Katholieke Universiteit Leuven

About: Katholieke Universiteit Leuven is a education organization based out in Leuven, Belgium. It is known for research contribution in the topics: Population & Transplantation. The organization has 61109 authors who have published 176584 publications receiving 6210872 citations.

A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task

Abstract: Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

Lysozymes in the animal kingdom

Abstract: Lysozymes (EC 3.2.1.17) are hydrolytic enzymes, characterized by their ability to cleave the β-(1,4)-glycosidic bond between N-acetylmuramic acid and N-acetylglucosamine in peptidoglycan, the major bacterial cell wall polymer. In the animal kingdom, three major distinct lysozyme types have been identified — the c-type (chicken or conventional type), the g-type (goose-type) and the i-type (invertebrate type) lysozyme. Examination of the phylogenetic distribution of these lysozymes reveals that c-type lysozymes are predominantly present in the phylum of the Chordata and in different classes of the Arthropoda. Moreover, g-type lysozymes (or at least their corresponding genes) are found in members of the Chordata, as well as in some bivalve mollusks belonging to the invertebrates. In general, the latter animals are known to produce i-type lysozymes. Although the homology in primary structure for representatives of these three lysozyme types is limited, their three-dimensional structures show striking similarities. Nevertheless, some variation exists in their catalytic mechanisms and the genomic organization of their genes. Regarding their biological role, the widely recognized function of lysozymes is their contribution to antibacterial defence but, additionally, some lysozymes (belonging to different types) are known to function as digestive enzymes.

Buckled two-dimensional Xene sheets

Abstract: Silicene, germanene and stanene are part of a monoelemental class of two-dimensional (2D) crystals termed 2D-Xenes (X = Si, Ge, Sn and so on) which, together with their ligand-functionalized derivatives referred to as Xanes, are comprised of group IVA atoms arranged in a honeycomb lattice - similar to graphene but with varying degrees of buckling. Their electronic structure ranges from trivial insulators, to semiconductors with tunable gaps, to semi-metallic, depending on the substrate, chemical functionalization and strain. More than a dozen different topological insulator states are predicted to emerge, including the quantum spin Hall state at room temperature, which, if realized, would enable new classes of nanoelectronic and spintronic devices, such as the topological field-effect transistor. The electronic structure can be tuned, for example, by changing the group IVA element, the degree of spin-orbit coupling, the functionalization chemistry or the substrate, making the 2D-Xene systems promising multifunctional 2D materials for nanotechnology. This Perspective highlights the current state of the art and future opportunities in the manipulation and stability of these materials, their functions and applications, and novel device concepts.

Influence of dry–wet cycles on the interrelationship between aggregate, particulate organic matter, and microbial community dynamics

Abstract: Aggregate dynamics and their relationship to the microbial community have been suggested as key factors controlling SOM dynamics. Dry–wet (DW) cycles are thought to enhance aggregate turnover and decomposition of soil organic matter (SOM), particularly in tilled soils. The objective of this study was to evaluate the effects of DW cycles on aggregate stability, SOM dynamics, and fungal and bacterial populations in a Weld silt loam soil (Aridic Paleustoll). Samples, taken from 250 μm sieved air-dried soil (i.e. free of macroaggregates > 250 μm), were incubated with 13C-labeled wheat residue. In one set of soil samples, fungal growth was suppressed using a fungicide (Captan) in order to discern the effect of dry–wet cycles on fungal and bacterial populations. Aggregate formation was followed during the first 14 d of incubation. After this period, one set of soil samples was subjected to four DW cycles, whereas another set, as a control, was kept at field capacity (FC). Over 74 d, total and wheat-derived respiration, size distribution of water stable aggregates and fungal and bacterial biomass were measured. We determined native and labeled C dynamics of three particulate organic matter (POM) fractions related to soil structure: the free light fraction (LF), and the coarse (250–2000 μm) and fine (53–250 μm) intra-aggregate POM fraction (iPOM). In the fungicide treated soil samples, fungal growth was significantly reduced and no large macroaggregates (> 2 mm) were formed, whereas without addition of fungicide, fungi represented the largest part of the microbial biomass (66%) and 30% of the soil dry weight was composed of large macroaggregates. During macroaggregate formation, labeled free LF-C significantly decreased whereas labeled coarse iPOM-C increased, indicating that macroggregates are formed around fresh wheat residue (free LF), which is consequently incorporated and becomes coarse iPOM. The first drying and wetting event reduced the amount of large macroaggregates from 30 to 21% of the total soil weight. However, macroaggregates became slake-resistant after two dry-wet cycles. Fine iPOM-C was significantly lower in soil after two dry–wet cycles compared to soil kept at FC. We conclude that more coarse iPOM is decomposed into fine iPOM in macroaggregates not exposed to DW cycles due to a slower macroaggregate turnover. In addition, when macroaggregates, subjected to dry–wet cycles, became slake-resistant (d 44) and consequently macroaggregate turnover decreased, fine iPOM accumulated. In conclusion, differences in fine iPOM accumulation in DW vs. control macroaggregates are attributed to differences in macroaggregate turnover.

Decision trees for hierarchical multi-label classification

Abstract: Hierarchical multi-label classification (HMC) is a variant of classification where instances may belong to multiple classes at the same time and these classes are organized in a hierarchy. This article presents several approaches to the induction of decision trees for HMC, as well as an empirical study of their use in functional genomics. We compare learning a single HMC tree (which makes predictions for all classes together) to two approaches that learn a set of regular classification trees (one for each class). The first approach defines an independent single-label classification task for each class (SC). Obviously, the hierarchy introduces dependencies between the classes. While they are ignored by the first approach, they are exploited by the second approach, named hierarchical single-label classification (HSC). Depending on the application at hand, the hierarchy of classes can be such that each class has at most one parent (tree structure) or such that classes may have multiple parents (DAG structure). The latter case has not been considered before and we show how the HMC and HSC approaches can be modified to support this setting. We compare the three approaches on 24 yeast data sets using as classification schemes MIPS's FunCat (tree structure) and the Gene Ontology (DAG structure). We show that HMC trees outperform HSC and SC trees along three dimensions: predictive accuracy, model size, and induction time. We conclude that HMC trees should definitely be considered in HMC tasks where interpretable models are desired.