Gamma frequency entrainment attenuates amyloid load and modifies microglia

Changes in gamma oscillations (20-50 Hz) have been observed in several neurological disorders. However, the relationship between gamma oscillations and cellular pathologies is unclear. Here we show reduced, behaviourally driven gamma oscillations before the onset of plaque formation or cognitive decline in a mouse model of Alzheimer's disease. Optogenetically driving fast-spiking parvalbumin-positive (FS-PV)-interneurons at gamma (40 Hz), but not other frequencies, reduces levels of amyloid-β (Aβ)[subscript 1-40] and Aβ [subscript 1-42] isoforms. Gene expression profiling revealed induction of genes associated with morphological transformation of microglia, and histological analysis confirmed increased microglia co-localization with Aβ. Subsequently, we designed a non-invasive 40 Hz light-flickering regime that reduced Aβ[subscript 1-40] and Aβ[subscript 1-42] levels in the visual cortex of pre-depositing mice and mitigated plaque load in aged, depositing mice. Our findings uncover a previously unappreciated function of gamma rhythms in recruiting both neuronal and glial responses to attenuate Alzheimer's-disease-associated pathology.National Institutes of Health (U.S.) (Grant 1R01EY023173)National Institutes of Health (U.S.) (Grant 1DP1NS087724)National Institutes of Health (U.S.) (Grant RF1AG047661)National Institutes of Health (U.S.) (Grant ROIGM104948

Analysis of Convergence of Adaptive Single­step Algorithms for the Identification of Non­stationary Objects

The study deals with the problem of identification of non-stationary parameters of a linear object which can be described by first-order Markovian model, with the help of the simplest in computational terms single-step adaptive identification algorithms – modified algorithms by Kaczmarz and Nagumo-Noda. These algorithms do not require knowledge of information on the degree of non-stationarity of the studied object. When building the model, they use the information only about one step of measurements. Modification involves the use of the regularizing addition in the algorithms to improve their computing properties and avoid division by zero. Using a Markovian model is quite effective because it makes it possible to obtain analytic estimates of the properties of algorithms.It was shown that the use of regularizing additions in identification algorithms, while improving stability of algorithms, leads to some slowdown of the process of model construction. The conditions for convergence of regularizing algorithms by Kaczmarz and Nagumo-Noda at the evaluation of stationary parameters in mean and root-mean-square and existing measurement interference were determined.The obtained estimates differ from the existing ones by higher accuracy. Despite this, they are quite general and depend both on the degree of non-stationarity of an object, and on statistical characteristics of interference. In addition, the expressions for the optimal values of the parameters of algorithms, ensuring their maximum rate of convergence under conditions of non-stationarity and the presence of Gaussian interferences, were determined. The obtained analytical expressions contain a series of unknown parameters (estimation error, degree of non-stationarity of an object, statistical characteristics of interferences). For their practical application, it is necessary to use any recurrent procedure for estimation of these unknown parameters and apply the obtained estimates to refine the parameters that are included in the algorithm

Formation of nanocrystalline BaTiO3 thin films by pulsed laser deposition

The paper shows the experimental results of the substrate temperature effect on the morphological and electro-physical parameters of nanocrystalline BaTiO3 films fabricated by pulsed laser deposition. It was found increasing in the substrate temperature from 300 °C to 600 °C results in decreasing in surface roughness from (6.1±0.6) nm to (0.8±0.1) nm and increasing in the films grain size from (39.1±3.1) nm to (212.1± 17.2) nm. Increasing in the substrate temperature leads to a change in electro-physical parameters: the concentration of charge carriers increases from (1.85±0.16)×1013 cm-3 to (2.77±0.25)×1013 cm-3, the mobility of charge carriers decreases from (10.1±0.9) cm2/(V·s) to (7.2±0.6) cm2/(V·s), and the resistivity of the films changes insignificantly from (3.4±0.2)×103 Ω·cm to (3.1±0.2)×103 Ω·cm under increase in the temperature from 300 °C to 600 °C. The obtained results make it possible to get BaTiO3 films with target parameters, which can be used to develop promising lead-free energy harvesters for alternative energy devices

Breed Recognition and Estimation of Live Weight of Cattle Based on Methods of Machine Learning and Computer Vision

A method of measuring cattle parameters using neural network methods of image processing was proposed. To this end, several neural network models were used: a convolutional artificial neural network and a multilayer perceptron. The first is used to recognize a cow in a photograph and identify its breed followed by determining its body dimensions using the stereopsis method. The perceptron was used to estimate the cow's weight based on its breed and size information. Mask RCNN (Mask Regions with CNNs) convolutional network was chosen as an artificial neural network. To clarify information on the physical parameters of animals, a 3D camera (Intel RealSense D435i) was used. Images of cows taken from different angles were used to determine the parameters of their bodies using the photogrammetric method. The cow body dimensions were determined by analyzing animal images taken with synchronized cameras from different angles. First, a cow was identified in the photograph and its breed was determined using the Mask RCNN convolutional neural network. Next, the animal parameters were determined using the stereopsis method. The resulting breed and size data were fed to a predictive model to determine the estimated weight of the animal. When modeling, Ayrshire, Holstein, Jersey, Krasnaya Stepnaya breeds were considered as cow breeds to be recognized. The use of a pre-trained network with its subsequent training applying the SGD algorithm and Nvidia GeForce 2080 video card has made it possible to significantly speed up the learning process compared to training in a CPU. The results obtained confirm the effectiveness of the proposed method in solving practical problems

Genes associated with testicular germ cell tumors and testicular dysgenesis in patients with testicular microlithiasis

Testicular microlithiasis (TM) is one of the symptoms of testicular dysgenesis syndrome (TDS). TM is particularly interesting as an informative marker of testicular germ cell tumors (TGCTs). KIT ligand gene (KITLG), BCL2 antagonist/killer 1 (BAK1), and sprouty RTK signaling antagonist 4 (SPRY4) genes are associated with a high risk of TGCTs, whereas bone morphogenetic protein 7 gene (BMP7), transforming growth factor beta receptor 3 gene (TGFBR3), and homeobox D cluster genes (HOXD) are related to TDS. Using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis, we investigated allele and genotype frequencies for KITLG (rs995030, rs1508595), SPRY4 (rs4624820, rs6897876), BAK1 (rs210138), BMP7 (rs388286), TGFBR3 (rs12082710), and HOXD (rs17198432) in 142 TGCT patients, 137 TM patients, and 153 fertile men (control group). We found significant differences in the KITLG GG_rs995030 genotype in TM (P = 0.01) and TGCT patients (P = 0.0005) compared with the control. We also revealed strong associations between KITLG_rs1508595 and TM (G allele, P = 0.003; GG genotype, P = 0.01) and between KITLG_rs1508595 and TGCTs (G allele, P = 0.0001; GG genotype, P = 0.0007). Moreover, there was a significant difference in BMP7_rs388286 between the TGCT group and the control (T allele, P = 0.00004; TT genotype, P = 0.00006) and between the TM group and the control (T allele, P = 0.04). HOXD also demonstrated a strong association with TGCTs (rs17198432 A allele, P = 0.0001; AA genotype, P = 0.001). Furthermore, significant differences were found between the TGCT group and the control in the BAK1_rs210138 G allele (P = 0.03) and the GG genotype (P = 0.01). KITLG and BMP7 genes, associated with the development of TGCTs, may also be related to TM. In summary, the KITLG GG_rs995030, GG_rs1508595, BMP7 TT_rs388286, HOXD AA_rs17198432, and BAK1 GG_rs210138 genotypes were associated with a high risk of TGCT development