Notes

Storing wine at Attic vs. Area Conditions: Modifications in the Aroma Composition of Riesling Wine.
e. across jurisdictions and sectors), adaptive management that supports equitable and sustainable stewardship of marine environments. Conserving marine ecosystems will require recalibrating our social, financial, and industrial relationships with the marine environment. While a sustainable future requires long-term planning and commitment beyond 2030, immediate action is needed to avoid tipping points and avert trajectories of ecosystem decline. By acting now to optimise management and protection of marine ecosystems, building upon existing technologies, and conserving the remaining biodiversity, we can create the best opportunity for a sustainable future in 2030 and beyond.Semantic knowledge about individual entities (i.e., the referents of proper names such as Jacinta Ardern) is fine-grained, episodic, and strongly social in nature, when compared with knowledge about generic entities (the referents of common nouns such as politician). We investigate the semantic representations of individual entities in the brain; and for the first time we approach this question using both neural data, in the form of newly-acquired EEG data, and distributional models of word meaning, employing them to isolate semantic information regarding individual entities in the brain. We ran two sets of analyses. The first set of analyses is only concerned with the evoked responses to individual entities and their categories. We find that it is possible to classify them according to both their coarse and their fine-grained category at appropriate timepoints, but that it is hard to map representational information learned from individuals to their categories. In the second set of analyses, we learn to decode from evoked responses to distributional word vectors. These results indicate that such a mapping can be learnt successfully this counts not only as a demonstration that representations of individuals can be discriminated in EEG responses, but also as a first brain-based validation of distributional semantic models as representations of individual entities. Finally, in-depth analyses of the decoder performance provide additional evidence that the referents of proper names and categories have little in common when it comes to their representation in the brain.Words typically form the basis of psycholinguistic and computational linguistic studies about sentence processing. However, recent evidence shows the basic units during reading, i.e., the items in the mental lexicon, are not always words, but could also be sub-word and supra-word units. To recognize these units, human readers require a cognitive mechanism to learn and detect them. In this paper, we assume eye fixations during reading reveal the locations of the cognitive units, and that the cognitive units are analogous with the text units discovered by unsupervised segmentation models. We predict eye fixations by model-segmented units on both English and Dutch text. The results show the model-segmented units predict eye fixations better than word units. This finding suggests that the predictive performance of model-segmented units indicates their plausibility as cognitive units. The Less-is-Better (LiB) model, which finds the units that minimize both long-term and working memory load, offers advantages both in terms of prediction score and efficiency among alternative models. Our results also suggest that modeling the least-effort principle for the management of long-term and working memory can lead to inferring cognitive units. Overall, the study supports the theory that the mental lexicon stores not only words but also smaller and larger units, suggests that fixation locations during reading depend on these units, and shows that unsupervised segmentation models can discover these units.A spiking neural network model inspired by synaptic pruning is developed and trained to extract features of hand-written digits. The network is composed of three spiking neural layers and one output neuron whose firing rate is used for classification. The model detects and collects the geometric features of the images from the Modified National Institute of Standards and Technology database (MNIST). In this work, a novel learning rule is developed to train the network to detect features of different digit classes. For this purpose, randomly initialized synaptic weights between the first and second layers are updated using average firing rates of pre- and postsynaptic neurons. Then, using a neuroscience-inspired mechanism named, "synaptic pruning" and its predefined threshold values, some of the synapses are deleted. Hence, these sparse matrices named, "information channels" are constructed so that they show highly specific patterns for each digit class as connection matrices between the first and second layers. The "information channels" are used in the test phase to assign a digit class to each test image. In addition, the role of feed-back inhibition as well as the connectivity rates of the second and third neural layers are studied. Similar to the abilities of the humans to learn from small training trials, the developed spiking neural network needs a very small dataset for training, compared to the conventional deep learning methods that have shown a very good performance on the MNIST dataset. This work introduces a new class of brain-inspired spiking neural networks to extract the features of complex data images.Recent progress in machine-learning-based distributed semantic models (DSMs) offers new ways to simulate the apperceptive mass (AM; Kintsch, 1980) of reader groups or individual readers and to predict their performance in reading-related tasks. The AM integrates the mental lexicon with world knowledge, as for example, acquired via reading books. Following pioneering work by Denhière and Lemaire (2004), here, we computed DSMs based on a representative corpus of German children and youth literature (Jacobs et al., 2020) as null models of the part of the AM that represents distributional semantic input, for readers of different reading ages (grades 1-2, 3-4, and 5-6). After a series of DSM quality tests, we evaluated the performance of these models quantitatively in various tasks to simulate the different reader groups' hypothetical semantic and syntactic skills. In a final study, we compared the models' performance with that of human adult and children readers in two rating tasks. Overall, the results show that with increasing reading age performance in practically all tasks becomes better. The approach taken in these studies reveals the limits of DSMs for simulating human AM and their potential for applications in scientific studies of literature, research in education, or developmental science.The adoption and use of artificial intelligence, and the application of this concept through the development and implementation of intelligent automation is not considered simply as an option, but rather as an obligation in current times, due to the considerable change caused by the COVID 19 pandemic and responses to it. This study is an attempt to more thoroughly understand and clarify how the adoption of such intelligent automation can work to improve customer engagement in the food and restaurant domain. To attend to this objective, a theoretical framework is developed and tested based on an integrative approach of determinants of customer engagement through artificial intelligence to increase trust levels when intelligent automation is used. This paper will contribute to the construction of a matrix of customer engagement based on the different steps identified in the customer engagement cycle, and build a co-constructive and dynamic model of customer engagement in relation to mutual' trust and intelligent automation.A significant fraction of Brazil's population has been exposed to drought in recent years, a situation that is expected to worsen in frequency and intensity due to climate change. This constitutes a current key environmental health concern, especially in densely urban areas such as several big cities and suburbs. For the first time, a comprehensive assessment of the short-term drought effects on weekly non-external, circulatory, and respiratory mortality was conducted in 13 major Brazilian macro-urban areas across 2000-2019. We applied quasi-Poisson regression models adjusted by temperature to explore the association between drought (defined by the Standardized Precipitation-Evapotranspiration Index) and the different mortality causes by location, sex, and age groups. We next conducted multivariate meta-analytical models separated by cause and population groups to pool individual estimates. Impact measures were expressed as the attributable fractions among the exposed population, from the relative risks (RRs). Overall, a positive association between drought exposure and mortality was evidenced in the total population, with RRs varying from 1.003 [95% CI 0.999-1.007] to 1.010 [0.996-1.025] for non-external mortality related to moderate and extreme drought conditions, from 1.002 [0.997-1.007] to 1.008 [0.991-1.026] for circulatory mortality, and from 1.004 [0.995-1.013] to 1.013 [0.983-1.044] for respiratory mortality. Females, children, and the elderly population were the most affected groups, for whom a robust positive association was found. The study also revealed high heterogeneity between locations. We suggest that policies and action plans should pay special attention to vulnerable populations to promote efficient measures to reduce vulnerability and risks associated with droughts.
To determine the efficacy and safety of vitamin D
supplementation in reducing depressive symptoms in women with type 2 diabetes (T2D), depression, and low vitamin D.

In this double-blind randomized active comparator-controlled trial, women with significant depressive symptoms as assessed by the Center for Epidemiologic Studies Depression (CES-D) scale received weekly oral vitamin D
supplementation (50,000 IU) or an active comparator (5,000 IU) for 6 months. Assessments of vitamin D, 25-hydroxyvitamin D [25 (OH) D], and depression were measured at baseline, 3 months, and 6 months.

A total of 129 women were randomized, from which 119 completed the study (57 in lower dose and 62 in higher dose). Participants had an average 25 (OH) D and HbA1c of 20.8 ng/mL and 7.8%, respectively, at baseline. They were diverse (48% Black) and had a mean age of 50 and T2D for about 8 years. Upon completion of vitamin D
supplementation, serum 25 (OH) D levels increased with 50,000 IU (+34 ng/mL) and 5,000 IU (+10 ng/mLgnificant symptoms and low vitamin D. Regardless of the dose, participants' mood improved over time. Further study of vitamin D to target depressive symptoms in comorbid populations is needed.This study investigated game-related statistics differentiating the winning and losings teams of matches during the 2019 African Cup of Nations (AFCON) soccer tournament. The sample consisted of 38 games, with the data obtained from the InStat Scout platform. Data were analyzed using mean (M), SD, effect size (ES), structure coefficients (SCs), and the Wilcoxon signed-rank test. The results showed that the winning teams performed significantly better than the losing teams in terms of shots (M = 12.13, SD = 4.67, Z = -2.26, ES = 0.62), shots on target (M = 5.05, SD = 2.54, Z = -4.22, ES = 1.13), and shots from counter-attacks (M = 2.24, SD = 1.42, Z = -2.48, ES = 0.57). FIN56 Shots on target (SC = 1.22), shots (SC = -0.73), fouls (SC = 0.60), total passes (SC = 0.44), and yellow cards (SC = -0.32) presented the highest discriminatory power. These findings highlight the key match performance variables which influence the game results and may assist coaches in developing and implementing team strategies to improve the likelihood of winning the AFCON championship.
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