep-learning-driven multi-image super-resolution (MISR) reconstruction techniques have significant application value in the field of aerospace remote sensing. In particular, Transformer-based models have shown outstanding performance in super-resolution tasks. However, current MISR models have some deficiencies in the application of multi-scale information and the modeling of the attention mechanism, leading to an insufficient utilization of complementary information in multiple images. In this context, we innovatively propose a Multi-Attention Multi-Image Super-Resolution Transformer (MAST), which involves improvements in two main aspects. Firstly, we present a Multi-Scale and Mixed Attention Block (MMAB). With its multi-scale structure, the network is able to extract image features from different scales to obtain more contextual information. Additionally, the introduction of mixed attention allows the network to fully explore high-frequency features of the images in both channel and spatial dimensions. Secondly, we propose a Collaborative Attention Fusion Block (CAFB). By incorporating channel attention into the self-attention layer of the Transformer, we aim to better establish global correlations between multiple images. To improve the network’s perception ability of local detailed features, we introduce a Residual Local Attention Block (RLAB). With the aforementioned improvements, our model can better extract and utilize non-redundant information, achieving a superior restoration effect that balances the global structure and local details of the image. The results from the comparative experiments reveal that our approach demonstrated a notable enhancement in cPSNR, with improvements of 0.91 dB and 0.81 dB observed in the NIR and RED bands of the PROBA-V dataset, respectively, in comparison to the existing state-of-the-art methods. Extensive experiments demonstrate that the method proposed in this paper can provide a valuable reference for solving multi-image super-resolution tasks for remote sensing.

AimsThe cost of drug regimens prescribed to Chinese patients has not been evaluated. This study aims to evaluate the medical costs and hypoglycemic agents for diabetes mellitus patients with or without chronic respiratory disease in Beijing, and to investigate the changes in the costs and number of antidiabetic medications used for diabetes patients with chronic respiratory disease from 2016 to 2018.MethodsThis observational, retrospective study included diabetes patients with outpatient medication records from Beijing Medical Insurance between 2016 and 2018. The medications, including hypoglycemic and nonhypoglycemic drugs, insulin dosage, comorbidities, diabetes-related complications, treatment strategies, and annual medical costs, were recorded.ResultsThis study included 2,853,036 diabetes patients from 2016 to 2018. About 18.95%–20.53% of patients with chronic respiratory disease were predominantly distributed among those aged 45–84 years (88.7%–89.1%). Diabetes patients with chronic respiratory disease used more medications (4.48 ± 2.41 vs. 3.76 ± 2.33) and had higher total annual drug costs (¥12,286 ± 10,385 vs. ¥9700 ± 9202) to treat more comorbidities (2.52 ± 1.53 vs. 2.05 ± 1.85) than those without chronic respiratory disease (p

Offline perceptions are self-generated sensations that do not involve physical stimulus. These perceptions can be induced by external hallucinated objects or internal imagined objects. However, how the brain dissociates these visual sensations remains unclear. We aimed to map the brain areas involved in internal and external visual sensations induced by intracranial electrical stimulation and further investigate their neural differences. In this study, we collected subjective reports of internal and external visual sensations elicited by electrical stimulation in 40 drug-refractory epilepsy during presurgical evaluation. The response rate was calculated and compared to quantify the dissociated distribution of visual responses. We found that internal and external visual sensations could be elicited when different brain areas were stimulated, although there were more overlapping brain areas. Specifically, stimulation of the hippocampus and inferior temporal cortex primarily induces internal visual sensations. In contrast, stimulation of the occipital visual cortex mainly triggers external visual sensations. Furthermore, compared to that of the dorsal visual areas, the ventral visual areas show more overlap between the two visual sensations. Our findings show that internal and external visual sensations may rely on distinct neural representations of the visual pathway. This study indicated that implantation of electrodes in ventral visual areas should be considered during the evaluation of visual sensation aura epileptic seizures.

A rich and effective dataset is an important foundation for the development of AI algorithms, and the quantity and quality of the dataset determine the upper limit level of the algorithms. For aerospace remote sensing datasets, due to the high cost of data collection and susceptibility to meteorological and airway conditions, the existing datasets have two problems: firstly, the number of datasets is obviously insufficient, and, secondly, there is large unevenness between different categories in datasets. One of the effective solutions is to use neural networks to generate fake data by learning from real data, but existing methods still find difficulty in generating remote sensing sample images with good texture detail and geometric distortion. To address the shortcomings of existing image generation algorithms, this paper proposes a gradient structure information-guided attention generative adversarial network (SGA-GAN) for remote sensing image generation, which contains two innovative initiatives: on the one hand, a learnable gradient structure information extraction branch network can be added to the generator network to obtain complex structural information in the sample image, thus alleviating the distortion of the sample geometric structure in remote sensing image generation; on the other hand, a multidimensional self-attention feature selection module is proposed to further improve the quality of the generated remote sensing images by connecting cross-attentive modules as well as spatial and channel attention modules in series to guide the generator to better utilize global information. The algorithm proposed in this paper outperformed other methods, such as StyleGAN-XL and FastGAN, in both the qualitative and quantitative evaluation, whereby the FID on the DOTA dataset decreased by 23.927 and the IS was improved by 2.351. The comparison experiments show that the method proposed in this paper can generate more realistic sample images, and images generated by this method can improve object detection metrics by increasing the number of single-category datasets and the number of targets in fewer categories in multi-category datasets, which means it can be effectively used in the field of intelligent processing of remote sensing images.

Convolutional neural networks (CNNs) have been developed as an effective strategy for hyperspectral image (HSI) classification. However, the lack of feature extraction by CNN networks is due to the network failing to effectively extract global features and poor capability in distinguishing between different feature categories that are similar. In order to solve these problems, this paper proposes a novel approach to hyperspectral image classification using a multidimensional spectral transformer with channel-wise correlation. The proposed method consists of two key components: an input mask and a channel correlation block. The input mask is used to extract relevant spectral information from hyperspectral images and discard irrelevant information, reducing the dimensionality of the input data and improving classification accuracy. The channel correlation block captures the correlations between different spectral channels and is integrated into the transformer network to improve the model’s discrimination power. The experimental results demonstrate that the proposed method achieves great performance with several benchmark hyperspectral image datasets. The input mask and channel correlation block effectively improve classification accuracy and reduce computational complexity.

Blind deblurring is a typical challenge in image processing, carried out to correct various complex types of distortions that occur in the real world. Although learning-based deblurring methods have substantially outperformed the traditional algorithms, they fail to make full use of image priors, leading to inconsistent data distributions between the restored images and sharp images. In this paper, we divide the deblurring process into two steps and propose a two-stage network. The first stage generates an initial deblurred image using a common convolutional network. The second stage converts the initial data distribution into a latent sharp image distribution to obtain sharp edges through a prior network. Moreover, we propose a relativistic training strategy to train the prior network, which aims to learn the latent sharp image priors. In addition, we use a coarse-to-fine multiscale framework that shares weights between the different scales. The experimental results show that compared with other state-of-the-art methods, our method achieves competitive performances on benchmark datasets.

Abstract Background Renal cell carcinoma (RCC) is a highly vascular tumor and patients with low risk metastatic RCC of clear-cell histological sub-type (mccRCC) are treated with tyrosine-kinase inhibitors (TKIs), sunitinib, as the first-line of treatment. Unfortunately, TKI resistance eventually develops, and the underlying molecular mechanism is not well understood. Methods RCC cell-line with metastatic clear-cell histology (Caki-1), and patient samples were analysed to identify the role of Y-box binding protein 1 (YB-1) and ATP-binding cassette sub-family B member 1 (ABCB-1) in acquired sunitinib-resistance development. Caki-1 was conditioned with increasing sunitinib doses to recapitulate acquired resistance development in clinics. Sunitinib-conditioned and wild-type Caki-1 were subjected to cell viability assay, scratch assay, chicken embryo chorioallantoic membrane engraftment and proteomics analysis. Classical biochemical assays like flow cytometry, immunofluorescent staining, immunohistochemical staining, optical coherence tomography imaging, Western Blot and RT-PCR assays were applied to determine the possible mechanism of sunitinib-resistance development and the effect of drug treatments. Publicly available data was also used to determine the role of YB-1 upregulation in ccRCC and the patients’ overall survival. Results We demonstrate that YB-1 and ABCB-1 are upregulated in sunitinib-resistant in vitro, ex vivo, in vivo and patient samples compared to the sensitive samples. This provides evidence to a mechanism of acquired sunitinib-resistance development in mccRCC. Furthermore, our results establish that inhibiting ABCB-1 with elacridar, in addition to sunitinib, has a positive impact on reverting sunitinib-resistance development in in vitro, ex vivo and in vivo models. Conclusion This work proposes a targeted therapy (elacridar and sunitinib) to re-sensitize sunitinib-resistant mccRCC and, possibly, slow disease progression.

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of blurred aerial remote sensing images. Moreover, the average PSNR of this paper’s algorithm tested on the publicly available dataset RealBlur-R reached 41.02 dB, surpassing the latest SOTA algorithm.

To address the problems of large storage requirements, computational pressure, untimely data supply of off-chip memory, and low computational efficiency during hardware deployment due to the large number of convolutional neural network (CNN) parameters, we developed an innovative hardware-friendly CNN pruning method called KRP, which prunes the convolutional kernel on a row scale. A new retraining method based on LR tracking was used to obtain a CNN model with both a high pruning rate and accuracy. Furthermore, we designed a high-performance convolutional computation module on the FPGA platform to help deploy KRP pruning models. The results of comparative experiments on CNNs such as VGG and ResNet showed that KRP has higher accuracy than most pruning methods. At the same time, the KRP method, together with the GSNQ quantization method developed in our previous study, forms a high-precision hardware-friendly network compression framework that can achieve “lossless” CNN compression with a 27× reduction in network model storage. The results of the comparative experiments on the FPGA showed that the KRP pruning method not only requires much less storage space, but also helps to reduce the on-chip hardware resource consumption by more than half and effectively improves the parallelism of the model in FPGAs with a strong hardware-friendly feature. This study provides more ideas for the application of CNNs in the field of edge computing.

Abstract Background Rapid stratification and appropriate treatment on admission are critical to saving lives of patients with acute pulmonary embolism (PE). None of the clinical prediction tools perform well when applied to all patients with acute PE. It may be important to integrate respiratory features into the 2014 European Society of Cardiology model. First, we aimed to assess the relationship between the arterial partial pressure of oxygen/fraction of inspired oxygen (PaO2/FIO2) ratio and in-hospital mortality, determine the optimal cutoff value of PaO2/FIO2, and determine if this value, which is quick and easy to obtain on admission, is a predictor of in-hospital mortality in this population. Second, we aimed to evaluate the potential additional determinants including laboratory parameters that may affect the in-hospital mortality. We hypothesized that the PaO2/FiO2 ratio would be a clinical prediction tool for in-hospital mortality in patients with acute PE. Methods A prospective single-center observational cohort study was conducted in Beijing Hospital from January 2010 to November 2017. Arterial blood gas analysis data captured on admission, clinical characteristics, risk factors, laboratory data, imaging findings, and in-hospital mortality were compared between survivors and non-survivors. The area under the receiver operating characteristic curve (AUC) for in-hospital mortality based on the PaO2/FiO2 value was determined, and the association between the parameters and in-hospital mortality was analyzed by using logistic regression analysis. Results Body mass index, history of cancer, PaO2/FiO2 value, pulse rate, cardiac troponin I level, lactate dehydrogenase level, white blood cell count, D-dimer level, and risk stratification measurements differed between survivors and non-survivors. The optimal cutoff value of PaO2/FiO2 for predicting mortality was 265 (AUC = 0.765, P

Haze and mist caused by air quality, weather, and other factors can reduce the clarity and contrast of images captured by cameras, which limits the applications of automatic driving, satellite remote sensing, traffic monitoring, etc. Therefore, the study of image dehazing is of great significance. Most existing unsupervised image-dehazing algorithms rely on a priori knowledge and simplified atmospheric scattering models, but the physical causes of haze in the real world are complex, resulting in inaccurate atmospheric scattering models that affect the dehazing effect. Unsupervised generative adversarial networks can be used for image-dehazing algorithm research; however, due to the information inequality between haze and haze-free images, the existing bi-directional mapping domain translation model often used in unsupervised generative adversarial networks is not suitable for image-dehazing tasks, and it also does not make good use of extracted features, which results in distortion, loss of image details, and poor retention of image features in the haze-free images. To address these problems, this paper proposes an end-to-end one-sided unsupervised image-dehazing network based on a generative adversarial network that directly learns the mapping between haze and haze-free images. The proposed feature-fusion module and multi-scale skip connection based on residual network consider the loss of feature information caused by convolution operation and the fusion of different scale features, and achieve adaptive fusion between low-level features and high-level features, to better preserve the features of the original image. Meanwhile, multiple loss functions are used to train the network, where the adversarial loss ensures that the network generates more realistic images and the contrastive loss ensures a meaningful one-sided mapping from the haze image to the haze-free image, resulting in haze-free images with good quantitative metrics and visual effects. The experiments demonstrate that, compared with existing dehazing algorithms, our method achieved better quantitative metrics and better visual effects on both synthetic haze image datasets and real-world haze image datasets.

Motion blur recovery is a common method in the field of remote sensing image processing that can effectively improve the accuracy of detection and recognition. Among the existing motion blur recovery methods, the algorithms based on deep learning do not rely on a priori knowledge and, thus, have better generalizability. However, the existing deep learning algorithms usually suffer from feature misalignment, resulting in a high probability of missing details or errors in the recovered images. This paper proposes an end-to-end generative adversarial network (SDD-GAN) for single-image motion deblurring to address this problem and to optimize the recovery of blurred remote sensing images. Firstly, this paper applies a feature alignment module (FAFM) in the generator to learn the offset between feature maps to adjust the position of each sample in the convolution kernel and to align the feature maps according to the context; secondly, a feature importance selection module is introduced in the generator to adaptively filter the feature maps in the spatial and channel domains, preserving reliable details in the feature maps and improving the performance of the algorithm. In addition, this paper constructs a self-constructed remote sensing dataset (RSDATA) based on the mechanism of image blurring caused by the high-speed orbital motion of satellites. Comparative experiments are conducted on self-built remote sensing datasets and public datasets as well as on real remote sensing blurred images taken by an in-orbit satellite (CX-6(02)). The results show that the algorithm in this paper outperforms the comparison algorithm in terms of both quantitative evaluation and visual effects.

Fourier transform spectroscopy with bandpass sampling (BPS-FTS) increases the interferogram sampling step and reduces sampling frequency, thereby reducing system implementation difficulty. However, this technology has only been introduced in principle and lacks analysis of the actual system error. In this paper, we present a theoretical analysis of the optical system diffraction effect, derive the bandpass spectrum bandwidth and interferogram sampling step under the influence of the diffraction effect, and conduct modeling and a simulation analysis with and without the diffraction effect. Through simulation, we found that when the diffraction effect was considered, the relative deviation of the recovered spectrum from the input spectrum was 1.30%, and the spectral similarity was 0.9669. However, without considering the diffraction effect, the relative deviation of the recovered spectrum from the input spectrum was 6.23%, and the spectral similarity was only −0.0056. The simulation results demonstrate that the diffraction effect has a significant influence on BPS-FTS interferogram sampling and recovery spectra. The results of this study have reference significance for their technical realization of BPS-FTS.

To address the problems of convolutional neural networks (CNNs) consuming more hardware resources (such as DSPs and RAMs on FPGAs) and their accuracy, efficiency, and resources being difficult to balance, meaning they cannot meet the requirements of industrial applications, we proposed an innovative low-bit power-of-two quantization method: the global sign-based network quantization (GSNQ). This method involves designing different quantization ranges according to the sign of the weights, which can provide a larger quantization-value range. Combined with the fine-grained and multi-scale global retraining method proposed in this paper, the accuracy loss of low-bit quantization can be effectively reduced. We also proposed a novel convolutional algorithm using shift operations to replace multiplication to help to deploy the GSNQ quantized models on FPGAs. Quantization comparison experiments performed on LeNet-5, AlexNet, VGG-Net, ResNet, and GoogLeNet showed that GSNQ has higher accuracy than most existing methods and achieves “lossless” quantization (i.e., the accuracy of the quantized CNN model is higher than the baseline) at low-bit quantization in most cases. FPGA comparison experiments showed that our convolutional algorithm does not occupy on-chip DSPs, and it also has a low comprehensive occupancy in terms of on-chip LUTs and FFs, which can effectively improve the computational parallelism, and this proves that GSNQ has good hardware-adaptation capability. This study provides theoretical and experimental support for the industrial application of CNNs.

Background: Sphingomyelin synthase 1 (SMS1) has been reported to participate in hepatitis and atherosclerosis. However, its role in autoimmune response is not clear. This study investigates the possible involvement of SMS1 in B-cell activation and lupus-like autoimmunity. Methods: SMS1 knockout lupus-like animal model and SLE patient samples were utilized. B-cell activation and associated signal transduction were detected by flow cytometry, confocal analysis and western blotting. The SMS1 expression in B cells was measured by real-time qPCR. Findings: SMS1 deficiency suppressed B-cell activation in culture, which was restored by exogenous SM supplementation. The BCR-mediated early signal transduction including the colocalization of BCR with F-actin or pY/pBtk, and the phosphorylation of intracellular Fyn and Syk were impaired in SMS1 knockout B cells. Furthermore, SMS1 knockout mice showed reduced production and deposition of autoantibodies, accompanied by less severe kidney pathological changes after pristane induction. SMS1 deficiency also displayed lower autoantibody titers and 24 h urine protein excretion in bm12-induced lupus, which were associated with reduced B-cell activation. Adoptively transferred wide-type B cells partially recovered B-cell activation and autoantibody production in SMS1 deficient bm12-induced lupus mice. Moreover, the SMS1 mRNA level in B cells of SLE patients was increased and positively correlated with the serum anti-dsDNA level, IgG and globulin titers. Interpretation: These data suggest that SMS1 is involved in lupus-like autoimmunity via regulating BCR signal transduction and B cell activation. (Word count for the abstract: 230). Keywords: Systemic lupus erythematosus, Sphingomyelin synthase 1, B cell, B cell receptor

Metal substrates are widely used in engineering production. However, material life reduction and economic loss due to chemical and electrochemical corrosion are a major problem facing people. Electrochemical corrosion is the main corrosion mode of metals, such as seawater corrosion. It is found that the superhydrophobic surface treated by laser texturing plays an important role in the corrosion resistance of the substrate, with the laser texturing process and post-treatment affecting the corrosion resistance. The corrosion resistance is positively correlated with the superhydrophobic property of the surface. For the mechanism of corrosion resistance, this paper summarizes the effect of micro-nano structure, surface-modified coating, oxidation layer or new product layer, surface inhomogeneity, crystal structure, and slippery surface on corrosion resistance. Superhydrophobic surface and slippery surface are two common types of bioinspired, special wetting surfaces. In order to prepare better superhydrophobic and corrosion-resistant surfaces, this paper summarizes the selection and optimization of laser parameters, surface structure, processing media, and post-treatment from the point of view of mechanism and law. In addition, after summarizing the corrosion resistance mechanism, this paper introduces a series of characterization experiments that can measure the corrosion resistance, providing a reference for preparation and evaluation of the surface.

As transcriptional co-activator of AP-1/Jun, estrogen receptors and NF-κB, nuclear protein RBM39 also involves precursor mRNA (pre-mRNA) splicing. Porcine reproductive and respiratory syndrome virus (PRRSV) causes sow reproductive disorders and piglet respiratory diseases, which resulted in serious economic losses worldwide. In this study, the up-regulated expression of RBM39 and down-regulated of inflammatory cytokines (IFN-β, TNFα, NF-κB, IL-1β, IL-6) were determined in PRRSV-infected 3D4/21 cells, and accompanied with the PRRSV proliferation. The roles of RBM39 altering phosphorylation of c-Jun to inhibit the AP-1 pathway to promote PRRSV proliferation were further verified. In addition, the nucleocytoplasmic translocation of RBM39 and c-Jun from the nucleus to cytoplasm was enhanced in PRRSV-infected cells. The three RRM domain of RBM39 are crucial to support the proliferation of PRRSV. Several PRRSV RNA (nsp4, nsp5, nsp7, nsp10-12, M and N) binding with RBM39 were determined, which may also contribute to the PRRSV proliferation. Our results revealed a complex mechanism of RBM39 by altering c-Jun phosphorylation and nucleocytoplasmic translocation, and regulating binding of RBM39 with viral RNA to prompt PRRSV proliferation. The results provide new viewpoints to understand the immune escape mechanism of PRRSV infection.

High mobility group box 1 protein (HMGB1) is known to be a trigger of inflammation in experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis (MS). However, it may play a different role in some way. Here we investigated the effect of HMGB1 on promoting sonic hedgehog (shh) release from astrocytes as well as the possible signal pathway involved in it. Firstly, shh increased in astrocytes after administration of recombinant HMGB1 or decreased after HMGB1 was blocked when stimulated by homogenate of the onset stage of EAE. Moreover, the expression of HMGB1 receptors, toll-like receptor (TLR) 2 and receptor for advanced glycation end products (RAGE) increased after HMGB1 administration in primary astrocytes. However, the enhancing effect of HMGB1 on shh release from astrocytes was suppressed only after RAGE was knocked out or blocked. Mechanistically, HMGB1 functioned by activating RAGE-mediated JNK, p38, stat3 phosphorylation. Moreover, HMGB1 could induce shh release in EAE. Additionally, intracerebroventricular injection of recombinant shh protein on the onset stage of EAE alleviated the progress of disease and decreased demylination, compared to the mice with normal saline treatment. Overall, HMGB1 promoted the release of shh from astrocytes through signal pathway JNK, p38 and stat3 mediated by receptor RAGE, which may provide new insights of HMGB1 function in EAE.

Evidences have suggested that Sjogren's syndrome (SS) is associated with viral infection. The aim of this study was to investigate the involvement of respiratory viral poly(I:C) in the pathogenesis of SS and potential mechanisms using a SS-like NOD/ShiLtJ (NOD) mouse model. 5-week female NOD mice were intratracheally administered poly(I:C) every other day for 5 times to mimic viral infection. Pilocarpine induced saliva secretion was determined every 8 days. Submandibular glands (SMG) and lungs were harvested for the detection of pathological changes. We found that intratracheal administration of poly(I:C) significantly advanced and enhanced the reduction of saliva flow rate in NOD mice. Furthermore, poly(I:C) treatment aggravated the histopathological lesions and inflammatory cells infiltration in SMG. Accompanied by elevated expression of IFN cytokines and IL-33, Th1 activation was enhanced in SMG of poly(I:C)-treated NOD mice, but Th17 cells activation was unchanged among the groups. In addition, intratracheal poly(I:C) exposure promoted the expression of IL-33 and increased T cells proportion in the lung, which were consistent with the change in SMG. Therefore, intratracheal poly(I:C) exposure aggravated the immunological and function disorder of SMG in NOD mice.

Cardiac allograft vasculopathy (CAV) charactered with aberrant remodeling and fibrosis usually leads to the loss of graft after heart transplantation. Our previous work has reported that extracellular high-mobility group box 1 (HMGB1) participated in the CAV progression via promoting inflammatory cells infiltration and immune damage. The aim of this study was to investigate the involvement of HMGB1 in the pathogenesis of CAV/fibrosis and potential mechanisms using a chronic cardiac rejection model in mice. We found high levels of transforming growth factor (TGF)-β1 in cardiac allografts after transplantation. Treatment with HMGB1 neutralizing antibody markedly prolonged the allograft survival accompanied by attenuated fibrosis of cardiac allograft, decreased fibroblasts-to-myofibroblasts conversion, and reduced synthesis and release of TGF-β1. In addition, recombinant HMGB1 stimulation promoted release of active TGF-β1 from cardiac fibroblasts and macrophages in vitro, and subsequent phosphorylation of Smad2 and Smad3 which were downstream of TGF-β1 signaling. These data indicate that HMGB1 contributes to the CAV/fibrosis via promoting the activation of TGF-β1/Smad signaling. Targeting HMGB1 might become a new therapeutic strategy for inhibiting cardiac allograft fibrosis and dysfunction.

The role of IL-33/ST2 signaling in cardiac allograft vasculopathy (CAV) is not fully addressed. Here, we investigated the role of IL-33/ST2 signaling in allograft or recipient in CAV respectively using MHC-mismatch murine chronic cardiac allograft rejection model. We found that recipients ST2 deficiency significantly exacerbated allograft vascular occlusion and fibrosis, accompanied by increased F4/80+ macrophages and CD3+ T cells infiltration in allografts. In contrast, allografts ST2 deficiency resulted in decreased infiltration of F4/80+ macrophages, CD3+ T cells and CD20+ B cells and thus alleviated vascular occlusion and fibrosis of allografts. These findings indicated that allografts or recipients ST2 deficiency oppositely affected cardiac allograft vasculopathy/fibrosis via differentially altering immune cells infiltration, which suggest that interrupting IL-33/ST2 signaling locally or systematically after heart transplantation leads different outcome.

Aiming at the motion blur restoration of large-scale dual-channel space-variant images, this paper proposes a dual-channel image deblurring method based on the idea of block aggregation, by studying imaging principles and existing algorithms. The study first analyzed the model of dual-channel space-variant imaging, reconstructed the kernel estimation process using the side prior information from the correlation of the two-channel images, and then used a clustering algorithm to classify kernels and restore the images. In the kernel estimation process, the study proposed two kinds of regularization terms. One is based on image correlation, and the other is based on the information from another channel input. In the image restoration process, the mean-shift clustering algorithm was used to calculate the block image kernel weights and reconstruct the final restored image according to the weights. As the experimental section shows, the restoration effect of this algorithm was better than that of the other compared algorithms.

Environmental pollution and clean water production are challenges to the development of human society. In this paper, devices consisting of a superhydrophobic Ni-CoFe2O4 foam layer (floating layer), a hydrophilic channel and a superhydrophilic Ni-CoFe2O4 foam layer (photothermal conversion layer) were designed. The light energy was converted into heat on the photothermal layer, for which the hydrophilic channel provided a small amount of water. The superhydrophobic layer ensured the floating and selective adsorption of organic solvents on the water surface, whose contact angle reached 157°, and the steam production rate reached 1.68 kg·m−2·h−1. Finally, the LSV curve demonstrated that the Ni-CoFe2O4 foam prepared had a minimum starting potential, achieving the multifunctionality of the Ni foam.

Coal mining, regarded as a high-risk industry, has a strong demand for virtual reality (VR) to fulfill safety and emergency rescue training. In the past ten years, VR technology has significantly improved miner training on both the hardware and software side. However, it still has some drawbacks, such as expensive and unsuitable hardware, lack of satisfactory user experience, without direct browser access, and lack of humanized and intelligent design. To solve these problems, a cloud-based VR system is designed for the training of coal miners in this paper. The system, with browser/client architecture, includes eight modules demonstrating the full procedure of an underground coal mine. The online cloud-rendered video streaming is adopted to provide enough computing and rendering power and hence a better browser-based user experience. Furthermore, game artificial intelligence (AI) is also introduced into the system to increase the emotional exchange between the system and users. Unlike traditional VR training software, this system designs two virtual miners to enhance the experience of trainees. The first virtual miner is a task-oriented non-player-character (NPC) which conveys general knowledge about the mine and guides the users in visiting the underground work sites. The second virtual miner is a disaster-oriented character which prepares the users for typical disasters. The system has been successfully implemented in a laboratory environment, and its performance has been validated. Yet, further practices are needed to stimulate more innovative applications of VR-based miner training and disaster drilling.