Showing papers by "University of North Carolina at Greensboro published in 2019"
TL;DR: The challenges to bringing PDT into mainstream cancer therapy are summarized, the chemical and photophysical solutions that transition metal complexes offer are considered, and the multidisciplinary effort needed to bring a new drug to clinical trial is put into context.
Abstract: Transition metal complexes are of increasing interest as photosensitizers in photodynamic therapy (PDT) and, more recently, for photochemotherapy (PCT). In recent years, Ru(II) polypyridyl complexes have emerged as promising systems for both PDT and PCT. Their rich photochemical and photophysical properties derive from a variety of excited-state electronic configurations accessible with visible and near-infrared light, and these properties can be exploited for both energy- and electron-transfer processes that can yield highly potent oxygen-dependent and/or oxygen-independent photobiological activity. Selected examples highlight the use of rational design in coordination chemistry to control the lowest-energy triplet excited-state configurations for eliciting a particular type of photoreactivity for PDT and/or PCT effects. These principles are also discussed in the context of the development of TLD1433, the first Ru(II)-based photosensitizer for PDT to enter a human clinical trial. The design of TLD1433 arose from a tumor-centered approach, as part of a complete PDT package that includes the light component and the protocol for treating non-muscle invasive bladder cancer. Briefly, this review summarizes the challenges to bringing PDT into mainstream cancer therapy. It considers the chemical and photophysical solutions that transition metal complexes offer, and it puts into context the multidisciplinary effort needed to bring a new drug to clinical trial.
740 citations
TL;DR: This report represents a critical review with commentary about the current state of the scientific literature as it relates to studying combination effects in natural product extracts, with particular emphasis on analytical and Big Data approaches for identifying synergistic or antagonistic combinations and elucidating the mechanisms that underlie their interactions.
362 citations
TL;DR: An overview and classification of the cannabinoid ligands that have been reported to modulate TRP channels and their therapeutic potential is provided.
Abstract: Transient receptor potential (TRP) channels are a group of membrane proteins involved in the transduction of a plethora of chemical and physical stimuli. These channels modulate ion entry, mediating a variety of neural signaling processes implicated in the sensation of temperature, pressure, and pH, as well as smell, taste, vision, and pain perception. Many diseases involve TRP channel dysfunction, including neuropathic pain, inflammation, and respiratory disorders. In the pursuit of new treatments for these disorders, it was discovered that cannabinoids can modulate a certain subset of TRP channels. The TRP vanilloid (TRPV), TRP ankyrin (TRPA), and TRP melastatin (TRPM) subfamilies were all found to contain channels that can be modulated by several endogenous, phytogenic, and synthetic cannabinoids. To date, six TRP channels from the three subfamilies mentioned above have been reported to mediate cannabinoid activity: TRPV1, TRPV2, TRPV3, TRPV4, TRPA1, and TRPM8. The increasing data regarding cannabinoid interactions with these receptors has prompted some researchers to consider these TRP channels to be "ionotropic cannabinoid receptors." Although CB1 and CB2 are considered to be the canonical cannabinoid receptors, there is significant overlap between cannabinoids and ligands of TRP receptors. The first endogenous agonist of TRPV1 to be discovered was the endocannabinoid, anandamide (AEA). Similarly, N-arachidonyl dopamine (NADA) and AEA were the first endogenous TRPM8 antagonists discovered. Additionally, Δ9-tetrahydrocannabinol (Δ9-THC), the most abundant psychotropic compound in cannabis, acts most potently at TRPV2, moderately modulates TRPV3, TRPV4, TRPA1, and TRPM8, though Δ9-THC is not reported to modulate TRPV1. Moreover, TRP receptors may modulate effects of synthetic cannabinoids used in research. One common research tool is WIN55,212-2, a CB1 agonist that also exerts analgesic effects by desensitizing TRPA1 and TRPV1. In this review article, we aim to provide an overview and classification of the cannabinoid ligands that have been reported to modulate TRP channels and their therapeutic potential.
338 citations
VU University Medical Center1, Public Health Research Institute2, National University of Singapore3, Open University in the Netherlands4, VU University Amsterdam5, Bergen University College6, National Taiwan Normal University7, University of British Columbia8, Malmö University9, University of North Carolina at Greensboro10, University of Kentucky11, Northeastern University12, University of Chichester13, University of Basel14, University of Georgia15
TL;DR: There is strong evidence for beneficial effects of PA on maths performance, and recommendations focus on adequate control groups and sample size, the use of valid and reliable measurement instruments for physical activity and cognitive performance, measurement of compliance and data analysis.
Abstract: Objective To summarise the current evidence on the effects of physical activity (PA) interventions on cognitive and academic performance in children, and formulate research priorities and recommendations. Design Systematic review (following PRISMA guidelines) with a methodological quality assessment and an international expert panel. We based the evaluation of the consistency of the scientific evidence on the findings reported in studies rated as of high methodological quality. Data sources PubMed, PsycINFO, Cochrane Central, Web of Science, ERIC, and SPORTDiscus. Eligibility criteria for selecting studies PA-intervention studies in children with at least one cognitive or academic performance assessment. Results Eleven (19%) of 58 included intervention studies received a high-quality rating for methodological quality: four assessed effects of PA interventions on cognitive performance, six assessed effects on academic performance, and one on both. All high-quality studies contrasted the effects of additional/adapted PA activities with regular curriculum activities. For cognitive performance 10 of 21 (48%) constructs analysed showed statistically significant beneficial intervention effects of PA, while for academic performance, 15 of 25 (60%) analyses found a significant beneficial effect of PA. Across all five studies assessing PA effects on mathematics, beneficial effects were reported in six out of seven (86%) outcomes. Experts put forward 46 research questions. The most pressing research priority cluster concerned the causality of the relationship between PA and cognitive/academic performance. The remaining clusters pertained to PA characteristics, moderators and mechanisms governing the ‘PA–performance’ relationship and miscellaneous topics. Conclusion There is currently inconclusive evidence for the beneficial effects of PA interventions on cognitive and overall academic performance in children. We conclude that there is strong evidence for beneficial effects of PA on maths performance. The expert panel confirmed that more ‘high-quality’ research is warranted. By prioritising the most important research questions and formulating recommendations we aim to guide researchers in generating high-quality evidence. Our recommendations focus on adequate control groups and sample size, the use of valid and reliable measurement instruments for physical activity and cognitive performance, measurement of compliance and data analysis. PROSPERO registration number CRD42017082505.
277 citations
TL;DR: It is shown that engineering a hairpin secondary structure onto the spacer region of single guide RNAs (hp-sgRNAs) can increase specificity by several orders of magnitude when combined with various CRISPR effectors.
Abstract: CRISPR (clustered regularly interspaced short palindromic repeat) systems have been broadly adopted for basic science, biotechnology, and gene and cell therapy. In some cases, these bacterial nucleases have demonstrated off-target activity. This creates a potential hazard for therapeutic applications and could confound results in biological research. Therefore, improving the precision of these nucleases is of broad interest. Here we show that engineering a hairpin secondary structure onto the spacer region of single guide RNAs (hp-sgRNAs) can increase specificity by several orders of magnitude when combined with various CRISPR effectors. We first demonstrate that designed hp-sgRNAs can tune the activity of a transactivator based on Cas9 from Streptococcus pyogenes (SpCas9). We then show that hp-sgRNAs increase the specificity of gene editing using five different Cas9 or Cas12a variants. Our results demonstrate that RNA secondary structure is a fundamental parameter that can tune the activity of diverse CRISPR systems.
219 citations
TL;DR: In this paper, a reliable sol-gel approach, which combines the formation of ZnO nanocrystals and a solvent driven, shape-controlled, crystal growth process to form well-organized ZnOsO nanostructures at low temperature is presented.
Abstract: A reliable sol–gel approach, which combines the formation of ZnO nanocrystals and a solvent driven, shape-controlled, crystal-growth process to form well-organized ZnO nanostructures at low temperature is presented. The sol of ZnO nanocrystals showed shape-controlled crystal growth with respect to the solvent type, resulting in either nanorods, nanoparticles, or nanoslates. The solvothermal process, along with the solvent polarity facilitate the shape-controlled crystal growth process, augmenting the concept of a selective adhesion of solvents onto crystal facets and controlling the final shape of the nanostructures. The XRD traces and XPS spectra support the concept of selective adhesion of solvents onto crystal facets that leads to yield different ZnO morphologies. The shift in optical absorption maxima from 332 nm in initial precursor solution, to 347 nm for ZnO nanocrystals sol, and finally to 375 nm for ZnO nanorods, evidenced the gradual growth and ripening of nanocrystals to dimensional nanostructures. The engineered optical band gaps of ZnO nanostructures are found to be ranged from 3.10 eV to 3.37 eV with respect to the ZnO nanostructures formed in different solvent systems. The theoretical band gaps computed from the experimental XRD spectral traces lie within the range of the optical band gaps obtained from UV-visible spectra of ZnO nanostructures. The spin-casted thin film of ZnO nanorods prepared in DMF exhibits the electrical conductivity of 1.14 × 10−3 S cm−1, which is nearly one order of magnitude higher than the electrical conductivity of ZnO nanoparticles formed in hydroquinone and ZnO sols. The possibility of engineering the band gap and electrical properties of ZnO at nanoscale utilizing an aqueous-based wet chemical synthesis process presented here is simple, versatile, and environmentally friendly, and thus may applicable for making other types of band-gap engineered metal oxide nanostructures with shape-controlled morphologies and optoelectrical properties.
177 citations
TL;DR: Findings show how members of the normal human skin microbiome can contribute to epithelial barrier homeostasis by using quorum sensing to inhibit S. aureus toxin production.
Abstract: Colonization of the skin by Staphylococcus aureus is associated with exacerbation of atopic dermatitis (AD), but any direct mechanism through which dysbiosis of the skin microbiome may influence the development of AD is unknown. Here, we show that proteases and phenol-soluble modulin α (PSMα) secreted by S. aureus lead to endogenous epidermal proteolysis and skin barrier damage that promoted inflammation in mice. We further show that clinical isolates of different coagulase-negative staphylococci (CoNS) species residing on normal skin produced autoinducing peptides that inhibited the S. aureus agr system, in turn decreasing PSMα expression. These autoinducing peptides from skin microbiome CoNS species potently suppressed PSMα expression in S. aureus isolates from subjects with AD without inhibiting S. aureus growth. Metagenomic analysis of the AD skin microbiome revealed that the increase in the relative abundance of S. aureus in patients with active AD correlated with a lower CoNS autoinducing peptides to S. aureus ratio, thus overcoming the peptides’ capacity to inhibit the S. aureus agr system. Characterization of a S. hominis clinical isolate identified an autoinducing peptide (SYNVCGGYF) as a highly potent inhibitor of S. aureus agr activity, capable of preventing S. aureus–mediated epithelial damage and inflammation on murine skin. Together, these findings show how members of the normal human skin microbiome can contribute to epithelial barrier homeostasis by using quorum sensing to inhibit S. aureus toxin production.
154 citations
TL;DR: In this article, the authors investigated whether and how gamification in loyalty programs impacts consumer loyalty toward loyalty programs and behavioral intentions, along with the role of the type of rewards (self-oriented vs. altruistic rewards).
135 citations
TL;DR: In this paper, the authors evaluated the capabilities of machine learning models in detecting at-risk patients using survey data (and laboratory results), and identified key variables within the data contributing to these diseases among the patients.
Abstract: Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of machine learning models in detecting at-risk patients using survey data (and laboratory results), and identify key variables within the data contributing to these diseases among the patients. Our research explores data-driven approaches which utilize supervised machine learning models to identify patients with such diseases. Using the National Health and Nutrition Examination Survey (NHANES) dataset, we conduct an exhaustive search of all available feature variables within the data to develop models for cardiovascular, prediabetes, and diabetes detection. Using different time-frames and feature sets for the data (based on laboratory data), multiple machine learning models (logistic regression, support vector machines, random forest, and gradient boosting) were evaluated on their classification performance. The models were then combined to develop a weighted ensemble model, capable of leveraging the performance of the disparate models to improve detection accuracy. Information gain of tree-based models was used to identify the key variables within the patient data that contributed to the detection of at-risk patients in each of the diseases classes by the data-learned models. The developed ensemble model for cardiovascular disease (based on 131 variables) achieved an Area Under - Receiver Operating Characteristics (AU-ROC) score of 83.1% using no laboratory results, and 83.9% accuracy with laboratory results. In diabetes classification (based on 123 variables), eXtreme Gradient Boost (XGBoost) model achieved an AU-ROC score of 86.2% (without laboratory data) and 95.7% (with laboratory data). For pre-diabetic patients, the ensemble model had the top AU-ROC score of 73.7% (without laboratory data), and for laboratory based data XGBoost performed the best at 84.4%. Top five predictors in diabetes patients were 1) waist size, 2) age, 3) self-reported weight, 4) leg length, and 5) sodium intake. For cardiovascular diseases the models identified 1) age, 2) systolic blood pressure, 3) self-reported weight, 4) occurrence of chest pain, and 5) diastolic blood pressure as key contributors. We conclude machine learned models based on survey questionnaire can provide an automated identification mechanism for patients at risk of diabetes and cardiovascular diseases. We also identify key contributors to the prediction, which can be further explored for their implications on electronic health records.
133 citations
Smithsonian Environmental Research Center1, Johns Hopkins University2, Marquette University3, University of Minnesota4, Eastern Michigan University5, Wichita State University6, University of North Carolina at Greensboro7, Virginia Institute of Marine Science8, University of Wyoming9, Qatar University10, New Mexico State University11, VU University Amsterdam12, Southern Illinois University Carbondale13, University of Maryland, College Park14, United States Department of Agriculture15, University of Bayreuth16, The Wilderness Society17, Kansas State University18, Institut national de la recherche agronomique19, University of Central Florida20, University of Alberta21, Archbold Biological Station22, University of Colorado Boulder23, James Hutton Institute24, University of Buenos Aires25, Stanford University26, Northwest A&F University27, University of New Mexico28, Lanzhou University29, University of Oulu30, Queensland University of Technology31, University of Kansas32, Towson University33, Michigan State University34, University of Oregon35, Chinese Academy of Sciences36, University of Tasmania37, Jönköping University38, Norwegian University of Life Sciences39, Colorado State University40, University of Greifswald41, Northern Arizona University42, University of Kentucky43, University of Texas at El Paso44, La Trobe University45, University of Houston46, Charles Sturt University47, University of Sydney48, University of Manchester49, Arizona State University50, Leiden University51, University of Oklahoma52, University of California, Santa Cruz53, Oregon State University54, University of British Columbia55, Inner Mongolia University56, Utrecht University57, Georgia Institute of Technology58
TL;DR: An unprecedented global synthesis of over 100 experiments that manipulated factors linked to GCDs shows that herbaceous plant community responses depend on experimental manipulation length and number of factors manipulated, and finds that plant communities are fairly resistant to experimentally manipulated G CDs in the short term.
Abstract: Global change drivers (GCDs) are expected to alter community structure and consequently, the services that ecosystems provide. Yet, few experimental investigations have examined effects of GCDs on plant community structure across multiple ecosystem types, and those that do exist present conflicting patterns. In an unprecedented global synthesis of over 100 experiments that manipulated factors linked to GCDs, we show that herbaceous plant community responses depend on experimental manipulation length and number of factors manipulated. We found that plant communities are fairly resistant to experimentally manipulated GCDs in the short term (<10 y). In contrast, long-term (≥10 y) experiments show increasing community divergence of treatments from control conditions. Surprisingly, these community responses occurred with similar frequency across the GCD types manipulated in our database. However, community responses were more common when 3 or more GCDs were simultaneously manipulated, suggesting the emergence of additive or synergistic effects of multiple drivers, particularly over long time periods. In half of the cases, GCD manipulations caused a difference in community composition without a corresponding species richness difference, indicating that species reordering or replacement is an important mechanism of community responses to GCDs and should be given greater consideration when examining consequences of GCDs for the biodiversity–ecosystem function relationship. Human activities are currently driving unparalleled global changes worldwide. Our analyses provide the most comprehensive evidence to date that these human activities may have widespread impacts on plant community composition globally, which will increase in frequency over time and be greater in areas where communities face multiple GCDs simultaneously.
122 citations
University of Washington1, Seattle Children's Research Institute2, Philippines National Collegiate Athletic Association3, University of North Carolina at Greensboro4, University of Arizona5, University of Chicago6, University of Pittsburgh7, United States Naval Institute8, University of St. Thomas (Minnesota)9
TL;DR: A narrative review of four topics central to collegiate athlete sleep, including sleep patterns and disorders among collegiate athletes; sleep and optimal functioning among athletes; screening, tracking and assessment of athlete sleep; and interventions to improve sleep are provided.
Abstract: Sleep is an important determinant of collegiate athlete health, well-being and performance. However, collegiate athlete social and physical environments are often not conducive to obtaining restorative sleep. Traditionally, sleep has not been a primary focus of collegiate athletic training and is neglected due to competing academic, athletic and social demands. Collegiate athletics departments are well positioned to facilitate better sleep culture for their athletes. Recognising the lack of evidence-based or consensus-based guidelines for sleep management and restorative sleep for collegiate athletes, the National Collegiate Athletic Association hosted a sleep summit in 2017. Members of the Interassociation Task Force on Sleep and Wellness reviewed current data related to collegiate athlete sleep and aimed to develop consensus recommendations on sleep management and restorative sleep using the Delphi method. In this paper, we provide a narrative review of four topics central to collegiate athlete sleep: (1) sleep patterns and disorders among collegiate athletes; (2) sleep and optimal functioning among athletes; (3) screening, tracking and assessment of athlete sleep; and (4) interventions to improve sleep. We also present five consensus recommendations for colleges to improve their athletes’ sleep.
TL;DR: In this article, the authors investigated Twitter usage during Hurricane Sandy following the survey of the general population and exploring communication dynamics on Twitter through different modalities, finding that Twitter is a highly valuable source of disaster-related information particularly during the power outage.
Abstract: This study investigates Twitter usage during Hurricane Sandy following the survey of the general population and exploring communication dynamics on Twitter through different modalities. The results suggest that Twitter is a highly valuable source of disaster-related information particularly during the power outage. With a substantial increase in the number of tweets and unique users during the Hurricane Sandy, a large number of posts contained firsthand information about the hurricane showing the intensity of the event in real-time. More specifically, a number of images of damage and flooding were shared on Twitter through which researchers and emergency managers can retrieve valuable information to help identify storm damages and plan relief efforts. The social media analysis revealed the most important information that can be derived from twitter during disasters so that authorities can successfully utilize such data. The findings provide insights into the choice of keywords and sentiments and identifying the influential actors at different stages of disasters. A number of key influencers and their followers from different domains including political, news, weather, and relief organizations participated in Twitter-based discussions related to Hurricane Sandy. The connectivity of the influencers and their followers on Twitter plays a vital role in information sharing and dissemination throughout the hurricane. These connections can provide an effective vehicle for emergency managers towards establishing better bi-directional communication during disasters. However, while government agencies were among the prominent Twitter users during the Hurricane Sandy, they primarily relied on one-way communication rather than engaging with their audiences, a challenge that need to be addressed in future research.
TL;DR: Findings from this EMA study do not support the narrative that young adolescents’ digital technology usage is associated with elevated mental-health symptoms.
Abstract: This study examines whether adolescents' digital technology use is associated with mental health symptoms (N=388) during early to mid-adolescence. Adolescents completed an initial Time 1 (T1) assessment in 2015, followed by a 14-day ecological momentary assessment (EMA) via mobile phone in 2016-2017 which yielded 13,017 total observations over 5270 study days. Adolescents' T1 technology use did not predict later mental health symptoms. Adolescents' reported mental health was also not worse on days when they reported spending more versus less time on technology. Little was found to support daily quadratic associations (whereby adolescent mental health was worse on days with little or excessive use). Adolescents at higher risk for mental health problems also exhibited no signs of increased risk for mental health problems on higher technology use days. Findings from this EMA study do not support the narrative that young adolescents' digital technology usage is associated with elevated mental health symptoms.
TL;DR: This study investigates the item-level relations of four Openness to Experience inventories using a network science approach, which allowed items to form an emergent taxonomy of facets and aspects, and establishes a broader consensus of Opennesses to Experience at the aspect and facet level.
Abstract: Openness to Experience is a complex trait, the taxonomic structure of which has been widely debated. Previous research has provided greater clarity of its lower order structure by synthesizing face...
01 Nov 2019
TL;DR: While Nrf2 is a protector against oxidative and electrophilic tissue injury, persistent activation of NRF2 signaling may also contribute to disease pathophysiology, such as cancer progression.
Abstract: Regulation of antioxidant gene expression is essential for controlling oxidative stress and maintaining physiological homeostasis. In this context, the nuclear factor E2-related factor 2 (Nrf2) has been identified as the chief regulator of the transcription of diverse antioxidant genes as well as many other cytoprotective genes. Nrf2 activity is subjected to the regulation at various levels including protein stability, transcription, and post-transcription. Among the various regulatory pathways, the Keap1-Cul3-Rbx1 axis is the most prominent regulator of Nrf2 activity. Being a tightly controlled transcriptional activator of antioxidant genes, Nrf2 signaling is intimately involved in health and disease. While Nrf2 is a protector against oxidative and electrophilic tissue injury, persistent activation of Nrf2 signaling may also contribute to disease pathophysiology, such as cancer progression.
University of Illinois at Chicago1, University of Denver2, University of California, Santa Cruz3, University of Eastern Piedmont4, Federal University of Paraná5, Université Paris-Saclay6, Universidade Federal de Santa Catarina7, University of Southern California8, Griffith University9, University of Queensland10, Leiden University11, University of North Carolina at Chapel Hill12, Huntington Medical Research Institutes13, Simon Fraser University14, Paris Descartes University15, Scripps Institution of Oceanography16, University of Montana17, University of Tübingen18, University of Utah19, University of New South Wales20, University of Geneva21, Chinese Academy of Sciences22, Virginia Tech23, Oregon State University24, Yeungnam University25, University of Hawaii at Manoa26, University of Reims Champagne-Ardenne27, University of North Carolina at Greensboro28, Scripps Research Institute29, National Autonomous University of Mexico30, University of Vienna31, University of California, Davis32, University of Münster33, University of Innsbruck34, Schiller International University35, University of Valle36, University of California, Los Angeles37
TL;DR: In this article, the authors present a comprehensive review of the scientific value of disseminating raw nuclear magnetic resonance (NMR) data, independently of, and in parallel with, classical publishing outlets.
TL;DR: The urban expansion model based on SVM method can substantially improve the prediction accuracy and would be helpful for making appropriate plans and policies to mitigate the adverse impacts of urban expansion.
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07 Mar 2019TL;DR: The Chilean Transition The Pinochet Regime From Critics to Celebrants Democracy and Poverty or the Poverty of Democracy? The Political Economy of the Aylwin Government Social Movements and Electoral Politics Limits to Aylwins Growth with Equity as discussed by the authors.
Abstract: Introduction The Chilean Transition The Pinochet Regime From Critics to Celebrants Democracy and Poverty or the Poverty of Democracy? The Political Economy of the Aylwin Government Social Movements and Electoral Politics Limits to Aylwins Growth with Equity. Conclusion Epilogue.
TL;DR: The use of ML on supervised regression tasks in studies of coastal morphodynamics and sediment transport and a set of best practices for coastal researchers using machine learning methods are outlined.
TL;DR: A review of the strengths and weaknesses of these analogs for the study of plant growth and development compared to spaceflight experiments and considers reduced or partial gravity effects via spaceflight and analog methods.
Abstract: Life on Earth has evolved under the influence of gravity. This force has played an important role in shaping development and morphology from the molecular level to the whole organism. Although aquatic life experiences reduced gravity effects, land plants have evolved under a 1-g environment. Understanding gravitational effects requires changing the magnitude of this force. One method of eliminating gravity''s influence is to enter into a free-fall orbit around the planet, thereby achieving a balance between centripetal force of gravity and the centrifugal force of the moving object. This balance is often mistakenly referred to as microgravity, but is best described as weightlessness. In addition to actually compensating gravity, instruments such as clinostats, random-positioning machines (RPM), and magnetic levitation devices have been used to eliminate effects of constant gravity on plant growth and development. However, these platforms do not reduce gravity but constantly change its direction. Despite these fundamental differences, there are few studies that have investigated the comparability between these platforms and weightlessness. Here, we provide a review of the strengths and weaknesses of these analogs for the study of plant growth and development compared to spaceflight experiments. We also consider reduced or partial gravity effects via spaceflight and analog methods. While these analogs are useful, the fidelity of the results relative to spaceflight depends on biological parameters and environmental conditions that cannot be simulated in ground-based studies.
TL;DR: In this article, it is argued that the opportunity to learn in consequential ways are shaped by the historicized injustices students encounter in relation to participation in STEM and schooling, and that the...
Abstract: Opportunities to learn in consequential ways are shaped by the historicized injustices students encounter in relation to participation in STEM and schooling. In this article, it is argued that the ...
TL;DR: Black males are disproportionately the victims of police killings in the United States, yet few studies have examined their personal narratives of trauma and bereavement resulting from police viole... as mentioned in this paper.
Abstract: Black males are disproportionately the victims of police killings in the United States, yet few studies have examined their personal narratives of trauma and bereavement resulting from police viole...
University of California, Berkeley1, Michigan State University2, Auburn University3, University of California, San Francisco4, University of Connecticut Health Center5, University of North Carolina at Greensboro6, University of Wisconsin-Madison7, University of Miami8, San Francisco State University9, University of North Carolina at Chapel Hill10
TL;DR: The findings affirm the need to consider individual variability in coping and potentially other psychosocial processes involved in the stress response process, and offer several insights that may help elucidate the mechanisms by which racial discrimination gets “under the skin.”
Abstract: Racial discrimination has been linked to allostatic load (i.e., cumulative biological stress) among African American women. However, limited attention has been given to psychosocial processes involved in the stress response-critical for understanding biological pathways to health-in studies examining racial discrimination as a social determinant of health. We examined whether the superwoman schema (SWS), a multidimensional culture-specific framework characterizing psychosocial responses to stress among African American women, modifies the association between racial discrimination and allostatic load. We used purposive sampling to recruit a community sample of African American women ages 30-50 from five San Francisco Bay Area counties (n = 208). Path analysis was used to test for interactions while accounting for the covariance among SWS subscales using both linear and quadratic models. Significant interactions were observed between racial discrimination and four of the five SWS subscales. Feeling obligated to present an image of strength and an obligation to suppress emotions were each protective whereas feeling an intense motivation to succeed and feeling an obligation to help others exacerbated the independent health risk associated with experiencing racial discrimination. Our findings affirm the need to consider individual variability in coping and potentially other psychosocial processes involved in the stress response process, and offer several insights that may help elucidate the mechanisms by which racial discrimination gets "under the skin."
TL;DR: It is concluded that HMR is a valid approach for membrane systems, but a 9 Å cutoff is not, finding significant deviations in many properties tested.
Abstract: The time step of atomistic molecular dynamics (MD) simulations is determined by the fastest motions in the system and is typically limited to 2 fs. An increasingly popular approach is to increase the mass of the hydrogen atoms to ∼3 amu and decrease the mass of the parent atom by an equivalent amount. This approach, known as hydrogen-mass repartitioning (HMR), permits time steps up to 4 fs with reasonable simulation stability. While HMR has been applied in many published studies to date, it has not been extensively tested for membrane-containing systems. Here, we compare the results of simulations of a variety of membranes and membrane-protein systems run using a 2 fs time step and a 4 fs time step with HMR. For pure membrane systems, we find almost no difference in structural properties, such as area-per-lipid, electron density profiles, and order parameters, although there are differences in kinetic properties such as the diffusion constant. Conductance through a porin in an applied field, partitioning of a small peptide, hydrogen-bond dynamics, and membrane mixing show very little dependence on HMR and the time step. We also tested a 9 A cutoff as compared to the standard CHARMM cutoff of 12 A, finding significant deviations in many properties tested. We conclude that HMR is a valid approach for membrane systems, but a 9 A cutoff is not.
01 Apr 2019
TL;DR: This paper derives a pragmatic energy transfer model that outperforms the Set Cover baseline method by an average of 34.2% and introduces a method of charging power discretization, which significantly reduces the search space and bounds the performance gap to the optimal one with a $\displaystyle \frac{1}{1-\epsilon^{2}}$ approximation ratio.
Abstract: The discovery of Wireless Power Transfer (WPT) technologies makes charging more convenient and reliable. Among all the existing WPT technologies, directional WPT is more efficient and has been successfully applied to supply energy for wireless rechargeable sensor networks (WRSNs). However, the state-of-the-art methods ignore the anisotropic energy receiving property of rechargeable sensors, resulting in energy wastage. In order to address this issue, in this paper, we point out that the received energy of a sensor is not only relative to the distance, but also relative to the angle between the sensor and the charger’s orientation in directional WPT. Towards this end, we derive a pragmatic energy transfer model verified by experiments. In particular, we focus on a Minimal chArging Delay (MAD) problem to reduce chArging delays. To obtain the optimal solution, we formulate the problem as a linear programming problem. Moreover, we introduce a method of charging power discretization, which significantly reduces the search space and bounds the performance gap to the optimal one with a $\displaystyle \frac{1}{1-\epsilon^{2}}$ approximation ratio. Besides, a merging method is introduced for a more practical application scenario. Finally, we demonstrate that our methods outperform the Set Cover baseline method by an average of 34.2% through simulations and experiments.
TL;DR: This review provides recommended approaches for addressing the critical questions that researchers must address prior to in vitro or in vivo (including clinical) evaluation of botanical natural products.
Johns Hopkins University1, University of New Mexico2, Wichita State University3, University of Oregon4, University of Minnesota5, University of North Carolina at Greensboro6, Smithsonian Environmental Research Center7, Colorado State University8, University of Wyoming9, United States Department of Agriculture10
TL;DR: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Abstract: Univariate and multivariate methods are commonly used to explore the spatial and temporal dynamics of ecological communities, but each has limitations, including oversimplification or abstraction of communities. Rank abundance curves (RACs) potentially integrate these existing methodologies by detailing species-level community changes. Here, we had three goals: first, to simplify analysis of community dynamics by developing a coordinated set of R functions, and second, to demystify the relationships among univariate, multivariate, and RACs measures, and examine how each is influenced by the community parameters as well as data collection methods. We developed new functions for studying temporal changes and spatial differences in RACs in an update to the R package library(“codyn”), alongside other new functions to calculate univariate and multivariate measures of community dynamics. We also developed a new approach to studying changes in the shape of RAC curves. The R package update presented here increases the accessibility of univariate and multivariate measures of community change over time and difference over space. Next, we use simulated and real data to assess the RAC and multivariate measures that are output from our new functions, studying (1) if they are influenced by species richness and evenness, temporal turnover, and spatial variability and (2) how the measures are related to each other. Lastly, we explore the use of the measures with an example from a long-term nutrient addition experiment. We find that the RAC and multivariate measures are not sensitive to species richness and evenness and that all the measures detail unique aspects of temporal change or spatial differences. We also find that species reordering is the strongest correlate of a multivariate measure of compositional change and explains most community change observed in long-term nutrient addition experiment. Overall, we show that species reordering is potentially an understudied determinant of community changes over time or differences between treatments. The functions developed here should enhance the use of RACs to further explore the dynamics of ecological communities.
TL;DR: Blockchain has the potential to provide solutions to problems such as product recalls due to faulty parts or security vulnerabilities by addressing visibility and traceability challenges and reducing the costs of a supply chain.
Abstract: Reports on how blockchain can impact industries. IN 2009, Toyota announced a recall of four million vehicles due to faulty gas pedals.1 The recall cost an estimated US$2 billion. The company had received pedals from many suppliers. It lacked mechanisms to track the suppliers that were responsible for the faulty pedals. There was, thus, no way to know which cars had the defective pedals. Similar problems are found in the food industry. Blockchain has the potential to provide solutions to problems such as mentioned above by addressing visibility and traceability challenges. Blockchain technology enables companies to record every event or transaction within a supply chain (SC) on a distributed ledger, which is shared among all participants, making it secure, immutable, and irrevocable. It makes it possible to deal with a crisis in a targeted way after such a crisis is discovered and provides distributed trust. Blockchain can facilitate handling and dealing with crisis situations such as product recalls due to faulty parts or security vulnerabilities. Blockchain’s public availability means that it is possible to trace back every product to the origin of the raw materials, and transactions can be linked to identify users of vulnerable parts and devices. Also, blockchain can reduce the costs of a supply chain.
TL;DR: Although the term sustainability did not gain traction until the 1980s, concerns about the consequences of transportation technology started long before as discussed by the authors, and a review of the literature on sustainability in transportation technology can be found in this paper.
Abstract: Although the term “sustainability” did not gain traction until the 1980s, concerns about the consequences of transportation technology started long before. This paper reviews the literature on urba...
TL;DR: A novel sparse regression algorithm for inference of the integrated hyper‐connectivity networks from BOLD fMRI and ASL fMRI is proposed, which outperforms the existing single modality based sparse functional connectivity inference methods.