UTUPub
Not a member yet
55238 research outputs found
Sort by
Metabolic engineering of Streptomyces peucetius for biosynthesis of N,N-dimethylated anthracyclines
Full text linkIntroduction: Daunorubicin and doxorubicin, two anthracycline polyketides produced by S. peucetius, are potent anticancer agents that are widely used in chemotherapy, despite severe side effects. Recent advances have highlighted the potential of producing improved derivatives with reduced side effects by incorporating l-rhodosamine, the N,N-dimethyl analogue of the native amino sugar moiety.Method: In this study, we aimed to produce N,N-dimethylated anthracyclines by engineering the doxorubicin biosynthetic pathway in the industrial Streptomyces peucetius strain G001. To achieve this, we introduced genes from the aclarubicin biosynthetic pathway encoding the sugar N-methyltransferases AclP and AknX2. Furthermore, the native gene for glycosyltransferase DnrS was replaced with genes encoding the aclarubicin glycosyltransferases AknS and AknT. Additionally, the gene for methylesterase RdmC from the rhodomycin biosynthetic pathway was introduced.Results: A new host was engineered successfully, whereby genes from the aclarubicin pathway were introduced and expressed. LC-MS/MS analysis of the engineered strains showed that dimethylated sugars were efficiently produced, and that these were incorporated ino the anthracycline biosynthetic pathway to produce the novel dimethylated anthracycline N,N-dimethyldaunorubicin. Further downstream tailoring steps catalysed by the cytochrome P450 monooxygenase DoxA exhibited limited efficacy with N,N-dimethylated substrates. This resulted in only low production levels of N,N-dimethyldaunorubicin and no N,N-dimethyldoxorubicin, most likely due to the low affinity of DoxA for dimethylated substrates.Discussion: S. peucetius G001 was engineered such as to produce N,N-dimethylated sugars, which were incorporated into the biosynthetic pathway. This allowed the successful production of N,N-dimethyldaunorubicin, an anticancer drug with reduced cytotoxicity. DoxA is the key enzyme that determines the efficiency of the biosynthesis of N,N-dimethylated anthracyclines, and engineering of this enzyme will be a major step forwards towards the efficient production of more N,N-dimethylated anthracyclines, including N,N-dimethyldoxorubicin. This study provides valuable insights into the biosynthesis of clinically relevant daunorubicin derivatives, highlighting the importance of combinatorial biosynthesis.</p
Distributed Denial of Service Attack Detection for the Internet of Things Using Hybrid Deep Learning Model
Full text linkAs a result of the widespread adoption of the Internet of Things, there are now hundreds of millions of connected devices, increasing the likelihood that they may be vulnerable to various types of cyberattacks. In recent years, distributed denial of service (DDoS) has emerged as one of the most destructive tools utilized by attackers. Traditional machine learning approaches are typically ineffective and unable to cope with actual traffic properties when used to identify DDoS attacks. This paper introduces a novel deep learning-based intrusion detection system, specifically designed for deployment at either the Cloud or Fog level in the IoT environment. The proposed model aims to detect all types of DDoS attacks with their specific subcategory. Our hybrid model combines different types of deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Deep Autoencoder, and Deep Neural Networks (DNNs). Our proposed model is made up of two main levels. The first one contains different parallel sub-neural networks trained with specific algorithms. The second level uses the output of the frozen first level combined with the initial data as input. The idea behind the combination of these various types of deep neural networks is to exploit their different properties to achieve very high performance. To evaluate our model, we used the CIC-DDoS2019 dataset, which satisfies all the constraints of an intrusion detection dataset. The results obtained demonstrate that our proposed model outperformed various well-known machine learning and deep learning models in terms of the true positive rate, accuracy, false alarm rate, average accuracy, and average detection rate.</p
Active learning of molecular data for task-specific objectives
Full text linkActive learning (AL) has shown promise to be a particularly data-efficient machine learning approach. Yet, its performance depends on the application, and it is not clear when AL practitioners can expect computational savings. Here, we carry out a systematic AL performance assessment for three diverse molecular datasets and two common scientific tasks: compiling compact, informative datasets and targeted molecular searches. We implemented AL with Gaussian processes (GP) and used the many-body tensor as molecular representation. For the first task, we tested different data acquisition strategies, batch sizes, and GP noise settings. AL was insensitive to the acquisition batch size, and we observed the best AL performance for the acquisition strategy that combines uncertainty reduction with clustering to promote diversity. However, for optimal GP noise settings, AL did not outperform the randomized selection of data points. Conversely, for targeted searches, AL outperformed random sampling and achieved data savings of up to 64%. Our analysis provides insight into this task-specific performance difference in terms of target distributions and data collection strategies. We established that the performance of AL depends on the relative distribution of the target molecules in comparison to the total dataset distribution, with the largest computational savings achieved when their overlap is minimal.</p
Contextuality with Disturbance and without: Neither Can Violate Substantive Requirements the Other Satisfies
Full text linkLipid and volatile profiles of Finnish oat batches of pure cultivars : Effect of storage on the volatile formation
Full text linkRecent data showing the compositional variation and storage behavior among different oat batches for the purpose of food remains limited. Lipids of twenty oat flour samples of pure cultivars grown in Finland during 2019 were extracted and fractionated into neutral and polar-rich lipids. Flour was stored for nine months, and profiles of volatiles and tocols were analyzed to reveal oxidative stability. The lipid content was 5.9–8.9 g per 100 g of flour [DW] and consisted of 78.7 ± 2.5 % neutral and 21.3 ± 2.5 % polar lipids. Palmitic (16 %), oleic (36 %), and linoleic (39 %) acids were the most abundant fatty acids. Neutral lipids had more oleic and less linoleic and palmitic acids than polar lipids. The fresh samples correlated with tocols, pentanal, 2-pentylfuran, 2-heptanone, nonanal, 2-butanone, and heptanal, while stored samples were associated with 3-octen-2-one, 2-octenal, hexanal, and octanal. Lipid composition and oxidative stability are essential factors for selecting oat batches for food applications
Disentangling relationships between Alzheimer's disease plasma biomarkers and established biomarkers in patients of tertiary memory clinics
Full text linkBackground Several plasma biomarkers for Alzheimer's disease (AD) have demonstrated diagnostic and analytical robustness. Yet, contradictory results have been obtained regarding their association with standard diagnostic markers of AD. This study aims to investigate the specific relationship between the AD biomarkers currently used in clinical practice and the plasma biomarkers.Methods In a memory clinic cohort, we analysed plasma pTau181, pTau217, pTau231, respectively, GFAP, NfL, CSF pTau181, Aβ-PET scans, and MRI/CT visual read of atrophy. We utilized methods based on multiple linear regression to evaluate the specific associations between clinically used and recently developed plasma biomarkers, while also considering demographic variables such as age and sex.Findings Although plasma pTau181, pTau217, pTau231, and GFAP were significantly associated with both Aβ-PET and CSF pTau181, Aβ-PET explained more variance in the levels of these biomarkers. The effect of CSF pTau181 on plasma GFAP and pTau181 was completely attenuated by Aβ-PET, whereas pTau231 and pTau217 were affected by both Aβ-PET and CSF pTau181 levels. Unlike these biomarkers, increased NfL was rather indicative of brain atrophy and older age. Based on the effect sizes, plasma pTau217 emerged as highly effective in distinguishing between A+ and A-, and T+ and T- individuals, with 60% of variance in plasma pTau217 explained by clinical AD biomarkers.Interpretation Amyloid burden primarily drives the changes in plasma pTau181, pTau217, pTau231, and GFAP. In contrast to plasma pTau217, a significant portion of variance in plasma pTau181, pTau231, GFAP, NfL remains unexplained by clinical AD biomarkers.Funding This research is supported by the Swedish Research Council VR: 2017-06086, 2020-4-3018, 2024-2027; Swedish Brain Foundation, Swedish Alzhzeimer Foundation, CIMED Region Stockholm/Karolinska Institutet; the Region Stockholm - Karolinska Institutet regional agreement on medical training and clinical research (ALF), Fondation Recherche sur Alzheimer (France).</p
Empowering Healthcare Through User Feedback: A Multidimensional Analysis of the Knowledge
Full text linkPurpose: Feedback from service users is a valuable source for improving the quality of care and services, potentially reflecting the successes and failures in providing empowering healthcare. In supporting empowerment, the multidimensionality of knowledge of service users is assumed to be a crucial factor, yet feedback has not been explored from the perspective of empowering knowledge. In this study, the aim was to analyze the knowledge areas expressed in the service users’ feedback from the point of view of empowering knowledge.Patients and Methods: This was a retrospective study utilizing systematically collected service-user feedback from a feedback register of one university hospital district in Finland. Free-form feedback (n = 26,374) along with structured evaluative feedback was given by the patients themselves or their significant others, either by text message or using a feedback form, in 2019. The content of the feedback was analyzed according to the empowering knowledge areas (biophysiological, cognitive, functional, experiential, ethical, social, and financial), quantified, and analyzed statistically in relation to the background characteristics of service users.Results: Service users gave multidimensional free-form feedback about the knowledge and educational practices in care and services. In the free-form feedback, the most common empowering knowledge areas were biophysiological and cognitive ones, whilst experiential, ethical, social, and financial areas were the least common. The highest ratings of structured evaluative feedback were associated with the cognitive and ethical areas.Conclusion: Register-based feedback is systematic data for quality evaluation. In this study, service users seem to actively evaluate the knowledge procession in care and services, and therefore, they can be actors involved in developing the quality of educational practices. It does, however, indicate a need to add multidimensionality and improve the quality of the knowledge, and by that, advance the potential of empowerment among diverse service users.</p
BMI is positively associated with accelerated epigenetic aging in twin pairs discordant for body mass index
Full text linkBackground Obesity is a heritable complex phenotype that can increase the risk of age-related outcomes. Biological age can be estimated from DNA methylation (DNAm) using various "epigenetic clocks." Previous work suggests individuals with elevated weight also display accelerated aging, but results vary by epigenetic clock and population. Here, we utilize the new epigenetic clock GrimAge, which closely correlates with mortality. Objectives We aimed to assess the cross-sectional association of body mass index (BMI) with age acceleration in twins to limit confounding by genetics and shared environment. Methods and results Participants were from the Finnish Twin Cohort (FTC; n = 1424), including monozygotic (MZ) and dizygotic (DZ) twin pairs, and DNAm was measured using the Illumina 450K array. Multivariate linear mixed effects models including MZ and DZ twins showed an accelerated epigenetic age of 1.02 months (p-value = 6.1 x 10(-12)) per one-unit BMI increase. Additionally, heavier twins in a BMI-discordant MZ twin pair (Delta BMI >3 kg/m(2)) had an epigenetic age 5.2 months older than their lighter cotwin (p-value = 0.0074). We also found a positive association between log (homeostatic model assessment of insulin resistance) and age acceleration, confirmed by a meta-analysis of the FTC and two other Finnish cohorts (overall effect = 0.45 years, p-value = 4.1 x 10(-25)) from adjusted models. Conclusion We identified significant associations of BMI and insulin resistance with age acceleration based on GrimAge, which were not due to genetic effects on BMI and aging. Overall, these results support a role of BMI in aging, potentially in part due to the effects of insulin resistance.</p
Early diagnosis is associated with improved clinical outcomes in benign esophageal perforation: an individual patient data meta-analysis
Full text linkBackground: Time of diagnosis (TOD) of benign esophageal perforation is regarded as an important risk factor for clinical outcome, although convincing evidence is lacking. The aim of this study is to assess whether time between onset of perforation and diagnosis is associated with clinical outcome in patients with iatrogenic esophageal perforation (IEP) and Boerhaave's syndrome (BS).Methods: We searched MEDLINE, Embase and Cochrane library through June 2018 to identify studies. Authors were invited to share individual patient data and a meta-analysis was performed (PROSPERO: CRD42018093473). Patients were subdivided in early (≤ 24 h) and late (> 24 h) TOD and compared with mixed effects multivariable analysis while adjusting age, gender, location of perforation, initial treatment and center. Primary outcome was overall mortality. Secondary outcomes were length of hospital stay, re-interventions and ICU admission.Results: Our meta-analysis included IPD of 25 studies including 576 patients with IEP and 384 with BS. In IEP, early TOD was not associated with overall mortality (8% vs. 13%, OR 2.1, 95% CI 0.8-5.1), but was associated with a 23% decrease in ICU admissions (46% vs. 69%, OR 3.0, 95% CI 1.2-7.2), a 22% decrease in re-interventions (23% vs. 45%, OR 2.8, 95% CI 1.2-6.7) and a 36% decrease in length of hospital stay (14 vs. 22 days, p Conclusions: This individual patient data meta-analysis confirms the general opinion that an early (≤ 24 h) compared to a late diagnosis (> 24 h) in benign esophageal perforations, particularly in IEP, is associated with improved clinical outcome.</p