Project DECIDE, part 1: increasing the amount of valid advance directives in people with Alzheimer’s disease by offering advance care planning - a prospective double-arm intervention study

Finanziert im Rahmen der DEAL-Verträge durch die Universitätsbibliothek SiegenBackground Everybody has the right to decide whether to receive specific medical treatment or not and to provide their free, prior and informed consent to do so. As dementia progresses, people with Alzheimer’s dementia (PwAD) can lose their capacity to provide informed consent to complex medical treatment. When the capacity to consent is lost, the autonomy of the affected person can only be guaranteed when an interpretable and valid advance directive exists. Advance directives are not yet common in Germany, and their validity is often questionable. Once the dementia diagnosis has been made, it is assumed to be too late to write an advance directive. One approach used to support the completion of advance directives is ‘Respecting Choices’®—an internationally recognised, evidence-based model of Advance Care Planning (ACP), which, until now, has not been evaluated for the target group of PwAD. This study’s aims include (a) to investigate the proportion of valid advance directives in a memory clinic population of persons with suspected AD, (b) to determine the predictors of valid advance directives, and (c) to examine whether the offer of ACP can increase the proportion of valid advance directives in PwAD. Method We intend to recruit at least N = 250 participants from two memory clinics in 50 consecutive weeks. Of these, the first 25 weeks constitute the baseline phase (no offer of ACP), the following 25 weeks constitute the intervention phase (offer of ACP). The existence and validity of an advance directive will be assessed twice (before and after the memory clinic appointment). Moreover, potential predictors of valid advance directives are assessed. Discussion The results of this study will enhance the development of consent procedures for advance directives of PwAD based on the ACP/Respecting Choices (R) approach. Therefore, this project contributes towards increasing the autonomy and inclusion of PwAD and the widespread acceptance of valid advance directives in PwAD

Dense and taxonomically detailed habitat maps of coral reef benthos machine-generated from underwater hyperspectral transects in Curaçao

This dataset contains 248 benthic habitat maps, that were created from 31 underwater hyperspectral images captured with the HyperDiver device in 8 reef sites across the western coastline of Curacao (see https://doi.org/10.3390/data5010019 for information on the acquisition of the transects). The maps were produced by 8 combinations of two semantic labelspaces (detailed and reefgroups), two machine learning classifiers (patched and segmented), and two spectral signals (radiance and reflectance). Maps in the detailed labelspace have each pixel assigned to one of 43 labels, which are taxonomic labels at family, genus and species levels for biotic components of the reef (corals, sponges, macroalgae, etc.), as well as substrate labels (sediment, cyanobacterial mats, turf algae) and survey material labels (transect tape, reference board, etc.). The set of maps in the reefgroups labelspace cluster the labels in the detailed labelspace into 11 classes that describe reef functional groups (i.e. corals, sponges, algae, etc.). All habitat maps were produced with high accuracy (Fbeta 87%), by two different machine learning methods: a random forest ensemble classifier (segmented method) and a deep learning neural network classifier (patched method). The maps are further divided by the signal type from the hyperspectral image that was used, either radiance or reflectance (the latter was calculated with a reference board located at the beginning and end of each transect). These benthic habitat maps can be used to obtain accurate descriptions of the benthic community and habitat structure of coral reef sites in Curacao. The dataset also contains: an assessment of the accuracy and data efficiency of the machine learning methods, a consistency assessment of the mapped regions, a comparison of habitat metrics (class coverage, biodiversity indices, composition and configuration) between habitat maps produced by each method, and an effort-vs-error analysis of sparse sampling techniques on the densely classified maps

Digitizing the coral reef: Machine learning of underwater spectral images enables dense taxonomic mapping of benthic habitats

Abstract Coral reefs are the most biodiverse marine ecosystems, and host a wide range of taxonomic diversity in a complex spatial community structure. Existing coral reef survey methods struggle to accurately capture the taxonomic detail within the complex spatial structure of benthic communities. We propose a workflow to leverage underwater hyperspectral image transects and two machine learning algorithms to produce dense habitat maps of 1150 m2 of reefs across the Curaçao coastline. Our multi‐method workflow labelled all 500+ million pixels with one of 43 classes at taxonomic family, genus or species level for corals, algae, sponges, or to substrate labels such as sediment, turf algae and cyanobacterial mats. With low annotation effort (only 2% of pixels) and no external data, our workflow enables accurate (Fbeta of 87%) survey‐scale mapping, with unprecedented thematic detail and with fine spatial resolution (2.5 cm/pixel). Our assessments of the composition and configuration of the benthic communities of 23 image transects showed high consistency. Digitizing the reef habitat and community structure enables validation and novel analysis of pattern and scale in coral reef ecology. Our dense habitat maps reveal the inadequacies of point sampling methods to accurately describe reef benthic communities

Determination of Cycle to Cycle Battery Cell Degradation with High-Precision Measurements

Due to the long life of lithium ion cells, it is difficult to measure their low capacity degradation from cycle to cycle. In order to accelerate the measurements, cells are often exposed to extreme stress conditions, which usually means elevated temperatures and high charging currents. This raises doubts as to whether the results obtained in this way are representative for real world applications. This work shows that, with the help of very precise capacity measurements, it is possible to determine cell aging in a few days even under normal operating conditions from cycle to cycle. To verify this, a self-built measurement system is used. After demonstrating the capabilities of the system, two different cycling schemes are used simultaneously to determine the various causes of aging—namely cycle aging, calendrical aging and self-discharge due to leakage currents

Detailed tree inventory and area coverage of remote mangrove forests (species: Pelliciera rhizophorae and Rhizophora mangle) in the Utría National Park in the Colombian Pacific Coast

This dataset contains detailed inventories of 7 large plots of mangrove forests in the Utría National Park in the Colombian Pacific Coast. The inventory consists of individual geo-referenced tree masks for the endemic Pelliciera rhizophorae species (/pelliciera_trees/Pelliciera.shp), and area coverages for the Rhizophora mangle species, as well as Mud and Water areas (/other_classes_coverage/.tiff). For each individual tree of the Pelliciera rhizophorae species we provide the predicted height, crown diameter and crown area (/pelliciera_trees/trees.csv). We also provide the cover area of the other predicted classes (/other_area_coverage/area_coverages.csv). The inventories were automatically produced with trained Artificial Intelligence (AI) algorithms. The algorithms were trained with orthomosaic images and digital surface models (DSMs) produced from Unoccupied Aerial System (UAS) imagery with Structure-from-Motion software, both paired with expert annotations of the trees and areas (/annotations/.shp). In this dataset we provide all the input data for the algorithms, as well as the predicted geo-referenced data products, such as: predicted Pelliciera rhizophorae tree masks, Rhizophora mangle areas, Water areas, Mud areas, canopy height models (CHM), digital elevation models (DEM), digital terrain models (DTM) and various ancillary images. We also provide the initial orthomosaic files (/orthomosaic.tif) and the DSM files (/DSM.tif), that were produced with SfM software Agisoft Metashape v1.6.2 from the aerial footage captured in 2019 (19–22 February) using two consumer-grade UASs: the DJI Phantom 4 and DJI Mavic Pro (SZ DJI Technology Co., Ltd—Shenzhen, China). The DJI Phantom 4 has an integrated photo camera, the DJI FC330 and the DJI Mavic Pro was equipped with the integrated DJI FC220. The flights were programmed to follow the trajectories in an automated mode by means of the commercial application "DroneDeploy". Ground control points (GCPs) were positioned in the field, and their geographic location was acquired. We used two single-band global navigation satellite system (GNSS) receivers: an Emlid Reach RS+ single-band real-time kinematics (RTK) GNSS receiver (Emlid Tech Kft.—Budapest, Hungary) as a base station, and a Bad Elf GNSS Surveyor handheld GPS (Bad Elf, LLC—West Hartford, AZ, USA). RINEX static data from the base station was processed with the Precise Point Positioning Service (PPP) of the Natural Resources of Canada, while rover position was processed using the RTKLib software through a post processed kinematics (PPK) workflow. The final absolute positional accuracy of the products is below one meter because the results of the PPP workflow has a positional accuracy between 0.2 m and 1 m

Therapeutical Administration of Peptide Pep19-2.5 and Ibuprofen Reduces Inflammation and Prevents Lethal Sepsis

Sepsis is still a major cause of death and many efforts have been made to improve the physical condition of sepsis patients and to reduce the high mortality rate associated with this disease. While achievements were implemented in the intensive care treatment, all attempts within the field of novel therapeutics have failed. As a consequence new medications and improved patient stratification as well as a thoughtful management of the support therapies are urgently needed. In this study, we investigated the simultaneous administration of ibuprofen as a commonly used nonsteroidal anti-inflammatory drug (NSAID) and Pep19-2.5 (Aspidasept), a newly developed antimicrobial peptide. Here, we show a synergistic therapeutic effect of combined Pep19-2.5-ibuprofen treatment in an endotoxemia mouse model of sepsis. In vivo protection correlates with a reduction in plasma levels of both tumor necrosis factor α and prostaglandin E, as a likely consequence of Pep19-2.5 and ibuprofen-dependent blockade of TLR4 and COX pro-inflammatory cascades, respectively. This finding is further characterised and confirmed in a transcriptome analysis of LPS-stimulated human monocytes. The transcriptome analyses showed that Pep19-2.5 and ibuprofen exerted a synergistic global effect both on the number of regulated genes as well as on associated gene ontology and pathway expression. Overall, ibuprofen potentiated the anti-inflammatory activity of Pep19-2.5 both in vivo and in vitro, suggesting that NSAIDs could be useful to supplement future anti-sepsis therapies