bluuebunny/crossref_metadata_2025 huggingface dataset:
Toward Waveform‐Based Characterization of Slab & Mantle Wedge (SAM) Earthquakes
Authors: Halpaap Felix , Rondenay Stéphane , Liu Qinya , Millet Florian , Ottemöller Lars
Published: August 2021

Earthquakes in subduction zones occur in the slab mantle, in the subducting crust, on the subduction plate interface, and, in some cases, in the mantle wedge–regions that are separated by strong seismic discontinuities. These discontinuities are typically imaged with techniques using teleseismic waves, while local earthquakes are located based on arrival times. While this combination of imaging and earthquake location provides a good initial overview of where the earthquakes are located, the uncertainties associated with the two approaches are too large (i.e., few kilometers) to robustly identify on which side of a discontinuity (with thickness 100 m) the earthquakes occurred. Here we investigate how the waveforms of local earthquakes, which contain secondary phases arising from wave scattering at discontinuities, can be exploited to determine the source region of subduction zone earthquakes more robustly. Our investigation involves a three‐step approach and includes an application to data from western Greece. First, to identify characteristic secondary phases, we analyzed synthetic seismograms from a generic 2‐D subduction zone. Second, to enhance the visibility of secondary phases in field data, we implemented a workflow to process three‐component seismograms. Third, to identify individual secondary phases in the data, we matched their timing to arrivals computed in a 3‐D velocity model. We identified on average two to three secondary arrivals per station. These include P‐ and S‐reflections from the plate interface which indicate hypocenters in the mantle wedge, and P‐reflections from the slab Moho which indicate hypocenters on the plate interface and in the subducting crust.
Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM
Authors: Dunbar Oliver R. A. , Garbuno‐Inigo Alfredo , Schneider Tapio , Stuart Andrew M.
Published: August 2021

Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow uncertainty quantification are too expensive for climate models; they are also not robust in the presence of internal climate variability. For example, Markov chain Monte Carlo (MCMC) methods typically require model runs and are sensitive to internal variability noise, rendering them infeasible for climate models. Here we demonstrate an approach to model calibration and uncertainty quantification that requires only model runs and can accommodate internal climate variability. The approach consists of three stages: (a) a calibration stage uses variants of ensemble Kalman inversion to calibrate a model by minimizing mismatches between model and data statistics; (b) an emulation stage emulates the parameter‐to‐data map with Gaussian processes (GP), using the model runs in the calibration stage for training; (c) a sampling stage approximates the Bayesian posterior distributions by sampling the GP emulator with MCMC. We demonstrate the feasibility and computational efficiency of this calibrate‐emulate‐sample (CES) approach in a perfect‐model setting. Using an idealized general circulation model, we estimate parameters in a simple convection scheme from synthetic data generated with the model. The CES approach generates probability distributions of the parameters that are good approximations of the Bayesian posteriors, at a fraction of the computational cost usually required to obtain them. Sampling from this approximate posterior allows the generation of climate predictions with quantified parametric uncertainties.
GLOBE Mosquito Habitat Mapper Citizen Science Data 2017–2020
Authors: Low Russanne , Boger Rebecca , Nelson Peder , Kimura Matteo
Published: August 2021

The GLOBE Program's GLOBE Observer application is a free citizen science mobile data collection and visualization tool compatible with iOS and Android operating systems. Citizen scientists armed with the app can report the mosquito larval habitats they identify using the GLOBE Mosquito Habitat Mapper tool. This data can complement the climate, weather, and land cover data obtained from satellite measurements by scientists who develop risk models for mosquito‐borne diseases. Public participation in mosquito surveillance research provides the opportunity to obtain the volume, velocity and variety of data needed to fight the threat of vector‐borne diseases, especially in under‐resourced communities with minimal to no municipal surveillance and mitigation services. GLOBE Mosquito Habitat Mappers document and describe potential and active mosquito larval habitats in and around their homes and communities. An easy‐to‐use pictorial interface enables users to geolocate and describe oviposition sites encountered, count and identify mosquito larvae, and when appropriate, eliminate the larval habitats. During Mosquito Habitat Mapper's first 3 years of use, over 24,000 data observations have been reported throughout the world. This technical report summarizes GLOBE Mosquito Habitat Mapper data reported by GLOBE citizen scientists from three regions: Africa, Asia and the Pacific Islands, and Latin America and the Caribbean. Localized mosquito larvae distribution patterns were examined by comparing data collected in three cities in Senegal–Dakar, Touba, and Thilmakha. The Senegal data show habitat and genera differences among mosquitoes identified by citizen scientists in the cities and illustrates the potential of the app for community‐based surveillance and research.
Co‐occurrence of California Drought and Northeast Pacific Marine Heatwaves Under Climate Change
Authors: Shi Hui , García‐Reyes Marisol , Jacox Michael G. , Rykaczewski Ryan R. , Black Bryan A. , Bograd Steven J. , Sydeman William J.
Published: August 2021

Motivated by the uncommon yet highly impactful co‐occurrence of California (CA) drought and a northeast Pacific (NEP) marine heatwave (MHW) during 2013–2016, we examined such compound extremes in Coupled Model Intercomparison Project Phase 6 projections, comparing the end of the 21st century with the preindustrial period, and separating effects of long‐term trends from interannual variability. Here, we show that long‐term trends due to anthropogenic climate change will dramatically increase the co‐occurrence of extreme dry CA and warm NEP conditions. When trends are removed, the co‐occurrence of CA drought and Gulf of Alaska (GOA) MHW will increase while the co‐occurrence of CA drought and California Current (CC) MHW remains unchanged. A stronger link between GOA MHW and subsequent CC MHW as well as decreased persistence in CA drought are also projected under anthropogenic warming. These frequency changes are consistent with shifts in distributions of sea surface temperature and soil moisture anomalies associated with individual extremes.
The Ekman Streamfunction and the Eulerian and Residual Overturning Circulations of the Southern Ocean
Authors: Stewart K. D. , Hogg A. McC. , England M. H. , Waugh D. W. , Kiss A. E.
Published: August 2021

The Ekman streamfunction is a wind‐derived metric that can be used to infer the Southern Ocean overturning circulations (SOOCs) in both latitude‐depth and latitude‐potential density spaces. The Ekman streamfunction integrates the Ekman pumping zonally and northwards from Antarctica, either to a given latitude or potential density. Here, we evaluate the relationship between the Ekman streamfunction and SOOCs in a global 0. ocean‐sea‐ice model driven by interannual forcing (1958–2018). In certain regions of the Southern Ocean, strong correlations () exist between the Ekman streamfunction and the Eulerian and residual SOOCs on monthly and annual timescales. Regression analysis identifies regions where Ekman streamfunction variability coincides with  Sv changes in the overturning; one such location is where the wind stress curl changes sign and the Ekman pumping is highly variable.
Large‐Scale Atmospheric Drivers of Snowfall Over Thwaites Glacier, Antarctica
Authors: Maclennan Michelle L. , Lenaerts Jan T. M.
Published: August 2021

High snowfall events on Thwaites Glacier (TG, West Antarctica) are a key influencer of its mass balance, and can act to mitigate sea level rise due to ocean warming‐induced ice loss. We use the output of a high‐resolution regional climate model, RACMO2, in conjunction with MERRA‐2 and ERA5 atmospheric reanalyses for the period 1980–2015 and show that there is a pronounced seasonal cycle in snowfall over TG, driven by the Amundsen Sea Low (ASL). We find that the total annual snowfall does not correlate significantly with the Southern Annular Mode or El Niño Southern Oscillation, but it does relate to the zonal wave three pattern over Antarctica through the coupling of the ASL with a blocking high over the Antarctic Peninsula during high snowfall events. Our results highlight that atmospheric circulation and consequent high snowfall events on TG are highly variable, and recognizing their future change will aid to improve predictions of mass balance.
Subseasonal Prediction of the State and Evolution of the North Pacific Jet Stream
Authors: Winters Andrew C.
Published: August 2021

The state and evolution of the North Pacific jet (NPJ) stream strongly influences the character of the downstream synoptic‐scale flow pattern over North America. This study employs data from nine models within the Subseasonal‐to‐Seasonal Reforecast Database hosted by the European Centre for Medium‐Range Weather Forecasts to examine the subseasonal (2 weeks–1 month) predictability of the NPJ through the lens of an NPJ phase diagram. The NPJ phase diagram provides a visual representation of the state and evolution of the NPJ with respect to the two leading modes of NPJ variability. The first mode of NPJ variability corresponds to a zonal extension or retraction of the climatological jet‐exit region, whereas the second mode corresponds to a poleward or equatorward shift of the climatological jet‐exit region. The analysis reveals that ensemble forecasts of the prevailing NPJ regime, as determined from the NPJ phase diagram, are skillful into week 3 of the forecast period. Forecasts initialized during a jet retraction, or verifying during a jet retraction and equatorward shift, generally feature the largest errors during the forecast period. Examination of the worst‐performing 21‐day forecasts from each model demonstrates that the worst‐performing forecasts are uniformly associated with development, maintenance, and decay of upper‐tropospheric ridges over the high‐latitude North Pacific. These results demonstrate that bias‐corrected NPJ phase diagram forecasts have the potential to identify periods that may exhibit enhanced forecast skill at subseasonal lead times based on the anticipated NPJ evolution.
Pliocene to Earliest Pleistocene (5–2.5 Ma) Reconstruction of the Kuroshio Current Extension Reveals a Dynamic Current
Authors: Lam Adriane R. , MacLeod Kenneth G. , Schilling Solveig H. , Leckie R. Mark , Fraass Andrew J. , Patterson Molly O. , Venti Nicholas L.
Published: August 2021

The Kuroshio Current Extension (KCE) is the major western boundary current of the North Pacific Subtropical Gyre. To better understand how the KCE behaved under elevated CO2conditions and how it came into its modern configuration, we use stable isotopic analyses from mixed‐layer planktic foraminifera from three Ocean Drilling Program sites that lie to the north, underneath of, and south of the present day KCE. Our data support previous hypotheses suggesting that the KCE played a significant role in delivering moisture to the high latitudes and contributed to extensive ice growth. Samples span from 5 to 2.5 Ma and cover three tectonic and climate events. In response to constriction of the Central American Seaway, the KCE region warmed then cooled, with a potential equatorward shift of the current and intensification of the North Pacific Subtropical Gyre. During the mid‐Piacenzian Warm Period, we define the mid‐Piacenzian 1 and 2 events, which were times when the KCE warmed and subsequently developed a strong temperature/salinity gradient between the two northernmost sites. Northern Hemisphere ice growth at ∼2.9 Ma brought the KCE into its modern‐day configuration. Paradoxically, the southernmost Hole 1209A appears coolest through the study interval. This paradox is resolved by invoking seasonal preferences of the species analyzed, a finding that highlights the need to consider seasonal and ecological preferences of plankton when interpreting geochemical records in midlatitude regions. We propose several hypotheses for drivers of KCE behavior that require further testing using higher‐resolution proxy data tied to additional regional and global records.
Geomagnetically Induced Currents at Middle Latitudes: 1. Quiet‐Time Variability
Authors: Kellerman Adam C. , Mcgranaghan Ryan , Bortnik Jacob , Carter Brett A. , Hughes Joseph , Arritt Robert F. , Venkataramani Karthik , Perry Charles H. , McCormick Jackson , Ngwira Chigomezyo M. , Cohen Morris , Yue Jia
Published: August 2021

Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long‐period and short‐period characteristics?” is addressed. The study focuses on the long‐term variability of measured GIC, and establishes the extent to which the variability relates to quiet‐time geomagnetic activity. GIC quiet‐day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of thermosphere‐ionosphere‐electrodynamics general circulation model (TIE‐GCM)‐simulated neutral wind and height‐integrated current density. The results show strong evidence that the middle‐latitude nodes routinely respond to the tidal‐driven Sq variation, with a local time and seasonal dependence on the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC‐based significance score computed in this study provides power utilities with a node‐specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.
A Bayesian Hierarchical Network Model for Daily Streamflow Ensemble Forecasting
Authors: Ossandón Álvaro , Rajagopalan Balaji , Lall Upmanu , Nanditha J. S. , Mishra Vimal
Published: August 2021

A novel Bayesian Hierarchical Network Model (BHNM) for ensemble forecasts of daily streamflow is presented that uses the spatial dependence induced by the river network topology and hydrometeorological variables from the upstream contributing area between station gauges. Model parameters are allowed to vary with time as functions of selected covariates for each day. Using the network structure to incorporate flow information from upstream gauges and precipitation from the immediate contributing area as covariates allows one to model the spatial correlation of flows simultaneously and parsimoniously. An application to daily monsoon period (July–August) streamflow at three gauges in the Narmada basin in central India for the period 1978–2014 is presented. The best set of covariates include daily streamflow from upstream gauges or from the gauge above the upstream gauges depending on travel times and daily precipitation from the area between two stations. The model validation indicates that the model is highly skillful relative to a null‐model of generalized linear regression, which represents the analogous non‐Bayesian forecast. The ensemble spread of BHNM accounts for the forecast uncertainty leading to reliable and skillful streamflow predictions.
Conductivity Estimates of Fractal Models of Geological Media
Authors: Aarão Reis Fábio D. A. , Voller Vaughan R.
Published: August 2021

Geological media are inherently fractal over multiple length scales, exhibiting a wide range of fractal dimensions . We study the size dependent conductivity of models of geological media based on so called regular random fractals. Specifically, we consider generalizations of the classic Menger sponges where the impermeable cells can take random locations. This results, for a given random fractal generator and a given construction order, in multiple realizations of the target media, each one maintaining the same . We use direct numerical simulation to estimate the conductivity, over a number of patterns, with orders . The conductivity bounds and estimates for regular fractal media are, in general, not applicable to these random fractal models. The conductivity of a specific realization may differ from the ensemble average by a few percents, which suggests a lack of self‐averaging; however, the self‐averaging is regained if the size of the domain of interest is beyond the maximal fractality length scale. We propose a low computational cost approach for estimating the ensemble average conductivity of a given random fractal at a given order, which matches the order of magnitude predicted from more intensive calculations. Through tabulation of conductivities of generators of random fractals with small scaling factors and fitting models, we explain how these fractals can model geological media in which fractal dimensions and conductivities (or effective diffusion coefficients) are measured.
Retro-enantio isomer of angiopep-2 assists nanoprobes across the blood-brain barrier for targeted magnetic resonance/fluorescence imaging of glioblastoma
Authors: Xie Ruoxi , Wu Zijun , Zeng Fanxin , Cai Huawei , Wang Dan , Gu Lei , Zhu Hongyan , Lui Su , Guo Gang , Song Bin , Li Jinxing , Wu Min , Gong Qiyong
Published: August 2021

Glioblastoma (GBM), one of the most common primary intracranial malignant tumours, is very difficult to be completely excised by surgery due to its irregular shape. Here, we use an MRI/NIR fluorescence dual-modal imaging nanoprobe that includes superparamagnetic iron oxide nanoparticles (SPIONs) modified with indocyanine (Cy7) molecules and peptides (ANG or DANG) to locate malignant gliomas and guide accurate excision. Both peptides/Cy7-SPIONs probes displayed excellent tumour-homing properties and barrier penetrating abilities in vitro, and both could mediate precise aggregation of the nanoprobes at gliomas sites in in vivo magnetic resonance imaging (MRI) and ex vivo near-infrared (NIR) fluorescence imaging. However, compared with ANG/Cy7-SPIONs probes, DANG/Cy7-SPIONs probes exhibited better enhanced MR imaging effects. Combining all these features together, this MRI/NIR fluorescence imaging dual-modal nanoprobes modified with retro-enantio isomers of the peptide have the potential to accurately display GBMs preoperatively for precise imaging and intraoperatively for real-time imaging.
Sumoylation regulates the assembly and activity of the SMN complex
Authors: Riboldi Giulietta M. , Faravelli Irene , Kuwajima Takaaki , Delestrée Nicolas , Dermentzaki Georgia , De Planell-Saguer Mariangels , Rinchetti Paola , Hao Le Thi , Beattie Christine C. , Corti Stefania , Przedborski Serge , Mentis George Z. , Lotti Francesco
Published: August 2021

SMN is a ubiquitously expressed protein and is essential for life. SMN deficiency causes the neurodegenerative disease spinal muscular atrophy (SMA), the leading genetic cause of infant mortality. SMN interacts with itself and other proteins to form a complex that functions in the assembly of ribonucleoproteins. SMN is modified by SUMO (Small Ubiquitin-like Modifier), but whether sumoylation is required for the functions of SMN that are relevant to SMA pathogenesis is not known. Here, we show that inactivation of a SUMO-interacting motif (SIM) alters SMN sub-cellular distribution, the integrity of its complex, and its function in small nuclear ribonucleoproteins biogenesis. Expression of a SIM-inactivated mutant of SMN in a mouse model of SMA slightly extends survival rate with limited and transient correction of motor deficits. Remarkably, although SIM-inactivated SMN attenuates motor neuron loss and improves neuromuscular junction synapses, it fails to prevent the loss of sensory-motor synapses. These findings suggest that sumoylation is important for proper assembly and function of the SMN complex and that loss of this post-translational modification impairs the ability of SMN to correct selective deficits in the sensory-motor circuit of SMA mice.
The risk of indoor sports and culture events for the transmission of COVID-19
Authors: Moritz Stefan , Gottschick Cornelia , Horn Johannes , Popp Mario , Langer Susan , Klee Bianca , Purschke Oliver , Gekle Michael , Ihling Angelika , Zimmermann Frank D. L. , Mikolajczyk Rafael
Published: August 2021

Nearly all mass gathering events worldwide were banned at the beginning of the COVID-19 pandemic, as they were suspected of presenting a considerable risk for the transmission of SARS-CoV-2. We investigated the risk of transmitting SARS-CoV-2 by droplets and aerosols during an experimental indoor mass gathering event under three different hygiene practices, and used the data in a simulation study to estimate the resulting burden of disease under conditions of controlled epidemics. Our results show that the mean number of measured direct contacts per visitor was nine persons and this can be reduced substantially by appropriate hygiene practices. A comparison of two versions of ventilation with different air exchange rates and different airflows found that the system which performed worst allowed a ten-fold increase in the number of individuals exposed to infectious aerosols. The overall burden of infections resulting from indoor mass gatherings depends largely on the quality of the ventilation system and the hygiene practices. Presuming an effective ventilation system, indoor mass gathering events with suitable hygiene practices have a very small, if any, effect on epidemic spread.
Methane from microbial hydrogenolysis of sediment organic matter before the Great Oxidation Event
Authors: Xia Xinyu , Gao Yongli
Published: August 2021

Methane, along with other short-chain alkanes from some Archean metasedimentary rocks, has unique isotopic signatures that possibly reflect the generation of atmospheric greenhouse gas on early Earth. We find that alkane gases from the Kidd Creek mines in the Canadian Shield are microbial products in a Neoarchean ecosystem. The widely varied hydrogen and relatively uniform carbon isotopic compositions in the alkanes infer that the alkanes result from the biodegradation of sediment organic matter with serpentinization-derived hydrogen gas. This proposed process is supported by published geochemical data on the Kidd Creek gas, including the distribution of alkane abundances, stable isotope variations in alkanes, and CH2D2 signatures in methane. The recognition of Archean microbial methane in this work reveals a biochemical process of greenhouse gas generation before the Great Oxidation Event and improves the understanding of the carbon and hydrogen geochemical cycles.
Genome dependent Cas9/gRNA search time underlies sequence dependent gRNA activity
Authors: Moreb E. A. , Lynch M. D.
Published: August 2021

CRISPR-Cas9 is a powerful DNA editing tool. A gRNA directs Cas9 to cleave any DNA sequence with a PAM. However, some gRNA sequences mediate cleavage at higher efficiencies than others. To understand this, numerous studies have screened large gRNA libraries and developed algorithms to predict gRNA sequence dependent activity. These algorithms do not predict other datasets as well as their training dataset and do not predict well between species. Here, to better understand these discrepancies, we retrospectively examine sequence features that impact gRNA activity in 44 published data sets. We find strong evidence that gRNA sequence dependent activity is largely influenced by the ability of the Cas9/gRNA complex to find the target site rather than activity at the target site and that this drives sequence dependent differences in gRNA activity between different species. This understanding will help guide future work to understand Cas9 activity as well as efforts to identify optimal gRNAs and improve Cas9 variants.
Professional actors demonstrate variability, not stereotypical expressions, when portraying emotional states in photographs
Authors: Le Mau Tuan , Hoemann Katie , Lyons Sam H. , Fugate Jennifer M. B. , Brown Emery N. , Gendron Maria , Barrett Lisa Feldman
Published: August 2021

It is long hypothesized that there is a reliable, specific mapping between certain emotional states and the facial movements that express those states. This hypothesis is often tested by asking untrained participants to pose the facial movements they believe they use to express emotions during generic scenarios. Here, we test this hypothesis using, as stimuli, photographs of facial configurations posed by professional actors in response to contextually-rich scenarios. The scenarios portrayed in the photographs were rated by a convenience sample of participants for the extent to which they evoked an instance of 13 emotion categories, and actors’ facial poses were coded for their specific movements. Both unsupervised and supervised machine learning find that in these photographs, the actors portrayed emotional states with variable facial configurations; instances of only three emotion categories (fear, happiness, and surprise) were portrayed with moderate reliability and specificity. The photographs were separately rated by another sample of participants for the extent to which they portrayed an instance of the 13 emotion categories; they were rated when presented alone and when presented with their associated scenarios, revealing that emotion inferences by participants also vary in a context-sensitive manner. Together, these findings suggest that facial movements and perceptions of emotion vary by situation and transcend stereotypes of emotional expressions. Future research may build on these findings by incorporating dynamic stimuli rather than photographs and studying a broader range of cultural contexts.
Laser-annealing Josephson junctions for yielding scaled-up superconducting quantum processors
Authors: Hertzberg Jared B. , Zhang Eric J. , Rosenblatt Sami , Magesan Easwar , Smolin John A. , Yau Jeng-Bang , Adiga Vivekananda P. , Sandberg Martin , Brink Markus , Chow Jerry M. , Orcutt Jason S.
Published: August 2021

As superconducting quantum circuits scale to larger sizes, the problem of frequency crowding proves a formidable task. Here we present a solution for this problem in fixed-frequency qubit architectures. By systematically adjusting qubit frequencies post-fabrication, we show a nearly tenfold improvement in the precision of setting qubit frequencies. To assess scalability, we identify the types of “frequency collisions” that will impair a transmon qubit and cross-resonance gate architecture. Using statistical modeling, we compute the probability of evading all such conditions, as a function of qubit frequency precision. We find that, without post-fabrication tuning, the probability of finding a workable lattice quickly approaches 0. However, with the demonstrated precisions it is possible to find collision-free lattices with favorable yield. These techniques and models are currently employed in available quantum systems and will be indispensable as systems continue to scale to larger sizes.
Effects of a large-scale social media advertising campaign on holiday travel and COVID-19 infections: a cluster randomized controlled trial
Authors: Breza Emily , Stanford Fatima Cody , Alsan Marcella , Alsan Burak , Banerjee Abhijit , Chandrasekhar Arun G. , Eichmeyer Sarah , Glushko Traci , Goldsmith-Pinkham Paul , Holland Kelly , Hoppe Emily , Karnani Mohit , Liegl Sarah , Loisel Tristan , Ogbu-Nwobodo Lucy , Olken Benjamin A. , Torres Carlos , Vautrey Pierre-Luc , Warner Erica T. , Wootton Susan , Duflo Esther
Published: August 2021

During the Coronavirus Disease 2019 (COVID-19) epidemic, many health professionals used social media to promote preventative health behaviors. We conducted a randomized controlled trial of the effect of a Facebook advertising campaign consisting of short videos recorded by doctors and nurses to encourage users to stay at home for the Thanksgiving and Christmas holidays (NCT04644328 and AEARCTR-0006821). We randomly assigned counties to high intensity (n = 410 (386) at Thanksgiving (Christmas)) or low intensity (n = 410 (381)). The intervention was delivered to a large fraction of Facebook subscribers in 75% and 25% of randomly assigned zip codes in high- and low-intensity counties, respectively. In total, 6,998 (6,716) zip codes were included, and 11,954,109 (23,302,290) users were reached at Thanksgiving (Christmas). The first two primary outcomes were holiday travel and fraction leaving home, both measured using mobile phone location data of Facebook users. Average distance traveled in high-intensity counties decreased by −0.993 percentage points (95% confidence interval (CI): –1.616, −0.371; P = 0.002) for the 3 days before each holiday compared to low-intensity counties. The fraction of people who left home on the holiday was not significantly affected (adjusted difference: 0.030; 95% CI: −0.361, 0.420; P = 0.881). The third primary outcome was COVID-19 infections recorded at the zip code level in the 2-week period starting 5 days after the holiday. Infections declined by 3.5% (adjusted 95% CI: −6.2%, −0.7%; P = 0.013) in intervention compared to control zip codes. Social media messages recorded by health professionals before the winter holidays in the United States led to a significant reduction in holiday travel and subsequent COVID-19 infections.
Synthesis of triarylpyridines with sulfonate and sulfonamide moieties via a cooperative vinylogous anomeric-based oxidation
Authors: Torabi Morteza , Zolfigol Mohammad Ali , Yarie Meysam , Notash Behrouz , Azizian Saeid , Azandaryani Mina Mirzaei
Published: August 2021

Herein, novel magnetic nanoparticles with pyridinium bridges namely Fe3O4@SiO2@PCLH-TFA through a multi-step pathway were designed and synthesized. The desired catalyst and its corresponding precursors were characterized with different techniques such as Fourier transform infrared (FT-IR) spectroscopy, 1H NMR, 13C NMR, Mass spectroscopy, energy dispersive X-ray (EDX) analysis, thermogravimetric/derivative thermogravimetry (TG/DTG) analysis, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and vibrating sample magnetometer (VSM). In addition, the catalytic application of the prepared catalyst in the synthesis of new series of triarylpyridines bearing sulfonate and sulfonamide moieties via a cooperative vinylogous anomeric-based oxidation was highlighted. The current trend revealed that the mentioned catalyst shows high recoverability in the reported synthesis.
Structural and optical properties of Nd:YAB-nanoparticle-doped PDMS elastomers for random lasers
Authors: Hlil Antsar R. , Thomas Jyothis , Garcia-Puente Yalina , Boisvert Jean-Sebastien , Lima Bismarck C. , Rakotonandrasana Ando , Maia Lauro J. Q. , Tehranchi Amirhossein , Loranger Sebastien , Gomes Anderson S. L. , Messaddeq Younes , Kashyap Raman
Published: August 2021

We report the structural and optical properties of Nd:YAB (NdxY1−x Al3(BO3)4)-nanoparticle-doped PDMS elastomer films for random lasing (RL) applications. Nanoparticles with Nd ratios of x = 0.2, 0.4, 0.6, 0.8, and 1.0 were prepared and then incorporated into the PDMS elastomer to control the optical gain density and scattering center content over a wide range. The morphology and thermal stability of the elastomer composites were studied. A systematic investigation of the lasing wavelength, threshold, and linewidth of the laser was carried out by tailoring the concentration and optical gain of the scattering centers. The minimum threshold and linewidth were found to be 0.13 mJ and 0.8 nm for x = 1 and 0.8. Furthermore, we demonstrated that the RL intensity was easily tuned by controlling the degree of mechanical stretching, with strain reaching up to 300%. A strong, repeatable lasing spectrum over ~ 50 cycles of applied strain was observed, which demonstrates the high reproducibility and robustness of the RL. In consideration for biomedical applications that require long-term RL stability, we studied the intensity fluctuation of the RL emission, and confirmed that it followed Lévy-like statistics. Our work highlights the importance of using rare-earth doped nanoparticles with polymers for RL applications.
Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images
Authors: Jiang Shuai , Zanazzi George J. , Hassanpour Saeed
Published: August 2021

We developed end-to-end deep learning models using whole slide images of adults diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to predict prognosis and the mutation status of a somatic biomarker, isocitrate dehydrogenase (IDH) 1/2. The models, which utilize ResNet-18 as a backbone, were developed and validated on 296 patients from The Cancer Genome Atlas (TCGA) database. To account for the small sample size, repeated random train/test splits were performed for hyperparameter tuning, and the out-of-sample predictions were pooled for evaluation. Our models achieved a concordance- (C-) index of 0.715 (95% CI: 0.569, 0.830) for predicting prognosis and an area under the curve (AUC) of 0.667 (0.532, 0.784) for predicting IDH mutations. When combined with additional clinical information, the performance metrics increased to 0.784 (95% CI: 0.655, 0.880) and 0.739 (95% CI: 0.613, 0.856), respectively. When evaluated on the WHO grade 3 gliomas from the TCGA dataset, which were not used for training, our models predicted survival with a C-index of 0.654 (95% CI: 0.537, 0.768) and IDH mutations with an AUC of 0.814 (95% CI: 0.721, 0.897). If validated in a prospective study, our method could potentially assist clinicians in managing and treating patients with diffusely infiltrating gliomas.
Implications of zonal architecture on differential gene expression profiling and altered pathway expressions in mandibular condylar cartilage
Authors: Basudan Aisha M. , Aziz Mohammad Azhar , Yang Yanqi
Published: August 2021

Mandibular condylar cartilage (MCC) is a multi-zonal heterogeneous fibrocartilage containing different types of cells, but the factors/mechanisms governing the phenotypic transition across the zones have not been fully understood. The reliability of molecular studies heavily rely on the procurement of pure cell populations from the heterogeneous tissue. We used a combined laser-capture microdissection and microarray analysis approach which allowed identification of differential zone-specific gene expression profiling and altered pathways in the MCC of 5-week-old rats. The bioinformatics analysis demonstrated that the MCC cells clearly exhibited distinguishable phenotypes from the articular chondrocytes. Additionally, a set of genes has been determined as potential markers to identify each MCC zone individually; Crab1 gene showed the highest enrichment while Clec3a was the most downregulated gene at the superficial layer, which consists of fibrous (FZ) and proliferative zones (PZ). Ingenuity Pathway Analysis revealed numerous altered signaling pathways; Leukocyte extravasation signaling pathway was predicted to be activated at all MCC zones, in particular mature and hypertrophic chondrocytes zones (MZ&HZ), when compared with femoral condylar cartilage (FCC). Whereas Superpathway of Cholesterol Biosynthesis showed predicted activation in both FZ and PZ as compared with deep MCC zones and FCC. Determining novel zone-specific differences of large group of potential genes, upstream regulators and pathways in healthy MCC would improve our understanding of molecular mechanisms on regional (zonal) basis, and provide new insights for future therapeutic strategies.
An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm
Authors: Sardesai Abha Umesh , Tanak Ambalika Sanjeev , Krishnan Subramaniam , Striegel Deborah A. , Schully Kevin L. , Clark Danielle V. , Muthukumar Sriram , Prasad Shalini
Published: August 2021

Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10 and TRAIL where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed good accuracy of 96.64% and 94.64%. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.
A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos
Authors: Li Wen , Liang Yuan , Zhang Xuan , Liu Chao , He Lei , Miao Leiying , Sun Weibin
Published: August 2021

Routine dental visit is the most common approach to detect the gingivitis. However, such diagnosis can sometimes be unavailable due to the limited medical resources in certain areas and costly for low-income populations. This study proposes to screen the existence of gingivitis and its irritants, i.e., dental calculus and soft deposits, from oral photos with a novel Multi-Task Learning convolutional neural network (CNN) model. The study can be meaningful for promoting the public dental health, since it sheds light on a cost-effective and ubiquitous solution for the early detection of dental issues. With 625 patients included in this study, the classification Area Under the Curve (AUC) for detecting gingivitis, dental calculus and soft deposits were 87.11%, 80.11%, and 78.57%, respectively; Meanwhile, according to our experiments, the model can also localize the three types of findings on oral photos with moderate accuracy, which enables the model to explain the screen results. By comparing to general-purpose CNNs, we showed our model significantly outperformed on both classification and localization tasks, which indicates the effectiveness of Multi-Task Learning on dental disease detection. In all, the study shows the potential of deep learning for enabling the screening of dental diseases among large populations.
SARS-CoV-2 infection initiates interleukin-17-enriched transcriptional response in different cells from multiple organs
Authors: Hasan Md Zobaer , Islam Syful , Matsumoto Kenichi , Kawai Taro
Published: August 2021

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has emerged as a pandemic. Paucity of information concerning the virus and therapeutic interventions have made SARS-CoV-2 infection a genuine threat to global public health. Therefore, there is a growing need for understanding the molecular mechanism of SARS-CoV-2 infection at cellular level. To address this, we undertook a systems biology approach by analyzing publicly available RNA-seq datasets of SARS-CoV-2 infection of different cells and compared with other lung pathogenic infections. Our study identified several key genes and pathways uniquely associated with SARS-CoV-2 infection. Genes such as interleukin (IL)-6, CXCL8, CCL20, CXCL1 and CXCL3 were upregulated, which in particular regulate the cytokine storm and IL-17 signaling pathway. Of note, SARS-CoV-2 infection strongly activated IL-17 signaling pathway compared with other respiratory viruses. Additionally, this transcriptomic signature was also analyzed to predict potential drug repurposing and small molecule inhibitors. In conclusion, our comprehensive data analysis identifies key molecular pathways to reveal underlying pathological etiology and potential therapeutic targets in SARS-CoV-2 infection.
Diversity of MHC IIB genes and parasitism in hybrids of evolutionarily divergent cyprinoid species indicate heterosis advantage
Authors: Šimková Andrea , Gettová Lenka , Civáňová Kristína , Seifertová Mária , Janáč Michal , Vetešník Lukáš
Published: August 2021

The genes of the major histocompatibility complex (MHC) are an essential component of the vertebrate immune system and MHC genotypes may determine individual susceptibility to parasite infection. In the wild, selection that favors MHC variability can create situations in which interspecies hybrids experience a survival advantage. In a wild system of two naturally hybridizing leuciscid fish, we assessed MHC IIB genetic variability and its potential relationships to hosts’ ectoparasite communities. High proportions of MHC alleles and parasites were species-specific. Strong positive selection at specific MHC codons was detected in both species and hybrids. MHC allele expression in hybrids was slightly biased towards the maternal species. Controlling for a strong seasonal effect on parasite communities, we found no clear associations between host-specific parasites and MHC alleles or MHC supertypes. Hybrids shared more MHC alleles with the more MHC-diverse parental species, but expressed intermediate numbers of MHC alleles and positively selected sites. Hybrids carried significantly fewer ectoparasites than either parent species, suggesting a hybrid advantage via potential heterosis.
A siamese network with adaptive gated feature fusion for individual knee OA features grades prediction
Authors: Wang Kang , Niu Xin , Dou Yong , Xie Dongxing , Yang Tuo
Published: August 2021

Grading individual knee osteoarthritis (OA) features is a fine-grained knee OA severity assessment. Existing methods ignore following problems: (1) more accurately located knee joints benefit subsequent grades prediction; (2) they do not consider knee joints’ symmetry and semantic information, which help to improve grades prediction performance. To this end, we propose a SE-ResNext50-32x4d-based Siamese network with adaptive gated feature fusion method to simultaneously assess eight tasks. In our method, two cascaded small convolution neural networks are designed to locate more accurate knee joints. Detected knee joints are further cropped and split into left and right patches via their symmetry, which are fed into SE-ResNext50-32x4d-based Siamese network with shared weights, extracting more detailed knee features. The adaptive gated feature fusion method is used to capture richer semantic information for better feature representation here. Meanwhile, knee OA/non-knee OA classification task is added, helping extract richer features. We specially introduce a new evaluation metric (top±1 accuracy) aiming to measure model performance with ambiguous data labels. Our model is evaluated on two public datasets: OAI and MOST datasets, achieving the state-of-the-art results comparing to competing approaches. It has the potential to be a tool to assist clinical decision making.
Genetics of PlGF plasma levels highlights a role of its receptors and supports the link between angiogenesis and immunity
Authors: Ruggiero Daniela , Nutile Teresa , Nappo Stefania , Tirozzi Alfonsina , Bellenguez Celine , Leutenegger Anne-Louise , Ciullo Marina
Published: August 2021

Placental growth factor (PlGF) is a member of the vascular endothelial growth factor family and is involved in bone marrow-derived cell activation, endothelial stimulation and pathological angiogenesis. High levels of PlGF have been observed in several pathological conditions especially in cancer, cardiovascular, autoimmune and inflammatory diseases. Little is known about the genetics of circulating PlGF levels. Indeed, although the heritability of circulating PlGF levels is around 40%, no studies have assessed the relation between PlGF plasma levels and genetic variants at a genome-wide level. In the current study, PlGF plasma levels were measured in a population-based sample of 2085 adult individuals from three isolated populations of South Italy. A GWAS was performed in a discovery cohort (N = 1600), followed by a de novo replication (N = 468) from the same populations. The meta-analysis of the discovery and replication samples revealed one signal significantly associated with PlGF circulating levels. This signal was mapped to the PlGF co-receptor coding gene NRP1, indicating its important role in modulating the PlGF plasma levels. Two additional signals, at the PlGF receptor coding gene FLT1 and RAPGEF5 gene, were identified at a suggestive level. Pathway and TWAS analyses highlighted genes known to be involved in angiogenesis and immune response, supporting the link between these processes and PlGF regulation. Overall, these data improve our understanding of the genetic variation underlying circulating PlGF levels. This in turn could lead to new preventive and therapeutic strategies for a wide variety of PlGF-related pathologies.
Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information
Authors: Li Yang , Wang Zheng , Li Li-Ping , You Zhu-Hong , Huang Wen-Zhun , Zhan Xin-Ke , Wang Yan-Bin
Published: August 2021

Various biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.