Biomedical subjects

Yuanhang Zhang

Publications and source records attributed to Yuanhang Zhang.

8 recordsLinked to original sources

PubMed2026

3D chromatin remodeling during domestication defines novel targets for crop improvement.

Three-dimensional (3D) genome folding shapes gene regulation, yet the genetic underpinnings linking 3D genome evolution to phenotypic innovation during domestication remain elusive. Using population-scale Hi-C profiling of 34 semi-wild and 267 cultivated allotetraploid cottons, we generated a pan-3D genome atlas capturing extensive diversity in topologically associating domains (TADs) and chromatin loops. Chromatin interactome-wide association studies identified 105 TAD reconfigurations and 58 loop rewirings that were established as the 3D chromatin basis of fiber quality, boosting heritability estimates for fiber strength by 16% and fiber length by 20%. We reveal that domestication selection within sequence-defined sweeps fixed 57% of 3D conformation signatures, thereby decoupling sequence-level from chromatin-level selection and shifting the subgenome expression balance of 39 homoeologs in cultivated cotton. Sequence-based modeling and mutational analyses identified the C2H2 zinc-finger protein YY1 as a conserved mediator of 3D genome organization. This study provides a resource for redefining precision-breeding paradigms by harnessing cryptic 3D chromatin targets.

3D genome↗

PubMed2024

Diagnosis with Metagenomic Next-Generation Sequencing (mNGS) technology and real-time PCR for SARS-CoV-2 Omicron detection using various nasopharyngeal swabs in SARS-CoV-2 Omicron.

BACKGROUND: The SARS-CoV-2 Omicron variant, with the main subtypes BA.5.2 and BF.7 in China, led to off-target effects on the S and N genes from December 1, 2022, to January 31, 2023. The kits used for studying and developing these agents were not adequately and independently evaluated. It is important to verify the performance of commercial Real-Time quantitative PCR (RT-qPCR) tests. OBJECTIVE: We conducted a clinical evaluation of two Real Time SARS-CoV-2 Omicron assays to verify their performance using various detection reagents and clinical specimens. METHODS: We performed clinical evaluations of two existing Chinese SARS-CoV-2 Omicron RT-qPCR kits 2019-nCoV nucleic acid diagnostic kits (Fosun Biotechnology, National instrument registration 20203400299, Shanghai, China) and COVID-19 nucleic acid detection kits (eDiagnosis Biomedicine, National instrument registration 20203400212, Wuhan, China) and using BSD (Bondson) (Guangzhou Bondson Biotechnology Co. Ltd, batch number 2022101), quality controls provided by the inspection center and a large number of clinically confirmed specimens. RESULTS: The concordance rates for the Fosun and eDiagnosis kits were 95% and 100%, respectively. The detection limit for the Fosun and eDiagnosis kits was verified to be 300 copies/mL and 500 copies/mL. The Fosun assay exhibited the largest coefficient of variation (CV) for ORF1ab and N gene at the detection limit concentration (4.80%, 3.49%), whereas eDiagnosis showed a smaller CV (0.93%, 1.10%). In the reference product from the Hangzhou Clinical Laboratory Center test, it was found that Fosun had the lowest sensitivity of 93.47% and a specificity of 100%, while eDiagnosis exhibited 100% for both sensitivity and specificity. The lowest single target gene detection rate of Fosun reagents was 68.7% for the ORF1ab gene and 87.5% for the N gene, while eDiagnosis detection rate was 100%. Among the clinical group S specimens, the missed detection rate of the Fosun reagent was 10.9%, which was higher than the 3.9% of eDiagnosis. However, there was no significant difference in the clinical diagnostic efficiency of the two reagents. CONCLUSIONS: The ORF1ab and N assays of SARS-CoV-2 Omicron on the eDiagnosis platform yielded higher values compared to those on the Fosun platform. Consequently, the eDiagnosis kit has also been used as standard detection reagents. Considering that the Fosun reagent has a relatively low detection limit and targets three single genes, it is more advantageous as a confirmatory reagent for the new museum.

Humans↗

PubMed2006

Source apportionment of PM2.5 in Beijing in 2004.

Based on measurements of fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5 microm or less) in January and August 2004, serious air pollution persists in Beijing. The chemical analysis included organic and elemental carbon, water-soluble ions, and elemental compositions. The positive matrix factorization (PMF) method was used to apportion the PM2.5 sources. The sources contributing dominantly to PM2.5 mass concentrations are coal combustion in winter and the secondary products in summer. Furthermore, the contributions from motor vehicles, road dusts and biomass burning could not be neglected. The products of biomass burning for winter heating in the area around Beijing could enter the urban area during quasi-quiescent weather conditions. In conclusion, some effective control measures were proposed to reduce the PM2.5 pollution in Beijing.

Air Pollutants↗

PubMed2006

Source apportionment of PM2.5 in Beijing using principal component analysis/absolute principal component scores and UNMIX.

Source apportionment of fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5 microm or less) in Beijing, China, was determined using two eigenvector models, principal component analysis/absolute principal component scores (PCA/APCS) and UNMIX. The data used in this study were from the chemical analysis of 24-h samples, which were collected at 6-day intervals in January, April, July, and October 2000 in the Beijing metropolitan area. Both models identified five sources of PM2.5: secondary sulfate and secondary nitrate, a mixed source of coal combustion and biomass burning, industrial emission, motor vehicles exhaust, and road dust. On average, the PCA/APCS and UNMIX models resolved 73% and 85% of the PM2.5 mass concentrations, respectively. The results were comparable to previous estimate using the positive matrix factorization (PMF) and chemical mass balance (CMB) receptor models. Secondary products and the emissions from coal combustion and biomass burning dominated PM2.5. Such comparison among various receptor models, which contain different physical constraints, is important for better understanding PM2.5 sources.

Air Pollutants↗

PubMed2004

Characteristics of PM10, SO2, NO(x) and O3 in ambient air during the dust storm period in Beijing.

In this study, the hourly variations of the mass concentrations of PM10, SO2, NO(x) and O3 at three sampling sites were observed in Beijing during dust storm occurrence period in April 2000. The PM2.5 samples were simultaneously collected. By comparing the hourly variations of the pollutant concentrations before, during and after dust storm event and haze pollution episode, the variation characteristics of the mass concentrations of PM10, SO2, NO(x) and O3 during dust storm events were presented. The results show that the mass concentration of PM10 reached 1500 microg m(-3) during dust storm events on April 6 and 25, 2000, which was 5-10 times that of the non-dust weather conditions, and this period of high mass concentration of PM10 lasted for about 14 h, and then the concentration level prior to the dust event was recovered in 6-h time period. Due to the strong wind, the concentrations of SO2, NO(x), NO2 and O3 during dust storm period were maintained at low levels, which was significantly different from those on non-dust storm and haze pollution conditions. A lot of coarse particles as well as a very large amount of fine particles were contained in the atmospheric particulates during dust storm period, and the concentration level of PM2.5 was comparable to that during haze pollution episode. During the dust storm period, the PM2.5 concentration was approximately 230 microg m(-3), accounting for 30% of the total PM10 mass concentration, was four times that of non-dust weather conditions, and the crustal elements constituted about 66.4% of the chemical composition of PM2.5 while sulfate and nitrate contributed much less, which was quite different from the chemical composition of PM2.5 primarily constituted by sulfate, nitrate and organics on haze pollution day.

Air↗

PubMed2002

Diurnal variations of aerosol chemical compositions and related gaseous pollutants in Beijing and Guangzhou.

A new apparatus, Steam Jet Aerosol Collector (SJAC) combined with on-line Mobile Ion Chromatograph provides a reliable way to measure both aerosol components and related gas species simultaneously. The SJAC was applied in the field campaigns in Beijing and Guangzhou to investigate aerosol chemical compositions and related gaseous pollutants. The concentration levels and diurnal variations are discussed, which implies differences in atmospheric chemistry of gas-to-particle conversions in Beijing and Guangzhou.

Aerosols↗

PubMed2002

[Effects on fine particles by the continued high temperature weather in Beijing].

The continued high temperature weather (CHTW) appeared in Beijing since June 23 1999. The measurements showed the PM2.5 mass concentrations were 2 to 3 times of that in non-CHTW period. However the diffusion conditions were still fine in these days. Both the highest hourly concentrations of O3 and the SO4(2-) composition in PM2.5 can identify that the photochemical action was very active. It is the very reason to produce more fine particles in atmosphere.

Air Pollutants↗

PubMed2002

[Source apportionment on fine particles in Beijing].

The positive matrix factorization(PMF) method was used to apportion the sources for fine particles in Beijing. The data were obtained from four samplings from 1999 to 2000. The number of this data set was 40. The chemical compositions used in analysis include element carbon(EC), organics, SO4(2-), F-, Cl-, Fe, Ca, K, Mg, Al, Na, Zn, Mn, Ti, Pb, Ba and P. Six main sources, soil dust, construction dust, secondary source (by photochemical action), biomass burning, motor vehicle and coal burning were found.

Air Pollutants↗