Investigating the predictability of essential genes across distantly related organisms using an integrative approach

Rapid and accurate identification of new essential genes in under-studied microorganisms will significantly improve our understanding of how a cell works and the ability to re-engineer microorganisms. However, predicting essential genes across distantly related organisms remains a challenge. Here, we present a machine learning-based integrative approach that reliably transfers essential gene annotations between distantly related bacteria. We focused on four bacterial species that have well-characterized essential genes, and tested the transferability between three pairs among them. For each pair, we trained our classifier to learn traits associated with essential genes in one organism, and applied it to make predictions in the other. The predictions were then evaluated by examining the agreements with the known essential genes in the target organism. Ten-fold cross-validation in the same organism yielded AUC scores between 0.86 and 0.93. Cross-organism predictions yielded AUC scores between 0.69 and 0.89. The transferability is likely affected by growth conditions, quality of the training data set and the evolutionary distance. We are thus the first to report that gene essentiality can be reliably predicted using features trained and tested in a distantly related organism. Our approach proves more robust and portable than existing approaches, significantly extending our ability to predict essential genes beyond orthologs

Amyloid Oligomer Conformation in a Group of Natively Folded Proteins

Recent in vitro and in vivo studies suggest that destabilized proteins with defective folding induce aggregation and toxicity in protein-misfolding diseases. One such unstable protein state is called amyloid oligomer, a precursor of fully aggregated forms of amyloid. Detection of various amyloid oligomers with A11, an anti-amyloid oligomer conformation-specific antibody, revealed that the amyloid oligomer represents a generic conformation and suggested that toxic β-aggregation processes possess a common mechanism. By using A11 antibody as a probe in combination with mass spectrometric analysis, we identified GroEL in bacterial lysates as a protein that may potentially have an amyloid oligomer conformation. Surprisingly, A11 reacted not only with purified GroEL but also with several purified heat shock proteins, including human Hsp27, 40, 70, 90; yeast Hsp104; and bovine Hsc70. The native folds of A11-reactive proteins in purified samples were characterized by their anti-β-aggregation activity in terms of both functionality and in contrast to the β-aggregation promoting activity of misfolded pathogenic amyloid oligomers. The conformation-dependent binding of A11 with natively folded Hsp27 was supported by the concurrent loss of A11 reactivity and anti-β-aggregation activity of heat-treated Hsp27 samples. Moreover, we observed consistent anti-β-aggregation activity not only by chaperones containing an amyloid oligomer conformation but also by several A11-immunoreactive non-chaperone proteins. From these results, we suggest that the amyloid oligomer conformation is present in a group of natively folded proteins. The inhibitory effects of A11 antibody on both GroEL/ES-assisted luciferase refolding and Hsp70-mediated decelerated nucleation of Aβ aggregation suggested that the A11-binding sites on these chaperones might be functionally important. Finally, we employed a computational approach to uncover possible A11-binding sites on these targets. Since the β-sheet edge was a common structural motif having the most similar physicochemical properties in the A11-reactive proteins we analyzed, we propose that the β-sheet edge in some natively folded amyloid oligomers is designed positively to prevent β aggregation

Practical training and the audit expectations gap: The case of accounting undergraduates of Universiti Utara Malaysia

The accounting profession has long faced the issue of an audit expectation gap; being the gap between the quality of the profession’s performance, its objectives and results, and that which the society expects.The profession believes that the gap could be reduced over time through education.Studies have been carried out overseas and in Malaysia to determine the effect of education in narrowing the audit expectation gap. Extending the knowledge acquired, this paper investigates whether academic internship programs could reduce the audit expectation gap in Malaysia.Using a pre-post method, the research instrument adapted from Ferguson et al.(2000) is administered to the Universiti Utara Malaysia’s accounting students at the beginning and end of their internship program.The results show there is a significant change in perceptions among students after the internship program. However, changes in perceptions do not warrant an internship program as a means of reducing the audit expectation gap as misperceptions are still found among respondents on issues of auditing after the completion of the internship program. Nevertheless, an internship program can still be used to complement audit education in a university as it is an ideal way to expose students to professional issues and enables them to have a better insight of the actual performance and duties of auditors

COMPUTATIONAL NEUROSCIENCE: A BRIEF OVERVIEW

Computational and mathematical modeling is an increasingly useful approach for investigating the functionality of the nervous system. Though such modeling has been used for decades, recent advances in computational power and numerical techniques have greatly expanded its scope, with a corresponding increase in research activity. This paper presents a brief – and necessarily incomplete – review of methods and applications in computational neuroscience. Computational neuroscience refers to the use of mathematical and computational models in the study of neural systems. It is part of the larger – increasingly active – discipline of computational biology, which applies computational modeling to all aspects of biological organisms. Quantitative modeling has been a key component of research in neuroscience for many decades. Indeed, one of the most celebrated achievements in the field – the Hodgkin-Huxley model for the generation of action potentials 1 – was a triumph of the quantitative approach. Also, much of what is understood about the functionality of the visual, auditory and olfactory systems, as well as the neural basis of learning an

Covariance Learning of Correlated Patterns in Competitive Networks

(To appearinNeural Computation) Covariance learning is a specially powerful type of Hebbian learning, allowing both potentiation and depression of synaptic strength. It is used for associative memory in feed-forward and recurrent neural network paradigms. This letter describes a variant ofcovariance learning which works particularly well for correlated stimuli in feed-forward networks with competitive K-of-N ring. The rule, which is nonlinear, has an intuitive mathematical interpretation, and simulations presented in this letter demonstrate its utility.

Network capacity for latent attractor computation

Attractor networks have been one of the most successful paradigms in neural computation, and have been used as models of computation in the nervous system. Many experimentally observed phenomena -- such as coherent population codes, contextual representations, and replay of learned neural activity patterns -- are explained well by attractor dynamics. Recently, we proposed a paradigm called "latent attractors" where attractors embedded in a recurrent network via Hebbian learning are used to channel network response to external input rather than becoming manifest themselves. This allows the network to generate context-sensitive internal codes in complex situations. Latent attractors are particularly helpful in explaining computations within the hippocampus -- a brain region of fundamental significance for memory and spatial learning. The performance of latent attractor networks depends on the number of such attractors that a network can sustain. Following methods developed for associative memory networks, we present analytical and computational results on the capacity of latent attractor networks