BACKGROUND: P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. RESULTS: We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). CONCLUSIONS: Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.

Most sciences conduct experiments with a thorough understanding of the accuracy and precision of the instruments used for making measurements. Time is the most frequently used measurement in systems research, yet most of the literature does not consider the precision and accuracy of clocks. In this paper, we argue for the importance of understanding timekeeping and providing precise and accurate time for general systems research.

In the 5G-envisioned Internet of vehicles (IoV), a significant volume of data is exchanged through networks between intelligent transport systems (ITS) and clouds or fogs. With the introduction of Software-Defined Networking (SDN), the problems mentioned above are resolved by high-speed flow-based processing of data in network systems. To classify flows of packets in the SDN network, high throughput packet classification systems are needed. Although software packet classifiers are cheaper and more flexible than hardware classifiers, they could only deliver limited performance. A key idea to resolve this problem is parallelizing packet classification on graphical processing units (GPUs). In this paper, we study parallel forms of Tuple Space Search and Pruned Tuple Space Search algorithms for the flow classification suitable for GPUs using CUDA (Compute Unified Device Architecture). The key idea behind the offered methodology is to transfer the stream of packets from host memory to the global memory of the CUDA device, then assigning each of them to a classifier thread. To evaluate the proposed method, the GPU-based versions of the algorithms were implemented on two different CUDA devices, and two different CPU-based implementations of the algorithms were used as references. Experimental results showed that GPU computing enhances the performance of Pruned Tuple Space Search remarkably more than Tuple Space Search. Moreover, results evinced the computational efficiency of the proposed method for parallelizing packet classification algorithms.

Wireless protocol design for IoT networks is an active area of research. We demonstrate tinySDR which is a low-power software-defined radio platform tailored to the needs of IoT endpoints. TinySDR is a standalone, fully programmable software-defined radio platform which has the requirements of IoT protocols. We present the physical layer implementations of BLE beacon and LoRa protocols to demonstrate the capabilities of tinySDR.

Modern Systems-on-Chip(SoCs) frequently power-off individual voltage domains to save leakage power across a variety of applications, from large-scale heterogeneous computing to ultra-low power systems in IoT applications. However, the considerable energy stored within the capacitance of the powered-off domain is lost through leakage. In this paper, we present an approach to leverage existing voltage regulators to recover this energy from the disabled voltage-domain back into the supply using a low-overhead all-digital runtime control system. Simulation experiments conducted in an industrial 65nm CMOS process indicate that over 90% of the stored energy can be recovered across a range of operating system voltages from 0.4V--1V.