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Metabolomic analysis of murine tissues infected with Brucella melitensis

Brucella is a gram negative, facultative intracellular bacterial pathogen that constitutes a substantial threat to human and animal health. Brucella can replicate in a variety of tissues and can induce immune responses that alter host metabolite availability. Here, mice were infected with B. melitensis and murine spleens, livers, and female reproductive tracts were analyzed by GC-MS to determine tissue-specific metabolic changes at one-, two- and four- weeks post infection. The most remarkable changes were observed at two-weeks post-infection when relative to uninfected tissues, 42 of 329 detected metabolites in reproductive tracts were significantly altered by Brucella infection, while in spleens and livers, 68/205 and 139/330 metabolites were significantly changed, respectively. Several of the altered metabolites in host tissues were linked to the GABA shunt and glutaminolysis. Treatment of macrophages with GABA did not alter control of B. melitensis infection, and deletion of the putative GABA transporter BMEI0265 did not alter B. melitensis virulence. While glutaminolysis inhibition did not affect control of B. melitensis in macrophages, glutaminolysis was required for macrophage IL-1β production in response to B. melitensis. In summary, these results indicate that Brucella infection alters host tissue metabolism and that these changes could have effects on inflammation and the outcome of infection.

Optimizing key parameters for grinding energy efficiency and modeling of particle size distribution in a stirred ball mill

Abstract Fine grinding using a stirred ball mill can enhance ore liberation but incurs high energy consumption, which can be minimized by optimizing operating conditions. This study explores the impact of key operational parameters—grinding time, stirrer tip speed, solid concentration, and feed size—on grinding efficiency, evaluated using specific energy inputs, in stirred milling of Egyptian copper ore. The particle size distribution (PSD) of ground products was simulated using the Gates–Gaudin-Schuhmann model (GGS) and the Rosin-Rammler-Benne (RRB) function. Taking minimum energy consumption into account, the finest particles (100% ~1 μm) were achieved at the maximum stirrer speed of 500 rpm and a moderate solid concentration of 33.3% after 17 h of grinding, consuming approximately 1225 kWh/t. Experimental data demonstrated a linear correlation between the natural logarithm of the cumulative retained fraction and particle size (µm). The proposed model accurately describes PSDs across different solid concentrations and grinding durations.

Site-selective photo-crosslinking for the characterisation of transient ubiquitin-like protein-protein interactions

Non-covalent protein-protein interactions are one of the most fundamental building blocks in cellular signalling pathways. Despite this, they have been historically hard to identify using conventional methods due to their often weak and transient nature. Using genetic code expansion and incorporation of commercially available unnatural amino acids, we have developed a highly accessible method whereby interactions between biotinylated ubiquitin-like protein (UBL) probes and their binding partners can be stabilised using ultraviolet (UV) light-induced crosslinks. The stabilised protein complexes can be purified using affinity purification and identified by mass spectrometry. The resultant covalent bonds can withstand even the harshest washing conditions, allowing for the removal of indirect binders whilst retaining and capturing weak and transient interactors that are commonly lost during wash steps. This technique is widely applicable and highly effective for identifying site-selective non-covalent interactors. Members of our team have previously demonstrated the benefit of this method using the small ubiquitin-like modifier (SUMO). Here, we provide further proof-of-principle validation of the method and highlight its generality by applying an optimised workflow to a lesser studied UBL, interferon stimulated gene 15 (ISG15). We show that this method is able to capture known ISG15 interactors from a complex protein mixture in a site-selective manner, only capturing proteins that specifically interact with the region of ISG15 where the unnatural amino acid was incorporated. This exquisite degree of sensitivity and specificity greatly improves upon previous screens aimed at identifying downstream non-covalent binders, or readers, of ISG15. Taken together, the approach opens the possibility of characterising previously undetected protein-protein interactions, with the potential of elucidating molecular mechanisms behind the most complex and poorly understood processes in the cell.

Comparison of aesthetic perception and acceptability of silver diamine fluoride staining between Spanish and Italian parents

Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLP

Software-Defined Networks (SDN) provides more control and network operation over a network infrastructure as an emerging and revolutionary paradigm in networking. Operating the many network applications and preserving the network services and functions, the SDN controller is regarded as the operating system of the SDN-based network architecture. The SDN has several security problems because of its intricate design, even with all its amazing features. Denial-of-service (DoS) attacks continuously impact users and Internet service providers (ISPs). Because of its centralized design, distributed denial of service (DDoS) attacks on SDN are frequent and may have a widespread effect on the network, particularly at the control layer. We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. We use the SHAP feature selection technique to improve the detection procedure. By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. Fine-tuning the hyperparameters with the help of Bayesian optimization to obtain the best model performance is another important thing that we do in our model. Two datasets, InSDN and CICDDoS-2019, are utilized to assess the effectiveness of the proposed method, 99.95% for the true positive (TP) of the CICDDoS-2019 dataset and 99.98% for the InSDN dataset, the results show that the model is highly accurate.

Thermophysical analysis of time-dependent magnetized Casson hybrid nanofluid flow (Cu + GO/Kerosene Oil) using Darcy-Forchheimer and thermal radiative models for industrial cooling applications

Abstract This paper presents an in-depth analytical investigation into the time-dependent flow of a Casson hybrid nanofluid over a radially stretching sheet. The study introduces the effects of magnetic fields and thermal radiation, along with velocity and thermal slip, to model real-world systems for enhancing heat transfer in critical industrial applications. The hybrid nanofluid consists of three nanoparticles—Copper and Graphene Oxide—suspended in Kerosene Oil, selected for their stable and superior thermal properties. The theory of Darcy-Forchheimer, along with the suction and injection effect, is applied to refine the flow behaviour and enhance heat transfer efficiency. The governing nonlinear equations are solved using the Homotopy Analysis Method to provide a robust framework for solution accuracy. The graphical and tabulated results demonstrated that hybrid nanofluid outperforms mono and Casson hybrid nanofluids. The result shows that, at a nanoparticle volume concentration of 0.03, the Casson hybrid nanofluid showed a remarkable 19.99% increase in heat transfer, compared to 14.80% for simple nanofluid. The magnetic parameter and thermal radiation parameter further amplify thermal conductivity. This research provided a critical insight into optimizing thermal management systems for advanced engineering applications, positioning hybrid nanofluid as highly effective solutions for next-generation cooling technologies.

The customer satisfaction model in the mobile telecommunications sector after Covid-19 pandemic

The subject of this paper is modeling customer satisfaction in the mobile telecommunication industry following the Covid-19 pandemic. Based on standard customer satisfaction models, a specialized model tailored for the mobile telecommunication industry has been developed to account for its unique characteristics, including market concentration. This model was created within the Slovakian context using the Structural Equation Modelling method. The respondents were customers of all mobile operators in this market. The model revealed a positive relationship between image and perceived service quality and a negative relationship between customer expectations and perceived service value. However, it was not possible to demonstrate a relationship between image and customer loyalty or between customer expectations and customer satisfaction. Therefore, it seems that the factors influencing customer satisfaction in the telecommunications sector of an emerging EU economy differ from those in other sectors and economies in the post-Covid-19 context.

Spatiotemporal dynamics and driving factors of energy-related carbon emissions in the Yangtze River Delta region based on nighttime light data

Impact of nutritional status and pulmonary function on short- and long-term overall survival in hemodialysis patients

Background End-stage renal disease (ESRD) patients frequently experience protein-energy wasting (PEW), which increases their morbidity and mortality rates. Objective This study explores the effects of nutritional status and pulmonary function on the short- and long-term mortality of ESRD patients undergoing hemodialysis. Materials and methods 67 consecutive ESRD patients on maintenance hemodialysis were included in the study. The primary outcomes were all-cause one-year and five-year mortality. Data on demographic characteristics, comorbidities, and laboratory findings were collected. Pulmonary function tests were conducted along with body composition measurements using bioelectrical impedance analysis (BIA). Malnutrition was assessed using the Prognostic Nutritional Index (PNI). Results The median age of the patients was 60.9 ±  12.4 years, with 58.3% being male. Pulmonary function parameters (FEV1 and FVC) were significantly associated with short-term mortality. The PNI was a significant predictor of both short-term and long-term mortality. A PNI score ≤  39.01 was associated with increased short-term mortality (HR: 0.65, 95% CI: 0.48–0.88, p =  0.006), while a score ≤  40 was linked to increased long-term mortality (HR: 0.80, 95% CI: 0.67–0.95, p =  0.015). Additionally, older age (HR: 1.06, 95% CI: 1.01–1.12, p =  0.021) and higher glomerular filtration rate (GFR) (HR: 1.23, 95% CI: 1.02–1.42, p =  0.024) were related to increased long-term mortality risk. Conclusion The study demonstrates that PNI, age, and pulmonary function are critical factors influencing the survival of hemodialysis patients. These findings underscore the importance of comprehensive nutritional and pulmonary assessment to improve clinical outcomes in this population.

Enhancing trace DNA profile recovery in forensic casework using the amplicon RX post-PCR clean-up kit

Distinguishing benign and malignant myxoid soft tissue tumors: Performance of radiomics vs. radiologists

Introduction Benign and malignant myxoid soft tissue tumors have shared clinical, imaging, and histologic features that can make diagnosis challenging. The purpose of this study is comparison of the diagnostic performance of a radiomic based machine learning (ML) model to musculoskeletal radiologists. Methods Manual segmentation of 90 myxoid soft tissue tumors (45 myxomas and 45 myxofibrosarcomas) was performed on axial T1, and T2FS or STIR magnetic resonance imaging sequences. Eighty-seven radiomic features from each modality were extracted. Five ML models were trained to classify tumors as benign or malignant in 40 tumors and then tested with an additional 50 tumors using cross validation. The accuracy of the best ML model based on area under the receiver operating characteristic curve (AUC) was compared to the consensus diagnosis of three musculoskeletal radiologists. Correlation between radiologist confidence (equivocal, probably, consistent with) and accuracy was tested. Results The best ML classifier was a logistic regression model (AUC 0.792). Using T1 + T2/STIR images, the ML model classified 78% (39/50) of tumors correctly at a similar rate compared to 74% (37/50) by radiologists. When radiologists disagreed, the consensus diagnosis classified 50% of tumors (7/14) correctly compared to 86% (12/14) by the ML model, though this did not reach statistical significance. Radiologists had a cumulative accuracy of 91% (30/33) when they rated their confidence ‘consistent with’ compared to 61% (31/51) when they rated their confidence ‘equivocal/probably’ (P = 0.006). For cases when radiologists rated their confidence ‘equivocal/probably’, the ML model had 76% accuracy (39/51). Conclusions A radiomic based ML model predicted benign or malignant diagnosis in myxoid soft tissue tumors similarly to the consensus diagnosis by three musculoskeletal radiologists. Radiologist confidence in the diagnosis strongly correlated with their diagnostic accuracy. Though radiomics and radiologists perform similarly overall, radiomics may provide novel diagnostic utility when radiologist confidence is low, or when radiologists disagree.

Antihypertensive drug-related problems in Vietnamese ambulatory care

The impact of the reform of rural collective property rights system on villagers’ public participation: An empirical study based on CRRS 2020 data

The reform of rural collective property rights system is of great significance for protecting the collective asset rights and interests of villagers, activating rural resource elements, and achieving rural revitalization. This study is based on 284 village committee questionnaires and 7451 villager questionnaires from 10 provinces in China, and uses multi-layer linear regression models to explore the impact of the reform of rural collective property rights system on villagers’ public participation. Research has found that:(1) the reform of rural collective property rights system that has been completed at the rural level can significantly enhance the public participation of villagers, including total participation (β = 0.102, p ≤ 0.1), interest expression (β = 0.044, p ≤ 0.1), and election voting (β = 0.076, p ≤ 0.05). However, the impact on volunteer service is not significant (β = -0.004, p>0.1); (2)mechanism analysis shows that the reform can reduce the probability of migration for work, and thus enhance the level of public participation; (3)heterogeneity analysis reveals that the reform has a greater impact on the villagers’ public participation in suburban villages and villages with better collective economy; (4)further analysis discovers that villagers have heard that reform can significantly enhance their public participation. This study comprehensively explores the spillover effects of the reform at the village level on public participation at the individual level through the use of more accurate measurement indicators, empirical analysis methods, and large-scale authoritative data, providing strong support for proposing strategies to promote villages’ public participation.

Quality of orthodontic care in an academic setting in the Middle East

Examining Mammalian facial behavior using Facial Action Coding Systems (FACS) and combinatorics

There has been an increased interest in standardized approaches to coding facial movement in mammals. Such approaches include Facial Action Coding Systems (FACS), where individuals are trained to identify discrete facial muscle movements that combine to create a facial configuration. Some studies have utilized FACS to analyze facial signaling, recording the quantity of morphologically distinct facial signals a species can generate. However, it is unclear whether these numbers represent the total number of facial muscle movement combinations (which we refer to as facial configurations) that each species is capable of producing. If unobserved combinations of facial muscle movements are communicative in nature, it is crucial to identify them, as this information is important for testing research hypotheses related to the evolution of complex communication among mammals. Our study aimed to assess how well the existing literature represents the potential range of facial signals in two previously studied species: chimpanzees (Pan troglodytes) and domesticated cats (Felis silvestris catus). We adhered to the coding guidelines outlined in the FACS manuals, which are based on the anatomical constraints and capabilities of each mammal’s face, to create our comprehensive list of all potential facial configurations. Using this approach, we found that chimpanzees and domesticated cats may be capable of producing thousands of facial configurations, many of which have not yet been documented in the existing research literature. It is plausible that some of these facial configurations are communicative and could be discovered with further research and video recording. In addition to our findings having significant implications for future research on the communicative complexity of mammals, it can also assist researchers in evaluating FACS coding accuracy.

Efficient anomaly detection in tabular cybersecurity data using large language models

Assessment of health behaviors of primary school teachers based on their nutritional knowledge and physical activity: A cross-sectional study in the Asir Region

Background Primary school teachers play a critical role as educators in imparting healthy eating behaviour and the importance of physical activity to prevent health issues. However, the teachers’ health behaviors have not been studied much, particularly in Saudi Arabia. Understanding these factors is essential to developing interventions that enhance teachers’ well-being and their ability to influence students positively. This study aims to assess nutritional knowledge and physical activity; to determine correlations between health behaviour factors and Body Mass Index (BMI), and evaluate BMI status concerning demographic factors among primary school teachers in the Asir Region, KSA. Methods A cross-sectional design was employed, involving 370 primary school teachers. Data were collected using a self-administered questionnaire that covered sociodemographic details, anthropometric measurements, physical activity levels, and nutritional knowledge. Statistical analyses included Pearson’s correlation and Chi-square tests, with significance at p < 0.05. Results Among the participants, 38.6% were overweight, and 33.5% were obese, with 76% of married teachers classified as overweight or obese. Only 9.0% exhibited excellent nutritional knowledge, while 25.0% demonstrated poor knowledge. The majority (84.0%) engaged in low or minimal physical activity. A weak but significant correlation was observed between nutritional knowledge and BMI (p < 0.05), whereas no significant associations were found between physical activity levels and either BMI or nutritional knowledge. Conclusion The study highlights critical gaps in nutritional knowledge and physical activity among teachers, emphasizing the need for targeted school-based health education programs. Improving teacher health behaviors could enhance their well-being and enable them to act as positive role models for their students.

High-performance triboelectric nanogenerators based on Ag-doped ZnO loaded electrospun PVDF nanofiber mats for energy harvesting and healthcare monitoring

Abstract This study investigates the potential of zinc oxide (ZnO) and Ag-doped zinc oxide (Ag-ZnO) nanoparticles (NPs) (1, 3 and 5 wt%) electrospun into poly(vinylidene fluoride) (PVDF) based triboelectric nanogenerators (TENGs) to harness electrical energy from ambient mechanical vibrations. ZnO and Ag-ZnO NPs were developed using a co-precipitation method. 3 wt% Ag-ZnO doping was optimized to exhibit a higher β-crystalline phase in PVDF (PAZ3). The electrospun ZnO and Ag-ZnO loaded PVDF nanofiber mats (NFMs) served as tribo-negative layer, while thermoplastic polyurethane (TPU) served as tribo-positive layer in the TENG device fabrication. The electrical measurement results revealed that pristine PVDF/TPU-based TENG device exhibited lower performance (V oc = 9.0 V and I sc = 0.6 µA) compared to the optimized PAZ3/TPU-based TENG (V oc = 51 V and I sc = 1.2 µA), and utilized for concurrent applications such as powering over 10 light-emitting diodes, energy harvesting and healthcare monitoring applications. Overall, the study not only reveals the significant potential of PAZ3/TPU-based TENG for excellent output performance, but also provides a new insight into next-generation clean, affordable and sustainable energy harvesting materials.

Low-power artificial neuron networks with enhanced synaptic functionality using dual transistor and dual memristor

Artificial neurons with bio-inspired firing patterns have the potential to significantly improve the performance of neural network computing. The most significant component of an artificial neuron circuit is a large amount of energy consumption. Recent literature has proposed memristors as a promising option for synaptic implementation. In contrast, implementing memristive circuitry through neuron hardware presents significant challenges and is a relevant research topic. This paper describes an efficient circuit-level mixed CMOS memristor artificial neuron network with a memristor synapse model. From this perspective, the paper describes the design of artificial neurons in standard CMOS technology with low power utilization. The neuron circuit response is a modified version of the Morris-Lecar theoretical model. The suggested circuit employs memristor-based artificial neurons with Dual Transistor and Dual Memristor (DTDM) synapse circuit. The proposed neuron network produces a high spiking frequency and low power consumption. According to our research, a memristor-based Morris Lecar (ML) neuron with a DTDM synapse circuit consumes 12.55 pW of power, the spiking frequency is 22.72 kHz, and 2.13 fJ of energy per spike. The simulations were carried out using the Spectre tool with 45 nm CMOS technology.

Advances and prospects of cell-penetrating peptides in tumor immunotherapy