Indoor microbiome, environmental characteristics and asthma among junior high school students in Johor Bahru, Malaysia

Xi Fu; Dan Norback; Qianqian Yuan; Yanling Li; Xunhua Zhu; Yiqun Deng; Jamal Hisham Hashim; Zailina Hashim; Yi-Wu Zheng; Xu-Xin Lai; Michael Dho Spangfort; Yu Sun

doi:10.1016/j.envint.2020.105664

arXiv · 1911.06946

Indoor microbiome, environmental characteristics and asthma among junior high school students in Johor Bahru, Malaysia

Xi Fu·Dan Norback·Qianqian Yuan·Yanling Li·Xunhua Zhu·Yiqun Deng·Jamal Hisham Hashim·Zailina Hashim·Yi-Wu Zheng·Xu-Xin Lai·Michael Dho Spangfort·Yu Sun

View original source ↗Read source document ↗Export metadata ↓

Abstract

Indoor microbial diversity and composition are suggested to affect the prevalence and severity of asthma. In this study, we collected floor dust and environmental characteristics from 21 classrooms, and health data related to asthma symptoms from 309 students, in junior high schools in Johor Bahru, Malaysia. Bacterial and fungal composition was characterized by sequencing 16s rRNA gene and internal transcribed spacer (ITS) region, and the absolute microbial concentration was quantified by qPCR. In total, 326 bacterial and 255 fungal genera were characterized. Five bacterial (Sphingobium, Rhodomicrobium, Shimwellia, Solirubrobacter, Pleurocapsa) and two fungal (Torulaspora and Leptosphaeriaceae) taxa were protective for asthma severity. Two bacterial taxa, Izhakiella and Robinsoniella, were positively associated with asthma severity. Several protective bacterial taxa including Rhodomicrobium, Shimwellia and Sphingobium has been reported as protective microbes in previous studies, whereas other taxa were first time reported. Environmental characteristics, such as age of building, size of textile curtain per room volume, occurrence of cockroaches, concentration of house dust mite allergens transferred from homes by the occupants, were involved in shaping the overall microbial community but not asthma-associated taxa; whereas visible dampness and mold, which did not change the overall microbial community for floor dust, decreased the concentration of protective bacteria Rhodomicrobium (\b{eta}=-2.86, p=0.021) of asthma, indicating complex interactions between microbes, environmental characteristics and asthma symptoms. Overall, this is the first indoor microbiome study to characterize the asthma-associated microbes and their environmental determinant in tropical area, promoting the understanding of microbial exposure and respiratory health in this region.

Explore related subjects

q-bio.GN

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Xi Fu, Dan Norback, Qianqian Yuan, Yanling Li, Xunhua Zhu, Yiqun Deng, Jamal Hisham Hashim, Zailina Hashim, Yi-Wu Zheng, Xu-Xin Lai, Michael Dho Spangfort, Yu Sun. 2019-11-16. Indoor microbiome, environmental characteristics and asthma among junior high school students in Johor Bahru, Malaysia. https://doi.org/10.1016/j.envint.2020.105664

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

EMMA: an R/Bioconductor package to automate tracking of metadata in functional enrichment analyses

Summary: Functional enrichment analysis (FEA) is a widely used approach for interpreting high-throughput omics data. However, essential methodological details, such as software versions, analysis parameters, and annotation database releases among others, are often incompletely reported, limiting the reproducibility and transparency of enrichment analyses and complicating the assessment of potentially problematic methodological choices. Here we present EMMA, an R/Bioconductor package that integrates with existing FEA tools and automatically captures provenance metadata, such as annotation metadata, software version, and parameters, during the analysis runtime. Our package provides utilities for accessing and exporting the recorded metadata to facilitate transparent reporting and preserve provenance required for reproducible enrichment analyses. This also enables auditing of the results while remaining compatible with existing Bioconductor workflows. Availability and implementation: EMMA is available on Bioconductor under the MIT license (https: //bioconductor.org/packages/EMMA), with its development version also available on GitHub (https: //github.com/imbeimainz/EMMA).

q-bio.GN↗

arXiv2026

Motif-Vocab: StatisticallyCalibrated Transcription-Factor-Identity Tokenization forGenomic Language Models

Tokenization is a central design choice in genomic language models, yet most deoxyribonucleic acid (DNA) tokenizers use characters, fixed-length k-mers, or frequency-derived subwords without explicitly using prior information about the specificity of DNA-binding regulatory factors. We introduce Motif-Vocab, a biologically informed tokenizer that scans both DNA strands for statistically calibrated motif matches, emits transcription-factor (TF) identity tokens, and applies nucleotide, $k$-mer, or byte-pair encoding (BPE) to unmatched sequence. Motif-specific null distributions put position-weight matrices (PWMs) of different lengths and degeneracy on a common significance scale; deterministic overlap rules make the representation reproducible. In controlled Bidirectional Encoder Representations from Transformers (BERT) pretraining on two billion base pairs, real motif libraries outperform randomized-motif controls on 54 of 55 in-scope downstream tasks. On a motif-disjoint recognition task derived from DART-Eval Task 2, TF-specific tokens improve macro-F1 by 0.040 over a position-matched generic motif token and by 0.033 over a matched no-motif tokenizer (95\% bootstrap confidence interval: 0.027--0.038). Motif tokens also receive stronger attribution and produce larger occlusion effects than shuffled controls. Dense no-motif tokenizers remain strong general-purpose baselines, including a near-tie on the five-task BERT-base panel. Thus, Motif-Vocab is not a universal accuracy replacement; it is a targeted, interpretable inductive bias for motif-sensitive genomic modeling.

q-bio.GN↗

arXiv2026

Revolutionizing Genomics with Reinforcement Learning Techniques

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algorithms are capable of learning from experience with minimal human supervision, making them well-suited for genomic data analysis and interpretation. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data, which is required for supervised learning. While there have been numerous studies examining the applications of Machine Learning (ML) in genomics, this survey focuses exclusively on the use of RL in various genomics research fields, including gene regulatory networks (GRNs), genome assembly, and sequence alignment. We present a comprehensive technical overview of existing studies on the application of RL in genomics, highlighting the strengths and limitations of these approaches. We then discuss potential research directions that are worthy of future exploration, including the development of more sophisticated reward functions as RL heavily depends on the accuracy of the reward function, the integration of RL with other machine learning techniques, and the application of RL to new and emerging areas in genomics research. Finally, we present our findings and conclude by summarizing the current state of the field and the future outlook for RL in genomics.

q-bio.GN↗