Effects of Soil Moisture on Photosynthesis and Fluorescence in Heteromeles arbutifolia

Some plants have evolved to increase their chances of survival by being drought-adapted. Among those plant species is Heteromeles arbutifolia, native to California. Logically, the fact that Heteromeles arbutifolia tolerates the low supply of water makes this plant more likely to be within environments where the level of sun exposure is high. Thus, we hypothesized that lowering soil moisture will cause an increase in xylem pressure, causing an increase in photo-protection and florescence, and a decrease in photosynthetic rate. This has not been tested before on a native chaparral plant such as Heteromeles arbutifolia. The experiment was held as following: six H. arbutifolia were evenly divided up into a treatment group and a control group. Both groups were given equal hydration at the beginning of this experiment. From then on the control group maintained a constant daily rate of hydration as the experimental groups received no water. Data of photosynthesis, photo-protection, florescence, xylem pressure, and soil moisture were taken for both groups throughout the entire experiment. Data collection showed statistically significant results of comparing non-photosynthetic quenching to photosynthetic quenching in water-stressed plants and water-saturated plants. Significant correlations were drawn between the following: the fraction of non-photosynthetic quenching to xylem pressure, the fraction of non-photosynthetic quenching to soil moisture, the fraction of non-photosynthetic quenching versus the time since the last event of irrigation, and xylem pressure and the time since the plant was last irrigated. It was concluded that photo-protection rate increases with water stress, which supported the initial hypothesis in part. However, the study also concluded that there was not a significant difference between the two groups regarding the florescence rate

Smart e-Health System for Real-time Tracking and Monitoring of Patients, Staff and Assets for Healthcare Decision Support in Saudi Arabia

Healthcare in Saudi Arabia has been lagging behind the developed countries of the world, due to the insufficient number of healthcare practitioners and the lack of applications of tracking and monitoring technology. These shortages have contributed to problems such as patient misidentification, long patient waiting times, and the inability to locate medical equipment efficiently. The country’s Vision 2030 plan outlines ways to solve the deficient workforce problem by promoting more local health-related educational outlets, and by funding this expanding sector. Consequently, Saudi Arabia needs to adapt to the demanding nature of modern healthcare, which presents major problems that this research aims to help solve. The literature has shown that Information Technology systems have begun to be implemented in some hospitals across Saudi Arabia, but even in those hospitals these technologies are being under-utilised. The intention of this thesis is to provide an appropriate choice for a real-time tracking and monitoring technology in healthcare, in the form of an integrated RFID/ZigBee system. This thesis develops a holistic framework for healthcare institutions, to be followed for customised solutions in improving staff efficiency and productivity, and for better patient care, while minimising long-term costs. This holistic framework incorporates contextual elements from both the Information System Strategy Triangle (ISST) and the Human, Organisation and Technology-fit factors (HOT-fit) frameworks, in a way that ensures the new framework addresses technology, organisational, human and business factors. The holistic model is refined through Communities of Practice (CoPs), one of which was developed and utilised for the research purposes of this thesis, and assisted in the creation of a questionnaire for assessing the requirements and challenges of the KSA healthcare system. This questionnaire was based on 220 usable responses. It also helped to refine the framework for its final version, which included all identified factors relevant to the decision a healthcare institution faces in choosing a health information technology system. Various cases were analysed to improve the hospitals workflow, using the proposed technology and including processes such as relocating staff and medical assets. This led to the need for visualisation and knowledge management, to support real-time data analysis for business intelligence decision making. The end goal of this analysis is to provide interactive platforms to healthcare staff for use in improving efficiency and productivity. The outcomes of these improvements will be to ensure better patient care, lower patient waiting time, reduced healthcare costs, and to allow more time for staff to provide improved patient-centric care in the Saudi healthcare sector. Keywords: e-Health, Health Information Technology, Tracking and Monitoring System, Kingdom of Saudi Arabia, Holistic Framework, Communities of Practice, Knowledge Management, Visualisation, KFM

Primary Renal Synovial Sarcoma Presenting as Haemorrhagic Shock: A Rare Presentation

Primary synovial sarcoma (PSS) of the kidney is considered the rarest type of all renal sarcomas with specific chromosomal translocation t (X; 18) (p11.2; q11.2). We report the case of a 65-year-old man with no medical conditions who presented to the emergency department with sudden severe right flank pain associated with haemodynamic instability and haemorrhagic shock. Computed tomography (CT) of the abdomen and pelvis revealed a right renal mass. A right open radical nephrectomy was performed. Histopathology revealed a monophasic synovial sarcoma. The patient received six cycles of docetaxel and gemcitabine as adjuvant chemotherapy. No sign of recurrence was seen on a follow-up CT urogram. This rare tumour often presents atypically, and clear guidelines regarding appropriate treatment are lacking. Our case showed that treatment with docetaxel/gemcitabine after an open radical nephrectomy is promising

The association of vitamin D status with dyslipidaemia and biomarkers of endothelial cell activation in older Australians

© 2016 by the authors; licensee MDPI, Basel, Switzerland.Background/Aims: Vitamin D has been investigated for many non-skeletal effects. The objective of this study was to determine whether circulating lipids, systemic inflammation, and biomarkers of endothelial cell activation varied with the vitamin D status of older Australians. Methods: One hundred and one participants were proportionately and randomly sampled across tertiles of 25 hydroxy vitamin D (25(OH)D) from a larger cohort of free living older adults (T1 median = 97; T2 median = 74.5; T3 median = 56.8 nmol/L). Overnight fasting blood samples were assayed for 25(OH)D, parathyroid hormone (PTH), insulin, triacylglycerol (TAG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C) and high density lipoprotein cholesterol (HDL-C). Markers of systemic inflammation (high sensitivity C-reactive protein (hsCRP), tumour necrosis factor-a (TNF-a)) and endothelial activation (hepatocyte growth factor (HGF), P-selectin and soluble vascular cell adhesion molecule (sVCAM), soluble intracellular adhesion molecule (sICAM)) were determined. A general linear model multivariate analysis with a backward elimination procedure was performed. Results: Eighty-three participants (48 women, 35 men), aged 65 ± 7.7 years, BMI 28 ± 4.5 kg/m2, with complete data were analyzed. The final parsimonious model controlled for age, gender, BMI, and McAuley’s index, but excluded season, medications, and PTH. There were significant differences across 25(OH)D tertiles in TC (T1 < T3, p = 0.003; T2 < T3, p = 0.001), LDL-C (T1 < T3, p = 0.005; T2 < T3, p = 0.001), TAG (T2 < T3, p = 0.026), HGF (T1 > T3, p = 0.009) and sVCAM (T1 > T3, P = 0.04). Conclusions: Higher vitamin D status may protect the endothelium through reduced dyslipidaemia and increased HGF

Amygdala and subregion volume are associated with photoperiod and seasonal depressive symptoms : A cross sectional study in the UK Biobank cohort

ACKNOWLEDGEMENTS This research has been conducted using the UK Biobank Resource. This work was supported by the Aberdeen Biomedical Imaging Centre with financial support from the Roland Sutton Academic Trust (RSAT-0039/R/16) and Jazan University and Ministry of Health in Saudi Arabia.Peer reviewedPublisher PD

A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches

Diabetes in humans is a rapidly expanding chronic disease and a major crisis in modern societies. The classification of diabetics is a challenging and important procedure that allows the interpretation of diabetic data and diagnosis. Missing values in datasets can impact the prediction accuracy of the methods for the diagnosis. Due to this, a variety of machine learning techniques has been studied in the past. This research has developed a new method using machine learning techniques for diabetes risk prediction. The method was developed through the use of clustering and prediction learning techniques. The method uses Singular Value Decomposition for missing value predictions, a Self-Organizing Map for clustering the data, STEPDISC for feature selection, and an ensemble of Deep Belief Network classifiers for diabetes mellitus prediction. The performance of the proposed method is compared with the previous prediction methods developed by machine learning techniques. The results reveal that the deployed method can accurately predict diabetes mellitus for a set of real-world datasets

Sentiment Analysis of Semantically Interoperable Social Media Platforms Using Computational Intelligence Techniques

Competitive intelligence in social media analytics has significantly influenced behavioral finance worldwide in recent years; it is continuously emerging with a high growth rate of unpredicted variables per week. Several surveys in this large field have proved how social media involvement has made a trackless network using machine learning techniques through web applications and Android modes using interoperability. This article proposes an improved social media sentiment analytics technique to predict the individual state of mind of social media users and the ability of users to resist profound effects. The proposed estimation function tracks the counts of the aversion and satisfaction levels of each inter- and intra-linked expression. It tracks down more than one ontologically linked activity from different social media platforms with a high average success rate of 99.71%. The accuracy of the proposed solution is 97% satisfactory, which could be effectively considered in various industrial solutions such as emo-robot building, patient analysis and activity tracking, elderly care, and so on