Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Kinetic Monte Carlo Simulations of Crystal Growth in Ferroelectric Alloys

The growth rates and chemical ordering of ferroelectric alloys are studied with kinetic Monte Carlo (KMC) simulations using an electrostatic model with long-range Coulomb interactions, as a function of temperature, chemical composition, and substrate orientation. Crystal growth is characterized by thermodynamic processes involving adsorption and evaporation, with solid-on-solid restrictions and excluding diffusion. A KMC algorithm is formulated to simulate this model efficiently in the presence of long-range interactions. Simulations were carried out on Ba(Mg_{1/3}Nb_{2/3})O_3 (BMN) type materials. Compared to the simple rocksalt ordered structures, ordered BMN grows only at very low temperatures and only under finely tuned conditions. For materials with tetravalent compositions, such as (1-x)Ba(Mg_{1/3}Nb_{2/3})O_3 + xBaZrO_3 (BMN-BZ), the model does not incorporate tetravalent ions at low-temperature, exhibiting a phase-separated ground state instead. At higher temperatures, tetravalent ions can be incorporated, but the resulting crystals show no chemical ordering in the absence of diffusive mechanisms.Comment: 13 pages, 16 postscript figures, submitted to Physics Review B Journa

Commensurate stacking within confined ultramicropores boosting acetylene storage capacity and separation efficiency

Developing advanced porous materials possessing both a high storage capacity and selectivity for acetylene (C2H2) remains challenging but a sought-after endeavor. Herein we show a strategy involving synergic combination of spatial confinement and commensurate stacking for enhanced C2H2 storage and capture via maximizing the host—guest and guest—guest interactions. Two ultramicroporous metal-organic frameworks (MOFs), MIL-160 and MOF-303 are elaborately constructed to exhibit ultrahigh C2H2 uptakes of 235 and 195 cm3·g−1, respectively, due to the confinement effect of the suitable pore sizes and periodically dispersed molecular recognition sites. Specially, C2H2 capacity of MIL-160 sets a new benchmark for C2H2 storage. The exceptional separation performances of two materials for C2H2 over both CO2 and ethylene (C2H4), which is rarely observed, outperform most of the benchmark materials for C2H2 capture. We scrutinized the origins of ultrahigh C2H2 loading in the confined channels via theoretical investigations. The superior separation efficiency for C2H2/CO2 and C2H2/C2H4 mixtures with unprecedented C2H2 trapping capacity (&gt; 200 L·kg−1) was further demonstrated by dynamic breakthrough experiments. </p

Active RIS Versus Passive RIS: Which Is Superior with the Same Power Budget?

This letter theoretically compares the active reconfigurable intelligent surface (RIS)-aided system with the passive RIS-aided system. For a fair comparison, we consider that these two systems have the same overall power budget that can be used at both the base station (BS) and the RIS. For active RIS, we first derive the optimal power splitting between the BS&#x2019;s transmit signal power and RIS&#x2019;s output signal power. We also analyze the impact of various system parameters on the optimal power splitting ratio. Then, we theoretically and numerically compare the performance between the active RIS and the passive RIS, which demonstrates that the active RIS would be superior if the power budget is not very small and the number of RIS elements is not very large

Mice lacking C1q or C3 show accelerated rejection of minor H disparate skin grafts and resistance to induction of tolerance

Complement activation is known to have deleterious effects on organ transplantation. On the other hand, the complement system is also known to have an important role in regulating immune responses. The balance between these two opposing effects is critical in the context of transplantation. Here, we report that female mice deficient in C1q (C1qa(−/−)) or C3 (C3(−/−)) reject male syngeneic grafts (HY incompatible) at an accelerated rate compared with WT mice. Intranasal HY peptide administration, which induces tolerance to syngeneic male grafts in WT mice, fails to induce tolerance in C1qa(−/−) or C3(−/−) mice. The rejection of the male grafts correlated with the presence of HY D(b)Uty-specific CD8(+) T cells. Consistent with this, peptide-treated C1qa(−/−) and C3(−/−) female mice rejecting male grafts exhibited more antigen-specific CD8(+)IFN-γ(+) and CD8(+)IL-10(+) cells compared with WT females. This suggests that accumulation of IFN-γ- and IL-10-producing T cells may play a key role in mediating the ongoing inflammatory process and graft rejection. Interestingly, within the tolerized male skin grafts of peptide-treated WT mice, IFN-γ, C1q and C3 mRNA levels were higher compared to control female grafts. These results suggest that C1q and C3 facilitate the induction of intranasal tolerance

Highly Efficient Oxygen Reduction Catalysts by Rational Synthesis of Nanoconfined Maghemite in a Nitrogen-Doped Graphene Framework

The oxygen reduction reaction (ORR) is critical for electrochemical energy storage and conversion: e.g., in fuel cells and metal–air batteries. A major challenge is to develop cost-effective and durable ORR catalysts, to replace the relatively expensive platinum-loaded carbon (PtC) counterparts, particularly for large-scale applications. Despite progress over the past few decades in developing efficient non-precious-metal (NPM) catalysts, such as Fe/N/C-based materials (the best-known alternatives), most of the reported catalytic activities have yet to match that of PtC. Herein we propose a two-step process for the production of highly efficient NPM catalysts that outperform PtC in alkaline media: (1) a hierarchical porosity of a supporting substrate is generated and optimized in advance, especially to achieve a high total pore volume for rapid mass transfer, and (2) an appropriate amount of NPM precursor is added to the optimized substrate to boost the reduction potential while maintaining the hierarchically porous structure. Such a scheme was successfully applied to a case of nanoconfined maghemite (γ-Fe2O3) in a nitrogen-doped graphene framework. The resulting catalyst system surpasses the performance of the equivalent commercial PtC, in terms of a higher reduction potential, a significantly lower peroxide formation ratio, more than tripled kinetic current density, smaller Tafel slope, better durability, etc. The reported catalyst is also among the best of all the existing Fe-based ORR catalysts, indicating the great potential of γ-Fe2O3 for ORR in practical applications

Detention Properties of Subsurface Stormwater Modules Under Tropical Climate

Subsurface stormwater module is one of the components of a sustainable drainage system. However, the performance of subsurface stormwater module as on-site detention under tropical climate like Malaysia has not been extensively studied in the literature. The current study involves on-site installation of pilot scale subsurface stormwater modules exposed to tropical climate to simulate real conditions to evaluate the detention performance. Rainfall together with the changes in water level and volume of water detained in the installation were observed for six months between April 2021 to October 2021. The subsurface stormwater module used in the current study has a porosity of 94%. It was found that the subsurface stormwater module setup was able to detain between 35.2% to 95.6% of the rainfall volume generated from total rainfall between 11.1 mm to 56.8 mm. The findings can be used as design consideration for using subsurface stormwater module under tropical climate

Detention Properties of Subsurface Stormwater Modules Under Tropical Climate

Subsurface stormwater module is one of the components of a sustainable drainage system. However, the performance of subsurface stormwater module as on-site detention under tropical climate like Malaysia has not been extensively studied in the literature. The current study involves on-site installation of pilot scale subsurface stormwater modules exposed to tropical climate to simulate real conditions to evaluate the detention performance. Rainfall together with the changes in water level and volume of water detained in the installation were observed for six months between April 2021 to October 2021. The subsurface stormwater module used in the current study has a porosity of 94%. It was found that the subsurface stormwater module setup was able to detain between 35.2% to 95.6% of the rainfall volume generated from total rainfall between 11.1 mm to 56.8 mm. The findings can be used as design consideration for using subsurface stormwater module under tropical climate

Proteomic Analysis of Bacterial Expression Profiles Following Exposure to Organic Solvent Flower Extract of Melastoma candidum D Don (Melastomataceae)

Purpose: To identify potential antibacterial protein targets following exposure to Melastoma candidum extract.Methods: Plant extracts were prepared using sequential extraction method. Denaturing gel electrophoresis and MALDI TOF-TOF MS protein sequencing were used to identify differentialexpressed bacterial proteins. 96-well microplate method was used to determine the minimum inhibitory concentration (MIC) values. Thin layer chromatography (TLC) bio-autobiography and gaschromatography-mass spectrometry (GC-MS) were performed to determine the phytochemicals in the active fraction.Results: Five differentially expressed bacterial proteins (four from Escherichia coli and one from Staphylococcus aureus), were identified via proteomic approach. Among the bacterial proteins identified, glutamate decarboxylase, elongation factor-Tu and α-hemolysin are especially noteworthy, as they are implicated in critical bacterial pathways pertaining to survival in acidic environment, protein translation and virulence, respectively. Additionally, we tested and reported the minimum inhibition concentrations of different M. candidum fractions and gas chromatography-mass spectrometry GC-MS analysis of the active fraction.Conclusion: Glutamate decarboxylase, elongation factor-Tu and α-hemolysin represent potential antibacterial targets.Keywords: Escherichia coli, Staphylococcus aureus, Melastoma candidum, Glutamate decarboxylase, Elongation factor-Tu, α-Hemolysin, Protein expressio