Miracle-dz/UniFAR-FTUs huggingface dataset:
Circulating microRNAs (miR-423-5p, miR-208a and miR-1) in acute myocardial infarction and stable coronary heart disease.

AIM The microRNAs (miRs) are small non-coding RNAs which regulate expression of multiple genes involved in atherogenesis. MicroRNA are also present in circulation. The aims of this study were: 1) assessment of expression level of miR-1, miR-208a and miR-423-5p in plasma in patients with STEMI, stable CAD and healthy individuals; 2) evaluation of correlation between plasma miRs and left ventricle ejection fraction, end- systolic and end-diastolic diameters and troponin release in patients with STEMI. METHODS Study group consisted of 26 patients: 1) acute MI group (N.=17); 2) stable CAD group (N.=4); and 3) subjects with no history of CAD (control group, N.=5). Expression of miR-423-5p, miR-208 and miR-1 was measured in plasma before PCI, 6, 12 and 24 hours later. Expression level ofmiRs was measured using TaqMan® MicroRNA Assays. Expression was assessed by Pfaffl method, and miR-39 was used for normalization of the results. RESULTS In stable CAD in comparison to control group the expression level of miR-1, miR-208a and miR-423-5p did not show significant differences. Also there was no significant increase of number of miR copies at 6, 12 and 24 hours after PCI. There was a significantly higher number of miR-423-5p copies in patients with acute MI before the pPCI. After 6, 12 and 24 hours post-procedure the expression level was similar to the control group and significantly lower than the baseline level. Conversely, the expression level of miR-1 and miR-208a were not significantly different than in the control group. In patients with acute MI there were no significant correlations between the expression level of miRs and any of the echocardiographic parameters of LV as well as level of troponin I at any time-point of the follow-up. CONCLUSION Early in acute myocardial infarction the expression of miR-423-5p in plasma is significantly increased with subsequent normalization within 6 hours. Potentially it is an early marker of myocardial necrosis.
Determinants of Household Heads’ Enrolment in National Health Insurance Scheme in Central Region of Ghana: A Case Study

This study sought to determine household heads’ enrolment in the national health insurance scheme (NHIS) in the Twifo Hemang Lower Denkyira District in the Central Region of Ghana. A cross-sectional survey design was used and data were collected from both household heads who are enrolled in NHIS and those who have not been enrolled in the district for the purpose of analysis. An interview schedule was used for the collection of the data from the respondents. In total, 400 household heads were interviewed. A binary logit regression model was estimated to identify the determinants of household heads’ enrolment in the national health insurance scheme. The study found out that marital status, income, frequency of illness, distance from the nearest NHIS registration centre, education, occupation, age and religious affiliation were significant in explaining the decision of a household head to participate in the national health insurance scheme. In order to increase enrolment in the national health insurance scheme, it is recommended that National Health Insurance Authority (NHIA) should increase the number of National Health Insurance scheme registration centres. Also, government should strengthen policies to enhance income level.
Distributed Strain and Temperature Discrimination Using Two Types of Fiber in OFDR

We present a simple and effective method to achieve a distributed strain and temperature discrimination using two types of fiber by a Rayleigh backscattering spectra (RBS) shifts in optical frequency domain reflectometry. We paired two types of single mode fiber (SMF) side by side as the sensing fibers. One is the reduced-cladding (RC) SMF, and the other is the standard SMF. Since the RC SMF and the standard SMF have different sensitivity responses to strain and temperature variation, we can measure strain and temperature variation simultaneously by monitoring RBS shifts. We demonstrate that the measure errors are 0.31 °C in temperature and 7.97 με in strain with a measurement range of 50 m and a spatial resolution of 18 cm.
Quantitative analysis of retinal ganglion cell survival with Rbpms immunolabeling in animal models of optic neuropathies.

PURPOSE To investigate whether a recently described retinal ganglion cell (RGC) marker Rbpms (RNA binding protein with multiple splicing) could be used for RGC quantification in various models of RGC degeneration. METHODS Optic nerve crush, excitotoxicity, and elevated intraocular pressure (IOP) rat models were used. Topographic analysis of Rbpms immunolabeling was performed on retinal wholemounts. Retrograde labelings with Fluorogold (FG) and III β-tubulin immunohistochemistry were compared. RESULTS In the optic nerve crush model, 37%, 87%, and 93% of Rbpms-positive cells were lost 1, 2, and 4 weeks, respectively. Significant loss of Rbpms-positive cells was noted 1 week after intravitreal injection of 12, 30, and 120 nmol N-methyl-d-aspartate (NMDA), whereas coinjection of 120 nmol of NMDA along with MK-801 increased the cell number from 10% to 59%. Over 95% of Rbpms-positive cells were FG- and III β-tubulin-positive after injury caused by optic nerve crush and NMDA injection. In rats with elevated IOP, induced by trabecular laser photocoagulation, there was a significant loss of Rbpms-positive cells compared with that of contralateral controls (P = 0.0004), and cumulative IOP elevation showed a strong linear relationship with the quantification of RGCs by Rbpms immunolabeling and retrograde labeling with FG. More than 99% of the remaining Rbpms-positive cells were double-labeled with FG. CONCLUSIONS Rbpms can reliably be used as an RGC marker for quantitative evaluation in rat models of RGC degeneration, regardless of the nature and the location of the primary site of the injury and the extent of neurodegeneration.
Modeling preparation condition and composition-activity relationship of perovskite-type LaxSr1-xFeyCo1-yO3 nano catalyst.

In this paper, an artificial neural network (ANN) is first applied to perovskite catalyst design. A series of perovskite-type oxides with the LaxSr1-xFeyCo1-yO3 general formula were prepared with a sol-gel autocombustion method under different preparation conditions. A three-layer perceptron neural network was used for modeling and optimization of the catalytic combustion of toluene. A high R2 value was obtained for training and test sets of data: 0.99 and 0.976, respectively. Due to the presence of full active catalysts, there was no necessity to use an optimizer algorithm. The optimum catalysts were La0.9Sr0. 1Fe0.5Co0. 5O3 (Tc=700 and 800 °C and [citric acid/nitrate]=0.750), La0.9Sr0. 1Fe0.82Co0. 18O3 (Tc=700 °C, [citric acid/nitrate]=0.750), and La0.8Sr0. 2Fe0.66Co0. 34O3 (Tc=650 °C, [citric acid/nitrate]=0.525) exhibiting 100% conversion for toluene. More evaluation of the obtained model revealed the relative importance and criticality of preparation parameters of optimum catalysts. The structure, morphology, reducibility, and specific surface area of catalysts were investigated with XRD, SEM, TPR, and BET, respectively.
A survey of data mining applications in water quality management

The world today is facing the problem of ever-increasing level of pollution of water bodies. The seriousness of this problem calls for specialized attention to predict future trend. Data mining is the most popular technique for handling huge amount of geo-spatial data. The variety and size of information to be processed for proper trend analysis towards water quality management makes the use of data mining concepts all the more relevant. This paper summarizes the on-going researches involving data mining applications in this area. The applications have been observed to use the techniques of ANN, Fuzzy, or GIS modeling for feature-wise classification of water pollutants. The parameters need to be clustered depending upon their area of application. The respective advantages of each model in classifying the parameters have been identified. The authors have arrived at a set of parameters best suited for analyzing the effect of pollutants on the river Churni, an effluent of the river Bhagirathi.
Cross asset class applications of functional data analysis: evaluation with controls for data snooping bias

This thesis applies functional data analysis techniques to address a number of specific research questions in financial markets. Data snooping bias controls are adopted in parallel to provide statistical robustness to our inferences. Firstly, we conduct an investigation into U.S. exchange-traded fund outperformance during the 2008-2012 period. The funds are tested for net asset value premium, underlying index and market benchmark outperformance. The study serves as a platform to showcase the data snooping bias problem and application of generalised multiple hypothesis testing techniques, in advance of their use for functional data analysis evaluation. Secondly, as the first application of functional data analysis, we examine implied volatility, jump risk, and pricing dynamics within crude oil markets. Strong evidence is found of converse jump dynamics during periods of demand and supply side weakness. Next, we demonstrate the performance advantage over traditional benchmarks of adopting a functional linear model to forecast EUR-USD implied volatility. Our findings are shown to be robust across various moneyness segments, contract maturities and out-of-sample window lengths. The final chapter also uses a functional data framework to produce forecasts, demonstrating how information can be extracted from forward contracts to predict future spot foreign exchange rates. The evaluation of an out-of-sample framework leads to near systematic outperformance in terms of a direct comparison of performance measures, versus both the restricted vector error correction model and random walk. Overall, this thesis highlights the usefulness of adopting insightful and novel functional data analysis techniques across various asset classes where multiple hypothesis testing controls provide robustness around our conclusions. Each of the studies contributes to the literature individually, with the collection emphasising the benefits of adopting functional approaches to tackle a wide range of empirical finance problems.
Truth Discovery for SpatioTemporal Events from Crowdsourced Data

One of the greatest challenges in spatial crowdsourcing is determining the veracity of reports from multiple users about a particular event or phenomenon. In this paper, we address the difficulties of truth discovery in spatio-temporal tasks and present a new method based on recursive Bayesian estimation (BE) from multiple reports of users. Our method incorporates a reliability model for users, which improves as more reports arrive while increasing the accuracy of the model in labeling the state of the event. The model is further improved by Kalman estimation (BE+KE) that models the spatio-temporal correlations of the events and predicts the next state of an event and is corrected when new reports arrive. The methods are tested in a simulated environment, as well as using real-world data. Experimental results show that our methods are adaptable to the available data, can incorporate previous beliefs, and outperform existing truth discovery methods of spatio-temporal events.
A case of Beare–Stevenson syndrome with a broad spectrum of features and a review of the FGFR2 Y375C mutation phenotype

We present a case of Beare–Stevenson syndrome with a broad range of phenotypic features including craniosynostosis, cutis gyrata, choanal stenosis, bifid scrotum with perineal hypospadias and a caudal appendage. The paternal age at the time of conception was 62 years consistent with a paternal age effect. Mutation analysis was undertaken and demonstrated the FGFR2 Y375C mutation. This case, one of only nine with molecular analysis, confirms the significant morbidity associated with this syndrome.
Terminal Nerve-Derived Neuropeptide Y Modulates Physiological Responses in the Olfactory Epithelium of Hungry Axolotls (Ambystoma mexicanum)

The vertebrate brain actively regulates incoming sensory information, effectively filtering input and focusing attention toward environmental stimuli that are most relevant to the animal's behavioral context or physiological state. Such centrifugal modulation has been shown to play an important role in processing in the retina and cochlea, but has received relatively little attention in olfaction. The terminal nerve, a cranial nerve that extends underneath the lamina propria surrounding the olfactory epithelium, displays anatomical and neurochemical characteristics that suggest that it modulates activity in the olfactory epithelium. Using immunocytochemical techniques, we demonstrate that neuropeptide Y (NPY) is abundantly present in the terminal nerve in the axolotl (Ambystoma mexicanum), an aquatic salamander. Because NPY plays an important role in regulating appetite and hunger in many vertebrates, we investigated the possibility that NPY modulates activity in the olfactory epithelium in relation to the animal's hunger level. We therefore characterized the full-length NPY gene from axolotls to enable synthesis of authentic axolotl NPY for use in electrophysiological experiments. We find that axolotl NPY modulates olfactory epithelial responses evoked by l-glutamic acid, a food-related odorant, but only in hungry animals. Similarly, whole-cell patch-clamp recordings demonstrate that bath application of axolotl NPY enhances the magnitude of a tetrodotoxin-sensitive inward current, but only in hungry animals. These results suggest that expression or activity of NPY receptors in the olfactory epithelium may change with hunger level, and that terminal nerve-derived peptides modulate activity in the olfactory epithelium in response to an animal's changing behavioral and physiological circumstances.
Real-time vanishing point detection using the Local Dominant Orientation Signature

The vanishing point can be defined as a point generated by converged perspective lines, which are parallel in the real world. We propose a novel algorithm to detect the vanishing point in various images in real-time. The proposed algorithm unfolds into three steps. In the first step, we introduce the Local Dominant Orientation Signature (LDOS) descriptor to extract structural feature of an image. Then, we detect vanishing point candidates using dynamic programming. Finally, we estimate the location of the vanishing point from detected vanishing point candidates. Unlike the previous methods, the proposed method, which uses the dominant orientation of the local image structure, is fast and not limited to specific image contents. Experiments are performed on diverse images to confirm the efficiency of the proposed method and to show that it can be employed in various real-time applications.
EduAware: using tablet-based navigation gestures to predict learning module performance

ABSTRACT In this paper, we develop a context-aware, tablet-based learning module for adult education. Specifically, we focus on adult education in healthcare-teaching learners to perform a medical screening procedure. Based upon how learners navigate through the learning module (e.g. swipe-speed and click duration, among others), we use machine learning to predict what comprehensive test questions a user will answer correctly or incorrectly. Compared with other context aware learning applications, this is the first time tablet-based navigation gestures have been used to support learning assessment. We conducted a 21 participant user study, showing the system can predict how users respond to test questions with 87.7% accuracy without user-specific calibration data. This is compared to item response theory methods which achieve about 70% accuracy. We also investigate which attributes are most responsible for making predictions.
Influence of aromatic hydrocarbons on growth, plant growth promoting activities and survival in soil of Azotobacter chroococcum strain JL104

Abstract This study was undertaken to determine the effect of aromatic hydrocarbons on growth and plant growth promoting activities of Azotobacter chroococcum strain JL104. The organism was grown on Jensen’s media without sucrose, supplemented with different concentrations of aromatic hydrocarbons. Azotobacter chroococcum strain JL104 was able to grow in the presence of benzene, toluene, aniline and benzoic acid and was able to utilize these as sole carbon source as well. The culture showed the highest growth in presence of 0.5% concentrations of aniline and benzoic acid and 0.01% concentrations of benzene and toluene. Maximum indole acetic acid (IAA) production and acetylene reduction activity (ARA) were recorded with benzene and benzoic acid, respectively. Among other substituted benzene derivatives such as xylene, p-hydroxybenzoic acid, di-nitrophenol and di-chlorophenol, xylene was observed to be the least toxic and di-nitrophenol the most toxic hydrocarbon. The highest soil survival was found in soil amended with 1% sucrose however, the population of A. chroococccum strain JL104 declined continuously in unamended soil. Amongst various hydrocarbons, 0.1% toluene amended soil supported the maximum survival, indicating it to be least toxic aromatic hydrocarbon carbon in soil.
A Requirement for Microglial TLR4 in Leukocyte Recruitment into Brain in Response to Lipopolysaccharide

To study the mechanisms involved in leukocyte recruitment induced by local bacterial infection within the CNS, we used intravital microscopy to visualize the interaction between leukocytes and the microvasculature in the brain. First, we showed that intracerebroventricular injection of LPS could cause significant rolling and adhesion of leukocytes in the brain postcapillary venules of wild-type mice, while negligible recruitment was observed in TLR4-deficient C57BL/10ScCr mice and CD14 knockout mice, suggesting recruitment is mediated by TLR4/CD14-bearing cells. Moreover, we observed reduced but not complete inhibition of recruitment in MyD88 knockout mice, indicating both MyD88-dependent and -independent pathways are involved. The leukocyte recruitment responses in chimeric mice with TLR4-positive microglia and endothelium, but TLR4-negative leukocytes, were comparable to normal wild-type mice, suggesting either endothelium or microglia play a crucial role in the induction of leukocyte recruitment. LPS injection induced both microglial and endothelial activation in the CNS. Furthermore, minocycline, an effective inhibitor of microglial activation, completely blocked the rolling and adhesion of leukocytes in the brain and blocked TNF-α production in response to LPS in vivo. Minocycline did not affect activation of endothelium by LPS in vitro. TNFR p55/p75 double knockout mice also exhibited significant reductions in both rolling and adhesion in response to LPS, indicating TNF-α signaling is critical for the leukocyte recruitment. Our results identify a TLR4 detection system within the blood-brain barrier. The microglia play the role of sentinel cells detecting LPS thereby inducing endothelial activation and leading to efficient leukocyte recruitment to the CNS.
A performance improvement process to tackle tachysystole.

Inappropriate use of oxytocin is an important patient safety issue. In this article, the authors describe success in "tackling tachysystole" using an evidence-based algorithm for oxytocin administration. Monthly audits (N = 1160) reached 100% compliance. An algorithm posted at the bedside, multidisciplinary follow-up, education, and widely disseminated results helped counter resistance and normalization of deviance. Clinicians need standardized definitions and guidelines with measures of progress to manage safety concerns such as tachysystole.
Association of the type 2 deiodinase Thr92Ala polymorphism with type 2 diabetes: case-control study and meta-analysis.

OBJECTIVE The type 2 deiodinase (D2) is a key enzyme for intracellular triiodothyronine (T(3)) generation. A single-nucleotide polymorphism in D2 (Thr92Ala) has been associated with increased insulin resistance in nondiabetic and type 2 diabetes (DM2) subjects. Our aim was to evaluate whether the D2 Thr92Ala polymorphism is associated with increased risk for DM2. DESIGN AND METHODS A case-control study with 1057 DM2 and 516 nondiabetic subjects was performed. All participants underwent genotyping of the D2 Thr92Ala polymorphism. Additionally, systematic review and meta-analysis of the literature for genetic association studies of D2 Thr92Ala polymorphism and DM2 were performed in Medline, Embase, LiLacs, and SciELO, and major meeting databases using the terms 'rs225014' odds ratio (OR) 'thr92ala' OR 'T92A' OR 'dio2 a/g'. RESULTS In the case-control study, the frequencies of D2 Ala92Ala homozygous were 16.4% (n=173) versus 12.0% (n=62) in DM2 versus controls respectively resulting in an adjusted OR of 1.41 (95% confidence intervals (CI) 1.03-1.94, P=0.03). The literature search identified three studies that analyzed the association of the D2 Thr92Ala polymorphism with DM2, with the following effect estimates: Mentuccia (OR 1.40 (95% CI 0.78-2.51)), Grarup (OR 1.09 (95% CI 0.92-1.29)), and Maia (OR 1.22 (95% CI 0.78-1.92)). The pooled effect of the four studies resulted in an OR 1.18 (95% CI 1.03-1.36, P=0.02). CONCLUSIONS Our results indicate that in a case-control study, the homozygosity for D2 Thr92Ala polymorphism is associated with increased risk for DM2. These results were confirmed by a meta-analysis including 11 033 individuals, and support a role for intracellular T(3) concentration in skeletal muscle on DM2 pathogenesis.
Currency Regimes and Weak Interest Rate Parity

We consider the presence of regimes in currency markets and their implications for interest rate parity. A weak form of interest rate parity is postulated and tested which assumes that the hedged risk premiums are identical within each regime across currencies. Both the in-sample (January 2002 - December 2004) and the out-of-sample (January 2005 - December 2007) daily data support weak interest rate parity. Furthermore, using the Federal Exchange Rate Index as a proxy of the currency market portfolio and T-Bills as the risk free asset, we find strong evidence that the weak interest rate parity hypothesis is consistent with standard portfolio equilibrium theory. The similarity between the benchmark and the implied equilibrium portfolio provides strong evidence that regime switching with weak interest rate parity is appropriate for modeling currency returns.