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End-to-end CNN-based detection of permanent first molars and prediction of root development stages from panoramic radiographs
Report from MDE practice: An interview-based evaluation of model-driven engineering uses
In this study, we investigate the usability of Model-Driven Engineering (MDE) through interviews with fifteen practitioners from diverse roles (e.g., developers, researchers, architects) and domains, and with a range of expertise levels across academic and industrial software sectors, capturing in-depth perspectives on its practical application. Participants emphasized MDE’s benefits in enhancing project robustness, reliability, development speed, and system organization. However, they also identified challenges such as a steep learning curve, technological constraints, organizational resistance, and a shortage of skilled professionals. To address these issues, participants recommended simplifying tools and language, improving consistency and flexibility, enhancing integration with existing workflows, and raising awareness of MDE. These insights provide valuable guidance for improving MDE usability and encouraging broader MDE adoption.
Detection of commercial crop weeds using machine learning algorithms
Evaluating machine learning approaches for host prediction using H3 influenza genomic data
Background H3 influenza A viruses (IAV) have been shown to frequently cross the species barrier which can be an important factor in sustained transmission and spread. Machine learning methods have been widely explored for host prediction of IAV using genomic data; however, this is often done using data from only one of the eight IAV segments or by using all available IAV data to predict broad categories of hosts. Objective The objective of this study was to combine machine learning algorithms with H3 IAV sequence data from all eight segments to train predictive machine learning models for distinct host prediction and validate model performance. Methods Models were trained on both k-mers and amino acid properties alongside machine learning algorithms that included random forest and XGBoost for each of the eight IAV genome segments. Models were then validated on a test dataset through analytics of model class predicted probabilities and subsequently used to investigate between-species transmission patterns within case studies including canine H3N8, swine H3N2 2010.2, and duck H3 sequences. Results Models demonstrated strong performance in host prediction across all eight segments on the test dataset, with overall accuracies and κ (kappa) values ranging from 0.995–0.997, 0.984–0.990, respectively. Misclassified test dataset sequences with high predicted probabilities (> 90%) were validated using available literature and were identified to be frequently associated with between-species transmission events. Between-species transmission patterns within case study model class predicted probabilities were also identified to be consistent with the literature in cases of both correct and incorrect classification. Conclusions These models allow for rapid and accurate host prediction of H3 IAV datasets from any of the eight IAV segments and provide a solid framework that allows for identification of variants with higher than typical between-species transmission potential. However, results obtained on selected case studies suggest further improvements of the training and validation processes should be considered.
Bifidobacterium bifidum enhances hepatic mitochondrial β-oxidation and ameliorates MAFLD in a high-fat diet rat model
Evaluating continuous nanosecond pulsed electric field (nsPEF) treatment as a non-thermal alternative for human milk pasteurisation
This study investigated the application of nanosecond pulsed electric field (nsPEF) treatment as an alternative pasteurisation method for donor human milk (DHM). A 0.1% saline solution was identified as the closest imitation to the received DHM in terms of pulse waveform and conductivity, which was used for the optimisation of PEF parameters. Complete inactivation of inoculated Escherichia coli was achieved after nsPEF treatment in saline with an initial count of 5 log CFU/mL and nearly a 7 log CFU/mL reduction with an 8 log CFU/mL initial count. In DHM, nsPEF treatment resulted in a 3 log CFU/mL reduction with an initial 5 log CFU/mL count and a 5 log CFU/mL reduction at higher initial counts. However, no statistically significant difference in log reduction was observed across various initial bacterial counts in DHM samples. Microscopic analysis revealed potential protective effects of human milk fat globules and epithelial cells on E. coli , resulting in residual counts of 2–3 log CFU/mL post-treatment. Overall, the maximum temperature during nsPEF treatment was approximately 36°C, highlighting its advantage over thermal pasteurisation, and further optimisation could be conducted to evaluate the potential protective effects of the milk components.
Bioinformatics analysis and experimental validation reveal that heat HSP90AA1 enhances intestinal ischemia reperfusion induced necroptosis by inducing phosphorylated MLKL
Impact of agricultural subsidy on chemical fertilizer use: Empirical evidence of China’s Organic-Substitute-Chemical-Fertilizer policy based on double machine learning
The sustainable development of agriculture hinges on effective fertilizer management, and China’s experience with chemical fertilizer overuse highlights the challenges and opportunities in this domain. This study examines the impact of agricultural subsidy policy on chemical fertilizer use across 2319 counties from 2012 to 2022. By treating the “Action Plan for Organic-Substitute-Chemical-Fertilizer (OSCF) for Fruits, Vegetables and Tea” as a quasi-natural experiment, this study uses a Double Machine Learning model to analyze its effects on fertilizer use and the underlying mechanisms, considering technical and scale efficiency as mediating variables. The findings reveal that the OSCF policy has a significant negative effect on chemical fertilizer use, primarily by enhancing both technical and scale efficiency. This study further reveals regional heterogeneity in the policy’s effectiveness. The results imply that while the impact of the OSCF policy is generally beneficial, it is shaped by regional economic development, agricultural production structure and initial level of fertilizer use. This highlights the importance of tailored policy instruments to address regional disparities in agricultural practices and targeted strategies to maximize the OSCF policy’s impact on sustainable agricultural development. This study provides valuable insights for policymakers and farm managers to enhance the sustainability of agricultural practices.
Multimedia data-driven customer churn prediction using an enhanced extreme learning machine
An exploratory machine learning study on paediatric abdominal pain phenotyping and prediction
Background The exact mechanisms underlying paediatric abdominal pain (AP) remain unclear due to patient heterogeneity. This preliminary study aimed to identify AP phenotypes and develop predictive models to explore associated factors, with the goal of guiding future research. Methods In 13,790 children from a large birth cohort, data on paediatric and maternal demographics and comorbidities were extracted from general practitioner records. Machine learning (ML) clustering was used to identify distinct AP phenotypes, and an ML-based predictive model was developed using demographics and clinical features. Results 1,274 children experienced AP (9.2%) (average age: 8.4 ± 1.1 years, male/female: 615/659), who clustered into three distinct phenotypes: Phenotype 1 with an allergic predisposition (n = 137), Phenotype 2 with maternal comorbidities (n = 676), and Phenotype 3 with minimal other comorbidities (n = 340). As the number of allergic diseases or maternal comorbidities increased, so did the frequency of AP, with 17.6% of children with ≥ 3 allergic diseases and 25.6% of children with ≥ 3 maternal comorbidities. The predictive model demonstrated moderate performance in predicting paediatric AP (AUC 0.67), showing that a child’s ethnicity, paediatric allergic diseases, and maternal comorbidities were key predictive factors. When stratified by ML-predicted probability, observed AP rates were 18.9% in the < 40% group, 44.8% in the 40–50% group, 60.6% in the 50–60% group, and 100.0% in the > 60% group. Conclusions This study identified distinct AP phenotypes and key risk factors using ML. Furthermore, the predictive ML model enabled risk stratification for paediatric AP. These analyses provide valuable insights to guide future investigations into the mechanisms of AP and may facilitate research aimed at identifying targeted interventions to improve patient outcomes.
End-digit preference of readings of grip strength measured with analogue-display devices
Experiences of accessing primary care by those living with long Covid in New Zealand: A qualitative analysis
Background Long Covid is the persistence of symptoms beyond 12 weeks following acute Covid-19 infection. It is estimated to affect one in ten people and can be extremely debilitating. With few publicly funded long Covid clinics, most people rely on primary care providers as a first point of contact. There is currently limited understanding of the experience of accessing primary health care by adults living with long Covid in New Zealand. Purpose To explore the experiences of accessing primary health care by adults living with long Covid. Methods A narrative inquiry approach was used to capture participants lived experiences of accessing primary health care. Zoom interviews and discussions were conducted with study participants. The automatically generated transcripts were reviewed and corrected, and the collated data were analysed using Braun and Clarke’s thematic analysis. Results Eighteen people participated in the interviews. Codes were identified and, through an iterative process, themes were generated, reviewed, and named. The seven themes included lack of upskilling of primary care staff; let down by the Government; self-advocacy and its cost; and throwing money at it. Conclusion(s) The picture painted by participants was bleak with a sense that the world had moved on from Covid-19 and left them behind, with some experiencing a lack of support in primary health care. Reducing the likely long-term health and economic burden of long Covid requires targeted investment and action by Government at every level, along with better utilisation of the allied health workforce in primary care.
Lateral load capacity of flexible piles in the SDMC model with cylindrical cavity expansion theory
Barriers and enablers to help-seeking for common mental disorders among young people in low-income settings: Perspectives from Zimbabwe
Common Mental Disorders (CMDs), such as depression and anxiety are highly prevalent, particularly among young people globally. In Zimbabwe, contributing factors like poverty, unemployment, and the COVID-19 pandemic have exacerbated these challenges. Despite the pressing need for mental health support among young people, there remains a significant knowledge gap on barriers and enablers to help-seeking for CMDs among this demographic. This study addressed this gap by applying the Consolidated Framework for Implementation Research (CFIR) as an analytical framework to explore the unique factors influencing mental health help-seeking among young people in Zimbabwe. Methods We utilised a qualitative research design and conducted 32 semi-structured interviews with young people (15–24 years) across high schools and the Friendship Bench (FB) in Harare between 20 December 2022 and 30 September 2023. Interviews were audiotaped and transcribed verbatim and then coded using an inductive approach to capture patterns grounded in participants’ experiences. Thematic analysis was utilised to develop relevant codes and identify relevant themes. Results Nine themes were generated including six themes related to barriers (factors that hinder help-seeking for CMDs) and three themes related to enablers (factors that facilitate help-seeking for CMDs). Barriers identified include perceived stigma, privacy and confidentiality issues, unavailability of services, lack of awareness, financial challenges and lack of incentives. Enablers identified include raising awareness, implementing school based initiatives and enhancing accessibility and affordability of mental health services. Conclusion This study revealed significant barriers and enablers to help-seeking for CMDs among young people in Zimbabwe. Addressing these multifaceted barriers and leveraging the identified enablers is key to creating supportive systems that encourage young people in low-resource settings to seek and engage with mental health services, ultimately improving their mental wellbeing and overall quality of life.
Homeostatic response of phospholipid pathways to PCYT2 deficiency and impaired de Novo synthesis of phosphatidylethanolamine
Evidence for an indigenous female mouse urobiome
Mice have been used as a valuable model for understanding pathophysiological mechanisms of urinary tract infection for almost six decades. Mice offer many advantages including genetic manipulation to test the role of genes and mechanisms, the availability of germ-free mice, and similarities to humans in innate immune defenses and the strain-dependent presence of vesicoureteral reflux. However, like with humans, the mouse bladder urine above the urinary sphincter has generally been assumed to be sterile. Yet, given the presence of urobiomes in other mammals and the emerging role of the human urobiome in the defense of the urinary bladder and upper urinary tract, the existence of a mouse urobiome should be critically examined as indigenous microbiota may influence experimental results. To determine if an indigenous murine urobiome exists, we obtained voided urine from two sets of female C57BL/6J mice during three different intervals using two different extraction and sequencing methods and analyzed them simultaneously by a single method. For one set, we also obtained urine by suprapubic aspiration, which we compared to the paired voided urine samples. We conclude that an indigenous murine urobiome exists and that voided urine contains post-urethral microbes.
Effects of cold storage on the growth and development of Trichopria drosophilae (Hymenoptera: Diapriidae)
Testing semantic compositionality in baboons (Papio papio) through relearning and generalization
This study investigates whether baboons are capable of semantic compositionality, specifically, whether they can apply compositional rules to new situations (generalization). In language, semantic compositionality is linked to productivity, the generalization of a rule to new combinations. Across four experiments, baboons were trained to match visual stimuli based on either shape or color depending on symbolic cues. Experiments 1–3 tested generalization under different task complexities but consistently failed to show evidence that baboons understood or applied the matching rules beyond memorized combinations. Only in Experiment 4, which used a relearning paradigm rather than generalization, did baboons show improved performance when the rule remained consistent across phases. Four hypotheses were explored to explain the lack of generalization: an iconicity-novelty bias, the possibility that compositionality is present, but that training was not sufficient for generalization, rote memorization of cue-sample pairs, and a difference between implicit and explicit learning. The findings do not allow us to discriminate between these hypotheses.