Browse Articles

Continuous or discrete magnitudes? A comparative study between cats, dogs and humans

A long-standing question in the study of quantity discrimination is what stimulus properties are controlling choice. While some species have been found to do it based on the total amount of stimuli and without using numerical information, others prefer numeric rather than any continuous magnitude. Here, we tested cats, dogs, and humans using a simple two-way spontaneous choice paradigm (involving food for the first two, images for the latter) to see whether numerosity or total surface area has a greater influence on their decision. We found that cats showed preference for the larger amount of food when the ratio between the stimuli was 0.5, but not when it was 0.67; dogs did not differentiate between stimuli presenting the two options (smaller vs. larger amount of food) regardless of the ratio between them, but humans did so almost perfectly. When faced with two stimuli of the same area but different shapes, dogs and humans exhibited a preference for certain shapes, particularly the circle, while cats’ choices seemed to be at chance level. Furthermore, cats’ and dogs’ reaction times were equal across conditions, while humans were quicker when choosing between stimuli in trials where the shape was the same, but the surface area was different, and even more so when asked to choose between two differently sized circle shapes. Results suggest that there is no universal rule regarding how to process quantity, but rather that quantity estimation seems to be tied to the ecological context of each species. Future work should focus on testing quantity estimation in different contexts and different sources of motivation.

Real time fault diagnosis in industrial robotics using discrete and slantlet wavelet transformations

LncRNA RP11-818O24.3 regulates proliferation and differentiation of hair follicle stem cells by targeting FGF2/PI3K/AKT pathway

Hair follicle stem cells (HFSCs) play critical roles in adult hair regeneration, owing to its self-renewal and multipotent differentiation properties. Emerging evidence has shown that long noncoding RNAs (LncRNAs) are implicated in biological processes such as proliferation, differentiation and apoptosis. However, the specific role of LncRNA RP11-818O24.3 in regulating HFSCs remains unclear. To explore the effect of LncRNA RP11-818O24.3 on HFSCs, stable LncRNA RP11-818O24.3 overexpression and knockdown HFSCs were established using a lentivirus vector system. The effect of LncRNA RP11-818O24.3 on proliferation was evaluated by Cell Counting Kit-8 (CCK8) and EdU incorporation experiments. The differentiation of HFSCs into neurons and keratinocyte stem cells was detected by immunofluorescence staining. We showed that LncRNA RP11-818O24.3 overexpression promoted the proliferation and inhibited cell apoptosis in HFSCs. High levels of LncRNA RP11-818O24.3 promoted the differentiation of HFSCs into CD34+K15+ keratinocyte progenitors and CD34+Nestin+neuron-specific enolase (NSE)+ neural stem cells. Additionally, LncRNA RP11-818O24.3 increased fibroblast growth factor 2 (FGF2) expression and the subsequent activation of the PI3K/AKT signaling pathway. These data demonstrated that LncRNA RP11-818O24.3 promotes self-renewal, differentiation, and the capability to inhibit apoptosis of HFSCs via FGF2 mediated PI3K/AKT signaling pathway, highlighting its potential role as a therapeutic strategy for treating hair loss diseases.

Efficacy of antimicrobial photodynamic therapy with chitosan nanoparticles for decontamination of dental implants contaminated with Aggregatibacter actinomycetemcomitans

Seeking validation in the digital age: The impact of validation seeking on self-image and internalized stigma among self- vs. clinically diagnosed individuals on r/ADHD

The digital age has fueled a surge in ADHD self-diagnosis as people turn to online platforms for mental health information. However, the relationship between validation-seeking behaviors and self-perception in these online communities and users’ self-perception has received limited scholarly focus. Drawing on self-verification theory and utilizing natural language processing to analyze 452,026 posts from the r/ADHD subreddit, our study uncovers distinct patterns in validation-seeking behaviors. Results show that (a) self-diagnosed individuals with ADHD are more likely to seek social validation and media validation and to report higher levels of negative self-image and internalized stigma than clinically diagnosed individuals, (b) social validation was strongly associated with both positive and negative self-perceptions; and (c) diagnosis status significantly moderated these relationships, such that the effects of social validation on self-image and stigma were consistently weaker for the self-diagnosed group. Theoretically, this study extends self-verification theory by demonstrating that professional verification hierarchically moderates self-verification effectiveness. This implies a practical need for clinicians to acknowledge online validation seeking and for digital communities to affirm user experiences while mitigating stigma.

Safety and effectiveness of catheter ablation of atrial fibrillation in patients with mitral valve replacement mechanical versus bioprosthetic valves

“Skills for Resilience in Farming”; an evidence-based, theory driven educational intervention to increase mental health literacy and help-seeking intentions among Irish farmers

While mental health literacy is an important component to successful help-seeking, rural populations often face gaps in both knowledge and service provision. Informed by the Theory of Planned Behaviour and Self-Efficacy Theory, we designed the ‘Skills for Resilience’ as a brief, once-off, community-based educational intervention to increase Irish farmers’ mental health literacy and help-seeking intentions. We adopted a quasi-experimental between (group: intervention and control) and within-group design (time: baseline [T1], immediately post-intervention [T2], and ≥ 1 month post-intervention [T3]). A total of 72 participants (intervention n = 37; control n = 35) were recruited from knowledge-sharing discussion groups. Although recruitment was also open to women, all discussion groups consisted of men. A trained facilitator delivered a discussion lasting between 30 and 90 minutes. Five intervention participants also participated in a qualitative interview after T3. Our results identified intervention participants’ mental health literacy increased significantly at T2 and T3 compared to T1, but did not increase between T2 and T3. Mental health literacy was also significantly greater in the intervention group compared to the control group at T2 and T3. Help-seeking intentions and self-efficacy in seeking mental healthcare also increased significantly at T2 compared to T1, but did not increase between T1 and T3 or T2 and T3. There were no significant changes in outcome measures for the control group at any time point. Through reflexive thematic analysis we identified that the intervention also addressed stigma against mental health (Theme 1) and provided important resources for participants and their community’s present and future coping (Theme 2). At T3, 100% of participants enjoyed the discussion and would recommend the intervention to other farmers. This intervention provides a successful example of integrating the Theory of Planned Behaviour and Self-Efficacy Theory to improve mental health literacy in farmers using a brief, educational intervention.

Energy metabolism characteristics of sprinters in speed endurance training with different intermittent rest periods

RETRACTED: Shaping environmental responsibility through virtual wilderness: The key role of aesthetic psychology

Enhanced fault protection coordination in wind-solar distribution grids utilizing improved hyper spherical search based optimization

Bacteria isolated from the grape phyllosphere capable of degrading guaiacol, a main volatile phenol associated with smoke taint in wine

Recent wildfires near vineyards in the Pacific United States have caused devastating financial losses due to smoke taint in wine. When wine grapes (Vitis vinifera) are exposed to wildfire smoke, their berries absorb volatile phenols derived from the lignin of burning plant material. Volatile phenols are released during the winemaking process giving the finished wine an unpleasant, smokey, and ashy taste known as smoke taint. Bacteria are capable of undergoing a wide variety of metabolic processes and therefore present great potential for bioremediation applications in many industries. In this study, we identify two strains of the same species that colonize the grape phyllosphere and are able to degrade guaiacol, a main volatile phenol responsible for smoke taint in wine. We identify the suite of genes that enable guaiacol degradation in Gordonia alkanivorans via RNAseq of cells growing on guaiacol as a sole carbon source. Additionally, we knockout guaA, a cytochrome P450 gene involved in the conversion of guaiacol to catechol; ΔguaA cells cannot catabolize guaiacol in vitro, providing evidence that GuaA is necessary for this process. Furthermore, we analyze the microbiome of berries and leaves exposed to smoke in the vineyard to investigate the impact of smoke on the grape microbial community. We found smoke has a significant but small effect on the microbial community, leading to an enrichment of several genera belonging to the Bacilli class. Collectively, this research shows that studying microbes and their enzymes has the potential to identify novel tools for alleviating smoke taint.

Correction: A comprehensive analysis of YOLO architectures for tomato leaf disease identification

Study on the environmental adaptation characteristics of red panda release into the wild

Animals undergo a cognitive process when exposed to novel environments, which plays a crucial role in their ability to identify optimal habitats and support long-term survival. We conducted an initial investigation into the spatial utilization and habitat selection patterns of a femal red panda using GPS collar technology. Our research revealed that the home range and core activity area of the red panda was larger during initial 60 days after release, and markedly decreased thereafter. The red panda’s selection of altitude did not align with that of wild individuals until 60 days after release, whereas slope selection may require at least 30 days to stabilize and become consistent with wild individuals’ patterns. Our study further revealed that the home range and core activity area of the red panda showed low overlap with the suitable habitat of the wild population during the initial two months; however, this overlap increased significantly, reaching over 90% thereafter. We hypothesize that the red panda may require at least 60 days to acclimate to its new environment after release. Although our study was only based on a single individual, it provides the first evidence of the environmental adaptation process of red panda following released into the wild, thereby establishing a crucial foundation for future conservation and reintroduction initiatives.

Describing variability of intensively collected longitudinal ordinal data with latent spline models

Abstract Population health studies increasingly collect longitudinal, patient-reported symptom data via mobile devices, offering unique insights into experiences outside clinical settings, such as pain, fatigue or mood. However, such data present challenges due to ordinal measurement scales, irregular sampling and temporal autocorrelation. This paper introduces two novel summary measures for analysing ordinal outcomes: (1) the mean absolute deviation from the median (Madm) for cross-sectional analyses and (2) the mean absolute deviation from expectation (Made) for longitudinal data. The latter is based on a latent cumulative model with penalized splines, enabling smooth transitions between irregular time points while accounting for the ordinal nature of the data. Unlike black-box machine learning approaches, this method is interpretable, computationally efficient and easy to implement in standard statistical software. Through simulations, we demonstrate that the proposed measures outperform standard methods when the assumptions of normality or stationarity are violated. Application to real-world data from a national smartphone study, Cloudy with a Chance of Pain, highlights the utility of these measures in characterising symptom variability and trends over time. The methods developed here provide intuitive tools for analysing patient-reported outcomes in longitudinal studies, with potential applications in prediction modelling, causal discovery and evaluation of interventions.

Analysis of variable-order fractional enzyme kinetics model with time delay

Abstract In enzymatic reactions, studying reaction rates and mechanisms helps us understand how concentration, temperature, and catalysts influence the speed of chemical transformations. This field is critical for optimizing processes in biotechnology, pharmaceuticals, and food industries. Traditional enzyme kinetics models may overlook the influence of past system states. In this paper, we propose a variable-order Caputo fractional derivative enzyme kinetics model that incorporates constant time delays to capture memory effects and nonlocal behavior more accurately. We establish the existence and uniqueness of solutions using fixed-point theory. The proposed model stability is analyzed through Ulam–Hyers and generalized Ulam–Hyers concepts. A robust and an effective numerical approach is employed to reveal the intricate dynamics of the model and demonstrate the significance of the variable-order Caputo fractional derivative with time delay. Incorporating a delay term and employing the variable-order Caputo fractional derivative, this model refines conventional enzyme kinetics, leading to a more precise characterization of biological catalytic processes.

Comparative study on caesarian and normal vaginal delivery, Rajshahi Division, Bangladesh

Comprehensive assessment of privacy security of financial services in cloud environment

Integrated genomics and morphological approach reveals interspecific gene flow cases and decodes the origin of selected feathergrasses (Poaceae, Stipa)

Abstract Central Asia is a diversity hotspot of arid-adapted grasses from the genus Stipa , with approximately 100 taxa found in the region. Recent studies in the steppe areas of Kazakhstan revealed specimens displaying intermediate morphology, distinguishing them from other taxa that grow sympatrically. Using integrative taxonomy, we investigated whether these individuals resulted from natural speciation or hybridisation, and if so, we would like to know which species were involved in this process feathergrasses. Research conducted in steppes of central Kazakhstan (Kyzylorda region), revealed the existence of individuals morphologically intermediate between S. arabica and S. richteriana , suggesting that these are probably of hybrid origin. Morphology and SNP markers validated the specimens as F1 hybrid between the aforementioned species by cladding separately based on neighbor-joining phylogenetic tree. Moreover, genetic structure displayed a separate cluster and showed almost equal genetic admixture between S. arabica and S. richteriana . Additionally, fastStructure analysis detected two geographically separated cryptic genotypes within S. richteriana population and their involvement in the hybridisation resulted in occurrence of S . × heptapotamica , S. × czerepanovii and S . × korshinskyi which recently were suggested as hybrids. Based on these evidences, we described a new nothospecies S . × kyzylordensis , as F1 hybrid. Furthermore, morphologically, the nothospecies delimited with other hybrids in Kazakh steppe area, marking the first report of hybridisation between S. arabica and S. richteriana , along with molecular evidence for the origin of further species supposed to be hybrids. This finding is crucial to understanding species diversity and hybridisation process in morphologically and genetically distant Stipa species.

Digital literacy’s impact on digital village participation in rural left-behind women through serial mediation of political trust and self-efficacy

A hybrid deep learning model for detection and mitigation of DDoS attacks in VANETs

Abstract Intelligent transport systems are increasing in application for real-time communication between vehicles and the infrastructure, and along with that are increasing the popularity of vehicular ad-hoc networks (VANETs). However, the very open and dynamic environment gives rise to varied kinds of DDoS attacks that can disrupt safety–critical services. The existing mechanisms for detection of DDoS attacks in VANETs have been found to suffer from low efficacy of detection, high magnitude of false alarm rates, and poor adaptability to evolving patterns of attacks. To address this challenge, this paper introduces VANET-DDoSNet++, a novel, multi-layered defense framework that uniquely integrates optimized feature selection, advanced deep learning detection, adaptive reinforcement learning mitigation, and secure blockchain-based reporting. The preprocessing step ensures high quality of data by dealing with missing values, removing outliers, augmenting the data, and detecting outliers effectively, preparing for analysis. The features including network traffic statistics, spatiotemporal data, deep traffic embeddings, and behavioural patterns are extracted. To improve the detection performance, a hybrid selection strategy is introduced featuring an adaptive dragonfly algorithm (ADA) and an Enhanced grasshopper optimization algorithm (EGOA) for feature selection where the optimal features are determined. Finally, the detection part applies a hybrid architecture of deep learning referred to as VANET-DDoSNet++, where convolutional LSTM networks, attention layers, and residual/dense connections are used for reliable DDoS detection. An adaptive reinforcement learning-based intrusion mitigation approach with reward shaping tailors defense strategies dynamically with evolving attack vectors by all means. The decentralized trust management mechanism based on blockchain is intended for a secure and verifiable real-time threat reporting from vehicles. The CIC-DDoS2019 dataset, which includes real-world vehicular traffic data with modern reflective DDoS attacks, is utilized for evaluation. The experimental results show that VANET-DDoSNet++ surpasses other currently existing methodologies achieving 98.04% accuracy with 70% training data and 99.18% with 80% training data besides dramatically reducing false positive and negative rates as well as improving overall precision, F1-score, sensitivity, and specificity. The factor deals with the evolution of DDoS attacks whereas VANET networks offer a dynamic and secure intrusion detection and mitigation framework.