Prediction of lymph node metastasis in oral squamous cell carcinoma based on protein profile

Objective: Lymph node metastasis leads to high mortality rates of oral squamous cell carcinoma (OSCC). However, it is still controversial to define clinically negative neck (cN0) and positive neck (cN1-3).

Methods: We retrieved candidate biomarkers identified by proteomic analysis in OSCC from published works of literature. In training stage, immunohistochemistry (IHC) analysis was used to determine the expression of proteins and logistic regression models with stepwise variable selection were used to identify potential factors that might affect lymph node metastasis and life status. Furthermore, the prediction model was validated in validating stage.

Results: We screened eight highly expressed proteins related to lymph node metastasis in OSCC and found that the expression levels of SOD2, BST2, CAD, ITGB6, and PRDX4 were significantly elevated in patients with lymph node metastasis compared to the patients without lymph node metastasis. Furthermore, in training and validating stages, the prediction model base on the combination of CAD, SOD2 expression levels, and histopathologic grade was developed and validated in patients with OSCC.

Conclusions: Our findings showed that the developed model well predicts the lymph node metastasis and life status in patients with OSCC, independent of TNM stage.     

Li–Ti Cation Mixing Enhanced Structural and Performance Stability of Li‐Rich Layered Oxide

Li‐rich layered metal oxides are one type of the most promising cathode materials in lithium‐ion batteries but suffer from severe voltage decay during cycling because of the continuous transition metal (TM) migration into the Li layers. A Li‐rich layered metal oxide Li_1.2Ti_0.26Ni_0.18Co_0.18Mn_0.18O₂ (LTR) is hereby designed, in which some of the Ti⁴⁺ cations are intrinsically present in the Li layers. The native Li–Ti cation mixing structure enhances the tolerance for structural distortion and inhibits the migration of the TM ions in the TMO₂ slabs during (de)lithiation. Consequently, LTR exhibits a remarkable cycling stability of 97% capacity retention after 182 cycles, and the average discharge potential drops only 90 mV in 100 cycles. In‐depth studies by electron energy loss spectroscopy and aberration‐corrected scanning transmission electron microscopy demonstrate the Li–Ti mixing structure. The charge compensation mechanism is uncovered with X‐ray absorption spectroscopy and explained with the density function theory calculations. These results show the superiority of introducing transition metal ions into the Li layers in reinforcing the structural stability of the Li‐rich layered metal oxides. These findings shed light on a possible path to the development of Li‐rich materials with better potential retention and a longer lifespan.     

RAD‐sequencing for estimating genomic relatedness matrix‐based heritability in the wild: A case study in roe deer

Estimating the evolutionary potential of quantitative traits and reliably predicting responses to selection in wild populations are important challenges in evolutionary biology. The genomic revolution has opened up opportunities for measuring relatedness among individuals with precision, enabling pedigree‐free estimation of trait heritabilities in wild populations. However, until now, most quantitative genetic studies based on a genomic relatedness matrix (GRM) have focused on long‐term monitored populations for which traditional pedigrees were also available, and have often had access to knowledge of genome sequence and variability. Here, we investigated the potential of RAD‐sequencing for estimating heritability in a free‐ranging roe deer (Capreolous capreolus) population for which no prior genomic resources were available. We propose a step‐by‐step analytical framework to optimize the quality and quantity of the genomic data and explore the impact of the single nucleotide polymorphism (SNP) calling and filtering processes on the GRM structure and GRM‐based heritability estimates. As expected, our results show that sequence coverage strongly affects the number of recovered loci, the genotyping error rate and the amount of missing data. Ultimately, this had little effect on heritability estimates and their standard errors, provided that the GRM was built from a minimum number of loci (above 7,000). Genomic relatedness matrix‐based heritability estimates thus appear robust to a moderate level of genotyping errors in the SNP data set. We also showed that quality filters, such as the removal of low‐frequency variants, affect the relatedness structure of the GRM, generating lower h² estimates. Our work illustrates the huge potential of RAD‐sequencing for estimating GRM‐based heritability in virtually any natural population.     

Study and modeling of fluctuating dissolved oxygen concentration impact on Corynebacterium glutamicum growth in a scale-down bioreactor

In this study, the impact of dissolved oxygen concentrations oscillations on Corynebacterium glutamicum 2262 ΔldhA growth was studied experimentally and modeled. Aiming at this, a dedicated two-compartment scale down set-up composed of two interconnected aerobic/anaerobic stirred tank bioreactors was used. The mean residence time of bacteria in each compartment was modified by adapting circulation rates and culture volumes in each bioreactor and the resulting temporal ratio of aeration was calculated. The five growth kinetics were then modeled using an original kinetic model coupling Monod growth modeling and the Residence Time Distributions. Our study showed that the microbial growth rate and macroscopic yields were clearly linked to the temporal ratio of aeration, allowing the definition of simple but robust law for process scale-up purpose. It was also revealed that the model proposed precisely agreed with the experimental growth data, whatever the fractions of aeration time imposed experimentally.