Association between acoustic features and brain volumes: the Framingham Heart Study

IntroductionAlthough brain magnetic resonance imaging (MRI) is a valuable tool for investigating structural changes in the brain associated with neurodegeneration, the development of non-invasive and cost-effective alternative methods for detecting early cognitive impairment is crucial. The human voice has been increasingly used as an indicator for effectively detecting cognitive disorders, but it remains unclear whether acoustic features are associated with structural neuroimaging.MethodsThis study aims to investigate the association between acoustic features and brain volume and compare the predictive power of each for mild cognitive impairment (MCI) in a large community-based population. The study included participants from the Framingham Heart Study (FHS) who had at least one voice recording and an MRI scan. Sixty-five acoustic features were extracted with the OpenSMILE software (v2.1.3) from each voice recording. Nine MRI measures were derived according to the FHS MRI protocol. We examined the associations between acoustic features and MRI measures using linear regression models adjusted for age, sex, and education. Acoustic composite scores were generated by combining acoustic features significantly associated with MRI measures. The MCI prediction ability of acoustic composite scores and MRI measures were compared by building random forest models and calculating the mean area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation.ResultsThe study included 4,293 participants (age 57 ± 13 years, 53.9% women). During 9.3 ± 3.7 years follow-up, 106 participants were diagnosed with MCI. Seven MRI measures were significantly associated with more than 20 acoustic features after adjusting for multiple testing. The acoustic composite scores can improve the AUC for MCI prediction to 0.794, compared to 0.759 achieved by MRI measures.DiscussionWe found multiple acoustic features were associated with MRI measures, suggesting the potential for using acoustic features as easily accessible digital biomarkers for the early diagnosis of MCI

Nonlinear dimensionality reduction methods for synthetic biology biobricks’ visualization

Background: Visualizing data by dimensionality reduction is an important strategy in Bioinformatics, which could help to discover hidden data properties and detect data quality issues, e.g. data noise, inappropriately labeled data, etc. As crowdsourcing-based synthetic biology databases face similar data quality issues, we propose to visualize biobricks to tackle them. However, existing dimensionality reduction methods could not be directly applied on biobricks datasets. Hereby, we use normalized edit distance to enhance dimensionality reduction methods, including Isomap and Laplacian Eigenmaps. Results: By extracting biobricks from synthetic biology database Registry of Standard Biological Parts, six combinations of various types of biobricks are tested. The visualization graphs illustrate discriminated biobricks and inappropriately labeled biobricks. Clustering algorithm K-means is adopted to quantify the reduction results. The average clustering accuracy for Isomap and Laplacian Eigenmaps are 0.857 and 0.844, respectively. Besides, Laplacian Eigenmaps is 5 times faster than Isomap, and its visualization graph is more concentrated to discriminate biobricks. Conclusions: By combining normalized edit distance with Isomap and Laplacian Eigenmaps, synthetic biology biobircks are successfully visualized in two dimensional space. Various types of biobricks could be discriminated and inappropriately labeled biobricks could be determined, which could help to assess crowdsourcing-based synthetic biology databases’ quality, and make biobricks selection. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1484-4) contains supplementary material, which is available to authorized users

The Clinical and Medicolegal Analysis of Electrical Shocked Rats: Based on the Serological and Histological Methods

This research was aimed at discovering the serological and histological changes in cardiac and hepatic tissue after electric shock. The CK-MB, ALT, and AMS indexes were tested with serological methods. Moreover, the Bcl-2, Bax, and Hsp-60 expression levels were carefully measured. An electrical injury model was established by giving rats electric shocks at 110 V with alternating electric current. Blood samples from the rats were analyzed for the biochemical indexes. The degrees of pathological changes in the heart and liver were evaluated using IHC staining for Bcl-2, Bax, and Hsp-60. The levels of CK-MB in the electrical injury group rapidly peaked at 0.5 hours after the electric shock. Additionally, the levels of Bcl-2, Bax, and Hsp-60 in the cardiac and hepatic tissues changed regularly after the electrical injury and exhibited apparent differences from the levels in the control group. CK-MB, ALT, and AMS were altered regularly after electric shock, and these results provide significant information for clinical and medicolegal practice. This research has shed light on the assessment of electrical injury without obvious electrical burns. Furthermore, the findings obtained for Bcl-2/Bax and Hsp-60 can also facilitate pathological diagnosis and the identification of antemortem and postmortem electrical injury

Variation in the ovine KAP8-1 gene affects wool fibre uniformity in Chinese Tan sheep

Chinese Tan sheep are known for producing ‘spring-like’ crimped wool fleeces that consist of both fine wool fibres (non-medulated) and heterotypic wool fibres (frequently medulated). The genetic basis of these unique characteristics is poorly understood. Wool fibres are comprised of hard alpha-keratins embedded in keratin-associated proteins (KAPs). Variation in the KAP8-1 gene (KRTAP8-1) was investigated in Chinese Tan sheep, and the effect of variation in the gene on four key wool traits [mean fibre diameter (MFD), fibre diameter standard deviation (FDSD), coefficient of variation of fibre diameter (CVFD) and mean fibre curvature (MFC)] was investigated for both the fine and heterotypic wool fibres. Three previously identified ovine KRTAP8-1 variants (A, D and E) were detected, but two other previously identified variants (B and C) were not present in these Tan sheep. Of the six genotypes (AA, AD, AE, DD, DE and EE) observed, the effect of the genotype on wool traits were analysed for the three (AA, AE and EE) where the genotype frequency was greater than 5%. Genotype was found to have an effect (P =  0.022) on CVFD in the fine wool. These results suggest that variation in KRTAP8-1 affects fibre uniformity in Tan sheep

Variation in the KAP8-2 gene affects wool crimp and growth in Chinese Tan sheep

The keratin-associated protein KAP8-2, is present in wool from sheep. The effect of variation in KRTAP8-2 on wool traits was investigated in Chinese Tan sheep. Two previously identified KRTAP8-2 alleles (A and B) were detected, but the frequency of B in the Tan sheep was much higher than reported previously for New Zealand (NZ) sheep. Variant A was found to be associated with both a decrease in straightened fibre-length and the ratio of straightened length to crimped length at day-35 post-partum (conventionally called ‘Er-mao’). A trend was evident between the presence/absence of A and crimped fibre-length at Er-mao, and between the presence/absence of A and fibre growth-rate from birth to Er-mao. This suggests that variation in ovine KRTAP8-2 may affect wool crimping and growth in the early life of Tan sheep

Additional file 1 of Nonlinear dimensionality reduction methods for synthetic biology biobricks’ visualization

This file contains more experiments on other types of biobricks. Besides, classification validation for the dimensionality reduction results are also included. These results are illustrated in two figures and two tables in the file. Figure S1: Dimensionality reduction results for various combinations of Plasmid backbones, Promoters, Terminators, Translational units, Protein generators, Primers by applying Isomap algorithm. Figure S2: Dimensionality reduction results for various combinations of Plasmid backbones, Promoters, Terminators, Translational units, Protein generators, Primers by applying Laplacian Eigenmaps algorithm. Table S1: Clustering accuracy comparison of dimensionality reduction results in Figures S1 and S2. Table S2: Classification accuracy comparison of dimensionality reduction results by Isomap and Laplacian Eigenmaps. (PDF 123 kb

Association Between Acoustic Features and Neuropsychological Test Performance in the Framingham Heart Study: Observational Study

BackgroundHuman voice has increasingly been recognized as an effective indicator for the detection of cognitive disorders. However, the association of acoustic features with specific cognitive functions and mild cognitive impairment (MCI) has yet to be evaluated in a large community-based population. ObjectiveThis study aimed to investigate the association between acoustic features and neuropsychological (NP) tests across multiple cognitive domains and evaluate the added predictive power of acoustic composite scores for the classification of MCI. MethodsThis study included participants without dementia from the Framingham Heart Study, a large community-based cohort with longitudinal surveillance for incident dementia. For each participant, 65 low-level acoustic descriptors were derived from voice recordings of NP test administration. The associations between individual acoustic descriptors and 18 NP tests were assessed with linear mixed-effect models adjusted for age, sex, and education. Acoustic composite scores were then built by combining acoustic features significantly associated with NP tests. The added prediction power of acoustic composite scores for prevalent and incident MCI was also evaluated. ResultsThe study included 7874 voice recordings from 4950 participants (age: mean 62, SD 14 years; 4336/7874, 55.07% women), of whom 453 were diagnosed with MCI. In all, 8 NP tests were associated with more than 15 acoustic features after adjusting for multiple testing. Additionally, 4 of the acoustic composite scores were significantly associated with prevalent MCI and 7 were associated with incident MCI. The acoustic composite scores can increase the area under the curve of the baseline model for MCI prediction from 0.712 to 0.755. ConclusionsMultiple acoustic features are significantly associated with NP test performance and MCI, which can potentially be used as digital biomarkers for early cognitive impairment monitoring