BERTDom: Protein Domain Boundary Prediction Using BERT

The domains of a protein provide an insight on the functions that the protein can perform. Delineation of proteins using high-throughput experimental methods is difficult and a time-consuming task. Template-free and sequence-based computational methods that mainly rely on machine learning techniques can be used. However, some of the drawbacks of computational methods are low accuracy and their limitation in predicting different types of multi-domain proteins. Biological language modeling and deep learning techniques can be useful in such situations. In this study, we propose BERTDom for segmenting protein sequences. BERTDOM uses BERT for feature representation and stacked bi-directional long short term memory for classification. We pre-train BERT from scratch on a corpus of protein sequences obtained from UniProt knowledge base with reference clusters. For comparison, we also used two other deep learning architectures: LSTM and feed-forward neural networks. We also experimented with protein-to-vector (Pro2Vec) feature representation that uses word2vec to encode protein bio-words. For testing, three other bench-marked datasets were used. The experimental results on benchmarks datasets show that BERTDom produces the best F-score as compared to other template-based and template-free protein domain boundary prediction methods. Employing deep learning architectures can significantly improve domain boundary prediction. Furthermore, BERT used extensively in NLP for feature representation, has shown promising results when used for encoding bio-words. The code is available at https://github.com/maryam988/BERTDom-Code

Automatic code generation from UML diagrams: the state-of-the-art

The emergence of the Unified Modeling Language (UML) as the de-facto standard for modeling software systems has encouraged the development of automated software tools that facilitate automatic code generation. UML diagrams are used to diagrammatically model and specify the static structure as well as the dynamic behavior of object-oriented systems and the software tools then go ahead and automatically produce code from the given diagrams. In the last two decades substantial work has been done in this area of automatic code generation. This paper is aimed at identifying and classifying this work pertaining to automatic code generation from UML diagrams, restricting the search neither to a specific context nor to a particular programming language. A Systematic literature review (SLR) using the keywords “automatic code generation”, “MDE”, “code generation” and “UML” is used to identify 40 research papers published during the years 2000–2016 which are broadly classified into three groups: Approaches, Frameworks and Tools. For each paper, an analysis is made of the achievements and the gaps, the UML diagrams used the programming languages and the platform. This analysis helps to answer the main questions that the paper addresses including what techniques or implementation methods have been used for automatic code generation from UML Diagrams, what are the achievements and gaps in the field of automatic code generation from UML diagrams, which UML diagram is most used for automatic code generation from UML diagrams, which programming language source code is mostly automatically generated from the design models and which is the most used target platform? The answers provided in this paper will assist researchers, practitioners and developers to know the current state-of-the-art in automatic code generation from UML diagrams.Keywords: Automatic Code Generation (ACG); Unified Modeling Language (UML); Model Driven Engineering (MDE

Cognitive Architecture to Generate Motivational Feelings: A Way to Improve Visual Learning in Robots

Expressions and voice pitch of an individual play an indispensable role in different cognitive processes. These factor help humans to learn a lot about different things present in their environment. This paper proposes a way to motivate robot learning through their environment and human around them. This mechanism is based on recognition of other agent’s facial expressions and voice pitch analysis by robot. A motivational level can be calculated through these feelings. Motivational level can impel the robots to improve their past learning. This mechanism can possibly help a robot to apprehend its environment and interact with other agents effectively. Keywords: cognition; motivation; facial expression; voice pitch; perception; memory

Psychosocial predictors of breast self-examination among female students in Malaysia: a study to assess the roles of body image, self-efficacy and perceived barriers

Background: Early detection is a critical part of reducing the burden of breast cancer and breast self-examination (BSE) has been found to be an especially important early detection strategy in low and middle income countries such as Malaysia. Although reports indicate that Malaysian women report an increase in BSE activity in recent years, additional research is needed to explore factors that may help to increase this behavior among Southeastern Asian women. Objective: This study is the first of its kind to explore how the predicting variables of self-efficacy, perceived barriers, and body image factors correlate with self-reports of past BSE, and intention to conduct future breast self-exams among female students in Malaysia. Materials and methods: Through the analysis of data collected from a prior study of female students from nine Malaysian universities (n=842), this study found that self-efficacy, perceived barriers and specific body image sub-constructs (MBSRQ-Appearance Scales) were correlated with, and at times predicted, both the likelihood of past BSE and the intention to conduct breast self-exams in the future. Results: Self-efficacy (SE) positively predicted the likelihood of past self-exam behavior, and intention to conduct future breast self-exams. Perceived barriers (BR) negatively predicted past behavior and future intention of breast self-exams. The body image sub-constructs of appearance evaluation (AE) and overweight preoccupation (OWP) predicted the likelihood of past behavior but did not predict intention for future behavior. Appearance orientation (AO) had a somewhat opposite effect: AO did not correlate with or predict past behavior but did correlate with intention to conduct breast self-exams in the future. The body image sub-constructs of body area satisfaction (BASS) and self-classified weight (SCW) showed no correlation with the subjects' past breast self-exam behavior nor with their intention to conduct breast self-exams in the future. Conclusions: Findings from this study indicate that both self-efficacy and perceived barriers to BSE are significant psychosocial factors that influence BSE behavior. These results suggest that health promotion interventions that help enhance self-efficacy and reduce perceived barriers have the potential to increase the intentions of Malaysian women to perform breast self-exams, which can promote early detection of breast cancers. Future research should evaluate targeted communication interventions for addressing self-efficacy and perceived barriers to breast self-exams with at-risk Malaysian women and further explore the relationship between BSE and body image

A SYSTEMATIC REVIEW ON THE IMPACT OF FACEBOOK USAGE ON ACADEMIC PERFORMANCE

Facebook has become an essential part of nearly every individual’s daily life. Though it is beneficial for students in terms of connectivity such as exchanging information, socialization, and other constructive activities, the literature shows that Facebook has become dangerously addictive, causing disruption in routinely activities and academic goals of students. The purpose of this review is to investigate the impact of Facebook usage on the academic performance of university students and how can these be assimilated in order to enhance students’ academic performance. Papers were retrieved from academic databases and Google from 2011 to 2017. All studies that were included appraised critically by using Mixed Method Appraisal Tool Appraisal tool. The results showed that both positive and negative impacts of using Facebook on the academic performance of university students. The conclusions propose that, despite the variance of findings, the overall outcome is negative when it comes to the use of Facebook in academic performance. Furthermore, it highlights the ways that how Facebook can help to enhance the academic performance of university students

Comparative effects of organic manure sources and rates on performance of groundnut varieties

An experiment was conducted at the Teaching and Research farm of the Institute for Agricultural Research, Ahmadu Bello University Zaria. The aim was to study effects of different organic manure sources on performance of groundnut varieties. Treatment consisted of three organic manure source, (Poultry manure, (PM) cow dung (CD) and household waste (HW) each at two levels (1 ton and 2 tons), two varieties of groundnut SAMNUT 21 (V1) and SAMNUT 23 (V2) and a control. The treatments were factorially combined and assigned in a randomized complete block design and replicated three times. Growth data such as plant height, canopy spread and biomass weight and; yield data including, pod yield per plant, seed yield per plant, 100 seed weight were collected

Early predictors in language-based learning disabilities: a bibliometric analysis

IntroductionLanguage-based learning disabilities (LBLD) refers to a spectrum of neurodevelopmental-associated disorders that are characterized by cognitive and behavioral differences in comprehending, processing and utilizing spoken and/or written language. The focus of this work was on identifying early predictors of three main specific LBLD including dyslexia, dyscalculia, and dysgraphia.MethodsThe Web of Science (WoS) was searched for literature related to (neurocognitive, neurophysiological, and neuroimaging) measurements used to identify early predictors of LBLD from 1991 to 25 October 2021. A retrospective bibliometric analysis was performed to analyze collaboration among countries, institutions, authors, publishing journals, reference co-citation patterns, keyword co-occurrence, keyword clustering, and burst keywords using Biblioanalytics software.ResultsIn total, 921 publications related to the identification of LBLD using (neurocognitive, neurophysiological, and neuroimaging) modalities were included. The data analysis shows a slow growth in research on the topic in the 90s and early 2000 and growing trend in recent years. The most prolific and cited journal is Neuroimage, followed by Neuropsychologia. The United States and Finland’s Universities Jyvaskyla and Helsinki are the leading country and institution in this field, respectively. “Neuroimaging,” “brain,” “fMRI,” “cognitive predictor,” “comorbidity,” “cortical thickness” were identified as hotspots and trends of (neurocognitive, neurophysiological, and neuroimaging) modalities in the identification of LBLD.DiscussionEarly predictors of LBLDs would be useful as targets for specific prevention and intervention programs to be implemented at very young ages, which could have a significant clinical impact. A novel finding of neuroimaging predictors combined with neurocognitive and neuropsychological batteries may have implications for future research

Incidence of Gestational Diabetes Mellitus in the United Arab Emirates; Comparison of Six Diagnostic Criteria: The Mutaba’ah Study

BACKGROUND: For more than half a century, there has been much research and controversies on how to accurately screen for and diagnose gestational diabetes mellitus (GDM). There is a paucity of updated research among the Emirati population in the United Arab Emirates (UAE). The lack of a uniform GDM diagnostic criteria results in the inability to accurately combine or compare the disease burden worldwide and locally. This study aimed to compare the incidence of GDM in the Emirati population using six diagnostic criteria for GDM. METHODS: The Mutaba’ah study is the largest multi-center mother and child cohort study in the UAE with an 18-year follow-up. We included singleton pregnancies from the Mutaba’ah cohort screened with the oral glucose tolerance test (OGTT) at 24–32 weeks from May 2017 to March 2021. We excluded patients with known diabetes and with newly diagnosed diabetes. GDM cumulative incidence was determined using the six specified criteria. GDM risk factors were compared using chi-square and t-tests. Agreements among the six criteria were assessed using kappa statistics. RESULTS: A total of 2,546 women were included with a mean age of 30.5 ± 6.0 years. Mean gravidity was 3.5 ± 2.1, and mean body mass index (BMI) at booking was 27.7 ± 5.6 kg/m(2). GDM incidence as diagnosed by any of the six criteria collectively was 27.1%. It ranged from 8.4% according to the EASD 1996 criteria to 21.5% according to the NICE 2015 criteria. The two most inclusive criteria were the NICE 2015 and the IADPSG criteria with GDM incidence rates of 21.5% (95% CI: 19.9, 23.1) and 21.3% (95% CI: 19.8, 23.0), respectively. Agreement between the two criteria was moderate (k = 0.66; p < 0.001). The least inclusive was the EASD 1996 criteria [8.4% (95% CI: 7.3, 9.6)]. The locally recommended IADPSG/WHO 2013 criteria had weak to moderate agreement with the other criteria, with Cohen’s kappa coefficient ranging from (k = 0.51; p < 0.001) to (k = 0.71; p < 0.001). Most of the GDM risk factors assessed were significantly higher among those with GDM (p < 0.005) identified by all criteria. CONCLUSIONS: The findings indicate discrepancies among the diagnostic criteria in identifying GDM cases. This emphasizes the need to unify GDM diagnostic criteria in this population to provide accurate and reliable incidence estimates for healthcare planning, especially because the agreement with the recommended criteria was not optimal