Miracle-dz/UniFAR-FTUs huggingface dataset:
Surveillance of the potential drug-drug interactions in the medicine department of a tertiary care hospital

Introduction: Drug-Drug Interactions (DDIs) account for 6-30% of the adverse drug events, pose a significant risk to the patient’s health outcome and they put an economic burden on the health care system. Polypharmacy significantly contributes to these interactions. This study was aimed at assessing the drug-drug interactions in the in-patients in the medicine department in a tertiary care hospital. Materials and Methods: The case records of 250 patients who were admitted in the medicine ward were analyzed regarding the demography of the patient, the prescription and the interaction details like the object drug, severity, management and the outcome. Results: The patients received drugs which ranged from 5 to 18. 66% of the patients had DDIs, with a majority being moderate in severity and occurring in the patients who received cardiovascular drugs. Age and gender did not have a significant effect on the drug- drug interactions. Conclusion: Most of the DDIs are preventable. The frequently occurring DDIs are seen between fluoroquinolones and oral anti-diabetics, iron and pantoprazole, aspirin and clopidogrel.
Supporting Source Code Search with Context-Aware and Semantics-Driven Query Reformulation

Software bugs and failures cost trillions of dollars every year, and could even lead to deadly accidents (e.g., Therac-25 accident). During maintenance, software developers x numerous bugs and implement hundreds of new features by making necessary changes to the existing software code. Once an issue report (e.g., bug report, change request) is assigned to a developer, she chooses a few important keywords from the report as a search query, and then attempts to nd out the exact locations in the software code that need to be either repaired or enhanced. As a part of this maintenance, developers also often select ad hoc queries on the y, and attempt to locate the reusable code from the Internet that could assist them either in bug xing or in feature implementation. Unfortunately, even the experienced developers often fail to construct the right search queries. Even if the developers come up with a few ad hoc queries, most of them require frequent modi cations which cost signi cant development time and e orts. Thus, construction of an appropriate query for localizing the software bugs, programming concepts or even the reusable code is a major challenge. In this thesis, we overcome this query construction challenge with six studies, and develop a novel, e ective code search solution (BugDoctor) that assists the developers in localizing the software code of interest (e.g., bugs, concepts and reusable code) during software maintenance. In particular, we reformulate a given search query (1) by designing novel keyword selection algorithms (e.g., CodeRank) that outperform the traditional alternatives (e.g., TF-IDF), (2) by leveraging the bug report quality paradigm and source document structures which were previously overlooked and (3) by exploiting the crowd knowledge and word semantics derived from Stack Over ow Q&A site, which were previously untapped. Our experiment using 5000+ search queries (bug reports, change requests, and ad hoc queries) suggests that our proposed approach can improve the given queries signi cantly through automated query reformulations. Comparison with 10+ existing studies on bug localization, concept location and Internet-scale code search suggests that our approach can outperform the state-of-the-art approaches with a signi cant margin.
24-channel transcutaneous electrical sensory stimulation of the forearm: Effects on cognitive performance and autonomic arousal compared with single-electrode stimulation

Abstract We sought to establish the effects on cognitive performance and autonomic arousal of a multi-electrode transcutaneous electrical sensory stimulator compared with single-electrode stimulation. Progressing from a feasibility study (n = 10) to a repeated-measures, within-subject study (n = 67), healthy, right-handed participants (34 male, mean age 28 ± 11.5 yrs.) received four separate predictable (“lines” or “ring”) or unpredictable (“fly” or “random”) complex electrical stimulation patterns to the non-dominant forearm via 24 individually programmable electrodes, or single-electrode stimulation. During stimulation, participants in the main study performed a series of cognitively demanding tests or a two-point discrimination task and had their autonomic arousal (skin conductance response) monitored. Single-electrode “first felt” current intensity decreased from distal to proximal forearm, was positively correlated with forearm circumference and was lower in females. Single-electrode stimulation was associated with a significant decline in two-point discrimination performance. There were no significant autonomic arousal or cognitive performance differences between complex patterns, but all patterns were associated with greater autonomic arousal than single-electrode stimulation. In conclusion, gender and forearm location and circumference significantly influence perception-levels for electrical current. Future research should examine whether the induction of increased autonomic arousal, via multi-electrode complex patterns, has improved therapeutic application, compared with single-electrode stimulation.
testosterone, cardiovascular, men, trial

Background: Testosterone therapy is increasingly promoted. No randomized placebo-controlled trial has been implemented to assess the effect of testosterone therapy on cardiovascular events, although very high levels of androgens are thought to promote cardiovascular disease. Methods: A systematic review and meta-analysis was conducted of placebo-controlled randomized trials of testosterone therapy among men lasting 12+ weeks reporting cardiovascular-related events. We searched PubMed through the end of 2012 using “(“testosterone” or “androgen”) and trial and (“random*”)” with the selection limited to studies of men in English, supplemented by a bibliographic search of the World Health Organization trial registry. Two reviewers independently searched, selected and assessed study quality with differences resolved by consensus. Two statisticians independently abstracted and analyzed data, using random or fixed effects models, as appropriate, with inverse variance weighting. Results: Of 1,882 studies identified 27 trials were eligible including 2,994, mainly older, men who experienced 180 cardiovascular-related events. Testosterone therapy increased the risk of a cardiovascular-related event (odds ratio (OR) 1.54, 95% confidence interval (CI) 1.09 to 2.18). The effect of testosterone therapy varied with source of funding (P-value for interaction 0.03), but not with baseline testosterone level (P-value for interaction 0.70). In trials not funded by the pharmaceutical industry the risk of a cardiovascular-related event on testosterone therapy was greater (OR 2.06, 95% CI 1.34 to 3.17) than in pharmaceutical industry funded trials (OR 0.89, 95% CI 0.50 to 1.60). Conclusions: The effects of testosterone on cardiovascular-related events varied with source of funding. Nevertheless, overall and particularly in trials not funded by the pharmaceutical industry, exogenous testosterone increased the risk of cardiovascular-related events, with corresponding implications for the use of testosterone therapy.
Review of second-order optimization techniques in artificial neural networks backpropagation

Second-order optimization technique is the advances of first-order optimization in neural networks. It provides an addition curvature information of an objective function that adaptively estimate the step-length of optimization trajectory in training phase of neural network. With the additional information, it reduces training iteration and achieves fast convergence with less tuning of hyper-parameter. The current improved memory allocation and computing power further motivates machine learning practitioners to revisit the benefits of second-order optimization techniques. This paper covers the review on second-order optimization techniques that involve Hessian calculation for neural network training. It reviews the basic theory of Newton method, quasi-Newton, Gauss-Newton, Levenberg-Marquardt, Approximate Greatest Descent and Hessian-Free optimization. This paper summarizes the feasibility and performance of optimization techniques in deep neural network training. Comments and suggestions are highlighted for second-order optimization techniques in artificial neural network training in term of advantages and limitations.
Profile of road traffic accidents & head injury in Jaipur (Rajasthan)

The injuries and death due to head trauma are inescapable in the modern way of life and their correct interpretation is vital to the reconstruction of the events of Forensic Medicine and their proper management for treatment of the injured. The present study was conducted in the department of forensic medicine, SMS medical college, Jaipur in year 2003–2004 to analyze the quantity of the magnitude of deaths due to Road Traffic Accidents (RTAs) and to provide epidemiological data, so the preventive measures can be undertaken. Our study shows that majority of the victims of RTA were male of middle age group (20–40 years), when they were going on two wheelers with out wearing helmets.
A False-name-proof Combinatorial Auction for Resource Allocation in Cloud Computing

Cloud computing is an Internet-based computing and network model, which provides a market for sharing computing and storage resources. To motivate the cloud providers, the allocation of resources and their pricing becomes one of the challenges in this market. In this situation, designing auction-based mechanisms for cloud resource allocation have attracted a wide interest. Most of prior works focus on designing strategy-proof (a.k.a. truthful) cloud auction mechanisms to eliminate market manipulation. Strategy-proof auctions ensure that no bidders can improve their utilities by untruthful bidding. However, these strategy-proof auctions are fragile when false-name-bids are allowed. This is a new type of cheating where a bidder can gain profit by submitting bids using multiple fictitious names, and it is easy to form in cloud environment. Therefore, a new series of auction mechanisms are needed to guarantee the false-name-proofness. To tackle this issue, in this paper, we designed CACC, a combinatorial auction for resource allocation in Cloud Computing. To address the heterogeneity of cloud resource, the combinatorial auction model is adopted. We show CACC is both strategy-proof and false-name-proof. Moreover, CACC is computationally efficient. Through simulation experiments, we show that CACC is effective and efficient.
Human Activity Recognition Using Recurrent Neural Network

With the spread of smartphones incorporating various sensors, accelerometers and gyro sensors have become familiar to us. Based on such situations, sensor-based human activity recognition (HAR) that uses human sensor data to identify human activity has come to use smartphones as data acquisition sources. In the early studies of HAR using smartphones, handcrafted methods were used if various statistical values were required as feature quantities and high accuracy was realized. Meanwhile, the popularization of deep learning in recent years has not been discussed, and its application has been made to HAR. Although deep learning has the advantage of being able to automatically extract feature quantities from data, it has not reached a step beyond precision in handcrafted methods. Furthermore, in the previous research, to divide data by time window of a ﬁxed interval, except for some part, inference could not be performed unless the data for the time window was secured. We attempted to overcome these limitations using recurrent neural network. Our method records higher accuracy than previous studies using convolutional neural network and long short term memory, which are typical methods in deep learning and display results comparable to handcrafted methods. We also succeeded in pre-calculating many feature quantities, whose calculation was a problem in the previous research, and eliminating the time window.
Immediate effect of ice bag application to head and spine on cardiovascular changes in healthy volunteers

Background/Objectives: Ice application is one of the treatment procedures used in hydrotherapy. Though its various physiological/therapeutic effects were reported, ice bag application (IBA) to head and spine on cardiovascular changes were not reported. Hence, this study aims at evaluating the immediate effect of IBA to head and spine on cardiovascular changes in healthy volunteers. Materials and Methods: Twenty-eight subjects were randomized into three sessions ([i] IBA [ii] tap water bag application [TWBA] and [iii] control) and intervention was given in one of the 3-different orders. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse rate (PR) was assessed before and after 20-min of each intervention. Pulse pressure, mean pressure (MP), rate pressure product (RPP), and double product (Do-P) were derived by standard formula. Statistical analysis was performed by repeated measures of analysis of variance and post-hoc analysis with Bonferroni adjustment for multiple comparisons with the use of Statistical Package for Social Sciences version-16. Results: The results showed no significant difference between sessions in all variables. Within-group analysis showed significant reductions in SBP, PR, RPP, Do-P in IBA and TWBA sessions; Significant reduction in DBP, MP in IBA unlike TWBA; and no significant changes in all the variables of control session. Conclusions: Result of our study suggest that though both IBA and TWBA to head and spine might be considered as having effect on improving cardiovascular function in healthy volunteers, IBA to head and spine could be considered as a better choice than TWBA.
Identifying non-accidental injury cases in veterinary practice

Cases of non-accidental injury in veterinary patients remain underreported, with correct identification of such injuries presenting a significant diagnostic challenge. However, the veterinary profession has the potential to improve on the identification of animal abuse injury, and to contribute to the holistic management of the public health issue of violence within families. This article discusses the extent of the problem and reviews how veterinarians can identify and respond to such cases, and the possible links to family violence.
Build a 4D MRI blood-flow velocity atlas

Cardiovascular diseases(CVDs) are the global number one cause of death. Current diagnosis of CVDs is mainly based on functions and morphology of cardiovascular structures. Magnetic resonance imaging (MRI), especially the 4D flow MRI has become a new and powerful tool for CVD diagnosis, which allows both anatomical and functional analysis in a single measurement. However, it is still difficult to efficiently utilize this data source to diagnose CVDs. One of the main reasons is the lack of standard analysis and understanding of this rather new imaging modality. The common method to understand the healthy behaviour and its variation is to build an atlas. Current blood flow atlas does not support 3D velocity vector field full information. We have built a statistical vector field model (SVFM) based on 4D flow MRI, which is similar to statistical shape and appearance models by applying Principal Component Analysis (PCA) to get the mean and main variations of the blood flow fields. The visualization of this atlas would help physicians understand the 4D MRI data and assist them to identify whether the data is abnormal or not. In order to get more efficient representations of variations for vector fields, we also tested Complex PCA and Quaternion PCA, besides traditional Real PCA. Finally, we visualize the results such that they can be interpretable.
Embolizzazione pre-chirurgica del ramo portale di destra: indicazioni e risultati

Purpose. The purpose of this retrospective study was to evaluate the efficacy of right portal vein embolisation (PVE) in inducing contralateral liver hypertrophy before extended hepatectomy. Materials and methods. Twenty-six consecutive patients, 14 with liver metastases (ten from colorectal cancer; four from carcinoid tumours) and 12 with biliary cancers (ten Klatskin tumours; one gallbladder tumour; one intrahepatic cholangiocarcinoma) with insufficient predicted future remnant liver (FRL) underwent right PVE to induce hypertrophy of the contralateral hemiliver prior to surgical resection. Total liver volume, tumour volume and FRL volume were calculated on a 3D workstation. The ratio of the FRL to the total functional liver volume was <30% in all patients. Results. The FRL volume increased by 5%‐25% (15% on average) after right PVE in patients with liver metastases and by 9%‐19% (14% on average) in patients with biliary cancers. In all patients, the ratio of FRL to functional liver volume was ≥30% after right PVE. No postoperative deaths due to severe liver failure occurred in the 20 patients who underwent extended hepatectomy. Conclusions. Right PVE extends the indications for hepatectomy in patients with liver metastases and those with biliary cancers who have an insufficient potential hepatic functional reserve. Riassunto
Towards Data-Driven Modelling of Saulekilen Wastewater Treatment Plant based on Artificial Intelligence

The processes at a wastewater treatment plant (WWTP) are complex systems that clean the wastewater before it is released into the environment. Total phosphorous (TP), biological oxygen demand (BOD) and chemical oxygen demand (COD) are critical measurements of the water quality. Measuring BOD and COD are too slow to effectively control the wastewater treatment processes (5 days and a couple of hours respectively) as the wastewater treatment process at Saulekilen WWTP uses about 30-40 minutes from influent to effluent. Measurements of effluent TP are also performed and are quicker but controlling by the use of effluent TP is too slow. Hence it is a goal for this thesis to propose models that can predict the values of BOD, COD and TP quicker by the use of multiple linear regression (MLR) and artificial neural networks (ANN). Results of this thesis indicate that measurements of effluent TP can be predicted with reasonable accuracy. Further, the influent BOD and COD measurements can be predicted with good accuracy. These promising predictions may be important in further work for an improved wastewater treatment. A sensitivity analysis has been performed to determine which input parameters being the most effective in modelling the output. Input parameters in this thesis have consisted of TP, water discharge, the local temperature and the date for predictions of TP, COD and BOD. Measurements of influent BOD and COD has also been used as inputs for prediction of effluent BOD and COD, while total solids (TS), total percent solids (TS(%)), reject water, pH, the process additive PIX and turbidity has been used as inputs for predictions of effluent TP. The results of the models have been considered with respects to correlation, mean squared error (MSE) and mean absolute percentage error (MAPE).
Deep learning as an alternative to global optimization in diffusion model for conflict tasks

To apply mathematical models of decision making in psychological research, researchers need ways to extract model parameters from behavioural studies. The expansion of the drift diffusion model to con ict tasks (DMC) (Ulrich, Schroter, Leuthold, & Birngruber, 2015) resulted in the model being non-differentiable, which means that the parameters of DMC can only be estimated. The current methods for recovering parameters from DMC rely on comparing reaction time (RT) distributions. Such methods will struggle to recover all DMC parameters well due to the of the solution space of DMC, which means that some parameters can be confused with others when RT distributions are compared. Following that, five global optimization algorithms from different optimization families were compared to create a benchmark for parameter recovery from DMC. The results revealed that differential evolution outperformed the other four optimization algorithms in recovery of parameters from both distributions with high and low trial numbers. Even though differential evolution is capable of recovering parameters well, it is very expensive in computational time, which means that researchers who do not have access to vast computational resources cannot apply DMC in their research. Due to this, deep learning was investigated in application of parameter recovery from DMC. The results showed that deep learning recovered all model parameters exceptionally well from RT distributions with large trial numbers, and as well as differential evolution from RT distributions with low trial numbers, which allows application of deep learning models in deployment pipelines that take seconds rather than months. Finally, deep learning models were applied in several experimental studies investigating the effects of speed-accuracy trade-off (SAT) in response inhibition and perceptual decision making tasks, and how the performance relates between the tasks and over two different testing sessions, and demonstrated the effects of SAT on DMC parameters in different tasks.
Palliative Care Services Utilization and Location of Death

In this study, the utilization of palliative care services, acute care services, and location of death for clients who were palliative and receiving services from Champlain Community Care Access Centre (CCAC) in Ontario during their last month of life were investigated. An adaptation of Andersen‟s Behavioral Model of Health Services Utilization provided context and structure to this study. This is an historical, quantitative descriptive study using chart audits for data collection. The data on CCAC clients who were palliative and who died during the month of July 2009 were tracked during their last month of life. Collection of socio-demographic data, services provided through CCAC, emergency department visits, hospital admissions, and location of death provided the data for this study. The clients who died at home used more CCAC services than those who died at other locations and frequently community palliative care physicians provided their medical care. The findings of this study included: 1) The majority of the clients, who expressed a preference, died in their preferred location. 2) The role of community palliative care physicians was an important component of the services that supported the clients to die in their location of choice. 3) Over 25% of the study sample died in a hospital and the clients used a large number of in-patient hospital days with one quarter of the hospital deaths taking place in an emergency department or an intensive care unit. 4) During the last month of life, 25% of the clients received chemotherapy and/or radiation therapy.
Franconian tones fare better as tones than as feet: a reply to Köhnlein (2016)

Köhnlein (2016) proposes to represent the Franconian tone contrast as a difference in foot structure, whereby Accent 1 appears in lexically marked syllabic trochees and Accent 2 in default moraic trochees, as an alternative to analyses with an underlying privative tone for Accent 2. After sketching the two approaches, we argue against three arguments Köhnlein advances in favour of the metrical analysis. We then show that one of the disadvantages incurred by the metrically derived tonal representations is the introduction of a novel and otherwise unsupported concept of a single tone that incorporates two morphologically different but phonologically identical tones. We also evaluate Köhnlein's (2018) more recent proposal to use the syllabic trochee to account for subtractive plurals in tonal dialects. Finally, we compare the predictive powers of the metrical and tonal analyses of the Arzbach dialect.