A preliminary feasibility study of potential market applications for non-commercial technology magnets.

Climate change has spurred increased electrification through means of transportation, hydropower, and wind turbines which has caused an increased demand for permanent magnet materials. Current commercial magnet technologies rely heavily on several critical materials such as neodymium, praseodymium, dysprosium, samarium, and cobalt which are primarily sourced and refined outside of the United States (U.S.). To combat these problems, the Critical Materials Institute (CMI) has begun research into alternative magnet compositions to reduce critical material content. Additionally, these alternative magnets can fulfill a gap in the market between high performance neodymium-iron-boron (Nd-Fe-B) and samarium cobalt (Sm-Co) magnets and low performance ferrite or bonded Nd-Fe-B magnets, earning the term gap magnets. This research seeks to compile a simple strategy for identifying an application for these alternative magnets and assessing preliminary market impacts through substitution for two example magnets. The first magnet was identified to be applicable for ancillary motors and sensors in conventional gasoline vehicles with a maximum substitution of 4,825 metric tonnes (mt) per year by the year 2050. The second magnet was identified to be applicable for magnetic couplings in energy and industrial sectors with a maximum substitution of 978 mt per year by the year 2050.

Maternal diabetes independent of BMI is associated with altered accretion of adipose tissue in large for gestational age fetuses.

AIM: To analyse the effects of maternal diabetes mellitus (DM) and body mass Index (BMI) on central and peripheral fat accretion of large for gestational age (LGA) offspring. METHODS: This retrospective study included LGA fetuses (n = 595) with ultrasound scans at early (19.23 ± 0.68 weeks), mid (28.98 ± 1.62 weeks) and late (36.20 ± 1.59 weeks) stages of adipogenesis and measured abdominal (AFT) and mid-thigh (TFT) fat as surrogates for central and peripheral adiposity. Women were categorised according to BMI and DM status [pre-gestational (P-DM; n = 59), insulin managed (I-GDM; n = 132) and diet managed gestational diabetes (D-GDM; n = 29)]. Analysis of variance and linear regressions were applied. RESULTS: AFT and TFT did not differ significantly between BMI categories (normal, overweight and obese). In contrast, AFT was significantly higher in pregnancies affected by D-GDM compared to non-DM pregnancies from mid stage (0.44 mm difference, p = 0.002) and for all DM categories in late stage of adipogenesis (≥ 0.49 mm difference, p < 0.008). Late stage TFT accretion was higher than controls for P-DM and I-GDM but not for D-GDM (0.67 mm difference, p < 0.001; 0.49 mm difference, p = 0.001, 0.56 mm difference, p = 0.22 respectively). In comparison to the early non-DM group with an AFT to TFT ratio of 1.07, the I-GDM group ratio was 1.25 (p < 0.001), which normalised by 28 weeks becoming similar to control ratios. CONCLUSIONS: DM, independent of BMI, was associated with higher abdominal and mid-thigh fat accretion in fetuses. Use of insulin improved central to peripheral fat ratios in fetuses of GDM mothers.

spicyR: spatial analysis of in situ cytometry data in R.

MOTIVATION: High parameter histological techniques have allowed for the identification of a variety of distinct cell types within an image, providing a comprehensive overview of the tissue environment. This allows the complex cellular architecture and environment of diseased tissue to be explored. While spatial analysis techniques have revealed how cell-cell interactions are important within the disease pathology, there remains a gap in exploring changes in these interactions within the disease process. Specifically, there are currently few established methods for performing inference on cell-type co-localization changes across images, hindering an understanding of how cellular environments change with a disease pathology. RESULTS: We have developed the spicyR R package to perform inference on changes in the spatial co-localization of types across groups of images. Application to simulated data demonstrates a high sensitivity and specificity. We the utility of spicyR by applying it to a type 1 diabetes imaging mass cytometry dataset, revealing changes in cellular associations that were relevant to the disease progression. Ultimately, spicyR allows changes in cellular environments to be explored under different pathologies or disease states. AVAILABILITY AND IMPLEMENTATION: R package is freely available at http://bioconductor.org/packages/release/bioc/html/spicyR.html and shiny app implementation at http://shiny.maths.usyd.edu.au/spicyR/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational approaches for direct cell reprogramming: from the bulk omics era to the single cell era.

Recent advances in direct cell reprogramming have made possible the conversion of one cell type to another cell type, offering a potential cell-based treatment to many major diseases. Despite much attention, substantial roadblocks remain including the inefficiency in the proportion of reprogrammed cells of current experiments, and the requirement of a significant amount of time and resources. To this end, several computational algorithms have been developed with the goal of guiding the hypotheses to be experimentally validated. These approaches can be broadly categorized into two main types: transcription factor identification methods which aim to identify candidate transcription factors for a desired cell conversion, and transcription factor perturbation methods which aim to simulate the effect of a transcription factor perturbation on a cell state. The transcription factor perturbation methods can be broken down into Boolean networks, dynamical systems and regression models. We summarize the contributions and limitations of each method and discuss the innovation that single cell technologies are bringing to these approaches and we provide a perspective on the future direction of this field.

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model.

Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.

scDC: single cell differential composition analysis.

BACKGROUND: Differences in cell-type composition across subjects and conditions often carry biological significance. Recent advancements in single cell sequencing technologies enable cell-types to be identified at the single cell level, and as a result, cell-type composition of tissues can now be studied in exquisite detail. However, a number of challenges remain with cell-type composition analysis - none of the existing methods can identify cell-type perfectly and variability related to cell sampling exists in any single cell experiment. This necessitates the development of method for estimating uncertainty in cell-type composition. RESULTS: We developed a novel single cell differential composition (scDC) analysis method that performs differential cell-type composition analysis via bootstrap resampling. scDC captures the uncertainty associated with cell-type proportions of each subject via bias-corrected and accelerated bootstrap confidence intervals. We assessed the performance of our method using a number of simulated datasets and synthetic datasets curated from publicly available single cell datasets. In simulated datasets, scDC correctly recovered the true cell-type proportions. In synthetic datasets, the cell-type compositions returned by scDC were highly concordant with reference cell-type compositions from the original data. Since the majority of datasets tested in this study have only 2 to 5 subjects per condition, the addition of confidence intervals enabled better comparisons of compositional differences between subjects and across conditions. CONCLUSIONS: scDC is a novel statistical method for performing differential cell-type composition analysis for scRNA-seq data. It uses bootstrap resampling to estimate the standard errors associated with cell-type proportion estimates and performs significance testing through GLM and GLMM models. We have made this method available to the scientific community as part of the scdney package (Single Cell Data Integrative Analysis) R package, available from https://github.com/SydneyBioX/scdney.

scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets.

Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.

AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications.

Class labels are required for supervised learning but may be corrupted or missing in various applications. In binary classification, for example, when only a subset of positive instances is labeled whereas the remaining are unlabeled, positive-unlabeled (PU) learning is required to model from both positive and unlabeled data. Similarly, when class labels are corrupted by mislabeled instances, methods are needed for learning in the presence of class label noise (LN). Here we propose adaptive sampling (AdaSampling), a framework for both PU learning and learning with class LN. By iteratively estimating the class mislabeling probability with an adaptive sampling procedure, the proposed method progressively reduces the risk of selecting mislabeled instances for model training and subsequently constructs highly generalizable models even when a large proportion of mislabeled instances is present in the data. We demonstrate the utilities of proposed methods using simulation and benchmark data, and compare them to alternative approaches that are commonly used for PU learning and/or learning with LN. We then introduce two novel bioinformatics applications where AdaSampling is used to: 1) identify kinase-substrates from mass spectrometry-based phosphoproteomics data and 2) predict transcription factor target genes by integrating various next-generation sequencing data.

DCARS: differential correlation across ranked samples.

MOTIVATION: Genes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena such as the aetiology of cancer. We have developed an approach for quantifying how changes in the association between pairs of genes may inform the outcome of interest called Differential Correlation across Ranked Samples (DCARS). Modelling gene correlation across a continuous sample ranking does not require the dichotomisation of samples into two distinct classes and can identify differences in gene correlation across early, mid or late stages of the outcome of interest. RESULTS: When we evaluated DCARS against the typical Fisher Z-transformation test for differential correlation, as well as a typical approach testing for interaction within a linear model, on real TCGA data, DCARS significantly ranked gene pairs containing known cancer genes more highly across several cancers. Similar results are found with our simulation study. DCARS was applied to 13 cancers datasets in TCGA, revealing several distinct relationships for which survival ranking was found to be associated with a change in correlation between genes. Furthermore, we demonstrated that DCARS can be used in conjunction with network analysis techniques to extract biological meaning from multi-layered and complex data. AVAILABILITY AND IMPLEMENTATION: DCARS R package and sample data are available at https://github.com/shazanfar/DCARS. Publicly available data from The Cancer Genome Atlas (TCGA) was used using the TCGABiolinks R package. Supplementary Files and DCARS R package is available at https://github.com/shazanfar/DCARS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling and analysis of modular structure in diverse biological networks.

Biological networks, like most engineered networks, are not the product of a singular design but rather are the result of a long process of refinement and optimization. Many large real-world networks are comprised of well-defined and meaningful smaller modules. While engineered networks are designed and refined by humans with particular goals in mind, biological networks are created by the selective pressures of evolution. In this paper, we seek to define aspects of network architecture that are shared among different types of evolved biological networks. First, we developed a new mathematical model, the Stochastic Block Model with Path Selection (SBM-PS) that simulates biological network formation based on the selection of edges that increase clustering. SBM-PS can produce modular networks whose properties resemble those of real networks. Second, we analyzed three real networks of very different types, and showed that all three can be fit well by the SBM-PS model. Third, we showed that modular elements within the three networks correspond to meaningful biological structures. The networks chosen for analysis were a proteomic network composed of all proteins required for mitochondrial function in budding yeast, a mesoscale anatomical network composed of axonal connections among regions of the mouse brain, and the connectome of individual neurons in the nematode C. elegans. We find that the three networks have common architectural features, and each can be divided into subnetworks with characteristic topologies that control specific phenotypic outputs.

A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types.

Cancer research continues to highlight the extensive genetic diversity that exists both between and within tumors. This intrinsic heterogeneity poses one of the central challenges to predicting patient clinical outcome and the personalization of treatments. Despite progress in some individual tumor types, it is not yet possible to prospectively, accurately classify patients by expected survival. One hypothesis proposed to explain this is that the prognostic classifiers developed to date are insufficiently sensitive and specific; however it is also possible that patients are not equally easy to classify by any given biomarker. We demonstrate in a cohort of 45 AJCC stage III melanoma patients that clinico-pathologic biomarkers can identify those patients that are most likely to be misclassified by a molecular biomarker. The process of modelling the classifiability of patients was then replicated in a cohort of 49 stage II breast cancer patients and 53 stage III colon cancer patients. A multi-step procedure incorporating this information not only improved classification accuracy but also indicated the specific clinical attributes that had made classification problematic in each cohort. These findings show that, even when cohorts are of moderate size, including features that explain the patient-specific performance of a prognostic biomarker in a classification framework can improve the modelling and estimation of survival.

Big Area Additive Manufacturing of High Performance Bonded NdFeB Magnets.

Additive manufacturing allows for the production of complex parts with minimum material waste, offering an effective technique for fabricating permanent magnets which frequently involve critical rare earth elements. In this report, we demonstrate a novel method - Big Area Additive Manufacturing (BAAM) - to fabricate isotropic near-net-shape NdFeB bonded magnets with magnetic and mechanical properties comparable or better than those of traditional injection molded magnets. The starting polymer magnet composite pellets consist of 65 vol% isotropic NdFeB powder and 35 vol% polyamide (Nylon-12). The density of the final BAAM magnet product reached 4.8 g/cm3, and the room temperature magnetic properties are: intrinsic coercivity Hci = 688.4 kA/m, remanence Br = 0.51 T, and energy product (BH)max = 43.49 kJ/m3 (5.47 MGOe). In addition, tensile tests performed on four dog-bone shaped specimens yielded an average ultimate tensile strength of 6.60 MPa and an average failure strain of 4.18%. Scanning electron microscopy images of the fracture surfaces indicate that the failure is primarily related to the debonding of the magnetic particles from the polymer binder. The present method significantly simplifies manufacturing of near-net-shape bonded magnets, enables efficient use of rare earth elements thus contributing towards enriching the supply of critical materials.

Differential distribution improves gene selection stability and has competitive classification performance for patient survival.

A consistent difference in average expression level, often referred to as differential expression (DE), has long been used to identify genes useful for classification. However, recent cancer studies have shown that when transcription factors or epigenetic signals become deregulated, a change in expression variability (DV) of target genes is frequently observed. This suggests that assessing the importance of genes by either differential expression or variability alone potentially misses sets of important biomarkers that could lead to improved predictions and treatments. Here, we describe a new approach for assessing the importance of genes based on differential distribution (DD), which combines information from differential expression and differential variability into a unified metric. We show that feature ranking and selection stability based on DD can perform two to three times better than DE or DV alone, and that DD yields equivalent error rates to DE and DV. Finally, assessing genes via differential distribution produces a complementary set of selected genes to DE and DV, potentially opening up new categories of biomarkers.

A Variational Bayes Approach to the Analysis of Occupancy Models.

Detection-nondetection data are often used to investigate species range dynamics using Bayesian occupancy models which rely on the use of Markov chain Monte Carlo (MCMC) methods to sample from the posterior distribution of the parameters of the model. In this article we develop two Variational Bayes (VB) approximations to the posterior distribution of the parameters of a single-season site occupancy model which uses logistic link functions to model the probability of species occurrence at sites and of species detection probabilities. This task is accomplished through the development of iterative algorithms that do not use MCMC methods. Simulations and small practical examples demonstrate the effectiveness of the proposed technique. We specifically show that (under certain circumstances) the variational distributions can provide accurate approximations to the true posterior distributions of the parameters of the model when the number of visits per site (K) are as low as three and that the accuracy of the approximations improves as K increases. We also show that the methodology can be used to obtain the posterior distribution of the predictive distribution of the proportion of sites occupied (PAO).

Integrated single cell data analysis reveals cell specific networks and novel coactivation markers.

BACKGROUND: Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behavior of individual cells. Identifying genes with high levels of expression using data from single cell RNA sequencing can be useful to characterize very active genes and cells in which this occurs. In particular single cell RNA-Seq allows for cell-specific characterization of high gene expression, as well as gene coexpression. RESULTS: We offer a versatile modeling framework to identify transcriptional states as well as structures of coactivation for different neuronal cell types across multiple datasets. We employed a gamma-normal mixture model to identify active gene expression across cells, and used these to characterize markers for olfactory sensory neuron cell maturity, and to build cell-specific coactivation networks. We found that combined analysis of multiple datasets results in more known maturity markers being identified, as well as pointing towards some novel genes that may be involved in neuronal maturation. We also observed that the cell-specific coactivation networks of mature neurons tended to have a higher centralization network measure than immature neurons. CONCLUSION: Integration of multiple datasets promises to bring about more statistical power to identify genes and patterns of interest. We found that transforming the data into active and inactive gene states allowed for more direct comparison of datasets, leading to identification of maturity marker genes and cell-specific network observations, taking into account the unique characteristics of single cell transcriptomics data.

ClassifyR: an R package for performance assessment of classification with applications to transcriptomics.

UNLABELLED: Although a large collection of classification software packages exist in R, a new generic framework for linking custom classification functions with classification performance measures is needed. A generic classification framework has been designed and implemented as an R package in an object oriented style. Its design places emphasis on parallel processing, reproducibility and extensibility. Finally, a comprehensive set of performance measures are available to ease post-processing. Taken together, these important characteristics enable rapid and reproducible benchmarking of alternative classifiers. AVAILABILITY AND IMPLEMENTATION: ClassifyR is implemented in R and can be obtained from the Bioconductor project: http://bioconductor.org/packages/release/bioc/html/ClassifyR.html.

Effects of competition, exercise, and mental stress on secretory immunity.

It has been suggested that the psychological stress associated with competitive sports events may help to explain the increased susceptibility to respiratory infections due to reductions in secretory immunity. In the current study, we investigated the influence of competitive exercise and psychological stress on secretory immunoglobulin A (s-IgA). Salivary s-IgA and heart rate were measured in 62 healthy young recreationally active men at rest and, in a between-subjects design, following one of four 8-min tasks: mental arithmetic, cycling at workloads of 60 to 180 W (mean = 146 W), mental arithmetic while cycling, or competitive cycling. Mental arithmetic was associated with significant increases in s-IgA concentration (mean = 49 microg.min(-1)) and s-IgA secretion rate (mean = 25 microg.ml(-1)) compared with rest, while mental arithmetic combined with exercise was associated with a significant increase in s-IgA concentration only (mean = 124 microg.min(-1)). In contrast, competitive exercise and exercise alone did not influence s-IgA concentration or secretion rate. Heart rate increased modestly to mental arithmetic (mean = 7 beats.min(-1)) and substantially, and similarly, to the three exercise tasks (mean = 56(62 beats.min(-1)). The hypothesis that the psychological stress of competitive exercise contributes to increased susceptibility to infection via reductions in s-IgA requires further investigation.

The bchU gene of Chlorobium tepidum encodes the c-20 methyltransferase in bacteriochlorophyll c biosynthesis.

Bacteriochlorophylls (BChls) c and d, two of the major light-harvesting pigments in photosynthetic green sulfur bacteria, differ only by the presence of a methyl group at the C-20 methine bridge position in BChl c. A gene potentially encoding the C-20 methyltransferase, bchU, was identified by comparative analysis of the Chlorobium tepidum and Chloroflexus aurantiacus genome sequences. Homologs of this gene were amplified and sequenced from Chlorobium phaeobacteroides strain 1549, Chlorobium vibrioforme strain 8327d, and C. vibrioforme strain 8327c, which produce BChls e, d, and c, respectively. A single nucleotide insertion in the bchU gene of C. vibrioforme strain 8327d was found to cause a premature, in-frame stop codon and thus the formation of a truncated, nonfunctional gene product. The spontaneous mutant of this strain that produces BChl c (strain 8327c) has a second frameshift mutation that restores the correct reading frame in bchU. The bchU gene was inactivated in C. tepidum, a BChl c-producing species, and the resulting mutant produced only BChl d. Growth rate measurements showed that BChl c- and d-producing strains of the same organism (C. tepidum or C. vibrioforme) have similar growth rates at high and intermediate light intensities but that strains producing BChl c grow faster than those with BChl d at low light intensities. Thus, the bchU gene encodes the C-20 methyltransferase for BChl c biosynthesis in Chlorobium species, and methylation at the C-20 position to produce BChl c rather than BChl d confers a significant competitive advantage to green sulfur bacteria living at limiting red and near-infrared light intensities.

;Every dogma has its day': a personal look at carbon metabolism in photosynthetic bacteria.

Dogmas are unscientific. What is perhaps the greatest biological dogma of all time, the 'unity of biochemistry' is, in the main, still having its day. According to present knowledge, the exceptions to this dogma are mere details when seen in relation to the biosystem as a whole. Nevertheless the exceptions are scientifically interesting and the understanding of them has led to a better comprehension of photosynthesis and ecology. Until the discovery of (14)C, photosynthetic CO(2) fixation was like a slightly opened black box. With (14)C in hand scientists mapped out the path of carbon in green plant photosynthesis in the course of a few years. The impressive reductive pentose phosphate cycle was almost immediately assumed to be universal in autotrophs, including anoxygenic phototrophs, in spite of the odd observation to the contrary. A new dogma was born and held the field for about two decades. Events began to turn when green sulfur bacteria were found to contain ferredoxin-coupled ketoacid-oxidoreductases. This led to the formulation of a novel CO(2)-fixing pathway, the reductive citric acid cycle, but its general acceptance required much work by many investigators. However, the ice had now been broken and after some years a third mechanism of CO(2) fixation was discovered, this time in Chloroflexus,and then a fourth in the same genus. One consequence of these discoveries is that it has become apparent that oxygen is an important factor that determines the kind of CO(2)-fixing mechanism an organism uses. With the prospect of the characterization of hordes of novel bacteria forecast by molecular ecologists we can expect further distinctive CO(2) fixation mechanisms to turn up.

Biosynthesis of chlorosome proteins is not inhibited in acetylene-treated cultures of Chlorobium vibrioforme.

The composition, abundance and apparent molecular masses of chlorosome polypeptides from Chlorobium tepidum and Chlorobium vibrioforme 8327 were compared. The most abundant, low-molecular-mass chlorosome polypeptides of both strains had similar electrophoretic mobilities and abundances, but several of the larger proteins were different in both apparent mass and abundance. Polyclonal antisera raised against recombinant chlorosome proteins of Cb. tepidum recognized the homologous proteins in Cb. vibrioforme, and a one-to-one correspondence between the chlorosome proteins of the two species was confirmed. As previously shown [Ormerod et al. (1990) J Bacteriol 172: 1352-1360], acetylene strongly suppressed the synthesis of bacteriochlorophyll c in Cb. vibrioforme strain 8327. No correlation was found between the bacteriochlorophyll c content of cells and the cellular content of chlorosome proteins. Nine of ten chlorosome proteins were detected in acetylene-treated cultures, and the chlorosome proteins were generally present in similar amounts in control and acetylene-treated cells. These results suggest that the synthesis of chlorosome proteins and the assembly of the chlorosome envelope is constitutive. It remains possible that the synthesis of bacteriochlorophyll c and its insertion into chlorosomes might be regulated by environmental parameters such as light intensity.