Trajectory Data Mining in Mouse Models of Stroke

Contains fulltext : 273912.pdf (Publisher’s version ) (Open Access)Radboud University, 04 oktober 2022Promotor : Kiliaan, A.J. Co-promotor : Wiesmann, M.167 p

Pattern Discovery for climate and environmental policy indicators

Quantitative environmental policy indicators are useful for modeling the impact of environmental policy on the economy. They can be important tools for policy-makers, companies, investors, and researchers alike. Well-crafted environmental policies lead to cleaner environments whilst encouraging innovative behaviour to stimulate green growth and ‘win-wins’ for the economy and the environment. Such win-win policies are increasingly sought out by policymakers, evidenced in the growing number of green 'new deals' and 'net zero' carbon emissions pledges at a national level. But there is a gap between the needs for environmental policy data and the supply of reliable indicators and indexes. This disconnect has negative consequences for policy feedback as well as the inducement of potential innovators of environmental technologies. While there are now a wide range of indicators and indexes, these largely remain inadequate for various reasons. This is disappointing considering the immense progress that has been made in machine learning and pattern discovery methods—methods that are already fully deployed in other research disciplines. Such automated techniques can limit human biases which currently plague the environmental indicator's scholarship. Against this backdrop, the main objective of this paper is to highlight how researchers can carefully collect these data and augment the effectiveness of environmental policy indicators. This is an important research area that, apart from a handful of studies, is not sufficiently developed

Automated Analysis of Stroke Mouse Trajectory Data With Traja

Contains fulltext : 220275.pdf (publisher's version ) (Open Access

The Impact of Voluntary Exercise on Stroke Recovery

Stroke treatment is limited to time-critical thrombectomy and rehabilitation by physiotherapy. Studies report beneficial effects of exercise; however, a knowledge gap exists regarding underlying mechanisms that benefit recovery of brain networks and cognition. This study aims to unravel therapeutic effects of voluntary exercise in stroke-induced mice to develop better personalized treatments. Male C57Bl6/JOlaHsd mice were subjected to transient middle cerebral artery occlusion. After surgery, the animals were divided in a voluntary exercise group with access to running wheels (RW), and a control group without running wheels (NRW). During 6 days post-stroke, activity/walking patterns were measured 24/7 in digital ventilated cages. Day 7 post-surgery, animals underwent MRI scanning (11.7T) to investigate functional connectivity (rsfMRI) and white matter (WM) integrity (DTI). Additionally, postmortem polarized light imaging (PLI) was performed to quantify WM fiber density and orientation. After MRI the animals were sacrificed and neuroinflammation and cerebral vascularisation studied. Voluntary exercise promoted myelin density recovery corresponding to higher fractional anisotropy. The deteriorating impact of stroke on WM dispersion was detected only in NRW mice. Moreover, rsfMRI revealed increased functional connectivity, cerebral blood flow and vascular quality leading to improved motor skills in the RW group. Furthermore, voluntary exercise showed immunomodulatory properties post-stroke. This study not only helped determining the therapeutic value of voluntary exercise, but also provided understanding of pathological mechanisms involved in stroke

Non-destructive Measurement of Sugar Content in Chestnuts Using Near-Infrared Spectroscopy

International audienceThe chestnut (Castanea) is an important fruit in Europe and Asia. As a highly variable fruit, its quality is graded according to nutrition components, especially according to the sugar content, which are traditionally measured by using chemical methods. However, the traditional methods are time-consuming, laborious, and expensive. Here, we analyzed the sugar content of intact and peeled chestnuts by near-infrared spectroscopy. The spectra of intact and peeled chestnut samples were collected in the wavelength range from 833 nm to 2500 nm. The Sample Set Partitioning based on joint X–Y distances was used when the calibration and validation subsets were partitioned. The predictive models for intact and peeled chestnut samples respectively, were developed using partial least squares (PLS) regression based on the original spectra and the spectra derived from different pretreatments. The PLS models developed from the spectra of peeled samples gave accurate predictions. The correlation coefficient (R2) of the optimized model for calibration set and validation set were 0.90 and 0.86. Although the models established on the spectra of intact samples did not perform excellently, they were still qualified to measure sugar content of the chestnut kernel. The correlation coefficient (R2) of optimized model for calibration set and validation set were 0.89 and 0.59. These results suggested that NIR spectroscopy could be used as a fast and accurate alternative method for the nondestructive evaluation of sugar content in chestnuts during orchard and post-harvest processes