Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Advances in the Diels-Alder Reaction in Self-Healing Polymeric Materials

Polymeric functional materials are widely used in all aspects of everyday life. However, they are subject to a variety of environmental influences such as chemical attack, mechanical abrasion or impact and thermal decomposition, resulting in a significant shortening of the service life of polymer materials. Using traditional repair methods, the damaged part of the material is only reattached or reinforced at a macro level. Currently, more and more research is focused on smart composite materials with self-healing properties. This eliminates hidden problems in the use of the material, thereby extending its service life and broadening the range of applications. The Diels-Alder (DA) reaction is a [4+2] cycloaddition reaction between a conjugated diene and a substituted alkene. It has become a hot research topic in the field of self-healing due to its mild reaction conditions and lack of catalyst. In this paper, the progress of research on self-healing polymeric materials is reviewed with a focus on applications in DA reactions. The research progress in this field is reviewed based on the chemical structures of the prepared DA reaction-based polymers, and the development of DA reaction based self-healing polymers is investigated. Finally, the development of selfhealing polymers based on DA reactions is given as an outlook

The nth-order bias optimality for multichain Markov decision processes

In this paper, we propose a new approach to the theory of finite multichain Markov decision processes (MDPs) with different performance optimization criteria. We first propose the concept of nth-order bias; then, using the average reward and bias difference formulas derived in this paper, we develop an optimization theory for finite MDPs that covers a complete spectrum from average optimality, bias optimality, to all high-order bias optimality, in a unified way. The approach is simple, direct, natural, and intuitive; it depends neither on Laurent series expansion nor on discounted MDPs. We also propose one-phase policy iteration algorithms for bias and high-order bias optimal policies, which are more efficient than the two-phase algorithms in the literature. Furthermore, we derive high-order bias optimality equations. This research is a part of our effort in developing sensitivity-based learning and optimization theory

Event-based optimization of Markov systems

Recent research indicates that Markov decision processes (MDPs) and perturbation analysis (PA) based optimization can be derived easily from two fundamental performance sensitivity formulas. With this sensitivity point of view, an event-based optimization approach, including event-based sensitivity analysis and event-based policy iteration, was proposed via an example by X. R. Can (Discrete Event Dyn. Syst.: Theory Appl., vol. 15, pp. 169-197, 2005). This approach utilizes the special feature of a system and illustrates how the potentials can be aggregated using the special feature. The approach applies to many practical problems that do not fit well the standard MDP formulation. This note provides a mathematical formulation and proves the main results for this approach

Identification of COL1A1 associated with immune infiltration in brain lower grade glioma.

Brain low grade gliomas (LGG) often give serious clinical symptoms due to the invasion towards nervous system, affecting the life quality of patients. Collagen type I alpha 1(COL1A1) is the main component of type I collagen. Although there are many reports about abnormal expression of COL1A1 in various tumors, specific role and clinical significance of COL1A1 in LGG have not yet been elucidated. In this work, Tumor Immune Estimation Resource database was used for detecting the expression level of COL1A1 in cancer and normal tissues, and aimed to explore the relationship between COL1A1 and tumor immune infiltration. We applied Kaplan-Meier to analyze the role of COL1A1 in clinical prognosis. Univariate survival rate and multivariate Cox analysis were used to compare clinical characteristics and survival rate. The relativity between the expression of COL1A1 and the tumor microenvironment was evaluated using ESTIMATE algorithm. Finally, the relationship between expression level of COL1A1 and gene marker sets of immune cell infiltration was investigated via TIMER. According to TCGA, COL1A1 overexpression was correlated with overall survival (OS), progression free interval (PFI) and disease specific survival (DSS) of multiple tumors, especially in LGG. Multivariate analysis showed that COL1A1 expression was an independent prognostic factor for LGG. The expression of COL1A1 was positively correlated with the infiltration of CD4 + T and CD8 + T cells, neutrophils, macrophages and dendritic cells in LGG. In addition, there was a strong correlation between expression of COL1A1 and different immune marker sets in LGG. The results suggest that COL1A1 is related with tumor immune infiltration of LGG