bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preferences for Rent Control: Between Political Geography and Political Economy
Authors: Cohen Denis 
Published: June 2022

AbstractFollowing geographically concentrated changes in housing markets, real estate prices have skyrocketed in many cities and metropolitan areas across Germany. These developments have not only shifted the macro-level distribution of asset wealth among homeowners but have also resulted in price spikes in rental markets, which in turn have intensified social and economic risks among renters. This preregistered study aims to provide a theoretical rationale for, and first-time insights into, the determinants of individual preferences for rent control. It argues that policy preferences are shaped by individuals’ economic and geographic positions in the housing market. It not only explores differences between homeowners and renters but also considers how heterogeneity in exposure to the burden of rental costs—structured by local rents and disposable income—explains differences within the group of renters. The results reveal the precedence of egotropic considerations over geotropic effects of common market exposures. Homeowners oppose rent control far more strongly than renters do, whose support for rent control is primarily a function of income. Market rents, in contrast, only heighten support for rent control among low-income renters. These findings deepen our understanding of the politicization of housing policy in Germany and advance important debates on political reactions to housing markets.
Hybrid semiparametric Bayesian networks
Authors: Atienza David , Larrañaga Pedro , Bielza Concha 
Published: June 2022

AbstractThis paper presents a new class of Bayesian networks called hybrid semiparametric Bayesian networks, which can model hybrid data (discrete and continuous data) by mixing parametric and nonparametric estimation models. The parametric estimation models can represent a conditional linear Gaussian relationship between variables, while the nonparametric estimation model can represent other types of relationships, such as non-Gaussian and nonlinear relationships. This new class of Bayesian networks generalizes the conditional linear Gaussian Bayesian networks, including them as a special case. In addition, we describe a learning procedure for the structure and the parameters of our proposed type of Bayesian network. This learning procedure finds the best combination of parametric and nonparametric models automatically from data. This requires the definition of a cross-validated score. We also detail how new data can be sampled from a hybrid semiparametric Bayesian network, which in turn can be useful to solve other related tasks, such as inference. Furthermore, we intuitively relate our proposal with adaptive kernel density estimation models. The experimental results show that hybrid semiparametric Bayesian networks are a valuable contribution when dealing with data that do not meet the parametric assumptions that are expected for other models, such as conditional linear Gaussian Bayesian networks. We include experiments with synthetic data and real-world data from the UCI repository which demonstrate the good performance and the ability to extract useful information about the relationship between the variables in the model.
Comments on: hybrid semiparametric Bayesian networks
Authors: Sperlich Stefan 
Published: June 2022

AbstractThis note comments on the article of David Atienza, Pedro Larrañaga and Concha Bielza in which they first review recent contributions to Bayesian networks and then introduce a new hybrid version. It combines parametric and nonparametric density estimates for continuous variables by simultaneously allowing for discrete parents. We discuss straightforward extensions of the linear Gaussian parts and potential smoothing over the outcomes of discrete parents and conclude with some minor comments.
Thymic neuroendocrine tumors in patients with multiple endocrine neoplasia type 1
Authors: Yliaska Iina , Tokola Heikki , Ebeling Tapani , Kuismin Outi , Ukkola Olavi , Koivikko Minna L. , Lesonen Timo , Rimpiläinen Jussi , Felin Tuuli , Ryhänen Eeva , Metso Saara , Schalin-Jäntti Camilla , Salmela Pasi 
Published: June 2022

Abstract
Objective
MEN1 is associated with an increased risk of developing tumors in different endocrine organs. Neuroendocrine tumors of the thymus (TNETs) are very rare but often have an aggressive nature. We evaluated patients with MEN1 and TNET in three university hospitals in Finland.

Design/Methods
We evaluated patient records of 183 MEN1-patients from three university hospitals between the years 1985–2019 with TNETs. Thymus tumor specimens were classified according to the new WHO 2021 classification of TNET. We collected data on treatments and outcomes of these patients.

Results
There were six patients (3.3%) with MEN1 and TNET. Five of them had the same common gene mutation occurring in Finland. They originated from common ancestors encompassing two pairs of brothers from sequential generations. The mean age at presentation of TNET was 44.7 ± 11.9 years. TNET was classified as atypical carcinoid (AC) in five out of six patients. One patient had a largely necrotic main tumor with very few mitoses and another nodule with 25 mitoses per 2 mm2, qualifying for the 2021 WHO diagnosis of large cell neuroendocrine carcinoma (LCNEC). In our patients, the 5-year survival of the TNET patients was 62.5% and 10-year survival 31.3%.

Conclusion
In this study, TNETs were observed in one large MEN1 founder pedigree, where an anticipation-like earlier disease onset was observed in the most recent generation. TNET in MEN1 patients is an aggressive disease. The prognosis can be better by systematic screening. We also show that LCNEC can be associated with TNET in MEN1 patients.
Study of the Technology for Gold Recovery from Gravity-Flotation Concentrate from Ore Beneficiation with the use of Oxidizing Reagents
Authors: Koizhanova A. K. , Berkinbayeva A. N. , Magomedov D. R. , Azlan M. N. , Atanova O. V. , Kamalov E. M. , Erdenova M. B. 
Published: June 2022

AbstractGold-containing ore processing technology has been developed by using chemical, X-ray phase, mineralogical, X-ray fluorescence analyses. The results of experiments on the gold recovery from a concentrate with a particle size of 10 and 4 µm are presented, including options for direct cyanidation without preliminary oxygen oxidation, as well as with the use of preliminary oxidation of Trichloroisocyanuric acid (TCCA) and Sulfanol surfactant (SAS). It was found that the test sample contains 24.74 g/t Au; 11.66% S; 18.51% Fe. The content of sulfide minerals is 36.1%, the main sulfides are arsenopyrite (25.4%) and pyrite (10.7%). Microscopic examination of the combined gravity-flotation concentrate revealed ultrafine, fine and visible gold dust particles both in free form and in aggregations with waste rock. The particle size of Au in the product varies from 0.5 to 18.8 μm. 77.12% of gold is recovered from concentrate of 10 μm by direct cyanidation. “Sulfanol” was used to recover 83.71% of gold. Pre-treatment with TCCA yielded 85.61% of gold. Recovery of gold from the 4 μm concentrate was 75.38% for direct cyanidation, 78.79% for cyanidation with Sulfanol surfactant, 76.52% for cyanidation with TCCA.
How does firms' broadband adoption affect regional TFP in Italy?
Authors: Giannini Massimo , Martini Barbara , Fiorelli Cristiana 
Published: June 2022

AbstractIn the literature, the positive effect of ICT on labour productivity and, in general, economic growth is vast and well consolidated. This paper wants to go beyond the general term of ICT and look inside the "black box." In particular, broadband adoption among Italian firms is critical for productivity. Hence, we focus on broadband adoption and internet facilities and how they affect the firms' total factor productivity in the Italian business sector firms at NUTS2 over the period 2003–2018. Italy is indeed still characterized by a robust North–South divide. Our question is: can we exploit the digital advantage for filling the productivity gap? To answer, we are going to use a classical two-stage approach. In the first one, the TFP is filtered out using both a semi-parametric approach and a parametric one (spatial ML). The second step investigates its determinants, using broadband firms' adoption as a covariate in an ECM augmented by spatial spillovers, controlling for competitivity, internationalization, and human capital. Our results show a positive relationship between TFP and broadband adoption (cointegration), including regional spillovers; this positive effect spreads to GVA. Moreover, our results show that digitalization makes Southern regions more resilient to external shocks.
A Novel Method to Improve the Physical Property and Biocompatibility of Decellularized Heart Valve Scaffold with Sericin and Polydopamine
Authors: Bai Peng , Kong Gangcheng , Qiao Weihua , Song Yu , Wang Yixuan , Shi Jiawei , Zhang Nan , Liu Chungeng , Chu Chong , Xiong Tixiusi , Zhou Ying , Lu Cuifen , Wang Lin , Dong Nianguo 
Published: June 2022

AbstractCardiac valve replacement is an effective method to treat valvular heart disease. Artificial valves used routinely in clinic still have defects. In our study, we explored a novel method to modify the performance of Decellularized Heart Valve (DHV) scaffold. The decellularized porcine aortic valve was prepared using sequential hydrophile and lipophile solubilization method. The sericin was extracted from silk fibroin-deficient silkworm cocoon by lithium bromide method. First, DHV was immersed in sericin solution to produce the sericin–DHV composite scaffold. Then, we modified the DHV by making a Polydopamine (PDA) coating on the DHV first and then binding the sericin. The physical properties and biological compatibility of our composite scaffold were assessed in vitro and in vivo. Sericin were successfully prepared, combined to DHV and improved its biocompatibility. PDA coating further promoted the combination of sericin on DHV and improved the physical properties of scaffolds. The decay rate of our modified valve scaffold was decreased in vivo and it showed good compatibility with blood. In conclusion, our modification improved the physical properties and biocompatibility of the valve scaffold. The combination of PDA and sericin promoted the recellularization of decellularized valves, showing great potential to be a novel artificial valve.
High-density individually addressable platinum nanoelectrodes for biomedical applications
Authors: Raj Vidur , Gopakumar Aswani , Vaidya Gayatri , Scott John , Toth Milos , Jagadish Chennupati , Gautam Vini 
Published: June 2022

Abstract3-D vertical nanoelectrode arrays (NEAs) have found applications in several biomedical and sensing applications, including high-resolution neuronal excitation and measurement and single-molecule electrochemical biosensing. There have been several reports on high-density nanoelectrodes in recent years, with the filling ratio of electrodes reaching close to 0.002 (assuming the electrode diameter of 200 nm and pitch of 4 μm). Still, it is well below the nanowire filling ratio required to form interconnected neuronal networks, i.e., more than 0.14 (assuming the electrode diameter of 200 nm and pitch of 1.5 μm). Here, we employ a multi-step, large-area electron beam lithography procedure along with a targeted, focused ion beam based metal deposition technique to realize an individually addressable, 60-channel nanoelectrode chip with a filling ratio as high as 0.16, which is well within the limit required for the formation of interconnected neuronal networks. Moreover, we have designed the NEA chip to be compatible with the commercially available MEA2100-System, which can, in the future, enable the chip to be readily used for obtaining data from individual electrodes. We also perform an in-depth electrochemical impedance spectroscopy characterization to show that the electrochemical behavior and the charge transfer mechanism in the array are significantly influenced by changing the thickness of the SU-8 planarization layer (i.e., the thickness of the exposed platinum surface). In addition to neural signal excitation and measurement, we propose that these NEA chips have the potential for other future applications, such as high-resolution single-molecule level electrochemical and bio-analyte sensing.
Interlaboratory comparison of an intestinal triple culture to confirm transferability and reproducibility
Authors: Kämpfer Angela A. M. , Shah Ume-Kulsoom , Chu Shui L. , Busch Mathias , Büttner Veronika , He Ruiwen , Rothen-Rutishauser Barbara , Schins Roel P. F. , Jenkins Gareth J. 
Published: June 2022

Abstract
The development and improvement of advanced intestinal in vitro models has received increasing attention in recent years. While the availability of relevant in vitro models is pivotal to advance the replacement and reduction of animal use in research, their robustness is a crucial determinant for intra- and interlaboratory reproducibility. We have developed a standard protocol to build a triple culture model combining two types of human intestinal epithelial cells (Caco-2, HT29-MTX-E12) and macrophages (THP-1), which was tested for transferability and reproducibility between three laboratories. The epithelial tissue barrier development and triple culture stability were investigated as well as the models’ responses to the non-steroidal anti-inflammatory drug diclofenac in terms of barrier integrity, cytotoxicity, and cytokine release. The results of two partner laboratories were compared to previously established benchmark results and quality criteria. For the epithelial co-cultures, the results were overall highly comparable between the laboratories. The addition of THP-1 cells resulted in increased variability and reduced reproducibility. While good correlation was achieved in several endpoints, others showed substantial response differences between the laboratories. Some variations may be addressed with training or demonstrations, whereas others might be related to fundamental differences in the cell lines introduced during routine cell culture and maintenance. Our results underline the importance of interlaboratory transfer studies using standardised experimental procedures, including defined quality criteria and benchmarks, as well as of training when newly establishing complex in vitro models in laboratories.
Polyphenic risk score shows robust predictive ability for long-term future suicidality
Authors: Cheng M. , Roseberry K. , Choi Y. , Quast L. , Gaines M. , Sandusky G. , Kline J. A. , Bogdan P. , Niculescu A. B. 
Published: June 2022

AbstractSuicides are preventable tragedies, if risk factors are tracked and mitigated. We had previously developed a new quantitative suicidality risk assessment instrument (Convergent Functional Information for Suicidality, CFI-S), which is in essence a simple polyphenic risk score, and deployed it in a busy urban hospital Emergency Department, in a naturalistic cohort of consecutive patients. We report a four years follow-up of that population (n = 482). Overall, the single administration of the CFI-S was significantly predictive of suicidality over the ensuing 4 years (occurrence- ROC AUC 80%, severity- Pearson correlation 0.44, imminence-Cox regression Hazard Ratio 1.33). The best predictive single phenes (phenotypic items) were feeling useless (not needed), a past history of suicidality, and social isolation. We next used machine learning approaches to enhance the predictive ability of CFI-S. We divided the population into a discovery cohort (n = 255) and testing cohort (n = 227), and developed a deep neural network algorithm that showed increased accuracy for predicting risk of future suicidality (increasing the ROC AUC from 80 to 90%), as well as a similarity network classifier for visualizing patient’s risk. We propose that the widespread use of CFI-S for screening purposes, with or without machine learning enhancements, can boost suicidality prevention efforts. This study also identified as top risk factors for suicidality addressable social determinants.
Emotional distress, stress, anxiety, and the impact of the COVID-19 pandemic on early- to mid-career women in healthcare sciences research
Authors: Bittar Noor , Cohee Andrea , Bhamidipalli Surya Sruthi , Savoy April , Ismail Heba M. 
Published: June 2022

Abstract

Objectives:
The main objective of this study was to report stress and anxiety levels during the COVID-19 pandemic on early- to mid-career women researchers in healthcare sciences research and determine the associated factors.

Methods:
A 50-item self-administered internet questionnaire was developed using a mix of Likert-type scales and open-ended response questions. The survey was distributed June 10–August 3, 2020. Anxiety and stress as well as personal/family demands were assessed through validated measures (Patient Reported Outcomes Measurement Information System [PROMIS]-Anxiety Short Form and Perceived Stress Scale [PSS]) and open-ended responses.

Results:
One hundred and fifty-one early-career women in healthcare sciences research completed the survey; mean respondent age was 37.3 ± 5.2 years; and all had a college degree or higher, 50.3% holding a PhD and 35.8% MD. Race and ethnicity were reported in 128; the majority were White (74.0%). One-third (31.2%) reported being “very much” concerned about reaching their research productivity goals and 30.1% were “very much” concerned about academic promotion and tenure. Fifty percent reported a “moderate” PROMIS anxiety score and 72.1% reported a “moderate” PSS score. For the open-ended responses, 65.6% reported a worry about their professional goals because of the COVID-19 pandemic. Major concerns revolved around finances, childcare, and job security.

Conclusions:
Throughout the pandemic, early- and mid-career women in healthcare sciences research have reported moderate to high overall stress, anxiety, and worries. These concerns appear related to household settings, additional responsibilities, financial concerns, and reduced research productivity. Institutions and funding agencies should take these concerns into consideration and offer support.
Universal Digital Twin: Integration of national-scale energy systems and climate data
Authors: Savage Thomas , Akroyd Jethro , Mosbach Sebastian , Krdzavac Nenad , Hillman Michael , Kraft Markus 
Published: June 2022

Abstract
This article applies a knowledge graph-based approach to unify multiple heterogeneous domains inherent in climate and energy supply research. Existing approaches that rely on bespoke models with spreadsheet-type inputs are noninterpretable, static and make it difficult to combine existing domain specific models. The difficulties inherent to this approach become increasingly prevalent as energy supply models gain complexity while society pursues a net-zero future. In this work, we develop new ontologies to extend the World Avatar knowledge graph to represent gas grids, gas consumption statistics, and climate data. Using a combination of the new and existing ontologies we construct a Universal Digital Twin that integrates data describing the systems of interest and specifies respective links between domains. We represent the UK gas transmission system, and HadUK-Grid climate data set as linked data for the first time, formally associating the data with the statistical output areas used to report governmental administrative data throughout the UK. We demonstrate how computational agents contained within the World Avatar can operate on the knowledge graph, incorporating live feeds of data such as instantaneous gas flow rates, as well as parsing information into interpretable forms such as interactive visualizations. Through this approach, we enable a dynamic, interpretable, modular, and cross-domain representation of the UK that enables domain specific experts to contribute toward a national-scale digital twin.
Measure-theoretically mixing subshifts with low complexity
Authors: CREUTZ DARREN , PAVLOV RONNIE , RODOCK SHAUN 
Published: June 2022

AbstractWe introduce a class of rank-one transformations, which we call extremely elevated staircase transformations. We prove that they are measure-theoretically mixing and, for any
$f : \mathbb {N} \to \mathbb {N}$
with
$f(n)/n$
increasing and
$\sum 1/f(n) < \infty $
, that there exists an extremely elevated staircase with word complexity
$p(n) = o(f(n))$
. This improves the previously lowest known complexity for mixing subshifts, resolving a conjecture of Ferenczi.
Six kilowatt record all-fiberized and narrow-linewidth fiber amplifier with near-diffraction-limited beam quality
Authors: Wang Guangjian , Song Jiaxin , Chen Yisha , Ren Shuai , Ma Pengfei , Liu Wei , Yao Tianfu , Zhou Pu 
Published: June 2022

Abstract
In this work, an all-fiberized and narrow-linewidth fiber amplifier with record output power and near-diffraction-limited beam quality is presented. Up to 6.12 kW fiber laser with the conversion efficiency of approximately 78.8% is achieved through the fiber amplifier based on a conventional step-index active fiber. At the maximum output power, the 3 dB spectral linewidth is approximately 0.86 nm and the beam quality factor is M
x
2 = 1.43, M
y
2 = 1.36. We have also measured and compared the output properties of the fiber amplifier employing different pumping schemes. Notably, the practical power limit of the fiber amplifier could be estimated through the maximum output powers of the fiber amplifier employing unidirectional pumping schemes. Overall, this work could provide a good reference for the optimal design and potential exploration of high-power narrow-linewidth fiber laser systems.
Metabolic regulation of subcellular sucrose cleavage inferred from quantitative analysis of metabolic functions
Authors: Nägele Thomas 
Published: June 2022

AbstractQuantitative analysis of experimental metabolic data is frequently challenged by non-intuitive, complex patterns which emerge from regulatory networks. The complex output of metabolic regulation can be summarised by metabolic functions which comprise information about dynamics of metabolite concentrations. In a system of ordinary differential equations, metabolic functions reflect the sum of biochemical reactions which affect a metabolite concentration, and their integration over time reveals metabolite concentrations. Further, derivatives of metabolic functions provide essential information about system dynamics and elasticities. Here, invertase-driven sucrose hydrolysis was simulated in kinetic models on a cellular and subcellular level. Both Jacobian and Hessian matrices of metabolic functions were derived for quantitative analysis of kinetic regulation of sucrose metabolism. Model simulations suggest that transport of sucrose into the vacuole represents a central regulatory element in plant metabolism during cold acclimation which preserves control of metabolic functions and limits feedback-inhibition of cytosolic invertases by elevated hexose concentrations.