Pitfalls in measuring the endocannabinoid 2-arachidonoyl glycerol in biological samples

Background: The endocannabinoid 2-arachidonoyl glycerol (2-AG) undergoes spontaneous isomerization to biologically inactive 1-AG. This effect has not been adequately addressed in previous studies that reported 2-AG concentrations in biological samples. Methods: Liquid chromatography tandem-mass spectrometry (LC-MS/MS) was used for 1-AG and 2-AG analyses. Results: Identical collision-induced disintegration spectra were found for 1-AG and 2-AG. For specific detection of both compounds, which share a common mass transition, baseline chromatographic separation is mandatory, even when applying MS/MS technology with its generally high detection specificity. When using standard chromatographic conditions with the very short run times typically used in LC-MS/ MS methods, co-elution of 2-AG with 1-AG, which is present in human serum, causes false 2-AG results. Conclusions: Our data highlight that the analytical specificity of MS/MS can be limited by interference from isobaric isomers with identical disintegration patterns. The specificity of this technology must be carefully evaluated for each individual application

Motion sickness, stress and the endocannabinoid system

A substantial number of individuals are at risk for the development of motion sickness induced nausea and vomiting (N&V) during road, air or sea travel. Motion sickness can be extremely stressful but the neurobiologic mechanisms leading to motion sickness are not clear. The endocannabinoid system (ECS) represents an important neuromodulator of stress and N&V. Inhibitory effects of the ECS on N&V are mediated by endocannabinoid-receptor activation

Plasma concentrations of endocannabinoids and related primary Fatty Acid amides in patients with post-traumatic stress disorder.

Endocannabinoids (ECs) and related N-acyl-ethanolamides (NAEs) play important roles in stress response regulation, anxiety and traumatic memories. In view of the evidence that circulating EC levels are elevated under acute mild stressful conditions in humans, we hypothesized that individuals with traumatic stress exposure and post-traumatic stress disorder (PTSD), an anxiety disorder characterized by the inappropriate persistence and uncontrolled retrieval of traumatic memories, show measurable alterations in plasma EC and NAE concentrations. We determined plasma concentrations of the ECs anandamide (ANA) and 2-arachidonoylglycerol (2-AG) and the NAEs palmitoylethanolamide (PEA), oleoylethanolamide (OEA), stearoylethanolamine (SEA), and N-oleoyldopamine (OLDA) by HPLC-MS-MS in patients with PTSD (n = 10), trauma-exposed individuals without evidence of PTSD (n = 9) and in healthy control subjects (n = 29). PTSD was diagnosed according to DSM-IV criteria by administering the Clinician Administered PTSD Scale (CAPS), which also assesses traumatic events. Individuals with PTSD showed significantly higher plasma concentrations of ANA (0.48±0.11 vs. 0.36±0.14 ng/ml, p = 0.01), 2-AG (8.93±3.20 vs. 6.26±2.10 ng/ml, p<0.01), OEA (5.90±2.10 vs. 3.88±1.85 ng/ml, p<0.01), SEA (2.70±3.37 vs. 0.83±0.47, ng/ml, p<0.05) and significantly lower plasma levels of OLDA (0.12±0.05 vs. 0.45±0.59 ng/ml, p<0.05) than healthy controls. Moreover, PTSD patients had higher 2-AG plasma levels (8.93±3.20 vs. 6.01±1.32 ng/ml, p = 0.03) and also higher plasma concentrations of PEA (4.06±1.87 vs. 2.63±1.34 ng/ml, p<0.05) than trauma-exposed individuals without evidence of PTSD. CAPS scores in trauma-exposed individuals with and without PTSD (n = 19) correlated positively with PEA (r = 0.55, p = 0.02) and negatively with OLDA plasma levels (r = -0.68, p<0.01). CAPS subscores for intrusions (r = -0.65, p<0.01), avoidance (r = -0.60, p<0.01) and hyperarousal (r = -0.66, p<0.01) were all negatively related to OLDA plasma concentrations. PTSD appears to be associated with changes in plasma EC/NAE concentrations. This may have pathophysiological and diagnostic consequences but will need to be reproduced in larger cohorts

Enabling viewpoint learning through dynamic label generation

Optimal viewpoint prediction is an essential task in many computer graphics applications. Unfortunately, common viewpointqualities suffer from two major drawbacks: dependency on clean surface meshes, which are not always available, and the lack ofclosed-form expressions, which requires a costly search involving rendering. To overcome these limitations we propose to sepa-rate viewpoint selection from rendering through an end-to-end learning approach, whereby we reduce the in¿uence of the meshquality by predicting viewpoints from unstructured point clouds instead of polygonal meshes. While this makes our approachinsensitive to the mesh discretization during evaluation, it only becomes possible when resolving label ambiguities that arise inthis context. Therefore, we additionally propose to incorporate the label generation into the training procedure, making the labeldecision adaptive to the current network predictions. We show how our proposed approach allows for learning viewpoint pre-dictions for models from different object categories and for different viewpoint qualities. Additionally, we show that predictiontimes are reduced from several minutes to a fraction of a second, as compared to state-of-the-art (SOTA) viewpoint quality eval-uation. Code and training data is available at https://github.com/schellmi42/viewpoint_learning, whichis to our knowledge the biggest viewpoint quality dataset available.This work was supported in part by project TIN2017-88515-C2-1-R(GEN3DLIVE), from the Spanish Ministerio de Economía yCompetitividad, by 839 FEDER (EU) funds.Peer ReviewedPostprint (published version

Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow

Small RNA-Seq has emerged as a powerful tool in transcriptomics, gene expression profiling and biomarker discovery. Sequencing cell-free nucleic acids, particularly microRNA (miRNA), from liquid biopsies additionally provides exciting possibilities for molecular diagnostics, and might help establish disease-specific biomarker signatures. The complexity of the small RNA-Seq workflow, however, bears challenges and biases that researchers need to be aware of in order to generate high-quality data. Rigorous standardization and extensive validation are required to guarantee reliability, reproducibility and comparability of research findings. Hypotheses based on flawed experimental conditions can be inconsistent and even misleading. Comparable to the well-established MIQE guidelines for qPCR experiments, this work aims at establishing guidelines for experimental design and pre-analytical sample processing, standardization of library preparation and sequencing reactions, as well as facilitating data analysis. We highlight bottlenecks in small RNA-Seq experiments, point out the importance of stringent quality control and validation, and provide a primer for differential expression analysis and biomarker discovery. Following our recommendations will en-courage better sequencing practice, increase experimental transparency and lead to more reproducible small RNA-Seq results. This will ultimately enhance the validity of biomarker signatures, and allow reliable and robust clinical predictions

Learning human viewpoint preferences from sparsely annotated models

View quality measures compute scores for given views and are used to determine an optimal view in viewpoint selection tasks. Unfortunately, despite the wide adoption of these measures, they are rather based on computational quantities, such as entropy, than human preferences. To instead tailor viewpoint measures towards humans, view quality measures need to be able to capture human viewpoint preferences. Therefore, we introduce a large-scale crowdsourced data set, which contains 58k annotated viewpoints for 3220 ModelNet40 models. Based on this data, we derive a neural view quality measure abiding to human preferences. We further demonstrate that this view quality measure not only generalizes to models unseen during training, but also to unseen model categories. We are thus able to predict view qualities for single images, and directly predict human preferred viewpoints for 3D models by exploiting point-based learning technology, without requiring to generate intermediate images or sampling the view sphere. We will detail our data collection procedure, describe the data analysis and model training and will evaluate the predictive quality of our trained viewpoint measure on unseen models and categories. To our knowledge, this is the first deep learning approach to predict a view quality measure solely based on human preferences.This work was supported in part by The Federal Ministry of Education and Research funding program - AuCity 3 - Kollaborative und adaptive Mixed Reality in der Hochschullehre am Beispiel des Bauingenieurwesens, between Magdeburg-Stendal University of Applied Sciences, the Bauhaus University Weimar and Ulm University. Open access funding enabled and organized by Projekt DEAL.Peer ReviewedPostprint (published version

Parentage of Hydatidiform Moles

We were presented with the STR (short tandem repeat) profiles from two separate paternity trios. Each trio consisted of a mother, an alleged father, and products of conception (POC) that contained a hydatidiform mole but no visible fetus. In both cases, antecedent pregnancies had followed alleged sexual assaults. Mole classification and pathogenesis are described in order to explain the analyses and statistical reasoning used in each case. One mole exhibited several loci with two different paternal alleles, indicating it was a dispermic (heterozygous) mole. Maternal decidua contaminated the POC, preventing the identification of paternal obligate alleles (POAs) at some loci. The other mole exhibited only one paternal allele/locus at all loci and no maternal alleles, indicating it was a diandric and diploid (homozygous) mole. In each case, traditional calculations were used to determine paternity indices (PIs) at loci that exhibited one paternal allele/locus. PIs at mole loci with two different paternal alleles/locus were calculated from formulas first used for child chimeras that are always dispermic. Combined paternity indices in both mole cases strongly supported the paternity of each suspect.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155886/1/jfo14291.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155886/2/jfo14291_am.pd