Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple Machine Learning Algorithms

Allele specific antibody response against the polymorphic system of HLA is the allogeneic response marker determining the immunological risk for graft acceptance before and after organ transplantation and therefore routinely studied during the patient’s workup. Experimentally, bead bound antigen- antibody reactions are detected using a special multicolor flow cytometer (Luminex). Routinely for each sample, antibody responses against 96 different HLA antigen groups are measured simultaneously and a 96-dimensional immune response vector is created. Under a common experimental protocol, using unsupervised clustering algorithms, we analyzed these immune intensity vectors of anti HLA class II responses from a dataset of 1,748 patients before or after renal transplantation residing in a single country. Each patient contributes only one serum sample in the analysis. A population view of linear correlations of hierarchically ordered fluorescence intensities reveals patterns in human immune responses with striking similarities with the previously described CREGs but also brings new information on the antigenic properties of class II HLA molecules. The same analysis affirms that “public” anti-DP antigenic responses are not correlated to anti DR and anti DQ responses which tend to cluster together. Principal Component Analysis (PCA) projections also demonstrate ordering patterns clearly differentiating anti DP responses from anti DR and DQ on several orthogonal planes. We conclude that a computer vision of human alloresponse by use of several dimensionality reduction algorithms rediscovers proven patterns of immune reactivity without any a priori assumption and might prove helpful for a more accurate definition of public immunogenic antigenic structures of HLA molecules. Furthermore, the use of Eigen decomposition on the Immune Response generates new hypotheses that may guide the design of more effective patient monitoring tests

Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning

Detection of alloreactive anti-HLA antibodies is a frequent and mandatory test before and after organ transplantation to determine the antigenic targets of the antibodies. Nowadays, this test involves the measurement of fluorescent signals generated through antibody–antigen reactions on multi-beads flow cytometers. In this study, in a cohort of 1,066 patients from one country, anti-HLA class I responses were analyzed on a panel of 98 different antigens. Knowing that the immune system responds typically to “shared” antigenic targets, we studied the clustering patterns of antibody responses against HLA class I antigens without any a priori hypothesis, applying two unsupervised machine learning approaches. At first, the principal component analysis (PCA) projections of intralocus specific responses showed that anti-HLA-A and anti-HLA-C were the most distantly projected responses in the population with the anti-HLA-B responses to be projected between them. When PCA was applied on the responses against antigens belonging to a single locus, some already known groupings were confirmed while several new crossreactive patterns of alloreactivity were detected. Anti-HLA-A responses projected through PCA suggested that three cross-reactive groups accounted for about 70% of the variance observed in the population, while anti-HLA-B responses were mainly characterized by a distinction between previously described Bw4 and Bw6 cross-reactive groups followed by several yet undocumented or poorly described ones. Furthermore, anti-HLA-C responses could be explained by two major cross-reactive groups completely overlapping with previously described C1 and C2 allelic groups. A second featurebased analysis of all antigenic specificities, projected as a dendrogram, generated a robust measure of allelic antigenic distances depicting bead-array defined cross reactive groups. Finally, amino acid combinations explaining major population specific crossreactive groups were described. The interpretation of the results was based on the current knowledge of the antigenic targets of the antibodies as they have been characterized either experimentally or computationally and appear at the HLA epitope registry

Language Model Co-occurrence Linking for Interleaved Activity Discovery

As ubiquitous computer and sensor systems become abundant, the potential for automatic identification and tracking of human behaviours becomes all the more evident. Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. One possible solution to this problem is activity discovery: the identification of activities in an unlabelled dataset by means of an unsupervised algorithm. This paper presents a novel approach to activity discovery that utilises deep learning based language production models to construct a hierarchical, tree-like structure over a sequential vector of sensor events. Our approach differs from previous work in that it explicitly aims to deal with interleaving (switching back and forth between between activities) in a principled manner, by utilising the long-term memory capabilities of a recurrent neural network cell. We present our approach and test it on a realistic dataset to evaluate its performance. Our results show the viability of the approach and that it shows promise for further investigation. We believe this is a useful direction to consider in accounting for the continually changing nature of behaviours

Avermectins differentially affect ethanol intake and receptor function: implications for developing new therapeutics for alcohol use disorders

Abstract Our laboratory is investigating ivermectin (IVM) and other members of the avermectin family as new pharmacotherapeutics to prevent and/or treat alcohol use disorders (AUDs). Earlier work found that IVM significantly reduced ethanol intake in mice and that this effect likely reflects IVM's ability to modulate ligand-gated ion channels. We hypothesized that structural modifications that enhance IVM's effects on key receptors and/or increase its brain concentration should improve its anti-alcohol efficacy. We tested this hypothesis by comparing the abilities of IVM and two other avermectins, abamectin (ABM) and selamectin (SEL), to reduce ethanol intake in mice, to alter modulation of GABA A Rs and P2X 4 Rs expressed in Xenopus oocytes and to increase their ability to penetrate the brain. IVM and ABM significantly reduced ethanol intake and antagonized the inhibitory effects of ethanol on P2X 4 R function. In contrast, SEL did not affect either measure, despite achieving higher brain concentrations than IVM and ABM. All three potentiated GABA A R function. These findings suggest that chemical structure and effects on receptor function play key roles in the ability of avermectins to reduce ethanol intake and that these factors are more important than brain penetration alone. The direct relationship between the effect of these avermectins on P2X 4 R function and ethanol intake suggest that the ability to antagonize ethanol-mediated inhibition of P2X 4 R function may be a good predictor of the potential of an avermectin to reduce ethanol intake and support the use of avermectins as a platform for developing novel drugs to prevent and/or treat AUDs

Expanding Stereochemical and Skeletal Diversity Using Petasis Reactions and 1,3-Dipolar Cycloadditions

A short and modular synthetic pathway using intramolecular 1,3-dipolar cycloaddition reactions and yielding functionalized isoxazoles, isoxazolines, and isoxazolidines is described. The change in shape of previous compounds and those in this study is quantified and compared using principal moment-of-inertia shape analysis.Chemistry and Chemical Biolog

Requirements Analysis for an Open Research Knowledge Graph

Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get an overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KGs) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective by presenting a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications and outline possible solutions.Comment: Accepted for publishing in 24th International Conference on Theory and Practice of Digital Libraries, TPDL 202

Docosahexaenoic Acid-Derived Neuroprotectin D1 Induces Neuronal Survival via Secretase- and PPARγ-Mediated Mechanisms in Alzheimer's Disease Models

Neuroprotectin D1 (NPD1) is a stereoselective mediator derived from the omega-3 essential fatty acid docosahexaenoic acid (DHA) with potent inflammatory resolving and neuroprotective bioactivity. NPD1 reduces Aβ42 peptide release from aging human brain cells and is severely depleted in Alzheimer's disease (AD) brain. Here we further characterize the mechanism of NPD1's neurogenic actions using 3xTg-AD mouse models and human neuronal-glial (HNG) cells in primary culture, either challenged with Aβ42 oligomeric peptide, or transfected with beta amyloid precursor protein (βAPP)sw (Swedish double mutation APP695sw, K595N-M596L). We also show that NPD1 downregulates Aβ42-triggered expression of the pro-inflammatory enzyme cyclooxygenase-2 (COX-2) and of B-94 (a TNF-α-inducible pro-inflammatory element) and apoptosis in HNG cells. Moreover, NPD1 suppresses Aβ42 peptide shedding by down-regulating β-secretase-1 (BACE1) while activating the α-secretase ADAM10 and up-regulating sAPPα, thus shifting the cleavage of βAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway. Use of the thiazolidinedione peroxisome proliferator-activated receptor gamma (PPARγ) agonist rosiglitazone, the irreversible PPARγ antagonist GW9662, and overexpressing PPARγ suggests that the NPD1-mediated down-regulation of BACE1 and Aβ42 peptide release is PPARγ-dependent. In conclusion, NPD1 bioactivity potently down regulates inflammatory signaling, amyloidogenic APP cleavage and apoptosis, underscoring the potential of this lipid mediator to rescue human brain cells in early stages of neurodegenerations