Showing papers by "University of Akron published in 2022"

Highly active catalysis of methanol oxidative carbonylation over nano Cu2O supported on micropore-rich mesoporous carbon

Abstract: A micropore-rich mesoporous carbon (MMC) derived from ordered mesoporous carbon (OMC) is fabricated as support to prepare a highly active nano Cu2O catalyst for methanol oxidation carbonylation. The well-dispersed ~3.3 nm Cu nanoparticles with ca. 90% purity of Cu2O are obtained. The space-time yield and turnover frequency of DMC are significantly enhanced to 34.2 g g Cu − 1 h − 1 and 89.1 h−1, both of which are greater than that over the mesoporous-only Cu/OMC catalyst. It is found that plentiful under-coordinated carbon atoms are formed in the introduced micropores, which serve as binding sites to immobilize Cu precursors to form the well-dispersed Cu nanoparticles. A large number of these atoms are favorable to accelerate auto-reduction of CuO to Cu2O in kinetics and further promote to form high-purity Cu2O. Besides, the electrons of Cu2O are forced to transfer to the micropore surrounding, forming an electron-deficient Cu+ site in favor of intrinsic activity enhancement.

Optimal Dynamic Information Acquisition

Abstract: I study a dynamic model in which a decision‐maker (DM) acquires information about the payoffs of different alternatives prior to making a decision. The model's key feature is the flexibility of information: the DM can choose any dynamic signal process as an information source, subject to a flow cost that depends on the informativeness of the signal. Under the optimal policy, the DM acquires a signal that arrives according to a Poisson process . The optimal Poisson signal confirms the DM's prior belief and is sufficiently precise to warrant immediate action. Over time, given the absence of the arrival of a Poisson signal, the DM continues seeking an increasingly precise but less frequent Poisson signal.

Fundamentals and exploration of aggregation-induced emission molecules for amyloid protein aggregation

Abstract: The past decade has witnessed the growing interest and advances in aggregation-induced emission (AIE) molecules as driven by their unique fluorescence/optical properties in particular sensing applications including biomolecule sensing/detection, environmental/health monitoring, cell imaging/tracking, and disease analysis/diagnosis. In sharp contrast to conventional aggregation-caused quenching (ACQ) fluorophores, AIE molecules possess intrinsic advantages for the study of disease-related protein aggregates, but such studies are still at an infant stage with much less scientific exploration. This outlook mainly aims to provide the first systematic summary of AIE-based molecules for amyloid protein aggregates associated with neurodegenerative diseases. Despite a limited number of studies on AIE-amyloid systems, we will survey recent and important developments of AIE molecules for different amyloid protein aggregates of Aβ (associated with Alzheimer's disease), insulin (associated with type 2 diabetes), (α-syn, associated with Parkinson's disease), and HEWL (associated with familial lysozyme systemic amyloidosis) with a particular focus on the working principle and structural design of four types of AIE-based molecules. Finally, we will provide our views on current challenges and future directions in this emerging area. Our goal is to inspire more researchers and investment in this emerging but less explored subject, so as to advance our fundamental understanding and practical design/usages of AIE molecules for disease-related protein aggregates.

Preparation, characterization, and drug release study of ibuprofen-loaded poly (vinyl alcohol)/poly (vinyl pyrrolidone) bilayer antibacterial membrane

Abstract: Controlling wound pain, absorption of wound exudate, and minimizing the frequency of changing the dressing are necessary parameters to facilitate wound-healing process. Hence, stable hydrophilic hy...

Microscopic Dynamics and Viscoelasticity of Vitrimers

Abstract: We implement a hybrid molecular dynamics/Monte Carlo simulation to study the microscopic dynamics and the macroscopic rheology of vitrimers with a fast bond exchange rate. We show that the linear viscoelastic properties and mean squared displacement of the vitrimers collapse onto master curves by applying the same shift factors that follow the Williams–Landel–Ferry equation at low temperatures and Arrhenius-like behavior at high temperatures. The linkage between the microscopic dynamics and the linear rheology of vitrimers is established using the generalized Stokes–Einstein relationship, which efficiently extends the timescale of simulations and predicts the viscoelasticity. The values of the shift factors are related to the characteristic decay time of the intermediate scattering function, which is accessible in scattering experiments. The same results hold in the case of an all-atom model of an ionic liquid. Our methodology provides a microscopic basis for the time-superposition principle and predicts the macroscopic rheology of thermo-rheologically simple vitrimers.

Therapeutic Targeting of Stromal-Tumor HGF-MET Signaling in an Organotypic Triple-Negative Breast Tumor Model

Abstract: The tumor microenvironment (TME) promotes proliferation, drug resistance, and invasiveness of cancer cells. Therapeutic targeting of the TME is an attractive strategy to improve outcomes for patients, particularly in aggressive cancers such as triple-negative breast cancer (TNBC) that have a rich stroma and limited targeted therapies. However, lack of preclinical human tumor models for mechanistic understanding of tumor-stromal interactions has been an impediment to identify effective treatments against the TME. To address this need, we developed a three-dimensional organotypic tumor model to study interactions of patient-derived cancer-associated fibroblasts (CAF) with TNBC cells and explore potential therapy targets. We found that CAFs predominantly secreted hepatocyte growth factor (HGF) and activated MET receptor tyrosine kinase in TNBC cells. This tumor-stromal interaction promoted invasiveness, epithelial-to-mesenchymal transition, and activities of multiple oncogenic pathways in TNBC cells. Importantly, we established that TNBC cells become resistant to monotherapy and demonstrated a design-driven approach to select drug combinations that effectively inhibit prometastatic functions of TNBC cells. Our study also showed that HGF from lung fibroblasts promotes colony formation by TNBC cells, suggesting that blocking HGF-MET signaling potentially could target both primary TNBC tumorigenesis and lung metastasis. Overall, we established the utility of our organotypic tumor model to identify and therapeutically target specific mechanisms of tumor-stromal interactions in TNBC toward the goal of developing targeted therapies against the TME.Leveraging a state-of-the-art organotypic tumor model, we demonstrated that CAFs-mediated HGF-MET signaling drive tumorigenic activities in TNBC and presents a therapeutic target.

Evaluation of ArmedXpert software tools, MixtureAce and Mixture Interpretation, to analyze MPS-STR data.

Abstract: Massively parallel sequencing (MPS) technologies have revolutionized studies of genomic variations and transformed DNA analysis in multiple fields. Assays based on MPS must be capable of discriminating variations introduced by the method, i.e. artifacts from true polymorphisms. In PCR-MPS methods targeting microsatellite markers, artifacts can arise from PCR mis-incorporation, PCR strand slippage (stutter), and sequencing error. Reliable detection of artifacts in mixed DNA samples is a significant challenge that must be addressed in forensic DNA analysis. The ArmedXpert (NicheVision) software tools, MixtureAce™ and Mixture Interpretation, can analyze MPS data by categorizing sequence reads in alleles, stutter, and non-stutter artifacts and analyzing autosomal STR loci of mixed samples. In this study, we evaluated the ArmedXpert tools for the analysis of STR profiles of single-sourced and mixed samples generated by the ForenSeq™ DNA Signature Prep kit (Verogen). Data from eight experimental runs (240 samples) were analyzed: one benchmark run, two runs testing sensitivity with down to 50 pg DNA input, one run testing artificially degraded samples and DNA derived from bones, blood cards and teeth, as well as four runs with mixed DNA samples of varying ratios, sex, and different number of contributors (two to six). The MixtureAce stutter thresholds were initially set following the recommendations from Verogen, plus a non-stutter artifact threshold was set at 5% of allele read counts. A benchmark run, of 30 samples, plus two controls, containing 2310 total alleles, revealed over 5000 artifacts, above an analytical threshold of 10. A total of 4869 artifacts were correctly classified, while 435 were mis-classified as alleles due to exceedance of initial threshold settings. False positives must be resolved by an analyst, which can be time consuming. Stutter thresholds were adjusted based on the benchmark data and the samples were re-tested, resulting in only 57 false positive allele calls. The revised settings were then used in the analysis of the remaining seven experimental runs. Results show that MixtureAce can accurately classify artifacts and alleles when laboratory-specific threshold settings are used. The Mixture Interpretation tool was applied on two- and three-person mixtures. This tool utilized the analyzed data from MixtureAce to calculate, based on the number of alleles at a locus and their read counts, possible deconvolution outcomes with their respective ratios.

Testing the effects of body depth on fish maneuverability via robophysical models.

Abstract: Fish show a wide diversity of body shapes which affect many aspects of their biology, including swimming and feeding performance, and defense from predators. Deep laterally compressed bodies are particularly common, and have evolved multiple times in different families. Functional hypotheses that explain these trends include predator defense and increased maneuverability. While there is strong evidence that increasing body depth helps fish avoid gape-limited predators, the evidence that body shape increases a fish's maneuverability is ambiguous. We used a two-pronged approach to explore the effects of body shape on the control of maneuvers using both live fish and a robotic model that allowed us to independently vary body shape. We captured ventral video of two tetra species (Gymnocorymbus ternetziandAphyocharax anisitsi) performing a wide range of maneuvers to confirm that both species of live fish utilize fundamentally similar body deformations to execute a turn, despite their different body depths. Both species use a propagating 'pulse' of midline curvature that is qualitatively similar to prior studies and displayed similar trends in the relationships between body kinematics and performance. We then tested the robotic model's maneuverability, defined as the total heading change and maximum centripetal acceleration generated during a single pulse, at a range of different input kinematics across three body shapes. We found that deepening bodies increase the robot's ability to change direction and centripetal acceleration, though centripetal acceleration exhibits diminishing returns beyond a certain body depth. By using a robotic model, we were able to isolate the effects of body shape on maneuverability and clarify this confounded relationship. Studying the functional morphology of complex traits such as body shape and their interaction with complex behavior like maneuverability benefits from both the broad view provided by comprehensive comparative studies, and the control of variables enabled by robophysical experiments.

Laboratory Evaluation of Microsurfacing Bond Strength

Abstract: This paper summarizes the results of a study that was conducted to evaluate the influence of different factors on the bond strength between a microsurfacing layer and an existing pavement s...

Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases

Abstract: Abstract Proteome-wide Mendelian randomization (MR) shows value in prioritizing drug targets in Europeans, but limited data has made identification of causal proteins in other ancestries challenging. Here we present a multi-ancestry proteome-wide MR analysis pipeline based on cross-population data from the Global Biobank Meta-analysis Initiative (GBMI). We estimated the causal effects of 1,545 proteins on eight complex diseases in up to 32,658 individuals of African ancestries and 1.22 million individuals of European ancestries. We identified 45 and seven protein-disease pairs with MR and genetic colocalization evidence in the two ancestries respectively. 15 protein-disease pairs showed evidence of differential effects between males and females. A multi-ancestry MR comparison identified two protein-disease pairs with MR evidence of an effect in both ancestries, seven pairs with European-specific effects and seven with African-specific effects. Integrating these MR signals with observational and clinical trial evidence, we were able to evaluate the efficacy of one existing drug, identify seven drug repurposing opportunities and predict seven novel effects of proteins on diseases. Our results highlight the value of proteome-wide MR in informing the generalisability of drug targets across ancestries and illustrate the value of multi-cohort and biobank meta-analysis of genetic data for drug development. Graphical abstract Notation: genome-wide association study (GWAS); Mendelian randomization (MR); primary open-angle glaucoma (POAG); idiopathic pulmonary fibrosis (IPF); chronic obstructive pulmonary disease (COPD); heart failure (HF), venous thromboembolism (VTE). European ancestry (EUR); African ancestry (AFR)*For the seven protein-disease associations, one association passed FDR threshold of 0.05 in proteome-wide MR, six additional associations passed FDR of 0.05 in the multi-ancestry comparison analysis. Highlights A multi-ancestry proteome-wide Mendelian randomization (MR) analysis of 1,545 proteins on eight diseases in more than 1.26 million individuals from a disease GWAS meta-analysis of 19 biobanks. We find evidence for putative causal effects in 45 protein-disease pairs in European ancestry and seven protein-disease pairs in African ancestry, with 15 pairs showing sex specific effects. We identify evidence of causality for two protein-disease pairs that are common to both African and European ancestries, seven pairs with European-specific effects and seven pairs with African-specific effects. Triangulating with clinical trial and observational evidence prioritizes seven new targets, seven drug repurposing opportunities and one existing drug target that generalise to African ancestry.

Conceptualizing Q Methodology as an Integrated Analysis

Abstract: Q methodology [Q] is an 80-year-old research methodology developed by William Stephenson to scientifically study human subjectivity. Stephenson held a doctoral degree in psychology and a doctoral degree in physics; he often relied on concepts from the former of the two disciplines to explain Q’s underlying framework and processes. This chapter borrows from some of Stephenson’s seminal writings to situate the methodology within my conceptualization of mixed methods integration. It is apparent from Stephenson’s writings that a key to conceptualizing Q as an example of an integrated analytic methodology is to understand that its use of factor analysis is quite different from that used in R factor analysis. In Q, factors represent people, grouped based on participants’ Q-sorts. That is, a Q factor is a is a grouping of people based on how they responded to a Q-sample of items. Thus, each factor represents a different viewpoint about a given topic, and the interpretations of the factors require consideration of qualitative and quantitative information. When conducting Q factor analysis, it is almost always the case that complementarity is needed. Complementarity in physics represents the idea that two contrasted theories are sometimes necessary to explain phenomena (e.g., the wave-particle duality of light). Complementarity in mixed methods refers to the enriched, elaborated understanding of phenomena obtained by using qualitative and quantitative methods. In the context of Q, qualitative-quantitative interaction is almost always needed to understand phenomena. Q represents integrated analyses, leading to a conclusion that integration has been utilized for decades.

Emerging investigator series: moving beyond resilience by considering antifragility in potable water systems

Abstract: It is inherently difficult to plan water systems for a future that is non-predictive. This paper introduces a novel perspective for the design and operation of potable water systems under increasing water quality volatility (e.g., a relatively rapid and unpredicted deviation from baseline water quality). Increased water quality volatility and deep uncertainty stress water systems, confound design decisions, and increase the risk of decreased water system performance. Recent emphasis on resilience in drinking water treatment has partly addressed this issue, but still establishes an adversarial relationship with change. An antifragile system benefits from volatile change. By incorporating antifragility, water systems may move beyond resilience and improve performance with extreme events and other changes, rather than survive, or fail and quickly recover. Using examples of algal blooms, wildfires, and the COVID-19 pandemic, this work illustrates fragility, resilience, and antifragility within physicochemical process design including clarification, adsorption and disinfection. Methods for increasing antifragility, both individual process options and new system design tools, are discussed. Novel physicochemical processes with antifragile characteristics include ferrate preoxidation and magnetic iron (nano)particles. New design tools that allow for systematic evaluation of antifragile opportunities include artificial neural networks and virtual jar or pilot “stress testing”. Incorporating antifragile characteristics represents a trade-off with capital and/or operating cost. We present a real options analysis approach to considering costs in the context of antifragile design decisions. Adopting this antifragile perspective will help ensure water system improved performance during extreme events and a general increase in volatility.

Core and catalyst criteria for disclosing one’s burnout in the workplace

Abstract: A sample of 142 full-time employees who experienced job burnout during the COVID-19 pandemic completed an online, open-ended survey designed to explore the core and catalyst privacy rule criteria that individuals consider when disclosing or not disclosing their experiences of burnout in the workplace. Participant responses indicate that core criteria consisted of the quality of one’s relationship with colleagues and the organization’s culture. Catalyst criteria consisted of a sense of a shared experience of burnout, the risk of judgment toward the disclosure, and the severity of one’s burnout. These findings suggest that discussing job burnout in one’s workplace can create feelings of vulnerability that can potentially impact one’s job and, thus, employees are careful in choosing confidants and methods of disclosure for job burnout.

Second Erratum — Theory of thermal expansion: Quasi-harmonic approximation and corrections from quasi-particle renormalization

Abstract: Modern Physics Letters BVol. 36, No. 30n31, 2292002 (2022) Second ErratumFree AccessSecond Erratum — Theory of thermal expansion: Quasi-harmonic approximation and corrections from quasi-particle renormalizationis erratum ofTheory of thermal expansion: Quasi-harmonic approximation and corrections from quasi-particle renormalizationPhilip B. AllenPhilip B. AllenDepartment of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794-3800, USAE-mail Address: [email protected]https://doi.org/10.1142/S0217984922920026Cited by:1 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail [Mod. Phys. Lett. B, Vol. 34, No. 2 (2020) 2050025 (15 pages) DOI: 10.1142/S0217984920500256] References 1. A. R. Varma, S. Paul, A. Itale, P. Pable, R. Tibrewala, S. Dodal, H. Yerunkar, S. Bhaumik, V. Shah, M. P. Gururajan and T. R. S. Prasanna, arXiv:2204.08321. Google Scholar2. P. B. Allen, Mod. Phys. Lett. B 34 (2020) 2050025. ADS, Google Scholar3. P. B. Allen and V. Heine, J. Phys. C, Solid State Phys. 9 (1976) 2305. Crossref, ADS, Google Scholar4. P. B. Allen, Mod. Phys. Lett. B 35 (2021) 2192001. ADS, Google Scholar5. O. Delaire, M. S. Lucas, J. A. Muñoz, M. Kresch and B. Fultz, Phys. Rev. Lett. 101 (2008) 105504. ADS, Google Scholar6. N. Bock, D. Coffey and D. C. Wallace, Phys. Rev. B 72 (2005) 155120. ADS, Google Scholar7. N. Bock, D. C. Wallace and D. Coffey, Phys. Rev. B 73 (2006) 055114. Google Scholar Remember to check out the Most Cited Articles! Boost your collection with these New Books in Condensed Matter Physics today! FiguresReferencesRelatedDetailsCited By 1Electron–Phonon Interaction Contribution to the Total Energy of Group IV Semiconductor Polymorphs: Evaluation and ImplicationsArjun Varma Ramasimha Varma, Shilpa Paul, Anup Itale, Pranav Pable and Radhika Tibrewala et al.13 March 2023 | ACS Omega, Vol. 14Related articlesTheory of thermal expansion: Quasi-harmonic approximation and corrections from quasi-particle renormalization6 Dec 2019Modern Physics Letters B Recommended Vol. 36, No. 30n31 Metrics History Published: 10 December 2022 PDF download