On the Design of a Novel Joint Network-Channel Coding Scheme for the Multiple Access Relay Channel

This paper proposes a novel joint non-binary network-channel code for the Time-Division Decode-and-Forward Multiple Access Relay Channel (TD-DF-MARC), where the relay linearly combines -- over a non-binary finite field -- the coded sequences from the source nodes. A method based on an EXIT chart analysis is derived for selecting the best coefficients of the linear combination. Moreover, it is shown that for different setups of the system, different coefficients should be chosen in order to improve the performance. This conclusion contrasts with previous works where a random selection was considered. Monte Carlo simulations show that the proposed scheme outperforms, in terms of its gap to the outage probabilities, the previously published joint network-channel coding approaches. Besides, this gain is achieved by using very short-length codewords, which makes the scheme particularly attractive for low-latency applications.Comment: 28 pages, 9 figures; Submitted to IEEE Journal on Selected Areas in Communications - Special Issue on Theories and Methods for Advanced Wireless Relays, 201

An Introduction to MPEG-G: The First Open ISO/IEC Standard for the Compression and Exchange of Genomic Sequencing Data

The development and progress of high-throughput sequencing technologies have transformed the sequencing of DNA from a scientific research challenge to practice. With the release of the latest generation of sequencing machines, the cost of sequencing a whole human genome has dropped to less than 600. Such achievements open the door to personalized medicine, where it is expected that genomic information of patients will be analyzed as a standard practice. However, the associated costs, related to storing, transmitting, and processing the large volumes of data, are already comparable to the costs of sequencing. To support the design of new and interoperable solutions for the representation, compression, and management of genomic sequencing data, the Moving Picture Experts Group (MPEG) jointly with working group 5 of ISO/TC276 'Biotechnology' has started to produce the ISO/IEC 23092 series, known as MPEG-G. MPEG-G does not only offer higher levels of compression compared with the state of the art but it also provides new functionalities, such as built-in support for random access in the compressed domain, support for data protection mechanisms, flexible storage, and streaming capabilities. MPEG-G only specifies the decoding syntax of compressed bitstreams, as well as a file format and a transport format. This allows for the development of new encoding solutions with higher degrees of optimization while maintaining compatibility with any existing MPEG-G decoder

GABAC : An arithmetic coding solution for genomic data

Motivation: In an effort to provide a response to the ever-expanding generation of genomic data, the International Organization for Standardization (ISO) is designing a new solution for the representation, compression and management of genomic sequencing data: the Moving Picture Experts Group (MPEG)-G standard. This paper discusses the first implementation of an MPEG-G compliant entropy codec: GABAC. GABAC combines proven coding technologies, such as context-adaptive binary arithmetic coding, binarization schemes and transformations, into a straightforward solution for the compression of sequencing data. Results: We demonstrate that GABAC outperforms well-established (entropy) codecs in a significant set of cases and thus can serve as an extension for existing genomic compression solutions, such as CRAM. © 2019 The Author(s). Published by Oxford University Press

Frozen cancellous bone allografts: positive cultures of implanted grafts in posterior fusions of the spine

We have carried out a study on the behaviour pattern of implanted allografts initially stored in perfect conditions (aseptically processed, culture-negative and stored at -80 degrees C) but which presented positive cultures at the implantation stage. There is no information available on how to deal with this type of situation, so our aim was to set guidelines on the course of action which would be required in such a case. This was a retrospective study of 112 patients who underwent a spinal arthrodesis and in whom a total of 189 allograft pieces were used. All previous bone and blood cultures and tests for hepatitis B and C, syphilis and HIV (via PCR techniques) were negative. The allografts were stored by freezing them at -80 degrees C. A sample of the allograft was taken for culture in the operating theatre just before its implantation in all cases. The results of the cultures were obtained 3-5 days after the operation. There were 22 allografts with positive culture results (12%) after implantation. These allografts were implanted in 16 patients (14%). Cultures were positive for staphylococci coagulase negative (ECN) in 10 grafts (46%), Pseudomonas stutzeri in two grafts (9%), Corynebacterium jeikeium in two grafts (9%), staphylococci coagulase positive in two grafts (9%) and for each of the following organisms in one case each (4%): Corynebacterium spp., Actinomyces odontolyticus, Streptococcus mitis, Peptostreptococcus spp., Rhodococcus equi and Bacillus spp. No clinical infection was seen in any of these patients. Positive cultures could be caused by non-detected contamination at harvesting, storing or during manipulation before implantation. The lack of clinical signs of infection during the follow-up of our patients may indicate that no specific treatment different from our antibiotic protocol is required in the case of positive culture results of a graft piece after implantation

Optimization of universal allogeneic CAR-T cells combining CRISPR and transposon-based technologies for treatment of acute myeloid leukemia

Despite the potential of CAR-T therapies for hematological malignancies, their efficacy in patients with relapse and refractory Acute Myeloid Leukemia has been limited. The aim of our study has been to develop and manufacture a CAR-T cell product that addresses some of the current limitations. We initially compared the phenotype of T cells from AML patients and healthy young and elderly controls. This analysis showed that T cells from AML patients displayed a predominantly effector phenotype, with increased expression of activation (CD69 and HLA-DR) and exhaustion markers (PD1 and LAG3), in contrast to the enriched memory phenotype observed in healthy donors. This differentiated and more exhausted phenotype was also observed, and corroborated by transcriptomic analyses, in CAR-T cells from AML patients engineered with an optimized CAR construct targeting CD33, resulting in a decreased in vivo antitumoral efficacy evaluated in xenograft AML models. To overcome some of these limitations we have combined CRISPR-based genome editing technologies with virus-free gene-transfer strategies using Sleeping Beauty transposons, to generate CAR-T cells depleted of HLA-I and TCR complexes (HLA-IKO/TCRKO CAR-T cells) for allogeneic approaches. Our optimized protocol allows one-step generation of edited CAR-T cells that show a similar phenotypic profile to non-edited CAR-T cells, with equivalent in vitro and in vivo antitumoral efficacy. Moreover, genomic analysis of edited CAR-T cells revealed a safe integration profile of the vector, with no preferences for specific genomic regions, with highly specific editing of the HLA-I and TCR, without significant off-target sites. Finally, the production of edited CAR-T cells at a larger scale allowed the generation and selection of enough HLA-IKO/TCRKO CAR-T cells that would be compatible with clinical applications. In summary, our results demonstrate that CAR-T cells from AML patients, although functional, present phenotypic and functional features that could compromise their antitumoral efficacy, compared to CAR-T cells from healthy donors. The combination of CRISPR technologies with transposon-based delivery strategies allows the generation of HLA-IKO/TCRKO CAR-T cells, compatible with allogeneic approaches, that would represent a promising option for AML treatment

From transcriptomics to proteomics: Unraveling biological knowledge via Machine Learning

We start by highlighting basic concepts of both molecular biology and machine learning. This overview focuses on the key ideas that are required to comprehend the rest of the work, and thus, it does not attempt at providing a comprehensive review. We start with the basis of DNA and RNA, the genetic building bricks, until the formation of the proteins, the final actors of the genetic machinery. We also explore state-of-the-art technologies to measure those processes along with their limitations. After introducing the basic biological concepts, we will discuss the basics of machine learning methodologies and some of the most important models used in recent years to solve many biological problems

Identifying key multifunctional components shared by critical cancer and normal liver pathways via SparseGMM

Despite the abundance of multimodal data, suitable statistical models that can improve our understanding of diseases with genetic underpinnings are challenging to develop. Here, we present SparseGMM, a statistical approach for gene regulatory network discovery. SparseGMM uses latent variable modeling with sparsity constraints to learn Gaussian mixtures from multiomic data. By combining coexpression patterns with a Bayesian framework, SparseGMM quantitatively measures confidence in regulators and uncertainty in target gene assignment by computing gene entropy. We apply SparseGMM to liver cancer and normal liver tissue data and evaluate discovered gene modules in an independent single-cell RNA sequencing (scRNA-seq) dataset. SparseGMM identifies PROCR as a regulator of angiogenesis and PDCD1LG2 and HNF4A as regu-lators of immune response and blood coagulation in cancer. Furthermore, we show that more genes have significantly higher entropy in cancer compared with normal liver. Among high-entropy genes are key multifunctional components shared by critical pathways, including p53 and estrogen signaling