Miracle-dz/UniFAR-FTUs huggingface dataset:
Étude prospective des résultats cliniques et urodynamiques des injections intra-détrusoriennes de toxine botulique dans le traitement de l'hyperactivité détrusorienne neurogène Clinical and urodynamic evaluation of efficacy intradetrusor botulinum toxin A (BIA) injections in patients with neurogenic

Objective: To evaluate the clinical and urodynamic efficacy and duration of efficacy of intradetrusor botulinum toxin A (BTA) injections in patients with neurogenic overactive bladder refractory to anticholinergic therapy. Patients and methods: Between 2004 and 2005, 33 patients (19 men and 14 women), with neurogenic overactive bladder were treated by intradetrusor injections of 300 U of BTA (Botox) in 30 points. All patients were refractory to, intolerant of or presented contraindications to anticholinergic therapy. All patients voided by intermittent self-catheterization. Results: Six weeks after BTA injections, the success rate was 75.8%, with improvement in 12.1% of cases and failure in 9.1% of cases. The mean number of self-catheterizations per 24 h was significantly decreased (6.37 versus 5.2, P=0.02), the maximum voiding volume was increased (321.68 ml versus 536.25 ml, P=0.002), the mean number of episodes of incontinence per 24 h was decreased (7.39 versus 0.03, P<0.0001), the proportion of patients with incontinence was decreased (66.66% versus 6.04%, P<0.0001), the mean maximum cystomanometric capacity was increased (286.75 ml versus 554.16 ml, P=0.002) and the mean maximum intravesical pressure was decreased (54.8 cm H(2)O versus 5.3 cm H(2)O, P<0.0001). After BTA injections, 87.8 % of patients no longer experienced uninhibited contractions. The median duration of clinical efficacy was 7.03 months. At 12 months, injections were still effective clinically in 21.2 % of patients. Conclusion: Intradetrusor BTA injections are an effective and well tolerated treatment for neurogenic overactive bladder. Their clinical efficacy persisted for more than 12 months in more than 20% of cases. Mots cles ♦ Toxine botulique ♦ vessie neurologique ♦ incontinence ♦ hyperactivite detrusorienne neurogene
Follow-up of three large community-based programs to reduce anaemia among children 24-59 months in Ghana, Malawi and Tanzania

Childhood anaemia remains a problem of global health importance, despite decades of research to understand its aetiology and develop effective interventions to reduce its prevalence and consequences. While the individual risk factors for anaemia in young children are known, including factors related to undernutrition and morbidity, much less is known about the interaction amongst these in contexts where children are frequently exposed to several at the same time. This study seeks to document the anaemia control efforts of the Micronutrient and Health (MICAH) program implemented in Ghana, Malawi and Tanzania and use both process and evaluation data collected during the program to better understand the risk factors for anaemia in young children in these contexts and how these risk relationships may have changed over time during the intervention. Specifically, this study tests whether there is evidence of a reduction in child vulnerability to the risk factors associated with anaemia in each context. A review of program documentation was conducted to characterize the program contexts and interventions, including estimates of intensity and reach. Cross-sectional data on the nutrition and health status of children 24-59 mo (N=2405) obtained in 2000 and 2004 from community-based program evaluation surveys in Ghana, Malawi and Tanzania, were used to describe the prevalence of anaemia. Multinomial logistic and linear regression models were used to estimate the risk of mild and moderate/severe anaemia and low haemoglobin, respectively, associated with groups of variables. Population attributable risk (PAR) estimates were also calculated. Anaemia (haemoglobin <110 g/L) affected at least 60% of children in all three countries; moderate/severe anaemia (<100 g/L) accounted for the majority of cases. A large decrease in anaemia was observed between 2000 and 2004 in Ghana, but only a small decrease in Malawi and Tanzania. The risk of moderate/severe anaemia was associated with stunting in children from Ghana (OR 2.68; 95% CI 1.70, 4.23) and Malawi (OR 1.71; 1.29, 2.27) but not Tanzania (OR 1.29; 0.87, 1.92). Malaria and recent illness was associated with lower Hb overall; attenuation of this association in
Different Biomarkers address different colorectal cancer stem cell populations: who's the killer?

Recent findings suggested the presence in several cancers, comprising colorectal malignancies, of a small subset of tumor-initiating cells, or cancer stem cells. So the isolation and characterization of tumorigenic colorectal cancer cells was extremely important for the development of new diagnostic and therapeutic procedures. Even if a defined and universally approved phenotype of colorectal cancer stem cells has not been defined yet, several markers have been reported able to isolate cancer stem-like cells. For the majority of these markers, it has not been associated a clear function but more of them seem to be involved in colon cancer stem cell maintenance, survival and proliferation through well known pathways such as the Wnt-β-catenin pathway whose activity has been demonstrated to define colorectal cancer stem cells. Even if the knowledge on colorectal cancer stem cell “functioning” is still little detailed and elusive, the importance to develop new therapeutic strategies that precisely target colorectal cancer stem cells prompted researchers to test different approaches to this aim. We summarize here the phenotypic and functional characteristics associated with colon cancer stem cells along with the approaches experimented to selectively target and kill these cancer cells. We also reviewed innovative approaches targeting colorectal cancer stem cells based on new delivery systems to precisely kill them or also on the combination of conventional therapies with functional dietary elements.
The Death of Feature Engineering? —— BERT with Liguistic Features on SQuAD 2.0

Machine reading comprehension is an essential natural language processing task, which takes into a pair of context and query and predicts the corresponding answer to query. In this project, we developed an end-to-end question answering model incorporating BERT and additional linguistic features. We conclude that the BERT base model will be improved by incorporating the features. The EM score and F1 score are improved 2.17 and 2.14 compared with BERT(base). Our best single model reaches EM score 76.55 and F1 score 79.97 in the hidden test set. Our error analysis also shows that the linguistic architecture can help model understand the context better in that it can locate answers that BERT only model predicted "No Answer" wrongly.
Physiological adaptations following a four-week of high-intensity functional training Fiziološka adaptacija za vreme funkcionalnog treninga visokog intenziteta u trajanju od četiri nedelje

Background/Aim. High-Intensity Functional Training (HIFT) is a popular mixed-modal program that utilizes both resistance and aerobic based exercises. The aim of this study was to examine the physiological effects of the HIFT programing on physically active men (10) and women (10) over a four-week period through the measure of the aerobic capacity, anaerobic capacity, and maximal weight lifted. Methods. The participants first completed a maximal oxygen consumption (VO2max) test. After 48-hours of rest, the subjects completed the anaerobic capacity test via the Wingate protocol. Following the Wingate test, the subjects performed a 1-repetition maximum test for squat, snatch and clean at the offsite training location. After the pre-measurements were obtained, the subjects entered a four-week the HIFT intervention and returned to the lab for all post-measurements. Results. Significant improvements were observed in male and female: VO2 max (Pre: 46.7 ± 2.6, 33.7 ± 1.7 mL/kg/min; Post: 49.0 ± 3.0, 35.0 ± 1.8 mL/kg/min), Peak Wingate Power (Pre: 1206 ± 106, 708 ± 44 W; Post: 1283 ± 88, 809 ± 38 W) Mean Wingate Power (Pre: 680 ± 46, 704 ± 48 W; Post: 434 ± 15, 458 ± 18 W; p < 0.05), back squat (Pre: 128.8 ± 8.8 kg, 44.1 ± 6.8 kg; Post: 142.7 ± 9.8, 54.3 ± 6.2 kg) clean (Pre: 82.5 ± 6.2, 24.1 ± 3.4 kg; Post: 92.7 ± 5.8, 33.2 ± 3.3 kg) and snatch (Pre: 59.3 ± 4.4, 20.9 ± 1.7 kg; Post: 69.1 ± 5.3, 25.0 ± 2.3 kg; p < 0.05), respectively. No gender influence on interaction was observed over time (p > 0.05). Conclusion. The HIFT demonstrated the rapid physiological improvements in strength, aerobic and the anaerobic capacity following a four-week intervention in the physically active participants.
Hybrid Sensor Fusion Framework for Perception in Autonomous Vehicles

There are many sensor fusion frameworks proposed in the literature using different sensors and methods configurations. Most focus has been on improving the accuracy performance; the implementation feasibility of these frameworks in an autonomous vehicle is less explored. Some fusion architectures can perform very well in lab conditions using powerful computational resources; however, in real-world applications, they cannot be implemented in an embedded edge computer due to their high computational need. We propose a new hybrid multi-sensor fusion pipeline configuration that performs environment perception for autonomous vehicles such as road segmentation, obstacle detection, and tracking. This fusion framework uses an improved encoder-decoder based Fully Convolutional Neural Networks (FCN) and a traditional Extended Kalman Filter (EKF) nonlinear state estimator methods. It also uses a configuration of camera, lidar, and radar sensors that are best suited for each fusion method. The goal of this hybrid framework is to provide a relatively lightweight, modular, and robust fusion system solution. It uses modified algorithms that improve environment perception accuracy and real-time efficiency compared to benchmark models that can be used in an autonomous vehicle embedded computer. Our fusion algorithm shows better performance in various environment scenarios compared to baseline techniques. Moreover, the algorithm is implemented in a vehicle and tested using actual sensor data collected from a vehicle, performing real-time environment perception.
CS224n Final Project: SQuAD 2.0 with BERT

This paper adapts pre-trained models of Bidirectional Encoder Representations from Transformers (BERT) to the natural language processing task of answer span prediction ("QA") on the Stanford Question Answering Dataset (SQuAD 2.0). The original BERT implementation ("origBERT") presented in Devlin et al. [2018] achieved SOTA performance of 88.5 F1 on a SQuAD 1.1 dataset with 100,000+ answerable context-question-answer tuples. However, when origBERT is used on the much more challenging SQuAD 2.0 dataset, released subsequently with 50,000+ additional questions that are impossible to answer from the context paragraph, performance drops to 73.1 F1. This paper presents a combination of refinements to the task-specific tuning in origBERT, including: a multi-layer QA architecture, an ensemble with a binary sequence classification model trained to identify impossible questions, a larger fine-tune training data set including examples from TriviaQA, and optimized hyperparameters. SQuAD 2.0 performance improves from a baseline of 73.1 F1 to a best F1 score of 77.6 for BERTbase and 81.4 for BERTlarge. Finally, this paper concludes with the observation that these refinements allow an optimized BERTbase model to perform almost as well (-2.3 F1) as an origBERTlarge model, which has 3 times as many parameters and is considerably more challenging to train.
Proposal of a Data-to-text Natural Language Generation Approach to Create Stories for Dyslalic Children

During the development of children in their early years, several problems that affect the emotional and linguistic development may happen. These problems may arise as a difficulty of the child speech (e.g., pronunciation of some phonemes), or an emotional disorder (e.g., depression), and are normally treated with several types of therapies, some of which include the use of stories (Rvachew et al., 1999). Computational linguistics (CL) can be useful for studying methods and approaches capable to process and generate natural language, leading to the development of tools and resources that can support the therapies employed in the treatment of previously mentioned problems. In particular, Natural Language Generation (NLG) task can be especially useful for the (semi-)automatic creation of on-demand tales and stories that can take into account a specific child speech problem, and tailor the generated text to this problem in order to reinforce the child’s therapy. NLG is the CL’s area that aims to automatically develop techniques to produce human utterances, text and speech. The NLG process is frequently viewed as a pipeline of three stages (Reiter and Dale, 2000): document planning, microplanning and surface realisation. In these stages the necessary subtasks for generating language, such as content selection, discourse planning or lexical selection are addressed. In our research, we want to focus on creating stories for dyslalia disorder using NLG techniques for the Spanish language. Dyslalia is a disorder in phoneme articulation which implies the inability to correctly pronounce certain phonemes or groups of phonemes (in Spanish some of this phonemes are: /ch/, /ll/, /rr/ or in English are: /zh/, /ng/, /j/). This disorder is estimated to have a 5-10% incidence among the child population (Conde-Guzón et al., 2014). Therefore, the main goal of this paper is to propose a new thesis research idea, focused on the analysis and development of a data-to-text NLG hybrid approach (combining statistical techniques with semantic knowledge) that would be able to generate stories containing a huge number of difficult phonemes for dyslalia’s children, so these stories would help experts in the creation of resources, as well as in the treatment of this disorder. The high difficulty of building generic and open domain NLG systems has led us to design and plan our research in the context of such a concrete application, even though, our aim is to experiment with generic and flexible NLG techniques and methods, as much as possible, that could be also applied to other contexts.
Accelerating Action Potential Generation Using GPU Implementation of a Resonant Model of a Cell

Computational modelling of bioelectric phenomena in cardiac tissue has made significant contributions to our understanding of the electrical activity of the human heart. However, extending these simulations over large temporal and spatial ranges is an inherently computationally expensive task. Motivated by the apparent advantages of GPU over CPU in many areas of science, we have developed a new Resonant Model (RM) of an AP of a biological cell amenable to parallel computing. In this study, we have investigated the potential performance benefits of the implementation of the RM on a GPU over the sequential execution on a CPU. In the proposed implementation, all threads in the same GPU warp share data using a warp-shuffle operation, which eliminates the access of global or shared memory. By exploiting the memory model and computational strengths of GPU, the obtained performance of the RM on the GPU is far superior to that of the CPU. On the GPU, the RM demonstrate linear or sub-linear growth in the execution times with the increase in the number of cells. The speedup of the computations achieved on the GPU may, in the near future, facilitate the patient-specific electrophysiological studies and treatment planning.
Design and Performance Optimization of a Wide Band Millimeter-wave Power Amplifier in 130 nm SiGe BiCMOS Technology

This paper presents simple systematic way of designing and optimizing of a wide band millimeter-wave power amplifier using IHP 130 nm SiGe BiCMOS Technology, for applications that operate between 50 – 65 GHz and requires a high data rate. Single stage common emitter configuration with multiple emitter fingers are investigated, five configurations with variable number of emitter fingers are studied separately. A load-pull technique is used for determining the optimal input, output impedance at maximum output power as well as maximum power added efficiency (PAE). Based on the load-pull analysis, an input, output impedance matching network is designed for each configuration. Thereafter, the performance of single stage PA like gain, efficiency, PAE, and 1-dB compression are compared. Based on this comparison, a configuration of six emitter fingers is selected for designing three stages power amplifier, The three stages PA has a gain of 21 dB, PAE of 20.5%, saturation output power of 6.7 dBm, and reflections S11, S22 >-10 dB for the band width of (50 – 65 GHz) with unconditionally stability.
INTERGOVERNMENTAL FISCAL TRANSFER SYSTEM AND POLITI CALLY MOTIVATED TARGETING IN ALBANIA

Intergovernmental transfers from the central gover nment to the local level are an important source of revenues in Albania. While the need for capital investment and other local services is rapidly increasing, local b udget management has become a key point of discussion. Own revenues of l ocal governments are still unconsolidated and uncon diti al transfers cover about 50% of the local budget. Although Alban ia has undertaken major reforms in the field of int ergovernmental relations, still the transfer mechanism must resolv e issues such as the determination of total transfe r and distribution of this fund to the local government. Regarding the determi nation of the total fund, there is still no fixed f ormula, which complicates its prediction by local units and it also makes the distribution scheme less underst andable and transparent. This study is focused on the system of intergovernmental transfers in Albania, especially in unconditional transfers, utilizing qualitative and quantitative d ata and by applying econometric analysis to test th e system regarding politically motivated targeting of transfer allocat ion.
Discriminant Analysis for

The correlated data are of great importance in the practical life, for example we may want to track the case of a patient after taking a treatment for consecutive periods of time. Also, we may want to track a disease with the members of a certain family. Finally, we want to know the developement of a certain disease with a patient for different periods of time, … , etc. the binary data, (for example a person smokes = 1, a person does not smoke = 0), (female = 1, male = 0), (the effect of treatment is active =1, placebo = 0), is a type of the correlated data. In this paper, we are applying the classification and discriminant methods on the correlated data (some of them are binary data and the other are not). Usually we are using a one dependent variable and then we classify it into two or more classes. Contrary to what is traditionally followed, in this paper we will deal with two dependent correlated categorical variables each one is divided into two classes. The methods of discriminant analysis are investigated in this paper when we are dealing with the independent and dependent variables both of them is correlated variables.
Defense mechanisms and quality of life in military personnel with a burnout syndrome Mehanizmi odbrane i kvalitet života kod profesionalnih vojnih lica sa sindromom sagorevanja na radu

Background/Aim. Professional military personnel are exposed to a number of stressors during the war as well as in peacetime conditions that can cause some hidden or manifest disorders, especially anxiety and depression, but also the development of a burnout syndrome. The aim of our investigation was to determine the defense mechanisms and subjective assessment of quality of life and anxiety in professional military personnel of the Serbian Armed Forces with the burnout syndrome. Methods. The cross-sectional study included a total of 55 professional military personnel, from 25 to 55 years of age, without current mental problem. In the investigation, the Maslach Burnout Inventory (MBI), Defense Style Questionnaire (DSQ-40), World Health Organization Abbreviated Instrument for Quality of Life Assessment (WHOQOL-BREF) and Beck Anxiety Inventory were used. The statistical analysis included parametric and nonparametric descriptive statistics. Results. Emotional exhaustion and depersonalization were present in 10.9% of subjects and in 12.7% of subjects respectively, in moderate level, while personal accomplishment was present in 21.8% of subjects of high level. Humor was higher in the subjects with a moderate level of burnout on the personal accomplishment (PA) scale and altruism in the subjects with a low level of burnout on the depersonalization (DP) scale as well as acting-out and rationalization on the PA scale in the subjects with a moderate level of burnout. High level of anxiety was present in 14.5% of subjects. Conclusion. Professional military personnel with lower level of burnout were less anxious, use mature defense mechanisms and have a perception of better quality of life. When burnout is diagnosed, psychological interventions requires training through the adoption of mechanisms for overcoming everyday stress, which may affect the reduction of anxiety and the improvement of the quality of life. Professional assistance, including psychotherapy is required in severe cases.
Modelling Dependent Defaults in Static Credit Portfolios

This thesis studies the modelling of credit risk in static credit portfolios, where the emphasis lies on the modelling of the dependence between defaults of the obligors. Finding a proper way to model dependence between defaults is a central, however nontrivial, task in order to incorporate the possibilities of extreme events. Further, this default dependence greatly influences the portfolio loss distribution, and thus is vital to realistically capture the true credit risk in the portfolio. We will present several different approaches in order to introduce a default dependence, and the core ideas of these approaches are present in the most common industry models. Furthermore, the asymptotic behavior in static credit portfolios will be explored and we will derive large portfolio approximations in several model settings. Finally, it will be concluded that the different approaches of modelling dependence yield similar results for non-extreme scenarios. This implies that the choice of model is of less concern than the process of estimating the individual default probability and default correlation.
Organizations and ERP Systems: Conceptualizing Fit

The high failure rates and mixed results of Enterprise Resource Planning (ERP) projects necessitate investigations that enhance our understanding of the issues involved in implementing these complex systems. The prospect of identifying and concentrating on a small number of important priorities is essential throughout the implementation of ERP systems. We explore the conceptual applicability of Critical Success Factor (CSF) analysis and present a model that “conceptualizes the fit” of ERP in organizations by integrating organizational characteristics and CSFs to ensure the desired the value of enterprise systems. Appropriate propositions are drawn and implications for practice, research and theory are identified. Toni M. Somers Wayne State University School of Business Administration Department of Information Systems and Manufacturing 5201 Cass Avenue Detroit, MI 48202 313-577-9144 or 313-577-9145 FAX: 313-577-4880 aa3808@wayne.edu Klara G. Nelson The University of Tampa John H. Sykes College of Business Department of Information and Technology Management 401 West Kennedy Blvd. Tampa, Florida 33606-1490 813-253-6221 krnelson@alpha.utampa. edu