On the convergence of a Block-Coordinate Incremental Gradient method

In this paper, we study the convergence of a block-coordinate incremental gradient method. Under some specific assumptions on the objective function, we prove that the block-coordinate incremental gradient method can be seen as a gradient method with errors and convergence can be proved by showing the error at each iteration satisfies some standard conditions. Thus, we can prove convergence towards stationary points when the block incremental gradient method is coupled with a diminishing stepsize and towards an epsilon-approximate solution when a bounded away from zero stepsize is employed

Machine learning use for prognostic purposes in multiple sclerosis

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only “real world” data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given “confidence threshold”. For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how “real world” data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982

ONLINE BLOCK LAYER DECOMPOSITION SCHEMES FOR TRAINING DEEP NEURAL NETWORKS

Deep Feedforward Neural Networks’ (DFNNs) weights estimation relies on the solution of a very large nonconvex optimization problem that may have many local (no global) minimizers, saddle points and large plateaus. Furthermore, the time needed to find good solutions to the training problem heavily depends on both the number of samples and the number of weights (variables). In this work, we show how Block Coordinate Descent (BCD) methods can be applied to improve the performance of state-of-the-art algorithms by avoiding bad stationary points and flat regions. We first describe a batch BCD method able to effectively tackle difficulties due to the network’s depth; then we further extend the algorithm proposing an online BCD scheme able to scale with respect to both the number of variables and the number of samples. We perform extensive numerical results on standard datasets using different deep networks, and we showed how the application of (online) BCD methods to the training phase of DFNNs permits to outperform standard batch/online algorithms leading to an improvement on both the training phase and the generalization performance of the networks

Generation of a Yeast Cell Model Potentially Useful to Identify the Mammalian Mitochondrial N-Acetylglutamate Transporter

The human mitochondrial carrier family (MCF) consists of 53 members. Approximately onefifth of them are still orphans of a function. Most mitochondrial transporters have been functionally characterized by reconstituting the bacterially expressed protein into liposomes and transport assays with radiolabeled compounds. The efficacy of this experimental approach is constrained to the commercial availability of the radiolabeled substrate to be used in the transport assays. A striking example is that of N-acetylglutamate (NAG), an essential regulator of the carbamoyl synthetase I activity and the entire urea cycle. Mammals cannot modulate mitochondrial NAG synthesis but can regulate the levels of NAG in the matrix by exporting it to the cytosol, where it is degraded. The mitochondrial NAG transporter is still unknown. Here, we report the generation of a yeast cell model suitable for identifying the putative mammalian mitochondrial NAG transporter. In yeast, the arginine biosynthesis starts in the mitochondria from NAG which is converted to ornithine that, once transported into cytosol, is metabolized to arginine. The deletion of ARG8 makes yeast cells unable to grow in the absence of arginine since they cannot synthetize ornithine but can still produce NAG. To make yeast cells dependent on a mitochondrial NAG exporter, we moved most of the yeast mitochondrial biosynthetic pathway to the cytosol by expressing four E. coli enzymes, argB-E, able to convert cytosolic NAG to ornithine. Although argB-E rescued the arginine auxotrophy of arg8D strain very poorly, the expression of the bacterial NAG synthase (argA), which would mimic the function of a putative NAG transporter increasing the cytosolic levels of NAG, fully rescued the growth defect of arg8D strain in the absence of arginine, demonstrating the potential suitability of the model generated

Data of patients undergoing rehabilitation programs

In this data article, we present a dataset made up of personal, social and clinical records related to patients undergoing a rehabilitation program. Data refers to records registered in the "Acceptance/Discharge Report for the rehabilitation area” (ADR) which implements the Italian law (DGR 731/2005) and refer to hospitalization at the rehabilitation hospital of Rome "San Raffaele" in the years from 2015 to 2018 of patients suffering from orthopedic and neurological pathologies. For each ADR report, the clinical status of the patient at the date of acceptance and discharge is reported using, among other, the Barthel index as a measure of the Activities Daily Living of the patient. These data can be used to understand the influence of many different factors in the rehabilitation progress of clinical patients

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982

Topical hemostatic agents

Hemostasis in the operating theatre has always been an issue of fundamental importance in any surgical procedure. The amount of blood loss may greatly vary between different surgical procedures and depends on both surgical and non-surgical factors. Several different topical agents can be used to achieve or maintain hemostasis in surgical patients. Hemostatic agents can be divided into two categories: those that provide their mechanism of action on the clotting cascade in a biologically active manner (active agents) and those that act passively through contact activation and promotion of platelet aggregation (passive agents). These products are available in a wide range of devices and are nowadays accepted and extensively used in the vast majority of surgical specialties with appreciable results in terms of blood loss sparing