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The causes of terrace formation in active orogenic belts have long been studied. Here, we present a synthesis of 147 available papers on the ages of Quaternary terraces within the Tibetan Plateau and along the surrounding orogenic belts. More than 1500 ages were collected to establish a database for exploring the causes of fluvial terrace formation. Because the age data were collected from different settings, ages younger than 200 ka were grouped into three regional climate systems: the westerlies, Indian summer monsoon, and East Asian summer monsoon. We further examined the relationship between terrace formation and climate change, as well as the contribution of tectonic uplift. Our results indicate that the intervals of fluvial terrace formation are synchronous with transitions in global climate from cooler to warmer conditions (stadials to interstadials). We propose that the formation of fluvial terraces across the Tibetan Plateau has been controlled mainly by cyclical climate changes, although there is not a one-to-one correspondence. • An age database for fluvial terraces across the Tibetan Plateau since ∼3 Mya. • Fluvial terraces development during stadials-interstadials transition periods. • Complex interaction of climatic and tectonic processes in terrace generation. [ABSTRACT FROM AUTHOR]

• A novel Contrastive Multi-Task Convolutional Neural Network (CMT-CNN) is proposed for automatic COVID-19 diagnosis. • The main task is to diagnose COVID-19 from other pneumonia and normal controls. The auxiliary task is self-supervised contrastive learning to acquire transformation-invariant representations. • A series of interpretable transformations are defined for medical image augmentation. • Extensive experiments demonstrate that the auxiliary task can significantly improve the generalization of CNN on both CT and X-ray datasets. Computed tomography (CT) and X-ray are effective methods for diagnosing COVID-19. Although several studies have demonstrated the potential of deep learning in the automatic diagnosis of COVID-19 using CT and X-ray, the generalization on unseen samples needs to be improved. To tackle this problem, we present the contrastive multi-task convolutional neural network (CMT-CNN), which is composed of two tasks. The main task is to diagnose COVID-19 from other pneumonia and normal control. The auxiliary task is to encourage local aggregation though a contrastive loss: first, each image is transformed by a series of augmentations (Poisson noise, rotation, etc.). Then, the model is optimized to embed representations of a same image similar while different images dissimilar in a latent space. In this way, CMT-CNN is capable of making transformation-invariant predictions and the spread-out properties of data are preserved. We demonstrate that the apparently simple auxiliary task provides powerful supervisions to enhance generalization. We conduct experiments on a CT dataset (4,758 samples) and an X-ray dataset (5,821 samples) assembled by open datasets and data collected in our hospital. Experimental results demonstrate that contrastive learning (as plugin module) brings solid accuracy improvement for deep learning models on both CT (5.49%-6.45%) and X-ray (0.96%-2.42%) without requiring additional annotations. Our codes are accessible online. [ABSTRACT FROM AUTHOR]

[Display omitted] • Comprehensive immunity and ferroptosis in serous ovarian cancer prognosis has not been reported. • We constructed a prognostic prediction model with immunity- and ferroptosis-related genes (CXCL11, CX3CR1, FH, and DNAJB6) for SOC patients. Our results showed that risk score according to this model was an independent prognostic factor. • Patients with high risk score indicated an immunosuppression in the tumor microenvironment predicting poor prognosis. Serous ovarian cancer (SOC) is the most common type of ovarian cancer (OC), with bad outcomes. To improve the prognosis of SOC patients, a novel risk signature was developed by combining immunity- and ferroptosis-related genes. By means of comparing SOC tissues with normal tissues, we screened the differential expression of immunity-related genes (DE-IRGs) and ferroptosis-related genes(DE-FRGs) with the standards of |log 2 fold change| > 1 and false discovery rate (FDR) < 0.05. After obtaining the meaningful differentially expressed genes from immune and ferroptosis (DEGs), we established a prognostic risk signature by utilizing Cox regression analyses in TCGA training set, which was validated in TCGA testing set and GSE26712 dataset. Besides, the differential expression of immune-related markers, immunophenoscore (IPS), TIDE score,T cell dysfunction score and T cell exclusion score were also analyzed. We further verified the expression of target genes in ovarian tumor cells lines by QRT-PCR. A risk signature constructed by totally four immunity- and ferroptosis-related DEGs (CXCL11, CX3CR1, FH, and DNAJB6) was developed, which distinguished the SOC patients as high-risk and low-risk groups. Patients in the high-risk group showed a lower overall survival (OS) than those in the low-risk group. Furthermore, the risk score was independent when analyzed with clinical augments, which was significantly associated with 13 KEGG signaling pathways. The gene signature showed favorable predictive performance according to Receiver operating characteristic (ROC) curves. Notably, the expression of immune-related markers or IPS indicated a negative connection with the risk score. SOC patients had a lower score of TIDE and T cell dysfunction than Whom had a higher score. Nonetheless, there were no significant differences in T cell exclusion scores between the two groups.Compared with normal ovarian cell line IOSE-80,QRT-PCR experiments exhibited that CXCL11, CX3CR1 and FH were up-regulated in ovarian tumor cells lines(SK-OV-3,COC1,A2780),while DNAJB6 was down-regulated. Four-biomarker signature formed by immunity- and ferroptosis-related genes may be clinically used as risk stratifcation tool in serous ovarian cancer,which can help further clinical decision-making regarding prognostic prediction,individualized treatment and follow-up scheduling. [ABSTRACT FROM AUTHOR]