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In this paper we propose a functional account of the Welsh mood system, focussing on responsives in particular. The discourse functions of responsives are interpreted through the concept of negotiation within the systemic functional linguistic framework, which offers a rich model for accounting for both initiations and responses, including possible tracking and challenging moves. By examining the interaction of mood together with specific features of Welsh, e.g. a dominant VSO clause ordering, mood particles, Subject ellipsis and a complex system of negation, we are able to show that Welsh tends to highlight interpersonal meanings in clause initial position. As the first functional description of Welsh, we also set out important directions for future research, based on the findings presented in this paper.

In this paper we describe an automated approach to enriching sentiment analysis with idiom–based features. Specifically, we automated the development of the supporting lexico–semantic resources, which include (1) a set of rules used to identify idioms in text and (2) their sentiment polarity classifications. Our method demonstrates how idiom dictionaries, which are readily available general pedagogical resources, can be adapted into purpose–specific computational resources automatically. These resources were then used to replace the manually engineered counterparts in an existing system, which originally outperformed the baseline sentiment analysis approaches by 17 percentage points on average, taking the F–measure from 40s into 60s. The new fully automated approach outperformed the baselines by 8 percentage points on average taking the F–measure from 40s into 50s. Although the latter improvement is not as high as the one achieved with the manually engineered features, it has got the advantage of being more general in a sense that it can readily utilize an arbitrary list of idioms without the knowledge acquisition overhead previously associated with this task, thereby fully automating the original approach.

Machine learning based Intrusion Detection Systems (IDS) allow flexible and efficient automated detection of cyberattacks in Internet of Things (IoT) networks. However, this has also created an additional attack vector; the machine learning models which support the IDS's decisions may also be subject to cyberattacks known as Adversarial Machine Learning (AML). In the context of IoT, AML can be used to manipulate data and network traffic that traverse through such devices. These perturbations increase the confusion in the decision boundaries of the machine learning classifier, where malicious network packets are often miss-classified as being benign. Consequently, such errors are bypassed by machine learning based detectors, which increases the potential of significantly delaying attack detection and further consequences such as personal information leakage, damaged hardware, and financial loss. Given the impact that these attacks may have, this paper proposes a rule-based approach towards generating AML attack samples and explores how they can be used to target a range of supervised machine learning classifiers used for detecting Denial of Service attacks in an IoT smart home network. The analysis explores which DoS packet features to perturb and how such adversarial samples can support increasing the robustness of supervised models using adversarial training. The results demonstrated that the performance of all the top performing classifiers were affected, decreasing a maximum of 47.2 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks.

The proliferation in Internet of Things (IoT) devices, which routinely collect sensitive information, is demonstrated by their prominence in our daily lives. Although such devices simplify and automate every day tasks, they also introduce tremendous security flaws. Current insufficient security measures employed to defend smart devices make IoT the “weakest” link to breaking into a secure infrastructure, and therefore an attractive target to attackers. This paper proposes a three layer intrusion detection system (IDS) that uses a supervised approach to detect a range of popular network based cyber-attacks on IoT networks. The system consists of three main functions: 1) classify the type and profile the normal behavior of each IoT device connected to the network; 2) identifies malicious packets on the network when an attack is occurring; and 3) classifies the type of the attack that has been deployed. The system is evaluated within a smart home testbed consisting of eight popular commercially available devices. The effectiveness of the proposed IDS architecture is evaluated by deploying 12 attacks from 4 main network based attack categories, such as denial of service (DoS), man-in-the-middle (MITM)/spoofing, reconnaissance, and replay. Additionally, the system is also evaluated against four scenarios of multistage attacks with complex chains of events. The performance of the system’s three core functions result in an ${F}$ -measure of: 1) 96.2%; 2) 90.0%; and 3) 98.0%. This demonstrates that the proposed architecture can automatically distinguish between IoT devices on the network, whether network activity is malicious or benign, and detect which attack was deployed on which device connected to the network successfully.

The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. InterProScan is the underlying software that allows protein and nucleic acid sequences to be searched against InterPro's signatures. Signatures are predictive models which describe protein families, domains or sites, and are provided by multiple databases. InterPro combines signatures representing equivalent families, domains or sites, and provides additional information such as descriptions, literature references and Gene Ontology (GO) terms, to produce a comprehensive resource for protein classification. Founded in 1999, InterPro has become one of the most widely used resources for protein family annotation. Here, we report the status of InterPro (version 81.0) in its 20th year of operation, and its associated software, including updates to database content, the release of a new website and REST API, and performance improvements in InterProScan.

This article presents three-tiered intrusion detection systems, which uses a supervised approach to detect cyber-attacks in industrial control systems networks. The proposed approach does not only aim to identify malicious packets on the network but also attempts to identify the general and finer grain attack type occurring on the network. This is key in the industrial control systems environment as the ability to identify exact attack types will lead to an increased response rate to the incident and the defence of the infrastructure. More specifically, the proposed system consists of three stages that aim to classify: (i) whether packets are malicious; (ii) the general attack type of malicious packets (e.g. Denial of Service); and (iii) finer-grained cyber-attacks (e.g. bad cyclic redundancy check, attack). The effectiveness of the proposed intrusion detection systems is evaluated on network data collected from a real industrial gas pipeline system. In addition, an insight is provided as to which features are most relevant in detecting such malicious behaviour. The performance of the system results in an F-measure of: (i) 87.4%, (ii) 74.5% and (iii) 41.2%, for each of the layers, respectively. This demonstrates that the proposed architecture can successfully distinguish whether network activity is malicious and detect which general attack was deployed.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causesthe infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformaticstools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection,understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to getinsight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for theroutine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemicand evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets anddevelopment of therapeutic strategies. For each tool, we briefly describe its use case and how it advances researchspecifically for SARS-CoV-2. Fil: Hufsky, Franziska. Friedrich Schiller University Jena; Alemania Fil: Lamkiewicz, Kevin. Friedrich Schiller University Jena; Alemania Fil: Almeida, Alexandre. the Wellcome Sanger Institute; Reino Unido Fil: Aouacheria, Abdel. Centre National de la Recherche Scientifique; Francia Fil: Arighi, Cecilia. Biocuration and Literature Access at PIR; Estados Unidos Fil: Bateman, Alex. European Bioinformatics Institute. Head of Protein Sequence Resources; Reino Unido Fil: Baumbach, Jan. Universitat Technical Zu Munich; Alemania Fil: Beerenwinkel, Niko. Universitat Technical Zu Munich; Alemania Fil: Brandt, Christian. Jena University Hospital; Alemania Fil: Cacciabue, Marco Polo Domingo. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Chuguransky, Sara Rocío. European Bioinformatics Institute; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Drechsel, Oliver. Robert Koch-Institute; Alemania Fil: Finn, Robert D.. Biocurator for Pfam and InterPro databases; Reino Unido Fil: Fritz, Adrian. Helmholtz Centre for Infection Research; Alemania Fil: Fuchs, Stephan. Robert Koch-Institute; Alemania Fil: Hattab, Georges. University Marburg; Alemania Fil: Hauschild, Anne Christin. University Marburg; Alemania Fil: Heider, Dominik. University Marburg; Alemania Fil: Hoffmann, Marie. Freie Universität Berlin; Alemania Fil: Hölzer, Martin. Friedrich Schiller University Jena; Alemania Fil: Hoops, Stefan. University of Virginia; Estados Unidos Fil: Kaderali, Lars. University Medicine Greifswald; Alemania Fil: Kalvari, Ioanna. European Bioinformatics Institute; Reino Unido Fil: von Kleist, Max. Robert Koch-Institute; Alemania Fil: Kmiecinski, Renó. Robert Koch-Institute; Alemania Fil: Kühnert, Denise. Max Planck Institute for the Science of Human History; Alemania Fil: Lasso, Gorka. Albert Einstein College of Medicine; Estados Unidos Fil: Libin, Pieter. Hasselt University; Bélgica Fil: List, Markus. Universitat Technical Zu Munich; Alemania Fil: Löchel, Hannah F.. University Marburg; Alemania

The number of diverse interconnected Internet of Things (IoT) devices keeps increasing exponentially, introducing new security and privacy challenges. These devices tend to become more pervasive than mobile phones and already have access to very sensitive personal information such as usernames, passwords, etc., making them a target for cyber-attacks. Given that smart devices are vulnerable to a variety of attacks, they can be considered to be the weakest link for breaking into a secure infrastructure. For instance, IoT devices have recently been employed as part of botnets, such as Mirai, and have launched several of the largest Distributed Denial of Service (DDoS) and spam attacks in history. As a result, there is a need to develop an Intrusion Detection System (IDS) dedicated to monitor IoT ecosystems, which will be able to adapt to this heterogeneous environment and detect malicious activity on the network. In this paper, we describe the initial stages of developing Pulse; a novel IDS for the IoT, which employs Machine Learning (ML) methodologies and is capable of successfully identifying network scanning probing and simple forms of Denial of Service (DoS) attacks.

Idiom-based features significantly improve sentiment classification results.This study provides resources that can support further research into sentiment analysis.A comprehensive collection of 580 idioms annotated with sentiment polarity.A set of local grammars that can be used to recognize occurrences of these idioms.A corpus of 2521 annotated sentences in which idioms are used in context. In this paper we investigate the role of idioms in automated approaches to sentiment analysis. To estimate the degree to which the inclusion of idioms as features may potentially improve the results of traditional sentiment analysis, we compared our results to two such methods. First, to support idioms as features we collected a set of 580 idioms that are relevant to sentiment analysis, i.e. the ones that can be mapped to an emotion. These mappings were then obtained using a web-based crowdsourcing approach. The quality of the crowdsourced information is demonstrated with high agreement among five independent annotators calculated using Krippendorff's alpha coefficient (α=0.662). Second, to evaluate the results of sentiment analysis, we assembled a corpus of sentences in which idioms are used in context. Each sentence was annotated with an emotion, which formed the basis for the gold standard used for the comparison against two baseline methods. The performance was evaluated in terms of three measures - precision, recall and F-measure. Overall, our approach achieved 64% and 61% for these three measures in two experiments improving the baseline results by 20 and 15 percent points respectively. F-measure was significantly improved over all three sentiment polarity classes: Positive, Negative and Other. Most notable improvement was recorded in classification of positive sentiments, where recall was improved by 45 percent points in both experiments without compromising the precision. The statistical significance of these improvements was confirmed by McNemar's test.

In a longitudinal prospective study 58 schoolgirls with covert bacteriuria were followed up for an average of 11.2 years (range 8.8 to 13.5 years). Intravenous urography was carried out at the start of the study (aged 4 to 11 years) and after completion of the follow up period (aged 14.8 to 22.3 years). After random allocation 27 of these girls received intermittent treatment for covert bacteriuria for the first four years and the control group received no treatment. The effect of covert bacteriuria, treatment, vesicoureteric reflux, and reflux nephropathy at presentation on the final renal length, progression of scarring, and development of new scars was analysed. No new scars were found in girls with bilaterally normal kidneys. In girls with reflux nephropathy, three kidneys showed progression of existing scars and two kidneys developed new scars. It was shown that final renal length was not influenced by vesicoureteric reflux or treatment, but reduced renal length at final assessment was associated with the presence of kidney scarring at initial assessment.

The Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new families have been added in Pfam 33.1 and numerous improvements have been made to existing entries. To facilitate research on COVID-19, we have revised the Pfam entries that cover the SARS-CoV-2 proteome, and built new entries for regions that were not covered by Pfam. We have reintroduced Pfam-B which provides an automatically generated supplement to Pfam and contains 136 730 novel clusters of sequences that are not yet matched by a Pfam family. The new Pfam-B is based on a clustering by the MMseqs2 software. We have compared all of the regions in the RepeatsDB to those in Pfam and have started to use the results to build and refine Pfam repeat families. Pfam is freely available for browsing and download at http://pfam.xfam.org/.