Assessment of Soil Fertility Status under Soil Degradation Rate Using Geomatics in West Nile Delta

The presence of a noticeable rate of degradation in the land of the Nile Delta reduces the efficiency of crop production and hinders supply of the increasing demand of its growing population. For this purpose, knowledge of soil resources and their agricultural potential is important for determining their proper use and appropriate management. Thus, we investigated the state of soil fertility by understanding the effect of the physical and chemical properties of the soil and their impact on the state of land degradation for the years 1985, 2002 (ancillary data), and 2021 (our investigation). The study showed that there are clear changes in the degree of soil salinity as a result of agricultural management, water conditions, and climatic changes. The soil fertility is obtained in four classes: Class one (I) represents soils of a good fertility level with an area of about 39%. Class two (II) includes soils of an average fertility level, on an area of about 7%. Class three (III) includes soils with a poor level of fertility, with an area of about 17%. Class four (IV) includes soils of a very poor level of fertility with an area of about 37% of the total area. Principal component analysis (PCA) has revealed that the parameters that control fertility in the studied soils are: C/N, pH, Ca, CEC, OM, P, and Mg. Agro-pedo-ecological units are important units for making appropriate agricultural decisions in the long term, which contribute to improving soil quality and thus increasing the efficiency of soil fertility processes

An assessment of the impact of herb-drug combinations used by cancer patients

Background Herb/Dietary Supplements (HDS) are the most popular Complementary and Alternative Medicine (CAM) modality used by cancer patients and the only type which involves the ingestion of substances which may interfere with the efficacy and safety of conventional medicines. This study aimed to assess the level of use of HDS in cancer patients undergoing treatment in the UK, and their perceptions of their effects, using 127 case histories of patients who were taking HDS. Previous studies have evaluated the risks of interactions between HDS and conventional drugs on the basis on numbers of patient using HDSs, so our study aimed to further this exploration by examining the actual drug combinations taken by individual patients and their potential safety. Method Three hundred seventy-five cancer patients attending oncology departments and centres of palliative care at the Oxford University Hospitals Trust (OUH), Duchess of Kent House, Sobell House, and Nettlebed Hospice participated in a self-administered questionnaire survey about their HDS use with their prescribed medicines. The classification system of Stockley’s Herbal Medicine’s Interactions was adopted to assess the potential risk of herb-drug interactions for these patients. Results 127/375 (34 %; 95 % CI 29, 39) consumed HDS, amounting to 101 different products. Most combinations were assessed as ‘no interaction’, 22 combinations were categorised as ‘doubt about outcomes of use’, 6 combinations as ‘Potentially hazardous outcome’, one combination as an interaction with ‘Significant hazard’, and one combination as an interaction of “Life-threatening outcome”. Most patients did not report any adverse events. Conclusion Most of the patients sampled were not exposed to any significant risk of harm from interactions with conventional medicines, but it is not possible as yet to conclude that risks in general are over-estimated. The incidence of HDS use was also less than anticipated, and significantly less than reported in other areas, illustrating the problems when extrapolating results from one region (the UK), in one setting (NHS oncology) in where patterns of supplement use may be very different to those elsewhere

Batch and continuous removal of heavy metals from industrial effluents using microbial consortia

Bio-removal of heavy metals, using microbial biomass, increasingly attracting scientific attention due to their significant role in purification of different types of wastewaters making it reusable. Heavy metals were reported to have a significant hazardous effect on human health, and while the conventional methods of removal were found to be insufficient; microbial biosorption was found to be the most suitable alternative. In this work, an immobilized microbial consortium was generated using Statistical Design of Experiment (DOE) as a robust method to screen the efficiency of the microbial isolates in heavy metal removal process. This is the first report of applying Statistical DOE to screen the efficacy of microbial isolates to remove heavy metals instead of screening normal variables. A mixture of bacterial biomass and fungal spores was used both in batch and continuous modes to remove Chromium and Iron ions from industrial effluents. Bakery yeast was applied as a positive control, and all the obtained biosorbent isolates showed more significant efficiency in heavy metal removal. In batch mode, the immobilized biomass was enclosed in a hanged tea bag-like cellulose membrane to facilitate the separation of the biosorbent from the treated solutions, which is one of the main challenges in applying microbial biosorption at large scale. The continuous flow removal was performed using fixed bed mini-bioreactor, and the process was optimized in terms of pH (6) and flow rates (1 ml/min) using Response Surface Methodology. The most potential biosorbent microbes were identified and characterized. The generated microbial consortia and process succeeded in the total removal of Chromium ions and more than half of Iron ions both from standard solutions and industrial effluents

An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis σ66 promoters

<p>Abstract</p> <p>Background</p> <p>Promoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from <it>Escherichia coli</it>. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between <it>Escherichia coli </it>and <it>Chlamydia trachomatis </it>are large enough to recommend an organism-specific modeling effort.</p> <p>Results</p> <p>Here we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model <it>Chlamydia trachomatis </it>σ⁶⁶promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for <it>Chlamydia trachomatis </it>RNA polymerase σ⁶⁶/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability.</p> <p>Conclusion</p> <p>This strategy and resulting model support the conjecture that DNA biophysical properties, along with RNA polymerase σ-factor/DNA binding collaboratively, contribute to a sequence's ability to promote transcription. This work provides a baseline model that can evolve as new <it>Chlamydia trachomatis </it>σ⁶⁶promoters are identified with assistance from the provided genome-wide predictions. The proposed methodology is ideal for organisms with few identified promoters and relatively small genomes.</p

Elastic properties of TeO2-B2O3-Ag2O glasses.

A series of glasses [(TeO2) x (B2O3)1−x ]1−y [Ag2O] y with x = 70 and y = 10, 15, 20, 25 and 30 mol% were synthesised by rapid quenching. Longitudinal and shear ultrasonic velocity were measured at room temperature and at 5 MHz frequency. Elastic properties, Poisson's ratio, microhardness, softening temperature and Debye temperature have been calculated from the measured density and ultrasonic velocity at room temperature. The experimental results indicate that the elastic constants depend upon the composition of the glasses and the role of the Ag2O inside the glass network is discussed. Estimated parameters based on Makishima–Mackenzie theory and bond compression model were calculated in order to analyse the experimental elastic moduli. Comparison between the experimental elastic moduli data obtained in the study and the calculated theoretically by the mentioned above models has been discussed

Solvothermal synthesis and thermoelectric properties of indium telluride nanostring-cluster hierarchical structures

A simple solvothermal approach has been developed to successfully synthesize n-type α-In2Te3 thermoelectric nanomaterials. The nanostring-cluster hierarchical structures were prepared using In(NO3)3 and Na2TeO3 as the reactants in a mixed solvent of ethylenediamine and ethylene glycol at 200°C for 24 h. A diffusion-limited reaction mechanism was proposed to explain the formation of the hierarchical structures. The Seebeck coefficient of the bulk pellet pressed by the obtained samples exhibits 43% enhancement over that of the corresponding thin film at room temperature. The electrical conductivity of the bulk pellet is one to four orders of magnitude higher than that of the corresponding thin film or p-type bulk sample. The synthetic route can be applied to obtain other low-dimensional semiconducting telluride nanostructures

The Maristán stigma scale: a standardized international measure of the stigma of schizophrenia and other psychoses

Background: People with schizophrenia face prejudice and discrimination from a number of sources including professionals and families. The degree of stigma perceived and experienced varies across cultures and communities. We aimed to develop a cross-cultural measure of the stigma perceived by people with schizophrenia.Method: Items for the scale were developed from qualitative group interviews with people with schizophrenia in six countries. The scale was then applied in face-to-face interviews with 164 participants, 103 of which were repeated after 30 days. Principal Axis Factoring and Promax rotation evaluated the structure of the scale; Horn’s parallel combined with bootstrapping determined the number of factors; and intra-class correlation assessed test-retest reliability.Results: The final scale has 31 items and four factors: informal social networks, socio-institutional, health professionals and self-stigma. Cronbach’s alpha was 0.84 for the Factor 1; 0.81 for Factor 2; 0.74 for Factor 3, and 0.75 for Factor 4. Correlation matrix among factors revealed that most were in the moderate range [0.31-0.49], with the strongest occurring between perception of stigma in the informal network and self-stigma and there was also a weaker correlation between stigma from health professionals and self-stigma. Test-retest reliability was highest for informal networks [ICC 0.76 [0.67 -0.83]] and self-stigma [ICC 0.74 [0.64-0.81]]. There were no significant differences in the scoring due to sex or age. Service users in Argentina had the highest scores in almost all dimensions.Conclusions: The MARISTAN stigma scale is a reliable measure of the stigma of schizophrenia and related psychoses across several cultures. A confirmatory factor analysis is needed to assess the stability of its factor structure.We are also grateful for support from the Pan-American Health Office (PAHO), Camden and Islington NHS Foundation Trust and University College London (UCL)

Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best

<p>Abstract</p> <p>Background</p> <p>Associations between measures of subjective health and mortality risk have previously been shown. We assessed the impact and comparative predictive performance of a multi-biomarker panel on this association.</p> <p>Methods</p> <p>Data from 4,261 individuals aged 20-79 years recruited for the population-based Study of Health in Pomerania was used. During an average 9.7 year follow-up, 456 deaths (10.7%) occurred. Subjective health was assessed by SF-12 derived physical (PCS-12) and mental component summaries (MCS-12), and a single-item self-rated health (SRH) question. We implemented Cox proportional-hazards regression models to investigate the association of subjective health with mortality and to assess the impact of a combination of 10 biomarkers on this association. Variable selection procedures were used to identify a parsimonious set of subjective health measures and biomarkers, whose predictive ability was compared using receiver operating characteristic (ROC) curves, C-statistics, and reclassification methods.</p> <p>Results</p> <p>In age- and gender-adjusted Cox models, poor SRH (hazard ratio (HR), 2.07; 95% CI, 1.34-3.20) and low PCS-12 scores (lowest vs. highest quartile: HR, 1.75; 95% CI, 1.31-2.33) were significantly associated with increased risk of all-cause mortality; an association independent of various covariates and biomarkers. Furthermore, selected subjective health measures yielded a significantly higher C-statistic (0.883) compared to the selected biomarker panel (0.872), whereas a combined assessment showed the highest C-statistic (0.887) with a highly significant integrated discrimination improvement of 1.5% (p < 0.01).</p> <p>Conclusion</p> <p>Adding biomarker information did not affect the association of subjective health measures with mortality, but significantly improved risk stratification. Thus, a combined assessment of self-reported subjective health and measured biomarkers may be useful to identify high-risk individuals for intensified monitoring.</p