Miracle-dz/UniFAR-FTUs huggingface dataset:
Transferability of microsatellite markers of Capsicum annuum L. to C. frutescens L. and C. chinense Jacq.

In order to support further genetic, diversity, and phylogeny studies of Capsicum species, the transferability of a Capsicum annuum L. simple sequence repeat (SSR) microsatellite set was analyzed for C. frutescens L. ("malagueta" and "tabasco" peppers) and C. chinense Jacq. (smell peppers, among other types). A total of 185 SSR primers were evaluated in 12 accessions from 115 C. frutescens L. and 480 C. chinense Jacq, representing different types within each species. Transferability to C. frutescens L. and C. chinense Jacq. occurred for 116 primers (62.7%). Nineteen (16.37%) were polymorphic in C. frutescens L. and 36 (31.03%) in C. chinense Jacq. , 17 of which were coincident and could be used to analyze samples obtained for the 2 species. Among these primers, CA49 showed a different amplitude range of alleles between the 2 species (130-132 base pairs for C. frutescens L. and 120-128 base pairs for C. chinense Jacq.), and could differentiate the species. A total of 55 alleles were identified among the 19 polymorphic SSR loci among accessions of C. frutescens L. , with the number of alleles per locus ranging from 2 to 5, a mean of 2.89, and the polymorphic information content ranging from 0.30 to 0.65. The number of alleles identified in C. chinense Jacq. was 119, ranging from 2 to 5 alleles per locus, an average of 3.30, and polymorphic information content from 0.19 to 0.68. The C. annuum L. SSR primers were most often transfer-able and polymorphic for C. frutescens L. and C. chinense Jacq. , and we present a set of SSR for each species.
Prediction of coronary artery calcium in young adults using the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) risk score: the CARDIA study.

BACKGROUND Using data from autopsied young people aged 15 to 34 years, the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) study developed a risk score based on age, sex, smoking status, high-density lipoprotein and non-high-density lipoprotein cholesterol levels, and the presence of obesity, hyperglycemia, and hypertension to predict advanced coronary artery atherosclerosis. METHODS The Coronary Artery Risk Development in Young Adults (CARDIA) study assessed coronary artery calcium (CAC) by computed tomography in young adults participating in the 15-year examination. The PDAY risk score was calculated from risk factors measured at the CARDIA examinations at years 0, 5, 10, and 15. RESULTS Odds ratios for amount of CAC (6 ordinal categories) for a 1-point increase in risk score computed from the modifiable risk factors ranged from 1.10 to 1.16 (all statistically significant). Odds ratios for presence of any amount of CAC ranged from 1.09 to 1.15 (all statistically significant), with the highest odds ratio for the risk score at year 0. An increase in risk score between years 0 and 15 increased the odds of CAC, and a decrease in risk score decreased the odds of CAC. A positive family history of cardiovascular disease increased the odds of CAC. The c statistics ranged from 0.752 to 0.770, with the highest discrimination based on the year 0 revised PDAY risk score that included family history and increased the points for the sex differential. CONCLUSION The PDAY risk score predicts CAC up to 15 years before its assessment, and risk score change during 15 years affects the risk of CAC.
Formaldehyde Carcinogenicity Research

Formaldehyde is a widely used high production chemical that is also released as a byproduct of combustion, off-gassing of various building products, and as a fixative for pathologists and embalmers. What is not often realized is that formaldehyde is also produced as a normal physiologic chemical in all living cells. In 1980, chronic inhalation of high concentrations of formaldehyde was shown to be carcinogenic, inducing a high incidence of nasal squamous cell carcinomas in rats. Some epidemiologic studies have also found increased numbers of nasopharyngeal carcinoma and leukemia in humans exposed to formaldehyde that resulted in formaldehyde being considered a Known Human Carcinogen. This article reviews the data for rodent and human carcinogenicity, early Mode of Action studies, more recent molecular studies of both endogenous and exogenous DNA adducts, and epigenetic studies. It goes on to demonstrate the power of these research studies to provide critical data to improve our ability to develop science-based cancer risk assessments, instead of default approaches. The complexity of constant physiologic exposure to a known carcinogen requires that new ways of thinking be incorporated into determinations of cancer risk assessment for formaldehyde, other endogenous carcinogens, and the role of background endogenous DNA damage and mutagenesis.
Hypothesis Test of Fingerprint-Image Matching Algorithms in Operational ROC Analysis | NIST

To evaluate the performance of fingerprint-image matching algorithms on large datasets, a receiver operating characteristic (ROC) curve is applied. From the operational perspective, the true accept rate (TAR) of the genuine scores at a specified false accept rate (FAR) of the impostor scores is usually employed. And the equal error rate (EER) can also be used. The accuracies of the measurement TAR and EER in terms of standard errors and 95 % confidence intervals can be computed using the nonparametric two-sample bootstrap based on our studies of bootstrap variability on large fingerprint datasets. In this article, the hypothesis testing is performed to determine whether the difference between the performance of one algorithm and a hypothesized value, or the difference between the performances of two algorithms where the correlation is taken into account is statistically significant. In the case that the alternative hypothesis is accepted, the sign of the difference is employed to determine which is better than the other. Examples are provided.
A simple user interface system for recovering patterns repeating in time and frequency in mixtures of sounds

Repetition is a fundamental element in generating and perceiving structure in audio. Especially in music, structures tend to be composed of patterns that repeat through time (e.g., rhythmic elements in a musical accompaniment), and also frequency (e.g., different notes of the same instrument). The auditory system has the remarkable ability to parse such patterns by identifying repetitions within the audio mixture. On this basis, we propose a simple user interface system for recovering patterns repeating in time and frequency in mixtures of sounds. A user selects a region in the log-frequency spectrogram of an audio recording from which she/he wishes to recover a repeating pattern masked by an undesired element (e.g., a note masked by a cough). The selected region is then cross-correlated with the spectrogram to identify similar regions where the underlying pattern repeats. The identified regions are finally averaged over their repetitions and the repeating pattern is recovered.
New insights on postglacial colonization in western Europe: the phylogeography of the Leisler's bat (Nyctalus leisleri)

Despite recent advances in the understanding of the interplay between a dynamic physical environment and phylogeography in Europe, the origins of contemporary Irish biota remain uncertain. Current thinking is that Ireland was colonized post-glacially from southern European refugia, following the end of the last glacial maximum (LGM), some 20 000 years BP. The Leisler's bat (Nyctalus leisleri), one of the few native Irish mammal species, is widely distributed throughout Europe but, with the exception of Ireland, is generally rare and considered vulnerable. We investigate the origins and phylogeographic relationships of Irish populations in relation to those across Europe, including the closely related species N. azoreum. We use a combination of approaches, including mitochondrial and nuclear DNA markers, in addition to approximate Bayesian computation and palaeo-climatic species distribution modelling. Molecular analyses revealed two distinct and diverse European mitochondrial DNA lineages, which probably diverged in separate glacial refugia. A western lineage, restricted to Ireland, Britain and the Azores, comprises Irish and British N. leisleri and N. azoreum specimens; an eastern lineage is distributed throughout mainland Europe. Palaeo-climatic projections indicate suitable habitats during the LGM, including known glacial refugia, in addition to potential novel cryptic refugia along the western fringe of Europe. These results may be applicable to populations of many species.
Book Reviews: Dyadic Data Analysis by David A. Kenny, Deborah A. Kashy, and William. L Cook. New York: Guilford, 2006

In this section, recent books applicable to the broad field of program evaluation are reviewed. In most cases, a single book will be considered in a review, but in some instances, multiple books may be jointly reviewed to illuminate similarities and differences in intent, philosophy, and usefulness. In most cases, a single review will be commissioned for each book. Two or more reviews may be commissioned for books judged by the Editor and/or Book Review Editor to be especially noteworthy works in evaluation. Persons with suggestions of books to be reviewed, or those who wish to submit a review, should contact Lori A. Wingate at lori.wingate@wmich.edu.
Accelerating Data Migration: A Comparative Study of Apache Spark and Other Leading Tools

The process of transferring data from one system to another, known as data migration, is a critical task. As big data continues to grow, organizations encounter increasing complexity in managing data migration. While Apache Spark is an effective open-source big data processing framework that provides a versatile platform for data migration, there are also other tools and frameworks available, such as NiFi, Kafka, Hadoop, and A WS Glue. This study compares data migration using Apache Spark with other widely used tools and frameworks, offering a comprehensive overview of each tool and highlighting their strengths and weaknesses. The study also includes a real- world performance-based case study, in which we evaluate the data migration capabilities of each tool and provide detailed statistics for comparison. Our findings demonstrate that Apache Spark surpasses the other tools in terms of data transfer rates, processing times, and fault tolerance capabilities. This research paper presents a thorough analysis of data migration using Apache Spark and other popular tools and frameworks, enabling organizations to make informed decisions regarding their specific data migration requirements.
Decolonization is not a metaphor

Our goal in this article is to remind readers what is unsettling about decolonization. Decolonization brings about the repatriation of Indigenous land and life; it is not a metaphor for other things we want to do to improve our societies and schools. The easy adoption of decolonizing discourse by educational advocacy and scholarship, evidenced by the increasing number of calls to “decolonize our schools,” or use “decolonizing methods,” or, “decolonize student thinking”, turns decolonization into a metaphor. As important as their goals may be, social justice, critical methodologies, or approaches that decenter settler perspectives have objectives that may be incommensurable with decolonization. Because settler colonialism is built upon an entangled triad structure of settler-native-slave, the decolonial desires of white, non-white, immigrant, postcolonial, and oppressed people, can similarly be entangled in resettlement, reoccupation, and reinhabitation that actually further settler colonialism. The metaphorization of decolonization makes possible a set of evasions, or “settler moves to innocence”, that problematically attempt to reconcile settler guilt and complicity, and rescue settler futurity. In this article, we analyze multiple settler moves towards innocence in order to forward “an ethic of incommensurability” that recognizes what is distinct and what is sovereign for project(s) of decolonization in relation to human and civil rights based social justice projects. We also point to unsettling themes within transnational/Third World decolonizations, abolition, and critical space-place pedagogies, which challenge the coalescence of social justice endeavors, making room for more meaningful potential alliances.
Subject-oriented training for motor imagery brain-computer interfaces

Successful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the machine-learning aspects of BCI training. This work presents a novel co-adaptive training protocol shifting the focus on subject-related performances and the optimal accommodation of the interactions between the two learning agents of the BCI loop. Preliminary results with 8 able-bodied individuals demonstrate that the proposed method has been able to bring 3 naive users into control of a MI BCI within a few runs and to improve the BCI performances of 3 experienced BCI users by an average of 0.36 bits/sec.
Building Better Communities for Children: Community Implementation and Evaluation of the Australian Early Development Index

Since 2004 the Australian Early Development Index (AEDI) has been completed in 54 Australian communities over seven states and territories on more than 30,000 children. A concurrent systematic evaluation of community implementation and use of the AEDI was undertaken that included both a process and impact component. The purpose of this paper is to discuss the evaluation findings between 2004 and 2006. Results have shown there are a number of barriers and facilitators for communities in implementing the AEDI, disseminating results, and planning for community actions. The evaluation findings indicate AEDI implementation helps raise awareness of the importance of early childhood development, assists communities to work more collaboratively and to strategically plan actions to work towards improving outcomes for children.
Efficient Feature Selection Approach for Prediction of Cervical Cancer Using Machine Learning Algorithms

Diagnosing cervical cancer early is crucial for successful treatment. Early detection proves to be challenging in areas where medical infrastructure is scarce, as is the awareness and information regarding cervical cancer. Machine learning and deep learning are efficient ways of catering to the early detection of cervical cancer while providing accurate results. This study compares various machine-learning approaches for predicting cervical cancer in patients. Each model undergoes tests across various conditions, including data variations, balance adjustments, and feature selection metrics. The dataset used in this study is processed and cleaned by applying machine-learning-based imputations and data balancing techniques SMOTE and ADASYN. Feature selection metrics like ANOVA, MRMR and PCA are used to select the most relevant features for the study. Accuracy, Error Rate, Precision, Recall and Specificity are used to measure the performance of all models in this study. An enhanced KNN model along with a refined Ensemble model attained the highest performance, securing an approximate accuracy of 99.54% and an F1 Score of around 99%.
Image Binarization for End-to-End Text Understanding in Natural Images

While modern off-the-shelf OCR engines show particularly high accuracy on scanned text, text detection and recognition in natural images still remains a challenging problem. Here, we demonstrate that OCR engines can still perform well on this harder task as long as appropriate image binarization is applied to input photographs. For such binarization, we systematically evaluate the performance of 12 binarization methods as well as of a new binarization algorithm that we propose here. Our evaluation includes different metrics and uses established natural image text recognition benchmarks (ICDAR 2003 and ICDAR 2011). Our main finding is thus the fact that image binarization methods combined with additional filtering of generated connected components and off-the-shelf OCR engines can achieve state-of-the-art performance for end-to-end text understanding in natural images.
Efficient Approximate Vedic Multiplier: Design, Analysis, and Application in Image Blending

Approximate computing has become a widely recognized method for designing energy-efficient arithmetic architectures in the context of error-tolerant applications. This paper presents the design and analysis of a 4-bit approximate Vedic multiplier (AVMT) using the Urdhva Tiryagbhyam method. This Vedic approach, involving vertical and crosswise steps, outperforms traditional multiplication in terms of efficiency. An approximate 2-bit multiplier (AVM2) is designed, and an AVMT is proposed using AVM2. The proposed architecture has better propagation delay and less area utilization compared to other conventional multipliers. AVMT has an 11% reduction in area consumption and a 12% increase in processing speed compared to the exact Vedic multiplier. To assess its practicality in real-world scenarios, the proposed multiplier is integrated into an image-blending application. The results indicate that the system achieves a Structural Similarity Index (SSIM) average value of 0.91, which proves to be suitable for error-resilient image processing applications.
Classification of Malocclusion using Convolutional Neural Network and Knowledge-Based Systems

This paper presents a classification of malocclusion using convolutional neural networks (CNN) and knowledge-based systems (KBS). Malocclusion is a dental abnormality and occlusal feature that deviates from the ideal occlusion. Early detection and treatment can resolve the malocclusion problem. However, the process of detection takes time, and there are also cases of misdiagnosis of the malocclusion in referral letters for an orthodontist. Thus, the use of machine learning (ML) algorithms is needed to facilitate the detection and derive a more accurate classification of the malocclusion. The dataset has undergone pre-processing that includes standardising the image size and several processes of augmentation. The dataset used for this research is 1,064 intraoral images of 236 patients. The system was developed using the ResNet-50 model with an accuracy of 61.20%, precision, recall, and F1-score values of 68%, 61%, and 60%, respectively. This research has developed a system that is able to classify malocclusion based on the IOTN that is currently being used by orthodontists. The classification emphasises classifying the five main grades of IOTN, which include grades Very Great, Great, Moderate, Little, and None. In conclusion, all three objectives of this research have been accomplished, and each objective's deliverables are detailed in this research. In the future, a better system for classifying malocclusion based on the IOTN should be developed.
Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer–Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying.
Reversing the Atypical Valuation of Drug and Nondrug Rewards in Smokers Using Multi-modal Neuroimaging Supplemental Information Supplemental Methods

Supplemental Methods Participants Twenty right-handed male cigarette smokers (M= 25.8, SD = 4.1years of age), who were not currently trying or planning to quit smoking, participated in the study. Participants were compensated for their participation at the rate of $15 per-hour, totaling approximately $115 CAN for five experimental sessions spanning a 4 week interval. Participants were paid in full at the end of the final session and in the event that a subject wished to discontinue their participation, they were paid to the nearest half-hour. All experimental sessions took place at Notre-Dame Hospital MRI and neuromodulation facilities. To note, we focused exclusively on male participants in order to control for the interaction between sex hormones (e.g. estradiol, progesterone, and testosterone) and the mesocorticolimbic reward system across time (1). Future multi-session studies will need to take into account such sex differences, which has important implications for both basic and clinical research (2).
Anomaly Detection in Cloud Environments

Cloud environments compose unique operational characteristics and intrinsic capabilities such as service transparency and elasticity. By virtue of their exclusive properties as being outcomes of their virtualized nature, these environments are prone to a number of security threats either from malicious or legitimate intent. By virtue of the minimal proactive properties attained by off-the-shelf signature-based commercial detection solutions employed in various infrastructures, cloud-specific Intrusion Detection System (IDS) Anomaly Detection (AD)-based methodologies have been proposed in order to enable accurate identification, detection, and clustering of anomalous events that could manifest. Therefore, in this chapter the authors firstly aim to provide an overview in the state of the art related with cloud-based AD mechanisms and pinpoint their basic functionalities. They subsequently provide an insight and report some results derived by a particular methodology that jointly considers cloud-specific properties and relies on the Empirical Mode Decomposition (EMD) algorithm.