Identification of metabolism pathways directly regulated by sigma54 factor in Bacillus thuringiensis

Sigma54 (σ54) normally regulates nitrogen and carbon utilization in bacteria. Promoters that are σ54-dependent are highly conserved and contain short sequences located at the −24 and −12 positions upstream of the transcription initiation site. σ54 requires regulatory proteins known as bacterial enhancer-binding proteins (bEBPs) to activate gene transcription. We show that σ54 regulates the capacity to grow on various nitrogen sources using a Bacillus thuringiensis HD73 mutant lacking the sigL gene encoding σ54 (ΔsigL). A 2-fold-change cutoff and a false discovery rate cutoff of P < 0.05 were used to analyze the DNA microarray data, which revealed 255 genes that were downregulated and 121 that were upregulated in the ΔsigL mutant relative to the wild-type HD73 strain. The σ54 regulon (stationary phase) was characterized by DNA microarray, bioinformatics, and functional assay; 16 operons containing 47 genes were identified whose promoter regions contain the conserved −12/−24 element and whose transcriptional activities were abolished or reduced in the ΔsigL mutant. Eight σ54-dependent transcriptional bEBPs were found in the Bt HD73 genome, and they regulated night σ54-dependent promoters.The metabolic pathways activated by σ54 in this process have yet to be identified in Bacillus thuringiensis; nonetheless, the present analysis of the σ54 regulon provides a better understanding of the physiological roles of σ factors in bacteria

The differential impact of investor sentiment on the value relevance of book value versus earnings

Thesis (Ph.D.)--Boston UniversityThis study investigates the differential role of investor sentiment on the value relevance of book value versus earnings. I predict and find that the value relevance of book value is higher during low sentiment relative to high sentiment periods, and conversely that the value relevance of earnings is higher during high sentiment relative to low sentiment periods. These findings are consistent with investors-when optimistic-placing a higher weight on earnings, which represent an accounting proxy more indicative of future performance, whereas investors-when pessimistic-placing a higher weight on book value, which represents an accounting proxy (given historical cost conventions) that is more indicative of current value. Additional analyses suggest that this sentiment effect is more pronounced for book value components that are closely related to abandonment value, and for earnings components that have strong indication of future earnings (specifically, permanent earnings). Results are also robust to alternative measures of investor sentiment

IL-1 receptor like 1 protects against alcoholic liver injury by limiting NF-κB activation in hepatic macrophages

Background & Aim Alcohol consumption increases intestinal permeability and causes damage to hepatocytes, leading to the release of pathogen- and damage-associated molecular pattern molecules (PAMPs and DAMPs), stimulating hepatic macrophages and activating NF-κB. The resultant inflammation exacerbates alcoholic liver disease (ALD). However, much less is known about the mechanisms attenuating inflammation and preventing disease progression in most heavy drinkers. Interleukin (IL)-33 is a DAMP (alarmin) released from dead cells that acts through its receptor, IL-1 receptor like 1 (ST2). ST2 signaling has been reported to either stimulate or inhibit NF-κB activation. The role of IL-33/ST2 in ALD has not been studied. Methods Serum levels of IL-33 and its decoy receptor, soluble ST2 (sST2) were measured in ALD patients. Alcohol-induced liver injury, inflammation and hepatic macrophage activation were compared between wild-type, IL-33−/− and ST2−/− mice in several models. Results Elevation of serum IL-33 and sST2 were only observed in patients with severe decompensated ALD. Consistently, in mice with mild ALD without significant cell death and IL-33 release, IL-33 deletion did not affect alcohol-induced liver damage. However, ST2-deletion exacerbated ALD, through enhancing NF-κB activation in liver macrophages. In contrast, when extracellular IL-33 was markedly elevated, liver injury and inflammation were attenuated in both IL-33−/− and ST2−/− mice compared to wild-type mice. Conclusion Our data revealed a dichotomous role of IL-33/ST2 signaling during ALD development. At early and mild stages, ST2 restrains the inflammatory activation of hepatic macrophages, through inhibiting NF-κB, and plays a protective function in an IL-33-independent fashion. During severe liver injury, significant cell death and marked IL-33 release occur, which triggers IL-33/ST2 signaling and exacerbates tissue damage. Lay summary In mild ALD, ST2 negatively regulates the inflammatory activation of hepatic macrophages, thereby protecting against alcohol-induced liver damage, whereas in the case of severe liver injury, the release of extracellular IL-33 may exacerbate tissue inflammation by triggering the canonical IL-33/ST2L signaling in hepatic macrophages

AN INVESTIGATION ON OPERATIONAL PERFORMANCE OF THREE TYPICAL HVAC SYSTEMS IN WUHAN

Proceedings of the 2021 International Workshop on Modern Science and Technology; September 29, 2021conference pape

GOMCL: a t GOMCL: a toolkit t oolkit to cluster o cluster, evaluate, and extr aluate, and extract non-r act non-redundant edundant associations of Gene Ontology-based functions

Background Functional enrichment of genes and pathways based on Gene Ontology (GO) has been widely used to describe the results of various -omics analyses. GO terms statistically overrepresented within a set of a large number of genes are typically used to describe the main functional attributes of the gene set. However, these lists of overrepresented GO terms are often too large and contains redundant overlapping GO terms hindering informative functional interpretations. Results We developed GOMCL to reduce redundancy and summarize lists of GO terms effectively and informatively. This lightweight python toolkit efficiently identifies clusters within a list of GO terms using the Markov Clustering (MCL) algorithm, based on the overlap of gene members between GO terms. GOMCL facilitates biological interpretation of a large number of GO terms by condensing them into GO clusters representing non-overlapping functional themes. It enables visualizing GO clusters as a heatmap, networks based on either overlap of members or hierarchy among GO terms, and tables with depth and cluster information for each GO term. Each GO cluster generated by GOMCL can be evaluated and further divided into non-overlapping sub-clusters using the GOMCL-sub module. The outputs from both GOMCL and GOMCL-sub can be imported to Cytoscape for additional visualization effects. Conclusions GOMCL is a convenient toolkit to cluster, evaluate, and extract non-redundant associations of Gene Ontology-based functions. GOMCL helps researchers to reduce time spent on manual curation of large lists of GO terms, minimize biases introduced by redundant GO terms in data interpretation, and batch processing of multiple GO enrichment datasets. A user guide, a test dataset, and the source code of GOMCL are available at and

Multi-field coupled mathematical modeling and numerical simulation technique of gas transport in deep coal seams

Coalbed gas production contributes to energy diversification and effectively mitigates the risk of mine gas outbursts. However, the complexity and nonlinear characteristics of multifield coupled gas migration in deep coal seams pose significant challenges that traditional prediction and control methods struggle to address. This paper explores the effects of coupled multi-physics fields on gas migration and reviews a numerical simulation method that integrates fractal theory with discrete fracture network modeling, aiming to overcome the limitations of conventional models in capturing the interactions among seepage, heat transfer, stress distribution, and gas adsorption/desorption. The study highlights the interactions between fractures and pores, as well as the coupling effects between fluids f low, heat transfer, and solid mechanics. It further presents a more accurate prediction method to enhance the simulation accuracy of gas migration in deep coal seams.Document Type: PerspectiveCited as: Wang, Q., Ji, M., Liu, G., Fan, S. Multi-field coupled mathematical modeling and numerical simulation technique of gas transport in deep coal seams. Advances in Geo-Energy Research, 2025, 15(1): 87-90. https://doi.org/10.46690/ager.2025.01.0