13 research outputs found
An Interpretable Boosting-based Predictive Model for Transformation Temperatures of Shape Memory Alloys
In this study, we demonstrate how the incorporation of appropriate feature
engineering together with the selection of a Machine Learning (ML) algorithm
that best suits the available dataset, leads to the development of a predictive
model for transformation temperatures that can be applied to a wide range of
shape memory alloys. We develop a gradient boosting ML surrogate model capable
of predicting Martensite Start, Martensite Finish, Austenite Start, and
Austenite Finish transformation temperatures with an average accuracy of more
than 95% by explicitly taking care of potential distribution changes when
modeling different alloy systems. We included heat treatment, rolling,
extrusion processing parameters, and alloy system categorical features in the
model input features to achieve more accurate and realistic results. In
addition, using Shapley values, which are calculated based on the average
marginal contribution of features to all possible coalitions, this study was
able to gain insights into the governing features and their effect on predicted
transformation temperatures, providing a unique opportunity to examine the
critical parameters and features in martensite transformation temperatures
An artificial intelligence tool for heterogeneous team formation in the classroom
Nowadays, there is increasing interest in the development of teamwork skills
in the educational context. This growing interest is motivated by its
pedagogical effectiveness and the fact that, in labour contexts, enterprises
organize their employees in teams to carry out complex projects. Despite its
crucial importance in the classroom and industry, there is a lack of support
for the team formation process. Not only do many factors influence team
performance, but the problem becomes exponentially costly if teams are to be
optimized. In this article, we propose a tool whose aim it is to cover such a
gap. It combines artificial intelligence techniques such as coalition structure
generation, Bayesian learning, and Belbin's role theory to facilitate the
generation of working groups in an educational context. This tool improves
current state of the art proposals in three ways: i) it takes into account the
feedback of other teammates in order to establish the most predominant role of
a student instead of self-perception questionnaires; ii) it handles uncertainty
with regard to each student's predominant team role; iii) it is iterative since
it considers information from several interactions in order to improve the
estimation of role assignments. We tested the performance of the proposed tool
in an experiment involving students that took part in three different team
activities. The experiments suggest that the proposed tool is able to improve
different teamwork aspects such as team dynamics and student satisfaction
31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two
Background
The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd.
Methods
We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background.
Results
First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001).
Conclusions
In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival
Non-oncogenic Acute Viral Infections Disrupt Anti-cancer Responses and Lead to Accelerated Cancer-Specific Host Death
In light of increased cancer prevalence and cancer-specific deaths in patients with infections, we investigated whether infections alter anti-tumor immune responses. We report that acute influenza infection of the lung promotes distal melanoma growth in the dermis and leads to accelerated cancer-specific host death. Furthermore, we show that during influenza infection, anti-melanoma CD8+ T cells are shunted from the tumor to the infection site, where they express high levels of the inhibitory receptor programmed cell death protein 1 (PD-1). Immunotherapy to block PD-1 reverses this loss of anti-tumor CD8+ T cells from the tumor and decreases infection-induced tumor growth. Our findings show that acute non-oncogenic infection can promote cancer growth, raising concerns regarding acute viral illness sequelae. They also suggest an unexpected role for PD-1 blockade in cancer immunotherapy and provide insight into the immune response when faced with concomitant challenges