View-Driven Multi-View Clustering via Contrastive Double-Learning.

Multi-view clustering requires simultaneous attention to both consistency and the diversity of information between views. Deep learning techniques have shown impressive abilities to learn complex features when working with extensive datasets; however, existing deep multi-view clustering methods often focus only on either consistency information or diversity information, making it difficult to balance both aspects. Therefore, this paper proposes a view-driven multi-view clustering using the contrastive double-learning method (VMC-CD), aiming to generate better clustering results. This method first adopts a view-driven approach to consider information from other views to encourage diversity, thus guiding feature learning. Additionally, it presents the idea of dual contrastive learning to enhance the alignment of views at both the clustering and feature levels. The VMC-CD method's superiority over various cutting-edge methods is substantiated by experimental findings across three datasets, affirming its effectiveness.

Exploring the supply and demand imbalance of carbon and carbon-related ecosystem services for dual­carbon goal ecological management in the Huaihe River Ecological Economic Belt.

The measurement of carbon and carbon-related ecosystem services (CCESs) has garnered considerable global attention, primarily due to dual­carbon goals, which are crucial for the rational allocating of ecosystem service (ES) resources and the enhancement of terrestrial carbon sinks. This study developed a novel research framework on CCESs to quantitatively measure carbon storage (CS), food production (FS), habitat quality (HQ), soil conservation (SC), and water yield (WY), and examined the spatiotemporal patterns of the supply-demand and trade-off/synergy processes related to CCESs in the Huaihe River Ecological Economic Belt (HREEB). The findings are as follows: (1) From 2000 to 2020, the supply-demand of the CCESs generally increased, except for carbon storage and food demand. Overall, the supply level of the CCESs exceeds the demand level, with a median ratio of supply and demand ratio (ESDR) of 1.13. (2) During the study period, the synergy relationship of the CCESs is mainly determined by the supply side of the CS-HQ and CS-SC, while on the demand side, it is determined by the CD- FD. And the ESDR of all C-related ecosystem services showed a significant synergy strengthening with CS in the HREEB. (3) Spatially, "high-low" spatial matching of the ESDR decreased, suggesting a gradual reduction in the spatial mismatch of CCESs. (4) We identified seven ecological functional zones and proposed corresponding strategies for promoting ecological management. Our research emphasized the spatiotemporal patterns of supply and demand imbalance in CCESs and the spatial optimization paths of trade-offs/synergies, providing valuable insights for achieving regional dual­carbon goals.

Anisotropic magnetic property, magnetostriction, and giant magnetocaloric effect with plateau behavior in TbMn2Ge2 single crystal.

The ternary RMn2Ge2 (R = rare earth) intermetallic compounds have attracted great attention due to their interesting magnetic behaviors and magnetotransport responses. Here, we reported our observation of anisotropic magnetic property, magnetostriction, and magnetocaloric effect (MCE) in TbMn2Ge2 single crystal. Below the transition temperature of Tb magnetic sublattices ([Formula: see text] ~ 95 K), strong Ising-like magnetocrystalline anisotropy is observed with an out-of-plane ferromagnetic moments 5.98 µB/f.u. along the easy c axis, which is two orders of magnitude larger than that of field along a axis. Above [Formula: see text], a field-induced metamagnetic transition is observed from the spin-flip of Mn sublattices. During this transition, remarkable magnetostriction effect is observed, indicating of strong spin-lattice coupling. The responses of Tb and Mn sublattices to the magnetic field generate a giant magnetic entropy change ([Formula: see text]) and large values of relative cooling power (RCP) and temperature-averaged entropy change (TEC). The calculated maximum magnetic entropy change ([Formula: see text]), RCP, and TEC(10) with magnetic field change of 7 T along c axis reach 24.02 J kg-1 K-1, 378.4 J kg-1, and 21.39 J kg-1 K-1 near [Formula: see text], which is the largest among RMn2Ge2 families. More importantly, this giant MCE shows plateau behavior with wide window temperatures from 93 to 108 K, making it be an attractive candidate for magnetic refrigeration applications.

A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation.

Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain-computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.

Gram-Scale Synthesis of Graphitic Carbon Nitride Quantum Dots with Ultraviolet Photoluminescence for Fe3+ Ion Detection.

A method for gram-scale synthesis of graphitic carbon nitride quantum dots (g-C3N4QDs) was developed. The weight of the g-C3N4QDs was up to 1.32 g in each run with a yield of 66 wt%, and the purity was 99.96 wt%. The results showed that g-C3N4QDs exhibit a stable and strong ultraviolet photoluminescence at a wavelength of 365 nm. More interestingly, the g-C3N4QDs can be used as a high-efficiency, sensitive, and selective fluorescent probe to detect Fe3+ with a detection limit of 0.259 µM.

Subspace Clustering via Structured Sparse Relation Representation.

Due to the corruptions or noises that existed in real-world data sets, the affinity graphs constructed by the classical spectral clustering-based subspace clustering algorithms may not be able to reveal the intrinsic subspace structures of data sets faithfully. In this article, we reconsidered the data reconstruction problem in spectral clustering-based algorithms and proposed the idea of "relation reconstruction." We pointed out that a data sample could be represented by the neighborhood relation computed between its neighbors and itself. The neighborhood relation could indicate the true membership of its corresponding original data sample to the subspaces of a data set. We also claimed that a data sample's neighborhood relation could be reconstructed by the neighborhood relations of other data samples; then, we suggested a much different way to define affinity graphs consequently. Based on these propositions, a sparse relation representation (SRR) method was proposed for solving subspace clustering problems. Moreover, by introducing the local structure information of original data sets into SRR, an extension of SRR, namely structured sparse relation representation (SSRR) was presented. We gave an optimization algorithm for solving SRR and SSRR problems and analyzed its computation burden and convergence. Finally, plentiful experiments conducted on different types of databases showed the superiorities of SRR and SSRR.

Shared-Weight-Based Multi-Dimensional Feature Alignment Network for Oriented Object Detection in Remote Sensing Imagery.

Arbitrarily Oriented Object Detection in aerial images is a highly challenging task in computer vision. The mainstream methods are based on the feature pyramid, while for remote-sensing targets, the misalignment of multi-scale features is always a thorny problem. In this article, we address the feature misalignment problem of oriented object detection from three dimensions: spatial, axial, and semantic. First, for the spatial misalignment problem, we design an intra-level alignment network based on leading features that can synchronize the location information of different pyramid features by sparse sampling. For multi-oriented aerial targets, we propose an axially aware convolution to solve the mismatch between the traditional sampling method and the orientation of instances. With the proposed collaborative optimization strategy based on shared weights, the above two modules can achieve coarse-to-fine feature alignment in spatial and axial dimensions. Last but not least, we propose a hierarchical-wise semantic alignment network to address the semantic gap between pyramid features that can cope with remote-sensing targets at varying scales by endowing the feature map with global semantic perception across pyramid levels. Extensive experiments on several challenging aerial benchmarks show state-of-the-art accuracy and appreciable inference speed. Specifically, we achieve a mean Average Precision (mAP) of 78.11% on DOTA, 90.10% on HRSC2016, and 90.29% on UCAS-AOD.

Exploring the likely relationship between soil carbon change and environmental controls using nonrevisited temporal data sets: Mapping soil carbon dynamics across China.

The prediction of soil organic carbon (SOC) changes in response to environmental change is often limited by a scarcity of revisited temporal data, which constrains scientific understanding and realistic predictions of soil carbon change. The present study improved the potential of nonrevisited temporal data in the prediction of SOC stocks (SOCS) variations. We proposed a method to develop predictions of SOCS change using two independent temporal data sets (pertaining to the 1980s and 2010s) in China based on the digital soil mapping technique. Changes in SOCS over time at the site level were analyzed via the interpolation of missing SOCS values in each data set. Quantitative SOCS change predictions were generated by modeling the relationship between SOCS change and variables that represent changes in climate, vegetation indices, and land cover. The scale-dependent response of SOCS change to these environmental dynamics was assessed. On average, a slight increase was observed from 3.70 kg m-2 in the 1980s to 4.53 kg m-2 in the 2010s. The proposed approach attained moderate accuracy with an R2 value of 0.32 and a root mean squared error (RMSE) of 1.73 kg m-2. We found that changes in climate factors were dominant controls of SOCS change over time at the country scale. At the regional scale, the controlling factors of SOCS change were distinct and variable. Our case study may be of value in the application of independent temporal data sets to analyze soil carbon change on multiple scales. The method may be used to resolve questions of soil carbon change projections and provide an alternative solution to predict likely changes in soil carbon in response to future environmental change when no temporal data are available.

A Variable Search Space Strategy Based on Sequential Trust Region Determination Technique.

The complexity of an optimization problem is determined by its decision and objective spaces. Over the past few decades, a large number of works have focused on the performance improvement of metaheuristic algorithms via the objective space, whereas studies related to the decision space have attracted little attentions. Moreover, metaheuristic algorithms may not obtain satisfactory results within an entire feasible region, even if sufficient computational resources are available. Therefore, reducing the search space (i.e., finding a trust region) may be an effective method to ensure that the convergence is sufficiently close to the global optimal region. However, inappropriate subspace size may also weaken the performance of algorithms except for ones with a sufficiently small search space. To alleviate aforementioned problems, a variable search space (VSS) strategy based on a sequential trust region determination approach is proposed in this paper. In the VSS, the entire optimization process is divided into two stages: the first stage is to use an optimization approach for sequentially finding the trust domain of each variable and then determine the best-matched subspace; the second stage is to employ the optimization method for searching an optimal/near-optimal solution within the found trust region. The effectiveness of the VSS is evaluated using two widely used test suites, that is, IEEE CEC2014 and BBOB2012. Experimental results indicate that improving the algorithm performance is an important method for tackling problems, but locating a trust region is also beneficial for metaheuristic algorithms to improve the solution precision, especially for complex optimization problems.

Multilevel Resistive Switching Memory Based on a CH3NH3PbI 3-xClx Film with Potassium Chloride Additives.

High-quality CH3NH3PbI 3-xClx (MAPIC) films were prepared using potassium chloride (KCl) as an additive on indium tin oxide (ITO)-coated glass substrates using a simple one-step and low-temperature solution reaction. The Au/KCl-MAPIC/ITO/glass devices exhibited obvious multilevel resistive switching behavior, moderate endurance, and good retention performance. Electrical conduction analysis indicated that the resistive switching behavior of the KCl-doped MAPIC films was primarily attributed to the trap-controlled space-charge-limited current conduction that was caused by the iodine vacancies in the films. Moreover, the modulations of the barrier in the Au/KCl-MAPIC interface under bias voltages were thought to be responsible for the resistive switching in the carrier injection trapping/detrapping process.

Rapid Loss of Tidal Flats in the Yangtze River Delta since 1974.

As the home to national nature reserves and a Ramsar wetland, the tidal flats of the Yangtze River Delta are of great significance for ecological security, at both the local and global scales. However, a comprehensive understanding of the spatiotemporal conditions of the tidal flats in the Yangtze River Delta remains lacking. Here, we propose using remote sensing to obtain a detailed spatiotemporal profile of the tidal flats, using all available Landsat images from 1974 to 2018 with the help of the Google Earth Engine cloud platform. In addition, reclamation data were manually extracted from time series Landsat images for the same period. We found that approximately 40.0% (34.9-43.1%) of the tidal flats in the study area have been lost since 1980, the year in which the tidal flat area was maximal. The change in the tidal flat areas was consistent with the change in the riverine sediment supply. We also found that the cumulative reclamation areas totaled 816.6 km2 and 431.9 km2 in the Yangtze estuary zone and along the Jiangsu coast, respectively, between 1974 and 2018. Because of reclamation, some areas (e.g., the Hengsha eastern shoal and Pudong bank), which used to be quite rich, have lost most of their tidal flats. Currently, almost 70% of the remaining tidal flats are located in the shrinking branch (North Branch) and the two National Nature Reserves (Chongming Dongtan and Jiuduansha) in the Yangtze estuary zone. Consequently, the large-scale loss of tidal flats observed was primarily associated with reduced sediment supply and land reclamation at the time scale of the study. Because increasing demand for land and rising sea levels are expected in the future, immediate steps should be taken to prevent the further deterioration of this valuable ecosystem.

The Chinese Linguistic Validation of the Ureteral Stent Symptom Questionnaire.

OBJECTIVE: We validated the Chinese version of the Ureteral Stent Symptoms Questionnaire (USSQ) in patients with an indwelling ureteric stent. PATIENTS AND METHODS: The English version of the USSQ was translated into Chinese using a multi-step process by 3 urologists and 2 independent translators. The Chinese version was validated by asking 50 patients with temporary unilateral ureteral stent to complete the questionnaire at weeks 1 and 4 after stent placement. Thirty healthy individuals without a ureteral stent were also asked to complete the Chinese USSQ once. The psychometric properties of the questionnaire were analyzed. RESULTS: After revision of the initial 2 drafts after translation, back translation, and pilot testing, a final draft was developed that underwent field testing. Psychometric analyses revealed satisfactory internal consistencies and test-retest reliability for all domains except for sexual matters. Most USSQ domains showed moderate correlations with each other. It demonstrated satisfactory discriminant validity (sensitivity to change, p < 0.05) and test-retest reliability. Analysis of the domains of the sexual matters was limited because of the small proportion of the study population for whom it was applicable. CONCLUSION: The Chinese version of the USSQ is a reliable and valid instrument to evaluate symptoms and health-related quality of life in Chinese patients with ureteric stents.

Improved linear classifier model with Nyström.

Most data sets consist of interlaced-distributed samples from multiple classes and since these samples always cannot be classified correctly by a linear hyperplane, so we name them nonlinearly separable data sets and corresponding classifiers are named nonlinear classifiers. Traditional nonlinear classifiers adopt kernel functions to generate kernel matrices and then get optimal classifier parameters with the solution of these matrices. But computing and storing kernel matrices brings high computational and space complexities. Since INMKMHKS adopts Nyström approximation technique and NysCK changes nonlinearly separable data to linearly ones so as to reduce the complexities, we combines ideas of them to develop an improved NysCK (INysCK). Moreover, we extend INysCK into multi-view applications and propose multi-view INysCK (MINysCK). Related experiments validate the effectiveness of them in terms of accuracy, convergence, Rademacher complexity, etc.

Five microRNAs in serum as potential biomarkers for prostate cancer risk assessment and therapeutic intervention.

BACKGROUND: Prostate cancer (PCa) is a common malignant human tumor and one of the main causes of cancer-related deaths in men. At present, prostate-specific antigen levels are widely used to diagnose PCa in the clinic, but they are not sufficient for an accurate early diagnosis or prognosis. METHODS: To identify potential molecular markers for PCa, we used real-time PCR to measure the expression levels of various microRNAs, including miR-1825, miR-484, miR-205, miR-141, and let-7b, in the serum of 72 PCa patients and 34 healthy controls. RESULTS: miR-1825, miR-484, miR-205, miR-141, and let-7b were shown to be highly specific for PCa, suggesting that they could be used as PCa tumor screening biomarkers. miR-205 may also be used as a biomarker for indicating bone metastasis in PCa patients, miR-1825 levels may help indicate tumor-node-metastasis classification, the evaluation of treatment effects, and determining prognosis, while let-7b levels may indicate potential tumor malignancy and the hormone resistance status and could be used as a basis to adjust individual treatments for the high-risk, early diagnosis of refractory PCa. CONCLUSION: This study identified possible PCa tumor markers to more accurately predict the occurrence, progression, and prognosis of PCa, and which could be used in the development of tumor drug therapy.

Expression and phosphorylation of FOXO1 influences cell proliferation and apoptosis in the gastrointestinal stromal tumor cell line GIST-T1.

The mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase (PI3K) pathways are activated during pathogenesis of gastrointestinal stromal tumors (GISTs). Forkhead box protein O1 (FOXO1) is a transcription factor regulated by the MAPK and PI3K pathways and is associated with multiple metabolic reactions. The present study aims to investigate the association of FOXO1 with cell proliferation and apoptosis in the cell line, GIST-T1. Cell counting kit-8 assay revealed that cell growth was inhibited by the PI3K inhibitor, LY294002, and/or MAPK inhibitor, UO126. Western blotting demonstrated that the expression of p-FOXO1 and B-cell lymphoma 2 (Bcl2) were significantly reduced, whereas the expression of Bcl-2-associated X protein was significantly increased following treatment with LY294002 and/or UO126 (all P<0.05). However, no significant change was revealed in the level of total FOXO1. Flow cytometry revealed that apoptosis was significantly increased by the pathway inhibitors (P<0.05). Specifically, the proportion of cells in the G1 phase was increased whereas the proportion in the S phase was reduced. The changes of protein expression and cell apoptosis were more evident in the LY294002 + UO126 group than in either single-inhibitor group. The results indicated that FOXO1 was able to affect cell proliferation, apoptosis and the cell cycle of GISTs. The regulation of FOXO1 was part of the PI3K and MAPK signaling network, while this regulation was mostly activated by phosphorylation of FOXO1.

Analysis of Gene Expression Profiles in the Human Brain Stem, Cerebellum and Cerebral Cortex.

The human brain is one of the most mysterious tissues in the body. Our knowledge of the human brain is limited due to the complexity of its structure and the microscopic nature of connections between brain regions and other tissues in the body. In this study, we analyzed the gene expression profiles of three brain regions-the brain stem, cerebellum and cerebral cortex-to identify genes that are differentially expressed among these different brain regions in humans and to obtain a list of robust, region-specific, differentially expressed genes by comparing the expression signatures from different individuals. Feature selection methods, specifically minimum redundancy maximum relevance and incremental feature selection, were employed to analyze the gene expression profiles. Sequential minimal optimization, a machine-learning algorithm, was employed to examine the utility of selected genes. We also performed a literature search, and we discuss the experimental evidence for the important physiological functions of several highly ranked genes, including NR2E1, DAO, and LRRC7, and we give our analyses on a gene (TFAP2B) that have not been investigated or experimentally validated. As a whole, the results of our study will improve our ability to predict and understand genes related to brain regionalization and function.

Prediction of bioactive compound pathways using chemical interaction and structural information.

The functional screening of compounds is an important topic in chemistry and biomedicine that can uncover the essential properties of compounds and provide information concerning their correct use. In this study, we investigated the bioactive compounds reported in Selleckchem, which were assigned to 22 pathways. A computational method was proposed to identify the pathways of the bioactive compounds. Unlike most existing methods that only consider compound structural information, the proposed method adopted both the structural and interaction information from the compounds. The total accuracy achieved by our method was 61.79% based on jackknife analysis of a dataset of 1,832 bioactive compounds. Its performance was quite good compared with that of other machine learning algorithms (with total accuracies less than 46%). Finally, some of the false positives obtained by the method were analyzed to investigate the likelihood of compounds being annotated to new pathways.

EGF reverses multi-drug resistance via the p-ERK pathway in HepG2/ADM and SMMC7721/ADM hepatocellular carcinoma models.

AIM: To investigate signaling pathways for reversal of EGF-mediated multi-drug resistance (MDR) in hepatocellular carcinoma (HCC) models. MATERIALS AND METHODS: HCC MDR cell strain HepG2/adriamycin (ADM) and SMMC7721/ADM models were established using a method of exposure to medium with ADM between low and high concentration with gradually increasing concentration. Drug sensitivity and reversal of multi-drug resistance by EGF were determined and the cell cycle distribution and apoptosis were analyzed by flow cytometry. Phosphorylation of ERK1, ERK2, ERK5 and expression of Bim were detected by Western blotting. RESULTS: The results showed that HepG2/ADM and SMMC7721/ADM cells were resistant not only to ADM, but also to multiple anticancer drugs. When used alone, EGF had no anti-tumor activity in HepG2/ADM and SMMC7721/ADM cells in vitro, while it increased the cytotoxicity of ADM. EGF induced cell apoptosis and G0/G1 phase cell cycle arrest in HepG2/ADM And SMMC7721/ADM cells, while enhancing activity of p-ERKs and up-regulated expression of BimEL. CONCLUSIONS: EGF might enhance the chemosensitivity of HepG2/ADM and SMMC7721/ADM cells via up-regulating p-ERKs and BimEL protein.

[Human papillomavirus in the pubic hair follicles of men and cervical lesions in their female sexual partners].

OBJECTIVE: To investigate the association of human papillomavirus (HPV) in the pubic hair follicles of males with HPV infection in their female sexual partners. METHODS: We included in this study 21 female patients with HPV infection, including 8 cases of cervical cancer, 5 cases of atypical cervical hyperplasia, 5 cases of cervical condyloma, and 3 cases with unidentified causes. We also enlisted 52 men without visible condyloma acuminatum in the external genitalia as healthy controls. We detected HPV in the pubic hair follicles of the female patients' male sexual partners and the healthy male controls by PCR and reverse hybridization in situ. RESULTS: HPV positive was found in 6 (28.6%) of the 21 women's male partners, in whom the HPV types were correspondent situ. to those of the female patients. CONCLUSION: HPV in the pubic hair follicles of men might be one of the causes of HPV-related cervical lesions in their female sexual partners.

[A spatial adaptive algorithm for endmember extraction on multispectral remote sensing image].

Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks' landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What's more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.