bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fluorinating the graphene-like BeO monolayer: A spin-polarized first principles study of the electronic, magnetic and optical properties
Authors: Van On Vo , Hoat D M , Nguyen Duy Khanh , Ponce-Pérez R , Vu Tuan V , Rivas-Silva J F , Cocoletzi Gregorio H 
Published: September 2020

Abstract
Chemical functionalization of low-dimensional materials has been widely employed to create new multifunctional materials with novel properties. In this paper, we present the results of the fluorination effects on the structural, electronic, magnetic, and optical properties of the BeO monolayer. Materials stability is examined by means of the phonon dispersion curves and binding energy. Significant structural changes of the BeO monolayer are induced by the fluorination. The pristine single layer possesses a wide indirect K − Γ band gap of 5.23 eV with no spin-polarization. Either half-metallic or magnetic semiconductor structures with important ferromagnetism can be induced by the half-fluorination, and a total magnetic moment of 1 μ

B
may be obtained. The full-fluorination produces a magnetism-free material, however causes the indirect-direct gap transition and a considerable reduction of the energy gap to 0.97 eV. The fluorination enhances the BeO monolayer optical properties in the low energy regime, and also the interaction with the light polarized in the z-direction. Our simulations suggest that the fluorination may be an effective approach to tune the BeO single layer physical properties producing new materials, which are expected to find prospective applications in the spintronic and optoelectronic nano-devices.
Identifying Pre-ictal Period of Seizure and Better Brain Lobes for Seizure Detection using EEG Biomarkers
Authors: Adalarasu K. , Shrisowmya P. , Jagannath M. 
Published: September 2020

AbstractSeizures are one of the serious neuropsychiatric disorders that affect 50 million people across the world, and 80% of them live in the developing countries like India. In such kind of critical care, delays in the diagnosis of neurological conditions can have a significant impact on patients with seizure. To address this treatment gap, we propose an early identificationof pre-ictal period of seizure and brain lobes for seizure detection by using electroencephalogram (EEG) biomarkers. Five subjects were voluntarily involved in this study. The raw EEG signals were filtered and decomposed using wavelet techniques and the features such as relative delta energy, relative theta energy, total beta energy, heart rate, and neuronal activity were extracted for analysis. The results showed that the relative theta activity and the neuronal activity are found to be the better features in early predicting of period of pre-ictal seizures, as both the features can be seen with significant differences among the three different time periods in all the electrode positions, which are taken into consideration. It is also evident that the occipital lobe is better in indicating the pre-ictal period earlier, as the observed data shows expected outcome from the electrode positions O1 and O2 (occipital lobe). On national level, this study will enable the primary health centres to fulfil its dream of providing basic medical facilities to serve huge sections of population with seizure.
Environmental engagement, religion and spirituality in the context of secularization
Authors: Briguglio Marie , Garcia-Muñoz Teresa , Neuman Shoshana 
Published: September 2020

Abstract
Over the past half century, a literature has developed across a range of disciplines exploring the relationship between religion and environmental engagement, including pro-environmental attitudes and behaviours. Empirical results are diverse and the relationship seems to vary in size and direction, depending on definitions and the method of investigation adopted. An increasingly important phenomenon which has received far less attention is that of spirituality, within/out the context of a religion. This paper contributes to the literature by examining the question in a predominantly Roman Catholic European Union country where church attendance is in decline. It employs a nationally representative dataset (n = 1029) which includes diverse measures of religiosity and spirituality, as well as measures of interest in environmental issues, in wildlife and natural history, and engagement in countryside activities and gardening, together with relevant socio-economic control variables. Our findings confirm that the usual socio-economic determinants are associated with this type of environmental engagement. We find that church attendance adds no further explanatory power to environmental engagement. On the other hand, participation in socio-cultural religious activities and self-assessed spirituality are positively and significantly associated of various dimensions of environmental engagement.
Conditional maximum entropy and superstatistics
Authors: Davis Sergio 
Published: September 2020

Abstract
Superstatistics describes nonequilibrium steady states as superpositions of canonical ensembles with a probability distribution of temperatures. Rather than assume a certain distribution of temperature, recently [2020 J. Phys. A: Math. Theor.
53 045004] we have discussed general conditions under which a system in contact with a finite environment can be described by superstatistics together with a physically interpretable, microscopic definition of temperature. In this work, we present a new interpretation of this result in terms of the standard maximum entropy principle using conditional expectation constraints, and provide an example model where this framework can be tested.
Interfacial coupling effects on the thermal conductivity of few-layer graphene
Authors: Kan Yajing , Hong Feng , Wei Zhiyong , Bi Kedong 
Published: September 2020

Abstract
The thermal conductivities of both suspended and supported few-layer graphene (FLG) were investigated via molecular dynamics simulations. The results indicate that the thermal conductivity of a suspended FLG sample decreases by 3.9% from 511.2 W m−1 K−1 upon an increase in the number of layers from 1 to 20 layers, whereas it increases by 5.5% to 486.8 W m−1 K−1 in the case of supported FLG specimens on a smooth crystalline silicon surface. Both trends converge when the number of layers is higher than five. The effects of the substrate roughness on the supported FLG samples were also investigated. The results show that their thermal conductivity on a rough silicon surface is lower than that on a smooth silicon surface. In order to demonstrate the importance of interfacial coupling on the phonon transport properties, the coupling strength parameter was enhanced by a factor of 3 or 10 to see the influence on the thermal conductivity. The simulations show that the thermal conductivity decreases with an increasing coupling strength. Lastly, the phonon dispersion of a two-layer graphene specimen was calculated by varying the interlayer coupling strength. The calculations show that the coupling strength is mainly influenced by the out-of-plane phonon dispersion relation. The frequency of flexural acoustic (ZA’) phonons around the center of the first Brillouin zone increases significantly from 2.14 to 6.78 THz when the interlayer coupling strength is enhanced by a factor of 10. This may decrease the phonon group velocity and provide more scattering channels, and thus reduce the thermal conductivity.
Using models to improve optimizers for variational quantum algorithms
Authors: Sung Kevin J , Yao Jiahao , Harrigan Matthew P , Rubin Nicholas C , Jiang Zhang , Lin Lin , Babbush Ryan , McClean Jarrod R 
Published: September 2020

Abstract
Variational quantum algorithms are a leading candidate for early applications on noisy intermediate-scale quantum computers. These algorithms depend on a classical optimization outer-loop that minimizes some function of a parameterized quantum circuit. In practice, finite sampling error and gate errors make this a stochastic optimization with unique challenges that must be addressed at the level of the optimizer. The sharp trade-off between precision and sampling time in conjunction with experimental constraints necessitates the development of new optimization strategies to minimize overall wall clock time in this setting. In this work, we introduce two optimization methods and numerically compare their performance with common methods in use today. The methods are surrogate model-based algorithms designed to improve reuse of collected data. They do so by utilizing a least-squares quadratic fit of sampled function values within a moving trusted region to estimate the gradient or a policy gradient. To make fair comparisons between optimization methods, we develop experimentally relevant cost models designed to balance efficiency in testing and accuracy with respect to cloud quantum computing systems. The results here underscore the need to both use relevant cost models and optimize hyperparameters of existing optimization methods for competitive performance. The methods introduced here have several practical advantages in realistic experimental settings, and we have used one of them successfully in a separately published experiment on Google’s Sycamore device.
Coarse-grain cluster analysis of tensors with application to climate biome identification
Authors: DeSantis Derek , Wolfram Phillip J , Bennett Katrina , Alexandrov Boian 
Published: September 2020

Abstract
A tensor provides a concise way to codify the interdependence of complex data. Treating a tensor as a d-way array, each entry records the interaction between the different indices. Clustering provides a way to parse the complexity of the data into more readily understandable information. Clustering methods are heavily dependent on the algorithm of choice, as well as the chosen hyperparameters of the algorithm. However, their sensitivity to data scales is largely unknown.
In this work, we apply the discrete wavelet transform to analyze the effects of coarse-graining on clustering tensor data. We are particularly interested in understanding how scale affects clustering of the Earth’s climate system. The discrete wavelet transform allows classification of the Earth’s climate across a multitude of spatial-temporal scales. The discrete wavelet transform is used to produce an ensemble of classification estimates, as opposed to a single classification. Each element of the ensemble is a clustering at a different spatial-temporal scale. Information theoretic approaches are used to identify important scale lengths in clustering the L15 Climate Datset. We also discover a sub-collection of the ensemble that spans the majority of the variance observed, allowing for efficient consensus clustering techniques that can be used to identify climate biomes.
Adversarial reverse mapping of equilibrated condensed-phase molecular structures
Authors: Stieffenhofer Marc , Wand Michael , Bereau Tristan 
Published: September 2020

AbstractA tight and consistent link between resolutions is crucial to further expand the impact of multiscale modeling for complex materials. We herein tackle the generation of condensed molecular structures as a refinement—backmapping—of a coarse-grained (CG) structure. Traditional schemes start from a rough coarse-to-fine mapping and perform further energy minimization and molecular dynamics simulations to equilibrate the system. In this study we introduce DeepBackmap: A deep neural network based approach to directly predict equilibrated molecular structures for condensed-phase systems. We use generative adversarial networks to learn the Boltzmann distribution from training data and realize reverse mapping by using the CG structure as a conditional input. We apply our method to a challenging condensed-phase polymeric system. We observe that the model trained in a melt has remarkable transferability to the crystalline phase. The combination of data-driven and physics-based aspects of our architecture help reach temperature transferability with only limited training data.
Improving proton dose calculation accuracy by using deep learning
Authors: Wu Chao , Nguyen Dan , Xing Yixun , Montero Ana Barragan , Schuemann Jan , Shang Haijiao , Pu Yuehu , Jiang Steve 
Published: September 2020

Abstract
Pencil beam (PB) dose calculation is fast but inaccurate due to the approximations when dealing with inhomogeneities. Monte Carlo (MC) dose calculation is the most accurate method but it is time consuming. The aim of this study was to develop a deep learning model that can boost the accuracy of PB dose calculation to the level of MC dose by converting PB dose to MC dose for different tumor sites. The proposed model uses the PB dose and computed tomography image as inputs to generate the MC dose. We used 290 patients (90 head and neck, 93 liver, 75 prostate and 32 lung) to train, validate, and test the model. For each tumor site, we performed four numerical experiments to explore various combinations of training datasets. Training the model on data from all tumor sites together and using the dose distribution of each individual beam as input yielded the best performance for all four tumor sites. The average gamma passing rate (1 mm/1%) between the converted and the MC dose was 92.8%, 92.7%, 89.7% and 99.6% for head and neck, liver, lung, and prostate test patients, respectively. The average dose conversion time for a single field was less than 4 s. The trained model can be adapted to new datasets through transfer learning. Our deep learning-based approach can quickly boost the accuracy of PB dose to that of MC dose. The developed model can be added to the clinical workflow of proton treatment planning to improve dose calculation accuracy.
A nonlocal transport equation modeling complex roots of polynomials under differentiation
Authors: O’Rourke Sean , Steinerberger Stefan 
Published: September 2020

Letpn:C→Cp_n:\mathbb {C} \rightarrow \mathbb {C}be a random complex polynomial whose roots are sampled i.i.d. from a radial distribution2πru(r)dr2\pi r u(r) drin the complex plane. A natural question is how the distribution of roots evolves under repeated (sayn/2−n/2-times) differentiation of the polynomial. We conjecture a mean-field expansion for the evolution ofψ(s)=u(s)s\psi (s) = u(s) s:∂ψ∂t=∂∂x((1x∫0xψ(s)ds)−1ψ(x)).\begin{equation*} \frac {\partial \psi }{\partial t} = \frac {\partial }{\partial x} \left ( \left ( \frac {1}{x} \int _{0}^{x} \psi (s) ds \right )^{-1} \psi (x) \right ). \end{equation*}The evolution ofψ(s)≡1\psi (s) \equiv 1corresponds to the evolution of random Taylor polynomialspn(z)=∑k=0nγkzkk!whereγk∼NC(0,1).\begin{equation*} p_n(z) = \sum _{k=0}^{n}{ \gamma _k \frac {z^k}{k!}} \quad \text {where} \quad \gamma _k \sim \mathcal {N}_{\mathbb {C}}(0,1). \end{equation*}We discuss some numerical examples suggesting that this particular solution may be stable. We prove that the solution is linearly stable. The linear stability analysis reduces to the classical Hardy integral inequality. Many open problems are discussed.
Definable combinatorics at the first uncountable cardinal
Authors: Chan William , Jackson Stephen 
Published: September 2020

We work throughout in the theory

Z
F

\mathsf {ZF}

with the axiom of determinacy,

A
D

\mathsf {AD}

. We introduce and prove some club uniformization principles under

A
D

\mathsf {AD}

and

A
D

R

\mathsf {AD}_\mathbb {R}

. Using these principles, we establish continuity results for functions of the form

Φ
:
[

ω

1

]

ω

1

→

ω

1

\Phi \colon [{\omega _{1}}]^{\omega _{1}} \rightarrow {\omega _{1}}

and

Ψ
:
[

ω

1

]

ω

1

→

ω

1

ω

1

\Psi \colon [{\omega _{1}}]^{\omega _{1}} \rightarrow {}^{\omega _{1}}{\omega _{1}}

. Specifically, for every function

Φ
:
[

ω
1

]

ω
1

→

ω
1

\Phi \colon [\omega _1]^{\omega _1} \rightarrow \omega _1

, there is a club

C
⊆

ω
1

C \subseteq \omega _1

so that

Φ
↾
[
C

]
∗

ω
1

\Phi \upharpoonright [C]^{\omega _1}_*

is a continuous function. This has several consequences such as establishing the cardinal relation

|

[

ω

1

]

>

ω

1

|

>

|

[

ω

1

]

ω

1

|

|[{\omega _{1}}]^{>{\omega _{1}}}| > |[{\omega _{1}}]^{\omega _{1}}|

and answering a question of Zapletal by showing that if

⟨

X
α

:
α
>

ω
1

⟩

\langle X_\alpha : \alpha > \omega _1\rangle

is a collection of subsets of

[

ω
1

]

ω
1

[\omega _1]^{\omega _1}

with the property that

⋃

α
>

ω
1

X
α

=
[

ω
1

]

ω
1

\bigcup _{\alpha > \omega _1}X_\alpha = [\omega _1]^{\omega _1}

, then there is an

α
>

ω
1

\alpha > \omega _1

so that

X
α

X_\alpha

and

[

ω
1

]

ω
1

[\omega _1]^{\omega _1}

are in bijection.

We show that under

A
D

R

\mathsf {AD}_\mathbb {R}

everywhere

[

ω
1

]

>

ω
1

[\omega _1]^{>\omega _1}

-club uniformization holds which is the following statement: Let

c
l
u
b

ω
1

\mathsf {club}_{\omega _1}

denote the collection of club subsets of

ω
1

\omega _1

. Suppose

R
⊆
[

ω
1

]

>

ω
1

×

c
l
u
b

ω
1

R \subseteq [\omega _1]^{>\omega _1} \times \mathsf {club}_{\omega _1}

is

⊆
\subseteq

-downward closed in the sense that for all

σ
∈
[

ω
1

]

>

ω
1

\sigma \in [\omega _1]^{>\omega _1}

, for all clubs

C
⊆
D

C \subseteq D

,

R
(
σ
,
D
)

R(\sigma ,D)

implies

R
(
σ
,
C
)

R(\sigma ,C)

. Then there is a function

F
:

d
o
m

(
R
)
→

c
l
u
b

ω
1

F \colon {\mathrm {dom}}(R) \rightarrow \mathsf {club}_{\omega _1}

so that for all

σ
∈

d
o
m

(
R
)

\sigma \in {\mathrm {dom}}(R)

,

R
(
σ
,
F
(
σ
)
)

R(\sigma ,F(\sigma ))

.

We show that under

A
D

\mathsf {AD}

almost everywhere

[

ω

1

]

>

ω

1

[{\omega _{1}}]^{>{\omega _{1}}}

-club uniformization holds which is the statement that for every

R
⊆
[

ω

1

]

>

ω

1

×

c
l
u
b

ω

1

R \subseteq [{\omega _{1}}]^{>{\omega _{1}}} \times \mathsf {club}_{\omega _{1}}

which is

⊆
\subseteq

-downward closed, there is a club

C
C

and a function

F
:

d
o
m

(
R
)
∩
[
C

]
∗

>

ω

1

→

c
l
u
b

ω

1

F \colon {\mathrm {dom}}(R) \cap [C]^{>{\omega _{1}}}_* \rightarrow \mathrm {club}_{\omega _{1}}

so that for all

σ
∈

d
o
m

(
R
)
∩
[
C

]
∗

>

ω

1

\sigma \in {\mathrm {dom}}(R) \cap [C]^{>{\omega _{1}}}_*

,

R
(
σ
,
F
(
σ
)
)

R(\sigma ,F(\sigma ))

.
Effects of Huangjiu, Baijiu and Red Wine Combined With High-Fat Diet on Glucose and Lipid Metabolism: Aggravate or Alleviate?
Authors: Zhao Liang , Ouyang Hanying , Zhang Nanhai , Wang Chengtao , Ji Baoping , Zhou Feng 
Published: September 2020

Abstract

Aim
To compare effects on certain health indices in rodents of different doses of alcoholic beverages, huangjiu (Chinese yellow wine), red wine and baijiu (Chinese liquor) combined with high-fat diet (HFD) and the pure HFD.

Methods
A total of 80 rats were randomly divided into eight groups and treated with (a) basal diet (3.5 kcal/g); (b) HFD (19.5% w/w lard, 4.5 kcal/g) and (c) HFD with low or high doses of separate alcoholic beverages (2.5 and 5 g/kg ethanol, respectively) for 28 weeks.

Results
Chronic drinking when combined with HFD was associated with reduced body weight, fat accumulation and serum TNF-α level, serum TG, TC and LDL-C levels, and improved glucose tolerance (OGTT) and insulin sensitivity (ITT), hepatic enzymes; elevated levels or activities of the antioxidant enzymes like superoxide dismutase, catalase and glutathione reductase, reduced the content of lipid peroxidation productions such as malondialdehyde, in comparison with the pure HFD intake. In addition, compared with HFD, drinking plus HFD improved microbiota dysbiosis, down-regulated the ratio of Firmicutes/Bacteroidetes and promoted the growth of some probiotics including Prevotellaceae_UCG-001 and norank_f__Bacteroidales_S24-7_group.

Conclusion
Overall, the three beverages showed different impacts on indicators but red wine showed the most ‘beneficial’ effects. Of course, higher ethanol dosages can be expected to cause overall negative health effects, and harms of high fat intake can be prevented by healthier diet.
panRGP: a pangenome-based method to predict genomic islands and explore their diversity
Authors: Bazin Adelme , Gautreau Guillaume , Médigue Claudine , Vallenet David , Calteau Alexandra 
Published: September 2020

Abstract

Motivation
Horizontal gene transfer (HGT) is a major source of variability in prokaryotic genomes. Regions of genome plasticity (RGPs) are clusters of genes located in highly variable genomic regions. Most of them arise from HGT and correspond to genomic islands (GIs). The study of those regions at the species level has become increasingly difficult with the data deluge of genomes. To date, no methods are available to identify GIs using hundreds of genomes to explore their diversity.

Results
We present here the panRGP method that predicts RGPs using pangenome graphs made of all available genomes for a given species. It allows the study of thousands of genomes in order to access the diversity of RGPs and to predict spots of insertions. It gave the best predictions when benchmarked along other GI detection tools against a reference dataset. In addition, we illustrated its use on metagenome assembled genomes by redefining the borders of the leuX tRNA hotspot, a well-studied spot of insertion in Escherichia coli. panRPG is a scalable and reliable tool to predict GIs and spots making it an ideal approach for large comparative studies.

Availability and implementation
The methods presented in the current work are available through the following software: https://github.com/labgem/PPanGGOLiN. Detailed results and scripts to compute the benchmark metrics are available at https://github.com/axbazin/panrgp_supdata.
Padhoc: a computational pipeline for pathway reconstruction on the fly
Authors: Casaní-Galdón Salvador , Pereira Cecile , Conesa Ana 
Published: September 2020

Abstract

Motivation
Molecular pathway databases represent cellular processes in a structured and standardized way. These databases support the community-wide utilization of pathway information in biological research and the computational analysis of high-throughput biochemical data. Although pathway databases are critical in genomics research, the fast progress of biomedical sciences prevents databases from staying up-to-date. Moreover, the compartmentalization of cellular reactions into defined pathways reflects arbitrary choices that might not always be aligned with the needs of the researcher. Today, no tool exists that allow the easy creation of user-defined pathway representations.

Results
Here we present Padhoc, a pipeline for pathway ad hoc reconstruction. Based on a set of user-provided keywords, Padhoc combines natural language processing, database knowledge extraction, orthology search and powerful graph algorithms to create navigable pathways tailored to the user’s needs. We validate Padhoc with a set of well-established Escherichia coli pathways and demonstrate usability to create not-yet-available pathways in model (human) and non-model (sweet orange) organisms.

Availability and implementation
Padhoc is freely available at https://github.com/ConesaLab/padhoc.

Supplementary information
Supplementary data are available at Bioinformatics online.