The wide adoption of Machine Learning to solve a large set of real-life problems came with the need to collect and process large volumes of data, some of which are considered personal and sensitive, raising serious concerns about data protection. Privacy-enhancing technologies (PETs) are often indicated as a solution to protect personal data and to achieve a general trustworthiness as required by current EU regulations on data protection and AI. However, an off-the-shelf application of PETs is insufficient to ensure a high-quality of data protection, which one needs to understand. This work systematically discusses the risks against data protection in modern Machine Learning systems taking the original perspective of the data owners, who are those who hold the various data sets, data models, or both, throughout the machine learning life cycle and considering the different Machine Learning architectures. It argues that the origin of the threats, the risks against the data, and the level of protection offered by PETs depend on the data processing phase, the role of the parties involved, and the architecture where the machine learning systems are deployed. By offering a framework in which to discuss privacy and confidentiality risks for data owners and by identifying and assessing privacy-preserving countermeasures for machine learning, this work could facilitate the discussion about compliance with EU regulations and directives. We discuss current challenges and research questions that are still unsolved in the field. In this respect, this paper provides researchers and developers working on machine learning with a comprehensive body of knowledge to let them advance in the science of data protection in machine learning field as well as in closely related fields such as Artificial Intelligence.

Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.

Cholesteric Spherical Reflectors (CSRs) are made of droplets of cholesteric liquid crystals (the same material under the screen of our mobile phones) but molded in a spherical shape and hardened into a solid. CSRs have a peculiar behavior when illuminated: they reflect light and produce unique optical patterns whose full display is hardly predictable. They have been argued to behave like an optical Physical Unclonable Function (PUF), therefore finding application in anti-counterfeiting, in particular for object authentication. However, a fundamental challenge remains open: to understand what makes each optical response unique and how to extract this identifying information reliably and repeatedly. We study the problem, and we design and discuss two pivotal procedures to build authentication protocols for objects coated with CSRs. We test the quality of our procedures against large data sets of pattern images: images from CSRs are used to calculate inter- and intra-distance; simulated patterns created artificially are used to measure security in terms of false positive ratio. Our procedures successfully cluster images coming from the same CSR, distinguishing them from images of different CSRs and decoys. Our work is one of the few that has studied procedures of information extraction for materials derived from CSRs. It advances the state of the art in this area, closing the gap between the research on optical PUFs and practical applications.

SUMMARY: The decreasing cost in high-throughput technologies led to a number of sequencing projects consisting of thousands of whole genomes. The paradigm shift from exome to whole genome brings a significant increase in the size of output files. Most of the existing tools which are developed to analyse exome files are not adequate for larger VCF files produced by whole genome studies. In this work we present VCF-Explorer, a variant analysis software capable of handling large files. Memory efficiency and avoiding computationally costly pre-processing step enable to carry out the analysis to be performed with ordinary computers. VCF-Explorer provides an easy to use environment where users can define various types of queries based on variant and sample genotype level annotations. VCF-Explorer can be run in different environments and computational platforms ranging from a standard laptop to a high performance server. AVAILABILITY AND IMPLEMENTATION: VCF-Explorer is freely available at: http://vcfexplorer.sourceforge.net/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DNA deletions are one of the main genetic reasons of disease. Currently there are many tools which are capable of detecting structural variations. However, these tools usually require long running time and lack ease of use. It is generally not possible to restrict the search to a region of interest. The programs also yield excessive number of results which obstructs further analysis. In this work, we present LargeDEL, a tool which quickly scans aligned paired-end next generation sequencing (NGS) data for finding large deletions. The program is capable of extracting the candidate deletions according to desired criteria. It is a fast, easy to use tool for finding large deletions within the critical regions in the whole genome.

UNLABELLED: Due to the big data produced by next-generation sequencing studies, there is an evident need for methods to extract the valuable information gathered from these experiments. In this work, we propose GeneCOST, a novel scoring-based method to evaluate every gene for their disease association. Without any prior filtering and any prior knowledge, we assign a disease likelihood score to each gene in correspondence with their variations. Then, we rank all genes based on frequency, conservation, pedigree and detailed variation information to find out the causative reason of the disease state. We demonstrate the usage of GeneCOST with public and real life Mendelian disease cases including recessive, dominant, compound heterozygous and sporadic models. As a result, we were able to identify causative reason behind the disease state in top rankings of our list, proving that this novel prioritization framework provides a powerful environment for the analysis in genetic disease studies alternative to filtering-based approaches. AVAILABILITY AND IMPLEMENTATION: GeneCOST software is freely available at www.igbam.bilgem.tubitak.gov.tr/en/softwares/genecost-en/index.html. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract This paper examines Vaudenay's privacy model, which is one of the first and most complete privacy models that featured the notion of different privacy classes. We enhance this model by introducing two new generic adversary classes,k‐strong andk‐forward adversaries where the adversary is allowed to corrupt a tag at mostktimes. Moreover, we introduce an extended privacy definition that also covers all privacy classes of Vaudenay's model. In order to achieve highest privacy level, we study low cost primitives such as physically unclonable functions (PUFs). The common assumption of PUFs is that their physical structure is destroyed once tampered. This is an ideal assumption because the tamper resistance depends on the ability of the attacker and the quality of the PUF circuits. In this paper, we have weakened this assumption by introducing a new definitionk‐resistant PUFs.k‐PUFs are tamper resistant against at mostkattacks; that is, their physical structure remains still functional and correct until at mostkthphysical attack. Furthermore, we prove that strong privacy can be achieved without public‐key cryptography usingkPUF‐based authentication. We finally prove that our extended proposal achieves both reader authentication andk‐strong privacy. Copyright © 2014 John Wiley & Sons, Ltd.