Local maxima of the sample functions of the N-parameter Bessel process

AbstractIn this paper we show that almost every sample function of the N-parameter Bessel process associated with the N-parameter Wiener process has a local maximum. In addition some properties related to the local maxima are investigated

Dip Hopping Technique and Yeast Biotransformations in Craft Beer Productions

This paper evaluates the effects of an alternative hopping technique, called dip hopping, on beer. This technique involves infusing hops in hot water (or in a portion of wort) and subsequently combining the infusion with the wort (after wort cooling) directly in the fermenter when the yeast is added for fermentation. The reference beers were produced employing the “traditional” late hopping technique, and the experimental beers were produced using the dip hopping technique. A variety of hops with a significant concentration of essential oil and a strain of yeast with high β-glucosidic activity capable of releasing aromatic molecules from precursors supplied by hops were used. The samples were analysed in terms of alcohol content, degree of attenuation, colour, and bitterness. Sensory analysis and gas chromatography analysis were also performed. The data showed statistically significant differences between the reference beers and the experimental beers, with the latter featuring greater hints of citrus, fruity, floral, and spicy aromas. As an overall effect, there was an increase in the olfactory and gustatory pleasantness of the beers produced with the dip hopping technique

Biotransformations Performed by Yeasts on Aromatic Compounds Provided by Hop—A Review

The biodiversity of some Saccharomyces (S.) strains for fermentative activity and metabolic capacities is an important research area in brewing technology. Yeast metabolism can render simple beers very elaborate. In this review, we examine much research addressed to the study of how different yeast strains can influence aroma by chemically interacting with specific aromatic compounds (mainly terpenes) from the hop. These reactions are commonly referred to as biotransformations. Exploiting biotransformations to increase the product’s aroma and use less hop goes exactly in the direction of higher sustainability of the brewing process, as the hop generally represents the highest part of the raw materials cost, and its reduction allows to diminish its environmental impact

Important Interactions Promoting Recognition of Peptide Ligands at the Binding Pocket of the PDZ Signaling Domain of AF6

Afadin (AF6) is a scaffolding protein involved in the formation of protein complexes at tight and adherens junctions. AF6 plays vital roles in many cellular processes, such as structural development, cellular organization, directional cell movement, and polarity. The PDZ domain participates in multiple signaling networks to initiate and maintain cellular polarity, to facilitate protein trafficking, and allow neuronal communications; thus, misregulated PDZ domain-dependent pathways can induce cells to become cancerous. AF6 interacts with many other proteins via its PDZ signaling domain and exhibits multimodal specificity. The PDZ signaling domain of AF6 (AF6_PDZ) binds predominantly hydrophobic peptide ligands. A biological array binding study has shown that AF6_PDZ can also recognize peptide ligands that contain diverse features compared to those of cognate sequence. The plasticity of the AF6_PDZ to recognize chemically diverse ligands raises questions regarding how AF6_PDZ achieves specificity in peptide recognition, as well as which binding pocket interactions are important for peptide binding. Structures of the AF6_PDZ with several biological peptide ligands highlight intermolecular interactions that stabilize these ligands at the binding pocket. We hypothesize that these interactions play a key role in recruiting the ligand to a “hot spot” on the AF6_PDZ surface and promote binding. This hypothesis predicts that changing the interactions by introducing mutations into the peptide sequence will alter the peptide binding affinity. The protein-peptide interactions in this project were studied predominantly with 1H-15N-heteronuclear single quantum coherence nuclear magnetic resonance (HSQC NMR) titration. We selected two non-cognate peptides suspected of binding to AF6_PDZ but found that these peptides only weakly interact with AF6_PDZ. Based on this result, we suggest that AF6_PDZ cannot recognize peptide with a deviated residue at the extreme C-terminal position of the peptide (P0). In specific, an arginine or a proline amino acid is likely not preferable residue at this position of the peptide. Using knowledge-based approach, we introduced a valine at the P0 of the peptide, successfully restoring binding. This result highlights the crucial role of the valine in promote binding. The disassociation constants KD for binding interaction of AF6_PDZ with cognate peptide (Class I ligand) and designed peptide (Class II ligand) are 154 µM and 26.4 µM respectively, and these values are consistent with what others have observed previously. We also studied shifted motif ligands, in which we investigated whether AF6_PDZ can accommodate ligands with an additional C-terminal residue to the P0 valine. We conclude that the AF6_PDZ does not have the ability to recognize these peptides, and the shifted motif binding mode is likely not relevant for AF6_PDZ in biological environment. Comparison of available structures reveals the possibility that αA helix of AF6_PDZ may impose some structural constraint, and thereby inhibit the protein recognition of shifted motif peptides. We also suggest that the list of the top 100 binding sequences from the peptide screening experiment does not reflect all biological binders; thus, this list needs to be used with precaution in future experiments. Altogether, we confirm the important interactions that the valine at P0 forms at the binding pocket and these interactions help targeting the peptide to the binding pocket to promote binding. This finding will aid scientists in designing therapeutic drugs that targets the AF6_PDZ, in specific, and PDZ-domain containing proteins, in generally

Use of potassium polyaspartate on white wines: interaction with proteins and aroma compounds

The precipitation of tartaric salts represents one of the main visual sensory faults of white wines. It can be prevented by cold stabilization or adding some adjuvants, such as potassium polyaspartate (KPA). KPA is a biopolymer that can limit the precipitation of tartaric salts linking the potassium cation, however, it could interact also with other compounds affecting wine quality. The present work aims to study the effect of potassium polyaspartate on proteins and aroma compounds of two white wines, at different storage temperatures (4 °C and 16 °C). The KPA addition showed positive effects on the quality of wines, with a significant decrease of unstable proteins (up to 92%), also related to better wine protein stability indices. A Logistic function well described the effect of KPA and storage temperature on protein concentration (R2 > 0.93; NRMSD: 1.54-3.82%). Moreover, the KPA addition allowed the preservation of the aroma concentration and no adversely effects were pointed out. Alternatively to common enological adjuvants, KPA could be considered a multifunctional product against tartaric and protein instability of white wines, avoiding adverse effects on their aroma profil

Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction

Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit their risky assets, in order to not materially affect market prices. However, the task of multi-step stock price prediction is challenging, given the highly stochastic nature of stock data. Current solutions to tackle this problem are mostly designed for single-step, classification-based predictions, and are limited to low representation expressiveness. The problem also gets progressively harder with the introduction of the target price sequence, which also contains stochastic noise and reduces generalizability at test-time. To tackle these issues, we combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction through a stochastic generative process. The hierarchical VAE allows us to learn the complex and low-level latent variables for stock prediction, while the diffusion probabilistic model trains the predictor to handle stock price stochasticity by progressively adding random noise to the stock data. Our Diffusion-VAE (D-Va) model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance. More importantly, the multi-step outputs can also allow us to form a stock portfolio over the prediction length. We demonstrate the effectiveness of our model outputs in the portfolio investment task through the Sharpe ratio metric and highlight the importance of dealing with different types of prediction uncertainties.Comment: CIKM 202

Qserv: a distributed shared-nothing database for the LSST catalog

The LSST project will provide public access to a database catalog that, in its final year, is estimated to include 26 billion stars and galaxies in dozens of trillion detections in multiple petabytes. Because we are not aware of an existing open-source database implementation that has been demonstrated to efficiently satisfy astronomers' spatial self-joining and cross-matching queries at this scale, we have implemented Qserv, a distributed shared-nothing SQL database query system. To speed development, Qserv relies on two successful open-source software packages: the MySQL RDBMS and the Xrootd distributed file system. We describe Qserv's design, architecture, and ability to scale to LSST's data requirements. We illustrate its potential with test results on a 150-node cluster using 55 billion rows and 30 terabytes of simulated data. These results demonstrate the soundness of Qserv's approach and the scale it achieves on today's hardware