bluuebunny/crossref_metadata_2025_split huggingface dataset:
Annual and Seasonal Surface Circulation Over the Mid‐Atlantic Bight Continental Shelf Derived From a Decade of High Frequency Radar Observations
Authors: Roarty Hugh , Glenn Scott , Brodie Joseph , Nazzaro Laura , Smith Michael , Handel Ethan , Kohut Josh , Updyke Teresa , Atkinson Larry , Boicourt William , Brown Wendell , Seim Harvey , Muglia Mike , Wang Haixing , Gong Donglai 
Published: October 2020

AbstractA decade (2007–2016) of hourly 6‐km‐resolution maps of the surface currents across the Mid‐Atlantic Bight (MAB) generated by a regional‐scale High Frequency Radar network are used to reveal new insights into the spatial patterns of the annual and seasonal mean surface flows. Across the 10‐year time series, temporal means and interannual and intra‐annual variability are used to quantify the variability of spatial surface current patterns. The 10‐year annual mean surface flows are weaker and mostly cross‐shelf near the coast, increasing in speed and rotating to more alongshore directions near the shelfbreak, and increasing in speed and rotating to flow off‐shelf in the southern MAB. The annual mean surface current pattern is relatively stable year to year compared to the hourly variations within a year. The 10‐year seasonal means exhibit similar current patterns, with winter and summer more cross‐shore while spring and fall transitions are more alongshore. Fall and winter mean speeds are larger and correspond to when mean winds are stronger and cross‐shore. Summer mean currents are weakest and correspond to a time when the mean wind opposes the alongshore flow. Again, intra‐annual variability is much greater than interannual, with the fall season exhibiting the most interseasonal variability in the surface current patterns. The extreme fall seasons of 2009 and 2011 are related to extremes in the wind and river discharge events caused by different persistent synoptic meteorological conditions, resulting in more or less rapid fall transitions from stratified summer to well‐mixed winter conditions.
Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products
Authors: Arabzadeh Alireza , Ehsani Mohammad Reza , Guan Bin , Heflin Stella , Behrangi Ali 
Published: October 2020

AbstractAtmospheric rivers (ARs) play an important role in the total annual precipitation regionally and globally, delivering precious freshwater to many arid/semiarid regions. On the other hand, they may cause intense precipitation and floods with huge socioeconomic effects worldwide. In this study, we investigate AR‐related precipitation using 18 years (2001–2018) of globally gridded AR locations derived from Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2). AR precipitation features are explored regionally and seasonally using remote sensing (Integrated Multi‐satellitE Retrievals for GPM version 6 [IMERG V6], daily Global Precipitation Climatology Project version 1.3 [GPCP V1.3], bias‐adjusted CPC Morphing Technique version 1 [CMORPH V1], and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks [PERSIANN‐CDR]) and reanalysis (MERRA‐2 and ECMWF Reanalysis 5th Generation [ERA5]) precipitation products. The results show that most of the world (except the tropics) experience more intense precipitation from AR‐related events compared to non‐AR events. Over the oceans (especially the Southern Ocean), the contribution of ARs to the total precipitation and extreme events is larger than over land. However, some coastal areas over land are highly affected by ARs (e.g., the western and eastern United States and Canada, Western Europe, North Africa, and part of the Middle East, East Asia, and eastern South America and part of Australia). Although spatial correlations for pairs of IMERG/CMORPH and GPCP/PERSIANN‐CDR are fairly high, considerable discrepancies are shown in their estimation of AR‐related events (i.e., overall IMERG and CMORPH show a higher fraction of AR‐related precipitation). It was found that the degree of consistency between reanalysis and satellite‐based products is highly regionally dependent, partly due to the uneven distribution of in situ measurements.
A novel PPARɣ ligand, PPZ023, overcomes radioresistance via ER stress and cell death in human non-small-cell lung cancer cells
Authors: Kim Tae Woo , Hong Da-Won , Kang Chang-Mo , Hong Sung Hee 
Published: October 2020

AbstractPeroxisome proliferator-activated receptor gamma (PPARɣ) agonists exert powerful anticancer effects by suppressing tumor growth. In this study, we developed PPZ023 (1-(2-(ethylthio)benzyl)-4-(2-methoxyphenyl)piperazine), a novel PPAR ligand candidate, and investigated the underlying signaling pathways in both non-small-cell lung cancer (NSCLC) and radio-resistant NSCLC cells. To identify whether PPZ023 has anticancer effects in NSCLC and radioresistant NSCLC cells, we performed WST-1, LDH, Western blot, and caspase-3 and -9 activity assays. Furthermore, we isolated exosomes from PPZ023-treated NSCLC cells and studied cell death signaling. PPZ023 reduces cell viability and increases LDH cytotoxicity and caspase-3 activity in NSCLC cells. PPZ023 induces cell death by generating reactive oxygen species (ROS) and triggering mitochondrial cytochrome c release. PPZ023 treatment causes cell death via the PERK–eIF2α–CHOP axis in both NSCLC cell lysates and exosomes, and PERK and CHOP knockdown significantly blocks ER stress-mediated apoptosis by reducing cleaved caspase-3. Interestingly, diphenyleneiodonium (DPI, a Nox inhibitor) inhibits PPZ023-induced cell death via ER stress, and PPARɣ knockdown inhibits PPZ023-induced ROS, ER stress, and cell death. Moreover, PPZ023, in combination with radiation, causes synergic cell death via exosomal ER stress in radioresistant NSCLC cells, indicating that PPZ023/radiation overcomes radioresistance. Taken together, our results suggest that PPZ023 is a powerful anticancer reagent for overcoming radioresistance.
Influence of race/ethnicity and income on the link between adverse childhood experiences and child flourishing
Authors: Goldstein Ellen , Topitzes James , Miller-Cribbs Julie , Brown Roger L. 
Published: October 2020

Abstract
Background
The impact of early adversity increases the risk of poor outcomes across the life course. Identifying factors that protect against or contribute to deleterious life outcomes represents an important step in resilience promotion among children exposed to adversity. Informed by resilience science, we hypothesized that family resilience mediates the relationship between adverse childhood experiences (ACEs) and child flourishing, and these pathways vary by race/ethnicity and income.

Methods
We conducted a secondary data analysis using the 2016–17 National Survey of Children’s Health data reported by parents/guardians for 44,686 children age 6–17 years. A moderated-mediation model estimated direct, indirect, and total effects using a probit link function and stacked group approach with weighted least square parameter estimates.

Results
The main variables were related in expected directions. Family resilience partially mediated the ACEs-flourishing association. Although White and socioeconomically advantaged families were more likely to maintain family resilience, their children functioned more poorly at high-risk levels relative to Black and Hispanic children and across income groups.

Conclusion
Children suffer from cumulative adversity across race/ethnicity and income. Partial mediation of family resilience indicates that additional protective factors are needed to develop comprehensive strategies, while racial/ethnic differences underscore the importance of prevention and intervention programs that are culturally sensitive.

Impact

The key message of the article reinforces the notion that children suffer from cumulative adversity across race/ethnicity and income, and prevention of ACEs should be the number one charge of public policy, programs, and healthcare.

This is the first study to examine family resilience in the National Survey Children’s Health (NSCH) data set as mediating ACEs-flourishing by race/ethnicity and family poverty level.

Examining an ACEs dose–response effect using population-based data within the context of risk and protective factors can inform a public health response resulting in a greater impact on prevention efforts.
Osteocyte apoptosis: the roles and key molecular mechanisms in resorption-related bone diseases
Authors: Ru Jiang-ying , Wang Yan-fen 
Published: October 2020

AbstractVital osteocytes have been well known to function as an important orchestrator in the preservation of robustness and fidelity of the bone remodeling process. Nevertheless, some key pathological factors, such as sex steroid deficiency and excess glucocorticoids, and so on, are implicated in inducing a bulk of apoptotic osteocytes, subsequently resulting in resorption-related bone loss. As much, osteocyte apoptosis, under homeostatic conditions, is in an optimal state of balance tightly controlled by pro- and anti-apoptotic mechanism pathways. Importantly, there exist many essential signaling proteins in the process of osteocyte apoptosis, which has a crucial role in maintaining a homeostatic environment. While increasing in vitro and in vivo studies have established, in part, key signaling pathways and cross-talk mechanism on osteocyte apoptosis, intrinsic and complex mechanism underlying osteocyte apoptosis occurs in various states of pathologies remains ill-defined. In this review, we discuss not only essential pro- and anti-apoptotic signaling pathways and key biomarkers involved in these key mechanisms under different pathological agents, but also the pivotal role of apoptotic osteocytes in osteoclastogenesis-triggered bone loss, hopefully shedding new light on the attractive and proper actions of pharmacotherapeutics of targeting apoptosis and ensuing resorption-related bone diseases such as osteoporosis and fragility fractures.
Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium
Authors: Sajuthi Satria P. , DeFord Peter , Li Yingchun , Jackson Nathan D. , Montgomery Michael T. , Everman Jamie L. , Rios Cydney L. , Pruesse Elmar , Nolin James D. , Plender Elizabeth G. , Wechsler Michael E. , Mak Angel C. Y. , Eng Celeste , Salazar Sandra , Medina Vivian , Wohlford Eric M. , Huntsman Scott , Nickerson Deborah A. , Germer Soren , Zody Michael C. , Abecasis Gonçalo , Kang Hyun Min , Rice Kenneth M. , Kumar Rajesh , Oh Sam , Rodriguez-Santana Jose , Burchard Esteban G. , Seibold Max A. 
Published: October 2020

AbstractCoronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, an emerging virus that utilizes host proteins ACE2 and TMPRSS2 as entry factors. Understanding the factors affecting the pattern and levels of expression of these genes is important for deeper understanding of SARS-CoV-2 tropism and pathogenesis. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci for bothACE2andTMPRSS2, that vary in frequency across world populations. We findTMPRSS2is part of a mucus secretory network, highly upregulated by type 2 (T2) inflammation through the action of interleukin-13, and that the interferon response to respiratory viruses highly upregulatesACE2expression. IL-13 and virus infection mediated effects onACE2expression were also observed at the protein level in the airway epithelium. Finally, we define airway responses to common coronavirus infections in children, finding that these infections generate host responses similar to other viral species, including upregulation ofIL6andACE2. Our results reveal possible mechanisms influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.
Mechanical forces drive ordered patterning of hair cells in the mammalian inner ear
Authors: Cohen Roie , Amir-Zilberstein Liat , Hersch Micha , Woland Shiran , Loza Olga , Taiber Shahar , Matsuzaki Fumio , Bergmann Sven , Avraham Karen B. , Sprinzak David 
Published: October 2020

AbstractPeriodic organization of cells is required for the function of many organs and tissues. The development of such periodic patterns is typically associated with mechanisms based on intercellular signaling such as lateral inhibition and Turing patterning. Here we show that the transition from disordered to ordered checkerboard-like pattern of hair cells and supporting cells in the mammalian hearing organ, the organ of Corti, is likely based on mechanical forces rather than signaling events. Using time-lapse imaging of mouse cochlear explants, we show that hair cells rearrange gradually into a checkerboard-like pattern through a tissue-wide shear motion that coordinates intercalation and delamination events. Using mechanical models of the tissue, we show that global shear and local repulsion forces on hair cells are sufficient to drive the transition from disordered to ordered cellular pattern. Our findings suggest that mechanical forces drive ordered hair cell patterning in a process strikingly analogous to the process of shear-induced crystallization in polymer and granular physics.
Deep transfer learning for reducing health care disparities arising from biomedical data inequality
Authors: Gao Yan , Cui Yan 
Published: October 2020

AbstractAs artificial intelligence (AI) is increasingly applied to biomedical research and clinical decisions, developing unbiased AI models that work equally well for all ethnic groups is of crucial importance to health disparity prevention and reduction. However, the biomedical data inequality between different ethnic groups is set to generate new health care disparities through data-driven, algorithm-based biomedical research and clinical decisions. Using an extensive set of machine learning experiments on cancer omics data, we find that current prevalent schemes of multiethnic machine learning are prone to generating significant model performance disparities between ethnic groups. We show that these performance disparities are caused by data inequality and data distribution discrepancies between ethnic groups. We also find that transfer learning can improve machine learning model performance for data-disadvantaged ethnic groups, and thus provides an effective approach to reduce health care disparities arising from data inequality among ethnic groups.
Mechanical characterization and induced crystallization in nanocomposites of thermoplastics and carbon nanotubes
Authors: Cruz-Chú Eduardo R. , Villegas-Rodríguez Gonzalo J. , Jäger Tobias , Valentini Luca , Pugno Nicola M. , Gkagkas Konstantinos , Gräter Frauke 
Published: October 2020

AbstractNanocomposites built from polymers and carbon nanotubes (CNTs) are a promising class of materials. Computer modeling can provide nanoscale views of the polymer–CNT interface, which are much needed to foster the manufacturing and development of such materials. However, setting up periodic nanocomposite models is a challenging task. Here we propose a computational workflow based on Molecular Dynamics simulations. We demonstrate its capabilities and showcase its applications, focusing on two existing nanocomposite materials: polystyrene (PS) with CNT and polyether ether ketone with CNT. The models provide insights into the polymer crystallization inside CNTs. Furthermore, the PS+CNT nanocomposite models are mechanically tested and able to predict an enhancement in Young’s modulus due to the addition of highly dispersed CNTs. We accompany those results with experimental tests and provide a prediction model based on Dynamic Quantized Fracture Mechanics theory. Our study proposes representative simulations of polymer–CNT nanocomposites as promising tools to guide the rational design of this class of materials.
The influence of common polygenic risk and gene sets on social skills group training response in autism spectrum disorder
Authors: Li Danyang , Choque-Olsson Nora , Jiao Hong , Norgren Nina , Jonsson Ulf , Bölte Sven , Tammimies Kristiina 
Published: October 2020

AbstractSocial skills group training (SSGT) is a frequently used behavioral intervention in autism spectrum disorder (ASD), but the effects are moderate and heterogeneous. Here, we analyzed the effect of polygenic risk score (PRS) and common variants in gene sets on the intervention outcome. Participants from the largest randomized clinical trial of SSGT in ASD to date were selected (N = 188, 99 from SSGT, 89 from standard care) to calculate association between the outcomes in the SSGT trial and PRSs for ASD, attention-deficit hyperactivity disorder (ADHD), and educational attainment. In addition, specific gene sets were selected to evaluate their role on intervention outcomes. Among all participants in the trial, higher PRS for ADHD was associated with significant improvement in the outcome measure, the parental-rated Social Responsiveness Scale. The significant association was due to better outcomes in the standard care group for individuals with higher PRS for ADHD (post-intervention: β = −4.747, P = 0.0129; follow-up: β = −5.309, P = 0.0083). However, when contrasting the SSGT and standard care group, an inferior outcome in the SSGT group was associated with higher ADHD PRS at follow-up (β = 6.67, P = 0.016). Five gene sets within the synaptic category showed a nominal association with reduced response to interventions. We provide preliminary evidence that genetic liability calculated from common variants could influence the intervention outcomes. In the future, larger cohorts should be used to investigate how genetic contribution affects individual response to ASD interventions.
StableClim, continuous projections of climate stability from 21000 BP to 2100 CE at multiple spatial scales
Authors: Brown Stuart C. , Wigley Tom M. L. , Otto-Bliesner Bette L. , Fordham Damien A. 
Published: October 2020

AbstractPaleoclimatic data are used in eco-evolutionary models to improve knowledge of biogeographical processes that drive patterns of biodiversity through time, opening windows into past climate–biodiversity dynamics. Applying these models to harmonised simulations of past and future climatic change can strengthen forecasts of biodiversity change. StableClim provides continuous estimates of climate stability from 21,000 years ago to 2100 C.E. for ocean and terrestrial realms at spatial scales that include biogeographic regions and climate zones. Climate stability is quantified using annual trends and variabilities in air temperature and precipitation, and associated signal-to-noise ratios. Thresholds of natural variability in trends in regional- and global-mean temperature allow periods in Earth’s history when climatic conditions were warming and cooling rapidly (or slowly) to be identified and climate stability to be estimated locally (grid-cell) during these periods of accelerated change. Model simulations are validated against independent paleoclimate and observational data. Projections of climatic stability, accessed through StableClim, will improve understanding of the roles of climate in shaping past, present-day and future patterns of biodiversity.
Protein ontology on the semantic web for knowledge discovery
Authors: Chen Chuming , Huang Hongzhan , Ross Karen E. , Cowart Julie E. , Arighi Cecilia N. , Wu Cathy H. , Natale Darren A. 
Published: October 2020

AbstractThe Protein Ontology (PRO) provides an ontological representation of protein-related entities, ranging from protein families to proteoforms to complexes. Protein Ontology Linked Open Data (LOD) exposes, shares, and connects knowledge about protein-related entities on the Semantic Web using Resource Description Framework (RDF), thus enabling integration with other Linked Open Data for biological knowledge discovery. For example, proteins (or variants thereof) can be retrieved on the basis of specific disease associations. As a community resource, we strive to follow the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, disseminate regular updates of our data, support multiple methods for accessing, querying and downloading data in various formats, and provide documentation both for scientists and programmers. PRO Linked Open Data can be browsed via faceted browser interface and queried using SPARQL via YASGUI. RDF data dumps are also available for download. Additionally, we developed RESTful APIs to support programmatic data access. We also provide W3C HCLS specification compliant metadata description for our data. The PRO Linked Open Data is available at https://lod.proconsortium.org/.
Direct visualization of the extracellular binding structure of E-cadherins in liquid
Authors: Shibata-Seki Teiko , Nagaoka Masato , Goto Mitsuaki , Kobatake Eiry , Akaike Toshihiro 
Published: October 2020

AbstractE-cadherin is a key Ca-dependent cell adhesion molecule, which is expressed on many cell surfaces and involved in cell morphogenesis, embryonic development, EMT, etc. The fusion protein E-cad-Fc consists of the extracellular domain of E-cadherin and the IgG Fc domain. On plates coated with this chimeric protein, ES/iPS cells are cultivated particularly well and induced to differentiate. The cells adhere to the plate via E-cad-Fc in the presence of Ca2+ and detach by a chelating agent. For the purpose of clarifying the structures of E-cad-Fc in the presence and absence of Ca2+, we analyzed the molecular structure of E-cad-Fc by AFM in liquid. Our AFM observations revealed a rod-like structure of the entire extracellular domain of E-cad-Fc in the presence of Ca2+ as well as trans-binding of E-cad-Fc with adjacent molecules, which may be the first, direct confirmation of trans-dimerization of E-cadherin. The observed structures were in good agreement with an X-ray crystallographic model. Furthermore, we succeeded in visualizing the changes in the rod-like structure of the EC domains with and without calcium. The biomatrix surface plays an important role in cell culture, so the analysis of its structure and function may help promote cell engineering based on cell recognition.
Stability theory of nano-fluid over an exponentially stretching cylindrical surface containing microorganisms
Authors: Ferdows M. , Hossan Amran , Bangalee M. Z. I. , Sun Shuyu , Alzahrani Faris 
Published: October 2020

AbstractThis research is emphasized to describe the stability analysis in the form of dual solution of the flow and heat analysis on nanofluid over an exponential stretching cylindrical surface containing microorganisms. The research is also implemented to manifest the dual profiles of velocity, temperature and nanoparticle concentration in the effect of velocity ratio parameter ($$s = \frac{{U_{w} }}{{U_{\infty } }}$$

s
=

U
w

U
∞

). Living microorganisms’ cell are mixed into the nanofluid to neglect the unstable condition of nano type particles. The governing equations are transformed to non-linear ordinary differential equations with respect to pertinent boundary conditions by using similarity transformation. The significant differential equations are solved using build in function bvp4c in MATLAB. It is seen that the solution is not unique for vertical stretching sheet. This research is reached to excellent argument when found results are compared with available result. It is noticed that dual results are obtained demanding on critical value ($$s_{c}$$

s
c

), the meanings are indicated at these critical values both solutions are connected and behind these critical value boundary layer separates thus the solution are not stable.