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Paraoxonase-2 in osteosarcoma: potential for the development of a targeted anticancer therapy
No full textShort Stem vs. Standard Stem in Primary Total Hip Replacement: A Perioperative Prospective Invasiveness Study with Serum Markers
Get PDFBackground: Total hip arthroplasty (THA) is a well-established surgical procedure for end-stage hip arthrosis. Innovations such as minimally invasive approaches and new technologies have improved outcomes and reduced invasiveness. The introduction of short-stem prostheses, which offer potential benefits in bone preservation, has been a significant development in recent years. This prospective case series study aims to compare invasiveness of the short-stem (SS) and conventional-stem (CS) prostheses in THA with a posterolateral approach (PLA) by assessing perioperative serum markers. Methods: A prospective case series was conducted involving consecutive patients who underwent primary THA from January 2022 to December 2023. Demographics and preoperative, postoperative day 1 (POD1), and postoperative day 2 (POD2) serum levels of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), and white blood cells (WBCs) were measured. Results: The study included 21 patients with CS and 19 with SS, with no significant differences between groups in demographic. No statistically significant differences were found in serum markers between SS and CS groups at any time point. Both groups showed significant increases in ESR, CRP, and PCT from preoperative levels to POD2 (p < 0.001), while WBC values increased from preoperative to POD1 but decreased between POD1 and POD2. Conclusion: The short-stem prosthesis does not exhibit significantly different perioperative serum marker profiles compared to the conventional stem, suggesting similar levels of surgical invasiveness between the two implants. Further studies with larger sample sizes are needed to validate these findings and explore other aspects of short-stem THA
Interpretability and reliability-driven knowledge distillation for non-intrusive load monitoring on the edge
Get PDFThe deployment of deep neural networks (DNNs) on resource-constrained edge devices necessitates efficient, low-complexity algorithms. Knowledge distillation (KD) addresses this through a student-teacher paradigm, transferring knowledge from complex teacher models to simpler student models. Current KD methods often optimize student performance without adequately addressing the reliability and interpretability of transferred knowledge, thus presenting challenges in maintaining both robustness and decision transparency. This paper introduces an Interpretability and Reliability-driven Knowledge Distillation (IR-KD) framework that enhances teacher model interpretability through perception-aligned gradients while leveraging hidden information from weak labels to optimize knowledge transfer. Our approach ensures compressed models remain computationally efficient while improving interpretability, which is essential for trustworthy edge AI deployment. We demonstrate improved predictive performance and model interpretability in non-intrusive load monitoring (NILM) applications as a case study. Quantitative explainability metrics confirm that perception-aligned gradients provide more faithful explanations, validating our approach's effectiveness in developing reliable and transparent edge AI systems
DdRAD sequencing and morphometric data analysis reveal a clear differentiation among the Tunisian populations of Onopordum nervosum ssp. platylepis Murb
No full textOnopordum nervosum ssp. platylepis Murb, an endemic plant in Tunisia, Algeria, and Libya, with spiny leaves and distinctive spiny-winged stems from the Asteraceae (Compositae) family, is used as a vegetable rennet in the production of traditional cheese. In Tunisia, the species is threatened and occurs in fragmented small populations isolated from each other. This study aims to evaluate the germplasm diversity of wild Onopordum platylepis to support future breeding efforts and aid in discriminating among populations with interesting phenotypic traits. Morphological characterization was conducted on five populations identified in the North (Tunis, Nabeul) and the center of Tunisia (Sousse, Kairouan, Monastir) to identify relationships among them. This involved adapting certain UPOV (International Union for the Protection of New Varieties of Plants) descriptors for Cynara cardunculus L. (cardoon) along with additional specific traits. Results revealed significant differences among the studied populations for the majority of traits with Kairouan (KN) population producing the greatest amount of biomass and flower heads. Additionally, a double-digest restriction-associated DNA sequencing study was conducted on O. platylepis individuals to confirm population differentiation. Results showed that Kairouan (KN) population provided the highest genetic diversity (pi = 1.5 x 10-3). Comparisons of pairwise Fst values between populations ranged from 0.1 to 0.13 and negative inbreeding coefficient (FIS) values were observed. According to our findings, the studied populations revealed important differentiation and maintained a moderate level of genetic diversity through frequent outcrossing or sequential gene flow. Both morphological and molecular analyses unveiled a distinct correlation between population structure and geographical distribution. Two distinct clusters were identified for the populations from the north and the center, suggesting that spatial segregation has resulted in lower genetic relatedness between these populations. In this context, it is important to monitor and reduce anthropogenic activity to protect genetic diversity, as well as ensuring the resilience of O. platylepis to environmental and climatic changes
Insights into pedigree- and genome-based inbreeding patterns in Martina Franca donkey breed
Get PDFConserving small, endangered equine populations demands tools that capture both recent and historical inbreeding more accurately than pedigree alone. The Martina Franca donkey, a large indigenous breed from southern Italy that approached extinction in the 1980s, offers a relevant model for conservation genetics in equids. Here, we combine pedigree- and genome-based approaches to quantify inbreeding, disentangle its temporal components via runs of homozygosity (ROH), and highlight putative selection targets to inform management. We sampled 101 studbook-registered animals (70 females, 31 males) and generated genome-wide data by the double-digest restriction-site associated DNA sequencing, retaining 21 280 high-quality Single Nucleotide Polymorphisms after filtering. We estimated pedigree inbreeding (FPED1) and genomic indices based on excess homozygosity (FHET, FHAT1–3) and ROH-derived genomic inbreeding (FROH). FPED1 ranged from 0.029 to 0.245 (mean 0.114), whereas FROH ranged from 0.006 to 0.316 (mean 0.147), with weak concordance between pedigree and genomic metrics, consistent with incomplete pedigrees or founder effects. Across individuals, we detected 4 433 ROH segments; medium-to-long segments (4–16 Mb) predominated, indicating substantial recent inbreeding, while very short ROH (< 2 Mb) were rare (about 2%), suggesting limited ancient autozygosity. ROH were unevenly distributed across the genome: chromosomes 2 and 3 harboured the most segments, whereas chromosome 18 had the fewest. We identified ROH islands on chromosomes 2, 6, 8, 12, 13, and 19; candidate genes therein included SHH (development), EPAS1 (hypoxia response), OPRK1 (stress response), and BIRC5 (apoptosis/cell cycle), pointing to historical pressures on development, resilience, and reproduction. Overall, genomic measures—particularly FROH—provided a finer-grained portrait of autozygosity than pedigree alone and revealed focal regions likely shaped by selection. These results deliver actionable guidance for breeding schemes seeking to limit further inbreeding while preserving adaptive variation, and they illustrate how genomic surveillance can bolster conservation strategies for endangered donkey breeds and other small equid populations worldwide
Investigation into Knowledge and Adherence To Vaccination and Screening Campaigns among Immigrants in the Marche Region, Central Italy
Get PDFPromoting preventive health through vaccination and screening is a key public health goal. Immigrant populations often show lower uptake compared to the general population, highlighting the need for targeted interventions. This study examines knowledge and adherence to vaccination and screening programs among immigrants residing in the Marche Region, Central Italy. To evaluate the socio-economic and cultural characteristics of immigrants, as well as their participation in vaccination and screening programs. A structured questionnaire was developed ad hoc. Data collection was conducted via assisted interviews with a convenience sample of immigrants in the Province of Pesaro-Urbino. The study sample was mostly female 59 (84.3%), with participants primarily from Ukraine 30 (42.9%) and Moldova 15 (21.4%). The average age was 53 years, and they had lived in Italy for 17 years on average. The majority of immigrants had a high school diploma or higher degree 56 (52.9%). Most immigrants 66 (96%) reported receiving all mandatory childhood vaccinations in their country of origin, however, vaccination coverage declined after migration to Italy. Although 89% of participants were aware of disease prevention through screening, only 27% had participated in screening programs in their country of origin, whereas, 71% had a cancer screening in Italy, primarily through public health invitations. Screening and vaccine hesitancy were mainly linked to lack of information or absence of invitations (76%), time constraints (14%), low trust in screening efficacy (7%). This study highlights the low adherence to vaccination programs among the immigrant population in Italy, emphasizing the need for policymakers and health authorities to design local level interventions to improve communication strategies, enhance healthcare accessibility and ultimately contribute to better health outcomes, cost savings, and more equitable healthcare systems
Interface-Induced Synaptic Performance in CeO2/La0.8Ba0.2MnO3Oxygen Reservoir Junction
Get PDFRealizing next-generation intelligent applications requires novel resistive switching devices that can operate with low power, high stability, and desired neuromorphic performance. La0.8Ba0.2MnO3 (LBMO), a functional complex oxide exhibiting a room-temperature metal–insulator transition, shows promise in this context. In this work, we demonstrate interface-engineered resistive switching in the LBMO thin film junction by introducing an ultrathin CeO2 insertion layer. Compared to bare LBMO film, which requires higher forming voltages and suffers from limited stability and large cycle-to-cycle variability, the CeO2/LBMO (LBC) device exhibits stable, low-power bipolar resistive switching. The LBC device achieves a low forming voltage of 2.2 V, an ON/OFF ratio of ∼102, endurance of 600 switching cycles, and data retention of 103 seconds. The improved performance is attributed to controlled oxygen vacancy migration and redistribution facilitated by the CeO2 interlayer. Furthermore, the LBC device displays, for the first time, bioinspired synaptic behaviors, such as gradual potentiation and depression under pulsed stimuli, and exhibits linear plasticity under nonidentical pulse schemes, effectively emulating synaptic weight modulation. Our results demonstrate an interface-induced resistive switching device as a compelling candidate for next-generation neuromorphic components
Hop biomass waste against fire blight and black rot: an eco-friendly approach to crop protection
Get PDFHop is a perennial, deciduous climbing plant best known for its use in beer production. Recently, in line with the Green Strategy of Agenda 2030 and increasing market demand, research and industry interest in natural products has grown. In this context, hop biomass waste (HW), such as leaves, stems and non-standardized cones discarded after cone harvesting, has been shown to contain a wealth of bioactive compounds. These compounds offer promising applications in several productive sectors, including the pesticide industry. This study aims to evaluate the effect of extracts obtained from powdered HW (HWP) on two quarantine phytopathogenic bacteria: Erwinia amylovora (EA), the causal agent of fire blight in Rosaceae, and Xanthomonas campestris pv. campestris (Xcc), the causal agent of black rot in crucifers. Both diseases are highly destructive and economically impactful, necessitating the search for effective and eco-friendly alternatives. Various extracts were prepared, including two hydroalcoholic ones (ethanol/water 80:20 and 70:30), one ethanolic extract, and one methanolic extract. The potential mechanisms of action were investigated, with a focus on bacterial membrane permeability, biofilm formation and removal, and antioxidant activity, assessed using the Folin–Ciocalteu test. The in vitro analyses demonstrated that HWP extracts exhibit significant antibacterial activity against both EA and Xcc by altering bacterial membrane permeability, inhibiting biofilm formation, and promoting biofilm removal. The Folin–Ciocalteu test confirmed the presence of polyphenols, suggesting antioxidant activity. In planta tests provided realistic insights into the interaction between plants, bacteria, and extracts, showing promising results for the use of HWP extracts in controlling these phytopathogens. The study highlights the high efficacy, eco-friendliness, and sustainability of low-cost HWP extracts as a potential strategy for controlling fire blight and black rot. HWP extracts could serve as an alternative to traditional synthetic pesticides and antibiotics, promoting more sustainable and environmentally friendly agricultural practices, in line with Sustainable Development Goal 2 of the 2030 Agenda
Analytical solutions for piles subjected to a passive load
No full textThe paper analyses a single free-head flexible passive pile embedded in a soil profile consisting of an unstable layer overlying a stable one. The pile segment in the unstable layer is subjected to a linear distribution of the load, whereas the pile response is evaluated by assuming a uniform modulus of subgrade reaction in the stable layer. Closed-form expressions are derived for the pile head deflection and tabulated values are provided to evaluate the maximum bending moment as a function of dimensionless length of the pile segment in the unstable and stable layers. Analytical solutions for infinitely flexible and rigid piles have been obtained as special cases of the general solution. A novel rational rigidity criterion for passive piles is also proposed. A numerical example is finally presented to illustrate the application of the derived solution
Groundwater level forecasting using data-driven models and vadose zone: A comparative analysis of ARIMA, SARIMAX, Prophet, and NeuralProphet
Get PDFForecasting water availability is becoming increasingly vital due to rising human demands and changing climatic pressures have caused declines in groundwater levels across many regions. While numerous studies employ data-driven approaches to predict groundwater fluctuations using meteorological data and groundwater level observations, few incorporate measurements from the vadose zone into predictive models. This study proposes a novel method leveraging an advanced hydrogeological monitoring system with high spatio-temporal resolution to forecast groundwater levels in a shallow alluvial aquifer used for drinking purposes. The monitoring system comprises a thermo-pluviometric station and three probes that measure soil water content, electrical conductivity, and temperature at depths of 0.6, 0.9, and 1.7 meters, in addition to a piezometer with a permanent water-level sensor. Data was collected at 15 min intervals over two hydrological years and integrated as exogenous inputs to enhance model predictive performance. Statistical, machine learning and deep learning architectures were tested through ARIMA, SARIMAX, Prophet and NeuralProphet providing a comprehensive evaluation of different approaches. For a robust evaluation, a rolling K-fold cross-validation strategy was implemented and coupled with a grid search to fine-tune all the models. Evaluation metrics and correlation coefficients are employed to assess the predictive capabilities of each model. Our findings indicate that prediction accuracy improves across all models with increasing depth in the vadose zone, with machine learning and deep learning models showing the most significant improvements. Specifically, at 1.7 m depth, Prophet achieves a MAPE of 4.5%, and NeuralProphet achieves a MAPE of 4.1% compared to statistical models. This study has successfully highlighted the enhancement of AI-based models for estimating levels of groundwater incorporating subsurface information from the vadose zone at different depths and phreatic zones, alongside climatic variables