24 research outputs found

    Assessment of Past and Present Sediment Quality of Stoney Creek in Burnaby, British Columbia

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    In analyzing the sediment and water quality of the Stoney Creek habitat, four key aspects were investigated: lithology, sediment/water quality, salmon spawning/incubation, and particle size distribution. The lithology found the streambed sediment layer is 3 cm in depth (over bedrock) and consists mainly of sand and some coarser material including gravels, cobbles, and boulders. The sediment of the offchannel pond is mainly mud (fine material) with a moderate amount of sand and a very small percentage of coarser material including gravels and organic matter (leaf detritus and woody debris). Chemical analysis concluded a significant concentration of iron in the pond environment, with potential for adverse effects to salmon offspring. This report further aims to assess the influences of fine sediment on the quality of salmon spawning habitat and incubation success rate. Permeability of spawning gravels and dissolved oxygen concentrations are measured to see if they support the incubation and growth of salmon eggs. Particle size distributions are found significantly different between upstream pool and pond side. And the difference of particle size distributions can influence salmon production in the off-channel site

    Loss of MyoD Promotes Fate Transdifferentiation of Myoblasts Into Brown Adipocytes

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    Brown adipose tissue (BAT) represents a promising agent to ameliorate obesity and other metabolic disorders. How- ever, the abundance of BAT decreases with age and BAT paucity is a common feature of obese subjects. As brown adipocytes and myoblasts share a common Myf5 lineage origin, elucidating the molecular mechanisms underlying the fate choices of brown adipocytes versus myoblasts may lead to novel approaches to expand BAT mass. Here we identify MyoD as a key negative regulator of brown adipocyte development. CRISPR/CAS9-mediated deletion of MyoD in C2C12 myoblasts facilitates their adipogenic transdifferentiation. MyoD knockout downregulates miR- 133 and upregulates the miR-133 target Igf1r, leading to amplification of PI3K–Akt signaling. Accordingly, inhibition of PI3K or Akt abolishes the adipogenic gene expression of MyoD null myoblasts. Strikingly, loss of MyoD converts satellite cell-derived primary myoblasts to brown adipocytes through upregulation of Prdm16, a target of miR-133 and key determinant of brown adipocyte fate. Conversely, forced expression of MyoD in brown preadipocytes blocks brown adipogenesis and upregulates the expression of myogenic genes. Importantly, miR-133a knockout signifi- cantly blunts the inhibitory effect of MyoD on brown adipogenesis. Our results establish MyoD as a negative regu- lator of brown adipocyte development by upregulating miR-133 to suppress Akt signaling and Prdm16

    miR-133a Regulates Adipocyte Browning In Vivo.

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    Prdm16 determines the bidirectional fate switch of skeletal muscle/brown adipose tissue (BAT) and regulates the thermogenic gene program of subcutaneous white adipose tissue (SAT) in mice. Here we show that miR-133a, a microRNA that is expressed in both BAT and SATs, directly targets the 3′ UTR of Prdm16. The expression of miR-133a dramatically decreases along the commitment and differentiation of brown preadipocytes, accompanied by the upregulation of Prdm16. Overexpression of miR-133a in BAT and SAT cells significantly inhibits, and conversely inhibition of miR-133a upregulates, Prdm16 and brown adipogenesis. More importantly, double knockout of miR-133a1 and miR-133a2 in mice leads to elevations of the brown and thermogenic gene programs in SAT. Even 75% deletion of miR-133a (a1−/−a2+/−) genes results in browning of SAT, manifested by the appearance of numerous multilocular UCP1-expressing adipocytes within SAT. Additionally, compared to wildtype mice, miR-133a1−/−a2+/− mice exhibit increased insulin sensitivity and glucose tolerance, and activate the thermogenic gene program more robustly upon cold exposure. These results together elucidate a crucial role of miR-133a in the regulation of adipocyte browning in vivo

    Notch signaling regulates adipose browning and energy metabolism

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    Beige adipocytes in white adipose tissue (WAT) are similar to classical brown adipocytes in that they can burn lipids to produce heat. Thus, an increase in beige adipocyte content in WAT browning would raise energy expenditure and reduce adiposity. Here we report that adipose-specific inactivation of Notch1 or its signaling mediator Rbpj in mice results in browning of WAT and elevated expression of uncoupling protein 1 (Ucp1), a key regulator of thermogenesis. Consequently, as compared to wild-type mice, Notch mutants exhibit elevated energy expenditure, better glucose tolerance and improved insulin sensitivity and are more resistant to high fat diet-induced obesity. By contrast, adipose-specific activation of Notch1 leads to the opposite phenotypes. At the molecular level, constitutive activation of Notch signaling inhibits, whereas Notch inhibition induces, Ppargc1a and Prdm16 transcription in white adipocytes. Notably, pharmacological inhibition of Notch signaling in obese mice ameliorates obesity, reduces blood glucose and increases Ucp1 expression in white fat. Therefore, Notch signaling may be therapeutically targeted to treat obesity and type 2 diabetes

    Robust estimation of bacterial cell count from optical density

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    Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

    Antituberculosis Drugs (Rifampicin and Isoniazid) Induce Liver Injury by Regulating NLRP3 Inflammasomes

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    Patients being treated for pulmonary tuberculosis often suffer liver injury due to the effects of anti-TB drugs, and the underlying mechanisms for those injuries need to be clarified. In this study, rats and hepatic cells were administrated isoniazid (INH) and rifampin (RIF) and then treated with NLRP3-inflammasome inhibitors (INF39 and CP-456773) or NLRP3 siRNA. Histopathological changes that occurred in liver tissue were examined by H&E staining. Additionally, the levels IL-33, IL-18, IL-1β, NLRP3, ASC, and cleaved-caspase 1 expression in the liver tissues were also determined. NAT2 and CYP2E1 expression were identified by QRT-PCR analysis. Finally, in vitro assays were performed to examine the effects of siRNA targeting NLRP3. Treatment with the antituberculosis drugs caused significant liver injuries, induced inflammatory responses and oxidative stress (OS), activated NLRP3 inflammasomes, reduced the activity of drug-metabolizing enzymes, and altered the antioxidant defense system in rats and hepatic cells. The NLRP3 inflammasome was required for INH- and RIF-induced liver injuries that were produced by inflammatory responses, OS, the antioxidant defense system, and drug-metabolizing enzymes. This study indicated that the NLRP3 inflammasome is involved in antituberculosis drug-induced liver injuries (ATLIs) and suggests NLRP3 as a potential target for attenuating the inflammation response in ATLIs

    The upregulated LsKN1 gene transforms pinnately to palmately lobed leaves through auxin, gibberellin, and leaf dorsiventrality pathways in lettuce

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    Leaf shape represents a vital agronomic trait for leafy vegetables such as lettuce. Some lettuce cultivars produce lobed leaves, varying from pinnately to palmately lobed, but the genetic mechanisms remain unclear. In this study, we cloned one major quantitative trait locus (QTL) controlling palmately lobed leaves. The candidate gene, LsKN1, encodes a homeobox transcription factor, and has been shown previously to be critical for the development of leafy heads in lettuce. The LsKN1 allele that is upregulated by the insertion of a transposon promotes the development of palmately lobed leaves. We demonstrated that LsKN1 upregulated LsCUC2 and LsCUC3 through different mechanisms, and their upregulation was critical for the development of palmately lobed leaves. LsKN1 binds the promoter of LsPID to promote auxin biosynthesis, which positively contributes to the development of palmately lobed leaves. In contrast, LsKN1 suppresses GA biosynthesis to promote palmately lobed leaves. LsKN1 also binds to the promoter of LsAS1, a dorsiventrality gene, to downregulate its expression. Overexpression of the LsAS1 gene compromised the effects of the LsKN1 gene changing palmately to pinnately lobed leaves. Our study illustrated that the upregulated LsKN1 gene led to palmately lobed leaves in lettuce by integrating several downstream pathways, including auxin, gibberellin, and leaf dorsiventrality pathways

    Deep learning predicts malignancy and metastasis of solid pulmonary nodules from CT scans

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    In the clinic, it is difficult to distinguish the malignancy and aggressiveness of solid pulmonary nodules (PNs). Incorrect assessments may lead to delayed diagnosis and an increased risk of complications. We developed and validated a deep learning-based model for the prediction of malignancy as well as local or distant metastasis in solid PNs based on CT images of primary lesions during initial diagnosis. In this study, we reviewed the data from multiple patients with solid PNs at our institution from 1 January 2019 to 30 April 2022. The patients were divided into three groups: benign, Ia-stage lung cancer, and T1-stage lung cancer with metastasis. Each cohort was further split into training and testing groups. The deep learning system predicted the malignancy and metastasis status of solid PNs based on CT images, and then we compared the malignancy prediction results among four different levels of clinicians. Experiments confirmed that human–computer collaboration can further enhance diagnostic accuracy. We made a held-out testing set of 134 cases, with 689 cases in total. Our convolutional neural network model reached an area under the ROC (AUC) of 80.37% for malignancy prediction and an AUC of 86.44% for metastasis prediction. In observer studies involving four clinicians, the proposed deep learning method outperformed a junior respiratory clinician and a 5-year respiratory clinician by considerable margins; it was on par with a senior respiratory clinician and was only slightly inferior to a senior radiologist. Our human–computer collaboration experiment showed that by simply adding binary human diagnosis into model prediction probabilities, model AUC scores improved to 81.80–88.70% when combined with three out of four clinicians. In summary, the deep learning method can accurately diagnose the malignancy of solid PNs, improve its performance when collaborating with human experts, predict local or distant metastasis in patients with T1-stage lung cancer, and facilitate the application of precision medicine

    Exploring fullerene-based superlattices self-assembled via giant molecules

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    Fullerene, as a novel building block of functional materials, possesses a wide range of intriguing properties, some of which are closely related to hierarchical structures. Advancing the methodology for manipulating fullerene hierarchical structures could facilitate developments of novel fullerene-based materials with desired properties. In the present work, we report our exploration of a series of fullerene-based unconventional spherical packing nanostructures, including Frank-Kasper (FK) A15 and σ phases as well as dodecagonal quasicrystalline (DDQC) phase in unary self-assembly systems of fullerene-based giant molecules. Also, in giant molecule binary blends, alloy-type spherical phases with high volume asymmetries such as FK Laves C14 (MgZn2) and C15 (MgCu2) phases and quasi-FK AlB2 phase can also be found. These observations showcase the feasibility of fabricating a wide range of fullerene-based superlattices through self-assembly of specifically designed giant molecules. Superlattice engineering offers a promising avenue for constructing specific structures and functions in order to achieve desired properties, in particular, for developing innovative fullerene-based materials
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