What is the role of context in fair group recommendations?

We investigate the role played by the context, i.e. the situation the group is currently experiencing, in the design of a system that recommends sequences of activities as a multi-objective optimization problem, where the satisfaction of the group and the available time interval are two of the functions to be optimized. In particular, we highlight that the dynamic evolution of the group can be the key contextual feature that has to be considered to produce fair suggestions

A context-based approach for partitioning big data

In recent years, the amount of available data keeps growing at fast rate, and it is therefore crucial to be able to process them in an efficient way. The level of parallelism in tools such as Hadoop or Spark is determined, among other things, by the partitioning applied to the dataset. A common method is to split the data into chunks considering the number of bytes. While this approach may work well for text-based batch processing, there are a number of cases where the dataset contains structured information, such as the time or the spatial coordinates, and one may be interested in exploiting such a structure to improve the partitioning. This could have an impact on the processing time and increase the overall resource usage efficiency. This paper explores an approach based on the notion of context, such as temporal or spatial information, for partitioning the data. We design a context-based multi-dimensional partitioning technique that divides an n 12dimensional space into splits by considering the distribution of the each contextual dimension in the dataset. We tested our approach on a dataset from a touristic scenario, and our experiments show that we are able to improve the efficiency of the resource usage

Choice of costimulatory domains and of cytokines determines CAR T-cell activity in neuroblastoma

Chimeric antigen receptor (CAR) T-cell therapy has been shown to be dramatically effective in the treatment of B-cell malignancies. However, there are still substantial obstacles to overcome, before similar responses can be achieved in patients with solid tumors. We evaluated both in vitro and in a preclinical murine model the efficacy of different 2nd and 3rd generation CAR constructs targeting GD2, a disial-ganglioside expressed on the surface of neuroblastoma (NB) tumor cells. In order to address potential safety concerns regarding clinical application, an inducible safety switch, namely inducible Caspase-9 (iC9), was also included in the vector constructs. Our data indicate that a 3rd generation CAR incorporating CD28.4-1BB costimulatory domains is associated with improved anti-tumor efficacy as compared with a CAR incorporating the combination of CD28.OX40 domains. We demonstrate that the choice of 4-1BB signaling results into significant amelioration of several CAR T-cell characteristics, including: 1) T-cell exhaustion, 2) basal T-cell activation, 3) in vivo tumor control and 4) T-cell persistence. The fine-tuning of T-cell culture conditions obtained using IL7 and IL15 was found to be synergic with the CAR.GD2 design in increasing the anti-tumor activity of CAR T cells. We also demonstrate that activation of the suicide gene iC9, included in our construct without significantly impairing neither CAR expression nor anti-tumor activity, leads to a prompt induction of apoptosis of GD2.CAR T cells. Altogether, these findings are instrumental in optimizing the function of CAR T-cell products to be employed in the treatment of children with NB

GD2 redirected CAR T and activated NK-cell-mediated secretion of IFNγovercomes MYCN-dependent IDO1 inhibition, contributing to neuroblastoma cell immune escape

Immune escape mechanisms employed by neuroblastoma (NB) cells include secretion of immunosuppressive factors disrupting effective antitumor immunity. The use of cellular therapy to treat solid tumors needs to be implemented. Killing activity of anti-GD2 Chimeric Antigen Receptor (CAR) T or natural killer (NK) cells against target NB cells was assessed through coculture experiments and quantified by FACS analysis. ELISA assay was used to quantify interferon-gamma (IFN gamma) secreted by NK and CAR T cells. Real Time PCR and Western Blot were performed to analyze gene and protein levels modifications. Transcriptional study was performed by chromatin immunoprecipitation and luciferase reporter assays on experiments of mutagenesis on the promoter sequence. NB tissue sample were analyzed by IHC and Real Time PCR to perform correlation study. We demonstrate that Indoleamine-pyrrole 2,3-dioxygenase1 (IDO1), due to its ability to convert tryptophan into kynurenines, is involved in NB resistance to activity of immune cells. In NB, IDO1 is able to inhibit the anti-tumor effect displayed by of both anti-GD2 CAR (GD2.CAR) T-cell and NK cells, mainly by impairing their IFN gamma production. Furthermore, inhibition of MYCN expression in NB results into accumulation of IDO1 and consequently of kynurenines, which negatively affect the immune surveillance. Inverse correlation between IDO1 and MYCN expression has been observed in a wide cohort of NB samples. This finding was supported by the identification of a transcriptional repressive role of MYCN on IDO1 promoter. The evidence of IDO1 involvement in NB immune escape and its ability to impair NK and GD2.CAR T-cell activity contribute to clarify one of the possible mechanisms responsible for the limited efficacy of these immunotherapeutic approaches. A combined therapy of NK or GD2.CAR T-cells with IDO1 inhibitors, a class of compounds already in phase I/II clinical studies, could represent a new and still unexplored strategy capable to improve long-term efficacy of these immunotherapeutic approaches

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Background The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. Methods A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. Results Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. Conclusions In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

Semantic-enriched data mining techniques for intensional service representation

The adoption of Web service technologies to enable collaboration in distributed environments has been made possible by the availability of huge amount of service repositories, that, if not properly controlled, leads to information overload rather than facilitating collaboration. Data mining provides well known exploratory techniques to extract relevant and frequent information from data repositories. This paper presents a preliminary effort to apply data mining algorithms to service repositories, to properly extract useful information about services. Our purpose is two-fold: (i) we study a proper Web service representation extracted from available Web service standards, to enable the application of data mining techniques; (ii) we propose the application of data mining algorithms to infer patterns representing summarized and integrated representation of service functionalities. These patterns can be used to facilitate the formulation of service requests on the underlying repositories. Semantic heterogeneities will be also addressed, in order to improve the recall of data mining results