Miracle-dz/UniFAR-FTUs huggingface dataset:
Increased Accuracy of Genomic Prediction Using Preselected SNPs from GWAS with Imputed Whole-Genome Sequence Data in Pigs

Simple Summary By integrating prior biological information into genomic selection methods using appropriate models, it is possible to improve prediction accuracy for complex traits. In this context, we conducted a comparative assessment of two genomic prediction models, namely, genomic best linear unbiased prediction and genomic feature best linear unbiased prediction. The accuracy of these models in predicting the growth traits of backfat thickness and loin muscle area was evaluated. Our results revealed that the genomic feature best linear unbiased prediction model can effectively integrate prior information into the model, which is superior to the genomic best linear unbiased prediction model in some cases. These findings provide valuable ideas for enhancing the genomic prediction accuracy of growth traits in pigs. Abstract Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models.
An Evaluation and Annotation Methodology for Product Category Matching in E-Commerce Using GPT

In the rapidly evolving landscape of e-commerce, accurately matching products to their relevant categories is crucial for improving search functionality, enhancing user experience, and driving sales. To address this challenge, this research paper proposes an innovative evaluation and annotation methodology that leverages the power of GPT (Generative Pre-trained Transformer) for product category matching. The primary goal of this study is to develop an efficient and reliable system that can automatically categorize products into appropriate groups based on their descriptions, titles, and other relevant attributes. To achieve this, we employ GPT, a state-of-the-art natural language processing model known for its proficiency in understanding and generating human-like text. The research methodology follows a multi-step approach. Firstly, a large dataset comprising product descriptions and corresponding categories is collected from diverse e-commerce platforms. Next, this dataset is carefully annotated by domain experts to establish ground truth category assignments. During the annotation process, specific challenges related to ambiguous product descriptions and overlapping categories are addressed to ensure high-quality annotations. Subsequently, the pre-trained GPT model is fine-tuned on the annotated dataset using transfer learning techniques. The fine-tuned model is then evaluated using various performance metrics, including precision, recall, F1-score, and accuracy, to quantify its effectiveness in categorizing products accurately. To validate the proposed methodology, extensive experiments are conducted on a representative set of e-commerce products. A comparative analysis is performed by benchmarking the GPT-based approach against traditional rule-based methods and other popular deep learning models in the field of text classification. The results demonstrate the superiority of the GPT-based model in product category matching, exhibiting significant improvements over existing methods. The research findings highlight the model's ability to capture complex semantic relationships between products and categories, leading to more accurate and context-aware categorization. This research paper contributes a robust evaluation and annotation methodology for product category matching in e-commerce using GPT. The study establishes the effectiveness of GPT in enhancing the performance of product categorization systems and showcases its potential in revolutionizing the e-commerce landscape. The proposed methodology holds promising implications for online retailers seeking to optimize product discovery, customer engagement, and overall user satisfaction.
Gradient Balance Prior for Centrosymmetric Cross-Point Detection and Localization

We introduce a novel concept termed gradient balance prior to cross-point patterns and prove three related theorems. Using this prior, we develop vanishing power transform to convert gray-scale images into response maps, highlighting every cross-point satisfying centrosymmetry condition as humps. By calculating the hump centers, we can localize cross-points with subpixel accuracy. Our method surpasses existing benchmarks in both detection efficiency and subpixel accuracy, as demonstrated in both simulated and real-world experiments.
Cheaters or AI-Enhanced Learners: Consequences of ChatGPT for Programming Education

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where creating assessments has long been a challenging task due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT’s ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, this position paper investigates the potential opportunities and threats of ChatGPT for programming education, guided by the question: What could the potential consequences of ChatGPT be for programming education? This paper applies a methodological approach inspired by analytic autoethnography to investigate, experiment, and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. The study finds that there are several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be “lying” in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT have potential for a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast.
A Review on Sustainable Application of Tyre Derived Activated Carbon in Energy Storage Device

Tyre is a common form of non-degradable polymer-based solid waste. As it is abundant in quantity and takes a long time to decompose, it is complicated to manage this waste. This solid waste can be effectively managed by converting it into char through the pyrolysis process, and then converting char into activated carbon (AC) through physical and chemical activation processes. Tyre-derived activated carbon (TDAC) has multiple applications, such as absorber, catalyst, and electrode material, among others. This study reviews different production processes, and applications of TDAC. The abstracts of n= 197 papers were critically analysed for this review. Finally, n=11 full-length articles were selected through PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) bibliographic search method for a thorough investigation of production processes, properties, relationship between activation process and properties, and applications of TDAC. Both physical and chemical activation processes can produce a large surface area and a microporous structure on TDAC. The study found that both processes can produce AC with a surface area ranging from 400 to 900 m2/g. However, the chemical activation process with potassium hydroxides (KOH) is found preferable because it has the capability to increase surface area at less time and temperature. The focus of the study was evaluated the application of TDAC as electrode material. In several experimental studies, TDAC was utilised as an electrode in numerous energy storage devices, including supercapacitors, batteries, and fuel cells, as TDAC has high specific-capacitance properties.
Topological and Sequential Neural Network Model for Detecting Fake News

Fake news can be easily propagated through social media and cause negative societal effects. Since fake news is disinformation with malicious intent, manual fact-checking requires great effort. In order to cope with these challenges, many automatic fake news detection models have been introduced. Recent studies have shown that social network information along with news content can be used effectively for detecting fake news. In this paper, we propose a Topological and Sequential Neural Network model (TSNN) for detecting fake news by capturing the diffusion patterns between source news and users in social networks. We employ the supernode approach instead of simple graph pooling methods to extract representative features in graph topological structure. To better learn the representations in the supernode, we design two-staged graph neural networks reflecting the heterogeneity between news and Twitter users. Our model additionally captures sequential information on news diffusion path by using a transformer. We evaluate our model with two fake news benchmark datasets annotated by fact-checking websites: PolitiFact and GossipCop. TSNN achieves 92.15% accuracy and 92.11% F1-score on PolitiFact, and 97.91% accuracy and 97.88% F1-score on GossipCop. These results demonstrate that our model significantly outperforms other baselines, establishing it as a state-of-the-art solution for fake news detection. To verify the effectiveness following model configuration, we perform ablation studies to demonstrate how each component among our two-stage graph neural networks, and sequential information modules contribute to the performance improvements.
Spatial and temporal patterns of air quality in the three economic zones of China

ABSTRACT The air pollution problem in China continues to become more severe with dramatic economic development. The focus of this study was to create a map representing the spatial–temporal pattern of the air quality in the three major economic zones in China, including the Beijing–Tianjin–Hebei Economic Zone, the Yangtze River Delta Economic Zone, and the Pearl River Delta Economic Zone in 2014. A calendar view was used to visualize the daily condition of air quality and primary pollutant in each city in 2014, and geographic references were added to each visualization according to their spatial relationships. The map provides an efficient way to investigate and understand the current status of air quality and spatial–temporal patterns of air quality.
Enhancing Diagnostic Accuracy for Lung Disease in Medical Images: A Transfer-Learning Approach

To efficiently control healthcare costs, early and precise diagnosis of lung illnesses is essential. Using deep learning (DL) and transfer learning (TL), this study proposes a fresh method for categorizing five types of lung illnesses. Ten thousand chest X-ray pictures were used as a training and assessment dataset. To improve results, we used a TL model predicated on the MobileNet V2 structure. The testing findings show that the suggested model works, successfully diagnosing lung illnesses with an overall accuracy of 95.05%. Performance parameters showed that the evaluated performance of each class was superior to that of other state-of-the-art models. The model’s utility in diagnosing lung disorders exemplifies its applicability to imaging diagnostics. Furthermore, the TL strategy is shown to be more accurate and computationally efficient when compared to preexisting models. Testing the model on a variety of data sets attested to its sturdiness and generalizability. This study shows promise for improving the diagnosis and detection of lung diseases by utilizing DL and TL. The suggested paradigm makes it easier to implement preventative measures, individualize patient care, and boost health outcomes.
Efficacy of Enamel Matrix Derivative in Periodontal Regeneration Defects: A Systematic Review and Meta-Analysis

Aim: To evaluate the efficacy of enamel matrix derivative (EMD) on periodontal regeneration defects. Materials and Methods: Four databases were searched until October 2021. Experimental animal studies evaluating the efficacy of EMD were used. The primary outcomes were bone formation (BF) and cementum formation (CF). The secondary outcomes were junctional epithelium (JE), gingival recession (GR), and clinical attachment level (CAL). Measures of effect were mean difference (MD) with 95% confidence intervals (CIs). Random-effects model were used for all meta-analyses. The Systematic Review Centre for Laboratory animal Experimentation tool was used to assess the risk of bias. Results: Seven experimental animal studies (n = 40) used with a maximum follow-up period of 3 months. Compared to control, EMD did not significantly reduce BF (MD 0.02 mm; 95% CI − 1.91–1.96; I2 = 89%). However, it increased CF (MD 1.38 mm; 95% CI 0.01–2.74; I2 = 55%). For secondary outcomes it was found that compared to control, EMD only significantly reduced JE (MD − 0.54 mm; 95% CI − 1.06 to − 0.02; I2 = 55%). However, the other secondary outcomes were not significant as in the case of GR (MD − 3. 88 mm; 95% CI − 68.29–60.53; I2 = 82%), and in CAL (MD 0.02 mm; 95% CI − 0.29–0.39; I2 = 38%). Finally, according to the risk of bias assessment, all included studies had a high risk of bias. Conclusion: EMD had no effect on BF values while it did not reduce CF. Otherwise, in the secondary outcomes, EMD only significantly reduced JE values and had no effect on GR and CAL.
A high-fat eucaloric diet induces reprometabolic syndrome of obesity in normal weight women

Abstract We examined the effects of 1 month of a eucaloric, high-fat (48% of calories) diet (HFD) on gonadotropin secretion in normal-weight women to interrogate the role of free fatty acids and insulin in mediating the relative hypogonadotropic hypogonadism of obesity. Eighteen eumenorrheic women (body mass index [BMI] 18–25 kg/m2) were studied in the early follicular phase of the menstrual cycle before and after exposure to an HFD with frequent blood sampling for luteinizing hormone (LH) and follicle-stimulating hormone (FSH), followed by an assessment of pituitary sensitivity to gonadotropin-releasing hormone (GnRH). Mass spectrometry-based plasma metabolomic analysis was also performed. Paired testing and time-series analysis were performed as appropriate. Mean endogenous LH (unstimulated) was significantly decreased after the HFD (4.3 ± 1.0 vs. 3.8 ± 1.0, P < 0.01); mean unstimulated FSH was not changed. Both LH (10.1 ± 1.0 vs. 7.2 ± 1.0, P < 0.01) and FSH (9.5 ± 1.0 vs. 8.8 ± 1.0, P < 0.01) responses to 75 ng/kg of GnRH were reduced after the HFD. Mean LH pulse amplitude and LH interpulse interval were unaffected by the dietary exposure. Eucaloric HFD exposure did not cause weight change. Plasma metabolomics confirmed adherence with elevation of fasting free fatty acids (especially long-chain mono-, poly-, and highly unsaturated fatty acids) by the last day of the HFD. One-month exposure to an HFD successfully induced key reproductive and metabolic features of reprometabolic syndrome in normal-weight women. These data suggest that dietary factors may underlie the gonadotrope compromise seen in obesity-related subfertility and therapeutic dietary interventions, independent of weight loss, may be possible.
Use of Bio-Epoxies and Their Effect on the Performance of Polymer Composites: A Critical Review

This study comprehensively examines recent developments in bio-epoxy resins and their applications in composites. Despite the reliability of traditional epoxy systems, the increasing demand for sustainability has driven researchers and industries to explore new bio-based alternatives. Additionally, natural fibers have the potential to serve as environmentally friendly substitutes for synthetic ones, contributing to the production of lightweight and biodegradable composites. Enhancing the mechanical properties of these bio-composites also involves improving the compatibility between the matrix and fibers. The use of bio-epoxy resins facilitates better adhesion of natural composite constituents, addressing sustainability and environmental concerns. The principles and methods proposed for both available commercial and especially non-commercial bio-epoxy solutions are investigated, with a focus on promising renewable sources like wood, food waste, and vegetable oils. Bio-epoxy systems with a minimum bio-content of 20% are analyzed from a thermomechanical perspective. This review also discusses the effect of incorporating synthetic and natural fibers into bio-epoxy resins both on their own and in hybrid form. A comparative analysis is conducted against traditional epoxy-based references, with the aim of emphasizing viable alternatives. The focus is on addressing their benefits and challenges in applications fields such as aviation and the automotive industry.
Productivity of prefixation: A case of Lithuanian prefixes nu-, pri-, į- and iš-

The aim of this paper is to measure the productivity of four distinct morphological constructions – verbal derivatives with prefixes nu-, pri-, į- and iš-. It is measured in quantitative way, employing the calculation methods of Harald Baayen (e.g., 2009). The primary foundations of these measurements are the so-called hapax legomena or simply hapaxes. The data for the investigation was gathered from morphologically lemmatized corpus “LithuanianWaC v2”. The analysis reveals that the prefix iš- is the most productive prefix in terms of realized and expanding productivity, and nu- – in terms of potential. The analysis raises a question – what factors could possibly explain, why constructions with one prefix are more productive compared to others? This leads to a partly confirmed hypothesis that suggests a correlation between productivity and the number of meanings associated with each prefix. However, this investigation extends beyond mere quantitative measurements. Hapaxes were categorized on the basis of their meanings. This categorization aimed to identify the most productive semantic models. Meanings were identified using cognitive analysis of semantics associated with these prefixes. Categorization shows that the most productive ones are prototypical spatial meanings, as well as those meanings which are based on metaphors including CONTAINER as a source domain. Furthermore, this paper raises discussion about the nature of hapaxes – which part of them is real neologisms and which is just rarely used lexemes, and what are the tendencies and motivation of prefixal neologisms’ usage.
Three Years of the Coronavirus Disease 2019 Pandemic in a European Region: A Population-Based Longitudinal Assessment in Madrid Between 2020 and 2022

Abstract Background Our objective was to assess the health impact of coronavirus disease 2019 (COVID-19) during 2020–2022 in the Madrid region. Methods We included all individuals registered in the Madrid Health System Registry as of 31 December 2019, and followed them until 31 December 2022. Using a unique personal identifier, we linked the databases of primary care, hospitals, pharmacies, certified laboratories performing diagnostic tests, vaccines, and mortality. Results Of 6 833 423 individuals, 21.4% had a confirmed COVID-19 diagnosis, and 1.5% had a COVID-19 hospitalization (primary diagnosis). Thirty-day mortality was 1.6% for confirmed COVID-19 (from 11.4% in first semester 2020 to 0.4% in first semester 2022). Thirty-day mortality was 10.8% for COVID-19 hospitalizations (from 14.0% in first semester 2020 to 6.0% in second semester 2022). There were 24 073 deaths within 30 days of a confirmed COVID-19 diagnosis. Advanced age, male sex, higher socioeconomic deprivation, and comorbidities were associated with higher mortality. Conclusions By linking administrative and clinical databases, we characterized the burden of the COVID-19 pandemic in Madrid over 3 years. Our analysis proposes a high-level framework for comparisons of the burden of COVID-19 across areas worldwide.
Gas Permeability through Polyimides: Unraveling the Influence of Free Volume, Intersegmental Distance and Glass Transition Temperature

The relationships between gas permeability and free volume fraction, intersegmental distance, and glass transition temperature, are investigated. They are analyzed for He, CO2, O2, CH4, and N2 gases and for five similar polyimides with a wide range of permeabilities, from very low to extremely high ones. It has been established here that there is an exponential relationship between permeability and the free volume fraction, and between permeability and the most probable intersegmental distance as measured by WAXS; in both cases, with an exponential coefficient that depends on the kinetic gas diameter as a quadratic polynomial and with a preexponential positive constant. Moreover, it has been proven that the intersegmental distance increases linearly with the free volume fraction. Finally, it has been established that the free volume fraction increases with the glass transition temperature for the polymers tested, and that they depend on each other in an approximate linear way.