Emerging Artificial Societies Through Learning

The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs.Artificial Societies, Evolution of Language, Decision Trees, Peer-To-Peer Networks, Social Learning

The Epstein-Barr Virus Glycoprotein gp150 Forms an Immune-Evasive Glycan Shield at the Surface of Infected Cells

Cell-mediated immunity plays a key role in host control of viral infection. This is exemplified by life-threatening reactivations of e.g. herpesviruses in individuals with impaired T-cell and/or iNKT cell responses. To allow lifelong persistence and virus production in the face of primed immunity, herpesviruses exploit immune evasion strategies. These include a reduction in viral antigen expression during latency and a number of escape mechanisms that target antigen presentation pathways. Given the plethora of foreign antigens expressed in virus-producing cells, herpesviruses are conceivably most vulnerable to elimination by cell-mediated immunity during the replicative phase of infection. Here, we show that a prototypic herpesvirus, Epstein-Barr virus (EBV), encodes a novel, broadly acting immunoevasin, gp150, that is expressed during the late phase of viral replication. In particular, EBV gp150 inhibits antigen presentation by HLA class I, HLA class II, and the non-classical, lipid-presenting CD1d molecules. The mechanism of gp150-mediated T-cell escape does not depend on degradation of the antigen-presenting molecules nor does it require gp150’s cytoplasmic tail. Through its abundant glycosylation, gp150 creates a shield that impedes surface presentation of antigen. This is an unprecedented immune evasion mechanism for herpesviruses. In view of its likely broader target range, gp150 could additionally have an impact beyond escape of T cell activation. Importantly, B cells infected with a gp150-null mutant EBV displayed rescued levels of surface antigen presentation by HLA class I, HLA class II, and CD1d, supporting an important role for iNKT cells next to classical T cells in fighting EBV infection. At the same time, our results indicate that EBV gp150 prolongs the timespan for producing viral offspring at the most vulnerable stage of the viral life cycle

Optimized low-dose combinatorial drug treatment boosts selectivity and efficacy of colorectal carcinoma treatment.

The current standard of care for colorectal cancer (CRC) is a combination of chemotherapeutics, often supplemented with targeted biological drugs. An urgent need exists for improved drug efficacy and minimized side effects, especially at late-stage disease. We employed the phenotypically driven therapeutically guided multidrug optimization (TGMO) technology to identify optimized drug combinations (ODCs) in CRC. We identified low-dose synergistic and selective ODCs for a panel of six human CRC cell lines also active in heterotypic 3D co-culture models. Transcriptome sequencing and phosphoproteome analyses showed that the mechanisms of action of these ODCs converged toward MAP kinase signaling and cell cycle inhibition. Two cell-specific ODCs were translated to in vivo mouse models. The ODCs reduced tumor growth by ~80%, outperforming standard chemotherapy (FOLFOX). No toxicity was observed for the ODCs, while significant side effects were induced in the group treated with FOLFOX therapy. Identified ODCs demonstrated significantly enhanced bioavailability of the individual components. Finally, ODCs were also active in primary cells from CRC patient tumor tissues. Taken together, we show that the TGMO technology efficiently identifies selective and potent low-dose drug combinations, optimized regardless of tumor mutation status, outperforming conventional chemotherapy

Targeting the tumor vasculature to enhance T cell activity.

T cells play a critical role in tumor immune surveillance as evidenced by extensive mouse-tumor model studies as well as encouraging patient responses to adoptive T cell therapies and dendritic cell vaccines. It is well established that the interplay of tumor cells with their local cellular environment can trigger events that are immunoinhibitory to T cells. More recently it is emerging that the tumor vasculature itself constitutes an important barrier to T cells. Endothelial cells lining the vessels can suppress T cell activity, target them for destruction, and block them from gaining entry into the tumor in the first place through the deregulation of adhesion molecules. Here we review approaches to break this tumor endothelial barrier and enhance T cell activity

A different cup of TI? The added value of commercial threat intelligence

Commercial threat intelligence is thought to provide unmatched coverage on attacker behavior, but it is out of reach for many organizations due to its hefty price tag. This paper presents the first empirical assessment of the services of commercial threat intelligence providers. For two leading vendors, we describe what these services consist of and compare their indicators with each other. There is almost no overlap between them, nor with four large open threat intelligence feeds. Even for 22 specific threat actors – which both vendors claim to track – we find an average overlap of only 2.5% to 4.0% between the indicator feeds. The small number of overlapping indicators show up in the feed of the other vendor with a delay of, on average, a month. These findings raise questions on the coverage and timeliness of paid threat intelligence.We also conducted 14 interviews with security professionals that use paid threat intelligence. We find that value in this market is understood differently than prior work on quality metrics has assumed. Poor coverage and small volume appear less of a problem to customers. They seem to be optimizing for the workflow of their scarce resource – analyst time – rather than for the detection of threats. Respondents evaluate TI mostly through informal processes and heuristics, rather than the quantitative metrics that research has proposed

SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions

MOTIVATION: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here, we present a webserver that implements this method efficiently. RESULTS: With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than 10-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry. AVAILABILITY AND IMPLEMENTATION: Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.SCOPUS: ar.jDecretOANoAutActifinfo:eu-repo/semantics/publishe

SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions

MOTIVATION: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random-forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here we present a webserver that implements this method efficiently. RESULTS: With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than ten-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry. AVAILABILITY: Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/

A different cup of TI? The added value of commercial threat intelligence

Commercial threat intelligence is thought to provide unmatched coverage on attacker behavior, but it is out of reach for many organizations due to its hefty price tag. This paper presents the first empirical assessment of the services of commercial threat intelligence providers. For two leading vendors, we describe what these services consist of and compare their indicators with each other. There is almost no overlap between them, nor with four large open threat intelligence feeds. Even for 22 specific threat actors – which both vendors claim to track – we find an average overlap of only 2.5% to 4.0% between the indicator feeds. The small number of overlapping indicators show up in the feed of the other vendor with a delay of, on average, a month. These findings raise questions on the coverage and timeliness of paid threat intelligence.We also conducted 14 interviews with security professionals that use paid threat intelligence. We find that value in this market is understood differently than prior work on quality metrics has assumed. Poor coverage and small volume appear less of a problem to customers. They seem to be optimizing for the workflow of their scarce resource – analyst time – rather than for the detection of threats. Respondents evaluate TI mostly through informal processes and heuristics, rather than the quantitative metrics that research has proposed.Organisation and Governanc

MammaPrint and BluePrint comprehensively capture the cancer hallmarks in early‐stage breast cancer patients

International audienc