A TPDP-MPM-based approach to understanding the evolution mechanism of landslide-induced disaster chain

With complex topographic and hydrological characteristics,the landslide-induced surge disaster chain readily develops in mountainous and gorge areas,posing a huge challenge for infrastructure construction.This landslide-induced surge disaster chain involves a complex fluid-solid coupling between the landslide mass and a water body and exhibits complex energy conversion and dissipation characteristics,which is challenging to deal with using traditional finite element analysis.In this study,the energy evolution characteristics in the whole process of the disaster chain were first investigated,and the momentum-conservation equations for different stages were established.Then,the two-phase doublepoint material point method(TPDP-MPM)was used to model the landslide-induced surge disaster chain,and an experiment involving block-induced surge was modeled and simulated to validate this method.Finally,three generalized models were established for the landslide-induced surge process in a U-shaped valley,including subaerial,partly submerged,and submarine scenarios.The interaction mechanism between the landslide mass and the water body in the disaster chain was revealed by defining the system energy conversion ratio and the mechanism of evolution of the disaster chain from the perspective of energy.The results help further evaluate the secondary disasters,given the submerged position of the landslide mass.

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources,for which mathematical modeling is commonly adopted.In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling,we employed an ensemble machine learning(ML)model to identify the key nitrogen and phosphorus sources of lakes.Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality,environmental input,and meteorological conditions,among which the XGBoost model stood out as the best model for total nitrogen(TN)and total phosphorus(TP)prediction.The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality,while the lake TP is predominantly from endogenous sources.The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control.Finally,one-month-ahead prediction of lake TN and TP concentrations(R2 of 0.85 and 0.95,respectively)was achieved based on this model with sliding time window lengths of 9 and 6 months,respectively.Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction,which may provide valuable references for early warning and rational control of lake eutrophication.

Deciphering urban traffic impacts on air quality by deep learning and emission inventory

Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future metropolises.Here,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality.Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other models.And identifies the ranking of major factors,local meteorological conditions have become a nonnegligible factor.Layer-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of Shanghai.Meanwhile,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction.We also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 gradually.These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.

The RppC-AvrRppC NLR-effector interaction mediates the resistance to southern corn rust in maize

Southern corn rust(SCR),caused by the fungal pathogen Puccinia polysora,is a major threat to maize pro-duction worldwide.Efficient breeding and deployment of resistant hybrids are key to achieving durable control of SCR.Here,we report the molecular cloning and characterization of RppC,which encodes an NLR-type immune receptor and is responsible for a major SCR resistance quantitative trait locus.Further-more,we identified the corresponding avirulence effector,AvrRppC,which is secreted by P.polysora and triggers RppC-mediated resistance.Allelic variation of AvrRppC directly determines the effectiveness of RppC-mediated resistance,indicating that monitoring of AvrRppC variants in the field can guide the rational deployment of RppC-containing hybrids in maize production.Currently,RppC is the most frequently deployed SCR resistance gene in China,and a better understanding of its mode of action is crit-ical for extending its durability.

Atomistic characterization of binding modes and affinity of peptide inhibitors to amyloid-β protein

Abstract The aggregation of amyloid β-protein （Aβ） is tightly linked to the pathogenesis of Alzheimer＇s disease. Previous studies have found that three peptide inhibitors （i.e., KLVFF, VVIA, and LPFFD） can inhibit Aβ aggregation and alleviate Aβ-induced neurotoxicity. How- ever, atomic details of binding modes and binding affinities between these peptide inhibitors and Aβ have not been revealed. Here, using molecular dynamics simulations and molecular mechanics Poisson Boltzmann surface area （MM/PBSA） analysis, we examined the effect of three peptide inhibitors （KLVFF, VVIA, and LPFFD） on their sequence-specific interactions with Aβ and the molecular basis of their inhibition. All inhibitors exhibit varied binding affinity to Aβ, in which KLVFF has the highest binding affinity, whereas LPFFD has the least. MM/PBSA analysis further revealed that different peptide inhibitors have different modes of interaction with Aβ, consequently hotspot binding residues, and underlying driving forces. Specific residue-based interactions between inhibitors and Aβ were determined and compared for illustrating different binding and inhibition mechanisms. This work provides structure-based binding information for further modifica- tion and optimization of these three peptide inhibitors to enhance their binding and inhibitory abilities against Aβ aggregation.

A standardized dataset of built-up areas of China’s cities with populations over 300,000 for the period 1990-2015

China’s urbanization has attracted a lot of attention due to its unprecedented pace and intensity in terms of land,population,and economic impact.However,due to the lack of consistent and harmonized data,little is known about the patterns and dynamics of the interaction between these different aspects over the past few decades.Along with the implementation of the 2030 Agenda for Sustainable Development,a standardized dataset for assessing the sustainability of urbanization in China is needed.In this paper,we used remote sensing data from multiple sources(time-series of Landsat and Sentinel images)to map the impervious surface area(ISA)at five-year intervals from 1990 to 2015 and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more.This dataset was produced following the well-established rules adopted by the United Nations(UN).Validation of the ISA maps in urban areas based on the visual interpretation of Google Earth images showed that the average overall accuracy(OA),producer’s accuracy(PA)and user’s accuracy(UA)were 91.24%,92.58%and 89.65%,respec-tively.Comparisons with other existing urban built-up area datasets derived from the National Bureau of Statistics of China,the World Bank and UN-habitat indicated that our dataset,namely the stan-dardized urban built-up area dataset for China(SUBAD-China),provides an improved description of the spatiotemporal character-istics of the urbanization process and is especially applicable to a combined analysis of the spatial and socio-economic domains in urban areas.Potential applications of this dataset include combin-ing the spatial expansion and demographic information provided by UN to calculate sustainable development indicators such as SDG 11.3.1.The dataset could also be used in other multidimensional syntheses related to the study of urbanization in China.

Efficient replacement of long DNA fragments via non-homologous end joining at norrcoding regions

Targeted double-strand breaks(DSBs)in genomes can be introduced efficiently by endonucleases(Umov et al.,2010;Jinek et al.,2012;Joung and Sander,2013),including zinc-finger nucleases,tran scription activator-like effector nu cleases,and clustered regularly in terspaced palindromic repeats(CRISPR)/Cas9.After DSBs,DNA repair is mainly via homology-directed repair(HDR)and/or non-homologous end joining(NHEJ)(Hustedt and Durocher,2016).It was reported that genomic DNA replacement can be achieved via HDR at the site of DSBs in multiple organisms(Dickinson et al.,2013;Yang et al.,2013;Zu et al.,2013),but the efficiency is still not enough for general application,in particular for replacing long DNA fragment that is more than hundreds of base pairs(bps).As NHEJ is 10-fold more active than HDR at DSB sites(Mao et al.,2008),we speculated that NHEJ can be utilized to implement long genomic DNA replacement with high efficiency.