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Peach Narrow Leaf
No full textComparison of expression profiles in developing leaves of narrow leaf versus standard leaf trees
Supplementary document for Photoelectron jets in ionization beyond dipole approximation: carrier-envelope phase effects and vortex streets - 6796759.pdf
No full textSupplemental Documen
The hepatokine orosomucoid 2 mediates beneficial metabolic effects of bile acids
No full textBile acids (BAs) are pleiotropic regulators of metabolism. Elevated levels of hepatic and circulating BAs improve energy metabolism in peripheral organs, but the precise mechanisms underlying the metabolic benefits and harm still need to be fully understood. In the present study, we identified orosomucoid 2 (ORM2) as a liver-secreted hormone (i.e., hepatokine) induced by BAs and investigated its role in BA-induced metabolic improvements in mouse models of diet-induced obesity. Contrary to our expectation, under a high-fat diet (HFD), our Orm2 knockout (Orm2-KO) exhibited a lean phenotype compared to C57BL/6J control, partly due to the increased energy expenditure. However, when challenged with HFD supplemented with cholic acid (HFDCA), Orm2-KO eliminated the anti-obesity effect of BAs, indicating that ORM2 governs BA-induced metabolic improvements. Moreover, hepatic ORM2 overexpression partially replicated BA effects by enhancing insulin sensitivity. Mechanistically, ORM2 suppressed IFNγ/STAT1 activities in inguinal WAT (iWAT) depots, forming the basis for anti-inflammatory effects of BAs and improving glucose homeostasis. In conclusion, our study provides new insights into the molecular mechanisms of BA-induced liver-adipose crosstalk through ORM2 induction.</p
Humanities discourse in games classroom: research through design with Games4Impact
No full textAbstract: Game development education is commonly considered to focus on game production with branching areas of game design, game programming or game art; nonetheless, developing games include more than what these three disciplines—design, software development, art— bring to the table. Acknowledging the transdisciplinary nature of games, we present a classroom approach that explores the social, cultural and humanistic identity of games via research through design while also encouraging students to leverage the expressive power of games. The merit of this approach is using game making as a space to ignite inquiry on socio-cultural contexts and facilitate an exploration for complex topics in a playful manner. This paper presents a reflective practice, methods of inquiry, and case examples to effectively apply design research in a project-based classroom environment. Case examples demonstrate strengths of the application for student learning and for the actualisation of design research in the classroom.</p
"Nobody Makes Games for Us" - An Investigation Into the Independent Design of Audio Games Through the Development of the Audio Game Hub and Blind Cricket
No full textAccording to the World Health Organization (2019), an estimated 217 million people worldwide are visually impaired and 36 million are blind. Although approximately 114,000 video games are currently in active circulation (MobyGames, 2020), just over 700 of these are accessible to the visually impaired (AudioGames.net, n.d.-a).This practice-oriented research project investigates the potential of audio games through the design and development of the Audio Game Hub and Blind Cricket. The games were created through iterative cycles of prototyping and public releases and stimulated and refined through the agency of voluntary user feedback. They were released on iOS and Android platforms and over a period of two years were downloaded over 130,000 times. They gathered insightful user reviews and won multiple nominations and awards. The project was presented at several conferences and featured on television and the Internet.The research was activated by a form of agency I define as an Indie Designer/Developer. Here, one is an integrated agent who develops work through critical reflection from online reviews, relying heavily on the implementation of tacit knowing (Polanyi, 1967; Schön, 1984). As a ‘generalist’, the Indie Designer/Developer combines the role of researcher, designer, reflective practitioner, developer, publisher and entrepreneur.</p
Phase Equilibrium Calculation of Bio-Oil-Related Molecules Using Predictive Thermodynamic Models
No full textIn the present study, predictive thermodynamic models
including
original UNIFAC, Dortmund-modified UNIFAC (UNIFAC-DMD), NIST-modified
UNIFAC (NIST-UNIFAC), and the COSMO-segment activity coefficient (COSMA-SAC)
were used to predict the phase equilibrium of binary and ternary mixtures
relevant for the description of fast pyrolysis bio-oils. A total of
3371 binary vapor–liquid equilibrium (VLE) isothermal or isobaric
data sets were used to study the predictive power of the investigated
models. Based on the obtained deviation for VLE of binary mixtures,
the NIST-UNIFAC is recommended for class I mixtures (including non-bio
oil), while the COSMO-SAC model is suggested for class II mixtures
(bio-oil molecules). In sequence, 62 available ternary vapor–liquid
equilibrium (VLE) isobaric data including bio-oil-related and non-bio-oil
molecules were used to investigate the models. Results showed that
both the UNIFAC-DMD and NIST-UNIFAC provide the lowest deviation compared
to UNIFAC and COSMO-SAC. Also, the COSMO-SAC model requires a large
CPU time (51 min) for 62 ternary systems, while NIST-UNIFAC, with
a CPU time of 13.2 s, allows the fastest model calculations. To further
evaluate the ability of the models, 125 binary LLE systems with 850
binary data sets and 2543 data points were collected from the literature.
In terms of CPU time, the group contribution models demand run times
in the order of seconds, and those of COSMO-SAC require run times
in the order of hours (1.91 h). Also, the NIST-UNIFAC model provides
the lowest deviation (with a slight difference from the UNIFAC-DMD
model) compared with other models. In many cases, the COSMO-SAC model
cannot predict the phase separation of the binary LLE data. Finally,
157 ternary combinations with 276 experimental ternary LLE data were
collected, and the results show that UNIFAC-DMD and NIST-UNIFAC are
the models with the lowest deviation. In summary, according to the
obtained results in VLE systems for the bio-oil-related molecules
with polar and complex structures, the group contribution models should
be used with more investigation and the COSMO-SAC model is not recommended
for LLE systems at all
Supplementary data.docx
No full textSummary of previous studies designed to investigate the effects of sexual hormones on Ca2+-handling proteins related to sarcorplasmic reticulum and plasma membrane in vasculature</p
Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow
No full textMachine learning (ML) methods can train a model to predict
material
properties by exploiting patterns in materials databases that arise
from structure–property relationships. However, the importance
of ML-based feature analysis and selection is often neglected when
creating such models. Such analysis and selection are especially important
when dealing with multifidelity data because they afford a complex
feature space. This work shows how a gradient-boosted statistical
feature-selection workflow can be used to train predictive models
that classify materials by their metallicity and predict their band
gap against experimental measurements, as well as computational data
that are derived from electronic-structure calculations. These models
are fine-tuned via Bayesian optimization, using solely the features
that are derived from chemical compositions of the materials data.
We test these models against experimental, computational, and a combination
of experimental and computational data. We find that the multifidelity
modeling option can reduce the number of features required to train
a model. The performance of our workflow is benchmarked against state-of-the-art
algorithms, the results of which demonstrate that our approach is
either comparable to or superior to them. The classification model
realized an accuracy score of 0.943, a macro-averaged F1-score of
0.940, area under the curve of the receiver operating characteristic
curve of 0.985, and an average precision of 0.977, while the regression
model achieved a mean absolute error of 0.246, a root-mean squared
error of 0.402, and R2 of 0.937. This
illustrates the efficacy of our modeling approach and highlights the
importance of thorough feature analysis and judicious selection over
a “black-box” approach to feature engineering in ML-based
modeling
Results of mixed ANOVA on mean auditory localisation responses during the spatial ventriloquist task.
No full textResults of mixed ANOVA on mean auditory localisation responses during the spatial ventriloquist task.</p