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AbstractBarnacles are crustaceans that are critical model organisms in intertidal ecology and biofouling research. In this study, we present the first mitochondrial genome of Striatobalanus tenuis which is a circular molecule of 15,067 bp in length. Consistent with most barnacles, the mitochondrial genome of S. tenuis encodes 37 genes, including 13 PCGs, 22 tRNAs and 2 rRNAs. A novel insight into the phylogenetic analysis based on the nucleotide data of 13 PCGs showed that the S. tenuis clusters with Striatobalanus amaryllis (bootstrap value = 100) of the same genus, then groups with other Balanoidea species, the Chelonibiidae, Austrobalanidae and Tetraclitidae cluster together forming superfamily Coronuloidea. The result can help us to understand the novel classification within Balanomorpha.

Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.

Abstract Background Though articular cartilage stem cell (ACSC)-based therapies have been demonstrated to be a promising option in the treatment of diseased joints, the wide variety of cell isolation, the unknown therapeutic targets, and the incomplete understanding of the interactions of ACSCs with diseased microenvironments have limited the applications of ACSCs. Methods In this study, the human ACSCs have been isolated from osteoarthritic articular cartilage by advantage of selection of anatomical location, the migratory property of the cells, and the combination of traumatic injury, mechanical stimuli and enzymatic digestion. The protective effects of ACSC infusion into osteoarthritis (OA) rat knees on osteochondral tissues were evaluated using micro-CT and pathological analyses. Moreover, the regulation of ACSCs on osteoarthritic osteoclasts and the underlying mechanisms in vivo and in vitro were explored by RNA-sequencing, pathological analyses and functional gain and loss experiments. The one-way ANOVA was used in multiple group data analysis. Results The ACSCs showed typical stem cell-like characteristics including colony formation and committed osteo-chondrogenic capacity. In addition, intra-articular injection into knee joints yielded significant improvement on the abnormal subchondral bone remodeling of osteoarthritic rats. Bioinformatic and functional analysis showed that ACSCs suppressed osteoarthritic osteoclasts formation, and inflammatory joint microenvironment augmented the inhibitory effects. Further explorations demonstrated that ACSC-derived tumor necrosis factor alpha-induced protein 3 (TNFAIP3) remarkably contributed to the inhibition on osteoarhtritic osteoclasts and the improvement of abnormal subchondral bone remodeling. Conclusion In summary, we have reported an easy and reproducible human ACSC isolation strategy and revealed their effects on subchondral bone remodeling in OA rats by releasing TNFAIP3 and suppressing osteoclasts in a diseased microenvironment responsive manner. Graphical abstract

CFD offers advantages over wind tunnel experiments in the prediction and optimization of building wind environment; however, the computational costs associated with optimizing architectural wind environment remain a challenge. In this study, an approach that combines deep learning techniques with CFD simulations is proposed for the prediction and optimization of the architectural wind environment efficiently. A dataset of wind field is constructed using CFD simulation, considering various wind directions, wind speeds, and building spacing. Subsequently, a U-net deep learning model is trained as a surrogate model to rapidly predict the architectural wind field under different conditions. The results indicate that the model can accurately predict the wind field in buildings. The prediction time of building wind field is only 1/900 of that of CFD simulations, making it a viable surrogate model for wind environment optimization. Furthermore, considering all the building layouts and inflow conditions examined in this study, the maximum and minimum uniform wind speed area ratios Auni are 0.84 and 0.13, respectively. Under a single inflow speed, the maximum improvement in the Auni is 0.4, with an improvement rate of 48%. The results demonstrate the effectiveness of the proposed method as an efficient approach for optimizing architectural wind environment.

Two new species from China, Hymenogaster pseudoniveus and H. zunhuaensis, are described and illustrated based on morphological and molecular evidence. Hymenogaster pseudoniveus is distinguished from other species of the genus based on the color of peridium, which is snow white to white when unexcavated but then turns yellowish to earth yellow, and the basidiospores, which are ornamented with nearly longitudinally arranged ridges up to 2 μm high. H. zunhuaensis is diagnosed by its dirty white to pale yellow peridium, yellow brown to brown gleba, and the smaller (Lm × Wm = 11.7 μm × 9.8 μm) broadly ellipsoidal to subglobose basidiospores (Q = 1.1–1.3). ITS/LSU-based phylogenetic analysis supports the erection of the two new species, each placed in distinct clades with strong statistical support, suggesting that they represent two distinct species novel to science. Based on the morphological and molecular evidence, we have published two new species of Hymenogaster. A key for Hymenogaster species from China is provided.

Background: Clear cell renal cell carcinoma (ccRCC) is corelated with tumor-associated material (TAM), coagulation system and adipocyte tissue, but the relationships between them have been inconsistent. Our study aimed to explore the cut-off intervals of variables that are non-linearly related to ccRCC pathological T stage for providing clues to understand these discrepancies, and to effectively preoperative risk stratification. Methods: This retrospective analysis included 218 ccRCC patients with a clear pathological T stage between January 1st, 2014, and November 30th, 2021. The patients were categorized into two cohorts based on their pathological T stage: low T stage (T1 and T2) and high T stage (T3 and T4). Abdominal and perirenal fat variables were measured based on preoperative CT images. Blood biochemical indexes from the last time before surgery were also collected. The generalized sum model was used to identify cut-off intervals for nonlinear variables. Results: In specific intervals, fibrinogen levels (FIB) (2.63–4.06 g/L) and platelet (PLT) counts (>200.34 × 109/L) were significantly positively correlated with T stage, while PLT counts (

Barnacles have demonstrated adaptability to a range of habitats, spanning from shallow water to the deep sea. Given the harsh conditions present in hydrothermal vents, hydrothermal vent barnacles serve as the model organism for investigating the interplay between evolution and adaptability. In order to gain insights into barnacle adaptive characteristics, particularly within hydrothermal vents, we conducted a comprehensive analysis of the mitogenomes and transcriptome in a deep-sea barnacle (Vulcanolepas fijiensis), in comparison to its shallow-water related species. The mitogenomes with the same genetic skews and the non-synonymous/synonymous mutation ratios (Ka/Ks) of the mitogenomes indicate that the protein-coding genes (COIII, ND2, and ND6) of V. fijiensis are under positive selection. Meanwhile, the functional annotation shows that distinctly positive selected orthologs in V. fijiensis are predominately related to neural signal transduction, immunity, antiapoptotic, and energy metabolism. These results indicate that the mitogenomes and key genes found in transcriptomic analysis are under high-temperature and high-pressure conditions, and which may contribute V. fijiensis to have evolved to adapt to the extreme hydrothermal vent environments. The findings shed light on the mitogenome and transcriptome of V. fijiensis, which lays a foundation for the in-depth understanding of the adaptation mechanism of sessile invertebrates to the deep-sea environment.

IntroductionThe deep-sea environment is always characterized by high hydrostatic pressure, fluctuating temperatures, heavy metals, darkness, and others. The Vesicomyidae inhabit cold seep zones, hydrothermal vents, and other chemically reduced environments.MethodsTo enhance the understanding of the adaptation mechanisms of clams in extreme environments, a comprehensive proteomic study was conducted on the cold seep clam Archivesica marissinica and shallow water clam Ruditapes philippinarum.ResultsA total of 4,557 proteins were identified from the comparative groups. The Gene Ontology results indicated that the differentially expressed proteins (DEP) for the comparative group Rpgill vs. Amgill were enriched in the nitrogen compound metabolic process, and others. The comparative analysis for Amfoot vs. Amgill and Ammantle vs. Amgill revealed significant enrichment of the differential proteins that were involved in metal ion transport, divalent inorganic cation transport, and so on. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for the comparative group Rpgill vs. Amgill was significantly enriched in the regulation of actin cytoskeleton, lysosome, and others. The proteins that exhibited differential expression in the cold seep clam’ different tissues were also enriched in important pathways, such as lysosome, fatty acid degradation, nitrogen metabolism.DiscussionThe further analysis identified crucial response proteins involved in various biological pathways. For example, the pattern recognition receptors, such as galectin and peptidoglycan recognition protein, participated in recognition of symbiotic microorganisms. The lysosome pathway members, such as cathepsin and saposins, were engaged in the degradation process of symbiont proteins during symbiont digestion. Profilin and gelsolin from actin cytoskeleton pathway might be pressure-related proteins. Furthermore, carbonic anhydrases from nitrogen metabolism KEGG pathway provide inorganic carbon for symbiotic bacteria. Additionally, Mn superoxide dismutase plays a role in the scavenging of superoxide anion radicals and antioxidant activity. Then, arginine kinases facilitate the low temperature adaptation of deep-sea shellfish with its cold adaptation characters. These findings offer novel perspectives on the proteins that are implicated in A. marisica’s response to cold seep environments, thereby contributing to the understanding of deep-sea biological adaptation and the preservation of deep-sea ecosystems.

Accurate segmentation of skin lesions is a challenging task because the task is highly influenced by factors such as location, shape and scale. In recent years, Convolutional Neural Networks (CNNs) have achieved advanced performance in automated medical image segmentation. However, existing CNNs have problems such as inability to highlight relevant features and preserve local features, which limit their application in clinical decision-making. This paper proposes a CNN with an added attention mechanism (EA-Net) for more accurate medical image segmentation.EA-Net is based on the U-Net network model framework. Specifically, we added a pixel-level attention module (PA) to the encoder section to preserve the local features of the image during downsampling, making the feature maps input to the decoder more relevant to the ground-truth. At the same time, we added a spatial multi-scale attention module (SA) after the decoding process to increase the spatial weight of the feature maps that are more relevant to the ground-truth, thereby reducing the gap between the output results and the ground-truth. We conducted extensive segmentation experiments on skin lesion images from the ISIC 2017 and ISIC 2018 datasets. The results demonstrate that, when compared to U-Net, our proposed EA-Net achieves an average Dice score improvement of 1.94% and 5.38% for skin lesion tissue segmentation on the ISIC 2017 and ISIC 2018 datasets, respectively. The IoU also increases by 2.69% and 8.31%, and the ASSD decreases by 0.3783 pix and 0.5432 pix, indicating superior segmentation performance. EA-Net can achieve better segmentation results when the original image of skin lesions has an obscure boundary and the segmentation area contains interference factors, which proves that the addition of attention mechanism in the encoder and the application of comprehensive attention mechanism can improve the performance of neural network in the field of skin lesions image segmentation.

We present a two-stage scheduling approach including proactive and reactive scheduling to solve the ground resource scheduling problem with uncertain arrival time. In the first stage, an integer programming model is constructed to minimize the delay and transfer costs. After solving this model, we obtain a baseline scheduling plan that considers the service arrival time uncertainty. In the second stage, the feasibility of the subsequent benchmark plan is evaluated based on the current state of the services and resources. The reactive scheduling model is enabled when trigger conditions are met. Moreover, an improved adaptive large neighborhood search is designed to solve the proactive scheduling model effectively. Real data from an international airport in South China is used as a test case to compare different scheduling strategies. The results show that it is difficult to handle the uncertainty of the problem with the benchmark plan that simply considered buffer time. Compared with rolling time-domain scheduling, the average transfer cost of the scheduling strategy proposed in this paper increased slightly, but the average service delay cost can be reduced significantly. Algorithm-wise, instances of different scales are designed to verify the effectiveness of the improved adaptive large neighborhood search algorithm. The efficiency of the algorithm scheme is better than that of the Gurobi solver scheme in medium to large-scale problems. Therefore, the forward and reactive strategies can better handle the uncertainty of airport ground protection services as they can simultaneously guide the allocation and utilization of airport ground protection resources.

IntroductionAutism Spectrum Disorder (ASD) has a significant impact on the health of patients, and early diagnosis and treatment are essential to improve their quality of life. Machine learning methods, including multi-classifier fusion, have been widely used for disease diagnosis and prediction with remarkable results. However, current multi-classifier fusion methods lack the ability to measure the belief level of different samples and effectively fuse them jointly.MethodsTo address these issues, a multi-classifier fusion classification framework based on belief-value for ASD diagnosis is proposed in this paper. The belief-value measures the belief level of different samples based on distance information (the output distance of the classifier) and local density information (the weight of the nearest neighbor samples on the test samples), which is more representative than using a single type of information. Then, the complementary relationships between belief-values are captured via a multilayer perceptron (MLP) network for effective fusion of belief-values.ResultsThe experimental results demonstrate that the proposed classification framework achieves better performance than a single classifier and confirm that the fusion method used can effectively fuse complementary relationships to achieve accurate diagnosis.DiscussionFurthermore, the effectiveness of our method has only been validated in the diagnosis of ASD. For future work, we plan to extend this method to the diagnosis of other neuropsychiatric disorders.

Background: Statistics show that each year more than 100,000 patients pass away from brain tumors. Due to the diverse morphology, hazy boundaries, or unbalanced categories of medical data lesions, segmentation prediction of brain tumors has significant challenges. Purpose: In this thesis, we highlight EAV-UNet, a system designed to accurately detect lesion regions. Optimizing feature extraction, utilizing automatic segmentation techniques to detect anomalous regions, and strengthening the structure. We prioritize the segmentation problem of lesion regions, especially in cases where the margins of the tumor are more hazy. Methods: The VGG-19 network structure is incorporated into the coding stage of the U-Net, resulting in a deeper network structure, and an attention mechanism module is introduced to augment the feature information. Additionally, an edge detection module is added to the encoder to extract edge information in the image, which is then passed to the decoder to aid in reconstructing the original image. Our method uses the VGG-19 in place of the U-Net encoder. To strengthen feature details, we integrate a CBAM (Channel and Spatial Attention Mechanism) module into the decoder to enhance it. To extract vital edge details from the data, we incorporate an edge recognition section into the encoder. Results: All evaluation metrics show major improvements with our recommended EAV-UNet technique, which is based on a thorough analysis of experimental data. Specifically, for low contrast and blurry lesion edge images, the EAV-Unet method consistently produces forecasts that are very similar to the initial images. This technique reduced the Hausdorff distance to 1.82, achieved an F1 score of 96.1%, and attained a precision of 93.2% on Dataset 1. It obtained an F1 score of 76.8%, a Precision of 85.3%, and a Hausdorff distance reduction to 1.31 on Dataset 2. Dataset 3 displayed a Hausdorff distance cut in 2.30, an F1 score of 86.9%, and Precision of 95.3%. Conclusions: We conducted extensive segmentation experiments using various datasets related to brain tumors. We refined the network architecture by employing smaller convolutional kernels in our strategy. To further improve segmentation accuracy, we integrated attention modules and an edge enhancement module to reinforce edge information and boost attention scores.

Abstract. Background:. Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. Methods:. In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. Results:. A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94–0.99), 0.93 (95% CI: 0.85–0.98), and 0.94 (95% CI: 0.87–0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63–0.83), 0.72 (95% CI: 0.62–0.82), and 0.73 (95% CI: 0.62–0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model (P

In this study, five new species from China, Hymenogaster latisporus, H. minisporus, H. papilliformis, H. perisporius, and H. variabilis, are described and illustrated based on morphological and molecular evidence. Hymenogaster latisporus was distinguished from other species of the genus by the subglobose, broad ellipsoidal, ovoid basidiospores (average = 13.7 μm × 11.6 μm) with sparse verrucose and ridge-like ornamentation (1–1.2 μm high); H. minisporus by the ellipsoidal to broadly ellipsoidal and small basidiospores (average = 11.7 μm × 9.5 μm); H. papilliformis was characterized by the whitish to cream-colored basidiomes, and broadly fusiform to citriform basidiospores with a pronounced apex (2–3 μm, occasionally up to 4 μm high), papillary, distinct warts and ridges, and pronounced appendix (2–3 μm long); H. perisporius by the dirty white to pale yellow basidiomes, broad ellipsoidal to ellipsoidal, and yellow-brown to dark-brown basidiospores with warts and gelatinous perisporium; H. variabilis by the peridium with significant changes in thickness (167–351 μm), and broad ellipsoidal to subglobose basidiospores ornamented with sparse warts and ridges. An ITS/LSU-based phylogenetic analysis supported the erection of the five new species. A key for Hymenogaster species from northern China is provided.

The dinoflagellate Noctiluca scintillans is a globally distributed bloom-forming species. Previous studies have shown that the primary reason for the frequent occurrence of N. scintillans blooms may be the proliferation of microalgae due to eutrophication, which provides a sufficient source of food. Meanwhile, N. scintillans may release nutrients into the environment, thus affecting the population dynamics of microalgae. Thus, to investigate the interaction between N. scintillans and co-occurring microalgae, this study examined the population dynamics of N. scintillans and their interaction with three representative microalgae species in response to varying nutrient levels. The findings indicate that the growth of N. scintillans is slow when co-cultured with diatom Skeletonema costatum. Moreover, a high density and rapid growth rate of S. costatum may have an inhibitory effect on the growth of N. scintillans. Conversely, the population abundance of N. scintillans increased with the rise in the population density and nutritional level of Heterocapsa steinii (dinoflagellate) and Heterosigma akashiwo (raphidophyceae). Notably, N. scintillans can discharge specific nutrients into the aquatic environment, which can subsequently be assimilated and exploited by H. steinii. Thus, the interaction between the species and population dynamics of plankton, as well as changes in nutrient levels within the ecosystem, played a significant role in influencing the growth and population dynamics of N. scintillans. The mutualistic association between N. scintillans and microalgae may establish a transient closed loop, thereby fostering the sustained proliferation and subsequent expansion of N. scintillans.

A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses an AI sensor and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effect and introduced a recovery vector to suppress this effect. We improved the phase noise of the interference fringe by a factor of 1.9 by performing marine gravity measurements using an AI-based gravimeter and optimizing the recovery vector. Marine gravity measurements were performed, and high gravity measurement precision was achieved. The external and inner coincidence accuracies of the gravity measurement were ±0.42 mGal and ±0.46 mGal after optimizing the cross-coupling effect, which was improved by factors of 4.18 and 4.21 compared to the cases without optimization.

Background: Major depressive disorder (MDD) is associated with widespread, irregular cortical thickness (CT) reductions across the brain. However, little is known regarding mechanisms that govern spatial distribution of the reductions. Methods: We combined multimodal MRI and genetic, cytoarchitectonic and chemoarchitectonic data to examine structural covariance, functional synchronization, gene co-expression, cytoarchitectonic similarity and chemoarchitectonic covariance between regions atrophied in MDD. Results: Regions atrophied in MDD were associated with significantly higher structural covariance, functional synchronization, gene co-expression and chemoarchitectonic covariance. These results were robust against methodological variations in brain parcellation and null model, reproducible in patients and controls, and independent of age at onset of MDD. Despite no significant differences in the cytoarchitectonic similarity, MDD-related CT reductions were susceptible to specific cytoarchitectonic class of association cortex. Further, we found that nodal shortest path lengths to disease epicenters derived from structural (right supramarginal gyrus) and chemoarchitectonic covariance (right sulcus intermedius primus) networks of healthy brains were correlated with the extent to which a region was atrophied in MDD, supporting the transneuronal spread hypothesis that regions closer to the epicenters are more susceptible to MDD. Finally, we showed that structural covariance and functional synchronization among regions atrophied in MDD were mainly related to genes enriched in metabolic and membrane-related processes, driven by genes in excitatory neurons, and associated with specific neurotransmitter transporters and receptors. Conclusions: Altogether, our findings provide empirical evidence for and genetic and molecular insights into connectivity-constrained CT thinning in MDD.

Abstract Background Pneumonic-type invasive mucinous adenocarcinoma (IMA) was often misdiagnosed as pneumonia in clinic. However, the treatment of these two diseases is different. Methods A total of 341 patients with pneumonic-type IMA (n = 134) and infectious pneumonia (n = 207) were retrospectively enrolled from January 2017 to January 2022 at six centers. Detailed clinical and CT imaging characteristics of two groups were analyzed and the characteristics between the two groups were compared by χ2 test and Student’s t test. The multivariate logistic regression analysis was performed to identify independent predictors. Receiver operating characteristic curve analysis was used to determine the diagnostic performance of different variables. Results A significant difference was found in age, fever, no symptoms, elevation of white blood cell count and C-reactive protein level, family history of cancer, air bronchogram, interlobular fissure bulging, satellite lesions, and CT attenuation value (all p

(1) Background: The increasing complexity of marine organism habitats and the impact of climate change on the marine environment have garnered global attention towards the adaptive evolution of marine organisms. However, there still remains a lack of understanding regarding the historical development and research trends of adaptive evolution in marine organisms. (2) Methods: We conducted an analysis on the adaptive evolution of marine organisms published in the Web of Science (WOS) over the past two decades. Through a visualization of publications, authors, institutional affiliations, and keywords based on CiteSpace, this review examines research contributions, identifies research hotspots, and outlines future development trends in this field. (3) Results: The findings indicate a surge in the advancement of the adaptive evolution of marine organisms, particularly since 2017, with tight collaboration among European nations. Additionally, Chinese research institutions and scholars have emerged as some of the most significant contributors in this field. (4) Conclusions: This review provides an overview of the main research hotspots and frontiers in the field pertaining to adaptive evolution of marine organisms, as well as the historical timeline of research and development. This review has offered scientific guidance to scholars and institutions concerned with conducting research on marine organisms and adaptive evolution.

Abstract Nonsymmorphic symmetries open up horizons of exotic topological boundary states and even generalize the bulk–boundary correspondence, which, however, the third-order topological insulator in electronic materials are still unknown. Here, by means of the symmetry analysis and k · p models, we uncover the emergence of long-awaited third-order topological insulators and the wallpaper fermions in space group I4/mcm (No.140). Based on this, we present the hourglass fermion, fourfold-degenerate Dirac fermion, and Möbius fermion in the (001) surface of Tl4 XTe3 (X = Pb/Sn) with a nonsymmorphic wallpaper group p4g. Remarkably, 16 helical corner states reside on eight corners in Kramers pair, rendering the real electronic material of third-order topological insulators. More importantly, a time-reversal polarized octupole polarization is defined to uncover the nontrivial third-order topology, as is implemented by the 2nd and 3rd order Wilson loop calculations. Our results could considerably broaden the range of wallpaper fermions and lay the foundation for future experimental investigations of third-order topological insulators.

Abstract Background Repairing radiation-induced bone injuries remains a significant challenge in the clinic, and few effective medicines are currently available. Psoralen is a principal bioactive component of Cullen corylifolium (L.) Medik and has been reported to have antitumor, anti-inflammatory, and pro-osteogenesis activities. However, less information is available regarding the role of psoralen in the treatment of radiation-induced bone injury. In this study, we explored the modulatory effects of psoralen on skeletal stem cells and their protective effects on radiation-induced bone injuries. Methods The protective effects of psoralen on radiation-induced osteoporosis and irradiated bone defects were evaluated by microCT and pathological analysis. In addition, the cell proliferation, osteogenesis, and self-renewal of SSCs were explored. Further, the underlying mechanisms of the protective of psoralen were investigated by using RNA sequencing and functional gain and loss experiments in vitro and in vivo. Statistical significance was analyzed using Student's t test. The one-way ANOVA was used in multiple group data analysis. Results Here, we demonstrated that psoralen, a natural herbal extract, mitigated radiation-induced bone injury (irradiation-induced osteoporosis and irradiated bone defects) in mice partially by rescuing the stemness of irradiated skeletal stem cells. Mechanistically, psoralen restored the stemness of skeletal stem cells by alleviating the radiation-induced suppression of AKT/GSK-3β and elevating NRF2 expression in skeletal stem cells. Furthermore, the expression of KEAP1 in skeletal stem cells did not significantly change in the presence of psoralen. Moreover, blockade of NRF2 in vivo partially abolished the promising effects of psoralen in a murine model of irradiation-induced osteoporosis and irradiated bone regeneration. Conclusions In summary, our findings identified psoralen as a potential medicine to mitigate bone radiation injury. In addition, skeletal stem cells and AKT-GSK-3β and NRF2 may thus represent therapeutic targets for treating radiation-induced bone injury. Graphical Abstract

Abstract Background Radiomics plays an important role in the field of oncology. Few studies have focused on the identification of factors that may influence the classification performance of radiomics models. The goal of this study was to use contrast-enhanced mammography (CEM) images to identify factors that may potentially influence the performance of radiomics models in diagnosing breast lesions. Methods A total of 157 women with 161 breast lesions were included. Least absolute shrinkage and selection operator (LASSO) regression and the random forest (RF) algorithm were employed to construct radiomics models. The classification result for each lesion was obtained by using 100 rounds of five-fold cross-validation. The image features interpreted by the radiologists were used in the exploratory factor analyses. Univariate and multivariate analyses were performed to determine the association between the image features and misclassification. Additional exploratory analyses were performed to examine the findings. Results Among the lesions misclassified by both LASSO and RF ≥ 20% of the iterations in the cross-validation and those misclassified by both algorithms ≤5% of the iterations, univariate analysis showed that larger lesion size and the presence of rim artifacts and/or ripple artifacts were associated with more misclassifications among benign lesions, and smaller lesion size was associated with more misclassifications among malignant lesions (all p

Summary: Background: Breast cancer is the leading cause of cancer-related deaths in women. However, accurate diagnosis of breast cancer using medical images heavily relies on the experience of radiologists. This study aimed to develop an artificial intelligence model that diagnosed single-mass breast lesions on contrast-enhanced mammography (CEM) for assisting the diagnostic workflow. Methods: A total of 1912 women with single-mass breast lesions on CEM images before biopsy or surgery were included from June 2017 to October 2022 at three centres in China. Samples were divided into training and validation sets, internal testing set, pooled external testing set, and prospective testing set. A fully automated pipeline system (FAPS) using RefineNet and the Xception + Pyramid pooling module (PPM) was developed to perform the segmentation and classification of breast lesions. The performances of six radiologists and adjustments in Breast Imaging Reporting and Data System (BI-RADS) category 4 under the FAPS-assisted strategy were explored in pooled external and prospective testing sets. The segmentation performance was assessed using the Dice similarity coefficient (DSC), and the classification was assessed using heatmaps, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The radiologists’ reading time was recorded for comparison with the FAPS. This trial is registered with China Clinical Trial Registration Centre (ChiCTR2200063444). Findings: The FAPS-based segmentation task achieved DSCs of 0.888 ± 0.101, 0.820 ± 0.148 and 0.837 ± 0.132 in the internal, pooled external and prospective testing sets, respectively. For the classification task, the FAPS achieved AUCs of 0.947 (95% confidence interval [CI]: 0.916–0.978), 0.940 (95% [CI]: 0.894–0.987) and 0.891 (95% [CI]: 0.816–0.945). It outperformed radiologists in terms of classification efficiency based on single lesions (6 s vs 3 min). Moreover, the FAPS-assisted strategy improved the performance of radiologists. BI-RADS category 4 in 12.4% and 13.3% of patients was adjusted in two testing sets with the assistance of FAPS, which may play an important guiding role in the selection of clinical management strategies. Interpretation: The FAPS based on CEM demonstrated the potential for the segmentation and classification of breast lesions, and had good generalisation ability and clinical applicability. Funding: This study was supported by the Taishan Scholar Foundation of Shandong Province of China (tsqn202211378), National Natural Science Foundation of China (82001775), Natural Science Foundation of Shandong Province of China (ZR2021MH120), and Special Fund for Breast Disease Research of Shandong Medical Association (YXH2021ZX055).

The aircraft ferry service is an important link in the transfer of travelers between the remote parking stand and the terminal. By analyzing the process of inbound and outbound ferry service, the relationship between the in-place time of each ferry on the same flight is clarified. A ferry service splitting method considering the difference in in-place time is proposed. Based on this, a dynamic programming scheduling model for ferries is innovatively developed. Considering the impact of flight arrival and departure uncertainty on the in-place time of ferry service, the model is transformed into a dynamic programming stochastic model with opportunity constraints. The optimization objective of this model is to minimize the number of ferries used. To describe the model more easily, a sample average approximation technique is introduced to transform the stochastic model into a deterministic model with confidence. A genetic algorithm based on Monte Carlo random sampling is designed to solve the model. The experimental results based on the actual operation data of an airport in South China show that the dynamic splitting method considering the difference in the in-place time of the ferry can improve the resource utilization efficiency; the scheduling scheme based on the dynamic planning stochastic model has better robustness.

In order to solve the problem of uniform and precise fertilizer discharge, based on experimental analysis of the uneven nature of single-screw fertilizer discharge, a double arc groove screw fertilizer discharger was designed based on the principle of the half-cycle superposition of the fertilizer discharge curve. The fertilizer discharge amount and the instantaneous fertilizer discharge characteristics of the double arc groove screw fertilizer discharger were theoretically analyzed, and the factors affecting the fertilizer discharge uniformity of the double arc groove screw fertilizer discharger were obtained, taking the pitch S, arc groove radius Rp and center distance as the test factors. Using the uniformity variation coefficient and fertilization accuracy as test indexes, the experimental indicators were evaluated through a quadratic universal rotation combination design experiment with three factors and five levels. The optimal parameters were pitch S = 35 mm, arc groove radius Rp = 17 mm and center distance a = 40 mm. The fertilizer discharger was produced based on the optimal parameter combination, and a bench verification test and a comparative test were carried out. The test results show that the uniformity variation coefficient of the bench test and the relative error between the fertilization accuracy and the simulation test are 5.60% and 5.52%, respectively, and there is little difference between them, which verifies the correctness of the simulation. The comparative test results show that the uniformity variation coefficient of the optimized double arc groove screw fertilizer discharger is 7.16%, the fertilization accuracy is 3.44% and the fitting curve equation R2 of fertilizer discharge flow is 0.998, all of which are significantly better than in the single-screw fertilizer discharger. We developed an electronic fertilizer discharge controller and conducted bench verification tests on it. The test results show that the average deviation between the measured fertilizer discharge capacity and the preset value of the double arc groove screw fertilizer discharger based on our self-developed controller is 2.78%. This fertilizer discharge device can precisely control fertilizer discharge, effectively solving the problem of uneven fertilizer discharge in single-screw fertilizer dischargers.

Taking the Madoi MS 7.4 earthquake of 21 May 2021 as an example, this paper proposes using time series prediction models to predict the outgoing long-wave radiation (OLR) anomalies and study short-term pre-earthquake signals. Five time series prediction models, including autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM), were trained with the OLR time series data of the aseismic moments in the 5° × 5° spatial range around the epicenter. The model with the highest prediction accuracy was selected to retrospectively predict the OLR values during the aseismic period and before the earthquake in the area. It was found, by comparing the predicted time series values with the actual time series value, that the similarity indexes of the two time series before the earthquake were lower than the index of the aseismic period, indicating that the predicted time series before the earthquake significantly differed from the actual time series. Meanwhile, the temporal and spatial distribution characteristics of the anomalies in the 90 days before the earthquake were analyzed with a 95% confidence interval as the criterion of the anomalies, and the following was found: out of 25 grids, 18 grids showed anomalies—the anomalies of the different grids appeared on similar dates, and the anomalies of high values appeared centrally at the time of the earthquake, which supports the hypothesis that pre-earthquake signals may be associated with the earthquake.

In this paper, we propose a deterministic shift design model with task-based demand and give the corresponding stochastic version with a probability constraint such that the shift plan designed is staffed with the workforce with a certain probability of performing all given tasks. Since we currently find no suitable methods for solving this stochastic model from the literature related to solving stochastic shift design models, we developed a single-stage heuristic method based on statistics, whose main idea is to reduce the occurrence of manpower shortage by prolonging the resource occupation time of a task, but this leads to a serious waste of resources, which is common in solving resource allocation problems with uncertain durations. To reduce the cost of wastage, we also propose a two-stage heuristic approach that is a two-stage heuristic with an evolutionary strategy. The two heuristics show their effectiveness in solving the proposed stochastic model in numerical experiments, and the two-stage heuristic significantly outperforms the one-stage heuristic in cost optimization and solution time stability.

Two new species of Clitocella are proposed based on morphological and phylogenetic investigations. Clitocella borealichinensis sp. nov. is closely related to C. orientalis but distinguished from the latter by its slightly smaller basidiospores and hyphae of pileipellis with pale brown to brown intracellular or parietal pigment. Clitocella colorata sp. nov. is closely related to C. popinalis and C. mundula in macromorphology but is differentiated from C. popinalis by its slightly smaller basidiospores and the difference in genetic profile, and from C. mundula by its relatively colorful pileus (white to yellowish white, grayish white to grayish brown, pink white). Phylogenetic analyses based on sequence data from five different loci (ITS, nrLSU, tef1, rpb2 and atp6) support the taxonomic position of the two new species in the genus Clitocella. The illustrations and descriptions for the new taxa are provided.

Abstract The gut microbiome is essential for processing complex food compounds and synthesizing nutrients that the host cannot digest or produce, respectively. New model systems are needed to study how the metabolic capacity provided by the gut microbiome impacts the nutritional status of the host, and to explore possibilities for altering host metabolic capacity via the microbiome. Here, we colonized the nematode Caenorhabditis elegans gut with cellulolytic bacteria that enabled C. elegans to utilize cellulose, an otherwise indigestible substrate, as a carbon source. Cellulolytic bacteria as a community component in the worm gut can also support additional bacterial species with specialized roles, which we demonstrate by using Lactobacillus plantarum to protect C. elegans against Salmonella enterica infection. This work shows that engineered microbiome communities can be used to endow host organisms with novel functions, such as the ability to utilize alternate nutrient sources or to better fight pathogenic bacteria.

Currently it is highly desired to study coal pyrolysis based on weight loss behavior, mathematical model of pyrolysis kinetics and advanced multi-scale molecular simulations to realize clean utilization of coal. This work presents a comprehensive analysis of HSW vitrinite coal pyrolysis based on thermogravimetric mass spectrometry (TG-MS) experiments, kinetics models and reactive force field molecular dynamics (ReaxFF MD) simulations. The weight loss and gas release behavior of HSW vitrinite coal at different heating rates was investigated by TG-MS. The kinetic parameters of coal pyrolysis were analyzed by Coats-Redfern model and distributed activation energy model (DAEM). The pyrolysis reactive behaviors and thermal decomposition process of HSW vitrinite coal was observed and analysis with ReaxFF MD simulations at atomic scale. The results show that the weight loss rate of pyrolysis decreased with increased heating rate. The trend of higher heating rate delayed the release of pyrolysis gas was consistent with TG results. The pyrolysis activation energy increases from 12.30 to 25.83 kJ/mol with further pyrolysis stage by Coats-Redfern model, and from 59.85 to 328.24 kJ/mol with higher carbon conversion hate by DAEM model. The results of ReaxFF MD simulations give complex initial chemical process of coal pyrolysis process, which agrees with the actual and expected coal pyrolysis process. Also the secondary reaction of tar in later stage of pyrolysis is observed clearly via ReaxFF MD simulations.

Abstract The intertwined ferroelectricity and band topology will enable the non-volatile control of the topological states, which is of importance for nanoelectrics with low energy costing and high response speed. Nonetheless, the principle to design such system is unclear and the feasible approach to achieve the coexistence of two parameter orders is absent. Here, we propose a general paradigm to design 2D ferroelectric topological insulators by sliding topological multilayers on the basis of first-principles calculations. Taking trilayer Bi2Te3 as a model system, we show that in the van der Waals multilayer based 2D topological insulators, the in-plane and out-of-plane ferroelectricity can be induced through a specific interlayer sliding, to enable the coexistence of ferroelectric and topological orders. The strong coupling of the order parameters renders the topological states sensitive to polarization flip, realizing non-volatile ferroelectric control of topological properties. The revealed design-guideline and ferroelectric-topological coupling not only are useful for the fundamental research of the coupled ferroelectric and topological physics in 2D lattices, but also enable innovative applications in nanodevices.

Abstract Background Although lots of quantitative trait loci (QTLs) and genes present roles in litter size of some breeds, the information might not make it clear for the huge diversity of reproductive capability in pig breeds. To elucidate the inherent mechanisms of heterogeneity of reproductive capability in litter size of Xiang pig, we performed transcriptome analysis for the expression profile in ovaries using RNA-seq method. Results We identified 1,419 up-regulated and 1,376 down-regulated genes in Xiang pigs with large litter size. Among them, 1,010 differentially expressed genes (DEGs) were differently spliced between two groups with large or small litter sizes. Based on GO and KEGG analysis, numerous members of genes were gathered in ovarian steroidogenesis, steroid biosynthesis, oocyte maturation and reproduction processes. Conclusions Combined with gene biological function, twelve genes were found out that might be related with the reproductive capability of Xiang pig, of which, eleven genes were recognized as hub genes. These genes may play a role in promoting litter size by elevating steroid and peptide hormones supply through the ovary and facilitating the processes of ovulation and in vivo fertilization.

Abstract The reconstruction of irradiated bone defects after settlement of skeletal tumors remains a significant challenge in clinical applications. In this study, we explored radiation‐induced skeletal stem cell (SSC) stemness impairments and rescuing effects of ferulic acid (FA) on SSCs in vitro and in vivo. The immunophenotype, cell renewal, cell proliferation, and differentiation of SSCs in vitro after irradiation were investigated. Mechanistically, the changes in tissue regeneration‐associated gene expression and MAPK pathway activation in irradiated SSCs were evaluated. The regenerative capacity of SSCs in the presence of FA in an irradiated bone defect mouse model was also investigated. We found that irradiation reduced CD140a‐ and CD105‐positive cells in skeletal tissues and mouse‐derived SSCs. Additionally, irradiation suppressed cell proliferation, colony formation, and osteogenic differentiation of SSCs. The RNA‐Seq results showed that tissue regeneration‐associated gene expression decreased, and the Western blotting results demonstrated the suppression of phosphorylated p38/MAPK and ERK/MAPK in irradiated SSCs. Notably, FA significantly rescued the radiation‐induced impairment of SSCs by activating the p38/MAPK and ERK/MAPK pathways. Moreover, the results of imaging and pathological analyses demonstrated that FA enhanced the bone repair effects of SSCs in an irradiated bone defect mouse model substantially. Importantly, inhibition of the p38/MAPK and ERK/MAPK pathways in SSCs by specific chemical inhibitors partially abolished the promotive effect of FA on SSC‐mediated bone regeneration. In summary, our findings reveal a novel function of FA in repairing irradiated bone defects by maintaining SSC stemness and suggest that the p38/MAPK and ERK/MAPK pathways contribute to SSC‐mediated tissue regeneration postradiation.

Brain function networks (BFN) are widely used in the diagnosis of electroencephalography (EEG)-based major depressive disorder (MDD). Typically, a BFN is constructed by calculating the functional connectivity (FC) between each pair of channels. However, it ignores high-order relationships (e.g., relationships among multiple channels), making it a low-order network. To address this issue, a novel classification framework, based on matrix variate normal distribution (MVND), is proposed in this study. The framework can simultaneously generate high-and low-order BFN and has a distinct mathematical interpretation. Specifically, the entire time series is first divided into multiple epochs. For each epoch, a BFN is constructed by calculating the phase lag index (PLI) between different EEG channels. The BFNs are then used as samples, maximizing the likelihood of MVND to simultaneously estimate its low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN). In addition, to solve the problem of the excessively high dimensionality of Ho-BFN, Kronecker product decomposition is used for dimensionality reduction while retaining the original high-order information. The experimental results verified the effectiveness of Ho-BFN for MDD diagnosis in 24 patients and 24 normal controls. We further investigated the selected discriminative Lo-BFN and Ho-BFN features and revealed that those extracted from different networks can provide complementary information, which is beneficial for MDD diagnosis.

Today's brain imaging modality migration techniques are transformed from one modality data in one domain to another. In the specific clinical diagnosis, multiple modal data can be obtained in the same scanning field, and it is more beneficial to synthesize missing modal data by using the diversity characteristics of multiple modal data. Therefore, we introduce a self-supervised learning cycle-consistent generative adversarial network (BSL-GAN) for brain imaging modality transfer. The framework constructs multi-branch input, which enables the framework to learn the diversity characteristics of multimodal data. In addition, their supervision information is mined from large-scale unsupervised data by establishing auxiliary tasks, and the network is trained by constructing supervision information, which not only ensures the similarity between the input and output of modal images, but can also learn valuable representations for downstream tasks.

Background/PurposeCurrently, there are no effective tools to accurately assess acute biliary pancreatitis (ABP) risk in patients with gallstones. This study aimed to develop an ABP risk nomogram in patients with symptomatic gallstones.MethodsWe conducted a retrospective nested case-control study and data on 816 conservatively treated patients with symptomatic gallstones admitted to The First Affiliated Hospital of Harbin Medical University between January 6, 2007 and January 22, 2016 were retrospectively collected. We conducted a propensity-score matched (PSM) analysis based on follow-up time in a ratio of 1:4 between ABP group (n=65) and non-ABP group (n=260). These matched patients were randomly divided into study cohort (n=229) and validation cohort (n=96) according to a ratio of 7:3. In the study cohort, independent risk factors for ABP occurrence identified using Cox regression were included in nomogram. Nomogram performance and discrimination were assessed using the concordance index (C-index), area under the curve (AUC), calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC). The model was also validated in the validation cohort.ResultsNomogram was based on 7 independent risk factors: age, diabetes history, gallbladder wall thickness, gallstone diameter, coexisting common bile duct (CBD) stones, direct bilirubin (DBIL), and white blood cell count (WBC). The C-index of nomogram was 0.888, and the 10-year AUCs of nomogram was 0.955. In the validation cohort, nomogram still had good discrimination (C-index, 0.857; 10-year AUC, 0.814). The calibration curve showed good homogeneity between the prediction by nomogram and the actual observation. DCA and CIC demonstrated that nomogram was clinically useful.ConclusionsThe ABP risk nomogram incorporating 7 features is useful to predict ABP risk in symptomatic gallstone patients.