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Alzheimer’s disease (AD) is a progressive and relentless debilitating neurodegenerative disease. A post-mortem microscopic neuropathological examination of the brain revealed the existence of extracellular β-amyloid plaques and intracellular neurofibrillary tangles. An accurate early diagnosis of AD is difficult because various disorders share the initial symptoms of the disease. Based on system biology, the multi-omics approach captures and integrates information from genomics, transcriptomics, proteomics, cytokinomics, and metabolomics. This study developed an AD prediction model based on the integrated blood-based multi-omics dataset involving 32 AD patients and 15 non-AD subjects. The integrated multi-omics dataset consists of 16 transcript genes, 14 metabolites, and nine cytokines. Due to the complete separation and multicollinearity issues, Firth’s logistic regression model was then developed to predict AD using the principal components. The model revealed 18 potential biomarkers of AD, consisting of seven metabolites, two transcriptomes, and nine cytokines. These potential biomarkers show an upregulated risk in the AD group compared to the non-AD subjects. The possibility of using these biomarkers as early predictors of AD is discussed.

The present work focuses on the measured wave spectrum in the South China Sea (scs) during storms. The measured spectra have been obtained from surface elevation measurements undertaken at 2 locations in scs over 7 years, both of which are related to intermediate relative water depths. Shell’s stringent ts-211 quality control procedure was applied to the raw data and from the data that passed this procedure, the five largest storms for each of the locations were isolated. Analysis of this data in conjunction with the corresponding wind data confirmed that for both of the locations, the largest ‘storms’ comprised of mainly swell. In keeping with this trend, a double-peaked spectrum consisting of a Gaussian swell component and a jonswap wind component provided the best fits to the measured spectra. Further analysis of the spectral parameters resulting from the fits for the individual wave records revealed that no particular trend was identifiable for the shape parameters. Due to this, for each location, a pragmatic fit that encompasses the average spectral shape parameters obtained from all the sea states has been suggested as a typical design spectrum for each location.

Adults are constantly exposed to stressful conditions at their workplace, and this can lead to decreased job performance followed by detrimental clinical health problems. Advancement of sensor technologies has allowed the electroencephalography (EEG) devices to be portable and used in real-time to monitor mental health. However, real-time monitoring is not often practical in workplace environments with complex operations such as kindergarten, firefighting and offshore facilities. Integrating the EEG with virtual reality (VR) that emulates workplace conditions can be a tool to assess and monitor mental health of adults within their working environment. This paper evaluates the mental states induced when performing a stressful task in a VR-based offshore environment. The theta, alpha and beta frequency bands are analysed to assess changes in mental states due to physical discomfort, stress and concentration. During the VR trials, mental states of discomfort and disorientation are observed with the drop of theta activity, whilst the stress induced from the conditional tasks is reflected in the changes of low-alpha and high-beta activities. The deflection of frontal alpha asymmetry from negative to positive direction reflects the learning effects from emotion-focus to problem-solving strategies adopted to accomplish the VR task. This study highlights the need for an integrated VR-EEG system in workplace settings as a tool to monitor and assess mental health of working adults.

This study aims to explore the effects of flipped classroom implementation on students’ academic achievement. Three courses that was offered to Information System students implemented the flipped classroom approach with the support of ULearn Learning Management System (LMS) over twelve (12) weeks of learning. Students’ coursework assessments and examination scores were compared between controlled groups that employed traditional teachingapproach and experimental groups which introduced the flipped learning approach. The Mann-Whitney test was used to compare the median and mean total marks of the two learning approaches, as well as data consistency for the mean total marks. Results show that median and mean total marks were higher in experimental group when compared to the control group for Knowledge Management System (KMS) and Information System Strategy & Planning (ISP)courses. This is proof that flipped classroom approach is a potential student-centred learning approach that can improve students’ academic achievement by encouraging students to be deep learners and promoting self-directed learning and life-long learning through the time optimization of out-class lectures and in-class active and collaborative learning activities.

Tropical Storm (TS) Pabuk which occurred in South China Sea during the Northwest Monsoon season of 2018-2019 is a rare storm event. Even though it was originally considered weak, it was able to enter and remains active in the 5 degree latitude of weak Coriolis zone, then track further north, make a short land crossing before ending in Andaman Sea. At its early formation, it was in tune with the incoming monsoon's cold surge which perfectly align with the storm outer circulation that enable it to amplify like a little push that spun a spin-top going faster. The waves measured during this storm far exceeds the waves conditions associated with a 100 year Return Period (RP). In addition to being larger, they are also much steeper than the waves contained in the SEAFINE hindcast database spanning back over the past 59 years. However, interestingly, correlating the generated waves with the local wind field showed that at the locations where the data originated, the peaks in wave conditions are contributed by swell.

Hydroxyapatite (HA) powders were prepared via chemical solution through aqueous solution of calcium hydroxide and phosphoric acid. The calcium precursor was extracted from the calcium carbonate of Corbiculacea shells while the phosphate precursor originated from the commercially available phosphoric acid. The final product of HA powders is then manipulated through the sintering process at 500°C while the other sample was used as it is. XRD result shows significant changes in its crystallinity, crystallite size and lattice parameters after the sintering process. By sintering the HA, the crystallite size and crystallinity were increases as much as 6.25% and 5.31% respectively. SEM on the other hand showed different morphology for both sintering and non-sintering HA powders. For the sintering HA, the grains size is higher which is 5.00μm compared to non-sintering HA which is 3.91μm due to the agglomeration.

Background: Alzheimer’s disease (AD) is a neurodegenerative disorder where patients suffer from memory loss, cognitive impairment and progressive disability. Individual blood biomarkers have not been successful in defining the disease pathology, progression and diagnosis of AD. There is a need to identify multiplex panels of blood biomarkers for early diagnosis of AD with high sensitivity and specificity. This study focused on identification of cytokine biomarkers. The maximum likelihood estimates of the ordinary logistic regression model cannot be obtained when there is complete separation and the alternative is Firth logistic regression which uses a penalised Maximum Likelihood in parameter estimation. Methods: This paper reports a Firth logistic regression application in finding potential blood-based cytokine biomarkers for Alzheimer’s disease in a matched case control study. We used a principle component analysis to discriminate the correlated, completely separated covariates. Results: The Firth logistic regression results showed that nine individual biomarkers IL-1β, IL-6, IL-12, IFN-γ, IL-10, IL-13, IP-10, MCP-1 and MIP-1α had a significant relationshipwith elevated risk for AD as compared to the healthy control (HC). Principal component analysis with varimax rotation for the nine biomarkers revealed four factors (total variance explained=85.5%). The main principal component biomarkers were IL-1β, IL-6, IL-13 and MCP-1 (total variance explained=62.3%). Firth’s logistic regression model with the first principal component had accuracy of 78.2% with sensitivity and specificity of 71.8% and 75% respectively. Conclusion: Firth’s logistic regression is a useful technique in identification of significant biomarkers when there is an issue of data separation.

Sentiment analysis is a computational process to identify and classify subjective information such as positive, negative and neutral from the source material. It is able to extract feeling and emotion from a piece of a sentence. This technology has been widely used to extract valuable information from people’s views on social media. Hence, this project aims to classify movie reviews into positives, negatives and neutral polarity using lexicon-based method which used R as the language and development framework. Twitter data is used as the source material. Firstly, tweets were extracted using RStudio and Twitter API. Then data pre-processing was done by removing all the stop words and noises. Next was the tokenization process, which separates the words and matches the separated words with positive and negative words vocabulary. Finally, the result of the sentiment analysis is produced into positive, negative and neutral polarities. The results were evaluated using standard evaluation metrics that are the precision, recall, F1 score and accuracy. After all, it is found that the basic lexicon-based method is able to classify sentiment quite well with 52% accuracy. Apparently, the accuracy value achieved in our experiment is not impressive enough, but it is worth corresponding to the simplicity and minimal cost of development for sentiment analysis on Twitter data for movies.

Leprosy is a complex disease which will affect in many aspects of the patient. In dr. Soetomo hospital, there were two cases of pregnancy with leprosy and Lucio’s phenomenon from 2014 until 2018. Both had been receiving multidrug therapy (MDT) before pregnancy but stopped due to lack of compliance. First case was resulted with term. Second case was admitted with worse condition than the first case, fetal growth restriction and ended with preterm delivery probably because the severity of the case. Lucio’s phenomenon incidence is increased in pregnancy due to immunodeficient condition. This serial case report shows that the initial diagnosis and optimum treatment of leprosy is very important especially in women of child-bearing age.

In the learning process, teamwork is often a crucial part where students learn to work together using their individual skills despite any personal conflict. Students often enjoy learning in teams and developing teamwork skills, but criticize team assessment as unfair if there is equal reward for unequal contribution. In order to evaluate these teamwork skills, a peer assessment often used to measure the performance each of the team members. Peer assessment can be defined as the responsibility of assessing work of peers against various set of assessment criteria. It is one of the powerful tools for the students to have better understanding on assessment criteria and also transfer certain percentage of ownership of the assessment process and marks to them that subsequently will help increasing their self-motivation and teamwork skills. Group assignment is one type of assessments that is widely used by the lecturers in UiTM (Perak) especially in the Faculty of Architecture, Planning and Surveying (FSPU). However, no study has been conducted to measure the level of awareness among the lecturers. Thus, this research seeks to identify the level of awareness of peer assessment among the lecturers in UiTM (Perak). In addition, this research will determine the most preferable medium for peer assessment by the students. To achieve the objectives of this research, data collection through survey analysis method were used. Set of 50 questionnaires was distributed to the lecturers in UiTM (Perak). Data from the questionnaire survey was analyzed quantitatively using descriptive statistics involving frequency and percentage distribution using the Statistical Package for the Social Sciences (SPSS). The finding from this research clearly shows that most of the respondent answer completely agree and agree for all of the questions which lead to high awareness about peer review assessment for their students. We also found that most respondents who have high awareness preferred online and paper as their medium of peer assessment. It is hoped that this preliminary study will be used as indicator to shows that the need of development of online peer assessment.

Most studies regarding to air pollution were focused on forecasting Air Pollutant Index (API). Yet, there were no studies that conducted in Malaysia focused on Health-Related Quality of Life (HRQoL). The aim of this study was to investigate the HRQoL in two urban cities, which are Shah Alam and Putrajaya with different air pollution index. In doing so, SF36v2 questionnaire has been utilized to elicit data on HRQoL domains measured using eight domains (Physical Functioning, Role-Physical, Bodily Pain, General Health, Vitality, Social Functioning, Role-Emotional and Mental Health). A cross-sectional study was conducted and residents were selected using simple random sampling from Shah Alam and Putrajaya. The SF36v2 questionnaire with socio demographic information was distributed to the residents. A total of 266 participated in the study, of which 133 samples per groups. The statistical methods employed were descriptive analyses, independent samples t-test and MANOVA to analyze the HRQoL data. There was no d...

We aimed to determine the effect of Clinical Practice Guideline (CPG) implementation on length of hospital stay of children and adolescents with diabetic ketoacidosis (DKA).This was a 6-year (2008-2014) case-control retrospective study conducted at King Khalid University Hospital, Riyadh, that compared patients with DKA managed using CPG with those treated before CPG implementation.There were 63 episodes of DKA in 41 patients managed using CPG compared with 40 episodes in 33 patients treated before implementation of CPG. Baseline characteristics of the 2 groups were similar (age, sex, newly diagnosed patients, recurrent DKA, DKA severity, and mean glycosylated hemoglobin). The mean length of hospital stay (±SD) was 68.6 ± 53.1 hours after implementation of CPG compared with 107.4 ± 65.6 hours before implementation (P .001). The reduction in length of hospital stay equals to 1700 bed days saved per year per 1000 patients.Implementation of CPG for DKA decreased the length of hospital stay.