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Cannabis is the fourth psychoactive substance to be legalized which are of far-reaching significance to the world. We analyzed data from the 2019 Global Burden of Disease Study (GBD) to estimate the incidence and prevalence of cannabis use disorder (CUD) and calculated the disease burden of CUD in 204 countries and territories and 21 regions over the past three decades. We reported disease burden due to CUD in terms of disability-adjusted life years (DALYs), age-standardized rate (ASR), estimated annual percentage change (EAPC), and analyzed associations between the burden of CUD and sociodemographic index (SDI) quintiles. Globally, the number of incidence cases of CUD was estimated to be increasing by 32.3% from 1990 to 2019 and males are nearly double higher than that of female. DALYs increase 38.6% from 1990. Young people aged 20–24 years old with cannabis use disorder have the highest DALYs in 2019, followed by those younger than 20 years old. India, Canada, USA, Qatar, Kenya, and high SDI quintile areas showed a high burden of disease. Nearly 200 million individuals are cannabis users worldwide, and CUD is a notable condition of GBD. The global cultivation of cannabis, rooted in different cultures, diversified access to cannabis, legalization in controversy, the promotion of medical cannabis, and many other factors promote the global cannabis industry is constantly updated and upgraded. It deserves more discussion in the future in terms of pathophysiological mechanisms, socioeconomics, law, and policy improvement.

Clinical studies from WHO have demonstrated that only 50–70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390).

Understanding the underlying mechanisms of mindfulness has been a hot topic in recent years, not only in clinical fields but also in neuroscience. Most neuroimaging findings demonstrate that critical brain regions involved in mindfulness are responsible for cognitive functions and mental states. However, the brain is a complex system operating via multiple circuits and networks, rather than isolated brain regions solely responsible for specific functions. Mindfulness-based treatments for attention deficit hyperactivity disorder (ADHD) have emerged as promising adjunctive or alternative intervention approaches. We focus on four key brain circuits associated with mindfulness practices and effects on symptoms of ADHD and its cognitive dysfunction, including executive attention circuit, sustained attention circuit, impulsivity circuit, and hyperactivity circuit. We also expand our discussion to identify three key brain networks associated with mindfulness practices, including central executive network, default mode network, and salience network. We conclude by suggesting that more research efforts need to be devoted into identifying putative neuropsychological mechanisms of mindfulness on how it alleviates ADHD symptoms.

Background: Faced with surging infections and considering that 80% of cases were mild and moderate, the city government transformed stadiums and exhibition centers into the Fangcang shelter hospitals (temporary treatment centers) in Wuhan. Aim: The aims of this study were to evaluate psychological impact on health care workers in the Fangcang shelter hospitals and analyze potential risk factors associated with these symptoms, including PTSS, symptoms of anxiety, depression, insomnia, and perceived stress. Methods: This cross-sectional study used an online survey to assess mental health problems of 522 health care workers in a Jianghan Fangcang shelter from February 21 to February 28, 2020. Results: Overall, 25.3%, 25.3%, 51.0%, 58.0%, 14.8%, and 39.1% of all participants reported symptoms of posttraumatic stress, anxiety, depression, insomnia, and perceived stress, respectively. Multivariable logistic regression analysis showed that, occupation and technical title were associated with all investigated outcomes. Nurses were more likely to endorse posttraumatic stress symptoms (OR, 4.63; 95% CI, 2.43–8.82; p Conclusion: In this survey, participants reported high rates of symptoms of posttraumatic stress, anxiety, depression, insomnia, and perceived stress. Combining with the special environment of the shelter hospitals and the psychological needs, the health care workers should be given more effective interventions to relieve their stress and improve mental symptoms.

Electrified vehicles are increasingly being seen as a means of mitigating the pressing concerns of traffic-related pollution. Due to the nature of engine-assisted vehicle exhaust systems, pedestrians in close proximity to these vehicles may experience events where specific emission concentrations are high enough to cause health effects. To minimize pedestrians’ exposure to vehicle emissions and pollutants nearby, we present a pedestrian-aware supervisory control system for connected hybrid electric vehicles by proposing an interactive optimization methodology. This optimization methodology combines a novel fuzzy adaptive cost map and the Bees Algorithm to optimize power-split control parameters. It enables the self-regulation of inter-objective weights of fuel and exhaust emissions based on the real-time pedestrian density information during the optimization process. The evaluation of the vehicle performance by using the proposed methodology is conducted on the realistic trip map involving pedestrian density information collected from the University College Dublin campus. Moreover, two bootstrap sampling techniques and effect of communication quality are both investigated in order to examine the robustness of the improved vehicle system. The results demonstrate that 14.42% mass of exhaust emissions can be reduced for the involved pedestrians, by using the developed fuzzy adaptive cost map.

This paper proposes a congestion balancing parking guidance system that suggests to a driver a sequence of streets to follow around the desired destination with the aim to reduce the total distance that is travelled while searching for a free parking spot. The system requires only limited infrastructure information, and neither requires parking spaces to be instrumented, nor vehicles to communicate with each other. Specifically, the system utilizes parking vacancy information on each street. The system also accounts for the added cost of not finding a free space, which is typically expressed as the additional distance that needs to be travelled to find an available parking spot. To avoid local congestion, different drivers respond to different suggestions based on a probability distribution that considers the total distance that needs to be travelled. A mobility simulator is used to model the searching behaviors of vehicles for parking spaces with and without the smart parking algorithm and experimental results are provided using the road network of the city of Dublin, Ireland.

An intelligent speed advisory system can be used to recommend speed for vehicles travelling in a given road network in cities. In this paper, we extend our previous work where a distributed speed advisory system has been devised to recommend an optimal consensus speed for a fleet of Internal Combustion Engine Vehicles (ICEVs) in a highway scenario. In particular, we propose a novel optimisation framework where the exact format of each vehicle's cost function can be implicit, and our algorithm can be used to recommend multiple consensus speeds for vehicles travelling on different lanes in an urban highway scenario. Our studies show that the proposed scheme based on an improved whale optimisation algorithm can effectively reduce CO2 emission generated from ICEVs while providing different recommended speed options for groups of vehicles., This paper has been accepted by IEEE VTC2021-Spring for presentation

As a part of Advanced Driver Assistance Systems (ADASs), Consensus-based Speed Advisory Systems (CSAS) have been proposed to recommend a common speed to a group of vehicles for specific application purposes, such as emission control and energy management. With Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) technologies and advanced control theories in place, state-of-the-art CSAS can be designed to get an optimal speed in a privacy-preserving and decentralized manner. However, the current method only works for specific cost functions of vehicles, and its execution usually involves many algorithm iterations leading long convergence time. Therefore, the state-of-the-art design method is not applicable to a CSAS design which requires real-time decision making. In this paper, we address the problem by introducing MPC-CSAS, a Multi-Party Computation (MPC) based design approach for privacy-preserving CSAS. Our proposed method is simple to implement and applicable to all types of cost functions of vehicles. Moreover, our simulation results show that the proposed MPC-CSAS can achieve very promising system performance in just one algorithm iteration without using extra infrastructure for a typical CSAS., This manuscript has been accepted by the IEEE Transactions on Intelligent Transportation Systems

Introduction Faced with surging infections and considering that 80% of cases were mild and moderate, the city government transformed stadiums and exhibition centers into the Fangcang shelter hospitals in Wuhan. The aims of this study were to evaluate mental health outcomes among patients with COVID-19 treated in the Fangcang shelter hospitals and analyze potential risk factors associated with these symptoms, including PTSS, symptoms of anxiety, depression, insomnia, and perceived stress. Method This cross-sectional study used an online survey to assess mental health problems of 461 confirmed COVID-19 patients in a Jianghan Fangcang shelter from February 15 to 22, 2020. A multivariable logistic regression analysis was performed to explore potential risk factors. Results Overall, 25.2%, 50.1%, 54.4%, 10.2%, and 39.7% of all patients reported symptoms of posttraumatic stress, anxiety, depression, insomnia, and perceived stress, respectively. Multivariable logistic regression analysis showed that female sex was associated with all investigated outcomes except insomnia. Patients with lower education level were more likely to endorse anxiety, depression, and higher perceived stress, while patients' family members with other diseases were more likely to endorse severe insomnia. Patients with illness onset to admission less than 7 days were associated with higher perceived stress. Discussion In this survey study, patients reported high rates of symptoms of posttraumatic stress, anxiety, depression, insomnia, and perceived stress. Combining with the special environment of the shelter hospitals and patient's psychological needs, the health care workers should give more effective interventions to relieve the patients' stress and improve their mental symptoms.

Medication non-adherence is a widespread problem affecting over 50% of people who have chronic illness and need chronic treatment. Non-adherence exacerbates health risks and drives significant increases in treatment costs. In order to address these challenges, the importance of predicting patients' adherence has been recognised. In other words, it is important to improve the efficiency of interventions of the current healthcare system by prioritizing resources to the patients who are most likely to be non-adherent. Our objective in this work is to make predictions regarding individual patients' behaviour in terms of taking their medication on time during their next scheduled medication opportunity. We do this by leveraging a number of machine learning models. In particular, we demonstrate the use of a connected IoT device; a "Smart Sharps Bin", invented by HealthBeacon Ltd.; to monitor and track injection disposal of patients in their home environment. Using extensive data collected from these devices, five machine learning models, namely Extra Trees Classifier, Random Forest, XGBoost, Gradient Boosting and Multilayer Perception were trained and evaluated on a large dataset comprising 165,223 historic injection disposal records collected from 5,915 HealthBeacon units over the course of 3 years. The testing work was conducted on real-time data generated by the smart device over a time period after the model training was complete, i.e. true future data. The proposed machine learning approach demonstrated very good predictive performance exhibiting an Area Under the Receiver Operating Characteristic Curve (ROC AUC) of 0.86., This paper has been accepted by IEEE IoT World Forum

Supplemental material, sj-pdf-2-isp-10.1177_0020764020975805 for Factors associated with mental health outcomes among health care workers in the Fangcang shelter hospital in China by Yingqi Gu, Yi Zhu and Guangxing Xu in International Journal of Social Psychiatry

A key motivation to fasten roll-out of electric vehicles (EVs) to the market is to implement Vehicle-to-Grid (V2G) functionalities. With V2G in place, EV owners can have extra freedom to interact their battery energy with power grids, namely by selling their energy to the grid when their EVs are not in use. On the other hand, EV aggregators and utility companies can leverage the flexibility of the collected energy to implement various ancillary services to the grids, which may significantly reduce costs of, for instance, running spinning reserve of traditional power plants on the grid side. However, this extra freedom also poses practical challenges in terms of how to devise a discharging strategy for a group of EVs that is fair and in some sense optimal. In this paper, we present a new design of EV discharging strategy in a typical V2G energy trading framework whilst leveraging the whale optimization algorithm in a decentralized manner, a metaheuristic algorithm that has been shown effective in solving large-scale centralized optimization problems. We demonstrate that by using simple ideas of data shuffling and aggregation, one can design an EV discharging strategy in a fair, optimal and privacy-aware manner, where the privacy refers to the fact that no critical information of EVs should be exchanged with the EV aggregator, and vice versa. The fairness implies that a common discharge rate needs to be sought for all EVs so that no one gets better benefits than others in the same V2G programme. Simulation results are presented to illustrate the efficacy of our proposed system., Comment: This manuscript has been accepted by the IEEE System Journal

Supplemental material, sj-pdf-2-isp-10.1177_0020764020975805 for Factors associated with mental health outcomes among health care workers in the Fangcang shelter hospital in China by Yingqi Gu, Yi Zhu and Guangxing Xu in International Journal of Social Psychiatry

Supplemental material, sj-pdf-1-isp-10.1177_0020764020975805 for Factors associated with mental health outcomes among health care workers in the Fangcang shelter hospital in China by Yingqi Gu, Yi Zhu and Guangxing Xu in International Journal of Social Psychiatry

Electric Vehicles (EVs) and Plug-in Hybrid Electric Vehicles (PHEVs) are increasingly being seen as a means of mitigating the pressing concerns of traffic-related pollution. While hybrid vehicles are usually designed with the objective of minimising fuel consumption, in this paper we propose engine management strategies that also take into account environmental effects of the vehicles to pedestrians outside of the vehicles. Specifically, we present optimisation based engine energy management strategies for PHEVs, that attempt to minimise the environmental impact of pedestrians along the route of the vehicle, while taking account of route dependent uncertainties. We implement the proposed approach in a real PHEV, and evaluate the performance in a hardware-in-the-loop platform. A variety of simulation results are given to illustrate the efficacy of our proposed approach.

In this paper, we describe a distributed ledger re-cycling system to encourage responsible disposal of paper cups. A complete working prototype is described. Real measurements are presented to illustrate the potential suitability of the IOTA based distributed ledger for this application.

One of the key ideas to make intelligent transportation systems work effectively is to deploy advanced communication and cooperative control technologies among vehicles and road infrastructures. In this spirit, we propose a consensus-based distributed speed advisory system that optimally determines a recommended common speed for a given area in order that the group emissions, or group battery consumptions, are minimized. Our algorithms achieve this in a privacy-aware manner; that is, individual vehicles do not reveal in-vehicle information to other vehicles or to infrastructure. A mobility simulator is used to illustrate the efficacy of the algorithm, and hardware-in-the-loop tests involving a real vehicle are given to illustrate user acceptability and ease of deployment.

In this paper, we propose a new engine management system for hybrid vehicles to enable energy providers and car manufacturers to provide new services. Energy forecasts are used to collaboratively orchestrate the behaviour of engine management systems of a fleet of plug-in hybrid electric vehicle (PHEVs) to absorb oncoming energy in a smart manner. Cooperative algorithms are suggested to manage the energy absorption in an optimal manner for a fleet of vehicles, and the mobility simulator SUMO (Simulation of Urban MObility) is used to demonstrate the efficacy of the proposed idea.

We present here a general framework and a specific algorithm for predicting the destination, route, or more generally a pattern, of an ongoing journey, building on the recent work of [Y. Lassoued, J. Monteil, Y. Gu, G. Russo, R. Shorten, and M. Mevissen, "Hidden Markov model for route and destination prediction," in IEEE International Conference on Intelligent Transportation Systems, 2017]. In the presented framework, known journey patterns are modelled as stochastic processes, emitting the road segments visited during the journey, and the ongoing journey is predicted by updating the posterior probability of each journey pattern given the road segments visited so far. In this contribution, we use Markov chains as models for the journey patterns, and consider the prediction as final, once one of the posterior probabilities crosses a predefined threshold. Despite the simplicity of both, examples run on a synthetic dataset demonstrate high accuracy of the made predictions., Accepted at The 21st IEEE International Conference on Intelligent Transportation Systems (ITSC)

Traffic flow pattern identification, as well as anomaly detection, is known to be an important component for traffic operations and control. Alongside classical applications, mainly, to improve the safety and the comfort of drivers, more recently there is a growing interest in gathering personalised route information to provide customised services. With this latter application in mind, in this paper we investigate the ability of simple macroscopic information (i.e., time varying junction turning probabilities) to identify changes in nominal urban traffic flows, most likely due to the occurrence of external events (e.g., road works or traffic congestions). Some preliminary results obtained with the use of a realistic mobility simulator are also illustrated and discussed, and some candidate applications are briefly outlined.

Traffic-related pollution is becoming a major societal problem globally. Cyclists are particularly exposed to this form of pollution due to their proximity to vehicles' tailpipes. In a number of recent studies, it is been shown that exposure to this form of pollution eventually outweighs the cardio-vascular benefits associated with cycling. Hence during cycling there are conflicting effects that affect the cyclist. On the one hand, cycling effort gives rise to health benefits, whereas exposure to pollution clearly does not. Mathematically speaking, these conflicting effects give rise to convex utility functions that describe the health threats accrued to cyclists. More particularly, and roughly speaking, for a given level of background pollution, there is an optimal length of journey time that minimises the health risks to a cyclist. In this paper, we consider a group of cyclists that share a common route. This may be recreational cyclists, or cyclists that travel together from an origin to destination. Given this context, we ask the following question. What is the common speed at which the cyclists should travel, so that the overall health risks can be minimised? We formulate this as an optimisation problem with consensus constraints. More specifically, we design an intelligent speed advisory system that recommends a common speed to a group of cyclists taking into account different levels of fitness of the cycling group, or different levels of electric assist in the case that some or all cyclists use e-bikes (electric bikes). To do this, we extend a recently derived consensus result to the case of quasi-convex utility functions. Simulation studies in different scenarios demonstrate the efficacy of our proposed system., This paper has been submitted to the 21st IEEE International Conference on Intelligent Transportation Systems (ITSC) for publication

We present a simple model and algorithm for predicting driver destinations and routes, based on the input of the latest road links visited as part of an ongoing trip. The algorithm may be used to predict any clusters previously observed in a driver's trip history. It assumes that the driver's historical trips are grouped into clusters sharing similar patterns. Given a new trip, the algorithm attempts to predict the cluster in which the trip belongs. The proposed algorithm has low temporal complexity. In addition, it does not require the transition and emission matrices of the Markov chain to be computed. Rather it relies on the frequencies of co-occurrences of road links and trip clusters. We validate the proposed algorithm against an experimental dataset. We discuss the success and convergence of the algorithm and show that our algorithm has a high prediction success rate., 19 pages, 8 figures

Objective: Between 2% and 8% of college students meet criteria for ADHD, with increased incidence in recent decades. There are very few clinical trials conducted on the meaningful intervention of ADHD in college. Mindfulness-based cognitive therapy (MBCT) effectively treats college students with ADHD and could be more feasibly applied in college mental health clinics. Method: Fifty-four undergraduates with ADHD between ages 19 and 24 were randomized to receive either MBCT or wait-list (WL) during a 6-week intervention phase. ADHD symptoms, neuropsychological performance, and related outcomes were assessed at pre-treatment, post-treatment, and 3-month follow-up. Results: Participants receiving MBCT group showed greater treatment response rates (57%-71% vs. 23%-31%) and experience less anxiety and depression, and greater levels of mindfulness; MBCT participants show greater improvement on neuropsychological performance. Conclusion: MBCT may be a useful intervention for college students with ADHD, improving participants’ ADHD symptoms, mindfulness, and sustained attention.

The metabolic syndrome is now regarded as a contributor to coronary heart disease (CHD), and components of the syndrome have thought to be potential therapeutic targets. Previous study indicates neurotransmitter activation related to psychosocial stress as a key factor to the metabolic syndrome. The

Intelligent Transportation Systems (ITS) can be used to involve vehicles and the road infrastructure to cooperatively implement a number of innovative and useful applications. Here, we explore the possibility to adopt a consensus based distributed speed advisory system to determine the optimal recommended speed in urban areas where only electric vehicles are allowed to travel (e.g., in the city centre). The optimality criterion is to maximise the energy efficiency of a fleet of vehicles travelling in the restricted area, and we adopt recently proposed distributed privacy-preserving consensus algorithms to achieve the desired objective.

Electric vehicles can potentially be the best means of transportation for improving air quality, provided that they are powered by electricity from natural gas or wind, water or solar power. In this paper we describe a simple cooperative algorithm that exploits the energy management units of Plug-in Hybrid Electric Vehicles (PHEVs) to absorb the expected forthcoming energy available from renewable sources. The proposed approach bridges the gap between mobility patterns and power grid constraints, and allows to prevent green energy from being wasted while at the same time reducing the complexity burden of the power grid to charge unexpected loads of electric vehicles. Simulation results are given to show the efficacy of the proposed method.

Among the many motivations to encourage the use of Electric Vehicles (EVs) there is the attractive possibility to implement Vehicle-to-Grid (V2G) functionalities. They are attractive both for EV owners, who can sell their own energy to the grid when they do not need to travel, and also for the power grid, as the stored energy can be used to back-up the fluctuating energy produced from renewable sources or to improve the grid stability at critical times. In this paper we illustrate a distributed algorithm that solves the V2G problem in a fair manner, trying to achieve an optimal trade-off between power generation costs and inconvenience to the vehicle owner. Results are shown and discussed for a case study simulated in the OpenDSS power system environment.

To determine the effect of mindfulness-based stress reduction program, with routine medical care on psychosocial functioning and markers of cardiovascular risk. Randomized controlled trial of 146 patients (86 male and 60 female; aged 45-79 years) with stable ischemic heart disease (IHD) and

In this paper, an application based on the Intelligent Speed Adaptation (ISA) system is proposed to reduce CO 2 emissions of vehicles running on the highway. We apply the idea of optimised consensus to solve the emission optimisation problem in a simplified highway scenario through simulation study. Our approach shows that total CO 2 emissions of vehicles can be minimised if all vehicles follow the reference speed signal derived from the ISA.

The equilibration in relativistic heavy-ion collisions for the systems 16O + 16O, 40Ca + 40Ca and 139La + 139(La is studied with relativistic VUU theory. We have found that the mean field still plays an important role in addition to the collision term in the equilibration process in relativistic heavy-ion collisions at energy around 1 GeV/nucleon. The influence of the medium effect is studied. The light and medium systems do not reach complete equilibrium so that the concept based on it is questionable at the energy domain 1 GeV/nucleon.

The objective of this study was to analyze the phylogenetic composition of bacterial community in the soil of an earth-cave (Niu Cave) using a culture-independent molecular approach. 16S rRNA genes were amplified directly from soil DNA with universally conserved and Bacteria-specific rRNA gene primers and cloned. The clone library was screened by restriction fragment length polymorphism (RFLP), and representative rRNA gene sequences were determined. A total of 115 bacterial sequence types were found in 190 analyzed clones. Phylogenetic sequence analyses revealed novel 16S rRNA gene sequence types and a high diversity of putative bacterial community. Members of these bacteria included Proteobacteria (42.6%), Acidobacteria (18.6%), Planctomycetes (9.0%), Chloroflexi (Green nonsulfur bacteria, 7.5%), Bacteroidetes (2.1%), Gemmatimonadetes (2.7%), Nitrospirae (8.0%), Actinobacteria (High G+C Gram-positive bacteria, 6.4%) and candidate divisions (including the OP3, GN08, and SBR1093, 3.2%). Thirty-five clones were affiliated with bacteria that were related to nitrogen, sulfur, iron or manganese cycles. The comparison of the present data with the data obtained previously from caves based on 16S rRNA gene analysis revealed similarities in the bacterial community components, especially in the high abundance of Proteobacteria and Acidobacteria. Furthermore, this study provided the novel evidence for presence of Gemmatimonadetes, Nitrosomonadales, Oceanospirillales, and Rubrobacterales in a karstic hypogean environment.

Mammalian reoviruses are internalized into cells by receptor-mediated endocytosis. Within the endocytic compartment, the viral outer capsid undergoes acid-dependent proteolysis resulting in removal of the σ3 protein and proteolytic cleavage of the μ1/μ1C protein. Ammonium chloride (AC) is a weak base that blocks disassembly of reovirus virions by inhibiting acidification of intracellular vacuoles. To identify domains in reovirus proteins that influence pH-sensitive steps in viral disassembly, we adapted strain type 3 Dearing (T3D) to growth in murine L929 cells treated with AC. In comparison to wild-type (wt) T3D, AC-adapted (ACA-D) variant viruses exhibited increased yields in AC-treated cells. AC resistance of reassortant viruses generated from a cross of wt type 1 Lang and ACA-D variant ACA-D1 segregated with the σ3-encoding S4 gene. The deduced σ3 amino acid sequences of six independently derived ACA-D variants contain one or two mutations each, affecting a total of six residues. Four of these mutations, I180T, A246G, I347S, and Y354H, cluster in the virion-distal lobe of σ3. Linkage of these mutations to AC resistance was confirmed in experiments using reovirus disassembly intermediates recoated with wt or mutant σ3 proteins. In comparison to wt virions, ACA-D viruses displayed enhanced susceptibility to proteolysis by endocytic protease cathepsin L. Image reconstructions of cryoelectron micrographs of three ACA-D viruses that each contain a single mutation in the virion-distal lobe of σ3 demonstrated native capsid protein organization and minimal alterations in σ3 structure. These results suggest that mutations in σ3 that confer resistance to inhibitors of vacuolar acidification identify a specific domain that regulates proteolytic disassembly.

Cryo-electron microscopy was exploited to reveal and study the influence of pyruvate dehydrogenase (E 1 ) occupancy on the conformational states of the Saccharomyces cerevisiae pyruvate dehydrogenase complex (PDC). Structures representative of PDC preparations with ≈40% and full E 1 occupancy were determined after the electron microscopy images from each preparation were classified according to their sizes. The reconstructions derived from two size groups showed that the deposition of the E 1 molecules associated with the larger complex is, unexpectedly, not icosahedrally arranged, whereas in the smaller complex the E 1 molecules have an arrangement and architecture similar to their more ordered deposition in the WT bovine kidney PDC. This study also shows that the linker of dihydrolipamide acetyltransferase (E 2 ) that tethers E 1 to the E 2 core increases in length from ≈50 to 75 Å, accounting largely for the size difference of the smaller and larger structures, respectively. Extensive E 1 occupancy of its 60 E 2 binding sites favors the extended conformation of the linker associated with the larger complex and appears to be related to the loss of icosahedral symmetry of the E 1 molecules. However, the presence of a significant fraction of larger molecules also in the WT PDC preparation with low E 1 occupancy indicates that the conformational variability of the linker contributes to the overall protein dynamics of the PDC and the variable deposition of E 1 . The flexibility of the complex may enhance the catalytic proficiency of this macromolecular machine by promoting the channeling of the intermediates of catalysis between the active sites.