Remote Sensing as a Precision Farming Tool in the Nile Valley, Egypt

Detecting stress in plants resulting from different stressors including nitrogen deficiency, salinity, moisture, contamination and diseases, is crucial in crop production. In the Nile Valley, crop production is hindered perhaps more fundamentally by issues of water supply and salinity. Predicting stress in crops by conventional methods is tedious, laborious and costly and is perhaps unreliable in providing a spatial context of stress patterns. Accurate and quick monitoring techniques for crop status to detect stress in crops at early growth stages are needed to maximize crop productivity. In this context, remotely sensed data may provide a useful tool in precision farming. This research aims to evaluate the role of in situ hyperspectral and high spatial resolution satellite remote sensing data to detect stress in wheat and maize crops and assess whether moisture induced stress can be distinguished from salinity induced stress spectrally. A series of five greenhouse based experiments on wheat and maize were undertaken subjecting both crops to a range of salinity and moisture stress levels. Spectroradiometry measurements were collected at different growth stages of each crop to assess the relationship between crop biophysical and biochemical properties and reflectance measurements from plant canopies. Additionally, high spatial resolution satellite images including two QuickBird, one ASTER and two SPOT HRV were acquired in south-west Alexandria, Egypt to assess the potential of high spectral and spatial resolution satellite imagery to detect stress in wheat and maize at local and regional scales. Two field work visits were conducted in Egypt to collect ground reference data and coupled with Hyperion imagery acquisition, during winter and summer seasons of 2007 in March (8-30: wheat) and July (12-17: maize). Despite efforts, Hyperion imagery was not acquired due to factors out with the control of this research. Strong significant correlations between crop properties and different vegetation indices derived from both ground based and satellite platforms were observed. RDVI showed a sensitive index to different wheat properties (r > 0.90 with different biophysical properties). In maize, GNDVIbr and Cgreen had strong significant correlations with maize biophysical properties (r > 0.80). PCA showed the possibility to distinguish between moisture and salinity induced stress at the grain filling stages. The results further showed that a combined approach of high (2-5 m) and moderate (15-20) spatial resolution satellite imagery can provide a better mechanistic interpretation of the distribution and sources of stress, despite the typical small size of fields (20-50 m scale). QuickBird imagery successfully detects stress within field and local scales, whereas SPOT HRV imagery is useful in detecting stress at a regional scale, and therefore, can be a robust tool in identifying issues of crop management at a regional scale. Due to the limited spectral capabilities of high spatial resolution images, distinguishing different sources of stress is not directly possible, and therefore, hyperspectral satellite imagery (e.g. Hyperion or HyspIRI) is required to distinguish between moisture and salinity induced stress. It is evident from the results that remotely sensed data acquired by both in situ hyperspectral and high spatial resolution satellite remote sensing can be used as a useful tool in precision farming in the Nile Valley, Egypt. A combined approach of using reliable high spatial and spectral satellite remote sensing data could provide better insight about stress at local and regional scales. Using this technique as a precision farming and management tool will lead to improved crop productivity by limiting stress and consequently provide a valuable tool in combating issues of food supply at a time of rapid population growth

Antiproliferative Activity of Plant Extracts Used Against Cancer in Traditional Medicine

Forty four extracts from sixteen plants used traditionally as anticancer agents were evaluated in vitro for their antiproliferative activity against Hep-2, MCF-7, and Vero cell lines. Plants were fractionated using ethanol, methanol, chloroform, n-hexane, distilled water, and butanol. The antiproliferative activity was measured by MTT assay. TLC was used to identify active fractions. The apoptotic activity of active fractions was determined using TUNEL colorimetric assay. 20 of these extracts demonstrated significant antiproliferative activity against one or more of the cell lines. These extracts were prepared from Ononis hirta, Inula viscosa, Salvia pinardi, Verbascum sinaiticum and Ononis sicula. Methanol fractions of Ononis hirta (aerial parts) and Inula viscosa (flowers) were the most active fractions against MCF-7 cells with IC50 of 27.96 and 15.78 μg/ml respectively and they were less toxic against other cell lines. Other extracts showed lower activity against cancer cell lines. TLC analysis showed the presence of flavonoids and terpenoids in active plants while alkaloids were detected in Ononis hirta (aerial parts) extracts. Ononis hirta (aerial parts) and Inula viscosa (flowers) extracts exerted their antiproliferative activity by inducing apoptosis in cancer cell lines. Further studies are necessary for detailed chemical characterization and more extensive biological evaluation of the most active ingredients

A Bestiary of Higher Dimensional Taub-NUT-AdS Spacetimes

We present a menagerie of solutions to the vacuum Einstein equations in six, eight and ten dimensions. These solutions describe spacetimes which are either locally asymptotically adS or locally asymptotically flat, and which have non-trivial topology. We discuss the global structure of these solutions, and their relevance within the context of M-theory.Comment: 11 pages, LaTex(v4: Comments and references added

The role of transcranial grayscale and Doppler ultrasound examination in diagnosis of neonatal hypoxic-ischemic encephalopathy

Background: The role of transcranial grayscale ultrasound (TC-GSUS) and transcranial color Doppler (TCD) in the diagnosis and prognosis of neonatal hypoxic-ischemic encephalopathy (HIE) is still questionable.Objective: This study targeted to evaluate the role of TC-GSUS and TCD in diagnosis and prediction of the outcome of neonates with suspected HIE in comparison to Sarnat's clinical scoring.Patients and methods: 26 neonates with suspected HIE were clinically evaluated and the severity of HIE was categorized according to Sarnat's clinical staging. Then, all neonates underwent sonographic examinations. TC-GSUS was performed at levels of anterior, mastoid, and posterior fontanelles and the level of the temporal window.Results: Cranial biometry had negative and positive rates for HIE of 7.7% and 92.3%, respectively. Using TC-GSUS, periventricular leukomalacia, intraventricular hemorrhage, brain edema, and hydrocephalus were detected in 17, 19, 14, and 16 patients, respectively. According to the resistive index (RI) of intracranial vessels, TCD excluded HIE in 11 patients and assured diagnosis of HIE with varying severity in 15 patients. Five neonates died and four developed neurological affection during follow-up. The outcome was correlated with Sarnat’s scoring, ventricular-hemispheric ratio, and abnormalities of RI. Statistical analyses defined severity of HIE as judged by RI as the significant predictor for mortality and abnormal RI of anterior cerebral (ACA) and internal carotid arteries (ICA) are the most significant predictors of outcomes.Conclusion: TCD can diagnose HIE in neonates with high sensitivity and specificity and abnormal RI of ICA and ACA might be used as valuable diagnostic and prognostic tests

Isolation and characterisation of microorganisms contaminating herbal infusion sold in Minna, Nigeria

The microbiological assessment of ten herbal infusion samples from ten different locations in Minna, Niger State was investigated. The assessment of the microbial contamination on the herbal products was carried out, using standard methods. Pour plate method was used to cultivate serially diluted portions of the medicinal plant infusion samples. The results revealed that all the herbal preparations had the presence of microbial contaminants. The total heterotrophic counts of the different herbal samples ranged from 0 cfu/mL to 25.0 × 108cfu/mL while the total fungal counts ranged from 3.0×106cfu/mL to 3.5×108cfu/mL. The total viable bacteria counts showed that the highest counts of 25.0 × 108cfu/mL was recorded in the sample from Bosso and the least counts of 0 cfu/mL from Kasuwan-Gwari while the total fungal counts showed that the highest count of 3.5×108cfu/mL was found in the sample obtained from FUT campus and the least counts of 3.0×106cfu/mL in the sample from Mai-Kunkele. One way analysis of variance (ANOVA) showed that there was significant difference (p<0.05) in the microbial load of the herbal infusions from each location. The microbial isolates identified were E. coli, Staphylococcus aureus, Shigella sp, Klebsiella sp, Pseudomonas sp, Micrococcus sp, Salmonella sp, Aspergillus sp, Penicillium sp and Saccharomyces cerevisaie. Members of the genus Aspergillus were found to be predominant. This suggests that the herbal infusion harbors microorganisms that could be hazardous to human health and hence producers should maintain the highest possible level of hygiene during the processing and packaging of the products in order to ensure safety of the products

On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech

Advanced neural network models have penetrated Automatic Speech Recognition (ASR) in recent years, however, in language modeling many systems still rely on traditional Back-off N-gram Language Models (BNLM) partly or entirely. The reason for this are the high cost and complexity of training and using neural language models, mostly possible by adding a second decoding pass (rescoring). In our recent work we have significantly improved the online performance of a conversational speech transcription system by transferring knowledge from a Recurrent Neural Network Language Model (RNNLM) to the single pass BNLM with text generation based data augmentation. In the present paper we analyze the amount of transferable knowledge and demonstrate that the neural augmented LM (RNN-BNLM) can help to capture almost 50% of the knowledge of the RNNLM yet by dropping the second decoding pass and making the system real-time capable. We also systematically compare word and subword LMs and show that subword-based neural text augmentation can be especially beneficial in under-resourced conditions. In addition, we show that using the RNN-BNLM in the first pass followed by a neural second pass, offline ASR results can be even significantly improved.Comment: 8 pages, 2 figures, accepted for publication at TSD 202