Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble

<p>Abstract</p> <p>Background</p> <p>Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi) or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP) as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM), which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS) ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting.</p> <p>Results</p> <p>Experimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The ensemble model produces better classification performance compared to the component neural networks trained. For the three images sets HeLa, CHO and RNAi, the Random Subspace Ensembles offers the classification rates 91.20%, 98.86% and 91.03% respectively, which compares sharply with the published result 84%, 93% and 82% from a multi-purpose image classifier WND-CHARM which applied wavelet transforms and other feature extraction methods. We investigated the problem of estimation of ensemble parameters and found that satisfactory performance improvement could be brought by a relative medium dimensionality of feature subsets and small ensemble size.</p> <p>Conclusions</p> <p>The characteristics of curvelet transform of being multiscale and multidirectional suit the description of microscopy images very well. It is empirically demonstrated that the curvelet-based feature is clearly preferred to wavelet-based feature for bioimage descriptions. The random subspace ensemble of MLPs is much better than a number of commonly applied multi-class classifiers in the investigated application of phenotype recognition.</p

Spectroscopic Observations and Analysis of the Peculiar SN 1999aa

We present an extensive new time-series of spectroscopic data of the peculiar SN 1999aa in NGC 2595. Our data set includes 25 optical spectra between -11 and +58 days with respect to B-band maximum light, providing an unusually complete time history. The early spectra resemble those of a SN 1991T-like object but with a relatively strong Ca H&K absorption feature. The first clear sign of Si II 6355, characteristic of Type Ia supernovae, is found at day -7 and its velocity remains constant up to at least the first month after B-band maximum light. The transition to normal-looking spectra is found to occur earlier than in SN 1991T suggesting SN 1999aa as a possible link between SN 1991T-like and Branch-normal supernovae. Comparing the observations with synthetic spectra, doubly ionized Fe, Si and Ni are identified at early epochs. These are characteristic of SN 1991T-like objects. Furthermore, in the day -11 spectrum, evidence is found for an absorption feature which could be identified as high velocity C II 6580 or H-alpha. At the same epoch C III 4648.8 at photospheric velocity is probably responsible for the absorption feature at 4500 A. High velocity Ca is found around maximum light together with Si II and Fe II confined in a narrow velocity window. Implied constraints on supernovae progenitor systems and explosion hydrodynamical models are briefly discussed.Comment: 46 pages including 23 figures. Accepted for publication by AJ. For full-resolution figures see http://www.physto.se/~gabri/sn99aa

Brain Changes in Long-Term Zen Meditators Using Proton Magnetic Resonance Spectroscopy and Diffusion Tensor Imaging: A Controlled Study

Introduction: This work aimed to determine whether 1H magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are correlated with years of meditation and psychological variables in long-term Zen meditators compared to healthy non-meditator controls. Materials and Methods: Design. Controlled, cross-sectional study. Sample. Meditators were recruited from a Zen Buddhist monastery. The control group was recruited from hospital staff. Meditators were administered questionnaires on anxiety, depression, cognitive impairment and mindfulness. 1H-MRS (1.5 T) of the brain was carried out by exploring four areas: both thalami, both hippocampi, the posterior superior parietal lobule (PSPL) and posterior cingulate gyrus. Predefined areas of the brain were measured for diffusivity (ADC) and fractional anisotropy (FA) by MR-DTI. Results: Myo-inositol (mI) was increased in the posterior cingulate gyrus and Glutamate (Glu), N-acetyl-aspartate (NAA) and N-acetyl-aspartate/Creatine (NAA/Cr) was reduced in the left thalamus in meditators. We found a significant positive correlation between mI in the posterior cingulate and years of meditation (r = 0.518; p = .019). We also found significant negative correlations between Glu (r =20.452; p = .045), NAA (r =20.617; p = .003) and NAA/Cr (r =20.448; P = .047) in the left thalamus and years of meditation. Meditators showed a lower Apparent Diffusion Coefficient (ADC) in the left posterior parietal white matter than did controls, and the ADC was negatively correlated with years of meditation (r =20.4850, p = .0066). Conclusions: The results are consistent with the view that mI, Glu and NAA are the most important altered metabolites. This study provides evidence of subtle abnormalities in neuronal function in regions of the white matter in meditators

Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis

IMPORTANCE: Amyloid-β positron emission tomography (PET) imaging allows in vivo detection of fibrillar plaques, a core neuropathological feature of Alzheimer disease (AD). Its diagnostic utility is still unclear because amyloid plaques also occur in patients with non-AD dementia. OBJECTIVE: To use individual participant data meta-analysis to estimate the prevalence of amyloid positivity on PET in a wide variety of dementia syndromes. DATA SOURCES: The MEDLINE and Web of Science databases were searched from January 2004 to April 2015 for amyloid PET studies. STUDY SELECTION: Case reports and studies on neurological or psychiatric diseases other than dementia were excluded. Corresponding authors of eligible cohorts were invited to provide individual participant data. DATA EXTRACTION AND SYNTHESIS: Data were provided for 1359 participants with clinically diagnosed AD and 538 participants with non-AD dementia. The reference groups were 1849 healthy control participants (based on amyloid PET) and an independent sample of 1369 AD participants (based on autopsy). MAIN OUTCOMES AND MEASURES: Estimated prevalence of positive amyloid PET scans according to diagnosis, age, and apolipoprotein E (APOE) ε4 status, using the generalized estimating equations method. RESULTS: The likelihood of amyloid positivity was associated with age and APOE ε4 status. In AD dementia, the prevalence of amyloid positivity decreased from age 50 to 90 years in APOE ε4 noncarriers (86% [95% CI, 73%-94%] at 50 years to 68% [95% CI, 57%-77%] at 90 years; n = 377) and to a lesser degree in APOE ε4 carriers (97% [95% CI, 92%-99%] at 50 years to 90% [95% CI, 83%-94%] at 90 years; n = 593; P < .01). Similar associations of age and APOE ε4 with amyloid positivity were observed in participants with AD dementia at autopsy. In most non-AD dementias, amyloid positivity increased with both age (from 60 to 80 years) and APOE ε4 carriership (dementia with Lewy bodies: carriers [n = 16], 63% [95% CI, 48%-80%] at 60 years to 83% [95% CI, 67%-92%] at 80 years; noncarriers [n = 18], 29% [95% CI, 15%-50%] at 60 years to 54% [95% CI, 30%-77%] at 80 years; frontotemporal dementia: carriers [n = 48], 19% [95% CI, 12%-28%] at 60 years to 43% [95% CI, 35%-50%] at 80 years; noncarriers [n = 160], 5% [95% CI, 3%-8%] at 60 years to 14% [95% CI, 11%-18%] at 80 years; vascular dementia: carriers [n = 30], 25% [95% CI, 9%-52%] at 60 years to 64% [95% CI, 49%-77%] at 80 years; noncarriers [n = 77], 7% [95% CI, 3%-18%] at 60 years to 29% [95% CI, 17%-43%] at 80 years. CONCLUSIONS AND RELEVANCE: Among participants with dementia, the prevalence of amyloid positivity was associated with clinical diagnosis, age, and APOE genotype. These findings indicate the potential clinical utility of amyloid imaging for differential diagnosis in early-onset dementia and to support the clinical diagnosis of participants with AD dementia and noncarrier APOE ε4 status who are older than 70 years

Models of Traumatic Cerebellar Injury

Traumatic brain injury (TBI) is a major cause of morbidity and mortality worldwide. Studies of human TBI demonstrate that the cerebellum is sometimes affected even when the initial mechanical insult is directed to the cerebral cortex. Some of the components of TBI, including ataxia, postural instability, tremor, impairments in balance and fine motor skills, and even cognitive deficits, may be attributed in part to cerebellar damage. Animal models of TBI have begun to explore the vulnerability of the cerebellum. In this paper, we review the clinical presentation, pathogenesis, and putative mechanisms underlying cerebellar damage with an emphasis on experimental models that have been used to further elucidate this poorly understood but important aspect of TBI. Animal models of indirect (supratentorial) trauma to the cerebellum, including fluid percussion, controlled cortical impact, weight drop impact acceleration, and rotational acceleration injuries, are considered. In addition, we describe models that produce direct trauma to the cerebellum as well as those that reproduce specific components of TBI including axotomy, stab injury, in vitro stretch injury, and excitotoxicity. Overall, these models reveal robust characteristics of cerebellar damage including regionally specific Purkinje cell injury or loss, activation of glia in a distinct spatial pattern, and traumatic axonal injury. Further research is needed to better understand the mechanisms underlying the pathogenesis of cerebellar trauma, and the experimental models discussed here offer an important first step toward achieving that objective