analysis of brain nmr images for age estimation with deep learning

Abstract During the last decade, deep learning and Convolutional Neural Networks (CNNs) have produced a devastating impact on computer vision, yielding exceptional results on a variety of problems, including analysis of medical images. Recently, these techniques have been extended to 3D images with the downside of a large increase in the computational load. In particular, state-of-the-art CNNs have been used for brain Nuclear Magnetic Resonance (NMR) imaging, with the aim of estimating the patients' age. In fact, a large discrepancy between the real and the estimated age is a clear alarm for the onset of neurodegenerative diseases, such as some types of early dementia and Alzheimer's disease. In this paper, we propose an effective alternative to 3D convolutions that guarantees a significant reduction of the computational requirements for this kind of analysis. The proposed architectures achieve comparable results with the competitor 3D methods, requiring only a fraction of the training time and GPU memory

Validation of an Automated System for the Extraction of a Wide Dataset for Clinical Studies Aimed at Improving the Early Diagnosis of Candidemia

: There is increasing interest in assessing whether machine learning (ML) techniques could further improve the early diagnosis of candidemia among patients with a consistent clinical picture. The objective of the present study is to validate the accuracy of a system for the automated extraction from a hospital laboratory software of a large number of features from candidemia and/or bacteremia episodes as the first phase of the AUTO-CAND project. The manual validation was performed on a representative and randomly extracted subset of episodes of candidemia and/or bacteremia. The manual validation of the random extraction of 381 episodes of candidemia and/or bacteremia, with automated organization in structured features of laboratory and microbiological data resulted in ≥99% correct extractions (with confidence interval < ±1%) for all variables. The final automatically extracted dataset consisted of 1338 episodes of candidemia (8%), 14,112 episodes of bacteremia (90%), and 302 episodes of mixed candidemia/bacteremia (2%). The final dataset will serve to assess the performance of different ML models for the early diagnosis of candidemia in the second phase of the AUTO-CAND project

Tolerogenic IL-10-engineered dendritic cell-based therapy to restore antigen-specific tolerance in T cell mediated diseases

Tolerogenic dendritic cells play a critical role in promoting antigen-specific tolerance via dampening of T cell responses, induction of pathogenic T cell exhaustion and antigen-specific regulatory T cells. Here we efficiently generate tolerogenic dendritic cells by genetic engineering of monocytes with lentiviral vectors co-encoding for immunodominant antigen-derived peptides and IL-10. These transduced dendritic cells (designated DCIL-10/Ag) secrete IL-10 and efficiently downregulate antigen-specific CD4+ and CD8+ T cell responses from healthy subjects and celiac disease patients in vitro. In addition, DCIL-10/Ag induce antigen-specific CD49b+LAG-3+ T cells, which display the T regulatory type 1 (Tr1) cell gene signature. Administration of DCIL-10/Ag resulted in the induction of antigen-specific Tr1 cells in chimeric transplanted mice and the prevention of type 1 diabetes in pre-clinical disease models. Subsequent transfer of these antigen-specific T cells completely prevented type 1 diabetes development. Collectively these data indicate that DCIL-10/Ag represent a platform to induce stable antigen-specific tolerance to control T-cell mediated diseases

Machine learning methods for the prediction of translation speed

Ribosomes carry out protein synthesis from mRNA templates by a highly regulated process called translation. Translational control plays a key role in the regulation of gene expression, under physiological and pathological conditions. How translation is regulated under different conditions and what factors greatly influence the translation speed remains open questions in molecular biology. In recent years, Ribosome profiling technique (Ribo-seq) has emerged as a powerful method for globally monitoring the translation process in vivo at single nucleotide resolution [1]. Ribo-seq is based on deep sequencing of mRNA fragments covered by ribosomes, called Ribosome Protected Fragments (RPFs). Sequencing of RPFs allows to record the precise position of the ribosomes at the time in which the translation was blocked. However, the exploitation of the full power of this technique is hindered by notable weaknesses (e.g. a low signal to noise ratio), influencing the reproducibility of Ribo-seq experiment. [2]. The aim of this thesis is the development of a newly designed statistical approach integrated with machine learning methodologies for a comprehensive understanding of the information contained in Ribosome Profiling data and for prediction of translation speed. Our data analysis approach consists of a systematic comparison of Ribo-seq profiles referring to several publically available Ribo-seq datasets generated in different laboratories, in different time but under the same experimental conditions. In the E.coli case studio, the analysis of 3588 Ribo-seq profiles across eight independent datasets revealed that only 40 profiles are significantly reproducibles. The identification of reproducible Ribo-seq profiles allows us to build consensus sequences which highlighted the nucleotides located within fast and slow regions. The density of the RPFs along the mRNAs reflects the different time spent by ribosomes in translating each part of the ORF. Therefore slow regions, extremely rich of ribosomes, and fast regions, characterized by few ribosomes, can be easily identified by Ribo-seq. We analysed the occurrences of nucleotides, dinucleotides, and codons of consensus sequences in order to conjecture the existence (or not) of signals in the sequence that could modulate the speed of translation. To this aim, we implemented different neural network architectures that let us classify the translation speed of the previously identified consensus sequences with high accuracy. Although the limited amount of data, the results clearly demonstrate that the models can extract useful information. Furthermore, we used the significantly reproducible profiles as a reference for comparative analyses aimed at detecting whether modifications in experimental conditions (heat shock stress and aminoacid starvation) could affect the reproducibility of our Ribo-seq workflow and thus influence the translation control. A preliminary analysis on Ribo-seq human data suggests that our method provides a rich resource for further in-depth studies about translation control of gene expression in all kind of Ribo-seq datasets, including those related to highly differentiated organisms like humans

A Neural Network Approach for the Analysis of Reproducible Ribo–Seq Profiles

In recent years, the Ribosome profiling technique (Ribo–seq) has emerged as a powerful method for globally monitoring the translation process in vivo at single nucleotide resolution. Based on deep sequencing of mRNA fragments, Ribo–seq allows to obtain profiles that reflect the time spent by ribosomes in translating each part of an open reading frame. Unfortunately, the profiles produced by this method can vary significantly in different experimental setups, being characterized by a poor reproducibility. To address this problem, we have employed a statistical method for the identification of highly reproducible Ribo–seq profiles, which was tested on a set of E. coli genes. State-of-the-art artificial neural network models have been used to validate the quality of the produced sequences. Moreover, new insights into the dynamics of ribosome translation have been provided through a statistical analysis on the obtained sequences

Point-Wise Ribosome Translation Speed Prediction with Recurrent Neural Networks

Escherichia coli is a benchmark organism, which has been deeply studied by the scientific community for decades, obtaining a vast amount of metabolic and genetic data. Among these data, estimates of the translation speed of ribosomes over their genome are available. These estimates are based on Ribo-Seq profiles, where the abundance of a particular fragment of mRNA in a profile indicates that it was sampled many times inside a cell. Various measurements of Ribo-Seq profiles are available for Escherichia coli, yet they do not always show a high degree of correspondence, which means that they can vary significantly in different experimental setups, being characterized by poor reproducibility. Indeed, within Ribo-Seq profiles, the translation speed for some sequences is easier to estimate, while for others, an uneven distribution of consensus among the different estimates is evidenced. Our goal is to develop an artificial intelligence method that can be trained on a small pool of highly reproducible sequences to establish their translation rate, which can then be exploited to calculate a more reliable estimate of the translation speed on the rest of the genome

Poly(l-lactic acid) Scaffold Releasing an α4β1 Integrin Agonist Promotes Nonfibrotic Skin Wound Healing in Diabetic Mice

Skin wound healing is a highly complex process that continues to represent a major medical problem, due to chronic nonhealing wounds in several classes of patients and to possible fibrotic complications, which compromise the function of the dermis. Integrins are transmembrane receptors that play key roles in this process and that offer a recognized druggable target. Our group recently synthesized GM18, a specific agonist for alpha 4 beta 1, an integrin that plays a role in skin immunity and in the migration of neutrophils, also regulating the differentiated state of fibroblasts. GM18 can be combined with poly(L-lactic acid) (PLLA) nanofibers to provide a controlled release of this agonist, resulting in a medication particularly suitable for skin wounds. In this study, we first optimized a GM18-PLLA nanofiber combination with a 7-day sustained release for use as skin wound medication. When tested in an experimental pressure ulcer in diabetic mice, a model for chronic nonhealing wounds, both soluble and GM18-PLLA formulations accelerated wound healing, as well as regulated extracellular matrix synthesis toward a nonfibrotic molecular signature. In vitro experiments using the adhesion test showed fibroblasts to be a principal GM18 cellular target, which we then used as an in vitro model to explore possible mechanisms of GM18 action. Our results suggest that the observed antifibrotic behavior of GM18 may exert a dual action on fibroblasts at the alpha 4 beta 1 binding site and that GM18 may prevent profibrotic EDA-fibronectin-alpha 4 beta 1 binding and activate outside-in signaling of the ERK1/2 pathways, a critical component of the wound healing process

SlAide2Voice: a new educational tool for students with visual disabilities

Online lessons had become more and more frequent and this new way of teaching, forced by the Covid-19 epidemic, implies many problems for all of the students and above all for those ones affected by disabilities. It is therefore absolutely necessary to address this issue and provide practical solutions for it. This paper proposes SlAIde2Voice, a new software pipeline architecture to help visually impaired students to overcome some difficulties related to online lessons. Our aim is to develop a tool, based on three simple components, a client–server architecture, an OpenOffice add-on and an artificial intelligence module, which will be able to help visually impaired students not only during online lessons, but also during their independent study. The proposed methodology promises to improve the learning quality of students with visual difficulties and aims to improve their inclusion and independence. SlAIde2Voice will be designed to be used together with any existing videoconference tools and its use can also be extended to conferences and meetings in general

A Neural Network Approach for the Analysis of Reproducible Ribo–Seq Profiles

In recent years, the Ribosome profiling technique (Ribo–seq) has emerged as a powerful method for globally monitoring the translation process in vivo at single nucleotide resolution. Based on deep sequencing of mRNA fragments, Ribo–seq allows to obtain profiles that reflect the time spent by ribosomes in translating each part of an open reading frame. Unfortunately, the profiles produced by this method can vary significantly in different experimental setups, being characterized by a poor reproducibility. To address this problem, we have employed a statistical method for the identification of highly reproducible Ribo–seq profiles, which was tested on a set of E. coli genes. State-of-the-art artificial neural network models have been used to validate the quality of the produced sequences. Moreover, new insights into the dynamics of ribosome translation have been provided through a statistical analysis on the obtained sequences.</jats:p