Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain

In preclinical research, histology images are produced using powerful optical microscopes to digitize entire sections at cell scale. Quantification of stained tissue relies on machine learning driven segmentation. However, such methods require multiple additional information, or features, which are increasing the quantity of data to process. As a result, the quantity of features to deal with represents a drawback to process large series or massive histological images rapidly in a robust manner. Existing feature selection methods can reduce the amount of required information but the selected subsets lack reproducibility. We propose a novel methodology operating on high performance computing (HPC) infrastructures and aiming at finding small and stable sets of features for fast and robust segmentation of high-resolution histological images. This selection has two steps: (1) selection at features families scale (an intermediate pool of features, between spaces and individual features) and (2) feature selection performed on pre-selected features families. We show that the selected sets of features are stables for two different neuron staining. In order to test different configurations, one of these dataset is a mono-subject dataset and the other is a multi-subjects dataset to test different configurations. Furthermore, the feature selection results in a significant reduction of computation time and memory cost. This methodology will allow exhaustive histological studies at a high-resolution scale on HPC infrastructures for both preclinical and clinical research.

Autumn destabilization of deep porewater CO2 store in a northern peatland driven by turbulent diffusion

The deep porewater of northern peatlands stores large amounts of carbon dioxide (CO2). This store is viewed as a stable feature in the peatland CO2 cycle. Here, we report large and rapid fluctuations in deep porewater CO2 concentration recurring every autumn over four consecutive years in a boreal peatland. Estimates of the vertical diffusion of heat indicate that CO2 diffusion occurs at the turbulent rather than molecular rate. The weakening of porewater thermal stratification in autumn likely increases turbulent diffusion, thus fostering a rapid diffusion of deeper porewater CO2 towards the surface where net losses occur. This phenomenon periodically decreases the peat porewater CO2 store by between 29 and 90 g C m−2 throughout autumn, which is comparable to the peatland’s annual C-sink. Our results establish the need to consider the role of turbulent diffusion in regularly destabilizing the CO2 store in peat porewater.

Can Gram-Negative Bacteria Develop Resistance to Antimicrobial Blue Light Treatment?

Antimicrobial blue light (aBL) treatment is considered low risk for the development of bacterial resistance and tolerance due to its multitarget mode of action. The aim of the current study was to demonstrate whether tolerance development occurs in Gram-negative bacteria. We evaluated the potential of tolerance/resistance development in Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa and demonstrated that representative Gram-negative bacteria may develop tolerance to aBL. The observed adaption was a stable feature. Assays involving E. coli K-12 tolC-, tolA-, umuD-, and recA-deficient mutants revealed some possible mechanisms for aBL tolerance development.

A Critical Study on Stability Measures of Feature Selection with a Novel Extension of Lustgarten Index

Stability of feature selection algorithm refers to its robustness to the perturbations of the training set, parameter settings or initialization. A stable feature selection algorithm is crucial for identifying the relevant feature subset of meaningful and interpretable features which is extremely important in the task of knowledge discovery. Though there are many stability measures reported in the literature for evaluating the stability of feature selection, none of them follows all the requisite properties of a stability measure. Among them, the Kuncheva index and its modifications, are widely used in practical problems. In this work, the merits and limitations of the Kuncheva index and its existing modifications (Lustgarten, Wald, nPOG/nPOGR, Nogueira ) are studied and analysed with respect to the requisite properties of stability measure. One more limitation of the most recent modified similarity measure, Nogueira’s measure, has been pointed out. Finally, corrections to Lustgarten’s measure have been proposed to define a new modified stability measure that satisfies the desired properties and overcomes the limitations of existing popular similarity based stability measures. The effectiveness of the newly modified Lustgarten’s measure has been evaluated with simple toy experiments.

Overcome the Brightness and Jitter Noises in Video Inter-Frame Tampering Detection

Digital video forensics plays a vital role in judicial forensics, media reports, e-commerce, finance, and public security. Although many methods have been developed, there is currently no efficient solution to real-life videos with illumination noises and jitter noises. To solve this issue, we propose a detection method that adapts to brightness and jitter for video inter-frame forgery. For videos with severe brightness changes, we relax the brightness constancy constraint and adopt intensity normalization to propose a new optical flow algorithm. For videos with large jitter noises, we introduce motion entropy to detect the jitter and extract the stable feature of texture changes fraction for double-checking. Experimental results show that, compared with previous algorithms, the proposed method is more accurate and robust for videos with significant brightness variance or videos with heavy jitter on public benchmark datasets.

Phenotypic variation of yielding of medium-early cultivars of edible potato

The aim of the study was to analyse the genotypic and environmental variation in yield, as well as the structure of the yield of tubers of medium-early cultivars of edible potato. The field study was carried out in the years 2015-2019 at the experimental station in Prusy near Krakow (50o07ʼN, 20o05ʼE) on chernozem. The following five potato cultivars were evaluated: Finezja, Oberon, Laskara, Satina and Tajfun. The total and commercial yield of tubers, the average tuber mass, the quantity of tubers from a plant, as well as the share of the fraction of large, commercial and small tubers were determined in the study. Of the potato features that were evaluated, the share of the commercial tuber fraction was the most stable. A low diversity was also found in the case of total and commercial yield of tubers, whereas the share of the fraction of small tubers was the least stable feature. The size of yields and their structure were determined mostly by environmental factors. Only in the case of the share of fractions of large and small tubers were the varietal properties more decisive than the genotypic-environmental interaction. The conducted cluster analysis identified two groups of cultivars: first—with a high yielding potential, substantial average tuber mass, a substantial share of commercial and large tubers; and second—with significantly lower tuber mass, substantial quantity of set tubers, particularly the fine ones with a simultaneous small share of large tubers.