Industrial SO2 emission monitoring through a portable multichannel gas analyzer with an optimized retrieval algorithm

SO2 variability over a large concentration range and interferences from other gases have been major limitations in industrial SO2 emission monitoring. This study demonstrates accurate industrial SO2 emission monitoring through a portable multichannel gas analyzer with an optimized retrieval algorithm. The proposed analyzer features a large dynamic measurement range and correction of interferences from other coexisting infrared absorbers such as NO, CO, CO2, NO2, CH4, HC, N2O, and H2O. The multichannel gas analyzer measures 11 different wavelength channels simultaneously to correct several major problems of an infrared gas analyzer including system drift, conflict of sensitivity, interferences among different infrared absorbers, and limitation of measurement range. The optimized algorithm uses a third polynomial instead of a constant factor to quantify gas-to-gas interference. Measurement results show good performance in the linear and nonlinear ranges, thereby solving the problem that the conventional interference correction is restricted by the linearity of the intended and interfering channels. The results imply that the measurement range of the developed multichannel analyzer can be extended to the nonlinear absorption region. The measurement range and accuracy are evaluated through experimental laboratory calibration. Excellent agreement was achieved, with a Pearson correlation coefficient (r(2)) of 0.99977 with a measurement range from approximately 5 to 10 000 ppmv and a measurement error of less than 2 %. The instrument was also deployed for field measurement. Emissions from three different factories were measured. The emissions of these factories have been characterized by different coexisting infrared absorbers, covering a wide range of concentration levels. We compared our measurements with commercial SO2 analyzers. Overall, good agreement was achieved

Substantial Fat Loss in Physique Competitors Is Characterized by Increased Levels of Bile Acids, Very-Long Chain Fatty Acids, and Oxylipins

Weight loss and increased physical activity may promote beneficial modulation of the metabolome, but limited evidence exists about how very low-level weight loss affects the metabolome in previously non-obese active individuals. Following a weight loss period (21.1 ± 3.1 weeks) leading to substantial fat mass loss of 52% (−7.9 ± 1.5 kg) and low body fat (12.7 ± 4.1%), the liquid chromatography-mass spectrometry-based metabolic signature of 24 previously young, healthy, and normal weight female physique athletes was investigated. We observed uniform increases (FDR < 0.05) in bile acids, very-long-chain free fatty acids (FFA), and oxylipins, together with reductions in unsaturated FFAs after weight loss. These widespread changes, especially in the bile acid profile, were most strongly explained (FDR < 0.05) by changes in android (visceral) fat mass. The reported changes did not persist, as all of them were reversed after the subsequent voluntary weight regain period (18.4 ± 2.9 weeks) and were unchanged in non-dieting controls (n = 16). Overall, we suggest that the reported changes in FFA, bile acid, and oxylipin profiles reflect metabolic adaptation to very low levels of fat mass after prolonged periods of intense exercise and low-energy availability. However, the effects of the aforementioned metabolome subclass alteration on metabolic homeostasis remain controversial, and more studies are warranted to unravel the complex physiology and potentially associated health implications. In the end, our study reinforced the view that transient weight loss seems to have little to no long-lasting molecular and physiological effects

Substantial Fat Loss in Physique Competitors Is Characterized by Increased Levels of Bile Acids, Very-Long Chain Fatty Acids, and Oxylipins

Weight loss and increased physical activity may promote beneficial modulation of the metabolome, but limited evidence exists about how very low-level weight loss affects the metabolome in previously non-obese active individuals. Following a weight loss period (21.1 ± 3.1 weeks) leading to substantial fat mass loss of 52% (−7.9 ± 1.5 kg) and low body fat (12.7 ± 4.1%), the liquid chromatography-mass spectrometry-based metabolic signature of 24 previously young, healthy, and normal weight female physique athletes was investigated. We observed uniform increases (FDR < 0.05) in bile acids, very-long-chain free fatty acids (FFA), and oxylipins, together with reductions in unsaturated FFAs after weight loss. These widespread changes, especially in the bile acid profile, were most strongly explained (FDR < 0.05) by changes in android (visceral) fat mass. The reported changes did not persist, as all of them were reversed after the subsequent voluntary weight regain period (18.4 ± 2.9 weeks) and were unchanged in non-dieting controls (n = 16). Overall, we suggest that the reported changes in FFA, bile acid, and oxylipin profiles reflect metabolic adaptation to very low levels of fat mass after prolonged periods of intense exercise and low-energy availability. However, the effects of the aforementioned metabolome subclass alteration on metabolic homeostasis remain controversial, and more studies are warranted to unravel the complex physiology and potentially associated health implications. In the end, our study reinforced the view that transient weight loss seems to have little to no long-lasting molecular and physiological effects

Substantial Fat Loss in Physique Competitors Is Characterized by Increased Levels of Bile Acids, Very-Long Chain Fatty Acids, and Oxylipins

Weight loss and increased physical activity may promote beneficial modulation of the metabolome, but limited evidence exists about how very low-level weight loss affects the metabolome in previously non-obese active individuals. Following a weight loss period (21.1 ± 3.1 weeks) leading to substantial fat mass loss of 52% (−7.9 ± 1.5 kg) and low body fat (12.7 ± 4.1%), the liquid chromatography-mass spectrometry-based metabolic signature of 24 previously young, healthy, and normal weight female physique athletes was investigated. We observed uniform increases (FDR < 0.05) in bile acids, very-long-chain free fatty acids (FFA), and oxylipins, together with reductions in unsaturated FFAs after weight loss. These widespread changes, especially in the bile acid profile, were most strongly explained (FDR < 0.05) by changes in android (visceral) fat mass. The reported changes did not persist, as all of them were reversed after the subsequent voluntary weight regain period (18.4 ± 2.9 weeks) and were unchanged in non-dieting controls (n = 16). Overall, we suggest that the reported changes in FFA, bile acid, and oxylipin profiles reflect metabolic adaptation to very low levels of fat mass after prolonged periods of intense exercise and low-energy availability. However, the effects of the aforementioned metabolome subclass alteration on metabolic homeostasis remain controversial, and more studies are warranted to unravel the complex physiology and potentially associated health implications. In the end, our study reinforced the view that transient weight loss seems to have little to no long-lasting molecular and physiological effects

Substantial Fat Loss in Physique Competitors Is Characterized by Increased Levels of Bile Acids, Very-Long Chain Fatty Acids, and Oxylipins

Weight loss and increased physical activity may promote beneficial modulation of the metabolome, but limited evidence exists about how very low-level weight loss affects the metabolome in previously non-obese active individuals. Following a weight loss period (21.1 +/- 3.1 weeks) leading to substantial fat mass loss of 52% (-7.9 +/- 1.5 kg) and low body fat (12.7 +/- 4.1%), the liquid chromatography-mass spectrometry-based metabolic signature of 24 previously young, healthy, and normal weight female physique athletes was investigated. We observed uniform increases (FDR < 0.05) in bile acids, very-long-chain free fatty acids (FFA), and oxylipins, together with reductions in unsaturated FFAs after weight loss. These widespread changes, especially in the bile acid profile, were most strongly explained (FDR < 0.05) by changes in android (visceral) fat mass. The reported changes did not persist, as all of them were reversed after the subsequent voluntary weight regain period (18.4 +/- 2.9 weeks) and were unchanged in non-dieting controls (n = 16). Overall, we suggest that the reported changes in FFA, bile acid, and oxylipin profiles reflect metabolic adaptation to very low levels of fat mass after prolonged periods of intense exercise and low-energy availability. However, the effects of the aforementioned metabolome subclass alteration on metabolic homeostasis remain controversial, and more studies are warranted to unravel the complex physiology and potentially associated health implications. In the end, our study reinforced the view that transient weight loss seems to have little to no long-lasting molecular and physiological effects

Evolution and transmission of antibiotic resistance is driven by Beijing lineage Mycobacterium tuberculosis in Vietnam

A previous investigation has elucidated the landscape of Mtb genomic diversity and transmission dynamics in Ho Chi Minh City, Vietnam. Here, we expand the scope of this survey by adding a substantial number of additional genomes (total sample size: 2,542) and phenotypic drug susceptibility data for the majority of isolates. We aim to explore the prevalence and evolutionary dynamics of drug resistance and our ability to predict drug resistance from sequencing data. Among isolates tested phenotypically against first-line drugs, we observed high rates of streptomycin [STR, 37.7% ( N = 573/1,520)] and isoniazid resistance [INH, 25.7% ( N = 459/1,786)] and lower rates of resistance to rifampicin [RIF, 4.9% ( N = 87/1,786)] and ethambutol [EMB, 4.2% ( N = 75/1,785)]. Relative to global benchmarks, resistance to STR and INH was predicted accurately when applying the TB-Profiler algorithm to whole genome sequencing data (sensitivities of 0.81 and 0.87, respectively), while resistance to RIF and EMB was predicted relatively poorly (sensitivities of 0.70 and 0.44, respectively). Exploring the evolution of drug resistance revealed the main phylogenetic lineages to display differing dynamics and tendencies to evolve resistance via mutations in certain genes. The Beijing sublineage L2.2.1 was found to acquire de novo resistance mutations more frequently than isolates from other lineages and to suffer no apparent fitness cost acting to impede the transmission of resistance. Mutations conferring resistance to INH and STR arose earlier, on average, than those conferring resistance to RIF and are now more widespread across the phylogeny. The high prevalence of “background” INH resistance, combined with high rates of RIF mono-resistance (20.7%, N = 18/87), suggests that rapid assays for INH resistance will be valuable in this setting. These tests will allow the detection of INH mono-resistance and will allow multi-drug-resistant isolates to be distinguished from isolates with RIF mono-resistance. IMPORTANCE Drug-resistant tuberculosis (TB) infection is a growing and potent concern, and combating it will be necessary to achieve the WHO’s goal of a 95% reduction in TB deaths by 2035. While prior studies have explored the evolution and spread of drug resistance, we still lack a clear understanding of the fitness costs (if any) imposed by resistance-conferring mutations and the role that Mtb genetic lineage plays in determining the likelihood of resistance evolution. This study offers insight into these questions by assessing the dynamics of resistance evolution in a high-burden Southeast Asian setting with a diverse lineage composition. It demonstrates that there are clear lineage-specific differences in the dynamics of resistance acquisition and transmission and shows that different lineages evolve resistance via characteristic mutational pathways

The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis.

Background: Molecular diagnostics are considered the most promising route to achievement of rapid, universal drug susceptibility testing for Mycobacterium tuberculosis complex (MTBC). We aimed to generate a WHO-endorsed catalogue of mutations to serve as a global standard for interpreting molecular information for drug resistance prediction. Methods: In this systematic analysis, we used a candidate gene approach to identify mutations associated with resistance or consistent with susceptibility for 13 WHO-endorsed antituberculosis drugs. We collected existing worldwide MTBC whole-genome sequencing data and phenotypic data from academic groups and consortia, reference laboratories, public health organisations, and published literature. We categorised phenotypes as follows: methods and critical concentrations currently endorsed by WHO (category 1); critical concentrations previously endorsed by WHO for those methods (category 2); methods or critical concentrations not currently endorsed by WHO (category 3). For each mutation, we used a contingency table of binary phenotypes and presence or absence of the mutation to compute positive predictive value, and we used Fisher's exact tests to generate odds ratios and Benjamini-Hochberg corrected p values. Mutations were graded as associated with resistance if present in at least five isolates, if the odds ratio was more than 1 with a statistically significant corrected p value, and if the lower bound of the 95% CI on the positive predictive value for phenotypic resistance was greater than 25%. A series of expert rules were applied for final confidence grading of each mutation. Findings: We analysed 41 137 MTBC isolates with phenotypic and whole-genome sequencing data from 45 countries. 38 215 MTBC isolates passed quality control steps and were included in the final analysis. 15 667 associations were computed for 13 211 unique mutations linked to one or more drugs. 1149 (7·3%) of 15 667 mutations were classified as associated with phenotypic resistance and 107 (0·7%) were deemed consistent with susceptibility. For rifampicin, isoniazid, ethambutol, fluoroquinolones, and streptomycin, the mutations' pooled sensitivity was more than 80%. Specificity was over 95% for all drugs except ethionamide (91·4%), moxifloxacin (91·6%) and ethambutol (93·3%). Only two resistance mutations were identified for bedaquiline, delamanid, clofazimine, and linezolid as prevalence of phenotypic resistance was low for these drugs. Interpretation: We present the first WHO-endorsed catalogue of molecular targets for MTBC drug susceptibility testing, which is intended to provide a global standard for resistance interpretation. The existence of this catalogue should encourage the implementation of molecular diagnostics by national tuberculosis programmes. Funding: Unitaid, Wellcome Trust, UK Medical Research Council, and Bill and Melinda Gates Foundation

Genomic analysis of the metabolic and microbial basis of human disease

© 2020 Youwen QinGenetics influences the phenotype and behavior of living organism. Human genetic variants have been widely linked to diseases, while the biological pathways through which genetic variants affect the physiology remain unclear. Genetic associations with molecular traits, for example levels of plasma metabolites, may aid the interpretation of genetic associations on higher-order phenotypes. The human body is a symbiont with a large number of microbial organisms, which have a close relationship with human health. In particular, the gut microbiota can influence various organs of human host through the synthesis of bioactive metabolites. Understanding the microbial organisms living in the human body holds great promise for increasing our understanding of human health and disease. This thesis focuses on genetic aspects of metabolic and microbial traits and has four general aims: 1. To identify genetic determinants of human plasma metabolite levels and prioritize metabolites with putative causality on complex diseases. 2. To identify genetic associations on gut microbiota and determine how these are relevant to host-microbe interactions. 3. To identify causal associations between gut microbes and plasma metabolites and characterize how these relate to diseases. 4. To identify novel genetic determinants of drug resistance in Mycobacterium tuberculosis and characterize the lineage-specific profiles of drug-resistant mutations. Through this research, I uncovered novel insights into communicable and non-communicable diseases in humans. In addition to identifying novel genetic loci associated with metabolism, my work prioritized plasma metabolites with putative causal effects on complex diseases. The robust genetic associations with gut microbiota expand our understanding of host-microbe interactions and may facilitate precision intervention of the gut microbiota. Causal relationships between gut microbes and plasma metabolites suggest that plasma metabolites may be involved in the microbial effects on human diseases. In infectious diseases, distinct lineage-specific profiles on drug-resistant associated mutations may guide public health decisions on tuberculosis control and treatment. In summary, this thesis demonstrates how genomic information can be leveraged to generate hypotheses, prioritize biomarkers, and uncover disease causality. The results expand our understanding of the human body as a complex symbiont

Statistical analysis of human gastrointestinal microbiota using next generation sequencing data

The human gastrointestinal tract is the niche of both commensal and pathogenic microbes which play an important role in human health. This thesis includes two independent studies relevant to analyzing next-generation sequencing data on the human gastrointestinal microbiota. The first study conducted a comparative analysis on 16S rRNA gene sequencing data obtained from gastritis and gastric cancer patients in the Hong Kong (HK) and Korean cohorts. Neisseriaceae and Lachnospiraceae were the important families in segregating gastritis and cancer samples in the HK dataset while it was Streptococcaceae in the Korean dataset. Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria and Fusobacteria were the major phyla in the two cohorts, where they made up ≥ 99% of the total relative abundance. However, when narrowed down to the family level, the two datasets only shared 5 major families among the 15 and 13 major families in the HK and Korean datasets, respectively. Hierarchical clustering showed that samples were segregated into two major clusters according to the relative abundance of Helicobacteria pylori (H. pylori) in the two datasets. Moreover, the cross-prediction results for gastritis versus cancer between two datasets yielded up to 3 times larger error rates compared to the prediction results within the training set. Taken together, the differences between the HK and Korean cohorts in the gastric microbiota outweighed the similarities. The second study developed a computational workflow to improve the draft genomes assembled from shotgun metagenomic sequencing data. The publicly available sequencing data of 396 human stool samples were downloaded for this purpose. Firstly, 3.9 million genes assembled from 396 samples were clustered into 7,381 co-abundance gene groups (CAGs) according to their pairwise correlations. The CAGs (741 CAGs) with more than 700 genes were defined as metagenomic species (MGSs), while the others (6,640 CAGs) were defined as metagenomic units (MGUs). In order to recover the relevant MGSs of the MGUs, the metagenomic deconvolution framework which decomposes the community-level gene content into taxon-specific gene profile was applied. Overall, 377 MGUs were assigned to 354 relevant MGSs, achieving a 9.57% mean improvement in the gene count of MGSs. Most of these MGSs were annotated to phylum Firmicutes. Specifically, the augmented results of 9 MGSs annotated to genus Faecalibacterium by their relative MGUs achieved average improvement of 21.08% and 17.84% in sensitivity and specificity. Importantly, MGUs included essential genes that were missed in MGSs, such as ribosomal genes, metabolism and transport system genes. Hence, the implementation of metagenomic deconvolution after binning improves the draft genomes of metagenomic species.published_or_final_versionPsychiatryMasterMaster of Philosoph