bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrating the exploration-exploitation dilemma and bad institutions to the Austrian theory of destructive entrepreneurship: a new perspective
Authors: Aimar Thierry 
Published: January 2023

AbstractContemporary Austrian theory has expanded widely on the relationship between entrepreneurship and the structure of production, yet it has never touched on the existence of an exploration-exploitation dilemma within organizations. The objective of the article is to show that the integration of the exploration-exploitation dilemma into the Austrian theory adds new fruitful elements to the function of the destructive entrepreneur, as presented by the Austrian economists of the firm. By showing how an organization's complexity can motivate destructive entrepreneurship on competition, it firstly explains how their analytical tools, which were limited in this area to the infra-organizational field, can also be applied to the catallactic field; it secondly enriches the traditional Austrian vision of the relation between institutions and entrepreneurship, in highlighting a reverse causal relationship, that has not yet been pointed by Austrian literature on entrepreneurship, between bad institutions and the destructive entrepreneur.
Treating trauma-driven OCD with narrative exposure therapy alongside cognitive behavioural therapy
Authors: Airdrie J.N. , Lambe S. , Cooper K. 
Published: January 2023

Abstract
When post-traumatic stress disorder (PTSD) co-occurs with obsessive compulsive disorder (OCD), symptoms of the former can interfere with evidence-based treatment of the latter. As a result, exposure-based treatments are recommended for both OCD and PTSD, potentially facilitating a concurrent treatment approach. This case study describes the application of concurrent cognitive behaviour therapy (CBT including exposure and response prevention; ERP) for OCD and narrative exposure therapy to treat a patient whose PTSD symptoms of intrusive images of memories and hyperarousal were interfering with standard CBT (including ERP) treatment for OCD. Following this concurrent approach, the patient’s symptoms of OCD reduced to non-clinical levels and showed reliable improvement in PTSD symptoms. Whilst further methodologically robust research is required, this case study highlights that this approach may be beneficial to the treatment of OCD where PTSD symptoms are impacting on treatment.

Key learning aims

(1)
To explore the literature considering explanations of the co-occurrence of OCD and PTSD symptomology.

(2)
To consider how symptoms of two mental health conditions can maintain one another and attenuate the effectiveness of evidence-based treatment for the other mental health condition.

(3)
Consider the use of concurrent therapeutic approaches to treat co-occurring mental health conditions.
Exploring the Genetic Association between Obesity and Serum Lipid Levels Using Bivariate Methods
Authors: Ke Ji , Gao Wenjing , Wang Biqi , Cao Weihua , Lv Jun , Yu Canqing , Huang Tao , Sun Dianjianyi , Liao Chunxiao , Pang Yuanjie , Pang Zengchang , Cong Liming , Wang Hua , Wu Xianping , Liu Yu , Li Liming 
Published: January 2023

AbstractIt is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.
Significant Diurnal Warming Events Observed by Saildrone at High Latitudes
Authors: Jia Chong , Minnett Peter J. , Luo Bingkun 
Published: January 2023

AbstractThe sea surface temperature (SST) is one of the essential parameters needed to understand the climate change in the Arctic. Saildrone, an advanced autonomous surface vehicle, has proven to be a useful tool for providing accurate SST data at high latitudes. Here, data from two Saildrones, deployed in the Arctic in the summer of 2019, are used to investigate the diurnal variability of upper ocean thermal structure. An empirical cool skin effect model with dependence on the wind speed with new coefficients was generated. Several local large diurnal warming events were observed, the amplitudes of warming in the skin layer >5 K, rarely reported in previous studies. Furthermore, the warming signals could persist beyond 1 day. For those cases, it was found surface warm air suppressed the surface turbulent heat loss to maintain the persistence of diurnal warming under low wind conditions. Salinity also plays an important role in the formation of upper ocean density stratification during diurnal warming at high latitudes. A less salty and hence less dense surface layer was likely created by precipitation or melting sea ice, providing favorable conditions for the formation of upper ocean stratification. Comparisons with two prognostic diurnal warming models showed the simulations match reasonably well with Saildrone measurements for moderate wind speeds but exhibit large differences at low winds. Both schemes show significant negative biases in the early morning and late afternoon. It is necessary to improve the model schemes when applied at high latitudes.
Data‐Driven Stochastic Lie Transport Modeling of the 2D Euler Equations
Authors: Ephrati Sagy R. , Cifani Paolo , Luesink Erwin , Geurts Bernard J. 
Published: January 2023

AbstractIn this paper, we propose and assess several stochastic parametrizations for data‐driven modeling of the two‐dimensional Euler equations using coarse‐grid SPDEs. The framework of Stochastic Advection by Lie Transport (SALT) (Cotter et al., 2019, https://doi.org/10.1137/18m1167929) is employed to define a stochastic forcing that is decomposed in terms of a deterministic basis (empirical orthogonal functions, EOFs) multiplied by temporal traces, here regarded as stochastic processes. The EOFs are obtained from a fine‐grid data set and are defined in conjunction with corresponding deterministic time series. We construct stochastic processes that mimic properties of the measured time series. In particular, the processes are defined such that the underlying probability density functions (pdfs) or the estimated correlation time of the time series are retained. These stochastic models are compared to stochastic forcing based on Gaussian noise, which does not use any information of the time series. We perform uncertainty quantification tests and compare stochastic ensembles in terms of mean and spread. Reduced uncertainty is observed for the developed models. On short timescales, such as those used for data assimilation (Cotter et al., 2020a, https://doi.org/10.1007/s10955-020-02524-0), the stochastic models show a reduced ensemble mean error and a reduced spread. Particularly, using estimated pdfs yields stochastic ensembles which rarely fail to capture the reference solution on small time scales, whereas introducing correlation into the stochastic models improves the quality of the coarse‐grid predictions with respect to Gaussian noise.
Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind Using the Alfvén Wave Solar Atmosphere Model
Authors: Jivani Aniket , Sachdeva Nishtha , Huang Zhenguang , Chen Yang , van der Holst Bart , Manchester Ward , Iong Daniel , Chen Hongfan , Zou Shasha , Huan Xun , Toth Gabor 
Published: January 2023

AbstractModeling the impact of space weather events such as coronal mass ejections (CMEs) is crucial to protecting critical infrastructure. The Space Weather Modeling Framework is a state‐of‐the‐art framework that offers full Sun‐to‐Earth simulations by computing the background solar wind, CME propagation, and magnetospheric impact. However, reliable long‐term predictions of CME events require uncertainty quantification (UQ) and data assimilation. We take the first steps by performing global sensitivity analysis (GSA) and UQ for background solar wind simulations produced by the Alfvén Wave Solar atmosphere Model (AWSoM) for two Carrington rotations: CR2152 (solar maximum) and CR2208 (solar minimum). We conduct GSA by computing Sobol' indices that quantify contributions from model parameter uncertainty to the variance of solar wind speed and density at 1 au, both crucial quantities for CME propagation and strength. Sobol' indices also allow us to rank and retain only the most important parameters, which aids in the construction of smaller ensembles for the reduced‐dimension parameter space. We present an efficient procedure for computing the Sobol' indices using polynomial chaos expansion surrogates and space‐filling designs. The PCEs further enable inexpensive forward UQ. Overall, we identify three important model parameters: the multiplicative factor applied to the magnetogram, Poynting flux per magnetic field strength constant used at the inner boundary, and the coefficient of the perpendicular correlation length in the turbulent cascade model in AWSoM.
Improving Precise Orbit Determination of LEO Satellites Using Enhanced Solar Radiation Pressure Modeling
Authors: Wang Youcun , Li Min , Jiang Kecai , Li Wenwen , Zhao Qile , Fang Rongxin , Wei Na , Mu Renhai 
Published: January 2023

AbstractPrecise orbit knowledge is a fundamental requirement for low Earth orbit (LEO) satellites. High‐precision non‐gravitational force modeling directly improves the overall quality of LEO precise orbit determination (POD). To address the potential systematic errors in solar radiation pressure (SRP), we introduce observed radiation data and modeled physical effects to describe the real in‐flight environment of satellites. Time‐dependent solar irradiance data and a highly physical shadow model are considered for SRP modeling. We develop an advanced thermal reradiation model for satellite solar panels. A set of improved non‐gravitational force models is performed for LEO POD, and we discuss the benefits of the enhanced dynamic models on orbit quality and dependence on empirical parameters. The Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO), Jason‐3, and Haiyang‐2B missions are selected for the POD process. Estimated empirical acceleration and scale parameters and independent satellite laser ranging (SLR) are used to validate the final orbit solutions. The magnitude of empirical acceleration estimated in POD is reduced by 19% with the enhanced dynamic modeling, and the estimated scale factor for the SRP converges to stable and reasonable level. Furthermore, the steady‐state temperature model used in thermal reradiation can effectively reduce mismodeled effects in the SRP, and the systematic linear dependency revealed by the SLR residuals is significantly reduced for the GRACE‐C and Jason‐3 satellites, with improvements of approximately 61% and 49%, respectively. Overall, advances are made in the explicit modeling of non‐gravitational forces to pursue superior satellite orbits, suggesting a more dynamic orbit solution.
Decoding Sequence Classification Models for Acquiring New Biological Insights
Authors: Bodenhofer Ulrich , Kothmeier Andreas , Abfalter Ingrid , Mahrenholz Carsten , Hochreiter Sepp 
Published: January 2023

AbstractClassifying biological sequences is one of the most important tasks in computational biology. In the last decade, support vector machines (SVMs) in combination with sequence kernels have emerged as a de-facto standard. These methods are theoretically well-founded, reliable, and provide high-accuracy solutions at low computational cost. However, obtaining a highly accurate classifier is rarely the end of the story in many practical situations. Instead, one often aims to acquire biological knowledge about the principles underlying a given classification task. SVMs with traditional sequence kernels do not offer a straightforward way of accessing this knowledge.In this contribution, we propose a new approach to analyzing biological sequences on the basis of support vector machines with sequence kernels. We first extract explicit pattern weights from a given SVM. When classifying a sequence, we then compute a prediction profile by distributing the weight of each pattern to the sequence positions that match the pattern. The final profile not only allows assessing the importance of a position, but also determining for which class it is indicative. Since it is unfeasible to analyze profiles of all sequences in a given data set, we advocate using affinity propagation (AP) clustering to narrow down the analysis to a small set of typical sequences.The proposed approach is applicable to a wide range of biological sequences and a wide selection of sequence kernels. To illustrate our framework, we present the prediction of oligomerization tendencies of coiled coil proteins as a case study.
Rule Responder: A Rule-Based Semantic eScience Service Infrastructure
Authors: Paschke Adrian , Zhao Zhili 
Published: January 2023

AbstractTo a large degree information and services for chemical e-Science have become accessible -anytime, anywhere -but not necessarily useful. The Rule Responder eScience middleware is about providing information consumers with rule-based agents to transform existing information into relevant information of practical consequences, hence providing control to the end-users to express in a declarative rule-based way how to turn existing information into personally relevant information and how to react or make automated decisions on top of it.
ChemCloud: Chemical e-Science Information Cloud
Authors: Todor Alexandru , Paschke Adrian , Heineke Stephan 
Published: January 2023

AbstractOur Chemical e-Science Information Cloud (ChemCloud) – a Semantic Web based eScience infrastructure – integrates and automates a multitude of databases, tools and services in the domain of chemistry, pharmacy and bio-chemistry available at the Fachinformationszentrum Chemie (FIZ Chemie), at the Freie Universitaet Berlin (FUB), and on the public Web. Based on the approach of the W3C Linked Open Data initiative and the W3C Semantic Web technologies for ontologies and rules it semantically links and integrates knowledge from our W3C HCLS knowledge base hosted at the FUB, our multi-domain knowledge base DBpedia (Deutschland) implemented at FUB, which is extracted from Wikipedia (De) providing a public semantic resource for chemistry, and our well-established databases at FIZ Chemie such as ChemInform for organic reaction data, InfoTherm the leading source for thermophysical data, Chemisches Zentralblatt, the complete chemistry knowledge from 1830 to 1969, and ChemgaPedia the largest and most frequented e-Learning platform for Chemistry and related sciences in German language.
Integrative Network-based Association Studies: Leveraging cell regulatory models in the post-GWAS era
Authors: Califano Andrea , Butte Atul , Friend Stephen , Ideker Trey , Schadt Eric 
Published: January 2023

AbstractAfter completion of a number of large scale Genome-Wide Association Studies (GWAS), there is still a significant amount of trait and disease variance that cannot be explained by existing genetic variability. This review introduces new, Integrative Network-based Association Study (INAS) approaches that aim to minimize the impact from multiple hypothesis testing statistics, thus allowing the identification of rare variants/alterations and epistatic interactions. In particular we discuss methods that rely on the de novo computational, experimental, and integrative dissection of context specific molecular interaction networks (or interactomes, for short). We provide several examples of how these approaches may be used to tackle discovery of genetic variants and somatic alterations causally related to the presentation of specific traits and diseases. We also discuss how more complex systems, including a variety of non-cell-autonomous traits and diseases will require new multicellular networks that explicitly represent short distance paracrine and long distance endocrine interactions.
Neoantigens: promising targets for cancer therapy
Authors: Xie Na , Shen Guobo , Gao Wei , Huang Zhao , Huang Canhua , Fu Li 
Published: January 2023

AbstractRecent advances in neoantigen research have accelerated the development and regulatory approval of tumor immunotherapies, including cancer vaccines, adoptive cell therapy and antibody-based therapies, especially for solid tumors. Neoantigens are newly formed antigens generated by tumor cells as a result of various tumor-specific alterations, such as genomic mutation, dysregulated RNA splicing, disordered post-translational modification, and integrated viral open reading frames. Neoantigens are recognized as non-self and trigger an immune response that is not subject to central and peripheral tolerance. The quick identification and prediction of tumor-specific neoantigens have been made possible by the advanced development of next-generation sequencing and bioinformatic technologies. Compared to tumor-associated antigens, the highly immunogenic and tumor-specific neoantigens provide emerging targets for personalized cancer immunotherapies, and serve as prospective predictors for tumor survival prognosis and immune checkpoint blockade responses. The development of cancer therapies will be aided by understanding the mechanism underlying neoantigen-induced anti-tumor immune response and by streamlining the process of neoantigen-based immunotherapies. This review provides an overview on the identification and characterization of neoantigens and outlines the clinical applications of prospective immunotherapeutic strategies based on neoantigens. We also explore their current status, inherent challenges, and clinical translation potential.
SEVs-mediated miR-6750 transfer inhibits pre-metastatic niche formation in nasopharyngeal carcinoma by targeting M6PR
Authors: Zhang Caiming , Chen Wenhui , Pan Si , Zhang Siyu , Xie Haijing , Zhang Zixiang , Lei Wei , Bao Lili , You Yiwen 
Published: January 2023

AbstractReliable detection of circulating small extracellular vesicles (SEVs) and their miRNA cargo has been needed to develop potential specific non-invasive diagnostic and therapeutic marker for cancer metastasis. Here, we detected miR-6750, the precise molecular function of which was largely unknown, was significantly enriched in serum-SEVs from normal volunteers vs. patients with nasopharyngeal carcinoma (NPC). And we determined that miR-6750-SEVs attenuated NPC metastasis. Subsequently, miR-6750-SEVs was proven to inhibit angiogenesis and activate macrophage toward to M1 phenotype to inhibit pre-metastatic niche formation. After analyzing the expression level of miR-6750 in NPC cells, HUVECs and macrophage, we found that once miR-6750 level in NPC cells was close to or higher than normal nasopharyngeal epithelial cells (NP69), miR-6750-SEVs would be transferred from NPC cells to macrophage and then to HUVECs to modulate metastatic niche. Moreover, in vitro assays and BALB/c mouse tumor models revealed that miR-6750 directly targeted mannose 6-phosphate receptor (M6PR). Taken together, our findings revealed that miR-6750-M6PR axis can mediate NPC metastasis by remodeling tumor microenvironment (TME) via SEVs, which give novel sights to pathogenesis of NPC.
Extracellular vesicles mediate antibody-resistant transmission of SARS-CoV-2
Authors: Xia Bingqing , Pan Xiaoyan , Luo Rong-Hua , Shen Xurui , Li Shuangqu , Wang Yi , Zuo Xiaoli , Wu Yan , Guo Yingqi , Xiao Gengfu , Li Qiguang , Long Xin-Yan , He Xiao-Yan , Zheng Hong-Yi , Lu Ying , Pang Wei , Zheng Yong-Tang , Li Jia , Zhang Lei-Ke , Gao Zhaobing 
Published: January 2023

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. Antibody resistance dampens neutralizing antibody therapy and threatens current global Coronavirus (COVID-19) vaccine campaigns. In addition to the emergence of resistant SARS-CoV-2 variants, little is known about how SARS-CoV-2 evades antibodies. Here, we report a novel mechanism of extracellular vesicle (EV)-mediated cell-to-cell transmission of SARS-CoV-2, which facilitates SARS-CoV-2 to escape from neutralizing antibodies. These EVs, initially observed in SARS-CoV-2 envelope protein-expressing cells, are secreted by various SARS-CoV-2-infected cells, including Vero E6, Calu-3, and HPAEpiC cells, undergoing infection-induced pyroptosis. Various SARS-CoV-2-infected cells produce similar EVs characterized by extra-large sizes (1.6–9.5 μm in diameter, average diameter > 4.2 μm) much larger than previously reported virus-generated vesicles. Transmission electron microscopy analysis and plaque assay reveal that these SARS-CoV-2-induced EVs contain large amounts of live virus particles. In particular, the vesicle-cloaked SARS-CoV-2 virus is resistant to neutralizing antibodies and able to reinfect naïve cells independent of the reported receptors and cofactors. Consistently, the constructed 3D images show that intact EVs could be taken up by recipient cells directly, supporting vesicle-mediated cell-to-cell transmission of SARS-CoV-2. Our findings reveal a novel mechanism of receptor-independent SARS-CoV-2 infection via cell-to-cell transmission, provide new insights into antibody resistance of SARS-CoV-2 and suggest potential targets for future antiviral therapeutics.