Scalable computation of intracellular metabolite concentrations

Current mathematical frameworks for predicting the flux state and macromolecular composition of the cell do not rely on thermodynamic constraints to determine the spontaneous direction of reactions. These predictions may be biologically infeasible as a result. Imposing thermodynamic constraints requires accurate estimations of intracellular metabolite concentrations. These concentrations are constrained within physiologically possible ranges to enable an organism to grow in extreme conditions and adapt to its environment. Here, we introduce tractable computational techniques to characterize intracellular metabolite concentrations within a constraint-based modeling framework. This model provides a feasible concentration set, which can generally be nonconvex and disconnected. We examine three approaches based on polynomial optimization, random sampling, and global optimization. We leverage the sparsity and algebraic structure of the underlying biophysical models to enhance the computational efficiency of these techniques. We then compare their performance in two case studies, showing that the global-optimization formulation exhibits more desirable scaling properties than the random-sampling and polynomial-optimization formulation, and, thus, is a promising candidate for handling large-scale metabolic networks

Positively charged mineral surfaces promoted the accumulation of organic intermediates at the origin of metabolism.

Identifying plausible mechanisms for compartmentalization and accumulation of the organic intermediates of early metabolic cycles in primitive cells has been a major challenge in theories of life's origins. Here, we propose a mechanism, where positive membrane potentials elevate the concentration of the organic intermediates. Positive membrane potentials are generated by positively charged surfaces of protocell membranes due to accumulation of transition metals. We find that (i) positive membrane potentials comparable in magnitude to those of modern cells can increase the concentration of the organic intermediates by several orders of magnitude; (ii) generation of large membrane potentials destabilize ion distributions; (iii) violation of electroneutrality is necessary to induce nonzero membrane potentials; and (iv) violation of electroneutrality enhances osmotic pressure and diminishes reaction efficiency, resulting in an evolutionary driving force for the formation of lipid membranes, specialized ion channels, and active transport systems

Metode Pemantau Posisi Dan Arah Gerak Helikopter Tanpa Awak Dengan Google Maps API

Makalah ini membahas metode pemantau posisi dan arah gerak helikopter tanpa awak dengan bantuan layanan Google Maps API dan sensor GPS. Sinyal yang dihasilkan oleh GPS berupa data posisi dalam koordinat lintang dan bujur. Arah gerak helikopter diperoleh dari modul GPS dengan mengkalkulasi data riwayat posisi. Sinyal dari GPS selanjutnya dipancarkan oleh modem radio di helikopter dan diterima oleh modem radio di komputer. Data kemudian diolah oleh program aplikasi yang dibuat dengan Visual Basic 6. Data hasil olahan digunakan oleh program javascript untuk menampilkan posisi dan arah gerak helikopter ke dalam peta yang diperoleh dari layanan Google Maps API. Sebagai hasil akhir, pengguna dapat melihat posisi dan arah gerak helikopter pada browser seperti Mozilla Firefox, Google Chrome maupun browser lain yang mendukung HTML5. Kecepatan pembaharuan data posisi pada jaringan lokal dapat mencapai duapuluh data perdetik atau 20 Hz. Kemampuan ini sangat lebih dari cukup mengingat kemampuan akusisi sensor GPS komersial yang paling mutakhir hanya berkisar antara 5-10 Hz. Dengan menggunakan metode ini data GPS dapat diproses tanpa ada data yang terlewat

Policies and Programs for Prevention and Control of Diabetes in Iran: A Document Analysis

Trend analysis in 2005 to 2011 showed high growth in diabetes prevalence in Iran. Considering the high prevalence of diabetes in the country and likely to increase its prevalence in the future, the analysis of diabetes-related policies and programs is very important and effective in the prevention and control of diabetes. Therefore, the aim of the study was an analysis of policies and programs related to prevention and control of diabetes in Iran in 2014. This study was a policy analysis using deductive thematic content analysis of key documents. The health policy triangle framework was used in the data analysis. PubMed and ScienceDirect databases were searched to find relevant studies and documents. Also, hand searching was conducted among references of the identified studies. MAXQDA 10 software was used to organize and analyze data. The main reasons to take into consideration diabetes in Iran can be World Health Organization (WHO) report in 1989, and high prevalence of diabetes in the country. The major challenges in implementing the diabetes program include difficulty in referral levels of the program, lack of coordination between the private sector and the public sector and the limitations of reporting system in the specialized levels of the program. Besides strengthening referral system, the government should allocate more funds to the program and more importance to the educational programs for the public. Also, Non-Governmental Organizations (NGOs) and the private sector should involve in the formulation and implementation of the prevention and control programs of diabetes in the future

Mass Deformation of the Multiple M2 Branes Theory

Based on recent developments, in this letter we study the one parameter deformation of 2+1 dimensional gauge theories with scale invariance and N = 8 supersymmetry, which is expected to be the field theory living on a stack of M2 branes. The deformed gauge theory is defined by a Lagrangian and is based on an infinite set of novel 3-algebras constructed by relaxing the assumption that the invariant metric is positive definite. Under the Higgs mechanism, we can obtain the D-branes world volume theory in the presence of background fluxes.Comment: 13pages, no figures, reference adde

Inferred regulons are consistent with regulator binding sequences in E. coli

The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Regulons are typically determined either from resource-intensive experimental measurement of functional binding sites, or inferred from analysis of high-throughput gene expression datasets. Recently, independent component analysis (ICA) of RNA-seq compendia has shown to be a powerful method for inferring bacterial regulons. However, it remains unclear to what extent regulons predicted by ICA structure have a biochemical basis in promoter sequences. Here, we address this question by developing machine learning models that predict inferred regulon structures in E. coli based on promoter sequence features. Models were constructed successfully (cross-validation AUROC > = 0.8) for 85% (40/47) of ICA-inferred E. coli regulons. We found that: 1) The presence of a high scoring regulator motif in the promoter region was sufficient to specify regulatory activity in 40% (19/47) of the regulons, 2) Additional features, such as DNA shape and extended motifs that can account for regulator multimeric binding, helped to specify regulon structure for the remaining 60% of regulons (28/47); 3) investigating regulons where initial machine learning models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, we found that strong regulatory binding sequences underlie both the genes shared between ICA-inferred and experimental regulons as well as genes in the E. coli core pan-regulon of Fur. This work demonstrates that the structure of ICA-inferred regulons largely can be understood through the strength of regulator binding sites in promoter regions, reinforcing the utility of top-down inference for regulon discovery

Effects of self-talk on football players performance in official competitions

One of the factors influencing the athletes’ performance in critical situations is their awareness of the strategies entailing mental skills. One such skill is self-talk that has been identified as an effective mental training tool in controlling human beings’ behaviors. This study aims to examine the perceived positive and negative effects of self-talk on the athletes’ performance. Data were collected through survey questionnaire from a group of Iranian elite football players qualified for national football team. The players’ responses were thematically analyzed for both positive and negative effects of ST in different occasions around official football competitions. The analysis indicated the perceived effects could be characterized at two levels: mental and behavioural. Most important positive effects of ST at mental level included its cognitive benefits such as enhancing focus and attention, promote decision making skills and decreasing reaction time. Mental level benefits also comprised emotional effects of ST such as motivating players to increase efforts, coping with difficult situations, and decreasing anxiety and psyching up. Emotional effects had negative aspects too. Weakening confidence by self-criticism, and dwelling on negative thoughts and increased stress were among negative effects. At behavioral level, ST was perceived to benefit execution of tasks by increased attentional focus and creating an awareness of the negative consequences of certain behaviors thereby benefiting the overall performance of the individuals and that of the team. Implications for football players and team managers have been discussed

A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique

Background: Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning. Methods: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies. Results: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine. Conclusions: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare. © 2018 The Author