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Understanding the spatial and temporal patterns of drought is essential for mitigating drought‐induced impacts. To date, less attention is paid to drought characterization and changes across global river basins within a 3D clustering drought identification framework. Here, we characterized drought events across 59 global river basins during 1979–2020 based on standardized precipitation evapotranspiration index and a three‐dimensional clustering method, together with exploration of relationships between drought indicators. The results show that drought characteristics did not change significantly over time in most basins, but the frequency tended to decrease in the Middle East and North Africa and showed increase at high latitudes. Droughts in Amazon, Nile and La Plata basins are severer than other basins with higher severities on the whole. Moreover, for most all basins, drought affected area and severity both increased with duration. Plain Language Summary Understanding space and time characteristics of drought is crucial for reducing the impacts of drought. Until now, we have paid less attention to drought characterization and change in river basins around the world from the three‐dimensional perspective. Here, we used the Standardized Precipitation Evapotranspiration Index tool and a three‐dimensional clustering method, to identify drought events between 1979 and 2020 in 59 global river basins, and explored the relationships between drought intensity, severity, duration and affected area. Our findings showed that most basins did not experience significant changes in drought characteristics over time. However, the frequency of droughts decreased in the Middle East and North Africa, while the affected area of droughts increased at high latitudes. Particularly, the Amazon, Nile and La Plata basins generally have experienced severer droughts than other basins. Meanwhile, the drought affected area and severity generally increase with duration over most basins. Key Points Drought changes between 1979–1999 and 2000–2020 are insignificant in most basins Amazon, Nile and La Plata basins experienced higher drought severity than other basins Drought affected area and severity generally increased with duration for most basins

Raised erosive gastritis (REG) is a chronic gastritis with a high risk of malignant transformation. Current treatments often result in high recurrence rates and complications. Jianpi Qinghua Sanyu Yin (JPQHSYY), a traditional Chinese medicine, shows promise in treating REG. However, the underlying molecular mechanisms remain unclear. This study aimed to investigate the potential mechanism of JPQHSYY's therapeutic effects on REG. RNA-seq was employed to systematically analyze mRNA, lncRNA, and miRNA profiles in gastric mucosal tissues from REG patients before and after JPQHSYY treatment. The pivotal lncRNA-miRNA and miRNA-mRNA networks were predicted from sequencing data and bioinformatic analysis, and the results were exported using Cytoscape software. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used for functional exploration. Real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to validate RNA-seq analysis results. CCK8, cell cycle, apoptosis and western blot were performed to detect the effects of miR-122-5p in GES-1 cells . RNA-seq analysis revealed 576 differentially expressed lncRNAs (269 upregulated, 307 downregulated), 33 differentially expressed miRNAs (13 upregulated, 20 downregulated), and 1717 differentially expressed mRNAs (777 upregulated, 940 downregulated) in JPQHSYY-treated REG patients. GO and KEGG analyses highlighted key pathways, including the PI3K/AKT signaling pathway, involved in cell cycle and apoptosis regulation. The ceRNA network analysis suggested that JPQHSYY impacts the miRNA-lncRNA interactions. Validation experiments confirmed that JPQHSYY inhibits the PI3K/AKT pathway, reducing cell viability, colony formation, and promoting apoptosis in miR-122-5p transfected GES-1 cells. The therapeutic efficacy of JPQHSYY in treating REG might be mediated by the ceRNA-driven PI3K/AKT pathway signaling pathways, which is implicated in the proliferation of gastric mucosal epithelial cells. Furthermore, the investigation of miRNA-lncRNA networks could reveal more information on potential new mechanisms and targets for JPQHSYY in the management of REG.

Background Pien Tze Huang (PZH), a traditional Chinese medicine formulation, is recognized for its therapeutic effect on colitis and colorectal cancer. However, its protective role and underlying mechanism in colitis-associated colorectal cancer (CAC) remain to be elucidated. Methods A CAC mouse model was established using AOM/DSS. Twenty mice were randomly divided into four groups ( n  = 5/group): Control, PZH, AOM/DSS, and AOM/DSS + PZH groups. Mice in the PZH and AOM/DSS + PZH group were orally administered PZH (250 mg/kg/d) from the first day of experiment, while the control and AOM/DSS group received an equivalent volume of distilled water. Parameters such as body weight, disease activity index (DAI), colon weight, colon length, colon histomorphology, intestinal tumor formation, serum concentrations of pro-inflammatory cytokines, proliferation and apoptosis in colon tissue were assessed. RNA sequencing was employed to identify the differentially expressed transcripts (DETs) in colonic tissues and related signaling pathways. Wnt/β-Catenin Pathway-Related genes in colon tissue were detected by QPCR and immunohistochemistry (IHC). Results PZH significantly attenuated AOM/DSS-induced weight loss, DAI elevation, colonic weight gain, colon shortening, histological damage, and intestinal tumor formation in mice. PZH also notably decreased serum concentration of IL-6, IL-1β, and TNF-α. Furthermore, PZH inhibited cell proliferation and promote apoptosis in tumor tissues. RNA-seq and KEGG analysis revealed key pathways influenced by PZH, including Wnt/β-catenin signaling pathway. IHC staining confirmed that PZH suppressed the expression of β-catenin, cyclin D1 and c-Myc in colonic tissues. Conclusions PZH ameliorates AOM/DSS-induced CAC in mice by suppressing the activation of Wnt/β-catenin signaling pathway.

Mid-infrared (MIR) frequency combs based on integrated photonic microresonators (micro combs) have attracted increasing attention in chip-scale spectroscopy due to their high spectral resolution and broadband wavelength coverage. However, up to date, there are no perfect solutions for the effective generation of MIR micro combs because of the lack of proper MIR materials as the core and cladding of the integrated microresonators, thereby hindering accurate and flexible dispersion engineering. Here, we have firstly demonstrated a MIR micro comb generation covering from 6.94 μm to 12.04 μm based on a sandwich-integrated all-ChG microresonator composed of GeAsTeSe and GeSbSe as the core and GeSbS as cladding. The novel sandwich microresonator is proposed to achieve a symmetrically uniform distribution of the mode field in the microresonator core, precise dispersion engineering, and low optical loss, which features a wide transmission window, high Kerr nonlinearity, and hybrid-fabrication flexibility on a silicon wafer. A MIR Kerr frequency comb with a 5.1 μm bandwidth has been numerically demonstrated, assisted by dispersive waves. Additionally, a feasible fabrication scheme is proposed to realize the on-demand ChG microresonators. These demonstrations characterize the advantages of integrated ChG photonic devices in MIR nonlinear photonics and their potential applications in MIR spectroscopy.

Heme oxygenase-1 (HO-1) is a stress-inducible enzyme with diverse cytoprotective effects, and reported to have an important role in angiogenesis recently. Here we investigated whether HO-1 transduced by mesenchymal stem cells (MSCs) can induce angiogenic effects in infarcted myocardium. HO-1 was transfected into cultured MSCs using an adenoviral vector. 1 × 10 6 Ad-HO-1-transfected MSCs (HO-1-MSCs) or Ad-Null-transfected MSCs (Null-MSCs) or PBS was respectively injected into rat hearts intramyocardially at 1 h post-myocardial infarction. The results showed that HO-1-MSCs were able to induce stable expression of HO-1 in vitro and in vivo . The capillary density and expression of angiogenic growth factors, VEGF and FGF2 were significantly enhanced in HO-1-MSCs-treated hearts compared with Null-MSCs-treated and PBS-treated hearts. However, the angiogenic effects of HO-1 were abolished by treating the animals with HO inhibitor, zinc protoporphyrin. The myocardial apoptosis was marked reduced with significantly reduced fibrotic area in HO-1-MSCs-treated hearts; Furthermore, the cardiac function and remodeling were also significantly improved in HO-1-MSCs-treated hearts. Our current findings support the premise that HO-1 transduced by MSCs can induce angiogenic effects and improve heart function after acute myocardial infarction.

Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. In this paper, we propose a Cross-Reference and Local–Global Conditional Networks (CRCNet) for few-shot segmentation. Unlike previous works that only predict the query image’s mask, our proposed model concurrently makes predictions for both the support image and the query image. Our network can better find the co-occurrent objects in the two images with a cross-reference mechanism, thus helping the few-shot segmentation task. To further improve feature comparison, we develop a local-global conditional module to capture both global and local relations. We also develop a mask refinement module to refine the prediction of the foreground regions recurrently. Experiments on the PASCAL VOC 2012, MS COCO, and FSS-1000 datasets show that our network achieves new state-of-the-art performance.

Gastrointestinal stromal tumors (GISTs) are commonly known to be derived from the gastrointestinal (GI) tract, but recently there have been more and more literature describing lesions with similar pathological and immunohistochemical resembling GISTs but located outside the GI tract, and they have been termed as extra-GISTs (eGISTs). However, due to the rare incidence of eGISTs, its association with survival outcomes is poorly understood, especially in the Chinese population. Here, we aimed to identify the risk factors of eGISTs and to assess their association with overall survival (OS) and disease-free survival (DFS). Data of pathologically confirmed eGISTs cases, without radiological and perioperative evidence of other primary lesions, and with no microscopically identified adhesion between the tumor and the gastrointestinal serosa, which were surgically treated between January 2006 and September 2017 were retrieved from the database of four high-volume hospitals. Immunohistochemical and genetic testing were performed on the postoperative lesions and were staged using the National Institutes of Health (NIH) criteria. A total of 55 cases were retrieved. eGISTs were identified from the retroperitoneum (36.4%), mesocolon (25.5%), small bowel mesentery (12.7%), abdominopelvic cavity (12.7%), lesser omental sac (5.5%), ovary (3.6%), pancreatic capsule (1.8%), or urinary bladder (1.8%). Based on the NIH risk classification, majority of the lesion were classified as high risk (85.5%). was the most common mutation site (76.5%) and 25.0% of the cases were wild-type eGISTs. Multivariate analyses showed that tumor location and size were independent factors affecting prognoses. Patients with tumors in the retroperitoneum had significantly poorer OS and DFS as compared to those in the non-retroperitoneum (HR [95% CI] for OS and DFS: 2.546 [1.023-6.337] [ = 0.037] and 2.475 [0.975-6.273] [ = 0.049], respectively). Similar findings were found for tumors of size >15 cm, compared to ≤15 cm (HR [95% CI] for OS and DFS: 5.350 [2.022-14.156] [ < 0.001] and 3.861 [1.493-9.988] [ = 0.003], respectively). eGISTs were predominantly found from the retroperitoneum and mostly classified as high risk. Those located in the retroperitoneum and of size >15 cm had the poorer OS and DFS as compared to those in the non-retroperitoneum and of size <15 cm.

Weakly-supervised point cloud segmentation with extremely limited labels is highly desirable to alleviate the expensive costs of collecting densely annotated 3D points. This paper explores applying the consistency regularization that is commonly used in weakly-supervised learning, for its point cloud counterpart with multiple data-specific augmentations, which has not been well studied. We observe that the straightforward way of applying consistency constraints to weakly-supervised point cloud segmentation has two major limitations: noisy pseudo labels due to the conventional confidence-based selection and insufficient consistency constraints due to discarding unreliable pseudo labels. Therefore, we propose a novel Reliability-Adaptive Consistency Network (RAC-Net) to use both prediction confidence and model uncertainty to measure the reliability of pseudo labels and apply consistency training on all unlabeled points while with different consistency constraints for different points based on the reliability of corresponding pseudo labels. Experimental results on the S3DIS and ScanNet-v2 benchmark datasets show that our model achieves superior performance in weakly-supervised point cloud segmentation. The code will be released publicly at https://github.com/wu-zhonghua/RAC-Net.

Before deploying a monocular depth estimation (MDE) model in real-world applications such as autonomous driving, it is critical to understand its generalization and robustness. Although the generalization of MDE models has been thoroughly studied, the robustness of the models has been overlooked in previous research. Existing state-of-the-art methods exhibit strong generalization to clean, unseen scenes. Such methods, however, appear to degrade when the test image is perturbed. This is likely because the prior arts typically use the primary 2D data augmentations (e.g., random horizontal flipping, random cropping, and color jittering), ignoring other common image degradation or corruptions. To mitigate this issue, we delve deeper into data augmentation and propose utilizing strong data augmentation techniques for robust depth estimation. In particular, we introduce 3D-aware defocus blur in addition to seven 2D data augmentations. We evaluate the generalization of our model on six clean RGB-D datasets that were not seen during training. To evaluate the robustness of MDE models, we create a benchmark by applying 15 common corruptions to the clean images from IBIMS, NYUDv2, KITTI, ETH3D, DIODE, and TUM. On this benchmark, we systematically study the robustness of our method and 9 representative MDE models. The experimental results demonstrate that our model exhibits better generalization and robustness than the previous methods. Specifically, we provide valuable insights about the choices of data augmentation strategies and network architectures, which would be useful for future research in robust monocular depth estimation. Our code, model, and benchmark can be available at https://github.com/KexianHust/Robust-MonoDepth.

The goal of this work is to present a systematic solution for RGB-D salient object detection, which addresses the following three aspects with a unified framework: modal-specific representation learning, complementary cue selection, and cross-modal complement fusion. To learn discriminative modal-specific features, we propose a hierarchical cross-modal distillation scheme, in which we use the progressive predictions from the well-learned source modality to supervise learning feature hierarchies and inference in the new modality. To better select complementary cues, we formulate a residual function to incorporate complements from the paired modality adaptively. Furthermore, a top-down fusion structure is constructed for sufficient cross-modal cross-level interactions. The experimental results demonstrate the effectiveness of the proposed cross-modal distillation scheme in learning from a new modality, the advantages of the proposed multi-modal fusion pattern in selecting and fusing cross-modal complements, and the generalization of the proposed designs in different tasks.