| 1. |
- Wylensek, David, et al.
(författare)
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A collection of bacterial isolates from the pig intestine reveals functional and taxonomic diversity
- 2020
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Ingår i: Nature Communications. - : Nature Publishing Group. - 2041-1723. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Our knowledge about the gut microbiota of pigs is still scarce, despite the importance of these animals for biomedical research and agriculture. Here, we present a collection of cultured bacteria from the pig gut, including 110 species across 40 families and nine phyla. We provide taxonomic descriptions for 22 novel species and 16 genera. Meta-analysis of 16S rRNA amplicon sequence data and metagenome-assembled genomes reveal prevalent and pig-specific species within Lactobacillus, Streptococcus, Clostridium, Desulfovibrio, Enterococcus, Fusobacterium, and several new genera described in this study. Potentially interesting functions discovered in these organisms include a fucosyltransferase encoded in the genome of the novel species Clostridium porci, and prevalent gene clusters for biosynthesis of sactipeptide-like peptides. Many strains deconjugate primary bile acids in in vitro assays, and a Clostridium scindens strain produces secondary bile acids via dehydroxylation. In addition, cells of the novel species Bullifex porci are coccoidal or spherical under the culture conditions tested, in contrast with the usual helical shape of other members of the family Spirochaetaceae. The strain collection, called 'Pig intestinal bacterial collection' (PiBAC), is publicly available at www.dsmz.de/pibac and opens new avenues for functional studies of the pig gut microbiota. The authors present a public collection of 117 bacterial isolates from the pig gut, including the description of 38 novel taxa. Interesting functions discovered in these organisms include a new fucosyltransferease and sactipeptide-like molecules encoded by biosynthetic gene clusters.
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| 2. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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A multiple motion sensors index for motor state quantification in Parkinson's disease
- 2020
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 189
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks. Method: Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients’ videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS. Results: The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89. Conclusion: Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results. © 2019
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| 3. |
- Al-Hammadi, Mustafa, 1995-, et al.
(författare)
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Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis : A Systematic Literature Review
- 2024
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Ingår i: Journal of Alzheimer's Disease. - 1387-2877 .- 1875-8908. ; 100:1, s. 1-27
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Forskningsöversikt (refereegranskat)abstract
- BACKGROUND: Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer's disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection.OBJECTIVE: Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods.METHODS: A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review.RESULTS: The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results.CONCLUSIONS: The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.
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| 4. |
- Anckarsäter, Henrik, et al.
(författare)
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Mental health and international crimes
- 2014
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Ingår i: Criminological approaches to international criminal law. - 9781107060036 ; , s. 263-286
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Bokkapitel (refereegranskat)abstract
- This volume is one of the few books to explain in-depth the international crimes behind the scenes of substantive or procedural law. The contributors place a particular focus on what motivates participation in international crime, how perpetrators, witnesses and victims see their predicament and how international crimes should be investigated at local and international level, with an emphasis on context. The book engages these questions with a broad interdisciplinary approach that is accessible to both lawyers and non-lawyers alike. It discusses international crime through the lens of anthropology, neuroscience, psychology, state crime theory and information systems theory and draws upon relevant investigative experience from experts in international and domestic law prosecutions.
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| 5. |
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| 6. |
- Bures, Tomas, et al.
(författare)
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Software Engineering for Smart Cyber-Physical Systems - Towards a Research Agenda : Report on the First International Workshop on Software Engineering for Smart CPS
- 2015
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Ingår i: Software Engineering Notes. - : ACM Press. - 0163-5948 .- 1943-5843. ; 40:6, s. 28-32
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Cyber-Physical Systems (CPS) are large interconnected softwareintensivesystems that influence, by sensing and actuating, thephysical world. Examples are traffic management and power grids.One of the trends we observe is the need to endow such systemswith the “smart” capabilities, typically in the form of selfawarenessand self-adaptation, along with the traditional qualitiesof safety and dependability. These requirements combined withspecifics of the domain of smart CPS – such as large scale, the roleof end-users, uncertainty, and open-endedness – render traditionalsoftware engineering (SE) techniques not directly applicable; makingsystematic SE of smart CPS a challenging task. This paperreports on the results of the First International Workshop on SoftwareEngineering of Smart Cyber-Physical Systems (SEsCPS2015), where participants discussed characteristics, challenges andopportunities of SE for smart CPS, with the aim to outline anagenda for future research in this important area.
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| 7. |
- Filieri, Antonio, et al.
(författare)
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Control Strategies for Self-Adaptive Software Systems
- 2017
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Ingår i: ACM Transactions on Autonomous and Adaptive Systems. - : Association for Computing Machinery (ACM). - 1556-4665 .- 1556-4703. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical grounding, allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes.This article discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and nonfunctional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the article highlights a set of open challenges, both for the software engineering and the control theory research communities.
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| 8. |
- Filieri, Antonio, et al.
(författare)
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Software Engineering Meets Control Theory
- 2015
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Ingår i: 2015 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. - Piscataway, NJ, USA : IEEE Press. - 9780769555676 ; , s. 71-82
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Konferensbidrag (refereegranskat)abstract
- The software engineering community has proposed numerous approaches for making software self-adaptive. These approaches take inspiration from machine learning and control theory, constructing software that monitors and modifies its own behavior to meet goals. Control theory, in particular, has received considerable attention as it represents a general methodology for creating adaptive systems. Control-theoretical software implementations, however, tend to be ad hoc. While such solutions often work in practice, it is difficult to understand and reason about the desired properties and behavior of the resulting adaptive software and its controller. This paper discusses a control design process for software systems which enables automatic analysis and synthesis of a controller that is guaranteed to have the desired properties and behavior. The paper documents the process and illustrates its use in an example that walks through all necessary steps for self-adaptive controller synthesis.
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| 9. |
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| 10. |
- Gerostathopoulos, Ilias, et al.
(författare)
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Continuous Data-driven Software Engineering : Towards a Research Agenda
- 2019
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Ingår i: Software Engineering Notes. - : Association for Computing Machinery (ACM). - 0163-5948 .- 1943-5843. ; 44:3, s. 60-64
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Tidskriftsartikel (refereegranskat)abstract
- The rapid pace with which software needs to be built, together with the increasing need to evaluate changes for end users both quantitatively and qualitatively calls for novel software engineering approaches that focus on short release cycles, continuous deployment and delivery, experiment-driven feature development, feedback from users, and rapid tool-assisted feedback to developers. To realize these approaches there is a need for research and innovation with respect to automation and tooling, and furthermore for research into the organizational changes that support flexible data-driven decision-making in the development lifecycle. Most importantly, deep synergies are needed between software engineers, managers, and data scientists. This paper reports on the results of the joint 5th International Workshop on Rapid Continuous Software Engineering (RCoSE 2019) and the 1st International Workshop on Data-Driven Decisions, Experimentation and Evolution (DDrEE 2019), which focuses on the challenges and potential solutions in the area of continuous data-driven software engineering.
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