bluuebunny/crossref_metadata_2025_split huggingface dataset:
DFENS: Diffusion Chronometry Using Finite Elements and Nested Sampling
Authors: Mutch Euan J. F. , Maclennan John , Shorttle Oliver , Rudge John F. , Neave David A. 
Published: April 2021

AbstractIn order to reconcile petrological and geophysical observations of magmatic processes in the temporal domain, the uncertainties in diffusion timescales need to be rigorously assessed. Here, we present a new diffusion chronometry method: Diffusion chronometry using Finite Elements and Nested Sampling (DFENS). This method combines a finite element numerical model with a nested sampling Bayesian inversion, meaning that uncertainties in the parameters contributing to diffusion timescale estimates can be obtained and that observations from multiple elements can be used to better constrain individual timescales. Uncertainties associated with diffusion timescales can be reduced by accounting for covariance in the uncertainty structure of diffusion parameters rather than assuming that they are independent of each other. We applied the DFENS method to the products of the Skuggafjöll eruption from the Bárðarbunga volcanic system in Iceland, which contains zoned macrocrysts of olivine and plagioclase that record a shared magmatic history. Olivine and plagioclase provide consistent pre‐eruptive mixing and mush disaggregation timescales of less than 1 year. The DFENS method goes some way toward improving our ability to rigorously address the uncertainties of diffusion timescales, but efforts still need to be made to understand other systematic sources of uncertainty such as crystal morphology, appropriate choice of diffusion coefficients, initial conditions, crystal growth, and the petrological context of diffusion timescales.
Global Lightning Quanta
Authors: Füllekrug Martin 
Published: April 2021

AbstractThe World Meteorological Organization recently declared lightning an essential climate variable which makes the global lightning flash rate density a key quantity, currently assessed by geostationary satellites and ground‐based lightning location networks. Yet, no theory has been put forward to explain the physical relationships between the thermodynamic temperature of the Earth’s atmosphere T, the global lightning flash occurrence frequency fg, and its radiant energy E of resonant electromagnetic waves within the Earth ionosphere cavity. These three parameters are combined here by adapting the rigorous framework of quantum physics. The minimum amount of radiant energy produced by the lightning flash occurrence frequency is the global lightning quantum E = hfg, h being Planck’s constant. The superposition of numerous global lightning quanta distributes its radiant energy around the world as Earth ionosphere cavity resonances. The novel theory is in agreement with measurements using a radiometer at Arrival Heights, Antarctica, as part of the Stanford ELF/VLF Radio Noise Survey. It is found that the measurements agree with the theory within ∼30%. The operation of the theory is illustrated with an interpretation of the measurements for an exemplary thermodynamic energy. In this case, the measurements correspond to a radiant temperature ∼−30°C akin to the mixed phase region of thunderclouds where lightning discharges are initiated. The theory can help to assess the mutual impact of climate change and global lightning on each other as proposed by the World Meteorological Organization.
Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System
Authors: Xue Yuan , Houser Paul R. , Maggioni Viviana , Mei Yiwen , Kumar Sujay V. , Yoon Yeosang 
Published: April 2021

AbstractThis first paper of the two‐part series focuses on demonstrating the accuracy of a hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° (∼1‐km) and 0.25° (∼25‐km). The assessment is conducted via comparisons against ground‐based observations and satellite‐derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground‐based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper‐resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse‐resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse‐resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper‐resolution precipitation products to drive model simulations.
Impact of the 2016 Southeastern US Wildfires on the Vertical Distribution of Ozone and Aerosol at Huntsville, Alabama
Authors: Wang Bo , Kuang Shi , Pfister Gabriele G. , Pour‐Biazar Arastoo , Buchholz Rebecca R. , Langford Andrew O. , Newchurch Michael J. 
Published: April 2021

AbstractWe present an integrated analysis of measurements from ozonesonde, ozone (O3) Differential Absorption Lidar (DIAL), ceilometer, surface monitors, and space‐borne observations in conjunction with the regional chemical transport model Weather Research and Forecast Model with Chemistry (WRF‐Chem) to investigate the effect of biomass burning emissions on the vertical distribution of ozone and aerosols during an episode of the 2016 Southeastern United States wildfires. The ceilometer and DIAL measurements capture the vertical extent of the smoke plumes affecting the surface and upper air over Huntsville, AL. The model evaluation results suggest a scaling factor of 3–4 for the wildfire aerosol emissions in order to better match observed aerosol optical depth, fine particulate matter (PM2.5), and DIAL aerosol extinction. We use the scaled emissions together with WRF‐Chem tendency diagnostics to quantify the fire impacts and characterize the processes affecting the vertical ozone budget downstream of the wildfires. During the daytime at Huntsville on November 12 and 13, we estimate that fire emissions contribute 12–32 μg/m3 (44%–70%) to hourly surface PM2.5 and 7–8 ppb/10 h (30%–37%) to the surface ozone increase (ΔO3), respectively. Net chemical ozone production (PO3) is the main contributor to upper‐air ozone, which reaches 17–19 ppb/10 h with an estimated 14%–25% contribution from fire sources. Vertical mixing and advection are the major drivers of changes in surface ozone. Model analysis indicates that advection dominates ΔO3 due to fire emissions below 1 km on November 12, while local photochemistry dominates on November 13. These results quantify the different mechanisms through which fires can influence the vertical ozone budget and point out uncertainties in fire inventories that need to be addressed in light of the increasing role of wildfires on air quality.
Insulin-positive ductal cells do not migrate into preexisting islets during pregnancy
Authors: Liu Qun , Jiang Yinan , Zhu Lingyan , Qian Jieqi , Wang Chaoban , Yang Tianlun , Prasadan Krishna , Gittes George K. , Xiao Xiangwei 
Published: April 2021

AbstractThe adult pancreatic ductal system was suggested to harbor facultative beta-cell progenitors similar to the embryonic pancreas, and the appearance of insulin-positive duct cells has been used as evidence for natural duct-to-beta-cell reprogramming. Nevertheless, the phenotype and fate of these insulin-positive cells in ducts have not been determined. Here, we used a cell-tagging dye, CFDA-SE, to permanently label pancreatic duct cells through an intraductal infusion technique. Representing a time when significant increases in beta-cell mass occur, pregnancy was later induced in these CFDA-SE-treated mice to assess the phenotype and fate of the insulin-positive cells in ducts. We found that a small portion of CFDA-SE-labeled duct cells became insulin-positive, but they were not fully functional beta-cells based on the in vitro glucose response and the expression levels of key beta-cell genes. Moreover, these insulin-positive cells in ducts expressed significantly lower levels of genes associated with extracellular matrix degradation and cell migration, which may thus prevent their budding and migration into preexisting islets. A similar conclusion was reached through analysis of the Gene Expression Omnibus database for both mice and humans. Together, our data suggest that the contribution of duct cells to normal beta-cells in adult islets is minimal at best.
Salmonella nomenclature in the genomic era: a time for change
Authors: Chattaway Marie A. , Langridge Gemma C. , Wain John 
Published: April 2021

AbstractSalmonella enterica nomenclature has evolved over the past one hundred years into a highly sophisticated naming convention based on the recognition of antigens by specific antibodies. This serotyping scheme has led to the definition of over 2500 serovars which are well understood, have standing in nomenclature and, for the majority, biological relevance. Therefore, it is highly desirable for any change in naming convention to maintain backwards compatibility with the information linked to these serovars. The routine use of whole genome sequencing and the well-established link between sequence types and serovars presents an opportunity to update the scheme by incorporating the phylogenetically relevant sequence data whilst preserving the best of serotyping nomenclature. Advantages include: overcoming the variability in antibody preparations; removing the need to use laboratory animals and implementing a truly universal system. However, the issue of trying to reproduce the phenotyping gold standard needs to be relaxed if we are to fully embrace the genomic era. We have used whole genome sequence data from over 46,000 isolates of Salmonella enterica subspecies enterica to define clusters in two stages: Multi Locus Sequence Typing followed by antigen prediction. Sequence type—serotype discrepancies were resolved using core SNP clustering to determine the phylogenetic groups and this was confirmed by overlaying the antigenic prediction onto the core SNP clusters and testing the separation of clusters using cgMLST Hierarchical Clustering. This allowed us to define any major antigenic clusters within an ST—here called the MAC type and written as ST-serovar. Using this method, 99.96% of Salmonella isolates reported in the UK were assigned a MAC type and linked to a serovar name taken from the Kauffmann and White scheme. We propose a change for reporting of Salmonella enterica sub-types using the ST followed by serovar.
Prediction of PCR amplification from primer and template sequences using recurrent neural network
Authors: Kayama Kotetsu , Kanno Miyuki , Chisaki Naoto , Tanaka Misaki , Yao Reika , Hanazono Kiwamu , Camer Gerry Amor , Endoh Daiji 
Published: April 2021

AbstractWe have developed a novel method to predict the success of PCR amplification for a specific primer set and DNA template based on the relationship between the primer sequence and the template. To perform the prediction using a recurrent neural network, the usual double-stranded formation between the primer and template nucleotide sequences was herein expressed as a five-lettered word. The set of words (pseudo-sentences) was placed to indicate the success or failure of PCR targeted to learn recurrent neural network (RNN). After learning pseudo-sentences, RNN predicted PCR results from pseudo-sentences which were created by primer and template sequences with 70% accuracy. These results suggest that PCR results could be predicted using learned RNN and the trained RNN could be used as a replacement for preliminary PCR experimentation. This is the first report which utilized the application of neural network for primer design and prediction of PCR results.
Plasma biomarkers for prediction of early tumor recurrence after resection of pancreatic ductal adenocarcinoma
Authors: Rittmann Marie-Claire , Hussung Saskia , Braun Lukas M. , Klar Rhena F. U. , Biesel Esther A. , Fichtner-Feigl Stefan , Fritsch Ralph , Wittel Uwe A. , Ruess Dietrich A. 
Published: April 2021

AbstractPancreatic ductal adenocarcinoma (PDAC) is a disease with a very unfavorable prognosis. Surgical resection represents the only potentially curative treatment option, but recurrence after complete resection is almost certain. In an exploratory attempt we here aimed at identifying preoperative plasma protein biomarkers with the potential to predict early recurrence after resection of PDAC. Peripheral blood samples from 14 PDAC patients divided into three groups according to their time to tumor recurrence after curatively intended resection (early: < 6 months, medium: 6–12 months, late: > 12 months) underwent targeted proteome analysis. Proteins most strongly discriminating early and late recurrence were then examined in a number of established PDAC cell lines and their culture supernatants. Finally, PDAC organoid lines from primary tumors of patients with early and late recurrence were analyzed for confirmation and validation of results. In total, 23 proteins showed differential abundance in perioperative plasma from PDAC patients with early recurrence when compared to patients with late recurrence. Following confirmation of expression on a transcriptional and translational level in PDAC cell lines we further focused on three upregulated (MAEA, NT5E, AZU1) and two downregulated proteins (ATP6AP2, MICA). Increased expression of NT5E was confirmed in a subset of PDAC organoid cultures from tumors with early recurrence. MICA expression was heterogeneous and ATP6AP2 levels were very similar in both organoids from early and late recurrent tumors. Most strikingly, we observed high MAEA expression in all tested PDAC (n = 7) compared to a non-cancer ductal organoid line. MAEA also demonstrated potential to discriminate early recurrence from late recurrence PDAC organoids. Our study suggests that identification of plasma protein biomarkers released by tumor cells may be feasible and of value to predict the clinical course of patients. Prediction of recurrence dynamics would help to stratify up-front resectable PDAC patients for neoadjuvant chemotherapy approaches in an individualized fashion. Here, MAEA and NT5E were the most promising candidates for further evaluation.
Common genetic variants and pathways in diabetes and associated complications and vulnerability of populations with different ethnic origins
Authors: Shoily Sabrina Samad , Ahsan Tamim , Fatema Kaniz , Sajib Abu Ashfaqur 
Published: April 2021

AbstractDiabetes mellitus is a complex and heterogeneous metabolic disorder which is often pre- or post-existent with complications such as cardiovascular disease, hypertension, inflammation, chronic kidney disease, diabetic retino- and nephropathies. However, the frequencies of these co-morbidities vary among individuals and across populations. It is, therefore, not unlikely that certain genetic variants might commonly contribute to these conditions. Here, we identified four single nucleotide polymorphisms (rs5186, rs1800795, rs1799983 and rs1800629 in AGTR1, IL6, NOS3 and TNFA genes, respectively) to be commonly associated with each of these conditions. We explored their possible interplay in diabetes and associated complications. The variant allele and haplotype frequencies at these polymorphic loci vary among different super-populations (African, European, admixed Americans, South and East Asians). The variant alleles are particularly highly prevalent in different European and admixed American populations. Differential distribution of these variants in different ethnic groups suggests that certain drugs might be more effective in selective populations rather than all. Therefore, population specific genetic architectures should be considered before considering a drug for these conditions.
The role of mechanical control of biofilm in the salivary pH after sucrose exposure in children with early childhood caries
Authors: Lima-Holanda Aline Tavares , de Sousa Emerson Tavares , Nobre-dos-Santos Marinês , Steiner-Oliveira Carolina 
Published: April 2021

AbstractThis quasi-experimental study sought to investigate if the mechanical control of biofilm (3-times-a-day) modifies the saliva’s ability to buffer the oral environment after 20% sucrose rinse (SR20%) in children with early childhood caries (ECC). Here, SR20% reduced the saliva’s pH in both groups and the mechanical control of biofilm had a greater effect on this parameter after SR20% in CF children. The mechanical control of biofilm evidenced a higher buffering capacity in CF children before SR20%, which was not observed after SR20%. Otherwise, the absence of mechanical control of biofilm showed that buffering capacity was comparable in the two groups before SR20%, whereas after SR20% the saliva’s buffering capacity of CF children was higher than ECC children. When biofilm was mechanically controlled, carbonic anhydrase VI activity did not change after SR20% whereas the absence of mechanical control of biofilm reduced this enzyme activity after SR20%. In conclusion, the mechanical control of biofilm did not change saliva’s ability to buffer the oral environment after SR20% in children with ECC. On the other hand, CF children appeared to regulate more effectively the saliva’s pH than ECC children while the absence of mechanical control of biofilm mediated their pH-modifying ability after SR20%.
Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings
Authors: Martini Michael L. , Valliani Aly A. , Sun Claire , Costa Anthony B. , Zhao Shan , Panov Fedor , Ghatan Saadi , Rajan Kanaka , Oermann Eric Karl 
Published: April 2021

AbstractReal-time seizure detection is a resource intensive process as it requires continuous monitoring of patients on stereoelectroencephalography. This study improves real-time seizure detection in drug resistant epilepsy (DRE) patients by developing patient-specific deep learning models that utilize a novel self-supervised dynamic thresholding approach. Deep neural networks were constructed on over 2000 h of high-resolution, multichannel SEEG and video recordings from 14 DRE patients. Consensus labels from a panel of epileptologists were used to evaluate model efficacy. Self-supervised dynamic thresholding exhibited improvements in positive predictive value (PPV; difference: 39.0%; 95% CI 4.5–73.5%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.03) with similar sensitivity (difference: 14.3%; 95% CI − 21.7 to 50.3%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.42) compared to static thresholds. In some models, training on as little as 10 min of SEEG data yielded robust detection. Cross-testing experiments reduced PPV (difference: 56.5%; 95% CI 25.8–87.3%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.002), while multimodal detection significantly improved sensitivity (difference: 25.0%; 95% CI 0.2–49.9%; Wilcoxon–Mann–Whitney test; N = 14; p < 0.05). Self-supervised dynamic thresholding improved the efficacy of real-time seizure predictions. Multimodal models demonstrated potential to improve detection. These findings are promising for future deployment in epilepsy monitoring units to enable real-time seizure detection without annotated data and only minimal training time in individual patients.
Diminished growth and vitality in juvenile Hydractinia echinata under anticipated future temperature and variable nutrient conditions
Authors: Tschink Daniel , Gerlach Gabriele , Winklhofer Michael , Kohlmeier Cora , Blasius Bernd , Eickelmann Laura , Schadewell Yvonne , Strahl Julia 
Published: April 2021

AbstractIn a warming climate, rising seawater temperatures and declining primary and secondary production will drastically affect growth and fitness of marine invertebrates in the northern Atlantic Ocean. To study the ecological performance of juvenile hydroids Hydractinia echinata we exposed them to current and predicted water temperatures which reflect the conditions in the inter- and subtidal in combination with changing food availability (high and low) in laboratory experiments. Here we show, that the interplay between temperature stress and diminished nutrition affected growth and vitality of juvenile hydroids more than either factor alone, while high food availability mitigated their stress responses. Our numerical growth model indicated that the growth of juvenile hydroids at temperatures beyond their optimum is a saturation function of energy availability. We demonstrated that the combined effects of environmental stressors should be taken into consideration when evaluating consequences of climate change. Interactive effects of ocean warming, decreasing resource availability and increasing organismal energy demand may have major impacts on biodiversity and ecosystem function.
Effect of polyaniline content and protonating dopants on electroconductive composites
Authors: Bednarczyk Katarzyna , Matysiak Wiktor , Tański Tomasz , Janeczek Henryk , Schab-Balcerzak Ewa , Libera Marcin 
Published: April 2021

AbstractElastic constructive elements prepared by electrospinning using polyacrylonitrile/polyaniline (PAN/PANI) electroconductive composites were prepared and investigated in terms of their thermal and mechanical properties. This study was focused on the impact of the type of counterion of polyaniline and the PANI content in composites on the thermal, conductive and morphological properties of electrospun fibers. In this study, composites obtained from PANI doped with sulfuric acid showed the highest conductivity, and composites obtained from PANI doped with hydrochloric acid showed the highest thermal stability. All obtained composites exhibited good thermal stability, with T5values in the range of 230–268 °C that increased with increasing PANI content. The prepared composites exhibited comparable PAN Tgvalues, which indicates their suitability for processing. Instrumental analysis of polymers and composites was carried out using UV–visible spectroscopy, thermogravimetric analysis, differential scanning calorimetry, dynamic mechanical thermal analysis and scanning electron microscopy.
Critical role of SMG7 in activation of the ATR-CHK1 axis in response to genotoxic stress
Authors: Ho Kathleen , Luo Hongwei , Zhu Wei , Tang Yi 
Published: April 2021

AbstractCHK1 is a crucial DNA damage checkpoint kinase and its activation, which requires ATR and RAD17, leads to inhibition of DNA replication and cell cycle progression. Recently, we reported that SMG7 stabilizes and activates p53 to induce G1 arrest upon DNA damage; here we show that SMG7 plays a critical role in the activation of the ATR-CHK1 axis. Following genotoxic stress, SMG7-null cells exhibit deficient ATR signaling, indicated by the attenuated phosphorylation of CHK1 and RPA32, and importantly, unhindered DNA replication and fork progression. Through its 14-3-3 domain, SMG7 interacts directly with the Ser635-phosphorylated RAD17 and promotes chromatin retention of the 9-1-1 complex by the RAD17-RFC, an essential step to CHK1 activation. Furthermore, through maintenance of CHK1 activity, SMG7 controls G2-M transition and facilitates orderly cell cycle progression during recovery from replication stress. Taken together, our data reveals SMG7 as an indispensable signaling component in the ATR-CHK1 pathway during genotoxic stress response.