Discovery proteomics has limited quantification capabilities because of stochastic precursor-ion selection. Several data-independent acquisition (DIA) methods have been proposed to overcome this limitation1, 2, 3, 4, including the sequential-window acquisition of all theoretical mass spectra (SWATH-MS)4.the National Science Foundation (NSF) of China (grants 91429301 and 31221065), 973 Program 2015CB553800, National Major Project 2013ZX10002-002, 111 Project B12001, funding from Xiamen City (grant 3502Z20130027) and the NSF of China for Fostering Talents in Basic Research (grant J1310027)

Mycotoxins have the potential to enter the human food chain through carry-over of contaminants from feed into animal-derived products. The objective of the study was to develop a reliable and sensitive method for the analysis of 30 mycotoxins in animal feed and animal-derived food (meat, edible animal tissues, and milk) using liquid chromatography-tandem mass spectrometry (LC-MS/MS). In the study, three extraction procedures, as well as various cleanup procedures, were evaluated to select the most suitable sample preparation procedure for different sample matrices. In addition, timed and highly selective reaction monitoring on LC-MS/MS was used to filter out isobaric matrix interferences. The performance characteristics (linearity, sensitivity, recovery, precision, and specificity) of the method were determined according to Commission Decision 2002/657/EC and 401/2006/EC. The established method was successfully applied to screening of mycotoxins in animal feed and animal-derived food. The results indicated that mycotoxin contamination in feed directly influenced the presence of mycotoxin in animal-derived food. Graphical abstract Multi-mycotoxin analysis of animal feed and animal-derived food using LC-MS/MS.

One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available prediction methods can only predict whether two drugs interact or not, whereas few methods can predict interaction events between two drugs. Accurately predicting interaction events of two drugs is more useful for researchers to study the mechanism of the interaction of two drugs. In the present study, we propose a novel method, MDF-SA-DDI, which predicts drug–drug interaction (DDI) events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. MDF-SA-DDI is mainly composed of two parts: multi-source drug fusion and multi-source feature fusion. First, we combine two drugs in four different ways and input the combined drug feature representation into four different drug fusion networks (Siamese network, convolutional neural network and two auto-encoders) to obtain the latent feature vectors of the drug pairs, in which the two auto-encoders have the same structure, and their main difference is the number of neurons in the input layer of the two auto-encoders. Then, we use transformer blocks that include self-attention mechanism to perform latent feature fusion. We conducted experiments on three different tasks with two datasets. On the small dataset, the area under the precision–recall-curve (AUPR) and F1 scores of our method on task 1 reached 0.9737 and 0.8878, respectively, which were better than the state-of-the-art method. On the large dataset, the AUPR and F1 scores of our method on task 1 reached 0.9773 and 0.9117, respectively. In task 2 and task 3 of two datasets, our method also achieved the same or better performance as the state-of-the-art method. More importantly, the case studies on five DDI events are conducted and achieved satisfactory performance. The source codes and data are available at https://github.com/ShenggengLin/MDF-SA-DDI.

In the present study, we developed a quick, highly specific method for detection of Shigella species by combining immunocapturing of the bacteria and a universal primer PCR. The method drastically enhances test sensitivity, and it can be used not only for identification of Shigella species in the environment but also for rapid detection of other pathogens.Dysentery caused by Shigella species is one of the most common infectious diseases in developing countries and in travelers to tropical countries (18,19). One statistic showed that about 50% of Spanish travelers who visited developing countries developed dysentery, and Shigella species were among the main etiological agents (3, 4). In the People's Republic of China, Shigella species are the second leading cause of intestinal infectious diseases. The dysentery bacilli include four Shigella species, S. dysenteriae, S. flexneri, S. sonnei, and S. boydii; S. sonnei, S. dysenteriae, S. flexneri, and S. boydii have 15, 10, 8, and 1 serotypes, respectively. The symptoms of shigellosis are mild or severe depending on the species causing the infection. Therefore, early rapid identification of Shigella species and their serotypes is very important for public health, epidemiological investigations, and clinical treatment.Traditionally, identification of pathogenic bacteria has been based on phenotypic characteristics and mainly involves analyses of differential metabolic properties and reactions of specific antibodies. Recently, molecular analysis of phylogenetic markers has been recognized as a very useful tool for identification of bacterial genera, species, or subspecies (2,4,14,17). Among these markers, 16S rRNAs are particularly useful because these molecules are present in every living cell and their function is highly conserved. However, an approach based on utilization of universal primer PCR (UPPCR) for conserved regions, such as 16S rRNA genes, can be used to study almost all bacteria (5, 8). The bacteria have to be characterized further by subsequent steps, including restriction fragment length polymorphism analysis, single-strand conformation polymorphism analysis, or sequencing analysis (4,10,11,12). These extra steps make the detection procedure more complex and tedious.In this paper, we report development of a new technique for rapid and efficient detection and differentiation of dysentery bacilli in environmental sewage. The new method, termed immunocapture UPPCR (iUPPCR), employs UPPCR amplification to detect bacteria captured by specific antibodies coupled to polystyrene 96-well plates. The specificity of coating antibodies distinguishes specific cell types, while the conserved 16S rRNA contributes to the universality of bacterial detection. We believe that this method will have broad application for detection and differentiation of pathogenic organisms in the environment.The bacteria used in this study included S. dysenteriae serotype 1, S. flexneri serotypes 1a, 2a, 3a, 4, 5, and Y variant, S. sonnei, and S. boydii serotype 1; these organisms were purc...

A new banana leaf spot disease (named as exserohilum leaf spot) caused by Exserohilum rostratum was found in Guangxi, China during a series of surveys on fungal species causing banana leaf diseases between 2005-2009. Three single spore derived isolates (CLER09, D087 and JL05) were obtained from diseased banana leaves and the pathogenicity of each isolate to banana plants was confirmed by inoculation tests based on Koch's postulates. Three distinctive types of conidia (A, B and C) were produced by all isolates when subjected to a range of light environments. The isolates were identified as members of E. rostratum based on their morphological characters as well as rDNA-ITS (internal transcribed spacer) sequences. The effects of temperature and pH on vegetative growth and sporulation of the E. rostratum isolates were also characterized during this study. A similar host spectrum among the isolates was also observed in the host range tests on 128 plant species covering 47 families using a detached leaf inoculation technique.