Miracle-dz/UniFAR-FTUs huggingface dataset:
Trafficking of APP by Alix-Syntenin-1 Alix and Syntenin-1 direct amyloid precursor protein and amyloid beta trafficking

33 Background: Endosomal trafficking and amyloidogenic cleavage of amyloid precursor protein (APP) is 34 believed to play a role in the neurodegeneration observed in Alzheimer’s disease (AD). Recent evidence 35 has suggested that packaging and secretion of APP and amyloid beta into small extracellular vesicles 36 (EVs) may facilitate uptake of these neurotoxic factors during disease progression. However, the 37 molecular mechanisms underlying trafficking of APP into EVs are poorly understood. 38 Results: In this study, the mechanism and impact of amyloid precursor protein (APP) trafficking into 39 extracellular vesicles (EVs) were assessed by a series of inducible gene knockdowns. We demonstrate 40 that vesicle-associated proteins Alix and Syntenin-1 are essential for proper subcellular localization and 41 efficient EV secretion of APP via an endosomal sorting complexes required for transport (ESCRT)42 independent pathway. The neurotoxic metabolite amyloid beta (Aβ) is similarly secreted in association 43 with small vesicles. These mechanisms are conserved in terminally differentiated neuron-like cells. 44 Furthermore, knockdown of Alix and Syntenin-1 alters the subcellular localization of APP, sequestering 45 the precursor protein to endoplasmic reticulum and endolysosomal compartments, respectively. Finally, 46 transfer of small EVs containing APP confers an increase in reactive oxygen species production and 47 neurotoxicity to human induced pluripotent stem cell-derived cortical neurons and naïve primary 48 neurons, an effect that is ameliorated by Alix and Syntenin-1 depletion. 49 Conclusions: Altogether these findings elucidate a novel mechanism for understanding the intracellular 50 trafficking of APP and Aβ into secreted extracellular vesicles, and the resultant potential impact on 51 neurotoxicity in the context of Alzheimer’s disease amyloidopathy. 52 53 54
Haematological management of major bleeding associated with direct oral anticoagulants â•fi <fc>UK</fc> experience

Laura Green,* Joachim Tan,* Sotiris Antoniou, Raza Alikhan, Nicola Curry, Tamara Everington, Khalid Saja, Simon Stanworth, Campbell Tait, Joan K. Morris and Peter MacCallum Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Barts Health NHS Trust, NHS Blood and Transplant, Colindale, London, University Hospital of Wales, Cardiff and Vale University Health Board, Cardiff, Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Radcliffe Department of Medicine, University of Oxford, Oxford BRC Haematology Theme, Oxford, Hampshire Hospitals NHS Foundation Trust, Basingstoke, Salisbury NHS Foundation Trust, Salisbury, Barking, Havering and Redbridge University Hospitals NHS Trust, Romford, Transfusion Medicine, NHS Blood and Transplant, Oxford, and Glasgow Royal Infirmary, NHS Greater Glasgow and Clyde, Glasgow, UK
Exploring the capabilities of deep learning in sea surveillance

In this master thesis deep learning is proven to be applicable in the field of sea surveillance. Commercial ships using the AIS system have to report the type of the vessel such as fishing ship or cargo ship. A problem with AIS data is that it can be easily manipulated and therefore deliberately or accidentally incorrect. This thesis will focus on detecting false ship types. To detect a false ship type 19 different methods were tested on the 1100 hour long AIS data set. Three of these methods were baseline methods using a more conventional approach to the sea surveillance problem. The testing showed that the best performing method was one of the deep learning methods proving that deep learning is indeed suitable in sea surveillance.
Preparation of Manuscripts for Technical Soaring

All papers require a summary-type abstract consisting of a single, self-contained paragraph. Suggest 100 to 150 words. Acronyms introduced in the abstract, but do not cite Abstract With increasing span or wing loading, it becomes increasingly difﬁcult to fulﬁll all CS22 certiﬁcation requirements for airbrakes simultaneously. Aerodynamic forces upon Schempp-Hirth airbrakes usually create pilot control forces, which limit the size of the airbrake. A certain design of the airbrake allows to reduce the aerodynamic source of this force considerably. This also solves two safety issues: It allows the pilot to retract the airbrakes at high speed and also reduces the risk of self extension of unlocked airbrakes at take-off.
BCTT_A_293867 189..197

1Ethiopia Public Health Institute, Addis Ababa, Ethiopia; 2Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia Background: Breast cancer, one of the chronic diseases, is the most common cancer among women in the world. In Ethiopia, late-stage breast cancer is widely diagnosed and women have very little access to adequate medication, pain relief, or palliative care. In addition, there are few research on risk factors for breast cancer in Ethiopia. A safer way out of this is to concentrate on prevention; one of the prevention methods is to recognize risk factors sooner. The study therefore aimed to establish risk factors for breast cancer among women in hospitals in the city of Addis Ababa. Methods: Hospital-based, unmatched case-control research conducted in selected Addis Ababa hospitals from April to September 2017 (Case=110 and Control=110). Data were collected by standardized questionnaires for both cases and controls. Cases were initially detected by mammography screening accompanied by histopathological examinations, while controls were those women who were negative by mammogram testing, stress levels were assessed by perceived stress scale (PSS) and body mass index measured by WHO norm. Summary statistics Computed and binary logistic regression analysis to classify risk factors for breast cancer modified odds ratio (AOR) with 95% confidence interval (CI) calculated. Results: The participants’ mean age of was 43.80 years (SD ±12.63) and 39.64 years (SD ±12.91) for cases and controls, respectively. Physical activity (AOR=0.2, 95% CI: 0.10, 0.41) was correlated with lower probability of breast cancer, while never breastfeed (AOR=3.4, 95% CI: 1.21, 9.67); menopausal state (AOR=6.8, 95% CI: 1.92, 24.16), and body mass index above 25. 1 kg/m (AOR=5.9, 95% CI: 2.16, 16.48) were factors risk factors for breast cancer. Conclusion: This research shows the importance of physical exercise, breastfeeding, menopausal status, and nutritional status in the occurrence of breast cancer. Actions aimed at increasing physical activity, breastfeeding habits and keeping a balanced diet will help to minimize the incidence of breast cancer.
Pengaruh pembelajaran socio-scientific problem-based learning terhadap literasi sains, keterampilan metakognitif, dan hasil belajar biologi siswa kelas X di Kota Malang / Lutfi Rizkita

This study was aimed at investigating the effect of Socio-scientific Problem Based Learning towards metacognitive skills and achievement in class. As a study implementing quasi experiment with non-randomized control group pretest-posttest design. The samples used in the study consisted of an experimental group (X-F) and a control group (X-G) and were selected randomly from the population of the 10 th grade students of SMAN 4 Malang whose linearity had been tested. The variables examined consisted of two independent variables which are Socio-scientific Problem Based Learning in the experimental group and discovery strategy in the control group; and two dependent variables namely metacognitive skills and student’s cognitive achievement. The hypothesis testing used anakova with significance level of 0,05 (p<0,05) and employed the pretest and posttest score as data. The study finding show that Socio-scientific Problem Based Learning affects the student’s metacognitive skills and cognitive achievement Penelitian ini bertujuan untuk mengetahui pengaruh pembelajaran Socio-scientific Problem Based Learning terhadap keterampilan metakognitif dan hasil belajar kognitif siswa kelas X SMAN di Kota Malang. Penelitian yang dilakukan merupakan penelitian kuasi eksperimen dengan desain penelitian nonrandomized control group pretest-posttest . Sampel yang digunakan dalam penelitian terdiri atas kelas eksperimen (X-F) dan kelas kontrol (X-G) yang dipilih secara acak dari populasi siswa kelas X SMAN 4 Malang yang telah teruji kesetaraannya. Variabel penelitian terdiri atas variabel bebas berupa strategi Socio-scientific Problem-Based Learning pada kelas eksperimen dan discovery pada kelas kontrol; dan variabel terikat terdiri atas keterampilan metakognitif dan hasil belajar kognitif siswa. Uji hipotesis dengan menggunakan anakova dengan taraf signifikansi 0,05 (P < 0,05) menggunakan data skor pretes dan postes. Hasil penelitian menunjukkan strategi pembelajaran Socio-scientific Problem Based Learning berpengaruh meningkatkan keterampilan metakognitif sebesar 154,2 % dan hasil belajar kognitif sebesar 62,9 % lebih tinggi dibandingkan kelas kontrol.
Practical Use Cases for Progressive Visual Analytics

Progressive Visual Analytics (PVA) is meant to allow visual analytics application to scale to large amounts of data while remaining interactive and steerable. The visualization community might believe that building progressive systems is difficult since there is no general purpose toolkit yet to build PVA applications, but it turns out that many existing libraries and data structures can be used effectively to help building PVA applications. They are just not well known by the visual analytics community. We report here on some of these techniques and libraries we use to handle “larger than RAM” data efficiently on three applications: Cartolabe, a system for visualizing large document corpora, ParcoursVis, a system for visualizing large event sequences from the French social security, and PPCA, a progressive PCA visualization system for large amounts of timesequences. We explain how PVA can benefit from compressed bitset to manage sets internally and perform extremely fast Boolean operations, data sketching to compute approximate results over streaming data, and use Online algorithms to perform analyzes on large data.
IL-6 trans signalling drives a STAT3 dependant pathway that leads to hyperactive TGF-β signalling promoting SMAD3 activation and fibrosis via gremlin

Fibrosis is a common and intractable condition associated with various pathologies. It is characterised by accumulation of an excessive amount of extracellular matrix molecules that primarily include collagen type I. IL-6 is a profibrotic cytokine that is elevated in the prototypic fibrotic autoimmune condition systemic sclerosis and is known to induce collagen I expression but the mechanism(s) behind this induction are currently unknown. Using healthy dermal fibroblasts in vitro we analysed the signalling pathways that underscore the IL-6 mediated induction of collagen. We show that IL-6 trans signalling is important and that the effect is dependent on STAT3, however the effect is indirect and mediated through enhanced TGF-β signalling and the classic downstream cellular mediator Smad3. This is due to induction of the BMP antagonist Gremlin1 and we show that Gremlin-1 is pro-fibrotic and is mediated through canonical TGF-β signalling. Introduction Fibrosis is characterised by excessive accumulation of extracellular matrix (ECM) components that include primarily collagen type I. Tissue fibrosis results from excessive scarring that ultimately leads to loss of tissue function through destruction of tissue architecture. Fibrosis is a critical component of many diseases including liver fibrosis, idiopathic pulmonary fibrosis, myleofibrosis, rheumatoid arthritis (RA) and systemic sclerosis. Fibrosis may be the final common outcome of many autoimmune diseases and fibrotic disease is estimated to contribute up to 45% of all deaths in the Western world and is therefore a need for therapeutics. Systemic sclerosis is a prototypical fibrotic disease that is characterised by activation of the immune system, autoimmunity, vasculopathy, chronic inflammation and ultimately fibrosis [1]. The cause of the disease is unknown and there is currently no accepted treatment. However, there is now a consensus that there is an interplay between the innate and adaptive immune system that leads to differentiation of resident fibroblasts to ‘myofibroblasts’ and the excessive deposition of ECM, primarily collagen I . Although the effector cell is the activated myofibroblast the interplay between the immune system (innate and adaptive) and secreted profibrotic factors appears important in the pathogenesis of the disease [2]. The observation of perivascular infiltrates of leukocytes at sites of fibrosis early in the disease course points to by gest on M arch 3, 2020 hp://w w w . jb.org/ D ow nladed from
University of Birmingham Evaluation of a standard provision versus an autonomy promotive exercise referral programme : rationale and study design

Background: The National Institute of Clinical Excellence in the UK has recommended that the effectiveness of ongoing exercise referral schemes to promote physical activity should be examined in research trials. Recent empirical evidence in health care and physical activity promotion contexts provides a foundation for testing the utility of a Self Determination Theory (SDT)-based exercise referral consultation. Methods/Design: Design: An exploratory cluster randomised controlled trial comparing standard provision exercise on prescription with a Self Determination Theory-based (SDT) exercise on prescription intervention. Participants: 347 people referred to the Birmingham Exercise on Prescription scheme between November 2007 and July 2008. The 13 exercise on prescription sites in Birmingham were randomised to current practice (n = 7) or to the SDT-based intervention (n = 6). Outcomes measured at 3 and 6-months: Minutes of moderate or vigorous physical activity per week assessed using the 7-day Physical Activity Recall; physical health: blood pressure and weight; health status measured using the Dartmouth CO-OP charts; anxiety and depression measured by the Hospital Anxiety and Depression Scale and vitality measured by the subjective vitality score; motivation and processes of change: perceptions of autonomy support from the advisor, satisfaction of the needs for competence, autonomy, and relatedness via physical activity, and motivational regulations for exercise. Discussion: This trial will determine whether an exercise referral programme based on Self Determination Theory increases physical activity and other health outcomes compared to a standard programme and will test the underlying SDT-based process model (perceived autonomy support, need satisfaction, motivation regulations, outcomes) via structural equation modelling. Trial registration: The trial is registered as Current Controlled trials ISRCTN07682833. Published: 8 June 2009 BMC Public Health 2009, 9:176 doi:10.1186/1471-2458-9-176 Received: 1 May 2009 Accepted: 8 June 2009 This article is available from: http://www.biomedcentral. com/1471-2458/9/176 © 2009 Jolly et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Application of TRMM PR and TMI Measurements to Assess Cloud Microphysical Schemes in the MM5 for a Winter Storm

This paper uses observations from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI) to evaluate the cloud microphysical schemes in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; version 3.7.4) for a wintertime frontal precipitation system over the eastern Pacific Ocean. By incorporating a forward radiative transfer model, the radar reflectivity and brightness temperatures are simulated and compared with the observations at PR and TMI frequencies. The main purpose of this study is to identify key differences among the five schemes [including Simple ice, Reisner1, Reisner2, Schultz, and Goddard Space Flight Center (GSFC) microphysics scheme] in the MM5 that may lead to significant departures of simulated precipitation properties from both active (PR) and passive (TMI) microwave observations. Radiative properties, including radar reflectivity, attenuation, and scattering in precipitation liquid and ice layers are investigated. In the rain layer, most schemes are capable of reproducing the observed radiative properties to a reasonable degree; the Reisner2 simulation, however, produces weaker reflectivity and stronger attenuation than the observations, which is possibly attributable to the larger intercept parameter (N0r) applied in this run. In the precipitation ice layer, strong evidence regarding the differences in the microphysical and radiative properties between a narrow cold-frontal rainband (NCFR) and a wide cold-frontal rainband (WCFR) within this frontal precipitation system is found. The performances of these schemes vary significantly on simulating the microphysical and radiative properties of the frontal rainband. The GSFC scheme shows the least bias, while the Reisner1 scheme has the largest bias in the reflectivity comparison. It appears more challenging for the model to replicate the scattering signatures obtained by the passive sensor (TMI). Despite the common problem of excessive scattering in the WCFR (stratiform precipitation) region in every simulation, the magnitude of the scattering maximum seems better represented in the Reisner2 scheme. The different types of precipitation ice, snow, and graupel are found to behave differently in the relationship of scattering versus reflectivity. The determinative role of the precipitation ice particle size distribution (intercept parameters) is extensively discussed through sensitivity tests and a single-layer radiative transfer model. Corresponding author address: Mei Han, Mesoscale Atmospheric Process Branch, Laboratory for Atmospheres, NASA/GSFC, Code 613.1, Greenbelt, MD 20771. E-mail: mei.han@nasa.gov JUNE 2010 H A N E T A L . 1129 DOI: 10.1175/2010JAMC2327.1 2010 American Meteorological Society
Factorization (cid:2)

Maximum Margin Matrix Factorization (MMMF) has been proposed as a learning approach to the task of collaborative ﬁltering with promising results. In our recent paper [2], we proposed to extend the general MMMF framework to allow for structured (ranking) losses in addition to the squared error loss. In this paper, we introduce a novel algorithm to compute the ordinal regression ranking loss which is signiﬁcantly faster than the state of the art. In addition, we propose severals extensions to the MMMF model: We introduce oﬀset terms to cater for user and item biases. Users exhibit vastly diﬀerent rating frequencies ranging from only one rating per user to thousands of them. Similarly, some items get thousands of ratings while others get rated only once. We introduce an adaptive regularizer to allow for more complex models for those items and users with many ratings. Finally, we show equivalence between a recent extension introduced in [3] and a graph kernel approach described in [4]. Both aim at providing meaningful predictions for users with very little training data by virtue of the recommender graph. We performed an evaluation of these extensions on two standard data sets: Eachmovie and Movielens. These experiments show that the introduced extensions do improve the predictive performance over the original MMMF formula-tion, even though we did not formally optimize the parameters.
Extracting Cyber Threat Intelligence From Hacker Forums

The use of more sophisticated tools and methods from cyber criminals has urged the cyber security community to look for enhancements to traditional security controls. Cyber Threat Intelligence represents one such proactive approach and includes the collection and analysis of information for potential threats from multiple diverse sources of data. The objective is to understand the methodology that different threat actors are using to launch their campaigns, and proactively adapt security controls to detect and prevent such activity. In addition to proprietary feeds, open sources such as social networks, news, online blogs, etc. represent valuable sources of such information. Among them, hacker forums and other platforms used as means of communication between hackers may contain vital information about security threats. The amount of data in such platforms, however, is enormous. Furthermore, their contents are not necessarily related to cyber security. Consequently, the discovery of relevant information using manual analysis is time consuming, ineffective, and requires a significant amount of resources. In this thesis, we explore the capabilities of Machine Learning methods in the task of locating relevant threat intelligence from hacker forums. We propose the combination of supervised and unsupervised learning in a two-phase process for this purpose. In the first phase, the recent developments in Deep Learning are compared against more traditional methods for text classification. The second phase involves the application of unsupervised topic models to discover the latent themes of the information deemed as relevant from the first phase. An optional third phase which includes the combination of manual analysis with other (semi)automated methods for exploring text data is applied to validate the results and get more details from the data. We tested these methods on a real hacker forum. The results of the experiments performed on manually labeled datasets show that even simple traditional methods such as Support Vector Machines with n-grams as features yield high performance on the task of classifying the contents of hacker posts. In addition, the experiments support our assumption that a considerable amount of data in such platforms is of general purpose and not relevant to cyber security. The findings from the security related data however include zero-day exploits, leaked credentials, IP addresses of malicious proxy servers, etc. Therefore, the hacker community should be considered an important source of threat intelligence.
ATOMIC LAYER DEPOSITION FOR EMERGING THIN-FILM APPLICATIONS Epitaxial growth of Mg x Ca 1 − x O on 4H – SiC(0001) and b -Ga 2 O 3 (cid:1) 201 ð Þ wide band gap semiconductors with atomic layer deposition

SiC and Ga 2 O 3 are promising wide band gap semiconductors for applications in power electronics because of their high breakdown electric ﬁ eld and normally off operation. However, lack of a suitable dielectric material that can provide high interfacial quality remains a problem. This can potentially lead to high leakage current and conducting loss. In this work, we present a novel atomic layer deposition process to grow epitaxially Mg x Ca 1 − x O dielectric layers on 4H-SiC(0001) and b -Ga 2 O 3 (cid:1) 201 ð Þ substrates. By tuning the composition of Mg x Ca 1 − x O toward the substrate lattice constant, better interfacial epitaxy can be achieved. The interfacial and epitaxy qualities were investigated and con ﬁ rmed by cross-sectional transmission electron microscopy and X-ray diffraction studies. Mg 0.72 Ca 0.28 O ﬁ lm showed the highest epitaxy quality on 4H-SiC(0001) because of its closest lattice match with the substrate. Meanwhile, highly textured Mg 0.25 Ca 0.75 O ﬁ lms can be grown on