Optimal sample selection applied to information rich, dense data

Dense data can be classified into superdense information-poor data (type 1 dense data) and dense information-rich data (type 2 dense data). Arbitrary, random, or optimal thinning may be applied to type 1 dense data to minimise computational burden and statistical issues (such as autocorrelation). In contrast, a prospective or retrospective optimal design can be applied to type 2 dense data to maximise information gain from limited resources (capital and/or time). Here we describe a retrospective optimal selection strategy for quantification of unbound drug concentration from a discrete set of plasma samples where the total drug concentration has been measured.

Graph neural network based unsupervised influential sample selection for brain multigraph population fusion

Graph neural networks (GNNs) have witnessed remarkable proliferation due to the increasing number of applications where data is represented as graphs. GNN-based multigraph population fusion methods for estimating population representative connectional brain templates (CBT) have recently led to improvements, especially in network neuroscience. However, prior studies do not consider how an individual training brain multigraph influences the quality of GNN training for brain multigraph population fusion. To address this issue, we propose two major sample selection methods to quantify the influence of a training brain multigraph on the brain multigraph population fusion task using GNNs, in a fully unsupervised manner: (1) GraphGradIn, in which we use gradients w.r.t GNN weights to trace changes in the centeredness loss of connectional brain template during the training phase; (2) GraphTestIn, in which we exclude a training brain multigraph of interest during the refinement process in the test phase to infer its influence on the CBT centeredness loss. Next, we select the most influential multigraphs to build the training set for brain multigraph population fusion into a CBT. We conducted extensive experiments on brain multigraph datasets to show that using a dataset of influential training samples improves the learned connectional brain template in terms of centeredness, discriminativeness, and topological soundness. Finally, we demonstrate the use of our methods by discovering the connectional fingerprints of healthy and neurologically disordered brain multigraph populations including Alzheimer's disease and Autism spectrum disorder patients. Our source code is available at https://github.com/basiralab/GraphGradIn.

Gone with the pandemic: How did Covid-19 affect the academic performance of Colombian students?

The Covid-19 pandemic is an unprecedented shock that has tested the responsiveness of education systems around the world. The international literature that has studied the Covid-19 pandemic and school performance is still limited. Colombia presents an interesting scenario for the analysis due to the prolonged school closures and long lockdowns it experienced in 2020, and the availability of a rich dataset on the results of a high school exit examination (Saber11) that was administered even during the pandemic. Using this data, we estimate whether the COVID-19 pandemic is associated to lower levels of performance amongst graduating high school students using a school and time fixed effects model, finding a negative relation. Due to the significant reduction in the number of students taking the national standardized high school exit exam in 2020, we use inverse probability weighting (IPW) regressions to control for possible selection bias. The results of these regressions show that the Covid-19 pandemic has a negative and statistically significant relation with the scores obtained by students on the Saber11 exam. These results are relevant because they provide evidence to motivate the design of public policies that mitigate the side effects of the pandemic on educational outcomes.

Understanding Covid-19 Mobility Through Human Capital: A Unified Causal Framework

This paper seeks to identify the causal impact of educational human capital on social distancing behavior at workplace in Turkey using district-level data for the period of April 2020 - February 2021. We adopt a unified causal framework, predicated on domain knowledge, theory-justified constraints anda data-driven causal structure discovery using causal graphs. We answer our causal query by employing machine learning prediction algorithms; instrumental variables in the presence of latent confounding and Heckman's model in the presence of selection bias. Results show that educated regions are able to distance-work and educational human capital is a key factor in reducing workplace mobility, possibly through its impact on employment. This pattern leads to higher workplace mobility for less educated regions and translates into higher Covid-19 infection rates. The future of the pandemic lies in less educated segments of developing countries and calls for public health action to decrease its unequal and pervasive impact.

Examining pathogen DNA recovery across the remains of a 14th century Italian friar (Blessed Sante) infected with Brucella melitensis

OBJECTIVE

To investigate variation in ancient DNA recovery of Brucella melitensis, the causative agent of brucellosis, from multiple tissues belonging to one individual MATERIALS: 14 samples were analyzed from the mummified remains of the Blessed Sante, a 14 ^th century Franciscan friar from central Italy, with macroscopic diagnosis of probable brucellosis.

METHODS

Shotgun sequencing data from was examined to determine the presence of Brucella DNA.

RESULTS

Three of the 14 samples contained authentic ancient DNA, identified as belonging to B. melitensis. A genome (23.81X depth coverage, 0.98 breadth coverage) was recovered from a kidney stone. Nine of the samples contained reads classified as B. melitensis (7-169), but for many the data quality was insufficient to withstand our identification and authentication criteria.

CONCLUSIONS

We identified significant variation in the preservation and abundance of B. melitensis DNA present across multiple tissues, with calcified nodules yielding the highest number of authenticated reads. This shows how greatly sample selection can impact pathogen identification.

SIGNIFICANCE

Our results demonstrate variation in the preservation and recovery of pathogen DNA across tissues. This study highlights the importance of sample selection in the reconstruction of infectious disease burden and highlights the importance of a holistic approach to identifying disease.

LIMITATIONS

Study focuses on pathogen recovery in a single individual.

SUGGESTIONS FOR FURTHER RESEARCH

Further analysis of how sampling impacts aDNA recovery will improve pathogen aDNA recovery and advance our understanding of disease in past peoples.

Transfer learning: a friendly introduction

Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.

Classification of various nutrient deficiencies in tomato plants through electrophysiological signal decomposition and sample space reduction

Plants leave testimonies of undergoing physical state by depicting distinct variations in their electrophysiological data. Adequate nutrition of plants signifies their role in the growth and a plentiful harvest. Plant signal data carries enough information to detect and analyse nutrient deficiency. Classification of nutrient deficiencies through signal decomposition and bilevel measurements has not been reported earlier. The proposed work explores tomato plants in four-time cycles (Early Morning, Morning, After Noon, Night) of macronutrients Calcium (Ca), Nitrogen (N) and micronutrients Manganese (Mn), Iron (Fe). Using the Empirical Mode Decomposition method (EMD), signals are decomposed into Intrinsic Mode Functions (IMF) in 10-levels. Further, Intrinsic mode functions are grouped into two clusters to extract descriptive data statistics and bi-level measurements. Then a novel sample selection method is proposed to achieve a better classification rate by reducing sample space. A binary classification model is built to train and test 15 features individually using discriminant analysis and naïve-Bayes classifier variants. The reported results achieved a classification rate up to 98% after 5-fold cross-validation. Attained findings endorse novel pathways for detection and classification of nutrient deficiencies in the early stages, consequently promoting prevention and treatment approaches earliest to the appearance of symptoms, also helping to enhance plant growth.

Online and offline health information seeking and the demand for physician services

We examine the relationship between Internet-based health information seeking and the demand for physician services, using data collected from the 28 European Union states in 2014. Unlike previous research, our analysis distinguishes seekers of health information into those who use only non-Internet sources and those who use the Internet and possibly non-Internet sources. By comparing the frequencies of physician visits among the two groups of health information seekers and non-seekers, we infer the net association between online health information and the demand for physician services while partially controlling for the effects of concurrent seeking of offline health information. The following are the two key findings: (1) individuals' health status and sociodemographic factors shape online and offline health information seeking patterns in similar ways; and (2) the demand for physician services is positively associated with offline health information seeking and not with online health information. The net association with online health information would be even smaller after controlling for the effect of concurrent offline health information seeking. These results suggest that extending the availability of online health information would potentially reinforce the unequal access to health information, which could create greater variation in individuals' health management skills and benefits from health care in the long term. However, it would be associated with little or no increase in the demand for physician services, unlike the implications of previous research.

Predicting cognitive scores with graph neural networks through sample selection learning

Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In existing literature, functional brain connectomes have been used successfully to predict cognitive measures such as intelligence quotient (IQ) scores in both healthy and disordered cohorts using machine learning models. However, existing methods resort to flattening the brain connectome (i.e., graph) through vectorization which overlooks its topological properties. To address this limitation and inspired from the emerging graph neural networks (GNNs), we design a novel regression GNN model (namely RegGNN) for predicting IQ scores from brain connectivity. On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task. However, since such deep learning architectures are computationally expensive to train, we further propose a learning-based sample selection method that learns how to choose the training samples with the highest expected predictive power on unseen samples. For this, we capitalize on the fact that connectomes (i.e., their adjacency matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results on full-scale and verbal IQ prediction outperforms comparison methods in autism spectrum disorder cohorts and achieves a competitive performance for neurotypical subjects using 3-fold cross-validation. Furthermore, we show that our sample selection approach generalizes to other learning-based methods, which shows its usefulness beyond our GNN architecture.

Does the adoption of emerging technologies improve technical efficiency? Evidence from Korean manufacturing SMEs

Despite the proliferation of innovative technologies during the Fourth Industrial Revolution (4IR), there is a severe lack of quantitative and empirical studies that deal with the effectiveness of recently emerging technologies. This study examines the impact of employing core technologies of the 4IR on small and medium enterprises (SMEs). We used the firm-level cross-sectional data on Korean manufacturing SMEs, including the information on technology utilization. The stochastic production frontier estimation with selectivity correction is employed, besides matching technique to obtain unbiased estimates on technology efficiency. The empirical analysis finds that adopting emerging technologies enhances the productivity of SMEs. After observed and unobserved factors are controlled, the technical efficiency of adopters is higher by more than 26% on average, compared to non-adopters. Moreover, if the gap among production frontiers is considered, the difference in productivity would rise further. Additionally, a strategic alliance is a crucial route for SMEs to accept new technologies.

A sample selection method specific to unknown test samples for calibration and validation sets based on spectra similarity

As is known to all, the construction of calibration and validation sets is of great importance for how to select representative samples into subsets so that the calibration model can be built, evaluated and predicted effectively for model development. In this study, a method was proposed for the calibration and validation sets constructed by selecting samples maximally similar to the test samples based on the spectra data. Both the Euclidean distance and Mahalanobis distance were attempted to estimate the spectra similarity. The method to select samples for calibration is more suitable and specific to unknown test samples in practical applications, thus improving the measurement accuracy. In addition, the optimization of calibration set size was carried out to avoid the influence of unnecessary samples. Two data sets of Salvia miltiorrhiza (S. miltiorrhiza) and corn by near infrared spectroscopy (NIR) were used to test the performance of the proposed method compared with two typical sample-selection algorithms, Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). The experimental results indicated that the proposed method could select a more targeted set of samples for the unknown test samples and had the superior predictive performance to the KS and SPXY methods.

Race, ethnicity and nativity and the prestige of colleges attended

Although much literature examines racial/ethnic variation in college attendance, comparable research on the prestige of colleges attended is quite limited. Of particular interest are the colleges attended by Asian and Hispanic Americans, two populations with varied education outcomes across ethnicity and nativity. The analysis draws on a diverse sample from the National Longitudinal Study of Adolescent to Adult Health to estimate OLS and Heckman selection models of prestige of the bachelor's institution attended among current college enrollees (Wave III) and graduates (Wave IV). Across all model specifications Chinese Americans tend to enroll and graduate from more prestigious colleges than Whites and most other racial/ethnic-nativity groups in the analysis. In contrast, economic disadvantage accounts for Mexican Americans' enrollment at less prestigious colleges than Whites. These findings suggest the important role of college prestige in stratification, especially for specific Asian American populations.

Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the conversion time that patients possibly convert to the severe stage, for designing effective treatment plans and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time formulated as a classification task, and if yes, the conversion time will be predicted formulated as a classification task. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of the high-dimensional data and learn the shared information across two tasks, i.e., the classification and the regression. To our knowledge, this study is the first work to jointly predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 408 chest computed tomography (CT) scans. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the conversion time.

Feasibility of satellite image and GIS sampling for population representative surveys: a case study from rural Guatemala

Background

Population-representative household survey methods require up-to-date sampling frames and sample designs that minimize time and cost of fieldwork especially in low- and middle-income countries. Traditional methods such as multi-stage cluster sampling, random-walk, or spatial sampling can be cumbersome, costly or inaccurate, leading to well-known biases. However, a new tool, Epicentre’s Geo-Sampler program, allows simple random sampling of structures, which can eliminate some of these biases. We describe the study design process, experiences and lessons learned using Geo-Sampler for selection of a population representative sample for a kidney disease survey in two sites in Guatemala.

Results

We successfully used Epicentre’s Geo-sampler tool to sample 650 structures in two semi-urban Guatemalan communities. Overall, 82% of sampled structures were residential and could be approached for recruitment. Sample selection could be conducted by one person after 30 min of training. The process from sample selection to creating field maps took approximately 40 h.

Conclusion

In combination with our design protocols, the Epicentre Geo-Sampler tool provided a feasible, rapid and lower-cost alternative to select a representative population sample for a prevalence survey in our semi-urban Guatemalan setting. The tool may work less well in settings with heavy arboreal cover or densely populated urban settings with multiple living units per structure. Similarly, while the method is an efficient step forward for including non-traditional living arrangements (people residing permanently or temporarily in businesses, religious institutions or other structures), it does not account for some of the most marginalized and vulnerable people in a population–the unhoused, street dwellers or people living in vehicles.

No gain in pain: psychological well-being, participation, and wages in the BHPS

Accounting for endogeneity, unobserved heterogeneity, and sample selection in an unified framework, we investigate the effect of psychological well-being on wages and labour market participation using a panel from the British Household Panel Survey. We find the effect of psychological well-being on labour market outcomes to differ across gender. In particular, psychological distress significantly reduces participation across genders, but, conditional on participation, has a significant negative effect on hourly wages only in the female sample.

Ethnic density, immigrant enclaves, and Latino health risks: A propensity score matching approach

Whether minority concentration in a neighborhood exposes residents to, or protects them from, health risks has generated burgeoning scholarly interests; yet endogeneity as a result of neighborhood selection largely remains unclear in the literature. This study addresses such endogeneity and simultaneously investigates the roles of co-ethnic density and immigrant enclaves in influencing high blood pressure and high cholesterol level among Latinos, the largest minority group in the United States. Pooled cross-sectional data that included both native and foreign-born Latinos of Puerto Rican, Mexican, and other origins (N = 1563) from the 2006 and 2008 Southeastern Pennsylvania Household Health Survey were linked to census-tract profiles from the 2005-2009 American Community Survey. Results from both multilevel regression and propensity score matching analysis confirmed the deleterious effect of residential co-ethnic density on Latino adults' health risks over and above individual risk factors. We also found selection bias associated with the observed protective effect of immigrant concentration, which is likely a result of residential preference.

Why sample selection matters in exploratory factor analysis: implications for the 12-item World Health Organization Disability Assessment Schedule 2.0

BACKGROUND

Sample selection can substantially affect the solutions generated using exploratory factor analysis. Validation studies of the 12-item World Health Organization (WHO) Disability Assessment Schedule 2.0 (WHODAS 2.0) have generally involved samples in which substantial proportions of people had no, or minimal, disability. With the WHODAS 2.0 oriented towards measuring disability across six life domains (cognition, mobility, self-care, getting along, life activities, and participation in society), performing factor analysis with samples of people with disability may be more appropriate. We determined the influence of the sampling strategy on (a) the number of factors extracted and (b) the factor structure of the WHODAS 2.0.

METHODS

Using data from adults aged 50+ from the six countries in Wave 1 of the WHO's longitudinal Study on global AGEing and adult health (SAGE), we repeatedly selected samples (n = 750) using two strategies: (1) simple random sampling that reproduced nationally representative distributions of WHODAS 2.0 summary scores for each country (i.e., positively skewed distributions with many zero scores indicating the absence of disability), and (2) stratified random sampling with weights designed to obtain approximately symmetric distributions of summary scores for each country (i.e. predominantly including people with varying degrees of disability).

RESULTS

Samples with skewed distributions typically produced one-factor solutions, except for the two countries with the lowest percentages of zero scores, in which the majority of samples produced two factors. Samples with approximately symmetric distributions, generally produced two- or three-factor solutions. In the two-factor solutions, the getting along domain items loaded on one factor (commonly with a cognition domain item), with remaining items loading on a second factor. In the three-factor solutions, the getting along and self-care domain items loaded separately on two factors and three other domains (mobility, life activities, and participation in society) on the third factor; the cognition domain items did not load together on any factor.

CONCLUSIONS

High percentages of participants with no disability (i.e., zero scores) produce heavily censored data (i.e., floor effects), limiting data heterogeneity and reducing the numbers of factors retained. The WHODAS 2.0 appears to have multiple closely-related factors. Samples of convenience and those collected for other purposes (e.g., general population surveys) would usually be inadequate for validating measures using exploratory factor analysis.

Multimodal manifold-regularized transfer learning for MCI conversion prediction

As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.

Consumer response to media information: the case of grapefruit-medicine interaction

This study measured the effect of media exposure on grapefruit/grapefruit juice consumption changes, in particular grapefruit-medicine interaction. Respondents' attitudes about health news on television and the internet were measured to account for consumers exposed versus not exposed to such information. Results of a sample selection model show that consumer attitudes toward health news were significantly related to exposure to media information. Also, news exposure about grapefruit-medicine interaction has a tendency to result in reduced grapefruit consumption. Consumers who are directly affected by the medication interaction significantly react to the news, and the effect varies by age. Even though consumer's age was positively related to the probability of increased grapefruit consumption, when consumers took the medication, consumer's age was negatively related to the probability of increased grapefruit consumption.

Active travel intervention and physical activity behaviour: an evaluation

A physically active lifestyle is an important contributor to individual health and well-being. The evidence linking higher physical activity levels with better levels of morbidity and mortality is well understood. Despite this, physical inactivity remains a major global risk factor for mortality and, consequently, encouraging individuals to pursue physically active lifestyles has been an integral part of public health policy in many countries. Physical activity promotion and interventions are now firmly on national health policy agendas, including policies that promote active travel such as walking and cycling. This study evaluates one such active travel initiative, the Smarter Choices, Smarter Places programme in Scotland, intended to encourage uptake of walking, cycling and the use of public transport as more active forms of travel. House to house surveys were conducted before and after the programme intervention, in May/June 2009 and 2012 (12,411 surveys in 2009 and 9542 in 2012), for the evaluation of the programme. This paper analyses the physical activity data collected, focussing on what can be inferred from the initiative with regards to adult uptake of physical activity participation and whether, for those who participated in physical activity, the initiative impacted on meeting recommended physical activity guidelines. The results suggest that the initiative impacted positively on the likelihood of physical activity participation and meeting the recommended physical activity guidelines. Individuals in the intervention areas were on average 6% more likely to meet the physical activity guidelines compared to individuals in the non intervention areas. However, the absolute prevalence of physical activity participation declined in both intervention and control areas over time. Our evaluation of this active transport initiative indicates that similar programmes may aid in contributing to achieving physical activity targets and adds to the international evidence base on the benefits of active travel interventions.

Hospital readmission rates: signal of failure or success?

Hospital readmission rates are increasingly used as signals of hospital performance and a basis for hospital reimbursement. However, their interpretation may be complicated by differential patient survival rates. If patient characteristics are not perfectly observable and hospitals differ in their mortality rates, then hospitals with low mortality rates are likely to have a larger share of un-observably sicker patients at risk of a readmission. Their performance on readmissions will then be underestimated. We examine hospitals' performance relaxing the assumption of independence between mortality and readmissions implicitly adopted in many empirical applications. We use data from the Hospital Episode Statistics on emergency admissions for fractured hip in 290,000 patients aged 65 and over from 2003 to 2008 in England. We find evidence of sample selection bias that affects inference from traditional models. We use a bivariate sample selection model to allow for the selection process and the dichotomous nature of the outcome variables.