Wisdom of the crowd from unsupervised dimension reduction

Wisdom of the crowd, the collective intelligence derived from responses of multiple human or machine individuals to the same questions, can be more accurate than each individual, and improve social decision-making and prediction accuracy. This can also integrate multiple programs or datasets, each as an individual, for the same predictive questions. Crowd wisdom estimates each individual's independent error level arising from their limited knowledge, and finds the crowd consensus that minimizes the overall error. However, previous studies have merely built isolated, problem-specific models with limited generalizability, and mainly for binary (yes/no) responses. Here we show with simulation and real-world data that the crowd wisdom problem is analogous to one-dimensional unsupervised dimension reduction in machine learning. This provides a natural class of crowd wisdom solutions, such as principal component analysis and Isomap, which can handle binary and also continuous responses, like confidence levels, and consequently can be more accurate than existing solutions. They can even outperform supervised-learning-based collective intelligence that is calibrated on historical performance of individuals, e.g. penalized linear regression and random forest. This study unifies crowd wisdom and unsupervised dimension reduction, and thereupon introduces a broad range of highly-performing and widely-applicable crowd wisdom methods. As the costs for data acquisition and processing rapidly decrease, this study will promote and guide crowd wisdom applications in the social and natural sciences, including data fusion, meta-analysis, crowd-sourcing, and committee decision making.Comment: 12 pages, 4 figures. Supplementary in sup folder of source files. 5 sup figures, 2 sup table

Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm

The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cells of this series. We employed a "pan-cell-state" strategy, and analyzed jointly microarray profiles obtained from different state-specific cell populations from this progression and reversion model of the breast cells using a tree-lineage multi-network inference algorithm, Treegl. We found that different breast cell states contain distinct gene networks. The network specific to non-malignant HMT3522-S1 cells is dominated by genes involved in normal processes, whereas the T4-2-specific network is enriched with cancer-related genes. The networks specific to various conditions of the reverted T4-2 cells are enriched with pathways suggestive of compensatory effects, consistent with clinical data showing patient resistance to anticancer drugs. We validated the findings using an external dataset, and showed that aberrant expression values of certain hubs in the identified networks are associated with poor clinical outcomes. Thus, analysis of various reversion conditions (including non-reverted) of HMT3522 cells using Treegl can be a good model system to study drug effects on breast cancer. © 2014 Parikh et al

Heterotic domain wall solutions and SU(3) structure manifolds

We examine compactifications of heterotic string theory on manifolds with SU(3) structure. In particular, we study N = 1/2 domain wall solutions which correspond to the perturbative vacua of the 4D, N =1 supersymmetric theories associated to these compactifications. We extend work which has appeared previously in the literature in two important regards. Firstly, we include two additional fluxes which have been, heretofore, omitted in the general analysis of this situation. This allows for solutions with more general torsion classes than have previously been found. Secondly, we provide explicit solutions for the fluxes as a function of the torsion classes. These solutions are particularly useful in deciding whether equations such as the Bianchi identities can be solved, in addition to the Killing spinor equations themselves. Our work can be used to straightforwardly decide whether any given SU(3) structure on a six-dimensional manifold is associated with a solution to heterotic string theory. To illustrate how to use these results, we discuss a number of examples taken from the literature.Comment: 34 pages, minor corrections in second versio

Microalgae production in fresh market wastewater and its utilization as a protein substitute in formulated fish feed for oreochromis spp.

Rapid growing of human population has led to increasing demand of aquaculture production. Oreochromis niloticus or known as tilapia is one of the most globally cultured freshwater fish due to its great adaptation towards extreme environment. Besides, farming of tilapia not only involves small scales farming for local consumption but also larger scales for international market which contributes to a foreign currency earning. Extensive use of fishmeal as feed for fish and for other animals indirectly caused an increasing depletion of the natural resource and may consequently cause economic and environmental unstable. Microalgae biomass seems to be a promising feedstock in aquaculture industry. It can be used for many purposes such as live food for fish larvae and dried microalgae to substitute protein material in fish feed. The microalgae replacement in fish feed formulation as protein alternative seem potentially beneficial for long term aqua-business sustainability. The present chapter discussed the potential of microalgae as an alternative nutrition in fish feed formulations, specifically Tilapia

Differences between <i>Trypanosoma brucei gambiense</i> groups 1 and 2 in their resistance to killing by Trypanolytic factor 1

&lt;p&gt;&lt;b&gt;Background:&lt;/b&gt; The three sub-species of &lt;i&gt;Trypanosoma brucei&lt;/i&gt; are important pathogens of sub-Saharan Africa. &lt;i&gt;T. b. brucei&lt;/i&gt; is unable to infect humans due to sensitivity to trypanosome lytic factors (TLF) 1 and 2 found in human serum. &lt;i&gt;T. b. rhodesiense&lt;/i&gt; and &lt;i&gt;T. b. gambiense&lt;/i&gt; are able to resist lysis by TLF. There are two distinct sub-groups of &lt;i&gt;T. b. gambiense&lt;/i&gt; that differ genetically and by human serum resistance phenotypes. Group 1 &lt;i&gt;T. b. gambiense&lt;/i&gt; have an invariant phenotype whereas group 2 show variable resistance. Previous data indicated that group 1 &lt;i&gt;T. b. gambiense&lt;/i&gt; are resistant to TLF-1 due in-part to reduced uptake of TLF-1 mediated by reduced expression of the TLF-1 receptor (the haptoglobin-hemoglobin receptor (&lt;i&gt;HpHbR&lt;/i&gt;)) gene. Here we investigate if this is also true in group 2 parasites.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Methodology:&lt;/b&gt; Isogenic resistant and sensitive group 2 &lt;i&gt;T. b. gambiense&lt;/i&gt; were derived and compared to other T. brucei parasites. Both resistant and sensitive lines express the &lt;i&gt;HpHbR&lt;/i&gt; gene at similar levels and internalized fluorescently labeled TLF-1 similar fashion to &lt;i&gt;T. b. brucei&lt;/i&gt;. Both resistant and sensitive group 2, as well as group 1 &lt;i&gt;T. b. gambiense&lt;/i&gt;, internalize recombinant APOL1, but only sensitive group 2 parasites are lysed.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Conclusions:&lt;/b&gt; Our data indicate that, despite group 1 &lt;i&gt;T. b. gambiense&lt;/i&gt; avoiding TLF-1, it is resistant to the main lytic component, APOL1. Similarly group 2 &lt;i&gt;T. b. gambiense&lt;/i&gt; is innately resistant to APOL1, which could be based on the same mechanism. However, group 2 &lt;i&gt;T. b. gambiense&lt;/i&gt; variably displays this phenotype and expression does not appear to correlate with a change in expression site or expression of &lt;i&gt;HpHbR&lt;/i&gt;. Thus there are differences in the mechanism of human serum resistance between &lt;i&gt;T. b. gambiense&lt;/i&gt; groups 1 and 2.&lt;/p&gt

Partial Covering Arrays: Algorithms and Asymptotics

A covering array $\mathsf{CA}(N;t,k,v)$ is an $N\times k$ array with entries in $\{1, 2, \ldots , v\}$, for which every $N\times t$ subarray contains each $t$-tuple of $\{1, 2, \ldots , v\}^t$ among its rows. Covering arrays find application in interaction testing, including software and hardware testing, advanced materials development, and biological systems. A central question is to determine or bound $\mathsf{CAN}(t,k,v)$, the minimum number $N$ of rows of a $\mathsf{CA}(N;t,k,v)$. The well known bound $\mathsf{CAN}(t,k,v)=O((t-1)v^t\log k)$ is not too far from being asymptotically optimal. Sensible relaxations of the covering requirement arise when (1) the set $\{1, 2, \ldots , v\}^t$ need only be contained among the rows of at least $(1-\epsilon)\binom{k}{t}$ of the $N\times t$ subarrays and (2) the rows of every $N\times t$ subarray need only contain a (large) subset of $\{1, 2, \ldots , v\}^t$. In this paper, using probabilistic methods, significant improvements on the covering array upper bound are established for both relaxations, and for the conjunction of the two. In each case, a randomized algorithm constructs such arrays in expected polynomial time

Predicting participation in group parenting education in an Australian sample: The role of attitudes, norms, and control factors

We examined the theory of planned behavior (TPB) in predicting intentions to participate in group parenting education. One hundred and seventy-six parents (138 mothers and 38 fathers) with a child under 12 years completed TPB items assessing attitude, subjective norms, perceived behavioral control (PBC), and two additional social influence variables (self-identity and group norm). Regression analyses supported the TPB predictors of participation intentions with self-identity and group norm also significantly predicting intentions. These findings offer preliminary support for the TPB, along with additional sources of social influence, as a useful predictive model of participation in parenting education

High Energy Gamma-Ray Emission From Blazars: EGRET Observations

We will present a summary of the observations of blazars by the Energetic Gamma Ray Experiment Telescope (EGRET) on the Compton Gamma Ray Observatory (CGRO). EGRET has detected high energy gamma-ray emission at energies greater than 100 MeV from more that 50 blazars. These sources show inferred isotropic luminosities as large as $3\times 10^{49}$ ergs s$^{-1}$. One of the most remarkable characteristics of the EGRET observations is that the gamma-ray luminosity often dominates the bolometric power of the blazar. A few of the blazars are seen to exhibit variability on very short time-scales of one day or less. The combination of high luminosities and time variations seen in the gamma-ray data indicate that gamma-rays are an important component of the relativistic jet thought to characterize blazars. Currently most models for blazars involve a beaming scenario. In leptonic models, where electrons are the primary accelerated particles, gamma-ray emission is believed to be due to inverse Compton scattering of low energy photons, although opinions differ as to the source of the soft photons. Hardronic models involve secondary production or photomeson production followed by pair cascades, and predict associated neutrino production.Comment: 16 pages, 7 figures, style files included. Invited review paper in "Observational Evidence for Black Holes in the Universe," 1999, ed. S. K. Chakrabarti (Dordrecht: Kluwer), 215-23

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads

Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation