The redundancy and diversity between two novel PKC isotypes that regulate learning in Caenorhabditis elegans

The nematode Caenorhabditis elegans learns the concentration of NaCl and moves toward the previously experienced concentration. In this behavior, the history of NaCl concentration change is reflected in the level of diacylglycerol and the activity of protein kinase C, PKC-1, in the gustatory sensory neuron ASER and determines the direction of migration. Here, through a genetic screen, we found that the activation of Gq protein compensates for the behavioral defect of the loss-of-function mutant of pkc-1. We found that Gq activation results in hyperproduction of diacylglycerol in ASER sensory neuron, which leads to recruitment of TPA-1, an nPKC isotype closely related to PKC-1. Unlike the pkc-1 mutants, loss of tpa-1 did not obviously affect migration directions in the conventional learning assay. This difference was suggested to be due to cooperative functions of the C1 and C2-like domains of the nPKC isotypes. Furthermore, we investigated how the compensatory capability of tpa-1 contributes to learning and found that learning was less robust in the context of cognitive decline or environmental perturbation in tpa-1 mutants. These results highlight how two nPKC isotypes contribute to the learning system.

Identification of ionospheric earthquake precursors in Kamchatka region based on correlation analysis

Сейсмическая активность является одним из источников изменчивости ионосферы. В данной работе на основе методики [1] исследовано изменение концентрации электронов в ионосфере, предшествующее наступлению сильных землетрясений с M ≥ 6.0 в Камчатском регионе. Данная методика основана на вычислении коэффициента корреляции между значениями критической частоты foF2 двух ионосферных станций, одна из которых расположена внутри зоны подготовки землетрясения, а другая – за ее пределами. Рассмотрены данные, полученные на двух станциях PETROPAVLOVSK (PK553) и EARECKSON (EA653) за период 01.09.2018–30.04.2021 гг. Статистический анализ критических частот foF2 показал, что для 6 из 8 землетрясений с M ≥ 6.0, произошедших на глубинах до 100 км и эпицентральных расстояниях до 500 км от расположения PK553, за 1–12 суток до их наступления предшествовало заметное уменьшение коэффициента корреляции. Seismic activity is one of the sources of ionospheric variability. In this work, we investigate electron concentration change in the ionosphere, preceding the onset of strong earthquakes with M ≥ 6.0 in Kamchatka region. The research technique is based on calculating the correlation coefficient between the critical frequency foF2 values at two ionospheric stations. One of them is located inside the earthquake preparation zone, and the other is outside it. The data obtained at two stations PETROPAVLOVSK (PK553) and EARECKSON (EA653) for the period 01.09.2018–30.04.2021 are considered. Statistical analysis of the critical frequencies foF2 showed that a noticeable decrease in the correlation coefficient was observed 1–7 days before the earthquakes for 6 out of 8 events with M ≥ 6.0 that occurred at depths of up to 100 km and epicentral distances of up to 500 km from the PK553 location.

Function Representation for Basic Processes Modeling in 3D Bioprinting

We propose a non-invasive approach to control the quality of spheroids and their fusion into a complex bioconstruct. The proposed method is based on the union of the nutrient concentration change calculated using Fick's law and the "reaction-diffusion" equations, taking into account absorption coefficient with specifying the exact fusion geometry using implicit Function Repre-sentation (FRep) functions. The proposed approach allows us to analyze the viability of cells within the spheroid, predict the fusion of spheroids, and accurately model complex heteroge-neous biostructures as a future task for our research. These results will significantly accelerate the development of such a promising field of additive biotechnologies as 3D bioprinting.

Planetary emittance and feedback parameters through varying climates in basic modelling

In search for reproducibility of the results from sophisticated scientific research, the present work focuses on the planetary (longwave) emittance variabilities. A simple model appears applicable through the entire range from very cold to extremely warm climates and for different climate driving forces, i.e. solar luminosity variation and CO2 concentration change. The results interrelate effects from lapse rate, water vapor, CO2, and clouds for equilibrium climate states. Feedback parameters are analysed for the emittance decomposition into the atmospheric window, clouds, and the cloud-free atmosphere. A view is devoted to the faint young Sun problem.

Analysis of Spatial Heterogeneity and the Scale of the Impact of Changes in PM2.5 Concentrations in Major Chinese Cities between 2005 and 2015

Deteriorating air quality is one of the most important environmental factors posing significant health risks to urban dwellers. Therefore, an exploration of the factors influencing air pollution and the formulation of targeted policies to address this issue are critically needed. Although many studies have used semi-parametric geographically weighted regression and geographically weighted regression to study the spatial heterogeneity characteristics of influencing factors of PM2.5 concentration change, due to the fixed bandwidth of these methods and other reasons, those studies still lack the ability to describe and explain cross-scale dynamics. The multi-scale geographically weighted regression (MGWR) method allows different variables to have different bandwidths, which can produce more realistic and useful spatial process models. By applying the MGWR method, this study investigated the spatial heterogeneity and spatial scales of impact of factors influencing PM2.5 concentrations in major Chinese cities during the period 2005–2015. This study showed the following: (1) Factors influencing changes in PM2.5 concentrations, such as technology, foreign investment levels, wind speed, precipitation, and Normalized Difference Vegetation Index (NDVI), evidenced significant spatial heterogeneity. Of these factors, precipitation, NDVI, and wind speed had small-scale regional effects, whose bandwidth ratios are all less than 20%, while foreign investment levels and technologies had medium-scale regional effects, whose bandwidth levels are 23% and 32%, respectively. Population, urbanization rates, and industrial structure demonstrated weak spatial heterogeneity, and the scale of their influence was predominantly global. (2) Overall, the change of NDVI was the most influential factor, which can explain 15.3% of the PM2.5 concentration change. Therefore, an enhanced protection of urban surface vegetation would be of universal significance. In some typical areas, dominant factors influencing pollution were evidently heterogeneous. Change in wind speed is a major factor that can explain 51.6% of the change in PM2.5 concentration in cities in the Central Plains, and change in foreign investment levels is the dominant influencing factor in cities in the Yunnan-Guizhou Plateau and the Sichuan Basin, explaining 30.6% and 44.2% of the PM2.5 concentration change, respectively. In cities located within the lower reaches of the Yangtze River, NDVI is a key factor, reducing PM2.5 concentrations by 9.7%. Those results can facilitate the development of region-specific measures and tailored urban policies to reduce PM2.5 pollution levels in different regions such as Northeast China and the Sichuan Basin.