Complex sequential data analysis: A systematic literature review of existing algorithms

This paper provides a review of past approaches to the use of deep-learning frameworks for the analysis of discrete irregularpatterned complex sequential datasets. A typical example of such a dataset is financial data where specific events trigger sudden irregular changes in the sequence of the data. Traditional deep-learning methods perform poorly or even fail when trying to analyse these datasets. The results of a systematic literature review reveal the dominance of frameworks based on recurrent neural networks

Multilayer structures of graphene and Pt nanoparticles: a multiscale computational study

Multiscale simulation study results of multilayer structures consisting of graphene sheets with embedded Pt nanoparticles is reported. Density functional theory is used to understand the energetics of Pt–graphene interfaces and provide reference data for the parameterization of a Pt–graphene interaction potential. Molecular dynamics simulations then provide the conformation and energetics of graphene sheets with embedded Pt nanoparticles of varying density, form, and size. These results are interpreted using a continuum mechanical model of sheet deformation, and serve to parameterize a meso‐scale Monte Carlo model to investigate the question under which conditions the free volume around the Pt nanoparticles forms a percolating cluster, such that the structures can be used in catalytic applications. This article is concluded with a discussion of potential applications of such multilayer structures

Biomassa e estoques de nutrientes em vegetação de pousio sob diferentes manejos em sistema agroflorestal seqüencial de corte-e-trituração na Amazônia Oriental.

O manejo da vegetação de pousio é importante para manutenção da produtividade em sistemas agroflorestais seqüenciais. Durante o período de pousio, o sistema acumula nutrientes para as culturas agrícolas subseqüentes. A introdução de espécies leguminosas associadas à adubação fosfatada de baixa solubilidade pode promover o acúmulo de biomassa e os estoques de nutrientes influenciando positivamente na produtividade das culturas agrícolas. O estudo da biomassa e dos estoques de nutrientes nesses agroecossistemas pode fornecer subsídios para o seu manejo. Este artigo compara estimativas da biomassa e estoques de nutrientes de três vegetações de pousio submetidos a diferentes tratamentos: (1) pousio espontâneo; (2) pousio enriquecido com leguminosas arbóreas (Sclerolobium paniculatum Vogel e Inga edulis Mart.), e (3) pousio enriquecido com leguminosas arbóreas submetidas à adubação fosfatada de baixa solubilidade. O experimento foi conduzido por 23 meses, em um sistema agroflorestal seqüencial de corte-e-trituração no município de Marapanim, Amazônia Oriental. Os resultados mostraram que o sistema de pousio enriquecido com leguminosas arbóreas, submetidas ou não à adubação fosfatada de baixa solubilidade, acumula maiores massas secas e estoques de nutrientes que o sistema com pousio espontâneoEditores técnicos: Roberto Porro, Milton Kanashiro, Maria do Socorro Gonçalves Ferreira, Leila Sobral Sampaio e Gladys Ferreira de Sousa

MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response

Predicting clinical outcome is remarkably important but challenging. Research efforts have been paid on seeking significant biomarkers associated with the therapy response or/and patient survival. However, these biomarkers are generally costly and invasive, and possibly dissatifactory for novel therapy. On the other hand, multi-modal, heterogeneous, unaligned temporal data is continuously generated in clinical practice. This paper aims at a unified deep learning approach to predict patient prognosis and therapy response, with easily accessible data, e.g., radiographics, laboratory and clinical information. Prior arts focus on modeling single data modality, or ignore the temporal changes. Importantly, the clinical time series is asynchronous in practice, i.e., recorded with irregular intervals. In this study, we formalize the prognosis modeling as a multi-modal asynchronous time series classification task, and propose a MIA-Prognosis framework with Measurement, Intervention and Assessment (MIA) information to predict therapy response, where a Simple Temporal Attention (SimTA) module is developed to process the asynchronous time series. Experiments on synthetic dataset validate the superiory of SimTA over standard RNN-based approaches. Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy. The proposed method achieves promising performance on predicting the immunotherapy response. Notably, our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.Comment: MICCAI 2020 (Early Accepted; Student Travel Award

Getting aligned on representational alignment

Biological and artificial information processing systems form representations that they can use to categorize, reason, plan, navigate, and make decisions. How can we measure the extent to which the representations formed by these diverse systems agree? Do similarities in representations then translate into similar behavior? How can a system's representations be modified to better match those of another system? These questions pertaining to the study of representational alignment are at the heart of some of the most active research areas in cognitive science, neuroscience, and machine learning. For example, cognitive scientists measure the representational alignment of multiple individuals to identify shared cognitive priors, neuroscientists align fMRI responses from multiple individuals into a shared representational space for group-level analyses, and ML researchers distill knowledge from teacher models into student models by increasing their alignment. Unfortunately, there is limited knowledge transfer between research communities interested in representational alignment, so progress in one field often ends up being rediscovered independently in another. Thus, greater cross-field communication would be advantageous. To improve communication between these fields, we propose a unifying framework that can serve as a common language between researchers studying representational alignment. We survey the literature from all three fields and demonstrate how prior work fits into this framework. Finally, we lay out open problems in representational alignment where progress can benefit all three of these fields. We hope that our work can catalyze cross-disciplinary collaboration and accelerate progress for all communities studying and developing information processing systems. We note that this is a working paper and encourage readers to reach out with their suggestions for future revisions.Comment: Working paper, changes to be made in upcoming revision

The interaction between the proliferating macroalga Asparagopsis taxiformis and the coral Astroides calycularis induces changes in microbiome and metabolomic fingerprints

Mediterranean Sea ecosystems are considered as hotspots of biological introductions, exposed to possible negative effects of non-indigenous species. In such temperate marine ecosystems, macroalgae may be dominant, with a great percentage of their diversity represented by introduced species. Their interaction with temperate indigenous benthic organisms have been poorly investigated. To provide new insights, we performed an experimental study on the interaction between the introduced proliferative red alga Asparagopsis taxiformis and the indigenous Mediterranean coral Astroides calycularis. The biological response measurements included meta-barcoding of the associated microbial communities and metabolomic fingerprinting of both species. Significant changes were detected among both associated microbial communities, the interspecific differences decreasing with stronger host interaction. No short term effects of the macroalga on the coral health, neither on its polyp activity or its metabolism, were detected. In contrast, the contact interaction with the coral induced a change in the macroalgal metabolomic fingerprint with a significant increase of its bioactivity against the marine bacteria Aliivibrio fischeri. This induction was related to the expression of bioactive metabolites located on the macroalgal surface, a phenomenon which might represent an immediate defensive response of the macroalga or an allelopathic offense against coral.ERA-NET Biome project "SEAPROLIF"; CNRS; Provence Alpes Cote d'Azur Region; TOTAL Fundation; Fundacao para a Ciencia e a Tecnologia (FCT) [Netbiome/0002/2011]; FCT fellowships [SFRH/BPD/63703/2009, SFRH/BPD/107878/2015]info:eu-repo/semantics/publishedVersio

Targeting vascular endothelial growth factor receptor 2 and protein kinase d1 related pathways by a multiple kinase inhibitor in angiogenesis and inflammation related processes in vitro.

Emerging evidence suggests that the vascular endothelial growth factor receptor 2 (VEGFR2) and protein kinase D1 (PKD1) signaling axis plays a critical role in normal and pathological angiogenesis and inflammation related processes. Despite all efforts, the currently available therapeutic interventions are limited. Prior studies have also proved that a multiple target inhibitor can be more efficient compared to a single target one. Therefore, development of novel inflammatory pathway-specific inhibitors would be of great value. To test this possibility, we screened our molecular library using recombinant kinase assays and identified the previously described compound VCC251801 with strong inhibitory effect on both VEGFR2 and PKD1. We further analyzed the effect of VCC251801 in the endothelium-derived EA.hy926 cell line and in different inflammatory cell types. In EA.hy926 cells, VCC251801 potently inhibited the intracellular activation and signaling of VEGFR2 and PKD1 which inhibition eventually resulted in diminished cell proliferation. In this model, our compound was also an efficient inhibitor of in vitro angiogenesis by interfering with endothelial cell migration and tube formation processes. Our results from functional assays in inflammatory cellular models such as neutrophils and mast cells suggested an anti-inflammatory effect of VCC251801. The neutrophil study showed that VCC251801 specifically blocked the immobilized immune-complex and the adhesion dependent TNF-alpha -fibrinogen stimulated neutrophil activation. Furthermore, similar results were found in mast cell degranulation assay where VCC251801 caused significant reduction of mast cell response. In summary, we described a novel function of a multiple kinase inhibitor which strongly inhibits the VEGFR2-PKD1 signaling and might be a novel inhibitor of pathological inflammatory pathways