A novel pyruvate kinase and its application in lactic acid production under oxygen deprivation in Corynebacterium glutamicum

BACKGROUND: Pyruvate kinase (Pyk) catalyzes the generation of pyruvate and ATP in glycolysis and functions as a key switch in the regulation of carbon flux distribution. Both the substrates and products of Pyk are involved in the tricarboxylic acid cycle, anaplerosis and energy anabolism, which places Pyk at a primary metabolic intersection. Pyks are highly conserved in most bacteria and lower eukaryotes. Corynebacterium glutamicum is an industrial workhorse for the production of various amino acids and organic acids. Although C. glutamicum was assumed to possess only one Pyk (pyk1, NCgl2008), NCgl2809 was annotated as a pyruvate kinase with an unknown role. RESULTS: Here, we identified that NCgl2809 was a novel pyruvate kinase (pyk2) in C. glutamicum. Complementation of the WTΔpyk1Δpyk2 strain with the pyk2 gene restored its growth on d-ribose, which demonstrated that Pyk2 could substitute for Pyk1 in vivo. Pyk2 was co-dependent on Mn(2+) and K(+) and had a higher affinity for ADP than phosphoenolpyruvate (PEP). The catalytic activity of Pyk2 was allosterically regulated by fructose 1,6-bisphosphate (FBP) activation and ATP inhibition. Furthermore, pyk2 and ldhA, which encodes l-lactate dehydrogenase, were co-transcribed as a bicistronic mRNA under aerobic conditions and pyk2 deficiency had a slight effect on the intracellular activity of Pyk. However, the mRNA level of pyk2 in the wild-type strain under oxygen deprivation was 14.24-fold higher than that under aerobic conditions. Under oxygen deprivation, pyk1 or pyk2 deficiency decreased the generation of lactic acid, and the overexpression of either pyk1 or pyk2 increased the production of lactic acid as the activity of Pyk increased. Fed-batch fermentation of the pyk2-overexpressing WTΔpyk1 strain produced 60.27 ± 1.40 g/L of lactic acid, which was a 47% increase compared to the parent strain under oxygen deprivation. CONCLUSIONS: Pyk2 functioned as a pyruvate kinase and contributed to the increased level of Pyk activity under oxygen deprivation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12896-016-0313-6) contains supplementary material, which is available to authorized users

Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework

Existing deep learning approaches for travel mode choice modeling fail to inform modelers about their prediction uncertainty. Even when facing scenarios that are out of the distribution of training data, which implies high prediction uncertainty, these approaches still provide deterministic answers, potentially leading to misguidance. To address this limitation, this study introduces the concept of uncertainty from the field of explainable artificial intelligence into travel mode choice modeling. We propose a Bayesian neural network-based travel mode prediction model (BTMP) that quantifies the uncertainty of travel mode predictions, enabling the model itself to "know" and "tell" what it doesn't know. With BTMP, we further propose an uncertainty-guided active survey framework, which dynamically formulates survey questions representing travel mode choice scenarios with high prediction uncertainty. Through iterative collection of responses to these dynamically tailored survey questions, BTMP is iteratively trained to achieve the desired accuracy faster with fewer questions, thereby reducing survey costs. Experimental validation using synthetic datasets confirms the effectiveness of BTMP in quantifying prediction uncertainty. Furthermore, experiments, utilizing both synthetic and real-world data, demonstrate that the BTMP model, trained with the uncertainty-guided active survey framework, requires 20% to 50% fewer survey responses to match the performance of the model trained on randomly collected survey data. Overall, the proposed BTMP model and active survey framework innovatively incorporate uncertainty quantification into travel mode choice modeling, providing model users with essential insights into prediction reliability while optimizing data collection for deep learning model training in a cost-efficient manner

An Empirical Study of Untangling Patterns of Two-Class Dependency Cycles

Dependency cycles pose a significant challenge to software quality and maintainability. However, there is limited understanding of how practitioners resolve dependency cycles in real-world scenarios. This paper presents an empirical study investigating the recurring patterns employed by software developers to resolve dependency cycles between two classes in practice. We analyzed the data from 38 open-source projects across different domains and manually inspected hundreds of cycle untangling cases. Our findings reveal that developers tend to employ five recurring patterns to address dependency cycles. The chosen patterns are not only determined by dependency relations between cyclic classes, but also highly related to their design context, i.e., how cyclic classes depend on or are depended by their neighbor classes. Through this empirical study, we also discovered three common counterintuitive solutions developers usually adopted during cycles' handling. These recurring patterns and common counterintuitive solutions observed in dependency cycles' practice can serve as a taxonomy to improve developers' awareness and also be used as learning materials for students in software engineering and inexperienced developers. Our results also suggest that, in addition to considering the internal structure of dependency cycles, automatic tools need to consider the design context of cycles to provide better support for refactoring dependency cycles.Comment: Preprint accepted for publication in Empirical Software Engineering, 202

Wide-area measurement-based supervision of the cerebral venous hemodynamic in a novel rat model

Abstract(#br)Background(#br)Traumatic brain injury (TBI) includes primary and secondary injuries, while monitoring intracranial pressure (ICP) and cerebral blood flow (CBF) is conducive to improve the prognosis of patients. However, the function of cerebral venous in this process is still unclear.(#br)New Method(#br)An acute epidural hematoma (AEDH) model was developed by placing a controllable microballoon in the right epidural space of a rat. The laser speckle contrast imaging (LSCI) system was used to observe CBF in real time, while ICP was monitored simultaneously. And the stability of this model was examined by magnetic resonance imaging (MRI).(#br)Results(#br)The blood perfusion rate (BPR) of venous was significantly negatively correlated with ICP. In the 100 μL group, the ipsilateral cerebral venous and microcirculation blood flow significantly decreased. According to the gross observations and pathological results, ischemic brain injury was the most serious on this condition.(#br)Comparison with Existing Method(s)(#br)Modeling method is relatively simple, which effectively reduces the cost. The volume of the microballoon is adjusted to simulate the volume of different size of hematomas. In addition, LSCI, as an advanced blood flow monitoring technology, has high sensitivity to detect subtle changes in CBF.(#br)Conclusion(#br)This study successfully developed a stable and reproducible AEDH rat model. Based on this model, it is preliminarily demonstrated that local intracranial hypertension can cause cerebral venous return restriction, which is an indispensable factor leading to the aggravation of secondary brain injury

Wide-area measurement-based supervision of the cerebral venous hemodynamic in a novel rat model.

BACKGROUND(#br)Traumatic brain injury (TBI) includes primary and secondary injuries, while monitoring intracranial pressure (ICP) and cerebral blood flow (CBF) is conducive to improve the prognosis of patients. However, the function of cerebral venous in this process is still unclear.(#br)NEW METHOD(#br)An acute epidural hematoma (AEDH) model was developed by placing a controllable micro balloon in the right epidural space of a rat. The laser speckle contrast imaging (LSCI) system was used to observe CBF in real time, while ICP was monitored simultaneously. And the stability of this model was examined by magnetic resonance imaging (MRI).(#br)RESULTS(#br)The blood perfusion rate (BPR) of venous was significantly negatively correlated with ICP. In the 100 μ L group, the ipsilateral cerebral venous and microcirculation blood flow significantly decreased. According to the gross observations and pathological results, ischemic brain injury was the most serious on this condition.(#br)COMPARISON WITH EXISTING METHOD(S)(#br)Modeling method is relatively simple, which effectively reduces the cost. The volume of the micro balloon is adjusted to simulate the volume of different size of hematomas. In addition, LSCI, as an advanced blood flow monitoring technology, has high sensitivity to detect subtle changes in CBF.(#br)CONCLUSION(#br)This study successfully developed a stable and reproducible AEDH rat model. Based on this model, it is preliminarily demonstrated that local intracranial hypertension can cause cerebral venous return restriction, which is an indispensable factor leading to the aggravation of secondary brain injury

DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding

Visually-situated languages such as charts and plots are omnipresent in real-world documents. These graphical depictions are human-readable and are often analyzed in visually-rich documents to address a variety of questions that necessitate complex reasoning and common-sense responses. Despite the growing number of datasets that aim to answer questions over charts, most only address this task in isolation, without considering the broader context of document-level question answering. Moreover, such datasets lack adequate common-sense reasoning information in their questions. In this work, we introduce a novel task named document-level chart question answering (DCQA). The goal of this task is to conduct document-level question answering, extracting charts or plots in the document via document layout analysis (DLA) first and subsequently performing chart question answering (CQA). The newly developed benchmark dataset comprises 50,010 synthetic documents integrating charts in a wide range of styles (6 styles in contrast to 3 for PlotQA and ChartQA) and includes 699,051 questions that demand a high degree of reasoning ability and common-sense understanding. Besides, we present the development of a potent question-answer generation engine that employs table data, a rich color set, and basic question templates to produce a vast array of reasoning question-answer pairs automatically. Based on DCQA, we devise an OCR-free transformer for document-level chart-oriented understanding, capable of DLA and answering complex reasoning and common-sense questions over charts in an OCR-free manner. Our DCQA dataset is expected to foster research on understanding visualizations in documents, especially for scenarios that require complex reasoning for charts in the visually-rich document. We implement and evaluate a set of baselines, and our proposed method achieves comparable results

Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic reticulum stress-related genes

SubjectMajor depressive disorder (MDD) negatively affects patients’ behaviours and daily lives. Due to the high heterogeneity and complex pathological features of MDD, its diagnosis remains challenging. Evidence suggests that endoplasmic reticulum stress (ERS) is involved in the pathogenesis of MDD; however, relevant diagnostic markers have not been well studied. This study aimed to screen for ERS genes with potential diagnostic value in MDD.MethodsGene expression data on MDD samples were downloaded from the GEO database, and ERS-related genes were obtained from the GeneCards and MSigDB databases. Differentially expressed genes (DEGs) in MDD patients and healthy subjects were identified and then integrated with ERS genes. ERS diagnostic model and nomogram were developed based on biomarkers screened using the LASSO method. The diagnostic performance of this model was evaluated. ERS-associated subtypes were identified. CIBERSORT and GSEA were used to explore the differences between the different subtypes. Finally, WGCNA was performed to identify hub genes related to the subtypes.ResultsA diagnostic model was developed based on seven ERS genes: KCNE1, PDIA4, STAU1, TMED4, MGST1, RCN1, and SHC1. The validation analysis showed that this model had a good diagnostic performance. KCNE1 expression was positively correlated with M0 macrophages and negatively correlated with resting CD4+ memory T cells. Two subtypes (SubA and SubB) were identified, and these two subtypes showed different ER score. The SubB group showed higher immune infiltration than the SubA group. Finally, NCF4, NCF2, CSF3R, and FPR2 were identified as hub genes associated with ERS molecular subtypes.ConclusionOur current study provides novel diagnostic biomarkers for MDD from an ERS perspective, and these findings further facilitate the use of precision medicine in MDD

Identification of differentially expressed microRNAs and the potential of microRNA-455-3p as a novel prognostic biomarker in glioma.

Glioma is an aggressive central nervous system malignancy. MicroRNAs (miRNAs/miRs) have been reported to be involved in the tumorigenesis of numerous types of cancer, including glioma. The present study aimed to identify the differentially expressed miRNAs in glioma, and further explore the clinical value of miR-455-3p in patients with glioma. GEO2R was used for the identification of the differentially expressed miRNAs according to the miRNA expression profiles obtained from the Gene Expression Omnibus database. OncomiR was used to analyze the relationship of miRNAs with the survival outcomes of the patients with glioma. A total of 108 patients with glioma were recruited to examine the expression levels of miR-455-3p and further explore its clinical value. The bioinformatics analysis results suggested that a total of 64 and 48 differentially expressed miRNAs were identified in the GSE90603 and GSE103229 datasets, respectively. There were 12 miRNAs in the overlap of the two datasets, of which three were able to accurately predict overall cancer survival, namely hsa-miR-7-5p, hsa-miR-21-3p and hsa-miR-455-3p. In patients with glioma, miR-455-3p was determined to be significantly upregulated (P<0.001). Additionally, patients with high miR-455-3p expression had significantly lower 5-year overall survival than those with low miR-455-3p expression (log-rank test, P=0.001). Cox regression analysis further determined that miR-455-3p was an independent prognostic indicator for overall survival in patients with glioma (hazard ratio=2.136; 95% CI=1.177-3.877; P=0.013). In conclusion, the present study revealed a series of miRNAs with potential functional roles in the pathogenesis of glioma, and provides findings that indicate miR-455-3p as a promising biomarker for the prognosis of glioma

Antibiotic chlortetracycline causes transgenerational immunosuppression via NF-κB

The extensive and increasing global use of antibiotics results in the ubiquitous presence of antibiotics in the environment, which has made them “pseudo persistent organic contaminants.” Despite numerous studies showing wide adverse effects of antibiotics on organisms, the chronic environmental risk of their exposure is unknown, and the molecular and cellular mechanisms of antibiotic toxicity remain unclear. Here, we systematically quantified transgenerational immune disturbances after chronic parental exposure to environmental levels of a common antibiotic, chlortetracycline (CTC), using zebrafish as a model. CTC strongly reduced the antibacterial activities of fish offspring by transgenerational immunosuppression. Both innate and adaptive immunities of the offspring were suppressed, showing significant perturbation of macrophages and neutrophils, expression of immune-related genes, and other immune functions. Moreover, these CTC-induced immune effects were either prevented or alleviated by the supplementation with PDTC, an antagonist of nuclear factor-κB (NF-κB), uncovering a seminal role of NF-κB in CTC immunotoxicity. Our results provide the evidence in fish that CTC at environmentally relevant concentrations can be transmitted over multiple generations and weaken the immune defense of offspring, raising concerns on the population hazards and ecological risk of antibiotics in the natural environment