bluuebunny/crossref_metadata_2025_split huggingface dataset:
Toward efficient runs of nonlinear gyrokinetic simulations assisted by a convolutional neural network model recognizing wavenumber-space images
Authors: Narita E. , Honda M. , Maeyama S. , Watanabe T.-H. 
Published: May 2022

Abstract
A neural-network based innovative model recognizing the wavenumber space images has been developed to accurately forecast when the saturation of turbulent heat fluxes commences, i.e., the saturation time, in nonlinear gyrokinetic simulations. The wavenumber space images of the perturbed distribution function are focused on, which better represent the characteristics of turbulence. The model exploiting the state-of-the-art convolutional neural network model is capable of detecting minuscule differences between the images. Once the wavenumber space image is fed into the developed model, it can quickly and almost perfectly classify which phase of the turbulence evolution in nonlinear gyrokinetic simulations the image is in: the linearly and nonlinearly growing phases and the saturation phase. It can also predict the simulation time at which the image was processed with significantly high accuracy. The model enables us to forecast the saturation time of the gyrokinetic simulation in question by feeding an image at an early stage of the simulation and receiving the degree of progress toward the saturation. The ability of the model makes it possible to easily search out a desirable initial condition that rapidly conducts the simulation to a saturation phase. Such a pre-prediction model is important for running long time simulations on a large scale supercomputer like Fugaku in view of the efficient use of computational resources. In order to improve the predictive capability for the simulation that is going to be performed, several prediction models are trained by data with different major instabilities. The best predictor is selected to be in use based on the result of the pre-performed linear stability calculation with low computational cost.
A sharpened inequality for twisted convolution
Authors: O’Neill Kevin 
Published: May 2022

Consider the trilinear form for twisted convolution on

R

2
d

\mathbb {R}^{2d}

:

T

t

(

f

)
≔
∬

f
1

(
x
)

f
2

(
y
)

f
3

(
x
+
y
)

e

i
t
σ
(
x
,
y
)

d
x
d
y
,

\begin{equation*} \mathcal {T}_t(\mathbf {f})≔\iint f_1(x)f_2(y)f_3(x+y)e^{it\sigma (x,y)}dxdy, \end{equation*}

where

σ
\sigma

is a symplectic form and

t
t

is a real-valued parameter. It is known that in the case

t
≠
0

t\neq 0

the optimal constant for twisted convolution is the same as that for convolution, though no extremizers exist. Expanding about the manifold of triples of maximizers and

t
=
0

t=0

we prove a sharpened inequality for twisted convolution with an arbitrary antisymmetric form in place of

σ
\sigma

.
Chromosome-level assembly, annotation and phylome of Pelobates cultripes, the western spadefoot toad
Authors: Liedtke Hans Christoph , Cruz Fernando , Gómez-Garrido Jèssica , Fuentes Palacios Diego , Marcet-Houben Marina , Gut Marta , Alioto Tyler , Gabaldón Toni , Gomez-Mestre Ivan 
Published: May 2022

Abstract
Genomic resources for amphibians are still hugely under-represented in vertebrate genomic research, despite being a group of major interest for ecology, evolution and conservation. Amphibians constitute a highly threatened group of vertebrates, present a vast diversity in reproductive modes, are extremely diverse in morphology, occupy most ecoregions of the world, and present the widest range in genome sizes of any major group of vertebrates. We combined Illumina, Nanopore and Hi-C sequencing technologies to assemble a chromosome-level genome sequence for an anuran with a moderate genome size (assembly span 3.09 Gb); Pelobates cultripes, the western spadefoot toad. The genome has an N50 length of 330 Mb with 98.6% of the total sequence length assembled into 14 super scaffolds, and 87.7% complete BUSCO genes. We use published transcriptomic data to provide annotations, identifying 32,684 protein-coding genes. We also reconstruct the P. cultripes phylome and identify 2,527 gene expansions. We contribute the first draft of the genome of the western spadefoot toad, P. cultripes. This species represents a relatively basal lineage in the anuran tree with an interesting ecology and a high degree of developmental plasticity, and thus is an important resource for amphibian genomic research.
Human milk oligosaccharides induce acute yet reversible compositional changes in the gut microbiota of conventional mice linked to a reduction of butyrate levels
Authors: Holst Andrea Qvortrup , Jois Harshitha , Laursen Martin Frederik , Sommer Morten O A , Licht Tine Rask , Bahl Martin Iain 
Published: May 2022

AbstractHuman Milk Oligosaccharides (HMOs) are glycans with prebiotic properties known to drive microbial selection in the infant gut, which in turn influences immune development and future health. Bifidobacteria are specialized in HMO degradation and frequently dominate the gut microbiota of breastfed infants. However, some species of Bacteroidaceae also degrade HMOs, which may prompt selection also of these species in the gut microbiota. To investigate to what extent specific HMOs affect the abundance of naturally occurring Bacteroidaceae species in a complex mammalian gut environment, we conducted a study in 40 female NMRI mice administered three structurally different HMOs, namely 6’sialyllactose (6'SL, n = 8), 3-fucosyllactose (3FL, n = 16), and Lacto-N-Tetraose (LNT, n = 8), through drinking water (5%). Compared to a control group receiving unsupplemented drinking water (n = 8), supplementation with each of the HMOs significantly increased both the absolute and relative abundance of Bacteroidaceae species in faecal samples and affected the overall microbial composition analyzed by 16s rRNA amplicon sequencing. The compositional differences were mainly attributed to an increase in the relative abundance of the genus Phocaeicola (formerly Bacteroides) and a concomitant decrease of the genus Lacrimispora (formerly Clostridium XIVa cluster). During a 1-week washout period performed specifically for the 3FL group, this effect was reversed. Short-chain fatty acid analysis of faecal water revealed a decrease in acetate, butyrate and isobutyrate levels in animals supplemented with 3FL, which may reflect the observed decrease in the Lacrimispora genus. This study highlights HMO-driven Bacteroidaceae selection in the gut environment, which may cause a reduction of butyrate-producing clostridia.
Effect of area ratio and Reynolds number on the distribution of discharge in dividing manifold
Authors: Jiang Yingzi , Alawee Wissam Hameed , Essa Fadl Abdelmonem , Abdullah Abdelkader saad , Omara Zakaria Mohamed , Ahmad Hijaz , Ali Rifaqat , Wang Fuzhang , Menni Younes 
Published: May 2022

AbstractThe goal of this study is to determine the role of the area ratio (AR) and the Reynolds number on the distribution of flow and pressure in the dividing manifold. For this purpose, five different models have been used to be analyzed under the test conditions. The first physical model is of 101.6 mm (4 in) in diameter for the master manifold in a regular longitudinal section and five sidelong of 50.8 (2 in) in diameter with a spacing of 220 mm. This model has been utilized to determine the magnitude of maldistribution inflow that usually takes place in such a design. Four other models have employed with different diameters of the header of 101.6, 76.2 and 50.8 mm and lateral of 50.8, 38.1 and 25.4 mm. These models used for the purpose of testing the stream and pressure allocation based on the AR. Three different values of 625, 790 and 950 l/min from of inlet flows examined with the employed models. The outcomes show that the AR parameter has a notable impact on the consistency of the stream from the manifold in which the uniformity of the flow distribution improves by 76% when the AR decreases from 0.48 to 0.13. It is found that the degree of uniformity of the mass discharge from the laterals is unaffected within tested Reynolds numbers of 50,000–200,000.
Study on the performance and economy of the building-integrated micro-grid considering photovoltaic and pumped storage: a case study in Foshan
Authors: Sun Haitao , Ren Qingzheng , Hou Jili , Zhao Zimo , Xie Donghuan , Zhao Wei , Meng Fanbin 
Published: May 2022

Abstract
Micro-grid is a promising technology for the energy reform in urban areas. This paper takes a building as a case study to construct a micro-grid system, which includes rooftop photovoltaic and pumped storage systems according to the characteristics of the building. Moreover, the cost–benefit analysis of the commercial building micro-grid system is also performed to obtain an in-depth understanding of the feasibility of the project. According to the characteristics of power load of commercial buildings, the working characteristics of the developed micro-grid and the mathematical model of optimal operation of micro-grid in commercial buildings is constructed. The calculation results from the mathematical model show that the dynamic payback period (PBP) of the micro-grid system of commercial buildings is 4.74 years, while the dynamic PBP of the micro-grid system of battery energy storage is 7.95 years. Therefore, the micro-grid system pumped storage has a much better economy than that with battery energy storage.
Establishing a centralized data mart from the Rakai community cohort study to improve HIV research in Rakai, Uganda
Authors: Ndyanabo Anthony , Footer Kevin , Ahmed Tanvir , Glogowski Alex , Whalen Christopher , Ssekasanvu Joseph , Ssentongo Lloyd , Lutalo Tom , Nalugoda Fred , Ha Grace K , Rosenthal Alex 
Published: May 2022

Abstract
To improve timely access to quality HIV research data, the Rakai Health Sciences Program (RHSP) Data Mart was developed to store cohort study data from a legacy database platform in a modernized system using standard data management processes. The RHSP Data Mart was developed on a Microsoft SQL Server platform using Microsoft SQL Server Integration Services with custom data mappings and queries. The data mart stores 20+ years of longitudinal HIV research data and includes standard processes for managing data, data dictionary, training materials, and a library of queries to fulfill data requests and load new data from completed survey rounds. The RHSP Data Mart enables efficient querying and analysis of multidimensional research data by simplifying data integration and processing. A sustainable database platform with well-defined data management processes promotes data accessibility and reproducibility, enabling researchers to advance their understanding and management of infectious diseases.
Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers
Authors: Hirten Robert P , Tomalin Lewis , Danieletto Matteo , Golden Eddye , Zweig Micol , Kaur Sparshdeep , Helmus Drew , Biello Anthony , Pyzik Renata , Bottinger Erwin P , Keefer Laurie , Charney Dennis , Nadkarni Girish N , Suarez-Farinas Mayte , Fayad Zahi A 
Published: May 2022

Abstract

Objective
To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices.

Materials and Methods
Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app.

Results
We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84–89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±∼4%) and specificity of 77% (CI ±∼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age.

Discussion
We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection.

Conclusion
Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.
Shocks and dust formation in nova V809 Cep
Authors: Babul Aliya-Nur , Sokoloski Jennifer L , Chomiuk Laura , Linford Justin D , Weston Jennifer H S , Aydi Elias , Sokolovsky Kirill V , Kawash Adam M , Mukai Koji 
Published: May 2022

ABSTRACT
The discovery that many classical novae produce detectable GeV γ-ray emission has raised the question of the role of shocks in nova eruptions. Here, we use radio observations of nova V809 Cep (nova Cep 2013) with the Jansky Very Large Array to show that it produced non-thermal emission indicative of particle acceleration in strong shocks for more than a month starting about 6 weeks into the eruption, quasi-simultaneous with the production of dust. Broadly speaking, the radio emission at late times – more than 6 months or so into the eruption – is consistent with thermal emission from $10^{-4}\, {\rm M}_\odot$ of freely expanding, 104 K ejecta. At 4.6 and 7.4 GHz, however, the radio light curves display an initial early-time peak 76 d after the discovery of the eruption in the optical (t0). The brightness temperature at 4.6 GHz on day 76 was greater than 105 K, an order of magnitude above what is expected for thermal emission. We argue that the brightness temperature is the result of synchrotron emission due to internal shocks within the ejecta. The evolution of the radio spectrum was consistent with synchrotron emission that peaked at high frequencies before low frequencies, suggesting that the synchrotron from the shock was initially subject to free–free absorption by optically thick ionized material in front of the shock. Dust formation began around day 37, and we suggest that internal shocks in the ejecta were established prior to dust formation and caused the nucleation of dust.
The lively accretion disc in NGC 2992 – II. The 2019/2021 X-ray monitoring campaigns
Authors: Middei R , Marinucci A , Braito V , Bianchi S , De Marco B , Luminari A , Matt G , Nardini E , Perri M , Reeves J N , Vagnetti F 
Published: May 2022

ABSTRACT
We report on the short- and long-term X-ray properties of the bright nearby Seyfert 2 galaxy NGC 2992, which was extensively observed with Swift, XMM–Newton, and NuSTAR. Swift targeted the source more than 100 times between 2019 and 2021 in the context of two monitoring campaigns. Both time-averaged and time-resolved analyses are performed, and we find that the short-to-long term spectral properties of NGC 2992 are dominated by a highly variable nuclear continuum. The source varied in the 2–10 keV energy band from 0.6 to 12 × 10−11 erg cm−2 s−1 during the two year long Swift monitoring. The fastest 2–10 keV flux change (by a factor of $\sim 60{{\ \rm per\ cent}}$) occurred on a time-scale of a few hours. The overall emission spectrum of the source is consistent with a power law-like continuum (Γ = 1.69 ± 0.01) absorbed by a constant line-of-sight column density NH = (7.8 ± 0.1) × 1021$\rm cm^{-2}$. The reflected emission is likely due to matter with an average column density NH = (9.6 ± 2.7) × 1022$\rm cm^{-2}$, thus NGC 2992 appears to have a globally Compton-thin circumnuclear medium. This scenario is fully supported by an independent analysis of the fractional variability and by XMM–Newton multiyear spectra.
Accelerated Domestication of New Crops: Yield is Key
Authors: Luo Guangbin , Najafi Javad , Correia Pedro M P , Trinh Mai Duy Luu , Chapman Elizabeth A , Østerberg Jeppe Thulin , Thomsen Hanne Cecilie , Pedas Pai Rosager , Larson Steve , Gao Caixia , Poland Jesse , Knudsen Søren , DeHaan Lee , Palmgren Michael 
Published: May 2022

Abstract
Sustainable agriculture in the future will depend on crops that are tolerant to biotic and abiotic stresses, require minimal input of water and nutrients and can be cultivated with a minimal carbon footprint. Wild plants that fulfill these requirements abound in nature but are typically low yielding. Thus, replacing current high-yielding crops with less productive but resilient species will require the intractable trade-off of increasing land area under cultivation to produce the same yield. Cultivating more land reduces natural resources, reduces biodiversity and increases our carbon footprint. Sustainable intensification can be achieved by increasing the yield of underutilized or wild plant species that are already resilient, but achieving this goal by conventional breeding programs may be a long-term prospect. De novo domestication of orphan or crop wild relatives using mutagenesis is an alternative and fast approach to achieve resilient crops with high yields. With new precise molecular techniques, it should be possible to reach economically sustainable yields in a much shorter period of time than ever before in the history of agriculture.
Plant Hormonomics: A Key Tool for Deep Physiological Phenotyping to Improve Crop Productivity
Authors: Hirayama Takashi , Mochida Keiichi 
Published: May 2022

Abstract
Agriculture is particularly vulnerable to climate change. To cope with the risks posed by climate-related stressors to agricultural production, global population growth, and changes in food preferences, it is imperative to develop new climate-smart crop varieties with increased yield and environmental resilience. Molecular genetics and genomic analyses have revealed that allelic variations in genes involved in phytohormone-mediated growth regulation have greatly improved productivity in major crops. Plant science has remarkably advanced our understanding of the molecular basis of various phytohormone-mediated events in plant life. These findings provide essential information for improving the productivity of crops growing in changing climates. In this review, we highlight the recent advances in plant hormonomics (multiple phytohormone profiling) and discuss its application to crop improvement. We present plant hormonomics as a key tool for deep physiological phenotyping, focusing on representative plant growth regulators associated with the improvement of crop productivity. Specifically, we review advanced methodologies in plant hormonomics, highlighting mass spectrometry- and nanosensor-based plant hormone profiling techniques. We also discuss the applications of plant hormonomics in crop improvement through breeding and agricultural management practices.
Covered Interest Parity Arbitrage
Authors: Rime Dagfinn , Schrimpf Andreas , Syrstad Olav 
Published: May 2022

Abstract
To understand deviations from covered interest parity (CIP), it is crucial to account for heterogeneity in funding costs across both banks and currency areas. For most market participants, the no-arbitrage relation holds fairly well when implemented using marginal funding costs and risk-free investment instruments. However, a few high-rated banks do enjoy CIP-arbitrage opportunities. Dealers avert inventory imbalances stemming from lower-rated banks’ usage of FX swaps to obtain dollar funding by inducing opposite (arbitrage) flows from high-rated banks. Arbitrage trades are difficult to scale, however, because funding costs increase as soon as arbitrageurs increase positions.
Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
Biological and clinical insights from a randomized phase 2 study of an anti-oncostatin M monoclonal antibody in systemic sclerosis
Authors: Denton Christopher P , del Galdo Francesco , Khanna Dinesh , Vonk Madelon C , Chung Lorinda , Johnson Sindhu R , Varga John , Furst Daniel E , Temple Jane , Zecchin Chiara , Csomor Eszter , Lee Amy , Wisniacki Nicolas , Flint Shaun M , Reid Juliet 
Published: May 2022

Abstract

Objectives
The cytokine oncostatin M (OSM) is implicated in the pathology of SSc. Inhibiting OSM signalling using GSK2330811 (an anti-OSM monoclonal antibody) in patients with SSc has the potential to slow or stop the disease process.

Methods
This multicentre, randomized, double-blind, placebo-controlled study enrolled participants ≥18 years of age with active dcSSc. Participants were randomized 3:1 (GSK2330811:placebo) in one of two sequential cohorts to receive GSK2330811 (cohort 1: 100 mg; cohort 2: 300 mg) or placebo s.c. every other week for 12 weeks. The primary endpoint was safety; blood and skin biopsy samples were collected to explore mechanistic effects on inflammation and fibrosis. Clinical efficacy was an exploratory endpoint.

Results
Thirty-five participants were randomized to placebo (n = 8), GSK2330811 100 mg (n = 3) or GSK2330811 300 mg (n = 24). Proof of mechanism, measured by coordinate effects on biomarkers of inflammation or fibrosis, was not demonstrated following GSK2330811 treatment. There were no meaningful differences between GSK2330811 and placebo for any efficacy endpoints. The safety and tolerability of GSK2330811 were not favourable in the 300 mg group, with on-target, dose-dependent adverse events related to decreases in haemoglobin and platelet count that were not observed in the 100 mg or placebo groups.

Conclusion
Despite a robust and novel experimental medicine approach and evidence of target engagement, anticipated SSc-related biologic effects of GSK2330811 were not different from placebo and safety was unfavourable, suggesting OSM inhibition may not be a useful therapeutic strategy in SSc.

Trial registration number
ClinicalTrials.gov, NCT03041025; EudraCT, 2016-003417-95.
Bayesian Analyses of Comparative Data with the Ornstein–Uhlenbeck Model: Potential Pitfalls
Authors: Cornuault Josselin 
Published: May 2022

Abstract
The Ornstein–Uhlenbeck (OU) model is widely used in comparative phylogenetic analyses to study the evolution of quantitative traits. It has been applied to various purposes, including the estimation of the strength of selection or ancestral traits, inferring the existence of several selective regimes, or accounting for phylogenetic correlation in regression analyses. Most programs implementing statistical inference under the OU model have resorted to maximum-likelihood (ML) inference until the recent advent of Bayesian methods. A series of issues have been noted for ML inference using the OU model, including parameter nonidentifiability. How these problems translate to a Bayesian framework has not been studied much to date and is the focus of the present article. In particular, I aim to assess the impact of the choice of priors on parameter estimates. I show that complex interactions between parameters may cause the priors for virtually all parameters to impact inference in sometimes unexpected ways, whatever the purpose of inference. I specifically draw attention to the difficulty of setting the prior for the selection strength parameter, a task to be undertaken with much caution. I particularly address investigators who do not have precise prior information, by highlighting the fact that the effect of the prior for one parameter is often only visible through its impact on the estimate of another parameter. Finally, I propose a new parameterization of the OU model that can be helpful when prior information about the parameters is not available. [Bayesian inference; Brownian motion; Ornstein–Uhlenbeck model; phenotypic evolution; phylogenetic comparative methods; prior distribution; quantitative trait evolution.]