Over exploitation of groundwater in the centre of Amman Zarqa Basin-Jordan: evaluation of well data and GRACE satellite observations

Jordan faces a sincere water crisis. Groundwater is the major water resource in Jordan and most of the ground water systems are already exploited beyond their estimated safe yield. The Amman Zarqa Basin is one of the most important groundwater systems in Jordan, which supplies the three largest cities in Jordan with drinking and irrigation water. Based on new data the groundwater drawdown in the Amman Zarqa Basin is studied. This basin is the most used drainage area in Jordan. Groundwater drawdown in eight central representative monitoring wells is outlined. Based on almost continuous data for the last 15 years (2000–2015) an average drawdown for the whole basin in the order of 1.1 m·a−1 is calculated. This result is in accordance with results of previous studies in other areas in Jordan and shows that, until now, no sustainable water management is applied. Groundwater management in such a basin presents a challenge for water managers and experts. The applicability of satellite data for estimating large-scale groundwater over exploitation, such as gravity products of the Gravity Recovery and Climate Experiment (GRACE) mission, along with supplementary data, is discussed. Although the size of the basin is below the minimum resolution of GRACE, the data generally support the measured drawdown

Folliculogenesis and follicular fluid adiponectin in cows: Its alterations and relationships with ovarian function [Folikulogeneza i adiponektin u folikularnoj tekućini krava: Promjene i odnos s funkcijom jajnika]

The objectives of the present study were to study the dynamic changes in adiponectin concentration in the growing luteal as well as the preovulatory follicles in dairy cows. In the first study, the ovaries and blood of 15 Holstein dairy cows in the luteal phase were collected from a slaughterhouse. Clear antral follicles were divided into three diameter groups (small, 3-5 mm; medium, 6-9 mm and large, ≥10 mm) and their fluid was aspirated. In the second study, the coccygeal blood and fluid of the preovulatory follicles of eight live Holstein dairy cows were aspirated transrectally, using a transrectal-guided fine-needle. Concentrations of adiponectin in the serum, and follicular fluid and progesterone in the serum were measured. Serum adiponectin concentrations in both luteal and follicular phases were higher than the follicular fluid adiponectin concentrations in all types of follicles (P0.05), and the reduction was seen in preovulatory follicles in comparison with small follicles (P = 0.001). In the luteal phase, a significant positive correlation was observed between the adiponectin concentrations in different sized follicles, and also in the serum progesterone and follicular fluid adiponectin of follicles (P<0.05). In conclusion, lower adiponectin concentrations in blood serum and preovulatory follicles in comparison to luteal growing follicles reflect the effect of ovarian stage on adiponectin alterations

Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects

We propose a segmentation and feature extraction method for trajectories of moving objects. The methodology consists of three stages: trajectory data preparation; global descriptors computation; and local feature extraction. The key element is an algorithm that decomposes the profiles generated for different movement parameters (velocity, acceleration, etc.) using variations in sinuosity and deviation from the median line. Hence, the methodology enables the extraction of local movement features in addition to global ones that are essential for modeling and analyzing moving objects in applications such as trajectory classification, simulation and extraction of movement patterns. As a case study, we show how the method can be employed in classifying trajectory data generated by unknown moving objects and assigning them to known types of moving objects, whose movement characteristics have been previously learned. We have conducted a series of experiments that provide evidence about the similarities and differences that exist among different types of moving objects. The experiments show that the methodology can be successfully applied in automatic transport mode detection. It is also shown that eye-movement data cannot be successfully used as a proxy of full-body movement of humans, or vehicles

Estimating and predicting corrections for empirical thermospheric models

Quantifying spatial and temporal changes in thermospheric neutral density is important for various applications such as precise orbit determination, estimating mission lifetime and re-entry prediction of Earth orbiting objects. It is also crucial for analysis of possible collisions between active satellite missions and space debris. Empirical models are frequently applied to estimate neutral densities at the position of satellites. But their accuracy is severely constrained by model simplifications and the sampling limitation of solar and geomagnetic indices used as inputs. In this study, we first estimate thermospheric neutral density by processing the high-accuracy accelerometer measurements on-board of the twin-satellite mission Gravity Recovery And Climate Experiment (GRACE). Daily density corrections (in terms of scales) are then computed for the commonly used NRLMSISE-00 empirical model. The importance of these daily scales is examined within an orbit determination practice. Finally, three data-driven prediction techniques based on Artificial Neural Network (ANN) are applied to forecast the daily density corrections for few days to months. Our numerical results indicate that GRACE derived scales are correlated with solar and geomagnetic indices and can improve the timing (from few hours to days) and magnitude of model simulations (up to 10–100 times) during high solar or geomagnetic activity when they usually perform poorly. We found that the Non-linear Autoregressive with Exogenous (External) Input (NARX) ANN technique performs well in predicting the corrections with an average fit of 0.8 or more in terms of squared correlation coefficients for time-scales of 7–90 days

Multivariate Prediction of Total Water Storage Changes Over West Africa from Multi-Satellite Data

West African countries have been exposed to changes in rainfall patterns over the last decades, including a significant negative trend. This causes adverse effects on water resources of the region, for instance, reduced freshwater availability. Assessing and predicting large-scale total water storage (TWS) variations are necessary for West Africa, due to its environmental, social, and economical impacts. Hydrological models, however, may perform poorly over West Africa due to data scarcity. This study describes a new statistical, data-driven approach for predicting West African TWS changes from (past) gravity data obtained from the gravity recovery and climate experiment (GRACE), and (concurrent) rainfall data from the tropical rainfall measuring mission (TRMM) and sea surface temperature (SST) data over the Atlantic, Pacific, and Indian Oceans. The proposed method, therefore, capitalizes on the availability of remotely sensed observations for predicting monthly TWS, a quantity which is hard to observe in the field but important for measuring regional energy balance, as well as for agricultural, and water resource management.Major teleconnections within these data sets were identified using independent component analysis and linked via low-degree autoregressive models to build a predictive framework. After a learning phase of 72 months, our approach predicted TWS from rainfall and SST data alone that fitted to the observed GRACE-TWS better than that from a global hydrological model. Our results indicated a fit of 79 % and 67 % for the first-year prediction of the two dominant annual and inter-annual modes of TWS variations. This fit reduces to 62 % and 57 % for the second year of projection. The proposed approach, therefore, represents strong potential to predict the TWS over West Africa up to 2 years. It also has the potential to bridge the present GRACE data gaps of 1 month about each 162days as well as a—hopefully—limited gap between GRACE and the GRACE follow-on mission over West Africa. The method presented could also be used to generate a near real-time GRACE forecast over the regions that exhibit strong teleconnections

An Optimized Approach to Determine Alternative Cluster Head in Wireless Sensor Network Clustering Structure

Wireless sensor networks consist of hundreds or thousands tiny nodes that work together to do some special tasks. Generally, to reduce energy consumption in the network, just some of the nodes send the data to the sink. This structure is called clustering and the nodes linked to the sink are called cluster head. The other nodes send their data to the nearest cluster head. Choosing a node as alternative cluster head can improve the network efficiency because clustering formation is a costly approach. When the cluster head stops working it is necessary to do the clustering formation but when we have alternative for the cluster head no clustering formation is needed, just the alternative introduces itself as the new cluster head and informs the other nodes in the cluster about this matter. In this paper we propose a novel approach to determine alternative node for cluster head. The existing methods impose high overhead on the network and some of them has very low accuracy while the proposed method has high accuracy also imposes almost no overhead to the network by using in-network data and eliminating the array data structur

Understanding the decline of water storage across the Ramser-Lake Naivasha using satellite-based methods

It has been postulated that Lake Naivasha, Kenya, has experienced a rapid decrease (and fluctuations) in its spatial extent and level between the years 2002 to 2010. Many factors have been advanced to explain this, with horticultural and floricultural activities, as well as climatic change, featuring prominently. This study offers a multi-disciplinary approach based on several different types of space-borne observations to look at the problem bedeviling Lake Naivasha, which is a Ramsar listed wetland of international importance. The data includes: (1) Gravity Recovery and Climate Experiment (GRACE) time-variable gravity field products to derive total water storage (TWS) variations within a region covering the Lakes Naivasha and Victoria basins; (2) precipitation records based on Tropical Rainfall Measurement Mission (TRMM) products to evaluate the impact of climate change; (3) satellite remote sensing (Landsat) images to map shoreline changes and to correlate these changes over time with possible causes; and (4) satellite altimetry observations to assess fluctuations in the lake’s level. In addition, data from an in situ tide gauge and rainfall stations as well as the output from the African Drought Monitor (ADM) model are used to evaluate the results.This study confirms that Lake Naivasha has been steadily declining with the situation being exacerbated from around the year 2000, with water levels falling at a rate of 10.2 cm/year and a shrinkage in area of 1.04 km2/year. GRACE indicates that the catchment area of 4°×4° that includes Lake Naivasha loses water at a rate of 1.6 cm/year for the period from August 2002 to May 2006, and 1.4 cm/year for the longer period of May 2002 to 2010. Examining the ADM outputs also supports our results of GRACE. Between the time periods 2000–2006 and 2006–2010, the lake surface area decreased by 14.43% and 10.85%, respectively, with a corresponding drop in the water level of 192 cm and 138 cm, respectively, over the same periods. Our results show a correlation coefficient value of 0.68 between the quantity of flower production and the lake’s level for the period 2002–2010 at 95% confidence level, indicating the probable impact of anthropogenic activities on the lake’s level drop

Correction: Inhibitor SBFI26 suppresses the malignant progression of castration-resistant PC3-M cells by competitively binding to oncogenic FABP5.

[This corrects the article DOI: 10.18632/oncotarget.16055.]

siRNA Knockdown of Ribosomal Protein Gene RPL19 Abrogates the Aggressive Phenotype of Human Prostate Cancer

We provide novel functional data that posttranscriptional silencing of gene RPL19 using RNAi not only abrogates the malignant phenotype of PC-3M prostate cancer cells but is selective with respect to transcription and translation of other genes. Reducing RPL19 transcription modulates a subset of genes, evidenced by gene expression array analysis and Western blotting, but does not compromise cell proliferation or apoptosis in-vitro. However, growth of xenografted tumors containing the knocked-down RPL19 in-vivo is significantly reduced. Analysis of the modulated genes reveals induction of the non-malignant phenotype principally to involve perturbation of networks of transcription factors and cellular adhesion genes. The data provide evidence that extra-ribosomal regulatory functions of RPL19, beyond protein synthesis, are critical regulators of cellular phenotype. Targeting key members of affected networks identified by gene expression analysis raises the possibility of therapeutically stabilizing a benign phenotype generated by modulating the expression of an individual gene and thereafter constraining a malignant phenotype while leaving non-malignant tissues unaffected