Multiparametric cardiac magnetic resonance in patients with thalassemia intermedia: new insights from the E-MIOT network.

PURPOSE: In a relatively large cohort of thalassemia intermedia (TI) patients, we systematically investigated myocardial iron overload (MIO), function, and replacement fibrosis using cardiac magnetic resonance (CMR), we assessed the clinical determinants of global heart T2* values, and we explored the association between multiparametric CMR findings and cardiac complications. MATERIALS AND METHODS: We considered 254 beta-TI patients (43.14 ± 13.69 years, 138 females) consecutively enrolled in the Extension-Myocardial Iron Overload in Thalassemia project. MIO was quantified by T2* technique and biventricular function and atrial areas by cine images. Macroscopic myocardial fibrosis was detected by late gadolinium enhancement technique. RESULTS: Compared to never/sporadically transfused patients, regularly transfused (RT)-TI patients exhibited significantly lower global heart T2* values, biventricular end-diastolic volume indexes, left ventricular mass index, and cardiac index. In RT-TI patients, age and serum ferritin levels were the strongest predictors of global heart T2* values. Independently from the transfusional state, cardiac T2* values were not associated with biventricular function. Of the 103 (40.6%) patients in whom the contrast medium was administrated, 27 (26.2%) had replacement myocardial fibrosis. Age, sex distribution, cardiac iron, and biventricular function parameters were comparable between patients without and without replacement myocardial fibrosis. Twenty-five (9.8%) patients had a history of cardiac complications (heart failure and arrhythmias). Increased age and replacement myocardial fibrosis emerged as significant risk markers for cardiac complications. CONCLUSIONS: In TI, regular transfusions are associated with less pronounced cardiac remodeling but increase the risk of MIO. Replacement myocardial fibrosis is a frequent finding associated with cardiac complications.

Prognostic Role of Multiparametric Cardiac Magnetic Resonance in Neo Transfusion-Dependent Thalassemia.

BACKGROUND: We prospectively evaluated the predictive value of multiparametric cardiac magnetic resonance (CMR) for cardiovascular complications in non-transfusion-dependent ß-thalassemia (ß-NTDT) patients who started regular transfusions in late childhood/adulthood (neo ß-TDT). METHODS: We considered 180 patients (38.25 ± 11.24 years; 106 females). CMR was used to quantify cardiac iron overload, biventricular function, and atrial dimensions, and to detect left ventricular (LV) replacement fibrosis. RESULTS: During a mean follow-up of 76.87 ± 41.60 months, 18 (10.0%) cardiovascular events were recorded: 2 heart failures, 13 arrhythmias (10 supraventricular), and 3 cases of pulmonary hypertension. Right ventricular (RV) end-diastolic volume index (EDVI), RV mass index (MI), LV replacement fibrosis, and right atrial (RA) area index emerged as significant univariate prognosticators of cardiovascular complications. The low number of events prevented us from performing a multivariable analysis including all univariable predictors simultaneously. Firstly, a multivariable analysis including the two RV size parameters (mass and volume) was carried out, and only the RV MI was proven to independently predict cardiovascular diseases. Then, a multivariable analysis, including RV MI, RA atrial area, and LV replacement fibrosis, was conducted. In this model, RV MI and LV replacement fibrosis emerged as independent predictors of cardiovascular outcomes (RV MI: hazard ratio (HR) = 1.18; LV replacement fibrosis: HR = 6.26). CONCLUSIONS: Our results highlight the importance of CMR in cardiovascular risk stratification.

Uveal melanoma: Recent advances in immunotherapy.

Uveal melanoma (UM) is the most common primary intraocular cancer in adults. The incidence in Europe and the United States is 6-7 per million population per year. Although most primary UMs can be successfully treated and locally controlled by irradiation therapy or local tumor resection, up to 50% of UM patients develop metastases that usually involve the liver and are fatal within 1 year. To date, chemotherapy and targeted treatments only obtain minimal responses in patients with metastatic UM, which is still characterized by poor prognosis. No standard therapeutic approaches for its prevention or treatment have been established. The application of immunotherapy agents, such as immune checkpoint inhibitors that are effective in cutaneous melanoma, has shown limited effects in the treatment of ocular disease. This is due to UM's distinct genetics, natural history, and complex interaction with the immune system. Unlike cutaneous melanomas characterized mainly by BRAF or NRAS mutations, UMs are usually triggered by a mutation in GNAQ or GNA11. As a result, more effective immunotherapeutic approaches, such as cancer vaccines, adoptive cell transfer, and other new molecules are currently being studied. In this review, we examine novel immunotherapeutic strategies in clinical and preclinical studies and highlight the latest insight in immunotherapy and the development of tailored treatment of UM.

Bridging functional and anatomical neural connectivity through cluster synchronization.

The dynamics of the brain results from the complex interplay of several neural populations and is affected by both the individual dynamics of these areas and their connection structure. Hence, a fundamental challenge is to derive models of the brain that reproduce both structural and functional features measured experimentally. Our work combines neuroimaging data, such as dMRI, which provides information on the structure of the anatomical connectomes, and fMRI, which detects patterns of approximate synchronous activity between brain areas. We employ cluster synchronization as a tool to integrate the imaging data of a subject into a coherent model, which reconciles structural and dynamic information. By using data-driven and model-based approaches, we refine the structural connectivity matrix in agreement with experimentally observed clusters of brain areas that display coherent activity. The proposed approach leverages the assumption of homogeneous brain areas; we show the robustness of this approach when heterogeneity between the brain areas is introduced in the form of noise, parameter mismatches, and connection delays. As a proof of concept, we apply this approach to MRI data of a healthy adult at resting state.

Algebraic Decomposition of Model Predictive Control Problems.

This paper is concerned with the application of model predictive control (MPC) to large-scale linear dynamical systems with linear inequality constraints. A decomposition is proposed of such problems into sets of independent MPCs of lower dimensions that preserves all information about the system, cost function, and constraints. Different from previous work, the constraints are incorporated in the decomposition procedure, which is attained by generalizing a previously developed technique to simultaneously block diagonalize a set of matrices. This approach is applied to practical examples involving large-scale systems with inequality constraints. It is shown that the computational complexity and the CPU time required to solve the transformed MPC problems are lower than those required by the solution of the original MPC problem.

Synchronizing chaos using reservoir computing.

We attempt to achieve complete synchronization between a drive system unidirectionally coupled with a response system, under the assumption that limited knowledge on the states of the drive is available at the response. Machine-learning techniques have been previously implemented to estimate the states of a dynamical system from limited measurements. We consider situations in which knowledge of the non-measurable states of the drive system is needed in order for the response system to synchronize with the drive. We use a reservoir computer to estimate the non-measurable states of the drive system from its measured states and then employ these measured states to achieve complete synchronization of the response system with the drive.

Impact of the COVID-19 Pandemic on Iron Overload Assessment by MRI in Patients with Hemoglobinopathies: The E-MIOT Network Experience.

BACKGROUND: The E-MIOT (Extension-Myocardial Iron Overload in Thalassemia) project is an Italian Network assuring high-quality quantification of tissue iron overload by magnetic resonance imaging (MRI). We evaluated the impact of the COVID-19 pandemic on E-MIOT services. METHODS: The activity of the E-MIOT Network MRI centers in the year 2020 was compared with that of 2019. A survey evaluated whether the availability of MRI slots for patients with hemoglobinopathies was reduced and why. RESULTS: The total number of MRI scans was 656 in 2019 and 350 in 2020, with an overall decline of 46.4% (first MRI: 71.7%, follow-up MRI: 36.9%), a marked decline (86.9%) in the period March-June 2020, and a reduction in the gap between the two years in the period July-September. A new drop (41.4%) was recorded in the period October-December for two centers, due to the general reduction in the total amount of MRIs/day for sanitization procedures. In some centers, patients refused MRI scans for fear of getting COVID. Drops in the MRI services >80% were found for patients coming from a region without an active MRI site. CONCLUSIONS: The COVID-19 pandemic had a strong negative impact on MRI multi-organ iron quantification, with a worsening in the management of patients with hemoglobinopathies.

Time-shift selection for reservoir computing using a rank-revealing QR algorithm.

Reservoir computing, a recurrent neural network paradigm in which only the output layer is trained, has demonstrated remarkable performance on tasks such as prediction and control of nonlinear systems. Recently, it was demonstrated that adding time-shifts to the signals generated by a reservoir can provide large improvements in performance accuracy. In this work, we present a technique to choose the time-shifts by maximizing the rank of the reservoir matrix using a rank-revealing QR algorithm. This technique, which is not task dependent, does not require a model of the system and, therefore, is directly applicable to analog hardware reservoir computers. We demonstrate our time-shift selection technique on two types of reservoir computer: an optoelectronic reservoir computer and the traditional recurrent network with a t a n h activation function. We find that our technique provides improved accuracy over random time-shift selection in essentially all cases.

Reservoir computing with noise.

This paper investigates in detail the effects of measurement noise on the performance of reservoir computing. We focus on an application in which reservoir computers are used to learn the relationship between different state variables of a chaotic system. We recognize that noise can affect the training and testing phases differently. We find that the best performance of the reservoir is achieved when the strength of the noise that affects the input signal in the training phase equals the strength of the noise that affects the input signal in the testing phase. For all the cases we examined, we found that a good remedy to noise is to low-pass filter the input and the training/testing signals; this typically preserves the performance of the reservoir, while reducing the undesired effects of noise.

Exact Decomposition of Optimal Control Problems via Simultaneous Block Diagonalization of Matrices.

In this paper, we consider optimal control problems (OCPs) applied to large-scale linear dynamical systems with a large number of states and inputs. We attempt to reduce such problems into a set of independent OCPs of lower dimensions. Our decomposition is 'exact' in the sense that it preserves all the information about the original system and the objective function. Previous work in this area has focused on strategies that exploit symmetries of the underlying system and of the objective function. Here, instead, we implement the algebraic method of simultaneous block diagonalization of matrices (SBD), which we show provides advantages both in terms of the dimension of the subproblems that are obtained and of the computation time. We provide practical examples with networked systems that demonstrate the benefits of applying the SBD decomposition over the decomposition method based on group symmetries.

Multidisciplinary long-term survey of Manila clam grown in farming sites subjected to different environmental conditions.

In recent years recurrent bivalve mass mortalities considerably increased around the world, causing the collapse of natural and farmed populations. Venice Lagoon has historically represented one of the major production areas of the Manila clam Ruditapes philippinarum in Europe. However, in the last 20 years a 75 % decrease in the annual production has been experienced. While climate change and anthropogenic interventions may have played a key role in natural and farmed stocks reductions, no studies investigated at multiple levels the environmental stressors affecting farmed Manila clam to date. In this work we carried out a long-term monitoring campaign on Manila clam reared in four farming sites located at different distances from the southern Venice Lagoon inlet, integrating (meta)genomic approaches (i.e. RNA-seq; microbiota characterization), biometric measurements and chemical-physical parameters. Our study allowed to characterize the molecular mechanisms adopted by this species to cope with the different environmental conditions characterizing farming sites and to propose hypotheses to explain mortality events observed in recent years. Among the most important findings, the disruption of clam's immune response, the spread of Vibrio spp., and the up-regulation of molecular pathways involved in xenobiotic metabolism suggested major environmental stressors affecting clams farmed in sites placed close to Chioggia's inlet, where highest mortality was also observed. Overall, our study provides knowledge-based tools for managing Manila clam farming on-growing areas. In addition, the collected data is a snapshot of the time immediately before the commissioning of MoSE, a system of mobile barriers aimed at protecting Venice from high tides, and will represent a baseline for future studies on the effects of MoSE on clams farming and more in general on the ecology of the Venice Lagoon.

Pinning control of networks: Dimensionality reduction through simultaneous block-diagonalization of matrices.

In this paper, we study the network pinning control problem in the presence of two different types of coupling: (i) node-to-node coupling among the network nodes and (ii) input-to-node coupling from the source node to the "pinned nodes." Previous work has mainly focused on the case that (i) and (ii) are of the same type. We decouple the stability analysis of the target synchronous solution into subproblems of the lowest dimension by using the techniques of simultaneous block diagonalization of matrices. Interestingly, we obtain two different types of blocks, driven and undriven. The overall dimension of the driven blocks is equal to the dimension of an appropriately defined controllable subspace, while all the remaining undriven blocks are scalar. Our main result is a decomposition of the stability problem into four independent sets of equations, which we call quotient controllable, quotient uncontrollable, redundant controllable, and redundant uncontrollable. Our analysis shows that the number and location of the pinned nodes affect the number and the dimension of each set of equations. We also observe that in a large variety of complex networks, the stability of the target synchronous solution is de facto only determined by a single quotient controllable block.

Supermodal Decomposition of the Linear Swing Equation for Multilayer Networks.

We study the swing equation in the case of a multilayer network in which generators and motors are modeled differently; namely, the model for each generator is given by second order dynamics and the model for each motor is given by first order dynamics. We also remove the commonly used assumption of equal damping coefficients in the second order dynamics. Under these general conditions, we are able to obtain a decomposition of the linear swing equation into independent modes describing the propagation of small perturbations. In the process, we identify symmetries affecting the structure and dynamics of the multilayer network and derive an essential model based on a 'quotient network.' We then compare the dynamics of the full network and that of the quotient network and obtain a modal decomposition of the error dynamics. We also provide a method to quantify the steady-state error and the maximum overshoot error. Two case studies are presented to illustrate application of our method.

Relationship between pancreatic iron overload, glucose metabolism and cardiac complications in sickle cell disease: An Italian multicentre study.

OBJECTIVES: Evidence about the cross-talk between iron, glucose metabolism, and cardiac disease is increasing. We aimed to explore the link of pancreatic iron by Magnetic Resonance Imaging (MRI) with glucose metabolism and cardiac complications (CC) in sickle cell disease (SCD) patients. METHODS: We considered 70 SCD patients consecutively enrolled in the Extension-Myocardial Iron Overload in Thalassemia Network. Iron overload was quantified by R2* technique and biventricular function by cine images. Macroscopic myocardial fibrosis was evaluated by late gadolinium enhancement technique. Glucose metabolism was assessed by the oral glucose tolerance test. RESULTS: Patients with an altered glucose metabolism showed a significantly higher pancreas R2* than patients with normal glucose metabolism. Pancreatic siderosis emerged as a risk factor for the development of metabolic alterations (OddsRatio 8.25, 95%confidence intervals 1.51-45.1; p = .015). Global pancreas R2* values were directly correlated with mean serum ferritin levels and liver iron concentration. Global pancreas R2* was not significantly associated with global heart R2* and macroscopic myocardial fibrosis. Patients with history of CC showed a significantly higher global pancreas R2* than patients with no CC. CONCLUSIONS: Our findings support the evaluation of pancreatic R2* by MRI in SCD patients to prevent the development of metabolic and cardiac disorders.

Matryoshka and disjoint cluster synchronization of networks.

The main motivation for this paper is to characterize network synchronizability for the case of cluster synchronization (CS), in an analogous fashion to Barahona and Pecora [Phys. Rev. Lett. 89, 054101 (2002)] for the case of complete synchronization. We find this problem to be substantially more complex than the original one. We distinguish between the two cases of networks with intertwined clusters and no intertwined clusters and between the two cases that the master stability function is negative either in a bounded range or in an unbounded range of its argument. Our proposed definition of cluster synchronizability is based on the synchronizability of each individual cluster within a network. We then attempt to generalize this definition to the entire network. For CS, the synchronous solution for each cluster may be stable, independent of the stability of the other clusters, which results in possibly different ranges in which each cluster synchronizes (isolated CS). For each pair of clusters, we distinguish between three different cases: Matryoshka cluster synchronization (when the range of the stability of the synchronous solution for one cluster is included in that of the other cluster), partially disjoint cluster synchronization (when the ranges of stability of the synchronous solutions partially overlap), and complete disjoint cluster synchronization (when the ranges of stability of the synchronous solutions do not overlap).

Looking beyond community structure leads to the discovery of dynamical communities in weighted networks.

A fundamental question is whether groups of nodes of a complex network can possibly display long-term cluster-synchronized behavior. While this question has been addressed for the restricted classes of unweighted and labeled graphs, it remains an open problem for the more general class of weighted networks. The emergence of coordinated motion of nodes in natural and technological networks is directly related to the network structure through the concept of an equitable partition, which determines which nodes can show long-term synchronized behavior and which nodes cannot. We provide a method to detect the presence of nearly equitable partitions in weighted networks, based on minimal information about the network structure. With this approach we are able to discover the presence of dynamical communities in both synthetic and real technological, biological, and social networks, to a statistically significant level. We show that our approach based on dynamical communities is better at predicting the emergence of synchronized behavior than existing methods to detect community structure.

Failure of the simultaneous block diagonalization technique applied to complete and cluster synchronization of random networks.

We discuss here the application of the simultaneous block diagonalization (SBD) of matrices to the study of the stability of both complete and cluster synchronization in random (generic) networks. For both problems, we define indices that measure success (or failure) of application of the SBD technique in decoupling the stability problem into problems of lower dimensionality. We then see that in the case of random networks the extent of the dimensionality reduction achievable is the same as that produced by application of a trivial transformation.

Molecular Monitoring of SARS-CoV-2 in Different Sewage Plants in Venice and the Implications for Genetic Surveillance.

Wastewater-based epidemiology is now widely used as an indirect tool to monitor the spread of SARS-CoV-2. In this study, five different sample matrices representing diverse phases of the wastewater treatment process were collected during the second wave of SARS-CoV-2 from two wastewater treatment plants (WWTPs) serving the Civil Hospital and Sacca Fisola island in Venice, Italy. Positive SARS-CoV-2 detections occurred at both WWTPs, and data on viral genome detection rate and quantification suggest that the pellet (i.e., the particulate resulting from the influent) is a sensitive matrix that permits reliable assessment of infection prevalence while reducing time to results. On the contrary, analysis of post-treatment matrices provides evidence of the decontamination efficacy of both WWTPs. Finally, direct sequencing of wastewater samples enabled us to identify B.1.177 and B.1.160 as the prevalent SARS-CoV-2 lineages circulating in Venice at the time of sampling. This study confirmed the suitability of wastewater testing for studying SARS-CoV-2 circulation and established a simplified workflow for the prompt detection and characterization of the virus.