bluuebunny/crossref_metadata_2025_split huggingface dataset:
Does promoting plant-based products in Veganuary lead to increased sales, and a reduction in meat sales? A natural experiment in a supermarket setting
Authors: Trewern Joanna , Chenoweth Jonathan , Christie Ian , Halevy Sarah 
Published: September 2022

AbstractObjective:To explore changes in plant-based and meat product sales during and after implementation of a multi-component in-store intervention implemented by a major UK food retailer. Secondary objectives included exploring differences by store format and area affluence.Design:The intervention increased the visibility, accessibility, affordability and availability of a selection of plant-based products. Unit sales of plant-based and meat products during the intervention (January 2021) were compared with pre- (November 2020) and post-intervention (February and March 2021). Non-meat product sales were assessed as a control. Negative binomial mixed models were used to explore sales changes and differences by store format or affluence.Setting:The intervention was applied in a real-world supermarket setting during Veganuary.Participants:Stores that applied the full intervention (n 154) were included for analysis. Weekly sales data for each store were obtained from the retailer.Results:Average weekly unit sales of plant-based products increased significantly (57 %) during the intervention period (incidence rate ratio (IRR) 1·52 (95 % CI1·51, 1·55)). Plant-based product sales decreased post-intervention but remained 15 % higher than pre-intervention (IRR 1·13 (95 % CI 1·12, 1·14)). There was no significant change in meat sales according to time period. The increase in plant-based product sales was greatest at superstores (58 %), especially those located in below average affluence areas (64 %).Conclusions:Results suggest that increasing visibility, accessibility, affordability and availability of plant-based products led to increased sales, with evidence of lasting effects. No significant changes in meat sales were observed. Variation according to store format and area affluence indicates targeted intervention approaches are needed.
The association between sleep and depressive symptoms in US adults: data from the NHANES (2007–2014)
Authors: Chunnan Li , Shaomei Shang , Wannian Liang 
Published: September 2022

Abstract

Aims
To assess the association of sleep factors (sleep duration, trouble sleeping, sleep disorder) and combined sleep behaviours with the risk of clinically relevant depression (CRD).

Methods
A total of 17 859 participants (8806 males and 9053 females) aged 20–79 years from the National Health and Nutrition Examination Survey (NHANES) 2007–2014 waves were included. Sleep duration, trouble sleeping and sleep disorder were asked in the home by trained interviewers using the Computer-Assisted Personal Interviewing (CAPI) system. The combined sleep behaviours were referred to as ‘sleep patterns (healthy, intermediate and poor)’, with a ‘healthy sleep pattern’ defined as sleeping 7–9 h per night with no self-reported trouble sleeping or sleep disorders. And intermediate and poor sleep patterns indicated 1 and 2–3 sleep problems, respectively. Weighted logistic regression was performed to evaluate the association of sleep factors and sleep patterns with the risk of depressive symptoms.

Results
The total prevalence of CRD was 9.5% among the 17 859 participants analysed, with females having almost twice as frequency than males. Compared to normal sleep duration (7–9 h), both short and long sleep duration were linked with a higher risk of CRD (short sleep: OR: 1.66, 95% CI: 1.39–1.98; long sleep: OR: 2.75, 95% CI: 1.93–3.92). The self-reported sleep complaints, whether trouble sleeping or sleep disorder, were significantly related with CRD (trouble sleeping: OR: 3.04, 95% CI: 2.59–3.56; sleep disorder: OR: 1.83, 95% CI: 1.44–2.34). Furthermore, the correlations appeared to be higher for individuals with poor sleep pattern (OR: 5.98, 95% CI: 4.91–7.29).

Conclusions
In this national representative survey, it was shown that there was a dose-response relationship between sleep patterns and CRD.
Modeling the Dynamic Response of River Deltas to Sea‐Level Rise Acceleration
Authors: Hariharan Jayaram , Passalacqua Paola , Xu Zhongyuan , Michael Holly A. , Steel Elisabeth , Chadwick Austin , Paola Chris , Moodie Andrew J. 
Published: September 2022

AbstractClimate change is raising sea levels across the globe. On river deltas, sea‐level rise (SLR) may result in land loss, saline intrusion into groundwater aquifers, and other problems that adversely impact coastal communities. There is significant uncertainty surrounding future SLR trajectories and magnitudes, even over decadal timescales. Given this uncertainty, numerical modeling is needed to explore how different SLR projections may impact river delta evolution. In this work, we apply the pyDeltaRCM numerical model to simulate 350 years of deltaic evolution under three different SLR trajectories: steady rise, an abrupt change in SLR rate, and a gradual acceleration of SLR. For each SLR trajectory, we test a set of six final SLR magnitudes between 5 and 40 mm/yr, in addition to control runs with no SLR. We find that both surface channel dynamics as well as aspects of the subsurface change in response to higher rates of SLR, even over centennial timescales. In particular, increased channel mobility due to SLR corresponds to higher sand connectivity in the subsurface. Both the trajectory and magnitude of SLR change influence the evolution of the delta surface, which in turn modifies the structure of the subsurface. We identify correlations between surface and subsurface properties, and find that inferences of subsurface structure from the current surface configuration should be limited to time spans over which the sea level forcing is approximately steady. As a result, this work improves our ability to predict future delta evolution and subsurface connectivity as sea levels continue to rise.
A New Flow‐Kinematics‐Based Model for Time‐Dependent Effective Dispersion in Mixing‐Limited Reactions
Authors: Deucher Ricardo H. , Durlofsky Louis J. 
Published: September 2022

AbstractA new upscaling procedure that provides 1D representations of 2D mixing‐limited reactive transport systems is developed and applied. A key complication with upscaled models in this setting is that the procedure must differentiate between interface spreading, driven by the spatially variable velocity field, and mixing, through which components contact one another and react. Our model captures the enhanced mixing caused by spreading through use of a time‐dependent effective dispersion term. The early‐time behavior of this dispersion is driven by flow kinematics, while at late times it reaches a Taylor‐dispersion‐like limit. The early‐time behavior is modeled here using a very fast (purely advective) particle tracking procedure. The only free parameter in the model is the late‐time asymptotic effective dispersion. This quantity is estimated using a fit involving a dimensionless grouping of system variables and a few reference results, or by calibrating with the corresponding conservative (non‐reacting) case. Numerical results for bimolecular reaction systems are generated using a pseudo‐spectral approach capable of resolving fronts at high Peclet numbers. Results are presented for three different types of 2D velocity fields over a wide range of parameters. The upscaled model is shown to provide highly accurate results for the conversion factor, along with reasonable approximations of the spatial distribution of reaction occurrence. The model is also shown to be valid to upscale mixing in non‐reacting systems.
Prospective validation of a biomarker-driven response prediction model to romiplostim in lower-risk myelodysplastic neoplasms – results of the EUROPE trial by EMSCO
Authors: Kubasch Anne Sophie , Giagounidis Aristoteles , Metzgeroth Georgia , Jonasova Anna , Herbst Regina , Diaz Jose Miguel Torregrosa , De Renzis Benoit , Götze Katharina S. , Huetter-Kroenke Marie-Luise , Gourin Marie-Pierre , Slama Borhane , Dimicoli-Salazar Sophie , Cony-Makhoul Pascale , Laribi Kamel , Park Sophie , Jersemann Katja , Schipp Dorothea , Metzeler Klaus H. , Tiebel Oliver , Sockel Katja , Gloaguen Silke , Mies Anna , Chermat Fatiha , Thiede Christian , Sapena Rosa , Schlenk Richard F. , Fenaux Pierre , Platzbecker Uwe , Adès Lionel 
Published: September 2022

AbstractThe EUROPE phase 2 trial investigated the predictive value of biomarkers on the clinical efficacy of single agent romiplostim (ROM) treatment in patients with lower-risk myelodysplastic neoplasms (LR-MDS) and thrombocytopenia within the ‘European Myelodysplastic Neoplasms Cooperative Group‘ (EMSCO) network. A total of 77 patients with LR-MDS and a median platelet count of 25/nl were included, all patients received ROM at a starting dose of 750 μg by SC injection weekly. Thirty-two patients (42%) achieved a hematologic improvement of platelets (HI-P) with a median duration of 340 days. Neutrophil (HI-N) and erythroid (HI-E) responses were observed in three (4%) and seven (9%) patients, respectively. We could not confirm previous reports that HI-P correlated with baseline endogenous thrombopoietin levels and platelet transfusion history, but SRSF2 mutation status and hemoglobin levels at baseline were significantly linked to HI-P. Sequential analysis of variant allelic frequency of mutations like SRSF2 did not reveal an impact of ROM on clonal evolution in both responders and non-responders. In summary, our study confirms the safety and efficacy of ROM in LR-MDS patients and may allow to better define subgroups of patients with a high likelihood of response.
Pathway-specific contribution of parvalbumin interneuron NMDARs to synaptic currents and thalamocortical feedforward inhibition
Authors: Lewis Eastman M. , Spence Hayli E. , Akella Neha , Buonanno Andres 
Published: September 2022

AbstractPrefrontal cortex (PFC) is a site of information convergence important for behaviors relevant to psychiatric disorders. Despite the importance of inhibitory GABAergic parvalbumin-expressing (PV+) interneurons to PFC circuit function and decades of interest in N-methyl-D-aspartate receptors (NMDARs) in these neurons, examples of defined circuit functions that depend on PV+ interneuron NMDARs have been elusive. Indeed, it remains controversial whether all PV+ interneurons contain functional NMDARs in adult PFC, which has major consequences for hypotheses of the pathogenesis of psychiatric disorders. Using a combination of fluorescent in situ hybridization, pathway-specific optogenetics, cell-type-specific gene ablation, and electrophysiological recordings from PV+ interneurons, here we resolve this controversy. We found that nearly 100% of PV+ interneurons in adult medial PFC (mPFC) express transcripts encoding GluN1 and GluN2B, and they have functional NMDARs. By optogenetically stimulating corticocortical and thalamocortical inputs to mPFC, we show that synaptic NMDAR contribution to PV+ interneuron EPSCs is pathway-specific, which likely explains earlier reports of PV+ interneurons without synaptic NMDAR currents. Lastly, we report a major contribution of NMDARs in PV+ interneurons to thalamus-mediated feedforward inhibition in adult mPFC circuits, suggesting molecular and circuit-based mechanisms for cognitive impairment under conditions of reduced NMDAR function. These findings represent an important conceptual advance that has major implications for hypotheses of the pathogenesis of psychiatric disorders.
The emergence of psychoanalytical electrochemistry: the translation of MDD biomarker discovery to diagnosis with electrochemical sensing
Authors: Nadar Priyanka M. , Merrill Mckenna A. , Austin Katherine , Strakowski Stephen M. , Halpern Jeffrey M. 
Published: September 2022

AbstractThe disease burden and healthcare costs of psychiatric diseases along with the pursuit to understand their underlying biochemical mechanisms have led to psychiatric biomarker investigations. Current advances in evaluating candidate biomarkers for psychiatric diseases, such as major depressive disorder (MDD), focus on determining a specific biomarker signature or profile. The origins of candidate biomarkers are heterogenous, ranging from genomics, proteomics, and metabolomics, while incorporating associations with clinical characterization. Prior to clinical use, candidate biomarkers must be validated by large multi-site clinical studies, which can be used to determine the ideal MDD biomarker signature. Therefore, identifying valid biomarkers has been challenging, suggesting the need for alternative approaches. Following validation studies, new technology must be employed to transition from biomarker discovery to diagnostic biomolecular profiling. Current technologies used in discovery and validation, such as mass spectroscopy, are currently limited to clinical research due to the cost or complexity of equipment, sample preparation, or measurement analysis. Thus, other technologies such as electrochemical detection must be considered for point-of-care (POC) testing with the needed characteristics for physicians’ offices. This review evaluates the advantages of using electrochemical sensing as a primary diagnostic platform due to its rapidity, accuracy, low cost, biomolecular detection diversity, multiplexed capacity, and instrument flexibility. We evaluate the capabilities of electrochemical methods in evaluating current candidate MDD biomarkers, individually and through multiplexed sensing, for promising applications in detecting MDD biosignatures in the POC setting.
Information flow in first-order potts model phase transition
Authors: Brown Joshua M. , Bossomaier Terry , Barnett Lionel 
Published: September 2022

AbstractPhase transitions abound in nature and society, and, from species extinction to stock market collapse, their prediction is of widespread importance. In earlier work we showed that Global Transfer Entropy, a general measure of information flow, was found to peaks away from the transition on the disordered side for the Ising model, a canonical second order transition. Here we show that (a) global transfer entropy also peaks on the disordered side of the transition of finite first order transitions, such as ecology dynamics on coral reefs, which have latent heat and no correlation length divergence, and (b) analysis of information flow across state boundaries unifies both transition orders. We obtain the first information-theoretic result for the high-order Potts model and the first demonstration of early warning of a first order transition. The unexpected earlier finding that global transfer entropy peaks on the disordered side of a transition is also found for finite first order systems, albeit not in the thermodynamic limit. By noting that the interface length of clusters in each phase is the dominant region of information flow, we unify the information theoretic behaviour of first and second order transitions.
Effect of fique fibers and its processing by-products on morphology, thermal and mechanical properties of epoxy based biocomposites
Authors: Centeno-Mesa Nicolas , Lombana-Toro Oscar , Correa-Aguirre Juan P. , Hidalgo-Salazar Miguel A. 
Published: September 2022

AbstractThis work examines the morphology, mechanical and thermal properties of biocomposites based on epoxy resin-EP and fique (Furcraea andina), a native crop of South America. The EP-fique biocomposites were prepared using fique powder-FP an industrial waste generated during fique processing, nonwoven fique fiber mats-NWF and unidirectional fique fiber mats-UF oriented at 0° and 90°. The addition of fique into EP matrix restricts EP macromolecule chains movement and enhance the thermal stability of EP. SEM images showed that fique form used (powder or fiber) and mat arrangement can generate changes in the biocomposites morphology. Mechanical characterization show that fique powder and fique fibers oriented at 90° acts as fillers for the epoxy matrix while the fique fibers oriented at 0° reinforce EP matrix increasing the tensile and flexural modulus up to 5700 and 1100% respectively and tensile and flexural strength up to 277% and 820% in comparison with neat EP. The obtained results can increase the interest in researching and developing products from fique Powders and other natural fibers processing byproducts thus reducing the abundance of waste in soil and landfills and environmental concerns and suggest that the EP-fique biocomposites are promising to be used in the automotive sector.
Improving ascertainment of suicidal ideation and suicide attempt with natural language processing
Authors: Bejan Cosmin A. , Ripperger Michael , Wilimitis Drew , Ahmed Ryan , Kang JooEun , Robinson Katelyn , Morley Theodore J. , Ruderfer Douglas M. , Walsh Colin G. 
Published: September 2022

AbstractMethods relying on diagnostic codes to identify suicidal ideation and suicide attempt in Electronic Health Records (EHRs) at scale are suboptimal because suicide-related outcomes are heavily under-coded. We propose to improve the ascertainment of suicidal outcomes using natural language processing (NLP). We developed information retrieval methodologies to search over 200 million notes from the Vanderbilt EHR. Suicide query terms were extracted using word2vec. A weakly supervised approach was designed to label cases of suicidal outcomes. The NLP validation of the top 200 retrieved patients showed high performance for suicidal ideation (area under the receiver operator curve [AUROC]: 98.6, 95% confidence interval [CI] 97.1–99.5) and suicide attempt (AUROC: 97.3, 95% CI 95.2–98.7). Case extraction produced the best performance when combining NLP and diagnostic codes and when accounting for negated suicide expressions in notes. Overall, we demonstrated that scalable and accurate NLP methods can be developed to identify suicidal behavior in EHRs to enhance prevention efforts, predictive models, and precision medicine.
Control of monomeric Vo’s versus Vo clusters in ZrO2−x for solar-light H2 production from H2O at high-yield (millimoles gr−1 h−1)
Authors: Deligiannakis Yiannis , Mantzanis Asterios , Zindrou Areti , Smykala Szymon , Solakidou Maria 
Published: September 2022

AbstractPristine zirconia, ZrO2, possesses high premise as photocatalyst due to its conduction band energy edge. However, its high energy-gap is prohibitive for photoactivation by solar-light. Currently, it is unclear how solar-active zirconia can be designed to meet the requirements for high photocatalytic performance. Moreover, transferring this design to an industrial-scale process is a forward-looking route. Herein, we have developed a novel Flame Spray Pyrolysis process for generating solar-light active nano-ZrO2−x via engineering of lattice vacancies, Vo. Using solar photons, our optimal nano-ZrO2−x can achieve milestone H2-production yield, > 2400 μmolg−1 h−1 (closest thus, so far, to high photocatalytic water splitting performance benchmarks). Visible light can be also exploited by nano-ZrO2−x at a high yield via a two-photon process. Control of monomeric Vo versus clusters of Vo’s is the key parameter toward Highly-Performing-Photocatalytic ZrO2−x. Thus, the reusable and sustainable ZrO2−x catalyst achieves so far unattainable solar activated photocatalysis, under large scale production.
A microblog content credibility evaluation model based on collaborative key points
Authors: Xing Ling , Yao Jinglong , Wu Honghai , Ma Huahong 
Published: September 2022

AbstractThe spread of false content on microblogging platforms has created information security threats for users and platforms alike. The confusion caused by false content complicates feature selection during credibility evaluation. To solve this problem, a collaborative key point-based content credibility evaluation model, CECKP, is proposed in this paper. The model obtains the key points of the microblog text from the word level to the sentence level, then evaluates the credibility according to the semantics of the key points. In addition, a rumor lexicon constructed collaboratively during word-level coding strengthens the semantics of related words and solves the feature selection problem when using deep learning methods for content credibility evaluation. Experimental results show that, compared with the Att-BiLSTM model, the F1 score of the proposed model increases by 3.83% and 3.8% when the evaluation results are true and false respectively. The proposed model accordingly improves the performance of content credibility evaluation based on optimized feature selection.
Using global navigation satellite systems for modeling athletic performances in elite football players
Authors: Imbach Frank , Ragheb Waleed , Leveau Valentin , Chailan Romain , Candau Robin , Perrey Stephane 
Published: September 2022

AbstractThis study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019–2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on average (p= 0.001). Reference and multivariate models did not show significant differences in error rates (p= 0.124), regardless of the nature of predictors (commercial features or extracted from signal processing methods) or the pooling method used. In addition, models built over a larger population did not provide significantly more accurate predictions. In conclusion, GNSS features seemed to be of limited relevance for predicting individual A-V profiles. However, new signal processing features open up new perspectives in athletic performance or injury occurrence modeling, mainly if higher sampling rate tracking systems are considered.
Economic evaluation of single-photon emission-computed tomography versus stress echocardiography in stable chest pain patients
Authors: Javan-Noughabi Javad , Rezapour Aziz , Hajahmadi Marjan , Alipour Vahid 
Published: September 2022

AbstractThe timely diagnosis of coronary artery disease (CAD) is an important medical problem. This study aims to assess the cost-effectiveness of Single-Photon Emission-Computed Tomography (SPECT) compared with stress echocardiography in stable chest pain patients. An economic evaluation study was conducted to assess the cost-effectiveness of SPECT versus stress echocardiography in stable chest pain patients without known CAD between April 1, 2017, and September 1, 2018 in Tehran, Iran. This study was performed from a societal perspective. The incremental cost-effectiveness ratio was calculated using a decision tree model. In addition, the robustness of results was examined by deterministic and probabilistic sensitivity analysis. This study showed that the expected cost and expected QALY for Stress echocardiography was $1106.75 and 0.83 respectively. Also, SPECT had expected cost and expected QALY equal to $1622.39 and 0.80 respectively. Finally, Stress echocardiography was the dominant strategy for CAD, with a lower cost and greater effectiveness than SPECT. The stress echocardiography can saved $18,528.17 per QALY. A deterministic and probabilistic sensitivity analysis confirmed the robustness of the results. Stress echocardiography was a more cost-effective method for diagnosing CAD disease in stable chest pain patients without known CAD compared to SPECT.