Physio-chemical assessment of beauty leaf (Calophyllum inophyllum) as second-generation biodiesel feedstock

Recently, second-generation (non-vegetable oil) feedstocks for biodiesel production are receiving significant attention due to the cost and social effects connected with utilising food products for the production of energy products. The Beauty leaf tree (Calophyllum inophyllum) is a potential source of non-edible oil for producing second-generation biodiesel because of its suitability for production in an extensive variety of atmospheric condition, easy cultivation, high fruit production rate, and the high oil content in the seed. In this study, oil was extracted from Beauty leaf tree seeds through three different oil extraction methods. The important physical and chemical properties of these extracted Beauty leaf oils were experimentally analysed and compared with other commercially available vegetable oils. Biodiesel was produced using a two-stage esterification process combining of an acid catalysed pre-esterification process and an alkali catalysed transesterification process. Fatty acid methyl ester (FAME) profiles and important physicochemical properties were experimentally measured and estimated using equations based on the FAME analysis. The quality of Beauty leaf biodiesels was assessed and compared with commercially available biodiesels through multivariate data analysis using PROMETHEE-GAIA software. The results show that mechanical extraction using a screw press produces oil at a low cost, however, results in low oil yields compared with chemical oil extraction. High pressure and temperature in the extraction process increase oil extraction performance. On the contrary, this process increases the free fatty acid content in the oil. A clear difference was found in the physical properties of Beauty leaf oils, which eventually affected the oil to biodiesel conversion process. However, Beauty leaf oils methyl esters (biodiesel) were very consistent physicochemical properties and able to meet almost all indicators of biodiesel standards. Overall this study found that Beauty leaf is a suitable feedstock for producing second-generation biodiesel in commercial scale. Therefore, the findings of this study are expected to serve as the basis for further development of Beauty leaf as a feedstock for industrial scale second-generation biodiesel production.

Algorithm of medication review in frail older people: Focus on minimizing the use of high-risk medications

Aim Frail older people typically suffer several chronic diseases, receive multiple medications and are more likely to be institutionalized in residential aged care facilities. In such patients, optimizing prescribing and avoiding use of high-risk medications might prevent adverse events. The present study aimed to develop a pragmatic, easily applied algorithm for medication review to help clinicians identify and discontinue potentially inappropriate high-risk medications. Methods The literature was searched for robust evidence of the association of adverse effects related to potentially inappropriate medications in older patients to identify high-risk medications. Prior research into the cessation of potentially inappropriate medications in older patients in different settings was synthesized into a four-step algorithm for incorporation into clinical assessment protocols for patients, particularly those in residential aged care facilities. Results The algorithm comprises several steps leading to individualized prescribing recommendations: (i) identify a high-risk medication; (ii) ascertain the current indications for the medication and assess their validity; (iii) assess if the drug is providing ongoing symptomatic benefit; and (iv) consider withdrawing, altering or continuing medications. Decision support resources were developed to complement the algorithm in ensuring a systematic and patient-centered approach to medication discontinuation. These include a comprehensive list of high-risk medications and the reasons for inappropriateness, lists of alternative treatments, and suggested medication withdrawal protocols. Conclusions The algorithm captures a range of different clinical scenarios in relation to potentially inappropriate medications, and offers an evidence-based approach to identifying and, if appropriate, discontinuing such medications. Studies are required to evaluate algorithm effects on prescribing decisions and patient outcomes.

High-density fine-mapping of a chromosome 10q26 linkage peak suggests association between endometriosis and variants close to CYP2C19

OBJECTIVE To refine a previously reported linkage peak for endometriosis on chromosome 10q26, and conduct follow-up analyses and a fine-mapping association study across the region to identify new candidate genes for endometriosis. DESIGN Case-control study. SETTING Academic research. PATIENT(S) Cases=3,223 women with surgically confirmed endometriosis; controls=1,190 women without endometriosis and 7,060 population samples. INTERVENTION(S) Analysis of 11,984 single nucleotide polymorphisms on chromosome 10. MAIN OUTCOME MEASURE(S) Allele frequency differences between cases and controls. RESULT(S) Linkage analyses on families grouped by endometriosis symptoms (primarily subfertility) provided increased evidence for linkage (logarithm of odds score=3.62) near a previously reported linkage peak. Three independent association signals were found at 96.59 Mb (rs11592737), 105.63 Mb (rs1253130), and 124.25 Mb (rs2250804). Analyses including only samples from linkage families supported the association at all three regions. However, only rs11592737 in the cytochrome P450 subfamily C (CYP2C19) gene was replicated in an independent sample of 2,079 cases and 7,060 population controls. CONCLUSION(S) The role of the CYP2C19 gene in conferring risk for endometriosis warrants further investigation.

Assessment of the severity of Ebola virus disease in Sierra Leone in 2014–2015

The current Ebola virus disease (EVD) epidemic in West Africa is unprecedented in scale, and Sierra Leone is the most severely affected country. The case fatality risk (CFR) and hospitalization fatality risk (HFR) were used to characterize the severity of infections in confirmed and probable EVD cases in Sierra Leone. Proportional hazards regression models were used to investigate factors associated with the risk of death in EVD cases. In total, there were 17 318 EVD cases reported in Sierra Leone from 23 May 2014 to 31 January 2015. Of the probable and confirmed EVD cases with a reported final outcome, a total of 2536 deaths and 886 recoveries were reported. CFR and HFR estimates were 74·2% [95% credibility interval (CrI) 72·6–75·5] and 68·9% (95% CrI 66·2–71·6), respectively. Risks of death were higher in the youngest (0–4 years) and oldest (≥60 years) age groups, and in the calendar month of October 2014. Sex and occupational status did not significantly affect the mortality of EVD. The CFR and HFR estimates of EVD were very high in Sierra Leone.

Variability in exposure to ambient ultrafine particles in urban schools: Comparative assessment between Australia and Spain

Ambient ultrafine particle number concentrations (PNC) have inhomogeneous spatio-temporal distributions and depend on a number of different urban factors, including background conditions and distant sources. This paper quantitatively compares exposure to ambient ultrafine particles at urban schools in two cities in developed countries, with high insolation climatic conditions, namely Brisbane (Australia) and Barcelona (Spain). The analysis used comprehensive indoor and outdoor air quality measurements at 25 schools in Brisbane and 39 schools in Barcelona. PNC modes were analysed with respect to ambient temperature, land use and urban characteristics, combined with the measured elemental carbon concentrations, NOx (Brisbane) and NO2 (Barcelona). The trends and modes of the quantified weekday average daily cycles of ambient PNC exhibited significant differences between the two cities. PNC increases were observed during traffic rush hours in both cases. However, the mid-day peak was dominant in Brisbane schools and had the highest contribution to total PNC for both indoors and outdoors. In Barcelona, the contribution from traffic was highest for ambient PNC, while the mid-day peak had a slightly higher contribution for indoor concentrations. Analysis of the relationships between PNC and land use characteristics in Barcelona schools showed a moderate correlation with the percentage of road network area and an anti-correlation with the percentage of green area. No statistically significant correlations were found for Brisbane. Overall, despite many similarities between the two cities, school-based exposure patterns were different. The main source of ambient PNC at schools was shown to be traffic in Barcelona and mid-day new particle formation in Brisbane. The mid-day PNC peak in Brisbane could have been driven by the combined effect of background and meteorological conditions, as well as other local/distant sources. The results have implications for urban development, especially in terms of air quality mitigation and management at schools.

Assessment of signs of foot infection in diabetes patients using photographic foot imaging and infrared thermography

Background Patients with diabetic foot disease require frequent screening to prevent complications and may be helped through telemedical home monitoring. Within this context, the goal was to determine the validity and reliability of assessing diabetic foot infection using photographic foot imaging and infrared thermography. Subjects and Methods For 38 patients with diabetes who presented with a foot infection or were admitted to the hospital with a foot-related complication, photographs of the plantar foot surface using a photographic imaging device and temperature data from six plantar regions using an infrared thermometer were obtained. A temperature difference between feet of > 2.2 °C defined a ''hotspot.'' Two independent observers assessed each foot for presence of foot infection, both live (using the Perfusion-Extent-Depth- Infection-Sensation classification) and from photographs 2 and 4 weeks later (for presence of erythema and ulcers). Agreement in diagnosis between live assessment and (the combination of ) photographic assessment and temperature recordings was calculated. Results Diagnosis of infection from photographs was specific (> 85%) but not very sensitive (< 60%). Diagnosis based on hotspots present was sensitive (> 90%) but not very specific (<25%). Diagnosis based on the combination of photographic and temperature assessments was both sensitive (> 60%) and specific (> 79%). Intra-observer agreement between photographic assessments was good (Cohen's j = 0.77 and 0.52 for both observers). Conclusions Diagnosis of foot infection in patients with diabetes seems valid and reliable using photographic imaging in combination with infrared thermography. This supports the intended use of these modalities for the home monitoring of high-risk patients with diabetes to facilitate early diagnosis of signs of foot infection.

Risk assessment for airborne infectious diseases between natural ventilation and split-system air conditioner in a university classroom

Indoor air quality is a critical factor in the classroom due to high people concentration in a unique space. Indoor air pollutant might increase the chance of both long and short-term health problems among students and staff, reduce the productivity of teachers and degrade the student’s learning environment and comfort. Adequate air distribution strategies may reduce risk of infection in classroom. So, the purpose of air distribution systems in a classroom is not only to maximize conditions for thermal comfort, but also to remove indoor contaminants. Natural ventilation has the potential to play a significant role in achieving improvements in IAQ. The present study compares the risk of airborne infection between Natural Ventilation (opening windows and doors) and a Split-System Air Conditioner in a university classroom. The Wells-Riley model was used to predict the risk of indoor airborne transmission of infectious diseases such as influenza, measles and tuberculosis. For each case, the air exchange rate was measured using a CO2 tracer gas technique. It was found that opening windows and doors provided an air exchange rate of 2.3 air changes/hour (ACH), while with the Split System it was 0.6 ACH. The risk of airborne infection ranged between 4.24 to 30.86 % when using the Natural Ventilation and between 8.99 to 43.19% when using the Split System. The difference of airborne infection risk between the Split System and the Natural Ventilation ranged from 47 to 56%. Opening windows and doors maximize Natural Ventilation so that the risk of airborne contagion is much lower than with Split System.

Assessment of motivation to control alcohol use: The motivational thought frequency and state motivation scales for alcohol control

Introduction The Elaborated Intrusion Theory of Desire holds that desires for functional and dysfunctional goals share a common form. Both are embodied cognitive events, characterised by affective intensity and frequency. Accordingly, we developed scales to measure motivational cognitions for functional goals (Motivational Thought Frequency, MTF; State Motivation, SM), based on the existing Craving Experience Questionnaire (CEQ). When applied to increasing exercise, MTF and SM showed the same three-factor structure as the CEQ (Intensity, Imagery, Availability). The current study tested the internal structure and concurrent validity of the MTF and SM Scales when applied to control of alcohol consumption (MTF-A; SM-A). Methods Participants (N = 417) were adult tertiary students, staff or community members who had recently engaged in high-risk drinking or were currently trying to control alcohol consumption. They completed an online survey comprising the MTF-A, SM-A, Alcohol Use Disorders Identification Test (AUDIT), Readiness to Change Questionnaire (RCQ) and demographics. Results Confirmatory Factor Analysis gave acceptable fit for the MTF-A, but required the loss of one SM-A item, and was improved by intercorrelations of error terms. Higher scores were associated with more severe problems on the AUDIT and with higher Contemplation and Action scores on the RCQ. Conclusions The MTF-A and SM-A show potential as measures of motivation to control drinking. Future research will examine their predictive validity and sensitivity to change. The scales' application to both increasing functional and decreasing dysfunctional behaviours is consistent with EI Theory's contention that both goal types operate in similar ways.

Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF) is a valid screening tool in chemotherapy outpatients

Purpose In the oncology population where malnutrition prevalence is high, more descriptive screening tools can provide further information to assist triaging and capture acute change. The Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF) is a component of a nutritional assessment tool which could be used for descriptive nutrition screening. The purpose of this study was to conduct a secondary analysis of nutrition screening and assessment data to identify the most relevant information contributing to the PG-SGA SF to identify malnutrition risk with high sensitivity and specificity. Methods This was an observational, cross-sectional study of 300 consecutive adult patients receiving ambulatory anti-cancer treatment at an Australian tertiary hospital. Anthropometric and patient descriptive data were collected. The scored PG-SGA generated a score for nutritional risk (PG-SGA SF) and a global rating for nutrition status. Receiver operating characteristic curves (ROC) were generated to determine optimal cut-off scores for combinations of the PG-SGA SF boxes with the greatest sensitivity and specificity for predicting malnutrition according to scored PG-SGA global rating. Results The additive scores of boxes 1–3 had the highest sensitivity (90.2 %) while maintaining satisfactory specificity (67.5 %) and demonstrating high diagnostic value (AUC = 0.85, 95 % CI = 0.81–0.89). The inclusion of box 4 (PG-SGA SF) did not add further value as a screening tool (AUC = 0.85, 95 % CI = 0.80–0.89; sensitivity 80.4 %; specificity 72.3 %). Conclusions The validity of the PG-SGA SF in chemotherapy outpatients was confirmed. The present study however demonstrated that the functional capacity question (box 4) does not improve the overall discriminatory value of the PG-SGA SF.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.