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Nevin Manimala Statistics

Evaluation and categorisation of individual patients based on white matter profiles: Single-patient diffusion data interpretation in neurodegeneration

J Neurol Sci. 2021 Jul 21;428:117584. doi: 10.1016/j.jns.2021.117584. Online ahead of print.

ABSTRACT

The majority of radiology studies in neurodegenerative conditions infer group-level imaging traits from group comparisons. While this strategy is helpful to define phenotype-specific imaging signatures for academic use, the meaningful interpretation of single scans of individual subjects is more important in everyday clinical practice. Accordingly, we present a computational method to evaluate individual subject diffusion tensor data to highlight white matter integrity alterations. Fifty white matter tracts were quantitatively evaluated in 132 patients with amyotrophic lateral sclerosis (ALS) with respect to normative values from 100 healthy subjects. Fractional anisotropy and radial diffusivity alterations were assessed individually in each patient. The approach was validated against standard tract-based spatial statistics and further scrutinised by the assessment of 78 additional data sets with a blinded diagnosis. Our z-score-based approach readily detected white matter degeneration in individual ALS patients and helped to categorise single subjects with a ‘blinded diagnosis’ as likely ‘ALS’ or ‘control’. The group-level inferences from the z-score-based approach were analogous to the standard TBSS output maps. The benefit of the z-score-based strategy is that it enables the interpretation of single DTI datasets as well as the comparison of study groups. Outputs can be summarised either visually by highlighting the affected tracts, or, listing the affected tracts in a text file with reference to normative data, making it particularly useful for clinical applications. While individual diffusion data cannot be visually appraised, our approach provides a viable framework for single-subject imaging data interpretation.

PMID:34315000 | DOI:10.1016/j.jns.2021.117584

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Nevin Manimala Statistics

Elucidation of familial relationships using hair shaft proteomics

Forensic Sci Int Genet. 2021 Jul 17;54:102564. doi: 10.1016/j.fsigen.2021.102564. Online ahead of print.

ABSTRACT

This study examines the potential of hair shaft proteomic analysis to delineate genetic relatedness. Proteomic profiling and amino acid sequence analysis provide information for quantitative and statistically-based analysis of individualization and sample similarity. Protein expression levels are a function of cell-specific transcriptional and translational programs. These programs are greatly influenced by an individual’s genetic background, and are therefore influenced by familial relatedness as well as ancestry and genetic disease. Proteomic profiles should therefore be more similar among related individuals than unrelated individuals. Likewise, profiles of genetically variant peptides that contain single amino acid polymorphisms, the result of non-synonymous SNP alleles, should behave similarly. The proteomically-inferred SNP alleles should also provide a basis for calculation of combined paternity and sibship indices. We test these hypotheses using matching proteomic and genetic datasets from a family of two adults and four siblings, one of which has a genetic condition that perturbs hair structure and properties. We demonstrate that related individuals, compared to those who are unrelated, have more similar proteomic profiles, profiles of genetically variant peptides and higher combined paternity indices and combined sibship indices. This study builds on previous analyses of hair shaft protein profiling and genetically variant peptide profiles in different real-world scenarios including different human hair shaft body locations and pigmentation status. It also validates the inclusion of proteomic information with other biomolecular substrates in forensic hair shaft analysis, including mitochondrial and nuclear DNA.

PMID:34315035 | DOI:10.1016/j.fsigen.2021.102564

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Nevin Manimala Statistics

Statistical models for firearm and tool mark image comparisons based on the congruent matching cells (CMC) method

Forensic Sci Int. 2021 Jul 20;326:110912. doi: 10.1016/j.forsciint.2021.110912. Online ahead of print.

ABSTRACT

In the branch of forensic science known as firearm evidence identification, various similarity scores have been proposed to compare firearm marks. Some similarity score comparisons, for example, congruent matching cells (CMC) method, are based on pass-or-fail tests. The CMC method compares the pairwise topography images of breech face impressions, from which the similarity score is derived for quantifying their topography similarity. For an image pair, the CMC method determines a certain number of correlated cell pairs. Next, each correlated pair is determined to be a congruent match cell (CMC) pair, or not based on several identification parameters. The number of CMC pairs as a threshold is required so that the two images of surface topographies can be either identified as matching or determined to be non-matching. To reliably estimate error rates or evaluate likelihood ratio (LR), the key is to find an appropriate probability distribution for the frequency distribution of the observed CMC results. This paper discusses four statistical models for CMC measurements, which are binomial and three binomial-related probability distributions. In previous studies, for a sequence of binomial distributed or other binomial-related distributed random variables (r.v.), the number of Bernoulli trials N for each r.v. is assumed to be the same. However, in practice, N(the number of cell pairs in an image pair) varies from one r.v. (or one image pair) to another. In that case, the term, frequency function, of the CMC results is not appropriate. In this paper, the generalized frequency function is introduced to depict the behavior of the CMC values and its limiting distribution is provided. Based on that, nonlinear regression models are used to estimate the model parameters. The methodology is applied to a set of actual CMC values of fired cartridge cases.

PMID:34314987 | DOI:10.1016/j.forsciint.2021.110912

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Nevin Manimala Statistics

Psychosocial functioning in integrated treatment of co-occurring posttraumatic stress disorder and alcohol use disorder

J Psychiatr Res. 2021 Jul 22;142:40-47. doi: 10.1016/j.jpsychires.2021.07.036. Online ahead of print.

ABSTRACT

Co-occurring posttraumatic stress disorder and alcohol use disorder (PTSD/AUD) is associated with poorer psychosocial functioning than either disorder alone; however, it is unclear if psychosocial functioning improves in treatment for PTSD/AUD. This study examined if psychosocial functioning improved in integrated treatments for PTSD/AUD, and if changes in PTSD severity and percentage heavy drinking days (PHDD) during treatment were associated with functioning outcomes. 119 veterans with PTSD/AUD randomized to receive either Concurrent Treatment of PTSD and Substance Use Disorders using Prolonged Exposure or Seeking Safety completed measures of functioning (Medical Outcomes Survey SF-36), PTSD (Clinician Administered PTSD Scale for DSM-5), and alcohol use (Timeline Follow-Back) at baseline, posttreatment, 3- and 6-month follow-ups. Our findings suggest that psychosocial functioning improved to a statistically significant degree with no significant differences between conditions. Reductions in PTSD severity during treatment were associated with psychosocial functioning improvements, whereas reductions in PHDD were associated with improvement in role impairment at posttreatment. Although psychosocial functioning improves to a statistically significant degree in interventions designed to treat PTSD/AUD, these improvements do not represent clinically meaningful improvements in patients’ abilities to navigate important roles. Findings underscore the need to study how to best treat psychosocial functioning impairment in PTSD/AUD.

PMID:34314993 | DOI:10.1016/j.jpsychires.2021.07.036

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Nevin Manimala Statistics

Visually Navigated Bronchoscopy using three cycle-Consistent generative adversarial network for depth estimation

Med Image Anal. 2021 Jul 18;73:102164. doi: 10.1016/j.media.2021.102164. Online ahead of print.

ABSTRACT

[Background] Electromagnetically Navigated Bronchoscopy (ENB) is currently the state-of-the art diagnostic and interventional bronchoscopy. CT-to-body divergence is a critical hurdle in ENB, causing navigation error and ultimately limiting the clinical efficacy of diagnosis and treatment. In this study, Visually Navigated Bronchoscopy (VNB) is proposed to address the aforementioned issue of CT-to-body divergence. [Materials and Methods] We extended and validated an unsupervised learning method to generate a depth map directly from bronchoscopic images using a Three Cycle-Consistent Generative Adversarial Network (3cGAN) and registering the depth map to preprocedural CTs. We tested the working hypothesis that the proposed VNB can be integrated to the navigated bronchoscopic system based on 3D Slicer, and accurately register bronchoscopic images to pre-procedural CTs to navigate transbronchial biopsies. The quantitative metrics to asses the hypothesis we set was Absolute Tracking Error (ATE) of the tracking and the Target Registration Error (TRE) of the total navigation system. We validated our method on phantoms produced from the pre-procedural CTs of five patients who underwent ENB and on two ex-vivo pig lung specimens. [Results] The ATE using 3cGAN was 6.2 +/- 2.9 [mm]. The ATE of 3cGAN was statistically significantly lower than that of cGAN, particularly in the trachea and lobar bronchus (p < 0.001). The TRE of the proposed method had a range of 11.7 to 40.5 [mm]. The TRE computed by 3cGAN was statistically significantly smaller than those computed by cGAN in two of the five cases enrolled (p < 0.05). [Conclusion] VNB, using 3cGAN to generate the depth maps was technically and clinically feasible. While the accuracy of tracking by cGAN was acceptable, the TRE warrants further investigation and improvement.

PMID:34314953 | DOI:10.1016/j.media.2021.102164

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Nevin Manimala Statistics

How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region

J Environ Manage. 2021 Jul 24;297:113344. doi: 10.1016/j.jenvman.2021.113344. Online ahead of print.

ABSTRACT

Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms. In the present study, fifteen flood conditioning parameters were generated from both coarse and high resolution datasets. Then, the ANN-multilayer perceptron (MLP), random forest (RF), bagging (B)-MLP, B-gaussian processes (B-GP) and B-SMOreg algorithms were used to integrate the flood conditioning parameters for generating the flood susceptible models. Furthermore, the influence of flood conditioning parameters on the modelling of flood susceptibility was investigated by proposing an ROC based sensitivity analysis. The validation of flood susceptibility models is also another challenge. In the present study, we proposed an index of flood vulnerability model to validate flood susceptibility models along with conventional statistical techniques, such as the ROC curve. Results showed that the coarse resolution based flood susceptibility MLP model has appeared as the best model (area under curve: 0.94) and it has predicted 11.65 % of the area as very high flood susceptible zones (FSz), followed by RF, B-MLP, B-GP, and B-SMOreg. Similarly, the high resolution based flood susceptibility model using MLP has predicted 19.34 % of areas as very high flood susceptible zones, followed by RF (14.32 %),B-MLP (14.88 %), B-GP, and B-SMOreg. On the other hand, ROC based sensitivity analysis showed that elevation influences flood susceptibility largely for coarse and high resolution based models, followed by drainage densityand flow accumulation. In addition, the accuracy assessment using the IFV model revealed that the MLP model outperformed all other models in the case of a high resolution imageThe coarser resolution image’s performance level is acceptable but quite low. So, the study recommended the use of high resolution images for developing a machine learning algorithm based flood susceptibility model. As the study has clearly identified the areas of higher flood susceptibility and the dominant influencing factors for flooding, this could be used as a good database for flood management.

PMID:34314957 | DOI:10.1016/j.jenvman.2021.113344

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Nevin Manimala Statistics

Genetically determined selenium concentrations and risk for autoimmune diseases

Nutrition. 2021 Jun 24;91-92:111391. doi: 10.1016/j.nut.2021.111391. Online ahead of print.

ABSTRACT

OBJECTIVE: Observational epidemiologic studies have reported a relationship between selenium status and risk for autoimmune diseases. However, the associations are susceptible to confounding or reverse causality. Thus, the aim of this study was to investigate the potential causal associations of selenium concentrations with the risk for common autoimmune diseases using a two-sample Mendelian randomization (MR) design.

METHODS: A meta-analysis of genome-wide association studies (GWASs) of selenium among 9639 individuals of European ancestry was used to identify genetic instruments. Summary statistics of systemic lupus erythematosus, rheumatoid arthritis, and inflammatory bowel disease were obtained from publicly available GWASs, respectively. We conducted MR study using the inverse-variance weighted method, supplemented with weighted median and likelihood-based methods as sensitivity analysis. Cochran Q test and MR-Egger regression were used to detect heterogeneity and potential directional pleiotropy. MR-Pleiotropy RESidual Sum and Outlier test was used to identify outlier single-nucleotide polymorphisms.

RESULTS: Genetically predicted high selenium level was associated with a decreased risk for SLE (odds ratio, 0.85; 95% confidence interval, 0.77-0.93; P = 0.001) per natural log-transformed selenium concentrations, with similar results in sensitivity analyses. No evidence of heterogeneity, pleiotropy, or outlier single-nucleotide polymorphisms were detected (all P > 0.05). However, genetically determined selenium concentrations may be not associated with risk for rheumatoid arthritis or inflammatory bowel disease in the primary analysis and subsequent sensitivity analyses.

CONCLUSIONS: The present study suggested a protective role of selenium on the risk for systemic lupus erythematosus. Further studies are warranted to elucidate the underlying mechanisms.

PMID:34314985 | DOI:10.1016/j.nut.2021.111391

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Nevin Manimala Statistics

The association between personalities, alternative breeding strategies, and reproductive success in dunnocks

J Evol Biol. 2021 Jul 27. doi: 10.1111/jeb.13906. Online ahead of print.

ABSTRACT

Although consistent between-individual differences in behaviour (i.e. animal personality) are ubiquitous in natural populations, relatively few studies have examined how personalities influence the formation of social relationships. Yet, behavioural characteristics of both sexes might be key when it comes to pair-bond formation, and the cooperation with partners to successfully rear offspring. We here use a wild population of dunnocks (Prunella modularis) to first investigate whether individuals mate non-randomly (i.e. assortative mating) with regard to four behavioural traits – flight-initiation distance (FID), provisioning, activity, and vigilance – that differ in repeatability and have previously been associated with mating patterns and fitness in other species. Second, we test whether an individual’s FID is associated with variability in the dunnocks’ mating system (i.e. monogamous pairs vs. polygamous groups). Finally, we determine whether FID and provisioning of males and females associate with their reproductive success. We found no statistical support for assortative mating in FID between males and females. Interestingly, in polygamous groups, co-breeding males differed in their FIDs with dominant alpha-males having significantly shorter FIDs compared to subordinate beta-males. Moreover, there was evidence for assortative mating in provisioning for alpha-males and females in polygamous groups. We also found that male provisioning influenced reproductive success of both sexes, while female provisioning rates only positively correlated with her own but not their partner(s) reproductive output. Our results suggest that personality differences may have important implications for social relationships, the emergence of different mating patterns and ultimately reproductive success within populations.

PMID:34314544 | DOI:10.1111/jeb.13906

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Nevin Manimala Statistics

Sudden cardiac death in childhood hypertrophic cardiomyopathy is best predicted by a combination of ECG Risk-score and HCMRisk-Kids score

Acta Paediatr. 2021 Jul 27. doi: 10.1111/apa.16045. Online ahead of print.

ABSTRACT

AIM: To compare risk-algorithms (HCMRisk-Kids, ECG Risk-score) in hypertrophic cardiomyopathy (HCM) without syndrome association (ns-HCM), and with Noonan-like syndromes (RAS-HCM).

METHODS: A national paediatric HCM-cohort (n=151), presenting <19y of age, mean follow-up 13.3y, from all Swedish centres of Paediatric Cardiology (presenting 1972-2015), with 41 RAS-HCM-patients (61% males), and 110 ns-HCM-patients (68% familial; 65% males). The end-point was a composite of sudden cardiac death and re-suscitated cardiac arrest (SCD/CA). Risk-factors were studied with Cox-hazard regression, and ROC-curve analysis (C-statistic).

RESULTS: There were 33 SCD/CA, 27/110 in ns-HCM and 6/41 in RAS-HCM (p=0.27). In ns-HCM HCMRisk-Kids ≥6% at diagnosis had C-statistic of 0.69 for predicting SCD/CA during first 5y of follow-up, and positive predictive value (PPV) of 22%. After 7y of age (HCMRisk-Kids7plus) C-statistic was 0.76. ECG Risk-score ≥6 at diagnosis had C-statistic 0.87 and PPV of 31%. Independent risk factors for SCD/CA were HCMRisk-Kids7plus score (p=0.005) and ECG risk-score (p<0.001), whereas early beta-blocker dose (p=0.001) and myectomy (p=0.004) reduced risk. The sum of HCMRisk-Kids7yplus and ECG Risk-score7yplus ≥14 best predicted SCD/CA within 5y in ns-HCM with C-statistic of 0.90 [0.83-0.96], sensitivity 100%, and PPV 38%.

CONCLUSIONS: Combining the ECG Risk-score with HCMRisk-Kids improves risk-stratification in ns-HCM, and shows promise in RAS-HCM.

PMID:34314540 | DOI:10.1111/apa.16045

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Nevin Manimala Statistics

Assessment of the global noise algorithm for automatic noise measurement in head CT examinations

Med Phys. 2021 Jul 27. doi: 10.1002/mp.15133. Online ahead of print.

ABSTRACT

PURPOSE: The global noise (GN) algorithm has been previously introduced as a method for automatic noise measurement in clinical CT images. Accuracy of the GN algorithm has been assessed in abdomen CT exams, but not in any other body part until now. This work assesses the GN algorithm accuracy in automatic noise measurement in head CT exams.

METHODS: A publicly available image dataset of 99 head CT exams was used to evaluate the accuracy of the GN algorithm in comparison to reference noise values. Reference noise values were acquired using a manual noise measurement procedure. The procedure used a consistent instruction protocol and multiple observers to miti-gate the influence of intra- and inter-observer variation, resulting in precise reference values. Optimal GN algorithm parameter values were determined. The GN algorithm accuracy and the corresponding statistical confidence interval were determined. The GN measurements were compared across the 6 different scan protocols used in this dataset. The correlation of GN to patient head size was also assessed using a linear regression model, and the CT scanner’s x-ray beam quality was inferred from the model fit parameters.

RESULTS: Across all head CT exams in the dataset, the range of reference noise was 2.9 – 10.2 HU. A precision of ±0:33 HU was achieved in the reference noise measurements. After optimization, then GN algorithm had a RMS error 0.34 HU corresponding to a percent RMS error of 6.6%. The GN algorithm had a bias of +3.9%. Statistically significant differences in GN were detected in 11 out of the 15 different pairs of scan protocols. The GN measurements were correlated with head size with a statistically significant regression slope parameter (p < 10-7 ). The CT scanner x-ray beam quality estimated from the slope parameter was 3.5 cm water HVL (2.8{4.8 cm 95% C.I.).

CONCLUSION: The GN algorithm was validated for application in head CT exams. The GN algorithm was accurate in comparison to reference manual measurement, with errors comparable to inter-observer variation in manual measurement. The GN algorithm can detect noise differences in exams performed on different scanner models or using different scan protocols. The trend of GN across patients of different head sizes closely follows that predicted by a physical model of x-ray attenuation.

PMID:34314528 | DOI:10.1002/mp.15133