Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of roundworm species prevalence in cattle, sheep and goats slaughtered at Lafia Abattoir, Nasarawa State, Nigeria

Intestinal parasites pose a great constraint to livestock production and cause high incidence of mortality and low production in livestock. This study was designed to identify the roundworm species infections in cattle, sheep and goats that were slaughtered at Lafia abattoir during the rainy seasonwithr theidespread of the infections and the methodology used was (April to June, 2016). The aim was to sensitize livestock stakeholders to strategize effective means of disrupting the life cycles of the roundworms in order to prevent their widespread within the herds. Faecal samples were collected from 22 cattle, 25 sheep and 23 goats and were examined by simple floatation method and microscopy for the presence of roundworms. Results showed that out of a total of 70 animals examined, 36 representing 51.4% of the animals were infected with various species of roundworms. Those found in the animals examined included Haemonchus contortus, Bunostomum phlebotomum, Ascaris suum and Neoascaris vitulorum. It was observed that both sexes of all the animal breeds examined were infected, though males were more susceptible than the females. Animals in all the age categories were infected and those on extensive management system were more infected than those in semi-intensive. Since different species of roundworms were found in cattle, sheep and goats slaughtered at Lafia abattoir, it indicated that there was probably high prevalent rate of roundworms in the area. More so, mixed species infection of Ascaris suum and Neoascaris vitulorum were recorded in goats, suggesting severe burden that may hinder optimum physiological performance. It could be necessary therefore to advise that careful disposal of the gastrointestinal contents, regular deworming, implementation of strict biosecurity measures and intensive animal production system should be adopted in the study area. This may disrupt the life cycles of the roundworm species thus, preventing parasitic infections from spreading to other animals in the study area.
Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis

Background With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including cardiovascular disease. Facts have proved that AI has broad application prospects in rapid and accurate diagnosis. Objective This study mainly summarizes the research on the application of AI in the field of cardiovascular disease through bibliometric analysis and explores possible future research hotpots. Methods The articles and reviews regarding application of AI in cardiovascular disease between 2000 and 2023 were selected from Web of Science Core Collection on 30 December 2023. Microsoft Excel 2019 was applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 6.2.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field. Results A total of 4,611 articles were selected in this study. AI-related research on cardiovascular disease increased exponentially in recent years, of which the USA was the most productive country with 1,360 publications, and had close cooperation with many countries. The most productive institutions and researchers were the Cedar sinai medical center and Acharya, Ur. However, the cooperation among most institutions or researchers was not close even if the high research outputs. Circulation is the journal with the largest number of publications in this field. The most important keywords are “classification”, “diagnosis”, and “risk”. Meanwhile, the current research hotpots were “late gadolinium enhancement” and “carotid ultrasound”. Conclusions AI has broad application prospects in cardiovascular disease, and a growing number of scholars are devoted to AI-related research on cardiovascular disease. Cardiovascular imaging techniques and the selection of appropriate algorithms represent the most extensively studied areas, and a considerable boost in these areas is predicted in the coming years.
Teaching Values to Secondary School Students: An Analysis of Teachers' Practices

In this multiple case study, the focus was on how teachers in the selected schools in Narowal cultivate values in their secondary school students. To attain a comprehensive understanding, twenty teachers were purposefully selected for interviews. Qualitative content analysis using NVivo 12 software was used to analyse the gathered data. The research found that these teachers employed diverse approaches to teaching values education. They incorporated storytelling, moral dilemmas, Islamic quotes, examples, activities, games, and projects into their practices. Furthermore, they aimed to foster a positive atmosphere in the classroom by endorsing respect, cooperation, tolerance, and empathy among students. However, the teachers felt hindered by several challenges. They highlighted time constraints due to curriculum overload, the presence of large class sizes, test-oriented educational systems, a diverse student population, and limited resources as factors impeding their effectiveness. Following these findings, it is recommended across society and administration levels that support should be provided to aid secondary school teachers in delivering effective values education.
Tissue-Based Diagnostic Biomarkers of Aggressive Variant Prostate Cancer: A Narrative Review

Simple Summary Metastatic prostate cancer is traditionally treated with androgen deprivation therapy. The introduction of second-generation antiandrogens into clinical practice elicits prolonged responses but also gives rise to mechanisms of resistance that do not rely on androgens. The emergent phenotype of androgen-indifferent prostate cancer is associated with an aggressive, atypical clinical course. The term “aggressive variant prostate cancer” (AVPC) has been coined in order to separate this phenotype from hormone-responsive tumors. Unfortunately, morphology alone cannot reliably predict virulent behavior. The development of prognostic and predictive biomarkers is therefore crucial. In line with this, research has been focusing on unraveling the biological identity of AVPC. Drawing from the current knowledge about AVPC molecular pathogenesis and evolution, we attempt to identify candidate tissue-based AVPC biomarkers. Abstract Prostate cancer (PC) is a common malignancy among elderly men, characterized by great heterogeneity in its clinical course, ranging from an indolent to a highly aggressive disease. The aggressive variant of prostate cancer (AVPC) clinically shows an atypical pattern of disease progression, similar to that of small cell PC (SCPC), and also shares the chemo-responsiveness of SCPC. The term AVPC does not describe a specific histologic subtype of PC but rather the group of tumors that, irrespective of morphology, show an aggressive clinical course, dictated by androgen receptor (AR) indifference. AR indifference represents an adaptive response to androgen deprivation therapy (ADT), driven by epithelial plasticity, an inherent ability of tumor cells to adapt to their environment by changing their phenotypic characteristics in a bi-directional way. The molecular profile of AVPC entails combined alterations in the tumor suppressor genes retinoblastoma protein 1 (RB1), tumor protein 53 (TP53), and phosphatase and tensin homolog (PTEN). The understanding of the biologic heterogeneity of castration-resistant PC (CRPC) and the need to identify the subset of patients that would potentially benefit from specific therapies necessitate the development of prognostic and predictive biomarkers. This review aims to discuss the possible pathophysiologic mechanisms of AVPC development and the potential use of emerging tissue-based biomarkers in clinical practice.
TENTATIVE CLASSIFICATION SCHEME OF EVERGREEN BROAD-LEAVED FORESTS OF CHINA

Vegetation classification is a highly complex and very contentious issue in the vegetation science. Up to now, there is no common classification system that is accepted by all vegetation scientists. The evergreen broad-leaved forests (EBLFs) is only next in complexity to tropical rain forests. Human activities have altered these forests tremendously, and there are many transitional communities which make their classification even more challenging. The classification of EBLFs of China has undergone many changes over the years. In the 1950s, we learned from the Russian school; in the 1960s, the concept of "importance value" and methods from the Wisconsin school were introduced into the study of EBLFs; additionally, the theory and method of the Braun-Blanquet school were used in th e study on the Cyclobalanopsis glaucoides forest. In the 1980s the authors of "Vegetation of China" (1980) published a principle system of vegetation classification for China which emphasized the use of "diagnostic species" in classifying the "formation" and "association" levels of vegetation in the south region. However, owing to limited resources, this principle was never adopted. Based on the previous classification schemes a new classification system including "association", "formation", "formation group", "vegetation subtype" and "vegetation type" was formalized in this paper. The high-rank units of classification are based on eco-physiognomy, while the median and lower units are based on species composition. At the same time, the role of dominant species and diagnostic species was considered. According to above guiding principles, the EBLFs of China were divided into 3 vegetation types, 8 vegetation subtypes, 14 formation groups and 53 formations. Each classification type occupied a specific geographical region and specific environmental conditions.
Evaluating the performance of WRF-CMAQ air quality modelling system in Bulgaria by means of the DELTA tool

Model evaluation is performed based on comparison of simulated and observed air pollution concentrations at 25 background stations of the Bulgarian national air quality monitoring network for the year 2013. The DELTA software package, developed within the forum for air quality modelling in Europe - FAIRMODE, is used to analyse model results focusing on maximum daily eight-hour mean ozone concentrations, hourly nitrogen dioxide (NO2) and daily particulate matter (PM10) concentrations. Ozone is overestimated by 38%, minimum daily values are overestimated by a factor of 3. NO2 and PM10 are underestimated by a factor of, respectively, 4 and (5 to 9). The main DELTA model quality objective is respected at 26% of the stations for ozone, 58% of the stations for NO2, and nowhere for PM10. The best model performance is for ozone at rural sites during summer. Possible ways for improvement of model results are discussed.
MARCKS Inhibition Alters Bovine Neutrophil Responses to Salmonella Typhimurium

Neutrophils are innate immune cells that respond quickly to sites of bacterial infection and play an essential role in host defense. Interestingly, some bacterial pathogens benefit from exuberant neutrophil inflammation. Salmonella is one such pathogen that can utilize the toxic mediators released by neutrophils to colonize the intestine and cause enterocolitis. Because neutrophils can aid gut colonization during Salmonella infection, neutrophils represent a potential host-directed therapeutic target. Myristoylated alanine-rich C-kinase substrate (MARCKS) is an actin-binding protein that plays an essential role in many neutrophil effector responses. We hypothesized that inhibition of MARCKS protein would alter bovine neutrophil responses to Salmonella Typhimurium (STm) ex vivo. We used a MARCKS inhibitor peptide to investigate the role of MARCKS in neutrophil responses to STm. This study demonstrates that MARCKS inhibition attenuated STm-induced neutrophil adhesion and chemotaxis. Interestingly, MARCKS inhibition also enhanced neutrophil phagocytosis and respiratory burst in response to STm. This is the first report describing the role of MARCKS protein in neutrophil antibacterial responses.
Morpho-physiological traits associated with leaf senescence at peak fruiting stage in tomato under water deficit condition

Leaf senescence is a crucial trait in crop improvement programme. Utilization of this trait in breeding work has immense importance. The present investigation was undertaken in tomato genotypes to study variation and association of leaf traits with leaf senescence in tomato. Twenty five tomato genotypes were evaluated in CRD with two replications. Plants were grown in pot under limited water supply condition. Water supply was restricted starting from peak flowering stage to peak fruiting stage. Observations were recorded on leaf traits like number of green leaves per plant, green leaf area, chlorophyll content, leaf water retention capacity (WRC), relative water content of leaf (RWC), water saturation deficit (WSD), leaf moisture content (LMC) along with fruit yield per plant. Leaf senescence was expressed in terms of senescence index. At peak fruiting stage, leaf senescence index had significant negative correlation with chlorophyll a & total chlorophyll content. The physiological traits like WRC and RWC had negative correlation with SI parameter but WSD and LMC had positive correlation with SI. The genetic variation present in the genotypes in respect of leaf senescence traits could be exploited to identify tomato genotypes with delayed leaf senescence.
Ghana's Evolutionary Role in a Changing World Economic Order

The world economy has been passing through interesting and significant changes for several years now promising for some (the emerging countries in particular) and not so promising for others (the developed countries). Huge transfer of resources has taken place and the process is continuing. In this context, it is but natural for African observers to raise the question: African share of world resources remaining somewhat unchanged (as per the World Bank data), what is the role and future of the African countries in this changing scenario? Are they going to be partners in the transfer of resources that is being experienced? An attempt has been made here to examine the case of Ghana – a leading economy in the continent. The author wouldcontendthatalthoughthecountryispoisedtogrowspectacularlyassomeforecasterswouldenvisage,thanks to the prospect of oil revenues, Ghana has to traverse a long way if it is to be noticed in the world bodies. Growth and development must occur in substantive measures from the present state of affairs. Agriculture, almost the backbone for any developing country, must take the lead to offer meaningful support to the more promising industry. The real economy, in its basic form, must be properly understood, developed and managed with strategic investments in place. And this must be followed by or accompanied with a vibrant knowledge-based service economy. Failing this, the burgeoning oil wealth may only be hallucinatory at best. Keywords: Ghanaianeconomy;Worldeconomicorder;Economicforecast
An Aspect-Based Review Analysis Using ChatGPT for the Exploration of Hotel Service Failures

In this study, we employed ChatGPT, an advanced large language model, to analyze hotel reviews, focusing on aspect-based feedback to understand service failures in the hospitality industry. The shift from traditional feedback analysis methods to natural language processing (NLP) was initially hindered by the complexity and ambiguity of hotel review texts. However, the emergence of ChatGPT marks a significant breakthrough, offering enhanced accuracy and context-aware analysis. This study presents a novel approach to analyzing aspect-based hotel complaint reviews using ChatGPT. Employing a dataset from TripAdvisor, we methodically identified ten hotel attributes, establishing aspect–summarization pairs for each. Customized prompts facilitated ChatGPT’s efficient review summarization, emphasizing explicit keyword extraction for detailed analysis. A qualitative evaluation of ChatGPT’s outputs demonstrates its effectiveness in succinctly capturing crucial information, particularly through the explicitation of key terms relevant to each attribute. This study further delves into topic distributions across various hotel market segments (budget, midrange, and luxury), using explicit keyword analysis for the topic modeling of each hotel attribute. This comprehensive approach using ChatGPT for aspect-based summarization demonstrates a significant advancement in the way hotel reviews can be analyzed, offering deeper insights into customer experiences and perceptions.
Exploration of the Delivery of Oncolytic Newcastle Disease Virus by Gelatin Methacryloyl Microneedles

Oncolytic Newcastle disease virus is a new type of cancer immunotherapy drug. This paper proposes a scheme for delivering oncolytic viruses using hydrogel microneedles. Gelatin methacryloyl (GelMA) was synthesized by chemical grafting, and GelMA microneedles encapsulating oncolytic Newcastle disease virus (NDV) were prepared by micro-molding and photocrosslinking. The release and expression of NDV were tested by immunofluorescence and hemagglutination experiments. The experiments proved that GelMA was successfully synthesized and had hydrogel characteristics. NDV was evenly dispersed in the allantoic fluid without agglomeration, showing a characteristic virus morphology. NDV particle size was 257.4 ± 1.4 nm, zeta potential was −13.8 ± 0.5 mV, virus titer TCID50 was 107.5/mL, and PFU was 2 × 107/mL, which had a selective killing effect on human liver cancer cells in a dose and time-dependent manner. The NDV@GelMA microneedles were arranged in an orderly cone array, with uniform height and complete needle shape. The distribution of virus-like particles was observed on the surface. GelMA microneedles could successfully penetrate 5% agarose gel and nude mouse skin. Optimal preparation conditions were freeze-drying. We successfully prepared GelMA hydrogel microneedles containing NDV, which could effectively encapsulate NDV but did not detect the release of NDV.
Wrist EMG Monitoring Using Neural Networks Techniques

In rehabilitation, the correct performance of the exercises the specialist prescribes wrist movement is crucial. However, this may have the disadvantage of the patient’s subjectivity. Moreover, recent studies show that feedback through electrostimulation devices is beneficial during the process that leads to neuromotor rehabilitation. Besides, the electromyographic (EMG) signals give information about the actual degree of rehabilitation. This work examines whether temporal features can be used to classify wrist movements using back-propagation artificial neural networks and superficial EMG (sEMG) signals. The data for the evaluation were based on the information acquired from sEMG signals of two forearm muscles: the flexor carpi ulnaris (FCU) and the brachioradialis (B). These sEMG signals were analyzed to find the most critical parameters for classifying the wrist’s movement and to configure a multilayer perceptron (MLP) capable of classifying such movements.
Exploring multi-decadal time series of temperature extremes in Australian coastal waters

Abstract. The intensity and frequency of extreme ocean temperature events, such as marine heatwaves (MHWs) and marine cold spells (MCSs), are expected to change as our oceans warm. Little is known about marine extremes in Australian coastal waters, particularly below the surface. Here we introduce a multi-decadal observational record of extreme ocean temperature events starting in the 1940s and 1950s between the surface and the bottom (50–100 m) at four long-term coastal sites around Australia: the Australian Multi-Decadal Ocean Time Series EXTreme (AMDOT-EXT) data products (https://doi.org/10.26198/wbc7-8h24, Hemming et al., 2024). The data products include indices indicating the timing of extreme warm and cold temperature events, their intensity and the corresponding temperature time series and climatology thresholds. We include MHWs, MCSs and shorter-duration heat spikes and cold spikes. For MHWs and MCSs, which are defined as anomalies above the daily varying 90th and 10th percentiles, respectively, and lasting more than 5 d, we also provide further event information, such as their category and onset and decline rates. The four data products are provided as CF-compliant NetCDF files, and it is our intention that they be updated periodically. It is advised that data users seek the latest data product version. Using these multi-decadal data products, we show the most intense and longest extreme temperature events at these sites, which have occurred below the surface. These data records highlight the value of long-term full water column ocean data for the identification of extreme temperature events below the surface.
Project management approaches and their selection in the digital age: Overview, challenges and decision models

Digital transformation is a challenge that also impacts the selection of tools for implementing projects. Which tools are suitable for handling complex digital twins? Project management must respond to this with suitable approaches. The challenge for decision-makers is to choose the right one. Based on literature research and a case study, influencing factors are derived and practice-relevant project management approaches are collected. Furthermore, a decision model is developed that, on the one hand, supports the decision-maker in selecting tools before and during the project, and on the other hand makes empirical values from past projects usable for future decisions. The results show that the number of influencing factors is large, and the approaches are di-verse. In complex projects, this can lead to complex decision-making situations that require appropriate decision models. The developed “Supervised Decision Model – L5” is based on five levels (L): (L1) Building a database; (L2) Derivation of algorithms; (L3) Initial approach selection; (L4) Review of the initial selection; (L5) Using experiences for future decisions. In practice it turns out that complex projects – like Digital Twins - often fail. Modified decision models for selecting suitable approaches should therefore take the following as-pects into account: (a) decision-makers are actively supported in the initial decision phase; (b) initial decisions once made are checked in the early phase of the project and corrected if necessary; (c) the lessons learned are recorded in the database as empirical value and used for future decisions.