Malocclusion of the premolar and molar teeth in the guinea pig.

Malocclusions of the premolar and molar teeth of inbred Strain 2/N and Strain 13/N and outbred Dunkin-Harley guinea pigs were examined. A higher incidence of malocclusion observed in the inbred strains suggested a genetic basis for the disorder.     

A Human Active Lower Limb Model for Chinese Pedestrian Safety Evaluation

A subsystem impactor test for pedestrian lower limb injury evaluation has been brought in China New Car Assessment Protocol(CNCAP).Concerning large anthropometr...     

Design,synthesis, and biological evaluation of novel carbazole derivatives as potent DNMT1 inhibitors with reasonable PK properties

The DNA methyltransferases (DNMTs) were found in mammals to maintain DNA methylation. Among them, DNMT1 was the first identified, and it is an attractive target for tumour chemotherapy. DC_05 and DC_517 have been reported in our previous work, which is non-nucleoside DNMT1 inhibitor with low micromolar IC₅₀ values and significant selectivity towards other S-adenosyl-_L-methionine (SAM)-dependent protein methyltransferases. In this study, through a process of similarity-based analog searching, a series of DNMT1 inhibitors were designed, synthesized, and evaluated as anticancer agents. SAR studies were conducted based on enzymatic assays. And most of the compounds showed strong inhibitory activity on human DNMT1, especially WK-23 displayed a good inhibitory effect on human DNMT1 with an IC₅₀ value of 5.0 µM. Importantly, the pharmacokinetic (PK) profile of WK-23 was obtained with quite satisfying oral bioavailability and elimination half-life. Taken together, WK-23 is worth developing as DNMT1-selective therapy for the treatment of malignant tumour.     

Association between long-term exposure to ambient air pollution and COVID-19 severity: a prospective cohort study

Background:The tremendous global health burden related to COVID-19 means that identifying determinants of COVID-19 severity is important for prevention and intervention. We aimed to explore long-term exposure to ambient air pollution as a potential contributor to COVID-19 severity, given its known impact on the respiratory system.Methods:We used a cohort of all people with confirmed SARS-CoV-2 infection, aged 20 years and older and not residing in a long-term care facility in Ontario, Canada, during 2020. We evaluated the association between long-term exposure to fine particulate matter (PM_2.5), nitrogen dioxide (NO₂) and ground-level ozone (O₃), and risk of COVID-19-related hospital admission, intensive care unit (ICU) admission and death. We ascertained individuals’ long-term exposures to each air pollutant based on their residence from 2015 to 2019. We used logistic regression and adjusted for confounders and selection bias using various individual and contextual covariates obtained through data linkage.Results:Among the 151 105 people with confirmed SARS-CoV-2 infection in Ontario in 2020, we observed 8630 hospital admissions, 1912 ICU admissions and 2137 deaths related to COVID-19. For each interquartile range increase in exposure to PM_2.5 (1.70 μg/m³), we estimated odds ratios of 1.06 (95% confidence interval [CI] 1.01–1.12), 1.09 (95% CI 0.98–1.21) and 1.00 (95% CI 0.90–1.11) for hospital admission, ICU admission and death, respectively. Estimates were smaller for NO₂. We also estimated odds ratios of 1.15 (95% CI 1.06–1.23), 1.30 (95% CI 1.12–1.50) and 1.18 (95% CI 1.02–1.36) per interquartile range increase of 5.14 ppb in O₃ for hospital admission, ICU admission and death, respectively.Interpretation:Chronic exposure to air pollution may contribute to severe outcomes after SARS-CoV-2 infection, particularly exposure to O₃.

By November 2021, COVID-19 had caused more than 5 million deaths globally¹ and more than 29 400 in Canada.² The clinical manifestations of SARS-CoV-2 infection range from being asymptomatic to multiple organ failure and death. Identifying risk factors for COVID-19 severity is important to better understand etiological mechanisms and identify populations to prioritize for screening, vaccination and medical treatment. Risk factors for severity of COVID-19 include male sex, older age, pre-existing medical conditions and being from racialized communities.^3–5 More recently, ambient air pollution has been implicated as a potential driver of COVID-19 severity.^6–10Long-term exposure to ambient air pollution, a major contributor to global disease burden,¹¹ could increase the risk of severe COVID-19 outcomes by several mechanisms. Air pollutants can reduce individuals’ pulmonary immune responses and antimicrobial activities, boosting viral loads.⁸ Air pollution can also induce chronic inflammation and overexpression of the alveolar angiotensin-converting enzyme 2 (ACE) receptor,⁷ the key receptor that facilitates SARS-CoV-2 entry into cells.^12,13 Exposure to air pollution contributes to chronic conditions, such as cardiovascular disease, that are associated with unfavourable COVID-19 prognosis, possibly owing to persistent immune activation and excessive amplification of cytokine development.¹⁰ Thus, greater exposure to long-term air pollution may lead to severe COVID-19 outcomes.Reports exist of positive associations between long-term exposure to particulate matter with diameters equal to or smaller than 2.5 or 10 μm (PM_2.5 and PM₁₀), ground-level ozone (O₃) and nitrogen dioxide (NO₂), and metrics of COVID-19 severity (e.g., mortality and case fatality rate).^8–10 However, most studies to date have used ecological and cross-sectional designs, owing to limited access to individual data, which leads to ambiguity in interpreting the results, thus hindering their influence on policy. ^6,14 Ecological designs do not allow for disentangling the relative impacts of air pollution on individual susceptibility to infection and disease severity.¹⁴ Residual confounding by factors such as population mobility and social interactions is also problematic. Therefore, a cohort study with data on individuals with SARS-CoV-2 is a more appropriate design.^6,14 Studies that have used individual data were conducted in specific subpopulations^15,16 or populations with few severe cases,¹⁷ or had limited data on individual exposure to air pollutants.¹⁸ In Canada, 1 ecological study found a positive association between long-term exposure to PM_2.5 and COVID-19 incidence,¹⁹ but no published study has explored the association between air pollution and COVID-19 severity.We aimed to examine the associations between long-term exposure to 3 common air pollutants (PM_2.5, NO₂ and O₃) and key indicators of COVID-19 severity, including hospital admission, intensive care unit (ICU) admission and death, using a large prospective cohort of people with confirmed SARS-CoV-2 infection in Ontario, Canada, in 2020. The air contaminants PM_2.5, NO₂ and O₃ are regularly monitored by the Canadian government, and are key pollutants that are considered when setting air-quality policies. They originate from varying sources (NO₂ is primarily emitted during combustion of fuel, O₃ is primarily formed in air by chemical reactions of nitrogen oxides and volatile organic compounds, and PM_2.5 can be emitted during combustion or formed by reactions of chemicals like sulphur dioxide and nitrogen oxides in air) and they may affect human health differently.^20,21,22     

谭清苏铁性别相关的RAPD标记研究

以谭清苏铁(Cycas tanqingii D.Y.Wang)雌雄植株半年生羽叶为材料,用优化的CTAB法分别提取其全基因组DNA,进行RAPD单因子梯度实验和正交实验以优化扩增条件。应用160个RAPD随机引物检测基因组DNA,雌雄植株均扩增出1450多条带,其中引物S0465扩增出与谭清苏铁雌株高度相关的RAPD标记,其大小约为500bp,该标记与雄株没有关联。     

使用冷冻方法防治昆虫标本虫害

皮蠹幼虫对昆虫标本的蛀蚀是我国北方地区标本保藏时需要注意的首要问题。经12次的试验观察表明,花斑皮蠹幼虫TrogodermavariabileBallion在冰柜中放置位置不同其冷冻致死率亦不相同置于冰柜表层的,死亡率介于0～50%;上层的死亡率为95%～100%;中上层及中层死亡率达100%。放置在表层及上层的皮蠹幼虫,在经2d以上的冷冻处理后部分个体出现复活。因此,对那些原先放置上层及表层的标本,第1次冷冻结束后,间隔数天应再进行第2次冷冻,以提高和巩固冷冻杀虫效果。经3年的实践证明,采用冷冻方法治理皮蠹幼虫为害效果明显,可以推广普及。     

Fast classification and compositional analysis of cornstover fractions using Fourier transform near-infrared techniques

The objectives of this research were to determine the variation of chemical composition across botanical fractions of cornstover, and to probe the potential of Fourier transform near-infrared (FT-NIR) techniques in qualitatively classifying separated cornstover fractions and in quantitatively analyzing chemical compositions of cornstover by developing calibration models to predict chemical compositions of cornstover based on FT-NIR spectra. Large variations of cornstover chemical composition for wide calibration ranges, which is required by a reliable calibration model, were achieved by manually separating the cornstover samples into six botanical fractions, and their chemical compositions were determined by conventional wet chemical analyses, which proved that chemical composition varies significantly among different botanical fractions of cornstover. Different botanic fractions, having total saccharide content in descending order, are husk, sheath, pith, rind, leaf, and node. Based on FT-NIR spectra acquired on the biomass, classification by Soft Independent Modeling of Class Analogy (SIMCA) was employed to conduct qualitative classification of cornstover fractions, and partial least square (PLS) regression was used for quantitative chemical composition analysis. SIMCA was successfully demonstrated in classifying botanical fractions of cornstover. The developed PLS model yielded root mean square error of prediction (RMSEP %w/w) of 0.92, 1.03, 0.17, 0.27, 0.21, 1.12, and 0.57 for glucan, xylan, galactan, arabinan, mannan, lignin, and ash, respectively. The results showed the potential of FT-NIR techniques in combination with multivariate analysis to be utilized by biomass feedstock suppliers, bioethanol manufacturers, and bio-power producers in order to better manage bioenergy feedstocks and enhance bioconversion.     

Linking seasonal inorganic nitrogen shift to the dynamics of microbial communities in the Chesapeake Bay

Seasonal shifts of dissolved inorganic nitrogen (DIN) and the dynamics of microbial communities for nitrogen transformation were investigated in the water column of Chesapeake Bay. The relative abundance of nitrogen over phosphorus (N*) showed a strong seasonal and spatial pattern: gradually decreased from upstream to downstream; high in winter and low in summer. Because the phosphorus concentration remained relatively stable, the spatiotemporal pattern of N* implied that a substantial fraction of DIN was removed in the bay, especially in summer. Correlation analyses indicated the functional microbial communities and environmental variables, such as temperature, dissolved oxygen, salinity, played important roles for connecting the seasonal variation of N*. Among them, temperature was the trigger factor. High temperature in the summer induced the growth of functional microbes, which subsequently consumed a large portion of DIN inputted from the tributaries and reduced the N*. The current study provided the relative importance of microbial communities and environmental variables in driving the DIN loss in the bay.