bluuebunny/crossref_metadata_2025_split huggingface dataset:
Turn up the volume: listening to phase transitions in hot dark sectors
Authors: Ertas Fatih , Kahlhoefer Felix , Tasillo Carlo 
Published: February 2022

Abstract
Stochastic gravitational wave (GW) backgrounds from first-order phase transitions are an exciting target for future GW observatories and may enable us to study dark sectors with very weak couplings to the Standard Model. In this work we show that such signals may be significantly enhanced for hot dark sectors with a temperature larger than the one of the SM thermal bath. The need to transfer the entropy from the dark sector to the SM after the phase transition can however lead to a substantial dilution of the GW signal. We study this dilution in detail, including the effect of number-changing processes in the dark sector (so-called cannibalism), and show that in large regions of parameter space a net enhancement remains. We apply our findings to a specific example of a dark sector containing a dark Higgs boson and a dark photon and find excellent detection prospects for LISA and the Einstein telescope.
Fifth forces and broken scale symmetries in the Jordan frame
Authors: Copeland Edmund J. , Millington Peter , Sevillano Muñoz Sergio 
Published: February 2022

Abstract
We study the origin of fifth forces in scalar-tensor theories of gravity in the so-called Jordan frame, where the modifications to the gravitational sector are manifest. We focus on theories of Brans-Dicke type in which an additional scalar field is coupled directly to the Ricci scalar of General Relativity. We describe how the necessary diffeomorphism invariance of the modified gravitational sector leads to a modification of the usual gauge fixing term (for the harmonic gauge), as compared to Einstein gravity. This allows us to perform a consistent linearization of the gravitational sector in the weak-field limit, which gives rise to a kinetic mixing between the non-minimally coupled scalar field and the graviton. It is through this mixing that a fifth force can arise between matter fields. We are then able to compute the matrix elements for fifth-force exchanges directly in the Jordan frame, without the need to perform a conformal transformation to the so-called Einstein frame, wherein the gravitational sector is of Einstein-Hilbert form. We obtain results that are in agreement with the equivalent Einstein-frame calculations and illustrate, still in the Jordan frame, the pivotal role that sources of explicit scale symmetry breaking in the matter sector play in admitting fifth-force couplings.
Enhancement of feasibility of macroscopic quantum superposition state with the quantum Rabi-Stark model
Authors: Wang Jing-Jing , Ding Ming-Song , Xiong Li , Zheng Li 
Published: February 2022

Abstract
We propose an efficient scheme to generate a macroscopical quantum superposition state with a cavity optomechanical system, which is composed of a quantum Rabi-Stark model coupling to a mechanical oscillator. In a low-energy subspace of the Rabi-Stark model, the dressed states and then the effective Hamiltonian of the system are given. Due to the coupling of the mechanical oscillator and the atom-cavity system, if the initial state of the atom-cavity system is one of the dressed states, the mechanical oscillator will evolve into a corresponding coherent state. Thus, if the initial state of the atom-cavity system is a superposition of two dressed states, a coherent state superposition of the mechanical oscillator can be generated. The quantum coherence and their distinguishable properties of the two coherent states are exhibited by Wigner distribution. We show that the Stark term can enhance significantly the feasibility and quantum coherence of the generated macroscopic quantum superposition state of the oscillator.
Confronting cosmic ray electron and positron excesses with hybrid triplet Higgs portal dark matter *
Authors: Chen Shao-Long , Banik Amit Dutta , Liu Ze-Kun 
Published: February 2022

Abstract
We perform a detailed study of scalar dark matter with triplet Higgs extensions of the Standard Model in order to explain the cosmic ray electron and positron excesses reported by AMS-02 and DAMPE. A detailed analysis of the AMS-02 positron excess reveals that for different orderings (normal, inverted, and quasi-degenerate) of neutrino mass, the hybrid triplet Higgs portal framework is more favored with respect to the single triplet Higgs portal for TeV scale dark matter. We also show that the resonant peak and continuous excess in DAMPE cosmic ray data can be well explained with the hybrid triplet Higgs portal dark matter when a dark matter sub-halo nearby is taken into account.
Global temperature goals should determine the time horizons for greenhouse gas emission metrics
Authors: Abernethy Sam , Jackson Robert B 
Published: February 2022

Abstract
Emission metrics, a crucial tool in setting effective exchange rates between greenhouse gases, currently require an arbitrary choice of time horizon. Here, we propose a novel framework to calculate the time horizon that aligns with scenarios achieving a specific temperature goal. We analyze the Intergovernmental Panel on Climate Change Special Report on Global Warming of 1.5 °C Scenario Database to find that time horizons aligning with the 1.5 °C and 2 °C global warming goals of the Paris Agreement are 24 [90% prediction interval: 7, 41] and 58 [90% PI: 41, 74] years, respectively. We then use these time horizons to quantify time-dependent emission metrics for methane. We find that the Global Warming Potential (GWP) values that align with the 1.5 °C and 2 °C goals are GWP1.5 °C = 75 [90% PI: 54, 107] and GWP2 °C = 42 [90% PI: 35, 54]. For the Global Temperature change Potential (GTP) they are GTP1.5 °C = 41 [90% PI: 16, 102] and GTP2 °C = 9 [90% PI: 7, 16]. The most commonly used time horizon, 100 years, underestimates methane’s GWP and GTP by 34% and 38%, respectively, relative to the values we calculate that align with the 2 °C goal and by 63% and 87%, respectively, relative to the 1.5 °C goal. To best align emission metrics with the Paris Agreement 1.5 °C goal, we recommend a 24 year time horizon, using 2045 as the endpoint time, with its associated GWP1.5 °C = 75 and GTP1.5 °C = 41.
Size-dependent aggression towards kin in a cannibalistic species
Authors: Fouilloux Chloe A , Fromhage Lutz , Valkonen Janne K , Rojas Bibiana 
Published: February 2022

Abstract
In juveniles extreme intraspecies aggression can seem counter-intuitive, as it might endanger their developmental goal of surviving until reproductive stage. Ultimately, aggression can be vital for survival, although the factors (e.g., genetic or environmental) leading to the expression and intensity of this behavior vary across taxa. Attacking (and sometimes killing) related individuals may reduce inclusive fitness; as a solution to this problem, some species exhibit kin discrimination and preferentially attack unrelated individuals. Here, we used both experimental and modeling approaches to consider how physical traits (e.g., size in relation to opponent) and genetic relatedness mediate aggression in dyads of cannibalistic Dendrobates tinctorius tadpoles. We paired full-sibling, half-sibling, and non-sibling tadpoles of different sizes together in an arena and recorded their aggression and activity. We found that the interaction between relative size and relatedness predicts aggressive behavior: large individuals in non-sibling dyads are significantly more aggressive than large individuals in sibling dyads. Unexpectedly, although siblings tended to attack less overall, in size-mismatched pairs they attacked faster than in non-sibling treatments. Using a theoretical model to complement these empirical findings, we propose that larval aggression reflects a balance between relatedness and size where individuals trade-off their own fitness with that of their relatives.
Familiarity, dominance, sex and season shape common waxbill social networks
Authors: Gomes Ana Cristina R , Beltrão Patrícia , Boogert Neeltje J , Cardoso Gonçalo C 
Published: February 2022

AbstractIn gregarious animals, social network positions of individuals may influence their life-history and fitness. Although association patterns and the position of individuals in social networks can be shaped by phenotypic differences and by past interactions, few studies have quantified their relative importance. We evaluated how phenotypic differences and familiarity influence social preferences and the position of individuals within the social network. We monitored wild-caught common waxbills (Estrilda astrild) with radio-frequency identifiers in a large mesocosm during the non-breeding and breeding seasons of two consecutive years. We found that social networks were similar, and that the centrality of individuals was repeatable, across seasons and years, indicating a stable social phenotype. Nonetheless, there were seasonal changes in social structure: waxbills associated more strongly with opposite-sex individuals in breeding seasons, whereas in non-breeding seasons they instead assorted according to similarities in social dominance. We also observed stronger assortment between birds that were introduced to the mesocosm at the same time, indicating long-lasting bonds among familiar individuals. Waxbills that had been introduced to the mesocosm more recently occupied more central network positions, especially during breeding seasons, perhaps indicating that these birds had less socially-differentiated associations with flock members. Finally, individual differences in color ornamentation and behavioral assays of personality, inhibitory control, and stress were not related to network centrality or association patterns. Together, these results suggest that, in gregarious species like the common waxbill, social networks may be more strongly shaped by long-lasting associations with familiar individuals than by phenotypic differences among group members.
LRcell: detecting the source of differential expression at the sub–cell-type level from bulk RNA-seq data
Authors: Ma Wenjing , Sharma Sumeet , Jin Peng , Gourley Shannon L , Qin Zhaohui S 
Published: February 2022

Abstract
Given most tissues are consist of abundant and diverse (sub-)cell types, an important yet unaddressed problem in bulk RNA-seq analysis is to identify at which (sub-)cell type(s) the differential expression occurs. Single-cell RNA-sequencing (scRNA-seq) technologies can answer the question, but they are often labor-intensive and cost-prohibitive. Here, we present LRcell, a computational method aiming to identify specific (sub-)cell type(s) that drives the changes observed in a bulk RNA-seq experiment. In addition, LRcell provides pre-embedded marker genes computed from putative scRNA-seq experiments as options to execute the analyses. We conduct a simulation study to demonstrate the effectiveness and reliability of LRcell. Using three different real datasets, we show that LRcell successfully identifies known cell types involved in psychiatric disorders. Applying LRcell to bulk RNA-seq results can produce a hypothesis on which (sub-)cell type(s) contributes to the differential expression. LRcell is complementary to cell type deconvolution methods.
pyFoldX: enabling biomolecular analysis and engineering along structural ensembles
Authors: Radusky Leandro G , Serrano Luis 
Published: February 2022

Abstract

Summary
Recent years have seen an increase in the number of structures available, not only for new proteins but also for the same protein crystallized with different molecules and proteins. While protein design software has proven to be successful in designing and modifying proteins, they can also be overly sensitive to small conformational differences between structures of the same protein. To cope with this, we introduce here pyFoldX, a python library that allows the integrative analysis of structures of the same protein using FoldX, an established forcefield and modelling software. The library offers new functionalities for handling different structures of the same protein, an improved molecular parametrization module and an easy integration with the data analysis ecosystem of the python programming language.

Availability and implementation
pyFoldX rely on the FoldX software for energy calculations and modelling, which can be downloaded upon registration in http://foldxsuite.crg.eu/ and its licence is free of charge for academics. The pyFoldX library is open-source. Full details on installation, tutorials covering the library functionality and the scripts used to generate the data and figures presented in this paper are available at https://github.com/leandroradusky/pyFoldX.

Supplementary information
Supplementary data are available at Bioinformatics online.
EMBER: multi-label prediction of kinase-substrate phosphorylation events through deep learning
Authors: Kirchoff Kathryn E , Gomez Shawn M 
Published: February 2022

Abstract

Motivation
Kinase-catalyzed phosphorylation of proteins forms the backbone of signal transduction within the cell, enabling the coordination of numerous processes such as the cell cycle, apoptosis, and differentiation. Although on the order of 105 phosphorylation events have been described, we know the specific kinase performing these functions for &lt;5% of cases. The ability to predict which kinases initiate specific individual phosphorylation events has the potential to greatly enhance the design of downstream experimental studies, while simultaneously creating a preliminary map of the broader phosphorylation network that controls cellular signaling.

Results
We describe Embedding-based multi-label prediction of phosphorylation events (EMBER), a deep learning method that integrates kinase phylogenetic information and motif-dissimilarity information into a multi-label classification model for the prediction of kinase–motif phosphorylation events. Unlike previous deep learning methods that perform single-label classification, we restate the task of kinase–motif phosphorylation prediction as a multi-label problem, allowing us to train a single unified model rather than a separate model for each of the 134 kinase families. We utilize a Siamese neural network to generate novel vector representations, or an embedding, of peptide motif sequences, and we compare our novel embedding to a previously proposed peptide embedding. Our motif vector representations are used, along with one-hot encoded motif sequences, as input to a classification neural network while also leveraging kinase phylogenetic relationships into our model via a kinase phylogeny-weighted loss function. Results suggest that this approach holds significant promise for improving the known map of phosphorylation relationships that underlie kinome signaling.

Availability and implementation
The data and code underlying this article are available in a GitHub repository at https://github.com/gomezlab/EMBER.

Supplementary information
Supplementary data are available at Bioinformatics online.
Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis
Authors: Xing Xiaohan , Yang Fan , Li Hang , Zhang Jun , Zhao Yu , Gao Mingxuan , Huang Junzhou , Yao Jianhua 
Published: February 2022

AbstractMotivationAdvanced deep learning techniques have been widely applied in disease diagnosis and prognosis with clinical omics, especially gene expression data. In the regulation of biological processes and disease progression, genes often work interactively rather than individually. Therefore, investigating gene association information and co-functional gene modules can facilitate disease state prediction.ResultsTo explore the gene modules and inter-gene relational information contained in the omics data, we propose a novel multi-level attention graph neural network (MLA-GNN) for disease diagnosis and prognosis. Specifically, we format omics data into co-expression graphs via weighted correlation network analysis, and then construct multi-level graph features, finally fuse them through a well-designed multi-level graph feature fully fusion module to conduct predictions. For model interpretation, a novel full-gradient graph saliency mechanism is developed to identify the disease-relevant genes. MLA-GNN achieves state-of-the-art performance on transcriptomic data from TCGA-LGG/TCGA-GBM and proteomic data from coronavirus disease 2019 (COVID-19)/non-COVID-19 patient sera. More importantly, the relevant genes selected by our model are interpretable and are consistent with the clinical understanding.Availabilityand implementationThe codes are available at https://github.com/TencentAILabHealthcare/MLA-GNN.
Social Support Effectiveness of the Mental Health of Immigrants Living in Belgium
Authors: Snoubar Yaser , Zengin Oğuzhan 
Published: February 2022

Abstract
The increase in immigration has required social workers to focus more on this field and understand the social support systems of immigrants and the effects of their relationships with these systems to improve their effectiveness. Subsequently, this study examined the perceived social support, psychological resilience and coping strategies of immigrants living in Belgium, which has a significant immigrant population compared with its population. Using a cross-sectional design, data were collected from 289 participants living in Belgium. According to our findings, immigrants with a high perceived level of social support also have high psychological resilience. However, immigrants’ high levels of perceived social support lead to more intense use of problem-focused and emotion-focused coping styles. These findings emphasise the functionality of the social support system for immigrants and shed light on the issues that social workers should focus on when working with immigrants.
Simultaneous PET/fMRI revealed increased motor area input to subthalamic nucleus in Parkinson’s disease
Authors: Zang Zhenxiang , Song Tianbin , Li Jiping , Nie Binbin , Mei Shanshan , Zhang Chun , Wu Tao , Zhang Yuqing , Lu Jie 
Published: February 2022

AbstractInvasive electrophysiological recordings in patients with Parkinson’s disease (PD) are extremely difficult for cross-sectional comparisons with healthy controls. Noninvasive approaches for identifying information flow between the motor area and the subthalamic nucleus (STN) are critical for evaluation of treatment strategy. We aimed to investigate the direction of the cortical-STN hyperdirect pathway using simultaneous 18F-FDG-PET/functional magnetic resonance imaging (fMRI). Data were acquired during resting state on 34 PD patients and 25 controls. The ratio of standard uptake value for PET images and the STN functional connectivity (FC) maps for fMRI data were generated. The metabolic connectivity mapping (MCM) approach that combines PET and fMRI data was used to evaluate the direction of the connectivity. Results showed that PD patients exhibited both increased FDG uptake and STN-FC in the sensorimotor area (PFDR &lt; 0.05). MCM analysis showed higher cortical-STN MCM value in the PD group (F = 6.63, P = 0.013) in the left precentral gyrus. There was a high spatial overlap between the increased glucose metabolism and increased STN-FC in the sensorimotor area in PD. The MCM approach further revealed an exaggerated cortical input to the STN in PD, supporting the precentral gyrus as a target for treatment such as the repetitive transcranial magnetic stimulation.
Modulation in alpha band activity reflects syntax composition: an MEG study of minimal syntactic binding
Authors: Hardy Sophie M , Jensen Ole , Wheeldon Linda , Mazaheri Ali , Segaert Katrien 
Published: February 2022

Abstract
Successful sentence comprehension requires the binding, or composition, of multiple words into larger structures to establish meaning. Using magnetoencephalography, we investigated the neural mechanisms involved in binding at the syntax level, in a task where contributions from semantics were minimized. Participants were auditorily presented with minimal sentences that required binding (pronoun and pseudo-verb with the corresponding morphological inflection; “she grushes”) and pseudo-verb wordlists that did not require binding (“cugged grushes”). Relative to no binding, we found that syntactic binding was associated with a modulation in alpha band (8–12 Hz) activity in left-lateralized language regions. First, we observed a significantly smaller increase in alpha power around the presentation of the target word (“grushes”) that required binding (−0.05 to 0.1 s), which we suggest reflects an expectation of binding to occur. Second, during binding of the target word (0.15–0.25 s), we observed significantly decreased alpha phase-locking between the left inferior frontal gyrus and the left middle/inferior temporal cortex, which we suggest reflects alpha-driven cortical disinhibition serving to strengthen communication within the syntax composition neural network. Altogether, our findings highlight the critical role of rapid spatial–temporal alpha band activity in controlling the allocation, transfer, and coordination of the brain’s resources during syntax composition.
A Novel Approach For CT-Based COVID-19 Classification and Lesion Segmentation Based On Deep Learning
Authors: Truong Hieu Minh , Huynh Hieu Trung 
Published: February 2022

AbstractThe coronavirus disease 2019 (COVID-19) pandemic has been a globally dangerous crisis that causes an increasingly high death rate. Applying machine learning to the computed-tomography (CT)-based COVID-19 diagnosis is essential and attracts the attention of the research community. This paper introduces an approach for simultaneously identifying COVID-19 disease and segmenting its manifestations on lung images. The proposed method is an asymmetric U-Net-like model improved with skip connections. The experiment was conducted on a light-weighted feature extractor called CRNet with a feature enhancement technique called atrous spatial pyramid pooling. Classifying between COVID-19 and non-COVID-19 cases recorded the highest mean scores of 97.1, 94.4, and 97.0% for accuracy, dice similarity coefficient (DSC) and F1 score, respectively. Alternatively, the respective highest mean scores of the classification between COVID-19 and community-acquired pneumonia were 99.89, 99.79, and 99.97%. The lesion segmentation performance was with the highest mean of 99.6 and 84.7% for, respectively, accuracy and DSC.