MATERIAL AND METHODS: This was a prospective cohort study conducted in a tertiary referral hospital in Sydney, Australia. In all, 212 women with a low-risk pregnancy or with gestational diabetes were recruited including 158 nulliparous and 54 parous women. Maternal demographic, clinical and ultrasound characteristics were collected at 37 weeks of gestation. Semi-Bayesian logistic regression and Markov chain Monte Carlo simulation were used to assess the relation between cervical length and cesarean section in labor.
RESULTS: Rates of cesarean section were 5% (2/55) for cervical length ≤20 mm, 17% (17/101) for cervical length 20-32 mm, and 27% (13/56) for cervical length >32 mm. These rates were 4, 22 and 33%, respectively, in nulliparous women. In the semi-Bayesian analysis, the odds ratio for cesarean section was 6.2 (95% confidence interval 2.2-43) for cervical length 20-32 mm and 10 (95% confidence interval 4.8-74) for cervical length >32 mm compared with the lowest quartile of cervical length, after adjusting for maternal age, parity, height, prepregnancy body mass index, gestational diabetes, induction of labor, neonatal sex and birthweight centile.
CONCLUSIONS: Cervical length at 37 weeks of gestation is associated with intrapartum cesarean section.
OBJECTIVE: The aim of the present study was to compare the TM-ECochG results obtained when the electrode was placed on the superior region versus the inferior region of TM.
MATERIALS AND METHODS: Forty healthy adults (aged 29 to 50 years) participated in this comparative study. The TM-ECochG testing was conducted with the electrode placed on the superior and inferior regions of TM.
RESULTS: SP and AP amplitudes were statistically higher for the inferior region of TM (p < .05). In contrast, SP/AP ratios were comparable between the two regions of TM (p = .417).
CONCLUSIONS AND SIGNIFICANCE: In TM-ECochG recording, when the electrode was placed on the inferior region of TM, SP and AP amplitudes were greater than when the electrode was placed on the superior region of TM. On the other hand, SP/AP amplitude ratio was not affected by the location of electrode on TM. The findings from the present study could be useful to guide clinicians in optimizing TM-ECochG recording when testing their respective patients.
METHOD: In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used.
RESULTS: The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95.
CONCLUSION: The SL features could be utilized as objective markers to screen the AUD patients and healthy controls.
RESULTS: We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data.
CONCLUSIONS: Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .
RESULTS: Based on Y-DNA, we confirm the presence of two lineages of M. fascicularis: the Indochinese and Sundaic lineages. The Indochinese lineage is represented by M. fascicularis located northwards of the Surat Thani-Krabi depression region and is introgressed by the Macaca mulatta Y-DNA. The Sundaic lineage is free from such hybridization event, thus defined as the original carrier of the M. fascicularis Y-DNA. We further revealed that the Sundaic lineage differentiated into two forms: the insular and the continental forms. The insular form, which represents the ancestral form of M. fascicularis, consists of two haplotypes: a single homogenous haplotype occupying the island of Borneo, Philippines, and southern Sumatra; and the Javan haplotype. The more diverse continental form consists of 17 haplotypes in which a dominant haplotype was shared by individuals from southern Thai Peninsular (south of Surat Thani-Krabi depression), Peninsular Malaysia, and Sumatra. Uniquely, Sumatra contains both the continental and insular Y-DNA which can be explained by a secondary contact hypothesis.
CONCLUSIONS: Overall, the findings in this study are important: (1) to help authority particularly in Malaysia on the population management activities including translocation and culling of conflict M. fascicularis, (2) to identify the unknown origin of captive M. fascicularis used in biomedical research, and; (3) the separation between the continental and insular forms warrants for the treatment as separate management units.
RESULTS: Apart from several named species of malaria parasites, long-tailed macaques were found to be potentially infected with novel species of Plasmodium, namely one we refer to as "P. inui-like." This group of parasites bifurcated into two monophyletic clades indicating the presence of two distinct sub-populations. Further analyses, which relied on the assumption of strict co-phylogeny between hosts and parasites, estimated a population expansion event of between 150,000 to 250,000 years before present of one of these sub-populations that preceded that of the expansion of P. knowlesi. Furthermore, both sub-populations were found to have diverged from a common ancestor of P. inui approximately 1.5 million years ago. In addition, the phylogenetic analyses also demonstrated that long-tailed macaques are new hosts for P. simiovale.
CONCLUSIONS: Malaria infections of long-tailed macaques of Sarawak, Malaysian Borneo are complex and include a novel species of Plasmodium that is phylogenetically distinct from P. inui. These macaques are new natural hosts of P. simiovale, a species previously described only in toque monkeys (Macaca sinica) in Sri Lanka. The results suggest that ecological factors could affect the evolution of malaria parasites.
RESULTS: The kinship coefficient between individuals in this family ranged from 0.35 to 0.62. S/F and O/DM had the highest genomic heritability, whereas F/B and O/P had the lowest. The accuracies using 135 SSRs were low, with accuracies of the traits around 0.20. The average accuracy of machine learning methods was 0.24, as compared to 0.20 achieved by other methods. The trait with the highest mean accuracy was F/B (0.28), while the lowest were both M/F and O/P (0.18). By using whole genomic SNPs, the accuracies for all traits, especially for O/DM (0.43), S/F (0.39) and M/F (0.30) were improved. The average accuracy of machine learning methods was 0.32, compared to 0.31 achieved by other methods.
CONCLUSION: Due to high genomic resolution, the use of whole-genome SNPs improved the efficiency of GS dramatically for oil palm and is recommended for dura breeding programs. Machine learning slightly outperformed other methods, but required parameters optimization for GS implementation.
METHODS: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of 4-year data (January 2010 to December 2013) collected in Malaysia.
RESULTS: This research has two major contributions. A new risk factor, called the TempeRain factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% for predicting dengue outbreaks.
CONCLUSIONS: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.
RESULT: We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, SVM, and Deep Neural Network algorithms to ASD screening dataset and compared the classifiers' based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic, area under the curve, and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn may impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset.
CONCLUSION: We found that J48 produced promising results as compared to other classifiers when tested in both circumstances, with and without missing values. Our findings also suggested that SVM does not necessarily perform well for small and simple datasets. The outcome is hoped to assist health practitioners in making accurate diagnosis of ASD occurrences in patients.