Many-box locality

There is an ongoing search for a physical or operational definition for quantum mechanics. Several informational principles have been proposed which are satisfied by a theory less restrictive than quantum mechanics. Here, we introduce the principle of "many-box locality", which is a refined version of the previously proposed "macroscopic locality". These principles are based on coarse-graining the statistics of several copies of a given box. The set of behaviors satisfying many-box locality for $N$ boxes is denoted $MBL_N$. We study these sets in the bipartite scenario with two binary measurements, in relation with the sets $\mathcal{Q}$ and $\mathcal{Q}_{1+AB}$ of quantum and "almost quantum" correlations. We find that the $MBL_N$ sets are in general not convex. For unbiased marginals, by working in the Fourier space we can prove analytically that $MBL_{N}\subsetneq\mathcal{Q}$ for any finite $N$, while $MBL_{\infty}=\mathcal{Q}$. Then, with suitably developed numerical tools, we find an example of a point that belongs to $MBL_{16}$ but not to $\mathcal{Q}_{1+AB}$. Among the problems that remain open, is whether $\mathcal{Q}\subset MBL_{\infty}$.Comment: 10 pages, 4 figures, 2 ancillary files; v2: similar to published versio

Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data

Long non-coding RNA, microRNA, and messenger RNA enable key regulations of various biological processes through a variety of diverse interaction mechanisms. Identifying the interactions and cross-talk between these heterogeneous RNA classes is essential in order to uncover the functional role of individual RNA transcripts, especially for unannotated and newly-discovered RNA sequences with no known interactions. Recently, sequence-based deep learning and network embedding methods are becoming promising approaches that can either predict RNA-RNA interactions from a sequence or infer missing interactions from patterns that may exist in the network topology. However, the majority of these methods have several limitations, eg, the inability to perform inductive predictions, to distinguish the directionality of interactions, or to integrate various sequence, interaction, and annotation biological datasets. We proposed a novel deep learning-based framework, rna2rna, which learns from RNA sequences to produce a low-dimensional embedding that preserves the proximities in both the interactions topology and the functional affinity topology. In this proposed embedding space, we have designated a two-part" source and target contexts" to capture the targeting and receptive fields of each RNA transcript, while encapsulating the heterogenous cross-talk interactions between lncRNAs and miRNAs. From experimental results, our method exhibits superior performance in AUPR rates compared to state-of-art approaches at predicting missing interactions in different RNA-RNA interaction databases and was shown to accurately perform link predictions to novel RNA sequences not seen at training time, even without any prior information. Additional results suggest that our proposed framework can capture a manifold for heterogeneous RNA sequences to discover novel functional annotations

Elliptic periods for finite fields

We construct two new families of basis for finite field extensions. Basis in the first family, the so-called elliptic basis, are not quite normal basis, but they allow very fast Frobenius exponentiation while preserving sparse multiplication formulas. Basis in the second family, the so-called normal elliptic basis are normal basis and allow fast (quasi linear) arithmetic. We prove that all extensions admit models of this kind

High-fat feeding reprograms maternal energy metabolism and induces long-term postpartum obesity in mice.

BackgroundExcessive gestational weight gain (EGWG) closely associates with postpartum obesity. However, the causal role of EGWG in postpartum obesity has not been experimentally verified. The objective of this study was to determine whether and how EGWG causes long-term postpartum obesity.MethodsC57BL/6 mice were fed with high-fat diet during gestation (HFFDG) or control chow, then their body composition and energy metabolism were monitored after delivery.ResultsWe found that HFFDG significantly increased gestational weight gain. After delivery, adiposity of HFFDG-treated mice (Preg-HF) quickly recovered to the levels of controls. However, 3 months after parturition, Preg-HF mice started to gain significantly more body fat even with regular chow. The increase of body fat of Preg-HF mice was progressive with aging and by 9 months after delivery had increased 2-fold above the levels of controls. The expansion of white adipose tissue (WAT) of Preg-HF mice was manifested by hyperplasia in visceral fat and hypertrophy in subcutaneous fat. Preg-HF mice developed low energy expenditure and UCP1 expression in interscapular brown adipose tissue (iBAT) in later life. Although blood estrogen concentrations were similar between Preg-HF and control mice, a significant decrease in estrogen receptor α (ERα) expression and hypermethylation of the ERα promoter was detected in the fat of Preg-HF mice 9 months after delivery. Interestingly, hypermethylation of ERα promoter and low ERα expression were only detected in adipocyte progenitor cells in both iBAT and WAT of Preg-HF mice at the end of gestation.ConclusionsThese results demonstrate that HFFDG causes long-term postpartum obesity independent of early postpartum fat retention. This study also suggests that HFFDG adversely programs long-term postpartum energy metabolism by epigenetically reducing estrogen signaling in both BAT and WAT

Inference of SNP-Gene Regulatory Networks by Integrating Gene Expressions and Genetic Perturbations

In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data

Homogenization of Thin and Thick Metamaterials and Applications

The wave propagation in structures involving metamaterials can be described owing to homogenization approaches which allow to replace the material structured at the subwavelength scale by an equivalent and simpler, effective medium. In its simplest form, homogenization predicts that the equivalent medium is homogeneous and anisotropic and it is associated to the usual relations of continuity for the electric and magnetic fields at the boundaries of the metamaterial structure. However, such prediction has a range of validity which remains limited to relatively thick devices and it is not adapted to more involved geometries (notably three-dimensional). The following two aspects are considered: (i) we study how the homogenization at the leading order can be improved when the thickness of the device becomes small and (ii) we propose a heuristic extension of the solution given by the leading order homogenization in order to deal with a complex geometry; in the latter case, an application to a demultiplexer device is proposed