Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in Nucleic Acid Research: Exploring the Potential of Oligonucleotides for Therapeutic Applications and Biological Studies

In recent years, nucleic acids have emerged as powerful biomaterials, revolutionizing the field of biomedicine. This review explores the multifaceted applications of nucleic acids, focusing on their pivotal role in various biomedical applications. Nucleic acids, including deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), possess unique properties such as molecular recognition ability, programmability, and ease of synthesis, making them versatile tools in biosensing and for gene regulation, drug delivery, and targeted therapy. Their compatibility with chemical modifications enhances their binding affinity and resistance to degradation, elevating their effectiveness in targeted applications. Additionally, nucleic acids have found utility as self-assembling building blocks, leading to the creation of nanostructures whose high order underpins their enhanced biological stability and affects the cellular uptake efficiency. Furthermore, this review delves into the significant role of oligonucleotides (ODNs) as indispensable tools for biological studies and biomarker discovery. ODNs, short sequences of nucleic acids, have been instrumental in unraveling complex biological mechanisms. They serve as probes for studying gene expression, protein interactions, and cellular pathways, providing invaluable insights into fundamental biological processes. By examining the synergistic interplay between nucleic acids as powerful biomaterials and ODNs as indispensable tools for biological studies and biomarkers, this review highlights the transformative impact of these molecules on biomedical research. Their versatile applications not only deepen our understanding of biological systems but also are the driving force for innovation in diagnostics and therapeutics, ultimately advancing the field of biomedicine.
Septic arthritis in the pediatric hip joint: a systematic review of diagnosis, management, and outcomes

Septic arthritis of the pediatric hip joint (SAH) is a rare but serious orthopedic emergency requiring immediate diagnosis and management. Delayed recognition can lead to severe complications, emphasizing the need for timely intervention. This systematic review aims to provide a comprehensive analysis of SAH in the pediatric population, focusing on its diagnosis, management, and outcomes. The review included 11 studies involving 391 patients with SAH, aged between three months and 12 years. Staphylococcus aureus was identified as the most common causative pathogen, with increasing cases of methicillin-resistant strains. Diagnosis is challenging due to nonspecific clinical presentations, necessitating validated criteria and a multidisciplinary approach. Ultrasound emerged as a valuable tool for early detection, and MRI was used in challenging cases. Treatment options include hip aspiration, arthrotomy, and arthroscopy, often combined with appropriate antibiotic therapy. Success rates were comparable among different surgical procedures. Early intervention is vital for optimal outcomes. However, the review highlights the need for standardized protocols and further prospective studies to address limitations and improve understanding and management of SAH in the pediatric hip joint.
DIssemination and adoption status of agroforestry practices in Mufindi district, Iringa region, Tanzania

The current study was carried out during September 2006 to March 2007 in six villages namely Sawala, Kisada, Ikongosi, Sao-Hill, Ihowanza and Igomaa in Mufindi District, Iringa Region, Tanzania. The objective of the study was to determine the extent of dissemination and adoption of Agroforestry by the local communities and indicate the mechanism of scaling up its performance. Specifically it checked on the current status of dissemination and adoption of agroforestry practices, identified Agroforestry systems, technologies, and woody perennials preferred by farmers, determined factors influencing adoption of Agroforestry systems, technologies and find out corrective measures required for improving their adoption by the local communities. The methods used include reconnaissance, social surveys using questionnaires on the household heads and checklists of probe questions on Government and NGOs officials at the various levels from village to the Regional. Data collected was analysed using the Statistical Package for Social Sciences (SPSS) and Microsoft Excel Program (MEP). The results showed that the adoption status of Agroforestry in the district was 65%, although most of the people adopted during the latter 16 years. The main agents that spearheaded the dissemination process included CONCERN, HIMA and various government extension agents. The most adopted agroforestry systems are Agrosilviculture and Agrosilvopasture, with Taungya, Mixed intercropping, and Homegarden being the most widely adopted technologies. Woody perennials species that people currently have shown to prefer are Eucalyptus and Pines. Insufficiend provision of germplasm, land scarcity and limited knowledge indicated to be the main factors limiting dissemination and adoption of Agroforestry in Mufindi District. Based on the results and subsequent discussion it clear that although encouraging the adoption rate of Agroforestry in the district is still low. The study therefore recommends that the Government should continue with a stepped up provision ii
Digital Pedagogy for Self-Paced Learning in Mathematics Education

This paper examined the contribution of digital pedagogy (DP) to self-paced learning in higher mathematics education. The research question was: In what ways does DP contribute to a resourceful learning environment to address individual learning styles and preferences in higher mathematics education? The study used a quantitative approach, employing a critical Action Research design with pre-test and post-test measures. The study participants were 126 third-semester students taking a Differential Geometry course in 2017. The tool used was the self-paced learning (SPL) index survey questionnaire. Based on analysis and discussion with relevant literature, this paper highlights the benefits of digital pedagogy, including the promotion of 21st-century skills, the provision of anytime and anywhere learning opportunities, and the creation of an e-resource repository to ensure SPL. Moreover, digital pedagogy provides adaptive learning experiences that can be adjusted to each student’s learning pace and style, enabling them to learn effectively and achieve their full potential.
Glimpse on 21st century new phobias; a predictive model of nomophobia

The main purpose of this study was to examine the explanatory power of a predictive model of nomophobia consisting of rumination, fear of missing out (FoMO), mindfulness and non-pathological compulsions. The research involved a cross-sectional design exploring the prevalence of nomophobia in a Romanian university students’ cohort. The quantitative methodology was used to collect and analyse the data obtained from all the respondents. Researchers adapted and pretested the questionnaire NMP-Q, before distributing it to 194 university students. SPSS (V. 20) and Hayes’s PROCESS tool were used to analyse the data. The findings demonstrated that the above-mentioned psychological variables have a direct and significant relationship with nomophobia. Specifically, within the multidimensional mechanism that explains nomophobia, fear of missing out (FoMO), non-pathological compulsions, and rumination, included in the predictive model in this order, played the most important role, as together cover 34% of nomophobia variance. Furthermore, the fear of missing out has the highest explanatory contribution to nomophobia. The current study gives a better understanding of the dynamics of nomophobia in young people by focusing on psychological factors that play an important role in this phenomenon.
Growth performance, blood lipids, and fat digestibility of broilers fed diets supplemented with bile acid and xylanase

Abstract This study aimed to show the effect of bile acid (BA) and xylanase (Xyl) supplementation on the growth, fat digestibility, serum lipid metabolites, and ileal digesta viscosity of broilers. A total of 720 1 d old male broilers were allocated to one of nine treatments with four replicates in each under a factorial design arrangement of three levels of BA (0 %, 0.25 %, and 0.50 %) and three levels of Xyl (0 %, 0.05 %, and 0.10 %) supplementation. The duration of the experiment was 35 d (7–42 d). Growth performance, blood lipids, fat digestibility, and ileal digesta viscosity were determined. The experimental treatments did not affect feed intake (FI) and weight gain (WG). Supplementation of BA or Xyl did not significantly ameliorate the feed conversion rate (FCR) ( p<0.05 ). The addition of BA linearly increased fat digestibility. At 7–21 d of age, the addition of BA or Xyl had a significant ( p<0.05 ) increase in serum cholesterol (Chol) but no significant difference for other serum lipid parameters in broiler chickens fed with Xyl in the starter and grower periods. However, the supplementation of 0.5 % BA at 7–21 d of age significantly increased the Chol and low-density-lipoprotein (LDL) levels. The results of this trial revealed that the supplementation of xylanases had a great effect on the degradation of arabinoxylan from wheat, which led to a relatively greater reduction in ileal digesta viscosity; it was also found that supplementation of BA significantly increased the concentration of serum lipid metabolites, whereas BA and Xyl supplementation linearly increased the fat digestibility of the birds fed wheat and tallow diets.
Nucleus Detection Using Deep Learning Approach on Pap Smear Images

Cervical cancer is caused by the abnormal growth of female cervix cells. It is one of the most familiar factors for women”s death worldwide. Therefore. , early detection of cervical cancer leads to a reduced mortality rate and increased chance of being alive. The Papanicolaou is a common method for screening and identifying the cancerous cells in a woman”s cervix. The resultant pap smear images may help the physician diagnose the cervix cells. The crucial part of the cell is the nucleus. Therefore. , auto-detection of the nucleus is the core point in this paper. A deep learning algorithm is employed to segment the nucleus in pap smear images. Two network structures. , known as ResNet18 and ResNet50. , are exploited to detect the nucleus part in the cell. The results are compared with ground truth and between the two structures. Both networks. , ResNet18 and ResNet50. , perform almost the same. , with test accuracy reaching 92%. This work distinguishes it from other work in simplicity. , fast. , and accuracy. Therefore. , it can be recommended to be used in clinical units and rural countries which suffer from the lack of specialist physician.
Evaluating of Football Player 1v1 Abilities based on the Glicko-2 with Modifications

In this paper, the authors propose an approach to measure the 1v1 abilities of football players. The authors utilize the Glicko-2 rating system as the foundational model but enhance it by incorporating the football game context of duels. As a result, ratings for players’ aerial dueling abilities were calculated, along with ratings of players’ skills of dribbling and defense against opponent dribbling.
Artificial Intelligence Engineering for Predictive Analytics

Artificial Intelligence Engineering is now a core trend in the field of technology. It combines both artificial intelligence and engineering concepts. In this study, an Artificial Intelligence Engineering process for predictive analytics was developed by synthesizing the predictive process of Machine Learning and the system development process of Software Engineering. The results revealed that the artificial intelligence engineering process consists of 5 phases: Phase 1 - Requirements analysis, which consists of Data collection, Data preprocessing, and Feature selection; Phase 2 - Design, which consists of Choosing ML Algorithms and Data Splitting; Phase 3 - Implementation, which consists of Model Development and Training Dataset; Phase 4 - Testing and Verification, which consists of Model Performance Evaluation (Testing Dataset) and Validation; and Phase 5 - Prediction and Maintenance.
TLR agonists as vaccine adjuvants in the prevention of viral infections: an overview

Tol-like receptor (TLR) agonists, as potent adjuvants, have gained attention in vaccine research for their ability to enhance immune responses. This study focuses on their application in improving vaccine efficacy against key viral infections, including hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), SARS-CoV-2, influenza virus, and flaviviruses, including West Nile virus, dengue virus, and chikungunya virus. Vaccines are crucial in preventing microbial infections, including viruses, and adjuvants play a vital role in modulating immune responses. However, there are still many diseases for which effective vaccines are lacking or have limited immune response, posing significant threats to human health. The use of TLR agonists as adjuvants in viral vaccine formulations holds promise in improving vaccine effectiveness. By tailoring adjuvants to specific pathogens, such as HBV, HCV, HIV, SARS-CoV-2, influenza virus, and flavivirus, protective immunity against chronic and emerging infectious disease can be elicited.
Identifying and regulating emotions after acquired brain injury: the role of interoceptive sensibility

Introduction Interoceptive deficits are associated with difficulties in identifying and regulating emotions. However, research on interoception after acquired brain injury (ABI) is scarce, and its relationship with emotional difficulties in this population is unknown. This study aimed to (1) examine differences in self-reported alexithymia, performance-based emotional awareness, emotion regulation, depression, and interoceptive sensibility between ABI and control individuals; and (2) analyze the role of adaptive interoceptive dimensions in these emotional processes after ABI. Methods Forty-three individuals with ABI and 42 matched control individuals completed the Multidimensional Assessment of Interoceptive Awareness-2, the Toronto Alexithymia Scale, the Levels of Emotional Awareness Scale, the Difficulties in Emotion Regulation Scale, and the Hospital Anxiety and Depression Scale. Results Compared to the control group, individuals with ABI showed reduced tendency to ignore unpleasant sensations increased severity of depressive symptoms, as well as tendencies to have greater difficulties in emotion regulation and lower emotional awareness. Additionally, interoceptive dimensions such as trusting, as well as not-distracting from and not-worrying about bodily sensations, played a relevant role in explaining lower alexithymia and difficulties in emotion regulation. Moreover, lower alexithymia and emotion dysregulation were related to less depressive symptoms. These relationships were invariant across ABI and control individuals. Discussion Although individuals with ABI may have different levels of emotional abilities compared to non-ABI individuals, the relationship patterns between interoceptive and emotional processes appear to be similar between the two groups. This study suggests the potential benefit of addressing both interoceptive and emotional difficulties in treatments targeting such prevalent sequelae of ABI as depressive symptoms.
An Epidemic Model with Virtual Mass Transportation: The Case of Smallpox in a Large City

Smallpox was “eradicated’’ in 1980 but two official repositories were left in the world: one at the CDC in Atlanta (U.S.) and the second at the State Research Center of Virology and Biotechnology in Koltsovo, Novosibirsk (Russia). U.S. concerns as to the actual number or location of sources of smallpox are due, in part, to the fact that the Soviet government began growing and stockpiling large quantities of smallpox, specially adapted for use in bombs and missiles, right after the elimination of the virus from human populations [55]. Hence, concerns about our nation’s readiness in the event of the deliberate release of a biological agent, like smallpox, have been discussed prior to the tragic events of September 11, 2001. The response, at least at some level, as the probability of such an event seems to have increased. For example, there were about 50 million smallpox vaccine doses stockpiled worldwide before September 11. Now there are about 300 million in the U.S. alone. The necessity for such ameasure is “obvious’’because smallpox kills about 30 percent of infected (unvaccinated) persons. The absence of specific treatment combined with the loss of the world’s population herd immunity make smallpox a possible weapon of terror [31]. Of course, smallpox is not the only concern. Past human epidemics and pandemics of influenza combined with its high genetic variability, its potential genetic manipulation (engineering of new strains), and past lethal impact on human and animal populations bring influenza viruses to the forefront of candidate for deliberate releases [8, 9, 34, 37, 15, 14, 42, 47]. TheFirst National Symposium onMedical andPublicHealth Response to Bioterrorism held on February 16–17, 1999 inArlington, Virginia wasmotivated, in part, by the 1995 sarin gas attack by a Japanese religious cult, that actually attempted to aerosolize anthrax in the Tokyo area; the establishment of Russia’s bioweapons research program; and the emigration of a large number of scientists after the crash of former Soviet Union. At this meeting,
Approximation of Hodgkin-Huxley Model Using Neural Networks

Simulation of electrophysiological cardiac models plays a critical role in enabling researchers to investigate the heart’s activity under various conditions. In this domain, the cell model assumes a significant position, as its accuracy and complexity have a direct impact on the reliability of simulation outcomes. Presently, most cell models are grounded in the Hodgkin-Huxley theory. However, some of these models prove sensitive to the initial values or time steps utilized in solving ordinary differential equations (ODEs). With the rapid advancements in deep learning, the application of this technology has expanded beyond image processing. In this paper, we propose a novel method to construct a cell model based on a neural network. Through experiments, we demonstrate that our new model exhibits high accuracy and robustness when compared to traditional cell models. The results validate the potential of employing deep learning techniques to enhance the reliability and performance of cardiac simulations, opening new avenues for future research in this domain.
Biological effects of background radiation and their risk of humans

Background : Exposure rate measurements of the natural background radiation are found in some selected locations at the university of Kufa in Najaf city, Iraq. Materials and Methods: The experimental results of this study are found using a G-M survey meter [SEI Inspector EXP (Digital Radiation Detector, USA)]. Two radioactive sources ( 137 Cs and 60 Co) are used to calibrate the GM tube exposure rate meter. Results: The most frequently recorded readings of the gamma-ray dose rate were observed between 74 and 93 nGy h -1 . The absorbed dose rates are found to be from 55 nGyh -1 at Science College (Chemistry Store) to 189 nGy h -1 at Science College (Classroom) (mean= 99 nGyh -1 ). Conclusions: Meanwhile, absorbed dose rates of background radiation are fell within the range reported in other listed regions worldwide. This finding indicates that selected locations in the present study have normal values and may not be harmful and have not biological effects on people in this region. The university of Kufa classified as an area of normal background radiation.
Carbon Stock in Gullele Botanical Garden: Implications for Carbon Emission Reduction, North Western Addis Ababa, Ethiopia

This study was aimed to examine the carbon Stock in the Gullele Botanical Garden. To meet the objective of the research, the study was entirely based on quantitative study design by considering 60 sample plot i.e., 10*20 m 2 which was systematically located at every 600m vertical height and 300m horizontal width in the Garden. The results shown the maximum carbon per tree is 37.84 tones for 2.9% of Eucalyptus globules and Juniperus procera. This indicates planting one tree could be sinking 0.56 + 1.71 SD tones mean carbon in the above ground biomasses. In addition, the litter carbon concentration analyzed in the laboratory was 54.67 % maximum and 47.71% minimum per sample with mean carbon stock of 59.78%. Whereas, the analysis of soil organic carbon, The laboratory result had shown . 3% minimum and 12.6% maximum soil organic carbon, while 121.49 t ha -1 maximum and 2.67 t ha -1 minimum value carbon stocks were recorded. Accordingly, 37.2608 + 19.65541t ha -1 mean carbon stock Captured in soils, this indicates mean carbon captured in the soil pool was relatively greater than the other pools. Moreover CO 2 equivalent valves showed the same trend as soil organic carbon which was 445.45t ha -1 maximum and 9.79t ha -1 minimum value sequestered in the soils. Comparatively, the lowest carbon stock is found in dead litter pool while the highest carbon stock found in soil pool i.e., soil pool 61.19%, above ground biomass pool 28.09 %, below ground biomass pool 5.62 % and dead litter pool 5.10 % which is in conformity with the already established knowledge. Thus, Undertaking different studies considering such issues have paramount importance for carbon trade, climate change mitigation and fund attraction. Consequently, intensifying indigenous knowledge for biodiversity conservation, climate change mitigation and urban greening option is very important in enhancing carbon sequestration potentials. Key words: Biomass, Carbon stock, Carbon Stock equivalent, DBH, Gullele Botanical Garden
Knowledge Graph-Based Language Question and Answer Model Design for Laser Welding

Considering that a large amount of process knowledge in laser welding process design is not effectively utilized and collected, this paper proposes a knowledge graph-based question and answering method for laser welding process design, and puts forward a text representation method combining sentence vectors and word vectors. A deep learning model that combines a Bidirectional Gated Recurrent Unit (BiGRU) with a Multi-Head Attention (MHA) mechanism is used to identify the intention of problem texts. Model comparison and ablation experiments were conducted on a constructed process problem dataset, which proved that the model achieved significantly better results and can provide valuable assistance to process personnel in laser welding process design.