Predicting human protein function with multitask deep neural networks

Machine learning methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remains unannotated despite the extensive use of functional assignments based on sequence similarity. One major bottleneck supervised learning faces in protein function prediction is the structured, multi-label nature of the problem, because biological roles are represented by lists of terms from hierarchically organised controlled vocabularies such as the Gene Ontology. In this work, we build on recent developments in the area of deep learning and investigate the usefulness of multitask deep neural networks (MTDNN), which consist of upstream shared layers upon which are stacked in parallel as many independent modules (additional hidden layers with their own output units) as the number of output GO terms (the tasks). MTDNN learns individual tasks partially using shared representations and partially from task-specific characteristics. When no close homologues with experimentally validated functions can be identified, MTDNN gives more accurate predictions than baseline methods based on annotation frequencies in public databases or homology transfers. More importantly, the results show that MTDNN binary classification accuracy is higher than alternative machine learning-based methods that do not exploit commonalities and differences among prediction tasks. Interestingly, compared with a single-task predictor, the performance improvement is not linearly correlated with the number of tasks in MTDNN, but medium size models provide more improvement in our case. One of advantages of MTDNN is that given a set of features, there is no requirement for MTDNN to have a bootstrap feature selection procedure as what traditional machine learning algorithms do. Overall, the results indicate that the proposed MTDNN algorithm improves the performance of protein function prediction. On the other hand, there is still large room for deep learning techniques to further enhance prediction ability

Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique.

To enable application of non-Gaussian diffusion magnetic resonance imaging (dMRI) techniques in large-scale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multi-shell b-value diffusion data, Quasi-Diffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1-4 min). We also describe a coherent framework for multi-directional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasi-diffusion tensor imaging (QDTI) maps. QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of non-Gaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i.e. diffusion coefficient, D in mm2 s-1) and the shape of the power law tail (i.e. the fractional exponent, α). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, Hn, but does so without the limitations of a maximum b-value. We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228 s. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena

A study of the combustion chemistry of petroleum and bio-fuel oil asphaltenes

The combustion of heavy fuel oils such as Bunker C and vacuum residual oil (VRO) are widely used for industrial applications such as furnaces, power generation and for large marine engines. There is also the possible use of bio-oils derived from biomass. Combustion of these oils generates carbonaceous particulate emissions and polynuclear aromatic hydrocarbons (PAH) that are both health hazards and have an adverse effect on the climate. This paper explores the mechanism of the formation of fine particulate soot and cenospheres. The chemical structure of petroleum asphaltene have been investigated via pyrolysis techniques. The results are consistent with a structure made up of linked small aromatic and naphthenic clusters with substituent alkyl groups, some in the long chains, with the building blocks held together by bridging groups. Other functional groups also play a role. The corresponding bio-asphaltene is made up of similar aromatic and oxygenated species and behave in an analogous way

Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique.

BACKGROUND: There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. METHODS: DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. RESULTS: Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. CONCLUSIONS: D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning

(Re)fashioning Biafra: identity, authorship and the politics of dress in half of a yellow sun and other narratives of the Nigeria-Biafra war

Chimamanda Ngozi Adichie’s second novel, Half of a Yellow (2006), is one in a long line of works by Nigerian authors to portray the Nigeria-Biafra War (1967-1970). While Adichie has stated that she wanted to make modern Nigeria aware of its history by writing the novel, the writer has also revealed that she drew from past literary portrayals to construct her narrative. In order to untangle the complex construction of Half of a Yellow Sun, this article explores the way the novel negotiates the literary legacy of Biafra through material fashion, which I argue elucidates this complex intertextuality. Furthermore, I contend that the novel draws attention to and critiques the way that understanding of Biafra has been dominated by novels written by male authors, and weaves threads of material fashion in order to offer a new way of negotiating Nigerian history

Stress and visual function in infantile nystagmus syndrome.

PURPOSE: Infantile nystagmus syndrome (INS) is an involuntary oscillation of the eyes that has been reported to impair vision and worsen under stress. This investigation aimed to measure visual function in terms of visual acuity (VA) and response time (RT), when INS subjects are placed under stress. METHODS: A total of 23 subjects with INS and 20 control subjects performed a 2-alternative forced choice (2AFC) staircase procedure identifying the gap in a Landolt C, under 4 experimental conditions: initial acclimatization (A); task demand (TD), during which subjects received a small electrical shock for every incorrect answer; anticipatory anxiety (AA), during which subjects received a small shock at random intervals; and relaxed (R). Arousal was monitored with galvanic skin conductance (SkC). In addition to VA and eye movements, RTs were recorded. RESULTS: The SkC was higher in the TD and AA periods and lower during A and R. Shock significantly increased nystagmus amplitude (P < 0.01) and intensity (P < 0.007), and reduced foveation periods (FPs, P < 0.022). In both groups, VA was not reduced, but showed a slight improvement. However, shock increased RT (P < 0.009), and INS subjects were slower than controls (P < 0.0005). CONCLUSIONS: Increased arousal ("stress") provoked more intense nystagmus eye movements. As seen in other studies, stress did not reduce VA despite the shorter FPs. Although VA and FP can correlate across subjects, there would appear to be little correlation, if any, within a subject. However, RTs did increase with stress and shorter FPs, which may have an adverse impact on the visual performance of those with INS

The Impact of Fuel Properties on the Composition of Soot Produced by the Combustion of Residential Solid Fuels in a Domestic Stove

Soot is formed from the incomplete combustion of biomass and conventional fossil fuels. It consists largely of a carbonaceous core termed Elemental Carbon (EC) with adsorbed volatile organic species, commonly termed Organic Carbon (OC). Estimation of the ratio of BC/OC is critical as climate models have recognised the Global Warming Potential (GWP) of BC as the second most important climate forcing agent after carbon dioxide. This paper presents values of EC, OC and EC/TC (where TC = EC + OC) for three different soot types: Firstly, soots collected on filters from the combustion of eight fossil fuel and biomass residential solid fuels (RSF), burned in a 6 kW heating stove. Secondly, chimney soot deposits taken from 'real-life' stoves installed in domestic homes; and finally wick burner soots generated from biomass model compounds; namely eugenol, furfural and anisole. Values of the EC/TC ratios for wood logs, torrefied briquettes, coal and smokeless fuel are given. These ratios are highly dependent on burning conditions; namely the flaming and smouldering phases. The results of this study suggest that EC and OC emissions from various solid fuels differ substantially in composition and relative proportion, which is useful information for climate models

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. RESULTS: The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. CONCLUSION: The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management

Incomplete reversibility of estimated glomerular filtration rate decline following tenofovir disoproxil fumarate exposure.

BACKGROUND: Tenofovir disoproxil fumarate (TDF) has been linked to renal impairment, but the extent to which this impairment is reversible is unclear. We aimed to investigate the reversibility of renal decline during TDF therapy. METHODS: Cox proportional hazards models assessed factors associated with discontinuing TDF in those with an exposure duration of >6 months. In those who discontinued TDF therapy, linear piecewise regression models estimated glomerular filtration rate (eGFR) slopes before initiation of, during, and after discontinuation of TDF therapy. Factors associated with not achieving eGFR recovery 6 months after discontinuing TDF were assessed using multivariable logistic regression. RESULTS: We observed declines in the eGFR during TDF exposure (mean slopes, -15.7 mL/minute/1.73 m(2)/year [95% confidence interval {CI}, -20.5 to -10.9] during the first 3 months and -3.1 mL/minute/1.73 m(2)/year [95% CI, -4.6 to -1.7] thereafter) and evidence of eGFR increases following discontinuation of TDF therapy (mean slopes, 12.5 mL/minute/1.73 m(2)/year [95% CI, 8.9-16.1] during the first 3 months and 0.8 mL/minute/1.73 m(2)/year [95% CI,.1-1.5] thereafter). Following TDF discontinuation, 38.6% of patients with a decline in the eGFR did not experience recovery. A higher eGFR at baseline, a lower eGFR after discontinuation of TDF therapy, and more-prolonged exposure to TDF were associated with an increased risk of incomplete recovery 6 months after discontinuation of TDF therapy. CONCLUSIONS: This study shows that a decline in the eGFR during TDF therapy was not fully reversible in one third of patients and suggests that prolonged TDF exposure at a low eGFR should be avoided