Research on prediction of coal water medium separation effect based on multi-models.

To improve the separation efficiency of raw coal and ensure clean use, the accurate calculation of the partition coefficients (PCs) in coal water medium sorting is required. Single models have been used to predict the partition coefficient (PC) for decades, but their accuracy remains constrained. This study proposes a multi-model (MM) calculation method based on the Gompertz model (GM), the Logistic model (LM), the Arctangent model (AM), and the Approximate formula (AFM) to improve the accuracy of the predicted coal water medium sorting PCs. Four groups of coal samples and two specific cases were used to verify the accuracy of the MM calculation method. The PCs of the MM method had a minimal Ef (0.91-8.84), a maximal R2 (0.9648-0.9994), a maximal F-value (199.17-11352.31), and the highest significance of all the models. The MM method was found to be the most suitable of all the models for predicting any coal water medium separation process. Further, when calculating the PC for cleaned coal ash, the separation density of MM is closer to the actual separation density than that of either the GM, LM, AM, or AFM models. The MM method, therefore, produces more accurate results compared to a single model. MM is expected to predict the PC based on the required cleaned coal ash, and then regulate the sorting density to improve the production efficiency.

A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions.

OBJECTIVE: The aim of this study is to conduct a systematic evaluation of the diagnostic efficacy of Breast Imaging Reporting and Data System (BI-RADS) 4 benign and malignant breast lesions using magnetic resonance imaging (MRI) radiomics. METHODS: A systematic search identified relevant studies. Eligible studies were screened, assessed for quality, and analyzed for diagnostic accuracy. Subgroup and sensitivity analyses explored heterogeneity, while publication bias, clinical relevance and threshold effect were evaluated. RESULTS: This study analyzed a total of 11 studies involving 1,915 lesions in 1,893 patients with BI-RADS 4 classification. The results showed that the combined sensitivity and specificity of MRI radiomics for diagnosing BI-RADS 4 lesions were 0.88 (95% CI 0.83-0.92) and 0.79 (95% CI 0.72-0.84). The positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 4.2 (95% CI 3.1-5.7), 0.15 (95% CI: 0.10-0.22), and 29.0 (95% CI 15-55). The summary receiver operating characteristic (SROC) analysis yielded an area under the curve (AUC) of 0.90 (95% CI 0.87-0.92), indicating good diagnostic performance. The study found no significant threshold effect or publication bias, and heterogeneity among studies was attributed to various factors like feature selection algorithm, radiomics algorithms, etc. Overall, the results suggest that MRI radiomics has the potential to improve the diagnostic accuracy of BI-RADS 4 lesions and enhance patient outcomes. CONCLUSION: MRI-based radiomics is highly effective in diagnosing BI-RADS 4 benign and malignant breast lesions, enabling improving patients' medical outcomes and quality of life.

Development and validation of a nomogram to predict medication risk based on a knowledge, attitude and practice (KAP) survey of residents in Shanxi Province, China.

Objective: Unsafe medication practices and medication errors are a major cause of harm in healthcare systems around the world. This study aimed to explore the factors that influence the risk of medication and provide medication risk evaluation model for adults in Shanxi province, China. Methods: The data was obtained from the provincial questionnaire from May to December 2022, relying on the random distribution of questionnaires and online questionnaires by four hospitals in Shanxi Province. Multiple linear regression analysis was used to explore the factors affecting the KAP score of residents. Univariate and multivariate logistic regression was used to determine the independent risk factors, and the nomogram was verified by receiver operating characteristic curve, calibration and decision curve analysis. Results: A total of 3,388 questionnaires were collected, including 3,272 valid questionnaires. The average scores of drugs KAP were 63.2 ± 23.04, 33.05 ± 9.60, 23.67 ± 6.75 and 33.16 ± 10.87, respectively. On the evaluation criteria of the questionnaire, knowledge was scored "fair", attitude and practice were scored "good". Sex, monthly income, place of residence, insurance status, education level, and employment were regarded as independent risk factors for medication and a nomogram was established by them. Conclusion: Males, low-income, and low-educated people are important factors affecting the risk of medication. The application of the model can help residents understand the risk of their own medication behavior and reduce the harm of medication.

Initial [18F]DCFPyL PET/CT in treatment-naïve prostate cancer: correlation with post-ADT PSA outcomes and recurrence.

PURPOSE: Positron emission tomography (PET) with prostate-specific membrane antigen (PSMA) targeting tracers has emerged as a valuable diagnostic tool for prostate cancer (PCa), androgen deprivation therapy (ADT) stands as the cornerstone treatment for advanced PCa, yet forecasting the response to hormonal therapy poses a significant clinical hurdle. METHODS: In a prospective cohort of 86 PCa patients undergoing short-term ADT, this study evaluated the prognostic potential of [18F]DCFPyL PET/CT scans. Comprehensive data encompassing clinical profiles, baseline prostate-specific antigen (PSA) levels, and imaging metrics were assessed. We developed predictive models for assessing decreases in PSA levels (PSA50 and PSA70) based on a combination of PET-related parameters and clinical factors. Kaplan-Meier survival analysis was utilized to ascertain the prognostic value of PET-based metrics. RESULTS: In this study, elevated [18F]DCFPyL uptake within the primary tumor, as indicated by a SUV ≥ 6.78 (p = 0.0024), and a reduction in the tumor volume (TV) of primary PSMA-avid tumor with PSMA-TV < 41.96 cm3 (p = 0.038), as well as an increased burden of metastatic PSMA-avid tumor, with PSMA-TV (PSMA-TV ≥ 71.39 cm3) (p = 0.012) were identified in association with diminished progression-free survival (PFS). PET and clinical parameters demonstrated constrained predictive capacity for PSA50 response as indicated by an area under the curve (AUC) of 0.442. CONCLUSION: Our study revealed that pretreatment [18F]DCFPyL uptake in primary or metastatic tumor sites is prognostically relevant in high-risk PCa patients undergoing ADT. Further research is needed to develop robust predictive models in this multifaceted landscape of PCa management.

Investigating the Mechanism of Diethylenetriamine Pentamethylene Phosphonic Acid as a Depressant for Calcite Flotation of Fluorite.

Fluorite and calcite have been attracting research attention for a long time. This paper reports on an investigation of the use of diethylene triamine pentamethylphosphonic acid (DTPMPA) as a chelating inhibitor. DTPMPA was used as a chelating inhibitor to study the flotation, separation, and adsorption behaviors of fluorite and calcite minerals. The microflotation experiment showed that the maximum separation of fluorite and calcite can be achieved with a DTPMPA dosage of 1.5 × 10-4 mol/L under weakly alkaline conditions (pH = 8). Zeta potential measurement, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy were used to confirm that DTPMPA was adsorbed on the surface of calcite, inhibiting NaOl adsorption. Additionally, density functional theory calculations showed that oxygen in the DTPMPA phosphate group formed the most stable bidentate binuclear adsorption configuration by chelating with calcium on the calcite surface. Through detection analysis and simulation calculations, the results showed that DTPMPA exhibited significantly weaker adsorption on fluorite compared to that on calcite, highlighting its selective inhibition ability on calcite.

Point Cloud Registration Method Based on Geometric Constraint and Transformation Evaluation.

Existing point-to-point registration methods often suffer from inaccuracies caused by erroneous matches and noisy correspondences, leading to significant decreases in registration accuracy and efficiency. To address these challenges, this paper presents a new coarse registration method based on a geometric constraint and a matrix evaluation. Compared to traditional registration methods that require a minimum of three correspondences to complete the registration, the proposed method only requires two correspondences to generate a transformation matrix. Additionally, by using geometric constraints to select out high-quality correspondences and evaluating the matrix, we greatly increase the likelihood of finding the optimal result. In the proposed method, we first employ a combination of descriptors and keypoint detection techniques to generate initial correspondences. Next, we utilize the nearest neighbor similarity ratio (NNSR) to select high-quality correspondences. Subsequently, we evaluate the quality of these correspondences using rigidity constraints and salient points' distance constraints, favoring higher-scoring correspondences. For each selected correspondence pair, we compute the rotation and translation matrix based on their centroids and local reference frames. With the transformation matrices of the source and target point clouds known, we deduce the transformation matrix of the source point cloud in reverse. To identify the best-transformed point cloud, we propose an evaluation method based on the overlap ratio and inliers points. Through parameter experiments, we investigate the performance of the proposed method under various parameter settings. By conducting comparative experiments, we verified that the proposed method's geometric constraints, evaluation methods, and transformation matrix computation consistently outperformed other methods in terms of root mean square error (RMSE) values. Additionally, we validated that our chosen combination for generating initial correspondences outperforms other descriptor and keypoint detection combinations in terms of the registration result accuracy. Furthermore, we compared our method with several feature-matching registration methods, and the results demonstrate the superior accuracy of our approach. Ultimately, by testing the proposed method on various types of point cloud datasets, we convincingly established its effectiveness. Based on the evaluation and selection of correspondences and the registration result's quality, our proposed method offers a solution with fewer iterations and higher accuracy.

A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.

Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.

Preparation of Ce-Doped Gd3(Al, Ga)5O12 Nanopowders via Microwave-Assisted Homogenization Precipitation for Transparent Ceramic Scintillators.

Ce-doped gadolinium gallium aluminum oxide (Ce: GGAG) precursors were first prepared by the microwave-assisted homogeneous precipitation method (MAHP). Thermal gravity-differential thermal analysis (TG-DTA), X-ray diffraction (XRD), specific surface area analysis (BET) and field emission scanning electron microscopy (FE-SEM) were employed to investigate the crystal structure, phase evolution and morphologies of the Ce: GGAG precursors and powders. The influence of Ga ion concentration in the salt solution on the properties of Ce: GGAG powders was investigated. All the precursors were transformed into single-phase GGAG after being calcined at 950 °C in a furnace for 3 h. Monodispersed Ce: GGAG powders were obtained as the Ga ion concentration was lower than 0.06 mol/L. Single-phase and dense Ce: GGAG ceramics were obtained after sintering at 1600 °C in a flowing oxygen atmosphere for 10 h. Specifically, the Ce: GGAG ceramic reached its maximum density of ~6.68 g/cm3, which was close to its theoretical density of 6.70 g/cm3, and exhibited the highest optical transmittance of 65.2% at 800 nm after hot isostatic pressing sintering (HIP) as the Ga ion concentration was 0.02 mol/L. The decay time and light yield of the GGAG ceramic were 35 ns and 35,000 ± 1250 ph/MeV, respectively, suggesting that Ce: GGAG ceramics prepared using MAHP-synthesized nanopowders are promising for scintillation applications.

MXene and AgNW based flexible transparent conductive films with sandwich structure for high-performance EMI shielding and electrical heaters.

With the popularization of 5G technology and the development of science and technology, flexible and transparent conductive films (TCF) are increasingly used in the preparation of optoelectronic devices such as electromagnetic shielding devices, transparent flexible heaters, and solar cells. Silver nanowires (AgNW) are considered the best material for replacing indium tin oxide to prepare TCFs due to their excellent comprehensive properties. However, the loose overlap between AgNWs is a significant reason for the high resistance. This article investigates a sandwich structured conductive network composed of AgNW and Ti3C2Tx MXene for high-performance EMI shielding and transparent electrical heaters. Polyethylene pyrrolidone (PVP) solution was used to hydrophilic modify PET substrate, and then MXene, AgNW, and MXene were assembled layer by layer using spin coating method to form a TCF with a sandwich structure. One-dimensional AgNW is used to provide electron transfer channels and improve light penetration, while two-dimensional MXene nanosheets are used for welding AgNWs and adding additional conductive channels. The flexible TCF has excellent transmittance (85.1 % at 550 nm) and EMI shielding efficiency (27.1 dB). At the voltage of 5 V, the TCF used as a heater can reach 85.6 °C. This work offers an innovative approach to creating TCFs for the future generation.

Multifunctional polyaniline modified calcium alginate aerogel membrane with antibacterial, oil-water separation, dye and heavy metal ions removal properties for complex water purification.

With the rapid development of urbanization, the discharge of industrial wastewater has led to increasingly critical water pollution issues. Additionally, heavy metals, organic dyes, microorganisms and oil pollution often coexist and have persistence and harmfulness. Developing materials that can treat these complex pollutants simultaneously has important practical significance. In this study, a calcium alginate-based aerogel membrane (PANI@CA membrane) was prepared by spraying, polymerization, Ca2+ cross-linking and freeze-drying using aniline and sodium alginate as raw materials. Oil-water emulsion can be separated by PANI@CA membrane only under gravity, and the separation efficiency was as high as 99 %. At the same time, the membrane can effectively intercept or adsorb organic dyes and heavy metal ions. The removal rates of methylene blue and Congo red were above 92 % and 63 % respectively even after ten times of cyclic filtration. The removal rate of Pb2+ was up to 95 %. In addition, PANI@CA membrane shows excellent photothermal conversion ability, and it can effectively kill Staphylococcus aureus under 808 nm laser irradiation. PANI@CA membrane has the advantages of low cost, simple preparation, good stability and high recycling ability, and has potential application prospects in wastewater treatment.

Protective Mechanism of Rosa roxburghii Tratt Fermentation Broth against Ultraviolet-A-Induced Photoaging of Human Embryonic Skin Fibroblasts.

This study takes the fruit of Rosa roxburghii Tratt (RRT) as a fermentation substrate and carries out a quantitative visual analysis of the domestic and foreign literature on screenings of five different lactic acid bacteria to obtain a fermentation broth. Systemic anti-photoaging effects are analyzed at the biochemical, cellular, and molecular biological levels. DPPH and ABTS free radical scavenging activities are used to verify the antioxidant capacity of the RRT fruit fermentation broth in vitro. Human embryonic skin fibroblasts (HESs) are used to establish a UVA damage model, and the antioxidant capacity of the RRT fruit fermentation broth is verified in terms of intracellular reactive oxygen species (ROS) and antioxidant enzyme activity. RT-qPCR and ELISA are used to detect the expression of TGF-ß/Smad, MMPs, and the MAPK/AP-1 and Nrf2/Keap-1 signaling pathways in order to explore the anti-oxidation and anti-photoaging effects of the RRT fruit fermentation broth by regulating different signaling pathways. The results show that an RRT fruit fermentation broth can effectively protect cells from oxidative stress caused by UVA and has significant anti-photoaging effects, with the co-cultured Lactobacillus Yogurt Starter LYS-20297 having the highest overall effect.

Population Structure and Selection Signal Analysis of Nanyang Cattle Based on Whole-Genome Sequencing Data.

With a rich breeding history, Nanyang cattle (NY cattle) have undergone extensive natural and artificial selection, resulting in distinctive traits such as high fertility, excellent meat quality, and disease resistance. This makes them an ideal model for studying the mechanisms of environmental adaptability. To assess the population structure and genetic diversity of NY cattle, we performed whole-genome resequencing on 30 individuals. These data were then compared with published whole-genome resequencing data from 432 cattle globally. The results indicate that the genetic structure of NY cattle is significantly different from European commercial breeds and is more similar to North-Central Chinese breeds. Furthermore, among all breeds, NY cattle exhibit the highest genetic diversity and the lowest population inbreeding levels. A genome-wide selection signal analysis of NY cattle and European commercial breeds using Fst, θπ-ratio, and θπ methods revealed significant selection signals in genes associated with reproductive performance and immunity. Our functional annotation analysis suggests that these genes may be responsible for reproduction (MAP2K2, PGR, and GSE1), immune response (NCOA2, HSF1, and PAX5), and olfaction (TAS1R3). We provide a comprehensive overview of sequence variations in the NY cattle genome, revealing insights into the population structure and genetic diversity of NY cattle. Additionally, we identify candidate genes associated with important economic traits, offering valuable references for future conservation and breeding efforts of NY cattle.

Overexpression of KAT8 induces a failure in early embryonic development in mice.

Embryo quality is strongly associated with subsequent embryonic developmental efficiency. However, the detailed function of lysine acetyltransferase 8 (KAT8) during early embryonic development in mice remains elusive. In this study, we reported that KAT8 played a pivotal role in the first cleavage of mouse embryos. Immunostaining results revealed that KAT8 predominantly accumulated in the nucleus throughout the entire embryonic developmental process. Kat8 overexpression (Kat8-OE) was correlated with early developmental potential of embryos to the blastocyst stage. We also found that Kat8-OE embryos showed spindle-assembly defects and chromosomal misalignment, and that Kat8-OE in embryos led to increased levels of reactive oxygen species (ROS), accumulation of phosphorylated γH2AX by affecting the expression of critical genes related to mitochondrial respiratory chain and antioxidation pathways. Subsequently, cellular apoptosis was activated as confirmed by TUNEL (Terminal Deoxynucleotidyl Transferase mediated dUTP Nick-End Labeling) assay. Furthermore, we revealed that KAT8 was related to regulating the acetylation status of H4K16 in mouse embryos, and Kat8-OE induced the hyperacetylation of H4K16, which might be a key factor for the defective spindle/chromosome apparatus. Collectively, our data suggest that KAT8 constitutes an important regulator of spindle assembly and redox homeostasis during early embryonic development in mice.

Deep learning model for the detection of prostate cancer and classification of clinically significant disease using multiparametric MRI in comparison to PI-RADs score.

BACKGROUND: The Prostate Imaging Reporting and Data System (PI-RADS) is an established reporting scheme for multiparametric magnetic resonance imaging (mpMRI) to distinguish clinically significant prostate cancer (csPCa). Deep learning (DL) holds great potential for automating csPCa classification on mpMRI. METHOD: To compare the performance between a DL algorithm and PI-RADS categorization in PCa detection and csPCa classification, we included 1,729 consecutive patients who underwent radical prostatectomy or biopsy in Tongji hospital. We developed DL models by integrating individual mpMRI sequences and employing an ensemble approach for distinguishing between csPCa and CiSPCa (specifically defined as PCa with a Gleason group 1 or benign prostate disease, training cohort: 1,285 patients vs. external testing cohort: 315 patients). RESULTS: DL-based models exhibited higher csPCa detection rates than PI-RADS categorization (area under the curve [AUC]: 0.902; sensitivity: 0.728; specificity: 0.906 vs. AUC: 0.759; sensitivity: 0.761; specificity: 0.756) (P < 0.001) Notably, DL networks exhibited significant strength in the prostate-specific antigen (PSA) arm < 10 ng/ml compared with PI-RADS assessment (AUC: 0.788; sensitivity: 0.588; specificity: 0.883 vs. AUC: 0.618; sensitivity: 0.379; specificity: 0.763) (P = 0.041). CONCLUSIONS: We developed DL-based mpMRI ensemble models for csPCa classification with improved sensitivity, specificity, and accuracy compared with clinical PI-RADS assessment. In the PSA-stratified condition, the DL ensemble model performed better than PI-RADS in the detection of csPCa in both the high PSA group and the low PSA group.

Poly-p-dioxanone Thread Leads to Fat Metabolism Around the Thread in Pig Subcutaneous Back Fat.

Background: When poly-p-dioxanone (PDO) thread is implanted subcutaneously, in addition to collagen hyperplasia, it can also cause denaturation of surrounding adipocytes and reduce the thickness of the fat layer. Hitherto, no studies have thoroughly investigated the effects of PDO thread on adipose tissue. Objectives: In this study, the effect of PDO thread on adipose tissue was investigated in an animal model. Methods: In the current study, PDO thread was implanted into subcutaneous adipose tissue of the back in a miniature pig. Implantation site tissue and control site tissue were taken 12 weeks after implantation for hematoxylin and eosin (H&E) staining and transcriptome sequencing. Gene ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed to investigate the differential gene expression between PDO thread implantation and control site tissue. Results: An obvious decrease in the number, fusion, and denaturation of adipocytes can be seen by H&E staining. Sequencing analysis results showed that many of the genes identified, which were downregulated after PDO thread implantation, were involved in functions and pathways related to lipid metabolism, such as fatty acid metabolism, fatty acid degradation, and lipid cell lipolysis regulation. Some genes related to fatty acid metabolism were significantly downregulated in the PDO tissue at 12 weeks compared to the control tissue. Conclusions: Our results showed PDO thread implantation can cause a decrease in the number of adipocytes, as well as a significant alteration of the expression levels of some genes involved in lipid metabolism-related pathways. PDO thread might play an important role in promoting lipolysis.

Peptide-Functionalized Inorganic Oxide Nanomaterials for Solid Cancer Imaging and Therapy.

The diagnosis and treatment of solid tumors have undergone significant advancements marked by a trend toward increased specificity and integration of imaging and therapeutic functions. The multifaceted nature of inorganic oxide nanomaterials (IONs), which boast optical, magnetic, ultrasonic, and biochemical modulatory properties, makes them ideal building blocks for developing multifunctional nanoplatforms. A promising class of materials that have emerged in this context are peptide-functionalized inorganic oxide nanomaterials (PFIONs), which have demonstrated excellent performance in multifunctional imaging and therapy, making them potential candidates for advancing solid tumor diagnosis and treatment. Owing to the functionalities of peptides in tumor targeting, penetration, responsiveness, and therapy, well-designed PFIONs can specifically accumulate and release therapeutic or imaging agents at the solid tumor sites, enabling precise imaging and effective treatment. This review provides an overview of the recent advances in the use of PFIONs for the imaging and treatment of solid tumors, highlighting the superiority of imaging and therapeutic integration as well as synergistic treatment. Moreover, the review discusses the challenges and prospects of PFIONs in depth, aiming to promote the intersection of the interdisciplinary to facilitate their clinical translation and the development of personalized diagnostic and therapeutic systems by optimizing the material systems.

The synthetic NLR RGA5HMA5 requires multiple interfaces within and outside the integrated domain for effector recognition.

Some plant sensor nucleotide-binding leucine-rich repeat (NLR) receptors detect pathogen effectors through their integrated domains (IDs). Rice RGA5 sensor NLR recognizes its corresponding effectors AVR-Pia and AVR1-CO39 from the blast fungus Magnaporthe oryzae through direct binding to its heavy metal-associated (HMA) ID to trigger the RGA4 helper NLR-dependent resistance in rice. Here, we report a mutant of RGA5 named RGA5HMA5 that confers complete resistance in transgenic rice plants to the M. oryzae strains expressing the noncorresponding effector AVR-PikD. RGA5HMA5 carries three engineered interfaces, two of which lie in the HMA ID and the other in the C-terminal Lys-rich stretch tailing the ID. However, RGA5 variants having one or two of the three interfaces, including replacing all the Lys residues with Glu residues in the Lys-rich stretch, failed to activate RGA4-dependent cell death of rice protoplasts. Altogether, this work demonstrates that sensor NLRs require a concerted action of multiple surfaces within and outside the IDs to both recognize effectors and activate helper NLR-mediated resistance, and has implications in structure-guided designing of sensor NLRs.

Adaptive Clustering for Point Cloud.

The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to large-scale scenes. Therefore, this paper proposes an adaptive clustering segmentation method. In this method, the threshold for clustering points within the point cloud is calculated using the characteristic parameters of adjacent points. After completing the preliminary segmentation of the point cloud, the segmentation results are further refined according to the standard deviation of the cluster points. Then, the cluster points whose number does not meet the conditions are further segmented, and, finally, scene point cloud data segmentation is realized. To test the superiority of this method, this study was based on point cloud data from a park in Guilin, Guangxi, China. The experimental results showed that this method is more practical and efficient than other methods, and it can effectively segment all ground objects and ground point cloud data in a scene. Compared with other segmentation methods that are easily affected by parameters, this method has strong robustness. In order to verify the universality of the method proposed in this paper, we test a public data set provided by ISPRS. The method achieves good segmentation results for multiple sample data, and it can distinguish noise points in a scene.

The association between closed-eye unipedal standing and the risk of cognitive impairment in the elderly: a 7-year community-based cohort study in Wuhan, China.

Objectives: The prevention of cognitive impairment in the elderly is one of the public health priority areas. However, the relationship between closed-eye unipedal standing and cognitive impairment remains unclear. Methods: This study was conducted on a group of elderly individuals from a community, using a prospective cohort study design. Participants were monitored for 7 years and were diagnosed with new-onset cognitive impairment. Logistic regression models and restricted cubic spline (RCS) were used to investigate the relationship between closed-eye unipedal standing and cognitive impairment. Stratified analysis by baseline characteristics were also performed. Results: At baseline, 1,652 people aged 65 years or older were enrolled. Ultimately, 880 participants completed the follow-up and 155 (17.61%) of them satisfied the diagnostic criteria for cognitive impairment at follow-up. Compared to the closed-eye unipedal standing low group as the reference, the middle (OR = 0.601, 95% CI: 0.396-0.911) and high (OR = 0.508, 95% CI: 0.329-0.785) groups had significantly lower cognitive impairment risks. RCS analysis indicated a linear relationship (Pnon - linear = 0.177), with a reduced risk of developing cognitive impairment when the duration of closed-eye unipedal standing was exceeded ~2.920 s. Stratified analysis showed that for female, aged 70 years or younger, with 3 or more years of education, without lack of exercise and without falls within 1 year subgroup, the elderly in the high group of closed-eye unipedal standing had significantly reduced cognitive impairment risks. Conclusion: Among the elderly population, closed-eye unipedal standing duration was linearly and negatively associated with the cognitive impairment risk. The closed-eye unipedal standing duration might be a predictive index for cognitive impairment in the elderly.

Related cellular signaling and consequent pathophysiological outcomes of ubiquitin specific protease 24.

Ubiquitin-specific protease 24 (USP24) is an essential member of the deubiquitinating protease family found in eukaryotes. It engages in interactions with multiple proteins, including p53, MCL-1, E2F4, and FTH1, among others. Through these interactions, USP24 plays a critical role in regulating vital cellular processes such as cell cycle control, DNA damage response, cellular iron autophagy, and apoptosis. Increased levels of USP24 have been observed in various cancer types, including bladder cancer, lung cancer, myeloma, hepatocellular carcinoma, and gastric cancer. However, in certain tumors like kidney cancer, USP24 is significantly downregulated, and the specific mechanism behind this remains unclear. Currently, there are no officially approved USP24 inhibitors available for clinical use. Some existing inhibitors targeting USP24 have shown promising effects in treating malignancies; however, their precise mode of action and information regarding binding sites are not well understood. Moreover, further optimization is required to enhance the selectivity and efficacy of these inhibitors. This review aims to provide a comprehensive overview of recent advancements in understanding the cellular functions of USP24, its association with various diseases, and the development of small-molecule inhibitors that target this protein. In conclusion, USP24 represents a promising therapeutic target for various diseases, and ongoing research will contribute to validating its role and facilitating the development of effective treatments.