Interaction between androgen receptor and coregulator SLIRP is regulated by Ack1 tyrosine kinase and androgen

Aberrant activation of the androgen receptor (AR) may play a critical role in castration resistant prostate cancer. After ligand binding, AR is recruited to the androgen responsive element (ARE) sequences on the DNA where AR interaction with coactivators and corepressors modulates transcription. We demonstrated that phosphorylation of AR at Tyr-267 by Ack1/TNK2 tyrosine kinase results in nuclear translocation, DNA binding, and androgen-dependent gene transcription in a low androgen environment. In order to dissect downstream mechanisms, we searched for proteins whose interaction with AR was regulated by Ack1. SLIRP (SRA stem-loop interacting RNA binding protein) was identified as a candidate protein. Interaction between AR and SLIRP was disrupted by Ack1 kinase activity as well as androgen or heregulin treatment. The noncoding RNA, SRA, was required for AR-SLIRP interaction. SLIRP was bound to ARE’s of AR target genes in the absence of androgen. Treatment with androgen or heregulin led to dissociation of SLIRP from the ARE. Whole transcriptome analysis of SLIRP knockdown in androgen responsive LNCaP cells showed that SLIRP affects a significant subset of androgen-regulated genes. Our data suggest that Ack1 kinase and androgen regulate interaction between AR and SLIRP and that SLIRP functions as a coregulator of AR with properties of a corepressor in a context-dependent manner

AutoKnow: Self-driving knowledge collection for products of thousands of types

Applied Data Science Track PaperCan one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. The system includes a suite of novel techniques for taxonomy construction, product property identification, knowledge extraction, anomaly detection, and synonym discovery. AutoKnow is (a) automatic, requiring little human intervention, (b) multi-scalable, scalable in multiple dimensions (many domains, many products, and many attributes), and (c) integrative, exploiting rich customer behavior logs. AutoKnow has been operational in collecting product knowledge for over 11K product types.Xin Luna Dong, Xiang He, Andrey Kan, Xian Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, Saurabh Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao, Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang, Christos Faloutsos, Andrew McCallum, Jiawei Ha

Big GABA II: Water-referenced edited MR spectroscopy at 25 research sites

Accurate and reliable quantification of brain metabolites measured in vivo using 1H magnetic resonance spectroscopy (MRS) is a topic of continued interest. Aside from differences in the basic approach to quantification, the quantification of metabolite data acquired at different sites and on different platforms poses an additional methodological challenge. In this study, spectrally edited γ-aminobutyric acid (GABA) MRS data were analyzed and GABA levels were quantified relative to an internal tissue water reference. Data from 284 volunteers scanned across 25 research sites were collected using GABA+ (GABA + co-edited macromolecules (MM)) and MM-suppressed GABA editing. The unsuppressed water signal from the volume of interest was acquired for concentration referencing. Whole-brain T1-weighted structural images were acquired and segmented to determine gray matter, white matter and cerebrospinal fluid voxel tissue fractions. Water-referenced GABA measurements were fully corrected for tissue-dependent signal relaxation and water visibility effects. The cohort-wide coefficient of variation was 17% for the GABA + data and 29% for the MM-suppressed GABA data. The mean within-site coefficient of variation was 10% for the GABA + data and 19% for the MM-suppressed GABA data. Vendor differences contributed 53% to the total variance in the GABA + data, while the remaining variance was attributed to site- (11%) and participant-level (36%) effects. For the MM-suppressed data, 54% of the variance was attributed to site differences, while the remaining 46% was attributed to participant differences. Results from an exploratory analysis suggested that the vendor differences were related to the unsuppressed water signal acquisition. Discounting the observed vendor-specific effects, water-referenced GABA measurements exhibit similar levels of variance to creatine-referenced GABA measurements. It is concluded that quantification using internal tissue water referencing is a viable and reliable method for the quantification of in vivo GABA levels

Liposomes in Biology and Medicine

Drug delivery systems (DDS) have become important tools for the specific delivery of a large number of drug molecules. Since their discovery in the 1960s liposomes were recognized as models to study biological membranes and as versatile DDS of both hydrophilic and lipophilic molecules. Liposomes--nanosized unilamellar phospholipid bilayer vesicles--undoubtedly represent the most extensively studied and advanced drug delivery vehicles. After a long period of research and development efforts, liposome-formulated drugs have now entered the clinics to treat cancer and systemic or local fungal infections, mainly because they are biologically inert and biocompatible and practically do not cause unwanted toxic or antigenic reactions. A novel, up-coming and promising therapy approach for the treatment of solid tumors is the depletion of macrophages, particularly tumor associated macrophages with bisphosphonate-containing liposomes. In the advent of the use of genetic material as therapeutic molecules the development of delivery systems to target such novel drug molecules to cells or to target organs becomes increasingly important. Liposomes, in particular lipid-DNA complexes termed lipoplexes, compete successfully with viral gene transfection systems in this field of application. Future DDS will mostly be based on protein, peptide and DNA therapeutics and their next generation analogs and derivatives. Due to their versatility and vast body of known properties liposome-based formulations will continue to occupy a leading role among the large selection of emerging DDS

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

The impact of insect herbivory on biogeochemical cycling in broadleaved forests varies with temperature

Herbivorous insects alter biogeochemical cycling within forests, but the magnitude of these impacts, their global variation, and drivers of this variation remain poorly understood. To address this knowledge gap and help improve biogeochemical models, we established a global network of 74 plots within 40 mature, undisturbed broadleaved forests. We analyzed freshly senesced and green leaves for carbon, nitrogen, phosphorus and silica concentrations, foliar production and herbivory, and stand-level nutrient fluxes. We show more nutrient release by insect herbivores at non-outbreak levels in tropical forests than temperate and boreal forests, that these fluxes increase strongly with mean annual temperature, and that they exceed atmospheric deposition inputs in some localities. Thus, background levels of insect herbivory are sufficiently large to both alter ecosystem element cycling and influence terrestrial carbon cycling. Further, climate can affect interactions between natural populations of plants and herbivores with important consequences for global biogeochemical cycles across broadleaved forests

Study of Z → llγ decays at √s = 8 TeV with the ATLAS detector

This paper presents a study of Z → llγ decays with the ATLAS detector at the Large Hadron Collider. The analysis uses a proton–proton data sample corresponding to an integrated luminosity of 20.2 fb−1 collected at a centre-ofmass energy √s = 8 TeV. Integrated fiducial cross-sections together with normalised differential fiducial cross-sections, sensitive to the kinematics of final-state QED radiation, are obtained. The results are found to be in agreement with stateof-the-art predictions for final-state QED radiation. First measurements of Z → llγ γ decays are also reported

Search for leptoquark pair production decaying into te−te¯ + or tμ−t¯μ+ in multi-lepton final states in pp collisions at √s = 13 TeV with the ATLAS detector

A search for leptoquark pair production decaying into te−te¯ + or tμ−t¯μ+ in final states with multiple leptons is presented. The search is based on a dataset of pp collisions at √s = 13 TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb−1. Four signal regions, with the requirement of at least three light leptons (electron or muon) and at least two jets out of which at least one jet is identified as coming from a b-hadron, are considered based on the number of leptons of a given flavour. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No excess above the Standard Model background prediction is observed and 95% confidence level limits on the production cross section times branching ratio are derived as a function of the leptoquark mass. Under the assumption of exclusive decays into te− (tμ−), the corresponding lower limit on the scalar mixed-generation leptoquark mass mLQd mix is at 1.58 (1.59) TeV and on the vector leptoquark mass mU˜1 at 1.67 (1.67) TeV in the minimal coupling scenario and at 1.95 (1.95) TeV in the Yang–Mills scenario

Deep generative models for fast photon shower simulation in ATLAS

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques