Miracle-dz/UniFAR-FTUs huggingface dataset:
A Smartphone App for Patients With Acute Coronary Syndrome (MoTER-ACS): User-Centered Design Approach

Background Postdischarge interventions are limited for patients with acute coronary syndrome (ACS) due to few scheduled visits to outpatient clinics and the need to travel from remote areas. Smartphones have become viable lifestyle technology to deliver home-based educational and health interventions. Objective The aim of this study was to develop a smartphone-based intervention for providing postdischarge support to patients with ACS. Methods The content of Mobile Technology–Enabled Rehabilitation for Patients with ACS (MoTER-ACS) was derived from a series of small studies, termed prestudy surveys, conducted in 2017. The prestudy surveys were conducted in Prince Charles Hospital, Queensland, Australia, and consisted of questionnaires among a convenience sample of patients with ACS (n=30), a focus group discussion with health care professionals (n=10), and an online survey among cardiologists (n=15). Responses from the patient survey identified educational topics of MoTER-ACS. The focus group with health care professionals assisted with identifying educational materials, health monitoring, and self-management interventions. Based on the results of the cardiologists’ survey, monitoring of symptoms related to heart failure exacerbation was considered as a weekly diary. Results The MoTER-ACS app covers multimedia educational materials to adopt a healthy lifestyle and includes user-friendly tools to monitor physiological and health parameters such as blood pressure, weight, and pain, assisting patients in self-managing their condition. A web portal that is linked to the data from the smartphone app is available to clinicians to regularly access patients’ data and provide support. Conclusions The MoTER-ACS platform extends the capabilities of previous mobile health platforms by providing a home-based educational and self-management intervention for patients with ACS following discharge from the hospital. The MoTER-ACS intervention narrows the gap between existing hospital-based programs and home-based interventions by complementing the postdischarge program for patients with ACS.
Automated Essay Scoring using Word2vec and Support Vector Machine

Essay scoring is one of the most important tools for evaluating and assessing the level of achievement of educational goals. It aims to innovate performance, arrange, integrate ideas, and connect them by using the vocabulary of the particular subjects. Human essay scoring consumes a lot of time and effort, this leads to mistakes. Automated Essay Scoring (AES) solve to great extent problems. A new approach for AES is presented. It is based on Natural Language Processing (NLP) which is used to unify linguistic answers, word2vec model which converts words into features and synonyms in semantic space, Support Vector Machine(SVM) is used to classify students answers and estimate score levels. The system stages consist of preprocessing, feature extraction, classification and similarity algorithm. The results of proposed method reaches high precision (94%) relative to human resident scores.
Towards a General Purpose Political Communications Simulator

This thesis introduces a new way of modelling information for the purposes of agent-based political simulations. By representing information in the proposed fashion, it becomes possible for agents to compute the attitudinal effects of exposure to information. Moreover, the manner in which information is modelled allows for agents to hold attitudes towards an arbitrary number of objects – not only towards traditional entities of interest like the president of the United States, but also towards other agents and to individual pieces of information. By employing vector algebra to quantitatively represent attitudes, an arbitrary number of attitudinal dimensions such as sentiment or importance may also be represented in a simulation and influenced by exposure to information. As such, this method of representing information unlocks multi-dimensional multi-issue multi-agent simulations of attitude formation, reinforcement, and change. This approach to information enables future research into simulations where the critical elements of political communication theory are directly involved. A considerable amount of effort is devoted to recasting questions of priming, framing, agenda-setting, credibility, and salient characteristics of the American public into the computable language of this information model. I demonstrate that theoretical and empirically-validated results of the political communication literature with regards to attitude change and reinforcement – and formation, to a lesser extent – are guaranteed to be reproduced in simulation. The mathematical approach is complemented by a proof-ofconcept which shows the expected differential attitudinal effects of equivalent information on a polarized artificial population is naturally emergent and due to the manner in which information is represented in the simulation. Classical emergent phenomena of opinion leadership and attitude convergence were observed and these were also caused by the use of the information model.
Molecular approaches to better understand the epidemiology and host resistance to Nervous Necrosis Virus in Asian groupers

The grouper aquaculture industry in many Asian and Pacific countries makes a great contribution to fish products. However, outbreaks of infectious diseases, in particular viral nervous necrosis (VNN), can result in the high mortalities and severe financial losses. Although, considerable efforts have been invested in order to resolve the problems caused by VNN, including trials for disinfecting pathogens and vaccination, the grouper aquaculture industry still lacks an effective and commercial solution to manage VNN. Thus, prevention and control of VNN remains one of the main obstacles to the development of grouper aquaculture industry for both small and large scale farmers. Additional strategies for treatment and vaccination may need to be com-bined with traditional approaches to enhance VNN management in grouper aquacul-ture, including the prevention of NNV transmission as well as the production of more resistant animals through selective breeding and interspecies hybridization. ; Submitted in the fulfilment of the requirements of the degree of Master of Science, University of the Sunshine Coast, 2017.
Microtubule plus-end dynamics link wound repair to the innate immune response

As a first line of defence against the environment, the epidermis protect animals from infection and physical damage. In C. elegans, wounding the epidermal epithelium triggers both an immune reaction and a repair response. Exactly how these are controlled, and the degree to which they are inter-connected remains unclear. To address these questions, we established a simple system for simultaneously inflicting precise laser wounds and imaging at high spatial and temporal resolution. We show that in C. elegans, wounding provokes a rapid sealing of the plasma membrane, involving reorganisation of phosphatidylinositol 4,5- bisphosphate domains. This is followed by a radial recruitment at the wound site of EBP-2/EB1, a protein that binds the plus ends of microtubules. EB1 recruitment is accompanied by a reorganisation of microtubules, required for the subsequent recruitment of actin and wound closure. It is also required for the directed trafficking towards the site of injury of the key signalling protein SNF-12. In the absence of SNF-12 recruitment, there is an abrogation of the immune response. Our results suggest that microtubule dynamics coordinate the cytoskeletal changes required for wound repair and the concomitant activation of the innate immune response.
Sistem Informasi Pencarian Korban Pasca Bencana Berbasis Web pada BNPB Kota Padang

Indonesia is one of the countries that often experiencing natural disaster, both because of geological factors and because of human activities. For example, floods or landslides. These kind of disasters also experienced by West Sumatra, especially in the city of Padang. Post-disaster control that had happened in Padang before meet some difficulty in processing data victims. Family has to recognize the identity and characteristics of the victims while there are not enough hints about the location of the disaster. A government agency for Disaster Management (BPBD) made the information system for searching the disaster victims in the Padang City easier for some stakeholder especially the families of the victims to identify their families. Data will be fast and easy to accessed by anyone, especially by family in identifying their missing family members. This system also can provide information about the searching location.
Stomatal acclimation to dynamic light: implications for photosynthesis and water use efficiency

Although stomata typically occupy only a small portion of the leaf surface (0.3-5%), stomata control approximately 95% of all gas exchange between the leaf interior and external environment.Therefore, stomatal behaviour has major consequences for photosynthetic CO2fixation and water loss from leaf to canopy levels, influencing carbon and hydrological cycles at global scales. Plant acclimation to growth light environment has been studied extensively; however, the majority of these studies have focused on constant light intensity and photo-acclimation, with few studies exploring the impact of dynamic growth light on stomatal acclimation and behaviour. Initially, in this thesis natural variation in the response of stomatal conductance (gs) to light was assessed in the model tree species Populus nigra. Dynamic growth light regimes (varying in intensity and pattern) were subsequently used, to explore how stomatal acclimation to growth light impacts stomatal behaviour, photosynthesis (A) and water use efficiency (Wi). The rate, magnitude and diurnal behaviour of the response of gs to light varied significantly between genotypes and growth light treatments, which promoted differences in A and therefore Wiover the course of the day. The findings in this study illustrate the impact of growing plants in dynamic light regimes, similar to those experienced by plants in the natural environment, on the physiology and performance of model species Populus nigraand Arabidopsis thaliana. Furthermore, it emphasizes that growing plants under laboratory conditions and square-wave illumination does not accurately represent plant acclimation anddevelopment under a natural environment. Highlighting the need to potentially rethink how we grow plants as a community if we are to infer results from the lab to the field. Finally, this study highlights the importance of considering plant acclimation togrowth light, and the impact this has on the functional response of stomata, when attempting to model the response of gsacross leaf to ecosystem and global scales.
Transcriptomic analyses reveal putative genes associated with bibenzyl biosynthesis in the traditional Chinese herb Dendrobium officinale

Background: Dendrobium plants are well known for their uses in traditional Chinese herbal medicine. Bibenzyl compounds are the main active compounds in Dendrobium officinale. However, the physiological and molecular basis for the biosynthesis of bibenzyl compounds in Dendrobium plants remains underexplored. Results: In this study, the accumulation of erianin and gigantol were studied as representative compounds of bibenzyl. Their presence in plant tissues were investigated. Our results show that root tissues contained the highest content of bibenzyl (erianin and gigantol). Based on the pre-experimental result that exogenous application of Methyl-Jasmonate promotes the biosynthesis of bibenzyl compounds in D. officinale root tissues, comparative transcriptomic analyses were conducted between the bibenzyl-accumulated root tissues and a control. In total, we identified 1,342 differentially expressed genes (DEGs) with 912 up-regulated and 430 down-regulated genes. Most of the identified DEGs are functionally involved in the JA signaling pathway and the biosynthesis of secondary metabolites. In particular, we identified 11 enzymatic genes functionally involved in bibenzyl biosynthesis.Conclusions: Our study provide insights on the identification of putative genes associated with bibenzyl biosynthesis and accumulation in Dendrobium plants, and also paves the way for future research on dissecting the physiological and molecular mechanisms of bibenzyl synthesis in plants as well as on how to best utilize genetic engineering and molecular modification techniques to genetically improve Dendrobium varieties by increasing the content of bibenzyl for drug production and industrialization. Keywords: Bibenzyl, D. officinale , Differentially expressed genes, Secondary metabolites, Transcriptome analysis.
Estimación de la presión arterial pulmonar mediante ecocardiografía

Technological advances in echocardiography over last 30 years have increased the accuracy in anatomical and functional evaluation of right cardiac cavities, as well as in estimation of pulmonary artery pressure and pulmonary hemodynamic evaluation. Conclusions derived from an echocardiographic report should suggest the probability of pulmonary hypertension depending on the elements that allow to estimate the systolic, diastolic and/or mean pulmonary artery pressure in addition to the presence of indirect elements of pulmonary hypertension. We analyze the evidence that supports the usefulness of different echocardiographic methods for estimation of pulmonary pressure. It is very important that the cardiologist keep in mind the different methods -direct and indirect- that allow to evaluate it.
Privacy-Preserving Linked Data Integration

Individual privacy is a major and largely unexplored concern when publishing new datasets in the context of Linked Open Data (LOD). The LOD cloud forms a network of interconnected and publicly accessible datasets in the form of graph databases modeled using the RDF format and queried using the SPARQL language. This heavily standardized context is nowadays extensively used by academics, public institutions and some private organizations to make their data available. Yet, some industrial and private actors may be discouraged by potential privacy issues. To this end, we introduce and develop a declarative framework for privacy-preserving Linked Data publishing in which privacy and utility constraints are specified as policies, that is sets of SPARQL queries. Our approach is data-independent and only inspects the privacy and utility policies in order to determine the sequence of anonymization operations applicable to any graph instance for satisfying the policies. We prove the soundness of our algorithms and gauge their performance through experimental analysis. Another aspect to take into account is that a new dataset published to the LOD cloud is indeed exposed to privacy breaches due to the possible linkage to objects already existing in the other LOD datasets. In the second part of this thesis, we thus focus on the problem of building safe anonymizations of an RDF graph to guarantee that linking the anonymized graph with any external RDF graph will not cause privacy breaches. Given a set of privacy queries as input, we study the data-independent safety problem and the sequence of anonymization operations necessary to enforce it. We provide sufficient conditions under which an anonymization instance is safe given a set of privacy queries. Additionally, we show that our algorithms are robust in the presence of sameAs links that can be explicit or inferred by additional knowledge. To conclude, we evaluate the impact of this safety-preserving solution on given input graphs through experiments. We focus on the performance and the utility loss of this anonymization framework on both real-world and artificial data. We first discuss and select utility measures to compare the original graph to its anonymized counterpart, then define a method to generate new privacy policies from a reference one by inserting incremental modifications. We study the behavior of the framework on four carefully selected RDF graphs. We show that our anonymization technique is effective with reasonable runtime on quite large graphs (several million triples) and is gradual: the more specific the privacy policy is, the lesser its impact is. Finally, using structural graph-based metrics, we show that our algorithms are not very destructive even when privacy policies cover a large part of the graph. By designing a simple and efficient way to ensure privacy and utility in plausible usages of RDF graphs, this new approach suggests many extensions and in the long run more work on privacy-preserving data publishing in the context of Linked Open Data
Translation versus language model pre-training objectives for word sense disambiguation

Contextual word representations pre-trained on large text data have advanced state of the art in many tasks in Natural Language Processing. Most recent approaches pre-train such models using a language modeling (LM) objective. In this thesis, we compare and contrast such LM models with the encoder of an encoder-decoder model pre-trained using a machine translation (MT) objective. For certain tasks such as word-sense disambiguation the MT task provides an intuitively better pre-training objective since diﬀerent senses of a word tend to translate diﬀerently into a target language, while word senses might not always need to be distinguished when using an LM objective. Our experimental results on word sense disambiguation provide insight into pre-training objective functions and can help guide future work into large-scale pre-trained models for transfer learning in NLP.
Scalable probabilistic matrix factorization for single-cell RNA-seq analysis

Motivation The gene expression profile of a cell dictates its function in molecular processes, and can be used to probe its health status. This represents a step forward in the deep characterization of diseases such as cancer and may lead to breakthroughs in their treatment. The technology used to measure the gene expression of isolated cells, single-cell RNA-seq (scRNA-seq), has emerged in the last decade as a key enabler of this progress. However, the use of existing methods for dimensionality reduction, clustering and differential expression is limited by the specificities of the data obtained from scRNA-seq experiments, where technical factors may confound analyses of the true biological signal and contribute to spurious results. To overcome this issue, a possible approach is designing probabilistic generative models of the data with hidden variables encoding different underlying processes. Results We propose two novel probabilistic models for scRNA-seq data: modified probabilistic count matrix factorization (m-pCMF) and Bayesian zero-inflated negative binomial factorization (ZINBayes). These build upon previous models in the literature while leveraging scalable Bayesian inference via variational methods. We show that the proposed methods are competitive with the state-of-the-art models for robust dimensionality reduction in modern data sets, and improve upon the current best Bayesian model for small numbers of cells. The results show that building probabilistic models of latent variables which encode domain knowledge and using variational inference constitute a promising approach to analyse scRNA-seq data in a scalable way. Availability m-pCMF and ZINBayes are publicly available as Python packages at https://github.com/pedrofale/, along with the code to reproduce all the results. Contact susanavinga@tecnico.ulisboa. pt
Modélisation en grandes déformations du comportement hystérétique des renforts de composites : Application à l'estampage incrémental

Les materiaux composites connaissent une croissance exponentielle d'utilisation que ce soit dans le domaine de l'aerospatial, l'aeronautique, l'automobile ou encore le sport. Cette evolution significative s'explique notamment par les excellentes proprietes mecaniques que propose ce genre de materiaux. De plus, le ratio caracteristiques/poids est extremement favorable a ces derniers puisqu'ils restent plus legers que les materiaux usuellement employes par le passe. Cependant, ils sont aussi extremement couteux et moyennement maitrises compares aux connaissances scientifiques qui existent pour les materiaux cristallins. L'outil de simulation numerique est donc devenu partie integrante de l'amelioration des procedes de mise en forme, ce qui necessite, entre autres, l'elaboration de modeles mecaniques. Jusqu'a maintenant, compte tenu des strategies d'emboutissage utilisant un seul couple poincon/matrice, les chargements etaient supposes monotones et donc les lois de comportement associees etaient hyperelastiques ou viscoelastiques. Toutefois, etant donne que la demande industrielle ne cesse de croitre et la complexite des geometries demandees aussi, nous proposons au travers des travaux presentes ici, des approches innovantes et originales comme la mise en forme incrementale et la gestion des conditions aux limites. Ces nouvelles approches induisent des variations de chargement en cisaillement ou en flexion et les modeles hyperelastiques ne sont donc plus suffisamment riches pour correctement modeliser les procedes d'estampage. L'etude du comportement hysteretique etant nouvelle pour les materiaux composites, les travaux presentes se focalisent alors sur les renforts secs. C'est ainsi qu'une approche experimentale a ete realisee afin de connaitre la reaction du tisse soumis a ce type de chargements non monotones. Puis, des modeles dissipatifs hysteretiques ont ete etablis en vue de les integrer dans un logiciel de calcul par elements finis. Enfin, des simulations numeriques avec comparaisons experimentales sont proposees, basiques au debut pour valider les modeles puis plus complexes pour montrer l'interet de telles modelisations et de telles strategies.
Vegetable Waste-A Potent Substrate for Cultivation of P.Ostreatus

P. ostreatus is commonly called as oyster mushroom. It is one of the most commonly cultivated and second largest to be cultivated. They are known for their nutritive values and medicinal properties. It belongs to the Class Basidiomycetes and Family Agaricaceae. It is one of the most suitable fungal organism for producing protein rich food from various agro waste without composting. The present study describes the cultivation of oyster mushroom with the utilization of vegetable waste (peels of carrot, radish, potato, cucumber and onion) in combination with agro waste (paddy straw, rice husk, wood shaving and sugarcane bagasse) as substrate. Different ratios of both the substrates were used for the cultivation. When cultivation was carried out on vegetable waste alone, there was absence of mycelium spread and fructification. However, the combination of 50% vegetable waste and 50% paddy straw supported significant growth. Thus the study implies that vegetable waste can prove to be a potent substrate for cultivation of oyster mushroom.