Analysis of SARS-CoV-2 infection associated cell entry proteins ACE2, CD147, PPIA, and PPIB in datasets from non SARS-CoV-2 infected neuroblastoma patients, as potential prognostic and infection biomarkers in neuroblastoma

SARS-CoV-2 viral contagion has given rise to a worldwide pandemic. Although most children experience minor symptoms from SARS-CoV-2 infection, some have severe complications including Multisystem Inflammatory Syndrome in Children. Neuroblastoma patients may be at higher risk of severe infection as treatment requires immunocompromising chemotherapy and SARS-CoV-2 has demonstrated tropism for nervous cells. To date, there is no sufficient epidemiological data on neuroblastoma patients with SARS-CoV-2. Therefore, we evaluated datasets of non-SARS-CoV-2 infected neuroblastoma patients to assess for key genes involved with SARS-CoV-2 infection as possible neuroblastoma prognostic and infection biomarkers. We hypothesized that ACE2, CD147, PPIA and PPIB, which are associated with viral-cell entry, are potential biomarkers for poor prognosis neuroblastoma and SARS-CoV-2 infection. We have analysed three publicly available neuroblastoma gene expression datasets to understand the specific molecular susceptibilities that high-risk neuroblastoma patients have to the virus. Gene Expression Omnibus (GEO) GSE49711 and GEO GSE62564 are the microarray and RNA-Seq data, respectively, from 498 neuroblastoma samples published as part of the Sequencing Quality Control initiative. TARGET, contains microarray data from 249 samples and is part of the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative. ACE2, CD147, PPIA and PPIB were identified through their involvement in both SARS-CoV-2 infection and cancer pathogenesis. In-depth statistical analysis using Kaplan-Meier, differential gene expression, and Cox multivariate regression analysis, demonstrated that overexpression of ACE2, CD147, PPIA and PPIB is significantly associated with poor-prognosis neuroblastoma samples. These results were seen in the presence of amplified MYCN, unfavourable tumour histology and in patients older than 18 months of age. Previously, we have shown that high levels of the nerve growth factor receptor NTRK1 together with low levels of the phosphatase PTPN6 and TP53 are associated with increased relapse-free survival of neuroblastoma patients. Interestingly, low levels of expression of ACE2, CD147, PPIA and PPIB are associated with this NTRK1-PTPN6-TP53 module, suggesting that low expression levels of these genes are associated with good prognosis. These findings have implications for clinical care and therapeutic treatment. The upregulation of ACE2, CD147, PPIA and PPIB in poor-prognosis neuroblastoma samples suggests that these patients may be at higher risk of severe SARS-CoV-2 infection. Importantly, our findings reveal ACE2, CD147, PPIA and PPIB as potential biomarkers and therapeutic targets for neuroblastoma

TipTrap: A Co-located Direct Manipulation Technique for Acoustically Levitated Content

Acoustic levitation has emerged as a promising approach for mid-air displays, by using multiple levitated particles as 3D voxels, cloth and thread props, or high-speed tracer particles, under the promise of creating 3D displays that users can see, hear and feel with their bare eyes, ears and hands. However, interaction with this mid-air content always occurred at a distance, since external objects in the display volume (e.g. user's hands) can disturb the acoustic fields and make the particles fall. This paper proposes TipTrap, a co-located direct manipulation technique for acoustically levitated particles. TipTrap leverages the reflection of ultrasound on the users' skin and employs a closed-loop system to create functional acoustic traps 2.1 mm below the fingertips, and addresses its 3 basic stages: selection, manipulation and deselection. We use Finite-Differences Time Domain (FDTD) simulations to explain the principles enabling TipTrap, and explore how finger reflections and user strategies influence the quality of the traps (e.g. approaching direction, orientation and tracking errors), and use these results to design our technique. We then implement the technique, characterizing its performance with a robotic hand setup and finish with an exploration of the ability of TipTrap to manipulate different types of levitated content

Bi-Legendrian manifolds and paracontact geometry

We study the interplays between paracontact geometry and the theory of bi-Legendrian manifolds. We interpret the bi-Legendrian connection of a bi-Legendrian manifold M as the paracontact connection of a canonical paracontact structure induced on M and then we discuss many consequences of this result both for bi-Legendrian and for paracontact manifolds. Finally new classes of examples of paracontact manifolds are presented.Comment: to appear in Int. J. Geom. Meth. Mod. Phy

Proof-Pattern Recognition and Lemma Discovery in ACL2

We present a novel technique for combining statistical machine learning for proof-pattern recognition with symbolic methods for lemma discovery. The resulting tool, ACL2(ml), gathers proof statistics and uses statistical pattern-recognition to pre-processes data from libraries, and then suggests auxiliary lemmas in new proofs by analogy with already seen examples. This paper presents the implementation of ACL2(ml) alongside theoretical descriptions of the proof-pattern recognition and lemma discovery methods involved in it

On Induced Gravity in 2-d Topological Theories

We study 2-d $\phi F$ gauge theories with the objective to understand, also at the quantum level, the emergence of induced gravity. The wave functionals - representing the eigenstates of a vanishing flat potential - are obtained in the $\phi$ representation. The composition of the space they describe is then analyzed: the state corresponding to the singlet representation of the gauge group describes a topological universe. For other representations a metric which is invariant under the residual gauge group is induced, apart from possible topological obstructions. Being inherited from the group metric it is rather rigid.Comment: 38, tex, 160/93/e

Análise dos fluxos metabólicos em Saccharomyces cerevisiae a partir de D-xilulose como fonte de carbono utilizando Optflux

A viabilidade econômica da produção de etanol 2G depende da eficiente fermentação da fração hemicelulósica por S. cerevisiae. A xilulose, isômero da xilose e principal componente da hemicelulose do bagaço, pode ser convertida pela levedura em biomassa, etanol, xilitol ou outros metabólitos. A otimização da produção de etanol requer a análise do metabolismo da xilulose. Modelos metabólicos permitem efetuar simulações de sistemas biológicos, viabilizando o estudo in silico das respostas celulares perante perturbações ambientais e genéticas. Uma das técnicas mais usadas para estudos deste tipo é a Análise do Balanço de Fluxos Metabólicos (do inglês, FBA). Neste trabalho, usando o software OptFlux, foi aplicado o método “parcimonious FBA” ao modelo iND750 para estimar os fluxos metabólicos em condições de aerobiose e anaerobiose, utilizando xilulose como fonte de carbono. Os resultados das simulações foram comparados a dados experimentais e o modelo foi ajustado adicionando restrições de expressão em fluxos metabólicos da via pentose fosfato e de formação de biomassa. A produção de etanol só foi observada na condição anaeróbia, com favorecimento da seletividade para baixos fluxos de xilulose

Four-nucleon contact interactions from holographic QCD

We calculate the low energy constants of four-nucleon interactions in an effective chiral Lagrangian in holographic QCD. We start with a D4-D8 model to obtain meson-nucleon interactions and then integrate out massive mesons to obtain the four-nucleon interactions in 4D. We end up with two low energy constants at the leading order and seven of them at the next leading order, which is consistent with the effective chiral Lagrangian. The values of the low energy constants are evaluated with the first five Kaluza-Klein resonances.Comment: 28 page