Longitudinal Study of Alzheimer’s Disease Degeneration through EEG Data Analysis with aNeuCube Spiking Neural Network Model

Motivated by the dramatic rise of neurological disorders, we propose a SNN technique to model electroencephalography (EEG) data collected from people affected by Alzheimer’s Disease (AD) and people diagnosed with mild cognitive impairment (MCI). An evolving spatio-temporal data machine (eSTDM), named the NeuCube architecture, is used to analyse changes of neural activity across different brain regions. The model developed allows for studying AD progression and for predicting whether a patient diagnosed with MCI is more likely to develop AD

Morphological and Mechanical Characterization of P-Scaffolds with Different Porosity

The aim of this paper is to model and to compare the results of the mechanical characterization, carried out on numerical models and real specimens, of uniform P-scaffolds with different porosity values. The analysis includes the morphological characterization of 3D printed specimens and the implementation of a FEM shell model to reproduce a compressive test suitable for mechanical properties evaluation of PLA scaffolds. Young modulus and yield strength were also obtained, in order to verify the numerical model accuracy, by experimental tests on 3D printed PLA scaffolds. Numerical results showed that the shell model was able to reproduce, more efficiently compared to a solid model proposed in a previous work, both elastic and plastic behavior of the scaffolds, providing elastic modulus values very close to the experimental ones. On the other hand, the not very high quality of the 3D printing, detected by MicroCT analysis, caused a significant dispersion in the yield strength numerical values respect to the real data. Anyway, an inverse correlation between mechanical properties and porosity was found as expected. The elastic modulus values were similar to the typical values of the trabecular bone for whose regeneration this kind of scaffolds is usually employed

Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

Cyclin-dependent kinase 5 regulates PSD-95 ubiquitination in neurons

Cyclin-dependent kinase 5 (Cdk5) and its activator p35 have been implicated in drug addiction, neurodegenerative diseases such as Alzheimer\u27s, learning and memory, and synapse maturation and plasticity. However, the molecular mechanisms by which Cdk5 regulates synaptic plasticity are still unclear. PSD-95 is a major postsynaptic scaffolding protein of glutamatergic synapses that regulates synaptic strength and plasticity. PSD-95 is ubiquitinated by the ubiquitin E3 ligase Mdm2, and rapid and transient PSD-95 ubiquitination has been implicated in NMDA receptor-induced AMPA receptor endocytosis. Here we demonstrate that genetic or pharmacological reduction of Cdk5 activity increases the interaction of Mdm2 with PSD-95 and enhances PSD-95 ubiquitination without affecting PSD-95 protein levels in vivo in mice, suggesting a nonproteolytic function of ubiquitinated PSD-95 at synapses. We show that PSD-95 ubiquitination correlates with increased interaction with beta-adaptin, a subunit of the clathrin adaptor protein complex AP-2. This interaction is increased by genetic reduction of Cdk5 activity or NMDA receptor stimulation and is dependent on Mdm2. Together these results support a function for Cdk5 in regulating PSD-95 ubiquitination and its interaction with AP-2 and suggest a mechanism by which PSD-95 may regulate NMDA receptor-induced AMPA receptor endocytosis

Alexithymia and psychological distress affect perceived quality of life in patients with type 2 diabetes mellitus

Backgrounds: Psychological factors may affect patients' ability to cope with chronic illness, which occur with a high incidence as they represent age related disorder. Anxiety, depression and alexithymia could specifically interfere with compliance and adherence leading to predictable consequences and predicting morbidity and mortality independently of several confounders. The present work aims at investigating the relationship between alexithymia and affective dimension such as anxiety and depression levels, and health related quality of life in T2DM patients. Particularly, alexithymia was analyzed in its three main facets and time since diagnosis was considered with also metabolic control. Methods: Forty seven patients with T2DM were consecutively enrolled and assessed with a gold standard interview and with a psycho-diagnostic evaluation. Clinical psychological exploration consisted of HAM-A, BECK-II, SF-36 and TAS-20 administration. Statistical analysis was performed using IBM SPSS statistical version 25. Data were analyzed anonymously. Results: 47 participants showed moderate depressive symptoms as confirmed by the mean BDI-II and HAMA-A score (15.14 ± 8.95 and 24.31 ± 6.95, respectively), suggesting a high prevalence of anxiety in the enrolled subjects. It was observed a lower perceived QoL as resulted by the MCS and PCS mean values (37.68 ± 9.41 and 39.31 ± 12.29, respectively) and TAS-20 highlighted considerable mean values of 60.53 ± 7.93 in the recruited participants with a prevalence in EOT values (27.51± 4.27), in comparison with mean DID and DDF values (17.26 ± 5.52 and 15.48 ± 3.84, respectively). Conclusions: Our study may suggest a predictive role of alexithymia in patients with T2DM. Moreover, lower PCS and MCS, revealing worst perceived QoL were associated to both higher anxiety and disease duration

Nuclear regions as seen with LOFAR international baselines: A high-resolution study of the recurrent activity

Context. Radio galaxies dominate the sky at radio wavelengths and represent an essential piece in the galaxy evolution puzzle. High-resolution studies focussed on statistical samples of radio galaxies are expected to shed light on the triggering mechanisms of the active galactic nucleus in their centre, alternating between the phases of activity and quiescence.Aims. For this work, we zoomed in on the sub-arcsec radio structures in the central regions of the 35 radio galaxies in the area covering 6.6 deg2 of the Lockman Hole region. The sources studied here were previously classified as active, remnant, and candidate restarted radio galaxies based on the LOw Frequency ARray (LOFAR) observations at 150 MHz. We examined the morphologies and studied the spectral properties of their central regions to explore their evolutionary stages and to revise the morphological and spectral criteria used to select the initial sample.Methods. We used the newly available LOFAR 150 MHz image obtained using international baselines, yielding a resolution of 0.38″ × 0.30″, making this the first systematic study of the nuclear regions at such a high resolution and low frequency. We used publicly available images from the Faint Images of the Radio Sky at Twenty-cm survey at 1.4 GHz and the Karl G. Jansky Very Large Array (VLA) Sky Survey at 3 GHz to achieve our goals. In addition, for one of the restarted candidates, we present new dedicated observations with the VLA at 3 GHz.Results. We characterised the central regions of the radio galaxies in our sample and found various morphologies, some even mimicking well-known double-double radio galaxies but on a smaller scale, that is, a few tens of kiloparsecs for the size of the restarted activity. We also see the beginnings of active jets or distinct detections unrelated to the large-scale structure. Furthermore, we found a variety of radio spectra characterising the sources in our sample, such as flat, steep, or peaked in the frequency range between 150 MHz and 3 GHz, indicative of the different life-cycle phases of the sources in our sample. Based on these analyses, we confirm five out of six previously considered restarted candidates and identify three more restarted candidates from the active sample. As the number of restarted candidates still exceeds that of remnant candidates, this is consistent with previous results suggesting that the restarted phase can occur after a relatively short remnant phase (i.e. a few tens of millions of years)

External validation on a prospective basis of a nomogram for predicting the time to first treatment in patients with chronic lymphocytic leukemia

BACKGROUND: A nomogram that incorporates traditional and newer prognostic factors to identify patients with chronic lymphocytic leukemia (CLL) who are at high risk of receiving therapy was developed by investigators at The University of Texas M. D. Anderson Cancer Center (MDACC). Because the model required validation before its extensive use could be recommended, the authors sought to externally validate the nomogram in an independent, community-based cohort of patients with CLL. METHODS: In total, 328 previously untreated patients with newly diagnosed, asymptomatic, Binet stage A CLL from different primary hematology centers who were registered on a prospective basis during 2006 to 2010 on an observational database of the Italian Lymphoma Study Group were considered suitable for external validation of the model. RESULTS: A total point score was calculated for each patient using a formula proposed by MDACC investigators, and the median score was 19.9 (range, 0-69.5). Furthermore, when the score was evaluated as continuous variable (ie, by measuring the risk of each point increase), the total point score was associated with the time to first treatment (hazard ratio [HR], 1.04; 95% confidence interval [CI], 1.02-1.05; P < .0001). Receiver operating characteristic analysis identified a point score of 25 (area under curve; 0.64; sensitivity, 61.5; specificity, 72.1; P < .0001) as the best threshold capable of separating patients who needed therapy from patients who did not (HR, 3.27; 95% CI, 2,07-5.18; P < .0001). The prognostic index category also remained a predictor of the time to first treatment when the analysis was limited to patients with Rai stage 0 disease (HR, 4.05; 95% CI, 2.25-7.52; P < .0001). Finally, a goodness-of-fit test demonstrated that the nomogram model had a significantly good fit at 2 years (correlation coefficient [r2] = 0.966; P = .002). CONCLUSIONS: The current results confirmed the ability of a newly developed prognostic index to predict the time to first treatment among previously untreated patients with CLL who had early disease and extended the utility of the model to those with Rai stage 0 disease. In addition, the actual and predicted time to first treatment outcomes revealed good agreement, suggesting that, externally, the results provided by the model are well calibrated. Cancer 2013. © 2012 American Cancer Society