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Molecular development of a thermostable β-glucosidase for modification of natural products

The gene encoding a β-glucosidase originating from the extreme thermophile Thermotoga neopolitana has been cloned and expressed in Escherichia coli. The aim was to produce a thermostable enzyme that could be used to remove sugar residues from glucosylated natural products classified as flavonoids by applying a method combining extraction in hot pressurized water with enzymatic hydrolysis. The β-g

Dynamic spatio‐temporal flow modeling with raster DEMs

A user-friendly high-resolution intermediate complexity dynamic and spatially distributed flow model is crucial in urban flood modeling. Planners and consultants need to improve the accuracy of floods and estimation of risks. A new flow model will serve as a rapid tool to improve identification of these. This article provides a detailed explanation of a model based on a multiple flow algorithm. Mo

Cerina Rydälv

Forskningskoordinator Kontaktinformation E-post: cerina [dot] rydalv [at] mgeo [dot] lu [dot] se Mobil: +46 72 216 86 26Organisation Miljö- och geovetenskapliga institutionen (MGeo) Besöksadress: Geocentrum, Sölvegatan 12, Lund Rumsnummer: 282 Hämtställe: 16 WebbplatsCerina Rydälvs profil i Lunds universitets forskningsportalAndra roller Forskningskoordinator Centrum för miljö- och klimatvetenskap

https://www.cec.lu.se/sv/cerina-rydalv - 2026-05-14

Medeltidshuset vid Stora Torg i Halmstad

Up until the mid 1960s you could still findsignificant remains of a medieval brick housein Halmstad. Since the building was rediscoveredin the 1920s, numerous speculationsabout the house and its owners have beenpresented. It has been linked to the Kalenteguild as well as to the guild of St. Gertrud.These hypotheses rest, however, on veryflimsy basis. Thus, this article examines awhole new hypothes

Hybridizing spatial machine learning to explore the fine-scale heterogeneity between stunting prevalence and its associated risk determinants in Rwanda

Childhood stunting is a serious global public health issue that exhibits local spatial variations. Previous studies have used traditional statistical methods to identify stunting risk factors, and little is known about the application and usefulness of spatial machine learning techniques in identifying localized stunting risk factors based on complex datasets. This study assesses the performance o