A Study of the Vascular Dissemination of Tumor Cells in Cortisone-Treated Mice

IT has been reported that cortisone increases metastatic spread in mice bearing the following transplanted and induced tumors: mammary adenocarcinomas K7 (Agosin et al., 1952), DBA (Baserga and Shubik, 1954), K9, Sa and Sb (unpublished work of Vergara et al.), bladder carcinoma T150 (Baserga and Shubik, 1955), methylcholanthrene-induced squamous cell carcinoma and methylcholanthrene-induced sarcoma (Baserga and Shubik, 1954). The phenomenon, however, is not general and the following mouse transplantable tumors do not seem to be affected by treatment with cortisone: mammary adenocarcinomas Acc. 635 (Sparks et al., 1955), Eo771 and C3HBA (unpublished observations of Gasic and Gasic), Sarcoma 1 (Kaliss, Borges, and Day, 1954) an

Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces

In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. A part of this effort is the policy optimisation task, which attempts to find a policy describing how to respond to humans, in the form of a function taking the current state of the dialogue and returning the response of the system. In this paper, we investigate deep reinforcement learning approaches to solve this problem. Particular attention is given to actor-critic methods, off-policy reinforcement learning with experience replay, and various methods aimed at reducing the bias and variance of estimators. When combined, these methods result in the previously proposed ACER algorithm that gave competitive results in gaming environments. These environments however are fully observable and have a relatively small action set so in this paper we examine the application of ACER to dialogue policy optimisation. We show that this method beats the current state-of-the-art in deep learning approaches for spoken dialogue systems. This not only leads to a more sample efficient algorithm that can train faster, but also allows us to apply the algorithm in more difficult environments than before. We thus experiment with learning in a very large action space, which has two orders of magnitude more actions than previously considered. We find that ACER trains significantly faster than the current state-of-the-art.Toshiba Research Europe Ltd, Cambridge Research Laboratory - RG85875 EPSRC Research Council - RG8079

"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.Comment: 13 pages, 6 figures, IUI 201

Modulation of adipocyte G-protein expression in cancer cachexia by a lipid-mobilizing factor (LMF)

Adipocytes isolated from cachectic mice bearing the MAC 16 tumour showed over a 3-fold increase in lipolytic response to both low concentrations of isoprenaline and a tumour-derived lipid mobilizing factor (LMF). This was reflected by an enhanced stimulation of adenylate cyclase in plasma membrane fractions of adipocytes in the presence of both factors. There was no up-regulation of adenylate cyclase in response to forskolin, suggesting that the effect arose from a change in receptor number or G-protein expression. Immunoblotting of adipocyte membranes from mice bearing the MAC16 tumour showed an increased expression of Gαs up to 10% weight loss and a reciprocal decrease in Gα. There was also an increased expression of Gαs and a decrease in Gα in adipose tissue from a patient with cancer-associated weight loss compared with a non-cachectic cancer patient. The changes in G-protein expression were also seen in adipose tissue of normal mice administered pure LMF as well as in 3T3L1 adipocytes in vitro. The changes in G-protein expression induced by LMF were attenuated by the polyunsaturated fatty acid, eicosapentaenoic acid (EPA). This suggests that this tumour-derived lipolytic factor acts to sensitize adipose tissue to lipolytic stimuli, and that this effect is attenuated by EPA, which is known to preserve adipose tissue in cancer cachexia. © 2001 Cancer Research Campaig

Prunus genetics and applications after de novo genome sequencing: achievements and prospects

Prior to the availability of whole-genome sequences, our understanding of the structural and functional aspects of Prunus tree genomes was limited mostly to molecular genetic mapping of important traits and development of EST resources. With public release of the peach genome and others that followed, significant advances in our knowledge of Prunus genomes and the genetic underpinnings of important traits ensued. In this review, we highlight key achievements in Prunus genetics and breeding driven by the availability of these whole-genome sequences. Within the structural and evolutionary contexts, we summarize: (1) the current status of Prunus whole-genome sequences; (2) preliminary and ongoing work on the sequence structure and diversity of the genomes; (3) the analyses of Prunus genome evolution driven by natural and man-made selection; and (4) provide insight into haploblocking genomes as a means to define genome-scale patterns of evolution that can be leveraged for trait selection in pedigree-based Prunus tree breeding programs worldwide. Functionally, we summarize recent and ongoing work that leverages whole-genome sequences to identify and characterize genes controlling 22 agronomically important Prunus traits. These include phenology, fruit quality, allergens, disease resistance, tree architecture, and self-incompatibility. Translationally, we explore the application of sequence-based marker-assisted breeding technologies and other sequence-guided biotechnological approaches for Prunus crop improvement. Finally, we present the current status of publically available Prunus genomics and genetics data housed mainly in the Genome Database for Rosaceae (GDR) and its updated functionalities for future bioinformatics-based Prunus genetics and genomics inquiry.info:eu-repo/semantics/publishedVersio

Plant growth retardation and conserved miRNAs are correlated to hibiscus chlorotic ringspot virus infection

10.1371/journal.pone.0085476PLoS ONE812-POLN