Lattice-corrected strain-induced vector potentials in graphene

The electronic implications of strain in graphene can be captured at low energies by means of pseudovector potentials which can give rise to pseudomagnetic fields. These strain-induced vector potentials arise from the local perturbation to the electronic hopping amplitudes in a tight-binding framework. Here we complete the standard description of the strain-induced vector potential, which accounts only for the hopping perturbation, with the explicit inclusion of the lattice deformations or, equivalently, the deformation of the Brillouin zone. These corrections are linear in strain and are different at each of the strained, inequivalent Dirac points, and hence are equally necessary to identify the precise magnitude of the vector potential. This effect can be relevant in scenarios of inhomogeneous strain profiles, where electronic motion depends on the amount of overlap among the local Fermi surfaces. In particular, it affects the pseudomagnetic field distribution induced by inhomogeneous strain configurations, and can lead to new opportunities in tailoring the optimal strain fields for certain desired functionalities.Comment: Errata for version

Band Gap Engineering with Ultralarge Biaxial Strains in Suspended Monolayer MoS2

We demonstrate the continuous and reversible tuning of the optical band gap of suspended monolayer MoS2 membranes by as much as 500 meV by applying very large biaxial strains. By using chemical vapor deposition (CVD) to grow crystals that are highly impermeable to gas, we are able to apply a pressure difference across suspended membranes to induce biaxial strains. We observe the effect of strain on the energy and intensity of the peaks in the photoluminescence (PL) spectrum, and find a linear tuning rate of the optical band gap of 99 meV/%. This method is then used to study the PL spectra of bilayer and trilayer devices under strain, and to find the shift rates and Gr\"uneisen parameters of two Raman modes in monolayer MoS2. Finally, we use this result to show that we can apply biaxial strains as large as 5.6% across micron sized areas, and report evidence for the strain tuning of higher level optical transitions.Comment: Nano Lett., Article ASA

Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology

Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes

Epitaxial growth of Cu on Cu(001): experiments and simulations

A quantitative comparison between experimental and Monte Carlo simulation results for the epitaxial growth of Cu/Cu(001) in the submonolayer regime is presented. The simulations take into account a complete set of hopping processes whose activation energies are derived from semi-empirical calculations using the embedded-atom method. The island separation is measured as a function of the incoming flux and the temperature. A good quantitative agreement between the experiment and simulation is found for the island separation, the activation energies for the dominant processes, and the exponents that characterize the growth. The simulation results are then analyzed at lower coverages, which are not accessible experimentally, providing good agreement with theoretical predictions as well.Comment: Latex document. 7 pages. 3 embedded figures in separate PS files. One bbl fil

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

Background: Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets.Results and discussion: Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large ‘omics’ datasets are increasingly being used in the area of rheumatology.Conclusions: Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery

Model for long QT syndrome type 2 using human iPS cells demonstrates arrhythmogenic characteristics in cell culture

Peer reviewe

Informant Discrepancies in Clinical Reports of Youths and Interviewers' Impressions of the Reliability of Informants

In this study the authors examined whether discrepancies between parent and youth reports of the youth's emotional and behavioral functioning are related to interviewers' reliability ratings of parents and youths

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

Background: Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. Methods: This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. Results: BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. Conclusions: Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation

Biogeography of Southern Ocean prokaryotes: a comparison of the Indian and Pacific sectors

18 pages, 5 figures.-- This is an open access article under the terms of the Creative Commons Attribution LicenseWe investigated the Southern Ocean (SO) prokaryote community structure via zero-radius operational taxonomic unit (zOTU) libraries generated from 16S rRNA gene sequencing of 223 full water column profiles. Samples reveal the prokaryote diversity trend between discrete water masses across multiple depths and latitudes in Indian (71–99°E, summer) and Pacific (170–174°W, autumn-winter) sectors of the SO. At higher taxonomic levels (phylum-family) we observed water masses to harbour distinct communities across both sectors, but observed sectorial variations at lower taxonomic levels (genus-zOTU) and relative abundance shifts for key taxa such as Flavobacteria, SAR324/Marinimicrobia, Nitrosopumilus and Nitrosopelagicus at both epi- and bathy-abyssopelagic water masses. Common surface bacteria were abundant in several deep-water masses and vice-versa suggesting connectivity between surface and deep-water microbial assemblages. Bacteria from same-sector Antarctic Bottom Water samples showed patchy, high beta-diversity which did not correlate well with measured environmental parameters or geographical distance. Unconventional depth distribution patterns were observed for key archaeal groups: Crenarchaeota was found across all depths in the water column and persistent high relative abundances of common epipelagic archaeon Nitrosopelagicus was observed in deep-water masses. Our findings reveal substantial regional variability of SO prokaryote assemblages that we argue should be considered in wide-scale SO ecosystem microbial modellingThis work was funded by a CSIRO Office of the Chief Executive Science Leader Fellowship (R-04202) to L.B. as well as the Antarctic Science International Bursary 2017 to S.L.S.S.Peer reviewe

Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density.

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease