GBDT Method Integrating Feature-Enhancement and Active-Learning Strategies-Sea Ice Thickness Inversion in Beaufort Sea.

Sea ice, as an important component of the Earth's ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has important research significance. Due to environmental and equipment-related limitations, the number of samples available for remote sensing inversion is currently insufficient. At high spatial resolutions, remote sensing data contain limited information and noise interference, which seriously affect the accuracy of sea ice thickness inversion. In response to the above issues, we conducted experiments using ice draft data from the Beaufort Sea and designed an improved GBDT method that integrates feature-enhancement and active-learning strategies (IFEAL-GBDT). In this method, the incident angle and time series are used to perform spatiotemporal correction of the data, reducing both temporal and spatial impacts. Meanwhile, based on the original polarization information, effective multi-attribute features are generated to expand the information content and improve the separability of sea ice with different thicknesses. Taking into account the growth cycle and age of sea ice, attributes were added for month and seawater temperature. In addition, we studied an active learning strategy based on the maximum standard deviation to select more informative and representative samples and improve the model's generalization ability. The improved GBDT model was used for training and prediction, offering advantages in dealing with nonlinear, high-dimensional data, and data noise problems, further expanding the effectiveness of feature-enhancement and active-learning strategies. Compared with other methods, the method proposed in this paper achieves the best inversion accuracy, with an average absolute error of 8 cm and a root mean square error of 13.7 cm for IFEAL-GBDT and a correlation coefficient of 0.912. This research proves the effectiveness of our method, which is suitable for the high-precision inversion of sea ice thickness determined using Sentinel-1 data.

Automatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2.

BACKGROUND: Accurate segmentation of critical anatomical structures in fetal four-chamber view images is essential for the early detection of congenital heart defects. Current prenatal screening methods rely on manual measurements, which are time-consuming and prone to inter-observer variability. This study develops an AI-based model using the state-of-the-art nnU-NetV2 architecture for automatic segmentation and measurement of key anatomical structures in fetal four-chamber view images. METHODS: A dataset, consisting of 1,083 high-quality fetal four-chamber view images, was annotated with 15 critical anatomical labels and divided into training/validation (867 images) and test (216 images) sets. An AI-based model using the nnU-NetV2 architecture was trained on the annotated images and evaluated using the mean Dice coefficient (mDice) and mean intersection over union (mIoU) metrics. The model's performance in automatically computing the cardiac axis (CAx) and cardiothoracic ratio (CTR) was compared with measurements from sonographers with varying levels of experience. RESULTS: The AI-based model achieved a mDice coefficient of 87.11% and an mIoU of 77.68% for the segmentation of critical anatomical structures. The model's automated CAx and CTR measurements showed strong agreement with those of experienced sonographers, with respective intraclass correlation coefficients (ICCs) of 0.83 and 0.81. Bland-Altman analysis further confirmed the high agreement between the model and experienced sonographers. CONCLUSION: We developed an AI-based model using the nnU-NetV2 architecture for accurate segmentation and automated measurement of critical anatomical structures in fetal four-chamber view images. Our model demonstrated high segmentation accuracy and strong agreement with experienced sonographers in computing clinically relevant parameters. This approach has the potential to improve the efficiency and reliability of prenatal cardiac screening, ultimately contributing to the early detection of congenital heart defects.

Can low-carbon cities pilot policy promote enterprise sustainable development? Quasi-experimental evidence from China.

With the predicament of sustainable improvement in traditional cities, the low-carbon city pilot policy (LCCPP), as a novel development mode, provides thinking for resolving the tensions of green development, resource conservation and environmental protection among firms. Using Chinese A-share listed companies panel data during 2007-2019, this study adopts the difference-in-differences model to explore the impact of LCCPP on firm green innovation. Based on theoretical analysis, LCCPP-driven environmental rules have the impact of encouraging business green innovation. The relationship between LCCPP and green innovation is strengthened by external media attention and organizational redundancy resources. The mechanism study shows that the incentive effect of LCCPP on firm green innovation is mainly due to the improvement of enterprises' green total factor productivity and financial stability. In addition, the heterogeneity analysis shows that the LCCPP has significantly positive effects in promoting green innovation in high-carbon industries and state-owned enterprises. This research contributes to the understanding of city-level low-carbon policies as a driving force for corporate green innovation, offering practical implications for policymakers and businesses striving for sustainability.

A comparative study of progressive failure of granite and marble rock bridges under direct shearing.

Shear failure of rock bridges is an important process in geological phenomena, including landslides and earthquakes. However, the progressive failure of natural rock bridges has not yet been fully understood. In this work, we carried out direct shearing experiments on both granite and marble rock bridges, and applied acoustic emission (AE) monitoring throughout the experiments. With the mechanical curves and the evolution of AE activity (including AE energy rate and b value), the failure of rock bridges can be divided into three pre-failure phases and one ultimate failure phases. We analyzed the effects of normal stress and lithology on the pre-failure phases. We noted that with the increasing of normal stress, the length of stable cracking phase decreases and the length of unstable cracking phase slightly increases, except for marble rock bridges at high normal stress, which maintains a great proportion of stable cracking phase that possibly results from the great off-fault damage. Increasing normal stress also suppresses the dilation of granite rock bridges, but has a different effect on marble rock bridges, which also suggests the effect of lithology on failure modes.

An ultrasound-based ensemble machine learning model for the preoperative classification of pleomorphic adenoma and Warthin tumor in the parotid gland.

OBJECTIVES: The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT. METHODS: A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong's test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model. RESULTS: The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774-0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720-0.932) and clinical (0.814; 95% CI, 0.682-0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively. CONCLUSIONS: The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland. CLINICAL RELEVANCE STATEMENT: This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application. KEY POINTS: • Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy. • Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy. • The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.

Glycolysis­related lncRNA may be associated with prognosis and immune activity in grade II­III glioma.

Glucose metabolism, as a novel theory to explain tumor cell behavior, has been intensively studied in various tumors. The present study explored the long non-coding RNAs (lncRNAs) related to glycolysis in grade II-III glioma, aiming to provide a promising target for further research. Pearson correlation analysis was used to identify glycolysis-related lncRNAs. Univariate/multivariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator algorithm were applied to identify glycolysis-related lncRNAs to construct a prognosis prediction model. Subsequently, multi-dimensional evaluations were used to verify whether the risk model could predict the prognosis and survival rate of patients with grade II-III glioma. Finally, it was verified by functional experiments. The present study finally identified seven glycolysis-related lncRNAs (CRNDE, AC022034.1, RHOQ-AS1, AL159169.2, AL133215.2, AC007098.1 and LINC02587) to construct a prognosis prediction model. The present study further investigated the underlying immune microenvironment, somatic landscape and functional enrichment pathways. Additionally, individualized immunotherapeutic strategies and candidate compounds were identified to guide clinical treatment. The experimental results demonstrated that CRNDE could increase the proliferation of SHG-44 cells. In conclusion, a large sample of human grade II-III glioma in The Cancer Genome Atlas database was used to construct a risk model using glycolysis-related lncRNAs to predict the prognosis of patients with grade II-III glioma.

ANXA9 facilitates S100A4 and promotes breast cancer progression through modulating STAT3 pathway.

Breast cancer has the highest global incidence and mortality rates among all cancer types. Abnormal expression of the Annexin family has been observed in different malignant tumors, including upregulated ANXA9 in breast cancer. We found highly expressed ANXA9 in metastatic breast cancer tissues, which is correlated with breast cancer progression. In vitro, the functional experiments indicated ANXA9 influenced breast cancer proliferation, motility, invasion, and apoptosis; in vivo, downregulation of ANXA9 suppressed breast cancer xenograft tumor growth and lung metastasis. Mechanically, on one side, we found that ANXA9 could mediate S100A4 and therefore regulate AKT/mTOR/STAT3 pathway to participate p53/Bcl-2 apoptosis; on the other side, we found ANXA9 transferred S100A4 from cells into the tumor microenvironment and mediated the excretion of cytokines IL-6, IL-8, CCL2, and CCL5 to participate angiogenesis via self- phosphorylation at site Ser2 and site Thr69. Our findings demonstrate significant involvement of ANXA9 in promoting breast cancer progression, thereby suggesting that therapeutic intervention via targeting ANXA9 may be effective in treating metastatic breast cancer.

Urolithin A inhibits breast cancer progression via activating TFEB-mediated mitophagy in tumor macrophages.

INTRODUCTION: Urolithin A (UA) is a naturally occurring compound that is converted from ellagitannin-like precursors in pomegranates and nuts by intestinal flora. Previous studies have found that UA exerts tumor-suppressive effects through antitumor cell proliferation and promotion of memory T-cell expansion, but its role in tumor-associated macrophages remains unknown. OBJECTIVES: Our study aims to reveal how UA affects tumor macrophages and tumor cells to inhibit breast cancer progression. METHODS: Observe the effect of UA treatment on breast cancer progression though in vivo and in vitro experiments. Western blot and PCR assays were performed to discover that UA affects tumor macrophage autophagy and inflammation. Co-ip and Molecular docking were used to explore specific molecular mechanisms. RESULTS: We observed that UA treatment could simultaneously inhibit harmful inflammatory factors, especially for InterleuKin-6 (IL-6) and tumor necrosis factor α (TNF-α), in both breast cancer cells and tumor-associated macrophages, thereby improving the tumor microenvironment and delaying tumor progression. Mechanistically, UA induced the key regulator of autophagy, transcription factor EB (TFEB), into the nucleus in a partially mTOR-dependent manner and inhibited the ubiquitination degradation of TFEB, which facilitated the clearance of damaged mitochondria via the mitophagy-lysosomal pathway in macrophages under tumor supernatant stress, and reduced the deleterious inflammatory factors induced by the release of nucleic acid from damaged mitochondria. Molecular docking and experimental studies suggest that UA block the recognition of TFEB by 1433 and induce TFEB nuclear localization. Notably, UA treatment demonstrated inhibitory effects on tumor progression in multiple breast cancer models. CONCLUSION: Our study elucidated the anti-breast cancer effect of UA from the perspective of tumor-associated macrophages. Specifically, TFEB is a crucial downstream target in macrophages.

The Effects of Hypoxia-Preconditioned Dental Stem Cell-Derived Secretome on Tissue Regeneration.

Mesenchymal stroma cells derived from oral tissues are known as dental stem cells (DSCs). Owing to their unique therapeutic niche and clinical accessibility, DSCs serve as a promising treatment option for bone defects and oral tissue regeneration. DSCs exist in a hypoxic microenvironment in vivo, which is far lower than the current 20% oxygen concentration used in in vitro culture. It has been widely reported that the application of an oxygen concentration less than 5% in the culture of DSCs is beneficial for preserving stemness and promoting proliferation, migration, and paracrine activity. The paracrine function of DSCs involves the secretome, which includes conditioned media (CM) and soluble bioactive molecules, as well as extracellular vesicles extracted from CM. Hypoxia can play a role in immunomodulation and angiogenesis by altering the protein or nucleic acid components in the secretory group, which enhances the therapeutic potential of DSCs. This review summarizes the biological characteristics of DSCs, the influence of hypoxia on DSCs, the impact of hypoxia on the secretory group of DSCs, and the latest progress on the use of DSCs secretory group in tissue regeneration based on hypoxia pretreatment. We highlighted the multifunctional biological effect of hypoxia culture on tissue regeneration and provided a summary of the current mechanism of hypoxia in the pretreatment of DSCs.

Spinal Lymphatic Dysfunction Aggravates the Recovery Process After Spinal Cord Injury.

We aimed to evaluate the role of the spinal lymphatic system in spinal cord injury and whether it has an impact on recovery after spinal cord injury. Flow cytometry was used to evaluate the changes in the number of microvesicles after spinal cord injury. Evans blue extravasation was used to evaluate the function of the lymphatic system. Evans blue extravasation and immunofluorescence were used to evaluate the permeability of blood spinal cord barrier. The spinal cord edema was evaluated by dry and wet weight.Terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay was used to evaluate apoptosis after spinal cord injury. Nuclear factor-kappa B pathway was detected by Western blot. Behavioral tests were used to evaluate limb function. Microvesicles released after spinal cord injury can enter the thoracic duct and then enter the blood through the lymph around the spine. After ligation of the thoracic duct, it can aggravate the neuropathological manifestations and limb function after spinal cord injury. The potential mechanism may involve nuclear factor-kappa B pathway.

Data-driven assisted real-time optimal control strategy of submerged arc furnace via intelligent energy terminals considering large-scale renewable energy utilization.

This study presents a data-driven assisted real-time optimization model which is an innovative approach to address the challenges posed by integrating Submerged Arc Furnace (SAF) systems with renewable energy sources, specifically photovoltaic (PV) and wind power, with modern intelligent energy terminals. Specifically, the proposed method is divided into two stages. The first stage is related to data-driven prediction for addressing local time-varying renewable energy and electricity market prices with predicted information, and the second stage uses an optimization model for real-time SAF dispatch. Connections between intelligent energy terminals, demand-side devices, and load management systems are established to enhance local renewable resource utilization. Additionally, mathematical formulations of the operating resistance in SAF are explored, and deep neuron networks are employed and modified for dynamic uncertainty prediction. The proposed approach is validated through a case study involving an intelligent energy terminal with a 12.5 MVA SAF system and 12 MW capacity renewable generators in an electricity market with fluctuating prices. The findings of this research underscore the efficacy of the proposed optimization model in reducing operational costs and enhancing the utilization of localized renewable energy generation. By integrating four distinct dissatisfaction coefficients into the optimization framework, we demonstrate the model's adaptability and efficiency. The application of the optimization strategy delineated herein results in the SAF system's profitability oscillating between $111 and $416 across various time intervals, contingent upon the coefficient settings. Remarkably, an aggregate daily loss recovery amounting to $1,906.84 can be realized during the optimization period. Such outcomes not only signify considerable economic advantages but also contribute to grid stability and the diminution of renewable energy curtailment, thereby underscoring the dual benefits of economic efficiency and sustainability in energy management practices.

Human periodontal ligament stem cell sheets activated by graphene oxide quantum dots repair periodontal bone defects by promoting mitochondrial dynamics dependent osteogenic differentiation.

BACKGROUND: Bone defects in the maxillofacial region restrict the integrity of dental function, posing challenges in clinical treatment. Bone tissue engineering (BTE) with stem cell implants is an effective method. Nanobiomaterials can effectively enhance the resistance of implanted stem cells to the harsh microenvironment of bone defect areas by promoting cell differentiation. Graphene oxide quantum dots (GOQDs) are zero-dimensional nanoscale derivatives of graphene oxide with excellent biological activity. In the present study, we aimed to explore the effects of GOQDs prepared by two methods (Y-GOQDs and B-GOQDs) on the osteogenic differentiation of human periodontal ligament stem cells (hPDLSCs), as well as the effect of gelatin methacryloyl (GelMA)-encapsulated GOQD-induced hPDLSC sheets on the repair of mandibular periodontal defects in rats. We also explored the molecular biological mechanism through which GOQD promotes bone differentiation. RESULTS: There were significant differences in oxygen-containing functional groups, particle size and morphology between Y-GOQDs and B-GOQDs. Y-GOQDs promoted the osteogenic differentiation of hPDLSCs more effectively than did B-GOQDs. In addition, GelMA hydrogel-encapsulated Y-GOQD-induced hPDLSC cell sheet fragments not only exhibited good growth and osteogenic differentiation in vitro but also promoted the repair of mandibular periodontal bone defects in vivo. Furthermore, the greater effectiveness of Y-GOQDs than B-GOQDs in promoting osteogenic differentiation is due to the regulation of hPDLSC mitochondrial dynamics, namely, the promotion of fusion and inhibition of fission. CONCLUSIONS: Overall, Y-GOQDs are more effective than B-GOQDs at promoting the osteogenic differentiation of hPDLSCs by regulating mitochondrial dynamics, which ultimately contributes to bone regeneration via the aid of the GelMA hydrogels in vivo.

Research Progress on the Geomechanical Properties of Block-in-Matrix Rocks.

The differences in geomechanical properties and the uncertainty in the spatial distribution of Bimrock pose significant challenges to the construction and disaster prediction of geotechnical engineering. To clarify the geomechanical characteristics of Bimrock, this paper summarizes the basic concepts and classification methods of Bimrock at home and abroad. It discusses the methods and characteristics of determining the geometric features of Bimrock blocks and explores the influencing factors and laws of failure modes and strength under different stress states of Bimrock. The study finds that the failure mode of Bimrock is mainly influenced by factors such as block proportion, degree of welding between blocks and matrix, strength ratio between blocks and matrix, and geometric properties of blocks. Among these factors, block proportion is the most significant, and the degree of welding is a controlling factor. However, due to the complexity of Bimrock structures, there is a lack of applicable methods and mechanical models for the evaluation of geomechanical characteristics of Bimrock in engineering practice. This article also explores the influence and research methods of the geological characteristics of Bimrock in slope and tunnel engineering and, finally, provides prospects for the future research trends relating to Bimrock.

Development of Sustainable and Active Food Packaging Materials Composed by Chitosan, Polyvinyl Alcohol and Quercetin Functionalized Layered Clay.

In order to solve the problems of insufficient active functions (antibacterial and antioxidant activities) and the poor degradability of traditional plastic packaging materials, biodegradable chitosan (CS)/polyvinyl alcohol (PVA) nanocomposite active films reinforced with natural plant polyphenol-quercetin functionalized layered clay nanosheets (QUE-LDHs) were prepared by a solution casting method. In this study, QUE-LDHs realizes a combination of the active functions of QUE and the enhancement effect of LDHs nanosheets through the deposition and complexation of QUE and copper ions on the LDHs. Infrared and thermal analysis results revealed that there was a strong interface interaction between QUE-LDHs and CS/PVA matrix, resulting in the limited movement of PVA molecules and the increase in glass transition temperature and melting temperature. With the addition of QUE-LDHs, the active films showed excellent UV barrier, antibacterial, antioxidant properties and tensile strength, and still had certain transparency in the range of visible light. As QUE-LDHs content was 3 wt%, the active films exhibited a maximum tensile strength of 58.9 MPa, representing a significant increase of 40.9% compared with CS/PVA matrix. Notably, the UV barrier (280 nm), antibacterial (E. coli) and antioxidant activities (DPPH method) of the active films achieved 100.0%, 95.5% and 58.9%, respectively. Therefore, CS/PVA matrix reinforced with QUE-LDHs has good potential to act as an environmentally and friendly active packaging film or coating.

Impact of molar teeth distalization by clear aligners on maxillary alveolar bone thickness and root resorption: a three­dimensional study.

OBJECTIVE: This study aimed to evaluate the impact of molar teeth distalization movement by clear aligners on changes in the alveolar bone thickness and orthodontically induced inflammatory root resorption (OIIRR) in maxillary molars using conebeam computed tomography (CBCT). MATERIALS AND METHODS: Three-dimensional CBCT scans of 35 adult patients (one hundred forty maxillary molars) with pre-designed selection criteria and a mean age of 24.4 ± 7.1 years were included. The measured parameters, including alveolar bone thickness for maxillary molars and root resorption (OIIRR), were analyzed using pre-and post-treatment CBCT (T0 and T1, respectively) with Invivo 6.0 software. RESULT: Post-treatment, relevant statistically significant changes included deposition of bone in the average palatal surface of the 1st molars. The reduction of bone was seen in the average buccal surface of the first molars and both surfaces of the second molars. Regarding root length after treatment, the average maxillary 1st molar roots showed significant OIIRR (p < 0.001). CONCLUSION: Clear aligner treatment could effectively reduce the incidence of alveolar bone thickness reduction and OIIRR in treating Class II malocclusions compared to conventional braces, as shown in previous studies. This research will aid in fully grasping the benefits of clear aligners.

LINC01572 promotes triple-negative breast cancer progression through EIF4A3-mediated ß-catenin mRNA nuclear exportation.

Long noncoding RNAs have been reported to be involved in the development of breast cancer. LINC01572 was previously reported to promote the development of various tumors. However, the potential biological function of LINC01572 in breast cancer remains largely unknown. R language was used to perform bioinformatic analysis of The Cancer Genome Atlas data. The expression level of RNAs was examined by RT-qPCR. The effect of knocking down or overexpression LINC01572 in triple-negative breast cancer (TNBC) cell lines was evaluated by detecting cell proliferation, migrant action. RNA immunoprecipitation assay and RNA pull-down assay were performed to explore the regulatory relationship between LINC01572, EIF4A3, and ß-catenin. Bioinformatics analysis identifies LINC01572 as an oncogene of breast cancer. LINC01572 is over-expressed in TNBC tissues and cell lines, correlated with poor clinical prognosis in BC patients. Cell function studies confirmed that LINC01572 facilitated the proliferation and migration of TNBC cells in both vivo and vitro. Mechanistically, ß-catenin mRNA and EIF4A3 combine spatially to form a complex, LINC01572 helps transport this complex from the nucleus to the cytoplasm, thereby facilitating the translation of ß-catenin. Our findings confirm that LINC01572 acts as a tumor promoter and may act as a biomarker in TNBC. In addition, novel molecular regulatory relationships involving LINC01572/EIF4A3/ß-catenin are critical to the development of TNBC, which led to a new understanding of the mechanisms of TNBC progression and shows a new target for precision treatment for TNBC.

Predictive modeling for eosinophilic chronic rhinosinusitis: Nomogram and four machine learning approaches.

Eosinophilic chronic rhinosinusitis (ECRS) is a distinct subset of chronic rhinosinusitis characterized by heightened eosinophilic infiltration and increased symptom severity, often resisting standard treatments. Traditional diagnosis requires invasive histological evaluation. This study aims to develop predictive models for ECRS based on patient clinical parameters, eliminating the need for invasive biopsy. Utilizing logistic regression with lasso regularization, random forest (RF), gradient-boosted decision tree (GBDT), and deep neural network (DNN), we trained models on common clinical data. The predictive performance was evaluated using metrics such as area under the curve (AUC) for receiver operator characteristics, decision curves, and feature ranking analysis. In a cohort of 437 eligible patients, the models identified peripheral blood eosinophil ratio, absolute peripheral blood eosinophil, and the ethmoidal/maxillary sinus density ratio (E/M) on computed tomography as crucial predictors for ECRS. This predictive model offers a valuable tool for identifying ECRS without resorting to histological biopsy, enhancing clinical decision-making.