Deltas in arid environments

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Day, J., Goodman, R., Chen, Z., Hunter, R., Giosan, L., & Wang, Y. Deltas in arid environments. Water, 13(12), (2021): 1677, https://doi.org/10.3390/w13121677.Due to increasing water use, diversion and salinization, along with subsidence and sea-level rise, deltas in arid regions are shrinking worldwide. Some of the most ecologically important arid deltas include the Colorado, Indus, Nile, and Tigris-Euphrates. The primary stressors vary globally, but these deltas are threatened by increased salinization, water storage and diversion, eutrophication, and wetland loss. In order to make these deltas sustainable over time, some water flow, including seasonal flooding, needs to be re-established. Positive impacts have been seen in the Colorado River delta after flows to the delta were increased. In addition to increasing freshwater flow, collaboration among stakeholders and active management are necessary. For the Nile River, cooperation among different nations in the Nile drainage basin is important. River flow into the Tigris-Euphrates River delta has been affected by politics and civil strife in the Middle East, but some flow has been re-allocated to the delta. Studies commissioned for the Indus River delta recommended re-establishment of some monthly water flow to maintain the river channel and to fight saltwater intrusion. However, accelerating climate impacts, socio-political conflicts, and growing populations suggest a dire future for arid deltas.This research received no external funding

Deep learning methods for solving linear inverse problems: Research directions and paradigms

The linear inverse problem is fundamental to the development of various scientific areas. Innumerable attempts have been carried out to solve different variants of the linear inverse problem in different applications. Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance in many applications. In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse problems. We review how deep learning methods are used in solving different linear inverse problems, and explore the structured neural network architectures that incorporate knowledge used in traditional methods. Furthermore, we identify open challenges and potential future directions along this research line

Study on the adaptability and optimization of boom replacement methods for suspension bridges

To ensure the safe operation of bridges, the study of methods and techniques for boom replacement has become a crucial aspect of the scientific maintenance of suspension bridges. This study focuses on analyzing the bridge responses and evaluating the applicability of three different boom replacement methods: single-point, three-point and five-point, using finite element calculations. A sea-crossing suspension bridge is taken as a case study to simulate the process of boom replacement using temporary booms. Consequently, the optimal replacement method for booms of varying lengths is determined. Meanwhile, this research proposes a quantitative basis for classifying boom lengths based on calculation data and analysis results to determine the suitable boom lengths for different replacement methods. Besides, a comparison of the relationship between the force transmission efficiency of temporary booms and boom length reveals that longer booms exhibit lower force transmission efficiency, with the efficiency decreasing at a faster rate as boom length increases. Overall, these findings provide a theoretical basis for the study of boom replacement in suspension bridges

Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.Comment: 7 pages, 11 figures, accepted by the 2019 World Wide Web Conference (WWW '19

Immunoglobulin G Locus Events in Soft Tissue Sarcoma Cell Lines

Recently immunoglobulins (Igs) have been found to be expressed by cells other than B lymphocytes, including various human carcinoma cells. Sarcomas are derived from mesenchyme, and the knowledge about the occurrence of Ig production in sarcoma cells is very limited. Here we investigated the phenomenon of immunoglobulin G (IgG) expression and its molecular basis in 3 sarcoma cell lines. The mRNA transcripts of IgG heavy chain and kappa light chain were detected by RT-PCR. In addition, the expression of IgG proteins was confirmed by Western blot and immunofluorescence. Immuno-electron microscopy localized IgG to the cell membrane and rough endoplasmic reticulum. The essential enzymes required for gene rearrangement and class switch recombination, and IgG germ-line transcripts were also identified in these sarcoma cells. Chromatin immunoprecipitation results demonstrated histone H3 acetylation of both the recombination activating gene and Ig heavy chain regulatory elements. Collectively, these results confirmed IgG expression in sarcoma cells, the mechanism of which is very similar to that regulating IgG expression in B lymphocytes

Suppression of the RAC1/MLK3/p38 Signaling Pathway by β-Elemene Alleviates Sepsis-Associated Encephalopathy in Mice

It is still difficult to treat sepsis-associated encephalopathy (SAE) which is a diffuse brain dysfunction caused by sepsis, with excessive activation of microglia as one of the main mechanisms. Ras-related C3 botulinum toxin substrate 1 (RAC1) is proven to be a key molecule in the inflammatory signaling network. By using microglial cell line BV-2 and a mouse model of cecal ligation puncture (CLP), we herein evaluated the effects of β-elemene, an extract of Curcuma zedoaria Rosc., on RAC1 signaling in microglia. β-Elemene decreased the expressions of pro-inflammatory cytokines [tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and IL-6] and attenuated translocation of nuclear factor-κB (NF-κB) p65 from the cytosol to the nucleus in BV-2 cells after lipopolysaccharide (LPS) treatment. It also inhibited the activation of RAC1, mixed-lineage protein kinase 3 (MLK3) and p38 mitogen-activated protein kinase (MAPK). The phosphorylation of the RAC1 Ser71 site was increased by β-elemene. Moreover, the learning and memory abilities of CLP mice in the water maze test and fear conditioning test were improved after β-elemene treatment. It reduced the expression of the microglial marker IBA1, significantly increased RAC1 Ser71 phosphorylation, and suppressed the RAC1/MLK3/p38 signaling activation and inflammatory response in the hippocampus. In conclusion, β-elemene effectively alleviated SAE in mice and inhibited the RAC1/MLK3/p38 signaling pathway in microglia, and might be an eligible potential candidate for SAE treatment