Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems with a Kalman-Inspired Proposal Distribution

Bayesian analysis is widely used in science and engineering for real-time forecasting, decision making, and to help unravel the processes that explain the observed data. These data are some deterministic and/or stochastic transformations of the underlying parameters. A key task is then to summarize the posterior distribution of these parameters. When models become too difficult to analyze analytically, Monte Carlo methods can be used to approximate the target distribution. Of these, Markov chain Monte Carlo (MCMC) methods are particularly powerful. Such methods generate a random walk through the parameter space and, under strict conditions of reversibility and ergodicity, will successively visit solutions with frequency proportional to the underlying target density. This requires a proposal distribution that generates candidate solutions starting from an arbitrary initial state. The speed of the sampled chains converging to the target distribution deteriorates rapidly, however, with increasing parameter dimensionality. In this paper, we introduce a new proposal distribution that enhances significantly the efficiency of MCMC simulation for highly parameterized models. This proposal distribution exploits the cross-covariance of model parameters, measurements and model outputs, and generates candidate states much alike the analysis step in the Kalman filter. We embed the Kalman-inspired proposal distribution in the DREAM algorithm during burn-in, and present several numerical experiments with complex, high-dimensional or multi-modal target distributions. Results demonstrate that this new proposal distribution can greatly improve simulation efficiency of MCMC. Specifically, we observe a speed-up on the order of 10-30 times for groundwater models with more than one-hundred parameters

GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation

Birds-eye-view (BEV) semantic segmentation is critical for autonomous driving for its powerful spatial representation ability. It is challenging to estimate the BEV semantic maps from monocular images due to the spatial gap, since it is implicitly required to realize both the perspective-to-BEV transformation and segmentation. We present a novel two-stage Geometry Prior-based Transformation framework named GitNet, consisting of (i) the geometry-guided pre-alignment and (ii) ray-based transformer. In the first stage, we decouple the BEV segmentation into the perspective image segmentation and geometric prior-based mapping, with explicit supervision by projecting the BEV semantic labels onto the image plane to learn visibility-aware features and learnable geometry to translate into BEV space. Second, the pre-aligned coarse BEV features are further deformed by ray-based transformers to take visibility knowledge into account. GitNet achieves the leading performance on the challenging nuScenes and Argoverse Datasets. The code will be publicly available

Effects of the Largest Lake of the Tibetan Plateau on the Regional Climate

Qinghai Lake is the largest lake in China. However, its influence on the local climate remains poorly understood. By using an atmosphere-lake coupled model, we investigated the impact of the lake on the local climate. After the adjustment of four key parameters, the model reasonably reproduced the lake-air interaction. Superimposed by the orographic effects on lake-land breeze circulation, the presence of the lake enhanced precipitation over the southern part of the lake and its adjacent land, while slightly reduced precipitation along the northern shore of the lake. The lake effect on local precipitation revealed a distinct seasonal and diurnal variability, reducing precipitation in May (-6.6%) and June (-4.5%) and increasing it from July (5.7%) to November (125.6%). During the open water season, the lake's daytime cooling effect weakened and the nighttime warming effect strengthened, affecting spatial distribution and intensity of lake-induced precipitation. In early summer, precipitation slightly decreased over the north part of the lake due to the lake's daytime cooling. In turn, lake-induced nighttime warming increased precipitation over the southern section of the lake and its adjacent land. With the start of the autumn cooling in September, heat and moisture fluxes from the lake resulted in precipitation increase in both daytime and nighttime over the entire lake. In October, the background atmospheric circulation coupled with the strong lake effects lead to a small amount but high proportion of lake-induced precipitation spreading evenly over the lake.Peer reviewe

Excellent combination of HPLC-RSD-DAD-ESI/MS and HSCCC experiments to screen and identify radical scavengers from Neo-Taraxacum siphonanthun

Our previous research found that the crude extract of Neo-T. siphonanthun exhibited an effective DPPH (1,1-diphenyl-2-picryhydrazyl) radical scavenging activity. In this study an online rapid screening method, high-performance liquid chromatography-radical scavenging detection-diode array detector-electrospray ionization mass spectrometry (HPLC-RSD- DAD-ESI/MS) system, was developed for screening individual antioxidants from the most active fraction. Accordingly, three isomeric derivatives were detected. The target active compounds were isolated by high-speed counter-current chromatography (HSCCC) with the purity over 99%, and were identified as luteolin-3'-O-β-D-glucopyranoside (1), luteolin-7-O-β-D-glucopyranoside (2) and luteolin-4'-O-β-D-glucopyranoside (3) by analysis of its off-line nuclear magnetic resonance (NMR) spectra. Antioxidant activity of three compounds was assessed by off-line DPPH assay, and all of them showed potent activity

The mechanism of palmatine-mediated intestinal flora and host metabolism intervention in OA-OP comorbidity rats

BackgroundErXian decoction is a Chinese herbal compound that can prevent and control the course of osteoarthritis (OA) and osteoporosis (OP). OP and OA are two age-related diseases that often coexist in elderly individuals, and both are associated with dysregulation of the gut microbiome. In the initial study, Palmatine (PAL) was obtained by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and network pharmacological screening techniques, followed by 16S rRNA sequencing and serum metabolomics of intestinal contents, to explore the mechanism of PAL in the treatment of OA and OP.MethodsThe rats selected for this study were randomly divided into three groups: a sham group, an OA-OP group and a PAL group. The sham group was intragastrically administered normal saline solution, and the PLA group was treated with PAL for 56 days. Through microcomputed tomography (micro-CT), ELISA, 16S rRNA gene sequencing and non-targeted metabonomics research, we explored the potential mechanism of intestinal microbiota and serum metabolites in PAL treatment of OA-OP rats.ResultsPalmatine significantly repair bone microarchitecture of rat femur in OA-OP rats and improved cartilage damage. The analysis of intestinal microflora showed that PAL could also improve the intestinal microflora disorder of OA-OP rats. For example, the abundance of Firmicutes, Bacteroidota, Actinobacteria, Lactobacillus, unclassified_f_Lachnospiraceae, norank_f_Muribaculaceae, Lactobacillaceae, Lachnospiraceae and Muribaculaceae increased after PAL intervention. In addition, the results of metabolomics data analysis showed that PAL also change the metabolic status of OA-OP rats. After PAL intervention, metabolites such as 5-methoxytryptophol, 2-methoxy acetaminophen sulfate, beta-tyrosine, indole-3-carboxylic acid-O-sulfate and cyclodopa glucoside increased. Association analysis of metabolomics and gut microbiota (GM) showed that the communication of multiple flora and different metabolites played an important role in OP and OA.ConclusionPalmatine can improve cartilage degeneration and bone loss in OA-OP rats. The evidence we provided supports the idea that PAL improves OA-OP by altering GM and serum metabolites. In addition, the application of GM and serum metabolomics correlation analysis provides a new strategy for uncovering the mechanism of herbal treatment for bone diseases