Miracle-dz/UniFAR-FTUs huggingface dataset:
Treatment of interferon-α for chronic hepatitis C

Combination therapy with polyethylene glycosylated IFN-α2a or IFN-α2b and ribavirin is currently the standard therapy for chronic hepatitis C. However, even with this therapy, hepatitis C virus cannot be eradicated in 50% of patients with refractory chronic hepatitis C. In addition, withdrawal or dose reduction occurs in ∼ 40% of patients due to adverse effects. This treatment is also a contraindication in some patients, such as in patients with coexisting diseases or in elderly patients. For these patients, standard IFN-α monotherapy is even safer and more effective. In patients with chronic hepatitis C, IFN-α monotherapy results in a significant increase in the cumulative survival rate by suppressing the progression to hepatocellular carcinoma or liver failure. In addition, other efficacious therapeutic regimens have been employed, such as prolonged administration of standard IFN-α in elderly patients; prolonged low-dose continuous administration in patients with decompensated cirrhosis or hepatocellular carcinoma postoperative patients; and combination therapy with 5-fluorouracil and standard IFN-α for advanced hepatocellular carcinoma. Monotherapy with standard IFN-α should thus be recognised as one of the important therapeutic strategies for chronic hepatitis C.
Expertise in Radiology: Accounting for the Evidence and Implications for Instruction.

The problem-solving strategies used by 10 radiologists and 10 radiology residents during the interpretation of difficult mammograms were examined under two experimental conditions. In the "authentic" condition, standard unmarked mammograms were used. On a second set of the same cases for the "augmented" condition, mammographic findings were highlighted. Verbal protocols revealed that mammography interpretation predominantly used data-driven or mixed-strategies but depended on case typicality and clinical experience. Radiologists scanned the cases significantly faster than residents, but no group differences were found in the number of findings, observations, and diagnoses. The groups (1) used the same types of operators, control processes, and diagnostic plans; (2) committed the same number of errors; and (3) both groups committed case-dependent errors. Overall, the results indicated that mammogram interpretation is a well-constrained visual cognitive task. Results were applied to the design of a computer-based tutor for mammogram interpretation. (Author, DB) ******************************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * ********************************************************************************
Bioinformatics Methods for Diagnosis and Treatment of Human Diseases

The availability of large databases of genomic information has enabled research efforts focused on refining methods for diagnosis and treatment of human diseases. However, proper use of genomic databases can not be achieved without the development of sophisticated data analysis methods, which is by itself a challenging task due to the size and heterogeneity of the data. The focus of the research proposed in this document is on developing computational methods and software tools for diagnosis and treatment of human diseases. We describe a primers design tool for rapid virus subtype identification, applied to Avian Influenza called PrimerHunter, which takes as input sets of both target and non-target sequences and select primers that efficiently amplify any one of the targets, and none of the non-targets. PrimerHunter ensures the desired amplification properties by using accurate estimates of melting temperature with mismatches, computed based on the nearest-neighbor model via an efficient fractional programming algorithm We also present a bioinformatics pipeline for detection of immunogenic cancer mutations by high throughput mRNA sequencing. As part of this pipeline, we developed and integrated novel algorithms and strategies for mRNA reads mapping, SNV detection, genotyping and haplotyping. We show through validations on real data that our methods improve accuracy to identify expressed mutations over existing methods and that our haplotyping algorithm is more efficient than other solutions with comparable accuracy levels. Our pipeline predicted more than a thousand candidate epitopes for six different mouse cancer tumor cell lines, which are currently used to find stable protocols for immunotherapy. i Acknowledgments I will start by saying thanks to my advisor professor Ion M˘ andoiu for all his support and guidance during these years. Professor M˘ andoiu is a full time committed educator and an outstanding researcher. Working with him I have been able to acquire not just the knowledge needed to work as a bioinformatician but also the skills and discipline needed to conduct a successful research project. I would like to thank my associate advisors Yufeng Wu and Sanguthevar Ra-jasekaran and also the researchers that I had the privilege to collaborate with during these years. Thanks to professors I will always be thankful with my friends from the group Jarana for all the good and bad things that we went through during these years. Thanks also to my new friends from all over the world for all what I have learned. Thanks to my friends back …
The Current Trends of Augmented Reality in Early Childhood Education

Augmented Reality has been widely used in various level of education such as higher-level education, secondary education (lower/upper secondary level), primary education, and in informal learning. However, the implementation in early childhood education is still limited. By using library research methodology, the objective of this paper is to investigate the existing work of augmented reality in early childhood education between 2009-2018. Based on the results, it shows that the publication of augmented reality in early childhood education increased slowly within these past ten years. It has been found that the main advantage of augmented reality is to enhance motivation. Early literacy has been found to be the most used topic with sampling less than 30 children. Finally, ‘Marker-based’ augmented reality has been widely used with mobile devices and in term of data collection methods, ‘Test’ has been used the most in this field of research
Tourist arrivals versus tourist expenditures in modelling tourism demand

In empirical research, aggregate tourist arrivals and tourist expenditures are often indistinctly used as measures of tourism demand, depending on the aim of the analysis or, simply, on the availability of data. However, when a literature review was conducted, we found differences in the estimated elasticities, accordingly to the measure that was used. This article investigates these two measures, exploring the theoretical link between them in the context of tourism demand modelling at a destination level. Having established the theoretical connection between the two variables with implications on the estimated elasticities, we estimate tourism demand models using international arrivals and tourist expenditures for 191 countries from 1998 to 2016, providing evidence for the theoretical connection. Our results show that when both tourist demand measures are used, the estimated elasticities may differ.
Encrypted DNP3 Traffic Classification Using Supervised Machine Learning Algorithms

The Distributed Network Protocol (DNP3) is predominately used by the electric utility industry and, consequently, in smart grids. The Peekaboo attack was created to compromise DNP3 traffic, in which a man-in-the-middle on a communication link can capture and drop selected encrypted DNP3 messages by using support vector machine learning algorithms. The communication networks of smart grids are a important part of their infrastructure, so it is of critical importance to keep this communication secure and reliable. The main contribution of this paper is to compare the use of machine learning techniques to classify messages of the same protocol exchanged in encrypted tunnels. The study considers four simulated cases of encrypted DNP3 traffic scenarios and four different supervised machine learning algorithms: Decision tree, nearest-neighbor, support vector machine, and naive Bayes. The results obtained show that it is possible to extend a Peekaboo attack over multiple substations, using a decision tree learning algorithm, and to gather significant information from a system that communicates using encrypted DNP3 traffic.
Development of an Online Sleep Diary for Physician and Patient Use

This paper describes the development of an electronic sleep diary and outlines its advantages over the traditional paper based approach still used by many sleep centres throughout the world. A sleep diary is a record of sleep details filled in by a patient normally over a period of two weeks. This information is then used by a physician as a diagnostic tool for identify sleep disorders in the patient. The development method used was convergent interviews with sleep specialists in order to establish initial requirements. This was followed by a rapid prototyping approach in order to produce the final specification. This paper concludes that an online sleep diary is a low cost, viable alternative offering benefits to both patients and physicians. The benefits to patients include the ability to perform functional analysis of their own sleep habits (referred to as sleep hygiene) and to determine factors affecting their sleep patterns. This knowledge leads to greater patient understanding of their circumstances and can lead to a potential increase in patient, physician collaboration. The physician gains access to timely accessible patient information as well as to an evidence database that will allow for greater analysis of sleep disorders throughout the general public over time. https://doi.org/10.34105/j.kmel. 2010.02. 014
Centralized and Decentralized Organizational Models of Interprofessional Education for Physical Therapist and Medical Students

Background and Purpose. Although there has been extensive discussion on the need for interprofessional education (IPE), available literature that addresses organizational models for its structure and implementation is limited. The purpose of this article is to describe the implementation of IPE at the University of South Florida (USF) College of Medicine, through the lens of organizational structure and change. Model Description and Evaluation. The University of South Florida College of Medicine has employed IPE for students in the medicine and physical therapy programs since the fall of 2005. This article describes the IPE program at USF, the 2 underlying organizational models—centralized and decentralized—of IPE, and the advantages and disadvantages of each model. The authors make the case that interprofessional organizational structures and decision‐making processes are necessary for the long‐term sustainability of IPE. Like learning experiences, organizational support is a part of the interprofessional continuum and can be classified as uniprofessional, multiprofessional, or truly interprofessional. Outcomes. Preliminary results indicate that centralized and decentralized models have both benefits and drawbacks. Organizational decision‐making processes in our program have become more interactive and interprofessional, resulting in fewer logistical barriers, a greater number of champions, and more IPE opportunities for students. Results from the disability experiences suggest that the learning experiences have been successful in stimulating interaction between students from different professions around the shared issue of disability, but not as effective in stimulating discussion around professionspecific perspectives on disability, which is vital to future teamwork. Discussion and Conclusion. Implementation of a centralized model of IPE requires organizational commitment and culture change, but holds the greatest potential for sustainable long‐term change for students, faculty, the implicit curriculum, and educational outcomes.
Occupational Therapy Needs Assessment of Male United States Veterans who are Homeless

The current occupational therapy research base related to individuals who are homeless is sparse, and few studies have identified the various needs of subgroups within the population. To provide appropriate interventions, treatment, and program development for homeless veterans, an understanding of their occupational performance, including impact of prior experiences, roles, and meaningful and valued activities is necessary. This study used qualitative methodology to explore the needs of a small number of male United States veterans in urban Western Washington who are currently or were previously homeless, in order to determine in what way the occupational therapy profession can best meet the needs of this client population. Two previously homeless veterans, an occupational therapist, and a social worker were interviewed. Themes derived from the responses of participants included the impact of decreased self-efficacy, the use of roles, and environmental supports and barriers. An increased understanding of the challenges, barriers, and supports that impact the lives of homeless veterans may provide occupational therapists with the knowledge to create appropriate and valid interventions to increase occupational performance.
Model-based Hand Pose Estimation for Generalized Hand Shape with Spatial Transformer Network

Since the emergence of large annotated datasets [1], state-of-the-art hand pose estimation methods have been mostly based on discriminative learning [2]. Recently, a hybrid approach has embedded a kinematic layer into the deep learning structure in such a way that the pose estimates obey the physical constraints of human hand kinematics [3]. However, the existing approach relies on a single person’s hand shape parameters, which are fixed constants. Therefore, the existing hybrid method has problems to generalize to new, unseen hands. In this work, we extend the kinematic layer to make the hand shape parameters adaptable. In this way, the learnt network can generalize towards arbitrary hand shapes. Furthermore, we show that by applying Spatial Transformer Network [4], the performance of a regression task can be also improved. The effectiveness and limitations of our proposed approach are evaluated on the Hands 2017 challenge dataset [1].
An application of Thirlwalls law to the South African economy: Evidence from ARDL bounds testing approach

This paper applies Thirlwall’s basic balance-of-payments constraint growth model to South Africa economic growth for the period of 1984:1 - 2006:1 by using Autoregressive Distributed Lag (ARDL) Bounds Testing approach. The empirical results reveal that import is cointegrated with relative price and income, and the equilibrium growth rates coincide with actual growth rates. Our empirical results also support the Thirlwall’s hypothesis which states that balance of payments position of the South African economy is the main constraint on its economic growth. As a policy implication, a successful economic growth policy will permit South Africa to have a rapid growth in demand and supply without suffering deterioration in its balance of payments. Key words: Growth, balance of payments, Thirlwall´s Law, bounds testing approach, South Africa.
Exploring the Service Quality of Airbnb

Airbnb has become a strong competitor for hotels when it comes to accommodation choices. However, few studies have analyzed the service quality provided by Airbnb. In order to approach this gap and to support the industry to maintain competitiveness, this study identified related dimensions measuring the service quality based on a qualitative design, conducting a content analysis of Airbnbrelated reviews. The following six dimensions are identified and discussed: Accuracy, Cleanliness, Rooms and Facilities, Location, Personalized Service, and Value. Findings indicate that competitive advantage can be maintained by improving target group-related service quality, the accuracy and relevance of information provided, flexible check-in and check-out slots and services, as well as the organization of tours and events for guests.
Energy Efficient Resource Provisioning through Energy and SLA Aware(ESA) Algorithm in Cloud Computing

Cloud Computing is an Internet-based computing in which services provided by the Cloud Service Providers (CSPs) are in the same way as utilities i.e, pay-per-use model. It enables the clients and endeavors to store and process their information in a cloud infrastructure to give them a chance to concentrate better on their business needs. With the rapid increase in popularity of Cloud Computing growth of data centers also increases to accomplish the routine services. As a result, a great amount of energy is consumed by data centers even when they remain underutilized. Proper management of this energy consumption becomes a key issue. Virtualization plays a vital role in reducing energy consumption. The process of migration of Virtual Machine (VM) from one server to another is known as Virtual Machine Migration (VMM). Fault tolerance, load balancing, improved performance, and energy management are some beneﬁts of VMM. But it leads to degradation of performance and SLA violations that cannot be ignored. In our work, we propose an algorithm namely Energy and SLA Aware (ESA) VM placement algorithm that reduces the overall energy consumption based on 1) After allocation, host utilization 2) VM creation history and 3) the diﬀerence between power consumption, SLA violation by the host before and after allocation. It also considers energy eﬃciency which is a function of power and SLA. The implementation of the proposed algorithm is done in CloudSim. The results show that our method provides a considerable amount of reduction in energy consumption and average SLA violations.
Ist die psychologische Forschung durchlässig für aktuelle gesellschaftliche Themen?: Eine szientometrische Analyse am Beispiel Flucht und Migration mithilfe von Topic Modeling

In the context of the scientific investigation of social challenges, and drawing on the example of refugees and migration, we investigated to what extent psychological research takes up current social and political issues, whether it provides an evidence-based foundation for addressing social challenges, and to which subdisciplines of psychology the research topics can be assigned. Specifically, the abstracts of 4,073 publications from German-speaking countries, documented in the psychological reference database PSYNDEX and published between 1980 and 2017, were analyzed using structural topic modeling. A total of 19 topics were identified. The most strongly increasing trend was shown for topics concerning the traumatization of refugees, transcultural psychotherapy, and rehabilitative treatment. Topics concerning the situation of foreigners and guest workers, juvenile crime, as well as social integration and acculturation in relation to the former German Democratic Republic showed decreasing trends. The topic of language development of migrant children had the highest probability of being addressed by empirical studies; the topic of identity development was most probable for nonempirical contributions. In summary, important social and political developments in the field of refugees and migration are reflected in psychological literature. A mixed picture emerges with regard to empirical contributions on the topics. Most topics have a clinical-psychological focus, but other disciplines are also represented. From a methodological perspective, the topic modeling approach has proved to be a helpful method for scientometric investigations within large text corpora.
Objective and Subjective Indicators in Long-term Contracting

Abstract We study the optimal compensation contract in a dynamic moral hazard setting with limited liability and two stages of production. At the end of the first stage, both an objective signal (publicly observed) and a subjective signal (privately observed by the principal) realize, and they are informative of the second-stage productivity. We show that in the optimal long-term contract, the two signals are used interactively with three notable features. First, whereas the objective signal is always used in the optimal contract, the subjective signal is used only if the realized value of objective signal is below a cutoff. Second, the solicited effort may exhibit an upward distortion (relative to the first best) for an intermediate region of the objective signal realizations. Third, the use of subjectivity varies with a number of factors such as the signal quality and the severity of the moral hazard problem.
Extracting Top Trends from Twitter Discussions in Bulgarian

Social networks offer plenty opportunities and areas for scientific research to dabble in user opinion mining and text analysis. The short text messages that get posted online present unique challenges related to automatic categorization and annotation. An interesting problem is the natural language filtering of text messages. Due to the huge volumes and sparsity of textual data machine learning algorithms are being applied. In this paper we take a look at the way to extract twitter messages in real-time containing Bulgarian texts. We also measure Twitter`s accuracy in terms of language identification from a 10 day dataset between 1st and 10th of October 2017. We propose a step by step text preprocessing algorithm, suitable for sanitizing tweets. We apply kmeans++ algorithm to cluster the extracted data and choose representative words for each cluster during each day.
Detection Method for User Click Fraud Based on Garbled Bloom Filter

In order to solve the click fraud problem in the network, a detection method of user click fraud is presented with confusion Bloom filter. In this method, the user click fraud advertising set is found by the training data set attributes at first, and then use the Bloom filter and outlier mining algorithm for the detection and localization of suspected fraud. Finally, all suspected fraud members were classified by the Bias classification method to detect fraud. The experiment results show that this method can effectively shield visitors from accidental unconscious clicks, and significantly reduce the probability of click fraud.