Threshold Recognition Based on Non-stationarity of Extreme Rainfall in the Middle and Lower Reaches of the Yangtze River Basin

Analyzing the hydrological sequence from the non-stationary characteristics can better understand the responses of changes in extreme rainfall to climate change. Taking the plain area in the middle and lower reaches of the Yangtze River basin (MLRYRB) as the study area, this study adopted a set of extreme rainfall indices and used the Bernaola-Galvan Segmentation Algorithm (BGSA) method to test the non-stationarity of extreme rainfall events. The General Pareto Distribution (GPD) was used to fit extreme rainfall and was calculated to select the optimal threshold of extreme rainfall. In addition, the cross-wavelet technique was used to explore the correlations of extreme rainfall with El Niño-Southern Oscillation (ENSO) and Western Pacific Subtropical High (WPSH) events. The results showed that: (1) extreme rainfall under different thresholds had different non-stationary characteristics; (2) the GPD distribution could well fit the extreme rainfall in the MLRYRB, and 40–60 mm was considered as the suitable optimal threshold by comparing the uncertainty of the return period; and (3) ENSO and WPSH had significant periodic effects on extreme rainfall in the MLRYRB. These findings highlighted the significance of non-stationary assumptions in hydrological frequency analysis, which were of great importance for hydrological forecasting and water conservancy project management

DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning

Diffusion models have proven to be highly effective in generating high-quality images. However, adapting large pre-trained diffusion models to new domains remains an open challenge, which is critical for real-world applications. This paper proposes DiffFit, a parameter-efficient strategy to fine-tune large pre-trained diffusion models that enable fast adaptation to new domains. DiffFit is embarrassingly simple that only fine-tunes the bias term and newly-added scaling factors in specific layers, yet resulting in significant training speed-up and reduced model storage costs. Compared with full fine-tuning, DiffFit achieves 2$\times$ training speed-up and only needs to store approximately 0.12\% of the total model parameters. Intuitive theoretical analysis has been provided to justify the efficacy of scaling factors on fast adaptation. On 8 downstream datasets, DiffFit achieves superior or competitive performances compared to the full fine-tuning while being more efficient. Remarkably, we show that DiffFit can adapt a pre-trained low-resolution generative model to a high-resolution one by adding minimal cost. Among diffusion-based methods, DiffFit sets a new state-of-the-art FID of 3.02 on ImageNet 512$\times$512 benchmark by fine-tuning only 25 epochs from a public pre-trained ImageNet 256$\times$256 checkpoint while being 30$\times$ more training efficient than the closest competitor.Comment: Tech Repor

Fenofibrate suppresses corneal neovascularization by regulating lipid metabolism through PPARα signaling pathway

Purpose: The purpose of this study was to explore the potential underlying mechanism of anti-vascular effects of peroxisome proliferator–activated receptor α (PPARα) agonist fenofibrate against corneal neovascularization (CNV) through the changes of lipid metabolism during CNV.Methods: A suture-induced CNV model was established and the clinical indications were evaluated from day 1 to day 7. Treatments of vehicle and fenofibrate were performed for 5 days after suture and the CNV areas were compared among the groups. The eyeballs were collected for histological analysis, malondialdehyde (MDA) measurement, terminal deoxynucleotidyl transferase 2′-deoxyuridine 5′-triphosphate nick end labeling (TUNEL) staining, western blot, quantitative real-time PCR (qRT-PCR) assays and immunohistochemical (IHC) staining to elucidate pathological changes and the underlying mechanism.Results: Lipi-Green staining and MDA measurement showed that lipid deposition and peroxidation were increased in the CNV cornea while the expression of long-chain acyl-coenzyme A synthetase 1 (ACSL1), carnitine palmitoyltransterase 1A(CPT1A) and medium-chain acyl-coenzyme A dehydrogenase (ACADM), which are key enzymes of fatty acid β-oxidation (FAO) and targeted genes of peroxisome proliferator-activated receptor alpha (PPARα) pathway, were decreased in CNV cornea. Fenofibrate suppressed lipid accumulation and peroxidation damage in the CNV cornea. Fenofibrate upregulated the expression levels of PPARα, ACSL1, CPT1A, and ACADM compared with vehicle group. IHC staining indicated that fenofibrate also decreased the expression of VEGFa, VEGFc, TNFα, IL1β and CD68.Conclusion: Disorder of lipid metabolism may be involved in the formation of suture-induced CNV and fenofibrate played anti-neovascularization and anti-inflammatory roles on cornea by regulating the key enzymes of lipid metabolism and ameliorating lipid peroxidation damage of cornea through PPARα signaling pathway

Molecularly imprinted polymer based on MWCNTs-QDs as fluorescent biomimetic sensor for specific recognition of target protein

A novel molecularly imprinted optosensing material based on multi-walled carbon nanotube-quantum dots (MWCNT-QDs) has been designed and synthesized for its high selectivity, sensitivity and specificity in the recognition of a target protein bovine serum albumin (BSA). Molecularly imprinted polymer coated MWCNT-QDs using BSA as the template (BMIP-coated MWCNT-QDs) exhibits a fast mass-transfer speed with a response time of 25 min. It is found that the BSA as a target protein can significantly quench the luminescence of BMIP-coated MWCNT-QDs in a concentration-dependent manner that is best described by a Stem-Volmer equation. The K-SV for BSA is much higher than bovine hemoglobin and lysozyme, implying a highly selective recognition of the BMIP-coated MWCNT-QDs to BSA. Under optimal conditions, the relative fluorescence intensity of BMIP-coated MWCNT-QDs decreases linearly with the increasing target protein BSA in the concentration range of 5.0 x 10(-7)-35.0 x 10(-7) M with a detection limit of 80 nM

A global perspective on propagation from meteorological drought to hydrological drought during 1902-2014

Meteorological drought is generally regarded as the cause of other types of droughts. This study firstly analyzed the characteristics of meteorological drought and hydrological drought in different climate regions all over the world during a long time period (1902-2014); then, the maximum Pearson correlation coefficients (MPCC) of meteorological drought and hydrological drought at different time scales were calculated to determine the drought response time (DRT) in each climate region. The results revealed that: 1) meteorological drought in most climate regions intensified during 1902-1958 but showed a wetting trend during 1959-2014. Compared with the characteristics of meteorological drought, the change of hydrological drought was slightly different. Hydrolog-ical drought weakened during 1902-1958 but intensified slightly during 1959-2014; however, the magnitude of the changing rate was relatively small. 2) The drought response relationship in the Cf (i.e., continental wet warm) climate region was the strongest, and that in the E (i.e., polar) climate region was the weakest. 3) Globally, the DRTs in various climate regions were mainly 5-10 months, which were mainly related to the climate type. The outcomes of this study can provide a reference for further revealing the propagation mecha-nism from meteorological drought to hydrological drought in different climate regions.11Nsciescopu

A New Perspective on Drought Propagation: Causality

© 2022. American Geophysical Union. All Rights Reserved.The essence of propagation from meteorological to hydrological drought is the cause-effect relationship between precipitation and runoff. This study challenged the reliability of applying linear or non-linear correlation (i.e., closeness/similarity, a non-directional scalar) to study drought propagation (i.e., causality, a directional vector). Meanwhile, in the field of hydrometeorology, causality analysis is burgeoning in model simulations, but still rare in analyzing the observations. Therefore, this study aims to provide a new perspective on drought propagation (i.e., causality) using convergent cross mapping (CCM) based on pure observations. Compared with the results in previous studies, the effectiveness of applying causality analysis in drought propagation study was proven, indicating that causality analysis would be more powerful than correlation analysis, especially for detecting drought propagation direction.11Nsciescopu

Investigating the spatiotemporal variations of extreme rainfall and its potential driving factors with improved partial wavelet coherence

Extreme rainfall can be affected by various climatic factors such as the large-scale climate patterns (LCPs). Understanding the changing LCPs can improve the accuracy of extreme rainfall prediction. This study explores the variation trend of extreme rainfall in the middle and lower reaches of the Yangtze River Basin (MLRYRB) and the telecorrelation with four LCPs, namely WPSHI (Western Pacific Subtropical High Index), EAMI (East Asia Monsoon Index), ENSO (El Nino-Southern Oscillation) and PDO (Pacific Decadal Oscillation), through modified Mann-Kendall (MMK) analysis, Pearson correlation coefficient, wavelet coherence analysis (WTC) and improved partial wavelet analysis (PWC). Previous studies have ignored the interdependence between these climate indices when analyzing their effects on precipitation. This study introduces the improved PWC, which can remove the correlations between them and reveal the influence of a single LCP. The results show that: 1) extreme rainfall in the MLRYRB has an obvious increasing trend and has a significant correlation with the LCPs; 2) the LCPs have a significant cyclical relationship with extreme rainfall, which can be significantly affected by the intergenerational variation of the LCPs; and 3) the improved PWC can accurately reveal the influence of a single LCP. EAMI is the main influencing factor in the 1-year cycle, while WPSHI is the main influencing factor in the 5-year cycle. ENSO and PDO can always influence extreme rainfall by coupling WPSHI or EAMI.11Nsciescopu

Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method

© 2022 Elsevier B.V.Model development in groundwater simulation and physics informed deep learning (DL) has been advancing separately with limited integration. This study develops a general hybrid model for groundwater level (GWL) simulations, wherein water balance-based groundwater processes are embedded as physics constrained recurrent neural layers into prevalent DL architectures. Because of the automatic parameterizing process, physics-informed deep learning algorithm (DLA) equips the hybrid model with enhanced abilities of inferring geological structures of catchment and unobserved groundwater-related processes implicitly. The main purposes of this study are: 1) to explore an optimized data-driven method as alternative to complicated groundwater models; 2) to improve the awareness of hydrological knowledge of DL model for lumped GWL simulation; and 3) to explore the lumped data-driven groundwater models for cross-region applications. The 91 illustrative cases of GWL modeling across the middle eastern continental United States (CONUS) demonstrate that the hybrid model outperforms the pure DL models in terms of prediction accuracy, generality, and robustness. More specifically, the hybrid model outperforms the pure DL models in 78 % of catchments with the improved Δ NSE = 0.129. Meanwhile, the hybrid model simulates more stably with different input strategies. This study reveals the superiority and powerful simulation ability of the DL model with physical constraints, which increases trust in data-driven approaches on groundwater modellings.11Nsciescopu