Deterministic entangling gates with nonlinear quantum photonic interferometers

The quantum computing paradigm in photonics currently relies on the multi-port interference in linear optical devices, which is intrinsically based on probabilistic measurements outcome and thus non-deterministic. Devising a fully deterministic, universal, and practically achievable quantum computing platform based on integrated photonic circuits is still an open challenge. Here we propose to exploit weakly nonlinear photonic devices to implement deterministic entangling quantum gates, following the definition of dual rail photonic qubits. It is shown that a universal set of single- and two-qubit gates can be designed by a suitable concatenation of few optical interferometric elements, with optimal fidelities arbitrarily close to 100% theoretically demonstrated through a bound constrained optimization algorithm. The actual realization would require the concatenation of a few tens of elementary operations, as well as on-chip optical nonlinearities that are compatible with some of the existing quantum photonic platforms, as it is finally discussed

CpGmotifs : a tool to discover DNA motifs associated to CpG methylation events

BackgroundThe investigation of molecular alterations associated with the conservation and variation of DNA methylation in eukaryotes is gaining interest in the biomedical research community. Among the different determinants of methylation stability, the DNA composition of the CpG surrounding regions has been shown to have a crucial role in the maintenance and establishment of methylation statuses. This aspect has been previously characterized in a quantitative manner by inspecting the nucleotidic composition in the region. Research in this field still lacks a qualitative perspective, linked to the identification of certain sequences (or DNA motifs) related to particular DNA methylation phenomena.ResultsHere we present a novel computational strategy based on short DNA motif discovery in order to characterize sequence patterns related to aberrant CpG methylation events. We provide our framework as a user-friendly, shiny-based application, CpGmotifs, to easily retrieve and characterize DNA patterns related to CpG methylation in the human genome. Our tool supports the functional interpretation of deregulated methylation events by predicting transcription factors binding sites (TFBS) encompassing the identified motifs.ConclusionsCpGmotifs is an open source software. Its source code is available on GitHub https://github.com/Greco-Lab/CpGmotifs and a ready-to-use docker image is provided on DockerHub at https://hub.docker.com/r/grecolab/cpgmotifs.Peer reviewe

A General Approach to Dropout in Quantum Neural Networks

In classical Machine Learning, "overfitting" is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A commonly employed technique in Machine Learning is the so called "dropout", which prevents computational units from becoming too specialized, hence reducing the risk of overfitting. With the advent of Quantum Neural Networks as learning models, overfitting might soon become an issue, owing to the increasing depth of quantum circuits as well as multiple embedding of classical features, which are employed to give the computational nonlinearity. Here we present a generalized approach to apply the dropout technique in Quantum Neural Network models, defining and analysing different quantum dropout strategies to avoid overfitting and achieve a high level of generalization. Our study allows to envision the power of quantum dropout in enabling generalization, providing useful guidelines on determining the maximal dropout probability for a given model, based on overparametrization theory. It also highlights how quantum dropout does not impact the features of the Quantum Neural Networks model, such as expressibility and entanglement. All these conclusions are supported by extensive numerical simulations, and may pave the way to efficiently employing deep Quantum Machine Learning models based on state-of-the-art Quantum Neural Networks

Knowledge Generation with Rule Induction in Cancer Omics

The explosion of omics data availability in cancer research has boosted the knowledge of the molecular basis of cancer, although the strategies for its definitive resolution are still not well established. The complexity of cancer biology, given by the high heterogeneity of cancer cells, leads to the development of pharmacoresistance for many patients, hampering the efficacy of therapeutic approaches. Machine learning techniques have been implemented to extract knowledge from cancer omics data in order to address fundamental issues in cancer research, as well as the classification of clinically relevant sub-groups of patients and for the identification of biomarkers for disease risk and prognosis. Rule induction algorithms are a group of pattern discovery approaches that represents discovered relationships in the form of human readable associative rules. The application of such techniques to the modern plethora of collected cancer omics data can effectively boost our understanding of cancer-related mechanisms. In fact, the capability of these methods to extract a huge amount of human readable knowledge will eventually help to uncover unknown relationships between molecular attributes and the malignant phenotype. In this review, we describe applications and strategies for the usage of rule induction approaches in cancer omics data analysis. In particular, we explore the canonical applications and the future challenges and opportunities posed by multi-omics integration problems.Peer reviewe

Multi-omics analysis of ten carbon nanomaterials effects highlights cell type specific patterns of molecular regulation and adaptation

New strategies to characterize the effects of engineered nanomaterials (ENMs) based on omics technologies are emerging. However, given the intricate interplay of multiple regulatory layers, the study of a single molecular species in exposed biological systems might not allow the needed granularity to successfully identify the pathways of toxicity (PoT) and, hence, portraying adverse outcome pathways (AOPs). Moreover, the intrinsic diversity of different cell types composing the exposed organs and tissues in living organisms poses a problem when transferring in vivo experimentation into cell-based in vitro systems. To overcome these limitations, we have profiled genome-wide DNA methylation, mRNA and microRNA expression in three human cell lines representative of relevant cell types of the respiratory system, A549, BEAS-2B and THP-1, exposed to a low dose of ten carbon nanomaterials (CNMs) for 48 h. We applied advanced data integration and modelling techniques in order to build comprehensive regulatory and functional maps of the CNM effects in each cell type. We observed that different cell types respond differently to the same CNM exposure even at concentrations exerting similar phenotypic effects. Furthermore, we linked patterns of genomic and epigenomic regulation to intrinsic properties of CNM. Interestingly, DNA methylation and microRNA expression only partially explain the mechanism of action (MOA) of CNMs. Taken together, our results strongly support the implementation of approaches based on multi-omics screenings on multiple tissues/cell types, along with systems biology-based multi-variate data modelling, in order to build more accurate AOPs.Peer reviewe

Unsupervised Algorithms for Microarray Sample Stratification

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.Peer reviewe

INfORM : Inference of NetwOrk Response Modules

The Summary: Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps.Peer reviewe

Toxicogenomics analysis of dynamic dose-response in macrophages highlights molecular alterations relevant for multi-walled carbon nanotube-induced lung fibrosis

Toxicogenomics approaches are increasingly used to gain mechanistic insight into the toxicity of engineered nanomaterials (ENMs). These emerging technologies have been shown to aid the translation of in vitro experimentation into relevant information on real-life exposures. Furthermore, integrating multiple layers of molecular alteration can provide a broader understanding of the toxicological insult. While there is growing evidence of the immunotoxic effects of several ENMs, the mechanisms are less characterized, and the dynamics of the molecular adaptation of the immune cells are still largely unknown. Here, we hypothesized that a multi-omics investigation of dynamic dose-dependent (DDD) molecular alterations could be used to retrieve relevant information concerning possible long-term consequences of the exposure. To this end, we applied this approach on a model of human macrophages to investigate the effects of rigid multi-walled carbon nanotubes (rCNTs). THP-1 macrophages were exposed to increasing concentrations of rCNTs and the genome-wide transcription and gene promoter methylation were assessed at three consecutive time points. The results suggest dynamic molecular adaptation with a rapid response in the gene expression and contribution of DNA methylation in the long-term adaptation. Moreover, our analytical approach is able to highlight patterns of molecular alteration in vitro that are relevant for the pathogenesis of pulmonary fibrosis, a known long-term effect of rCNTs exposure in vivo.Peer reviewe

Microarray Data Preprocessing: From Experimental Design to Differential Analysis

DNA microarray data preprocessing is of utmost importance in the analytical path starting from the experimental design and leading to a reliable biological interpretation. In fact, when all relevant aspects regarding the experimental plan have been considered, the following steps from data quality check to differential analysis will lead to robust, trustworthy results. In this chapter, all the relevant aspects and considerations about microarray preprocessing will be discussed. Preprocessing steps are organized in an orderly manner, from experimental design to quality check and batch effect removal, including the most common visualization methods. Furthermore, we will discuss data representation and differential testing methods with a focus on the most common microarray technologies, such as gene expression and DNA methylation.Peer reviewe

Evaluation of Tumor Response after Short-Course Radiotherapy and Delayed Surgery for Rectal Cancer.

PURPOSE: Neoadjuvant therapy is able to reduce local recurrence in rectal cancer. Immediate surgery after short course radiotherapy allows only for minimal downstaging. We investigated the effect of delayed surgery after short-course radiotherapy at different time intervals before surgery, in patients affected by rectal cancer. METHODS: From January 2003 to December 2013 sixty-seven patients with the following characteristics have been selected: clinical (c) stage T3N0 ≤ 12 cm from the anal verge and with circumferential resection margin > 5 mm (by magnetic resonance imaging); cT2, any N, < 5 cm from anal verge; and patients facing tumors with enlarged nodes and/or CRM+ve who resulted unfit for chemo-radiation, were also included. Patients underwent preoperative short-course radiotherapy with different interval to surgery were divided in three groups: A (within 6 weeks), B (between 6 and 8 weeks) and C (after more than 8 weeks). Hystopatolgical response to radiotherapy was measured by Mandard's modified tumor regression grade (TRG). RESULTS: All patients completed the scheduled treatment. Sixty-six patients underwent surgery. Fifty-three of which (80.3%) received a sphincter saving procedure. Downstaging occurred in 41 cases (62.1%). The analysis of subgroups showed an increasing prevalence of TRG 1-2 prolonging the interval to surgery (group A-16.7%, group B-36.8% and 54.3% in group C; p value 0.023). CONCLUSIONS: Preoperative short-course radiotherapy is able to downstage rectal cancer if surgery is delayed. A higher rate of TRG 1-2 can be obtained if interval to surgery is prolonged to more than 8 weeks