Intranasal insulin treatment improves memory and learning in a rat amyloid-beta model of Alzheimer’s disease

Recently, insulin has been used as a pro-cognitive agent for the potential treatment of Alzheimer’s disease (AD), because of its ability to cross the brain–blood barrier (BBB) by a saturable transport system. This study has been designed to evaluate the effects of intranasal insulin regimen, as a bypass system of BBB, on spatial memory in amyloid-beta (Aβ) model of AD in rat. Unilateral infusion of Aβ25–35 (10 nmol/2 µl/rat) into the lateral ventricular region of brain was used to produce a rat model of AD. After a 24-h recovery period, rats received insulin or vehicle via intraperitoneal or intranasal route (0.1, 0.2, and 0.3 IU) for 14 days. Memory function in rats was assessed by Morris water maze test, with 5 days of training and consequent probe test protocol. Different doses of intraperitoneal insulin did not have a significant effect on learning and memory in AD rats. However, intranasal insulin at doses of 0.2 and 0.3 IU improved the learning and memory in Aβ-received rats. In conclusion, intranasal insulin as a non-invasive strategy improves spatial learning and memory in AD model

The Effect of Si and Extrusion Process on the Microstructure and Tensile Properties of Mg-Mg2Si Composite

AbstractThis investigation has been carried out to study the influence of extrusion process on microstructure and tensile properties of Mg-Mg2Si composite with different amounts of Si (3, 5 and 7wt.%). Microstructural examination was carried out using optical microscopy (OM). The cast specimens were extruded at 350°C at different extrusion ratios (6:1, 12:1 and 18:1). The results showed that the specimens with lower Si contents, have higher ultimate tensile strength (UTS) and tensile elongation values. Moreover, there was a considerable improvement in tensile properties for the specimens extruded with the ratio of 12:1 and 18:1 in comparison to the specimens of 6:1. This can be attributed to the significant reduction in size of primary Mg2Si particles from more than 200μm to 20μm, 10μm and 5μm after extrusion with the ratio of 6:1, 12:1 and 18:1, respectively. The highest UTS values were found to be 265MPa for extruded with 1:18 ratio of Mg-3Si composite

Crowd-Certain: Label Aggregation in Crowdsourced and Ensemble Learning Classification

Crowdsourcing systems have been used to accumulate massive amounts of labeled data for applications such as computer vision and natural language processing. However, because crowdsourced labeling is inherently dynamic and uncertain, developing a technique that can work in most situations is extremely challenging. In this paper, we introduce Crowd-Certain, a novel approach for label aggregation in crowdsourced and ensemble learning classification tasks that offers improved performance and computational efficiency for different numbers of annotators and a variety of datasets. The proposed method uses the consistency of the annotators versus a trained classifier to determine a reliability score for each annotator. Furthermore, Crowd-Certain leverages predicted probabilities, enabling the reuse of trained classifiers on future sample data, thereby eliminating the need for recurrent simulation processes inherent in existing methods. We extensively evaluated our approach against ten existing techniques across ten different datasets, each labeled by varying numbers of annotators. The findings demonstrate that Crowd-Certain outperforms the existing methods (Tao, Sheng, KOS, MACE, MajorityVote, MMSR, Wawa, Zero-Based Skill, GLAD, and Dawid Skene), in nearly all scenarios, delivering higher average accuracy, F1 scores, and AUC rates. Additionally, we introduce a variation of two existing confidence score measurement techniques. Finally we evaluate these two confidence score techniques using two evaluation metrics: Expected Calibration Error (ECE) and Brier Score Loss. Our results show that Crowd-Certain achieves higher Brier Score, and lower ECE across the majority of the examined datasets, suggesting better calibrated results.Comment: 49 pages, 5 figure

Toward the detection of the triatomic negative ion SPN−: Spectroscopy and potential energy surfaces

High level theoretical calculations using coupled-cluster theory were performed to provide an accurate description of the electronic structure, spectroscopic properties, and stability of the triatomic negative ion comprising S, N, and P. The adiabatic electron affinities (AEAs) and vertical detachment energies (VDEs) of PNS, SPN, PSN, and cyc-PSN were calculated. The predicted AEA and VDE of the linear SPN isomer are large: 2.24 and 3.04 eV, respectively. The potential energy surfaces (PESs) of the lowest-lying electronic states of the SPN isomer along the PN and SP bond lengths and bond angle were mapped. A set of spectroscopic parameters for SPN, PNS, and PSN in their electronic ground states is obtained from the 3D PESs to help detect these species in the gas phase. The electronic excited state SPN (12A”) is predicted to be stable with a long lifetime calculated to be 189.7 µs. The formation of SPN in its electronic ground state through the bimolecular collision between S + PN and N + PS is also discussed

Is there a relationship between periodontal conditions and number of medications among the elderly?

Objective: To investigate possible correlations of clinical attachment level and pocket depth with number of medications in elderly individuals.Methods: Intra-oral examinations for 139 patients visiting Tufts dental clinic were done. Periodontal assessments were performed with a manual UNC-15 periodontal probe to measure probing depth (PD) and clinical attachment level (CAL) at 6 sites. Complete lists of patients’ medications were obtained during the examinations. Statistical analysis involved Kruskal-Wallis, chi square and multivariate logistic regression analyses.Results: Age and health status attained statistical significance (p< 0.05), in contingency table analysis with number of medications. Number of medications had an effect on CAL: increased attachment loss was observed when 4 or more medications were being taken by the patient. Number of medications did not have any effect on periodontal PD. In multivariate logistic regression analysis, 6 or more medications had a higher risk of attachment loss (>3mm) when compared to the no-medication group, in crude OR (1.20, 95% CI:0.22-6.64), and age adjusted (OR=1.16, 95% CI:0.21-6.45), but not with the multivariate model (OR=0.71, 95% CI:0.11-4.39).Conclusion: CAL seems to be more sensitive to the number of medications taken, when compared to PD. However, it is not possible to discriminate at exactly what number of drug combinations the breakdown in CAL will happen. We need to do further analysis, including more subjects, to understand the possible synergistic mechanisms for different drug and periodontal responses.Keywords: periodontal disease, medications, elderly, clinical attachment level, probing dept

3D geological models and their hydrogeological applications : supporting urban development : a case study in Glasgow-Clyde, UK

Urban planners and developers in some parts of the United Kingdom can now access geodata in an easy-to-retrieve and understandable format. 3D attributed geological framework models and associated GIS outputs, developed by the British Geological Survey (BGS), provide a predictive tool for planning site investigations for some of the UK's largest regeneration projects in the Thames and Clyde River catchments. Using the 3D models, planners can get a 3D preview of properties of the subsurface using virtual cross-section and borehole tools in visualisation software, allowing critical decisions to be made before any expensive site investigation takes place, and potentially saving time and money. 3D models can integrate artificial and superficial deposits and bedrock geology, and can be used for recognition of major resources (such as water, thermal and sand and gravel), for example in buried valleys, groundwater modelling and assessing impacts of underground mining. A preliminary groundwater recharge and flow model for a pilot area in Glasgow has been developed using the 3D geological models as a framework. This paper focuses on the River Clyde and the Glasgow conurbation, and the BGS's Clyde Urban Super-Project (CUSP) in particular, which supports major regeneration projects in and around the City of Glasgow in the West of Scotland

Optimal type-3 fuzzy system for solving singular multi-pantograph equations

In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients