Evaluate the Distribution of Heavy Elements that Dissolved in Ground Water Using IDW in AL-Wafa City, Al-Ramadi,Iraq

Groundwater can be assessed by studying water wells. This study was conducted in Al-Wafa District, Anbar Governorate, Iraq. The water samples were collected from 24 different wells in the study area, in January 2021. A laboratory examination of the samples was conducted. Geographical information systems technique was relied on to determine the values of polluting elements in the wells. The chemical elements that were measured were [cadmium, lead, cobalt and chromium]. The output of this research were planned to be spatial maps that show the distribution of the elements with respect to their concentrations. The results show a variation in the heavy elements concentrations at the studied area groundwater. The samples show different values of concentrations; for Cadmium (0.218-1.624) ppm, Lead (0.217-1.157) ppm, Cobalt (0.014-0156) ppm, and for Chromium (0.045-0263) ppm. The distribution of the materials concentrations differs for each element which is refer to the sources of pollution are not relate to industry, but it could be relate to fertilization.

Neuronal sequences during theta rely on behavior-dependent spatial maps

Navigation through space involves learning and representing relationships between past, current and future locations. In mammals, this might rely on the hippocampal theta phase code, where in each cycle of the theta oscillation, spatial representations provided by neuronal sequences start behind the animal's true location and then sweep forward. However, the exact relationship between theta phase, represented position and true location remains unclear and even paradoxical. Here, we formalize previous notions of 'spatial' or 'temporal' theta sweeps that have appeared in the literature. We analyze single-cell and population variables in unit recordings from rat CA1 place cells and compare them to model simulations based on each of these schemes. We show that neither spatial nor temporal sweeps quantitatively accounts for how all relevant variables change with running speed. To reconcile these schemes with our observations, we introduce 'behavior-dependent' sweeps, in which theta sweep length and place field properties, such as size and phase precession, vary across the environment depending on the running speed characteristic of each location. These behavior-dependent spatial maps provide a structured heterogeneity that is essential for understanding the hippocampal code.

Hippocampus maintains a flexible and coherent map under reward flavor-landmark cue conflict.

Animals predominantly use salient visual cues (landmarks) for efficient navigation over other sensory modalities. When the relative position of the visual cues is altered, the hippocampal population exhibits heterogeneous responses and constructs context-specific spatial maps. Another critical factor that can strongly modulate spatial representation is the presence of reward. Reward features can drive behavior and are known to bias spatial attention. However, it is unclear whether reward flavors are used for spatial reference in the presence of distal cues and how the hippocampus population dynamics changes when the association between reward flavors and distal cues is altered. We investigated these questions by recording place cells from the CA1 while the rats ran in an environment with the conflicting association between reward flavors and distal cues. We report that the hippocampal place cells coherently and dynamically bind to reward flavors or distal cues across sessions, but not simultaneously suggesting the use of a single spatial map. We found that place cells maintained their spatial offset in the cue conflict conditions, thus showing a robust spatial coupling featuring an attractor-like property in the CA1. When the textures were added on the track, the coherency of the CA1 is degraded, as the hippocampus showed a heterogeneous response and weak spatial coupling of co-recorded cells suggesting a break away from the attractor network. These results indicate that reward flavors alone may be used for spatial reference but may not cause sufficient input difference to create context-specific spatial maps in the CA1.

Analisis Kondisi Atmosfer Saat Kejadian Hujan Lebat dan Angin Kencang di Probolinggo Berdasarkan Citra Satelit dan Citra Radar

The phenomenon of extreme weather, heavy rain and strong winds hit four sub-districts in Probolinggo Regency, East Java on January 3, 2020 at 17.00 WIB. Based on data from the East Java Regional Disaster Management Agency (BPBD), the incidence of heavy rain and strong winds resulted in damage to as many as 204 houses. This study uses remote sensing data in the form of C-Band Radar and Himawari-8 Satellite and Copernicus ECMWF renalysis data. The data is processed into spatial maps and graphs which are then analyzed descriptively. The results of data analysis show that the reflectivity value reaches 43 dBZ and the wind speed reaches 13.57 m / s with a rainfall of 15.83 mm / hour at 10.00 WIB. Based on the analysis of the Himawari-8 Satellite, the peak temperature of the clouds reached -73.1 oC and the atmospheric lability data showed that the atmosphere was unstable, which could indicate the possibility of heavy rain and strong winds. The value of vortices in the 1000 mb - 500 mb layer is negative and the humidity value ranges from 85% - 90% and a positive sea surface temperature anomaly value and the presence of windshields result in convergence of air masses which can support convective cloud growth as the cause of heavy rain events and strong winds in Probolinggo Regency, East Java

Learning Orientations: a Discrete Geometry Model

In the mammalian brain, many neuronal ensembles are involved in representing spatial structure of the environment. In particular, there exist cells that encode the animal's location and cells that encode head direction. A number of studies have addressed properties of the spatial maps produced by these two populations of neurons, mainly by establishing correlations between their spiking parameters and geometric characteristics of the animal's environments. The question remains however, how the brain may intrinsically combine the direction and the location information into a unified spatial framework that enables animals' orientation. Below we propose a model of such a framework, using ideas and constructs from algebraic topology and synthetic affine geometry.

Behaviorally emergent hippocampal place maps remain stable during memory recall

The hippocampus is critical for the formation and recall of episodic memory. Place cells, hippocampal pyramidal neurons which show location-specific modulation of firing rates during navigation3, 4, together form a map of environmental contingencies that is presumed to serve long-term memory of that environment. However, recent studies call to question this tenant of the field by demonstrating high levels of representational drift in the hippocampal population with respect to the duration of episodic memories in mice. In the present study, we aimed to resolve this fundamental challenge of theories of hippocampal function by examining the formation and stability of the hippocampal representation of an environment as animals experience explicit rule-based learning and memory recall. Leveraging the stability of two-photon calcium imaging, we tracked activity of the same set of CA1 pyramidal neurons during learning in an operant, head-fixed, spatial navigation task. We found that place cells are rapidly recruited into task-dependent spatial maps, resulting in emergence of orthogonal as well as overlapping representations of space. Further, task-selective place cells used a diverse set of remapping strategies to represent changing task demands that accompany learning. We found behavioral performance dependent divergence of spatial maps between trial types occurs during learning. Finally, imaging during remote recall spanning up to 30 days revealed increased stabilization of learnt place cell maps following memory consolidation. Our findings suggest that a subset of place cells is recruited by rule based spatial learning, actively reconfigured to represent task-relevant spatial relationships, and stabilized following successful learning and consolidation.