PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity

Background: Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking. Results: We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module. Conclusions: PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data

miR-SEA: miRNA Seed Extension based Aligner Pipeline for NGS Expression Level Extraction

The advent of Next Generation Sequencing (NGS) technology has enabled a new major approach for micro RNAs (miRNAs) expression profiling through the so called RNA-Sequencing (RNA-Seq). Different tools have been developed in the last years in order to detect and quantify miRNAs, especially in pathological samples, starting from the big amount of data deriving from RNA sequencing. These tools, usually relying on general purpose alignment algorithms, are however characterized by different sensitivity and accuracy levels and in the most of the cases provide not overlapping predictions. To overcome these limitations we propose a novel pipeline for miRNAs detection and quantification in RNA-Seq sample, miRNA Seed Extension Aligner (miR-SEA), based on an experimental evidence concerning miRNAs structure. The proposed pipeline was tested on three Colorectal Cancer (CRC) RNA-Seq samples and the obtained results compared with those provided by two well-known miRNAs detection tools showing good ability in performing detection and quantification more adherent to miRNAs structure

Benchmarking a many-core neuromorphic platform with an MPI-based DNA sequence matching algorithm

SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS)multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implementation of a parallel DNA sequence matching algorithm implemented by using the MPI programming paradigm ported to the SpiNNaker platform. In our implementation, all cores available in the board are configured for executing in parallel an optimised version of the Boyer-Moore (BM) algorithm. Exploiting this application, we benchmarked the SpiNNaker platform in terms of scalability and synchronisation latency. Experimental results indicate that the SpiNNaker parallel architecture allows a linear performance increase with the number of used cores and shows better scalability compared to a general-purpose multi-core computing platform

Exploration of Convolutional Neural Network models for source code classification

The application of Artificial Intelligence is becoming common in many engineering fields. Among them, one of the newest and rapidly evolving is software generation, where AI can be used to automatically optimise the implementation of an algorithm for a given computing platform. In particular, Deep Learning technologies can be used to the decide how to allocate pieces of code to hardware platforms with multiple cores and accelerators, that are common in high performance and edge computing applications. In this work, we explore the use of Convolutional Neural Networks (CNN)s to analyse the application source code and decide the best compute unit to minimise the execution time. We demonstrate that CNN models can be successfully applied to source code classification, providing higher accuracy with consistently reduced learning time with respect to state-of-the-art methods. Moreover, we show the robustness of the method with respect to source code pre-processing, compiler options and hyper-parameters selection

Cervico-manubrio-toracotomia secondo Grünenwald. Nostra esperienza in 3 casi di tumori dello stretto toracico superiore

Tumors of the cervical-thoracic area can be treated by the Grünenwald approach, which consists of an L-shaped cervical-manubrialthoracotomy without section of the clavicle. We used this access in three different tumors of the cervical-thoracic inlet: a tumor of T1 vertebral body, a tumor of the left superior sulcus, and a rare tumor originating from the root T1 of the brachial plexus. The first patient was a 39-years-old man with a somatic fracture of T1 and tumor invasion of the residual vertebral body by multiple myeloma. The 2nd patient was a 61-years-old man with a squamous cell carcinoma of S1 left upper lobe, infiltrating the parietal pleura and the chest wall, in the anterior-lateral part of the 2nd intercostal space. The 3rd patient was a 35-years-old woman with a glomic tumor originating from the T1 root of the right brachial plexus. The only post-operative complication was a modest diaphragm elevation in the 3rd patient, completely disappeared after 3-4 months. The 2nd patient is dead one year after the operation for cerebral metastases. The other two patients are presently in good conditions, without signs of relapse. Is our opinion the Grünenwald technique is technique for the treatment of tumors of the cervical-thoracic area allows a safe visibility of the anatomical structures without the necessity of a clavicle section

Mapping Spiking Neural Networks on Multi-core Neuromorphic Platforms: Problem Formulation and Performance Analysis

In this paper, we propose a methodology for efficiently mapping concurrent applications over a globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a Spiking Neural Network (SNN) in real-time. The problem of neuron-to-core mapping is relevant as a non-efficient allocation may impact real-time and reliability of the SNN execution. We designed a task placement pipeline capable of analysing the network of neurons and producing a placement configuration that enables a reduction of communication between computational nodes. We compared four Placement techniques by evaluating the overall post-placement synaptic elongation that represents the cumulative distance that spikes generated by neurons running on a core have to travel to reach their destination core. Results point out that mapping solutions taking into account the directionality of the SNN application provide a better placement configuration

Modeling an Industrial Revolution: How to Manage Large-Scale, Complex IoT Ecosystems?

Advancements around the modern digital industry gave birth to a number of closely interrelated concepts: in the age of the Internet of Things (IoT), System of Systems (SoS), Cyber-Physical Systems (CPS), Digital Twins and the fourth industrial revolution, everything revolves around the issue of designing well-understood, sound and secure complex systems while providing maximum flexibility, autonomy and dynamics.The aim of the paper is to present a concise overview of a comprehensive conceptual framework for integrated modeling and management of industrial IoT architectures, supported by actual evidence from the Arrowhead Tools project; in particular, we adopt a three-dimensional projection of our complex engineering space, from modeling the engineering process to SoS design and deployment.In particular, we start from modeling principles of the the engineering process itself. Then, we present a design-time SoS representation along with a toolchain concept aiding SoS design and deployment. This brings us to reasoning about what potential workflows are thinkable for specifying comprehensive toolchains along with their data exchange interfaces. We also discuss the potential of aligning our vision with RAMI4.0, as well as the utilization perspectives for real-life engineering use-cases

Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware

Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world applications. Recent deep learning approaches have reached outstanding accuracy in such tasks, but their implementation on conventional embedded solutions is still very computationally and energy expensive. Tactile sensing in robotic applications is a representative example where real-time processing and energy-efficiency are required. Following a brain-inspired computing approach, we propose a new benchmark for spatio-temporal tactile pattern recognition at the edge through braille letters reading. We recorded a new braille letters dataset based on the capacitive tactile sensors/fingertip of the iCub robot, then we investigated the importance of temporal information and the impact of event-based encoding for spike-based/event-based computation. Afterwards, we trained and compared feed-forward and recurrent spiking neural networks (SNNs) offline using back-propagation through time with surrogate gradients, then we deployed them on the Intel Loihi neuromorphic chip for fast and efficient inference. We confronted our approach to standard classifiers, in particular to a Long Short-Term Memory (LSTM) deployed on the embedded Nvidia Jetson GPU in terms of classification accuracy, power/energy consumption and computational delay. Our results show that the LSTM outperforms the recurrent SNN in terms of accuracy by 14%. However, the recurrent SNN on Loihi is 237 times more energy-efficient than the LSTM on Jetson, requiring an average power of only 31mW. This work proposes a new benchmark for tactile sensing and highlights the challenges and opportunities of event-based encoding, neuromorphic hardware and spike-based computing for spatio-temporal pattern recognition at the edge.Comment: 20 pages, submitted to Frontiers in Neuroscience - Neuromorphic Engineerin