Thermodynamics modeling of deep learning systems for a temperature based filter pruning technique

We analyse the dynamics of convolutional filters' parameters of a convolutional neural networks during and after training, via a thermodynamic analogy which allows for a sound definition of temperature. We show that removing high temperature filters has a minor effect on the performance of the model, while removing low temperature filters influences majorly both accuracy and loss decay. This result could be exploited to implement a temperature-based pruning technique for the filters and to determine efficiently the crucial filters for an effective learning

Geometric Deep Learning: a Temperature Based Analysis of Graph Neural Networks

We examine a Geometric Deep Learning model as a thermodynamic system treating the weights as non-quantum and non-relativistic particles. We employ the notion of temperature previously defined in [7] and study it in the various layers for GCN and GAT models. Potential future applications of our findings are discussed.Comment: Published on Proceedings of GSI 202

Ivar, an interpretation‐oriented tool to manage the update and revision of variant annotation and classification

The rapid evolution of Next Generation Sequencing in clinical settings, and the resulting challenge of variant reinterpretation given the constantly updated information, require robust data management systems and organized approaches. In this paper, we present iVar: a freely available and highly customizable tool with a user‐friendly web interface. It represents a platform for the unified management of variants identified by different sequencing technologies. iVar accepts variant call format (VCF) files and text annotation files and elaborates them, optimizing data organization and avoiding redundancies. Updated annotations can be periodically re‐uploaded and associated with variants as historically tracked attributes, i.e., modifications can be recorded whenever an updated value is imported, thus keeping track of all changes. Data can be visualized through variant‐centered and sample‐centered interfaces. A customizable search function can be exploited to periodically check if pathogenicity‐related data of a variant has changed over time. Patient recontacting ensuing from variant reinterpretation is made easier by iVar through the effective identification of all patients present in the database carrying a specific variant. We tested iVar by uploading 4171 VCF files and 1463 annotation files, obtaining a database of 4166 samples and 22,569 unique variants. iVar has proven to be a useful tool with good performance in terms of collecting and managing data from a medium‐throughput laboratory

Theory of Vibrationally Inelastic Electron Transport through Molecular Bridges

Vibrationally inelastic electron transport through a molecular bridge that is connected to two leads is investigated. The study is based on a generic model of vibrational excitation in resonant transmission of electrons through a molecular junction. Employing methods from electron-molecule scattering theory, the transmittance through the molecular bridge can be evaluated numerically exactly. The current through the junction is obtained approximately using a Landauer-type formula. Considering different parameter regimes, which include both the case of a molecular bridge that is weakly coupled to the leads, resulting in narrow resonance structures, and the opposite case of a broad resonance caused by strong interaction with the leads, we investigate the characteristic effects of coherent and dissipative vibrational motion on the electron transport. Furthermore, the validity of widely used approximations such as the wide-band approximation and the restriction to elastic transport mechanisms is investigated in some detail.Comment: Submited to PRB, revised version according to comments of referees (minor text changes and new citations

Homochirality and the need of energy

The mechanisms for explaining how a stable asymmetric chemical system can be formed from a symmetric chemical system, in the absence of any asymmetric influence other than statistical fluctuations, have been developed during the last decades, focusing on the non-linear kinetic aspects. Besides the absolute necessity of self-amplification processes, the importance of energetic aspects is often underestimated. Going down to the most fundamental aspects, the distinction between a single object -- that can be intrinsically asymmetric -- and a collection of objects -- whose racemic state is the more stable one -- must be emphasized. A system of strongly interacting objects can be described as one single object retaining its individuality and a single asymmetry; weakly or non-interacting objects keep their own individuality, and are prone to racemize towards the equilibrium state. In the presence of energy fluxes, systems can be maintained in an asymmetric non-equilibrium steady-state. Such dynamical systems can retain their asymmetry for times longer than their racemization time.Comment: 8 pages, 7 figures, submitted to Origins of Life and Evolution of Biosphere

Small Cofactors May Assist Protein Emergence from RNA World: Clues from RNA-Protein Complexes

It is now widely accepted that at an early stage in the evolution of life an RNA world arose, in which RNAs both served as the genetic material and catalyzed diverse biochemical reactions. Then, proteins have gradually replaced RNAs because of their superior catalytic properties in catalysis over time. Therefore, it is important to investigate how primitive functional proteins emerged from RNA world, which can shed light on the evolutionary pathway of life from RNA world to the modern world. In this work, we proposed that the emergence of most primitive functional proteins are assisted by the early primitive nucleotide cofactors, while only a minority are induced directly by RNAs based on the analysis of RNA-protein complexes. Furthermore, the present findings have significant implication for exploring the composition of primitive RNA, i.e., adenine base as principal building blocks

Neuropsychological intervention in kindergarten children with subtyped risks of reading retardation

Kindergarten children at risk of developing language problems were administered the Florida Kindergarten Screening Battery. A principal components analysis revealed a verbal and a visual-spatial component and subsequent discriminant function analyses a high verbal/low visual-spatial group (LAL: Latent L) and a high visual-spatial/low verbal group (LAP: Latent P). LAL- and LAP-children were considered at risk for developing an L- or P-type of dyslexia, respectively. As is common practice with children suffering from manifest L- or Pdyslexia, the LAL- and LAP-kindergartners received right and left hemisphere stimulation, respectively. The outcomes were compared with those of bilateral hemispheric stimulation and no intervention. Reading tests were administered in primary school Grades 1 and 5/6; teachers' evaluation of reading took place in Grade 5/6. Overall, the LAL- and LAP- groups showed significant backwardness in word and text reading, both at early and late primary school. Types of intervention made a difference though: not significantly backward in early word, late word, and late text reading were the LAL-children who had received right hemisphere stimulation. Nonintervened LAP-children did not show significant backwardness in early word reading and late text reading, nor did LAP-children who had received left hemisphere or bilateral stimulation. Early text reading was not affected by any treatment. Teacher's evaluations were in support of these findings. Copyright © 2005 by The International Dyslexia Association®

Neuropsychological patterns following lesions of the anterior insula in a series of forty neurosurgical patients

In the present study we investigated the effects of lesions affecting mainly the anterior insula in a series of 22 patients with lesions in the left hemisphere (LH), and 18 patients with lesions involving the right hemisphere (RH). The site of the lesion was established by performing an overlap of the probabilistic cytoarchitectonic maps of the posterior insula. Here we report the patients\u2019 neuropsychological profile and an analysis of their pre-surgical symptoms. We found that pre-operatory symptoms significantly differed in patients depending on whether the lesion affected the right or left insula and a strict parallelism between the patterns emerged in the pre-surgery symptoms analysis, and the patients\u2019 cognitive profile. In particular, we found that LH patients showed cognitive deficits. By contrast, the RH patients, with the exception of one case showing an impaired performance at the visuo-spatial planning test were within the normal range in performing all the tests. In addition, a sub-group of patients underwent to the post-surgery follow-up examination

Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images

Trained pathologists base colorectal cancer identification on the visual interpretation of microscope images. However, image labeling is not always straightforward and this repetitive task is prone to mistakes due to human distraction. Significant efforts are underway to develop informative tools to assist pathologists and decrease the burden and frequency of errors. The present study proposes a deep learning approach to recognize four different stages of cancerous tissue development, including normal mucosa, early preneoplastic lesion, adenoma and cancer. A dataset of human colon tissue images collected and labeled over a 10-year period by a team of pathologists was partitioned into three sets. These were used to train, validate and test the neural network, comprising several convolutional and a few linear layers. The approach used in the present study is ‘direct’; it labels raw images and bypasses the segmentation step. An overall accuracy of >95% was achieved, with the majority of mislabeling referring to a near category. Tests on an external dataset with a different resolution yielded accuracies >80%. The present study demonstrated that the neural network, when properly trained, can provide fast, accurate and reproducible labeling for colon cancer images, with the potential to significantly improve the quality and speed of medical diagnoses