scholarly journals Distinct effects of single plant-based vs. animal-based meals on satiety and mood revealed by real-world smartphone-embedded studies

2021 ◽  
Author(s):  
Evelyn Medawar ◽  
Marie Zedler ◽  
Larissa de Biasi ◽  
Arno Villringer ◽  
A. Veronica Witte
Keyword(s):  
Protein Content ◽  
Large Scale ◽  
Model Comparison ◽  
Dietary Habits ◽  
Real Life ◽  
Obesity Prevalence ◽  
Single Plant ◽  
Psychological Determinants ◽  
Meal Intake ◽  
Before And After

Adopting plant-based diets high in fiber may reduce global warming and obesity prevalence. Physiological and psychological determinants of plant-based food decision-making remain unclear, particularly in real-life settings. As fiber has been linked with improved gut-brain signaling, we hypothesized that a single plant-based compared to an animal-based meal, would induce higher satiety, higher mood and less stress. In three smartphone-based studies adults (nall = 16,379) ranked satiety and mood on 5/10-point Likert scales before and after meal intake. Statistical analyses comprised linear mixed models, extended by nutrient composition, taste ratings, gender, social interaction, type of decision and dietary adherence to consider potential confounding. Overall, meal intake induced satiety and higher mood. Against our hypotheses, plant-based meal choice did not explain differences in hunger after the meal. Considering mood, individuals choosing a plant-based meal reported slightly higher mood before the meal and smaller mood increases after the meal compared to those choosing animal-based meals (post-meal*plant-based: b = -0.06 , t = -3.6, model comparison p < .001). Protein content marginally mediated post-meal satiety, while gender and taste ratings had a strong effect on satiety and mood in general. In this series of large-scale online studies, we could not detect profound effects of plant-based vs. animal-based meals on satiety and mood. Instead of meal category, satiety and mood depended on taste and protein content of the meal, as well as dietary habits and gender. Our findings might help to develop strategies to increase acceptability of healthy and sustainable plant-based food choices.

scholarly journals The Mechanism Analysis of the Accelerator for Support Vector Regression Based on Data Partition

2020 ◽  
Author(s):  
Yunsheng Song ◽  
Fangyi Li ◽  
Jianyu Liu ◽  
Juao Zhang
Keyword(s):  
Support Vector Regression ◽  
Large Scale ◽  
Real Life ◽  
Support Vector ◽  
Mechanism Analysis ◽  
Data Partition ◽  
Price Forecast ◽  
Before And After ◽  
Important Solution ◽  
The Difference

Support vector regression is an important algorithm in machine learning, and it is widely used in real life for its good performance, such as house price forecast, disease prediction, weather forecast, and so on. However, it cannot efficiently process large-scale data, because it has a high time complexity in the training process. Data partition as an important solution to solve the large-scale learning problem mainly focuses on the classification task, it trains the classifiers over the divided subsets produced by data partition and obtain the final classifier by combining those classifiers. Meanwhile, the most existing method rarely study the influence of data partition on the regressor performance, so that it is difficult to keep its generation ability. To solve this problem, we obtain the estimation of the difference in objective function before and after the data partition. Mini-Batch K-Means clustering is adopted to largely reduce this difference, and an improved algorithm is proposed. This proposed algorithm includes training stage and prediction stage. In training stag, it uses Mini-Batch K-Means clustering to divide the input space into some disjoint sub-regions of equal sample size, then it trains the regressor on each divided sub-region using support vector regression algorithm. In the prediction stage, the regressor merely offers the predicted label for the unlabeled instances that are in the same sub-region. Experiment results on real datasets illustrate that the proposed algorithm obtains the similar generation ability as the original algorithm, but it has less execution time than other acceleration algorithms.

Weekly Exercise Consistently Reinstates Positive Mood

1998 ◽  
Vol 3 (4) ◽  
pp. 271-280 ◽  
Author(s):  
Hannah Steinberg ◽  
Briony R. Nicholls ◽  
Elizabeth A. Sykes ◽  
N. LeBoutillier ◽  
Nerina Ramlakhan ◽  
...  
Keyword(s):  
Physical Exercise ◽  
Negative Mood ◽  
Real Life ◽  
Positive Mood ◽  
Field Investigation ◽  
Single Bout ◽  
Before And After ◽  
Exercise Class ◽  
Negative Feelings ◽  
Over Time

Mood improvement immediately after a single bout of exercise is well documented, but less is known about successive and longer term effects. In a “real-life” field investigation, four kinds of exercise class (Beginners, Advanced, Body Funk and Callanetics) met once a week for up to 7 weeks. Before and after each class the members assessed how they felt by completing a questionnaire listing equal numbers of “positive” and “negative” mood words. Subjects who had attended at least five times were included in the analysis, which led to groups consisting of 18, 20, 16, and 16 subjects, respectively. All four kinds of exercise significantly increased positive and decreased negative feelings, and this result was surprisingly consistent in successive weeks. However, exercise seemed to have a much greater effect on positive than on negative moods. The favorable moods induced by each class seemed to have worn off by the following week, to be reinstated by the class itself. In the Callanetics class, positive mood also improved significantly over time. The Callanetics class involved “slower,” more demanding exercises, not always done to music. The Callanetics and Advanced classes also showed significantly greater preexercise negative moods in the first three sessions. However, these differences disappeared following exercise. Possibly, these two groups had become more “tolerant” to the mood-enhancing effects of physical exercise; this may be in part have been due to “exercise addiction.”

The Effect of Active Site-inhibited Factor VIIa on Tissue Factor-initiated Coagulation Using Platelets before and after Aspirin Administration

1997 ◽  
Vol 78 (04) ◽  
pp. 1202-1208 ◽  
Author(s):  
Marianne Kjalke ◽  
Julie A Oliver ◽  
Dougald M Monroe ◽  
Maureane Hoffman ◽  
Mirella Ezban ◽  
...  
Keyword(s):  
Tissue Factor ◽  
Active Site ◽  
Large Scale ◽  
Thrombin Generation ◽  
Factor Viia ◽  
Dependent Manner ◽  
Antithrombotic Agent ◽  
Before And After ◽  
Ic50 Values

SummaryActive site-inactivated factor VIIa has potential as an antithrombotic agent. The effects of D-Phe-L-Phe-L-Arg-chloromethyl ketone-treated factor VIla (FFR-FVIIa) were evaluated in a cell-based system mimicking in vivo initiation of coagulation. FFR-FVIIa inhibited platelet activation (as measured by expression of P-selectin) and subsequent large-scale thrombin generation in a dose-dependent manner with IC50 values of 1.4 ± 0.8 nM (n = 8) and 0.9 ± 0.7 nM (n = 7), respectively. Kd for factor VIIa binding to monocytes ki for FFR-FVIIa competing with factor VIIa were similar (11.4 ± 0.8 pM and 10.6 ± 1.1 pM, respectively), showing that FFR-FVIIa binds to tissue factor in the tenase complex with the same affinity as factor VIIa. Using platelets from volunteers before and after ingestion of aspirin (1.3 g), there were no significant differences in the IC50 values of FFR-FVIIa [after aspirin ingestion, the IC50 values were 1.7 ± 0.9 nM (n = 8) for P-selectin expression, p = 0.37, and 1.4 ± 1.3 nM (n = 7) for thrombin generation, p = 0.38]. This shows that aspirin treatment of platelets does not influence the inhibition of tissue factor-initiated coagulation by FFR-FVIIa, probably because thrombin activation of platelets is not entirely dependent upon expression of thromboxane A2.

Real-life Field Studies of the NOx Removing Properties of Photocatalytic Surfaces in Roskilde and Copenhagen Airport, Denmark

2020 ◽  
Vol 01 ◽  
Author(s):  
Henrik Jensen ◽  
Pernille D. Pedersen
Keyword(s):  
Air Quality ◽  
High Stability ◽  
Reference Site ◽  
Real Life ◽  
Field Studies ◽  
The Real ◽  
Before And After ◽  
Parking Lot ◽  
Photocatalytic Concrete

Aims: To evaluate the real-life effect of photocatalytic surfaces on the air quality at two test-sites in Denmark. Background: Poor air quality is today one of the largest environmental issues, due to the adverse effects on human health associated with high levels of air pollution, including respiratory issues, cardiovascular disease (CVD), and lung cancer. NOx removal by TiO2 based photocatalysis is a tool to improve air quality locally in areas where people are exposed. Methods: Two test sites were constructed in Roskilde and Copenhage airport. In Roskilde, the existing asphalt at two parking lots was treated with TiO2 containing liquid and an in-situ ISO 22197-1 test setup was developed to enable in-situ evaluation of the activity of the asphalt. In CPH airport, photocatalytic concrete tiles were installed at the "kiss and fly" parking lot, and NOx levels were continuously monitored in 0.5 m by CLD at the active site and a comparable reference site before and after installation for a period of 2 years. Results: The Roskilde showed high stability of the photocatalytic coating with the activity being largely unchanged over a period of 2 years. The CPH airport study showed that the average NOx levels were decreased by 12 % comparing the before and after NOx concentrations at the active and reference site. Conclusion: The joined results of the two Danish demonstration projects illustrate a high stability of the photocatalytic coating as well as a high potential for improvements of the real-life air quality in polluted areas.

scholarly journals Neural methods for effective, efficient, and exposure-aware information retrieval

2021 ◽  
Vol 55 (1) ◽  
pp. 1-2
Author(s):  
Bhaskar Mitra
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Web Search ◽  
Real Life ◽  
Inverted Index ◽  
Information Need ◽  
Product Model ◽  
Performance Improvements ◽  
Deep Model

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

scholarly journals THU0642-HPR EVOLUTION OF THE PERCEPTIONS OF RA PATIENTS AFTER EDUCATION PATIENT SESSION TEACHING METHOTREXATE SELF-INJECTION A PROSPECTIVE PILOT STUDY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 566.1-566
Author(s):  
S. Afilal ◽  
H. Rkain ◽  
B. Berchane ◽  
J. Moulay Berkchi ◽  
S. Fellous ◽  
...  
Keyword(s):  
Pilot Study ◽  
Patient Education ◽  
Median Duration ◽  
Large Scale ◽  
Education Session ◽  
Prospective Pilot Study ◽  
Mean Delay ◽  
Lack Of Information ◽  
Level Of Satisfaction ◽  
Before And After

Background:Methotrexate is a gold standard for treatment of RA. In our context, RA patients prefer to be injected by paramedics rather than self-injecting. This can be explained by patients’ bad perceptions of self-injection or lack of information. Appropriate self-injection education can therefore be an important element in overcoming these obstacles and improving disease self-management.Objectives:Compare the RA patients’ perceptions on methotrexate self-injection before and after a patient education session.Methods:Prospective pilot study that included 27 consecutive patients (81.5% female, mean age 44.4 years, illiteracy rate 40.7%) with RA (median duration of progression of 4 years, mean delay in referral for specialist of 6 months, median duration of methotrexate use of 1 year). The patients benefited from an individual patient education session to learn how to self-inject with methotrexate subcutaneously. The patient education session was supervised by a nurse and a rheumatologist with a control a week later. Perceptions of the reluctance to self-inject and the difficulties encountered by patients were assessed before the patient education session, after the 1st and 2nd self-injection of methotrexate using a 10 mm visual analog scale. Patients also reported their level of satisfaction (10 mm VAS) after the 1st and 2nd self-injection.Results:The mean duration of patient education session is 13 min.Table I compares the evolution of the degrees of reluctance to self-injection, the difficulties encountered, and the satisfaction experienced by the patients.Table 1.Evolution of RA patients’ perceptions on the methotrexate self-injection. (N = 27)BeforeAfter the 1stself-injectionAfter the 2end self-injectionpVAS reluctance (0-10mm)6,5 ± 3,62,2 ± 2,91,0 ± 2,3<0,0001VAS difficulty (0-10mm)7,5 ± 2,62,5 ± 2,71,0 ± 1,9<0,0001VAS satisfaction (0-10mm)-8,9 ± 1,89,5 ± 1,50,002Conclusion:This study suggests the effectiveness of a methotrexate self-injection patient education session in RA patients. It also highlights the value of patient education in rheumatologic care. A large-scale study is necessary to better interpret and complete these preliminary results from this pilot study.Disclosure of Interests:None declared

scholarly journals Changes in the Soundscape of the Public Space Close to a Highway by a Noise Control Intervention

2021 ◽  
Vol 13 (9) ◽  
pp. 5284
Author(s):  
Timothy Van Renterghem ◽  
Francesco Aletta ◽  
Dick Botteldooren
Keyword(s):  
Public Space ◽  
Real Life ◽  
Noise Abatement ◽  
Acoustic Environment ◽  
The Public ◽  
Control Intervention ◽  
Before And After ◽  
Noise Shielding ◽  
Visual Preferences ◽  
The Impact

The deployment of measures to mitigate sound during propagation outdoors is most often a compromise between the acoustic design, practical limitations, and visual preferences regarding the landscape. The current study of a raised berm next to a highway shows a number of common issues like the impact of the limited length of the noise shielding device, initially non-dominant sounds becoming noticeable, local drops in efficiency when the barrier is not fully continuous, and overall limited abatement efficiencies. Detailed assessments of both the objective and subjective effect of the intervention, both before and after the intervention was deployed, using the same methodology, showed that especially the more noise sensitive persons benefit from the noise abatement. Reducing the highest exposure levels did not result anymore in a different perception compared to more noise insensitive persons. People do react to spatial variation in exposure and abatement efficiency. Although level reductions might not be excessive in many real-life complex multi-source situations, they do improve the perception of the acoustic environment in the public space.

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

scholarly journals Robots for Elderly Care in the Home: A Landscape Analysis and Co-Design Toolkit

2021 ◽  
Author(s):  
Gianluca Bardaro ◽  
Alessio Antonini ◽  
Enrico Motta
Keyword(s):  
Large Scale ◽  
Daily Life ◽  
Elderly Care ◽  
Real Life ◽  
Robotic Platform ◽  
Holistic View ◽  
Personal Assistants ◽  
Healthcare Interventions ◽  
The Impact ◽  
The Eu

AbstractOver the last two decades, several deployments of robots for in-house assistance of older adults have been trialled. However, these solutions are mostly prototypes and remain unused in real-life scenarios. In this work, we review the historical and current landscape of the field, to try and understand why robots have yet to succeed as personal assistants in daily life. Our analysis focuses on two complementary aspects: the capabilities of the physical platform and the logic of the deployment. The former analysis shows regularities in hardware configurations and functionalities, leading to the definition of a set of six application-level capabilities (exploration, identification, remote control, communication, manipulation, and digital situatedness). The latter focuses on the impact of robots on the daily life of users and categorises the deployment of robots for healthcare interventions using three types of services: support, mitigation, and response. Our investigation reveals that the value of healthcare interventions is limited by a stagnation of functionalities and a disconnection between the robotic platform and the design of the intervention. To address this issue, we propose a novel co-design toolkit, which uses an ecological framework for robot interventions in the healthcare domain. Our approach connects robot capabilities with known geriatric factors, to create a holistic view encompassing both the physical platform and the logic of the deployment. As a case study-based validation, we discuss the use of the toolkit in the pre-design of the robotic platform for an pilot intervention, part of the EU large-scale pilot of the EU H2020 GATEKEEPER project.

scholarly journals Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3776
Author(s):  
Carsten Jaeschke ◽  
Marta Padilla ◽  
Johannes Glöckler ◽  
Inese Polaka ◽  
Martins Leja ◽  
...  
Keyword(s):  
Real Life ◽  
Sensor Arrays ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Calibration Transfer ◽  
Early Disease Detection ◽  
Exhaled Breath Analysis ◽  
Breath Analyzer ◽  
Analysis Experiment ◽  
Before And After

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.

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