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Nevin Manimala Statistics

High variability is result of complex data workflows

A new study offers new evidence that the complexity of contemporary analytical methods in science contributes to the variability of research outcomes.
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Nevin Manimala Statistics

New study estimates the odds of life and intelligence emerging beyond our planet

Despite knowing when life first appeared on Earth, scientists still do not understand how life occurred, which has important implications for the likelihood of finding life elsewhere in the universe. A new paper shows how an analysis using a statistical technique called Bayesian inference could shed light on how complex extraterrestrial life might evolve in alien worlds.
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Nevin Manimala Statistics

Identifying light sources using artificial intelligence

Identifying sources of light plays an important role in the development of many photonic technologies, such as lidar, remote sensing, and microscopy. Traditionally, identifying light sources as diverse as sunlight, laser radiation, or molecule fluorescence has required millions of measurements, particularly in low-light environments, which limits the realistic implementation of quantum photonic technologies. Researchers demonstrated a smart quantum technology that enables a dramatic reduction in the number of measurements required to identify light sources.
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Nevin Manimala Statistics

Deformable image registration of the treatment planning CT with proton radiographies in perspective of adaptive proton therapy.

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Deformable image registration of the treatment planning CT with proton radiographies in perspective of adaptive proton therapy.

Phys Med Biol. 2020 May 04;:

Authors: Palaniappan P, Meyer S, Kamp F, Belka C, Riboldi M, Parodi K, Gianoli C

Abstract
The purpose of the work is to investigate the potentiality of using a limited number of in-room proton radiographies to compensate anatomical changes in adaptive proton therapy. The treatment planning CT is adapted to the treatment delivery scenario relying on 2D-3D deformable image registration (DIR). The proton radiographies, expressed in water equivalent thickness (WET) are simulated for both list-mode and integration-mode detector configurations in pencil beam scanning. Geometrical and analytical simulations of an anthropomorphic phantom in presence of anatomical changes due to breathing are adopted. A Monte Carlo simulation of proton radiographies based on a clinical CT image in presence of artificial anatomical changes is also considered. The accuracy of the 2D-3D DIR, calculated as root mean square error, strongly depends on the considered anatomical changes and is considered adequate for promising adaptive proton therapy when comparable to the accuracy of conventional 3D-3D DIR. In geometrical simulation, this is achieved with a minimum of eight/nine radiographies (more than 90% accuracy). Negligible improvement (~1%) is obtained with the use of 180 radiographies. Comparing different detector configurations, superior accuracy is obtained with list-mode than integration-mode max (WET with maximum occurrence) and mean (average WET weighted by occurrences). Moreover, integration-mode max performs better than integration-mode mean. Results are minimally affected by proton statistics. In analytical simulation, the anatomical changes are approximately compensated (about 60-70% accuracy) with two proton radiographies and minor improvement is observed with nine proton radiographies. In clinical data, two proton radiographies from list-mode demonstrate to perform better than nine from integration-mode (more than 100% and about 50-70% accuracy, respectively), even avoiding the finer grid spacing of the last numerical optimization stage. In conclusion, the choice of detector configuration as well as the amount and the complexity of the considered anatomical changes determine the minimum number of radiographies to be used.

PMID: 32365335 [PubMed – as supplied by publisher]

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Nevin Manimala Statistics

Bayesian group selection in logistic regression with application to MRI data analysis.

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Bayesian group selection in logistic regression with application to MRI data analysis.

Biometrics. 2020 May 04;:

Authors: Lee K, Cao X

Abstract
We consider Bayesian logistic regression models with group-structured covariates. In high-dimensional settings, it is often assumed that only a small portion of groups are significant, thus consistent group selection is of significant importance. While consistent frequentist group selection methods have been proposed, theoretical properties of Bayesian group selection methods for logistic regression models have not been investigated yet. In this paper, we consider a hierarchical group spike and slab prior for logistic regression models in high-dimensional settings. Under mild conditions, we establish strong group selection consistency of the induced posterior, which is the first theoretical result in the Bayesian literature. Through simulation studies, we demonstrate that the proposed method outperforms existing state-of-the-art methods in various settings. We further apply our method to an MRI data set for predicting Parkinson’s disease and show its benefits over other contenders. This article is protected by copyright. All rights reserved.

PMID: 32365231 [PubMed – as supplied by publisher]

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Nevin Manimala Statistics

Dynamic statistical model for predicting the risk of death among older Chinese people, using longitudinal repeated measures of the frailty index: a prospective cohort study.

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Dynamic statistical model for predicting the risk of death among older Chinese people, using longitudinal repeated measures of the frailty index: a prospective cohort study.

Age Ageing. 2020 May 04;:

Authors: Chen Q, Tang B, Zhai Y, Chen Y, Jin Z, Han H, Gao Y, Wu C, Chen T, He J

Abstract
BACKGROUND: Frailty is a common characteristic of older people with the ageing process. We aimed to develop and validate a dynamic statistical prediction model to calculate the risk of death in people aged ≥65 years, using a longitudinal frailty index (FI).
METHODS: One training dataset and three validation datasets from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were used in our study. The training dataset and validation datasets 1 to 3 included data from 9,748, 7,459, 9,093 and 6,368 individuals, respectively. We used 35 health deficits to construct the FI and a longitudinal FI based on repeated measurement of FI at every wave of the CLHLS. A joint model was used to build a dynamic prediction model considering both baseline covariates and the longitudinal FI. Areas under time-dependent receiver operating characteristic curves (AUCs) and calibration curves were employed to assess the predictive performance of the model.
RESULTS: A linear mixed-effects model used time, sex, residence (city, town, or rural), living alone, smoking and alcohol consumption to calculate a subject-specific longitudinal FI. The dynamic prediction model was built using the longitudinal FI, age, residence, sex and an FI-age interaction term. The AUCs ranged from 0.64 to 0.84, and both the AUCs and the calibration curves showed good predictive ability.
CONCLUSIONS: We developed a dynamic prediction model that was able to update predictions of the risk of death as updated measurements of FI became available. This model could be used to estimate the risk of death in individuals aged >65 years.

PMID: 32365173 [PubMed – as supplied by publisher]

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Nevin Manimala Statistics

scDoc: correcting drop-out events in single-cell RNA-seq data.

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scDoc: correcting drop-out events in single-cell RNA-seq data.

Bioinformatics. 2020 May 04;:

Authors: Ran D, Zhang S, Lytal N, An L

Abstract
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of “drop-out” events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this paper, we present a novel Single-Cell RNA-seq Drop-Out Correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells.
RESULTS: scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification, and differential expression detection in scRNA-seq data.
AVAILABILITY: R code is available at https://github.com/anlingUA/scDoc.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 32365169 [PubMed – as supplied by publisher]

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Nevin Manimala Statistics

Evaluation of biochemical profile of Chronic Kidney Disease of uncertain etiology in Sri Lanka.

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Evaluation of biochemical profile of Chronic Kidney Disease of uncertain etiology in Sri Lanka.

PLoS One. 2020;15(5):e0232522

Authors: Fernando BNTW, Sudeshika TSH, Hettiarachchi TW, Badurdeen Z, Abeysekara TDJ, Abeysundara HTK, Jayasinghe S, Ranasighe S, Nanayakkara N

Abstract
Chronic Kidney Disease of uncertain etiology (CKDu) is an endemic, disease that mostly affects young agricultural workers in the rural dry zone of Sri Lanka. This study was designed to identify specific biochemical manifestations of CKDu cases. All (119) non-dialysis definite CKDu patients in Girandurukotte and Wilgamuwa were selected. Blood and urine samples were collected and measured biochemical parameters. All analyses were performed in IBM SPSS statistics version 23 (IBM Corp, USA). The median blood pressure was normal though nearly half of the patients (45.4%) who were in the advanced stages (Stage 3b, 4 and 5) of CKDu. Patients without a history of hypertension before the diagnosis of CKDu (100%) and minimal proteinuria (26%) are similar to the previous findings. Patients without a history of diabetes before the CKDu diagnosis had high percentages of diabetes (15.7%) and pre-diabetes (59.8%) and hence indicated the possibility of uremia induced impaired glucose intolerance in the rural areas of the country. There were 62.2% patients who had low vitamin D and only a minority had evidence of bone mineral diseases. Out of liver disease markers serum glutamic pyruvic transaminases (SGPT), serum glutamic oxaloacetic transaminases (SGOT), gamma-glutamyl transferase (GGT), and Lactic acid degydrogenase (LDH) had an inverse correlation with the advancement of the disease indicating subclinical liver disease. Osmolality in serum and urine showed a discrepancy despite > 50% of CKDu patients had increased their serum osmolality. The current study supports most of the previously described manifestations of CKDu. Moreover, some specific patterns have been identified which need to be validated in a larger group.

PMID: 32365131 [PubMed – in process]

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Nevin Manimala Statistics

Evaluation of participant reluctance, confidence, and self-reported behaviors since being trained in a pharmacy Mental Health First Aid initiative.

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Evaluation of participant reluctance, confidence, and self-reported behaviors since being trained in a pharmacy Mental Health First Aid initiative.

PLoS One. 2020;15(5):e0232627

Authors: Witry M, Karamese H, Pudlo A

Abstract
In the U.S., an estimated one in five individuals experience a mental illness annually which contribute to significant human and economic cost. Pharmacists serving in a public health capacity are positioned to provide first aid level intervention to people experiencing a mental health crisis. Research on pharmacy professionals (pharmacists, technicians, students) undergoing training in Mental Health First Aid (MHFA) can provide evidence of the potential benefits of such training. The objectives of this study were to 1) describe the reluctance and confidence to intervene in mental health crises of pharmacy professionals previously trained in MHFA, 2) describe their self-reported use of MHFA behaviors since becoming trained, and 3) describe participant open-ended feedback on their MHFA training.
MATERIALS AND METHODS: An electronic survey was disseminated in May and June, 2019 using a four-email sequence to pharmacy professionals who had completed MHFA training from one of five pharmacist MHFA trainers throughout 2018. Domains included demographics, six Likert-type reluctance items, seven Likert-type confidence items for performing MHFA skills, and frequency of using a set of nine MHFA skills since being trained. Prompts collected open-ended feedback related to MHFA experiences and training. Descriptive statistics were used for scaled and multiple-choice items and a basic content analysis was performed on the open-ended items to group them into similar topics.
RESULTS: Ninety-eight out of 227 participants responded to the survey yielding a response rate of 44%. Participants reported high levels of disagreement to a set of reluctance items for intervening and overall high levels of confidence in performing a range of MHFA skills. Participant self-reported use of a set of MHFA skills ranged from 19% to 82% since being trained in MHFA. Almost half (44%) of participants had asked someone if they were considering suicide. A majority (61%) also had referred someone to resources because of a mental health crisis. Open-ended responses included positive experiences alongside important challenges to using MHFA in practice and recommendations including additional training focused on the pharmacy setting.
CONCLUSIONS: Pharmacy professionals in this evaluation reported little reluctance and high confidence related to using MHFA training and reported use of MHFA skills since being trained.

PMID: 32365115 [PubMed – in process]

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Nevin Manimala Statistics

A new linear regression-like residual for survival analysis, with application to genome wide association studies of time-to-event data.

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A new linear regression-like residual for survival analysis, with application to genome wide association studies of time-to-event data.

PLoS One. 2020;15(5):e0232300

Authors: Vieland VJ, Seok SC, Stewart WCL

Abstract
In linear regression, a residual measures how far a subject’s observation is from expectation; in survival analysis, a subject’s Martingale or deviance residual is sometimes interpreted similarly. Here we consider ways in which a linear regression-like interpretation is not appropriate for Martingale and deviance residuals, and we develop a novel time-to-event residual which does have a linear regression-like interpretation. We illustrate the utility of this new residual via simulation of a time-to-event genome-wide association study, motivated by a real study seeking genetic modifiers of Duchenne Muscular Dystrophy. By virtue of its linear regression-like characteristics, our new residual may prove useful in other contexts as well.

PMID: 32365095 [PubMed – in process]