Parsimony Model Research Articles

Abstract 2326: Integrating single nucleotide variants (SNVs), copy number alterations (CNAs), and structural variants (SVs) into single-cell clonal lineage inference

Abstract Background: We extend algorithms for clonal lineage inference (``tumor phylogenetics'') from single-cell DNA sequence (scDNA-seq) data to accommodate broader classes of genetic variants, including single nucleotide variants (SNVs), copy number alterations (CNAs), and structural variants (SVs). Methods: We develop a computational method based on constrained optimization, posing a problem of inferring cellular lineage trees consistent with a given scDNA-seq data set constrained to follow a Dollo parsimony model for SNVs and SVs. We then seek an optimal tree consistent with these constraints so as to optimize for a CNA minimum evolution model balanced against measure of consistency of the different data types with one another relative to the reconstructed set of clones. We pose this problem as an integer linear program (ILP) that we solve heuristically via a coordinate descent algorithm. We tested the resulting method on a combination of synthetic scDNA-seq data and publicly available real data. Results: Results on synthetic data show the method to be effective at constructing lineage trees under a variety of parameter domains, including variations in noise levels, clone numbers, single cells, and mutation rates. We further show it to yield improved accuracy over widely used prior models that operate on only subsets of these variant types. Application to previously published scDNA-seq data demonstrate the method to be effective at inferring clonal lineage trees incorporating all three variant types. The results further show how the different kinds of variants can contribute in tandem to progression along single cell lineages or to the emergence of clones exhibiting hallmarks of distinct mechanisms of progression. Conclusion: The results support the effectiveness of our method in resolving accurate trees from scDNA-seq data including diverse variant types, as well as the value of considering these mutation types collectively in developing a comprehensive understanding of how various forms of somatic mutability together shape clonal evolution in cancers. Citation Format: Nishat Bristy, Xuecong Fu, Russell Schwartz. Integrating single nucleotide variants (SNVs), copy number alterations (CNAs), and structural variants (SVs) into single-cell clonal lineage inference [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2326.

Revisiting the phylogeny of the scolebythid wasps (Hymenoptera: Aculeata) through Bayesian model evaluation and parsimony, with description of a new fossil family of Chrysidoidea

Abstract Scolebythidae constitute the smallest family of Aculeata in number of extant species, but with a considerable diversity of extinct lineages. Twenty Cretaceous species are attributed to the family coming from deposits in Canada, Lebanon, Myanmar, New Jersey, and Spain. However, several of these taxa lack characters of the family groundplan. Although phylogenetic investigations have been conducted for the family based on a relatively small set of morphological characters, the substantial diversity of species from Burmese amber have not yet been accounted for in a phylogenetic perspective. In the present work, we reconstruct the phylogenetic relationships of Scolebythidae using a new morphological matrix and explore it through parsimony and Bayesian model evaluation. In Bayesian analyses, homoplasy-based partitioning outperformed anatomy-based partitioning and the unpartitioned models. Our phylogenetic results support the relocation of a few taxa previously attributed to Scolebythidae to the families Bethylidae and Chrysididae. We also describe †Chrysopsenellidae fam. nov., a new family of Chrysidoidea, based on an inclusion in Burmese amber. In all analyses the new family is recovered as closely related to Scolebythidae, with the whole clade being sister-group to Chrysididae + †Chrysobythidae. Finally, we discuss the origin of the Burmese amber biota distinctiveness.

Discovery of potential biomarkers for lung cancer classification based on human proteome microarrays using Stochastic Gradient Boosting approach.

Early identification of lung cancer (LC) will considerably facilitate the intervention and prevention of LC. The human proteome micro-arrays approach can be used as a "liquid biopsy" to diagnose LC to complement conventional diagnosis, which needs advanced bioinformatics methods such as feature selection (FS) and refined machine learning models. A two-stage FS methodology by infusing Pearson's Correlation (PC) with a univariate filter (SBF) or recursive feature elimination (RFE) was used to reduce the redundancy of the original dataset. The Stochastic Gradient Boosting (SGB), Random Forest (RF), and Support Vector Machine (SVM) techniques were applied to build ensemble classifiers based on four subsets. The synthetic minority oversampling technique (SMOTE) was used in the preprocessing of imbalanced data. FS approach with SBF and RFE extracted 25 and 55 features, respectively, with 14 overlapped ones. All three ensemble models demonstrate superior accuracy (ranging from 0.867 to 0.967) and sensitivity (0.917to 1.00) in the test datasets with SGB of SBF subset outperforming others. The SMOTE technique has improved the model performance in the training process. Three of the top selected candidate biomarkers (LGR4, CDC34, and GHRHR) were highly suggested to play a role in lung tumorigenesis. A novel hybrid FS method with classical ensemble machine learning algorithms was first used in the classification of protein microarray data. The parsimony model constructed by the SGB algorithm with the appropriate FS and SMOTE approach performs well in the classification task with higher sensitivity and specificity. Standardization and innovation of bioinformatics approach for protein microarray analysis need further exploration and validation.

Iterative Refinement to Improve Data Quality and Label Consistency by Synergizing Parsimony Model Guidance and Physician Interaction

<h3>Purpose/Objective(s)</h3> Clinical data and labeling are usually noisy and uncertain. Modeling based on such inconsistent data risks overfit and may generate faulty insight on prominent features and regression relationships. We hypothesize inter-sample consistency serves as a rational surrogate for data quality and can be used to guide improvement, in the absence of an absolute ground-truth. To this end, we aim to synergize a deep-learning setting and an iterative interactive refinement procedure for label refinement. <h3>Materials/Methods</h3> We proposed a novel approach by guiding physician-approved label perturbation with parsimonious deep network modeling. The proposed procedure alternates between fitting a parsimonious deep network model to human labels and guiding the human observer to review the labels to identify ill-fitted examples to perform label refinement when appropriate. Convergence is claimed when further iteration stops to produce clinically pronounced improvement, as defined from a statistical equivalence test. To evaluate the efficacy of the refinement, input-output agreement index (IOAI) based on partial ranking consistency is also calculated. We took as a use case in segmenting the lumen and vessel wall from MR vessel wall imaging (VWI) from 80 patients with intracranial atherosclerotic disease, on four locations with high likelihood of plaque presence: the intracranial internal carotid artery, the middle cerebral artery, the intracranial vertebral artery, and the basilar artery. Each segment contained 30 contiguous 2D cross-sectional slices with 0.55 mm slice thickness and 0.10 mm in-plane resolution. A lightweight 2.5D segmentation network was used as the low-dimensional model, and equivalence criterion was defined by one-sided superiority threshold of 0.03 in Dice similarity coefficient (DSC) based on reported performance from existing study. Clinical soundness was further assessed with the variation in normalized wall index as a quantitative imaging index, expected to low if segmented structures were piecewise smooth conforming to clinical insight. <h3>Results</h3> 5-fold cross validation based on the 80*4*30 slice samples showed enhanced modeling performance and better conformality to clinical insight with the final labels. In this use case, DSC improved from 0.893±0.108 to 0.938±0.078 for lumen, from 0.806±0.086 to 0.879±0.072 for vessel wall, the total variation in normalized wall index decreased from 0.757±0.181 to 0.586±0.182, and the input-output agreement index increased from 0.523 to 0.556 by the proposed refinement procedure. <h3>Conclusion</h3> This study demonstrates that inconsistency in clinical labels and physiological limitations can be addressed with an iterative interactive process with parsimonious modeling, taking advantage of flexibility from deep networks. The rationale generalizes to other label settings and tasks.

Is there a loophole in Dollo's law? A DevoEvo perspective on irreversibility (of felid dentition).

There is a longstanding interest in whether the loss of complex characters is reversible (so-called "Dollo's law"). Reevolution has been suggested for numerous traits but among the first was Kurtén, who proposed that the presence of the second lower molar (M2 ) of the Eurasian lynx (Lynx lynx) was a violation of Dollo's law because all other Felids lack M2 . While an early and often cited example for the reevolution of a complex trait, Kurténand Werdelinused an ad hoc parsimony argument to support their. Here I revisit the evidence that M2 reevolved lynx using explicit parsimony and maximum likelihood models of character evolution and find strong evidence that Kurténand Werdelinwere correct-M2 reevolved in E.lynx. Next, I explore the developmental mechanisms which may explain this violation of Dollo's law and suggest that the reevolution of lost complex traits may arise from the reevolution of cis-regulatory elements and protein-protein interactions, which have a longer half-life after silencing that protein coding genes. Finally, I present a developmental model to explain the reevolution M2 in E.lynx, which suggestthat the developmental programs required for the establishment of serially homologous characters may never really be lost so long as a single instance of the character remains-thus the gain and loss and regain of serially homologous characters, such mammalian molars, may be developmentally and evolutionarily "simple."

Comparison of simultaneously nonparametric regression based on spline and Fourier series estimator related social aid distribution in Indonesia

Several estimators in the nonparametric regression approach that are often used are the Spline estimator and the Fourier series. Both estimators have high flexibility and can adapt to the local nature of the data effectively. This study applies these two methods to compare the best performance of the formed model. The data used in this study are multi-response data in the form of types of government assistance and multi-predictor data in the form of factors that are thought to affect the distribution of aid receipts in the community. This research is important to optimize the distribution of welfare assistance in Indonesia, especially during the Coronavirus Disease-19 (Covid-19) pandemic. By using the Generalized Cross-Validation (GCV) criteria for the parsimony model, the estimator chosen to predict the distribution of welfare assistance in Indonesia is the Fourier series estimator with cosine and sine bases. The goodness of the nonparametric regression model with the Fourier series estimator can be seen from the minimum GCV value generated with one oscillation parameter, which is 1.483961, with a Mean Square Error (MSE) of 2.1257374, and R 2 of 0.923143. The prediction results are expected to be the government's recommendation for the restoration of community welfare through equitable distribution of aid to support the achievement of the Sustainable Development Goals (SDGs).

Combining genomic, phenotypic and Sanger sequencing data to elucidate the phylogeny of the two-clawed spiders (Dionycha)

The importance of morphology in the phylogenomic era has recently gained attention, but relatively few studies have combined both types of information when inferring phylogenetic relationships. Sanger sequencing legacy data can also be important for understanding evolutionary relationships. The possibility of combining genomic, morphological and Sanger data in one analysis seems compelling, permitting a more complete sampling and yielding a comprehensive view of the evolution of a group. Here we used these three data types to elucidate the systematics and evolution of the Dionycha, a highly diverse group of spiders relatively underrepresented in phylogenetic studies. The datasets were analyzed separately and combined under different inference methods, including a novel approach for analyzing morphological matrices with commonly used evolutionary models. We tested alternative hypotheses of relationships and performed simulations to investigate the accuracy of our findings. We provide a comprehensive and thorough phylogenetic hypothesis for Dionycha that can serve as a robust framework to test hypotheses about the evolution of key characters. We also show that morphological data might have a phylogenetic impact, even when massively outweighed by molecular data. Our approach to analyze morphological data may serve as an alternative to the proposed practice of arbitrarily partitioning, weighting, and choosing between parsimony and stochastic models. As a result of our findings, we propose Trachycosmidae new rank for a group of Australian genera formerly included in Trochanteriidae and Gallieniellidae, and consider Ammoxenidae as a junior synonym of Gnaphosidae. We restore the family rank for Prodidomidae, but transfer the subfamily Molycriinae to Gnaphosidae. Drassinella is transferred to Liocranidae, Donuea to Corinnidae, and Mahafalytenus to Viridasiidae.

Confirmatory Factor Analysis (CFA) Model for Testing Normality with the Weight Least Square (WLS) Estimation Method

To complete the analysis problem of the data which form ordinal scale, we used Structural Equation Modeling (SEM) or Lisrel. This research aims to evaluate the normality and covariant matrix estimation from the ordinal data which unknown the spreading form in each sample 100, 150, 200, and 300 by using Weighted Least Square (WLS). Ordinal data appeared from the spreading of lisrel aid and used matrix covariant aid. Path of diagram also used to test of the suitable model with WLS. The normally graphic of each sample test are the Kolmogorov-Smirnov test, Anderson-Darling test, and Ryan-Joiner test. Determination of model quality could be used based on three groups of the model, they are absolute model: and RMSEA, incremental model: GFI and AGFI, and parsimony model: PGFI. This research found the normality by using the WLS method in samples 100, 150, 200, and 300 by using Kolmogorov-Smirnov test, Anderson-Darling test, Ryan-Joiner test. According to the result, it could be found that for samples 100, 150, 200, and 300, the spread of the data was normal and the best test used the Kolmogorov-Smirnov test. This result shows that if the sample has a larger size, it means the sample has the best value in the test performed.

The reciprocal Bayesian LASSO.

A reciprocal LASSO (rLASSO) regularization employs a decreasing penalty function as opposed to conventional penalization approaches that use increasing penalties on the coefficients, leading to stronger parsimony and superior model selection relative to traditional shrinkage methods. Here we consider a fully Bayesian formulation of the rLASSO problem, which is based on the observation that the rLASSO estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters are assigned independent inverse Laplace priors. Bayesian inference from this posterior is possible using an expanded hierarchy motivated by a scale mixture of double Pareto or truncated normal distributions. On simulated and real datasets, we show that the Bayesian formulation outperforms its classical cousin in estimation, prediction, and variable selection across a wide range of scenarios while offering the advantage of posterior inference. Finally, we discuss other variants of this new approach and provide a unified framework for variable selection using flexible reciprocal penalties. All methods described in this article are publicly available as an R package at: https://github.com/himelmallick/BayesRecipe.

A canopy conductance model with temporal physiological and environmental factors

Canopy conductance, one of the key variables in simulating evapotranspiration, is strongly influenced by the physiological status of a plant and environmental factors, including photosynthetically active radiation, vapor pressure deficit, air temperature, soil moisture and so on. However, the restrictive functions used to represent these factors rarely consider the dynamics of physiological and environmental factors. This study proposed an improved canopy conductance model by regarding radiation and vapor pressure deficit as the two main influencing factors, quantifying the temporal variation in stomatal responses to radiation that notably adjust stomatal behavior, parameterizing maximum stomatal conductance with plant type-specific functions and proposing a new restrictive function for the VPD. The improved canopy conductance model was incorporated in a surface conductance model for estimating surface conductance and evapotranspiration at 8 flux stations at the Heihe River Basin and the Haihe River Basin. The estimated results were the most accurate when comparing to two other models. Furthermore, the model performance was acceptable when most of the parameters were assumed to be constant across the sites except the reference canopy conductance Gc, ref and the soil evaporation parameter αs, which suggests that the improved canopy conductance model could be used as a parsimony model for improving canopy conductance predictions and water use efficiency over typical climate zones and underlying surface types in North of China.

GRMT: Generative Reconstruction of Mutation Tree From Scratch Using Single-Cell Sequencing Data.

Single-cell sequencing (SCS) now promises the landscape of genetic diversity at single cell level, and is particularly useful to reconstruct the evolutionary history of tumor. There are multiple types of noise that make the SCS data notoriously error-prone, and significantly complicate tumor tree reconstruction. Existing methods for tumor phylogeny estimation suffer from either high computational intensity or low-resolution indication of clonal architecture, giving a necessity of developing new methods for efficient and accurate reconstruction of tumor trees. We introduce GRMT (Generative Reconstruction of Mutation Tree from scratch), a method for inferring tumor mutation tree from SCS data. GRMT exploits the k-Dollo parsimony model to allow each mutation to be gained once and lost at most k times. Under this constraint on mutation evolution, GRMT searches for mutation tree structures from a perspective of tree generation from scratch, and implements it to an iterative process that gradually increases the tree size by introducing a new mutation per time until a complete tree structure that contains all mutations is obtained. This enables GRMT to efficiently recover the chronological order of mutations and scale well to large datasets. Extensive evaluations on simulated and real datasets suggest GRMT outperforms the state-of-the-arts in multiple performance metrics. The GRMT software is freely available at https://github.com/qasimyu/grmt.

Taming the Duplication-Loss-Coalescence Model with Integer Linear Programming.

The duplication-loss-coalescence (DLC) parsimony model is invaluable for analyzing the complex scenarios of concurrent duplication loss and deep coalescence events in the evolution of gene families. However, inferring such scenarios for already moderately sized families is prohibitive owing to the computational complexity involved. To overcome this stringent limitation, we make the first step by describing a flexible integer linear programming (ILP) formulation for inferring DLC evolutionary scenarios. Then, to make the DLC model more scalable, we introduce four sensibly constrained versions of the model and describe modified versions of our ILP formulation reflecting these constraints. Our simulation studies showcase that our constrained ILP formulations compute evolutionary scenarios that are substantially larger than scenarios computable under our original ILP formulation and the original dynamic programming algorithm by Wu et al. Furthermore, scenarios computed under our constrained DLC models are remarkably accurate compared with corresponding scenarios under the original DLC model, which we also confirm in an empirical study with thousands of gene families.

The autoregressive integrated vector model approach for covid-19 data in Indonesia and Singapore

Almost all of the countries in the world were attacked by coronavirus disease 2019 (covid-19) which is started from pneumonia endemic cases in Wuhan China in December 2019. The virus spreads including in the Southeast Asian region where Singapore and Indonesia are two countries with the highest cases. Simultaneous modeling of cases in both countries is important as information on the development of covid-19 cases. The Vector Autoregressive Integrated (VARI) model is a multivariate time series model that can be used to build model non-stationary time series data in several locations simultaneously. This study aims to analyze the development of covid-19 cases and builds a VARI model of covid-19 cases in Indonesia and Singapore. The data used from daily covid-19 confirmed cases in the period March 16th until April 19th, 2020. Plotting and statistics testing covid-19 data series from both countries show non-stationary series which have trend and fluctuation. Based on optimum lag identification use the minimum Akaike Information Criteria correction (AICc), the parsimony model is obtained namely VARI(1,1) which has satisfied the multivariate normal, white noise, and homogenous assumption. The result shows that covid-19 cases in Indonesia and Singapore have a strong positive correlation. However, the covid-19 cases in both countries were only influenced by previous cases in each country. The accuracy shows that the model is good enough for forecasting covid-19 cases in both countries.

Moderating Role of Job Autonomy in the Relationship among Antecedents of Organizational Commitment: Empirical Study in Indonesia Social Security Administrator for Health

This study examines the effect of perceived work overload and work-family conflict on organizational commitment with job autonomy as a moderating variable. This study combines Moore (2000a) and Ahuja et al. (2007) IS Turnover model to develop a more parsimony model in explaining the phenomenon of organizational commitment in Social Security Administrator for Health (BPJS Kesehatan). Population of this study is employees of BPJS Kesehatan including Central Office, Deputy for Jakarta-Bogor-Depok-Tangerang-Bekasi (Jabodetabek) Region, Deputy for Central Java and DIY, Deputy for Papua and West Papua, Deputy for North Sulawesi, Central Sulawesi, Gorontalo and North Maluku, and Deputy for Region South Sulawesi, West Sulawesi, Southeast Sulawesi and Maluku. 1650 samples of employee from all level and job positions have gathered by online questionnaire survey. The Result shows that the perception of work overload and work-family conflict affects organizational commitment and is moderated by job autonomy. Implications for future research and stakeholders are further discussed.

Feature Calibration Network for Occluded Pedestrian Detection

Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the deep learning framework, referred to as Feature Calibration Network (FC-Net), to adaptively detect pedestrians under various occlusions. FC-Net is based on the observation that the visible parts of pedestrians are selective and decisive for detection, and is implemented as a self-paced feature learning framework with a self-activation (SA) module and a feature calibration (FC) module. In a new self-activated manner, FC-Net learns features which highlight the visible parts and suppress the occluded parts of pedestrians. The SA module estimates pedestrian activation maps by reusing classifier weights, without any additional parameter involved, therefore resulting in an extremely parsimony model to reinforce the semantics of features, while the FC module calibrates the convolutional features for adaptive pedestrian representation in both pixel-wise and region-based ways. Experiments on CityPersons and Caltech datasets demonstrate that FC-Net improves detection performance on occluded pedestrians up to 10% while maintaining excellent performance on non-occluded instances.

Validation of the Short Forms of Centrality of Religiosity Scale in Russia

Since the end of the Soviet Union, Christian Orthodoxy has regained importance in Russian society. Considering the religious dynamics in the decades after 1990, scholars working in the field have been debating about a reliable measuring tool for religiosity. The present study provides a validation of two short forms of the Centrality of Religiosity Scale (CRS), the CRS-5, and CRSi-7 in Russia, as well as its corresponding translated items. Therefore, data from two large-scale sociological surveys from 2008 (N = 894) and 2019 (N = 1768) were used. A multigroup confirmatory factor analysis with restrictions on the variance and covariance structure of the model shows good results in terms of absolute, parsimony, and relative model fit for the CRS-5 and CRSi-7. Moreover, the models indicate time-invariance, which is a consistent psychometric characteristic of both short forms. The time-invariance is accompanied by the good internal consistency of the scales: The CRS-5 with α=0.85 and the CRSi-7 with α=0.84. The results of the analysis encourage the use of the CRS-5 and the CRSi-7 for research on religiosity in Russia. While the CRS-5 is especially suitable for the Orthodox-dominated religious landscape, the CRSi-7 should be used if non-monotheistic private religious practice and religious experience are the focus of the scientific investigation.

Executive Functions Are Associated with Fall Risk but not Balance in Chronic Cerebrovascular Disease.

Background: Older people’s deficits in executive functions (EF) have been shown to lead to higher fall risk, postural sway, and reduced speed. Crucially, EF impairments are even more pronounced in individuals with chronic cerebrovascular disease (CVD), namely vascular cognitive impairment. Methods: In this retrospective cross-sectional study, we used a complete neuropsychological battery, including the Trail Making Test (TMT) and physical measures, such as the Morse fall and EQUI scales, to assess 66 individuals with chronic CVD. Linear regressions, Bayesian analyses, and model selection were performed to see the impact of EF, global cognition, and vascular parkinsonism/hemiplegia on physical measures (fall risk and balance). Results: The TMT part B and BA correlated (r = 0.44 and r = 0.45) with Morse fall scale. Only EF significantly explained fall risk, whereas global cognition and vascular parkinsonism/hemiplegia did not. These findings were confirmed by Bayesian evidence and parsimony model selection. Balance was not significantly correlated with any of the neuropsychological tests. Conclusions: This is the first study investigating the relationship between cognitive and physical measures in a sample of older people with chronic CVD. The results are consistent with previous findings that link EF with fall risk in CVD.

Accuracy of ancestral state reconstruction for non-neutral traits

The assumptions underpinning ancestral state reconstruction are violated in many evolutionary systems, especially for traits under directional selection. However, the accuracy of ancestral state reconstruction for non-neutral traits is poorly understood. To investigate the accuracy of ancestral state reconstruction methods, trees and binary characters were simulated under the BiSSE (Binary State Speciation and Extinction) model using a wide range of character-state-dependent rates of speciation, extinction and character-state transition. We used maximum parsimony (MP), BiSSE and two-state Markov (Mk2) models to reconstruct ancestral states. Under each method, error rates increased with node depth, true number of state transitions, and rates of state transition and extinction; exceeding 30% for the deepest 10% of nodes and highest rates of extinction and character-state transition. Where rates of character-state transition were asymmetrical, error rates were greater when the rate away from the ancestral state was largest. Preferential extinction of species with the ancestral character state also led to higher error rates. BiSSE outperformed Mk2 in all scenarios where either speciation or extinction was state dependent and outperformed MP under most conditions. MP outperformed Mk2 in most scenarios except when the rates of character-state transition and/or extinction were highly asymmetrical and the ancestral state was unfavoured.

The Application of Log-Linear Model to Selected Poison Patients

Log-linear model is a technique used to analyze the cross-classification categorical data or the contingency table. It is used to obtain the parsimony models that describe the interaction between the categorical variables in contingency tables. Log-linear models are commonly used in evaluating higher dimensional contingency tables that involves more than two categorical variables. This study focuses on analyzing data of poisoned patients from 2012 to 2014 using log-linear model. There are two model analyzed; model for demographic data of patients and model of poisoning information. For the first model, the variables involved are gender, age, race and state. Variables for the second model are circumstance of exposure, type of exposure, location of exposure, route of exposure and types of poison. Both log-linear models are developed to investigate the association between variables in the model. As a result of this study, the best model for demographic data and poisoning information are the model with three-ways interaction. For the best model of demographic data, there is an association between gender, age and race, race, gender and state as well as age, race and state. Meanwhile, the best model for poisoning information reveals that there is relationship between circumstance of exposure, route of exposure and type of poison, location of exposure, route of exposure and type of poison, circumstance of exposure, type of exposure and route of exposure, circumstance of exposure, location of exposure and route of exposure, circumstance of exposure, type of exposure and type of poison and also type of exposure, location of exposure and type of poison. Keywords: log-linear; demographic; gender; age; race; state; circumstance of exposure; type of exposure; location of exposure; route of exposure; types of poison

The adoption of financial technology in Ethiopia: a study of bank customers perspective

Developing countries like Ethiopia in Africa highly striving to adopt financial technologies in order to modernize the financial system. However, the acceptance of financial technologies from consumers’ side faces many challenges. Financial technologies include the electronic-based platforms that designed by bankers to allow their customers for accessing financial services whenever they demand it without time and distance barriers. Those technologies considered in this study includes; internet banking, mobile banking, and card banking. The aim of this study was to identify factors that determine bank customers’ intention to adopt e-finance technologies in Ethiopia. It was confirmatory study to validate the integration of technology acceptance model (TAM) with customer awareness and subjective norm factors. Self-administered questionnaires were distributed to 412 bank customers from technologically leading banks in Ethiopia. The structural equation model (SEM) analysis was used for testing the hypotheses. The major findings of the study revealed that bank customers’ intention to adopt the financial technology was influenced positively by customer awareness, subjective norm, and perceived usefulness. The integration of customer awareness and subjective norm with the TAM results in a parsimony model to measure the acceptance intention of financial technologies. The study forwarded scientific recommendations for bank practitioners and policy makers.