bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Hybrid Approach Using Design of Experiment and Artificial Neural Network in a Camless Heavy-Duty Engine
Authors: Jia Zhiqin , Koopmans Lucien 
Published: May 2022

Abstract
Increasingly stricter emission regulations and fleet CO2 targets drive the engine development toward clean combustion and high efficiency. To achieve this goal, planning and conducting experiments in a time- and cost-effective way play a vital role in finding the optimal combinations of all selectable parameters. This study investigated the effects of five engine parameters on two engine-out responses in a camless variable valve actuation (VVA) heavy-duty engine. Five engine parameters were intake valve lift (IVL), inlet valve closing (IVC), injection pressure, start of injection (SOI), and exhaust gas recirculation (EGR). Initially, a design of experiment (DoE) model was generated to predict both engine-out responses: brake-specific fuel consumption (BSFC) and BSNOx emissions. Due to a poor fit of the BSFC regression model from DoE analysis, an artificial neural network (ANN) model was developed to predict BSFC instead. A d-optimal design with five engine parameters at five levels was used to design the experiment. Extra test points together with d-optimal design points were utilized to train the ANN model. The well-trained ANN model for BSFC and DoE model for BSNOx were combined with a genetic algorithm (GA) to generate the Pareto-optimal front. The results proved the concept of using a hybrid statistical approach (DoE + ANN) with GA as an effective tool to generate a range of compromise design solutions. By extracting designs along the Pareto-optimal front, the impact of engine parameters on the system can be explained.
Design of a Morphing Compliant Mechanism With Separate Gripping and Retraction Modes Using a Single Actuation
Authors: McGowan Patrick , Hao Guangbo 
Published: May 2022

Abstract
A key aspect of robotics and automation is continuous repetition of predefined travel and actions. One such example is a robotic gripper, gripping an object, retracting, and displacing it. Usually, two separate actuations are needed to implement the decoupled gripping and retraction modes. In this article, a morphing compliant gripper is proposed that grips an object (X displacement) and retracts it linearly (Y displacement) based on one single actuation, to reduce the input efforts. The mechanism is first designed by the rigid body replacement method and is based on the double-slider mechanism. Morphing is successfully achieved through the use of contact-aided features and a pseudo spring beam to decouple the X and Y displacements. The design is comprehensively analyzed through the nonlinear finite element analysis method and is optimized using the integrated design exploration tools. Simulation results closely match an ideal X and Y displacement path and a displacement-activated transition from X displacement to Y displacement while minimizing the input actuation force. Finally, a 3D-printed prototype is made and preliminarily tested to verify the design.
Familial risk of epithelial ovarian cancer after accounting for gynaecological surgery: a population-based study
Authors: Barnard Mollie E , Meeks Huong , Jarboe Elke A , Albro James , Camp Nicola J , Doherty Jennifer A 
Published: May 2022

BackgroundUptake of risk-reducing surgery has increased among women at high risk of epithelial ovarian cancer. We sought to characterise familial risk of epithelial ovarian cancer histotypes in a population-based study after accounting for gynaecological surgeries, including bilateral oophorectomy.MethodsWe compared risk of epithelial ovarian cancer in relatives of 3536 epithelial ovarian cancer cases diagnosed in 1966–2016 and relatives of 35 326 matched controls. We used Cox competing risk models, incorporating bilateral oophorectomy as a competing risk, to estimate the relative risk of ovarian cancer in first-degree (FDR), second-degree (SDR) and third-degree (TDR) relatives from 1966 to 2016. We also estimated relative risks in time periods before (1966–1994, 1995–2004) and after (2005–2016) formal recommendations were made for prophylactic oophorectomy among women with pathogenic variants inBRCA1/2.ResultsThe relative risks of epithelial ovarian cancer in FDRs, SDRs and TDRs of cases versus controls were 1.68 (95% CI 1.39 to 2.04), 1.51 (95% CI 1.30 to 1.75) and 1.34 (95% CI 1.20 to 1.48), respectively. Relative risks were greatest for high-grade serous, mucinous and ‘other epithelial’ histotypes. Relative risks were attenuated for case FDRs, but not for SDRs or TDRs, from 2005 onwards, consistent with the timing of recommendations for prophylactic surgery.ConclusionFamilial risk of epithelial ovarian cancer extends to TDRs, especially for high-grade serous and mucinous histotypes. Distant relatives share genes but minimal environment, highlighting the importance of germline inherited genetics in ovarian cancer aetiology. Increased ovarian cancer risk in distant relatives has implications for counselling and recommendations for prophylactic surgeries that, from our data, appear only to reach FDRs.
Causal structure of singularity in non-spherical gravitational collapse
Authors: Dey Dipanjan , Joshi Pankaj S. , Mosani Karim , Vertogradov Vitalii 
Published: May 2022

AbstractWe investigate here the final state of gravitational collapse of a non-spherical and non-marginally bound dust cloud as modeled by the Szekeres spacetime. We show that a directionally globally naked singularity can be formed in this case near the collapsing cloud boundary and not at its geometric center, as is typically the case for a spherical gravitational collapse. This singularity is a strong curvature naked singularity in the sense of Tipler criterion on gravitational strength. The null geodesics escaping from the singularity would be less scattered in this case in certain directions since the singularity is close to the boundary of the cloud, as is the case in the current scenario. The physical implications are pointed out.
EH-DPD: a dissipative particle dynamics approach to electrohydrodynamics
Authors: Gubbiotti Alberto , Chinappi Mauro , Casciola Carlo Massimo 
Published: May 2022

Abstract
Electrohydrodynamics is crucial in many nanofluidic and biotechnological applications. In such small scales, the complexity due to the coupling of fluid dynamics with the dynamics of ions is increased by the relevance of thermal fluctuations. Here, we present a mesoscale method based on the Dissipative Particle Dynamics (DPD) model of the fluid. Two scalar quantities, corresponding to the number of positive and negative ions carried by each DPD particle, are added to the standard DPD formulation. We introduced a general framework that, given the definition of the free-energy of the DPD particle, allows to derive a fluctuation-dissipation relation and the expression for ionic fluxes between the DPD particles. This provides a link between the dynamics of the system and its equilibrium properties. The model is then validated simulating a planar electroosmotic flow for the cases of overlapping and non overlapping electric double layers. It is shown that using a Van der Waals equation of state the effect of ionic finite size can be accounted, leading to significant effects on the concentration and velocity profiles with respect to the ideal solution case.

Graphic abstract
Measuring the stability of fundamental constants with a network of clocks
Authors: Barontini G. , Blackburn L. , Boyer V. , Butuc-Mayer F. , Calmet X. , Crespo López-Urrutia J. R. , Curtis E. A. , Darquié B. , Dunningham J. , Fitch N. J. , Forgan E. M. , Georgiou K. , Gill P. , Godun R. M. , Goldwin J. , Guarrera V. , Harwood A. C. , Hill I. R. , Hendricks R. J. , Jeong M. , Johnson M. Y. H. , Keller M. , Kozhiparambil Sajith L. P. , Kuipers F. , Margolis H. S. , Mayo C. , Newman P. , Parsons A. O. , Prokhorov L. , Robertson B. I. , Rodewald J. , Safronova M. S. , Sauer B. E. , Schioppo M. , Sherrill N. , Stadnik Y. V. , Szymaniec K. , Tarbutt M. R. , Thompson R. C. , Tofful A. , Tunesi J. , Vecchio A. , Wang Y. , Worm S. 
Published: May 2022

AbstractThe detection of variations of fundamental constants of the Standard Model would provide us with compelling evidence of new physics, and could lift the veil on the nature of dark matter and dark energy. In this work, we discuss how a network of atomic and molecular clocks can be used to look for such variations with unprecedented sensitivity over a wide range of time scales. This is precisely the goal of the recently launched QSNET project: A network of clocks for measuring the stability of fundamental constants. QSNET will include state-of-the-art atomic clocks, but will also develop next-generation molecular and highly charged ion clocks with enhanced sensitivity to variations of fundamental constants. We describe the technological and scientific aims of QSNET and evaluate its expected performance. We show that in the range of parameters probed by QSNET, either we will discover new physics, or we will impose new constraints on violations of fundamental symmetries and a range of theories beyond the Standard Model, including dark matter and dark energy models.
ForSense: Accelerating Online Research Through Sensemaking Integration and Machine Research Support
Authors: Ramos Gonzalo , Rachatasumrit Napol , Suh Jina , Ng Rachel , Meek Christopher 
Published: May 2022

Online research is a frequent and important activity people perform on the Internet, yet current support for this task is basic, fragmented and not well integrated into web browser experiences. Guided by sensemaking theory, we present ForSense, a browser extension for accelerating people’s online research experience. The two primary sources of novelty of ForSense are the integration of multiple stages of online research and providing machine assistance to the user by leveraging recent advances in neural-driven machine reading. We use ForSense as a design probe to explore (1) the benefits of integrating multiple stages of online research, (2) the opportunities to accelerate online research using current advances in machine reading, (3) the opportunities to support online research tasks in the presence of imprecise machine suggestions, and (4) insights about the behaviors people exhibit when performing online research, the pages they visit, and the artifacts they create. Through our design probe, we observe people performing online research tasks, and see that they benefit from ForSense’s integration and machine support for online research. From the information and insights we collected, we derive and share key recommendations for designing and supporting imprecise machine assistance for research tasks.
Fairness in Ranking, Part I: Score-Based Ranking
Authors: Zehlike Meike , Yang Ke , Stoyanovich Julia 
Published: May 2022

In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities. In this survey, we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across sub-fields. An important contribution of our work is in developing a common narrative around the value frameworks that motivate specific fairness-enhancing interventions in ranking. This allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs.
In this first part of this survey, we describe four classification frameworks for fairness-enhancing interventions, along which we relate the technical methods surveyed in this article, discuss evaluation datasets, and present technical work on fairness in score-based ranking. In the second part of this survey, we present methods that incorporate fairness in supervised learning, and also give representative examples of recent work on fairness in recommendation and matchmaking systems. We also discuss evaluation frameworks for fair score-based ranking and fair learning-to-rank, and draw a set of recommendations for the evaluation of fair ranking methods.
Mitochondrial Micropeptide STMP1 Enhances Mitochondrial Fission to Promote Tumor Metastasis
Authors: Xie Chen , Wang Feng-Yi , Sang Ye , Chen Bin , Huang Jia-Hui , He Feng-Jun , Li Hui , Zhu Ying , Liu Xingguo , Zhuang Shi-Mei , Fang Jian-Hong 
Published: May 2022

Abstract

Micropeptides are a recently discovered class of molecules that play vital roles in various cellular processes, including differentiation, proliferation, and apoptosis. Here, we sought to identify cancer-associated micropeptides and to uncover their mechanistic functions. A micropeptide named short transmembrane protein 1 (STMP1) that localizes at the inner mitochondrial membrane was identified to be upregulated in various cancer types and associated with metastasis and recurrence of hepatocellular carcinoma. Both gain- and loss-of-function studies revealed that STMP1 increased dynamin-related protein 1 (DRP1) activation to promote mitochondrial fission and enhanced migration of tumor cells. STMP1 silencing inhibited in vivo tumor metastasis in xenograft mouse models. Overexpression of STMP1 led to redistribution of mitochondria to the leading edge of cells and enhanced lamellipodia formation. Treatment with a DRP1 inhibitor abrogated the promotive effect of STMP1 on mitochondrial fission, lamellipodia formation, and tumor cell migration in vitro and metastasis in vivo. Furthermore, STMP1 interacted with myosin heavy chain 9 (MYH9), the subunit of nonmuscle myosin II, and silencing MYH9 abrogated STMP1-induced DRP1 activation, mitochondrial fission, and cell migration. Collectively, this study identifies STMP1 as a critical regulator of metastasis and a novel unit of the mitochondrial fission protein machinery, providing a potential therapeutic target for treating metastases.

Significance:
This study identifies the mitochondrial micropeptide STMP1 as a regulator of metastasis that promotes mitochondrial fission and tumor cell migration via DRP1 and MYH9.
Revival and Recharacterization of a Preclinical Model of Hormone-Dependent Breast Cancer to Study Immunotherapy
Authors: Roussos Torres Evanthia T. 
Published: May 2022

Abstract
Immune checkpoint inhibitors have yet to significantly improve outcomes for hormone-dependent estrogen/progesterone receptor–positive breast cancer. To address this issue, there is a need for murine models that more closely mimic hormone receptor–positive breast cancer. In this issue, Gil Del Alcazar and colleagues provide an in-depth characterization of a Nitroso-N-methylurea–induced mammary tumor model in outbred Sprague-Dawley rats that meets these needs as it mimics the heterogeneity for mutational profiles, estrogen receptor expression, and immune evasive mechanisms observed in human breast cancer.
See related article by Gil Del Alcazar et al., p. 680 (1).
Associations of Long-Term Visit-to-Visit Blood Pressure Variability With Subclinical Kidney Damage and Albuminuria in Adulthood: a 30-Year Prospective Cohort Study
Authors: Wang Yang , Zhao Peng , Chu Chao , Du Ming-Fei , Zhang Xiao-Yu , Zou Ting , Hu Gui-Lin , Zhou Hao-Wei , Jia Hao , Liao Yue-Yuan , Chen Chen , Ma Qiong , Wang Dan , Yan Yu , Sun Yue , Wang Ke-Ke , Niu Ze-Jiaxin , Zhang Xi , Man Zi-Yue , Wu Yong-Xing , Wang Lan , Li Hui-Xian , Zhang Jie , Li Chun-Hua , Gao Wei-Hua , Gao Ke , Lu Wan-Hong , Desir Gary V. , Delles Christian , Chen Fang-Yao , Mu Jian-Jun 
Published: May 2022

Background:
Recent evidence indicates that long-term visit-to-visit blood pressure variability (BPV) may be associated with risk of cardiovascular disease. We, therefore, aimed to determine the potential associations of long-term BPV from childhood to middle age with subclinical kidney damage (SKD) and albuminuria in adulthood.

Methods:

Using data from the ongoing cohort of Hanzhong Adolescent Hypertension study, which recruited children and adolescents aged 6 to 18 years at baseline, we assessed BPV by SD and average real variability (ARV) for 30 years (6 visits). Presence of SKD was defined as estimated glomerular filtration rate between 30 and 60 mL/min per 1.73 m
2
or elevated urinary albumin-to creatinine ratio at least 30 mg/g. Albuminuria was defined as urinary albumin-to creatinine ratio ≥30 mg/g.

Results:

During 30 years of follow-up, of the 1771 participants, 204 SKD events occurred. After adjustment for demographic, clinical characteristics, and mean BP during 30 years, higher SD
SBP
, ARV
SBP
, SD
DBP
, ARV
DBP
, SD
MAP
, ARV
MAP
, and ARV
PP
were significantly associated with higher risk of SKD. When we used cumulative exposure to BP from childhood to adulthood instead of mean BP as adjustment factors, results were similar. In addition, greater long-term BPV was also associated with the risk of albuminuria. Long-term BPV from childhood to middle age was associated with higher risk of SKD and albuminuria in adulthood, independent of mean BP or cumulative exposure to BP during follow-up.

Conclusions:
Identifying long-term BPV from early age may assist in predicting kidney disease and cardiovascular disease in later life.
Remnant Cholesterol Variability and Incident Ischemic Stroke in the General Population
Authors: Li Weijian , Huang Zegui , Fang Wei , Wang Xianxuan , Cai Zefeng , Chen Guanzhi , Wu Weiqiang , Chen Zhichao , Wu Shouling , Chen Youren 
Published: May 2022

Background:
Studies have demonstrated that remnant cholesterol is correlated with the risk of ischemic stroke. However, it is unknown whether visit-to-visit variability in remnant cholesterol concentration affects ischemic stroke. We sought to examine the role of remnant cholesterol variability in the subsequent development of ischemic stroke in the general population.

Methods:
We performed a post hoc analysis including eligible participants from the Kailuan Study cohort who underwent 3 health examinations and were free of atrial fibrillation, myocardial infarction, stroke, cancer, or known lipid-medication use from 2006 to 2010. Participants were followed up until the end of 2017. Variability was quantified as variability independent of the mean, average real variability, and SD. Multivariate analysis was performed using the Fine and Gray competing risk model to estimate subhazard ratios assuming death as a competing risk.

Results:
The final study cohort comprised 38 556 participants. After a median follow-up of 7.0 years, 1058 individuals were newly diagnosed with ischemic stroke. After adjusting for age (time scale), sex, smoking status, alcohol consumption, physical activity, hypertension, diabetes, family history of cardiovascular disease, body mass index, estimated glomerular filtration rate, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, and mean remnant cholesterol, the highest quartile (quartile 4) of variability independent of the mean of remnant cholesterol was associated with an increased ischemic stroke risk compared with the lowest quartile (quartile 1), (subhazard ratio, 1.27 [95% CI, 1.06–1.53]). For each 1-SD increase in variability independent of the mean of remnant cholesterol, the risk increased by 9% (subhazard ratio, 1.09 [95% CI, 1.03–1.16]). The association was also significant using average real variability and SD as indices of variability.

Conclusions:
Greater remnant cholesterol variability was associated with a higher risk of ischemic stroke in the general population.
Fragility Index Meta-Analysis of Randomized Controlled Trials Shows Highly Robust Evidential Strength for Benefit of <3 Hour Intravenous Alteplase
Authors: Mun Katherine T. , Bonomo Jordan B. , Liebeskind David S. , Saver Jeffrey L. 
Published: May 2022

Background:
Cumulative fragility index (FI) analysis enables quantification of the evidential strength of intravenous alteplase’s core indication—treatment of disabling acute ischemic stroke within 3 hours of onset.

Methods:
Meta-analyses were performed (study level) or identified (individual participant level) for freedom-from-disability (modified Rankin Scale [mRS] score 0–1, primary efficacy), functional independence (mRS score 0–2, secondary efficacy), and mortality outcomes. Individual trial and cumulative FI analyses were serially conducted after each successive randomized controlled trial (RCT). FI scores were classified as follows: not robust (FI 0–4), somewhat robust (FI 5–12), robust (FI 13–33), and highly robust (FI >33).

Results:

Nine randomized controlled trials were identified from 1995 to 2021 of within-3-hour intravenous alteplase for acute ischemic stroke. In study-level meta-analyses, alteplase increased freedom-from-disability outcome (mRS score 0–1), 31.0% versus 22.3%, relative risk, 1.39 (95% CI, 1.20–1.61);
P
<0.00001; increased functional independence (mRS score 0–2), 39.7% versus 31.2%, relative risk, 1.29 (95% CI, 1.14–1.45),
P
<0.000; and did not alter mortality, 24.1% versus 26.1%;
P
=0.23. Overall FIs for study-level meta-analyses were both highly robust at 42 and 40 for mRS score 0–1 and mRS score 0–2, respectively. Serial FI analyses showed robust evidential strength for intravenous alteplase superiority with publication of the 2 NINDS-tPA trials (National Institute of Neurological Disorders and Stroke–tissue-type plasminogen activator) in 1995, increased to highly robust in 2012, and remains highly robust in 2021.

Conclusions:
Within-3-hour intravenous alteplase for acute ischemic stroke is one of the most robustly proven therapies in medicine. The initial concurrent trials 25 years ago showed robust evidence for benefit and, after additional studies, advanced to highly robust.
Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures
Authors: Wein S. , Schüller A. , Tomé A. M. , Malloni W. M. , Greenlee M. W. , Lang E. W. 
Published: May 2022

Abstract
Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph-structured signals like those observed in complex brain networks. In our study we compare different spatiotemporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN-based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multimodal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatiotemporal dynamics in brain networks.