Transposons Acting as Competitive Endogenous RNAs: In-Silico Evidence from Datasets Characterised by L1 Overexpression

LINE L1 are transposable elements that can replicate within the genome by passing through RNA intermediates. The vast majority of these element copies in the human genome are inactive and just between 100 and 150 copies are still able to mobilize. During evolution, they could have been positively selected for beneficial cellular functions. Nonetheless, L1 deregulation can be detrimental to the cell, causing diseases such as cancer. The activity of miRNAs represents a fundamental mechanism for controlling transcript levels in somatic cells. These are a class of small non-coding RNAs that cause degradation or translational inhibition of their target transcripts. Beyond this, competitive endogenous RNAs (ceRNAs), mostly made by circular and non-coding RNAs, have been seen to compete for the binding of the same set of miRNAs targeting protein coding genes. In this study, we have investigated whether autonomously transcribed L1s may act as ceRNAs by analyzing public dataset in-silico. We observed that genes sharing miRNA target sites with L1 have a tendency to be upregulated when L1 are overexpressed, suggesting the possibility that L1 might act as ceRNAs. This finding will help in the interpretation of transcriptomic responses in contexts characterized by the specific activation of transposons

A Methodology for Embedded Classification of Heartbeats Using Random Projections

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject’s bio-signals. One of its most relevant applications is the acquisition and analysis of Electrocardiograms (ECGs). These low-power WBSN designs, while able to perform advanced signal processing to extract information on hearth conditions of subjects, are usually constrained in terms of computational power and transmission bandwidth. It is therefore beneficial to identify in the early stages of analysis which parts of an ECG acquisition are critical and activate only in these cases detailed (and computationally intensive) diagnosis algorithms. In this paper, we introduce and study the performance of a real-time optimized neuro-fuzzy classifier based on random projections, which is able to discern normal and pathological heartbeats on an embedded WBSN. Moreover, it exposes high confidence and low computational and memory requirements. Indeed, by focusing on abnormal heartbeats morphologies, we proved that a WBSN system can effectively enhance its efficiency, obtaining energy savings of as much as 63% in the signal processing stage and 68% in the subsequent wireless transmission when the proposed classifier is employed

Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections

Power-Efficient Joint Compressed Sensing of Multi-Lead ECG Signals

Compressed Sensing (CS) is a new acquisition- compression paradigm for low-complexity energy-aware sensing and compression. By merging both sampling and compression, CS is very promising to develop practical ultra-low power read- out systems for wireless bio-signal monitoring devices, where large amounts of sensor data need to be transferred through power-hungry wireless links. Lately CS has been successfully applied for real-time energy- aware single-lead ECG compression on resource-constrained Wireless Body Sensor Network (WBSN) motes. Building on our previous work, in this paper we propose a new and promising approach for joint compression of multi-lead ECG signals, where strong correlations exist between them. This situation that exhibit strong correlations, can be exploited to reduce even further amount of data to be transmitted wirelessly, thus addressing the important challenge of ultra-low-power embedded monitoring of multi-lead ECG signals

Hardware/Software Approach for Code Synchronization in Low-Power Multi-Core Sensor Nodes

Latest embedded bio-signal analysis applications, targeting low-power Wireless Body Sensor Nodes (WBSNs), present conflicting requirements. On one hand, bio-signal analysis applications are continuously increasing their demand for high computing capabilities. On the other hand, long-term signal processing in WBSNs must be provided within their highly constrained energy budget. In this context, parallel processing effectively increases the power efficiency of WBSNs, but only if the execution can be properly synchronized among computing elements. To address this challenge, in this work we propose a hardware/software approach to synchronize the execution of bio-signal processing applications in multi-core WBSNs. This new approach requires little hardware resources and very few adaptations in the source code. Moreover, it provides the necessary flexibility to execute applications with an arbitrarily large degree of complexity and parallelism, enabling considerable reductions in power consumption for all multi-core WBSN execution conditions. Experimental results show that a multi-core WBSN architecture using the illustrated approach can obtain energy savings of up to 40%, with respect to an equivalent singlecore architecture, when performing advanced bio-signal analysi

System-level exploration of in-package wireless communication for multi-chiplet platforms

Multi-Chiplet architectures are being increasingly adopted to support the design of very large systems in a single package, facilitating the integration of heterogeneous components and improving manufacturing yield. However, chiplet-based solutions have to cope with limited inter-chiplet routing resources, which complicate the design of the data interconnect and the power delivery network. Emerging in-package wireless technology is a promising strategy to address these challenges, as it allows to implement flexible chiplet interconnects while freeing package resources for power supply connections. To assess the capabilities of such an approach and its impact from a full-system perspective, herein we present an exploration of the performance of in-package wireless communication, based on dedicated extensions to the gem5-X simulator. We consider different Medium Access Control (MAC) protocols, as well as applications with different runtime profiles, showcasing that current in-package wireless solutions are competitive with wired chiplet interconnects. Our results show how in-package wireless solutions can outperform wired alternatives when running artificial intelligence workloads, achieving up to a 2.64× speed-up when running deep neural networks (DNNs) on a chiplet-based system with 16 cores distributed in four clusters.This work has been partially supported by the EC H2020 WiPLASH project (GA No. 863337) and the EC H2020 FVLLMONTI project (GA No. 101016776)Peer ReviewedPostprint (author's final draft

Novel Mechanistic Insight into the Molecular Basis of Amyloid Polymorphism and Secondary Nucleation during Amyloid Formation

The formation of amyloid β (Aβ) fibrils is crucial in initiating the cascade of pathological events that culminates in Alzheimer's disease. In this study, we investigated the mechanism of Aβ fibril formation from hydrodynamically well defined species under controlled aggregation conditions. We present a detailed mechanistic model that furnishes a novel insight into the process of Aβ42 fibril formation and the molecular basis for the different structural transitions in the amyloid pathway. Our data reveal the structure and polymorphism of Aβ fibrils to be critically influenced by the oligomeric state of the starting materials, the ratio of monomeric-to-aggregated forms of Aβ42 (oligomers and protofibrils), and the occurrence of secondary nucleation. We demonstrate that monomeric Aβ42 plays an important role in mediating structural transitions in the amyloid pathway, and for the first time, we provide evidences that Aβ42 fibrillization occurs via a combined mechanism of nucleated polymerization and secondary nucleation. These findings will have significant implications to our understanding of the molecular basis of amyloid formation in vivo, of the heterogeneity of Aβ pathology (e.g., diffuse versus amyloid plaques), and of the structural basis of Aβ toxicity

Embedded Real-Time ECG Delineation Methods: a Comparative Evaluation

Wireless sensor nodes (WSNs) have recently evolved to include a fair amount of computational power, so that advanced signal processing algorithms can now be embedded even in this extremely low-power platforms. An increasingly successful field of application of WSNs is tele-healthcare, which enables continous monitoring of subjects, even outside a medical environment. In particular, the design of solutions for automated and remote electrocardiogram (ECG) analysis have attracted considerable research interest in recent years, and different algorithms for delineation of normal and pathological heart rhythms have been proposed. In this paper, some of the most promising techniques for filtering and delinations of ECG signals are explored and comparatively evaluated, describing their implementation on the state-of-the-art IcyHeart WSN. The goal of this paper is to explore the trade-offs implied in the different settings and the impact of design choices for implementing “smart” WSNs dedicated to monitoring ECG bio-signal

Hardware-Software Inexactness in Noise-aware Design of Low-Power Body Sensor Nodes

Wireless Body Sensor Nodes (WBSNs) are miniaturized and ultra-low-power devices, able to acquire and wirelessly trans- mit biosignals such as electrocardiograms (ECG) for extended periods of times and with little discomfort for subjects [1]. Energy efficiency is of paramount importance for WBSNs, because it allows a higher wearability (by requiring a smaller battery) and/or an increased mean time between charges. In this paper, we investigate how noise-aware design choices can be made to minimize energy consumption in WBSNs. Noise is unavoidable in biosignals acquisitions, either due to external factors (in case of ECGs, muscle contractions and respiration of subjects [2]) or to the design of the front- end analog acquisition block. From this observation stems the opportunity to apply inexact strategies such as on-node lossy compression to minimize the bandwidth over the energy- hungry wireless link [3], as long as the output quality of the signal, when reconstructed on the receiver side, is not constrained by the performed compression. To maximize gains, ultra-low-power platforms must be employed to perform the above-mentioned Digital Signal Processing (DSP) techniques. To this end, we propose an under-designed (but extremely efficient) architecture that only guarantees the correctness of operations performed on the most significant data (i.e., data most affecting the final results), while allowing sporadic errors for the less significant data

A Synchronization-Based Hybrid-Memory Multi-Core Architecture for Energy-Efficient Biomedical Signal Processing

In the last decade, improvements on technology scaling have enabled the design of a novel generation of wearable bio-sensing monitors. These smart Wireless Body Sensor Nodes (WBSNs) are able to acquire and process biological signals, such as electrocardiograms, for periods of time extending from hours to days. The energy required for the on-node digital signal processing (DSP) is a crucial limiting factor in the conception of these devices. To address this design challenge, we introduce a domain-specific ultra-low power (ULP) architecture dedicated to bio-signal processing. The platform features a light-weight strategy to support different operating modes and synchronization among cores. Our approach effectively reduces the power consumption, harnessing the intrinsic parallelism and the workload requirements characterizing the target domain. Operations at low voltage levels are supported by a heterogeneous memory subsystem comprising a standard-cell based ultra-low voltage reliable partition. Experimental results show that, when executing real-world bio-signal DSP applications, a state-of-the-art multi-core architecture can improve its energy efficiency in up to 50% by utilizing our proposed approach, outperforming traditional single-core alternatives