Using of remote sensing and aeromagnetic data for predicting potential areas of hydrothermal mineral deposits in the Central Eastern Desert of Egypt

This article explored mineral resources and their relation to structural settings in the Central Eastern Desert (CED) of Egypt. Integration of remote sensing (RS) with aeromagnetic (AMG) data was conducted to generate a mineral predictive map. Several image transformation and enhancement techniques were performed to Landsat Operational Land Imager (OLI) and Shuttle Radar Topography Mission (SRTM) data. Using band ratios and oriented principal component analysis (PCA) on OLI data allowed delineating hydrothermal alteration zones (HAZs) and highlighted structural discontinuity. Moreover, processing of the AMG using Standard Euler deconvolution and residual magnetic anomalies successfully revealed the subsurface structural features. Zones of hydrothermal alteration and surface/subsurface geologic structural density maps were combined through GIS technique. The results showed a mineral predictive map that ranked from very low to very high probability. Field validation allowed verifying the prepared map and revealed several mineralized sites including talc, talc-schist, gold mines and quartz veins associated with hematite. Overall, integration of RS and AMG data is a powerful technique in revealing areas of potential mineralization involved with hydrothermal processes.

MAPPING AND PREDICTION OF COMMUNITY HEALTH LEVEL IN THE EAST JAVA REGION USING ANN BACKPROPAGATION METHOD

Predicting the level of public health in an area is very important to know the development of public health in order to give consideration to local governments in making policies that can increase the level of health and health services for the community. So we need a system that is able to make predictions and describe the development of public health in the years to come. The data used in the study were the percentage of the population who had health complaints and outpatient treatment for a month, the percentage of households that had access to proper sanitation, the percentage of households that had access to decent drinking water sources, the percentage of poor people, the average length of schooling, infant mortality rate, number of health centers and life expectancy. Backpropagation neural network method is a method that is often used in forecasting or prediction. Where this method can identify activities based on past data. Past data will be studied by neural networks so that they have the ability to make decisions on data that has never been studied. This method is expected to provide a prediction of the level of health by studying data from previous years. The results of this study produced a predictive map of the level of public health in the East Java region in 2019, 2020 and 2021 based on the model obtained from the training results with an epoch of 100 and an MSE value of 0.0173254.

Modelling suitable breeding habitat and GAP analysis for the endangered Scaly-sided Merganser Mergus squamatus: implications for conservation

Summary The Scaly-sided Merganser Mergus squamatus is a globally ‘Endangered’ species breeding in north-east Asia. Limited by information on the geographic distribution of suitable habitat, the conservation management programme has not been comprehensive or spatially explicit for the breeding population. This study combines potentially important environmental variables with extensive data on species occurrence to create the first species distribution model for the breeding Scaly-sided Merganser, followed by a GAP analysis to highlight the unprotected areas containing suitable habitat. The predictive map showing the most suitable breeding habitat for the Scaly-sided Merganser covered broad-leaved deciduous forest distributed in six provincial regions in south-east Russia, north-east China, and North Korea. The conservation GAP, i.e. 90% (38,813 km2) of highly suitable habitat, is mainly concentrated in the Sikhote-Alin and Changbai mountain ranges. This study suggests that prioritizing conservation of unprotected broad-leaved deciduous riverine forests within the above two mountainous regions should be included in international conservation planning, and the remaining suitable patches need to be preserved to allow range expansion in future. This predictive map improves the expert global assessment of breeding Scaly-sided Merganser distribution and provides a basic reference for establishing conservation areas or implementing conservation actions for the breeding Scaly-sided Merganser in north-east Asia.

Predicting Land Use Changes within the Tazekka Park and Its Borders via a Cellular Automata-Markov Modeling of Satellite Images

This article describes a new approach of land cover study to predicting and combatting deforestation based on satellite imagery as environmental statistics. Specifically, a stochastic mathematical cellular automata-Markov model was used to predict land-use changes in the Tazekka Park and its borders in TAZA province in Morocco. The model was used mainly to create thematic forecast maps. Through the proposed approach, we derived data and statistics covering the period 2000 to 2020 and then constructed a predictive map for the year 2040 using ArcGIS 10.4. The evaluation of our model’s effectiveness was confirmed by calculating the Markov transition matrix in the derivation of the final map. These results can improve the management of forest areas and serve as a reference in addressing the direct effects of forests on the environment.

An ichnological predictive map of the Jezero Crater, Mars: searching for potential traces of life-substrate interactions based on terrestrial analogues (Liguria, Italy; Naturtejo UNESCO Geopark, Portugal; Ômnôgov, Mongolia)

<p>Seeking signs of past life in the geological record of Mars is one of the four primary goals of the NASA Mars 2020 mission. However, scant attention has been paid to the fossilized products of life-substrate interactions (ichnofossils), which are one of the most abundant and reliable biosignatures on Earth. This lack of attention is surprising because the ichnofossil heritage does not include only metazoan tracks, but also macroscopic burrows produced by bacteria, microborings ascribed to the activity of bacteria and fungi, and biostratification structures produced by archaea, cyanobacteria and euglenozoans. In light of this gap, the goal of the present study is evaluating the suitability of the Mars 2020 Landing Site for ichnofossils. To this goal, this work applies palaeontological predictive modelling, a technique used to predict the location of fossil sites in uninvestigated areas on Earth. Accordingly, a GIS of the landing site is developed. Each layer of the GIS maps the suitability for one or more ichnofossil types (bioturbation, bioerosion, biostratification structure) based on an assessment of a single attribute (suitability factor) of the Martian environment. Suitability criteria have been selected among the environmental attributes that control ichnofossil abundance, preservation, and accessibility in W Liguria (Italy), Naturtejo UNESCO Geopark (Portugal), and Ômnôgov district (Mongolia). The goal of this research will be delivered through a predictive map showing which areas of the Mars 2020 landing site are more likely to preserve ichnofossils. This map can be used to guide future efforts to the regions of the Mars 2020 Landing Site with the highest ichnological potential, realizing benefits in life-search efficiency and cost‐reduction.</p>

Prioritized experience replays on a hippocampal predictive map for learning

Hippocampal cells are central to spatial and predictive representations, and experience replays by place cells are crucial for learning and memory. Nonetheless, how hippocampal replay patterns dynamically change during the learning process remains to be elucidated. Here, we designed a spatial task in which rats learned a new behavioral trajectory for reward. We found that as rats updated their behavioral strategies for a novel salient location, hippocampal cell ensembles increased theta-sequences and sharp wave ripple-associated synchronous spikes that preferentially replayed salient locations and reward-related contexts in reverse order. The directionality and contents of the replays progressively varied with learning, including an optimized path that had never been exploited by the animals, suggesting prioritized replays of significant experiences on a predictive map. Online feedback blockade of sharp wave ripples during a learning process inhibited stabilizing optimized behavior. These results implicate learning-associated experience replays that act to learn and reinforce specific behavioral strategies.

Predictive Maps in Rats and Humans for Spatial Navigation

Much of our understanding of navigation has come from the study of rats, humans and simulated artificial agents. To date little attempt has been made to integrate these approaches into a common framework to understand mechanisms that may be shared across mammals and the extent to which different instantiations of agents best capture mammalian navigation behaviour. Here, we report a comparison of rats, humans and reinforcement learning (RL) agents in a novel open-field navigation task (‘Tartarus Maze’) requiring dynamic adaptation (shortcuts and detours) to changing obstructions in the path to the goal. We find humans and rats are remarkably similar in patterns of choice in the task. The patterns in their choices, dwell maps and changes over time reveal that both species show the greatest similarity to RL agents utilising a predictive map: the successor representation. Humans also display trajectory features similar to a model-based RL agent. Our findings have implications for models seeking to explain mammalian navigation in dynamic environments and highlight the utility of modelling the behaviour of different species in the same frame-work in comparison to RL agents to uncover the potential mechanisms used for behaviour.

Epidemiology of methicillin resistant Staphylococcus species carriage in companion animals in the Greater Brisbane Area, Australia

Background: Methicillin resistant Staphylococcus species such as S. aureus (MRSA) and S. pseudintermedius (MRSP) can be involved in life-threatening multidrug resistant infections in companion animals. Knowledge of methicillin resistant Staphylococcus (MRS) carriage and factors influencing carriage in companion animals from South East Queensland is limited. Nasal and rectal swab samples were collected from dogs, cats and horses upon admission or within 24 hours of hospitalisation to several primary accession and referral veterinary practices between November 2015 and December 2017. MRSA and MRSP were identified using standard microbiological (Brilliance selective medium) and molecular (mecA gene PCR) methods. Risk factors associated with methicillin resistant Staphylococcus (MRS) carriage were quantified using Bernoulli logistic regression models. A Bayesian geostatistical model was developed to predict the probability of MRS carriage in Brisbane and surrounding areas. Results: Our results indicated that while the prevalence of MRSP carriage in dogs was 8.7% (35/402) no MRSP was isolated from cats (0/69) and horses (0/60); no MRSA was isolated in any species. MRSP carriage in dogs was significantly associated with previous hospitalisation, previous bacterial infection, consultation type, average precipitation, and human population density. Our predictive map of MRSP carriage indicated that the probability of carriage was highest along the coastal areas of Greater Brisbane, particularly Brisbane city, Sunshine Coast and Gympie areas. Conclusions: This study determined that MRSP carriage in dog populations from South East Queensland is geographically clustered and associated with both clinical and environmental factors.

Prioritized experience replays on a hippocampal predictive map for learning

Hippocampal cells are central to spatial and predictive representations, and experience replays by place cells are crucial for learning and memory. Nonetheless, how hippocampal replay patterns dynamically change during the learning process remains to be largely elucidated. We found that when rats updated their behavioral strategies in response to a novel salient location, place cells increased theta sequences and sharp wave ripple-associated synchronous spikes that preferentially replayed salient locations and reward-related contexts in reverse order. The directionality and contents of the replays progressively varied with learning, including an optimized path that had never been exploited by the animals. Realtime suppression of sharp wave ripples during learning inhibited the rapid stabilization of optimized behavior. Our results suggest that hippocampal replays prioritize salient experiences and support the reinforcement of new behavioral policies.One-Sentence SummaryNeuronal network mechanisms to internally rehearse learned information for updating behavioral strategy are revealed

Comparing six different species distribution models with several subsets of environmental variables: Predicting the potential current distribution of Guettarda speciosa in Indonesia

Yudaputra A, Pujiastuti I, Cropper Jr. WP. 2019. Comparing six different species distribution models with several subsets of environmental variables: predicting the potential current distribution of zebra Guettarda speciosa in Indonesia. Biodiversitas 20: 2321-2328. There are many algorithms of species distribution modeling that widely used to predict the potential distribution pattern of diverse organisms. Finding the best model in terms of predicting the potential distribution of many species remains a challenge. The objective of this study is to compare six different algorithms for predicting the potential current distribution pattern of Guettarda speciosa (zebra wood). The occurrence records of G. speciosa are derived from herbarium database, Bogor Botanic Gardens’s plant inventory database and direct field surveys through NKRI expedition. Seven climatic variables and elevation data are extracted from global data. R open-source software is used to run those algorithms and QGIS is used to prepare the spatial data. The result shows that MAXENT outperforms other predictive models with the highest AUC score 0.89, followed by SVM (0.87), RF (0.86), and GLM (0.82), DOMAIN (0.73), and BIOCLIM (0.62). Based on the AUC score, the four predictive models (MAXENT, SVM, RF, GLM) are categorized into good predictive models, indicating those are quite better to predict the potential current distribution pattern of G. speciosa. Whereas, DOMAIN is fair predictive model and BIOCLIM is poor predictive model. The predictive map derived from four models (MAXENT, SVM, RF, and GLM) shows almost similar appearance in predicting of potential current distribution of G. speciosa. The predictive map of current distribution would be useful to provide information regarding the potential habitat of G. speciosa across the landscape of Indonesia.