Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural history, diagnosis and treatment approach to fibrostenosing Crohn’s disease

Stricturing Crohn’s disease (CD) is a significant clinical problem. The presence of a stricture may be suggested by clinical symptoms. Cross-sectional imaging using computed tomography or magnetic resonance enterography is essential in diagnosing strictures as it allows further characterization and evaluation for complications such as abscess, fistulizing disease or malignancy. Managing small bowel stricturing CD should be approached in a multidisciplinary fashion. Medical therapy can be considered in strictures which are not associated with complications, with most of the data supporting anti-TNF strategies in this setting. If the disease is refractory to medical therapy, endoscopic therapy or surgery should be performed. Endoscopic balloon dilation (EBD) is an option for short, uncomplicated and straight strictures that are within reach of a colonoscope. Although EBD has good short-term outcomes, repeat dilation is often required. Surgical options mainly include resection and strictureplasty. Strictures refractory to medical therapy, not amenable or refractory to EBD, or associated with complications or malignancy should be managed surgically. However, surgery may also be considered at an earlier stage depending on disease characteristics and patient preference. Postoperative recurrence is common, highlighting the importance of careful monitoring of the patient postoperatively and optimization of medical management accordingly. There is a pressing need to develop anti-fibrotics for the treatment of stricturing CD. This requires the development of standardized diagnostic criteria, patient-reported outcome measures and validation of endpoints in fibrostenotic CD. The STAR consortium is pioneering this effort in order to allow development and testing of anti-fibrotics in future clinical trials.
A privacy-preserving solution for storage and processing of personal health records against brute-force attacks

A PRIVACY-PRESERVING SOLUTION FOR STORAGE AND PROCESSING OF PERSONAL HEALTH RECORDS AGAINST BRUTE-FORCE ATTACKS Saharnaz Esmaeilzadeh Dilmaghani M.S. in Computer Engineering Advisor: Erman Ayday September 2017 There is a crucial need for protecting patient’s sensitive information, such as personal health record (PHR), from unauthorized users due to the increase in demands of electronic health records. Even though cryptography systems have been significantly developed, cyber attack is dramatically increased during the last couple of years. Although using high entropy passwords in the encryption methods can decrease the success of an adversarial attack, it is not popular among the users to choose such passwords. However, using a weak password makes the system vulnerable to brute-force attacks. Towards this end, we present a new framework as a solution for a secure storage of PHR data regardless of the password entropy. Our system is an application of Honey Encryption (HE) scheme which is a new approach that provides a security beyond the brute-force bound and therefore dominates the Password Based Encryption (PBE). We utilize almost 10K patients’ information from various datasets in order to construct a precise encoder/decoder model as a core element of HE. By providing the proposed model, we ensure that the encryption with invalid keys yields a valid-looking but incorrect health information of a patient to an adversary. The previous applications of HE are mainly on the static datasets that are not changing over the time. However, we were able to design an HE based model on a highly dynamic dataset of PHR. To the best of our knowledge, we are the first to provide a robust password based framework against brute-force attacks of health records regardless of the password entropy. The results of the comparing our proposed encoding method with the direct iii
Hardiness pada single mother

Single mother mempunyai peran ganda dalam keluarga. Bagi seorang ibu, menjadi single mother merupakan pengalaman yang luar biasa berat. Terlebih lagi di saat-saat lingkungan tidak berpihak, terkadang seorang ibu takut jika hal tersebut dapat mempengaruhi perkembangan anak-anaknya, sehingga diperlukan sikap kuat dan tegar terhadap setiap tantangan hidupnya sebagai teladan bagi anak-anaknya. Tujuan daripenelitian ini adalah untuk mengetahui gambaran kepribadian tahan banting (hardiness) pada single mother. Penlitian ini menggunkan pendekatan kualitatif model fenomenologi. Data diambil dengan teknik wawancara dan observasi. Daa yag diperoleh kemudian dianalisis dengan model deductive thematic analysis, yang melibatkan 2 proses dasar,yaitu: koding dan kategorisasi. Hasil dari penelitian ini menyatakan bahwa gambaran haridiness pada single mother terbntuk dengan melibatkan 3 aspek pokok yakni komitmen, tantangan, dan kontrol diri. Pada aspek komitmen, informan merasa sangat terpukul atas meninggalnya suami akan tetapi informan berkomitmen untuk tidak menikah lagi dan fokus kepada kelurga. Pada aspek tantangan informan terkadang mengalami permasalahan pada saat mendidik anak namun hal tersebut dapat diatasi informan dengan lebih belajar dan beradaptasi dengan lingkungan. Kemudian untuk aspek tantangan, informan lebih bisa mengendalikan emosi di saat lelah supaya tidak terlampiaskan kepada anak. Selain ketiga aspek tersebut, juga di pengaruhi dua faktor yang dikaji dalam penelitian ini, yaitu pada faktor internal informan lebih meyakinkan kepada dirinya sendirisupaya dapatmengambilhikmah darisetiap kejadian yang dialami. Sedangkan pada faktor eksternal, informan lebih bisa beradaptasi dengan lingkungan dengan memberikan citra diri yang positif terhadap masyarakat sekitar.
Analisa dan Perancangan Sistem Informasi Pendukung Keputusan Berbasis Web Untuk Pemilihan Penghuni Asrama UNAI Menggunakan Metode SAW

Tujuan dari penelitian ini adalah untuk merancang sebuah sistem berbasis komputer yang dapat digunakan untuk membantu dalam proses pengambilan keputusan. Kebutuhan sistem pendukung keputusan dalam hal penentuan mahasiswa penghuni asrama khusus yang diperuntukkan bagi mahasiswa yang kurang mampu yang berkuliah di Universitas Advent Indonesia (UNAI). Berhubung proses pengambilan keputusan sering melibatkan banyak kriteria maka digunakan sebuah metode yang dapat mendukung dalam pengambilan keputusan yang melibatkan multi kriteria atau Multi Criteria Decision Making (MCDM). Simple Additive Weighting (SAW) merupakan salah satu metode yang umum digunakan dalam mendukung pengambilan keputusan dengan multi kriteria. Model yang ada pada SAW ini digunakan untuk menentukan rengking dari alternatif-alternatif yang ada yang selanjutnya tampilkan dalam bentuk aplikasi komputer berbasis web sehingga memudahkan pengguna baik mahasiswa maupun manajemen UNAI. Hasil dari penelitian ini adalah sebuah sistem pendukung keputusan berbasis komputer berbasis web yang dapat membantu menentukan pengambilan keputusan pada seleksi pendaftaran Asrama Khusus di UNAI. Selanjutnya penelitian ini dapat dikembangkan dengan menggunakan metode yang lain untuk penentuan keputusan yang lebih efektif serta dapat pula ditampilkan dalam platform yang lain seperti aplikasi mobile. Kata Kunci: Sistem Pendukung Keputusan, Metode Simple Additive Weighting (SAW), Multi Criteria Decision Making (MCDM), Sistem Informasi
Facial cutaneous phaeohyphomycosis associated with Alternaria infectoriae infection

Summary Cutaneous nodular disease in the horse is relatively rare and usually associated with arthropod bites, dermal hyperplasia and inflammatory or neoplastic infiltrates. Equine phaeohyphomycosis, particularly alternariosis, has been previously described in the literature and is usually associated with Alternaria alternata. In this article we report a case of phaeohyphomycosis caused by a member of Alternaria section Infectoriae in a horse. To our knowledge this is the first time that this organism has been identified in equids. Despite A. Infectoriae being commonly isolated in individuals receiving immunosuppressive therapy and/or suffering from Cushing's syndrome, we could not determine a link between pituitary pars intermedia dysfunction and fungal infection in this case. The mare responded well to surgical excision of the cutaneous lesions and made a full recovery.
Do I need want to speak? Foreigners in a “small” language country

ABSTRACT This article draws on linguistic and sociolinguistic data that was gathered to study the acquisition of Lithuanian as a second language and considers the factors that are at work in the process of language acquisition. The article also presents an overview of the varieties of language acquisition. A combination of biological, social, psychological, and other factors plays a role in this process and the complexity is shown by considering sociolinguistic data and the reflections of the study’s participants.
RADAR, A FRAMEWORK FOR AUTOMATED REPORTING

Large companies and organizations periodically feed their information systems with large data flows. Apart from the classical operational activities, they are called to prepare aggregated reports to send to institutions and rating agencies. Unfortunately, organizations typically suffer for the lack of integrated data and for the lack of a standard data dictionary. The presented approach aims to tackle such a problem by building a bridge between employees that need to specify how to generate reports (on the basis of concepts and terms typical of the application domain) and the information system that stores the data to query and aggregate in order to automatically produce reports. The implemented framework, RADAR (Rich Advanced Design Approach for Reporting), moves from the notion of Operational Data Store, and it is posed in the middle between an ontology (of concepts and terms) and the actual operational (and relational) schema of source data. Then, in the defined schema allows for giving a high-level view of such source data, based on concepts described in the ontology for a specific application domain.
Fgf4 is critical for maintaining Hes7 levels and Notch oscillations in the somite segmentation clock

During vertebrate development, the presomitic mesoderm (PSM) is periodically segmented into somites, which will form the segmented vertebral column and associated muscle, connective tissue, and dermis. The periodicity of somitogenesis is regulated by a segmentation clock of oscillating Notch activity. Here, we examined mouse mutants lacking only Fgf4 or Fgf8, which we previously demonstrated act redundantly to prevent PSM differentiation. Fgf8 is not required for somitogenesis, but Fgf4 mutants display a range of vertebral defects. We analyzed Fgf4 mutants by quantifying mRNAs fluorescently labeled by hybridization chain reaction within Imaris-based volumetric tissue subsets. These data indicate that FGF4 controls Notch pathway oscillations through the transcriptional repressor, HES7. This hypothesis is supported by demonstrating a genetic synergy between Hes7 and Fgf4, but not with Fgf8. Thus, Fgf4 is an essential Notch oscillation regulator and potentially important in a spectrum of human Segmentation Defects of the Vertebrae caused by defective Notch oscillations.
Weaponizing white thymos: flows of rage in the online audiences of the alt-right

ABSTRACT The alt-right is a growing radical right-wing network that is particularly effective at mobilizing emotion through digital communications. Introducing ‘white thymos’ as a framework to theorize the role of rage, anger, and indignation in alt-right communications, this study argues that emotive communication connects alt-right users and mobilizes white thymos to the benefit of populist radical right politics. By combining linguistic, computational, and interpretive techniques on data collected from Twitter, this study demonstrates that the alt-right weaponizes white thymos in three ways: visual documentation of white victimization, processes of legitimization of racialized pride, and reinforcement of the rectitude of rage and indignation. The weaponization of white thymos is then shown to be central to the culture of the alt-right and its connectivity with populist radical right politics.
Combining Reconfiguration and Instruction Computations with an Asynchronous Crossbar

With the rapid development of artificial intelligence (AI) technology, a more flexible and efficient computing architecture is required. The coarse-grained reconfigurable architectures (CGRAs) as a co-processor that combines the flexibility and specificity of AI would be an appropriate solution. In this paper, we introduce an innovative asynchronous CRGA architecture that combines reconfiguration and specific instructions. We aim to accelerate a common computation with memorized calculation and keep the reconfiguration for rare ones, so that the whole efficiency could be enhanced. To show the feasibility of our method, we choose the sigmoid function that is essentially and widely used in AI algorithms, which can achieve about 40Mhz configuration throughput and about 669ns to finish the whole calculation.
Unsupervised preprocessing of medical imaging data with generative adversarial networks

Machine learning has in the last decade changed the way we do our daily tasks. In this new age of machine intelligence, the usage of computer assistance has skyrocketed in fields ranging from education to health care. In recent years, the medical field has seen significant improvements regarding the practice of computer-assisted medical diagnosis, and as computing power increases, the models used by medical professionals get more and more accurate. Within the medical field, the practice of automated disease detection in videos and images from the gastrointestinal tract has received much attention in the last years. However, the quality of image data is often reduced due to overlays of text, personal data, and black corners around the medical images. As an attempt to address the challenge of improving the field of computer-aided diagnosis, our work explores ways to help existing models to increase their accuracy when it comes to finding anomalies in medical images. In this thesis, we tackle the problems associated with the misclassification of data based on overlays and other artefacts in the medical image data. We will look at how we can use machine learning to develop a system to increase the classification accuracy of existing models, as well as going in-depth into the topic of preprocessing to see if it has a place in modern classification models based on deep learning. During this thesis, we will look at different tools that we can use to remove dataset specific artefacts, and we will look at the consequences of removing them. Our primary focus lies in the usage of generative adversarial neural networks to cover up parts of images that we have deemed unwanted in our medical images. In the end, we demonstrate that our system can be of great use as a tool for preprocessing of medical data, showing clearly that with our tools, pretrained networks can be generalised to a much greater extent. With the use of our preprocessing our models saw an increase in classification accuracy of 29.5% when training on new unseen data.
Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

Current traffic management systems in urban networks require real-time estimation of the traffic states.With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles.
Linseed oil: Characterization and study of its oxidative degradation

This paper proposes to characterize and monitor the degradation of linseed oil under two oxidation conditions using some traditional oxidative and quality parameters. The experimental section of this study was divided into 2 stages. In the first one, three commercial linseed oil samples (OL1, OL2, and OL3) were characterized according to oxidative stability (90 °C) and fatty acid composition. In the second stage, the OL1 sample, selected due to its availability, was subjected to the following oxidation procedures: storage at room temperature conditions with exposure to light and air (temperature ranging from 7 to 35 °C) for 140 days and accelerated oxidation at 100 °C for 7h. Samples were collected at different time intervals and analyzed for oxidative stability (90 °C), peroxide value, and acid value. The results showed that all the samples presented a similar fatty acid profile and that the OL3 sample showed a higher induction period (p < 0.05). Regarding the oxidative degradation, the induction period of the OL1 sample reduced from 9.7 to 5.7 and 9.7 to 6.3 during 140 days of storage under room temperature and 7 h of accelerated oxidation, respectively. The end of induction period of the OL1 sample is expected to occur within 229 days according to an exponential mathematical model fitted to the induction period values at different temperatures. In addition, the OL1 sample met the limits proposed by Codex and Brazilian regulations for peroxide and acid values during the oxidation time intervals.
Life style disorders in ophthalmology and their management

Lifestyle disorder defined as disease linked with the way people live their life. A WHO report opines that in many middle income and industrialized countries eye condition have emerged as potential threats to the status of sight and their populations. These being diabetic retinopathy, glaucoma, age related macular degeneration, cataract, retinal vein occlusion disease and dry eye. Out all these life style diseases the most contributing factors affecting visual health are metabolic syndrome, obesity, diabetes, hypertension, dyslipidemia etc. To combat these situations Ayurveda can also offer many management skills that are put forth in this article.
Design of SCADA for Protection System of Uncoiled DC Motor’s Temperature using PLC ABB AC 800PEC Based on Wonderware Intouch

Continuous Tandem Cold Mill (CTCM) operates many types of DC motor for various processes. The continuity of the DC motor is very important to ensure the automation process are working perfectly and safe. One of the abnormal condition when operating DC motor is overheating at Field winding and Field bearing. Therefore, this research is needed to avoid further damage in the DC motor by designing and implementing protection system of temperature based on PLC (Programmable Logic Controller) ABB AC 800PEC and PT100 sensor. SCADA (Supervisory Control and Data Acquisition) system was implemented so the operator can supervise the motors. When temperature is increasing and higher than the allowed maximum temperature, the system should generate Fast Stop signal to stop every process that have been running at entry section CTCM. The protection system was implemented successfully and generated Fast Stop signal when the Field winding temperature was higher than 100-degree Celsius or when the Field bearing temperature was higher than 75-degree Celsius.
Integrating Text Mining and Network Analysis for Topic Detection from Published Articles on Banana Sensory Characteristics

Published articles (28) from PubMed database on banana sensory characteristics were from 2002 to 2018. They were mined to detect the topic of discussion using the KNIME software. The texts were tagged with the Open Source Chemistry Analysis Routines (OSCAR) chemical named entity and preprocessed by filtering and stemming, thereafter the topic of discussion detected with the Latent Dirichlet Allocation and term co-occurrence was determined using KNIME data mining software. The co-occurrence terms were converted to node adjacency matrix and imported into Gephi Graph Visualisation and Manipulation software version 0.02. Network statistics such as modularity class, degree centrality, betweenness and closeness centrality were estimated. Majority of the OSCAR tagged words (50.8%) were chemical compounds and 47.3% ontology terms. The directed network consisted of 53 nodes and 904 edges. There were four modularity classes. The terms with high betweenness centrality (>45) were, accept, fruit, analysis, coat, food, composite and banana. Three topics were detected from the documents, namely (1) quality of banana fruit and peel; (2) use of banana fruit in food and wine and (3) sensory acceptability of banana peel and flour in food products. This chapter provides details each topic.
Self-Care Level in Diabetic Patients

Objective The present study was done with the objective of assessing the self-care practices among diagnosed type 2 diabetes mellitus patients in hospital of Islamic Azad University of Medical Sciences in Tehran, Iran. Methods In this cross-sectional study, 314 Iranian patients older than 18 years of age with type 2 diabetes were completed Diabetes Self-Management Questionnaire. Data was analyzed by ANOVA, t-test, correlation and liner regression. Results A significant association was found between the diabetes self-management sum scale and body mass index, Dietary control subscale and body mass index, Physical activity subscale and body mass index & job and Healthcare use subscale and diabetes treatment (p<0.05). Conclusion Due to the high BMI in respondents, Overweight and obese respondents need to be advised by health professionals on physical activities and proper diet to achieve healthy weights to avoid complications related to diabetes mellitus. Keywords Diabetes self-management, Type 2 diabetes, Diabetes Self-Management Questionnaire DOI : 10.7176/JMPB/63-02 Publication date: January 31 st 2020