Identifying And Classifying Unknown Words In Malay Texts.

In this paper, we propose a method based on a chain of filters to handle the problem of identifying and classifying unknown words in Malay texts. A word is identified as unknown when it is not listed in the lexicon

Genetic diversity of circumsporozoite protein in Plasmodium knowlesi isolates from Malaysian Borneo and Peninsular Malaysia

Understanding the genetic diversity of candidate genes for malaria vaccines such as circumsporozoite protein (csp) may enhance the development of vaccines for treating Plasmodium knowlesi. Hence, the aim of this study is to investigate the genetic diversity of non-repeat regions of csp in P. knowlesi from Malaysian Borneo and Peninsular Malaysia. A total of 46 csp genes were subjected to polymerase chain reaction amplification. The genes were obtained from P. knowlesi isolates collected from different divisions of Sabah, Malaysian Borneo, and Peninsular Malaysia. The targeted gene fragments were cloned into a commercial vector and sequenced, and a phylogenetic tree was constructed while incorporating 168 csp sequences retrieved from the GenBank database. The genetic diversity and natural evolution of the csp sequences were analysed using MEGA6 and DnaSP ver. 5.10.01. A genealogical network of the csp haplotypes was generated using NETWORK ver. 4.6.1.3. The phylogenetic analysis revealed indistinguishable clusters of P. knowlesi isolates across different geographic regions, including Malaysian Borneo and Peninsular Malaysia. Nucleotide analysis showed that the csp nonrepeat regions of zoonotic P. knowlesi isolates obtained in this study underwent purifying selection with population expansion, which was supported by extensive haplotype sharing observed between humans and macaques. Novel variations were observed in the C-terminal non-repeat region of csp. The csp non-repeat regions are relatively conserved and there is no distinct cluster of P. knowlesi isolates from Malaysian Borneo and Peninsular Malaysia. Distinctive variation data obtained in the C-terminal non-repeat region of csp could be beneficial for the design and development of vaccines to treat P. knowlesi

Optimal glucose, HbA1c, glucose-HbA1c ratio and stress-hyperglycaemia ratio cut-off values for predicting 1-year mortality in diabetic and non-diabetic acute myocardial infarction patients

Background Stress-induced hyperglycaemia at time of hospital admission has been linked to worse prognosis following acute myocardial infarction (AMI). In addition to glucose, other glucose-related indices, such as HbA1c, glucose-HbA1c ratio (GHR), and stress-hyperglycaemia ratio (SHR) are potential predictors of clinical outcomes following AMI. However, the optimal blood glucose, HbA1c, GHR, and SHR cut-off values for predicting adverse outcomes post-AMI are unknown. As such, we determined the optimal blood glucose, HbA1c, GHR, and SHR cut-off values for predicting 1-year all cause mortality in diabetic and non-diabetic ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) patients. Methods We undertook a national, registry-based study of patients with AMI from January 2008 to December 2015. We determined the optimal blood glucose, HbA1c, GHR, and SHR cut-off values using the Youden’s formula for 1-year all-cause mortality. We subsequently analyzed the sensitivity, specificity, positive and negative predictive values of the cut-off values in the diabetic and non-diabetic subgroups, stratified by the type of AMI. Results There were 5841 STEMI and 4105 NSTEMI in the study. In STEMI patients, glucose, GHR, and SHR were independent predictors of 1-year all-cause mortality [glucose: OR 2.19 (95% CI 1.74–2.76); GHR: OR 2.28 (95% CI 1.80–2.89); SHR: OR 2.20 (95% CI 1.73–2.79)]. However, in NSTEMI patients, glucose and HbA1c were independently associated with 1-year all-cause mortality [glucose: OR 1.38 (95% CI 1.01–1.90); HbA1c: OR 2.11 (95% CI 1.15–3.88)]. In diabetic STEMI patients, SHR performed the best in terms of area-under-the-curve (AUC) analysis (glucose: AUC 63.3%, 95% CI 59.5–67.2; GHR 68.8% 95% CI 64.8–72.8; SHR: AUC 69.3%, 95% CI 65.4–73.2). However, in non-diabetic STEMI patients, glucose, GHR, and SHR performed equally well (glucose: AUC 72.0%, 95% CI 67.7–76.3; GHR 71.9% 95% CI 67.7–76.2; SHR: AUC 71.7%, 95% CI 67.4–76.0). In NSTEMI patients, glucose performed better than HbA1c for both diabetic and non-diabetic patients in AUC analysis (For diabetic, glucose: AUC 52.8%, 95% CI 48.1–57.6; HbA1c: AUC 42.5%, 95% CI 37.6–47. For non-diabetic, glucose: AUC 62.0%, 95% CI 54.1–70.0; HbA1c: AUC 51.1%, 95% CI 43.3–58.9). The optimal cut-off values for glucose, GHR, and SHR in STEMI patients were 15.0 mmol/L, 2.11, and 1.68 for diabetic and 10.6 mmol/L, 1.72, and 1.51 for non-diabetic patients respectively. For NSTEMI patients, the optimal glucose values were 10.7 mmol/L for diabetic and 8.1 mmol/L for non-diabetic patients. Conclusions SHR was the most consistent independent predictor of 1-year all-cause mortality in both diabetic and non-diabetic STEMI, whereas glucose was the best predictor in NSTEMI patients

Trends and predictions of metabolic risk factors for acute myocardial infarction: findings from a multiethnic nationwide cohort

BACKGROUND: Understanding the trajectories of metabolic risk factors for acute myocardial infarction (AMI) is necessary for healthcare policymaking. We estimated future projections of the incidence of metabolic diseases in a multi-ethnic population with AMI. METHODS: The incidence and mortality contributed by metabolic risk factors in the population with AMI (diabetes mellitus [T2DM], hypertension, hyperlipidemia, overweight/obesity, active/previous smokers) were projected up to year 2050, using linear and Poisson regression models based on the Singapore Myocardial Infarction Registry from 2007 to 2018. Forecast analysis was stratified based on age, sex and ethnicity. FINDINGS: From 2025 to 2050, the incidence of AMI is predicted to rise by 194.4% from 482 to 1418 per 100,000 population. The largest percentage increase in metabolic risk factors within the population with AMI is projected to be overweight/obesity (880.0% increase), followed by hypertension (248.7% increase), T2DM (215.7% increase), hyperlipidemia (205.0% increase), and active/previous smoking (164.8% increase). The number of AMI-related deaths is expected to increase by 294.7% in individuals with overweight/obesity, while mortality is predicted to decrease by 11.7% in hyperlipidemia, 29.9% in hypertension, 32.7% in T2DM and 49.6% in active/previous smokers, from 2025 to 2050. Compared with Chinese individuals, Indian and Malay individuals bear a disproportionate burden of overweight/obesity incidence and AMI-related mortality. INTERPRETATION: The incidence of AMI is projected to continue rising in the coming decades. Overweight/obesity will emerge as fastest-growing metabolic risk factor and the leading risk factor for AMI-related mortality. FUNDING: This research was supported by the NUHS Seed Fund (NUHSRO/2022/058/RO5+6/Seed-Mar/03) and National Medical Research Council Research Training Fellowship (MOH-001131). The SMIR is a national, ministry-funded registry run by the National Registry of Diseases Office and funded by the Ministry of Health, Singapore

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Structural Semantic Correspondence For Example-Based Machine Translation

The main key challenge in Machine Translation (MT) is to preserve the meaning from the original source sentence to the target translation. This remains the core problem in Machine Translation, which leads to a number of issues, such as: how to analyze the meaning of text, what information should be captured as the representation of meaning, how the meaning of a sentence should be represented (into what form and structure), how do we derive and generate target sentence based on this meaning information, etc. As the natural language is ambiguous in nature, the task of meaning treatment in MT systems is hence difficult. The idea of this study is to introduce semantic knowledge to improve the selection and matching of best translation examples in the Example-based Machine Translation (EBMT) system, also to facilitate a deeper semantic similarity measurement and evaluation of the matching examples. The approach is by specifying a structural semantics or “meaning” explicitly to the translation examples representation structures. The creation of the structural semantics begins with the English examples in the existing Bilingual Knowledge Bank (BKB). The structural semantics of the English examples is transferred to other languages based on parallel alignment (aligned words and tree structures) in this BKB. With structural semantic annotation of both the source language (SL) and target language (TL), a Structural Semantic Correspondence between these aligned translation examples is created. This structurally synchronized “meaning” or semantics of the SL and TL allowed the EBMT system to perform semantic-based analysis, selection of translation examples based on semantics similarity, and finally target translation hypothesis derivation

Analogical-Based Translation Hypothesis Derivation with Structural Semantics for English to Malay Example-Based Machine Translation

An analogical-based approach towards meaning preservation by transferring the source language meaning structure to the target language in the recombination process of an English to Malay Example-based Machine Translation system is presented. The meaning structure is built on top of the current synchronized translation examples pair representation with the incorporation of a layer of semantic annotation at the structural level. This meaning structure provides a consistent medium allowing the derivation of translation hypothesis using analogical-based approach throughout the automated translation process. The complexity of the structural transformation in the final recombination process is relaxed with this analogical-based derivation approach. The preliminary experiment demonstrates that the English to Malay automated translation is improved using the analogical-based approach. The best evaluation score is obtained using the Bilingual Evaluation Understudy metric, showing improvement of 37.06%

Analogical-Based Translation Hypothesis Derivation with Structural Semantics for English to Malay Example-Based Machine Translation

An analogical-based approach towards meaning preservation by transferring the source language meaning structure to the target language in the recombination process of an English to Malay Example-based Machine Translation system is presented. The meaning structure is built on top of the current synchronized translation examples pair representation with the incorporation of a layer of semantic annotation at the structural level. This meaning structure provides a consistent medium allowing the derivation of translation hypothesis using analogical-based approach throughout the automated translation process. The complexity of the structural transformation in the final recombination process is relaxed with this analogical-based derivation approach. The preliminary experiment demonstrates that the English to Malay automated translation is improved using the analogical-based approach. The best evaluation score is obtained using the Bilingual Evaluation Understudy metric, showing improvement of 37.06%

Moth flame optimization for the maximum power point tracking scheme of photovoltaic system under partial shading conditions

The dwindling reserves of fossil fuels have spurred the expansion of photovoltaic power systems, widely regarded as an alluring solution. Yet, a formidable challenge arises when it comes to optimizing the output of PV systems exposed to irregular irradiance stemming from external environmental factors. Consequently, this research endeavors to advocate the use of the Moth Flame Optimization (MFO) algorithm to search for the highest power output of a solar energy harvesting system. The distinctive behavior of moths proves instrumental in thorough searching of the feasible space, mitigating the risk of entrapment in local optima. To evaluate its efficacy, this algorithm’s performance is validated by comparing its outcomes with those of the Butterfly Optimization Algorithm (BOA). Both algorithms are subjected to experimentation to search for the Global Maximum Power Points (GMPPs) of the PV system under two distinct Partial Shading Condition (PSC) scenarios: Case 1 and Case 2. The results indicate that BOA tends to produce outcomes with a broader data dispersion range relative to the mean, unlike MFO. Specifically, for Case 1, the standard deviation values for MFO and BOA are 2.327040E−02 and 5.777913E−02, respectively, while for Case 2, they are 5.0567340E−02 and 8.519362E−02, respectively. Hence, the proposed approach demonstrates faster and more precise convergence