6,668 research outputs found

    Musculoskeletal complaints in primary care:Constraining healthcare costs, rethinking the deployment of healthcare professionals

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    Worldwide policy makers are challenged to account for rising healthcare costs and increased healthcare demand. Also, in the Netherlands there is a growing concern how to maintain high-quality and accessible care while keeping costs in check. Access to care is under pressure as the demand for care is rising fast, due to an aging population and an increasing number of chronically ill people. Not only at the policy level, but also in clinical practice challenges exist. The workload in the health care sector is high, causing health workers, such as general practitioners (GPs), to leave this sector. To keep costs in check available resources need be allocated as efficiently as possible. A good starting point for evaluating healthcare costs may be assessing large patient groups that are responsible for high resource use and costs, such as patients with musculoskeletal conditions treated in general practice. Another point may be identifying prognostic factors for higher healthcare costs. Besides lowering costs, it is also of importance to keep GP care accessible by lowering GPs’ workload. One of the ways to address GPs’ high workload is task reallocation. Internationally, positive effects have been found for an Advanced Physiotherapy Practitioner (APP) model of care, in which APPs take over tasks from a physician in the care for patients with musculoskeletal conditions. This model of care could potentially be of value in reducing the workload of Dutch GPs and keeping GP care accessible. Besides lowering healthcare cost and decreasing GPs’ workload maintaining good quality care is essential. One of the most widely used Patient Reported Outcome Measures (PROMs) in assessing quality of healthcare is the EQ-5D, a preference-based measurement instrument that measures health related quality of life and is used to estimate utility values that represent the preferences of the general population of a country for given health states. These utility values are needed for estimating Quality-Adjusted Life-Years (QALYs) in cost effectiveness analysis. However, quality-of-life measurements are generally not available when data are collected for clinical purposes, such as data from GP electronic medical records. Therefore, researchers are exploring ways to estimate EQ-5D based utility values by means of outcomes on other available health related outcome measures. This thesis aimed to explore some of the challenges in Dutch primary care by evaluating 1) healthcare utilization and associated cost of GP-guided care in patients with musculoskeletal complaints, 2) the introduction of an APP model of care, and 3) different approaches to estimate missing EQ-5D based utility values

    Evaluation Methodologies in Software Protection Research

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    Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 572 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks

    Software Product Line Engineering via Software Transplantation

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    For companies producing related products, a Software Product Line (SPL) is a software reuse method that improves time-to-market and software quality, achieving substantial cost reductions.These benefits do not come for free. It often takes years to re-architect and re-engineer a codebase to support SPL and, once adopted, it must be maintained. Current SPL practice relies on a collection of tools, tailored for different reengineering phases, whose output developers must coordinate and integrate. We present Foundry, a general automated approach for leveraging software transplantation to speed conversion to and maintenance of SPL. Foundry facilitates feature extraction and migration. It can efficiently, repeatedly, transplant a sequence of features, implemented in multiple files. We used Foundry to create two valid product lines that integrate features from three real-world systems in an automated way. Moreover, we conducted an experiment comparing Foundry's feature migration with manual effort. We show that Foundry automatically migrated features across codebases 4.8 times faster, on average, than the average time a group of SPL experts took to accomplish the task

    An extensive survey on Diffusion models

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    Denoising Diffusion models are gaining growing popularity in the field of generative modeling for several reasons. These reasons include the straightforward and stable training, the outstanding generative quality, and the robust probabilistic foundation, picture synthesis, video production, and molecular design are all examples of what this tool can do. This thesis explores denoising diffusion models, which are statistical models that aim to remove noise from an image while preserving its important features. The study focuses on developing new techniques for improving the performance of denoising diffusion models, such as incorporating prior information about the image structure, designing more efficient numerical algorithms for solving the models, and evaluating the effectiveness of the denoising algorithms using various quality metrics. The research also investigates the application of denoising diffusion models in various image processing tasks, such as image restoration, feature extraction, and segmentation. The performance of the proposed methods is evaluated on a variety of benchmark datasets, and the results demonstrate significant improvements in denoising accuracy compared to existing state-of-the-art techniques. Overall, this thesis provides valuable insights into the development and application of denoising diffusion models, which have important applications in many fields, including medical imaging, computer vision, and remote sensing. The proposed techniques and algorithms can potentially lead to significant advances in image processing and analysis, with practical implications for improving the quality and reliability of image-based applications

    ATR-FTIR Spectroscopy-Linked Chemometrics:A Novel Approach to the Analysis and Control of the Invasive Species Japanese Knotweed

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    Japanese knotweed (Reynoutria japonica), an invasive plant species, causes negative environmental and socio-economic impacts. A female clone in the United Kingdom, its extensive rhizome system enables rapid vegetative spread. Plasticity permits this species to occupy a broad geographic range and survive harsh abiotic conditions. It is notoriously difficult to control with traditional management strategies, which include repetitive herbicide application and costly carbon-intensive rhizome excavation. This problem is complicated by crossbreeding with the closely related species, Giant knotweed (Reynoutria sachalinensis), to give the more vigorous hybrid, Bohemian knotweed (Fallopia x Bohemica) which produces viable seed. These species, hybrids, and backcrosses form a morphologically similar complex known as Japanese knotweed ‘sensu lato’ and are often misidentified. The research herein explores the opportunities offered by advances in the application of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy-linked chemometrics within plant sciences, for the identification and control of knotweed, to enhance our understanding of knotweed biology, and the potential of this technique. ATR-FTIR spectral profiles of Japanese knotweed leaf material and xylem sap samples, which include important biological absorptions due to lipids, proteins, carbohydrates, and nucleic acids, were used to: identify plants from different growing regions highlighting the plasticity of this clonal species; differentiate between related species and hybrids; and predict key physiological characteristics such as hormone concentrations and root water potential. Technical advances were made for the application of ATR-FTIR spectroscopy to plant science, including definition of the environmental factors that exert the most significant influence on spectral profiles, evaluation of sample preparation techniques, and identification of key wavenumbers for prediction of hormone concentrations and abiotic stress. The presented results cement the position of concatenated mid-infrared spectroscopy and machine learning as a powerful approach for the study of plant biology, extending its reach beyond the field of crop science to demonstrate a potential for the discrimination between and control of invasive plant species

    Investigating self-perception of emotion in individuals with non-epileptic seizures (NES)

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    Emotional processing difficulties are hypothesised to be involved in the aetiology and maintenance of non-epileptic seizures (NES). This thesis aimed to explore the relationship between aspects of emotional processing: interoception, alexithymia and executive functioning, in people with NES in comparison with healthy controls and to understand how people with NES experience their symptoms, live with their condition, and perceive the role of life events in relation to their seizures. Study 1 reviewed the evidence for a relationship between interoception and other key emotional factors in studies which employed heartbeat perception tasks to measure interoception. Study quality was found to be generally poor, with no consistent evidence for significant findings between interoception and emotional factors, including alexithymia, depression, and anxiety. Study 2 was a cross-sectional, online, study to investigate an interactional model of emotion processing, exploring relationships between interoceptive sensibility, alexithymia, and executive functioning (attentional bias) in NES participants and healthy controls. Measures included the Body Perception Questionnaire (BPQ-VSF), the Toronto Alexithymia Scale-20 (TAS-20) and the emotional Stroop task (eStroop). The NES group, compared to controls, reported higher BPQ-VSF and TAS-20 scores. There were no significant correlations between any of the measures of interest in either the NES or control group. There was no evidence to support the proposed model. Study 3 was a qualitative study using Interpretative Phenomenological Analysis to explore: how individuals with NES respond emotionally to recent life events; and how these events impact on seizures. Six themes were developed from the analysis which described how NES affected many aspects of people’s lives. Four models captured the different ways in which people perceived the relationship between life stressors, their emotional responses, and their seizures: event->emotional response-> seizure; event-> emotional response -x-> no seizure; no event ->emotional reaction/experience -> seizure; and no event -x->no emotional response->seizure

    Evolutionary Reinforcement Learning: A Survey

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    Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep learning has recently resulted in impressive achievements in a wide range of challenging tasks, including board games, arcade games, and robot control. Despite these successes, there remain several crucial challenges, including brittle convergence properties caused by sensitive hyperparameters, difficulties in temporal credit assignment with long time horizons and sparse rewards, a lack of diverse exploration, especially in continuous search space scenarios, difficulties in credit assignment in multi-agent reinforcement learning, and conflicting objectives for rewards. Evolutionary computation (EC), which maintains a population of learning agents, has demonstrated promising performance in addressing these limitations. This article presents a comprehensive survey of state-of-the-art methods for integrating EC into RL, referred to as evolutionary reinforcement learning (EvoRL). We categorize EvoRL methods according to key research fields in RL, including hyperparameter optimization, policy search, exploration, reward shaping, meta-RL, and multi-objective RL. We then discuss future research directions in terms of efficient methods, benchmarks, and scalable platforms. This survey serves as a resource for researchers and practitioners interested in the field of EvoRL, highlighting the important challenges and opportunities for future research. With the help of this survey, researchers and practitioners can develop more efficient methods and tailored benchmarks for EvoRL, further advancing this promising cross-disciplinary research field

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Characterizing the influence of various antimicrobials used for metaphylaxis against bovine respiratory disease on host transcriptome responses

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    Currently, control against bovine respiratory disease (BRD) primarily consists of mass administration of an antimicrobial upon arrival to facility, termed “metaphylaxis.” The objective of this study was to determine the influence of six different antimicrobials used as metaphylaxis on the whole blood host transcriptome in healthy steers upon and following arrival to the feedlot. One hundred and five steers were stratified by arrival body weight (BW = 247 ± 28 kg) and randomly and equally allocated to one of seven treatments: negative control (NC), ceftiofur (CEFT), enrofloxacin (ENRO), florfenicol (FLOR), oxytetracycline (OXYT), tildipirosin (TILD), or tulathromycin (TULA). On day 0, whole blood samples and BW were collected prior to a one-time administration of the assigned antimicrobial. Blood samples were collected again on days 3, 7, 14, 21, and 56. A subset of cattle (n = 6) per treatment group were selected randomly for RNA sequencing across all time points. Isolated RNA was sequenced (NovaSeq 6,000; ~35 M paired-end reads/sample), where sequenced reads were processed with ARS-UCD1.3 reference-guided assembly (HISAT2/StringTie2). Differential expression analysis comparing treatment groups to NC was performed with glmmSeq (FDR ≤ 0.05) and edgeR (FDR ≤ 0.1). Functional enrichment was performed with KOBAS-i (FDR ≤ 0.05). When compared only to NC, unique differentially expressed genes (DEGs) found within both edgeR and glmmSeq were identified for CEFT (n = 526), ENRO (n = 340), FLOR (n = 56), OXYT (n = 111), TILD (n = 3,001), and TULA (n = 87). At day 3, CEFT, TILD, and OXYT shared multiple functional enrichment pathways related to T-cell receptor signaling and FcεRI-mediated NF-kappa beta (kB) activation. On day 7, Class I major histocompatibility complex (MHC)-mediated antigen presentation pathways were enriched in ENRO and CEFT groups, and CEFT and FLOR had DEGs that affected IL-17 signaling pathways. There were no shared pathways or Gene Ontology (GO) terms among treatments at day 14, but TULA had 19 pathways and eight GO terms enriched related to NF- κβ activation, and interleukin/interferon signaling. Pathways related to cytokine signaling were enriched by TILD on day 21. Our research demonstrates immunomodulation and potential secondary therapeutic mechanisms induced by antimicrobials commonly used for metaphylaxis, providing insight into the beneficial anti-inflammatory properties antimicrobials possess

    Engineering Proteins by Domain Insertion

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    Protein domains are structural and functional subunits of proteins. The recombination of existing domains is a source of evolutionary innovation, as it can result in new protein features and functions. Inspired by nature, protein engineering commonly uses domain recombination in order to create artificial proteins with tailor-made properties. Customized control over protein activity, for instance, can be achieved by harnessing switchable domains and functionally linking them to effector domains. Many natural protein domains exhibit conformational changes in response to exogenous triggers. The insertion of light-switchable receptor domains into an effector protein of choice, for instance, allows the control of effector activity with light. The resulting optogenetic proteins represent powerful tools for the investigation of dynamic cellular processes with high precision in time and space. On top, optogenetic proteins enable manifold biotechnological applications and they are even considered potential candidates for future therapeutics. In this study, we first focused on CRISPR-Cas9 genome editing and applied a domain insertion strategy to genetically encoded inhibitors of the CRISPR nuclease from Neisseria meningitidis (NmeCas9), which due to its small size and high DNA sequence-specificity is of great interest for CRISPR genome editing applications. Fusing stabilizing domains to the NmeCas9 inhibitory protein AcrIIC1 allowed us to boost its inhibitory effect, thereby yielding a potent gene editing off-switch. Furthermore, the insertion of the light-responsive LOV2 domain from Avena sativa into AcrIIC3, the most potent inhibitor of NmeCas9, enabled the optogenetic control of gene editing via light-dependent NmeCas9 inhibition. Further investigation of the engineered inhibitors revealed the potential these proteins could have with respect to safe-guarding of the CRISPR technology by selectively reducing off-target editing. The laborious optimization of the engineered CRISPR inhibitors necessary by the time motivated us to more systematically investigate possibilities and constraints of protein engineering by domain insertion using an unbiased insertion approach. Previously, single protein domains were usually introduced only at a few rationally selected sites into target proteins. Here, we inserted up to five structurally and functionally unrelated domains into several different candidate effector proteins at all possible positions. The resulting libraries of protein hybrids were screened for activity by fluorescence-activated cell sorting (FACS) and subsequent next-generation sequencing (Flow-seq). Training machine learning models on the resulting, comprehensive datasets allowed us to dissect parameters that affect domain insertion tolerance and revealed that sequence conservation statistics are the most powerful predictors for domain insertion success. Finally, extending our experimental Flow-seq pipeline towards the screening of engineered, switchable effector variants yielded two potent optogenetic derivatives of the E. coli transcription factor AraC. These novel hybrids will enable the co-regulation of bacterial gene expression by light and chemicals. Taken together, our study showcases the design of functionally diverse protein switches for the control of gene editing and gene expression in mammalian cells and E. coli, respectively. In addition, the generation of a large domain insertion datasets enabled - for the first time - the unbiased investigation of domain insertion tolerance in several evolutionary unrelated proteins. Our study showcases the manifold opportunities and remaining challenges behind the engineering of proteins with new properties and functionalities by domain recombination
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