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Abstract The linear ubiquitin chain assembly complex (LUBAC) mediates the linear ubiquitination of various proteins and is involved in NF-κB signaling and immune regulation. However, the function and mechanism of linear ubiquitination in regulating oncogenic signaling and tumor growth have remained poorly understood. Herein, we identified Gli proteins, key transcription factors in the Hedgehog (Hh) signaling pathway, as novel substrates of LUBAC. Linear ubiquitination stabilizes Gli proteins, leading to the noncanonical activation of Hh signaling in CRC cells. Furthermore, LUBAC facilitates tumor growth in CRC cells. Additionally, elevated expression of LUBAC components in CRC tissues was observed, and higher expression levels of these components correlated with poor prognosis in CRC patients. Interestingly, inhibition of LUBAC using either a small molecule agonist or RNA silencing specifically suppressed cell growth in CRC cells but had no effect on normal intestinal cells. Taken together, aberrant expression of LUBAC components activates Hh signaling noncanonically by mediating linear ubiquitination, promoting tumor growth in CRC, demonstrating the novel function of linear ubiquitination in regulating the protein stability of its substrates and highlighting the potential of targeting LUBAC as a therapeutic strategy in CRC.

The precise monitoring of sea ice parameters, including sea ice concentration and stage of development, is imperative for tactical navigation. Recent studies have showcased the enhanced mapping accuracy achieved by incorporating multi-source auxiliary data, such as passive microwave data, with Synthetic Aperture Radar (SAR) images. However, there remains a lack of research assessing the impact of individual features on model performance. This paper addresses this knowledge gap through ablation studies and alternate comparisons of data inputs. Building on the success in the AutoIce Challenge, we leverage the AI4Arctic Sea Ice Challenge Dataset to train multitask sea ice mapping models employing a U-Net architecture. Results from cross-validation and testing sets with all season data reveal the significant enhancement in estimation accuracy for all parameters when utilizing most of the AMSR2 channels. Additionally, the incorporation of time and location information as ancillary channels further amplifies the classification accuracy of all major ice types. Furthermore, among the various available ERA5 weather parameters, the inclusion of wind speed data proves effective in mitigating misclassifications in ice regions, particularly under melting scenarios. The paper culminates with a feature importance ranking table encompassing all available features, providing valuable guidance for the selection of pertinent data inputs. This comprehensive comparative study not only contributes to advancing sea ice mapping methodologies but also offers valuable insights into the nuanced impact of individual features on model performance.

With the deepening of the integration of power system and communication network.The failure of the power communication network often leads to a cascading failure of the power system. Evaluating the potential risks in the power communication network is crucial to ensure the stable operation of the power system. This paper proposes a comprehensive risk assessment method for qualitative and quantitative measures. Firstly, an index system for risk assessment, considering both static structure and dynamic operation, is established. The optimal weight of each index is determined using the combination game weighting method. Additionally, a risk assessment model for the power communication network is established by coupling gray cloud theory and matter-element extension model. The fuzzy theory’s triangular modulus operator is utilized to fuse three types of fault probabilities, quantify the risk value of the power communication network, and assign it a grade. Finally, experimental results demonstrate that this method improves discrimination degree by 1.2%, 92%, and 85% respectively compared to other traditional algorithms.

Taiping Houkui (TPHK) is prevalent green tea in China, its flavor quality is significantly influenced by different production regions. However, the key flavor compounds responsible for these discrepancies remain unclearly. Here, TPHK samples were produced from fresh leaves of ‘Shidacha 2’ cultivar planted in 14 distinct production regions. In 14 TPHK samples, a total of 33 non-volatile compounds were identified and quantified. Partial least-squares discriminant analysis (PLS-DA) reveal that theanine and glutamate were the main umami compounds, caffeine imparted with bitterness, which collectively contributed to the variation in the taste flavor of TPHK across different production regions. Furthermore, the profiles of 51 volatile compounds were determined, integrated PLS-DA with odor activity values of volatiles indicated that linalool (165.7–888.5) and geraniol (11.9–141.4) affecting the floral aroma of TPHK among different production regions. Our findings revealed the critical compounds that contributed to the effect of production regions on flavor quality of TPHK.

Exploring the symbiotic potential between fungal and yeast species, this study investigates the co-cultivation dynamics of Monascus, a prolific producer of pharmacologically relevant secondary metabolites, and Wickerhamomyce anomalous. The collaborative interaction between these microorganisms catalyzed a substantial elevation in the biosynthesis of secondary metabolites, prominently Monacolin K and natural pigments. Central to our discoveries was the identification and enhanced production of oxylipins (13S-hydroxyoctadecadienoic acid，13S-HODE), putative quorum-sensing molecules, within the co-culture environment. Augmentation with exogenous oxylipins not only boosted Monacolin K production by over half but also mirrored morphological adaptations in Monascus, affecting both spores and mycelial structures. This augmentation was paralleled by a significant upregulation in the transcriptional activity of genes integral to the Monacolin K biosynthetic pathway, as well as genes implicated in pigment and spore formation. Through elucidating the interconnected roles of quorum sensing, G-protein-coupled receptors, and the G-protein-mediate signaling pathway, this study provides a comprehensive view of the molecular underpinnings facilitating these metabolic enhancements. Collectively, our findings illuminate the profound influence of Wickerhamomyces anomalous co-culture on Monascus purpureus, advocating for oxylipins as a pivotal quorum-sensing mechanism driving the observed symbiotic benefits.

Satellite-derived Chlorophyll-a concentration (Chla) time series products are essential for large-scale marine environmental monitoring. However, the plenty of missing pixels in current satellite Chla products severely hinder their applications for marine research, due to cloud contamination, solar glint, and unfavorable observation conditions. This study proposed a Chla time series gap-filling method for MODIS 8-day composite Chla product by integrating spatiotemporal information (STGF). This method employed spatially neighboring pixels with similar temporal variation to fill the missing values in time series, without involving training or auxiliary data. The performance of the STGF is assessed quantitatively and qualitatively. The correlation coefficients (CC) between gap-filled data and actual observations for years of 2004, 2010, 2016, and 2022 across the entire study area are all greater than 0.97. The mean absolute percentage error (MAPE) and root mean square error (RMSE) are less than 16.1 % and 0.233 mg/m3, respectively. The proposed STGF outperformed linear interpolation and the DINEOF algorithm from both spatial and temporal perspectives, suggesting the effectiveness of STGF in handling continuous data gaps and capturing detailed Chla variation patterns, especially in regions with significant variability. The findings suggest that the proposed STGF method offers a viable alternative for filling missing values in Chla time series data. This supports the demand for long-term, large-scale, and high-coverage ocean color remote sensing data in marine environmental studies.

Point cloud segmentation is one of the most important tasks in LiDAR remote sensing with widespread scientific, industrial, and commercial applications. The research thereof has resulted in many breakthroughs in 3D object and scene understanding. Existing methods typically utilize hierarchical architectures for feature representation. However, the commonly used sampling and grouping methods in hierarchical networks are not only time-consuming but also limited to point-wise 3D coordinates, ignoring the local semantic homogeneity of point clusters. To address these issues, we propose a novel 3D point cloud representation network, called Dynamic Clustering Transformer Network (DCTNet). It has an encoder–decoder architecture, allowing for both local and global feature learning. Specifically, the encoder consists of a series of dynamic clustering-based Local Feature Aggregating (LFA) blocks and Transformer-based Global Feature Learning (GFL) blocks. In the LFA block, we propose novel semantic feature-based dynamic sampling and clustering methods, which enable the model to be aware of local semantic homogeneity for local feature aggregation. Furthermore, instead of traditional interpolation approaches, we propose a new semantic feature-guided upsampling method in the decoder for dense prediction. To our knowledge, DCTNet is the first work to introduce semantic information-based dynamic clustering into 3D Transformers. Extensive experiments on an object-based dataset (ShapeNet), and an airborne multispectral LiDAR dataset demonstrate the State-of-the-Art (SOTA) segmentation performance of DCTNet in terms of both accuracy and efficiency. Our code will be made publicly available.

Objective. This study aims to evaluate a novel prone position ventilation device designed to enhance patient safety, improve comfort, and reduce adverse events, facilitating prolonged tolerance in critically ill patients. Methods. A randomized controlled trial was conducted on 60 critically ill patients from January 2020 to June 2023. Of which, one self-discharged during treatment and another was terminated due to decreased oxygenation, leaving an effective sample of 58 patients. Patients were allocated to either a control group receiving traditional prone positioning aids (ordinary sponge pads and pillows) or an intervention group using a newly developed adjustable prone positioning device. A subset of patients in each group also received life support technologies such as extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT). We assessed prone position ventilation tolerance, oxygen saturation increments postintervention, duration of prone positioning, CRRT filter lifespan, and the incidence of adverse events. Results. The intervention group exhibited significantly longer average tolerance to prone positioning (16.6 hours vs. 8.3 hours, P

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This study addressed enamel demineralization, a common complication in fixed orthodontic treatment, by evaluating a novel orthodontic adhesive with DMAHDM-PCL composite fibers. These fibers, produced through electrospinning, were incorporated into orthodontic adhesive to create experimental formulations at different concentrations and a control group. The study assessed antimicrobial properties, biosafety, and mechanical characteristics. New orthodontic adhesive exhibited significant bacteriostatic effects, reducing bacterial biofilm activity and concentrations. Incorporating 1% and 3% DMAHDM-PCL did not affect cytocompatibility. Animal tests confirmed no inflammatory irritation. Shear bond strength and adhesive residual index results indicated that antimicrobial fibers didn't impact bonding ability. In conclusion, orthodontic adhesives with 3% DMAHDM-PCL fibers are potential antimicrobial bonding materials, offering a comprehensive solution to enamel demineralization in orthodontic patients.

PurposeWith the advancement in early diagnosis and treatment, the prognosis for individuals diagnosed with breast cancer (BC) has improved significantly. The prognosis of primary breast cancer (PBC) survivors can be significantly influenced by the occurrence of colorectal cancer (CRC) as a secondary primary cancer (SPC). The objective of this study is to explore the possible genetic association between PBC and CRC, aiming to lay a groundwork for the development of preventive strategies against SPC-CRC following BC surgery.MethodsWe employed a bidirectional two-sample Mendelian randomization (MR) approach to thoroughly examine genetic instrumental variables (IVs) derived from genome-wide association studies (GWAS) conducted on PBC and CRC. And applied inverse variance weighted (IVW) and multiple other MR methods (weighted median, simple median, MR-PRESSO and MR-RAPS) to evaluate the association between the two cancers (PBC and CRC) at genetic level. Furthermore, the robustness of the findings was further confirmed through the utilization of the genetic risk score (GRS) method in a secondary analysis.ResultsForward MR analysis, a total of 179 BC genetic IVs, 25 estrogen receptor-negative (ER-) genetic IVs and 135 ER-positive (ER+) genetic IVs were screened. Reverse MR analysis, 179 genetic IVs of CRC, 25 genetic IVs of colon cancer, 135 genetic IVs of rectal cancer, 25 genetic IVs of left colon cancer and 135 genetic IVs of right colon cancer were screened. IVW and other MR methods found no significant genetic association between PBC and CRC (P > 0.05). Subgroup analysis also showed that ER- BC and ER+ BC were not correlated with the occurrence of CRC (P > 0.05). The findings of the secondary analysis using GRS were consistent with those obtained from the primary analysis, thereby confirming the robustness and reliability of this study.ConclusionsOur findings do not provide any evidence supporting the association between PBC and CRC at the genetic level. Further large-scale prospective studies are warranted to replicate our findings.

As an extensively employed plasticizer in industrial applications, di-2-ethylhexyl phthalate (DEHP) can induce apoptosis of mouse Leydig cells, yet the precise mechanism remains elusive. In the current study, we identified that DEHP could specially induced apoptosis in the Leydig cells of the testis tissue, accompanied with the upregulation of apoptosis-related protein in the TGF-β signaling pathway (ARTS) in the cells. Overexpression of ARTS significantly induced apoptosis of TM3 cells, while knockdown of ARTS inhibited apoptosis. Furthermore, DEHP-induced apoptosis of TM3 cells could be alleviated by knockdown of ARTS, which indicated that ARTS was involved in DEHP-induced apoptosis of mouse Leydig cells. Bioinformation assay predicts that there are four potential p53-responsive elements (p53-REs) located at − 6060, − 5726, − 5631 and − 5554 before the transcription start site of ARTS gene, implying that gene transcription of ARTS could be regulated by p53. Interestingly, DEHP was shown to specifically upregulate the expression of p53 in the Leydig cells of the testis tissue and TM3 cells. Consistently, p53 was proved to bind to the RE4 site of the ARTS gene promoter and transcriptionally activated the promoter-driven expression of the luciferase reporter gene. Overexpression of p53 could induce apoptosis of TM3 cells; while knockdown of p53 could not only rescue DEHP-induced apoptosis of the cells, but also inhibit DEHP-caused upregulation of ARTS. Meanwhile, we showed that oxidative stress could induce apoptosis of TM3 cells, accompanied with the increased protein levels of p53 and ARTS; while inhibition of oxidative stress dramatically alleviated DEHP-induced apoptosis and the up-regulation of p53 and ARTS. Taken together, these results indicated that DEHP-induced oxidative stress activates the p53-ARTS cascade to promote apoptosis of mouse Leydig cells.

Most strawberry plants have white flowers and red fruit. We developed a new strawberry selection with pink flowers and white fruit, and named it G23. Basic phenotypic data were recorded over years of observation and experimentation with the flower crown diameter, petal color, and rate of fruit set, as well as fruit skin color, flesh color, seed color and attachment status, fruit weight and shape, soluble solids contents, and firmness. We found that G23 bloomed with a stable pink flower and produced white fruit consistently with a relatively high fruit-set rate compared with its female parent, ‘Pink Panda’. G23 displayed high resistance to Fusarium wilt (Fusarium oxysporum) and anthracnose (Colletotrichum spp.). It is also tolerant of high temperatures (up to 40 °C) and long-term drought. The asexual propagation ability of G23 is high, with ∼60 to 100 stolon ramets formed during the summer. In summary, this new pink-flowered and white-fruited strawberry germplasm is suitable for ornamental use, as a result of its remarkable flowering and fruiting characteristics. In addition, it provides opportunities for innovative strawberry germplasm for future breeding.

Natural fiber-reinforced geopolymer (GP) is gaining popularity in the construction and building materials industries due to its natural renewability, accessibility, low cost, and excellent mechanical properties. In light of this, an investigation was conducted on the effect of bamboo fiber (BF) loading and fiber pretreatment methods on final composites. Mechanical tests showed that compared to pure GP, the compressive strength and flexural strength of GP composites were slightly negatively affected by the addition of either untreated or alkali-treated BFs. However, the loading of KH550 significantly improved the mechanical properties of GP with the addition of alkali-treated BFs more than untreated BFs. Scanning electron microscopy (SEM) analysis illustrated that the employment of KH550 effectively improved the interfacial performance between the BF and GP matrix. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) tests demonstrated the successful synthesis of GP and that the addition of KH550 had little effect on the main crystal structure and chemical composition of GP. This study provides a new method of bamboo fiber preparation and the feasibility of applying it to reinforce GP.

Monitoring sea ice in the Arctic region is crucial for polar maritime activities. The Canadian Ice Service (CIS) wants to augment its manual interpretation with machine learning-based approaches due to the increasing data volume received from newly launched synthetic aperture radar (SAR) satellites. However, fully supervised machine learning models require large training datasets, which are usually limited in the sea ice classification field. To address this issue, we propose a semi-supervised interactive system to classify sea ice in dual-pol RADARSAT-2 imagery using limited training samples. First, the SAR image is oversegmented into homogeneous regions. Then, a graph is constructed based on the segmentation results, and the feature set of each node is characterized by a convolutional neural network. Finally, a graph convolutional network (GCN) is employed to classify the whole graph using limited labeled nodes automatically. The proposed method is evaluated on a published dataset. Compared with referenced algorithms, this new method outperforms in both qualitative and quantitative aspects.

Histamine, a bioactive component in certain foods such as Huangjiu has been associated with liver injury and disrupted intestinal balance. This study explored the potential therapeutic effects of fucoidan (FCD) in mitigating histamine-induced imbalances in mice. We found that FCD mitigated liver injury, reducing transaminases, oxidative stress, and inflammation. Histological improvements included decreased cell infiltration and necrosis. FCD restored tight junction proteins and suppressed inflammation-related genes. Western blot analysis revealed FCD’s impact on TGF-β1, p-AKT, AKT, CYP2E1, Grp78, NLRP3, Cas-1, and GSDMD. Gut LPS levels decreased with FCD. Gut microbiota analysis showed FCD’s modulation effect, reducing Firmicutes and increasing Bacteroides. FCD demonstrates potential in alleviating histamine-induced liver injury, regulating inflammation, and influencing gut microbiota. Further research exploring higher dosages and additional parameters is warranted.

As one of the most important phthalates, di-isononyl phthalate (DINP) has been widely used as a common plasticizer in the food and personal care products sectors. In our previous study, we found that DINP can induce autophagy of ovarian granulosa cells; while the underlying mechanism is unclear. In the study, we showed that DINP exposure could induce autophagy of ovarian granulosa cells and KGN cells, accompanied with the increase in the mRNA and protein level of DDIT4. Furthermore, overexpression of DDIT4 were shown to induce autophagy of KGN cells; while knockdown of DDIT4 inhibited DINP-induced autophagy, implying that DDIT4 played an important role in DINP-induced autophagy of ovarian granulosa cells. There were three putative binding sites of transcription factor ATF4 in the promoter region of DDIT4 gene, suggesting that DDIT4 might be regulated by ATF4. Herein, we found that overexpression of ATF4 could upregulate the expression of DDIT4 in KGN cells, while knockdown of ATF4 inhibited its expression. Subsequently, ATF4 was identified to bind to the promoter region of DDIT4 gene and promote its transcription. The expression of ATF4 was also increased in the DINP-exposed granulosa cells, and ATF4 overexpression promoted autophagy of KGN cells; whereas knockdown of ATF4 alleviated DINP-induced upregulation of DDIT4 and autophagy of the cells. Taken together, DINP triggered autophagy of ovarian granulosa cells through activating ATF4/DDIT4 signals.

Abstract The polymer/wood functional products may not be true nanocomposites due to the poor permeability of non-polar monomers into the hydrophilic wood cell wall. In this paper, methyl methacrylate (MMA) chosen as the representative non-polar monomer was used to impregnate wood with different lignin removal and then polymerized in situ in delignified wood to obtain polymethyl methacrylate/delignified wood (PMMA/DW). The larger 10 nm mesopores reduction in PMMA/DW-29.9 and the disappearance of the smaller 3 nm mesopores of PMMA/DW-51.7 indicated that the removal of lignin was beneficial for the penetration of MMA in the wood cell wall. When the lignin removal reached between 29.9 and 51.7%, the dimensional stability of PMMA/DW was improved, and it could be speculated that the resin began to enter the wood cell wall at this stage. In addition, the indentation modulus and hardness of the PMMA/DW cell wall were significantly increased when the lignin removal reached 29.9%. This result was consistent with the conclusion of the pore size analysis, which further confirmed that the nanopores of the cell wall were filled with PMMA when the lignin removal was greater than 29.9%. The results of this paper indeed provide a basis for the design of biomass functional nanocomposites.

Hyperspectral image (HSI) classification is a difficult task due to the heterogeneous spatial–spectral information, high-dimensionality, and noise effect in the HSI. Lately, an enhanced convolutional approach, i.e., ConvNeXt, has demonstrated a stronger feature representation capability than the popular vision transformer approaches. This article presents a spatial–spectral ConvNeXt approach, called SS-ConvNeXt, for hyperspectral classification. To better learn the spatial and spectral information in the HSI, the Spatial-ConvNeXt block, Spectral-ConvNeXt block, and spectral projection module are, respectively, designed. The depthwise and pointwise convolutions are adopted to reduce the model size and prevent vanishing gradient. The proposed model is evaluated against 14 other state-of-the-art methods on four different HSI datasets. Moreover, extensive ablation studies are conducted to investigate the roles of building blocks in the proposed model. The results demonstrate that the proposed method not only can achieve a high classification accuracy but also can better preserve class boundaries and reduce within-class noise.

Each of the three satellites constituting the RADARSAT Constellation Mission (RCM) provides compact polarimetric synthetic aperture radar (CP SAR) data. The complex CP data have similar properties to the complex quad polarimetric (QP) data provided by prior RADARSAT missions. In this article, a land cover classification method using spatial information is designed based on the statistical characteristics of the complex CP and QP SAR data. First, the local spatial dependency among pixels is captured by superpixels. Second, a graph is constructed on the superpixels to model the global spatial dependency among superpixels. The land cover classification image with land cover type labels is then estimated by propagating labels from the few labeled superpixels to the unlabeled superpixels. Classification of two RCM complex CP and QP scenes demonstrates that the proposed method, with few labeled pixels, provides much higher classification accuracy than methods that do not exploit global spatial dependency.

The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model’s efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model’s mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R2) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model’s predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences.

Abstract Rapid and eco‐friendly preparation of biomedical materials is significant for practical applications. This study proposes a simple two‐step method for preparing silver nanoparticles (AgNPs)‐functionalized gauze dressings (Ag‐Q‐G) with antibacterial activity in a green and time‐efficient manner that requires

A wave of Omicron infections rapidly emerged in China in 2022, but large-scale data concerning the safety profile of vaccines and Coronavirus disease 2019 (COVID-19) infection features in liver transplant (LT) recipients have not been collected. Therefore, the aim of this study was to assess the protectiveness and safety profile of the inactivated vaccines in LT patients against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant infections. A multi-centre retrospective study was conducted in a cohort with a history of liver transplantation. A total of 1881 participants (487 vaccinated and 1394 unvaccinated patients) were enrolled from seven centres in China. Fourteen of the participants were infected by Omicron, and 50% patients had over 14 days of viral shedding duration. The protection rate of COVID-19 vaccinations to Omicron was 2.59%. The three breakthrough infections occurred more than 6 months after fully vaccinated. A total of 96 (19.7%) vaccinated patients had adverse events, including fatigue, myalgia, liver dysfunction, swelling, and scleroma. There were more Grade 3 adverse events in the preoperative vaccination group than those in the postoperative vaccination group. Inactivated whole-virion SARS-CoV-2 vaccines are safe in patients with post-liver transplantation. The efficacy of inactivated vaccines decreases after 6 months of vaccination, it is recommended that liver transplant patients get boosted vaccinations as early as possible even when they are fully vaccinated. Although clinical manifestations of Omicron infections were mild in LT patients, unvaccinated patients might have a higher risk of liver dysfunction during infections.

Abstract Background Continuous glucose monitoring systems have been widely used but discrepancies among various brands of devices are rarely discussed. This study aimed to explore differences in glycemic metrics between FreeStyle Libre (FSL) and iPro2 among adults with type 1 diabetes mellitus (T1DM). Methods Participants with T1DM and glycosylated hemoglobin of 7%–10% were included and wore FSL and iPro2 for 2 weeks simultaneously. Datasets collected on the insertion and detachment day, and those with insufficient quantity (

Lipase has great application potential in hydrolyzing residual yolk lipid in egg white liquid to restore its functional properties. In this study, a lipase gene from Bacillus subtilis was expressed in E. coli BL21 (DE3) and named Lip-IM. Results showed that although Lip-IM has stronger specificity for medium- and short-chain substrates than long-chain substrates (C16, C18), due to its excellent enzyme activity, it also has strong hydrolysis activity for long-chain substrates and maintained over 80% activity at 4–20 °C, but significantly reduced when the temperature exceeds 40 °C. The addition of 0.5% Lip-IM enhanced foaming ability by 26% (from 475 to 501%) and reduced liquid precipitation rate by 9% (from 57 to 48%). Furthermore, molecular dynamics (MD) simulations were run to investigate the conformational stability of Lip-IM at different temperatures. Results showed that Lip-IM maintained a stable conformation within the temperature range of 277–303 K. Fluctuations in the flexible area and backbone movement of proteins were identified as the main reasons for its poor thermal stability.

Although the compact polarimetric (CP) synthetic aperture radar (SAR) mode of the RADARSAT Constellation Mission (RCM) offers new opportunities for oil spill candidate detection, there has not been an efficient machine learning model explicitly designed to utilize this new CP SAR data for improved detection. This paper presents a conditional random field model based on the Wishart mixture model (CRF-WMM) to detect oil spill candidates in CP SAR imagery. First, a “Wishart mixture model” (WMM) is designed as the unary potential in the CRF-WMM to address the class-dependent information of oil spill candidates and oil free water. Second, we introduce a new similarity measure based on CP statistics designed as a pairwise potential in the CRF-WMM model so that pixels with strong spatial connections have the same class label. Finally, we investigate three different optimization approaches to solve the resulting maximum a posterior (MAP) problem, namely iterated conditional modes (ICM), simulated annealing (SA), and graph cuts (GC). The results show that our proposed CRF-WMM model can delineate oil spill candidates better than the traditional CRF approaches, and that the GC algorithm provides the best optimization.

ObjectiveTo analyze the application value of two postural fixation techniques.(styrofoam combined with head mask and fixed headrest combined with head mask) in intracranial tumor radiotherapy via cone beam computed tomography (CBCT).MethodsThis study included 104 patients with intracranial tumors undergoing radiotherapy. The patients were divided into two groups: Group A (54 cases with styrofoam fixation) and Group B (50 cases with fixed headrest fixation). The positional deviation in 3D space between the two groups was compared using CBCT. The set-up errors were expressed as median (25th percentile, 75th percentile)or M(p25, p75) since the set-up errors in all directions were not normally distributed,The Mann-Whitney U test was performed.ResultsThe age and gender of patients in the two groups were not significantly different. The set-up errors of A in lateral (X), longitudinal (Y), vertical (Z), and yaw(Rtn) axes were 1.0 (0,1) mm, 1.0 (0,1) mm, 1.0 (0,2) mm, and 0.4 (0.1, 0.8) degrees, respectively while the set-up errors of B were 1.0 (0,1) mm, 1.0 (1,2) mm, 1.0 (0,2) mm, and 0.5 (0.15,0.9) degrees, respectively. Moreover, patients in the styrofoam group had significantly smaller set-up errors in the Y-axis than patients in the headrest group (p=0.001). However, set-up errors in the X, Z, and Rtn axes were not significantly different between the two groups. The expansion boundaries of the target area in the X, Y, and Z directions were 1.77 mm, 2.45 mm, and 2.47 mm, respectively. The outer expansion boundaries of the headrest group were 2.03 mm, 3.88 mm, and 2.57 mm in X, Y, and Z directions, respectively. The set-up times of groups A and B were (32.71 ± 5.21) seconds and (46.57 ± 6.68) seconds, respectively (p=0.014). Patients in group A had significantly better comfort satisfaction than patients in group B (p=0.001).ConclusionStyrofoam plus head thermoplastic mask body fixation technique has a higher positional accuracy in intracranial tumor radiotherapy than headrest plus head thermoplastic mask fixation. Besides, styrofoam plus head thermoplastic mask body fixation technique is associated with improved positioning efficiency, and better comfort than headrest plus head thermoplastic mask fixation, and thus can be effectively applied for intracranial tumor radiotherapy positioning.

Background: Observational evidence has shown that smoking, alcohol consumption, type 2 diabetes, and body mass index (BMI) are risk factors for peptic ulcer disease (PUD), including gastric ulcer (GU) and duodenal ulcer (DU). However, the observed associations may be confounding factors. Herein, we use Mendelian randomization (MR) to examine causal associations such as smoking, alcohol, type 2 diabetes, BMI, and risks of PUD.Methods: We used 8,17,41,325,82, 231, and 616 identified genetic variants as proxies for age of smoking initiation (AgeSmk), smoking cessation (SmkCes, current/former), number of cigarettes smoked per day (CigDay), smoking status (SmkIni, ever/never), alcohol consumption, type 2 diabetes, and BMI to obtain unconfounded effect estimates on the GU and DU levels among 452,264 participants from the Gene ATLAS. The causal relationship was estimated by using inverse-variance weighted (IVW) as the main method. Sensitivity analysis includes Cochran’s Q test, the MR-Egger test, MR pleiotropy residual sum and outlier (MR-PRESSO), and MR-robust adjusted profile score (MR-RAPS). In addition, secondary MR analysis was conducted within summary data using genetic risk scores (GRSs) as instrumental variables (IVs).Results: In our two-sample MR analyses, genetic predisposition to smoking (SmkInit) and BMI were associated with an increased risk of GU. The beta values were 0.0035 (95% CI, 0.0021, 0.0049, p = 1.56E-06) for smoking (SmkInit) and 0.0021 (95% CI, 0.0009, 0.0033, p = 0.0008) for BMI. Genetic predisposition to smoking (SmkInit) and higher genetically predicted BMI were associated with an increased risk of DU. The beta values of DU were 0.0029 (95% CI, 0.0017, 0.0041, p = 2.43E-06) for smoking (SmkInit) and 0.0018 (95% CI, 0.0007, 0.0029, p = 0.001) for BMI. No other causal association between smoking (AgeSmk, CigDay, and SmkCes), alcohol consumption, type 2 diabetes, and GU or DU was observed. Consistent results were obtained in sensitivity analyses. Furthermore, the GRS approach showed similar results in the several MR methods.Conclusion: These findings do not support a causal role of AgeSmk, CigDay, SmkCes, alcohol consumption, and type 2 diabetes in the development of GU and DU. However, it is confirmed that SmkInit and BMI have a causal part in the development of GU and DU.

Aircraft classification via remote sensing images has many commercial and military applications. The Swin-Transformer has shown great promise, recently dominating general-purpose image classification benchmarks such as ImageNet. In this paper, we test whether the performance of the Swin-Transformer on general-purpose image classification translates to domain-specific aircraft classification using the Multi-Type Aircraft from the Remote Sensing Images dataset. We also investigate the effect of training procedure vs. model selection on the validation score. Our carefully trained Swin-Transformer model achieved an impressive 99.4 % validation set accuracy without super-resolution, and 99.5 % with super-resolution. Moreover, the generalization of models trained on the MTARSI dataset to real-world and synthetic aircraft classification is evaluated with some out-of-distribution samples. Our results demonstrate that the lack of complexity and heterogeneity of the MTARSI dataset, and the labeling errors resulted in models which struggle to achieve high accuracy on the adopted test samples despite near perfect validation scores.

Although Bayesian methods have been very effective for spatial–spectral analysis of hyperspectral imagery (HSI), they had not been fully explored for enhanced subpixel mapping (SPM) by simultaneously considering several key issues, i.e., endmember variability, the discrete nature of subpixel class labels, and the spatial information in HSI. Therefore, we propose a new Bayesian SPM method based on the discrete endmember variability mixture model (DEMM) and Markov random field (MRF), which has three main characteristics. First, DEMM allows the advanced SPM by completely accounting for the endmember–abundance patterns of each pixel to accommodate the endmember variability, the discrete hidden class label field of subpixels, while taking into account the noise heterogeneity effect. Second, the discrete class label fields modeled by MRF together with the DEMM, which can be integrated into a novel Bayesian model to better exploit the spatial contextual and spectral information. Third, the resulting Bayesian model can be efficiently solved by a designed expectation–maximization iteration, where E-step estimates the subpixel class label field using a simulated annealing algorithm and M-step estimates the endmembers for each pixel in HSI using the alternating non-negative least squares approach. The experimental results on three HSI datasets demonstrate that the proposed approach outperforms previously available SPM techniques.

Although a graph-based semisupervised learning (SSL) approach can utilize limited numbers of labeled samples for hyperspectral image (HSI) classification, it is difficult to use the large amount of pixels in an HSI to construct a large-scale graph. In this article, we therefore propose a superpixel-based graph model for HSI classification, using anchor graph regularization to improve the extraction of local and nonlocal spatial information. To avoid the large-scale graph problem, the superpixel-based graph model constructs a scalable anchor graph using a small number of anchor points for graph-based SSL. The proposed approach consists of three key components: First, local and nonlocal features are extracted from the principal components of an HSI using the locally grouped order pattern and the nonlocal binary pattern approaches. Second, the extracted features are combined with the original spectral information and used as input to an improved simple line iteration clustering method to obtain superpixels, the centers of which are used as anchors in the anchor graph approach. Third, we use superpixel-based graph regularization to model the global information among the superpixels for better predictions of the label of each unlabeled pixel sample. Experimental results demonstrate that the proposed approach outperforms other state-of-the-art semisupervised HSI classification approaches for cases with a limited quantity of labeled samples.

Mapping different types of sea ice that form, grow, and melt in polar oceans is essential for shipping navigation, climate change modeling, and local community safety. Currently, ice charts are manually generated by analysts at the Canadian Ice Service based on dual-polarized RADARSAT-2/RADARSAT Constellation Mission imagery on a daily basis. Inspired by the demand for a computer-based mapping system, we have developed an automatic sea-ice classification method that integrates spatial contexture (unsupervised segmentation) with textural features (supervised pixel-level labeling). First, the full-scene image is oversegmented, and the segments are merged into homogeneous regions across the entire scene. Second, pixel-based classifiers (support vector machine and random forest) are compared for their ability to label the generated homogeneous regions. Finally, the segmentation and labeling are combined using a proposed energy function. The proposed method was tested on 18 dual-polarization RADARSAT-2 scenes acquired over the Beaufort Sea. This dataset contains water, young ice, first-year ice, and multiyear ice covering melt, summer, and freeze-up seasons. The proposed method obtains an average classification accuracy of 86.33% based on the leave-one-out validation. The experimental results show that the proposed method achieves promising classification results in both the quantity and quality measurements compared with benchmark methods. The robustness against incidence angle variance indicates that the proposed method is well qualified for operational sea-ice mapping.

Soil moisture (SM) estimation is a critical part of environmental and agricultural monitoring, with satellite-based microwave remote sensing being the main SM source. However, the limited spatial resolution of most current remote sensing SM products reduces their utility for many applications, such as evapotranspiration modeling and agriculture management. In this article, to address this issue, we propose a Bayesian deep image prior (BDIP) downscaling approach to estimate the high-resolution SM from satellite products. More specifically, the high-resolution SM estimation problem is formulated as a maximum a posteriori problem, and solved via a neural network comprising of a deep fully convolutional neural network (FCNN) for modeling the prior spatial correlation distribution of the underlying high-resolution SM variables, and a forward model characterizing the SM map degeneration process for modeling the data likelihood. As such, the proposed BDIP approach provides a statistical framework that integrates deep learning with forward modeling in a coherent manner for combining different sources of information, i.e., the knowledge in the forward model, the spatial correlation prior in FCNN architecture, and the remote sensing data and products. Experiments on the downscaling of SM active passive SM products using the moderate resolution imaging spectroradiometer products show that SM maps estimated using the proposed method provide greater spatial detail information than other downscaling methods, with the SM estimates very close to in situ measurements.

Time-series remote sensing images are important in agricultural monitoring and investigation. However, most time-series data with high temporal resolution have the problem of insufficient spatial resolution which cannot meet the requirement of precision agriculture. The unmixing technique can obtain the object abundances with richer spatial information from the coarse-resolution images. Although the unmixing technique is widely used in hyperspectral data, it is insufficiently researched for time-series data. Temporal unmixing extends spectral unmixing to the time domain from the spectral domain, and describes the temporal characteristics rather than the spectral characteristics of different ground objects. Deep learning (DL) techniques have achieved promising performance for the unmixing problem in recent years, but there are still few studies on temporal mixture analysis (TMA), especially in the application of crop phenological monitoring. This paper presents a novel spatial–temporal deep image prior method based on a Bayesian framework (ST-Bdip), which innovatively combines the knowledge-driven TMA model and the DL-driven model. The normalized difference vegetation index (NDVI) time series of moderate resolution imaging spectroradiometer (MODIS) data is used as the object for TMA, while the extracted seasonal crop signatures and the fractional coverages are perceived as the temporal endmembers (tEMs) and corresponding abundances. The proposed ST-Bdip method mainly includes the following contributions. First, a deep image prior model based on U-Net architecture is designed to efficiently learn the spatial context information, which enhances the representation of abundance modeling compared to the traditional non-negative least squares algorithm. Second, The TMA model is incorporated into the U-Net training process to exploit the knowledge in the forward temporal model effectively. Third, the temporal noise heterogeneity in time-series images is considered in the model optimization process. Specifically, the anisotropic covariance matrix of observations from different time dimensions is modeled as a multivariate Gaussian distribution and incorporated into the calculation of the loss function. Fourth, the "purified means" approach is used to further optimize crop tEMs and the corresponding abundances. Finally, the expectation–maximization (EM) algorithm is designed to solve the maximum a posterior (MAP) problem of the model in the Bayesian framework. Experimental results on three synthetic datasets with different noise levels and two real MODIS datasets demonstrate the superiority of the proposed approach in comparison with seven traditional and advanced unmixing algorithms.

Molecular generation (MG) via machine learning (ML) has speeded drug structural optimization, especially for targets with a large amount of reported bioactivity data. However, molecular generation for structural optimization is often powerless for new targets. DNA-encoded library (DEL) can generate systematic, target-specific activity data, including novel targets with few or unknown activity data. Therefore, this study aims to overcome the limitation of molecular generation in the structural optimization for the new target. Firstly, we generated molecules using the structure-affinity data (2.96 million samples) for 3C-like protease (3CLpro) from our own-built DEL platform to get rid of using public databases (e.g., CHEMBL and ZINC). Subsequently, to analyze the effect of transfer learning on the positive rate of the molecule generation model, molecular docking and affinity model based on DEL data were applied to explore the enhanced impact of transfer learning on molecule generation. In addition, the generated molecules are subjected to multiple filtering, including physicochemical properties, drug-like properties, and pharmacophore evaluation, molecular docking to determine the molecules for further study and verified by molecular dynamics simulation.

Mitochondrial dysfunction plays a key role in the pathogenesis of Alzheimer's disease (AD). The translocase of the outer membrane (TOM) complex controls the input of mitochondrial precursor proteins to maintain mitochondrial function under pathophysiological conditions. However, its role in AD development remains unclear. TOM70 is an important translocase present in the TOM complex. In the current study, we found that TOM70 levels were reduced in the peripheral blood and hippocampus of the APP/PS1 mice. In addition, we examined the whole-blood mRNA levels of TOM70 in patients with AD, dementia with Lewy bodies (DLB), and post-stroke dementia (PSD). Our study revealed that the mRNA level of TOM70 was decreased in the blood samples of patients with AD, which was also correlated with the progression of clinical stages. Therefore, we proposed that the expression of TOM70 could be a promising biomarker for AD diagnosis and monitoring of disease progression.

ABSTRACT: Objectives: The aims of this study were to infer the phylogenetic relationship of OXA-232-producing Klebsiella pneumoniae (OXA232Kp) strains collected from a Chinese hospital and to determine the composition and genetic background of antimicrobial resistance genes (ARGs) among these strains. Methods: Three non-duplicate OXA232Kp strains were collected from a Chinese hospital. Whole-genome sequencing was used to determine their genome sequences and then a genomic comparison of ARG-carrying genetic elements from the three strains with related sequences was performed. Phylogenetic analysis was conducted by constructing a maximum-likelihood phylogenetic tree. Results: Compared with other Chinese sequence type 15 (ST15)-OXA232Kp strains, the three ST15-OXA232Kp strains in this study could be divided into a single subgroup in phylogenetic relationship. The composition and genetic background of ARGs were identical in the three strains. Three ARG-carrying genetic elements or multidrug resistance (MDR) regions were determined, including a truncated Tn2013-like IS-based transposition unit, a unit transposition Tn6867b and a 40.9-kb MDR region. Conclusion: This study reported clonal dissemination of ST15-OXA232Kp strains carrying multiple ARGs in a Chinese hospital. A comprehensive evolutionary and genomics analysis provided a deeper understanding of OXA232Kp. Further surveillance and study should be advocated to prevent the dissemination of OXA232Kp strains in China.

Glioblastoma (GBM) is the most malignant tumor of the central nervous system in adults. Irradiation (IR) and temozolomide (TMZ) play an extremely important role in the treatment of GBM. However, major impediments to effective treatment are postoperative tumor recurrence and acquired resistance to chemoradiotherapy. Our previous studies confirm that Yin Yang 1 (YY1) is highly expressed in GBM, whereby it is associated with cell dedifferentiation, survival, and therapeutic resistance. Targeted delivery of small interfering RNA (siRNA) without blood-brain barrier (BBB) restriction for eradication of GBM represents a promising approach for therapeutic interventions. In this study, we utilize the engineering technology to generate T7 peptide-decorated exosome (T7-exo). T7 is a peptide specifically binding to the transferrin receptor. T7-exo shows excellent packaging and protection of cholesterol-modified Cy3-siYY1 while quickly releasing payloads in a cytoplasmic reductive environment. The engineered exosomes T7-siYY1-exo could deliver more effciently to GBM cells both in vitro and in vivo. Notably, in vitro experiments demonstrate that T7-siYY1-exo can enhance chemoradiotherapy sensitivity and reverse therapeutic resistance. Moreover, T7-siYY1-exo and TMZ/IR exert synergistic anti-GBM effect and significantly improves the survival time of GBM bearing mice. Our findings indicate that T7-siYY1-exo may be a potential approach to reverse the chemoradiotherapy resistance in GBM.

Selective labeling of distinct bacteria and biofilm is poised for the fundamental understanding of bacterial activities, interactions, and coupled phenomena occurring at the microscale. However, a simple and effective way to achieve selective bacterial labeling is still lacking. Herein, we report a fluorescence probe with core-shell nanostructure that has polydopamine (PDA) coating on the surface of fluorescent silicon quantum dots (SiQDs@PDA). The surface of the SiQDs@PDA can be functionalized by various molecules (2-mercaptoethylamine hydrochloride, PEG, d-alanine, glucose amide) through different strategies (Michael addition, π-π interaction, and ion–ion interaction). Importantly, the d-alanine (D-Ala)- and gluconamide (Glc)-functionalized SiQDs@PDA fluorescence probes are capable of selectively labeling gram-positive and gram-negative bacteria, as well as their biofilms. The excellent performance in universal functionalization and selective labeling and imaging of bacteria and their biofilms demonstrate that SiQDs@PDA are a promising fluorescence tool in microbe research.

BackgroundCompelling evidence indicates that elevated peripheral serum lymphocytes are associated with a favorable prognosis in various cancers. However, the association between serum lymphocytes and glioma is contradictory. In this study, a nomogram was established to predict the diagnosis of glioma-grading through Ki-67 expression and serum lymphocytes.MethodsWe performed a retrospective analysis of 239 patients diagnosed with LGG and 178 patients with HGG. Immunohistochemistry was used to determine the Ki-67 expression. Following multivariate logistic regression analysis, a nomogram was established and used to identify the most related factors associated with HGG. The consistency index (C-index), decision curve analysis (DCA), and a calibration curve were used to validate the model.ResultsThe number of LGG patients with more IDH1/2 mutations and 1p19q co-deletion was greater than that of HGG patients. The multivariate logistic analysis identified Ki-67 expression, serum lymphocyte count, and serum albumin (ALU) as independent risk factors associated with HGG, and these factors were included in a nomogram in the training cohort. In the validation cohort, the nomogram demonstrated good calibration and high consistency (C-index = 0.794). The Spearman correlation analysis revealed a significant association between HGG and serum lymphocyte count (r = −0.238, P

Most liver diseases, including acute liver injury, drug-induced liver injury, viral hepatitis, metabolic liver diseases, and end-stage liver diseases, are strongly linked with hormonal influences. Thus, delineating the clinical manifestation and underlying mechanisms of the “sexual dimorphism” is critical for providing hints for the prevention, management, and treatment of those diseases. Whether the sex hormones (androgen, estrogen, and progesterone) and sex-related hormones (gonadotrophin-releasing hormone, luteinizing hormone, follicle-stimulating hormone, and prolactin) play protective or toxic roles in the liver depends on the biological sex, disease stage, precipitating factor, and even the psychiatric status. Lifestyle factors, such as obesity, alcohol drinking, and smoking, also drastically affect the involving mechanisms of those hormones in liver diseases. Hormones deliver their hepatic regulatory signals primarily via classical and non-classical receptors in different liver cell types. Exogenous sex/sex-related hormone therapy may serve as a novel strategy for metabolic liver disease, cirrhosis, and liver cancer. However, the undesired hormone-induced liver injury should be carefully studied in pre-clinical models and monitored in clinical applications. This issue is particularly important for menopause females with hormone replacement therapy (HRT) and transgender populations who want to receive gender-affirming hormone therapy (GAHT). In conclusion, basic and clinical studies are warranted to depict the detailed hepatoprotective and hepatotoxic mechanisms of sex/sex-related hormones in liver disease. Prolactin holds a promising perspective in treating metabolic and advanced liver diseases.

Operational sea ice maps are usually generated manually using dual-polarization (DP) synthetic aperture radar (SAR) satellite imagery, but there is strong interest in automating this process. Recently launched satellites offer compact polarimetry (CP) imagery that provides more comprehensive polarimetric information compared to DP, which compels the use of CP for automated classification of SAR sea ice imagery. Existing sea ice scene classification algorithms using CP imagery rely on handcrafted features, while neural networks offer the potential of features that are more discriminating. We have developed a new and effective sea ice classification algorithm that leverages the nature of CP data. First, a residual-based convolutional neural network (ResCNN) is implemented to classify each pixel. In parallel, an unsupervised segmentation is performed to generate regions based on CP statistical properties. Regions are assigned a single class label by majority voting using the ResCNN output. For testing, quad-polarimetric (QP) SAR sea ice scenes from the RADARSAT Constellation Mission (RCM) are used, and QP, DP, CP, and reconstructed QP modes are compared for classification accuracy, while also comparing them to other classification approaches. Using CP achieves an overall accuracy of 96.86%, which is comparable to QP (97.16%), and higher than reconstructed QP and DP data by about 2% and 10%, respectively. The implemented algorithm using CP imagery provides an improved option for automated sea ice mapping.

This paper investigates the deep neural networks for rapid and accurate detection of building rooftops in aerial orthoimages. The networks were trained using the manually labeled rooftop vector data digitized on aerial orthoimagery covering the Kitchener-Waterloo area. The performance of the three deep learning methods, U-Net, Fully Convolutional Network (FCN), and Deeplabv3+ were compared by training, validation, and testing sets in the dataset. Our results demonstrated that DeepLabv3+ achieved 63.8% in Intersection over Union (IoU), 77.8% in mean IoU (mIoU), 74% in precision, and 78% in F1-score. After improving the performance with focal loss, training loss was greatly cut down and the convergence rate experienced a significant growth. Meanwhile, rooftop detection also achieved higher performance, as Deeplabv3+ reached 93.6% in average pixel accuracy, with 65.4% in IoU, 79.0% in mIoU, 77.6% in precision, and 79.1% in F1-score. Lastly, in order to evaluate the effects of data volume, by changing data volume from 100% to 75% and 50% in ablation study, it shows that when data volume decreased, the performance of extraction also got worse, with IoU, mIoU, precision, and F1-score also mostly decreased.

The Cumberland Sound Beluga is a threatened population of belugas and the assessment of the population is done by a manual review of aerial surveys. The time-consuming and labor-intensive nature of this job motivates the need for a computer automated process to monitor beluga populations. In this paper, we investigate convolutional neural networks to detect whether a section of an aerial survey image contains a beluga. We use data from the 2014 and 2017 aerial surveys of the Cumberland Sound, conducted by the Fisheries and Oceans Canada to simulate two scenarios: (1) when one annotates part of a survey and uses it to train a pipeline to annotate the remainder and (2) when one uses annotations from a survey to train a pipeline to annotate another survey from another time period. We experimented with a number of different architectures and found that an ensemble of 10 CNN models that leverage Squeeze-Excitation and Residual blocks performed best. We evaluated scenarios (1) and (2) by training on the 2014 and 2017 surveys, respectively. In both scenarios, the performance on (1) is higher than (2) due to the uncontrolled variables in the scenes, such as weather and surface conditions.

Abstract Hedgehog (Hh) signaling plays a critical role in embryogenesis and tissue homeostasis, and its deregulation has been associated with tumor growth. The tumor suppressor SuFu inhibits Hh signaling by preventing the nuclear translocation of Gli and suppressing cell proliferation. Regulation of SuFu activity and stability is key to controlling Hh signaling. Here, we unveil SuFu Negating Protein 1 (SNEP1) as a novel Hh target, that enhances the ubiquitination and proteasomal degradation of SuFu and thus promotes Hh signaling. We further show that the E3 ubiquitin ligase LNX1 plays a critical role in the SNEP1-mediated degradation of SuFu. Accordingly, SNEP1 promotes colorectal cancer (CRC) cell proliferation and tumor growth. High levels of SNEP1 are detected in CRC tissues and are well correlated with poor prognosis in CRC patients. Moreover, SNEP1 overexpression reduces sensitivity to anti-Hh inhibitor in CRC cells. Altogether, our findings demonstrate that SNEP1 acts as a novel feedback regulator of Hh signaling by destabilizing SuFu and promoting tumor growth and anti-Hh resistance.

Hyperspectral images classification relies on the accurate and efficient extraction of discriminative features, detail preservation, and efficient learning with limited training samples. This article, therefore, presents an advanced neural network architecture combined with convolutional conditional random fields (ConvCRF) and region growing (RGW) approaches to address these key issues. First, a depthwise separable fully convolutional residual network (DFRes) is proposed for efficient feature learning, where a fully convolutional operation ensures a larger field of view, and residual learning and depthwise separable convolution can mitigate the problem of vanishing gradient and overfitting. Second, because the collection of ground-truth labels is usually difficult, the proposed architecture integrates the RGW method to effectively overcome the problem of limited training samples. Third, ConvCRF is used to preserve the image details for fine-grained predictions. Finally, the abovementioned key components are coherently integrated into the new semisupervised framework, i.e., DFRes with conditional random fields and RGW. Experimental results on three hyperspectral datasets demonstrate that the proposed approach outperforms other state-of-the-art methods.

The Canadian RADARSAT constellation mission (RCM) is represented by three synthetic aperture radar (SAR) satellites, each of which includes a compact polarimetry (CP) mode. CP is advantageous because it provides increased backscatter information relative to single and conventional dual-polarized modes and has larger swath widths relative to a quad polarization mode. CP captures single-look complex data which can be used to derive the multilook complex (MLC) coherence matrix, or, equivalently, the Stokes vector data of the backscattered field. The challenge is to develop computer vision algorithms that can be used to effectively segment the scene using this new data source. An unsupervised region-based segmentation approach has been designed and implemented that utilizes the complex Wishart distribution characteristic of the MLC CP data. The segmentation method is based on the iterative region growing with semantics algorithm originally designed for single and dual pol intensity SAR data. The algorithm has been tested using both simulated CP SAR images and a pair of available quad polarization SAR images. The results demonstrate that the CP-IRGS algorithm provides more accurate segmentation images than those using only the RH and RV channel intensity images.

The emitter is one of the most critical components in drip irrigation. The flow path geometry parameters have a significant effect on the emitter’s hydraulic performance and have a direct impact on the emitter’s irrigation uniformity and lifetime. The hydraulic characteristics of the emitter are the key indicators of its performance. However, obtaining the hydraulic characteristics of the emitter is complex. Typically, only a small number of calibrations are performed for specific equipment models, making it difficult to obtain the parameter. Therefore, limited data corresponding to the morphological parameters and the flow rate were simulated using the FLUENT software, and the influence of the characteristics was analyzeanalyzed, based on which a flow rate prediction model was constructed using the ensemble learning (CatBoost) model. The extended data set was generated by stochastic simulation and parameter fitting. The flow index and flow coefficient prediction model were built and evaluated using the CatBoost model again with the augmented data set as a benchmark. The results show that the significant correlation between the geometric structure and the flow index and flow coefficient provides the basis for the correlation model. CatBoost can fit the complex nonlinear relationships between the parameters well, achieving excellent simulation accuracy for the flow rate (R2 = 0.9987), flow index (R2 = 0.9961), and flow coefficient (R2 = 0.9946), where the path width has the highest importance score in the model construction for the flow index (score = 55.97) and flow coefficient (score = 45.2). Furthermore, the CatBoost models used in this study achieved the best prediction results compared to seven typical models (XGBoost, Bagging, Random Forest, Tree, Adaboost, and KNN).

In the growth and development of plants, some non-coding small RNAs (sRNAs) not only mediate RNA interference at the post-transcriptional level, but also play an important regulatory role in chromatin modification at the transcriptional level. In these processes, the protein factors Argonaute (AGO), Dicer-like (DCL), and RNA-dependent RNA polymerase (RDR) play very important roles in the synthesis of sRNAs respectively. Though they have been identified in many plants, the information about these gene families in strawberry was poorly understood. In this study, using a genome-wide analysis and a phylogenetic approach, 13 AGO, six DCL, and nine RDR genes were identified in diploid strawberry Fragaria vesca. We also identified 33 AGO, 18 DCL, and 28 RDR genes in octoploid strawberry Fragaria × ananassa, studied the expression patterns of these genes in various tissues and developmental stages of strawberry, and researched the response of these genes to some hormones, finding that almost all genes respond to the five hormone stresses. This study is the first report of a genome-wide analysis of AGO, DCL, and RDR gene families in Fragaria spp., in which we provide basic genomic information and expression patterns for these genes. Additionally, this study provides a basis for further research on the functions of these genes and some evidence for the evolution between diploid and octoploid strawberries.

Advance knowledge of soil water content (SWC) in the soil wetting layer of crop irrigation can help develop more reasonable irrigation plans and improve the efficiency of agricultural irrigation water use. To improve the accuracy of predicting SWC at multiple depths, the ResBiLSTM model was proposed, in which continuous meteorological and SWC data were gridded and transformed as model inputs, and then high-dimensional spatial and time series features were extracted by ResNet and BiLSTM, respectively, and integrated by a meta-learner. Meteorological, SWC and growth stage records data from seven typical maize monitoring stations in Hebei Province, China, during the 2016-2018 summer maize planting process were utilized for the training, evaluation and testing of the ResBiLSTM model, with model prediction targets set at 20cm, 30cm, 40cm and 50cm depths. Experimental results showed that: 1) ResBiLSTM model could achieve better model fit and prediction of meteorological and SWC data at all growth stages, with R2 within [0.818, 0.991], average MAE within [0.79%, 2.00%], and the overall prediction accuracy ranked as follows: anthesis maturity stage > seedling stage > tassel stage; 2) The average MSE of the ResBiLSTM model for the prediction of SWC in the next 1-6 days was within [3.91%, 15.82%], and the prediction accuracy decreased with the extension of the prediction time; 3) Compared with the classical machine learning model and related deep learning models, the ResBiLSTM model was able to obtain better prediction accuracy performance.