| 1. |
- Akbari, Ali, et al.
(författare)
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Free and hydrogel encapsulated exosome-based therapies in regenerative medicine.
- 2020
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Ingår i: Life sciences. - : Elsevier BV. - 1879-0631 .- 0024-3205. ; 249
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Tidskriftsartikel (refereegranskat)abstract
- Over the last few decades, mesenchymal stem cells-derived exosomes (MSCs-Ex) have attracted a lot of attention as a therapeutic tool in regenerative medicine. Exosomes are extracellular vehicles (EVs) that play important roles in cell-cell communication through various processes such as stress response, senescence, angiogenesis, and cell differentiation. Success in the field of regenerative medicine sparked exploration of the potential use of exosomes as key therapeutic effectors of MSCs to promote tissue regeneration. Various approaches including direct injection, intravenous injection, intraperitoneal injection, oral administration, and hydrogel-based encapsulation have been exploited to deliver exosomes to target tissues in different disease models. Despite significant advances in exosome therapy, it is unclear which approach is more effective for administering exosomes. Herein, we critically review the emerging progress in the applications of exosomes in the form of free or association with hydrogels as therapeutic agents for applications in regenerative medicine.
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| 2. |
- Ali-Eldin, Ahmed, 1985-, et al.
(författare)
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How will your workload look like in 6 years? : Analyzing Wikimedia's workload
- 2014
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Ingår i: Proceedings of the 2014 IEEE International Conference on Cloud Engineering (IC2E 2014). - : IEEE Computer Society. - 9781479937660 ; , s. 349-354
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Konferensbidrag (refereegranskat)abstract
- Accurate understanding of workloads is key to efficient cloud resource management as well as to the design of large-scale applications. We analyze and model the workload of Wikipedia, one of the world's largest web sites. With descriptive statistics, time-series analysis, and polynomial splines, we study the trend and seasonality of the workload, its evolution over the years, and also investigate patterns in page popularity. Our results indicate that the workload is highly predictable with a strong seasonality. Our short term prediction algorithm is able to predict the workload with a Mean Absolute Percentage Error of around 2%.
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| 3. |
- Amano, Tatsuya, et al.
(författare)
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Transforming Practice : Checklists for Delivering Change
- 2022
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Ingår i: Transforming Conservation : A Practical Guide to Evidence and Decision Making - A Practical Guide to Evidence and Decision Making. - 9781800648586 - 9781800648562 ; , s. 367-386
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Bokkapitel (refereegranskat)abstract
- Delivering a revolution in evidence use requires a cultural change across society. For a wide range of groups (practitioners, knowledge brokers, organisations, organisational leaders, policy makers, funders, researchers, journal publishers, the wider conservation community, educators, writers, and journalists), options are described to facilitate a change in practice, and a series of downloadable checklists is provided.
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| 4. |
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| 5. |
- Farajbakhsh, Ali, et al.
(författare)
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Sesame oil and vitamin E co-administration may improve cardiometabolic risk factors in patients with metabolic syndrome: a randomized clinical trial
- 2019
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Ingår i: European Journal of Clinical Nutrition. - : Springer Science and Business Media LLC. - 0954-3007 .- 1476-5640. ; 73:10, s. 1403-1411
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Metabolic syndrome (MetS) represents a clustering of metabolic abnormalities that are associated with an increased risk of type 2 diabetes and cardiovascular disease. We aimed to evaluate the effects of sesame oil enriched with vitamin E (vit E), sesame oil alone and sunflower oil on lipid profile, fasting blood glucose (FBG), malondialdehyde (MDA), high-sensitivity C-reactive protein (Hs-CRP), homeostatic model assessment (HOMA-IR), and blood pressure (BP) in patients with MetS. Subjects: Overall, 75 individuals with MetS (aged 30–70 years) participated in this randomized, single-blind controlled trial. Patients were randomly allocated to: (1) Group A (n = 25): sesame oil (30 ml/day) enriched with vit E (400 mg/day), (2) Group B (n = 25): sesame oil (30 ml/day), (3) Group C (n = 25): sunflower oil (30 ml/day). Anthropometric data, dietary intake, blood pressure, and biochemical markers, including fasting serum lipids, FBG, serum insulin, MDA, and hs-CRP were measured at baseline and at week 8. Results: In individuals in the sesame oil enriched with vit E group (Group A), there were significant reductions in serum total cholesterol (TC), triglycerides (TG), FBG, HOMA-IR, MDA, hs-CRP, high-density lipoprotein (HDL-C) systolic and diastolic BP (for all the comparison p < 0.02). Similarly, in Group B (taking sesame oil alone), TC, TG, FBG, HOMA-IR, MDA, systolic and diastolic BP were significantly improved (for all the comparison p < 0.025), while there were no significant changes in serum HDL (baseline = 35.9 ± 7.2 mg/dL vs. 36.4 ± 6.2 mg/dL, p = 0.432) and hs-CRP (baseline = 4.38 ± 1.34 mg/dL vs. week 8 = 3.96 ± 1.7 mg/dL, p = 0.057) in second group. No significant changes in any of the studied clinical and anthropometric data were found in Group C (on sunflower oil). Conclusion: Sesame oil (±vit E) was shown to beneficially affect several cardiometabolic indices (including lipids, FBG, BP, HOMA-IR, and MDA) in patients with MetS.
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| 6. |
- Ghayur Sadigh, Armin, et al.
(författare)
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Comparison of optimized data-driven models for landslide susceptibility mapping
- 2023
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Ingår i: Environment, Development and Sustainability. - 1387-585X .- 1573-2975.
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Tidskriftsartikel (refereegranskat)abstract
- Locations prone to landslides must be identified and mapped to prevent landslide-related damage and casualties. Machine learning approaches have proven effective for such tasks and have thus been widely applied. However, owing to the rapid development of data-driven approaches, deep learning methods that can exhibit enhanced prediction accuracies have not been fully evaluated. Several researchers have compared different methods without optimizing them, whereas others optimized a single method using different algorithms and compared them. In this study, the performances of different fully optimized methods for landslide susceptibility mapping within the landslide-prone Kermanshah province of Iran were compared. The models, i.e., convolutional neural networks (CNNs), deep neural networks (DNNs), and support vector machine (SVM) frameworks were developed using 14 conditioning factors and a landslide inventory containing 110 historical landslide points. The models were optimized to maximize the area under the receiver operating characteristic curve (AUC), while maintaining their stability. The results showed that the CNN (accuracy = 0.88, root mean square error (RMSE) = 0.37220, and AUC = 0.88) outperformed the DNN (accuracy = 0.79, RMSE = 0.40364, and AUC = 0.82) and SVM (accuracy = 0.80, RMSE = 0.42827, and AUC = 0.80) models using the same testing dataset. Moreover, the CNN model exhibiting the highest robustness among the three models, given its smallest AUC difference between the training and testing datasets. Notably, the dataset used in this study had a low spatial accuracy and limited sample points, and thus, the CNN approach can be considered useful for susceptibility assessment in other landslide-prone regions worldwide, particularly areas with poor data quality and quantity. The most important conditioning factors for all models were rainfall and the distances from roads and drainages.
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| 7. |
- Ibidunmoye, Olumuyiwa, et al.
(författare)
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Adaptive Anomaly Detection in Performance Metric Streams
- 2018
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Ingår i: IEEE Transactions on Network and Service Management. - : IEEE. - 1932-4537 .- 1932-4537. ; 15:1, s. 217-231
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Tidskriftsartikel (refereegranskat)abstract
- Continuous detection of performance anomalies such as service degradations has become critical in cloud and Internet services due to impact on quality of service and end-user experience. However, the volume and fast changing behaviour of metric streams have rendered it a challenging task. Many diagnosis frameworks often rely on thresholding with stationarity or normality assumption, or on complex models requiring extensive offline training. Such techniques are known to be prone to spurious false-alarms in online settings as metric streams undergo rapid contextual changes from known baselines. Hence, we propose two unsupervised incremental techniques following a two-step strategy. First, we estimate an underlying temporal property of the stream via adaptive learning and, then we apply statistically robust control charts to recognize deviations. We evaluated our techniques by replaying over 40 time-series streams from the Yahoo! Webscope S5 datasets as well as 4 other traces of real web service QoS and ISP traffic measurements. Our methods achieve high detection accuracy and few false-alarms, and better performance in general compared to an open-source package for time-series anomaly detection.
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| 8. |
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| 9. |
- Najmoddin, Najmeh, et al.
(författare)
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Effect of nanoconfinement on the formation, structural transition and magnetic behavior of mesoporous copper ferrite
- 2014
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Ingår i: Journal of Alloys and Compounds. - : Elsevier BV. - 0925-8388 .- 1873-4669. ; 598, s. 191-197
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Tidskriftsartikel (refereegranskat)abstract
- Superparamagnetic, cubic single phase, ordered mesoporous copper ferrite is synthesized through confinement in nanocages of mesoporous silica. The heat generated during the reaction is conserved in the silica template pore channels, which allows the formation of copper ferrite at a relatively low processing temperature. The Jahn-Teller distortion is suppressed due to the effect of nanoconfinement and thus the high temperature phase of cubic copper ferrite is stabilized at room temperature. The particle size obtained from TEM, the crystallite size calculated from XRD and the magnetic domain size estimated from magnetization measurements are all in good agreement, manifesting the significant role of the confinement in the growth and fabrication of crystalline, single magnetic domain, nanoparticles with superparamagnetic behavior at room temperature.
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| 10. |
- Najmoddin, Najmeh, et al.
(författare)
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Magnetic properties of crystalline mesoporous Zn-substituted copper ferrite synthesized under nanoconfinement in silica matrix
- 2014
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Ingår i: Microporous and Mesoporous Materials. - : Elsevier BV. - 1387-1811 .- 1873-3093. ; 190, s. 346-355
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Tidskriftsartikel (refereegranskat)abstract
- A series of ordered mesoporous single phase Cu1-xZnxFe2O4 spinel ferrites, with x ranging from 0.00 to 0.75 with a step increment of 0.25, are prepared by a novel nanocasting route with the aid of vinyl-functionalized mesoporous silica as a hard template. All samples display a relatively high surface area and narrow pore size distribution from nitrogen sorption analysis. The magnetic hysteresis loops of these samples measured at 300 K, the temperature dependence of the zero field cooled (ZFC) and field cooled (FC) magnetization curves and the Mossbauer Spectra show the presence of superparamagnetic nanopartides in all samples. The hysteresis data indicate that the maximum saturation magnetization of 52 emu g(-1), is obtained for the composition with x = 0.25. For x >= 0.5, the saturation magnetization decreases as a result of the cation redistribution within tetrahedral (A) and octahedral (B) sites which weakens the A-B interactions due to triangular Yafet-Kittel spin arrangement on the B-sublattice. The observed magnetic features are attributed to the confined spaces of the host material which acts as a nanoreactor, limiting the growth of the embedded oxide phase and significantly influencing the cation distribution of copper-zinc ferrite on the A and B sites.
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