Brain Age from the Electroencephalogram of Sleep

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age", which can be compared to an age norm to reflect the deviation from normal aging process. Here, we develop an interpretable machine learning model to predict brain age based on two large sleep EEG datasets: the Massachusetts General Hospital sleep lab dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean absolute deviation of 8.1 years between brain age and chronological age in the healthy participants in the MGH dataset. As validation, we analyze a subset of SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years difference in brain age. Participants with neurological and psychiatric diseases, as well as diabetes and hypertension medications show an older brain age compared to chronological age. The findings raise the prospect of using sleep EEG as a biomarker for healthy brain aging

Optimization of Switch Allocation Problems in Power Distribution Networks

This paper presents the implementation of the mono-objective Switch Allocation Problem (SAP) optimization model for electric power distribution networks, considering the equivalent interruption duration per consumer unit EIDCU and non-distributed energy END reliability indexes. We use the current summation algorithm to solve the power flow, and we employ an intelligent bee colony algorithm to solve the model. Two network topologies, one with 43 and another with 136 bars, adapted from the literature, are used to illustrate the solution. Results show a significant reduction in the financial cost of planning a power distribution network

Optimization of Switch Allocation Problems in Power Distribution Networks

This paper presents the implementation of the mono-objective Switch Allocation Problem (SAP) optimization model for electric power distribution networks, considering the equivalent interruption duration per consumer unit EIDCU and non-distributed energy END reliability indexes. We use the current summation algorithm to solve the power flow, and we employ an intelligent bee colony algorithm to solve the model. Two network topologies, one with 43 and another with 136 bars, adapted from the literature, are used to illustrate the solution. Results show a significant reduction in the financial cost of planning a power distribution network

Brain age from the electroencephalogram of sleep

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as “brain age (BA),” which can be compared to chronological age to reflect the degree of deviation from normal aging. Here, we develop an interpretable machine learning model to predict BA based on 2 large sleep EEG data sets: the Massachusetts General Hospital (MGH) sleep lab data set (N = 2532; ages 18–80); and the Sleep Heart Health Study (SHHS, N = 1974; ages 40–80). The model obtains a mean absolute deviation of 7.6 years between BA and chronological age (CA) in healthy participants in the MGH data set. As validation, a subset of SHHS containing longitudinal EEGs 5.2 years apart shows an average of 5.4 years increase in BA. Participants with significant neurological or psychiatric disease exhibit a mean excess BA, or “brain age index” (BAI = BA-CA) of 4 years relative to healthy controls. Participants with hypertension and diabetes have a mean excess BA of 3.5 years. The findings raise the prospect of using the sleep EEG as a potential biomarker for healthy brain aging

Brain age from the electroencephalogram of sleep

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age (BA)," which can be compared to chronological age to reflect the degree of deviation from normal aging. Here, we develop an interpretable machine learning model to predict BA based on 2 large sleep EEG data sets: the Massachusetts General Hospital (MGH) sleep lab data set (N = 2532; ages 18-80); and the Sleep Heart Health Study (SHHS, N = 1974; ages 40-80). The model obtains a mean absolute deviation of 7.6 years between BA and chronological age (CA) in healthy participants in the MGH data set. As validation, a subset of SHHS containing longitudinal EEGs 5.2 years apart shows an average of 5.4 years increase in BA. Participants with significant neurological or psychiatric disease exhibit a mean excess BA, or "brain age index" (BAI = BA-CA) of 4 years relative to healthy controls. Participants with hypertension and diabetes have a mean excess BA of 3.5 years. The findings raise the prospect of using the sleep EEG as a potential biomarker for healthy brain aging. Keywords: Brain age; EEG; Machine learning; Slee

AN INTEGRATED MODEL FOR EVALUATING SELF SUSTAINABILITY OF BIO-ENERGY SETTLEMENTS: TECHNOLOGICAL, ECONOMICAL AND SOCIAL ASPECTS

The proposed paper present a generalized model based on Monte-Carlo simulation able to support the feasibility study by effectively model the production process, the woods groove and the overall logistics. This model can be applied to quantitatively identify cost and benefits for an integrated biomass energetic district and identify, at the same time, potential and pitfalls that usually reduce the success of an ecologic initiative. A case study implementing the proposed methodology is presented and discussed.

Detection of Biofilms in Biopsies from Chronic Rhinosinusitis Patients: In Vitro Biofilm Forming Ability and Antimicrobial Susceptibility Testing in Biofilm Mode of Growth of Isolated Bacteria

Chronic rhinosinusitis (CRS) is the most common illness among chronic disorders that remains poorly understood from a pathogenic standpoint and has a significant impact on patient quality of life, as well as healthcare costs. Despite being widespread, little is known about the etiology of the CRS. Recent evidence, showing the presence of biofilms within the paranasal sinuses, suggests a role for biofilm in the pathogenesis. To elucidate the role of biofilm in the pathogenesis of CRS, we assessed the presence of biofilm at the infection site and the ability of the aerobic flora isolated from CRS patients to form biofilm in vitro. For selected bacterial strains the susceptibility profiles to antibiotics in biofilm condition was also evaluated.Staphylococci represented the majority of the isolates obtained from the infection site, with S. epidermidis being the most frequently isolated species. Other isolates were represented by Enterobacteriaceae or by species present in the oral flora. Confocal laser scanning microscopy (CLSM) of the mucosal biopsies taken from patients with CRS revealed the presence of biofilm in the majority of the samples. Strains isolated from the specific infection site of the CRS patients were able to form biofilm in vitro at moderate or high levels, when tested in optimized conditions. No biofilm was observed by CLSM in the biopsies from control patients, although the same biopsies were positive for staphylococci in microbiological culture analysis. Drug-susceptibility tests demonstrated that the susceptibility profile of planktonic bacteria differs from that of sessile bacteria in biofilms