The Effects of Prenatal Buprenorphine Exposure on the Neurobehavioral Development of the Child

Background: Current guidelines for pregnant women with substance use disorder advise prenatal maintenance of opioid agonist therapy with either buprenorphine or methadone. Despite this rise in prenatal opioid agonist therapy, little is known about the effect of prenatal buprenorphine on the neurobehavioral development of the child. This poses the question: does buprenorphine have a long-lasting effect on the central and peripheral nervous system development and behavior of children who were exposed prenatally? Methods: A comprehensive literature review identified articles relating to prenatal buprenorphine and neurobehavioral outcomes. Article searches were conducted on PubMed and Dynamed. Publications from 2002 through November 2021 were pulled for further analysis since buprenorphine was approved by the Food and Drug Administration for use in 2000. The references of pulled articles were also manually searched. The search was limited to peer-reviewed, full-length articles written in English were considered. All articles assessing buprenorphine-naloxone (Suboxone) were excluded from the review due to possible teratogenic effects of naloxone. Relevant information was summarized and included in the review. The full review of the literature identified human and animal studies to gather current knowledge on the neurobehavioral outcomes of children from birth to 5 years of age exposed to buprenorphine prenatally. Results: The literature review revealed that available studies covered three stages of life: fetal, neonatal/infant, and toddler. Neonatal and infant stages were combined into one category due to studies overlapping these similar ages or using the terms interchangeably. Fetal outcomes showed prenatal buprenorphine-exposed fetuses were more likely to exhibit higher level of fetal heart rate variability that eventually normalized later in gestation; fetal motor activity was consistently lower in buprenorphine-exposed fetuses regardless of gestational age. Exposed neonates were more likely to have a depressed initial ability to self-regulate with poor quality of movement. Infants showed no significant deficiency in neurological development. Studies of exposed toddlers showed various results ranging from no significant deviations from normal neurobehavioral development to significant cognitive and motor underdevelopment. Discussion: Prenatal exposure to buprenorphine has varying results on the development of the child, some of which can have long-term detrimental effects. No general conclusion could be made regarding the overall effect of prenatal buprenorphine exposure on an individual due to the wide range of evidence at the toddler stage. While there are multiple factors to consider when assessing neurobehavioral development, there is significant evidence to show there should be serious consideration for future controlled studies of prenatal buprenorphine exposure, from the neonatal stage to older children and beyond

A study of feature exraction techniques for classifying topics and sentiments from news posts

Recently, many news channels have their own Facebook pages in which news posts have been released in a daily basis. Consequently, these news posts contain temporal opinions about social events that may change over time due to external factors as well as may use as a monitor to the significant events happened around the world. As a result, many text mining researches have been conducted in the area of Temporal Sentiment Analysis, which one of its most challenging tasks is to detect and extract the key features from news posts that arrive continuously overtime. However, extracting these features is a challenging task due to post’s complex properties, also posts about a specific topic may grow or vanish overtime leading in producing imbalanced datasets. Thus, this study has developed a comparative analysis on feature extraction Techniques which has examined various feature extraction techniques (TF-IDF, TF, BTO, IG, Chi-square) with three different n-gram features (Unigram, Bigram, Trigram), and using SVM as a classifier. The aim of this study is to discover the optimal Feature Extraction Technique (FET) that could achieve optimum accuracy results for both topic and sentiment classification. Accordingly, this analysis is conducted on three news channels’ datasets. The experimental results for topic classification have shown that Chi-square with unigram have proven to be the best FET compared to other techniques. Furthermore, to overcome the problem of imbalanced data, this study has combined the best FET with OverSampling technology. The evaluation results have shown an improvement in classifier’s performance and has achieved a higher accuracy at 93.37%, 92.89%, and 91.92 for BBC, Al-Arabiya, and Al-Jazeera, respectively, compared to what have been obtained on original datasets. Similarly, same combination (Chi-square+Unigram) has been used for sentiment classification and obtained accuracies at rates of 81.87%, 70.01%, 77.36%. However, testing the recognized optimal FET on unseen randomly selected news posts has shown a relatively very low accuracies for both topic and sentiment classification due to the changes of topics and sentiments over time

Hybrid mesh for nasal airflow studies

The accuracy of the numerical result is closely related to mesh density as well as its distribution. Mesh plays a very significant role in the outcome of numerical simulation. Many nasal airflow studies have employed unstructured mesh and more recently hybrid mesh scheme has been utilized considering the complexity of anatomical architecture. The objective of this study is to compare the results of hybrid mesh with unstructured mesh and study its effect on the flow parameters inside the nasal cavity. A three-dimensional nasal cavity model is reconstructed based on computed tomographic images of a healthy Malaysian adult nose. Navier-Stokes equation for steady airflow is solved numerically to examine inspiratory nasal flow. The pressure drop obtained using the unstructured computational grid is about 22.6 Pa for a flow rate of 20 L/min, whereas the hybrid mesh resulted in 17.8 Pa for the same flow rate. The maximum velocity obtained at the nasal valve using unstructured grid is 4.18 m/s and that with hybrid mesh is around 4.76 m/s. Hybrid mesh reported lower grid convergence index (GCI) than the unstructured mesh. Significant differences between unstructured mesh and hybrid mesh are determined highlighting the usefulness of hybrid mesh for nasal airflow studies

Rancang Bangun Media Pembelajaran Android Untuk Pembelajaran Komputer dan Jaringan Dasar

Proses pembelajaran yang ada di SMK Negeri 1 Bulango Selatan tidak berjalan maksimal dikarenakan masih dalam kondisi pandemi. Pada mata pelajaran Komputer dan Jaringan Dasar yang merupakan mata pelajaran program keahlian yang seharusnya dipelajari dengan cara interaktif, tetapi proses pembelajarannya hanya sekedar mencatat materi yang diberikan oleh guru. Penelitian ini bertujuan untuk menghasilkan sebuah media pembelajaran yang berbasis android untuk membantu memfasilitasi guru dan siswa agar bisa digunakan dalam pembelajaran mata pelajaran komputer dan jaringan dasar kelas X TKJ di SMK Negeri 1 Bulango Selatan. Metode penelitian adalah Penelitian dan Pengembangan (RD) model MDLC. Hasil penelitian melalui pengujian kelayakan media pembelajaran berbasis android oleh dua orang ahli materi diperoleh presentase kelayakan sebesar 100% dengan kategori sangat layak. Untuk hasil pengujian kelayakan media oleh dua orang ahli media masing-masing memperoleh presentase kelayakan sebesar 100% yang dapat dikatakan “sangat layak”. Adapun untuk hasil uji coba pengguna yang dilakukan pada 29 orang siswa mengenai tanggapan siswa terhadap media pembelajaran mendapatkan skor rata-rata sebesar 90% dengan kategori sangat praktis. Pengolahan uji validitas instrumen pada 19 item pernyataan pada kuisioner penilaian pengguna dengan menggunakan SPSS 25 menunjukkan hasil valid dan uji reliabilitas memperoleh nilai Alpha Cronbach’s sebesar 0,838 yang menyatakan bahwa instrumen sangat reliabel. Berdasarkan data tersebut dapat disimpulkan bahwa media pembelajaran berbasis android pada mata pelajaran Komputer dan Jaringan Dasar yang dirancang di SMK Negeri 1 Bulango Selatan layak digunakan dalam proses pembelajaran

An enhanced binary bat and Markov clustering algorithms to improve event detection for heterogeneous news text documents

Event Detection (ED) works on identifying events from various types of data. Building an ED model for news text documents greatly helps decision-makers in various disciplines in improving their strategies. However, identifying and summarizing events from such data is a non-trivial task due to the large volume of published heterogeneous news text documents. Such documents create a high-dimensional feature space that influences the overall performance of the baseline methods in ED model. To address such a problem, this research presents an enhanced ED model that includes improved methods for the crucial phases of the ED model such as Feature Selection (FS), ED, and summarization. This work focuses on the FS problem by automatically detecting events through a novel wrapper FS method based on Adapted Binary Bat Algorithm (ABBA) and Adapted Markov Clustering Algorithm (AMCL), termed ABBA-AMCL. These adaptive techniques were developed to overcome the premature convergence in BBA and fast convergence rate in MCL. Furthermore, this study proposes four summarizing methods to generate informative summaries. The enhanced ED model was tested on 10 benchmark datasets and 2 Facebook news datasets. The effectiveness of ABBA-AMCL was compared to 8 FS methods based on meta-heuristic algorithms and 6 graph-based ED methods. The empirical and statistical results proved that ABBAAMCL surpassed other methods on most datasets. The key representative features demonstrated that ABBA-AMCL method successfully detects real-world events from Facebook news datasets with 0.96 Precision and 1 Recall for dataset 11, while for dataset 12, the Precision is 1 and Recall is 0.76. To conclude, the novel ABBA-AMCL presented in this research has successfully bridged the research gap and resolved the curse of high dimensionality feature space for heterogeneous news text documents. Hence, the enhanced ED model can organize news documents into distinct events and provide policymakers with valuable information for decision making

Effect of ceramic coating in combustion and cogeneration performance of Al2O3 porous medium

In this work, the effect of SiC-, Ni-, and Cr-based coating on the performance of porous medium burner are evaluated. A dip-coating technique was used to coat SiC, Ni, and Cr powders on a pre-sintered porous Al2O3 substrate. The morphological properties of the sintered Al2O3 plain substrates and coating layer were observed using a light microscope and scanning electron microscopy. The combustion analyzer has been calibrated and used to measure the emissions during the experiment. Thermoelectric cells were used in the cogeneration system to generate electricity from the porous medium burner. The results show a significant improvement in the maximum surface flame temperature and combustion emissions over the plain substrate. The highest recorded surface flame temperature at flow rate of 0.25 L/min was 750°C for SiC-coated, 741°C for Cr-coated, 739°C for Ni-coated and plain substrate registered a temperature of only 634 °C. An 18% increase in flame temperature was recorded for SiC-coated substrate when compared to the plain substrate. Moreover, the coated substrate reduced the emissions CO, COu and NOx. It was also found that; SiC-coated substrate reported the best overall power output when compared to the plain substrate

Automated analysis of internal quantum efficiency using chain order regression

Spectral analysis of internal quantum efficiency (IQE) measurements of solar cells is a powerful method to identify performance-limiting mechanisms in photovoltaic devices. This analysis is usually performed using complex curve-fitting methods to extract various electrical and optical performance parameters. As these traditional fitting methods are not easy to use and are often sensitive to measurement noise, many users do not utilize the full potential of the IQE measurements to provide the key properties of their solar cells. In this study, we propose a simplified approach to analyze IQE curves of silicon solar cells using machine learning models that are trained to extract valuable information regarding the cell's performance and decoupling the parasitic absorption of the anti-reflection coating. The proposed approach is demonstrated to be a powerful characterization tool for solar cells as machine learning unlocks the full potential of IQE measurements