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SUMMARY: The hypotheses currently considered the most likely causes of Alzheimer's disease (AD) are amyloid beta peptide deposition in the cerebral cortex and hyperphosphorylation of the Tau protein, with the consequent formation of neurofibrillary tangles. In clinical practice, although not accurate, AD diagnosis is based on the exclusion of other diseases, behavioural assessments and complementary examinations, such as imaging and blood tests. Advances in the field of biotechnology have created exciting prospects for the early detection of AD via biomarker assessment, which is considered a safer and more efficient procedure. Molecules recognised as biomarkers can be expressed in some body fluids, including cerebrospinal fluid, saliva and blood. The presence of amyloid beta peptide and Tau can be confirmed in saliva, which is also an easily and non-invasively collectable material with an accessible cost. The objective was evaluate the concentrations of the t-Tau protein and Ab42 peptide in the saliva of elderly individuals with and without dementia of the AD type Method: The objective of this case-control study, involving a total of 120 individuals, was to analyse whether a correlation exists between variations in the concentrations of the t-Tau and Ab42 biomarkers in the saliva of patients with confirmed AD and individuals in the inclusion group but without AD . We found that t-Tau expression in AD patients is significantly lower than that in individuals without AD, whereas the salivary concentration of Ab42 is higher in patients with AD but not significantly different from that of the group without AD. Conclusion: Thus, we demonstrate the feasibility of using salivary biomarkers as predictive markers for diagnosis of Alzheimer's disease.

Rail car safety and stability are important factors for good performance. The most used safety index to quantify railway safety is the ratio between the lateral and vertical wheel rail contact forces (L/Q). This study aims to use machine learning (ML) models to evaluate the viability of estimating the wheel rail contact forces from sensors in a regular instrumented railway vehicle (IRV). A virtual model of a BRA1 railway vehicle was created to generate an artificial dataset with variables measured with the actual BRA1 vehicle plus different variables measured by other IRVs found in the literature. The desired output variable is the L/Q ratio for each wheel of the leading bogie. Exploratory data analysis was done to clarify the correlation between the variables while model explainability techniques were applied to evaluate the contribution of these input variables to the output (L/Q). A total of 24 embedded machine learning models were trained and optimized using the tree-based pipeline optimization tool (TPOT) to generate a ML pipeline capable of producing an accurate L/Q ratio as the output for different cases of sampling rate, input variables and track irregularities. The results show that machine learning models can predict the wheel rail contact forces indirectly – without using instrumented wheelsets – with the highest mean squared error being equal to 0.01113.

Highlights: • A purely bipolar behavior on a Co-doped ZnO thin film has been demonstrated. • We have shown what can happen if a unipolar test is performed in a purely bipolar device. • An explanation for how a sample can show a purely bipolar switching behavior was suggested. • An important open issue about resistive switching effect was put in debate. - Abstract: In order to investigate the resistive switching effect we built devices in a planar structure in which two Al contacts were deposited on the top of the film and separated by a small gap using a shadow mask. Therefore, two samples of 10% Co-doped ZnO thin films were sputtered on glass substrate. High resolution X-ray diffraction (HRXRD) revealed a highly c-axis oriented crystalline structure, without secondary phase. The high resolution scanning electron microscopy (HRSEM) showed a flat surface with good coverage and thickness about 300 nm. A Keithley 2425 semiconductor characterization system was used to perform the resistive switching tests in the bipolar and unipolar modes. Considering only the effect of compliance current (CC), the devices showed a purely bipolar behavior since an increase in CC did not induce a transition to unipolar behavior.