Bayesian nonparametric forecasting of monotonic functional time series

We propose a Bayesian nonparametric approach to modelling and predicting a class of functional time series with application to energy markets, based on fully observed, noise-free functional data. Traders in such contexts conceive profitable strategies if they can anticipate the impact of their bidding actions on the aggregate demand and supply curves, which in turn need to be predicted reliably. Here we propose a simple Bayesian nonparametric method for predicting such curves, which take the form of monotonic bounded step functions. We borrow ideas from population genetics by defining a class of interacting particle systems to model the functional trajectory, and develop an implementation strategy which uses ideas from Markov chain Monte Carlo and approximate Bayesian computation techniques and allows to circumvent the intractability of the likelihood. Our approach shows great adaptation to the degree of smoothness of the curves and the volatility of the functional series, proves to be robust to an increase of the forecast horizon and yields an uncertainty quantification for the functional forecasts. We illustrate the model and discuss its performance with simulated datasets and on real data relative to the Italian natural gas market.Comment: To appear on the Electronic Journal of Statistic

Experimental quantum key distribution with finite-key security analysis for noisy channels

In quantum key distribution implementations, each session is typically chosen long enough so that the secret key rate approaches its asymptotic limit. However, this choice may be constrained by the physical scenario, as in the perspective use with satellites, where the passage of one terminal over the other is restricted to a few minutes. Here we demonstrate experimentally the extraction of secure keys leveraging an optimal design of the prepare-and-measure scheme, according to recent finite-key theoretical tight-bounds. The experiment is performed in different channel conditions, and assuming two distinct attack models: individual attacks, or general quantum attacks. The request on the number of exchanged qubits is then obtained as a function of the key size and of the ambient quantum bit error rate. The results indicate that viable conditions for effective symmetric, and even one-time-pad, cryptography are achievable.Comment: 20 pages, 4 figure

Classical processing algorithms for Quantum Information Security

In this thesis, we investigate how the combination of quantum physics and information theory could deliver solutions at the forefront of information security, and, in particular, we consider two focus applications: randomness extraction as applied to quantum random number generators and classical processing algorithms for quantum key distribution (QKD). We concentrate on practical applications for such tools. We detail the implementation of a randomness extractor for a commercial quantum random number generator, and we evaluate its performance based on information theory. Then, we focus on QKD as applied to a specific experimental scenario, that is, the one of free-space quantum links. Commercial solutions with quantum links operating over optical fibers, in fact, already exist, but suffer from severe infrastructure complexity and cost overheads. Free-space QKD allows for a higher flexibility, for both terrestrial and satellite links, whilst experiencing higher attenuation and noise at the receiver. In this work, its feasibility is investigated and proven in multiple experiments over links of different length, and in various channel conditions. In particular, after a thorough analysis of information reconciliation protocols, we consider finite-key effects as applied to key distillation, and we propose a novel adaptive real-time selection algorithm which, by leveraging the turbulence of the channel as a resource, extends the feasibility of QKD to new noise thresholds. By using a full-fledged software for classical processing tailored for the considered application scenario, the obtained results are analyzed and validated, showing that quantum information security can be ensured in realistic conditions with free-space quantum links

Targeting epigenetic mechanisms in gastric cancer

Gastric cancer (GC) is a hard challenge for medical oncology, with globally over one million of new diagnoses each year and low survival rates. Gastric carcinogenesis is guided by the interaction of several risk factors, exerting through sequential histopathologic steps, including chronic gastritis, atrophic gastritis, intestinal metaplasia, dysplasia and cancer. GC is classified on the basis of anatomical, histological or molecular classification, reflecting the wide cancer heterogeneity, also highlighted by the inefficacy of the actual treatment schedules. Epigenetic mechanisms alterations affecting DNA methylation, histone methylation and acetylation, are a recognized hallmark of cancer and stand at the basis of gastric carcinogenesis and tumor development. The pharmacological targeting of these altered mechanisms is an attractive option for new cancer treatments. Aim of this study was to test the therapeutic potential of the compound CM-272 for GC, a selective and strong dual inhibitor of DNMT1 and EHMT2, which reached important results in pre-clinical models of other gastrointestinal malignancies. Moreover, in a GC patients case series, the expression of the target of the compound was tested, to prove the rationale for inhibition of DNMT1, EHMT2 and their functional adaptor were over-expressed in the majority of GC patients tissues. Through in-vitro testing of CM-272 alone and in combination with the most used chemotherapeutic treatments for GC in a panel of GC cell lines, this study demonstrated that the compound has a strong ability in inhibiting GC cells growth. Even though not directly inducing apoptosis, CM-272 was able to induce a senescent phenotype in GC cells, and to epigenetically reprogram the transcription of genes involved in phosphorylation cascades and mitochondria metabolism, thus affecting the growth and energetic machinery of cancer cells. In conclusion, the pharmacological targeting of epigenetic mechanisms demonstrated good potential pre-clinical models of GC, and further investigations to test in-vivo efficacy are needed

Bayesian Functional Forecasting with Locally-Autoregressive Dependent Processes

International audienceMotivated by the problem of forecasting demand and offer curves, we introduce a class of nonparametric dynamic models with locally-autoregressive behaviour, and provide a full inferential strategy for forecasting time series of piecewise-constant non-decreasing functions over arbitrary time horizons. The model is induced by a non Markovian system of interacting particles whose evolution is governed by a resampling step and a drift mechanism. The former is based on a global interaction and accounts for the volatility of the functional time series, while the latter is determined by a neighbourhood-based interaction with the past curves and accounts for local trend behaviours, separating these from pure noise. We discuss the implementation of the model for functional forecasting by combining a population Monte Carlo and a semi-automatic learning approach to approximate Bayesian computation which require limited tuning. We validate the inference method with a simulation study, and carry out predictive inference on a real dataset on the Italian natural gas market

Adaptive real time selection for quantum key distribution in lossy and turbulent free-space channels

The unconditional security in the creation of cryptographic keys obtained by quantum key distribution (QKD) protocols will induce a quantum leap in free-space communication privacy in the same way that we are beginning to realize secure optical fiber connections. However, free-space channels, in particular those with long links and the presence of atmospheric turbulence, are affected by losses, fluctuating transmissivity, and background light that impair the conditions for secure QKD. Here we introduce a method to contrast the atmospheric turbulence in QKD experiments. Our adaptive real time selection (ARTS) technique at the receiver is based on the selection of the intervals with higher channel transmissivity. We demonstrate, using data from the Canary Island 143-km free-space link, that conditions with unacceptable average quantum bit error rate which would prevent the generation of a secure key can be used once parsed according to the instantaneous scintillation using the ARTS technique

Epigenetic Mechanisms in Gastric Cancer: Potential New Therapeutic Opportunities

: Gastric cancer (GC) is one of the deadliest malignancies worldwide. Complex disease heterogeneity, late diagnosis, and suboptimal therapies result in the poor prognosis of patients. Besides genetic alterations and environmental factors, it has been demonstrated that alterations of the epigenetic machinery guide cancer onset and progression, representing a hallmark of gastric malignancies. Moreover, epigenetic mechanisms undergo an intricate crosstalk, and distinct epigenomic profiles can be shaped under different microenvironmental contexts. In this scenario, targeting epigenetic mechanisms could be an interesting therapeutic strategy to overcome gastric cancer heterogeneity, and the efforts conducted to date are delivering promising results. In this review, we summarize the key epigenetic events involved in gastric cancer development. We conclude with a discussion of new promising epigenetic strategies for gastric cancer treatment

Radiofrequency Ablation of hepatocellular carcinoma: a meta-analysis of overall survival and recurrence-free survival

Background and aims So far, no randomized trial or meta-analysis has been conducted on overall survival (OS) and recurrence-free survival (RFS) factors in patients treated with radiofrequency ablation (RFA) alone. The purpose of this meta-analysis was to evaluate prognostic factors of OS and RFS in patients treated with RFA. Methods A primary analysis was planned to evaluate the clinical prognostic factor of OS. RFS was the secondary aim. Thirty-four studies published from 2003 to 2017 were analyzed. They included 11,216 hepatocellular carcinoma patients. Results The results showed that Child\u2013Pugh B vs Child\u2013Pugh A (HR =2.32; 95% CI: 2.201\u20132.69; P<0.0001) and albumin\u2013bilirubin score 1 vs 0 (HR =2.69; 95% CI: 2.10\u20133.44; P<0.0001) were predictive of poor OS. Tumor size as a continuous variable was not predictive of OS, although it was predictive of OS when we considered the size as a cutoff value (.2 cm vs <2 cm: HR =1.41; 95% CI: 1.23\u20131.61; P<0.0001; >3 cm vs <3 cm: HR =1.43; 95% CI: 1.17\u20131.74; P<0.0001) and in presence of >1 nodule (HR =1.59; 95% CI: 1.46\u20131.74; P<0.0001). Alpha-fetoprotein >20 ng/mL (HR =1.46; 95% CI: 1.25\u20131.70; P<0.0001) was the only predictive factor of poor prognosis. Conclusion Our meta-analysis highlighted that the maximum benefit of RFA in terms of OS and RFS is reached in the presence of Child\u2013Pugh A, albumin\u2013bilirubin score 1, single-nodule tumor sized <2 cm, and alpha-fetoprotein <20 ng/mL

Molecular insights into cell toxicity of a novel familial amyloidogenic variant of β2-microglobulin

The first genetic variant of β(2)‐microglobulin (b2M) associated with a familial form of systemic amyloidosis has been recently described. The mutated protein, carrying a substitution of Asp at position 76 with an Asn (D76N b2M), exhibits a strongly enhanced amyloidogenic tendency to aggregate with respect to the wild‐type protein. In this study, we characterized the D76N b2M aggregation path and performed an unprecedented analysis of the biochemical mechanisms underlying aggregate cytotoxicity. We showed that, contrarily to what expected from other amyloid studies, early aggregates of the mutant are not the most toxic species, despite their higher surface hydrophobicity. By modulating ganglioside GM1 content in cell membrane or synthetic lipid bilayers, we confirmed the pivotal role of this lipid as aggregate recruiter favouring their cytotoxicity. We finally observed that the aggregates bind to the cell membrane inducing an alteration of its elasticity (with possible functional unbalance and cytotoxicity) in GM1‐enriched domains only, thus establishing a link between aggregate‐membrane contact and cell damage