Palladium-catalyzed difluoromethylation of heteroaryl chlorides, bromides and iodides.

A palladium-catalyzed difluoromethylation of a series of heteroaryl chlorides, bromides and iodides under mild conditions is described. A wide range of heteroaryl halides such as pyridyl, pyrimidyl, pyrazyl, funanyl, thienyl, pyazolyl, imidazolyl, thiazolyl, and oxazolyl halides were efficiently difluoromethylated, thus providing medicinal chemists an alternative choice for the preparation of drug candidates with the difluoromethylated heteroarene unit

Predicting DNA-binding sites of proteins from amino acid sequence

BACKGROUND: Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions. RESULTS: We start with a Naïve Bayes classifier trained to predict whether a given amino acid residue is a DNA-binding residue based on its identity and the identities of its sequence neighbors. The input to the classifier consists of the identities of the target residue and 4 sequence neighbors on each side of the target residue. The classifier is trained and evaluated (using leave-one-out cross-validation) on a non-redundant set of 171 proteins. Our results indicate the feasibility of identifying interface residues based on local sequence information. The classifier achieves 71% overall accuracy with a correlation coefficient of 0.24, 35% specificity and 53% sensitivity in identifying interface residues as evaluated by leave-one-out cross-validation. We show that the performance of the classifier is improved by using sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs) as additional input. The classifier achieves 78% overall accuracy with a correlation coefficient of 0.28, 44% specificity and 41% sensitivity in identifying interface residues. Examination of the predictions in the context of 3-dimensional structures of proteins demonstrates the effectiveness of this method in identifying DNA-binding sites from sequence information. In 33% (56 out of 171) of the proteins, the classifier identifies the interaction sites by correctly recognizing at least half of the interface residues. In 87% (149 out of 171) of the proteins, the classifier correctly identifies at least 20% of the interface residues. This suggests the possibility of using such classifiers to identify potential DNA-binding motifs and to gain potentially useful insights into sequence correlates of protein-DNA interactions. CONCLUSION: Naïve Bayes classifiers trained to identify DNA-binding residues using sequence information offer a computationally efficient approach to identifying putative DNA-binding sites in DNA-binding proteins and recognizing potential DNA-binding motifs

Ti₃C₂ MXene-based Schottky Photocathode for Enhanced Photoelectrochemical Sensing

Nanomaterials are vital to the realization of photoelectrochemical (PEC) sensing platfrom that provides the sensitive detection and quantification of low-abundance biological samples. Here, this work reports a Schottky junction-based BiOI/Ti₃C₂ heterostructure, used as a photocathode for PEC bioanalysis. Specially, we realize in situ growth of flower-like BiOI on 2D intrinsically negatively charged Ti₃C₂ MXene nanosheet that endows BiOI/Ti₃C₂ heterostructure with admirably combined merits, noting in particular the generation of built-in electric field and the decrease of contact resistance between BiOI and Ti₃C₂. Under the visible light irradiation, the BiOI/Ti₃C₂ heterostructure-modified PEC platform displays superior cathodic photocurrent signal, while PEC response cuts down with the presence of L-Cysteine (L-Cys) as a representative analyte owing to the metal-S bond formation. The “signal-off” PEC sensing strategy shows good performance in terms of sensitivity, limit of detection (LOD, 0.005 nM) and stability. This research reveals the great potentials of MXene-based heterostructure in the application field of PEC sensor establishment

Hyperspectral LiDAR-Based Plant Spectral Profiles Acquisition : Performance Assessment and Results Analysis

In precision agriculture, efficient fertilization is one of the most important pursued goals. Vegetation spectral profiles and the corresponding spectral parameters are usually employed for vegetation growth status indication, i.e., vegetation classification, bio-chemical content mapping, and efficient fertilization guiding. In view of the fact that the spectrometer works by relying on ambient lighting condition, hyperspectral/multi-spectral LiDAR (HSL/MSL) was invented to collect the spectral profiles actively. However, most of the HSL/MSL works with the wavelength specially selected for specific applications. For precision agriculture applications, a more feasible HSL capable of collecting spectral profiles at wide-range spectral wavelength is necessary to extract various spectral parameters. Inspired by this, in this paper, we developed a hyperspectral LiDAR (HSL) with 10 nm spectral resolution covering 500~1000 nm. Different vegetation leaf samples were scanned by the HSL, and it was comprehensively assessed for wide-range wavelength spectral profiles acquirement, spectral parameters extraction, vegetation classification, and the laser incident angle effect. Specifically, three experiments were carried out: (1) spectral profiles results were compared with that from a SVC spectrometer (HR-1024, Spectra Vista Corporation); (2) the extracted spectral parameters from the HSL were assessed, and they were employed as the input features of a support vector machine (SVM) classifier with multiple labels to classify the vegetation; (3) in view of the influence of the laser incident angle on the HSL reflected laser intensities, we analyzed the laser incident angle effect on the spectral parameters values. The experimental results demonstrated the developed HSL was more feasible for acquiring spectral profiles with wide-range wavelength, and spectral parameters and vegetation classification results also indicated its great potentials in precision agriculture application

Analysis and Radiometric Calibration for Backscatter Intensity of Hyperspectral LiDAR Caused by Incident Angle Effect

Hyperspectral LiDAR (HSL) is a new remote sensing detection method with high spatial and spectral information detection ability. In the process of laser scanning, the laser echo intensity is affected by many factors. Therefore, it is necessary to calibrate the backscatter intensity data of HSL. Laser incidence angle is one of the important factors that affect the backscatter intensity of the target. This paper studied the radiometric calibration method of incidence angle effect for HSL. The reflectance of natural surfaces can be simulated as a combination of specular reflection and diffuse reflection. The linear combination of the Lambertian model and Beckmann model provides a comprehensive theory that can be applied to various surface conditions, from glossy to rough surfaces. Therefore, an adaptive threshold radiometric calibration method (Lambertian-Beckmann model) is proposed to solve the problem caused by the incident angle effect. The relationship between backscatter intensity and incident angle of HSL is studied by combining theory with experiments, and the model successfully quantifies the difference between diffuse and specular reflectance coefficients. Compared with the Lambertian model, the proposed model has higher calibration accuracy, and the average improvement rate to the samples in this study was 22.67%. Compared with the results before calibration with the incidence angle of less than 70 degrees, the average improvement rate of the Lambertian-Beckmann model was 62.26%. Moreover, we also found that the green leaves have an obvious specular reflection effect near 650-720 nm, which might be related to the inner microstructure of chlorophyll. The Lambertian-Beckmann model was more helpful to the calibration of leaves in the visible wavelength range. This is a meaningful and a breakthrough exploration for HSL.Peer reviewe

Carbon in Chinese grasslands : meta-analysis and theory of grazing effects

Unidad de excelencia María de Maeztu CEX2019-000940-MGlobally, livestock grazing is an important management factor influencing soil degradation, soil health and carbon (C) stocks of grassland ecosystems. However, the effects of grassland types, grazing intensity and grazing duration on C stocks are unclear across large geographic scales. To provide a more comprehensive assessment of how grazing drives ecosystem C stocks in grasslands, we compiled and analyzed data from 306 studies featuring four grassland types across China: desert steppes, typical steppes, meadow steppes and alpine steppes. Light grazing was the best management practice for desert steppes (< 2 sheep ha−1) and typical steppes (3 to 4 sheep ha−1), whereas medium grazing pressure was optimal for meadow steppes (5 to 6 sheep ha−1) and alpine steppes (7 to 8 sheep ha−1) leading to the highest ecosystem C stocks under grazing. Plant biomass (desert steppes) and soil C stocks (meadow steppes) increased under light or medium grazing, confirming the 'intermediate disturbance hypothesis'. Heavy grazing decreased all C stocks regardless of grassland ecosystem types, approximately 1.4 Mg ha−1 per year for the whole ecosystem. The regrowth and regeneration of grasslands in response to grazing intensity (i.e., grazing optimization) depended on grassland types and grazing duration. In conclusion, grassland grazing is a double-edged sword. On the one hand, proper management (light or medium grazing) can maintain and even increase C stocks above- and belowground, and increase the harvested livestock products from grasslands. On the other hand, human-induced overgrazing can lead to rapid degradation of vegetation and soils, resulting in significant carbon loss and requiring long-term recovery. Grazing regimes (i.e., intensity and duration applied) must consider specific grassland characteristics to ensure stable productivity rates and optimal impacts on ecosystem C stocks

Distributed Differential Privacy via Shuffling vs Aggregation: a Curious Study

How to achieve distributed differential privacy (DP) without a trusted central party is of great interest in both theory and practice. Recently, the shuffle model has attracted much attention. Unlike the local DP model in which the users send randomized data directly to the data collector/analyzer, in the shuffle model an intermediate untrusted shuffler is introduced to randomly permute the data, which have already been randomized by the users, before they reach the analyzer. The most appealing aspect is that while shuffling does not explicitly add more noise to the data, it can make privacy better. The privacy amplification effect in consequence means the users need to add less noise to the data than in the local DP model, but can achieve the same level of differential privacy. Thus, protocols in the shuffle model can provide better accuracy than those in the local DP model. What looks interesting to us is that the architecture of the shuffle model is similar to private aggregation, which has been studied for more than a decade. In private aggregation, locally randomized user data are aggregated by an intermediate untrusted aggregator. Thus, our question is whether aggregation also exhibits some sort of privacy amplification effect? And if so, how good is this ``aggregation model\u27\u27 in comparison with the shuffle model. We conducted the first comparative study between the two, covering privacy amplification, functionalities, protocol accuracy, and practicality. The results as yet suggest that the new shuffle model does not have obvious advantages over the old aggregation model. On the contrary, protocols in the aggregation model outperform those in the shuffle model, sometimes significantly, in many aspects