1,721,160 research outputs found
Keeping their own and integrating the other: medicinal plant use among Ormurs and Pathans in South Waziristan, Pakistan
Full text linkBackgroundIn multicultural societies, traditional knowledge among minorities faces several challenges. Minority groups often face difficult situations living in specific peripheral geographies and striving to retain their biocultural heritage, including medicinal plant knowledge and practices. Folk medicinal plant knowledge is a dynamic eco-cultural complex influenced by various environmental, socio-cultural, and political factors. Examining medicinal plant knowledge among minorities has been an increasingly popular topic in cross-cultural ethnobiology. It also helps understand the dynamics of local/traditional ecological knowledge (LEK/TEK) change within a given community. The current study was designed to investigate the status of medicinal plant knowledge among two linguistic groups, i.e. Ormurs and Pathans, living in a remote valley of West Pakistan.MethodsWe recruited 70 male study participants from the studied groups for semi-structured interviews to record the medicinal plant use of their communities. Data were compared among the two studied communities using the stacked charts employing the presence or absence of data with Past 4.03 and Venn diagrams. Use reports (URs) were counted for each recorded taxon.Results and DiscussionA total of seventy-four medicinal plants were quoted as used as ethnomedicines by the researched communities. Most of the reported plants were used to treat digestive and liver problems. The cross-cultural comparison revealed a considerable homogeneity of medicinal plant knowledge (the two groups commonly used more than seventy plants); however, comparing uses recorded for the widely utilised medicinal plants showed numerous idiosyncratic uses among Ormurs but very few among Pathans. Ormurs reported a higher number of cultivated, wild, and imported plant uses than did Pathans. These results indicate that, compared to Pathans, the Ormur linguistic minority retain more folk medicinal plant knowledge, which may be explained by the fact that they have incorporated different folk remedies: their "own knowledge" plus that of Pathans, with whom they have lived together for centuries. Moreover, the local plant nomenclature among Ormurs was highly affected by the plant nomenclature of Pathans.ConclusionThe current study revealed that living together for a few centuries has not implied sharing plant knowledge (as the Pathans do not seem to have learnt from the Ormurs) or, in other words, that plant knowledge exchanges have been unidirectional. The findings show that the Pashto dominant culture may have possibly put pressure on the minority groups and affected local plant-centred cultural practices, as we see in the case of local plant nomenclature hybridisation among Omuri speakers. Hence, it is imperative to employ diverse educational strategies to revitalise the decline of medicinal plant knowledge in the studied communities, especially among Ormurs, who need more attention as they face more challenges than the other group. Locally based strategies should be devised to restore the fading connection with nature, which will be advantageous for revitalising plant knowledge
A COMPUTATIONAL MODEL OF A HUMAN VENTRICULAR CARDIOMYOCYTE: POSSIBLE ROLES IN CARDIOVASCULAR DISEASE AND ARRHYTHMIAS
Full text linkStudying cardiac diseases using human tissue is proven difficult and limited, even in optimized clinical conditions. Computer simulation studies have been continuously researched by mimicking electrophysiological protocols which can now be accommodated in the present time due to their high computational load. In that regard, we have developed a stochastic human ventricular cardiomyocyte model for intracellular calcium ([Ca2+]i) handling to include 9 individual L-Type calcium (LCC) and 49 ryanodine receptor (RyR) channels as components of 20,000 Ca2+–release units (CRUs). The model presented here explores the intricacies of calcium-induced calcium-release (CICR) dynamics, with a particular focus on the interplay between LCCs and a cluster of RyRs within CRUs. This framework elucidates the fundamental aspects of excitation-contraction coupling. Various ionic pumps and currents contained in the cell membrane contribute to the overall electrophysiological behavior of the cardiac action potential (AP) morphology. Moreover, cardiac contractility is regulated by fine–tuning multiple fluxes involved in [Ca2+]i concentrations and impacts signaling pathways by spontaneous calcium release from the sarcoplasmic reticulum (SR). However, Ca2+ ions also indicate the presence of abnormalities observed in the behavior of the cardiomyocyte’s AP and intracellular Ca2+ dynamics which may ultimately result to arrhythmogenic disorders. The model presented here captures the spontaneous Ca2+ release events and can be further used to explore both normal and defective mechanisms in ventricular cardiac abnormalities
Advancements in Computational Digital Pathology for EGFR and pd-l1
Full text linkAnnually Inova Health Care System treats more than two million individuals through their integrated network. With the increasing need for fast and accurate results for its cancer patients, the Inova Laboratories Healthcare System’s reference laboratory, has acquired a Rapid Molecular Testing System, called BioCartis Idyllatm which is a real-time PCR bases molecular testing system. This system is user friendly and will help improve the turnaround time for diagnosis and treatment of cancer patients. Most of the existing molecular testing for Next-Generation Sequencing (NGS) platforms such as whole genome sequencing, Exon sequencing and Disease diagnosis panel takes approximately fourteen days or longer to obtain results. This is due to the need for high volume batched samples, specimen procurement, transportation and complex interpretation. While there have been great expansion of knowledge on molecular changes occurring in the cancer development, clinical utility of molecular tests for the diagnosis and especially for the treatment of cancers have been limited. The FDA has approved some treatments targeted for specific molecular changes such as EGFR mutation and KRAS mutation in lung cancers and BRAF mutation in melanomas. Molecular testing aids the pathologist to check for certain changes in a gene or chromosome that may cause or affect the chance of developing a specific disease or disorder, in this case lung cancer. Another area of rapid progress is the advancement of immune targeted therapy aimed at PD-L1. Initially applied to lung cancers and some GI cancers, and has been approved by FDA- for multiple cancer types recently. To be able to apply these advanced therapeutics there is a growing need for more efficient targeted biomarker testing, including fast TAT (turnaround time), user friendliness, remarkable accuracy, specificity and sensitivity which are equivalent to the NGS test results The objective of this project is to determine the use of rapid molecular tests for tumor mutations and the application of AI-based digital scoring methods of IHC (immunohistochemistry) test for PD-L1 for Keytruda® (Pembrolizumab)
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Machine Learning Estimation of Nonparametric Econometric Models and Marginal Effects
Full text linkNowadays, with advanced technology, it is easier to obtain data like never before. With more available data, comes new information that economists can extract to uncover relationships between economic variables. By using new state of the art machine learning algorithms and techniques that can handle data efficiently and can identify trends and patterns easily, we can help solve economic problems, theoretically and empirically. The primary goal of this dissertation is to help bridge the gap between machine learning and econometrics. With powerful machine learning models that exhibit great predictive ability, it would be useful to further explore machine learning methods and add them to our econometrics toolbox. In addition, we wish to extend these models to incorporate problems often faced in econometric models, including partial effects estimation using first derivatives, evaluating concavity of various economic functions using second derivatives, and allowing for heteroskedastic and autocorrelated errors in an econometric model. These issues are clearly often faced in economics, but not so much in machine learning. To incorporate machine learning techniques, machine learning estimation of nonparametric models and marginal effects are established throughout the dissertation. A derivative estimation procedure of smoothing weighted difference quotients based on random forest is proposed. The procedure of smoothing weighted difference quotients based on random forest is then used to estimate second derivatives. Lastly, a generalized framework for Kernel Regularized Least Squares that incorporates information in the error covariance when estimating the regression function is proposed
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Essays on Semiparametric Ridge-Type Shrinkage Estimation, Model Averaging and Nonparametric Panel Data Model Estimation
Full text linkThis dissertation is composed with 4 essays. They explore modelling uncertainty following two major directions. The former 2 contains topics on ordinary and general ridge-type shrinkage estimation developed from model averaging and kernel density estimation. The third one critically reviews recent literature in the areas of model averaging and model selection both parametrically and nonparametrically and proposes topics for future work. The last one focuses on nonparametric panel data estimation with random effects. In chapter 2, ordinary ridge-type shrinkage estimation is extensively studied, where a class of well-behaved ordinary ridge-type semiparametric estimators is proposed. Monte Carlo simulations, theoretical derivations, as well as empirical out-of-sample forecasts are all investigated to prove their usefulness in reducing mean squared errors, i.e. risks. Chapter 3 develops the works in Chapter 2 to the general ridge regressions. By connecting general ridge regression with kernel density estimation, an asymptotically optimal semiparametric ridge-type estimator is built. By connecting general ridge regression with model averaging, a class of model averaging ridge-type estimators are obtained. These estimators are observed to have different improvements upon the feasible general ridge estimators when model uncertainties, i.e., the error variances are different. To encourage better understanding on model averaging and model selection, Chapter 4 gives a comprehensive literature review and analysis on these topics from a frequentist's point of view. Parametric and nonparametric procedures in the recent developments are explored. Chapter 5 starts investigating panel data estimation by introducing nonparametrics in the picture. The proposed two-stage estimator shows good behaviors in Monte Carlo simulation. In addition, illustrative empirical examples in health economics and environmental economics are also introduced
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Essays on Panel Data and System of Equations under Model Uncertainty
Full text linkThis dissertation consists of four chapters that study estimation and inference in systemof equations and panel data under model uncertainty. In Chapter 2, I consider model
uncertainty in a panel data model, and introduce a Stein-like shrinkage estimator that is
a weighted average of an unrestricted estimator and a restricted estimator. The restricted
estimator represents a belief about where the parameters of the model are likely to be close.
Chapter 3 considers the estimation uncertainty from choosing different number of lagged
dependent variables as instruments in dynamic panel data models. Generalized method of
moments (GMM), the typical estimation method, can produce efficient estimators when
all lagged dependent variables are used as instruments. However, estimation using all
instruments can cause substantial bias. Conversely, the GMM estimators that use one lag
as instrument are asymptotically unbiased under forward demeaning transformation, but
at the cost of losing efficiency. Therefore, I introduce an averaging estimator which is a
weighted average of the two GMM estimators where the averaging weight is proportional
to a quadratic loss function that minimizes the asymptotic risk.
In Chapter 4, I consider simultaneous equations models (SEM), and develop an estimator
to deal with the model uncertainty about the magnitude of endogeneity. Ordinary
least squares (OLS) estimators are the most efficient estimators, however, may suffer from substantial bias when the degree of endogeneity is substantial. On the contrary,
two-stage least squares (2SLS) estimators are consistent but not efficient. Therefore, I
consider a Stein-like shrinkage estimator which is a weighted average of the OLS and 2SLS
estimators, where the weight is inversely related to a Wu-Hausman statistic that measures
the magnitude of the endogeneity.
Chapter 5 considers latent group structures to model uncertainty resulting from
unobserved heterogeneity in panel data models. Basically, I consider a panel data model
where the slope parameters are heterogenous across groups but homogenous within a group,
and the group identity is unknown. I provide a framework for estimation and identification
of the latent group structure using a pairwise fusion penalized approach
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Essays on Nonparametric and Semiparametric Models and Continuous Time Models
Full text linkMy dissertation consists of six essays which contribute new theoretical resultsto two econometrics frontiers: nonparametrics and finite sample econometrics. Chapters 2 to 3 discuss the estimation and inference of the nonparametric and semiparametric models. In chapter 2 an efficient two-step estimator is developed in single nonparametric regression model with a general parametric error covariance. By fully utilizing the information incorporated in the error covariance into estimation, the newly developed method is more efficient compared to the conventional local linear estimator (LLLS) and some other two-step estimator. The corresponding asymptotic theorems are derived. Monte Carlo study shows the relative efficiency gain of the newly proposed estimator. Chapter 3 systematically develops a new set of results for seemingly unrelated regression (SUR) analysis within nonparametric and semiparametric framework. We study the properties of LLLS and local linear weighted least squares (LLWLS) estimators, provide an efficient two-step estimation for the system and establish the asymptotic theorems under both unconditional and conditional error variance-covariance cases. The procedures of estimation for various nonparametric and semiparametric SUR models are proposed. In addition, two nonparametric goodness-of-fit measures for the system are given. Chapter 4 applies the estimation method developed in chapter 2 and 3 to an empirical analysis on return to public capital in U.S.Chapters 5 to 6 study the finite sample properties of the mean reversion parameter estimator in continuous time models. In chapter 5 we approximate the bias of the estimator for the Levy-based Ornstein-Uhlenbeck (OU) process, and propose bias corrected estimators. In chapter 6 the exact distribution of the MLE is investigated under different scenarios: known or unknown drift term, fixed or random start-up value, and zero or positive . The numerical calculations demonstrate the remarkably reliable performance of the proposed exact approach.In chapter 7 we study the efficiency of the coefficient of determination based onfinal prediction error and compare it with conventional goodness-of-fit measuresin linear regression models with both normal and non-normal disturbances. Theefficiency results show that R2 based onfinal prediction error has practical use in empirical analysis, for examples,panel data analysis and time series analysis
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Three Essays on Nonparametric Hypothesis Testing
Full text linkNonparametric approaches have widely been used due to their advancement in not making assumptions on the distribution of the data. Even with their extensive development, nonparametric hypothesis testing has not been developed as much as a nonparametric estimation even though it is one of the key components of the econometric analysis. This dissertation has mainly two parts. I first explore the systematic development of the current nonparametric tests and provide results on testing linearity as an illustration. Then I develop new nonparametric tests for detecting endogeneity in cross-sectional data and panel data respectively.Elaborating each test's performance can be meaningful in that we can decide which test to use depending on the hypothesis and even construct a new test based on such a relationship. Under the hypotheses for linearity, Chapter 2 will categorize the types of nonparametric tests and discuss the analytical relationship of those tests. By imposing some conditions, I can compare the local power of each test asymptotically. While examining the analytical relationship, I develop a nonparametric Rao-Score test and show it to be equivalent to the Su and Ullah (2013) test. Once analyzing the analytical relationship of the current nonparametric tests, I focus on developing a new nonparametric test for endogeneity. Since endogeneity is commonly observed in many economic contexts, detecting its presence is a preliminary step for choosing an estimation strategy. In Chapter 3, I construct a test using the control function approach under a triangular simultaneous equations model. My test can be summarized as being simple to implement as a test and being able to capture the locally nonlinear correlation with kernel weighting. Furthermore, I will apply these tests to the empirical analyses and show the contradicting results with the parametric test. Not only in triangular simulation equations model, but also is endogeneity important model specification issue in panel data setting. The estimation strategy differs depending on the presence of endogeneity between the individual specific effects and the variable. I propose a new estimation method for the nonparametric panel random effects model and construct a new test for endogeneity using the residuals from the proposed estimation method. By obtaining the individual specific effects in the random effects model, I construct a test over the i index instead of the i index and time. With a large T, the test performs well in terms of size and power
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