Ulvan Activates Chicken Heterophils and Monocytes Through Toll-Like Receptor 2 and Toll-Like Receptor 4

Responsiveness to invasive pathogens, clearance via the inflammatory response, and activation of appropriate acquired responses are all coordinated by innate host defenses. Toll-like receptor (TLR) ligands are potent immune-modulators with profound effects on the generation of adaptive immune responses. This property is being exploited in TLR-based vaccines and therapeutic agents in chickens. However, for administering the TLR agonist, all previous studies used in ovo, intra-muscular or intra-venous routes that cannot be performed in usual farming conditions, thus highlighting the need for TLR ligands that display systemic immune effects when given orally (per os). Here we have demonstrated that an ulvan extract of Ulva armoricana is able to activate avian heterophils and monocytes in vitro. Using specific inhibitors, we have evidenced that ulvan may be a new ligand for TLR2 and TLR4; and that they regulate heterophil activation in slightly different manner. Moreover, activation of heterophils as well as of monocytes leads to release pro-inflammatory cytokines, including interleukin1-β, interferon α and interferon γ, through pathways that we partly identified. Finally, when given per os to animals ulvan induces heterophils and monocytes to be activated in vivo thus leading to a transient release of pro-inflammatory cytokines with plasma concentrations returning toward baseline levels at day 3

Detection and dynamics of circulating tumor cells in patients with high-risk prostate cancer treated with radiotherapy and hormones: a prospective phase II study

BACKGROUND: Circulating tumor cells (CTCs) are an established prognostic marker in castration-resistant prostate cancer but have received little attention in localized high-risk disease. We studied the detection rate of CTCs in patients with high-risk prostate cancer before and after androgen deprivation therapy and radiotherapy to assess its value as a prognostic and monitoring marker. PATIENTS AND METHODS: We performed a prospective analysis of CTCs in the peripheral blood of 65 treatment-naive patients with high-risk prostate cancer. EpCAM-positive CTCs were enumerated using the CELLSEARCH system at 4 timepoints. A cut off of 0 vs >/= 1 CTC/7.5 ml blood was defined as a threshold for negative versus positive CTCs status. RESULTS: CTCs were detected in 5/65 patients (7.5%) at diagnosis, 8/62 (12.9%) following neoadjuvant androgen deprivation and 11/59 (18.6%) at the end of radiotherapy, with a median CTC count/7.5 ml of 1 (range, 1-136). Only 1 patient presented a positive CTC result 9 months after radiotherapy. Positive CTC status (at any timepoint) was not significantly associated with any clinical or pathologic factors. However, when we analyzed variations in CTC patterns following treatment, we observed a significant association between conversion of CTCs and stages T3 (P = 0.044) and N1 (P = 0.002). Detection of CTCs was not significantly associated with overall survival (P > 0.40). CONCLUSIONS: Our study showed a low detection rate for CTCs in patients with locally advanced high-risk prostate cancer. The finding of a de novo positive CTC count after androgen deprivation therapy is probably due to a passive mechanism associated with the destruction of the tumor. Further studies with larger samples and based on more accurate detection of CTCs are needed to determine the potential prognostic and therapeutic value of this approach in non-metastatic prostate cancer. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT01800058

Artificial Intelligence, Machine Learning and Modeling for Understanding the Oceans and Climate Change

International audienceThe ongoing transformation of climate and biodiversity will have a drastic impact on almost all forms of life in the ocean with further consequences on food security, ecosystem services in coastal and inland communities. Despite these impacts, scientific data and infrastructures are still lacking to understand and quantify the consequences of these perturbations on the marine ecosystem. Understanding this phenomenon is not only an urgent but also a scientifically demanding task. Consequently, it is a problem that must be addressed with a tific cohort approach, where multi-disciplinary teams collaborate to bring the best of different scientific areas. In this proposal paper, we describe our newly launched four-years project focusedon developing new artificial intelligence, machine learning, and mathematical modeling tools to contribute to the understanding of the structure, functioning, and underlying mechanisms and dynamics of the global ocean symbiome and its relation with climate change. These actions should enable the understanding of our oceans and predict and mitigate the consequences of climate and biodiversity changes

A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci.

We conducted a multi-stage, genome-wide association study of bladder cancer with a primary scan of 591,637 SNPs in 3,532 affected individuals (cases) and 5,120 controls of European descent from five studies followed by a replication strategy, which included 8,382 cases and 48,275 controls from 16 studies. In a combined analysis, we identified three new regions associated with bladder cancer on chromosomes 22q13.1, 19q12 and 2q37.1: rs1014971, (P = 8 × 10⁻¹²) maps to a non-genic region of chromosome 22q13.1, rs8102137 (P = 2 × 10⁻¹¹) on 19q12 maps to CCNE1 and rs11892031 (P = 1 × 10⁻⁷) maps to the UGT1A cluster on 2q37.1. We confirmed four previously identified genome-wide associations on chromosomes 3q28, 4p16.3, 8q24.21 and 8q24.3, validated previous candidate associations for the GSTM1 deletion (P = 4 × 10⁻¹¹) and a tag SNP for NAT2 acetylation status (P = 4 × 10⁻¹¹), and found interactions with smoking in both regions. Our findings on common variants associated with bladder cancer risk should provide new insights into the mechanisms of carcinogenesis

Search for New Physics in e mu X Data at D0 Using Sleuth: A Quasi-Model-Independent Search Strategy for New Physics

We present a quasi-model-independent search for the physics responsible for electroweak symmetry breaking. We define final states to be studied, and construct a rule that identifies a set of relevant variables for any particular final state. A new algorithm ("Sleuth") searches for regions of excess in those variables and quantifies the significance of any detected excess. After demonstrating the sensitivity of the method, we apply it to the semi-inclusive channel e mu X collected in 108 pb^-1 of ppbar collisions at sqrt(s) = 1.8 TeV at the D0 experiment during 1992-1996 at the Fermilab Tevatron. We find no evidence of new high p_T physics in this sample.Comment: 23 pages, 12 figures. Submitted to Physical Review

Ratio of the Isolated Photon Cross Sections at \sqrt{s} = 630 and 1800 GeV

The inclusive cross section for production of isolated photons has been measured in \pbarp collisions at $\sqrt{s} = 630$ GeV with the \D0 detector at the Fermilab Tevatron Collider. The photons span a transverse energy ($E_T$) range from 7-49 GeV and have pseudorapidity $|\eta| < 2.5$. This measurement is combined with to previous \D0 result at $\sqrt{s} = 1800$ GeV to form a ratio of the cross sections. Comparison of next-to-leading order QCD with the measured cross section at 630 GeV and ratio of cross sections show satisfactory agreement in most of the $E_T$ range.Comment: 7 pages. Published in Phys. Rev. Lett. 87, 251805, (2001

A new insight for monitoring ungulates : density surface modelling of roe deer in a Mediterranean habitat

We would like to thank the University of Aveiro (Department of Biology) and FCT/MEC for the financial support to CESAM RU (UID/AMB/50017) through national funds and, where applicable, co-financed by the FEDER, within the PT2020 Partnership Agreement. TAM is partially funded by FCT, Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013.Ungulates are especially difficult to monitor, and population estimates are challenging to obtain; nevertheless, such information is fundamental for effective management. This is particularly important for expanding species such as roe deer (Capreolus capreolus), whose populations dramatically increased in number and geographic distribution over the last decades. In an attempt to follow population trends and assess species ecology, important methodological advances were recently achieved by combining line or point sampling with geographic information systems (GIS). In this study, we combined density surface modelling (DSM) with line transect survey to predict roe deer density in northeastern Portugal. This was based on modelling pellet group counts as a function of environmental factors while taking into account the probability of detecting pellets and conversion factors to relate pellet density to animal density. We estimated a global density of 3.01 animals/100 ha (95 % CI 0.37–3.51) with a 32.82 % CV. Roe deer densities increased with increasing distance to roads as well as with higher percentage of cover areas and decreased with increasing distance to human populations. This recently developed spatial method can be advantageous to predict density over space through the identification of key factors influencing species abundance. Furthermore, surface maps for subset areas will enable to visually depict abundance distribution of wild populations. This will enable the assessment of areas where ungulate impacts should be minimized, allowing an adaptive management through time.PostprintPeer reviewe

Fine-Tuning Enhancer Models to Predict Transcriptional Targets across Multiple Genomes

Networks of regulatory relations between transcription factors (TF) and their target genes (TG)- implemented through TF binding sites (TFBS)- are key features of biology. An idealized approach to solving such networks consists of starting from a consensus TFBS or a position weight matrix (PWM) to generate a high accuracy list of candidate TGs for biological validation. Developing and evaluating such approaches remains a formidable challenge in regulatory bioinformatics. We perform a benchmark study on 34 Drosophila TFs to assess existing TFBS and cis-regulatory module (CRM) detection methods, with a strong focus on the use of multiple genomes. Particularly, for CRM-modelling we investigate the addition of orthologous sites to a known PWM to construct phyloPWMs and we assess the added value of phylogenentic footprinting to predict contextual motifs around known TFBSs. For CRM-prediction, we compare motif conservation with network-level conservation approaches across multiple genomes. Choosing the optimal training and scoring strategies strongly enhances the performance of TG prediction for more than half of the tested TFs. Finally, we analyse a 35th TF, namely Eyeless, and find a significant overlap between predicted TGs and candidate TGs identified by microarray expression studies. In summary we identify several ways to optimize TF-specific TG predictions, some of which can be applied to all TFs, and others that can be applied only to particular TFs. The ability to model known TF-TG relations, together with the use of multiple genomes, results in a significant step forward in solving the architecture of gene regulatory networks