Photothermal Superhydrophobic Chitosan-Based Cotton Fabric for Rapid Deicing and Oil/Water Separation.

Superhydrophobic cotton fabrics have a lot of potential for use in practical settings. The majority of superhydrophobic cotton fabrics, however, only serve one purpose and are made from fluoride or silane chemicals. Therefore, it remains a challenge to develop multifunctional superhydrophobic cotton fabrics using environmentally friendly raw materials. In this study, chitosan (CS), amino carbon nanotubes (ACNTs), and octadecylamine (ODA) were used as raw materials to create CS-ACNTs-ODA photothermal superhydrophobic cotton fabrics. The cotton fabric that was created showed a remarkable superhydrophobic property with a water contact angle of 160.3°. The surface temperature of CS-ACNTs-ODA cotton fabric can rise by up to 70 °C when exposed to simulated sunlight, demonstrating the fabric's remarkable photothermal capabilities. Additionally, the coated cotton fabric is capable of quick deicing. Ice particles (10 µL) melted and began to roll down in 180 s under the light of "1 sun". The cotton fabric exhibits good durability and adaptability in terms of mechanical qualities and washing tests. Moreover, the CS-ACNTs-ODA cotton fabric displays a separation efficacy of more than 91% when used to treat various oil and water mixtures. We also impregnate the coating on polyurethane sponges, which can quickly absorb and separate oil and water mixtures.

Gene Mapping and Identification of a Missense Mutation in One Copy of VRN-A1 Affects Heading Date Variation in Wheat.

Heading date (HD) is an important trait for wide adaptability and yield stability in wheat. The Vernalization 1 (VRN1) gene is a key regulatory factor controlling HD in wheat. The identification of allelic variations in VRN1 is crucial for wheat improvement as climate change becomes more of a threat to agriculture. In this study, we identified an EMS-induced late-heading wheat mutant je0155 and crossed it with wide-type (WT) Jing411 to construct an F2 population of 344 individuals. Through Bulk Segregant Analysis (BSA) of early and late-heading plants, we identified a Quantitative Trait Locus (QTL) for HD on chromosome 5A. Further genetic linkage analysis limited the QTL to a physical region of 0.8 Mb. Cloning and sequencing revealed three copies of VRN-A1 in the WT and mutant lines; one copy contained a missense mutation of C changed to T in exon 4 and another copy contained a mutation in intron 5. Genotype and phenotype analysis of the segregation population validated that the mutations in VRN-A1 contributed to the late HD phenotype in the mutant. Expression analysis of C- or T-type alleles in exon 4 of the WT and mutant lines indicated that this mutation led to lower expression of VRN-A1, which resulted in the late-heading of je0155. This study provides valuable information for the genetic regulation of HD and many important resources for HD refinement in wheat breeding programs.

The role of ERBB4 mutations in the prognosis of advanced non-small cell lung cancer treated with immune checkpoint inhibitors.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have witnessed the achievements of convincing clinical benefits that feature the significantly prolonged overall survival (OS) of patients suffering from advanced non-small cell lung cancer (NSCLC), according to reports recently. Sensitivity to immunotherapy is related to several biomarkers, such as PD-L1 expression, TMB level, MSI-H and MMR. However, a further investigation into the novel biomarkers of the prognosis on ICIs treatment is required. In addition, there is an urgent demand for the establishment of a systematic hazard model to assess the efficacy of ICIs therapy for advanced NSCLC patients. METHODS: In this study, the gene mutation and clinical data of NSCLC patients was obtained from the TCGA database, followed by the analysis of the detailed clinical information and mutational data relating to two advanced NSCLC cohorts receiving the ICIs treatment from the cBioPortal of Cancer Genomics. The Kaplan-Meier plot method was used to perform survival analyses, while selected variables were adopted to develop a systematic nomogram. The prognostic significance of ERBB4 in pan-cancer was analyzed by another cohort from the cBioPortal of Cancer Genomics. RESULTS: The mutation frequencies of TP53 and ERBB4 were 54% and 8% in NSCLC, respectively. The mutual exclusive analysis in cBioPortal has indicated that ERBB4 does show co-occurencing mutations with TP53. Patients with ERBB4 mutations were confirmed to have better prognosis for ICIs treatment, compared to those seeing ERBB4 wild type (PFS: exact p = 0.017; OS: exact p < 0.01) and only TP53 mutations (OS: p = 0.021). The mutation status of ERBB4 and TP53 was tightly linked to DCB of ICIs treatment, PD-L1 expression, TMB value, and TIICs. Finally, a novel nomogram was built to evaluate the efficacy of ICIs therapy. CONCLUSION: ERBB4 mutations could serve as a predictive biomarker for the prognosis of ICIs treatment. The systematic nomogram was proven to have the great potential for evaluating the efficacy of ICIs therapy for advanced NSCLC patients.

Aberrant expression of angiopoietin-like proteins 1 and 2 in cumulus cells is potentially associated with impaired oocyte developmental competence in polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder associated with obesity, insulin resistance, hyperandrogenism, alterations in ovarian angiogenesis and impaired oocyte competence. Emerging evidence demonstrates that angiopoietin-like protein 1 (ANGPTL1) and angiopoietin-like protein 2 (ANGPTL2) have an important influence on angiogenesis, androgen biosynthesis, insulin resistance and adipocytes function. In this study, we set out to determine the potential relationship between ANGPTL1, ANGPTL2 and oocyte competence in PCOS through analyzing the expression levels and dynamic pattern of the two genes in cumulus cells (CCs) during different phases of nuclear maturation of PCOS patients and control groups undergoing controlled ovarian hyperstimulation (COH) for in vitro fertilization and embryo transfer. We found that the relative abundance of ANGPTL1 and ANGPTL2 transcripts in CCs from patients with PCOS showed dynamic changes during oocyte maturation. Specifically, their expressions were increased significantly at the Metaphase II stage. In summary, the present novel evidence indicates that the expression patterns of ANGPTL1 and ANGPTL2 mRNAs are disordered during oocyte maturation in PCOS, which were potentially related to aberrant oocyte quality and developmental potency, at least in part, via pathological angiogenesis and metabolism.

DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images.

Segmentation of intracranial aneurysm images acquired using magnetic resonance angiography (MRA) is essential for medical auxiliary treatments, which can effectively prevent subarachnoid hemorrhages. This paper proposes an image segmentation model based on a dense convolutional attention U-Net, which fuses deep and rich semantic information with shallow-detail information for adaptive and accurate segmentation of MRA-acquired aneurysm images with large size differences. The U-Net model serves as a backbone, combining dense block and convolution block attention module (CBAM). The dense block is composed of a batch normalization layer, an randomly rectified linear unit activation function, and a convolutional layer, for mitigation of vanishing gradients, for multiplexing of aneurysm features, and for improving the network training efficiency. The CBAM is composed of a channel attention module and a spatial attention module, improving the segmentation performance of feature discrimination and enhancing the acquisition of key feature information. Owing to the large variation of aneurysm sizes, multi-scale fusion is performed during up-sampling, for adaptive segmentation of MRA-acquired aneurysm images. The model was tested on the MICCAI 2020 ADAM dataset, and its generalizability was validated on the clinical aneurysm dataset (aneurysm sizes: < 3 mm, 3-7 mm, and > 7 mm) supplied by the Affiliated Hospital of Qingdao University. A good clinical application segmentation performance was demonstrated.

Whole-brain structural magnetic resonance imaging-based classification of primary dysmenorrhea in pain-free phase: a machine learning study.

To develop a machine learning model to investigate the discriminative power of whole-brain gray-matter (GM) images derived from primary dysmenorrhea (PDM) women and healthy controls (HCs) during the pain-free phase and further evaluate the predictive ability of contributing features in predicting the variance in menstrual pain intensity. Sixty patients with PDM and 54 matched female HCs were recruited from the local university. All participants underwent the head and pelvic magnetic resonance imaging scans to calculate GM volume and myometrium-apparent diffusion coefficient (ADC) during their periovulatory phase. Questionnaire assessment was also conducted. A support vector machine algorithm was used to develop the classification model. The significance of model performance was determined by the permutation test. Multiple regression analysis was implemented to explore the relationship between discriminative features and intensity of menstrual pain. Demographics and myometrium ADC-based classifications failed to pass the permutation tests. Brain-based classification results demonstrated that 75.44% of subjects were correctly classified, with 83.33% identification of the patients with PDM (P < 0.001). In the regression analysis, demographical indicators and myometrium ADC accounted for a total of 29.37% of the variance in pain intensity. After regressing out these factors, GM features explained 60.33% of the remaining variance. Our results suggested that GM volume can be used to discriminate patients with PDM and HCs during the pain-free phase, and neuroimaging features can further predict the variance in the intensity of menstrual pain, which may provide a potential imaging marker for the assessment of menstrual pain intervention.