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Showing papers by "University of Michigan published in 2022"


Journal ArticleDOI
TL;DR: In this paper , the authors present a global-scale study of API pollution in 258 of the world's rivers, representing the environmental influence of 471.4 million people across 137 geographic regions.
Abstract: Environmental exposure to active pharmaceutical ingredients (APIs) can have negative effects on the health of ecosystems and humans. While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world's rivers, representing the environmental influence of 471.4 million people across 137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals.

272 citations



Journal ArticleDOI
TL;DR: In this article, a multi-scale investigation was conducted to gain an in-depth understanding of the microstructure and ductility enhancement mechanism of geopolymer aggregate ECC (GPA-ECC).
Abstract: In this study, Engineered/Strain-Hardening Cementitious Composites (ECC/SHCC) incorporating geopolymer fine aggregates were successfully developed with high strength and high ductility. A multi-scale investigation was conducted to gain an in-depth understanding of the microstructure and ductility enhancement mechanism of geopolymer aggregate ECC (GPA-ECC). The use of geopolymer fine aggregates enabled the high-strength ECC to achieve higher tensile ductility and finer crack width compared to existing ones with similar compressive strength in the literature. It was found that the GPA reacted with the cementitious matrix, and the width of the GPA/matrix interfacial transition zone (ITZ) was larger than that of the silica sand/matrix ITZ. Moreover, the GPA achieved a strong bond with the cementitious matrix and could behave as “additional flaws” in high-strength matrix, resulting in saturated multiple cracking and excellent tensile ductility of ECC. This study provides a new avenue for developing high-performance fiber-reinforced cementitious composites based on artificial geopolymer aggregates.

61 citations


Journal ArticleDOI
Arwa Wali1
TL;DR: In this paper , a prospective cohort of 100 healthcare professionals naïve for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who received two doses of CoronaVac was analyzed at four different timepoints.

50 citations


Journal ArticleDOI
TL;DR: In this article , an aluminum hydroxide (AH) and CpG adjuvant formulation (AH:CpG) was used to enhance RBD immunogenicity in young and aged mice.
Abstract: Global deployment of vaccines that can provide protection across several age groups is still urgently needed to end the COVID-19 pandemic, especially in low- and middle-income countries. Although vaccines against SARS-CoV-2 based on mRNA and adenoviral vector technologies have been rapidly developed, additional practical and scalable SARS-CoV-2 vaccines are required to meet global demand. Protein subunit vaccines formulated with appropriate adjuvants represent an approach to address this urgent need. The receptor binding domain (RBD) is a key target of SARS-CoV-2 neutralizing antibodies but is poorly immunogenic. We therefore compared pattern recognition receptor (PRR) agonists alone or formulated with aluminum hydroxide (AH) and benchmarked them against AS01B and AS03-like emulsion-based adjuvants for their potential to enhance RBD immunogenicity in young and aged mice. We found that an AH and CpG adjuvant formulation (AH:CpG) produced an 80-fold increase in anti-RBD neutralizing antibody titers in both age groups relative to AH alone and protected aged mice from the SARS-CoV-2 challenge. The AH:CpG-adjuvanted RBD vaccine elicited neutralizing antibodies against both wild-type SARS-CoV-2 and the B.1.351 (beta) variant at serum concentrations comparable to those induced by the licensed Pfizer-BioNTech BNT162b2 mRNA vaccine. AH:CpG induced similar cytokine and chemokine gene enrichment patterns in the draining lymph nodes of both young adult and aged mice and enhanced cytokine and chemokine production in human mononuclear cells of younger and older adults. These data support further development of AH:CpG-adjuvanted RBD as an affordable vaccine that may be effective across multiple age groups.

46 citations


Journal ArticleDOI
01 Jan 2022
TL;DR: In this article, the concept of tunable input-to-state safe control barrier functions (TISSf-CBFs) is introduced to guarantee safety for disturbances that vary with state and, therefore, provide less conservative means of accommodating uncertainty.
Abstract: To bring complex systems into real world environments in a safe manner, they will have to be robust to uncertainties—both in the environment and the system. This letter investigates the safety of control systems under input disturbances, wherein the disturbances can capture uncertainties in the system. Safety, framed as forward invariance of sets in the state space, is ensured with the framework of control barrier functions (CBFs). Concretely, the definition of input-to-state safety (ISSf) is generalized to allow the synthesis of non-conservative, tunable controllers that are provably safe under varying disturbances. This is achieved by formulating the concept of tunable input-to-state safe control barrier functions (TISSf-CBFs), which guarantee safety for disturbances that vary with state and, therefore, provide less conservative means of accommodating uncertainty. The theoretical results are demonstrated with a simple control system with input disturbance and also applied to design a safe connected cruise controller for a heavy duty truck.

43 citations


Journal ArticleDOI
TL;DR: In this paper , the authors used adaptive mesh refinement simulations to obtain an over three order of magnitude leap in dynamic range and provide evidence that axion strings radiate their energy with a scale-invariant spectrum, leading to a mass prediction in the range (40,180) microelectronvolts.
Abstract: Axions are hypothetical particles that may explain the observed dark matter density and the non-observation of a neutron electric dipole moment. An increasing number of axion laboratory searches are underway worldwide, but these efforts are made difficult by the fact that the axion mass is largely unconstrained. If the axion is generated after inflation there is a unique mass that gives rise to the observed dark matter abundance; due to nonlinearities and topological defects known as strings, computing this mass accurately has been a challenge for four decades. Recent works, making use of large static lattice simulations, have led to largely disparate predictions for the axion mass, spanning the range from 25 microelectronvolts to over 500 microelectronvolts. In this work we show that adaptive mesh refinement simulations are better suited for axion cosmology than the previously-used static lattice simulations because only the string cores require high spatial resolution. Using dedicated adaptive mesh refinement simulations we obtain an over three order of magnitude leap in dynamic range and provide evidence that axion strings radiate their energy with a scale-invariant spectrum, to within ~5% precision, leading to a mass prediction in the range (40,180) microelectronvolts.

42 citations


Journal ArticleDOI
TL;DR: In this paper , a multi-scale convolutional neural network (MS-CNN) was proposed to extract distinguishable features of several non-overlapping canonical frequency bands of EEG signals from multiple scales for MI-BCI classification.

41 citations


Journal ArticleDOI
TL;DR: In this article , a biocatalytic method for cross-coupling of biaryl C-H bonds was presented. But the method is not suitable for the formation of sterically hindered biaryl bonds.
Abstract: Biaryl compounds, with two connected aromatic rings, are found across medicine, materials science and asymmetric catalysis1,2. The necessity of joining arene building blocks to access these valuable compounds has inspired several approaches for biaryl bond formation and challenged chemists to develop increasingly concise and robust methods for this task3. Oxidative coupling of two C–H bonds offers an efficient strategy for the formation of a biaryl C–C bond; however, fundamental challenges remain in controlling the reactivity and selectivity for uniting a given pair of substrates4,5. Biocatalytic oxidative cross-coupling reactions have the potential to overcome limitations inherent to numerous small-molecule-mediated methods by providing a paradigm with catalyst-controlled selectivity6. Here we disclose a strategy for biocatalytic cross-coupling through oxidative C–C bond formation using cytochrome P450 enzymes. We demonstrate the ability to catalyse cross-coupling reactions on a panel of phenolic substrates using natural P450 catalysts. Moreover, we engineer a P450 to possess the desired reactivity, site selectivity and atroposelectivity by transforming a low-yielding, unselective reaction into a highly efficient and selective process. This streamlined method for constructing sterically hindered biaryl bonds provides a programmable platform for assembling molecules with catalyst-controlled reactivity and selectivity. A study presents a biocatalytic method for the formation of sterically hindered biaryl bonds, providing a tunable approach for assembling molecules with catalyst-controlled reactivity, site selectivity and atroposelectivity.

39 citations


Journal ArticleDOI
TL;DR: This article identifies key scientific and engineering advances needed to enable effective spoken language interaction with robotics, and makes 25 recommendations, involving eight general themes: putting human needs first, better modeling the social and interactive aspects of language, improving robustness, creating new methods for rapid adaptation, and improving research infrastructure and resources.

38 citations


Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors examined the driving factors of China carbon price in a systemic way with quantitative analysis, and selected three categories of driving forces as macro economy risk and uncertainty, energy and environment factors to investigate their impacts on carbon price.

Journal ArticleDOI
TL;DR: In this paper , a systematic review and meta-analysis aims to provide up-to-date pooled estimates of the prevalence of social isolation and loneliness among older adults during the COVID-19 pandemic and other pandemics in the last two decades.
Abstract: ABSTRACT Objectives: Pandemics and their public health control measures have generally substantially increased the level of loneliness and social isolation in the general population. Because of the circumstances of aging, older adults are more likely to experience social isolation and loneliness during pandemics. However, no systematic review has been conducted or published on the prevalence of loneliness and/or social isolation among the older population. This systematic review and meta-analysis aims to provide up-to-date pooled estimates of the prevalence of social isolation and loneliness among older adults during the COVID-19 pandemic and other pandemics in the last two decades. Design: EMBASE, PsychoINFO, Medline, and Web of Science were searched for relevant studies from January 1, 2000 to November 31, 2021 published in a variety of languages. Only studies conducted during the COVID-19 pandemic were selected in the review. Results: A total of 30 studies including 28,050 participants met the inclusion criteria. Overall, the pooled period prevalence of loneliness among older adults was 28.6% (95% CI: 22.9–35.0%) and 31.2% for social isolation (95% CI: 20.2–44.9%). Prevalence estimates were significantly higher for those studies conducted post 3-month from the start of the COVID-19 pandemic compared to those conducted within the first 3 months of the pandemic. Conclusions: This review identifies the need for good quality longitudinal studies to examine the long-term impact of pandemics on loneliness and social isolation among older populations. Health policymaking and healthcare systems should proactively address the rising demand for appropriate psychological services among older adults.

Journal ArticleDOI
TL;DR: Anaerobic mono-and co-digestion of coffee pulp (CP), cattle manure (CM), food waste (FW) and dewatered sewage sludge (DSS) were assessed using biochemical methane potential tests as discussed by the authors.

Journal ArticleDOI
TL;DR: In this paper , a dual-fluorescence reporter system was designed for detecting collateral effects and screening Cas13 variants in mammalian cells, including Cas13d and Cas13X, and the results showed that these variants showed similar RNA knockdown activity to wild-type Cas13.
Abstract: CRISPR-Cas13 systems have recently been used for targeted RNA degradation in various organisms. However, collateral degradation of bystander RNAs has limited their in vivo applications. Here, we design a dual-fluorescence reporter system for detecting collateral effects and screening Cas13 variants in mammalian cells. Among over 200 engineered variants, several Cas13 variants including Cas13d and Cas13X exhibit efficient on-target activity but markedly reduced collateral activity. Furthermore, transcriptome-wide off-targets and cell growth arrest induced by Cas13 are absent for these variants. High-fidelity Cas13 variants show similar RNA knockdown activity to wild-type Cas13 but no detectable collateral damage in transgenic mice or adeno-associated-virus-mediated somatic cell targeting. Thus, high-fidelity Cas13 variants with minimal collateral effects are now available for targeted degradation of RNAs in basic research and therapeutic applications.

Journal ArticleDOI
TL;DR: In this paper , a green antisolvent of ethyl acetate (EA) with acetylacetone (AA) additive is used to fine-tune perovskite crystallization and passivate defect.

Journal ArticleDOI
TL;DR: In this article, the vibration transmission characteristics of a plate strip embedded with multiple two-dimensional (2D) acoustic black hole (ABH) structures without additional damping material were investigated.

Journal ArticleDOI
TL;DR: In this paper , the InGaN quantum well with gradually varying indium (In) content was proposed for improving the performance of GaN-based green LEDs, which not only alleviated the quantum-confined Stark effect (QCSE), but also yields a low Auger recombination rate.
Abstract: High-efficiency GaN-based green LEDs are of paramount importance to the development of the monolithic integration of multicolor emitters and full-color high-resolution displays. Here, the InGaN quantum well with gradually varying indium (In) content was proposed for improving the performance of GaN-based green LEDs. The InGaN quantum well with gradually varying In content not only alleviates the quantum-confined Stark effect (QCSE), but also yields a low Auger recombination rate. Consequently, the gradual In content green LEDs exhibited increased light output power (LOP) and reduced efficiency droop as compared to constant In content green LEDs. At 60 A/cm2, the LOPs of the constant In content green LEDs and the gradual In content green LEDs were 33.9 mW and 55.2 mW, respectively. At 150 A/cm2, the efficiency droops for the constant In content green LEDs and the gradual In content green LEDs were 61% and 37.6%, respectively. This work demonstrates the potential for the gradual In content InGaN to replace constant In content InGaN as quantum wells in LED devices in a technologically and commercially effective manner.

Journal ArticleDOI
TL;DR: In this paper, a non-proportionality correlation function is proposed to interrelate the resulting power spectrum density (PSD) of normal and shear traction stresses and their cross-PSD for establishing an effective stress parameter.

Journal ArticleDOI
TL;DR: In this article , the authors show that the human caveolin-1 complex is composed of 11 protomers organized into a tightly packed disc with a flat membrane-embedded surface.
Abstract: Membrane-sculpting proteins shape the morphology of cell membranes and facilitate remodeling in response to physiological and environmental cues. Complexes of the monotopic membrane protein caveolin function as essential curvature-generating components of caveolae, flask-shaped invaginations that sense and respond to plasma membrane tension. However, the structural basis for caveolin’s membrane remodeling activity is currently unknown. Here, we show that, using cryo–electron microscopy, the human caveolin-1 complex is composed of 11 protomers organized into a tightly packed disc with a flat membrane-embedded surface. The structural insights suggest a previously unrecognized mechanism for how membrane-sculpting proteins interact with membranes and reveal how key regions of caveolin-1, including its scaffolding, oligomerization, and intramembrane domains, contribute to its function.

Journal ArticleDOI
TL;DR: In this paper , the authors explored how realism of environmental presentations impact affective responses and participant perceptions and found that more realistic VR environments evoked more positive affective and serenity responses, as well as a greater sense of presence.

Journal ArticleDOI
TL;DR: In this paper, a joint and deep learning framework was designed to predict clinical scores of Alzheimer's disease (AD) in middle-aged and elderly people with the gradual loss of cognitive ability.
Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disease that often grows in middle-aged and elderly people with the gradual loss of cognitive ability. Presently, there is no cure for AD. Furthermore, the current clinical diagnosis of AD is too time-consuming. In this paper, we design a joint and deep learning framework to predict clinical scores of AD. Specifically, the feature selection method combining group LASSO and correntropy is used to reduce dimensions and screen the features of brain regions related to AD. We explore the multi-layer independently recurrent neural network regression to study the internal connection between different brain regions and the time correlation between longitudinal data. The proposed joint deep learning network studies the relationship between the magnetic resonance imaging and clinical score, and predicts the clinical score. The predicted clinical score values allow doctors to perform early diagnosis and timely treatment of patients’ disease condition.

Journal ArticleDOI
TL;DR: In this article, the authors examined how realism of environmental presentations impact affective responses and participant perceptions and found that more realistic VR environments evoked more positive affective and serenity responses, as well as a greater sense of presence.

Journal ArticleDOI
TL;DR: In this article , the authors summarize various mechanisms of immune escape as a means to inform novel strategies that may restore and improve host anti-myeloma immunity and further support myelomagenesis, disease progression and the emergence of drug resistance.
Abstract: Multiple myeloma is an incurable cancer characterized by the uncontrolled growth of malignant plasma cells nurtured within a permissive bone marrow microenvironment. While patients mount numerous adaptive immune responses directed against their disease, emerging data demonstrate that tumor intrinsic and extrinsic mechanisms allow myeloma cells to subvert host immunosurveillance and resist current therapeutic strategies. Myeloma downregulates antigens recognized by cellular immunity and modulates the bone marrow microenvironment to promote uncontrolled tumor proliferation, apoptotic resistance, and further hamper anti-tumor immunity. Additional resistance often develops after an initial clinical response to small molecules, immune-targeting antibodies, immune checkpoint blockade or cellular immunotherapy. Profound quantitative and qualitative dysfunction of numerous immune effector cell types that confer anti-myeloma immunity further supports myelomagenesis, disease progression and the emergence of drug resistance. Identification of tumor intrinsic and extrinsic resistance mechanisms may direct the design of rationally-designed drug combinations that prevent or overcome drug resistance to improve patient survival. Here, we summarize various mechanisms of immune escape as a means to inform novel strategies that may restore and improve host anti-myeloma immunity.

Journal ArticleDOI
TL;DR: In this article , integrated exploratory spatial data analysis, regression analysis, and geographical information systems tools were used to associate the distribution of F- in groundwater with spatial variability in terrain slopes, volcanic deposits, recharge water/vadose materials contact time, groundwater resource development for irrigated agriculture in the Sanya alluvial plain (SAP) of northern Tanzania.

Journal ArticleDOI
16 Jun 2022
TL;DR: In this paper , the authors quantify the impact of bank market power on monetary policy transmission through banks to borrowers and find that market power explains much of the transmission of monetary policy to borrowers, with an effect comparable to bank capital regulation.
Abstract: We quantify the impact of bank market power on monetary policy transmission through banks to borrowers. We estimate a dynamic banking model in which monetary policy affects imperfectly competitive banks' funding costs. Banks optimize the pass-through of these costs to borrowers and depositors, while facing capital and reserve regulation. We find that bank market power explains much of the transmission of monetary policy to borrowers, with an effect comparable to that of bank capital regulation. When the federal funds rate falls below 0.9%, market power interacts with bank capital regulation to produce a reversal of the effect of monetary policy. This article is protected by copyright. All rights reserved

Journal ArticleDOI
TL;DR: In this paper , a survey was conducted to determine the prevalence of medical and medication-related problems reported by people with chronic diseases during the coronavirus disease 2019 (COVID-19) pandemic.
Abstract: The coronavirus disease 2019 (COVID-19) pandemic has drastically disrupted primary health care and pharmacy services, posing a challenge in people with chronic diseases who receive routine care. Currently, there exists limited literature on the indirect impact of the pandemic on chronic disease management, particularly related to accessibility to medications and health care resources.To determine the prevalence of medical- and medication-related problems reported by people with chronic diseases during the pandemic. The secondary objective was to identify the barriers and contributing factors related to these medical- and medication-related problems.The anonymous and voluntary, Web-based survey was filled out by interested adult respondents with chronic disease(s) across Michigan between September 1, 2020, and January 1, 2021. The primary outcome included self-reported medical- and medication-related problems during the pandemic. Secondary outcomes included potential risk factors for medical- and medication-related problems. Descriptive statistics was used to describe respondents' demographics, chronic disease characteristics, medication adherence, medical- and medication-related problems, and COVID-19-related factors. The multivariable Firth logistic regression was used to analyze correlations between potential risk factors associated with medical- and medication-related problems.A total of 1103 respondents completed the survey and were included in the analysis. Approximately, 51% of respondents reported a medication-related problem with 19.6% reported problems obtaining medication(s) and 31.7% reported forgetting or not taking their medication(s). The top reason for problems obtaining medication(s) was doctor's office being closed for in-person visit(s). In addition, of all responses, more than half reported worsening symptoms of their chronic disease(s) during the pandemic especially with psychiatric disorders (79.5%) and inflammatory bowel disease (60%). Respondents with a significantly higher risk of medication-related problems included those who were younger, were female, and had psychiatric disorder(s), diabetes, arthritis, or lupus, and respondents with a significantly higher risk of medical-related problems included those with multiple chronic diseases, psychiatric disorder(s), and heart failure.Understanding the consequences of the pandemic, such as medical- and medication-related problems, in this population is critical to improving health care accessibility and resources through potential outpatient pharmacy services during this and future pandemics.

DOI
14 Jan 2022
TL;DR: This work reviews one approach that relies on rigorously defined computational models to specify the links between linguistic features and neural signals and describes one such data set in detail in the Supplementary Appendix.
Abstract: Efforts to understand the brain bases of language face the mapping problem: at what level do linguistic computations and representations connect to human neurobiology? We review one approach to this problem that relies on rigorously defined computational models to specify the links between linguistic features and neural signals. Such tools can be used to estimate linguistic predictions, model linguistic features, or specify a sequence of processing steps that may be quantitatively fit to neural signals collected while participants use language. Progress has been helped by advances in machine learning, attention to linguistically interpretable models, and openly shared datasets that allow researchers to compare and contrast a variety of models. We describe one such dataset in detail in the supplementary materials.

Journal ArticleDOI
TL;DR: In this article , a fourteen-equation fluid-structure coupling model of an L-shaped liquid-filled pipe with elastic support is established by the transfer matrix method (TMM).

Journal ArticleDOI
TL;DR: In this paper , a cross-sectional analysis of pooled, individual-level data from nationally representative health surveys done in 41 low-income and middle-income countries between 2013 and 2019 was performed.

Journal ArticleDOI
TL;DR: In this paper , the authors conducted a choice-based conjoint survey to patients with cirrhosis at four institutions, where participants were provided with 15 scenarios in which they were asked to choose surveillance modalities based on five test attributes: benefits, i.e. sensitivity for early HCC (range: 35-95%), physical harm, false positives requiring additional testing, out-of-pocket costs, test logistics and convenience.