Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

Background: Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology.Results: We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model.Conclusions: After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis

Metabolic engineering with systems biology tools to optimize production of prokaryotic secondary metabolites

This Highlight examines current status of metabolic engineering and systems biology tools deployed for the optimal production of prokaryotic secondary metabolites.</p

Two-gap and paramagnetic pair-breaking effects on upper critical field of SmFeAsO0.85_{0.85} and SmFeAsO0.8_{0.8}F0.2_{0.2} single crystals

We investigated the temperature dependence of the upper critical field [$H_{c2}(T)$] of fluorine-free SmFeAsO$_{0.85}$ and fluorine-doped SmFeAsO$_{0.8}$F$_{0.2}$ single crystals by measuring the resistive transition in low static magnetic fields and in pulsed fields up to 60 T. Both crystals show that $H_{c2}(T)$'s along the c axis [$H_{c2}^c(T)$] and in an $ab$-planar direction [$H_{c2}^{ab}(T)$] exhibit a linear and a sublinear increase, respectively, with decreasing temperature below the superconducting transition. $H_{c2}(T)$'s in both directions deviate from the conventional one-gap Werthamer-Helfand-Hohenberg theoretical prediction at low temperatures. A two-gap nature and the paramagnetic pair-breaking effect are shown to be responsible for the temperature-dependent behavior of $H_{c2}^c$ and $H_{c2}^{ab}$, respectively.Comment: 21 pages, 8 figure

Tau functions as Widom constants

We define a tau function for a generic Riemann-Hilbert problem posed on a union of non-intersecting smooth closed curves with jump matrices analytic in their neighborhood. The tau function depends on parameters of the jumps and is expressed as the Fredholm determinant of an integral operator with block integrable kernel constructed in terms of elementary parametrices. Its logarithmic derivatives with respect to parameters are given by contour integrals involving these parametrices and the solution of the Riemann-Hilbert problem. In the case of one circle, the tau function coincides with Widom's determinant arising in the asymptotics of block Toeplitz matrices. Our construction gives the Jimbo-Miwa-Ueno tau function for Riemann-Hilbert problems of isomonodromic origin (Painlev\'e VI, V, III, Garnier system, etc) and the Sato-Segal-Wilson tau function for integrable hierarchies such as Gelfand-Dickey and Drinfeld-Sokolov.Comment: 26 pages, 6 figure

Current evidence and the potential role of proton beam therapy for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death, and external beam radiation therapy has emerged as a promising approach for managing HCC. Proton beam therapy (PBT) offers dosimetric advantages over X-ray therapy, with superior physical properties known as the Bragg peak. PBT holds promise for reducing hepatotoxicity and allowing safe dose-escalation to the tumor. It has been tried in various clinical conditions and has shown promising local tumor control and survival outcomes. A recent phase III trial demonstrated the non-inferiority of PBT in local tumor control compared to current standard radiofrequency ablation in early-stage HCC. PBT also tended to show more favorable outcomes compared to transarterial chemoembolization in the intermediate stage, and has proven effective in-field disease control and safe toxicity profiles in advanced HCC. In this review, we discuss the rationale, clinical studies, optimal indication, and future directions of PBT in HCC treatment

Effects of Distilled Cervi Pantotrichum Cornu and Rehmannia glutinosa Pharmacopuncture at GB21 (Jianjing) on Heart Rate Variability: A Randomized and Double-blind Clinical Trial

AbstractBackground/PurposeThe purpose of this study was to use heart rate variability (HRV) to investigate the effects of distilled Cervi Pantotrichum Cornu pharmacopuncture and Rehmannia glutinosa pharmacopuncture on the autonomic nervous system.Materials and methodsForty healthy male participants were divided into two groups: the participants of the C-group received distilled Cervi Pantotrichum Cornu pharmacopuncture and those of the R-group received Rehmannia glutinosa pharmacopuncture. The study design was a randomized, double-blind clinical trial. Each participant received one of the two solutions injected at GB21 (Jianjing). The changes in HRV were measured seven times using the QECG-3: LXC3203 system (LAXTHA Inc. Korea). Time-dependent changes in HRV for each group were analyzed using the paired t test (significance level: p < 0.05), and the difference in the HRV fluctuations between the two experimental groups was evaluated using the independent sample test (significance level: p < 0.05).Results and conclusionThe results showed that Cervi Pantotrichum Cornu pharmacopuncture and Rehmannia glutinosa pharmacopuncture tended to activate the autonomic nervous system within the normal range. Cervi Pantotrichum Cornu pharmacopuncture tended to activate the sympathetic nervous system, whereas Rehmannia glutinosa pharmacopuncture tended to activate both the sympathetic and parasympathetic nervous systems

Effect of Irradiation on the Degradation of Nucleotides in Turkey Meat

The degradation of nucleotides in cured ready-to-eat (RTE) as well as uncured raw and cooked turkey meat products by irradiation were determined to evaluate the potential impact of nucleotides on the taste changes in irradiated turkey meat. Four irradiation doses (0, 1.5, 3.0 and 4.5 kGy) were applied to cured RTE and uncured turkey meat products, and the amounts of nucleotides and their degradation products were measured. Results showed that irradiation had a significant impact to the amount of nucleotides (adenosine diphosphate, adenosine monophosphate and inosine monophosphate) and the breakdown of these nucleotides (inosine and hypoxanthine) in uncured turkey meat when irradiated at \u3c 3.0 kGy. However, significant decreases in inosine and hypoxanthine were observed when the uncured turkey meat were irradiated at \u3e 3.0 kGy might attribute to uric acid and other compounds formation. The increase in K-value (the percentage of inosine and hypoxanthine over the total content of adenosine triphosphate) at lower irradiation dose in uncured cooked than raw turkey meat indicated that cooked meat is more susceptible to oxidation. But little effect was found on the nucleotides and nucleotides degradation products in cured RTE turkey meat products because of the antioxidant effect of sodium nitrite

Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers

High-quality and high-throughput prediction of enzyme commission (EC) numbers is essential for accurate understanding of enzyme functions, which have many implications in pathologies and industrial biotechnology. Several EC number prediction tools are currently available, but their prediction performance needs to be further improved to precisely and efficiently process an ever-increasing volume of protein sequence data. Here, we report DeepEC, a deep learning-based computational framework that predicts EC numbers for protein sequences with high precision and in a high-throughput manner. DeepEC takes a protein sequence as input and predicts EC numbers as output. DeepEC uses 3 convolutional neural networks (CNNs) as a major engine for the prediction of EC numbers, and also implements homology analysis for EC numbers that cannot be classified by the CNNs. Comparative analyses against 5 representative EC number prediction tools show that DeepEC allows the most precise prediction of EC numbers, and is the fastest and the lightest in terms of the disk space required. Furthermore, DeepEC is the most sensitive in detecting the effects of mutated domains/binding site residues of protein sequences. DeepEC can be used as an independent tool, and also as a third-party software component in combination with other computational platforms that examine metabolic reactions