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Epidemiology of gastrointestinal parasites of dogs in four districts of central Ethiopia: Prevalence and risk factors

From February 2022 to April 2023, a cross-sectional study on dog gastrointestinal parasites was conducted in Bishoftu, Dukem, Addis Ababa, and Sheno, Central Ethiopia, with the aim of estimating the prevalence and evaluating risk factors. A total of 701 faecal samples were collected and processed using floatation and McMaster techniques. In dogs that were investigated, the overall prevalence of gastrointestinal parasites was 53.1% (372/701). Nematode (28.2%), cestode (8.4%), and protozoan (5.6%) parasite infections were detected in dogs in both single (42.2%) and combined (10.8%) infections. With respective prevalences of 16%, 9.8%, 5%, 3.9%, and 3.1% Ancylostoma spp., Toxocara canis, Dipylidium caninum, Giardia spp., and Taenia/Echinococcus spp. were the most common parasites. The prevalence of gastrointestinal parasites was significantly higher (P<0.05) in female dogs (73.8%, OR = 0.4), adult dogs (55.3%, OR = 0.4), dogs that were given raw food (57.9%, OR = 2.7), and dogs kept free outdoor (60.9%, OR = 2.4). The incidence of gastrointestinal parasites was also higher in dogs with diarrheal faecal consistency (89.1%, OR = 9.1) and dogs from highland areas (62.1%, OR = 1.8). In contrast, statistically significant variation in the prevalence of gastrointestinal parasites was not recorded among dogs of different breeds. The current study found that dogs in the studied locations had a high overall prevalence of gastrointestinal parasites. In conclusion, gastrointestinal parasites in dogs have the potential to pose a serious threat to public health, so addressing this issue requires a unified approach. Therefore, it is necessary to conduct detailed epidemiological and genetic research on dog parasites in vast study regions across various agro-ecologies zones and seasons in Ethiopia. Additionally, it is crucial to raise public awareness of the prevalence, effects on public health, and financial implications of dog gastrointestinal parasites in Ethiopia.

Analysis of RL electric circuits modeled by fractional Riccati IVP via Jacobi-Broyden Newton algorithm

This paper focuses on modeling Resistor-Inductor (RL) electric circuits using a fractional Riccati initial value problem (IVP) framework. Conventional models frequently neglect the complex dynamics and memory effects intrinsic to actual RL circuits. This study aims to develop a more precise representation using a fractional-order Riccati model. We present a Jacobi collocation method combined with the Jacobi-Newton algorithm to address the fractional Riccati initial value problem. This numerical method utilizes the characteristics of Jacobi polynomials to accurately approximate solutions to the nonlinear fractional differential equation. We obtain the requisite Jacobi operational matrices for the discretization of fractional derivatives, therefore converting the initial value problem into a system of algebraic equations. The convergence and precision of the proposed algorithm are meticulously evaluated by error and residual analysis. The theoretical findings demonstrate that the method attains high-order convergence rates, dependent on suitable criteria related to the fractional-order parameters and the solution’s smoothness. This study not only improves comprehension of RL circuit dynamics but also offers a solid numerical foundation for addressing intricate fractional differential equations.

Vitamin D, acute respiratory infections, and Covid-19: The curse of small-size randomised trials. A critical review with meta-analysis of randomised trials

Background Randomised trials conducted from 2006 to 2021 indicated that vitamin D supplementation (VDS) was able to prevent severe COVID-19 and acute respiratory infections (ARI). However, larger randomised trials published in 2022 did not confirm the health benefits of VDS in COVID-19 patients. Objective To examine through a systematic review with meta-analysis the characteristics of randomised trials on VDS to COVID-19 patients and admission to intensive care unit (ICU), and of randomised trials on VDS for the prevention of ARI. Method A systematic search retrieved randomised trials on VDS to COVID-19 patients and admission to ICU. Data on VDS and ARI were extracted from the meta-analysis of Jolliffe et al. 2021. Groups were formed including trials with total numbers of patients below or above the median size of all trials. The associations between VDS vs no VDS, and admission to ICU were evaluated using random-effects models from which summary odds ratios (SOR) and 95% confidence intervals (CI) were obtained. Meta-analyses were done for all trials and for each group of trials, which allowed testing a possible effect modification of trial size. Publication bias was assessed using the Louis-Furuya-Kanaruori (LFK) index (no bias if index between -1 and +1) and the trim and fill method. Results Nine trials on VDS for preventing admission to ICU were identified, including 50 to 548 patients. The summary odds ratio (SOR) was 0.61 (95% CI: 0.39–0.95) for all trials, 0.34 (0.13–0.93) for trials including 50 to <106 patients and 0.88 (0.62–1.24) for trials including 106 to 548 patients (interaction p = 0.04). The LFK index was -3.79, and after trim and fill, the SOR was 0.80 (0.40–1.61). The SOR for the 37 trials on VDS for ARI prevention included 25 to 16,000 patients. The SOR was 0.92 (0.86–0.99) for all trials, 0.69 (0.57–0.83) for trials including 25 to <248 patients and 0.98 (0.94–1.03) for trials including 248 to 16,000 patients (interaction p = 0.0001). The LFK index was -3.11, and after trim and fill, the SOR was 0.96 (0.88–1.05). Conclusion Strong publication bias affected small randomised trials on VDS for the prevention of severe COVID-19 and of ARI. Systematic reviews should beware of small-size randomised trials that generally exaggerate health benefits.

Efficiency of four trap types and human landing catch in the sampling of Mansonia (Diptera, Culicidae) in Porto Velho, Rondônia, Brazil

Entomological surveillance plays a crucial role in designing and implementing mosquito control measures. In this context, developing more effective collection strategies is essential to accurately estimate the entomological parameters necessary for effective control. In this study, we investigated the effectiveness of four traps: CDC light trap, MosqTent, BG-Sentinel, and SkeeterVac, compared to human landing catch (HLC) in the collection of Mansonia mosquitoes, known to cause discomfort to riverside populations along the Madeira River in the District of Jaci Paraná, Porto Velho, in Rondônia state, Brazil. Sampling was conducted, during three periods corresponding to two seasons, dry and rainy, over five consecutive days for each period. The captures using HLC and the installation of the traps took place on the grounds of five selected residences from 6 to 10 pm. Rotational exchanges between houses ensured that all traps and the HLC were used in each of the five residences, following a predetermined Latin square pattern. A total of 7,080 mosquitoes were collected, of which 90.5% belonged to the Mansonia genus, distributed in four species: Mansonia titillans (75.97%), Mansonia humeralis (18.91%), Mansonia amazonensis (1.90%), and Mansonia indubitans (1.37%). HLC captured the highest number of Mansonia mosquitoes (58.1%), followed by SkeeterVac (21.8%) and MosqTent (18.9%). CDC and BG-Sentinel showed a very low performance (0.92 and 0.23%, respectively). Although HLC performed better in capturing Mansonia, our results suggest that SkeeterVac and MosqTent can serve as valuable additional tools to entomological inventories or sentinels for detecting invasive species in areas with high epidemiological vulnerability, thereby providing evidence-based recommendations for improving mosquito control measures and entomological surveillance.

Analysis of the social-epistemological dimensions of a Clinical Teaching Unit and Small Private Online Course compared to a traditional clerkship Internal Medicine

Clinical workplace learning is often suboptimal due to the dynamics of the clinical learning environment and several challenges encountered in clinical practice. At LUMC, clinical teachers introduced a novel blended learning program that included both the introduction of a Clinical Teaching Unit (CTU) and Small Private Online Course (SPOC). This study aimed to analyze the educational content and design of our educational interventions, by categorizing and comparing the dimensions of the learning program before and after the interventions. The novel blended learning program was integrated into a clerkship internal medicine, aiming to optimize clerks’ clinical workplace learning. Therefore, a traditional inpatient ward was transformed into a CTU, featuring the introduction of learning activities that promoted multidisciplinary and interprofessional learning. It included an integrated SPOC, aiming to improve clinical reasoning skills, feedback and collaboration of the clerks. The authors analyzed the clerkships’ content and design, by categorizing the social-epistemological dimensions of the teaching modes offered before (traditional clerkship) and after (including CTU and SPOC) the intervention. These dimensions consisted of individual versus group (collaborative), and objectivist versus constructive learning categories. The CTU model added eleven group activities to the clinical workplace, of which nine were characterized as constructivist-group activities. It also led to more active learning of the clerks, compared to the traditional clerkship. Analysis of the SPOC revealed 344 teaching modes in total, which included 113 objectivist–individual, 205 constructive–individual and 23 constructive–group activities. Compared to a traditional clerkship, the CTU with SPOC led to more constructivist and collaborative learning and a more diverse educational program. This study illustrates a methodology to address active and collaborative learning activities in an educational program, and offers tools to evaluate and redesign one’s teaching program. The methods and models described can be applied to clinical environments in other areas of medicine.

In vivo imaging of mitochondrial function in normal, glaucoma suspect, and glaucoma eyes

To investigate macula and optic nerve head (ONH) mitochondrial metabolic activity using flavoprotein fluorescence (FPF) in normal, glaucoma suspect (GS), and open-angle glaucoma (OAG) eyes we performed a cross-sectional, observational study of FPF in normal, GS, and OAG eyes. The macula and ONH of each eye was scanned and analyzed with a commercially available FPF measuring device (OcuMet Beacon, OcuSciences Inc., Ann Arbor, MI). One-way analysis of variance was used to compare macula and ONH FPF scores between groups. Linear regression models investigated the correlation between FPF scores and structural and functional parameters. We included 25 normal, 16 GS, and 54 OAG eyes. The average age in years ± SD for normal, GS, and OAG groups was 60.6 ±17.4, 67.8 ± 10.3, and 67.9 ± 11.6, respectively (P = 0.064). There was no significant difference in gender, race/ethnicity, visual acuity, and intraocular pressure between groups. OAG eyes had larger cup-to-disc ratio, thinner retinal nerve fiber and macula thicknesses, and worse visual field indices compared to normal and GS eyes (P ≤ 0.018). There was no significant difference in any FPF metric between the study groups in either the macula or the ONH, despite normalizing FPF data for structural differences between groups (e.g. retinal nerve fiber layer and ganglion cell-inner plexiform layer thickness). In conclusion, no significant differences in metabolic activity as measured by FPF were found in macula and ONH FPF scores using the integrated clinician report generator between normal, GS, and OAG eyes. Further research is needed to evaluate the role of mitochondrial metabolic activity measurements in glaucoma.

Look-alike modelling in violence-related research: A missing data approach

Violence has been analysed in silo due to difficulties in accessing data and concerns for the safety of those exposed. While there is some literature on violence and its associations using individual datasets, analyses using combined sources of data are very limited. Ideally data from the same individuals would enable linkage and a longitudinal understanding of experiences of violence and their (health) impacts and consequences. This paper aims to provide proof of concept to create a synthetic dataset by combining data from the Crime Survey for England and Wales (CSEW) and administrative data from Rape Crisis England and Wales (RCEW), pertaining to victim-survivors of sexual violence in adulthood. Intuitively, the idea was to impute missing information from one dataset by borrowing the distribution from the other. In our analyses, we borrowed information from CSEW to impute missing data in the RCEW administrative dataset, creating a combined synthetic RCEW-CSEW dataset. Using look-alike modelling principles, we provide an innovative and cost-effective approach to exploring patterns and associations in violence-related research in a multi-sectorial setting. Methodologically, we approached data integration as a missing data problem to create a synthetic combined dataset. Multiple imputation with chained equations were employed to collate/impute data from the two different sources. To test whether this procedure was effective, we compared regressions analyses for the individual and combined synthetic datasets on binary, continuous and categorical variables. We extended our testing to an outcome measure and, finally, applied the technique to a variable fully missing in one data source. Our results show that the effect sizes for the combined dataset reflect those from the dataset used for imputation. The variance is higher, resulting in fewer statistically significant estimates. Our approach reinforces the possibility of combining administrative with survey datasets using look-alike methods to overcome existing barriers to data linkage.

Artificial intelligence and network science as tools to illustrate academic research evolution in interdisciplinary fields: The case of Italian design

In this paper, we explore the application of Artificial Intelligence and network science methodologies in characterizing interdisciplinary disciplines, with a specific focus on the field of Italian design, taken as a paradigmatic example. Exploratory data analysis and the study of academic collaboration networks highlight how the field is evolving towards increased collaboration. Text analysis and semantic topic modelling identified the evolution of research interest over time, defining a ranking of pairs of keywords and three prominent research topics: User-Centric Experience Design, Innovative Product Design and Sustainable Service Design. Our results revealed a significant transformation in the field, with a shift from individual to collaborative research, as evidenced by the increasing complexity and collaboration within groups. We acknowledge the limitations faced by this work, suggesting that the methodology may be primarily suitable for bibliometric and more silos-like disciplines. However, we emphasize the urgency for the scientific community to address the future of research not indexed by large open-access databases like OpenAlex.

Impact of communication anxiety on L2 WTC of middle school students: Mediating effects of growth language mindset and language learning motivation

Previous research has shown a connection between communication anxiety and willingness to communicate (WTC) among English as a foreign/second language (L2) learners. Nonetheless, the potential mediating roles of learners’ beliefs like growth language mindset and language learning motivation have not been thoroughly investigated, particularly in the context of middle school language learners. This study aimed to explore the relationship between communication anxiety and L2 WTC by considering the mediating roles of growth language mindset and language learning motivation. To achieve this goal, an online survey was administered to 847 participants from five middle schools in eastern China. Structural equation modeling was employed to analyze the collected data. The findings revealed that L2 WTC was negatively impacted by communication anxiety and growth language mindset, while positively influenced by language learning motivation. Additionally, communication anxiety had a negative effect on both growth language mindset and language learning motivation, whereas growth language mindset exerted a positive effect on language learning motivation. Moreover, either individually or synergistically, growth language mindset and language learning motivation played mediating roles in the relationship between communication anxiety and L2 WTC. These research findings have significant implications for understanding the interrelationship among the variables, offering an innovative perspective on the mediating effects of growth language mindset and language learning motivation on communication anxiety and WTC among secondary school students. Consequently, this study provides valuable insights for language learning and instruction among middle school students and teachers.

Correction to Supporting Information for Le et al., Motor neuron disease, TDP-43 pathology, and memory deficits in mice expressing ALS–FTD-linked <i>UBQLN2</i> mutations

Exploring cooking fuel choices among Ghanaian women of reproductive age: A socio-economic analysis from a statistical mechanics perspective

Access to clean and efficient cooking fuel is crucial for promoting good health, safeguarding the environment, and driving economic growth. Despite efforts to promote the adoption of cleaner alternatives, traditional solid fuels such as charcoal and firewood remain prevalent in Ghana. In this study, we utilized a statistical mechanical model as a framework to explore the statistical relationship between socio-economic factors such as educational attainment, wealth status, place of residence, and cooking fuel choices. We analysed data from the Ghana Malaria Indicator Survey (GMIS) conducted in 2019, involving a total of 2,942 women of reproductive age. The findings revealed that 13.77% of participants preferred using LPG fuels for cooking, while 86.23% preferred non-LPG fuels for their cooking needs. The data indicated that among LPG users, 96.54% are educated women of reproductive age, and 3.46% are non-educated women of reproductive age. Among these, 95.31% are non-poor, and 4.69% are poor. Additionally, 21.73% reside in rural areas, while 78.27% live in urban areas. The data also showed that among non-LPG fuel users, 68.70% are educated women of reproductive age, and 31.30% are non-educated women of reproductive age. Among this group, 16.04% are non-poor, and 83.96% are poor. Furthermore, 67.24% reside in rural areas, and 32.76% live in urban areas. Our findings showed that in the absence of social interaction, a woman’s wealth status has a relationship to her choice of fuel for cooking. Additionally, women of reproductive age in rural areas with some education demonstrated a significant private incentive (40.12%) to use LPG, implying a positive correlation between education and the use of LPG for cooking. However, when social interactions are considered, factors such as education, wealth status, and place of residence have significant relationships with a woman’s decision about fuel choice. The interaction strength among women of reproductive age in urban areas with some education shows a negative estimate (-4.06%), suggesting that there is no significant imitative effect. The study further suggests that urban women of reproductive age who are poor exert a greater influence on their urban counterparts who are not poor when social interaction is incorporated. Women of reproductive age in rural areas with some form of education exert a greater influence on women of reproductive age in rural areas with no form of education. We recommend that the government of Ghana and its stakeholders focus on leveraging the influence of urban poor women and educated rural women through community-led programs and educational campaigns. Financial support mechanisms like microfinance and subsidies, alongside reliable LPG infrastructure, can make access easier for these target groups. Tailored communication strategies, peer-to-peer learning, and collaboration with local institutions are crucial for spreading awareness and encouraging the adoption of LPG.

Assessing the global dynamics of Nipah infection under vaccination and treatment: A novel computational modeling approach

In biology and life sciences, fractal theory and fractional calculus have significant applications in simulating and understanding complex problems. In this paper, a compartmental model employing Caputo-type fractional and fractal-fractional operators is presented to analyze Nipah virus (NiV) dynamics and transmission. Initially, the model includes nine nonlinear ordinary differential equations that consider viral concentration, flying fox, and human populations simultaneously. The model is reconstructed using fractional calculus and fractal theory to better understand NiV transmission dynamics. We analyze the model’s existence and uniqueness in both contexts and instigate the equilibrium points. The clinical epidemiology of Bangladesh is used to estimate model parameters. The fractional model’s stability is examined using Ulam-Hyers and Ulam-Hyers-Rassias stabilities. Moreover, interpolation methods are used to construct computational techniques to simulate the NiV model in fractional and fractal-fractional cases. Simulations are performed to validate the stable behavior of the model for different fractal and fractional orders. The present findings will be beneficial in employing advanced computational approaches in modeling and control of NiV outbreaks.

Correction for Orellana et al., Childhood maltreatment influences adult brain structure through its effects on immune, metabolic, and psychosocial factors

Computational and experimental approaches to explore defense related enzymes conferring resistance in Fusarium infected chilli plants by regulating plant metabolism through nutritional products

Nutritional status being the first line of defense for host plants, determines their susceptibility or resistance against invading pathogens. In recent years, the applications of plant nutrient related products have been documented as one of the best performers and considered as alternatives or/and supplements in plant disease management compared to traditional chemicals. However, knowledge about application of plant nutrient related products for the management of destructive fungal pathogen Fusarium oxysporum f.sp. capsici and their impact on the components of the antioxidant defense system, especially in chilli plants, still needs to be discovered. Therefore, in this current study, we aimed to evaluate two nutrient fertilizers viz. Krystafeed and Micro Plus at three different concentrations by soil drenching method for their effects against the Fusarium wilt of chilli and investigate the components of the antioxidant defense system of chilli plants. Correlation and computational analysis on the components of antioxidant defense system in various pathways were performed to predict the suitable binding sites of mineral ions. Results indicated that the combination of Krystafeed and Micro Plus was found the most effective with (27.01, 26.59%) disease incidence, followed by Micro Plus (29.56, 32.35%) and Krystafeed (38.21, 41.15%), both in greenhouse and field conditions, respectively. Moreover, the combination of Krystafeed and Micro Plus significantly increased the concentration of SOD (27.53, 108.96)%, POD (37.29, 45.65)%, CAT (19.33, 95.33)%, H2O2 (22.13, 118.98)%, TPC (27.39, 17.37)%, chlorophyll a (21.80, 35.74)%, chlorophyll b (57.57, 18.25)%, total chlorophyll (30.21, 19.83)%, Tocopherol (13.08, 33.66)%, TrxR (5.03, 36.56)%, MDA (13.84, 54.79)%, ascorbate (4.72, 17.28)%, Proline (5.94, 59.31)%, and phytoalexin (Capsidiol) (11.33, 55.08)% in the treated plants of resistant and susceptible chilli varieties, respectively, as compared to the untreated plants. Pearson’s correlation heat-map analysis showed that all the enzymes of antioxidant defense system were found positively correlated with each other. It is concluded that the improvement of crop resistance by the application of plant nutrient related products may be viable alternatives to synthetic chemicals for managing Fusarium wilt disease of chilli and potentially other pathogens.

Multidimensional well-being and income inequality in Central and Eastern Europe: A comparative analysis of CEE North and CEE Continental countries

Central Eastern European countries (CEEc) are characterized both by huge diversity in income inequality and, on average, by lower levels of well-being than in the other European Union (EU) countries. Given that income inequality may affect well-being negatively, the present study aims to explore the links between income inequalities and different dimensions of well-being in the eight CEEc, i.e. Poland, Czech Republic, Slovakia, Slovenia, Hungary, Latvia, Lithuania, and Estonia. The analysis is conducted in the two groups of CEEc regarding low and high inequality in income distribution, namely CEE Continental group and CEE North group (corresponding to post-socialist corporatist and post-socialist liberal, respectively). The multidimensional concept of well-being is applied, enabling deep exploration of its links with income inequalities in the following dimensions: subjective well-being (happiness) and objective well-being (material, health, educational, and environmental dimensions). We estimate the vector autoregression (VAR) models based on annual data disaggregated into quarterly data covering 2004 to 2020. The empirical results of Granger causality testing, which was used to investigate the links between income inequality and multidimensional well-being, indicated that not only are there differences between the groups in the studied patterns of interconnectedness, but also the groups of CEE North and CEE Continental countries are not homogeneous in those links.

Correction for Mehmood et al., Teacher vaccinations enhance student achievement in Pakistan: The role of role models and theory of mind

Factors influencing secondary school students’ nutrition, mindfulness, and academic performance in Nan Province, Thailand

Academic performance is crucial for future educational endeavors of students. However, there has been a concerning decline over time. This study aimed to investigate the association between nutritional status, environmental factors, mindfulness, and academic performance among students at Bo Kluea School in Nan province, Thailand. A cross-sectional study was conducted involving 350 students in grades 8–11 using questionnaires, measurements, and academic records. Results showed that female students performed significantly better academically compared to males(71.9% vs 56.4% achieving good grades; compared to p &lt; 0.001, OR = 3.583, 95%CI = 1.663–7.719). Age, junk food consumption, BMI, and mindfulness were identified as factors influencing academic performance. Students aged 16–18 years were 2.224 times more likely to achieve good academic performance compared to younger students (p = 0.015, OR = 2.224, 95%CI = 1.164–4.247). Significant associations were found between gender, age, waist circumference, mindfulness, and nutritional status. Female students and those with normal waist circumference or good mindfulness were more likely to have a normal BMI (p = 0.019, OR = 1.794, 95%CI = 1.101–2.922). Positive attitudes towards nutrition were associated with better academic performance (60.1% achieving good grades;p = 0.044, AOR = 1.543, 95%CI = 1.010–2.356). This study highlights the interconnectedness of these factors and their importance in in improving academic results. Further research is need to confirm these findings and overcome study limitations.

Correction for Tran-Kiem and Bedford, Estimating the reproduction number and transmission heterogeneity from the size distribution of clusters of identical pathogen sequences

A twofold perspective on the quality of research publications: The use of ICTs and research activity models

Previous studies have highlighted the inherent subjectivity, complexity, and challenges associated with research quality leading to fragmented findings. We identified determinants of research publication quality in terms of research activities and the use of information and communication technologies by employing an interdisciplinary approach. We conducted web-based surveys among academic scientists and applied machine learning techniques to model behaviors during and after the COVID-19 pandemic. Using model-agnostic explanations, we identified the determinants of research publication quality across 66 activity models. These models reflect the variety of behaviors among academic scientists during and after the COVID-19 pandemic. Our two-fold perspective distinguishes between research activities of academic scientists who increase research publication quality and those who maintain it. Notably, our findings reveal a diversity within activity models in shaping research publication quality. Academic institutions can apply our approach to analyze research staff behavior, stimulate activities, and ensure alignment with institutional objectives, thereby fostering individual and team complementarity.

A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach

This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons. For the most challenging task, the 30-minute ahead forecasts, the model achieved a wind speed Mean Absolute Error (MAE) of 0.78 m/s and a wind direction MAE of 33.06°. Furthermore, the use of Gaussian noise concatenation to both input and label training data yielded the most consistent results. A case study further validated the model’s efficacy, with MAE values below 0.43 m/s for wind speed and between 33.93° and 35.03° for wind direction across different forecast horizons. This approach shows that combining strategically deployed sensor networks with machine learning techniques offers improvements in wind nowcasting for airports in complex environments, possibly enhancing operational efficiency and safety.