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1. Modeling acute and chronic vascular responses to a major arterial occlusion

   https://doi.org/10.1111/micc.12738  

   Zhao, Erin; Barber, Jared; Sen, Chandan K.; Arciero, Julia (November 2021, Microcirculation)

   Abstract ObjectiveTo incorporate chronic vascular adaptations into a mathematical model of the rat hindlimb to simulate flow restoration following total occlusion of the femoral artery. MethodsA vascular wall mechanics model is used to simulate acute and chronic vascular adaptations in the collateral arteries and collateral‐dependent arterioles of the rat hindlimb. On an acute timeframe, the vascular tone of collateral arteries and distal arterioles is determined by responses to pressure, shear stress, and metabolic demand. On a chronic timeframe, sustained dilation of arteries and arterioles induces outward vessel remodeling represented by increased passive vessel diameter (arteriogenesis), and low venous oxygen saturation levels induce the growth of new capillaries represented by increased capillary number (angiogenesis). ResultsThe model predicts that flow compensation to an occlusion is enhanced primarily by arteriogenesis of the collateral arteries on a chronic time frame. Blood flow autoregulation is predicted to be disrupted and to occur for higher pressure values following femoral arterial occlusion. ConclusionsStructural adaptation of the vasculature allows for increased blood flow to the collateral‐dependent region after occlusion. Although flow is still below pre‐occlusion levels, model predictions indicate that interventions which enhance collateral arteriogenesis would have the greatest potential for restoring flow. 

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2. Direct Quantification of Intervertebral Disc Water Content Using MRI

   https://doi.org/10.1002/jmri.27171  

   Yang, Bo; Wendland, Michael F.; O'Connell, Grace D. (April 2020, Journal of Magnetic Resonance Imaging)

   BackgroundWater content is a key parameter for simulating tissue swelling and nutrient diffusion. Accurately measuring water content throughout the intervertebral disc (NP = nucleus pulposus; AF = annulus fibrosus) is important for developing patient‐specific models. Water content is measured using destructive techniques, Quantitative MRI has been used to estimate water content and detect early degeneration, but it is dependent on scan parameters, concentration of free water molecules, and fiber architecture. PurposeTo directly measure disc‐tissue water content using quantitative MRI and compare MRI‐based measurements with biochemical assays, and to quantify changes in disc geometry due to compression. Study TypeBasic science, controlled. SpecimenTwenty bone‐disc‐bone motion segments from skeletally mature bovines. Field Strength/Sequence7T/3D fast low angle shot (FLASH) pulse sequence and a T₂rapid imaging with refocused echoes (RARE) sequence. AssessmentDisc volumes, NP and AF volumetric water content, and T₂relaxation times were measured through MRI; NP and AF tissue gravimetric water content, mass density, and glycosaminoglycan content were measured through a biochemical assay. Statistical TestsCorrelations between MRI‐based measurement and biochemical composition were evaluated using Pearson's linear regression. ResultsMechanical dehydration resulted in a decrease in disc volume by up to 20% and a decrease in disc height by up to 35%. Direct water content measurements for the NP was achieved by normalizing MRI‐based spin density by NP mass density (1.10 ± 0.03 g/cm³). However, the same approach underestimated water content in the AF by ~10%, which may be due to a higher concentration of collagen fibers and bound water molecules. Data ConclusionSpin density or spin density normalized by mass density to estimate NP and AF water content was more accurate than correlations between water content and relaxation times. Mechanical dehydration decreased disc volume and disc height, and increased maximum cross‐sectional area. Level of Evidence  Technical Efficacy Stage  J. Magn. Reson. Imaging 2020;52:1152–1162. 

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3. Updated site concordance factors minimize effects of homoplasy and taxon sampling

   https://doi.org/10.1093/bioinformatics/btac741  

   Mo, Yu K.; Lanfear, Robert; Hahn, Matthew W.; Minh, Bui Quang; Schwartz, ed., Russell (November 2022, Bioinformatics)

   Abstract MotivationSite concordance factors (sCFs) have become a widely used way to summarize discordance in phylogenomic datasets. However, the original version of sCFs was calculated by sampling a quartet of tip taxa and then applying parsimony-based criteria for discordance. This approach has the potential to be strongly affected by multiple hits at a site (homoplasy), especially when substitution rates are high or taxa are not closely related. ResultsHere, we introduce a new method for calculating sCFs. The updated version uses likelihood to generate probability distributions of ancestral states at internal nodes of the phylogeny. By sampling from the states at internal nodes adjacent to a given branch, this approach substantially reduces—but does not abolish—the effects of homoplasy and taxon sampling. Availability and implementationUpdated sCFs are implemented in IQ-TREE 2.2.2. The software is freely available at https://github.com/iqtree/iqtree2/releases. Supplementary informationSupplementary information is available at Bioinformatics online. 

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4. 3D-equivariant graph neural networks for protein model quality assessment

   https://doi.org/10.1093/bioinformatics/btad030  

   Chen, Chen; Chen, Xiao; Morehead, Alex; Wu, Tianqi; Cheng, Jianlin; Cowen, ed., Lenore (January 2023, Bioinformatics)

   Abstract MotivationQuality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. ResultsWe develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method—AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method—AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA. Availability and implementationThe source code is available at https://github.com/BioinfoMachineLearning/EnQA. Supplementary informationSupplementary data are available at Bioinformatics online. 

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5. Entrepreneurial intent of engineering and business undergraduate students

   https://doi.org/10.1002/jee.20283  

   Gilmartin, Shannon K.; Thompson, Marissa E.; Morton, Emily; Jin, Qu; Chen, Helen L.; Colby, Anne; Sheppard, Sheri D. (August 2019, Journal of Engineering Education)

   Abstract BackgroundIn recent years, technological innovation and entrepreneurship have been emphasized in engineering education. There is a need to better understand which individual‐ and contextual‐level factors are related to engineering students' entrepreneurial intentions. Purpose/HypothesisThis study explores individual and contextual predictors of entrepreneurial intent among undergraduate women and men in engineering and business majors. Entrepreneurial intent is defined as the personal importance that students ascribe to starting a new business or organization. Design/MethodThe participants included 518 engineering and 471 business undergraduates from 51 U.S. colleges and universities. We examined relationships first by discipline and then by gender in each discipline using regression models with interaction terms. ResultsInnovation orientation and participation in entrepreneurship activities tied to intent more strongly for engineering students than for business students; in contrast, being at a research institution and selection of novel goals tied to intent more strongly for business students than for their engineering peers. Among engineering students only, being able to switch gears and apply alternative means for reaching one's goal in the face of setbacks was positively related with women's entrepreneurial intent but not with men's. ConclusionsEntrepreneurial intent is a function of individual‐level characteristics and academic and social contexts, with some degree of discipline‐specific effects. Diversifying the community of aspiring engineering entrepreneurs is a critical issue that merits attention by the engineering education community. 

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