bluuebunny/crossref_metadata_2025_split huggingface dataset:
Shortened temperature‐relevant period of spring leaf‐out in temperate‐zone trees
Authors: Fu Yongshuo H. , Geng Xiaojun , Hao Fanghua , Vitasse Yann , Zohner Constantin M. , Zhang Xuan , Zhou Xuancheng , Yin Guodong , Peñuelas Josep , Piao Shilong , Janssens Ivan A. 
Published: August 2019

AbstractTemperature during a particular period prior to spring leaf‐out, the temperature‐relevant period (TRP), is a strong determinant of the leaf‐out date in temperate‐zone trees. Climatic warming has substantially advanced leaf‐out dates in temperate biomes worldwide, but its effect on the beginning and length of the TRP has not yet been explored, despite its direct relevance for phenology modeling. Using 1,551 species–site combinations of long‐term (1951–2016) in situ observations on six tree species (namely, Aesculus hippocastanum, Alnus glutinosa, Betula pendula, Fagus sylvatica, Fraxinus excelsior, and Quercus robur) in central Europe, we found that the advancing leaf‐out was accompanied by a shortening of the TRP. On average across all species and sites, the length of the TRP significantly decreased by 23% (p < .05), from 60 ± 4 days during 1951–1965 to 47 ± 4 days during 2002–2016. Importantly, the average start date of the TRP did not vary significantly over the study period (March 2–5, DOY = 61–64), which could be explained by sufficient chilling over the study period in the regions considered. The advanced leaf‐out date with unchanged beginning of the TRP can be explained by the faster accumulation of the required heat due to climatic warming, which overcompensated for the retarding effect of shortening daylength on bud development. This study shows that climate warming has not yet affected the mean TRP starting date in the study region, implying that phenology modules in global land surface models might be reliable assuming a fixed TRP starting date at least for the temperate central Europe. Field warming experiments do, however, remain necessary to test to what extent the length of TRP will continue to shorten and whether the starting date will remain stable under future climate conditions.
Does curriculum reform influence perceived preparedness for practice of graduates? A comparison of two cohorts
Authors: Parmar Harsha , Schafheutle Ellen , Willis Sarah , Silkstone Victoria 
Published: August 2019

AbstractObjectivesUsing performance standards (PS) set by the General Pharmaceutical Council (GPhC), this study compared the views of two consecutive cohorts of MPharm graduates from one pharmacy school, pre- and postcurriculum reform on preparedness for practice (PFP).MethodsPreparedness was investigated using the GPhC’s 76 PS, grouped into three domains of practice: professional activity, interpersonal skills and ability to provide an effective pharmaceutical service. Respondents were asked to (dis)agree with how they perceived the MPharm had sufficiently prepared them to meet each of the 76 PS. Differences in mean score between the two cohorts were analysed via the independent-samples t-test. Regression analysis was used to determine whether the year of graduation was a predictor of PFP once other variables were controlled for.Key findingsA response rate of 30.1 and 42.4% was achieved for the 2014 and 2015 cohort, respectively. Significantly more respondents of the 2015 cohort (postcurricular reform) felt prepared for practice than respondents of the 2014 cohort (precurricular reform), for all domains of preregistration performance standards. Multiple regression analysis demonstrated that year of graduation was the independent variable that made the strongest unique contribution to explaining PFP (β = 0.527, P ≤ 0.005).ConclusionsOverall findings of this study suggest that increasing adoption of undergraduate active learning opportunities and integrating learning of core subjects may enhance the overall feeling of preparedness for practice.
Effects of ultrasonic treatment on Maillard reaction and product characteristics of enzymatic hydrolysate derived from mussel meat
Authors: Dong Zhou Yong , Liu Wei , Zhou Ya Jun , Ren Hui , Li Meng Yao , Liu Yin 
Published: August 2019

AbstractMussel meat is rich in amino acids and is a good source of flavor enhancers. To find a high‐efficiency and suitable method for the Maillard reaction (MR) of enzymatic hydrolysate derived from mussel meat, we studied the changes of Maillard reaction between enzymatic hydrolysate and glucose/xylose under ultrasonic treatment (UsT), heat treatment (HT) and ultrasonic‐assisted heat treatment (Us‐HT). It was found the Us‐HT displayed the highest Maillard reaction rate and a significant increase in intermediate products and melanoidins compared to HT and UsT. The Maillard reaction products (MRPs) derived from the three treatments showed higher antioxidant activity, while the MRPs from Us‐HT had the highest antioxidant activity and ideal sensory properties. Therefore, Us‐HT is a potential method to prepare the Maillard flavor enhancer from mussel meat.Practical ApplicationsThe ultrasonic technology was applied as a novel food processing technique and can effectively promote the interaction of certain ingredients. Compared with single ultrasonic or heat treatment, ultrasonic‐assisted heat treatment can accelerate the reaction and improve sensory properties and antioxidant activities of MRPs derived from mussel meat hydrolysate. The ultrasonic‐assisted heat treatment is a potential technology for preparing the Maillard flavor enhancer. This efficient method supports the utilization of mussel meat and the development of flavoring essence and functional foods in the future.
Physical Activity and Risk of Postoperative Delirium
Authors: Lee Susie S. , Lo Yungtai , Verghese Joe 
Published: August 2019

BACKGROUND/OBJECTIVERegular physical activity (PA) has been associated with improved cognitive function, but its effect on postoperative delirium (POD) has not been established. Our objectives were to determine the effect of baseline PA on the incidence of POD in older patients undergoing elective orthopedic surgery and to determine whether these effects were independent of cognitive reserve. We hypothesize that PA protects against POD by bolstering physiologic reserve needed to withstand the stressors of surgery.DESIGNSecondary analysis of a prospective, single‐center, cohort study.SETTINGUrban academic hospital.PARTICIPANTSA total of 132 nondemented, English‐speaking adults older than 60 years undergoing elective orthopedic surgery.MEASUREMENTSSubjects were screened for POD and delirium severity using the Confusion Assessment Method and the Memorial Delirium Assessment Scale. Baseline cognitive activities and PAs were assessed with a validated Leisure Activity Scale. Regular PA was categorized as 6 to 7 days per week. The association of regular PA with incidence of POD was assessed using multivariable logistic regression, adjusting for age, sex, Charlson Comorbidity Index, cognitive reserve, and cognitive function. Linear regression was used to assess the association of delirium severity with regular PA.RESULTSOf 132 patients, 41 (31.1%) developed POD. Regular PA was associated with a 74% lower odds of developing POD (odds ratio [OR] = 0.26; 95% confidence interval [CI] = 0.08‐0.82). There was no significant interaction between PA and cognitive reserve (P = .70). Of 85 women, 25 (29.4%), and of 47 men, 16 (34.0%) developed POD. In stratified analysis, women who engaged in regular PA had dramatically lower odds of POD (OR = 0.08; 95% CI = 0.01‐0.63) compared with men (OR = 0.93; 95% CI = 0.18‐4.97).CONCLUSIONSRegular PA is associated with decreased incidence of POD, especially among women. Future studies should address the basis of sex differences in PA benefits on delirium. J Am Geriatr Soc 67:2260–2266, 2019
Usefulness of cardiac parasympathetic index in CPAP‐treated patients with obstructive sleep apnea: A preliminary study
Authors: Salsone Maria , Marelli Sara , Vescio Basilio , Quattrone Andrea , Gambardella Antonio , Castelnuovo Alessandra , Quattrone Aldo , Ferini Strambi Luigi 
Published: August 2019

AbstractCardiac autonomic indexes, including cardiac parasympathetic index and cardiac sympathetic index, have been reported to accurately identify patients with sleep disorders such as obstructive sleep apnea. Our study aimed to assess cardiac autonomic indexes in patients with obstructive sleep apnea before and during a single full‐night continuous positive airway pressure therapy using a combined approach. Our simultaneous heart rate variability‐polysomnographic study included 16 never‐treated obstructive sleep apnea patients. Two patients dropped out. Patients underwent combined recordings in two consecutive days, at baseline and during a single full‐night of acute continuous positive airway pressure treatment. We calculated cardiac parasympathetic index and cardiac sympathetic index as night/day ratio for high‐frequency and low‐frequency heart rate variability spectral components, respectively. Continuous positive airway pressure treatment significantly reduced cardiac autonomic indexes values in comparison with baseline values (cardiac parasympathetic index: p < .0001; cardiac sympathetic index: p = .001). After acute continuous positive airway pressure treatment, the percentage of decrease of cardiac parasympathetic index was greater than that of cardiac sympathetic index (51.02 ± 15.72 versus 34.64 ± 26.93). A positive statistical correlation was also found between decrease of cardiac parasympathetic index and decrease of apnea–hypopnea index after continuous positive airway pressure (p < .001). This study improves the knowledge on cardiac autonomic modulation during acute continuous positive airway pressure therapy in obstructive sleep apnea. Our results demonstrate that both autonomic indexes decreased significantly after a single‐night of acute continuous positive airway pressure therapy. Cardiac parasympathetic index more than cardiac sympathetic index was related to decrease of apnea–hypopnea index after continuous positive airway pressure therapy, thus representing a potential help in everyday clinical practice.
Evolution of l‐DOPA 4,5‐dioxygenase activity allows for recurrent specialisation to betalain pigmentation in Caryophyllales
Authors: Sheehan Hester , Feng Tao , Walker‐Hale Nathanael , Lopez‐Nieves Samuel , Pucker Boas , Guo Rui , Yim Won C. , Badgami Roshani , Timoneda Alfonso , Zhao Lijun , Tiley Helene , Copetti Dario , Sanderson Michael J. , Cushman John C. , Moore Michael J. , Smith Stephen A. , Brockington Samuel F. 
Published: August 2019

Summary

The evolution of l‐DOPA 4,5‐dioxygenase activity, encoded by the gene DODA, was a key step in the origin of betalain biosynthesis in Caryophyllales. We previously proposed that l‐DOPA 4,5‐dioxygenase activity evolved via a single Caryophyllales‐specific neofunctionalisation event within the DODA gene lineage. However, this neofunctionalisation event has not been confirmed and the DODA gene lineage exhibits numerous gene duplication events, whose evolutionary significance is unclear.

To address this, we functionally characterised 23 distinct DODA proteins for l‐DOPA 4,5‐dioxygenase activity, from four betalain‐pigmented and five anthocyanin‐pigmented species, representing key evolutionary transitions across Caryophyllales. By mapping these functional data to an updated DODA phylogeny, we then explored the evolution of l‐DOPA 4,5‐dioxygenase activity.

We find that low l‐DOPA 4,5‐dioxygenase activity is distributed across the DODA gene lineage. In this context, repeated gene duplication events within the DODA gene lineage give rise to polyphyletic occurrences of elevated l‐DOPA 4,5‐dioxygenase activity, accompanied by convergent shifts in key functional residues and distinct genomic patterns of micro‐synteny.

In the context of an updated organismal phylogeny and newly inferred pigment reconstructions, we argue that repeated convergent acquisition of elevated l‐DOPA 4,5‐dioxygenase activity is consistent with recurrent specialisation to betalain synthesis in Caryophyllales.
The antiquity and evolution of the neural bases of rhythmic activity
Authors: Lieberman Philip 
Published: August 2019

AbstractThe evolution of the anatomy and neural circuits that regulate the rhythm of speech can be traced back to the Devonian age, 400 million years ago. Epigenetic processes 100 million years later modified these circuits. Natural selection on similar genetic processes occurred during the evolution of archaic hominins and humans. The lungs and larynx—anatomy that produces the rhythmic fundamental frequency patterns of speech—have a deep evolutionary history. Neural circuits linking the cortex, basal ganglia, and other subcortical structures plan, sequence, and execute motor as well as cognitive acts. These neural circuits generate the rhythm of speech, singing, and chanting. The human form of the transcription factor FOXP2 increased synaptic connectivity and plasticity in basal ganglia circuits, enhancing motor control and cognitive and linguistic capabilities in humans as well as Neanderthals. The archeological record also suggests that Neanderthals passed spoken language. Homologous circuits existed in amphibians. In songbirds, the avian form of FOXP2 acted on similar neural circuits allowing birds to learn and produce new songs. Current studies point to natural selection on genetic events enhancing these and other neural circuits to yield fully human rhythmic speech, and motor, cognitive, and linguistic capabilities, rather than the saltation proposed by Noam Chomsky.
Computational Creativity Via Assisted Variational Synthesis of Mechanisms Using Deep Generative Models
Authors: Deshpande Shrinath , Purwar Anurag 
Published: August 2019

Abstract
Computational methods for kinematic synthesis of mechanisms for motion generation problems require input in the form of precision positions. Given the highly nonlinear nature of the problem, solutions to these methods are overly sensitive to the input—a small perturbation to even a single position of a given motion can change the topology and dimensions of the synthesized mechanisms drastically. Thus, the synthesis becomes a blind iterative process of maneuvering precision positions in the hope of finding good solutions. In this paper, we present a deep-learning-based framework which manages the uncertain user input and provides the user with a higher level control of the design process. The framework also imputes the input with missing information required by the computational algorithms. The approach starts by learning the probability distribution of possible linkage parameters with a deep generative modeling technique, called variational auto encoder (VAE). This facilitates capturing salient features of the user input and relating them with possible linkage parameters. Then, input samples resembling the inferred salient features are generated and fed to the computational methods of kinematic synthesis. The framework postprocesses the solutions and presents the concepts to the user along with a handle to visualize the variants of each concept. We define this approach as variational synthesis of mechanisms. In addition, we also present an alternate end-to-end deep neural network architecture for variational synthesis of linkages. This end-to-end architecture is a conditional-VAE, which approximates the conditional distribution of linkage parameters with respect to a coupler trajectory distribution. The outcome is a probability distribution of kinematic linkages for an unknown coupler path or motion. This framework functions as a bridge between the current state of the art theoretical and computational kinematic methods and machine learning to enable designers to create practical mechanism design solutions.
A Case Study of Deep Reinforcement Learning for Engineering Design: Application to Microfluidic Devices for Flow Sculpting
Authors: Lee Xian Yeow , Balu Aditya , Stoecklein Daniel , Ganapathysubramanian Baskar , Sarkar Soumik 
Published: August 2019

AbstractEfficient exploration of design spaces is highly sought after in engineering applications. A spectrum of tools has been proposed to deal with the computational difficulties associated with such problems. In the context of our case study, these tools can be broadly classified into optimization and supervised learning approaches. Optimization approaches, while successful, are inherently data inefficient, with evolutionary optimization-based methods being a good example. This inefficiency stems from data not being reused from previous design explorations. Alternately, supervised learning-based design paradigms are data efficient. However, the quality of ensuing solutions depends heavily on the quality of data available. Furthermore, it is difficult to incorporate physics models and domain knowledge aspects of design exploration into pure-learning-based methods. In this work, we formulate a reinforcement learning (RL)-based design framework that mitigates disadvantages of both approaches. Our framework simultaneously finds solutions that are more efficient compared with supervised learning approaches while using data more efficiently compared with genetic algorithm (GA)-based optimization approaches. We illustrate our framework on a problem of microfluidic device design for flow sculpting, and our results show that a single generic RL agent is capable of exploring the solution space to achieve multiple design objectives. Additionally, we demonstrate that the RL agent can be used to solve more complex problems using a targeted refinement step. Thus, we address the data efficiency limitation of optimization-based methods and the limited data problem of supervised learning-based methods. The versatility of our framework is illustrated by utilizing it to gain domain insights and to incorporate domain knowledge. We envision such RL frameworks to have an impact on design science.
Mining and Representing the Concept Space of Existing Ideas for Directed Ideation
Authors: He Yuejun , Camburn Bradley , Liu Haowen , Luo Jianxi , Yang Maria , Wood Kristin 
Published: August 2019

AbstractDesign innovation projects often generate large numbers of design ideas from designers, users, and, increasingly, the crowd over the Internet. Such idea data are often used for selection and implementation but, in fact, can 1also be used as sources of inspiration for further idea generation. In particular, the elementary concepts that underlie the original ideas can be recombined to generate new ideas. But it is not a trivial task to retrieve concepts from raw lists of ideas and data sources in a manner that can stimulate or generate new ideas. A significant difficulty lies in the fact that idea data are often expressed in unstructured natural languages. This paper develops a methodology that uses natural language processing to extract key words as elementary concepts embedded in massive idea descriptions and represents the elementary concept space in a core–periphery structure to direct the recombination of elementary concepts into new ideas. We apply the methodology to mine and represent the concept space underlying massive crowdsourced ideas and use it to generate new ideas for future transportation system designs in a real public sector-sponsored project via humans and automated computer programs. Our analysis of the human and computer recombination processes and outcomes sheds light on future research directions for artificial intelligence in design ideation.
A Machine Learning Enabled Multi-Fidelity Platform for the Integrated Design of Aircraft Systems
Authors: Garriga Ana Garcia , Mainini Laura , Ponnusamy Sangeeth Saagar 
Published: August 2019

Abstract
The push toward reducing the aircraft development cycle time motivates the development of collaborative frameworks that enable the more integrated design of aircraft and their systems. The ModellIng and Simulation tools for Systems IntegratiON on Aircraft (MISSION) project aims to develop an integrated modelling and simulation framework. This paper focuses on some recent advancements in the MISSION project and presents a design framework that combines a filtering process to down-select feasible architectures, a modeling platform that simulates the power system of the aircraft, and a machine learning-based clustering and optimization module. This framework enables the designer to prioritize different designs and offers traceability on the optimal choices. In addition, it enables the integration of models at multiple levels of fidelity depending on the size of the design space and the accuracy required. It is demonstrated for the electrification of the Primary Flight Control System (PFCS) and the landing gear braking system using different electric actuation technologies. The performance of different architectures is analyzed with respect to key performance indicators (fuel burn, weight, power). The optimization process benefits from a data-driven localization step to identify sets of similar architectures. The framework demonstrates the capability of optimizing across multiple, different system architectures in an efficient way that is scalable for larger design spaces and larger dimensionality problems.
Multiscale Mesoscale Modeling of Porosity Evolution in Oxide Fuels
Authors: Welland M. J. , Colins K. D. , Ofori-Opoku N. , Prudil A. A. , Thomas E. S. 
Published: August 2019

Abstract
The behavior of fission gas, notably accommodation within intra- and intergranular bubbles, influences the macroscopic properties and overall performance of oxide fuels. This work discusses progress to capture key fission gas-related phenomena with modern mesoscale techniques: the interaction of grain growth and irradiation by a phase-field crystal (PFC) method; overpressurized intragranular bubble migration in a vacancy gradient by a linearized phase-field model; and intergranular bubble interlinkage and percolation by the included phase model (IPM). An outlook on the impact of these models for the investigation of unit mechanisms of fission gas behavior and integration of them into fuel-performance codes is presented.
Estimation of Flow-Accelerated Corrosion Rate in Nuclear Piping System
Authors: Hazra Indranil , Pandey Mahesh D. , Jyrkama Mikko I. 
Published: August 2019

Abstract
Flow-accelerated corrosion (FAC) is a life-limiting factor for the piping network of the primary heat transport system (PHTS) in CANDU® reactors. The pipe wall thinning caused by FAC is monitored by carrying out periodic in-service inspections (ISI) to ensure the fitness-for-service of the piping system. Accurate prediction of the lifetime of various components in the PHTS piping network requires estimation of FAC thinning rate. The traditional Bayesian inference techniques commonly employed for parameter estimation are computationally costly. This paper presents an inexpensive and intuitive simulation-based Bayesian approach to FAC rate estimation, called approximate Bayesian computation using Markov chain Monte Carlo (ABC-MCMC). ABC-MCMC is a likelihood-free Bayesian computation scheme that generates samples directly from an approximate posterior distribution by simulating data sets from a forward model. The efficiency of ABC-MCMC is demonstrated by presenting a comparison with a likelihood-based Bayesian computation scheme, Metropolis-Hastings (MH) algorithm, using a practical data-based example. Furthermore, an innovative step has been proposed for reducing the Markov chain burn-in time in the proposed scheme. To indicate the need of a Bayesian approach in quantifying the uncertainties related to the FAC model parameters, results from the linear regression method, a common industrial approach, are also presented in this study. The numerical results show a notable reduction in computational time, suggesting that ABC-MCMC is an efficient alternative to the traditional Bayesian inference methods, specifically for handling noisy degradation data.