Feature selection for gene prediction in metagenomic fragments

Abstract Background Computational approaches, specifically machine-learning techniques, play an important role in many metagenomic analysis algorithms, such as gene prediction. Due to the large feature space, current de novo gene prediction algorithms use different combinations of classification algorithms to distinguish between coding and non-coding sequences. Results In this study, we apply a filter method to select relevant features from a large set of known features instead of combining them using linear classifiers or ignoring their individual coding potential. We use minimum redundancy maximum relevance (mRMR) to select the most relevant features. Support vector machines (SVM) are trained using these features, and the classification score is transformed into the posterior probability of the coding class. A greedy algorithm uses the probability of overlapped candidate genes to select the final genes. Instead of using one model for all sequences, we train an ensemble of SVM models on mutually exclusive datasets based on GC content and use the appropriated model to classify candidate genes based on their read’s GC content. Conclusion Our proposed algorithm achieves an improvement over some existing algorithms. mRMR produces promising results in gene prediction. It improves classification performance and feature interpretation. Our research serves as a basis for future studies on feature selection for gene prediction

Towards Accurate Children’s Arabic Handwriting Recognition via Deep Learning

Automatic handwriting recognition has received considerable attention over the past three decades. Handwriting recognition systems are useful for a wide range of applications. Much research has been conducted to address the problem in Latin languages. However, less research has focused on the Arabic language, especially concerning recognizing children’s Arabic handwriting. This task is essential as the demand for educational applications to practice writing and spelling Arabic letters is increasing. Thus, the development of Arabic handwriting recognition systems and applications for children is important. In this paper, we propose two deep learning-based models for the recognition of children’s Arabic handwriting. The proposed models, a convolutional neural network (CNN) and a pre-trained CNN (VGG-16) were trained using Hijja, a recent dataset of Arabic children’s handwriting collected in Saudi Arabia. We also train and test our proposed models using the Arabic Handwritten Character Dataset (AHCD). We compare the performance of the proposed models with similar models from the literature. The results indicate that our proposed CNN outperforms the pre-trained CNN (VGG-16) and the other compared models from the literature. Moreover, we developed Mutqin, a prototype to help children practice Arabic handwriting. The prototype was evaluated by target users, and the results are reported

Associations of Spexin and cardiometabolic parameters among women with and without gestational diabetes mellitus

Spexin (SPX) is a novel biomarker abundantly expressed in several animal and human tissues implicated in food intake and glucose control, respectively. As new roles for SPX are emerging, the present study explored for the first time, the associations of SPX to several cardiometabolic indices and inflammatory markers in pregnant women, a demographic not yet investigated with respect to SPX. A total of 117 Saudi women subdivided to those with gestational diabetes mellitus (GDM) (N = 63) and those without (N = 54) were included in this cross-sectional study. Anthropometry, glycemic, lipid, vitamin D, adipocytokines and inflammatory markers were measured consecutively at baseline and after the 2nd and 3rd trimesters. Age- and BMI adjusted comparisons revealed that levels of SPX were not significantly different in pregnant women with and without GDM. In all subjects, circulating levels of SPX showed modest associations with glucose (R = 0.18; p = .08) and HOMA β (R = −0.19; p = .09) as well as significant positive associations with total cholesterol (R = 0.25; p = .02), LDL-cholesterol (R = 0.25; p = .02), 25(OH)D (R = 0.22; p = .04), albumin (R = 0.30; p < .01) and IL1β (R = 0.41; p < .01). Stepwise regression analysis also suggested that IL1β, leptin and albumin were the significant predictors of SPX. In summary, SPX levels modestly affect glucose and insulin sensitivity in pregnant women but is not associated with GDM and obesity. The significant association of SPX to ILβ warrants further investigation as to the role of SPX in immune modulation. Keywords: Spexin, Cardiometabolic, Inflammatory markers, Gestational diabetes, Pregnant wome

Reported Benefits of Insulin Therapy for Better Glycemic Control in Type 2 Diabetic Patients–-Is this Applicable in Saudi Patients?

Aim To compare the effect of different treatment regimens (oral hypoglycemic agents [OHGs], insulin therapy, and combination of both) on glycemic control and other cardiometabolic risk factors in type 2 diabetes mellitus (T2DM) patients in Saudi. Subjects and Methods Patients with T2DM, but no serious diabetic complications, were randomly recruited from the diabetes clinics at two large hospitals in Jeddah, Saudi Arabia, during June 2013 to July 2014. Only those without change in treatment modality for the last 18 months were included. Blood pressure and anthropometric measurements were measured. Treatment plan was recorded from the patients' files. Fasting blood sample was obtained to measure glucose, HbA1c, and lipid profile. Results A total of 197 patients were recruited; 41.1% were men and 58.9% were women. The mean (±SD) age was 58.5 ± 10.5 years. Most patients (60.7%) were on OHGs, 11.5% on insulin therapy, and 27.7% were using a combination of insulin and OHGs. The mean HbA1c was lower in patients using OHGs only, compared with means in those using insulin, or combined therapy in patients with disease duration of #10 years ( P = 0.001) and also in those with a longer duration of the disease ( P < 0.001). A lower mean diastolic and systolic blood pressure was found among patients on insulin alone ( P < 0.01). No significant differences were found in lipid profiles among the groups. Conclusion Insulin therapy, without adequate diabetes education, fails to control hyperglycemia adequately in Saudi T2DM patients. There is a challenge to find out reasons for poor control and the ways as to how to improve glycemic control in T2DM

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population