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Background. The Revised International Staging system (R-ISS) is the standard risk stratification model used for newly diagnosed (ND) multiple myeloma (MM) (Palumbo et al. JCO 2015). R-ISS identifies 3 groups of patients (pts) with different PFS and OS. However, 60% of pts are considered as intermediate-risk (R-ISS II), possibly including pts with different risk of progression/death. Recently, 1q copy number alterations (CNAs), which were not included in the R-ISS, proved to be a poor prognostic factor in NDMM pts. The European Myeloma Network (EMN), under the umbrella of the HARMONY project, collected more than 7000 patient data from European clinical trials. The aim of this analysis is to revise the R-ISS risk stratification model, by analyzing the prognostic value of each single baseline risk feature, including 1q CNAs, to improve prognostication in NDMM pts. Methods. Data from 15 European clinical trials enrolling NDMM pts from 2005 to 2014 were collected through EMN and registered in HARMONY platform. HARMONY is a European public-private partnership focusing on hematologic malignancies with unmet medical needs, including MM. OMOP Common Data Model was used to harmonize data. All pts received an immunomodulatory agent (IMiD) and/or a proteasome inhibitor (PI) upfront. In a multivariate Cox regression analysis adjusted for age, sex and therapy, we evaluated the impact of each risk feature on overall survival (OS) and progression-free survival (PFS). The hazard of death conferred by the most significant variables was used to create an additive risk score. Results. 7077 NDMM pts were registered in the HARMONY platform and included in the analysis. Data were mature with a median follow-up of 75 months; median age was 62 years. The majority of pts were transplant-eligible (65%). 40% of the pts received IMiDs only, 15% PIs only, 46% both drug classes during their first-line treatment. In a multivariate Cox model, ISS (II vs I HR 1.55 p These prognostic variables were simultaneously present in 2227 pts and the most frequent reason of exclusion of the remaining pts was 1q CNAs that was missing in some of the trials. We exploited the OS impact of these risk features in pts with complete data to create an additive scoring system (Table 1). Pts were then stratified into 4 groups: Low [(n=429 (19.3%), score 0)], Low-Intermediate [(n=686 (30.8%), score 0.5-1], Intermediate-High [(n=917 (41.2%), score 1.5-2.5] and High [(n=195 (8.8%), score 3-5]. Each group showed significantly different OS (Figure 1A) and PFS (Figure 1B). Median OS was not reached vs 109.2 vs 68.5 vs 37.9 months and median PFS was 68 vs 45.5 vs 30.2 vs 19.9 months in the above 4 risk groups, respectively. With this new stratification model, R-ISS stage II pts (n=1372) were better distributed into Low-Intermediate (n=517), Intermediate-High (n=811) and High risk (n=44) groups, confirming that this wide group included heterogeneous pts with a different risk of progression and/or death. This new risk stratification maintained its prognostic value in subgroup analysis of transplant-eligible and transplant-ineligible pts and in pts receiving IMiDs, PIs or both. Conclusion. This analysis on a large number of patient data collected thanks to a well-established European collaboration demonstrated that the existing R-ISS stratification model may be improved. This additive scoring system based on the impact of single risk features could be the future risk stratification model for NDMM, so called "R2-ISS". About half of the pts can be classified as Low or Low-Intermediate risk and about half of the pts can be classified as Intermediate-High or High risk, representing an opportunity to design risk-adapted approaches in a meaningful number of pts. Moreover, such additive risk score easily allows the inclusion of new prognostic variables in the future as they continue to emerge. The inclusion of new patient data is ongoing, and validation in an independent cohort is planned. Disclosures D'Agostino: GSK: Membership on an entity's Board of Directors or advisory committees. Waage:Janssen: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy; Shire: Honoraria. Zamagni:Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Speakers Bureau; Takeda: Honoraria, Other: Travel, Accommodations, Expenses, Speakers Bureau; Celgene Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Speakers Bureau; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Speakers Bureau. Mateos:Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen-Cilag: Consultancy, Honoraria; PharmaMar-Zeltia: Consultancy; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Regeneron: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie/Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Consultancy; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Larocca:Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Honoraria. van de Donk:Takeda: Other: Ad Board; Genentech: Other: Ad Board; Bayer: Other: Ad Board; BMS: Other: Ad Board, Research Funding; Amgen: Other: Ad Board, Research Funding; Celgene: Other: Ad Board, Research Funding; Novartis: Other: Ad Board; Janssen: Other: Ad Board, Research Funding. Cairns:Celgene, Amgen, Merck: Research Funding; Celgene: Other: Travel Support. Salwender:Takeda: Honoraria; Bristol-Myers Squibb/Celgene: Honoraria; Janssen-Cilag: Honoraria; Amgen: Honoraria; Oncopeptides: Honoraria; Sanofi: Honoraria; GlaxoSmithKline: Honoraria; AbbVie: Honoraria. Blade Creixenti:Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Dürig:Janssen: Consultancy; AbbVie: Consultancy; Celgene: Consultancy. Bringhen:Karyopharm: Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Zweegman:Sanofi: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Cavo:AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel accomodations, Speakers Bureau; GlaxoSmithKline: Honoraria, Speakers Bureau; Karyopharm: Honoraria; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel accomodations, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Goldschmidt:Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; Dietmar-Hopp-Foundation: Other: Grants and/or provision of Investigational Medicinal Product:; Molecular Partners: Research Funding; Merck Sharp and Dohme (MSD): Research Funding; Novartis: Honoraria, Research Funding; Johns Hopkins University: Other: Grants and/or provision of Investigational Medicinal Product; Chugai: Honoraria, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; Incyte: Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; University Hospital Heidelberg, Internal Medicine V and National Center for Tumor Diseases (NCT), Heidelberg, Germany: Current Employment; GlaxoSmithKline (GSK): Honoraria; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product, Research Funding; Mundipharma GmbH: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding. Cook:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; IQVIA: Research Funding; Sanofi: Consultancy; Amgen: Consultancy; Roche: Consultancy; Karyopharm: Consultancy. San-Miguel:Amgen, BMS, Celgene, Janssen, MSD, Novartis, Takeda, Sanofi, Roche, Abbvie, GlaxoSmithKline and Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees. Boccadoro:Sanofi: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; AbbVie: Honoraria; Mundipharma: Research Funding; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees. Sonneveld:Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Skyline Dx: Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy. OffLabel Disclosure: The presentation includes discussion of off-label use of a drug or drugs for the treatment of multiple myeloma.

This report comprises the first contributions from different partners on Federated Learning (FL). Aftera preliminary introductory section where the fundamental procedures and limitations are described,we detail the well-known mathematical foundation of Federated Learning for convex problems. In thiscase, we present a key algorithm, Alternating Direction Multipliers Method (ADMM), which is ableto implement in a distributed way some fundamental problems such as regression (Ridge and LASSO)and classification (Logistic Regression and Support Vector Machines (SVM)). This procedure sharesthe fundamental approach of FL, which consists of performing some local processing, sharing someintermediate information and updating the local information with some global innovation. In a secondstep we introduce the extension of this approach to non-convexproblems using Bayesian Neural Networks(BNN) where the update is based on the cooperative construction of the posterior of weightsfrom different architectures. Several sections follow where different partners provide different contributionsdescribing our first initiatives on the topic. Some preliminary code from all partners hasbeen uploaded to a common repository to start creating a pool of methods and tools to foster incomingsynergies.

This deliverable contains the initial literature mining and the first version of the Artificial Intelligence (AI) softwarerelease. The literature mining consists of a review of the most importantAI methods. The software is available for the entire consortium in the GenoMed4all GitLab repository andwill be made publicly available at a later stage in the project.

Additional file 2: Figure 1. Representation of all executions (6, 12 and 24 processors) on all 3 samples based on processors usage along time for both NPS and strategies (Fig. 3 as well).

Additional file 1: Tables 1–4. Average execution time for pipeline's tasks, for all processors configuration and for each strategy.

Additional file 3. Figure 2. Representation of all executions (6, 12 and 24 processors) on all 3 samples based on memory usage along time for both NPS and strategies (Fig. 4 as well).

Additional file 4. Figure 3. Representation of all executions (4, 8 and 16 processors) on 2 samples based on processors usage along time for both NPS and strategies (all possible 2-samples combinations are reported).

Background: The development of new genetic profiling techniques such as Next Generation Sequencing (NGS) have helped to unravel the genomic landscape of a large number of hematological diseases. In acute myeloblastic leukemia (AML), many mutations have been found at diagnosis or during the course of the disease, either alone or in combination. Nevertheless, the clinical significance of most of them has not been well established. That is particularly true regarding infrequent gene mutations and their co-mutations as they are underrepresented in most case series that have been analyzed so far. The big data platform of HARMONY alliance provides the excellent basis for addressing this problem as it assimilates clinical and genomic information about AML patients from over 100 organisations in 18 European countries comprising more than 5000 patients. Anonymised and harmonized using OMOP standards, data collected in HARMONY are optimal for studying the impact of gene-gene-interactions overcoming differences related to data providers. Aims: To identify clinically significant genetic patterns of 2 or more concurrent mutations using the Harmony alliance AML database Methods: From the HARMONY alliance database, we selected ~3600 AML patients with NGS molecular panel analysis. We first performed survival analysis between each gene combination and then we rendered those with statistically significant differences in one easy-to-read graph using the Gephi platform (Fig. A). We then highlighted promising or unexpected associations and analyzed them one by one in greater detail. Finally, these results were validated on an independent cohort. Results: We found that the co-mutation of RAD21 (RAD21mut) in DNMT3A mutated (DNMT3Amut) AML impacted outcome compared to DNMT3Amut alone patients (Fig. B, 3-year survival, 81% vs 52%, p=0.016). However, this effect was exclusively seen in allogeneic transplant recipients. In order to identify possible bias that could be generated if RAD21mut were associated with other well-known favorable prognosis mutations, we compared the frequency of each mutation in our DNMT3Amut / RAD21mut subgroup with the global AML cohort. NPM1 co-mutation was more frequent in the DNMT3Amut / RAD21mut group (Fig. C 3, 84% of patients with NPM1 mutation (NPM1mut) vs 26% in the global cohort), potentially explaining the higher survival. Next, we tried to isolate the positive effect of NPM1 on outcome by comparing DNMT3Amut / NPM1mut patients with and without the RAD21 co-mutation. This analysis showed a favorable outcome only in RAD21mut patients compared to RAD21 wildtype (Fig. D, 3-year survival, 83% in RAD21mut / DNMT3Amut / NPM1mut vs 50% in DNMT3Amut / NPM1mut with RAD21 wildtype, p=0.016), one more time only in allogeneic transplant recipients. Finally in order to validate our results we reproduced this study from the beginning using an independent cohort of 3125 AML patients. The Gephi graph confirmed an association of DNMT3Amut / RAD21mut patients with better survival over DNMT3A alone (3 year-survival, 75% vs 37%, p Conclusions: Using the HARMONY alliance database we tested for potential gene co-mutations in AML patients, often very infrequently represented in other studies. Our data suggest that RAD21mut has a positive effect on outcome in patients receiving an allogeneic transplant with concurrent mutation of DNMT3A and NPM1. Even though NPM1mut is much more frequent in the DNMT3Amut / RAD21mut group, its association with favourable outcome seems to depend on the presence of an additional RAD21mut Keywords: AML , gene combinations, RAD21, DNMT3A, NPM1, HARMONY, big data. Figure: Graphical results. A. View obtained from the Gephi platform with the gene combinations and their effect on survival. B. Survival curves respectively of the DNMT3A+RAD21 cohort and the DNMT3A-only one. 1. Representation of the proportions of each mutation in the overall cohort (red) compared to the DNMT3A+RAD21 cohort (blue). D. Survival curves respectively of the NPM1+DNMT3A+RAD21 cohort and the NPM1+DNMT3A one. Figure 1 Figure 1. Disclosures Sobas: Novartis: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Heckman: Novartis: Research Funding; Orion Pharma: Research Funding; Celgene/BMS: Research Funding; Oncopeptides: Consultancy, Research Funding; Kronos Bio, Inc.: Research Funding. Ayala: Incyte Corporation: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astellas: Honoraria; Celgene: Honoraria. Dombret: Amgen: Honoraria, Research Funding; Incyte: Honoraria, Research Funding; Jazz Pharmaceuticals: Honoraria, Research Funding; Novartis: Research Funding; Pfizer: Honoraria, Research Funding; Servier: Research Funding; Abbvie: Honoraria; Daiichi Sankyo: Honoraria; BMS-Celgene: Honoraria. Sierra: Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz Pharmaceuticals: Research Funding; Novartis: Honoraria, Research Funding, Speakers Bureau; BMS Celgene: Honoraria, Research Funding; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Pfizer: Honoraria; Roche: Other: Educational grant; Janssen: Other: Educational grant; Amgen: Other: Educational grant; Alexion: Other: Educational grant. Mayer: Principia: Research Funding. Voso: Celgene: Consultancy, Research Funding, Speakers Bureau; Novartis: Speakers Bureau. Sanz: Helsinn Healthcare: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Boehringer Ingelheim: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses, Speakers Bureau; Gilead Sciences: Other: Travel, accommodations, and expenses; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses, Research Funding. Calado: Novartis: Current Employment. Döhner: Janssen: Honoraria, Other: Advisory Board; Jazz Roche: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Astellas: Research Funding; Agios and Astex: Research Funding; Daiichi Sankyo: Honoraria, Other: Advisory Board; Celgene/BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Gaidzik: Janssen: Speakers Bureau; Pfizer: Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Heuser: AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Research Funding; BergenBio: Research Funding; BMS/Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolremo: Membership on an entity's Board of Directors or advisory committees; Astellas: Research Funding; Bayer Pharma AG: Research Funding. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Turki: Jazz Pharma: Consultancy, Speakers Bureau; MSD: Consultancy, Speakers Bureau; CSL Behring: Consultancy. Schulze-Rath: Bayer: Current Employment. Hernández Rivas: Celgene/BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees. Bullinger: Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria; Daiichi Sankyo: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Hexal: Consultancy; Gilead: Consultancy; Abbvie: Consultancy, Honoraria; Menarini: Consultancy; Novartis: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Amgen: Honoraria; Astellas: Honoraria; Sanofi: Honoraria; Seattle Genetics: Honoraria; Bayer: Research Funding. Döhner: Jazz: Honoraria, Research Funding; Janssen: Honoraria; GEMoaB: Honoraria; Astellas: Honoraria, Research Funding; Astex: Honoraria; Agios: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding; Roche: Honoraria; Pfizer: Research Funding; Novartis: Honoraria, Research Funding; Oxford Biomedicals: Honoraria; Helsinn: Honoraria; BMS: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; AstraZeneca: Honoraria; Berlin-Chemie: Honoraria; Amgen: Honoraria, Research Funding. Ossenkoppele: Servier: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Abbvie, AGIOS, BMS/Celgene Astellas,AMGEN, Gilead,Servier,JAZZ,Servier Novartis: Consultancy, Honoraria; Jazz: Consultancy, Honoraria; BMS/Celgene: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Gilead: Consultancy, Honoraria.

Supplemental Material, sj-pdf-1-vet-10.1177_03009858211066862 for Canine smooth muscle tumors: A clinicopathological study by Giancarlo Avallone, Valeria Pellegrino, Luisa Vera Muscatello, Paola Roccabianca, Gastone Castellani, Claudia Sala, Marco Tecilla, Paola Valenti and Giuseppe Sarli in Veterinary Pathology

Additional file 5. Figure 4. Representation of all executions (4, 8 and 16 processors) on 2 samples based on memory usage along time for both NPS and strategies (all possible 2-samples combinations are reported).

WISDoM (Wishart Distributed Matrices) is a new framework for the quantification of deviation of symmetric positive-definite matrices associated to experimental samples, like covariance or correlation matrices, from expected ones governed by the Wishart distribution WISDoM can be applied to tasks of supervised learning, like classification, in particular when such matrices are generated by data of different dimensionality (e.g. time series with same number of variables but different time sampling). We show the application of the method in two different scenarios. The first is the ranking of features associated to electro encephalogram (EEG) data with a time series design, providing a theoretically sound approach for this type of studies. The second is the classification of autistic subjects of the ABIDE study, using brain connectivity measurements., Comment: 17 pages, 6 figures, submitted to Frontiers in Neuroinformatics

Understanding the factors that control the dynamics of interacting species is a fundamental problem in ecology. The nature of the interactions among different species is usually not completely understood, but it is assumed that the species interaction plays an important role in the ecosystem properties as predicted by the niches models for an ecosystem. However, recent studies point out as the neutral hypothesis proposed by Hubbell of non-interacting species with an external source from the surrounding environment, allows to explain the relative species abundance distribution when the ecosystem has reached a stationary situation. In this paper we use the concept of fitness landscape to introduce a random dynamical model that describes the evolution of different communities near a stationary situation. The average dynamics can be associated to a system of Lotka-Volterra equations with mutualistic interactions. Then we derive a Master equation that satisfies the detailed balance condition of thermodynamical equilibria and allows to analytically compute the relative species abundance distribution near the stationary state as a multinomial negative distribution. These results suggest a possible approach to a synthetic theory that joins the niche theories and the Hubbell's neutral theory for RSA distribution.

Complex systems display anomalous diffusion, whose signature is a space/time scaling $x\sim t^��$ with $��\ne 1/2$ in the Probability Density Function (PDF). Anomalous diffusion can emerge jointly with both Gaussian, e.g., fractional Brownian motion, and power-law decaying distributions, e.g., L��vy Flights (LFs) or L��vy Walks (LWs). LFs get anomalous scaling, but also infinite position variance and also infinite energy and discontinuous velocity. LWs are based on random trapping events, resemble a L��vy-type power-law distribution that is truncated in the large displacement range and have finite moments, finite energy and discontinuous velocity. However, both LFs and LWs cannot describe friction-diffusion processes. We propose and discuss a model describing a Heterogeneous Ensemble of Brownian Particles (HEBP) based on a linear Langevin equation. We show that, for proper distributions of relaxation time and velocity diffusivity, the HEBP displays features similar to LWs, in particular power-law decaying PDF, long-range correlations and anomalous diffusion, at the same time keeping finite position moments and finite energy. The main differences between the HEBP model and two LWs are investigated, finding that, even if the PDFs are similar, they differ in three main aspects: (i) LWs are biscaling, while HEBP is monoscaling; (ii) a transition from anomalous ($��\ne 1/2$) to normal ($��= 1/2$) diffusion in the long-time regime; (iii) the power-law index of the position PDF and the space/time diffusion scaling are independent in the HEBP, while they both depend on the scaling of the inter-event time PDF in LWs. The HEBP model is derived from a friction-diffusion process, it has finite energy and it satisfies the fluctuation-dissipation theorem.

Background: The heterogeneous and poor survival group of Philadelphia negative (Ph-) B-ALL patients (pts) that doesn't have the most recurrent adult rearrangements (BCR-ABL1 t(9;22); TCF3-PBX1 t(1;19); MLL-AF4 t(4;11)) are collectively referred to as "triple negative" (Ph-/-/-) ALL. CRLF2 is frequently altered in adult B-ALL, especially in Ph-like pts (50-75% of cases). Alterations that lead, in the majority of cases, to a CRLF2 overexpression. Adult pts with CRLF2 upregulated have poor outcome and novel strategies are needed to improve it. Aims: Clustering and biological characterization of Ph-/-/- ALL (that represents 61% of adult B-ALL; Roberts KG, J Clin Oncol. 2016), considering CRLF2 overexpression event, in order to define and assess biomarkers in this subgroup to test new drugs. Patients and Methods: Gene Expression Profiling (GEP; HTA 2.0 Affymetrix) were performed on 55 Ph-/-/- ALL, 29 B-ALL Ph+ at different time point of the disease and on 7 mononuclear cell of healthy donors. Data were normalized with the Expression Console Software. Successively we cluster triple negative GEP data with our validated pipeline, based on CRLF2 upregulation and in the top ten-gene list. Ph-/-/- ALL samples were then characterized for the presence of gene fusions, Copy Number Alterations (CNAs) and mutations using different approaches (TruSight Pancancer-Illumina; MLPA and/or dMLPA-MRC-Holland; SNP Array-Affymetrix; 454 Junior-Roche and PCR). Results: Clustering our Ph-/-/- gene expression data using the impact of the 10 single genes in our cohort, we could identify a defined 2-clusters-subdivision (Gr1 and Gr2; Fig 1A). The Gr2 is characterized by CTGF, CRLF2 and CD200 (Gr2=3C-up; Fig 1B) overexpression and it represents 14.1% of all B-ALL. The Gr2 GEP is similar to Ph+ one. Fusion copy number alteration and mutational screening done, detected that 3C-Up group has a higher frequency of Ph-like associated lesions (primarily CRLF2, JAK2, IL7R mutations or deletion), that mainly affect JAK-STAT pathway. Also IKZF1 and EBF1 deletions are significantly associated to Gr2 (p=0.003; p=0.016). RAS pathway genes are highly affected in Gr1. Molecular characterization shed light on a very heterogeneous scenario especially in the group 1, suggesting the need of a more discerning clustering for this group. In spite of the small number of cases is required, preliminary Gr1 subclustering discerns MLLr and ZNF384 gene expression subgroups. Notably p53 pathway is enriched in both groups but with different deregulated genes: CHEK2 is upregulated in the group1 and CDK6 in the Gr2. CRLF2 and CD200 immunoblotting and CD200 immunohistochemistry preliminary analyses suggest that protein expression of CRFL2 and CD200 are higher in Gr2 in comparison to Gr1. Conclusions: we identified a new signature, related to CRLF2 high expression, to classify Ph-/-/- ALL B-based on 10 genes. 3C-up represents 14.1% of all B-ALL and it is characterized by a) high co-expression of three main genes: CRLF2, CTGF and CD200; b) IKZF1 deletion; c) JAK-STAT pathway mutations/fusions/deletions. Gr1 represents 46.9% of all B-ALL. Gr2 GEP similarity to Ph+ one, suggests that this Gr2 could contain Ph-like pts. This new Ph-/-/- subclassification identify new potential therapeutic targets with available drug (α-CTGF, α-CD200, CDK2, CHK2 and CDK6 inhibitors; tyrosine kinase inhibitors already effective on Ph+ and Ph-like) to test. Supported by: ELN, AIL, AIRC, project Regione-Università 2010-12 (L. Bolondi), FP7 NGS-PTL project, HARMONY project, Fondazione del Monte BO e RA project. Figure. Figure. Disclosures Cavo: Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Martinelli:Novartis: Speakers Bureau; Abbvie: Consultancy; Jazz Pharmaceuticals: Consultancy; Janssen: Consultancy; Pfizer: Consultancy, Speakers Bureau; Roche: Consultancy; Celgene: Consultancy, Speakers Bureau; Ariad/Incyte: Consultancy; Amgen: Consultancy.

In this paper we study noise-induced bistability in a specific circuit with many biological implications, namely a single-step enzymatic cycle described by Michaelis Menten equations with quasi-steady state assumption. We study the system both with a Master Equation formalism, and with the Fokker-Planck continuous approximation, characterizing the conditions in which the continuous approach is a good approximation of the exact discrete model. An analysis of the stationary distribution in both cases shows that bimodality can not occur in such a system. We discuss which additional requirements can generate stochastic bimodality, by coupling the system with a chemical reaction involving enzyme production and turnover. This extended system shows a bistable behaviour only in specific parameter windows depending on the number of molecules involved, providing hints about which should be a feasible system size in order that such a phenomenon could be exploited in real biological systems., Comment: 6 pages, 4 figures

Down Syndrome (DS) is characterized by a wide spectrum of clinical signs, which include segmental premature aging of central nervous and immune systems. Although it is well established that the causative defect of DS is the trisomy of chromosome 21, the molecular bases of its phenotype are still largely unknown. We used the Infinium HumanMethylation450 BeadChip to investigate DNA methylation patterns in whole blood from 29 DS persons, using their relatives (mothers and unaffected siblings) as controls. This family-based model allowed us to monitor possible confounding effects on DNA methylation patterns deriving from genetic and environmental factors. Although differentially methylated regions (DMRs) displayed a genome-wide distribution, they were enriched on chromosome 21. DMRs mapped in genes involved in developmental functions, including embryonic development (HOXA family) and haematological (RUNX1 and EBF4) and neuronal (NCAM1) development. Moreover, genes involved in the regulation of chromatin structure (PRMD8, KDM2B, TET1) showed altered methylation. The data also showed that several pathways are affected in DS, including PI3K-Akt signaling. In conclusion, we identified an epigenetic signature of DS that sustains a link between developmental defects and disease phenotype, including segmental premature aging.

Aging is characterized by a profound remodeling of the epigenetic architecture in terms of DNA methylation patterns. To date the most effective tool to study genome wide DNA methylation changes is Infinium HumanMethylation450 BeadChip (Infinium 450k). Despite the wealth of tools for Infinium 450k analysis, the identification of the most biologically relevant DNA methylation changes is still challenging. Here we propose an analytical pipeline to select differentially methylated regions (DMRs), tailored on microarray architecture, which is highly effective in highlighting biologically relevant results. The pipeline groups microarray probes on the basis of their localization respect to CpG islands and genic sequences and, depending on probes density, identifies DMRs through a single-probe or a region-centric approach that considers the concomitant variation of multiple adjacent CpG probes. We successfully applied this analytical pipeline on 3 independent Infinium 450k datasets that investigated age-associated changes in blood DNA methylation. We provide a consensus list of genes that systematically vary in DNA methylation levels from 0 to 100 years and that have a potentially relevant role in the aging process.

Additional file 1 : Figure S1. Procrustes analysis projecting genomic information summarized by first and second principal components onto geographic coordinates of Italian population samples. Figure S2. Decay of the length of chromosome chunks inherited by Italian population clusters from possible pairs of parental groups calculated with the GLOBETROTTER pipeline. Figure S3. PCA projecting variation of 559 ancient samples onto the genetic space defined by 239 individuals belonging to 40 modern Euro-Mediterranean populations. Figure S4. Outgroup f3 biplot comparing shared genetic drift between the N_ITA and S_ITA population clusters and, in turn, all ancient population groups included in the “modern + aDNA dataset”. Figure S5. Representation of the Insulin secretion pathway and of its components subjected to positive selection in the Italian population. Figure S6. Representation of the Mucin type O-glycan biosynthesis pathway and of its components subjected to positive selection in the S_ITA cluster. Figure S7. Representation of the Basal cell carcinoma pathway and of its components subjected to positive selection in the S_ITA cluster. Table S1. Admixture proportions inferred for N_ITA and S_ITA population clusters with the GLOBETROTTER method. Table S2. Admixture dates inferred for N_ITA and S_ITA population clusters with the GLOBETROTTER method. Table S3. Gene networks showing significant signatures of positive selection according to signet analysis performed on the obtained genome-wide distribution of DIND scores. Table S4. Gene networks showing significant signatures of positive selection according to signet analysis performed on the obtained genome-wide distribution of nSL scores. Table S5. Gene networks showing significant signatures of balancing selection according to signet analysis performed on the obtained genome-wide distribution of BALLET scores. Supplementary Results.

Additional file 1 : Figure S1. Procrustes analysis projecting genomic information summarized by first and second principal components onto geographic coordinates of Italian population samples. Figure S2. Decay of the length of chromosome chunks inherited by Italian population clusters from possible pairs of parental groups calculated with the GLOBETROTTER pipeline. Figure S3. PCA projecting variation of 559 ancient samples onto the genetic space defined by 239 individuals belonging to 40 modern Euro-Mediterranean populations. Figure S4. Outgroup f3 biplot comparing shared genetic drift between the N_ITA and S_ITA population clusters and, in turn, all ancient population groups included in the “modern + aDNA dataset”. Figure S5. Representation of the Insulin secretion pathway and of its components subjected to positive selection in the Italian population. Figure S6. Representation of the Mucin type O-glycan biosynthesis pathway and of its components subjected to positive selection in the S_ITA cluster. Figure S7. Representation of the Basal cell carcinoma pathway and of its components subjected to positive selection in the S_ITA cluster. Table S1. Admixture proportions inferred for N_ITA and S_ITA population clusters with the GLOBETROTTER method. Table S2. Admixture dates inferred for N_ITA and S_ITA population clusters with the GLOBETROTTER method. Table S3. Gene networks showing significant signatures of positive selection according to signet analysis performed on the obtained genome-wide distribution of DIND scores. Table S4. Gene networks showing significant signatures of positive selection according to signet analysis performed on the obtained genome-wide distribution of nSL scores. Table S5. Gene networks showing significant signatures of balancing selection according to signet analysis performed on the obtained genome-wide distribution of BALLET scores. Supplementary Results.

Creutzfeldt-Jakob disease (CJD) is characterized by a broad phenotypic spectrum regarding symptoms, progression, and molecular features. Current sporadic CJD (sCJD) classification recognizes six main clinical-pathological phenotypes. This work investigates the molecular basis of the phenotypic heterogeneity of prion diseases through a multi-omics analysis of the two most common sCJD subtypes: MM1 and VV2. We performed DNA target sequencing on 118 genes on a cohort of 48 CJD patients and full exome RNA sequencing on post-mortem frontal cortex tissue on a subset of this cohort. DNA target sequencing identified multiple potential genetic contributors to the disease onset and phenotype, both in terms of coding, damaging-predicted variants, and enriched groups of SNPs in the whole cohort and the two subtypes. The results highlight a different functional impairment, with VV2 associated with higher impairment of the pathways related to dopamine secretion, regulation of calcium release and GABA signaling, showing some similarities with Parkinson’s disease both on a genomic and a transcriptomic level. MM1 showed a gene expression profile with several traits shared with different neurodegenerative, without an apparent distinctive characteristic or similarities with a specific disease. In addition, integrating genomic and transcriptomic data led to the discovery of several sites of ADAR-mediated RNA editing events, confirming and expanding previous findings in animal models. On the transcriptomic level, this work represents the first application of RNA sequencing on CJD human brain samples. Here, a good clusterization of the transcriptomic profiles of the two subtypes was achieved, together with the finding of several differently impaired pathways between the two subtypes. The results add to the understanding of the molecular features associated with sporadic CJD and its most common subtypes, revealing strain-specific genetic signatures and functional similarities between VV2 and Parkinson’s disease and providing preliminary evidence of RNA editing modifications in human sCJD.

One of the main objectives of high-throughput genomics studies is to obtain a low-dimensional set of observables—a signature—for sample classification purposes (diagnosis, prognosis, stratification). Biological data, such as gene or protein expression, are commonly characterized by an up/down regulation behavior, for which discriminant-based methods could perform with high accuracy and easy interpretability. To obtain the most out of these methods features selection is even more critical, but it is known to be a NP-hard problem, and thus most feature selection approaches focuses on one feature at the time (k-best, Sequential Feature Selection, recursive feature elimination). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised network-based signature identification method. This method implements a network-based heuristic to generate one or more signatures out of the best performing feature pairs. The algorithm is easily scalable, allowing efficient computing for high number of observables ($$10^3$$ 10 3 –$$10^5$$ 10 5 ). We show applications on real high-throughput genomic datasets in which our method outperforms existing results, or is compatible with them but with a smaller number of selected features. Moreover, the geometrical simplicity of the resulting class-separation surfaces allows a clearer interpretation of the obtained signatures in comparison to nonlinear classification models.

Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system’s economic burden. Among the instrumental measurement methods, the image analysis of a wound area is becoming one of the cornerstones of chronic ulcer management. Our study aim is to develop a solid AI method based on a convolutional neural network to segment the wounds efficiently to make the work of the physician more efficient, and subsequently, to lay the foundations for the further development of more in-depth analyses of ulcer characteristics. In this work, we introduce a fully automated model for identifying and segmenting wound areas which can completely automatize the clinical wound severity assessment starting from images acquired from smartphones. This method is based on an active semi-supervised learning training of a convolutional neural network model. In our work, we tested the robustness of our method against a wide range of natural images acquired in different light conditions and image expositions. We collected the images using an ad hoc developed app and saved them in a database which we then used for AI training. We then tested different CNN architectures to develop a balanced model, which we finally validated with a public dataset. We used a dataset of images acquired during clinical practice and built an annotated wound image dataset consisting of 1564 ulcer images from 474 patients. Only a small part of this large amount of data was manually annotated by experts (ground truth). A multi-step, active, semi-supervised training procedure was applied to improve the segmentation performances of the model. The developed training strategy mimics a continuous learning approach and provides a viable alternative for further medical applications. We tested the efficiency of our model against other public datasets, proving its robustness. The efficiency of the transfer learning showed that after less than 50 epochs, the model achieved a stable DSC that was greater than 0.95. The proposed active semi-supervised learning strategy could allow us to obtain an efficient segmentation method, thereby facilitating the work of the clinician by reducing their working times to achieve the measurements. Finally, the robustness of our pipeline confirms its possible usage in clinical practice as a reliable decision support system for clinicians.

PURPOSE Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. METHODS We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed. RESULTS We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations ( SF3B1, SRSF2, and U2AF1) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1- and SRSF2-related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia–like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features. CONCLUSION Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.

In this paper we compared taxonomic results obtained by metataxonomics (16S rRNA gene sequencing) and metagenomics (whole shotgun metagenomic sequencing) to investigate their reliability for bacteria profiling, studying the chicken gut as a model system. The experimental conditions included two compartments of gastrointestinal tracts and two sampling times. We compared the relative abundance distributions obtained with the two sequencing strategies and then tested their capability to distinguish the experimental conditions. The results showed that 16S rRNA gene sequencing detects only part of the gut microbiota community revealed by shotgun sequencing. Specifically, when a sufficient number of reads is available, Shotgun sequencing has more power to identify less abundant taxa than 16S sequencing. Finally, we showed that the less abundant genera detected only by shotgun sequencing are biologically meaningful, being able to discriminate between the experimental conditions as much as the more abundant genera detected by both sequencing strategies.

In this thesis we focus on some statistical and physical methods which attempts to tackle the problem of cancer genetic heterogeneity and its relationship to higher level biological properties. The interactome allows to gain a system level view of mutational patters, providing a framework to understand how mutations act together to give rise to the cancer phenotype. Since different reconstruction of the interactome exist, in the first chapter of this thesis, we compare them from a topological perspective by analyzing their properties and we then study their overall resilience under node perturbation. Cancer stems from the impairment of one or more biological functions due to mutations of genes taking part in them. The observation that different patterns of mutations lead to different responses to treatments highlights the importance of stratifying patients based on their genetics and cytogenetic alterations. To this end, in the second chapter, we focus on hierarchical non parametric bayesian methods. Latent topic models allow to model hidden structures in the data and fit well with the hypothesis that cancer mutations impact specific gene groups in different proportions. In the second part of the chapter, we study a cohort of 2043 patients affected by Myelodysplastic Syndromes. From a more general perspective, the view of cancer as an evolutionary process, frequently implies the assumption of a direct and univocal genotype-phenotype relationship. However, as for cell differentiation, such genetic deterministic view is not always satisfactory. In the third chapter, we focus on the hypothesis of cancer as an abnormal attractor in the epigenetic landscape of the cell. We study the connection between the empirical distribution of cell in the gene expression state space with network laplacian-based manifold reconstruction techniques and their application for inferring the epigenetic landscape from data.

Purpose: In this work, we propose an implementation of the Bienenstock–Cooper–Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, but its application has remained mainly confined to neuroscience simulations and few applications in data science. Methods: To improve the convergence efficiency of the BCM model, we combine the original plasticity rule with the optimization tools of modern deep learning. By numerical simulation on standard benchmark datasets, we prove the efficiency of the BCM model in learning, memorization capacity, and feature extraction. Results: In all the numerical simulations, the visualization of neuronal synaptic weights confirms the memorization of human-interpretable subsets of patterns. We numerically prove that the selectivity obtained by BCM neurons is indicative of an internal feature extraction procedure, useful for patterns clustering and classification. The introduction of competitiveness between neurons in the same BCM network allows the network to modulate the memorization capacity of the model and the consequent model selectivity. Conclusions: The proposed improvements make the BCM model a suitable alternative to standard machine learning techniques for both feature selection and classification tasks.

Background Current high-throughput technologies—i.e. whole genome sequencing, RNA-Seq, ChIP-Seq, etc.—generate huge amounts of data and their usage gets more widespread with each passing year. Complex analysis pipelines involving several computationally-intensive steps have to be applied on an increasing number of samples. Workflow management systems allow parallelization and a more efficient usage of computational power. Nevertheless, this mostly happens by assigning the available cores to a single or few samples’ pipeline at a time. We refer to this approach as naive parallel strategy (NPS). Here, we discuss an alternative approach, which we refer to as concurrent execution strategy (CES), which equally distributes the available processors across every sample’s pipeline. Results Theoretically, we show that the CES results, under loose conditions, in a substantial speedup, with an ideal gain range spanning from 1 to the number of samples. Also, we observe that the CES yields even faster executions since parallelly computable tasks scale sub-linearly. Practically, we tested both strategies on a whole exome sequencing pipeline applied to three publicly available matched tumour-normal sample pairs of gastrointestinal stromal tumour. The CES achieved speedups in latency up to 2–2.4 compared to the NPS. Conclusions Our results hint that if resources distribution is further tailored to fit specific situations, an even greater gain in performance of multiple samples pipelines execution could be achieved. For this to be feasible, a benchmarking of the tools included in the pipeline would be necessary. It is our opinion these benchmarks should be consistently performed by the tools’ developers. Finally, these results suggest that concurrent strategies might also lead to energy and cost savings by making feasible the usage of low power machine clusters.

Nuclear Magnetic Resonance (NMR) has been a powerful and widespread tool since its birth thanks to its flexibility in assessing properties of physical systems without being invasive and without using ionizing radiations. Although applications of NMR for medical purposes have rapidly developed since the introduction of MR imaging (MRI), most of the clinical protocols retrieve qualitative information about biological tissues. Being able to retrieve also quantitative information with NMR may be beneficial to identify biomarkers for understanding and describing the pathophysiology of complex diseases in many tissues. However, established quantitative MRI (qMRI) methods require long scan times that not only can represent more exposure to image artifacts and more discomfort for the patient, but they also increase the costs of MRI protocols. To improve the clinical feasibility of quantitative NMR, one can focus on optimizing qMRI protocols to increase data acquisition efficiency, i.e. minimizing the acquisition times and maximising the number of retrieved information. Alternatively, one can focus on the application of low-cost, portable and low maintenance NMR devices in the medical field, such as single-sided devices. This Ph.D thesis presents studies that aim to advance the role of quantitative NMR in medicine using the two directions stated above. The first part of the thesis proposes a deep learning approach based on deep Fully Connected Networks (NN), for pixel-wise MR parameter prediction task in Magnetic Resonance Fingerprinting (MRF) as a solution to overcome the curse of dimensionality affecting the gold standard dictionary approach. The second part proposes a methodology to assess the trabecular bone-volume-to-total-volume (BV/TV) ratio using single-side NMR by means of NMR relaxometry measurements. Nowadays there are not well-established methodologies to assess trabecular BV/TV that are suitable for wide screening campaigns of the population at risk of bone fractures related to diseases such as osteoporosis.

We consider an ensemble of Ornstein–Uhlenbeck processes featuring a population of relaxation times and a population of noise amplitudes that characterize the heterogeneity of the ensemble. We show that the centre-of-mass like variable corresponding to this ensemble is statistically equivalent to a process driven by a non-autonomous stochastic differential equation with time-dependent drift and a white noise. In particular, the time scaling and the density function of such variable are driven by the population of timescales and of noise amplitudes, respectively. Moreover, we show that this variable is equivalent in distribution to a randomly-scaled Gaussian process, i.e., a process built by the product of a Gaussian process times a non-negative independent random variable. This last result establishes a connection with the so-called generalized grey Brownian motion and suggests application to model fractional anomalous diffusion in biological systems., ”Marco Polo Programme” (University of Bologna)

In this work a previously published bioinformatics pipeline was reimplemented across various computational platforms, and the performances of its steps evaluated. The tested environments were: (I) dedicated bioinformatics-specific server (II) low-power single node (III) HPC single node (IV) virtual machine. The pipeline was tested on a use case of the analysis of a single patient to assess single-use performances, using the same configuration of the pipeline to be able to perform meaningful comparison and search the optimal environment/hybrid system configuration for biomedical analysis. Performances were evaluated in terms of execution wall time, memory usage and energy consumption per patient. Our results show that, albeit slower, low power single nodes are comparable with other environments for most of the steps, but with an energy consumption two to four times lower. These results indicate that these environments are viable candidates for bioinformatics clusters where long term efficiency is a factor.

The feasibility of liver transplantation from old healthy donors suggests that this organ is able to preserve its functionality during aging. To explore the biological basis of this phenomenon, we characterized the epigenetic profile of liver biopsies collected from 45 healthy liver donors ranging from 13 to 90 years old using the Infinium HumanMethylation450 BeadChip. The analysis indicates that a large remodeling in DNA methylation patterns occurs, with 8,823 age-associated differentially methylated CpG probes. Notably, these age-associated changes tended to level off after the age of 60, as confirmed by Horvath's clock. Using stringent selection criteria, we further identified a DNA methylation signature of aging liver including 75 genomic regions. We demonstrated that this signature is specific for liver compared to other tissues and that it is able to detect biological age-acceleration effects associated with obesity. Finally, we combined DNA methylation measurements with available expression data. Although the intersection between the two omic characterizations was low, both approaches suggested a previously unappreciated role of epithelial-mesenchymal transition and Wnt-signaling pathways in the aging of human liver.

// Carolina Terragna 1 , Daniel Remondini 2 , Marina Martello 1 , Elena Zamagni 1 , Lucia Pantani 1 , Francesca Patriarca 3 , Annalisa Pezzi 1 , Giuseppe Levi 2 , Massimo Offidani 4 , Ilaria Proserpio 5 , Giovanni De Sabbata 6 , Paola Tacchetti 1 , Clotilde Cangialosi 7 , Fabrizio Ciambelli 8 , Clara Virginia Vigano 9 , Flores Angela Dico 1 , Barbara Santacroce 1 , Enrica Borsi 1 , Annamaria Brioli 1 , Giulia Marzocchi 1 , Gastone Castellani 2 , Giovanni Martinelli 1 , Antonio Palumbo 10 , Michele Cavo 1 1 ”Seragnoli” Institute of Hematology, Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Bologna University School of Medicine, Bologna, Italy 2 Department of Physics and Astronomy (DIFA), Bologna University, Bologna, Italy 3 Clinica Ematologica, DISM, University of Udine, Udine, Italy 4 Clinica di Ematologia, A.O.U. Ospedali Riuniti di Ancona, Ancona, Italy 5 U.O Oncologia Medica, Ospedale di Circolo e Fondazione Macchi, Varese, Italy 6 Ematologia Clinica, A.O.U. Ospedali Riuniti, Trieste, Italy 7 Hematology Division UTMO, Azienda “Ospedali Riuniti Villa Sofia-Cervello” Presidio Ospedaliero V.Cervello, Palermo, Italy 8 S.C.Oncologia Medica, A.O. Sant’Antonio Abate, Gallarate, Varese, Italy 9 Unita Operativa di Ematologia, Istituti Ospitalieri di Cremona, Cremona, Italy 10 Myeloma Unit, Division of Hematology, University of Torino, A.O.U. Citta della Salute e della Scienza di Torino, Torino, Italy Correspondence to: Michele Cavo, e-mail: michele.cavo@unibo.it Keywords: multiple myeloma, gene expression profile, SNPs, VTD, complete response Received: August 06, 2015 Accepted: September 16, 2015 Published: November 09, 2015 ABSTRACT The prime focus of the current therapeutic strategy for Multiple Myeloma (MM) is to obtain an early and deep tumour burden reduction, up to the level of complete response (CR). To date, no description of the characteristics of the plasma cells (PC) prone to achieve CR has been reported. This study aimed at the molecular characterization of PC obtained at baseline from MM patients in CR after bortezomib-thalidomide-dexamethasone (VTD) first line therapy. One hundred and eighteen MM primary tumours obtained from homogeneously treated patients were profiled both for gene expression and for single nucleotide polymorphism genotype. Genomic results were used to obtain a predictor of sensitivity to VTD induction therapy, as well as to describe both the transcription and the genomic profile of PC derived from MM with subsequent optimal response to primary induction therapy. By analysing the gene profiles of CR patients, we identified a 5-gene signature predicting CR with an overall median accuracy of 75% (range: 72%–85%). In addition, we highlighted the differential expression of a series of genes, whose deregulation might explain patients’ sensitivity to VTD therapy. We also showed that a small copy number loss, covering 606Kb on chromosome 1p22.1 was the most significantly associated with CR patients.

We characterize different cell states, related to cancer and ageing phenotypes, by a measure of entropy of network ensembles, integrating gene expression profiling values and protein interaction network topology. In our case studies, network entropy, that by definition estimates the number of possible network instances satisfying the given constraints, can be interpreted as a measure of the ‘‘parameter space’’ available to the cell. Network entropy was able to characterize specific pathological conditions: normal versus cancer cells, primary tumours that developed metastasis or relapsed, and extreme longevity samples. Moreover, this approach has been applied at different scales, from whole network to specific subnetworks (biological pathways defined on a priori biological knowledge) and single nodes (genes), allowing a deeper understanding of the cell processes involved.

We propose an extension of the cable equation by introducing a Caputo time fractional derivative. The fundamental solutions of the most common boundary problems are derived analitically via Laplace Transform, and result be written in terms of known special functions. This generalization could be useful to describe anomalous diffusion phenomena with leakage as signal conduction in spiny dendrites. The presented solutions are computed in Matlab and plotted., Comment: 10 figures. arXiv admin note: substantial text overlap with arXiv:1702.05325

The problem of biological motion is a very intriguing and topical issue. Many efforts are being focused on the development of novel modelling approaches for the description of anomalous diffusion in biological systems, such as the very complex and heterogeneous cell environment. Nevertheless, many questions are still open, such as the joint manifestation of statistical features in agreement with different models that can also be somewhat alternative to each other, e.g. continuous time random walk and fractional Brownian motion. To overcome these limitations, we propose a stochastic diffusion model with additive noise and linear friction force (linear Langevin equation), thus involving the explicit modelling of velocity dynamics. The complexity of the medium is parametrized via a population of intensity parameters (relaxation time and diffusivity of velocity), thus introducing an additional randomness, in addition to white noise, in the particle’s dynamics. We prove that, for proper distributions of these parameters, we can get both Gaussian anomalous diffusion, fractional diffusion and its generalizations., V.S. acknowledges BCAM Internship Program, Bilbao, for the financial support to her internship research period during which she developed her master’s thesis research useful for her master’s degree in Physics at University of Bologna. S.V. acknowledges the University of Bologna for the financial support through the ‘Marco Polo Programme’ for her PhD research period abroad spent at BCAM, Bilbao, useful for her PhD degree in Physics at University of Bologna. P.P. acknowledges financial support from Bizkaia Talent and European Commission through COFUND scheme, 2015 Financial Aid Program for Researchers, project number AYD–000–252 hosted at BCAM, Bilbao.

Radiation therapy is a useful and standard tumor treatment strategy. Despite recent advances in delivery of ionizing radiation, survival rates for some cancer patients are still low because of recurrence and radioresistance. This is why many novel approaches have been explored to improve radiotherapy outcome. Some strategies are focused on enhancement of accuracy in ionizing radiation delivery and on the generation of greater radiation beams, for example with a higher dose rate. In the present study we proposed an in vitro research of the biological effects of very high dose rate beam on SK-Mel28 and A375, two radioresistant human melanoma cell lines. The beam was delivered by a pulsed plasma device, a “Mather type” Plasma Focus for medical applications. We hypothesized that this pulsed X-rays generator is significantly more effective to impair melanoma cells survival compared to conventional X-ray tube. Very high dose rate treatments were able to reduce clonogenic efficiency of SK-Mel28 and A375 more than the X-ray tube and to induce a greater, less easy-to-repair DNA double-strand breaks. Very little is known about biological consequences of such dose rate. Our characterization is preliminary but is the first step toward future clinical considerations.

The amount of cellular proteins is a crucial parameter that is known to vary between cells as a function of the replicative passages, and can be important during physiological aging. The process of protein degradation is known to be performed by a series of enzymatic reactions, ranging from an initial step of protein ubiquitination to their final fragmentation by the protea- some. In this paper we propose a stochastic dynamical model of nuclear proteins concen- tration resulting from a balance between a constant production of proteins and their degradation by a cooperative enzymatic reaction. The predictions of this model are com- pared with experimental data obtained by fluorescence measurements of the amount of nuclear proteins in murine tail fibroblast (MTF) undergoing cellular senescence. Our model provides a three-parameter stationary distribution that is in good agreement with the experi- mental data even during the transition to the senescent state, where the nuclear protein concentration changes abruptly. The estimation of three parameters (cooperativity, satura- tion threshold, and maximal velocity of the reaction), and their evolution during replicative passages shows that only the maximal velocity varies significantly. Based on our modeling we speculate the reduction of functionality of the protein degradation mechanism as a possi- ble competitive inhibition of the proteasome.