Exploring the differences between forward osmosis and reverse osmosis fouling

A comparison of alginate fouling in forward osmosis (FO) with that in reverse osmosis (RO) was made. A key experimental finding, corroborated by membrane autopsies, was that FO is essentially more prone to fouling than RO, which is opposite to a common claim in the literature where deductions on fouling are often based solely on the water flux profiles. Our theoretical analysis shows that, due to a decrease in the intensity of internal concentration polarization (ICP), and thus an increase in the effective osmotic driving force during FO fouling tests, the similarity of experimental water flux profiles for FO and RO is in accordance with there being greater fouling in FO than RO. The specific foulant resistance for FO was also found to be greater than that for RO. Possible explanations are discussed and these include the influence of reverse solute diffusion from draw solution. Whilst this explanation regarding specific foulant resistance is dependent on the draw solution properties, the finding of greater overall foulant accumulation in FO is considered to be a general finding. Additionally, the present study did not find evidence that hydraulic pressure in RO plays a critical role in foulant layer compaction. Overall this study demonstrated that although FO has higher fouling propensity, it offers superior water flux stability against fouling. For certain practical applications this resilience may be important

The Carbon Dioxide Removal Model Intercomparison Project (CDRMIP): rationale and experimental protocol for CMIP6

The recent IPCC reports state that continued anthropogenic greenhouse gas emissions are changing the climate, threatening "severe, pervasive and irreversible" impacts. Slow progress in emissions reduction to mitigate climate change is resulting in increased attention to what is called geoengineering, climate engineering, or climate intervention – deliberate interventions to counter climate change that seek to either modify the Earth's radiation budget or remove greenhouse gases such as CO2 from the atmosphere. When focused on CO2, the latter of these categories is called carbon dioxide removal (CDR). Future emission scenarios that stay well below 2 °C, and all emission scenarios that do not exceed 1.5 °C warming by the year 2100, require some form of CDR. At present, there is little consensus on the climate impacts and atmospheric CO2 reduction efficacy of the different types of proposed CDR. To address this need, the Carbon Dioxide Removal Model Intercomparison Project (or CDRMIP) was initiated. This project brings together models of the Earth system in a common framework to explore the potential, impacts, and challenges of CDR. Here, we describe the first set of CDRMIP experiments, which are formally part of the 6th Coupled Model Intercomparison Project (CMIP6). These experiments are designed to address questions concerning CDR-induced climate "reversibility", the response of the Earth system to direct atmospheric CO2 removal (direct air capture and storage), and the CDR potential and impacts of afforestation and reforestation, as well as ocean alkalinization.

Measurement and models accounting for cell death capture hidden variation in compound response.

Cancer cell sensitivity or resistance is almost universally quantified through a direct or surrogate measure of cell number. However, compound responses can occur through many distinct phenotypic outcomes, including changes in cell growth, apoptosis, and non-apoptotic cell death. These outcomes have divergent effects on the tumor microenvironment, immune response, and resistance mechanisms. Here, we show that quantifying cell viability alone is insufficient to distinguish between these compound responses. Using an alternative assay and drug-response analysis amenable to high-throughput measurement, we find that compounds with identical viability outcomes can have very different effects on cell growth and death. Moreover, additive compound pairs with distinct growth/death effects can appear synergistic when only assessed by viability. Overall, these results demonstrate an approach to incorporating measurements of cell death when characterizing a pharmacologic response

The INSIDEOUT framework provides precise signatures of the balance of intrinsic and extrinsic dynamics in brain states

Finding precise signatures of different brain states is a central, unsolved question in neuroscience. We reformulated the problem to quantify the 'inside out' balance of intrinsic and extrinsic brain dynamics in brain states. The difference in brain state can be described as differences in the detailed causal interactions found in the underlying intrinsic brain dynamics. We used a thermodynamics framework to quantify the breaking of the detailed balance captured by the level of asymmetry in temporal processing, i.e. the arrow of time. Specifically, the temporal asymmetry was computed by the time-shifted correlation matrices for the forward and reversed time series, reflecting the level of non-reversibility/non-equilibrium. We found precise, distinguishing signatures in terms of the reversibility and hierarchy of large-scale dynamics in three radically different brain states (awake, deep sleep and anaesthesia) in electrocorticography data from non-human primates. Significantly lower levels of reversibility were found in deep sleep and anaesthesia compared to wakefulness. Non-wakeful states also showed a flatter hierarchy, reflecting the diversity of the reversibility across the brain. Overall, this provides signatures of the breaking of detailed balance in different brain states, perhaps reflecting levels of conscious awareness

Effect on smoking quit rate of telling patients their lung age: the Step2quit randomised controlled trial

Objective To evaluate the impact of telling patients their estimated spirometric lung age as an incentive to quit smoking.Design Randomised controlled trial.Setting Five general practices in Hertfordshire, England.Participants 561 current smokers aged over 35.Intervention All participants were offered spirometric assessment of lung function. Participants in intervention group received their results in terms of "lung age" (the age of the average healthy individual who would perform similar to them on spirometry). Those in the control group received a raw figure for forced expiratory volume at one second (FEV1). Both groups were advised to quit and offered referral to local NHS smoking cessation services.Main outcome measures The primary outcome measure was verified cessation of smoking by salivary cotinine testing 12 months after recruitment. Secondary outcomes were reported changes in daily consumption of cigarettes and identification of new diagnoses of chronic obstructive lung disease.Results Follow-up was 89%. Independently verified quit rates at 12 months in the intervention and control groups, respectively, were 13.6% and 6.4% (difference 7.2%, P=0.005, 95% confidence interval 2.2% to 12.1%; number needed to treat 14). People with worse spirometric lung age were no more likely to have quit than those with normal lung age in either group. Cost per successful quitter was estimated at 280 pound ((euro) 365, $556). A new diagnosis of obstructive lung disease was made in 17% in the intervention group and 14% in the control group; a total of 16% (89/561) of participants.Conclusion Telling smokers their lung age significantly improves the likelihood of them quitting smoking, but the mechanism by which this intervention achieves its effect is unclear.Trial registration National Research Register N0096173751

Quantum Algorithm Implementations for Beginners

As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers. While currently available quantum computers have less than 100 qubits, quantum computing hardware is widely expected to grow in terms of qubit count, quality, and connectivity. This review aims to explain the principles of quantum programming, which are quite different from classical programming, with straightforward algebra that makes understanding of the underlying fascinating quantum mechanical principles optional. We give an introduction to quantum computing algorithms and their implementation on real quantum hardware. We survey 20 different quantum algorithms, attempting to describe each in a succinct and self-contained fashion. We show how these algorithms can be implemented on IBM's quantum computer, and in each case, we discuss the results of the implementation with respect to differences between the simulator and the actual hardware runs. This article introduces computer scientists, physicists, and engineers to quantum algorithms and provides a blueprint for their implementations

Steric trapping reveals a cooperativity network in the intramembrane protease GlpG.

Membrane proteins are assembled through balanced interactions among proteins, lipids and water. Studying their folding while maintaining the native lipid environment is necessary but challenging. Here we present methods for analyzing key elements of membrane protein folding including thermodynamic stability, compactness of the unfolded state and folding cooperativity under native conditions. The methods are based on steric trapping, which couples the unfolding of a doubly biotinylated protein to the binding of monovalent streptavidin (mSA). We further advanced this technology for general application by developing versatile biotin probes possessing spectroscopic reporters that are sensitized by mSA binding or protein unfolding. By applying these methods to the Escherichia coli intramembrane protease GlpG, we elucidated a widely unraveled unfolded state, subglobal unfolding of the region encompassing the active site, and a network of cooperative and localized interactions to maintain stability. These findings provide crucial insights into the folding energy landscape of membrane proteins

The Zero Emissions Commitment Model Intercomparison Project (ZECMIP) contribution to C4MIP: Quantifying committed climate changes following zero carbon emissions

The amount of additional future temperature change following a complete cessation of CO2 emissions is a measure of the unrealized warming to which we are committed due to CO2 already emitted to the atmosphere. This “zero emissions commitment” (ZEC) is also an important quantity when estimating the remaining carbon budget – a limit on the total amount of CO2 emissions consistent with limiting global mean temperature at a particular level. In the recent IPCC Special Report on Global Warming of 1.5 ∘C, the carbon budget framework used to calculate the remaining carbon budget for 1.5 ∘C included the assumption that the ZEC due to CO2 emissions is negligible and close to zero. Previous research has shown significant uncertainty even in the sign of the ZEC. To close this knowledge gap, we propose the Zero Emissions Commitment Model Intercomparison Project (ZECMIP), which will quantify the amount of unrealized temperature change that occurs after CO2 emissions cease and investigate the geophysical drivers behind this climate response. Quantitative information on ZEC is a key gap in our knowledge, and one that will not be addressed by currently planned CMIP6 simulations, yet it is crucial for verifying whether carbon budgets need to be adjusted to account for any unrealized temperature change resulting from past CO2 emissions. We request only one top-priority simulation from comprehensive general circulation Earth system models (ESMs) and Earth system models of intermediate complexity (EMICs) – a branch from the 1 % CO2 run with CO2 emissions set to zero at the point of 1000 PgC of total CO2 emissions in the simulation – with the possibility for additional simulations, if resources allow. ZECMIP is part of CMIP6, under joint sponsorship by C4MIP and CDRMIP, with associated experiment names to enable data submissions to the Earth System Grid Federation. All data will be published and made freely available

IAEA Atlas of Cardiac PET/CT

This open access book presents a wide portfolio of examples of positron emission tomography coupled with computer tomography (PET/CT) studies in various cardiac conditions in order to provide a rationale for the implementation of this technology in an array of clinical conditions. Cardiovascular diseases are a major contributor to premature morbidity and mortality worldwide. Low- and middle-income countries (LMICs) are particularly affected by cardiovascular diseases (CVDs), with more than 75% of all CVDs deaths occurring in these countries. For this reason, target 3.4 of the United Nations (UN) Sustainable Development Goals (SDGs) agenda aims at a 30% reduction in premature mortality due to non-communicable diseases (NCDs), which include CVDs, by 2030. Among CVDs, ischemic heart disease (IHD) plays an important role and, according to the Institute for Health Metrics and Evaluation (IHME), it was responsible for 15.96% of global deaths in 2017. Between 2000 and 2017, the number of IHD deaths worldwide increased by 0.26% per year. Several imaging tools help to non-invasively diagnose, stratify risk and guide management in cardiac disease. They include nuclear cardiology techniques, using either SPECT (single photon emission computed tomography) or PET/CT. While myocardial imaging with SPECT has been fully embraced by the cardiology community and is widely available worldwide, PET/CT introduction has been slower, due not only to its higher costs, but also to the limited availability of PET/VCT scanners, mostly utilized for oncological applications. This book is an invaluable tool for nuclear medicine physicians, cardiologists and radiologists

Non-linear changes in modelled terrestrial ecosystems subjected to perturbations

Perturbed ecosystems may undergo rapid and non-linear changes, resulting in ‘regime shifts’ to an entirely different ecological state. The need to understand the extent, nature, magnitude and reversibility of these changes is urgent given the profound effects that humans are having on the natural world. General ecosystem models, which simulate the dynamics of ecosystems based on a mechanistic representation of ecological processes, provide one novel way to project ecosystem changes across all scales and trophic levels, and to forecast impact thresholds beyond which irreversible changes may occur. We model ecosystem changes in four terrestrial biomes subjected to human removal of plant biomass, such as occurs through agricultural land-use change. We find that irreversible, non-linear responses commonly occur where removal of vegetation exceeds 80% (a level that occurs across nearly 10% of the Earth’s land surface), especially for organisms at higher trophic levels and in less productive ecosystems. Very large, irreversible changes to ecosystem structure are expected at levels of vegetation removal akin to those in the most intensively used real-world ecosystems. Our results suggest that the projected twenty-first century rapid increases in agricultural land conversion may lead to widespread trophic cascades and in some cases irreversible changes to ecosystem structure