bluuebunny/crossref_metadata_2025_split huggingface dataset:
A defined N6-methyladenosine (m6A) profile conferred by METTL3 regulates muscle stem cell/myoblast state transitions
Authors: Gheller Brandon J. , Blum Jamie E. , Fong Ern Hwei Hannah , Malysheva Olga V. , Cosgrove Benjamin D. , Thalacker-Mercer Anna E. 
Published: September 2020

AbstractMuscle-specific adult stem cells (MuSCs) are required for skeletal muscle regeneration. To ensure efficient skeletal muscle regeneration after injury, MuSCs must undergo state transitions as they are activated from quiescence, give rise to a population of proliferating myoblasts, and continue either to terminal differentiation, to repair or replace damaged myofibers, or self-renewal to repopulate the quiescent population. Changes in MuSC/myoblast state are accompanied by dramatic shifts in their transcriptional profile. Previous reports in other adult stem cell systems have identified alterations in the most abundant internal mRNA modification, N6-methyladenosine (m6A), conferred by its active writer, METTL3, to regulate cell state transitions through alterations in the transcriptional profile of these cells. Our objective was to determine if m6A-modification deposition via METTL3 is a regulator of MuSC/myoblast state transitions in vitro and in vivo. Using liquid chromatography/mass spectrometry we identified that global m6A levels increase during the early stages of skeletal muscle regeneration, in vivo, and decline when C2C12 myoblasts transition from proliferation to differentiation, in vitro. Using m6A-specific RNA-sequencing (MeRIP-seq), a distinct profile of m6A-modification was identified, distinguishing proliferating from differentiating C2C12 myoblasts. RNAi studies show that reducing levels of METTL3, the active m6A methyltransferase, reduced global m6A levels and forced C2C12 myoblasts to prematurely differentiate. Reducing levels of METTL3 in primary mouse MuSCs prior to transplantation enhanced their engraftment capacity upon primary transplantation, however their capacity for serial transplantation was lost. In conclusion, METTL3 regulates m6A levels in MuSCs/myoblasts and controls the transition of MuSCs/myoblasts to different cell states. Furthermore, the first transcriptome wide map of m6A-modifications in proliferating and differentiating C2C12 myoblasts is provided and reveals a number of genes that may regulate MuSC/myoblast state transitions which had not been previously identified.
A transient enhancement of Mercury’s exosphere at extremely high altitudes inferred from pickup ions
Authors: Jasinski Jamie M. , Regoli Leonardo H. , Cassidy Timothy A. , Dewey Ryan M. , Raines Jim M. , Slavin James A. , Coates Andrew J. , Gershman Daniel J. , Nordheim Tom A. , Murphy Neil 
Published: September 2020

AbstractMercury has a global dayside exosphere, with measured densities of 10−2 cm−3 at ~1500 km. Here we report on the inferred enhancement of neutral densities (<102 cm−3) at high altitudes (~5300 km) by the MESSENGER spacecraft. Such high-altitude densities cannot be accounted for by the typical exosphere. This event was observed by the Fast-Imaging Plasma Spectrometer (FIPS), which detected heavy ions of planetary origin that were recently ionized, and “picked up” by the solar wind. We estimate that the neutral density required to produce the observed pickup ion fluxes is similar to typical exospheric densities found at ~700 km altitudes. We suggest that this event was most likely caused by a meteroid impact. Understanding meteoroid impacts is critical to understanding the source processes of the exosphere at Mercury, and the use of plasma spectrometers will be crucial for future observations with the Bepi-Colombo mission.
Thousands of reptile species threatened by under-regulated global trade
Authors: Marshall Benjamin M. , Strine Colin , Hughes Alice C. 
Published: September 2020

AbstractWildlife trade is a key driver of the biodiversity crisis. Unregulated, or under-regulated wildlife trade can lead to unsustainable exploitation of wild populations. International efforts to regulate wildlife mostly miss ‘lower-value’ species, such as those imported as pets, resulting in limited knowledge of trade in groups like reptiles. Here we generate a dataset on web-based private commercial trade of reptiles to highlight the scope of the global reptile trade. We find that over 35% of reptile species are traded online. Three quarters of this trade is in species that are not covered by international trade regulation. These species include numerous endangered or range-restricted species, especially hotspots within Asia. Approximately 90% of traded reptile species and half of traded individuals are captured from the wild. Exploitation can occur immediately after scientific description, leaving new endemic species especially vulnerable. Pronounced gaps in regulation imply trade is having unknown impacts on numerous threatened species. Gaps in monitoring demand a reconsideration of international reptile trade regulations. We suggest reversing the status-quo, requiring proof of sustainability before trade is permitted.
Coastal flooding will disproportionately impact people on river deltas
Authors: Edmonds Douglas A. , Caldwell Rebecca L. , Brondizio Eduardo S. , Siani Sacha M. O. 
Published: September 2020

AbstractClimate change is intensifying tropical cyclones, accelerating sea-level rise, and increasing coastal flooding. River deltas are especially vulnerable to flooding because of their low elevations and densely populated cities. Yet, we do not know how many people live on deltas and their exposure to flooding. Using a new global dataset, we show that 339 million people lived on river deltas in 2017 and 89% of those people live in the same latitudinal zone as most tropical cyclone activity. We calculate that 41% (31 million) of the global population exposed to tropical cyclone flooding live on deltas, with 92% (28 million) in developing or least developed economies. Furthermore, 80% (25 million) live on sediment-starved deltas, which cannot naturally mitigate flooding through sediment deposition. Given that coastal flooding will only worsen, we must reframe this problem as one that will disproportionately impact people on river deltas, particularly in developing and least-developed economies.
Development of an ObLiGaRe Doxycycline Inducible Cas9 system for pre-clinical cancer drug discovery
Authors: Lundin Anders , Porritt Michelle J. , Jaiswal Himjyot , Seeliger Frank , Johansson Camilla , Bidar Abdel Wahad , Badertscher Lukas , Wimberger Sandra , Davies Emma J. , Hardaker Elizabeth , Martins Carla P. , James Emily , Admyre Therese , Taheri-Ghahfarokhi Amir , Bradley Jenna , Schantz Anna , Alaeimahabadi Babak , Clausen Maryam , Xu Xiufeng , Mayr Lorenz M. , Nitsch Roberto , Bohlooly-Y Mohammad , Barry Simon T. , Maresca Marcello 
Published: September 2020

AbstractThe CRISPR-Cas9 system has increased the speed and precision of genetic editing in cells and animals. However, model generation for drug development is still expensive and time-consuming, demanding more target flexibility and faster turnaround times with high reproducibility. The generation of a tightly controlled ObLiGaRe doxycycline inducible SpCas9 (ODInCas9) transgene and its use in targeted ObLiGaRe results in functional integration into both human and mouse cells culminating in the generation of the ODInCas9 mouse. Genomic editing can be performed in cells of various tissue origins without any detectable gene editing in the absence of doxycycline. Somatic in vivo editing can model non-small cell lung cancer (NSCLC) adenocarcinomas, enabling treatment studies to validate the efficacy of candidate drugs. The ODInCas9 mouse allows robust and tunable genome editing granting flexibility, speed and uniformity at less cost, leading to high throughput and practical preclinical in vivo therapeutic testing.
Homopolymer self-assembly of poly(propylene sulfone) hydrogels via dynamic noncovalent sulfone–sulfone bonding
Authors: Du Fanfan , Qiao Baofu , Nguyen Trung Dac , Vincent Michael P. , Bobbala Sharan , Yi Sijia , Lescott Chamille , Dravid Vinayak P. , Olvera de la Cruz Monica , Scott Evan Alexander 
Published: September 2020

AbstractNatural biomolecules such as peptides and DNA can dynamically self-organize into diverse hierarchical structures. Mimicry of this homopolymer self-assembly using synthetic systems has remained limited but would be advantageous for the design of adaptive bio/nanomaterials. Here, we report both experiments and simulations on the dynamic network self-assembly and subsequent collapse of the synthetic homopolymer poly(propylene sulfone). The assembly is directed by dynamic noncovalent sulfone–sulfone bonds that are susceptible to solvent polarity. The hydration history, specified by the stepwise increase in water ratio within lower polarity water-miscible solvents like dimethylsulfoxide, controls the homopolymer assembly into crystalline frameworks or uniform nanostructured hydrogels of spherical, vesicular, or cylindrical morphologies. These electrostatic hydrogels have a high affinity for a wide range of organic solutes, achieving >95% encapsulation efficiency for hydrophilic small molecules and biologics. This system validates sulfone–sulfone bonding for dynamic self-assembly, presenting a robust platform for controllable gelation, nanofabrication, and molecular encapsulation.
Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming
Authors: Guo Xue , Gao Qun , Yuan Mengting , Wang Gangsheng , Zhou Xishu , Feng Jiajie , Shi Zhou , Hale Lauren , Wu Linwei , Zhou Aifen , Tian Renmao , Liu Feifei , Wu Bo , Chen Lijun , Jung Chang Gyo , Niu Shuli , Li Dejun , Xu Xia , Jiang Lifen , Escalas Arthur , Wu Liyou , He Zhili , Van Nostrand Joy D. , Ning Daliang , Liu Xueduan , Yang Yunfeng , Schuur Edward. A. G. , Konstantinidis Konstantinos T. , Cole James R. , Penton C. Ryan , Luo Yiqi , Tiedje James M. , Zhou Jizhong 
Published: September 2020

AbstractSoil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration (Q10) in a temperate grassland ecosystem persistently decreases by 12.0 ± 3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5–19%, and reduces model parametric uncertainty by 55–71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6 ± 7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted.
Quantitative analysis of global protein stability rates in tissues
Authors: McClatchy Daniel B. , Martínez-Bartolomé Salvador , Gao Yu , Lavallée-Adam Mathieu , Yates John R. 
Published: September 2020

AbstractProtein degradation is an essential mechanism for maintaining proteostasis in response to internal and external perturbations. Disruption of this process is implicated in many human diseases. We present a new technique, QUAD (Quantification of Azidohomoalanine Degradation), to analyze the global degradation rates in tissues using a non-canonical amino acid and mass spectrometry. QUAD analysis reveals that protein stability varied within tissues, but discernible trends in the data suggest that cellular environment is a major factor dictating stability. Within a tissue, different organelles and protein functions were enriched with different stability patterns. QUAD analysis demonstrated that protein stability is enhanced with age in the brain but not in the liver. Overall, QUAD allows the first global quantitation of protein stability rates in tissues, which will allow new insights and hypotheses in basic and translational research.
A lectin-coupled porous silicon-based biosensor: label-free optical detection of bacteria in a real-time mode
Authors: Yaghoubi Mona , Rahimi Fereshteh , Negahdari Babak , Rezayan Ali Hossein , Shafiekhani Azizollah 
Published: September 2020

AbstractAccuracy and speed of detection, along with technical and instrumental simplicity, are indispensable for the bacterial detection methods. Porous silicon (PSi) has unique optical and chemical properties which makes it a good candidate for biosensing applications. On the other hand, lectins have specific carbohydrate-binding properties and are inexpensive compared to popular antibodies. We propose a lectin-conjugated PSi-based biosensor for label-free and real-time detection of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) by reflectometric interference Fourier transform spectroscopy (RIFTS). We modified meso-PSiO2 (10–40 nm pore diameter) with three lectins of ConA (Concanavalin A), WGA (Wheat Germ Agglutinin), and UEA (Ulex europaeus agglutinin) with various carbohydrate specificities, as bioreceptor. The results showed that ConA and WGA have the highest binding affinity for E. coli and S. aureus respectively and hence can effectively detect them. This was confirmed by 6.8% and 7.8% decrease in peak amplitude of fast Fourier transform (FFT) spectra (at 105 cells mL−1 concentration). A limit of detection (LOD) of about 103 cells mL−1 and a linear response range of 103 to 105 cells mL−1 were observed for both ConA-E. coli and WGA-S. aureus interaction platforms that are comparable to the other reports in the literature. Dissimilar response patterns among lectins can be attributed to the different bacterial cell wall structures. Further assessments were carried out by applying the biosensor for the detection of Klebsiella aerogenes and Bacillus subtilis bacteria. The overall obtained results reinforced the conjecture that the WGA and ConA have a stronger interaction with Gram-positive and Gram-negative bacteria, respectively. Therefore, it seems that specific lectins can be suggested for bacterial Gram-typing or even serotyping. These observations were confirmed by the principal component analysis (PCA) model.
A data-driven methodology to discover similarities between cocaine samples
Authors: Cascini Fidelia , De Giovanni Nadia , Inserra Ilaria , Santaroni Federico , Laura Luigi 
Published: September 2020

AbstractMachine learning has been used for distinct purposes in the science field but no applications on illegal drug have been done before. This study proposes a new web-based system for cocaine classification, profiling relations and comparison, that is capable of producing meaningful output based on a large amount of chemical profiling’s data. In particular, the Profiling Relations In Drug trafficking in Europe (PRIDE) system, offers several advantages to intelligence actions across Europe. Thus, it provides a standardized, broad methodology which uses machine learning algorithms to classify and compare drug profiles, highlight how similar drug samples are, and how probable it is that they share a common origin, batch, or preparation process. We evaluated the proposed algorithms using precision and recall metrics and analyzed the quality of predictions performed by the algorithms, with respect to our gold standard. In our experiments, we reached a value of 88% for F0.5-measure, 91% for precision, and 78% for recall, confirming our main hypothesis: machine learning can learn and be applied to have an automatic classification of cocaine profiles.
Machine learning prediction in cardiovascular diseases: a meta-analysis
Authors: Krittanawong Chayakrit , Virk Hafeez Ul Hassan , Bangalore Sripal , Wang Zhen , Johnson Kipp W. , Pinotti Rachel , Zhang HongJu , Kaplin Scott , Narasimhan Bharat , Kitai Takeshi , Baber Usman , Halperin Jonathan L. , Tang W. H. Wilson 
Published: September 2020

AbstractSeveral machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. We aim to assess and summarize the overall predictive ability of ML algorithms in cardiovascular diseases. A comprehensive search strategy was designed and executed within the MEDLINE, Embase, and Scopus databases from database inception through March 15, 2019. The primary outcome was a composite of the predictive ability of ML algorithms of coronary artery disease, heart failure, stroke, and cardiac arrhythmias. Of 344 total studies identified, 103 cohorts, with a total of 3,377,318 individuals, met our inclusion criteria. For the prediction of coronary artery disease, boosting algorithms had a pooled area under the curve (AUC) of 0.88 (95% CI 0.84–0.91), and custom-built algorithms had a pooled AUC of 0.93 (95% CI 0.85–0.97). For the prediction of stroke, support vector machine (SVM) algorithms had a pooled AUC of 0.92 (95% CI 0.81–0.97), boosting algorithms had a pooled AUC of 0.91 (95% CI 0.81–0.96), and convolutional neural network (CNN) algorithms had a pooled AUC of 0.90 (95% CI 0.83–0.95). Although inadequate studies for each algorithm for meta-analytic methodology for both heart failure and cardiac arrhythmias because the confidence intervals overlap between different methods, showing no difference, SVM may outperform other algorithms in these areas. The predictive ability of ML algorithms in cardiovascular diseases is promising, particularly SVM and boosting algorithms. However, there is heterogeneity among ML algorithms in terms of multiple parameters. This information may assist clinicians in how to interpret data and implement optimal algorithms for their dataset.
Bayesian approach for maize yield response to plant density from both agronomic and economic viewpoints in North America
Authors: Lacasa Josefina , Gaspar Adam , Hinds Mark , Jayasinghege Don Sampath , Berning Dan , Ciampitti Ignacio A. 
Published: September 2020

AbstractTargeting the right agronomic optimum plant density (AOPD) for maize (Zea maysL.) is a critical management decision, but even more when the seed cost and grain selling price are accounted for, i.e. economic OPD (EOPD). From the perspective of improving those estimates, past studies have focused on utilizing a Frequentist (classical) approach for obtaining single-point estimates for the yield-density models. Alternative analysis models such as Bayesian computational methods can provide more reliable estimation for AOPD, EOPD and yield at those optimal densities and better quantify the scope of uncertainty and variability that may be in the data. Thus, the aims of this research were to (i) quantify AOPD, EOPD and yield at those plant densities, (ii) obtain and compare clusters of yield-density for different attainable yields and latitudes, and (iii) characterize their influence on EOPD variability under different economic scenarios, i.e. seed cost to corn price ratios. Maize hybrid by seeding rate trials were conducted in 24 US states from 2010 to 2019, in at least one county per state. This study identified common yield-density response curves as well as plant density and yield optimums for 460 site-years. Locations below 40.5 N latitude showed a positive relationship between AOPD and maximum yield, in parallel to the high potential level of productivity. At these latitudes, EOPD depended mostly on the maximum attainable yield. For the northern latitudes, EOPD was not only dependent on the attainable yield but on the cost:price ratio, with high ratios favoring reductions in EOPD at similar yields. A significant contribution from the Bayesian method was realizing that the variability of the estimators for AOPD is sometimes greater than the adjustment accounting for seed cost. Our results point at the differential response across latitudes and commercial relative maturity, as well as the significant uncertainty in the prediction of AOPD, relative to the economic value of the crop and the seed cost adjustments.
Characterization of semi-polar (20$$\overline{2}$$1) InGaN microLEDs
Authors: Horng Ray-Hua , Sinha Shreekant , Wu Yuh-Renn , Tarntair Fu-Guo , Han Jung , Wuu Dong-Sing 
Published: September 2020

AbstractIn this paper, semi-polar (20$$\overline{2}$$2¯1) InGaN blue light-emitting diodes (LEDs) were fabricated and compared the performance with those of LEDs grown on c-plane sapphire substrate. LEDs with different chip sizes of 100 μm × 100 μm, 75 μm × 75 μm, 25 μm × 25 μm, and 10 μm × 10 μm were used to study the influence of chip size on the device performance. It was found that the contact behavior between the n electrode and the n-GaN layer for the semi-polar (20$$\overline{2}$$2¯1) LEDs was different from that for the LEDs grown on the c-plane device. Concerning the device performance, the smaller LEDs provided a larger current density under the same voltage and presented a smaller forward voltage. However, the sidewall’s larger surface to volume ratio could affect the IQE. Therefore, the output power density reached the maximum with the 25 μm × 25 μm chip case. In addition, the low blue-shift phenomenon of semi-polar (20$$\overline{2}$$2¯1) LEDs was obtained. The larger devices exhibited the maximum IQE at a lower current density than the smaller devices, and the IQE had a larger droop as the current density increased for the LEDs grown on c-plane sapphire substrate.
Reduction of fatigue and anger-hostility by the oral administration of 5-aminolevulinic acid phosphate: a randomized, double-blind, placebo-controlled, parallel study
Authors: Higashikawa Fumiko , Kanno Keishi , Ogata Akiko , Sugiyama Masanori 
Published: September 2020

AbstractAlthough large populations feel fatigue, the standardized medicinal therapy is currently absent. In this study, we determined whether 5-aminolevulinic acid (5-ALA) supplementation alleviates the feeling of fatigue in healthy subjects who feel chronic physical tiredness. Males and females between ages of 20 and 64 who felt physical fatigue on a daily basis, with a visual analogue scale (VAS) for fatigue ≥ 40 mm, a T-score of Fatigue-Inertia in the Profile of Mood States—Second Edition—Adult (POMS2-A) ≥ 50, and a T-score of Vigor-Activity in POMS2-A ≤ 60 were recruited. Seventy eligible participants were randomly assigned to either a 5-ALA or a placebo group. During the 8 weeks of consumption, the subjects completed VAS questionnaires for fatigue and POMS2-A at 4-week intervals. The VAS values for overall feeling of fatigue and feeling of work-related fatigue, and the Anger-Hostility subscale of POMS2-A were decreased by 5-ALA with significant time × group interaction effects (p = 0.040, 0.020, and 0.045, respectively). Besides, the 5-ALA group showed significant differences in Fatigue-Inertia, Depression-Dejection and Total Mood Disturbance scores, when compared between pre- and post-intervention, while the placebo group did not. In conclusion, the oral administration of 5-ALA improves fatigue and negative mood in subjects who constantly feel physical fatigue.This clinical trial was registered with University hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) as UMIN000031528 on 2/3/2018.