Gut microbiota in children with split-dose bowel preparations revealed by metagenomics

ObjectiveSplit-dose polyethylene glycol (PEG) is routinely used for bowel preparation before colonoscopy. This study aimed to investigate the composition of gut microbiota and its functions in pediatric patients undergoing split-dose PEG bowel preparation for colonoscopy to understand the stability and resilience of gut microbiota.Material and methodsFrom September to December 2021, 19 pediatric patients were enrolled at Shenzhen Children’s Hospital and 76 samples (4 time points) were analyzed using metagenomics. Time points included Time_1 (one day before bowel preparation), Time_2 (one day after colonoscopy), Time_3 (two weeks after bowel preparation), and Time_4 (four weeks after bowel preparation).ResultAlpha diversity comparison at both the species and gene levels showed a decrease in community richness after colonoscopy, with little statistical significance. However, the Shannon diversity index significantly decreased (P<0.05) and gradually returned to pre-preparation levels at two weeks after bowel preparation. The genus level analysis showed six genera (Eubacterium, Escherichia, Intertinibacter, Veillonella, Ruminococcaceae unclassified, and Coprobacillus) significantly different across the four time periods. Additionally, at the species level, the abundance of Escherichia coli, Bacteroides fragilis, and Veillonella parvula significantly increased at one day after colonoscopy before gradually decreasing at two weeks after bowel preparation. In contrast, the abundance of Intertinibacter bartlettii decreased at one day after colonoscopy but then recovered at two weeks after bowel preparation, reaching the preoperative level at four weeks after bowel preparation. Furthermore, five functional pathways (base excision repair, biosynthesis of ansamycins, biosynthesis of siderophore group nonribosomal peptide, flavonoid biosynthesis, and biosynthesis of type II polyketide products) were significantly different across the four time periods, with recovery at two weeks after bowel preparation and reaching preoperative levels at four weeks after bowel preparation.ConclusionsGut microbiota at the genus level, species level, and functional pathways are impacted in pediatric patients undergoing split-dose PEG bowel preparation and colonoscopy, with recovery two weeks following bowel preparation. However, the phylum level was not impacted. Modifications in gut microbiota composition and function may be investigated in future studies of bowel preparation. This study highlights the stability and resilience of gut microbiota among pediatric patients during bowel preparation

ABO genotype alters the gut microbiota by regulating GalNAc levels in pigs.

peer reviewedThe composition of the intestinal microbiome varies considerably between individuals and is correlated with health1. Understanding to what extend and how host genetics contributes to this variation is paramount yet has proven difficult as few associations have been replicated, particularly in humans2. We herein study the effect of host genotype on the composition of the intestinal microbiota in a large mosaic pig population. We show that, under conditions of exacerbated genetic diversity and environmental uniformity, microbiota composition and abundance of specific taxa are heritable. We map a quantitative trait locus affecting the abundance of Erysipelotrichaceae species and show that it is caused by a 2.3-Kb deletion in the N-acetyl-galactosaminyl-transferase gene underpinning the ABO blood group in humans. We show that this deletion is a ≥3.5 million years old trans-species polymorphism under balancing selection. We demonstrate that it decreases the concentrations of N-acetyl-galactosamine in the gut thereby reducing the abundance of Erysipelotrichaceae that can import and catabolize N-acetyl-galactosamine. Our results provide very strong evidence for an effect of host genotype on the abundance of specific bacteria in the intestine combined with insights in the molecular mechanisms that underpin this association. They pave the way towards identifying the same effect in human rural populations

Deep adaptation of CNN in Chinese named entity recognition

Abstract Named entity recognition (NER) is an important task in the field of natural language processing, but it is more challenging in Chinese because of the lack of natural delimiters. The traditional character‐based Chinese NER model directly uses long short‐term memory (LSTM), gated recurrent units, and other sequence models to extract sentence‐level information from character sequences, resulting in the lack of word‐level information in the model. Therefore, a Chinese NER model called ChineseBERT‐CNNs‐BiLSTM‐CRF was proposed, which uses the ChineseBERT pretrained model as the embedding layer so that the vector representation of each Chinese character contained pinyin, glyph, and conventional character information. In addition, a CNN‐based neural network structure called CNNs was presented to extract word‐level information from character sequences and alleviate the problem of entity boundary recognition. BiLSTM was used to extract global features (i.e., sentence‐level information) and predict the corresponding labels of character sequences. Further, conditional random field (CRF) was employed to impose certain rules on the prediction of BiLSTM to enhance the recognition effect of the model. The experimental results revealed that the F1 values of the model on MSRA, people's Daily, and Weibo datasets reached 95.76, 96.61, and 70.00%, respectively, highlighting the effectiveness of the model

A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR

Reducing the cumulative error is a crucial task in simultaneous localization and mapping (SLAM). Usually, Loop Closure Detection (LCD) is exploited to accomplish this work for SLAM and robot navigation. With a fast and accurate loop detection, it can significantly improve global localization stability and reduce mapping errors. However, the LCD task based on point cloud still has some problems, such as over-reliance on high-resolution sensors, and poor detection efficiency and accuracy. Therefore, in this paper, we propose a novel and fast global LCD method using a low-cost 16 beam Lidar based on “Simplified Structure”. Firstly, we extract the “Simplified Structure” from the indoor point cloud, classify them into two levels, and manage the “Simplified Structure” hierarchically according to its structure salience. The “Simplified Structure” has simple feature geometry and can be exploited to capture the indoor stable structures. Secondly, we analyze the point cloud registration suitability with a pre-match, and present a hierarchical matching strategy with multiple geometric constraints in Euclidean Space to match two scans. Finally, we construct a multi-state loop evaluation model for a multi-level structure to determine whether the two candidate scans are a loop. In fact, our method also provides a transformation for point cloud registration with “Simplified Structure” when a loop is detected successfully. Experiments are carried out on three types of indoor environment. A 16 beam Lidar is used to collect data. The experimental results demonstrate that our method can detect global loop closures efficiently and accurately. The average global LCD precision, accuracy and negative are approximately 0.90, 0.96, and 0.97, respectively

Asymmetric Bargaining Model for Water Resource Allocation over Transboundary Rivers

Sustainable transboundary water governance is often challenged by conflicts between agents, which necessitates the design of cooperative and self-enforcing alternatives to facilitate equitable water distribution. The Nash bargaining approach, which originated from game theory, could offer a good mathematical framework to simulate strategic interactions among involved agents by considering individual rational benefits. Given that river-sharing problems often involve multiple self-interested agents, the asymmetric Nash bargaining solution (ANBS) could be used to describe agents’ powers, as determined by disparate social, economic, and political as well as military status, and ensure win–win strategies based on individual rationality. This paper proposed an asymmetric bargaining model by combining multi-criteria decision making, bankruptcy theory, and the ANBS for water distribution in the transboundary river context. The Euphrates River Basin (ERB) with three littoral states was used as a case study. Turkey has the highest bargaining power in ERB negotiation since it dominates in terms of economic strength, political influence, and military capacity, whereas in the two downstream countries these aspects are limited due to their internal political fragmentation and weaker military status. The water satisfaction percentages of Turkey, Syria, and Iraq under the best alternative are 96.30%, 84.23%, and 40.88%, respectively. The findings highlight the necessity for synthetically considering the agent’s disagreement utility and asymmetrical power when negotiating over water allocation

Experiencing Blended Learning during COVID‐19

nonPeerReviewe

Alterations in gene expressions of Caco-2 cell responses to LPS and ploy(I:C) stimulation

The intestinal epithelium barrier serves as a highly dynamic immunologic frontier in the defense against invading pathogenic bacteria and viruses. Hence, understanding of the complicated underlying relationship between enteric pathogens and the intestinal epithelium barrier is vital for developing strategies to improve the intestinal health of farm animals. To this end, Caco-2 cells were stimulated by 1 µg/ml lipopolysaccharide (LPS) for 24 h and 5 µg/ml polyinosinic-polycytidylic acid (ploy(I:C)) for 4 h to imitate bacterial and viral infection processes, respectively. The specific alterations in gene expression of Caco-2 cells after stimulation were characterized by transcriptome sequencing. Seventy differentially expressed genes (DEGs) were identified under LPS exposure, and 17 DEGs were observed under ploy(I:C) exposure. We found that most DEGs were specific, and only one common DEG SPAG7 was observed. Gene Ontology (GO) annotation analysis indicated that all DEGs identified in the different treatments were mainly derived from GO terms related to the maintenance of cellular homeostasis. Moreover, specific DEGs such as SLC39A10, MT2A, and MT1E regulated by LPS treatment, while IFIT2 and RUNX2 mediated by ploy(I:C) treatment, which are derived from immune function modulation related GO terms, were confirmed by both transcriptome sequencing and qRT-PCR. In addition, both transcriptome sequencing and qRT-PCR results verified that LPS specifically down-regulated the DEGs INHBE and ARF6, which are involved in inflammation responses related to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway including the TGF-beta signaling pathways and the Ras signaling pathway. Ploy(I:C) uniquely suppressed the DEGs GABARAP and LAMTOR3, which participated in viral replication-associated pathways including autophagy and mTOR signaling pathway

Advances in crop phenotyping and multi-environment trials

Efficient evaluation of crop phenotypes is a prerequisite for breeding, cultivar adoption, genomics and phenomics study. Plant genotyping is developing rapidly through the use of high-throughput sequencing techniques, while plant phenotyping has lagged far behind and it has become the rate-limiting factor in genetics, large-scale breeding and development of new cultivars. In this paper, we consider crop phenotyping technology under three categories. The first is high-throughput phenotyping techniques in controlled environments such as greenhouses or specifically designed platforms. The second is a phenotypic strengthening test in semi-controlled environments, especially for traits that are difficult to be tested in multi-environment trials (MET), such as lodging, drought and disease resistance. The third is MET in uncontrolled environments, in which crop plants are managed according to farmer's cultural practices. Research and application of these phenotyping techniques are reviewed and methods for MET improvement proposed

An Automatic Counting Method of Maize Ear Grain Based on Image Processing

International audienceCorn variety testing is a process to pick and cultivate a high yield, disease resistant and outstandingly adaptive variety from thousands of corn hybrid varieties. In this process, we have to do a large number of comparative tests, observation and measurement. The workload of this measurement is very huge, for the large number of varieties under test. The grain numbers of maize ear is an important parameter to the corn variety testing. At present, the grain counting is mostly done by manpower. In this way, both the deviation and workload is unacceptable. In this paper, an automatic counting method of maize ear grain is established basing on image processing. Image segmentation is the basis and classic difficult part of image processing. This paper presents an image pre-processing method, which is based on the characteristics of maize ear image. This method includes median filter to eliminate random noise, wallis filter to sharpen the image boundary and histogram enhancement. It also mainly introduces an in-depth study of Otsu algorithms. To overcome the problems of Otsu algorithm that background information being erroneously divided when object size is small. A new method based on traditional Otsu method is proposed, which combines the multi-threshold segmentation and RBGM gradient descent. The implementation of RBGM gradient descent leads to a remarkable improvement on the efficiency of multi-threshold segmentation which is generally an extremely time-consuming task. Our experimental evaluations on 25 sets of maize ear image datasets show that the proposed method can produce more competitive results on effectiveness and speed in comparison to the manpower. The grain counting accuracy of ear volume can reach to 96.8%