Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multiparametric MRI for the Classification and Grading of Prostate Cancer

Author(s): Korn, Natalie | Advisor(s): Kurhanewicz, John | Abstract: Prostate cancer is one of the most common diseases in men worldwide, however only a small percentage of men with prostate cancer have aggressive cancer, and most men diagnosed with focal disease have indolent disease with low risk of progression compared to the risks associated with treatment. There is a current, pressing need to differentiate between aggressive and indolent prostate cancers in ways that are minimally invasive and not damaging to patients. This dissertation proposes the multiparametric MRI of the prostate, with its inherent non-invasive technique void of harmful radiation, as a solution. However, the current multiparametric MRI of the prostate is not widespread, due to lingering artifacts and ambiguous results in some acquisitions. In this work, we provide improvements to pulse sequence software, acquisition hardware, and image-processing software for the multiparametric MRI of the prostate. Additionally, we introduce hyperpolarized carbon-13 imaging in patients as part of a phase 2 clinical trial, with the unique ability to quantify tissue energetics as a signature of prostate cancer’s abnormal metabolism.
Agroforestry Extension Manual For Kenya

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8-bit 50ksps ULV SAR ADC

With the growing market of MCUs and embedded electronic powered by batteries, more energy efficient peripherals are needed. This project presents an Analog to Digital converter using ultra low supply voltage on transistor level. With a charge recycling spilt capacitive DAC using set and down switching method. And a comparator with a dynamic amplifier, resulted in a very energy efficient SAR ADC. The ADC got a samplings rate of 50k Hz and an ENOB of 7.89 bits, while only draining 75nW power from a 0.5V supply. This results in a Walden FOM of 7.39fJ/conv.step.
Postnatal care in Rwanda: facilitators and barriers to postnatal care attendance and recommendations to improve participation

Background Sub-Saharan Africa has the highest rates of neonatal mortality in the world with an estimated 1.2 million deaths within the first 28 days of life. Postnatal care (PNC) can contribute to reductions in morbidity and mortality in mothers and newborns through vital support that identifies danger signs and establishes valuable practices and referral processes. Methods This qualitative data was collected as a part the East Africa Preterm Birth Initiative (PTBi-EA) to guide development of a group antenatal (ANC) and PNC model in Rwanda. Key-informant in-depth interviews (IDIs) and focus group discussions (FGDs) were conducted in four districts. Sixteen FGDs with 180 participants and 22 IDIs were completed at the time of thematic saturation. Results Four themes highlighted facilitators and barriers to PNC attendance and recommendations to improve participation: 1) There is little awareness in the community of what the PNC package is; PNC 4 in particular is not well understood; 2) PNC visits by community health workers (CHWs) are well accepted and valued; 3) Providers perceive PNC 4 as an added burden to an already high workload; 4) Community structures exist to better disseminate key messages about PNC, but have not yet been effectively utilized. Conclusions This qualitative work provides evidence that the PNC package was not initially well understood. Regardless, PNC service delivery performed by CHWs in Rwanda is well accepted and appreciated by the population, providing assurance that the full package has potential to be well utilized and valued by the population.
Unsupervised Translation Disambiguation Based on Web Indirect Association of Bilingual Word

To solve the problems of data sparseness and knowledge acquisition in translation disambiguation and WSD (word sense disambiguation), this paper introduces a fully unsupervised method, which is based on Web mining and Web indirect association of bilingual words. It provides new knowledge of translation disambiguation. It assumes that word sense can be determined by indirect association of bilingual words. Based on Web, this paper revises four common methods of indirect association, and designs three decision methods. These methods are evaluated on a gold standard Multilingual Chinese English Lexical Sample Task dataset of SemEval- 2007. The experimental results show that the model gets the state-of-the-art results (Pmar=44.4%) and outperforms the best system in SemEval-2007.
Sentiment Classification from Word of Mouth Documents based on Chinese Collocations

Word of mouth (WOM) has become the main information resource while making business or buying strategies. Most WOM mining research studies focus on classification of WOM documents according to their sentimental orientations, i.e. positive and negative. Generally speaking a well-defined sentiment lexicon is used to provide the sentiment score for words. As a word may have different meanings when used in different domains so it may have different sentiment score. However such a lexicon is static and does not adapt to different domains. In this paper, we first build an adaptive Chinese sentiment lexicon from a real product review website. Then we identify feature words and opinion words of each sentence via the technique of mutual correlations between words. Based on association rules and mutual information, we extract the feature words and their associated collocation words. Finally the term frequency-inverse class frequency (TF-ICF) is used to extract word sentiment scores. According to experimental results, the usage and distribution of words are varied from different domains and our approach has a potential for Chinese WOM classification.
The CEFR and the dynamics of second language learning: trends and challenges

Abstract This paper discusses the pedagogical implications of Dynamic Systems Theory approaches to second language development. The main question addressed is whether it is possible to describe a learner's level of proficiency with a simple and unambiguous label, and if so, whether the CEFR can provide such a label. It is argued that considering the dynamic and multidimensional nature of second language development, only an approach that values the relevance of variability will be able to capture the crucial time dimension of development. When learning is defined as self-organization that follows the perturbation of a system, language learning cannot be seen as development that follows a predetermined sequence, but must ultimately be regarded as a contextualized individual trajectory. When this observation is applied to language teaching and language testing, it must be concluded that a longitudinal approach using a language portfolio that captures a range of language skills as proposed by the CEFR is of considerably greater value for the learner than a rigid assessment of some aspects of language proficiency at one moment in time.
Nocturnal Glycemic Control with New Insulin Glargine 300 U/mL

Insulin glargine 300 U/mL (Gla-300) is a new generation basal insulin product that has been demonstrated to have more stable pharmacokinetic and pharmacodynamic characteristics than insulin glargine 100 U/mL (Gla-100). To evaluate the real-world benefits of Gla-300 in reducing nocturnal fluctuations in blood glucose levels and nocturnal hypoglycemia, 10 Taiwanese patients using Gla-100 for insulin therapy were switched to Gla-300 and continuous glucose monitoring (CGM) was applied at nighttime to monitor changes to nocturnal glycemic variability parameters. Glycemic variability parameters measured to assess between- and within-night glycemic variability included mean 6-hour nocturnal (00:00–6:00 AM) glucose levels, standard deviation (SD), and coefficient of variance (CV) of mean nocturnal glucose levels and mean glucose excursion (MAGE). In this study, Gla-300 demonstrated comparable glycemic efficacy to Gla-100 and the potential to further reduce nocturnal hypoglycemia risk. Overall, nocturnal glycemic variability parameters measured during the Gla-300 treatment period were numerically smaller than those measured during the Gla-100 treatment phase although statistical significance was not reached. In terms of within-night glucose management, SD and CV values of mean nocturnal glucose levels were found to be statistically lower during the Gla-300 treatment phase than the Gla-100 treatment phase on nights individuals displayed normal blood glucose level readings at the beginning of the night. In summary, this study represents the first of its kind from Taiwan to evaluate the real-world clinical benefits of switching Taiwanese diabetes patients from Gla-100 to Gla-300 insulin therapy in reducing nighttime glucose variability by means of CGM.
Non-invasive assessment of autonomic nervous system through cardiovascular signals variability analysis in non-stationary environments

Autonomic nervous system (ANS) is in charge of the maintenance of homeostasis of the body and regulates different functions such as heart rate, blood pressure, gastrointestinal responses, and thermoregulation. It is interesting to have a regular ANS evaluation in different applications, some examples include understanding the response of ANS to stress to early detect or diagnose cardiovascular, metabolic or mental diseases. Heart rate variability (HRV) analysis is one of the most popular non-invasive methods of ANS evaluation and is easily obtained from the electrocardiogram (ECG) signal. HRV reflects purposely generated responses to internal and external stimuli, being ANS the primary regulator of cardiac chronotropy. Aditionally, other cardiovascular variability signals (such as blood pressure variability) and respiration can also provide added value to the assessment of ANS state. Most of the tools developed to analyze HRV have been designed in a stationary environment. When this premise is not valid, there is a necessity to develop robust methodologies adapted to each scenario to make sure that measurements allow us to correctly interpret changes in ANS activity. The objective of this thesis is to extract information about ANS state from cardiovascular signals, mainly HRV, and respiratory information in non-stationary environments. A robust methodological framework suitable for HRV analysis during exercise is presented which addresses many limitations in HRV analysis during exercise. To have a better QRS detection in the noisy ECG, an evolutionary algorithm is applied to optimize some of the parameters of the detector. The HRV signal is computed based on the time-varying threshold integral pulse frequency modulation (TVIPFM) model, which takes into account the high variations in the mean heart rate expected during exercise. To deal with the non-stationary nature of the signal, a time-frequency analysis is used which allows to extract instantaneous power parameters. Since the respiratory frequency varies during exercise, usually reaching high frequencies, the high frequency (HF) band is needed to be redefined. Moreover, spectral components related to cardiolocomotor coupling (CC) are identified in the HRV spectrum, and a correction is applied whenever this component overlaps with the HF component. This framework is then applied to three different exercise tests in order to study the response of ANS to the stress of exercise in healthy subjects: one maximal test on a treadmill, and two submaximal tests on a treadmill and on a cyclo ergometer. Different intervals are determined in each test: one related to the resting phase, one for the recovery phase, and the rest, during exercise, based on a similar metabolic
A High-Fidelity Simulation Is Effective in Improving Athletic Training Students' Self-Efficacy with Emergency Cardiovascular Care Skills

High-fidelity simulation can provide an ideal adjunct to clinical or real-world experience by providing a realistic and safe learning environment for the practice of low-incident encounters. Given that levels of perceived self-efficacy are malleable and high-fidelity simulation can provide many positive outcomes, the purpose of this study was to determine whether participation in a high-fidelity simulated cardiovascular emergency scenario using the Laerdal SimMan in a university simulation center in the United States increased undergraduate athletic training students' self-efficacy scores. Cohort design with repeated measures. Convenience sample of undergraduate athletic training students (n = 46) enrolled in a professional program at a National Collegiate Athletic Association Division I university in the Midwest. Participation in or observation of a high-fidelity cardiopulmonary resuscitation (CPR) simulation. Self-efficacy scores before, immediately after, and 6 months after simulation. There was a significant main effect for the 3 repeated measures, with the scores steadily increasing significantly from pretest (mean = 7.60, SD = 1.13) to posttest (mean = 8.04, SD = 1.22, P = .001), then again from immediate posttest to the 6-month posttest (mean = 8.38, SD = 1.04, P = .04). Scores among the participants (mean = 8.21, SD = 1.03) were not significantly higher than scores among the observers (mean = 7.85, SD = 1.40). Scores at the 6-month follow-up posttest (mean = 8.38, SD = 1.04) significantly increased from the posttest immediately after the simulation (P = .04). Participating in or observing high-fidelity CPR simulation is an effective method of providing deliberate practice opportunities for athletic training students to increase self-efficacy related to CPR techniques.
Modeling, Simulation and Analysis for Software-as-Service in Cloud

Software-as-a-Service (SaaS) has received significant attention in recent years as major computer companies such as Google, Microsoft, Amazon, and Salesforce are adopting this new approach to develop software and systems. Cloud computing is a computing infrastructure to enable rapid delivery of computing resources as a utility in a dynamic, scalable, and virtualized manner. Computer Simulations are widely utilized to analyze the behaviors of software and test them before fully implementations. Simulation can further benefit SaaS application in a cost-effective way taking the advantages of cloud such as customizability, configurability and multi-tendency. This research introduces Modeling, Simulation and Analysis for Software-as-Service in Cloud. The researches cover the following topics: service modeling, policy specification, code generation, dynamic simulation, timing, event and log analysis. Moreover, the framework integrates current advantages of cloud: configurability, Multi-Tenancy, scalability and recoverability. The following chapters are provided in the architecture: • Multi-Tenancy Simulation Software-as-a-Service. • Policy Specification for MTA simulation environment. • Model Driven PaaS Based SaaS modeling. • Dynamic analysis and dynamic calibration for timing analysis. • Event-driven Service-Oriented Simulation Framework. • LTBD: A Triage Solution for SaaS.
New Algorithms for Efficient High-Dimensional Nonparametric Classification

This chapter contains sections titled: Introduction, Balltree, Exact Near Neighbor, KNS1: Conventional k-NN Search with Balltree, KNS2: Faster k-NN Classification for Skewed-Class Data, KNS3: Are at Least t of the K-NN Positive? , Experimental Results, Comments and related work
Cloud Computing in Bioinformatics: current solutions and challenges

MOTIVATIONS The availability of high-throughput technologies and the application of genomics and pharmacogenomics studies of large populations, are producing an increasing amount of experimental and clinical data, as well as specialized databases spread over the Internet. The storage, preprocessing and analysis of experimental data is becoming the main bottleneck of the analysis pipeline. Managing omics data requires both space for data storing as well as services for data preprocessing, analysis, and sharing. The resulting scenario comprises a set of bioinformatics tools, often implemented as web services, for the management and analysis of data stored in geographically distributed biological databases [1]. Cloud computing may play an important role in many phases of the bioinformatics analysis pipeline, from data management and processing, to data integration and analysis, including data exploration and visualization because it offers massive scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, thus it may represent the key technology for facing those issues [2].
Esthetic rehabilitation of anterior teeth by fiber-reinforced composite: A case report

Patients refusing implant surgery for psychological reasons, where minimal reduction of tooth is preferred, a fiber-reinforced composite fixed partial denture can be a good alternative to conventional prosthetic techniques. In comparison to other restorative systems this is a conservative approach carries a minimum jeopardy of pulp exposure or sensitivity and periodontal inflammation, maintaining the health of supporting tissues. The aim of this case report is to describe the clinical procedure for fabricating an IFPD with a pre-impregnated glass fiber system and a hybrid composite. Fiber-reinforced composite, in combination with adhesive techniques, appears promising for an IFPD. Further clinical investigation will be required to provide additional information on this technique.
Microflora composition in the gastrointestinal tract in patients with Barrett's esophagus.

Background The incidence of esophageal adenocarcinoma (EAC) has been increasing over the last 40 years. While Barrett's esophagus is a known risk factor for the development of EAC, the role of the microflora in the development of EAC is still largely unknown and is being investigated further by multiple centers. Our goal was to identify trends in microflora composition along various aspects of the upper gastrointestinal tract in patients with Barrett's esophagus. Methods After obtaining institutional review board approval, 12 patients agreed to participate in the study. While endoscopy was performed for surveillance Barrett's monitoring, additional biopsies of esophageal mucosa were taken from the (I) proximal esophagus, (II) mid-esophagus, (III) distal esophagus, and (IV) Barrett's esophagus. Additional swabs were also taken from the uvula and the endoscope used during the procedure. The swabs from the uvula and endoscope were obtained prior to the endoscope entering the stomach, to prevent exposing the endoscope to the acidic environment of the stomach. The most common bacterial elements were identified by amplifying sample DNA using a panel of 5 "universal" fusion primer pairs. The 400-500 base pair fragments created an overlap which covered 95% of the bacterial 16s gene. Results Throughout the esophagus, 34 bacterial genera were found which had a relative abundance of >1.0. Streptococcal genera were prevalent in all aspects of the esophagus, ranging from 16% to 70% of the bacterial community. Haemophilus genera were uniquely abundant in the Barrett's esophageal tissue but relatively absent elsewhere in the upper gastrointestinal tract. Overall, the percentage of Gram-positive organisms was much higher in the proximal than distal esophagus. The microflora pattern obtained from the uvula and endoscopic swabs did not correlate with any of the tissue biopsies along any aspect of the esophagus. Conclusions In patients with Barrett's esophagus, Streptococcal genera are widespread throughout the esophagus. Gram-positive genera tend to decrease as a percentage of overall flora distally. Obtaining a simple swab of the oropharynx or endoscope itself appears to be a poor substitute for tissue biopsy of esophageal mucosa when evaluating microflora patterns.
Searching and Mining with Semantic Categories

A new model is proposed to retrieve information by building automatically a semantic metatext1 structure for texts that allow searching and extracting discourse and semantic information according to certain linguistic categorizations. This paper presents approaches for searching and mining full text with semantic categories. The model is built up from two engines: The first one, called EXCOM (Djioua et al., 2006; Alrahabi, 2010), is an automatic system for text annotation, related to discourse and semantic maps, which are specification of general linguistic ontologies founded on the Applicative and Cognitive Grammar. The annotation layer uses a linguistic method called Contextual Exploration, which handles the polysemic values of a term in texts. Several ‘semantic maps’ underlying ‘point of views’ for text mining guide this automatic annotation process. The second engine uses semantic annotated texts, produced previously in order to create a semantic inverted index, which is able to retrieve relevant documents for queries associated with discourse and semantic categories such as definition, quotation, causality, relations between concepts, etc. (Djioua & Desclés, 2007). This semantic indexation process builds a metatext layer for textual contents. Some data and linguistic rules sets as well as the general architecture that extend third-party software are expressed as supplementary information.