Shortlisting SARS‐CoV‐2 Peptides for Targeted Studies from Experimental Data‐Dependent Acquisition Tandem Mass Spectrometry Data

Detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a crucial tool for fighting the COVID‐19 pandemic. This dataset brief presents the exploration of a shotgun proteomics dataset acquired on SARS‐CoV‐2 infected Vero cells. Proteins from inactivated virus samples were extracted, digested with trypsin, and the resulting peptides were identified by data‐dependent acquisition tandem mass spectrometry. The 101 peptides reporting for six viral proteins were specifically analyzed in terms of their analytical characteristics, species specificity and conservation, and their proneness to structural modifications. Based on these results, a shortlist of 14 peptides from the N, S, and M main structural proteins that could be used for targeted mass‐spectrometry method development and diagnostic of the new SARS‐CoV‐2 is proposed and the best candidates are commented.     

Plasmonic Biosensor Controlled by DNAzyme for On‐Site Genetic Detection of Pathogens

On‐site predetection of pathogens could significantly decrease of a disease outbreak or national loss in most of the countries. However, conventional detection techniques are limited in use for on‐site detection due to the necessity of specialized skill or equipment. Therefore, it is necessary to develop a new technique that can predetect pathogens in the field without special skills or equipment. Here, a DNAzyme strategy to control a plasmonic biosensor for rapid and simple visual detection of Salmonella choleraesuis is adopted. Multicomponent DNAzyme formed by target addition can cleave the linker effectively at 50 °C. Linker cleavage induces dispersion of two DNA‐immobilized gold nanoparticles and color change. Under optimized assay conditions, the target could be detected via visual discrimination sensitively and specifically. Moreover, the biosensor shows the possibility of practical use with contaminants and a 16S rRNA real target. As a result, the proposed plasmonic biosensor can visually detect S. choleraesuis without unstable enzymes, a specialized technique, or equipment. Therefore, these advantages could allow that this biosensor would be used for on‐site predetection to lower the risk of transmission of infectious diseases.     

Anthropogenic noise reduces avian feeding efficiency and increases vigilance along an urban–rural gradient regardless of species’ tolerances to urbanisation

Anthropogenic noise can adversely impact urban bird populations by interfering with vocal communication. Less research has addressed if anthropogenic noise masks the adventitious sounds that birds use to aid predator detection, which may lead to increased vigilance and reduced feeding efficiency. We test this hypothesis using a controlled playback experiment along an urban–rural gradient in Sheffield (UK). We also test the related predictions that anthropogenic noise has the greatest impacts on vigilance and feeding efficiency in rural populations, and on species that are more sensitive to urbanisation. We focus on six passerines, in order from most to least urbanised (based on how urbanisation influences population densities): blue tit Cyanistes caeruleus, robin Erithacus rubeculla, great tit Parus major, chaffinch Fringilla coelebs, coal tit Periparus ater and nuthatch Sitta europaea. We used play-back of anthropogenic urban noise and a control treatment at 46 feeding stations located along the urban–rural gradient. We assess impacts on willingness to visit feeders, feeding and vigilance rates. Exposure to anthropogenic noise reduced visit rates to supplementary feeding stations, reduced feeding rates and increased vigilance. Birds at more urban sites exhibit less marked treatment induced reductions in feeding rates, suggesting that urban populations may be partially habituated or adapted to noisy environments. There was no evidence, however, that more urbanised species were less sensitive to the impacts of noise on any response variable. Our results support the adventitious sound masking hypothesis. Urban noise may thus interfere with the ability of birds to detect predators, reducing their willingness to use food rich environments and increase vigilance rates resulting in reduced feeding rates. These adverse impacts may compromise the quality of otherwise suitable foraging habitats in noisy urban areas. They are likely to be widespread as they arise in a range of species including common urban birds.     

Loop-mediated isothermal amplification assay for the detection of Plasmopara viticola infecting grapes

Grapes downy mildew caused by obligate oomycete plant pathogen Plasmopara viticola is a devastating disease worldwide, resulting in significant yield and quality losses. A field survey was conducted in two major grapes cultivated areas of Tamil Nadu for the incidence of grapevine downy mildew. The disease incidence was 43.42%–76.69%, and the highest disease incidence of 76.69% was observed in the Theni district. Totally eight P. viticola isolates were collected from different places in Coimbatore and Theni districts. These isolates were confirmed through microscopic observation and sequencing of COX 2 gene, and the phylogenetic tree was developed to study their phylogenetic relationship among the isolates which shows 97–100% sequence similarity with other P. viticola isolates and less sequence similarity with Plasmopara species. The loop-mediated isothermal amplification (LAMP) assay was developed based on the CesA4 gene sequence of P. viticola. The assay developed was more sensitive as it detected P. viticola genomic DNA up to 20 fmg. LAMP assay specificity was proved by carrying out the assay with genomic DNA extracted from other Oomycetes and fungal plant pathogens. Finally, LAMP assay was validated by testing seventy-eight grapevine leaf samples collected from seven different locations. LAMP assay showed a positive reaction in sixty-two samples tested out of seventy-eight samples tested. Therefore, the LAMP assay described should helpful for early and specific detection of downy mildew pathogen and help in mitigating disease incidence.     

植物原生质体在分子细胞生物学研究中的应用

植物原生质体是去除了细胞壁的裸露细胞,其具有细胞全能性,现广泛应用于植物分子细胞生物学的研究中,可以大大缩减实验周期,并有助于得到体内实验的实时检测数据。该文除了介绍植物原生质体的提取和纯化方法外,还对国内外利用各种植物的原生质体进行细胞瞬时转化、亚细胞定位、细胞融合和大分子复合物相互作用等试验进行了总结和讨论。植物原生质体还可用于基因表达模式的实时检测,并作为生物反应器的受体细胞进行代谢物的体外生产。此外,还对当前该技术所面临的瓶颈进行了分析,为植物原生质体在分子细胞生物学领域的应用提供帮助,为技术的优化和推广提供参考。     

水相中氢气浓度检测方法的建立

近年来,研究表明氢分子具有广泛的生物学效应,饮用富氢水（hydrogen-rich water,HRW）是其主要的摄取方法,但目前对于水相中氢气浓度检测方法的研究甚少。为了建立适用于测定水相中氢气浓度的检测方法,利用纯水氢气发生器制备饱和富氢水。然后,利用氢气微电极直接测定水相中的氢气浓度,结果表明,在不同氢气浓度范围内（0～1.620 0 mg·L-1和0～0.202 5 mg·L-1）,氢气浓度与微电极信号值均呈现良好的线性关系,方法检出限（method detection limit,MDL）为4.3×10-3 mg·L-1。同时,采用顶空方式将水相中的氢气转移到气相中,通过气相色谱法测定氢气的浓度,结果表明,在不同氢气浓度范围内（0～1.620 0 mg·L-1和0～0.202 5 mg·L-1）,氢气浓度与气相色谱峰面积均具有良好的线性关系,MDL为8.7×10-4 mg·L-1。研究结果表明,氢微电极法和气相色谱法均可用于水相中氢气浓度的精确定量,即成功建立了采用氢气微电极及顶空气相色谱测定水相中氢气含量的检测方法。     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

A Generic Approach for Efficient Detection of Vascular Structures

Vascular segmentation is often required in medical image analysis for various imaging modalities. Despite the rich literature in the field, the proposed methods need most of the time adaptation to the particular investigation and may sometimes lack the desired accuracy in terms of true positive and false positive detection rate. This paper proposes a general method for vascular segmentation based on locally connected filtering applied in a multiresolution scheme. The filtering scheme performs progressive detection and removal of the vessels from the image relief at each resolution level, by combining directional 2D-3D locally connected filters (LCF). An important property of the LCF is that it preserves (positive contrasted) structures in the image if they are topologically connected with other similar structures in their local environment. Vessels, which appear as curvilinear structures, can be filtered out by an appropriate LCF set-up which will minimally affect sheet-like structures. The implementation in a multiresolution framework allows dealing with different vessel sizes. The outcome of the proposed approach is illustrated on several image modalities including lung, liver and coronary arteries. It is shown that besides preserving high accuracy in detecting small vessels, the proposed technique is less sensitive with respect to noise and the presence of pathologies of positive-contrast appearance on the images. The detection accuracy is compared with a previously developed approach on the 20 patient database from the VESSEL12 challenge.