Methylation of microRNA genes and its effect on secondary xylem development of stem in poplar.

MicroRNAs (miRNAs) and DNA methylation are both vital regulators of gene expression. DNA methylation can affect the transcription of miRNAs, just like coding genes, through methylating the CpG islands in the gene regions of miRNAs. Although previous studies have shown that DNA methylation and miRNAs can each be involved in the process of wood formation, the relationship between the two has been relatively little studied in plant wood formation. Studies have shown that the second internode (IN2) (from top to bottom) of 3-month-old poplar trees can represent the primary stage of poplar stem development and IN8 can represent the secondary stage. There were also significant differences in DNA methylation patterns and miRNA expression patterns obtained from PS and SS. In this study, we first interactively analyzed methylation and miRNA sequencing data to identify 43 differentially expressed miRNAs regulated by differential methylation from the primary stage and secondary stage, which were found to be involved in multiple biological processes related to wood formation by enrichment analysis. In addition, six miRNA/target gene modules were finally identified as potentially involved in secondary xylem development of poplar stems through degradome sequencing and functional analysis. In conclusion, this study provides important reference information on the mechanism of interaction between different regulatory pathways of wood formation.

Identification of the prognostic value of LACTB2 and its correlation with immune infiltrates in ovarian cancer by integrated bioinformatics analyses.

Ovarian cancer (OC) is one of the most common reproductive tumors in women, whereas current treatment options are limited. ß-lactamase-like-protein 2 (LACTB2) has been observed to be associated with various cancers, but its function in OC is unknown. Therefore, we evaluate the prognostic value and the underlying function of LACTB2 in OC. In this study, high expression of LACTB2 was observed in OC compared with normal controls. Kaplan-Meier Plotter analysis revealed that overexpressed LACTB2 is strongly correlated with poor prognosis. We conducted GO/KEGG analysis to investigate the potential biological function of LACTB2 in OC. GESA analysis showed that LACTB2 was closely related to immune-related pathways. Subsequently, we explored the relationship between LACTB2 and 24 types of immune cells in OC. The results suggested that LACTB2 was positively associated with multiple tumor-infiltrating immune cells. Importantly, LACTB2 may modulate immune cell infiltration in OC to influence prognosis. In conclusion, LACTB2 can be used as a promising prognostic biomarker and immunotherapy target for OC.

Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics.

Objective: High-grade serous ovarian cancer (HGSOC) has the highest mortality rate among female reproductive system tumors. Accurate preoperative assessment is crucial for treatment planning. This study aims to develop multitask prediction models for HGSOC using radiomics analysis based on preoperative CT images. Methods: This study enrolled 112 patients diagnosed with HGSOC. Laboratory findings, including serum levels of CA125, HE-4, and NLR, were collected. Radiomic features were extracted from manually delineated ROI on CT images by two radiologists. Classification models were developed using selected optimal feature sets to predict R0 resection, lymph node invasion, and distant metastasis status. Model evaluation was conducted by quantifying receiver operating curves (ROC), calculating the area under the curve (AUC), De Long's test. Results: The radiomics models applied to CT images demonstrated superior performance in the testing set compared to the clinical models. The area under the curve (AUC) values for the combined model in predicting R0 resection were 0.913 and 0.881 in the training and testing datasets, respectively. De Long's test indicated significant differences between the combined and clinical models in the testing set (p = 0.003). For predicting lymph node invasion, the AUCs of the combined model were 0.868 and 0.800 in the training and testing datasets, respectively. The results also revealed significant differences between the combined and clinical models in the testing set (p = 0.002). The combined model for predicting distant metastasis achieved AUCs of 0.872 and 0.796 in the training and test datasets, respectively. The combined model displayed excellent agreement between observed and predicted results in predicting R0 resection, while the radiomics model demonstrated better calibration than both the clinical model and combined model in predicting lymph node invasion and distant metastasis. The decision curve analysis (DCA) for predicting R0 resection favored the combined model over both the clinical and radiomics models, whereas for predicting lymph node invasion and distant metastasis, DCA favored the radiomics model over both the clinical model and combined model. Conclusion: The identified radiomics signature holds potential value in preoperatively evaluating the R0, lymph node invasion and distant metastasis in patients with HGSC. The radiomics nomogram demonstrated the incremental value of clinical predictors for surgical outcome and metastasis estimation.

Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer.

PURPOSE: Significant advancements in improving ovarian cancer (OC) outcomes have been limited over the past decade. To predict prognosis and improve outcomes of OC, we plan to develop and validate a robust prognosis signature based on blood features. METHODS: We screened age and 33 blood features from 331 OC patients. Using ten machine learning algorithms, 88 combinations were generated, from which one was selected to construct a blood risk score (BRS) according to the highest C-index in the test dataset. RESULTS: Stepcox (both) and Enet (alpha = 0.7) performed the best in the test dataset with a C-index of 0.711. Meanwhile, the low RBS group possessed observably prolonged survival in this model. Compared to traditional prognostic-related features such as age, stage, grade, and CA125, our combined model had the highest AUC values at 3, 5, and 7 years. According to the results of the model, BRS can provide accurate predictions of OC prognosis. BRS was also capable of identifying various prognostic stratifications in different stages and grades. Importantly, developing the nomogram may improve performance by combining BRS and stage. CONCLUSION: This study provides a valuable combined machine-learning model that can be used for predicting the individualized prognosis of OC patients.

Anoikis-related signature predicts prognosis and characterizes immune landscape of ovarian cancer.

Ovarian cancer (OV) is the most lethal gynecological malignancy worldwide, with high recurrence rates. Anoikis, a newly-acknowledged form of programmed cell death, plays an essential role in cancer progression, though studies focused on prognostic patterns of anoikis in OV are still lacking. We filtered 32 potential anoikis-related genes (ARGs) among the 6406 differentially expressed genes (DEGs) between the 180 normal controls and 376 TCGA-OV samples. Through the LASSO-Cox analysis, a 2-gene prognostic signature, namely AKT2, and DAPK1, was finally distinguished. We then demonstrated the promising prognostic value of the signature through the K-M survival analysis and time-dependent ROC curves (p-value < 0.05). Moreover, based on the signature and clinical features, we constructed and validated a nomogram model for 1-year, 3-year, and 5-year overall survival, with reliable prognostic values in both TCGA-OV training cohort (p-value < 0.001) and ICGC-OV validation cohort (p-value = 0.030). We evaluated the tumor immune landscape through the CIBERSORT algorithm, which indicated the upregulation of resting Myeloid Dendritic Cells (DCs), memory B cells, and naïve B cells and high expression of key immune checkpoint molecules (CD274 and PDCD1LG2) in the high-risk group. Interestingly, the high-risk group exhibited better sensitivity toward immunotherapy and less sensitivity toward chemotherapies, including Cisplatin and Bleomycin. Especially, based on the IHC of tissue microarrays among 125 OV patients at our institution, we reported that aberrant upregulation of DAPK1 was related to poor prognosis. Conclusively, the anoikis-related signature was a promising tool to evaluate prognosis and predict therapy responses, thus assisting decision-making in the realm of OV precision medicine.

CSGALNACT2 restricts ovarian cancer migration and invasion by modulating MAPK/ERK pathway through DUSP1.

PURPOSE: Ovarian cancer is one of the leading causes of cancer-related death among women. CSGALNACT2 is a vital Golgi transferase and is related to a variety of human diseases. However, its expression pattern and function in ovarian cancer remain uncertain. METHODS: The Cancer Genome Atlas and GEPIA databases were used to assess the expression of CSGALNACT2 in ovarian cancer patients. RNA-seq, qRT-PCR, and IHC were used to verify the expression of CSGALNACT2 in ovarian cancer tissues. Then, in vivo and in vitro experiments were conducted to evaluate the role of CSGALNACT2 in the progression of ovarian cancer. RNA-seq and GSEA were used to reveal the potential biological function and oncogenic pathways of CSGALNACT2. RESULTS: We demonstrated that the mRNA expression and protein level of CSGALNACT2 were significantly downregulated in ovarian cancer and ovarian cancer metastatic tissues. CSGALNACT2 can significantly inhibit the migration, invasion, and clonogenic growth of ovarian cancer in vitro and is progressively lost during ovarian cancer progression in vivo. CSGALNACT2 suppresses ovarian cancer migration and invasion via DUSP1 modulation of the MAPK/ERK pathway through RNA-seq, KEGG analysis, and Western blotting. Moreover, CSGALNACT2 expression was correlated with immune cell infiltration and had prognostic value in different immune cell-enriched or decreased ovarian cancer. In addition, patients with CSGALNACT2 downregulation are less likely to benefit from immunotherapy. CONCLUSION: As an ovarian cancer suppressor gene, CSGALNACT2 inhibits the development of ovarian cancer, and it might be used as a prognostic biomarker in patients with ovarian cancer.

Clinical significance and immune infiltration analyses of a novel coagulation-related signature in ovarian cancer.

Ovarian cancer (OV) is the most lethal gynecological malignancies worldwide. The coagulation cascade could induce tumor cell infiltration and contribute to OV progression. However, coagulation-related gene (CRG) signature for OV prognosis hasn't been determined yet. In this study, we evaluated the prognostic value of coagulation scores through receiver operating characteristics (ROC) analysis and K-M curves, among OV patients at our institution. Based on the transcriptome data of TCGA-OV cohort, we stratified two coagulation-related subtypes with distinct differences in prognosis and tumor immune microenvironment (p < 0.05). Moreover, from the 6406 differentially-expressed genes (DEGs) between the GTEx (n = 180) and TCGA-OV cohorts (n = 376), we identified 138 potential CRGs. Through LASSO-Cox algorithm, we finally distinguished a 3-gene signature (SERPINA10, CD38, and ZBTB16), with promising prognostic ability in both TCGA (p < 0.001) and ICGC cohorts (p = 0.040). Stepwise, we constructed a nomogram based on the clinical features and coagulation-related signature for overall survival prediction, with the C-index of 0.6761, which was evaluated by calibration curves. Especially, based on tissue microarrays analysis, Quantitative real-time fluorescence PCR (qRT-PCR), and Western Blot, we found that aberrant upregulation of CRGs was related to poor prognosis in OV at both mRNA and protein level (p < 0.05). Collectively, the coagulation-related signature was a robust prognostic biomarker, which could provide therapeutic benefits for chemotherapy/immunotherapy and assist clinical decision in OV patients.

Endoplasmic Reticulum Stress-Related Ten-Biomarker Risk Classifier for Survival Evaluation in Epithelial Ovarian Cancer and TRPM2: A Potential Therapeutic Target of Ovarian Cancer.

Epithelial ovarian cancer (EOC) is the most lethal gynecological malignant tumor. Endoplasmic reticulum (ER) stress plays an important role in the malignant behaviors of several tumors. In this study, we established a risk classifier based on 10 differentially expressed genes related to ER stress to evaluate the prognosis of patients and help to develop novel medical decision-making for EOC cases. A total of 378 EOC cases with transcriptome data from the TCGA-OV public dataset were included. Cox regression analysis was used to establish a risk classifier based on 10 ER stress-related genes (ERGs). Then, through a variety of statistical methods, including survival analysis and receiver operating characteristic (ROC) methods, the prediction ability of the proposed classifier was tested and verified. Similar results were confirmed in the GEO cohort. In the immunoassay, the different subgroups showed different penetration levels of immune cells. Finally, we conducted loss-of-function experiments to silence TRPM2 in the human EOC cell line. We created a 10-ERG risk classifier that displays a powerful capability of survival evaluation for EOC cases, and TRPM2 could be a potential therapeutic target of ovarian cancer cells.

The dual effect of disodium anthraquinone-2,6-disulfonate (AQDS) on the Cr(VI) removal by biochar: The enhanced electron transfer and the inhibited adsorption.

Due to large specific surface area, abundant surface functional groups, and stable chemical structure, biochar has been widely used in many environmental fields, including the remediation of Cr pollution. Alternatively, electrochemically active organic matter (e-OM), which is prevalent in both natural environments and industrial wastewater, exerts an inevitable influence on the mechanisms underlying Cr(VI) removal by biochar. The synergistic interplay between biochar and e-OM in the context of Cr(VI) remediation remains to be fully elucidated. In this study, disodium anthraquinone-2,6-disulfonate (AQDS) was used as a model for e-OM, characterized by its quinone group's ability to either donate or accept electrons. We found that AQDS sped up the Cr(VI) removal process, but the enhancement effect decreased with the increase in pyrolysis temperature. With the addition of AQDS, the removal amount of Cr(VI) by BC300 and BC600 increased by 160.0% and 49.5%, respectively. AQDS could release more electrons trapped in the lower temperature biochar samples (BC300 and BC600) for Cr(VI) reduction. However, AQDS inhibited the Cr(VI) removal by BC900 due to the adsorption of AQDS on biochar surface. In the presence of the small molecule carbon source lactate, more AQDS was adsorbed onto the biochar surface. This led to an inhibition of the electron transfer between biochar and Cr(VI), resulting in an inhibitory effect. This study has elucidated the electron transfer mechanism involved in the removal of Cr(VI) by biochar, particularly in conjunction with e-OM. Furthermore, it would augment the efficacy of biochar in applications targeting the removal of heavy metals.

Regulation of friction pair to promote conversion of mechanical energy to chemical energy on Bi2WO6 and realization of enhanced tribocatalytic activity to degrade different pollutants.

Recently, friction-induced tribocatalysis has received tremendous attention through converting mechanical energy to chemical energy. However, its efficiency is much lower than those of photocatalysis and piezocatalysis, and its environmental application is limited in dye degradation. Herein, we developed a facile approach to improve the tribocatalytic activity of Bi2WO6 via adding trace polymer powders to form friction pairs with Bi2WO6. Among various polymers, PTFE was demonstrated to be the best counterpart of Bi2WO6. Subsequently, the PTFE dosage, stirring rate, magnetic bar size and number, and stirring mode were further optimized. The PTFE-promoted Bi2WO6 tribocatalysis was verified to possess excellent performance not only for removing different dyes, but also for degrading chlorophenols that are typical persistent organic pollutants. Multiple uses of the recycled catalysts indicated its good stability and prominent tribocatalytic durability. EPR measurements suggested the generation of hydroxyl radical and superoxide radical, which were determined to be continuously generated within 12 h at the rates of 0.88 µM h-1 and 85 µM h-1, respectively. Subsequently, a possible mechanism was proposed to explain the enhanced performance of the PTFE-promoted Bi2WO6 tribocatalysis. Finally, on basis of the detected intermediates, the degradation pathways of Rhodamine B and 2,4-Dichlorophenol during tribocatalysis were suggested.

DiffSeer: Difference-Based Dynamic Weighted Graph Visualization.

Existing dynamic weighted graph visualization approaches rely on users' mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based node reordering strategy is developed to group nodes with similar evolution patterns and highlight interesting graph structure details in each timeslice. We conducted two case studies on real-world graph datasets and in-depth interviews with 12 target users to evaluate DiffSeer. The results demonstrate its effectiveness in visualizing dynamic weighted graphs.

A novel autophagy-related gene signature associated with prognosis and immune microenvironment in ovarian cancer.

Ovarian cancer (OV), the most fatal gynecological malignance worldwide, has high recurrence rates and poor prognosis. Recently, emerging evidence supports that autophagy, a highly regulated multi-step self-digestive process, plays an essential role in OV progression. Accordingly, we filtered 52 potential autophagy-related genes (ATGs) among the 6197 differentially expressed genes (DEGs) identified in TCGA-OV samples (n = 372) and normal controls (n = 180). Based on the LASSO-Cox analysis, we distinguished a 2-gene prognostic signature, namely FOXO1 and CASP8, with promising prognostic value (p-value < 0.001). Together with corresponding clinical features, we constructed a nomogram model for 1-year, 2-year, and 3-year survival, which was validated in both in training (TCGA-OV, p-value < 0.001) and validation (ICGC-OV, p-value = 0.030) cohorts. Interestingly, we evaluated the immune infiltration landscape through the CIBERSORT algorithm, which indicated the upregulation of 5 immune cells, including CD8 + T cells, Tregs, and Macrophages M2, and high expression of critical immune checkpoints (CTLA4, HAVCR2, PDCD1LG2, and TIGIT) in high-risk group. Stepwise, high-risk group exhibited better sensitivity towards chemotherapies of Bleomycin, Sorafenib, Veliparib, and Vinblastine, though less sensitive to immunotherapy. Especially, based on the IHC of tissue microarrays among 125 patients in our institution, we demonstrated that aberrant upregulation of FOXO1 in OV was related to metastasis and poor prognosis. Moreover, FOXO1 could significantly promote tumor invasiveness, migration, and proliferation in OV cell lines, which was assessed through the Transwell, wound-healing, and CCK-8 assay, respectively. Briefly, the autophagy-related signature was a reliable tool to evaluate immune responses and predict prognosis in the realm of OV precision medicine.

Artificial intelligence-based preoperative prediction system for diagnosis and prognosis in epithelial ovarian cancer: A multicenter study.

Background: Ovarian cancer (OC) is the most lethal gynecological malignancy, with limited early screening methods and poor prognosis. Artificial intelligence technology has made a great breakthrough in cancer diagnosis. Purpose: We aim to develop a specific interpretable machine learning (ML) prediction model for the diagnosis and prognosis of epithelial ovarian cancer (EOC) based on a variety of biomarkers. Methods: A total of 521 patients with EOC and 144 patients with benign gynecological diseases were enrolled including derivation datasets and an external validation cohort. The predicted information was acquired by 9 supervised ML methods, through 34 parameters. Behind predicted reasons for the best ML were improved by using the SHapley Additive exPlanations (SHAP) algorithm. In addition, the prognosis of EOC was analyzed by unsupervised clustering and Kaplan-Meier (KM) survival analysis. Results: ML technology was superior to conventional logistic regression in predicting EOC diagnosis and XGBoost performed best in the external validation datasets. The AUC values of distinguishing EOC and benign disease patients, determining pathological type, grade and clinical stage were 0.958 (0.926-0.989), 0.792 (0.701-0.8834), 0.819 (0.687-0.950) and 0.68 (0.573-0.788) respectively. For negative CA-125 EOC patients, the AUC performance of XGBoost model was 0.835(0.763-0.907). We used unsupervised cluster analysis to identify EOC subgroups with significantly poor overall survival (p-value <0.0001) and recurrence-free survival (p-value <0.0001). Conclusions: Based on the preoperative characteristics, we proved that ML algorithm can provide an acceptable diagnosis and prognosis prediction model for EOC patients. Meanwhile, SHAP analysis can improve the interpretability of ML models and contribute to precision medicine.

Single-Step Fabrication of Longtail Glasswing Butterfly-Inspired Omnidirectional Antireflective Structures.

Most bio-inspired antireflective nanostructures are extremely vulnerable and suffer from complicated lithography-based fabrication procedures. To address the issues, we report a scalable and simple non-lithography-based approach to engineer robust antireflective structures, inspired by the longtail glasswing butterfly, in a single step. The resulting two-dimensional randomly arranged 80/130/180 nm silica colloids, partially embedded in a polymeric matrix, generate a gradual refractive index transition at the air/substrate interface to suppress light reflection. Importantly, the randomly arranged subwavelength silica colloids display even better antireflection performance for large incident angles than that of two-dimensional non-close-packed silica colloidal crystals. The biomimetic coating is of considerable technological importance in numerous practical applications.

Tribocatalysis of homogeneous material with multi-size granular distribution for degradation of organic pollutants.

Recently, tribocatalysis driven by mechanical energy has been developed by rubbing two kinds of different materials. In this work, we firstly demonstrated that the friction of the single material also could initiate the tribocatalysis for degrading organic dyes. Under magnetic stirring, the multi-size granular polytetrafluoroethylene (PTFE) particles were triboelectrically charged, among which the collision between large and small particles would cause high energy electrons on large particles to transfer to small ones. These triboelectric charges on PTFE particles could react with adsorbed oxygen molecules or water to generate reactive oxygen species, and then promoted the degradation process of organic dyes together with oxidant holes. We further investigated the experimental parameters, such as stirring speed, size and quantity of stirring bar, to optimize the tribocatalytic performance. What's more, the PTFE tribocatalysis possessed high durability for multiple recycling runs with > 90% degradation efficiency of Rhodamine B, as well as well universality for eliminating other pollutants. Finally, we proposed a plausible tribocatalytic mechanism of multi-size granular PTFE according to the detected reactive oxygen species and the determined intermediates. This study provides new insights into tribocatalysis, and demonstrates that the single material with different particle sizes can also be used as catalyst to drive tribocatalytic process.

Piezoelectric polarization promoted spatial separation of photoexcited electrons and holes in two-dimensional g-C3N4 nanosheets for efficient elimination of chlorophenols.

Graphitic carbon nitride (g-C3N4) has been proved to be a potential photocatalyst for environment purification, but the high recombination rate of photogenerated carriers leads to the low photocatalytic efficiency. Herein, we report the enhanced degradation of chlorophenols by 2D ultrathin g-C3N4 nanosheets with intrinsic piezoelectricity through photopiezocatalysis strategy. Under the simultaneous visible-light irradiation and ultrasonic vibration, the 2D g-C3N4 presented improved removal efficiency for elimination of 2,4-dichlorophenol (2,4-DCP) with an apparent rate constant of 6.65 × 10-2 min-1, which was 6.7 and 2.2 times of the photocatalysis and piezocatalysis, respectively. The improved removal efficiency was attributed to the sufficient separation of free charges driven by the ultrasound-induced piezoelectric field in the 2D g-C3N4, which was demonstrated by the enhanced current response under photopiezocatalysis mode. Additionally, the photopiezocatalysis of 2D g-C3N4 was proved to possess well universality for removing different chlorophenols, as well as high durability and dechlorination efficiency. Finally, a possible photopiezocatalytic mechanism for removal of 2,4-DCP was proposed based on the electron paramagnetic resonance (EPR) technique and the determination of intermediates through liquid chromatography-mass spectrometry (LC-MS) analysis. This work provides a promising strategy for the design of energy-conversion materials towards capturing solar and mechanical energy in ambient environment.

Omnidirectional / Unidirectional Antireflection-Switchable Structures Inspired by Dragonfly Wings.

Randomly arranged irregular inclined conical structure-covered dragonfly wings, distinguished from periodic conical structure-covered cicada wings, are with high optical transparency for wide viewing angles. Bioinspired by the antireflective structures, we develop a colloidal lithography approach for engineering randomly arranged irregular conical structures with shape memory polymer-based tips. The structures establish a gradual refractive index transition to suppresses optical reflection in the visible spectrum. By manipulating the configuration of structure tips through applying common solvent stimulations or contact pressures under ambient conditions, the resulting unidirectional antireflection and omnidirectional antireflection performances are able to be instantaneously and reversibly switched. The dependences of structure shape, structure inclination, structure arrangement, and structure composition on the switchable antireflection capability are also systematically investigated in this study.

Exploring prognostic indicators in the pathological images of ovarian cancer based on a deep survival network.

Background: Tumor pathology can assess patient prognosis based on a morphological deviation of tumor tissue from normal. Digitizing whole slide images (WSIs) of tissue enables the use of deep learning (DL) techniques in pathology, which may shed light on prognostic indicators of cancers, and avoid biases introduced by human experience. Purpose: We aim to explore new prognostic indicators of ovarian cancer (OC) patients using the DL framework on WSIs, and provide a valuable approach for OC risk stratification. Methods: We obtained the TCGA-OV dataset from the NIH Genomic Data Commons Data Portal database. The preprocessing of the dataset was comprised of three stages: 1) The WSIs and corresponding clinical data were paired and filtered based on a unique patient ID; 2) a weakly-supervised CLAM WSI-analysis tool was exploited to segment regions of interest; 3) the pre-trained model ResNet50 on ImageNet was employed to extract feature tensors. We proposed an attention-based network to predict a hazard score for each case. Furthermore, all cases were divided into a high-risk score group and a low-risk one according to the median as the threshold value. The multi-omics data of OC patients were used to assess the potential applications of the risk score. Finally, a nomogram based on risk scores and age features was established. Results: A total of 90 WSIs were processed, extracted, and fed into the attention-based network. The mean value of the resulting C-index was 0.5789 (0.5096-0.6053), and the resulting p-value was 0.00845. Moreover, the risk score showed a better prediction ability in the HRD + subgroup. Conclusion: Our deep learning framework is a promising method for searching WSIs, and providing a valuable clinical means for prognosis.

Environmental persistent free radicals in diesel engine exhaust particles at different altitudes and engine speeds.

The occurrence of environmental persistent free radicals (EPFRs) in the environment has attracted a great deal of research attention. Although the major sources of EPFRs in the environment is diesel engine exhaust, the study on the emission characteristics of EPFRs at different working conditions is still very limited. An integrated engine system was adopted to simulate different working conditions of various altitudes and engine speeds, and to examine the emission process of a diesel engine. The results suggested that low engine speed and high altitude are generally associated with high PM10 emission with more stable and ordered structures. Based on the analysis of PAHs on solid and gas phases, PM10 generated from diesel engine at altitude higher than 2000 m may contain substantial amounts of PAHs embedded inside particles, but not adsorbed on the surface. EPFRs signal up to 1.66 × 1020 spins/g were detected in PM10 of the diesel exhaust. Higher engine speed and lower altitude were associated with stronger EPR signals on PM10. However, the accumulated EPR signal intensities after consuming 1 L of diesel were higher at lower engine speed and higher altitude, suggesting higher overall risks. A positive correlation between R value (signal strength ratio of D and G peaks on the Raman spectra) and EPFRs intensity indicated that the EPR signals were associated with the defects of carbon structure. EPFRs intensity in particles showed no significant change in dark, and over 70% of the EPR signals survived under UV light in a one-month aging simulation. The strong persistence of these EPFRs suggested their potential long lasting and widespread risks, which should be investigated extensively.

Synergistic catalysis of BiOIO3 catalyst for elimination of organic pollutants under simultaneous photo-irradiation and ultrasound-vibration treatment.

Development of efficiently catalytic strategy for oxidative purification of organic pollutants is of great significance. Photocatalysis has become one of the most important technologies in the past half a century, but the inefficiency of photocatalysts drastically suppresses its practical application. This work proposes a synergistic photopiezocatalysis of BiOIO3 under simultaneous photo-irradiation and ultrasound-vibration treatment to degrade various organic pollutants. Different from the high recombination of photo-excited charges in photocatalysis, the ultrasound-induced stress deforms the pyroelectric BiOIO3 to form a piezoelectric potential that drives photo-/thermo-generated free electrons and holes in catalyst to diffuse along opposite directions. In comparison with the single photocatalysis and piezocatalysis, the photopiezocatalysis possesses a synergistic effect, presenting evidently enhanced catalytic performance for decomposing a variety of organic dyes and a persistent organic pollutant 2,4-DCP. No apparent decrease in activity during successive five runs demonstrates that the photopiezocatalysis of BiOIO3 has a high stability and reusability. Finally, a plausible photopiezocatalysis mechanism is proposed based on the determination of active species produced on catalyst and intermediates during pollutant degradation. Our findings provide a new insight to promote charge separation and meanwhile develop an efficient synergistic photopiezocatalysis for environment remediation.