Miracle-dz/UniFAR-FTUs huggingface dataset:
False Discovery Rate and Correction for Multiple Comparisons in Linkage Disequilibrium Genome Screens

Author(s): Sabatti, Chiara; Service, Susan; Freimer, Nelson | Abstract: Population based linkage disequilibrium genome screens represent one of the most recent approaches for the lo- calization of genes responsible for complex diseases. One open problem in this context is represented by the definition of an appropriate significance threshold that takes into account the multiple comparison problem. We explore the con- ceptual and practical implications of the multiple testing procedure known as False Discovery Rate (FDR). We argue that controlling the FDR better represents the interest of researcher in this area than more traditional approaches. We then explore the applicability of the Benjamini-Hochberg (BH) FDR controlling procedure in the specific context of association mapping from case-control data. We analyze the nature of dependency between the test statistics with an- alytic work and simulations and we conclude that the BH rule effectively controls FDR in our context of interest. The dependency between test statistics translates into a decrease of power, which highlights the necessity of developing resampling based rules to control FDR.
Dopamine Concentration Changes Associated with the Retrodialysis of Methylone and 3,4-Methylenedioxypyrovalerone (MDPV) into the Caudate Putamen

Structural modifications to synthetic psychoactive cathinones (SPCs), a class of drugs that contain a β-keto modification of the phenethylamine pharmacophore of amphetamine, induce differences in dopamine transporter (DAT) activity. Here, in vivo retrodialysis was utilized to deliver the SPCs 3,4-methylenedioxypyrovalerone (MDPV, a DAT inhibitor) or methylone (a DAT substrate) into the caudate putamen of male Sprague-Dawley rats. Dialysate samples were collected prior to and post drug administration, and temporal changes in dopamine concentration were quantified using HPLC-EC methods. Methylone elicited a 200% increase and MDPV a 470% increase in dopamine levels at the 10 min time point. The findings demonstrate that in vivo retrodialysis can be used to evaluate the effects of SPCs on neurotransmission in the brain.
ECG classification through complexity measures using random forests

Aging causes changes in the cardiovascular structure, increasing the risk of diseases, and making people more dependent and vulnerable. According to the National Institute of Social Security, there are approximately 250,000 new cases of stroke in Brazil every year, and around 40% of retirement requests are due to heart attacks or strokes. Heart diseases are one of the most prominent causes of death in the world. Most sudden death cases occur without previous symptoms, whereas some non-lethal types of arrhythmias such as ventricular extrasystoles precede other directly related cases. In this context, it is advisable to monitor high-risk individuals, who are not hospitalized daily. Considering the aging population and the increasing number of people living alone, it is important to monitor for various types of biomedical signals remotely. This study aims to evaluate and compare three approaches to the QRS classification using the MIT-BIH Arrhythmia Database.
Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging

Simple Summary Deep-learning-based radiogenomic (DLR) models show promising performance in assisting with lung cancer care. The primary aim of our study was to develop and validate a DLR model to predict EGFR mutation status in non-small-cell lung cancer (NSCLC) patients. Using 990 patients from two clinical trials, the study employed a machine learning pipeline that analysed CT images with manually selected tumour regions. Two deep convolutional neural networks segmented lung masses and nodules from 3D regions of the CT image. The combined radiomics and DLR model achieved 88% accuracy in predicting EGFR mutations, outperforming individual models. The semantic features extracted from CT images also contributed to accurate predictions. The study suggests that this AI-based model in combination with CT semantic features could serve as a non-invasive biomarker that aids in predicting EGFR mutation status with significant accuracy. Abstract Purpose: The authors aimed to develop and validate deep-learning-based radiogenomic (DLR) models and radiomic signatures to predict the EGFR mutation in patients with NSCLC, and to assess the semantic and clinical features that can contribute to detecting EGFR mutations. Methods: Using 990 patients from two NSCLC trials, we employed an end-to-end pipeline analyzing CT images without precise segmentation. Two 3D convolutional neural networks segmented lung masses and nodules. Results: The combined radiomics and DLR model achieved an AUC of 0.88 ± 0.03 in predicting EGFR mutation status, outperforming individual models. Semantic features further improved the model’s accuracy, with an AUC of 0.88 ± 0.05. CT semantic features that were found to be significantly associated with EGFR mutations were pure solid tumours with no associated ground glass component (p < 0.03), the absence of peripheral emphysema (p < 0.03), the presence of pleural retraction (p = 0.004), the presence of fissure attachment (p = 0.001), the presence of metastatic nodules in both the tumour-containing lobe (p = 0.001) and the non-tumour-containing lobe (p = 0.001), the presence of ipsilateral pleural effusion (p = 0.04), and average enhancement of the tumour mass above 54 HU (p < 0.001). Conclusions: This AI-based radiomics and DLR model demonstrated high accuracy in predicting EGFR mutation, serving as a non-invasive and user-friendly imaging biomarker for EGFR mutation status prediction.
Sex differences in waist circumference obesity and eating speed: a cross-sectional study of Japanese people with normal body mass index

Background Fast eating has been positively associated with visceral fat accumulation in normal-weight individuals according to body mass index (BMI). However, previous studies have not examined energy and nutrients, or adjusted for food intake. We examined the relationship between eating speed and visceral fat accumulation, using waist circumference as an index, in middle-aged participants who were considered to be of standard weight according to BMI, with nutrient intake added as an adjustment factor. Methods We included 6,548 Japanese participants (3,875 men and 2,673 women) aged 40–74 years with BMI 18.5–25.0 kg/m2 who were enrolled in the Yamagata Cohort Study. Participants were divided into “fast,” “normal,” and “slow” eaters according to self-reported eating speed. Nutrient and food intake were evaluated using a food frequency questionnaire, and the difference in intake by eating speed and sex was compared. Logistic regression analysis was used to examine the relationship between waist circumference obesity (men ≥85 cm, women ≥90 cm, according to Japanese criteria) and eating speed, adjusted for nutrient intake and other lifestyle habits. Results In men, slow eaters had greater intakes of dietary protein, fat, polyunsaturated fatty acids (PUFA), omega-3 PUFA, total dietary fiber, soluble dietary fiber, insoluble dietary fiber, soybean products, fish, green and yellow vegetables, other vegetables, mushrooms, and seaweed in comparison with normal-speed eaters. In men, waist circumference obesity was significantly lower among slow eaters than in the group with normal eating speed. In women, waist circumference obesity was not significantly associated with eating speed and was not also associated with nutrient/food consumption except omega-6 PUFA. Conclusion Eating slowly was associated with healthy dietary habits. Our results could help prevent waist circumference obesity in men with a BMI between 18.5 and 25.0 kg/m2. However, similar findings were not observed in women, suggesting a sex difference.
EFL TEACHERS’ PERCEPTION OF THE USE OF ROLE PLAY IN SPEAKING CLASS

This research aims to describe two English teachers’ perceptionsregarding their experience in using role play to facilitate the students’ speaking practice at different English courses in Yogyakarta. The research participants are two teachers teaching speaking skills and have used role-play as one of the tasks in their classes. The researchers employed a descriptive qualitative design and thematic analysis to collect and examine the data obtained from interviews. The findings show that the use of role-play was: driven by the classes’ learning objectives, necessarily taking real life-like situation set up, having generally a significant impact on the students’ speaking fluency whilst boosting their confidence and on occasion, gaining various response from the students.
Constitutive Overexpression of an NB-ARC Gene from Wild Chinese Vitis quinquangularis in Arabidopsis thaliana Enhances Resistance to Phytopathogenic Oomycete and Bacteria

Resistance (R) genes were used to recognize pathogen effectors directly or indirectly in plants and activate defense signal pathways. Most of these R proteins consist of a nucleotide-binding adaptor (NB-ARC) domain, a leucine-rich repeat (LRR) domain and some also have a coiled-coil (CC) structure. In this study, we cloned a gene which encodes the CC-NB-ARC-LRR R protein (VqCNL) from Chinese wild grapevine Vitis. quinquangularis accession ‘Dan-2’. The transcript of VqCNL was obviously induced by inoculation with Plasmopara viticola and the salicylic acid (SA) treatment. The results of sequence analysis showed that the VqCNL gene contained a CC domain at the N-terminus, along with an NB-ARC and an LRR domain at the C-terminus. We transferred this gene into wildtype Arabidopsis and treated transgenic lines with Hyaloperonospora arabidopsidis (Hpa) and Pseudomonas syringae pv. tomato DC3000 (Pst DC3000); the results demonstrated that VqCNL promotes broad spectrum resistance to pathogens. Furthermore, qPCR analysis displayed that VqCNL may display a significant function in disease resistance via activating SA signaling pathways. In general, these conclusions primarily demonstrated that VqCNL enhances the disease resistance level in plants and contributes to future research of the R gene identification for grape breeding biotechnology.
Progressive muscle relaxation in pandemic times: bolstering medical student resilience through IPRMP and Gagne's model

Background Medical education, already demanding, has been further strained by the COVID-19 pandemic's challenges and the shift to distance learning. This context underscores the need for effective stress reduction techniques in competency-based medical curricula (CBMC). Objective We assessed the feasibility and benefits of integrating a Progressive Muscle Relaxation (PMR) module—a known effective stress-reducing technique—into a time-restricted CBMC, particularly given such modules often find placement as elective rather than mandatory. Methods Adapting Gagne's nine events of instruction, a 2-h PMR program was designed and implemented during the pandemic. Twenty participants were engaged on a first-come, first-served basis, ensuring adherence to social distancing measures. Feedback was continuously gathered, leading to two post-program focus group sessions. Qualitative data underwent thematic analysis following Braun and Clarke's approach, with study quality maintained by the Standards for Reporting Qualitative Research (SRQR). To gauge adaptability, we aligned the program with various learning outcomes frameworks and explored its fit within CBMC using Bourdieu's Theory of Practice. Results The pilot PMR program was well-received and effectively incorporated into our CBMC. Our analysis revealed five central themes tied to PMR's impact: Self-control, Self-realization, Liberation, Awareness, and Interpersonal relationships. Feedback indicated the program's capacity to mitigate stress during the pandemic. The SRQR confirmed the study's alignment with qualitative research standards. Further, the PMR program's contents resonated with principal domains of learning outcomes, and its integration into CBMC was supported by Bourdieu's Theory. These observations led us to propose the Integrative Psychological Resilience Model in Medical Practice (IPRMP), a model that captures the intricate interplay between the identified psychological constructs. Conclusion This research showcases an innovative, theory-guided approach to embed a wellbeing program within CBMC, accentuating PMR's role in fostering resilience among medical students. Our PMR model offers a feasible, cost-effective strategy suitable for global adoption in medical institutions. By instilling resilience and advanced stress-management techniques, PMR ensures that upcoming healthcare professionals are better equipped to manage crises like pandemics efficiently.
On-Device Feeding Behavior Analysis of Grazing Cattle

Precision livestock farming (PLF) leverages cutting-edge technologies and data-driven solutions to enhance the efficiency of livestock production, its associated management, and its welfare. Continuous monitoring of the masticatory sound of cattle allows the estimation of dry-matter intake, classification of jaw movements (JMs), and recognition of grazing and rumination bouts. Over the past two decades, algorithms for analyzing feeding sounds have seen improvements in performance and computational requirements. Nevertheless, in some cases, these algorithms have been implemented on resource-constrained electronic devices, limiting their functionality to one specific task: either classifying JMs or recognizing feeding activities (such as grazing and rumination). In this work, we present an acoustic monitoring system that comprehensively analyzes grazing cattle’s feeding behavior at multiple scales. This embedded system classifies different types of JMs, identifies feeding activities, and provides predictor variables for estimating dry-matter intake. Results are transmitted remotely to a base station using long-range communication (LoRa). Two variants of the system have been deployed on a Raspberry Pi Pico board, based on a low-power ARM Cortex-M0+ microcontroller. Both firmware versions make use of direct access memory, sleep mode, and clock-gating techniques to minimize energy consumption. In laboratory experiments, the first deployment consumes 20.1 mW and achieves an F1-score of 87.3% for the classification of JMs and 87.0% for feeding activities. The second deployment consumes 19.1 mW and reaches an F1-score of 84.1% for JMs and 83.5% for feeding activities. The modular design of the proposed embedded monitoring system facilitates integration with energy-harvesting power sources for autonomous operation in field conditions.
How Hot, How Deep, How Long: Constraints on the Tectono-Metamorphic Evolution of Granulite Terranes

Granulites are the dense, strong metamorphic rocks that are produced during high(HT) to ultrahigh-temperature metamorphism (UHT) and partial melting of Earth’s crust. Granulites are ubiquitous in exhumed Archean cratons and are thought to comprise much of Earth’s stable lower crust. Understanding the mechanisms responsible for crustal heating in Archean terranes is thus paramount to understanding the stabilisation of early continental crust, and whether such mechanisms resemble modern tectonic processes. It is therefore important to quantify the pressure–temperature–time (P–T–t) paths of Archean granulites, as such paths can be diagnostic of heating mechanism. This dissertation explores: 1) novel approaches to reconstructing the P– T–t paths of granulites, and 2) what the deciphered P–T–t paths of rocks from two Archean granulite terranes reveal about Archean crustal heating. The first chapter shows how petrologic modelling at multiple scales from a texturally heterogeneous granulite can provide “snapshots” of the P–T path, which would be difficult to reconstruct otherwise. The remaining chapters are focused on reconstructing the P–T–t paths of two Archean granulite terranes: the Beartooth Mountains, and the Pikwitonei granulite domain (PGD). The second and third chapters present evidence for cryptic HT metamorphism of the Beartooth granulites at ~2.7 Ga characterized by rapid (< 1 Ma) exhumation at HT and fast cooling (~10-100 °C/Ma) in the middle crust. This suggests advective/conductive heating over short length-scales. In the fourth chapter, thermobarometric data suggest the western PGD experienced UHT decompression followed by cooling in the lower crust. High-precision zircon and monazite dates reveal apparently episodic crystallization over at least ~24 Ma. This episodicity could reflect multiple thermal cycles or the control of local reactions on zircon/monazite crystallization during cooling. High-spatial resolution petrochronology provides temporal constraints on prograde metamorphism. These data suggest metamorphism in the PGD was driven by a long-lived heat source over large lengthscales near the base of the lithosphere. Disparities in the timescales, length-scales, and the depth and amount of heating between the terranes may suggest different crustal heating mechanisms in each, and that the late Archean Earth may have been tectonically diverse. How Hot, How Deep, How Long: Constraints on the Tectono-Metamorphic Evolution of Granulite Terranes
Towards an AI-driven soft toy for automatically detecting and classifying infant-toy interactions using optical force sensors

Introduction: It is crucial to identify neurodevelopmental disorders in infants early on for timely intervention to improve their long-term outcomes. Combining natural play with quantitative measurements of developmental milestones can be an effective way to swiftly and efficiently detect infants who are at risk of neurodevelopmental delays. Clinical studies have established differences in toy interaction behaviors between full-term infants and pre-term infants who are at risk for cerebral palsy and other developmental disorders. Methods: The proposed toy aims to improve the quantitative assessment of infant-toy interactions and fully automate the process of detecting those infants at risk of developing motor delays. This paper describes the design and development of a toy that uniquely utilizes a collection of soft lossy force sensors which are developed using optical fibers to gather play interaction data from infants laying supine in a gym. An example interaction database was created by having 15 adults complete a total of 2480 interactions with the toy consisting of 620 touches, 620 punches—“kick substitute,” 620 weak grasps and 620 strong grasps. Results: The data is analyzed for patterns of interaction with the toy face using a machine learning model developed to classify the four interactions present in the database. Results indicate that the configuration of 6 soft force sensors on the face created unique activation patterns. Discussion: The machine learning algorithm was able to identify the distinct action types from the data, suggesting the potential usability of the toy. Next steps involve sensorizing the entire toy and testing with infants.
Serum 25-Hydroxyvitamin D Status and Vitamin D Supplements Use Are Not Associated with Low Back Pain in the Large UK Biobank Cohort

Longitudinal studies assessing the association of vitamin D deficiency, defined by serum 25-hydroxyvitamin D levels < 30 nmol/L, and vitamin D supplement (VDS) use with low back pain (LBP) are sparse. This investigation assessed the cross-sectional and longitudinal association of vitamin D status and VDS use with LBP among 135,934 participants from the UK Biobank cohort. Approximately 21.6% of the participants had vitamin D deficiency, while only 4% regularly took VDS. In the month before study enrollment, 3.8% of the population reported experiencing LBP. An additional 3.3% of the population were diagnosed with LBP by their general practitioners for the first time during a median follow-up of 8.5 years. Vitamin D deficiency and VDS use were cross-sectionally associated with LBP in age- and sex-adjusted models, but these associations were not evident in comprehensively adjusted models. In longitudinal analyses, both vitamin D deficiency and VDS use were not associated with LBP in any model after correction for multiple testing. In conclusion, not unexpectedly due to the fact that LBP is multifactorial, our findings provide no evidence for the role of the vitamin D status in the etiology of LBP.
“Creating security and a good relationship with the student” – Exploring ECEC teachers’ relational professionalism during mentor training

Previous research on mentor training in early childhood education and care (ECEC) teacher education has shown that relationships are at the core of mentoring (Kupila et al., 2017; Ukkonen-Mikkola et al., 2021). This article presents findings from a qualitative study that aims to explore ECEC teachers’ relational professionalism (Furu & Sandvik, 2019) during mentor training. Our research material consisted of essays (n = 77) from the final reflective essay of a mentor training course for ECEC teachers, which were analysed through narrative analysis (Clandinin et al., 2016). The results show that ECEC teachers’ relational professionalism is built in three types of entangled relationships: (1) relation to oneself, (2) relation to the student and (3) relation to colleagues. The results imply that relationally professional mentors are a key factor that contributes to students continuing in the profession after graduation. This requires that the university engages in cooperation and relationships with the ECEC centres that receive students in the practicum. The university needs to offer mentor training and develop rich opportunities for mentors, their colleagues, and leaders in ECEC to establish relationships with all parties involved on all levels.
Hybrid Machine Learning Model for Efficient Botnet Attack Detection in IoT Environment

Cyber attacks are growing with the rapid development and wide use of internet technology. Botnet attack emerged as one of the most harmful attacks. Botnet identification is becoming challenging due to the numerous attack vectors and the ongoing evolution of viruses. As the Internet of Things (IoT) technology is developing rapidly, many network devices have been subject to botnet attacks leading to substantial losses in different sectors. Botnets pose serious risks to network security and deep learning models have shown potential for efficiently identifying botnet activity from network traffic data. In this research, a botnet identification system is proposed based on the stacking of artificial neural network (ANN), convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) (ACLR). The experiments are conducted by employing both the individual models, as well as, the proposed ACLR model for performance comparison. The UNSW-NB15 dataset is used for botnet attacks and contains nine different attack types including ‘Normal’, ‘Generic’, ‘Exploits’, ‘Fuzzers’, ‘DoS’, ‘Reconnaissance’, ‘Analysis’, ‘Backdoor’, ‘Shell code’ and ‘Worms’. Experimental results indicate the proposed ACLR model gains 0.9698 testing accuracy showing that it is successful in capturing the intricate patterns and characteristics of botnet attacks. The proposed ACLR model’s k values (3, 5, 7, and 10) for a K-fold cross-validation accuracy score is 0.9749 indicating that the model’s robustness and generalizability are demonstrated by k = 5. In addition, the proposed model detects botnets with a high receiver operating characteristic area under the curve (ROC-AUC) of 0.9934 and a precision-recall area under the curve (PR-AUC) of 0.9950. Performance comparison with existing state-of-the-art models further corroborates the superior performance of the proposed approach. The results of this research can be helpful against evolving threats and enhance cyber security procedures.