bluuebunny/crossref_metadata_2025_split huggingface dataset:
zELDA: fitting Lyman alpha line profiles using deep learning
Authors: Gurung-López Siddhartha , Gronke Max , Saito Shun , Bonoli Silvia , Orsi Álvaro A 
Published: December 2021

ABSTRACT
We present zELDA (redshift Estimator for Line profiles of Distant Lyman Alpha emitters), an open source code to fit Lyman α (Ly α) line profiles. The main motivation is to provide the community with an easy to use and fast tool to analyse Ly α line profiles uniformly to improve the understating of Ly α emitting galaxies. zELDA is based on line profiles of the commonly used ‘shell-model’ pre-computed with the full Monte Carlo radiative transfer code LyaRT. Via interpolation between these spectra and the addition of noise, we assemble a suite of realistic Ly α spectra which we use to train a deep neural network.We show that the neural network can predict the model parameters to high accuracy (e.g. ≲ 0.34 dex H i column density for R ∼ 12 000) and thus allows for a significant speedup over existing fitting methods. As a proof of concept, we demonstrate the potential of zELDA by fitting 97 observed Ly α line profiles from the LASD data base. Comparing the fitted value with the measured systemic redshift of these sources, we find that Ly α determines their rest frame Ly α wavelength with a remarkable good accuracy of ∼0.3 Å ($\sim 75\,\, {\rm km\, s}^{-1}$). Comparing the predicted outflow properties and the observed Ly α luminosity and equivalent width, we find several possible trends. For example, we find an anticorrelation between the Ly α luminosity and the outflow neutral hydrogen column density, which might be explained by the radiative transfer process within galaxies.
Formation of galactic bulges from the cold gas filaments in high-redshift dark matter haloes
Authors: Noguchi Masafumi 
Published: December 2021

ABSTRACT
Formation process(es) of galactic bulges are not yet clarified, although several mechanisms have been proposed. In a previous study, we suggested one possibility that galactic bulges have been formed from the cold gas inflowing through surrounding hot halo gas in massive dark matter haloes at high redshifts. It was shown that this scenario leads to the bulge-to-total stellar mass ratio increasing with the galaxy mass, in agreement with the well-known observed trend. We here indicate that it also reproduces recent observational results that the mean stellar age of the bulge increases with the galaxy mass while the age gradient across the bulge decreases. We infer that this formation path applies mainly to high-mass galaxies and the bulges in lower mass galaxies have different origins such as secular formation from the disc material.
Near-infrared detection of H2 flows in the core of the Mon R1 association
Authors: Magakian T Yu , Tatarnikov A M , Movsessian T A , Andreasyan H R 
Published: December 2021

ABSTRACT
We report the discovery of four new H2 jets in the Mon R1 star-forming region in images obtained with the 2.5-m telescope of the Caucasian Mountain Observatory of Sternberg Astronomical Institute (SAI), Moscow State University (MSU) through a filter centred on the H2 1–0 S(1) emission line. This discovery confirms the nature of these flows, the existence of which was previously suspected from archival Spitzer GLIMPSE360 and Wide-field Infrared Survey Explorer (WISE) survey images. Also, two infrared reflection nebulae were revealed. On Herschel Photodetecting Array Camera and Spectrometer (PACS) survey images, we found a small group of far-infrared sources, mostly unknown ones; among them are the possible exciting objects of these outflows. Spectral energy distributions of the new sources show their extremely red colour and bolometric luminosities reaching 3 L⊙ and even 10 L⊙. They are thought to be pre-main-sequence (PMS) objects in the very early evolutionary stages.
Characterizing the turbulent multiphase haloes with periodic box simulations
Authors: Mohapatra Rajsekhar , Jetti Mrinal , Sharma Prateek , Federrath Christoph 
Published: December 2021

ABSTRACT
Turbulence in the intracluster medium (ICM) is driven by active galactic nuclei (AGNs) jets, by mergers, and in the wakes of infalling galaxies. It not only governs gas motion but also plays a key role in the ICM thermodynamics. Turbulence can help seed thermal instability by generating density fluctuations, and mix the hot and cold phases together to produce intermediate temperature gas (104–107 K) with short cooling times. We conduct high resolution (3843–7683 resolution elements) idealized simulations of the multiphase ICM and study the effects of turbulence strength, characterized by fturb (0.001–1.0), the ratio of turbulent forcing power to the net radiative cooling rate. We analyse density and temperature distribution, amplitude and nature of gas perturbations, and probability of transitions across the temperature phases. We also study the effects of mass and volume weighted thermal heating and weak ICM magnetic fields. For low fturb, the gas is distribution is bimodal between the hot and cold phases. The mixing between different phases becomes more efficient with increasing fturb, producing larger amounts of the intermediate temperature gas. Strong turbulence (fturb ≥ 0.5) generates larger density fluctuations and faster cooling, The rms logarithmic pressure fluctuation scaling with Mach number $\sigma _{\ln {\bar{P}}}^2\approx \ln (1+b^2\gamma ^2\mathcal {M}^4)$ is unaffected by thermal instability and is the same as in hydro turbulence. In contrast, the density fluctuations characterized by $\sigma _s^2$ are much larger, especially for $\mathcal {M}\lesssim 0.5$. In magnetohydrodynamic runs, magnetic fields provide significant pressure support in the cold phase but do not have any strong effects on the diffuse gas distribution, and nature and amplitude of fluctuations.
Hierarchical generative models for star clusters from hydrodynamical simulations
Authors: Torniamenti Stefano , Pasquato Mario , Di Cintio Pierfrancesco , Ballone Alessandro , Iorio Giuliano , Artale M Celeste , Mapelli Michela 
Published: December 2021

ABSTRACT
Star formation in molecular clouds is clumpy, hierarchically subclustered. Fractal structure also emerges in hydrodynamical simulations of star-forming clouds. Simulating the formation of realistic star clusters with hydrodynamical simulations is a computational challenge, considering that only the statistically averaged results of large batches of simulations are reliable, due to the chaotic nature of the gravitational N-body problem. While large sets of initial conditions for N-body runs can be produced by hydrodynamical simulations of star formation, this is prohibitively expensive in terms of computational time. Here, we address this issue by introducing a new technique for generating many sets of new initial conditions from a given set of star masses, positions, and velocities from a hydrodynamical simulation. We use hierarchical clustering in phase space to inform a tree representation of the spatial and kinematic relations between stars. This constitutes the basis for the random generation of new sets of stars which share the clustering structure of the original ones but have individually different masses, positions, and velocities. We apply this method to the output of a number of hydrodynamical star-formation simulations, comparing the generated initial conditions to the original ones through a series of quantitative tests, including comparing mass and velocity distributions and fractal dimension. Finally, we evolve both the original and the generated star clusters using a direct N-body code, obtaining a qualitatively similar evolution.
The interaction between the Spt6-tSH2 domain and Rpb1 affects multiple functions of RNA Polymerase II
Authors: Connell Zaily , Parnell Timothy J , McCullough Laura L , Hill Christopher P , Formosa Tim 
Published: December 2021

Abstract
The conserved transcription elongation factor Spt6 makes several contacts with the RNA Polymerase II (RNAPII) complex, including a high-affinity interaction between the Spt6 tandem SH2 domain (Spt6-tSH2) and phosphorylated residues of the Rpb1 subunit in the linker between the catalytic core and the C-terminal domain (CTD) heptad repeats. This interaction contributes to generic localization of Spt6, but we show here that it also has gene-specific roles. Disrupting the interface affected transcription start site selection at a subset of genes whose expression is regulated by this choice, and this was accompanied by changes in a distinct pattern of Spt6 accumulation at these sites. Splicing efficiency was also diminished, as was apparent progression through introns that encode snoRNAs. Chromatin-mediated repression was impaired, and a distinct role in maintaining +1 nucleosomes was identified, especially at ribosomal protein genes. The Spt6-tSH2:Rpb1 interface therefore has both genome-wide functions and local roles at subsets of genes where dynamic decisions regarding initiation, transcript processing, or termination are made. We propose that the interaction modulates the availability or activity of the core elongation and histone chaperone functions of Spt6, contributing to coordination between RNAPII and its accessory factors as varying local conditions call for dynamic responses.
Recent Experimental Philosophy on Joint Action: Do We Need a New Normativism About Collective Action?
Authors: Löhr Guido 
Published: December 2021

Abstract
There are two general views that social ontologists currently defend concerning the nature of joint intentional action. According to ‘non-normativists’, for a joint action to be established, we need to align certain psychological states in certain ways. ‘Normativists’ argue that joint action essentially involves normative relations that cannot be reduced to the intentional states of individuals. In two ground-breaking publications, Javier Gomez-Lavin and Matthew Rachar empirically investigate the relation between normativity and joint action in several survey studies. They argue that people's intuitions support neither current normativists nor current non-normativists. They suggest that there is a need for a ‘new normativism of joint action’. I first explore what a new normativism could amount to and conclude that the authors’ findings cannot support a demand for such a view. Finally, I suggest some ideas about how to move the field forward.
Seropositivity to Shiga toxin 2 among Argentinian urban and rural residents. Association with sociodemographic and exposure factors
Authors: Rivero Mariana Alejandra , Krüger Alejandra , Rodríguez Edgardo Mario , Signorini Porchietto Marcelo Lisandro , Lucchesi Paula María Alejandra 
Published: December 2021

AbstractBackgroundShiga toxin-producing Escherichia coli (STEC) are enteric pathogens that cause hemolytic-uremic syndrome (HUS). Ruminants, especially cattle, are their main reservoir. This study describes the seroepidemiology of STEC in rural and urban populations in Argentina, a country with a high HUS incidence.MethodsA cross-sectional study was performed in patients without gastrointestinal symptoms. IgG antibodies against Stx2 were detected by western blotting.ResultsAnti-Stx2 antibodies were detected in 14.56% of serum samples, more frequently in rural (19.38%) than urban residents (12%). Seropositivity was associated with lower socioeconomic status (SES). Among the other variables considered, thawing homemade hamburgers before cooking them, and the lack of knowledge about HUS were also associated with seropositivity. A multivariate logistic regression analysis performed with the variables that were statistically significant showed that only the SES index remained significant. As SES was measured based on several variables, we further analyzed each one of them and found that the lack of a high education level was statistically associated with seropositivity.ConclusionsThe present findings have implications for STEC prevention efforts, highlighting the importance of considering SES and risks factors linked to different SES levels when targeting consumer-level public health interventions.
Long-Term Outcomes of First-Admission Psychosis: A Naturalistic 21-Year Follow-Up Study of Symptomatic, Functional and Personal Recovery and Their Baseline Predictors
Authors: Peralta Victor , García de Jalón Elena , Moreno-Izco Lucía , Peralta David , Janda Lucía , Sánchez-Torres Ana M , Cuesta Manuel J ,   SEGPEPs Group, Ballesteros A , Gil-Berrozpe G , Hernández R , Lorente R , Fañanás L , Papiol S , Ribeiro M , Rosero A , Zandio M 
Published: December 2021

Abstract
This study was aimed at characterizing long-term outcomes of first-admission psychosis and examining their baseline predictors. Participants were assessed at baseline for 38 candidate predictors and re-assessed after a median follow-up of 21 years for symptomatic, functional, and personal recovery. Associations between the predictors and the outcomes were examined using univariate and multivariate Cox regression models. At baseline, 623 subjects were assessed for eligibility, 510 met the inclusion/exclusion criteria and 243 were successfully followed-up (57.3% of the survivors). At follow-up, the percentages of subjects achieving symptomatic, functional, and personal recovery were 51.9%, 52.7%, and 51.9%, respectively; 74.2% met at least one recovery criterion and 32.5% met all three recovery criteria. Univariate analysis showed that outcomes were predicted by a broad range of variables, including sociodemographics, familial risk, early risk factors, premorbid functioning, triggering factors, illness-onset features, neurological abnormalities, deficit symptoms and early response to treatment. Many of the univariate predictors became nonsignificant when entered into a hierarchical multivariate model, indicating a substantial degree of interdependence. Each single outcome component was independently predicted by parental socioeconomic status, family history of schizophrenia spectrum disorders, early developmental delay, childhood adversity, and mild drug use. Spontaneous dyskinesia/parkinsonism, neurological soft signs and completion of high school remained specific predictors of symptomatic, functional, and personal outcomes, respectively. Predictors explained between 27.5% and 34.3% of the variance in the outcomes. In conclusion, our results indicate a strong potential for background and first-episode characteristics in predicting long-term outcomes of psychotic disorders, which may inform future intervention research.
Like worker, like union? Labor market risk exposure, white-collar predominance and trade unions’ policy advocacy
Authors: Cronert Axel , Forsén Richard 
Published: December 2021

AbstractThis study seeks to advance the scholarship on trade union heterogeneity. Expanding on previous research, we develop a theoretical framework that distinguishes unions along two dimensions—members’ labor market risk exposure and the predominance of white-collar workers—that help shape their labor market policy preferences and advocacy. The framework is then assessed in a within-country mixed-methods analysis. We first document how members in 35 Swedish unions map onto the two dimensions, using fine-grained survey data, and then qualitatively analyze the advocacy of seven unions with different membership characteristics. Our results confirm the relevance of isolating the two dimensions in analyses of both membership preferences and elite advocacy. These findings contrast with existing accounts of the contemporary Swedish union movement and carry implications for many other countries with fragmented union landscapes. They particularly demonstrate the importance of disaggregating union membership in micro- and macro-level research involving unions and their power resources.
Microstructural Properties of Human Brain Revealed by Fractional Anisotropy Can Predict the After-Effect of Intermittent Theta Burst Stimulation
Authors: Kimura Ikko , Oishi Hiroki , Hayashi Masamichi J , Amano Kaoru 
Published: December 2021

Abstract
Intermittent theta burst stimulation (iTBS) delivered by transcranial magnetic stimulation (TMS) produces a long-term potentiation-like after-effect useful for investigations of cortical function and of potential therapeutic value. However, the iTBS after-effect over the primary motor cortex (M1) as measured by changes in motor evoked potential (MEP) amplitude exhibits a largely unexplained variability across individuals. Here, we present evidence that individual differences in white matter (WM) and gray matter (GM) microstructural properties revealed by fractional anisotropy (FA) predict the magnitude of the iTBS-induced after-effect over M1. The MEP amplitude change in the early phase (5–10 min post-iTBS) was associated with FA values in WM tracts such as right superior longitudinal fasciculus and corpus callosum. By contrast, the MEP amplitude change in the late phase (15–30 min post-iTBS) was associated with FA in GM, primarily in right frontal cortex. These results suggest that the microstructural properties of regions connected directly or indirectly to the target region (M1) are crucial determinants of the iTBS after-effect. FA values indicative of these microstructural differences can predict the potential effectiveness of repetitive TMS for both investigational use and clinical application.
Are Ayurvedic medications store house of heavy metals?
Authors: Bhalla A , Pannu A K 
Published: December 2021

Abstract
Ayurvedic formulations are widely used and perceived as safer medicine and subjected to be self-prescribed. However, recent reports have demonstrated adulterating these drugs with toxic quantities of heavy metals. To study the magnitude of the problem in Indian-manufactured Ayurvedic medications, we randomly collected common over-the-counter Ayurvedic preparations from the licensed Ayurvedic shops in the local markets of Chandigarh in 2017. The samples were analyzed to identify and quantify eight metal ions, including mercury, arsenic, lead, cadmium, zinc, iron, copper, and chromium, using inductively coupled plasma mass spectrometry in Postgraduate Institute of Medical Education and Research, Chandigarh. The permissible limit set by the Food and Agriculture Organization/World Health Organization (FAO/WHO) for herbal medicines was followed to define the high metal concentrations. Out of 43 Ayurvedic preparations, 42 were analyzed. Heavy metals were detected in all formulations. The median (range) concentrations (in μg/g or mg/kg) of the metals were quantified as follows- mercury, 13.52 (0.00–61 095.99); arsenic, 0.00 (0.00–1038.83); lead, 1.40 (0.00–57.09); zinc, 84.2200 (26.48–22 519.03); iron, 1356.21 (128.24–136 835.25); copper, 17.1450 (0.00–12 756.86) and chromium, 20.9050 (0.00–2717.58). The metal contents above the FAO/WHO-mandated limit for zinc, mercury, arsenic, and lead were detected in 35, 29, 6, and 2 formulations, respectively. All medications contained detectable quantities of zinc and iron. Copper was detected in all except one. Cadmium was not found in any sample. Ayurvedic medications have a high prevalence of heavy metals. An evaluation of the sources of contamination and the necessary drug safety regulations are required.
Antibody prevalence and immunotherapy response in Chinese patients with epilepsy and encephalopathy scores for patients with different neuronal surface antibodies
Authors: Jia Yu , Wang Hui-Fang , Zhang Meng-Yao , Wang Yu-Ping 
Published: December 2021

Abstract

Background:
The scale assessment was helpful in predicting the presence of antibodies to autoimmune encephalitis. This study aimed to evaluate the application of antibody prevalence in Chinese patients with epilepsy and encephalopathy (APE2-CHN) and response to immunotherapy in Chinese patients with epilepsy and encephalopathy (RITE2-CHN) for patients with different neuronal surface antibodies.

Methods:
A total of 1365 patients with epileptic seizures as the prominent feature in Xuanwu Hospital, Capital Medical University, from June 2016 to June 2020 were enrolled in our study. Of these, 915 patients with epilepsy of unknown etiology whose serum and/or cerebrospinal fluid samples were examined for autoimmune antibodies were selected. All patients were scored with antibody prevalence in patients with epilepsy and encephalopathy (APE2), response to immunotherapy with epilepsy and encephalopathy (RITE2), APE2-CHN, and RITE2-CHN scores.

Results:
Of the 915 patients, 191 patients were positive for neural-surface specific antibodies (115 N-methyl-D-aspartate receptor (NMDAR) Ab, 47 leucine-rich glioma-inactivated protein 1 (LGI1) Ab, 8 contactin-associated protein 2 (CASPR2) Ab, 4 AMPA2R-Ab, and 11 GABAR-B-Ab; 3 CASPR2-Ab and LGI1-Ab, 2 NMDAR-Ab and CASPR2-Ab, and 1 NMDAR-Ab and myelin-oligodendrocyte glycoprotein [MOG] Ab). The sensitivity and specificity of APE2 ≥4 in predicting the presence of neural-surface specific antibodies in our study were 74.35% and 81.77%, respectively, and the sensitivity and specificity of APE2-CHN ≥4 were 75.92% and 84.53%, respectively. Eight cases had an APE2 score <4 and APE2-CHN score ≥5; all these patients had memory decline as the prominent manifestation. We divided the patients into six groups according to the different antibodies. APE2-CHN scores showed higher sensitivity for the prediction of NMDAR-Ab, but lower sensitivity for LGI1-Ab. A total of 187/191 (97.91%) patients received immunotherapy and 142/191 (74.35%) patients benefited from the treatments. The patients who were positive for LGI1-Ab with RITE2-CHN ≥8 responded well to immunotherapy.

Conclusions:
APE2-CHN had the highest value for predicting the positivity of NMDAR-Ab and RITE2-CHN evaluated the response of immunotherapy for anti-LGI1 encephalitis appropriately. However, RITE2 and RITE2-CHN do not appear to be good predictors of immunotherapy outcomes for patients with specific neuronal-surface antibodies and high APE2-CHN scores are often indicative of a poor response to immunotherapy.
Artificial intelligence for diabetic retinopathy
Authors: Li Sicong , Zhao Ruiwei , Zou Haidong 
Published: December 2021

Abstract
Diabetic retinopathy (DR) is an important cause of blindness globally, and its prevalence is increasing. Early detection and intervention can help change the outcomes of the disease. The rapid development of artificial intelligence (AI) in recent years has led to new possibilities for the screening and diagnosis of DR. An AI-based diagnostic system for the detection of DR has significant advantages, such as high efficiency, high accuracy, and lower demand for human resources. At the same time, there are shortcomings, such as the lack of standards for development and evaluation and the limited scope of application. This article demonstrates the current applications of AI in the field of DR, existing problems, and possible future development directions.