Growth characteristics of freshwater pearl mussels,Margaritifera margaritifera (L.)

• 1 Shell growth in the freshwater pearl mussel, Margaritifera margaritifera, was investigated. Three non‐linear growth models (i.e. power, logistic and von Bertalanffy) were fitted to Scottish length‐at‐age data sets and compared.
• 2 Overall, the von Bertalanffy model outperformed the other two approaches, generating the smallest residuals in eight out of 11 samples (the logistic model provided slightly better fits to the other three). It was concluded that individual M. margaritifera appear to grow in an approximately asymptotic fashion and that the von Bertalanffy equation is an appropriate growth model to fit to freshwater pearl mussel length‐at‐age data.
• 3 The ranges in von Bertalanffy parameter estimates observed (k = 0.023–0.075 year^‐1, L_∞ = 77–158 mm, t_o = ‐3.93–4.33 years) are typical of those reported in northern European populations.
• 4 Most of the populations investigated had relatively low k‐values and high maximum age (A_max) estimates. This feature, which suggests high long‐term productivity and less vulnerability to decline (i.e. larger, longer‐living mussels produce more offspring), may be a reason why these populations have survived until now. The population which appears to be the most vulnerable (i.e. which has the highest k and lowest A_max) is probably not recruiting adequately at present.
• 5 An index of absolute growth (mean shell length‐at‐age) was also used for comparing different populations. Observed between‐ and within‐river differences in mussel growth patterns may be associated with a number of environmental factors, particularly water temperature and productivity.
• 6 A significant positive relationship between river length and mean mussel length‐at‐age was observed. In general, mussels grow large in large, cold rivers and vice versa, although there are exceptions which suggest that additional factors may be involved.

     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.     

ALS/FTD‐associated FUS activates GSK‐3β to disrupt the VAPB–PTPIP51 interaction and ER–mitochondria associations

Defective FUS metabolism is strongly associated with amyotrophic lateral sclerosis and frontotemporal dementia (ALS/FTD), but the mechanisms linking FUS to disease are not properly understood. However, many of the functions disrupted in ALS/FTD are regulated by signalling between the endoplasmic reticulum (ER) and mitochondria. This signalling is facilitated by close physical associations between the two organelles that are mediated by binding of the integral ER protein VAPB to the outer mitochondrial membrane protein PTPIP51, which act as molecular scaffolds to tether the two organelles. Here, we show that FUS disrupts the VAPB–PTPIP51 interaction and ER–mitochondria associations. These disruptions are accompanied by perturbation of Ca²⁺ uptake by mitochondria following its release from ER stores, which is a physiological read‐out of ER–mitochondria contacts. We also demonstrate that mitochondrial ATP production is impaired in FUS‐expressing cells; mitochondrial ATP production is linked to Ca²⁺ levels. Finally, we demonstrate that the FUS‐induced reductions to ER–mitochondria associations and are linked to activation of glycogen synthase kinase‐3β (GSK‐3β), a kinase already strongly associated with ALS/FTD.     

The small regulatory RNA molecule MicA is involved in <Emphasis Type="Italic">Salmonella enterica</Emphasis> serovar Typhimurium biofilm formation

Background  

LuxS is the synthase enzyme of the quorum sensing signal AI-2. In Salmonella Typhimurium, it was previously shown that a luxS deletion mutant is impaired in biofilm formation. However, this phenotype could not be complemented by extracellular addition of quorum sensing signal molecules.     

Quirks of dye nomenclature. 1. Evans blue

The history, origin, identity, chemistry and use of Evans blue dye are described along with the first application to staining by Herbert McLean Evans in 1914. In the 1930s, the dye was marketed under the name, Evans blue dye, which was profoundly more acceptable than the ponderous chemical name.     

Temporal variation and characterization of grunt sounds produced by Atlantic cod Gadus morhua and pollack Pollachius pollachius during the spawning season

Fine‐scale temporal patterning in grunt production and variation in grunt attributes in Atlantic cod Gadus morhua and pollack Pollachius pollachius was examined. Pollachius pollachius produced only a single sound type, the grunt, similar to that previously described for G. morhua. Sound production and egg production were correlated in P. pollachius but not in G. morhua. Only G. morhua displayed a strongly cyclical pattern, producing more grunts at night. Finer‐scale temporal patterning in grunt production was observed in both species which produced significantly fewer grunts following a period of high grunt production. These quieter periods lasted up to 45 min for P. pollachius and up to 1 h in G. morhua. Grunts were not always produced in isolation but organized into bouts in both species. Longer bouts were more frequent during periods of increased sound activity and were linked with changes in grunt characteristics including increased grunt duration, pulse duration and repetition period of each pulse combined with decreased dominant frequency. This study provides the first evidence of acoustic signalling being used by spawning P. pollachius and presents the most detailed analysis of the complexity of gadoid sound production.     

Quirks of dye nomenclature. 3. Trypan blue

Trypan blue is colorant from the 19^th century that has an association with Africa as a chemotherapeutic agent against protozoan (Trypanosomal) infections, which cause sleeping sickness. The dye still is used for staining biopsies, living cells and organisms, and it also has been used as a colorant for textiles.