Medicinal Plants of the Indigenous Tribes in Peninsular Malaysia: Current and Future Perspectives

The main aim of this paper is to compile information on plant that is known to be medicinal to the indigenous tribes in Peninsular Malaysia. Information is compiled from various sources. Current trends on studies of medicinal plants of the indigenous tribes and threats to the sustainability of the plants are also discussed. Focus of future studies on medicinal plants utilized by the indigenous tribes will also be discussed

Fishmeal replacement with Spirulina Platensis and Chlorella vulgaris in African catfish (Clarias gariepinus) diet: Effect on antioxidant enzyme activities and haematological parameters

This study explored fishmeal replacement with two freshwater microalgae: Spirulina Platensis and Chlorella vulgaris in African catfish (Clarias gariepinus) diet. The effect of inclusion of the two microalgae on biomarkers of oxidative stress, haematological parameters, enzyme activities and growth performance were investigated. The juvenile fish were given 3 distinct treatments with isonitrogenous (35.01–36.57%) and isoenergetic (417.24–422.27 Kcal 100 g−1) diets containing 50% S. platensis (50SP), 75% S. platensis (75SP), 50% C. vulgaris (50CL), 75% C. vulgaris (75CL) and 100% fishmeal (100% FM) was used as the control diet. The result shows that all the diets substituted with both S. platensis, and C. vulgaris boosted the growth performance based on specific growth rate (SGR) and body weight gain (BDWG) when compared with the control diet. The feed conversion ratio (FCR) and protein efficiency ratio (PER) was significantly influenced by all the supplementations. The haematological analysis of the fish shows a significant increase in the value of red and white blood cells upon supplementation with 50SP and 50CL but decrease slightly when increased to 75SP and 75CL. Furthermore, the value of haematocrit and haemoglobin also increased upon supplementation with 50SP and 50CL but decrease slightly when increased to 75SP and 75CL. The white blood cell (WBC), red blood cell (RBC) increased, while total cholesterol (TCL), and Plasma glucose levels decreased significantly upon supplementation of algae. This is a clear indication that S. platensis and C. vulgaris are a promising replacement for fishmeal, which is a source protein in the C. gariepinus diet

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

<p>Abstract</p> <p>Background</p> <p>This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.</p> <p>Results</p> <p>Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task.</p> <p>Conclusions</p> <p>Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.</p

The potential of microphytobenthos in sediment biostabilisation of aquatic ecosystems: An overview

Microphytobenthos represents microscopic primary producers, primarily diatoms that often form heterogeneous biofilms on sediment surface. Microphytobenthos assemblages have been documented and reported to be closely linked with the biostabilisation of the sediment surface in the intertidal flat of Europe. Flood and ebb tides cause vertical movement of the benthic diatoms in the surface of the top sediment, which contributes to stabilizing the sediment. Light, salinity and other environmental stressors further initiate microphytobenthos to move up and down vertically in the surface of the top sediment. Diatoms produce and secrete extracellular polymeric substances in response to their locomotion, which favorably stabilize the sediment surface during high tide immersion. Frequent storms caused by climate change have intensified the erosion problem along coastlines. Unfortunately, structures such as wave breakers and breakwaters that are built along some coastlines have proven to worsen the erosion rate. More environmental and biological-friendly solutions are needed to tackle the erosion problem worldwide. The widely distributed tidal flats and mangrove forests along the coastlines must harbor the diatom species that have biostabilisation potential. This review presents data that supports the biostabilisation of sediment by diatoms, provides information on this process and initiates more studies regarding the potential of microphytobenthos in biologically reducing sediment erosion along the coastlines, rather than structurally. © 2019, BIOFLUX SRL. All rights reserved

Skeletonema costatum of mangrove ecosystem: Its dynamics across physico-chemical parameters variability

As part of a monitoring programme, twelve months total phytoplankton sampling were carried out starting from April 2009 to March 2010 in Carey Island mangrove ecosystem across measured physico-chemical and total phytoplankton diversity parameters. Across the one year sampling period, we found three major occurrences of mono-species bloom of Skeletonema costatum in the month of April, May and August 2009, in which the water body at study site displayed dark brown colour. This diatom species that displayed significant temporal variation has been found to grow rapidly when the nitrate concentration and temperature of the water column were > 0.6 mg L -1 and > 31°C, respectively. This study shows that S. costatum displayed negative correlation with dissolved oxygen in the water column. Increase in S. costatum cells abundance at the study site proved to destabilize total phytoplankton diversity by decreasing the equitability (measured by evenness) of total phytoplankton. © 2019, BIOFLUX SRL. All rights reserved

Developing an ecological visualization system for biodiversity data

Data visualisation is essential for communicating and interpreting biodiversity data effectively. When compared to numerical values, visualising information with images is easier. Citizen Science has facilitated the collection of biodiversity data that can be used to conserve and preserve biodiversity sites. Google Earth provides a visualisation platform that can be used for biodiversity site monitoring. The latter has frequently been expressed in terms of biodiversity indices. The use of biodiversity indices for sites can be improved by incorporating visualisation elements. Previous studies that attempted to incorporate the calculation of biodiversity indices into biodiversity monitoring systems lacked the visualisation feature. This novel study aims to create an online module that combines biodiversity data from citizen science with a visualisation component. The observation data is imported from iNaturalist (https://www.inaturalist.org/) using the REST API method, which includes the species name and location. Species richness, Shannon-Wiener index, and Simpson index, as well as Hill Numbers, are automatically calculated and displayed on the Google Map alongside the green space area. The University of Malaya, which is located in an urban area, will be used as the study site for the demonstration of the developed prototype. The online biodiversity module prototype is available at http://www.umlivinglabsystem.com/Map/multipoly

Determining hypertensive patients’ beliefs towards medication and associations with medication adherence using machine learning methods

Background This study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients’ adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients’ adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature. Methods Data from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients’ adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients’ adherence levels and variables were generated using SOM. Result Machine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern. Conclusion This study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients’ adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension

Random forest and Self Organizing Maps application for analysis of pediatric fracture healing time of the lower limb

In this study, we examined the lower limb fracture healing time in children using random forest (RF) and Self Organizing feature Maps (SOM) methods. The study sample was obtained from the pediatric orthopedic unit in University Malaya Medical Centre. Radiographs of long bones of lower limb fractures involving the femur, tibia and fibula from children ages 0–12 years, with ages recorded from the date and time of initial injury. Inputs assessment extracted from radiographic images included the following features: type of fracture, angulation of the fracture, contact area percentage of the fracture, age, gender, bone type, type of fracture, and number of bone involved. RF is initially used to rank the most important variables that effecting bone healing time. Then, SOM was applied for analysis of the relationship between the selected variables with fracture healing time. Due to the limitation of available dataset, leave one out technique was applied to enhance the reliability of RF. Results showed that age and contact area percentage of fracture were identified as the most important variables in explaining the fracture healing time. RF and SOM applications have not been reported in the field of pediatric orthopedics. We concluded that the combination of RF and SOM techniques can be used to assist in the analysis of pediatric fracture healing time efficiently

Dietary spirulina platensis and chlorella vulgaris effects on survival and haemato-immunological responses of clarias gariepinus juveniles to aeromonas hydrophila infection

This study investigated the effects of Spirulina platensis and Chlorella vulgaris on the growth, non-specific immunity and survival of Clarias gariepinus. C. gariepinus juveniles were infected with Aeromonas hydrophila after a 16-week feeding period with 5 different diets which served as replacements for fishmeal diet. The diets are 50% C. vulgaris (CL50%), 75% C. vulgaris (CL75%), 50% S. platensis (SP50%), 75% S. platensis (SP75%), and control (100% fishmeal). Fish (n=225; 41.85± 0.05 g) were randomly divided into fifteen tanks (150 L capacity each) with triplicate groups of 15 fish per tank. CL50% had the most significant (P0.05) in growth performance among the algae treatments. Similarly, lysozyme and respiratory burst activity and post-challenge haemoglobin, haematocrit, red blood cells, serum total protein, albumin and higher density lipoprotein cholesterol were significantly higher (P<0.05) in CL50% treatment. The present study indicates that replacing 50%-75% of fishmeal with C. vulgaris or S. platensis could improve the growth and confer significant protection against A. hydrophila in the algae-fed C. gariepinus. © 2019, BIOFLUX SRL. All rights reserved