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

Validation of T2* in-line analysis for tissue iron quantification at 1.5 T.

BACKGROUND: There is a need for improved worldwide access to tissue iron quantification using T2* cardiovascular magnetic resonance (CMR). One route to facilitate this would be simple in-line T2* analysis widely available on MR scanners. We therefore compared our clinically validated and established T2* method at Royal Brompton Hospital (RBH T2*) against a novel work-in-progress (WIP) sequence with in-line T2* measurement from Siemens (WIP T2*). METHODS: Healthy volunteers (n = 22) and patients with iron overload (n = 78) were recruited (53 males, median age 34 years). A 1.5 T study (Magnetom Avanto, Siemens) was performed on all subjects. The same mid-ventricular short axis cardiac slice and transaxial slice through the liver were used to acquire both RBH T2* images and WIP T2* maps for each participant. Cardiac white blood (WB) and black blood (BB) sequences were acquired. Intraobserver, interobserver and interstudy reproducibility were measured on the same data from a subset of 20 participants. RESULTS: Liver T2* values ranged from 0.8 to 35.7 ms (median 5.1 ms) and cardiac T2* values from 6.0 to 52.3 ms (median 31 ms). The coefficient of variance (CoV) values for direct comparison of T2* values by RBH and WIP were 6.1-7.8 % across techniques. Accurate delineation of the septum was difficult on some WIP T2* maps due to artefacts. The inability to manually correct for noise by truncation of erroneous later echo times led to some overestimation of T2* using WIP T2* compared with the RBH T2*. Reproducibility CoV results for RBH T2* ranged from 1.5 to 5.7 % which were better than the reproducibility of WIP T2* values of 4.1-16.6 %. CONCLUSIONS: Iron estimation using the T2* CMR sequence in combination with Siemens' in-line data processing is generally satisfactory and may help facilitate global access to tissue iron assessment. The current automated T2* map technique is less good for tissue iron assessment with noisy data at low T2* values

Comparison of 3 T and 1.5 T for T2* magnetic resonance of tissue iron.

BACKGROUND: T2* magnetic resonance of tissue iron concentration has improved the outcome of transfusion dependant anaemia patients. Clinical evaluation is performed at 1.5 T but scanners operating at 3 T are increasing in numbers. There is a paucity of data on the relative merits of iron quantification at 3 T vs 1.5 T. METHODS: A total of 104 transfusion dependent anaemia patients and 20 normal volunteers were prospectively recruited to undergo cardiac and liver T2* assessment at both 1.5 T and 3 T. Intra-observer, inter-observer and inter-study reproducibility analysis were performed on 20 randomly selected patients for cardiac and liver T2*. RESULTS: Association between heart and liver T2* at 1.5 T and 3 T was non-linear with good fit (R (2) = 0.954, p < 0.001 for heart white-blood (WB) imaging; R (2) = 0.931, p < 0.001 for heart black-blood (BB) imaging; R (2) = 0.993, p < 0.001 for liver imaging). R2* approximately doubled between 1.5 T and 3 T with linear fits for both heart and liver (94, 94 and 105 % respectively). Coefficients of variation for intra- and inter-observer reproducibility, as well as inter-study reproducibility trended to be less good at 3 T (3.5 to 6.5 %) than at 1.5 T (1.4 to 5.7 %) for both heart and liver T2*. Artefact scores for the heart were significantly worse with the 3 T BB sequence (median 4, IQR 2-5) compared with the 1.5 T BB sequence (4 [3-5], p = 0.007). CONCLUSION: Heart and liver T2* and R2* at 3 T show close association with 1.5 T values, but there were more artefacts at 3 T and trends to lower reproducibility causing difficulty in quantifying low T2* values with high tissue iron. Therefore T2* imaging at 1.5 T remains the gold standard for clinical practice. However, in centres where only 3 T is available, equivalent values at 1.5 T may be approximated by halving the 3 T tissue R2* with subsequent conversion to T2*

Thermal photons in QGP and non-ideal effects

We investigate the thermal photon production-rates using one dimensional boost-invariant second order relativistic hydrodynamics to find proper time evolution of the energy density and the temperature. The effect of bulk-viscosity and non-ideal equation of state are taken into account in a manner consistent with recent lattice QCD estimates. It is shown that the \textit{non-ideal} gas equation of state i.e $\epsilon-3\,P\,\neq 0$ behaviour of the expanding plasma, which is important near the phase-transition point, can significantly slow down the hydrodynamic expansion and thereby increase the photon production-rates. Inclusion of the bulk viscosity may also have similar effect on the hydrodynamic evolution. However the effect of bulk viscosity is shown to be significantly lower than the \textit{non-ideal} gas equation of state. We also analyze the interesting phenomenon of bulk viscosity induced cavitation making the hydrodynamical description invalid. We include the viscous corrections to the distribution functions while calculating the photon spectra. It is shown that ignoring the cavitation phenomenon can lead to erroneous estimation of the photon flux.Comment: 11 pages, 13 figures; accepted for publication in JHE

Analysis of cod-liver oil adulteration using Fourier Transform Infrared (FTIR) spectroscopy.

Analysis of the adulteration of cod-liver oil with much cheaper oil-like animal fats has become attractive in recent years. This study highlights an application of Fourier transform infrared (FTIR) spectroscopy as a nondestructive and fast technique for the determination of adulterants in cod-liver oil. Attenuated total reflectance measurements were made on pure cod-liver oil and cod-liver oil adulterated with different concentrations of lard (0.5–50% v/v in cod-liver oil). A chemometrics partial least squares (PLS) calibration model was developed for quantitative measurement of the adulterant. Discriminant analysis method was used to classify cod-liver oil samples from common animal fats (beef, chicken, mutton, and lard) based on their infrared spectra. Discriminant analysis carried out using seven principal components was able to classify the samples as pure or adulterated cod-liver oil based on their FTIR spectra at the selected fingerprint regions (1,500–1,030 cm−1)

The impact of Stieltjes' work on continued fractions and orthogonal polynomials

Stieltjes' work on continued fractions and the orthogonal polynomials related to continued fraction expansions is summarized and an attempt is made to describe the influence of Stieltjes' ideas and work in research done after his death, with an emphasis on the theory of orthogonal polynomials

RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach.

The security and privacy risks posed by unmanned aerial vehicles (UAVs) have become a significant cause of concern in today's society. Due to technological advancement, these devices are becoming progressively inexpensive, which makes them convenient for many different applications. The massive number of UAVs is making it difficult to manage and monitor them in restricted areas. In addition, other signals using the same frequency range make it more challenging to identify UAV signals. In these circumstances, an intelligent system to detect and identify UAVs is a necessity. Most of the previous studies on UAV identification relied on various feature-extraction techniques, which are computationally expensive. Therefore, this article proposes an end-to-end deep-learning-based model to detect and identify UAVs based on their radio frequency (RF) signature. Unlike existing studies, multiscale feature-extraction techniques without manual intervention are utilized to extract enriched features that assist the model in achieving good generalization capability of the signal and making decisions with lower computational time. Additionally, residual blocks are utilized to learn complex representations, as well as to overcome vanishing gradient problems during training. The detection and identification tasks are performed in the presence of Bluetooth and WIFI signals, which are two signals from the same frequency band. For the identification task, the model is evaluated for specific devices, as well as for the signature of the particular manufacturers. The performance of the model is evaluated across various different signal-to-noise ratios (SNR). Furthermore, the findings are compared to the results of previous work. The proposed model yields an overall accuracy, precision, sensitivity, and F1-score of 97.53%, 98.06%, 98.00%, and 98.00%, respectively, for RF signal detection from 0 dB to 30 dB SNR in the CardRF dataset. The proposed model demonstrates an inference time of 0.37 ms (milliseconds) for RF signal detection, which is a substantial improvement over existing work. Therefore, the proposed end-to-end deep-learning-based method outperforms the existing work in terms of performance and time complexity. Based on the outcomes illustrated in the paper, the proposed model can be used in surveillance systems for real-time UAV detection and identification

Identification of Andesite Rock Distribution Using Resistivity Geoelectric Method in Tapalang Area of Mamuju District West Sulawesi Province

Volcanoes make Indonesia have andesite rock reserves in several areas, including this research area carried out in Labuang Rano Village, West Tapalang District, Mamuju Regency. The geological complex of ancient volcanoes of the Adang Formation includes the morphology of hills from the eruption. The research aims to identify the subsurface layers of the earth using geoelectricity. The geoelectric method used in this research is the resistivity method with Wenner schlumberger configuration. With a stretch of track area of 320 meters each. The data obtained as apparent resistivity values are then processed in Res2Dinv Demo, producing an inversion cross-section. The results of the XYZC variable interpretation in Voxler Demo display andesite rocks at different depths from each track ranging from depths between 15-45 meters with resistivity values of 100-200 Ωm. The first layer is sandstone and clay resistivity &lt;100 Ωm, the second layer is andesite rock resistivity of 100-200 Ωm, and the base layer is tuff resistivity of 20-100. Based on the type of layer, it is identified that the rock layer in this area is andesite rock

A search for the decay modes B+/- to h+/- tau l

We present a search for the lepton flavor violating decay modes B+/- to h+/- tau l (h= K,pi; l= e,mu) using the BaBar data sample, which corresponds to 472 million BBbar pairs. The search uses events where one B meson is fully reconstructed in one of several hadronic final states. Using the momenta of the reconstructed B, h, and l candidates, we are able to fully determine the tau four-momentum. The resulting tau candidate mass is our main discriminant against combinatorial background. We see no evidence for B+/- to h+/- tau l decays and set a 90% confidence level upper limit on each branching fraction at the level of a few times 10^-5.Comment: 15 pages, 7 figures, submitted to Phys. Rev.