Precocious hippocampal structural and functional changes in gestational protein-restricted male elderly offspring: an Alzheimer-simile disease model?

Background: Maternal undernutrition has been associated with psychiatric and neurological disorders characterized by learning and memory impairment. Considering the lack of evidence for this, we aimed to analyze the effects of gestational protein restriction on learning and memory function later in life. This research associates behavioral findings with hippocampal cell numbers and protein content related to neurodegenerative brain disease. Methods: Experiments were conducted in animals subjected to a low-protein (LP, 6% casein) or regular-protein (NP, 17% casein) diet throughout their pregnancy. Behavioral tests, isolated hippocampal isotropic fractionator cell studies, immunoblotting, and survival lifetime tests were performed. The results confirmed that the birthweight of LP male pups significantly reduced relative to NP male pups and that hippocampal mass increased in 88-week-old LP compared to age-matched NP offspring. We used the Morris water maze proximity measure, which is the sum of 10 distances each second between rat position and location of a hidden platform target, as a suitable test for assessing age-related learning or memory impairment in aged offspring. Results: The results showed an increased proximity measure in 87-week-old LP rats (52.6 x 104 ± 10.3 x 104 mm) as compared to NP rats (47.0 x 104 ± 10.6 x 103 mm, p = 0.0007). In addition, LP rats exhibited anxiety-like behaviors compared to NP rats at 48 and 86 weeks of life. Additionally, the estimated neuron number was unaltered in LP rats; however, glial and other cell numbers increased in LP compared to NP rats. Here, we showed unprecedented hippocampal deposition of brain-derived neurotrophic factor, β-amyloid peptide (Aβ), and tau protein in 88-week-old LP compared to age-matched NP offspring. To date, no predicted studies showed changes in hippocampal neuron and glial cell numbers in maternal protein-restricted elderly offspring. The current data suggest that maternal protein restriction has a high impact on lifespan and brain structure, and function. Conclusion: the gestational protein restriction may accelerate hippocampal function loss, impacting learning/memory performance, and supposedly developing diseases similar to Alzheimer's disease (AD) in elderly offspring. Thus, we propose that maternal protein restriction could be a probable, elegant, and novel method for constructing an AD-like model in adult male offspring.

Methods of classification and algorithms of graph coloring

We study the connection between classifications on finite set and the problem of graph coloring. We consider the optimality criterion for classification of special type: h-classifications, which are built on the base of proximity measure. It is shown that the problem of finding the optimal h-classification can be reduced to the problem of coloring of non-adjacency graph vertices by the smallest possible number of colors. We consider algorithms of proper coloring of graph vertices.

ANALYSIS OF ROTATION AND SCALE INVARIANCE FOR PROXIMITY MEASURES IN TARGET DETECTION

The problem of stability of object detection in images using proximity measures is considered. The purpose of the work is to determine the degree of invariance of various proximity measures for detecting objects by reference when rotating and zooming the scanned image. The proximity measure that is most resistant to these geometric transformations of the image is found out. The proximity measures are analyzed: correlation, comparison, Chamfer Distance. The target location is based on the coordinates of the extremum of the target function. Modeling is performed in the Matlab software package. A database of thirty television images was created to test the proximity measures. Test images contain the required objects and imitations of both complex and simple backgrounds. It was determined that all considered proximity measures steadily determine the target with small turns and scaling factors.

Comparing temporal graphs using dynamic time warping

Within many real-world networks, the links between pairs of nodes change over time. Thus, there has been a recent boom in studying temporal graphs. Recognizing patterns in temporal graphs requires a proximity measure to compare different temporal graphs. To this end, we propose to study dynamic time warping on temporal graphs. We define the dynamic temporal graph warping (dtgw) distance to determine the dissimilarity of two temporal graphs. Our novel measure is flexible and can be applied in various application domains. We show that computing the dtgw-distance is a challenging (in general) -hard optimization problem and identify some polynomial-time solvable special cases. Moreover, we develop a quadratic programming formulation and an efficient heuristic. In experiments on real-world data, we show that the heuristic performs very well and that our dtgw-distance performs favorably in de-anonymizing networks compared to other approaches.

Using Different Qualitative Scales in a Multi-Criteria Decision-Making Procedure

Many decision problems manage linguistic information assessed through several ordered qualitative scales. In these contexts, the main problem arising is how to aggregate this qualitative information. In this paper, we present a multi-criteria decision-making procedure that ranks a set of alternatives assessed by means of a specific ordered qualitative scale for each criterion. These ordered qualitative scales can be non-uniform and be formed by a different number of linguistic terms. The proposed procedure follows an ordinal approach by means of the notion of ordinal proximity measure that assigns an ordinal degree of proximity to each pair of linguistic terms of the qualitative scales. To manage the ordinal degree of proximity from different ordered qualitative scales, we provide a homogenization process. We also introduce a stochastic approach to assess the robustness of the conclusions.

K-nearest neighbor performance for Nusantara scripts image transliteration

The concept of classification using the k-nearest neighbor (KNN) method is simple, easy to understand, and easy to be implemented in the system. The main challenge in classification with KNN is determining the proximity measure of an object and how to make a compact reference class. This paper studied the implementation of the KNN for the automatic transliteration of Javanese, Sundanese, and Bataknese script images into Roman script. The study used the KNN algorithm with the number k set to 1, 3, 5, 7, and 9. Tests used the image dataset of 2520 data. With the 3-fold and 10-fold cross-validation, the results exposed the accuracy differences if the area of the extracted image, the number of neighbors in the classification, and the number of data training were different.