bluuebunny/crossref_metadata_2025_split huggingface dataset:
A fully Lagrangian, non-parametric bias model for dark matter halos
Authors: Wu Xiaohan , Muñoz Julian B. , Eisenstein Daniel 
Published: February 2022

Abstract
We present a non-parametric Lagrangian biasing model and fit the ratio of the halo and mass densities at the field level using the mass-weighted halo field in the AbacusSummit simulations at z=0.5. Unlike the perturbative halo bias model that has been widely used in interpreting the observed large-scale structure traced by galaxies, we find a non-negative halo-to-mass ratio that increases monotonically with the linear overdensity δ1 in the initial Lagrangian space. The bias expansion, however, does not guarantee non-negativity of the halo counts, and may lead to rising halo number counts at negative overdensities. The shape of the halo-to-mass ratio is unlikely to be described by a polynomial function of δ1 and other quantities. Especially for massive halos with 6×1012 h-1 M⊙, the halo-to-mass ratio starts soaring up at δ1>0, substantially different from the predictions of the bias expansion. We show that for the halo masses we consider (M>3×1011 h-1 M⊙) a non-parametric halo-to-mass ratio as a function of δ1 and its local derivative ∇^2δ1 can recover the halo power spectra to sub-percent level accuracy for wavenumbers k=0.01-0.1 h Mpc-1 given a proper smoothing scale to filter the initial density field, even though we do not fit the power spectrum directly. However, there is mild dependence of the recovery of the halo power spectrum on the smoothing scale and other input parameters. At k<0.01 h Mpc-1 and for massive halos with M>6×1012 h-1 M⊙, our non-parametric model leads to a few percent overestimation of the halo power spectrum, indicating the need for larger or multiple smoothing scales. The halo-to-mass ratios obtained qualitatively agree with intuitions from extended Press-Schechter theory. We compare our framework to the bias expansion and discuss possible extensions.
Clustering of Gravitational Wave and Supernovae events: a multitracer analysis in Luminosity Distance Space
Authors: Libanore S. , Artale M.C. , Karagiannis D. , Liguori M. , Bartolo N. , Bouffanais Y. , Mapelli M. , Matarrese S. 
Published: February 2022

Abstract
We study the clustering of Gravitational Wave (GW) merger events and Supernovae IA (SN), as cosmic tracers in Luminosity Distance Space. We modify the publicly available CAMB code to numerically evaluate auto- and cross- power spectra for the different sources, including Luminosity Distance Space distortion effects generated by peculiar velocities and lensing convergence. We perform a multitracer Fisher analysis to forecast expected constraints on cosmological and GW bias coefficients, using outputs from hydrodynamical N-body simulations to determine the bias fiducial model and considering future observations from the Vera Rubin Observatory and Einstein Telescope (ET), both single and in a 3 detector network configuration. We find that adding SN to the GW merger dataset considerably improves the forecast, mostly by breaking significant parameter degeneracies, with final constraints comparable to those obtainable from a Euclid-like survey. GW merger bias is forecasted to be detectable with good significance even in the single ET case.
jULIEs: nanostructured polytrodes for low traumatic extracellular recordings and stimulation in the mammalian brain
Authors: Racz Romeo R , Kollo Mihaly , Racz Gabriella , Bulz Ciprian , Ackels Tobias , Warner Tom , Wray William , Kiskin Nikolai , Chen Chi , Ye Zhiwen , de Hoz Livia , Rancz Ede , Schaefer Andreas T 
Published: February 2022

Abstract

Objective. Extracellular microelectrode techniques are the most widely used approach to interrogate neuronal populations. However, regardless of the manufacturing method used, damage to the vasculature and circuit function during probe insertion remains a concern. This issue can be mitigated by minimising the footprint of the probe used. Reducing the size of probes typically requires either a reduction in the number of channels present in the probe, or a reduction in the individual channel area. Both lead to less effective coupling between the probe and extracellular signals of interest. Approach. Here, we show that continuously drawn SiO2-insulated ultra-microelectrode fibres offer an attractive substrate to address these challenges. Individual fibres can be fabricated to >10 m continuous stretches and a selection of diameters below 30 µm with low resistance (<100 Ω mm−1) continuously conductive metal core of <10 µm and atomically flat smooth shank surfaces. To optimize the properties of the miniaturised electrode-tissue interface, we electrodeposit rough Au structures followed by ∼20 nm IrOx film resulting in the reduction of the interfacial impedance to <500 kΩ at 1 kHz. Main results. We demonstrate that these ultra-low impedance electrodes can record and stimulate both single and multi-unit activity with minimal tissue disturbance and exceptional signal-to-noise ratio in both superficial (∼40 µm) and deep (∼6 mm) structures of the mouse brain. Further, we show that sensor modifications are stable and probe manufacturing is reproducible. Significance. Minimally perturbing bidirectional neural interfacing can reveal circuit function in the mammalian brain in vivo.
A network information theoretic framework to characterise muscle synergies in space and time
Authors: Ó’ Reilly David , Delis Ioannis 
Published: February 2022

Abstract

Objective. Current approaches to muscle synergy extraction rely on linear dimensionality reduction algorithms that make specific assumptions on the underlying signals. However, to capture nonlinear time varying, large-scale but also muscle-specific interactions, a more generalised approach is required. Approach. Here we developed a novel framework for muscle synergy extraction that relaxes model assumptions by using a combination of information and network theory and dimensionality reduction. We first quantify informational dynamics between muscles, time-samples or muscle-time pairings using a novel mutual information formulation. We then model these pairwise interactions as multiplex networks and identify modules representing the network architecture. We employ this modularity criterion as the input parameter for dimensionality reduction, which verifiably extracts the identified modules, and also to characterise salient structures within each module. Main results. This novel framework captures spatial, temporal and spatiotemporal interactions across two benchmark datasets of reaching movements, producing distinct spatial groupings and both tonic and phasic temporal patterns. Readily interpretable muscle synergies spanning multiple spatial and temporal scales were identified, demonstrating significant task dependence, ability to capture trial-to-trial fluctuations and concordance across participants. Furthermore, our framework identifies submodular structures that represent the distributed networks of co-occurring signal interactions across scales. Significance. The capabilities of this framework are illustrated through the concomitant continuity with previous research and novelty of the insights gained. Several previous limitations are circumvented including the extraction of functionally meaningful and multiplexed pairwise muscle couplings under relaxed model assumptions. The extracted synergies provide a holistic view of the movement while important details of task performance are readily interpretable. The identified muscle groupings transcend biomechanical constraints and the temporal patterns reveal characteristics of fundamental motor control mechanisms. We conclude that this framework opens new opportunities for muscle synergy research and can constitute a bridge between existing models and recent network-theoretic endeavours.
The radial phase variation of reversed-shear and toroidicity-induced Alfvén eigenmodes in DIII-D
Authors: Heidbrink W.W. , Hansen E.C. , Austin M.E. , Kramer G.J. , Van Zeeland M.A. 
Published: February 2022

Abstract
The eigenfunction of an instability contains information about energy flow in the wave. In this study, the amplitude and phase of electron cyclotron emission radiometer data from hundreds of DIII-D reversed shear Alfvén eigenmodes (RSAE) and toroidicity-induced Alfvén eigenmodes (TAE) are analyzed along the outboard horizontal midplane. The radial phase profile can be flat, linearly rising or falling, convex or concave; in other words, a wide variety of shapes is observed. For a particular mode, often the radial phase profile remains approximately constant as the mode evolves in time but sometimes it changes rapidly. Many TAEs and some RSAEs have phase profiles that are rather flat where the mode amplitude is largest but rise steadily by

∼
2
π

at large major radius. Rapid phase changes are observed when the frequencies of an RSAE and TAE overlap and the modes couple. The phase profile depends weakly on the fast-ion gradient that would appear in the absence of wave-induced transport. Linear and quadratic fits to the phase profiles, together with many plasma parameters, are assembled into RSAE and TAE databases. In both cases, large variability is observed. For RSAEs, the strongest phase dependencies are on electron temperature T
e, RSAE mode frequency, and the density of carbon impurities. For TAEs, the strongest dependencies are on beam power and major radius of the mode. In general, the average RSAE radial phase profile is essentially flat but the TAE profile has non-zero slope and curvature.
Pairs of commuting quadratic elements in the universal enveloping algebra of Euclidean algebra and integrals of motion*
Authors: Marchesiello A , Šnobl L 
Published: February 2022

Abstract
Motivated by the consideration of integrable systems in three spatial dimensions in Euclidean space with integrals quadratic in the momenta we classify three-dimensional Abelian subalgebras of quadratic elements in the universal enveloping algebra of the Euclidean algebra under the assumption that the Casimir invariant

p

→

⋅

l

→

vanishes in the relevant representation. We show by means of an explicit example that in the presence of magnetic field, i.e. terms linear in the momenta in the Hamiltonian, this classification allows for pairs of commuting integrals whose leading order terms cannot be written in the famous classical form of Makarov et al [17]. We consider limits simplifying the structure of the magnetic field in this example and corresponding reductions of integrals, demonstrating that singularities in the integrals may arise, forcing structural changes of the leading order terms.
Design of a broadband reflection mirror for the water window region using a stacked multilayer with optimized structure
Authors: Yang Taolve , Chen Shujing , Lin Chengyou 
Published: February 2022

Abstract
A broadband reflection mirror that can realize maximum uniform reflectivity is an important optical element in the water window region. In this paper, a stacked multilayer (SM) mirror with optimized structure for achieving broadband reflection in the water window region is presented. A genetic algorithm with a two-parametric merit function is employed to achieve maximum uniform reflection in the desired spectral band. A 4.90% average reflectivity with 0.25% root-mean-square deviation in the range of 370–390 eV is achieved by a Cr/Sc SM with 40 periodic multilayer structures. Compared with aperiodic multilayers, proposed SMs exhibit better optimization efficiency, design robustness, and thickness error tolerance. This research provides an efficient method for designing broadband reflection mirrors in the water window region, and may be helpful for steering broadband water window sources, such as high order harmonics and attosecond pulse.
Glucagon-Like Peptide 1 Receptor Agonists and Risk of Anaphylactic Reaction Among Patients With Type 2 Diabetes: A Multisite Population-Based Cohort Study
Authors: Pradhan Richeek , Patorno Elisabetta , Tesfaye Helen , Schneeweiss Sebastian , Yin Hui , Franklin Jessica , Pawar Ajinkya , Santella Christina , Yu Oriana H Y , Renoux Christel , Azoulay Laurent 
Published: February 2022

AbstractCase reports and a pharmacovigilance analysis have linked glucagon-like peptide 1 receptor agonists (GLP-1 RAs) with anaphylactic reactions, but real-world evidence for this possible association is lacking. Using databases from the United Kingdom (Clinical Practice Research Datalink) and the United States (Medicare, Optum (Optum, Inc., Eden Prairie, Minnesota), and IBM MarketScan (IBM, Armonk, New York)), we employed a new-user, active comparator study design wherein initiators of GLP-1 RAs were compared with 2 different active comparator groups (initiators of dipeptidyl peptidase 4 (DPP-4) inhibitors and initiators of sodium-glucose cotransporter 2 (SGLT-2) inhibitors) between 2007 and 2019. Propensity score fine stratification weighted Cox proportional hazards models were fitted to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for an anaphylactic reaction. Database-specific HRs were pooled using random-effects models. Compared with the use of DPP-4 inhibitors (n = 1,641,520), use of GLP-1 RAs (n = 324,098) generated a modest increase in the HR for anaphylactic reaction, with a wide 95% CI (36.9 per 100,000 person-years vs. 32.1 per 100,000 person-years, respectively; HR = 1.15, 95% CI: 0.94, 1.42). Compared with SGLT-2 inhibitors (n = 366,067), GLP-1 RAs (n = 259,929) were associated with a 38% increased risk of anaphylactic reaction (40.7 per 100,000 person-years vs. 29.4 per 100,000 person-years, respectively; HR = 1.38, 95% CI: 1.02, 1.87). In this large, multisite population-based cohort study, GLP-1 RAs were associated with a modestly increased risk of anaphylactic reaction when compared with DPP-4 inhibitors and SGLT-2 inhibitors.
Research on Health Disparities: Strategies and Findings From the Black Women’s Health Study
Authors: Palmer Julie R , Cozier Yvette C , Rosenberg Lynn 
Published: February 2022

Abstract
The American Journal of Epidemiology has been a platform for findings from the Black Women’s Health Study (BWHS) that are relevant to health disparities. Topics addressed have included methods of follow-up of a large cohort of Black women, disparities in health-care delivery, modifiable risk factors for health conditions that disproportionately affect Black women, associations with exposures that are highly prevalent in Black women, and methods for genetic research. BWHS papers have also highlighted the importance of considering social context, including perceived experiences of racism, in understanding health disparities. In the future, BWHS investigators will contribute to documentation of the role that structural racism plays in health disparities.
A Differential Response to Antihypertensive Therapy in African Men and Women: Insights From the CREOLE Trial
Authors: Ojji Dike B , Shedul Gabriel L , Sani Mahmoud , Ogah Okechukwu S , Dzudie Anastase , Barasa Felix , Mondo Charles , Ingabire Prossie M , Jones Erika S W , Rayner Brian , Albertino Damasceno , Ogola Elijah , Smythe Wynand , Hickman Nicky , Francis Veronica , Shahiemah Pandie , Shedul Grace , Aje Akinyemi , Sliwa Karen , Stewart Simon 
Published: February 2022

Abstract

Background
We sought to address the paucity of data to support the evidence-based management of hypertension to achieve optimal blood pressure (BP) control on a sex-specific basis in Africa.

Methods
We undertook a post hoc analysis of the multicenter, randomized CREOLE (Comparison of Three Combination Therapies in Lowering Blood Pressure in Black Africans) Trial to test the hypothesis that there would be clinically important differences in office BP control between African men and women. We compared the BP levels of 397 and 238 hypertensive women (63%, 50.9 ± 10.5 years) and men (51.2 ± 11.3 years) from 10 sites across sub-Saharan Africa who completed baseline and 6-month profiling according to their randomly allocated antihypertensive treatment.

Results
Overall, 442/635 (69.6%) participants achieved an office BP target of &lt;140/90 mm Hg at 6 months; comprising more women (286/72.0%) than men (156/65.5%) (adjusted odds ratio [OR] 1.59, 95% confidence interval [CI] 1.07–2.39; P = 0.023). Women randomized to amlodipine–hydrochlorothiazide (HCTZ) (adjusted OR 3.03, 95% CI 1.71–5.35; P &lt; 0.001) or amlodipine–perindopril (adjusted OR 2.62, 95% CI 1.49–4.58; P = 0.01) were more likely to achieve this target compared with perindopril–HCTZ. Among men, there were no equivalent treatment differences—amlodipine–HCTZ (OR 1.54, 95% CI 0.76–3.12; P = 0.23) or amlodipine–perindopril (OR 1.32, 95% CI 0.65–2.67; P = 0.44) vs. perindopril–HCTZ. Among the 613 participants (97%) with 24-hour ambulatory BP monitoring, women had significantly lower systolic (124.1 ± 18.1 vs. 127.3 ± 16.9; P = 0.028) and diastolic (72.7 ± 10.4 vs. 75.1 ± 10.5; P = 0.007) BP levels at 6 months compared with men.

Conclusions
These data suggest clinically important differences in the therapeutic response to antihypertensive combination therapy among African women compared with African men.
Biosynthetic machineries of anthraquinones and bisanthraquinones in Talaromyces islandicus
Authors: Fukaya Mitsunori , Ozaki Taro , Minami Atsushi , Oikawa Hideaki 
Published: February 2022

ABSTRACT
Talaromyces islandicus is a unique fungus that produces more than 20 numbers of anthraquinones (AQs) and their dimeric natural products, bisanthraquinones (BQs). These compounds share a 9,10-anthracenedione core derived from emodin. The biosynthetic pathway of emodin has been firmly established, while that of other AQs and BQs is still unclear. In this study, we identified the biosynthetic gene clusters for chrysophanol and skyrin. The function of key modification enzymes was examined by performing biotransformation experiments and in vitro enzymatic reactions with emodin and its derivatives, allowing us to propose a mechanism for the modification reactions. The present study provides insight into the biosynthesis of AQs and BQs in T. islandicus.
BDK knockout skeletal muscle satellite cells exhibit enhanced protein translation initiation signal in response to BCAA in vitro
Authors: Nakai Naoya , Iida Noriko , Kitai Saki , Shimomura Yoshiharu , Kitaura Yasuyuki , Higashida Kazuhiko 
Published: February 2022

ABSTRACT
We examined the effects of branched-chain amino acids (BCAAs) and electrical pulse stimulation (EPS) on the mTORC1 pathway in muscle satellite cells (MSCs) isolated from branched-chain α-keto acid dehydrogenase kinase (BDK) knockout (KO) mice in vitro. MSCs were isolated from BDK KO and wild-type (WT) mice, proliferated, and differentiated into myotubes. BCAA stimulation increased the phosphorylation of p70 S6 kinase (p70S6K), a marker of protein translation initiation, in MSCs from WT and BDK KO mice, but the rate of the increase was higher in MSCs isolated from BDK KO mice. Contrarily, there was no difference in the increase in p70S6K phosphorylation by EPS. Acute BDK knockdown in MSCs from WT mice using shRNA decreased p70S6K phosphorylation in response to BCAA stimulation. Collectively, the susceptibility of mTORC1 to BCAA stimulation was elevated by chronic, but not acute, enhancement of BCAA catabolism.
A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling
Authors: Zhao Weizhong , Luo Shujie , Wu Haifang , Jiang Xingpeng , He Tingting , Hu Xiaohua 
Published: February 2022

Abstract
The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.
Drug repositioning based on multi-view learning with matrix completion
Authors: Yan Yixin , Yang Mengyun , Zhao Haochen , Duan Guihua , Peng Xiaoqing , Wang Jianxin 
Published: February 2022

Abstract
Determining drug indications is a critical part of the drug development process. However, traditional drug discovery is expensive and time-consuming. Drug repositioning aims to find potential indications for existing drugs, which is considered as an important alternative to the traditional drug discovery. In this article, we propose a multi-view learning with matrix completion (MLMC) method to predict the potential associations between drugs and diseases. Specifically, MLMC first learns the comprehensive similarity matrices from five drug similarity matrices and two disease similarity matrices based on the multi-view learning (ML) with Laplacian graph regularization, and updates the drug-disease association matrix simultaneously. Then, we introduce matrix completion (MC) to add some positive entries in original association matrix based on low-rank structure, and re-execute the multi-view learning algorithm for association prediction. At last, the prediction results of the above two operations are integrated as the final output. Evaluated by 10-fold cross-validation and de novo tests, MLMC achieves higher prediction accuracy than the current state-of-the-art methods. Moreover, case studies confirm the ability of our method in novel drug-disease association discovery. The codes of MLMC are available at https://github.com/BioinformaticsCSU/MLMC. Contact: jxwang@mail.csu.edu.cn
Unfolding Social Work Research to Address the COVID-19 Impact: A Text Mining literature Analysis
Authors: Casquilho-Martins Inês , Belchior-Rocha Helena , Moro Sérgio 
Published: February 2022

AbstractThis article aims to contribute to understanding the main social impacts of the pandemic caused by COVID-19 by highlighting the present and future challenges of social intervention and proposing a research agenda for social workers. Based on main indicators collected from international reports, we categorise the terms associated and analyse 284 Scopus articles that address social work issues in face of the COVID-19 through a text mining literature analysis. By applying topic modelling, we are able to identify relations within the body of knowledge between the main indicators. The results enable to highlight the current trends of research, contributing to leverage knowledge in social work in face of a complex and uncertain society. We find that most articles are focused on professional practice, as well as areas such as health, education and employment. In contrast, we argue that issues related to women or migrants have been less explored. These aspects could bring new perspectives in future research within the pandemic context.