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Background Identification and tracking of important communicable diseases is pivotal to our understanding of the geographical distribution of disease, the emergence and spread of novel and resistant infections, and are of particular importance for public health policy planning. Moreover, understanding of current clinical practice norms is essential to audit clinical care, identify areas of concern, and develop interventions to improve care quality. However, there are several barriers to obtaining these research data. For example current disease surveillance mechanisms make it difficult for the busy doctor to know which diseases to notify, to whom and how, and are also time consuming. Consequently, many cases go un-notified. In addition assessments of current clinical practice are typically limited to small retrospective audits in individual hospitals. Therefore, we developed a free smartphone application to try to increase the identification of major infectious diseases and other acute medical presentations and improve our understanding of clinical practice. Description Within the first month there were over 1000 downloads and over 600 specific disease notifications, coming from a broad range of specialities, grades and from all across the globe, including some resource poor settings. Notifications have already provided important information, such as new cases of TB meningitis, resistant HIV and rabies, and important clinical information, such as where patient with myocardial infarctions are and are not receiving potentially life-saving therapy. The database generated can also answer new, dynamic and targeted questions. When a new guideline is released, for example for a new pandemic infection, we can track, in real-time, the global usage of the guideline and whether the recommendations are being followed. In addition this allows identification of where cases with key markers of severe disease are occurring. This is a potential resource for guideline-producing bodies, clinical governance and public health institutions and also for patient recruitment into ongoing studies. Conclusions Further parallel studies are needed to assess the clinical and epidemiological utility of novel disease surveillance applications, such as this, with direct comparisons made to data collected through routine surveillance routes. Nevertheless, current disease surveillance mechanisms do not always comprehensively and accurately reflect disease distribution for many conditions. Smartphone applications, such as ClickClinica, are a novel approach with the potential to generate real-time disease surveillance data that may augment current methods.

IntroductionCurrently, rare neurological diseases are under-reported and difficult to recruit to trials and research, in part due to difficulties with current surveillance mechanisms. Smartphones applications are increasingly being used by medical professionals to access up-to-date guidelines, but the potential to use them for data collection has yet to be fully explored.MethodWe developed a free smartphone application containing clinical guidelines, which collects data as the doctor reads, and asks readers specific research questions and to notify diseases. The application could also improve recruitment of patients to trials by automatically contacting research teams.ResultsIn the first 6 months the application has been viewed nearly 6000 times to access guidelines, with over 2000 specific disease notifications. Notifications have come from a wide range of countries, specialities, and grades. New cases of TB meningitis and rabies have been reported and also important clinical practice information collected, such as where patients with herpes encephalitis are, and are not, receiving aciclovir within the recommended timeframe.ConclusionParallel studies are needed to assess the utility of such applications. Nevertheless, current surveillance mechanisms miss many cases, and this novel approach has the potential to generate real-time disease surveillance data to augment current methods.

In computer science, ontologies define a domain to facilitate knowledge representation and sharing, in a machine processable way. Ontologies approximate an actual world representation, and thus ontologies will differ for many reasons. Therefore knowledge sharing, and in general semantic interoperability, is inherently hindered or even precluded between heterogenous ontologies. Ontology matching addresses this fundamental issue by producing alignments, i.e. sets of correspondences that describe relations between semantically related entities of different ontologies. However, alignments are typically incomplete. In order to support and improve ontology alignment, and semantic interoperability in general, this thesis exploits the notion of implicit definability. Implicit definability is a semantic property of ontologies, signatures, and concepts (and roles) stating that whenever the signature is fixed under a given ontology then the definition of a particular concept (or role) is also fixed. This thesis introduces the notion of minimal definition signature (MDS) from which a given entity is implicitly definable, and presents a novel approach that provides an efficient way to compute in practice all MDSs of the definable entities. Furthermore, it investigates the application of MDSs in the context of alignment generation, evaluation, and negotiation (whereby agents cooperatively establish a mutually acceptable alignment to support opportunistic communication within open environments). As implicit definability permits defined entities to be removed without semantic loss, this thesis argues, that if the meaning of the defined entity is wholly fixed by the terms of its definition, only the terms in the definition are required to be mapped in order to map the defined entity itself; thus implicit definability entails a new type of definability-based correspondence correspondence. Therefore this thesis defines and explores the properties of definability- based correspondences, and extends several ontology alignment evaluation metrics in order to accommodate their assessment. As task signature coverage is a prerequisite of many knowledge-based tasks (e.g. service invocation), a definability-based, efficient approximation approach to obtaining minimal signature cover sets is presented. Moreover, this thesis outlines a specific alignment negotiation approach and shows that by considering definability, agents are better equipped to: (i) determine whether an alignment provides the necessary coverage to achieve a particular task (align the whole ontology, formulate a message or query); (ii) adhere to privacy and confidentiality constraints; and (iii) minimalise the cardinality of the resulting mutual alignment.

Duplication of nodes is a common problem encountered when building knowledge graphs (KGs) from heterogeneous datasets, where it is crucial to be able to merge nodes having the same meaning. OntoMerger is a Python ontology integration library whose functionality is to deduplicate KG nodes. Our approach takes a set of KG nodes, mappings and disconnected hierarchies and generates a set of merged nodes together with a connected hierarchy. In addition, the library provides analytic and data testing functionalities that can be used to fine-tune the inputs, further reducing duplication, and to increase connectivity of the output graph. OntoMerger can be applied to a wide variety of ontologies and KGs. In this paper we introduce OntoMerger and illustrate its functionality on a real-world biomedical KG.

The use of knowledge graphs as a data source for machine learning methods to solve complex problems in life sciences has rapidly become popular in recent years. Our Biological Insights Knowledge Graph (BIKG) combines relevant data for drug development from public as well as internal data sources to provide insights for a range of tasks: from identifying new targets to repurposing existing drugs. Besides the common requirements to organisational knowledge graphs such as being able to capture the domain precisely and give the users the ability to search and query the data, the focus on handling multiple use cases and supporting use case-specific machine learning models presents additional challenges: the data models must also be streamlined for the performance of downstream tasks; graph content must be easily customisable for different use cases; different projections of the graph content are required to support a wider range of different consumption modes. In this paper we describe our main design choices in implementation of the BIKG graph and discuss different aspects of its life cycle: from graph construction to exploitation.

Membrane contact sites between organelles are critical for the transfer of biomolecules. Lipid droplets store fatty acids and form contacts with mitochondria, which regulate fatty acid oxidation and adenosine triphosphate production. Protein compartmentalization at lipid droplet-mitochondria contact sites and their effects on biological processes are poorly described. Using proximity-dependent biotinylation methods, we identify 71 proteins at lipid droplet-mitochondria contact sites, including a multimeric complex containing extended synaptotagmin (ESYT) 1, ESYT2, and VAMP Associated Protein B and C (VAPB). High resolution imaging confirms localization of this complex at the interface of lipid droplet-mitochondria-endoplasmic reticulum where it likely transfers fatty acids to enable β-oxidation. Deletion of ESYT1, ESYT2 or VAPB limits lipid droplet-derived fatty acid oxidation, resulting in depletion of tricarboxylic acid cycle metabolites, remodeling of the cellular lipidome, and induction of lipotoxic stress. These findings were recapitulated in Esyt1 and Esyt2 deficient mice. Our study uncovers a fundamental mechanism that is required for lipid droplet-derived fatty acid oxidation and cellular lipid homeostasis, with implications for metabolic diseases and survival. Protein-mediated transport is implicated in trafficking fatty acids at contact sites of lipid droplets and mitochondria. Here, the authors use proteomics to catalogue the proteins at this contact site and report a mechanism of fatty acid transfer that regulates fatty acid oxidation and lipid homeostasis.

Transforming an up-to-date vehicle into a measurement system is a rewarding task due to the large number of different sensors in the onboard control and diagnostic systems. These procedures are not performed by a single control unit; it is necessary to share the signal values over a communication network, to which an external device can be connected to record the real traffic. The paper aims to use these recorded data for 1 DOF longitudinal vehicle and powertrain model validation. For repeatability, three city routes are selected: plain road, smaller road grade, and higher road grade in both directions. Therefore, the drivetrain system is tested in a high load range, even with long-term recuperation. The altitude changes are recorded with a DGPS system. By the recorded measurements, the vehicle and the drivetrain model can be calibrated, such as the air drag parameters, the rolling resistances, and the efficiencies of the drivetrain. The validation criteria are defined for speed tracking, and the relative tolerance of the cumulated energy should be below 10%. At the end of the day, a developed model is ready for energetic analysis or control strategy design. The energy balance of the applied cycles is also presented to prove that.

Red clover (Trifolium pratense) is a perennial forage legume wildly used in temperate regions, including northern Europe. Its breeders are under increasing pressure to obtain rapid genetic gains to meet the high demand for improved forage yield and quality. One solution to increase genetic gain by reducing time and increasing accuracy is genomic selection. Thus, efficient genomic prediction (GP) models need to be developed, which are unbiased to traits and harvest time points. This study aimed to develop and evaluate single-trait (ST) and multi-trait (MT) models that simultaneously target more than one trait or cut. The target traits were dry matter yield, crude protein content, net energy for lactation, and neutral detergent fiber. The MT models either combined dry matter yield with one forage quality trait, all traits at one cut, or one trait across all cuts. The results show an increase with MT models where the traits had a genetic correlation of 0.5 or above. This study indicates that non-additive genetic effects have significant but varying effects on the predictive ability and reliability of the models. The key conclusion of this study was that these non-additive genetic effects could be better described by incorporating genetically correlated traits or cuts.

Several neglected tropical diseases (NTDs) employ mass drug administration (MDA) as part of their control or elimination strategies. This has historically required multiple distinct campaigns, each targeting one or more NTDs, representing a strain on both the recipient communities and the local health workforce implementing the distribution. We explored perceptions and attitudes surrounding combined MDA among these two groups of stakeholders. Our qualitative study was nested within a cluster randomized non-inferiority safety trial of combined ivermectin, albendazole and azithromycin MDA. Using semi-structured question guides, we conducted 16 key informant interviews with selected individuals involved in implementing MDA within the participating district. To better understand the perceptions of recipient communities, we also conducted four focus group discussions with key community groups. Individuals were selected from both the trial arm (integrated MDA) and the control arm (standard MDA) to provide a means of comparison and discussion. All interviews and focus group discussions were led by fluent Afaan oromo speakers. Interviewers transcribed and later translated all discussions into English. The study team synthesized and analyzed the results via a coding framework and software. Most respondents appreciated the time and effort saved via the co-administered MDA strategy but there were some misgivings amongst community beneficiaries surrounding pill burden. Both the implementing health work force members and beneficiaries reported refusals stemming from lack of understanding around the need for the new drug regimen as well as some mistrust of government officials among the youth. The house-to-house distribution method, adopted as a COVID-19 prevention strategy, was by far preferred by all beneficiaries over central-point MDA, and may have led to greater acceptability of co-administration. Our data demonstrate that a co-administration strategy for NTDs is acceptable to both communities and health staff.

Maintenance of mitochondrial protein homeostasis is critical for proper cellular function. Under normal conditions resident molecular chaperones and proteases maintain protein homeostasis within the organelle. Under conditions of stress however, misfolded proteins accumulate leading to the activation of the mitochondrial unfolded protein response (UPR mt ). While molecular chaperone assisted refolding of proteins in mammalian mitochondria has been well documented, the contribution of AAA+ proteases to the maintenance of protein homeostasis in this organelle remains unclear. To address this gap in knowledge we examined the contribution of human mitochondrial matrix proteases, LONM and CLPXP, to the turnover of OTC-∆, a folding incompetent mutant of ornithine transcarbamylase, known to activate UPR mt . Contrary to a model whereby CLPXP is believed to degrade misfolded proteins, we found that LONM and not CLPXP is responsible for the turnover of OTC-∆ in human mitochondria. To analyse the conformational state of proteins that are recognised by LONM, we examined the turnover of unfolded and aggregated forms of malate dehydrogenase (MDH) and OTC. This analysis revealed that LONM specifically recognises and degrades unfolded, but not aggregated proteins. Since LONM is not upregulated by UPR mt , this pathway may preferentially act to promote chaperone mediated refolding of proteins.