Retrieving Soil Water Contents from Soil Temperature Measurements by Using Linear Regression

A simple linear regression method is developed to retrieve daily averaged soil water content from diurnal variations of soil temperature measured at three or more depths. The method is applied to Oklahoma Mesonet soil temperature data collected at the depths of 5, 10, and 30 cm during 11–20 June 1995. The retrieved bulk soil water contents are compared with direct measurements for one pair of nearly collocated Mesonet and ARM stations and also compared with the retrievals of a previous method at 14 enhanced Oklahoma Mesonet stations. The results show that the current method gives more persistent retrievals than the previous method. The method is also applied to Oklahoma Mesonet soil temperature data collected at the depths of 5, 25, 60, and 75 cm from the Norman site during 20–30 July 1998 and 1–31 July 2000. The retrieved soil water contents are verified by collocated soil water content measurements with rms differences smaller than the soil water observation error (0.05 m3 m−3). The retrievals are found to be moderately sensitive to random errors (±0.1 K) in the soil temperature observations and errors in the soil type specifications.

TSNAdb: A Database for Tumor-specific Neoantigens from Immunogenomics Data Analysis

Tumor-specific neoantigens have attracted much attention since they can be used as biomarkers to predict therapeutic effects of immune checkpoint blockade therapy and as potential targets for cancer immunotherapy. In this study, we developed a comprehensive tumor-specific neoantigen database （TSNAdb v1.0）, based on pan-cancer immunogenomic analyses of somatic mutation data and human leukocyte antigen （HLA） allele information for 16 tumor types with 7748 tumor samples from The Cancer Genome Atlas （TCGA） and The Cancer Immunome Atlas （TCIA）. We predicted binding affinities between mutant/wild-type peptides and HLA class I molecules by NetMHCpan v2.8/v4.0, and presented detailed information of 3,707,562/1,146,961 potential neoantigens generated by somatic mutations of all tumor samples. Moreover, we employed recurrent mutations in combination with highly frequent HLA alleles to predict potential shared neoantigens across tumor patients,which would facilitate the discovery of putative targets for neoantigen-based cancer immunotherapy.TSNAdb is freely available at http：//biopharm.zju.edu.cn/tsnadb.

Self-templated formation of twin-like metal-organic framework nanobricks as pre-catalysts for efficient water oxidation

Fabrication of single-crystalline metal-organic framework(MOF)hollow nanostructures with two-dimensional(2D)morphologies is a challenging task.Herein,twin-like MOF nanobricks,a quasi-hollow 2D architecture,with multi-metal nodes and replaceable organic ligands,are uniformly and firmly grown on conductive Ni foam through a generic one-pot approach.The formation process of twin-like MOF nanobricks mainly includes selective epitaxial growth of Fe-rich MOF layer and simultaneously dissolution of the pre-formed Ni-rich metal-organic frameworks(MOFs),all of which can be ascribed to a special self-templated mechanism.The fantastic structural merits of twin-like MOF nanobrick arrays,featuring highly exposed active sites,remarkable electrical conductivity,and hierarchical porosities,enable this material for efficient electrocatalysis.Using bimetallic NiFe-MOFs grown on Ni foam as an example,the resultant twin-like nanobrick arrays can be directly utilized as three-dimensional(3D)integrated electrode for high-performance water oxidation in 1 M KOH with a low overpotential,fast reaction kinetics(28.5 mV·dec^(-1)),and superb stability.Interestingly,the unstable NiFe-MOFs were served as an oxygen evolution reaction(OER)pre-catalyst and the single-crystalline NiFe-MOF precursor can be in-situ topochemically regulated into porous and lowcrystalline NiFeOx nanosheets during the OER process.This work extends the hollowing strategy to fabricate hollow MOFs with 2D architectures and highlights their direct utilization for advanced electrocatalysis.

An anti-freezing biomineral hydrogel of high strain sensitivity for artificial skin applications

Mineral hydrogels have caught a lot of attention for their strong competency as artificial skin-like materials.Nonetheless,it remains a great difficulty in fulfilling in one hydrogel system a range of key functionalities that are needed for practical artificial skin applications,i.e.,to be biocompatible,strain-sensitive,ion-conductive,elastic and robust,anti-swelling,and anti-freezing.Here we present a such type of versatile hydrogel that is not only capable to deliver all the above-mentioned key functionalities but also highly stable.This novel hydrogel is constructed by introducing a gelatinous and amorphous multi-ionic biomineral(denoted as Mg-ACCP,containing Mg^(2+),Ca^(2+),CO_(3)^(2−),and PO_(4)^(3−))into the network of biocompatible polyvinyl alcohol(PVA)and sodium alginate(SA).The presence of Mg^(2+)and PO_(4)^(3−)in this hydrogel helps prohibit the crystallization of the biominerals,leading to significantly improved stability.The hydrogel thus obtained delivers excellent mechanical performance due to the chelation between the mineral ions and the organic matrix,and high sensitivity even to subtle pressure and strain applied,such as slight finger bending and gentle tapping.Furthermore,the novel hydrogel features high ionic conductivity,high resistance to swelling,and extraordinary anti-freezing property,holding great promise for applications in different practical scenarios,particularly in aqueous or cold environments.

四丁基溴化铵催化炔基酮的氢化反应

炔丙醇类化合物因其同时具有炔基与羟基两种官能团,在医药、农药及功能分子材料方面有很多的应用,同时也是有机合成中一类用途很广泛的中间体.目前获得炔丙醇类化合物的方法通常依赖于过渡金属催化的氢化反应和羰基化合物的炔基化反应,制备成本较高,基团耐受性差.本文报道了一类四丁基溴化铵催化的炔基酮与频那醇硼烷的氢化反应,以高达94%收率合成了一系列炔丙醇化合物.该反应为炔丙醇类化合物的合成提供了一种便捷、经济、高效的方法.

Dynamic road crime risk prediction with urban open data

Crime risk prediction is helpful for urban safety and citizens’life quality.However,existing crime studies focused on coarse-grained prediction,and usually failed to capture the dynamics of urban crimes.The key challenge is data sparsity,since that 1)not all crimes have been recorded,and 2)crimes usually occur with low frequency.In this paper,we propose an effective framework to predict fine-grained and dynamic crime risks in each road using heterogeneous urban data.First,to address the issue of unreported crimes,we propose a cross-aggregation soft-impute(CASI)method to deal with possible unreported crimes.Then,we use a novel crime risk measurement to capture the crime dynamics from the perspective of influence propagation,taking into consideration of both time-varying and location-varying risk propagation.Based on the dynamically calculated crime risks,we design contextual features(i.e.,POI distributions,taxi mobility,demographic features)from various urban data sources,and propose a zero-inflated negative binomial regression(ZINBR)model to predict future crime risks in roads.The experiments using the real-world data from New York City show that our framework can accurately predict road crime risks,and outperform other baseline methods.