Characterizing Phantom Arteries with Multi-Channel Laser Ultrasonics and Photo-Acoustics

Multi-channel photo-acoustic and laser ultrasonic waves are used to sense the characteristics of proxies for healthy and diseased vessels. The acquisition system is non-contacting and non-invasive with a pulsed laser source and a laser vibrometer detector. As the wave signatures of our targets are typically low in amplitude, we exploit multi-channel acquisition and processing techniques. These are commonly used in seismology to improve the signal-to-noise ratio of data. We identify vessel proxies with a diameter on the order of 1 mm, at a depth of 18 mm. Variations in scattered and photo-acoustic signatures are related to differences in vessel wall properties and content. The methods described have the potential to improve imaging and better inform interventions for atherosclerotic vessels, such as the carotid artery

Sensitivity to Experiencing Alcohol Hangovers: Reconsideration of the 0.11% Blood Alcohol Concentration (BAC) Threshold for Having a Hangover

The 2010 Alcohol Hangover Research Group consensus paper defined a cutoff blood alcohol concentration (BAC) of 0.11% as a toxicological threshold indicating that sufficient alcohol had been consumed to develop a hangover. The cutoff was based on previous research and applied mostly in studies comprising student samples. Previously, we showed that sensitivity to hangovers depends on (estimated) BAC during acute intoxication, with a greater percentage of drinkers reporting hangovers at higher BAC levels. However, a substantial number of participants also reported hangovers at comparatively lower BAC levels. This calls the suitability of the 0.11% threshold into question. Recent research has shown that subjective intoxication, i.e., the level of severity of reported drunkenness, and not BAC, is the most important determinant of hangover severity. Non-student samples often have a much lower alcohol intake compared to student samples, and overall BACs often remain below 0.11%. Despite these lower BACs, many non-student participants report having a hangover, especially when their subjective intoxication levels are high. This may be the case when alcohol consumption on the drinking occasion that results in a hangover significantly exceeds their “normal” drinking level, irrespective of whether they meet the 0.11% threshold in any of these conditions. Whereas consumers may have relative tolerance to the adverse effects at their “regular” drinking level, considerably higher alcohol intake—irrespective of the absolute amount—may consequentially result in a next-day hangover. Taken together, these findings suggest that the 0.11% threshold value as a criterion for having a hangover should be abandoned

Physician–Patient Communication About Prescription Medication Nonadherence: A 50-state Study of America’s Seniors

CONTEXT: Understanding and improving the quality of medication management is particularly important in the context of the Medicare prescription drug benefit that took effect last January 2006. OBJECTIVE: To determine the prevalence of physician–patient dialogue about medication cost and medication adherence among elderly adults nationwide. DESIGN: Cross-sectional survey. PARTICIPANTS: National stratified random sample of community-dwelling Medicare beneficiaries aged 65 and older. MAIN OUTCOME MEASURES: Rates of physician–patient dialogue about nonadherence and cost-related medication switching. RESULTS: Forty-one percent of seniors reported taking five or more prescription medications, and more than half has 2 or more prescribing physicians. Thirty-two percent overall and 24% of those with 3 or more chronic conditions reported not having talked with their doctor about all their different medicines in the last 12 months. Of seniors reporting skipping doses or stopping a medication because of side effects or perceived nonefficacy, 27% had not talked with a physician about it. Of those reporting cost-related nonadherence, 39% had not talked with a physician about it. Thirty-eight percent of those with cost-related nonadherence reported switching to a lower priced drug, and in a multivariable model, having had a discussion about drug cost was significantly associated with this switch (odds ratio [OR] 5.04, 95% confidence interval [CI] 4.28–5.93, P < .001). CONCLUSIONS: We show that there is a communication gap between seniors and their physicians around prescription medications. This communication problem is an important quality and safety issue, and takes on added salience as physicians and patients confront new challenges associated with coverage under new Medicare prescription drug plans. Meeting these challenges will require that more attention be devoted to medication management during all clinical encounters

Mechanism and disease-association of E2 conjugating enzymes:lessons from UBE2T and UBE2L3

Ubiquitin signalling is a fundamental eukaryotic regulatory system, controlling diverse cellular functions. A cascade of E1, E2, and E3 enzymes is required for assembly of distinct signals, whereas an array of deubiquitinases and ubiquitin-binding modules edit, remove, and translate the signals. In the centre of this cascade sits the E2-conjugating enzyme, relaying activated ubiquitin from the E1 activating enzyme to the substrate, usually via an E3 ubiquitin ligase. Many disease states are associated with dysfunction of ubiquitin signalling, with the E3s being a particular focus. However, recent evidence demonstrates that mutations or impairment of the E2s can lead to severe disease states, including chromosome instability syndromes, cancer predisposition, and immunological disorders. Given their relevance to diseases, E2s may represent an important class of therapeutic targets. In the present study, we review the current understanding of the mechanism of this important family of enzymes, and the role of selected E2s in disease

Data-driven Steering Torque Behaviour Modelling with Hidden Markov Models

Modern Advanced Driver Assistance Systems (ADAS) are limited in their ability to consider the drivers intention, resulting in unnatural guidance and low customer acceptance. In this research, we focus on a novel data-driven approach to predict driver steering torque. In particular, driver behavior is modeled by learning the parameters of a Hidden Markov Model (HMM) and estimation is performed with Gaussian Mixture Regression (GMR). An extensive parameter selection framework enables us to objectively select the model hyper-parameters and prevents overfitting. The final model behavior is optimized with a cost function balancing between accuracy and smoothness. Naturalistic driving data covering seven participants is obtained using a static driving simulator at Toyota Motor Europe for the training, evaluation, and testing of the proposed model. The results demonstrate that our approach achieved a 92% steering torque accuracy with a 37% increase in signal smoothness and 90% fewer data compared to a baseline. In addition, our model captures the complex and nonlinear human behavior and inter-driver variability from novice to expert drivers, showing an interesting potential to become a steering performance predictor in future user-oriented ADAS.Comment: submitted to IFAC HMM 202

Data-driven Steering Torque Behaviour Modelling with Hidden Markov Models

Modern Advanced Driver Assistance Systems (ADAS) are limited in their ability to consider the driver's intention, resulting in unnatural guidance and low customer acceptance. In this research, we focus on a novel data-driven approach to predict driver steering torque. In particular, driver behavior is modeled by learning the parameters of a Hidden Markov Model (HMM) and estimation is performed with Gaussian Mixture Regression (GMR). An extensive parameter selection framework enables us to objectively select the model hyper-parameters and prevents overfitting. The final model behavior is optimized with a cost function balancing between accuracy and smoothness. Naturalistic driving data covering seven participants is obtained using a static driving simulator at Toyota Motor Europe for the training, evaluation, and testing of the proposed model. The results demonstrate that our approach achieved a 92% steering torque accuracy with a 37% increase in signal smoothness and 90% fewer data compared to a baseline. In addition, our model captures the complex and nonlinear human behavior and inter-driver variability from novice to expert drivers, showing an interesting potential to become a steering performance predictor in future user-oriented ADAS.Intelligent Vehicle

STF-62247 and pimozide induce autophagy and autophagic cell death in mouse embryonic fibroblasts

Induction of autophagy can have beneficial effects in several human diseases, e.g. cancer and neurodegenerative diseases (ND). Here, we therefore evaluated the potential of two novel autophagy-inducing compounds, i.e. STF-62247 and pimozide, to stimulate autophagy as well as autophagic cell death (ACD) using mouse embryonic fibroblasts (MEFs) as a cellular model. Importantly, both STF-62247 and pimozide triggered several hallmarks of autophagy in MEFs, i.e. enhanced levels of LC3B-II protein, its accumulation at distinct cytosolic sites and increase of the autophagic flux. Intriguingly, autophagy induction by STF-62247 and pimozide resulted in cell death that was significantly reduced in ATG5- or ATG7-deficient MEFs. Consistent with ACD induction, pharmacological inhibitors of apoptosis, necroptosis or ferroptosis failed to protect MEFs from STF-62247- or pimozide-triggered cell death. Interestingly, at subtoxic concentrations, pimozide stimulated fragmentation of the mitochondrial network, degradation of mitochondrial proteins (i.e. mitofusin-2 and cytochrome c oxidase IV (COXIV)) as well as a decrease of the mitochondrial mass, indicative of autophagic degradation of mitochondria by pimozide. In conclusion, this study provides novel insights into the induction of selective autophagy as well as ACD by STF-62247 and pimozide in MEFs

Loperamide, pimozide, and STF-62247 trigger autophagy-dependent cell death in glioblastoma cells

Autophagy is a well-described degradation mechanism that promotes cell survival upon nutrient starvation and other forms of cellular stresses. In addition, there is growing evidence showing that autophagy can exert a lethal function via autophagic cell death (ACD). As ACD has been implicated in apoptosis-resistant glioblastoma (GBM), there is a high medical need for identifying novel ACD-inducing drugs. Therefore, we screened a library containing 70 autophagy-inducing compounds to induce ATG5-dependent cell death in human MZ-54 GBM cells. Here, we identified three compounds, i.e. loperamide, pimozide, and STF-62247 that significantly induce cell death in several GBM cell lines compared to CRISPR/Cas9-generated ATG5- or ATG7-deficient cells, pointing to a death-promoting role of autophagy. Further cell death analyses conducted using pharmacological inhibitors revealed that apoptosis, ferroptosis, and necroptosis only play minor roles in loperamide-, pimozide- or STF-62247-induced cell death. Intriguingly, these three compounds induce massive lipidation of the autophagy marker protein LC3B as well as the formation of LC3B puncta, which are characteristic of autophagy. Furthermore, loperamide, pimozide, and STF-62247 enhance the autophagic flux in parental MZ-54 cells, but not in ATG5 or ATG7 knockout (KO) MZ-54 cells. In addition, loperamide- and pimozide-treated cells display a massive formation of autophagosomes and autolysosomes at the ultrastructural level. Finally, stimulation of autophagy by all three compounds is accompanied by dephosphorylation of mammalian target of rapamycin complex 1 (mTORC1), a well-known negative regulator of autophagy. In summary, our results indicate that loperamide, pimozide, and STF-62247 induce ATG5- and ATG7-dependent cell death in GBM cells, which is preceded by a massive induction of autophagy. These findings emphasize the lethal function and potential clinical relevance of hyperactivated autophagy in GBM