Characterization of a Novel One-Domain Halotolerant Laccase from Parageobacillus thermoglucosidasius and Its Application in Dye Decolorization.

Laccases are widespread multi-copper oxidases and generally classified into three-domain laccases and two-domain laccases. In this study, a novel laccase PthLac from Parageobacillus thermoglucosidasius harbored only one domain of Cu-oxidase_4 and showed no sequence relatedness or structure similarity to three-domain and two-domain laccases. PthLac was heterologously expressed in Escherichia coli, purified, and characterized. The optimum temperature and pH of PthLac on guaiacol were at 60 ℃ and pH 6, respectively. The effects of various metal ions on PthLac were analyzed. All the tested metal ions did not suppress the activity of PthLac, except for 10 mM Cu2+, which increased the activity of PthLac to 316%, indicating that PthLac was activated by Cu2+. Meanwhile, PthLac kept 121% and 69% activity when incubated at concentrations of 2.5 and 3 M NaCl for 9 h, suggesting the long-term halotolerancy of this enzyme. In addition, PthLac showed resistance to the organic solvents and surfactants, and displayed dye decolorization capacity. This study enriched our knowledge about one-domain laccase and its potential industrial applications.

Paper vs. practice: How legal and ethical frameworks influence public sector data professionals in the Netherlands.

Recent years have seen a massive growth in ethical and legal frameworks to govern data science practices. Yet one of the core questions associated with ethical and legal frameworks is the extent to which they are implemented in practice. A particularly interesting case in this context comes to public officials, for whom higher standards typically exist. We are thus trying to understand how ethical and legal frameworks influence the everyday practices on data and algorithms of public sector data professionals. The following paper looks at two cases: public sector data professionals (1) at municipalities in the Netherlands and (2) at the Netherlands Police. We compare these two cases based on an analytical research framework we develop in this article to help understanding of everyday professional practices. We conclude that there is a wide gap between legal and ethical governance rules and the everyday practices.

Animal behavior and animal personality from a non-human perspective: Getting help from the machine.

We can now track the position of every fly's leg or immerse a tiny fish inside a virtual world by monitoring its gaze in real time. Yet capturing animals' posture or gaze is not like understanding their behavior. Instead, behaviors are still often interpreted by human observers in an anthropomorphic manner. Even newer tools that automatically classify behaviors rely on human observers for the choice of behaviors. In this perspective, we suggest a roadmap toward a "human-free" interpretation of behavior. We present several recent advances, including our recent work on animal personalities. Personality both underlies behavioral differences among individuals and is consistent over time. A mathematical formulation of this idea has allowed us to measure mouse traits objectively, map behaviors across species (humans included), and explore the biological basis of behavior. Our goal is to enable "machine translation" of raw movement data into intelligible human concepts en route to improving our understanding of animals and people.

Using Variational Multi-view Learning for Classification of Grocery Items.

An essential task for computer vision-based assistive technologies is to help visually impaired people to recognize objects in constrained environments, for instance, recognizing food items in grocery stores. In this paper, we introduce a novel dataset with natural images of groceries-fruits, vegetables, and packaged products-where all images have been taken inside grocery stores to resemble a shopping scenario. Additionally, we download iconic images and text descriptions for each item that can be utilized for better representation learning of groceries. We select a multi-view generative model, which can combine the different item information into lower-dimensional representations. The experiments show that utilizing the additional information yields higher accuracies on classifying grocery items than only using the natural images. We observe that iconic images help to construct representations separated by visual differences of the items, while text descriptions enable the model to distinguish between visually similar items by different ingredients.

Recommendations for Services in a FAIR Data Ecosystem.

The development and growing adoption of the FAIR data principles and associated standards as a part of research policies and practices place novel demands on research data services. This article highlights common challenges and priorities and proposes a set of recommendations on how data infrastructures can evolve and collaborate to provide services that support the implementation of the FAIR data principles, in particular in the context of building the European Open Science Cloud (EOSC). The recommendations cover a broad area of topics, including certification, infrastructure components, stewardship, costs, rewards, collaboration, training, support, and data management. These recommendations were prioritized according to their perceived urgency by different stakeholder groups and associated with actions as well as suggested action owners. This article is the output of three workshops organized by the projects FAIRsFAIR, RDA Europe, OpenAIRE, EOSC-hub, and FREYA designed to explore, discuss, and formulate recommendations among stakeholders in the scientific community. While the results are a work-in-progress, the challenges and priorities outlined provide a detailed and unique overview of current issues seen as crucial by the community that can sharpen and improve the roadmap toward a FAIR data ecosystem.

One-domain approach for studying multiphase transport phenomena in biofilm growing systems.

The one-domain approach (ODA) was used as an alternative to solve fluid-biofilm interfacial behavior in a 2-D model for diffusion-reaction-convection coupled with prediction of irregular growth of biofilms via a cellular automaton strategy. The simulations exhibited errors of <7% compared with the porosity of a previously reported capillary experimental system. Additionally, biofilm surface geometrical aspects were satisfactorily compared with reports of experimental and similar rigorously simulated benchmark systems. The method developed was applied to simulate typical biofilm systems predicting recirculation flow patterns, interface concentration profiles, and clogging of the inlet section of the capillary tube, which are phenomena that affect the efficiency of diverse biotechnological applications, including membrane bioreactors and biofilters. The ODA method applied to the governing equations of momentum and mass transfer combined with a cellular automaton algorithm is a suitable and straightforward approach for modeling solid-state fermentation at different sophistication levels.