Glaucoma severity at first presentation to an ophthalmologist and risk factors for late presentation in rural Australia: the S1P study.

CLINICAL RELEVANCE: Well-targeted referrals and timely commencement of treatment are essential to limiting vision loss in glaucoma. Optometrists, primary care providers, and public health policymakers can utilise predictors of late to identify and target at-risk populations. BACKGROUND: This study, which aimed to evaluate glaucoma severity at first presentation to an ophthalmologist in a rural Australian population, is the first of its kind in an Australian population. METHODS: Patient records from a large ophthalmology clinic in Port Macquarie, NSW were retrospectively reviewed using the Fight Glaucoma Blindness registry to identify patients who were first diagnosed with glaucoma at an ophthalmology practice in 2020 or 2021. Associations with glaucoma severity at presentation, measured with visual field index (VFI), were analysed using a beta-regression model. Retinal nerve fibre layer measurements were evaluated as a secondary outcome measure using linear regression. RESULTS: From 3548 new patients seen, 110 cases of glaucoma were diagnosed, 95 of whom met inclusion criteria. These comprised 41.8% primary open-angle glaucoma, 32.7% normal-tension glaucoma, 11.8% secondary open-angle glaucoma, 12.7% primary angle closure glaucoma, and 0.9% secondary angle closure glaucoma. The median VFI at presentation was 94.5%, and 71.9% of patients had a VFI ≥ 90%. However, 6.3% of patients presented with a VFI below 50%. Older age, higher intraocular pressure, and worse visual acuity were significantly associated with severity at presentation. No associations were found for remoteness, sex, family history, or glaucoma type. CONCLUSIONS: Glaucoma appears to be diagnosed relatively early in this population. Severity at presentation was associated with age, intraocular pressure, and visual acuity, but not influenced by the social determinants assessed. These findings underscore the importance of frequent comprehensive eye examinations in older patients. Replication in other Australian populations is recommended as the generalisability of these findings is limited.

A dynamic compositional equilibrium governs mRNA recognition by eIF3.

Eukaryotic translation initiation factor (eIF) 3 is a multi-subunit protein complex that binds both ribosomes and messenger RNAs (mRNAs) in order to drive a diverse set of mechanistic steps during translation. Despite its importance, a unifying framework explaining how eIF3 performs these numerous activities is lacking. Using single-molecule light scattering microscopy, we demonstrate that Saccharomyces cerevisiae eIF3 is an equilibrium mixture of the full complex, subcomplexes, and subunits. By extending our microscopy approach to an in vitro reconstituted eIF3 and complementing it with biochemical assays, we define the subspecies comprising this equilibrium and show that, rather than being driven by the full complex, mRNA binding by eIF3 is instead driven by the eIF3a subunit within eIF3a-containing subcomplexes. Our findings provide a mechanistic model for the role of eIF3 in the mRNA recruitment step of translation initiation and establish a mechanistic framework for explaining and investigating the other activities of eIF3.

Increasing the accuracy of single-molecule data analysis using tMAVEN.

Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physicochemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule data set and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series modeling, analysis, and visualization environment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from preprocessing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule data set with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule data sets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physicochemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule data sets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses.

Increasing the accuracy of single-molecule data analysis using tMAVEN.

Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physico-chemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule dataset and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from pre-processing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule dataset with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule datasets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physico-chemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule datasets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses.

The mechanism of mRNA activation.

During translation initiation, messenger RNA molecules must be identified and activated for loading into a ribosome. In this rate-limiting step, the heterotrimeric protein eukaryotic initiation factor eIF4F must recognize and productively interact with the 7-methylguanosine cap at the 5' end of the messenger RNA and subsequently activate the message. Despite its fundamental, regulatory role in gene expression, the molecular events underlying cap recognition and messenger RNA activation remain mysterious. Here, we generate a unique, single-molecule fluorescence imaging system to interrogate the dynamics with which eIF4F discriminates productive and non-productive locations on full-length, native messenger RNA molecules. At the single-molecule level, we observe stochastic sampling of eIF4F along the length of the messenger RNA and identify allosteric communication between the eIF4F subunits which ultimately drive cap-recognition and subsequent activation of the message. Our experiments uncover novel functions for each subunit of eIF4F and we conclude by presenting a model for messenger RNA activation which precisely defines the composition of the activated message. This model provides a general framework for understanding how messenger RNA molecules may be discriminated from one another, and how other RNA-binding proteins may control the efficiency of translation initiation.

Entropic control of the free-energy landscape of an archetypal biomolecular machine.

Biomolecular machines are complex macromolecular assemblies that utilize thermal and chemical energy to perform essential, multistep, cellular processes. Despite possessing different architectures and functions, an essential feature of the mechanisms of action of all such machines is that they require dynamic rearrangements of structural components. Surprisingly, biomolecular machines generally possess only a limited set of such motions, suggesting that these dynamics must be repurposed to drive different mechanistic steps. Although ligands that interact with these machines are known to drive such repurposing, the physical and structural mechanisms through which ligands achieve this remain unknown. Using temperature-dependent, single-molecule measurements analyzed with a time-resolution-enhancing algorithm, here, we dissect the free-energy landscape of an archetypal biomolecular machine, the bacterial ribosome, to reveal how its dynamics are repurposed to drive distinct steps during ribosome-catalyzed protein synthesis. Specifically, we show that the free-energy landscape of the ribosome encompasses a network of allosterically coupled structural elements that coordinates the motions of these elements. Moreover, we reveal that ribosomal ligands which participate in disparate steps of the protein synthesis pathway repurpose this network by differentially modulating the structural flexibility of the ribosomal complex (i.e., the entropic component of the free-energy landscape). We propose that such ligand-dependent entropic control of free-energy landscapes has evolved as a general strategy through which ligands may regulate the functions of all biomolecular machines. Such entropic control is therefore an important driver in the evolution of naturally occurring biomolecular machines and a critical consideration for the design of synthetic molecular machines.

Spore exines increase vitamin D clinical bioavailability by mucoadhesion and bile triggered release.

Sporopollenin exine capsules (SpECs) are microcapsules derived from the outer shells (exines) of plant spore and pollen grains. This work reports the first clinical study on healthy volunteers to show enhanced bioavailability of vitamin D encapsulated in SpECs from Lycopodium clavatum L. spore grains vs vitamin D alone, and the first evidence (in vitro, ex vivo and in vivo) of mechanisms to account for the enhancement and release of the active in the small intestine. Evidence for mucoadhesion of the SpECs contributing to the mechanism of the enhancement is based on: (i) release profile over time of vitamin D in a double blind cross-over human study showing significant release in the small intestine; (ii) in vivo particle counting data in rat showing preferred retention of SpECs vs synthetic beads; (iii) ex vivo99mTc labelling and counting data using rat small intestine sections showing preferred retention of SpECs vs synthetic beads; (iv) in vitro mucoadhesion data. Triggered release by bile in the small intestine was shown in vitro using solid state NMR and HPLC.

Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences.

A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N-dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results-especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes' rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated 'engine' capable of computationally executing analysis decisions currently made by visual inspection across the sciences.

Publicly reported health performance measures 2010-2020.

AIM: Performance measurement is central to healthcare management in many countries. The aim of this study was to determine whether performance measurement in a New Zealand healthcare organisation met a range of criteria supported by healthcare management literature. METHOD: Performance expectations published in statements of intent and annual plans from an 11-year period were analysed for evidence of continuity, accuracy, effectiveness, patient centredness and clinical relevance. RESULTS: 731 distinct performance measurements were identified. 48% were measured only once. Of those where comparison was possible, 21.9% met at least one expected target or range. In published reports there was limited reference to data verification methods, tests of significance, prospective linkage to actions, counterbalancing measures, application of benchmarks or standards, or patient measure prioritisation. CONCLUSIONS: These findings suggest that healthcare organisations do not find performance measurement easy. This may be due to the wrong choice of measures, inappropriate targets, incomplete analyses or difficulty in linking measurement results to actions.

Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments.

Biophysics experiments performed at single-molecule resolution provide exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. In this review, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous method of incorporating information from multiple experiments into a single analysis and finding the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment.

Screening method for the detection of residues of amphenicol antibiotics in bovine milk by optical biosensor.

An immunobiosensor assay was developed for multi-residue screening in bovine milk of the parent amphenicols, thiamphenicol and florfenicol, along with the metabolite florfenicol amine. A polyclonal antibody raised in a rabbit after immunisation with a florfenicol amine-protein conjugate was employed in the assay. Milk samples were subjected to acetonitrile extraction, reconstituted in buffer and diluted prior to biosensor analysis. Validation data obtained from the analysis of fortified samples has shown that the method has a detection capability of less than 0.25 µg kg-1 for florfenicol and less than 0.5 µg kg-1 for florfenicol amine and thiamphenicol. The cross-reactivity profile and validation data for the detection of these amphenicols is presented together with results obtained following the analysis of florfenicol incurred samples using the developed screening method along with a comparison of results obtained from the analysis of the same incurred samples using an MRM3 UPLC-MS/MS confirmatory method. Results are also presented obtained from the analysis of samples from both treated and non-treated animals which were co-housed and which show the potential for cross-contamination.

Additional qualifications of trainees in specialist training programs in Australia.

BACKGROUND: In Australia, the number of medical graduates per year has increased at a greater rate than the increase in the number of specialist training places. Consequently, competition for training positions is intensifying. There is anecdotal evidence to suggest that medical graduates are acquiring additional qualifications to compete with their peers Stevenson 2017 ( https://insightplus.mja.com.au/2017/36/specialty-training-places-the-other-looming-crisis/ ). Our study investigates this phenomenon of additional credentialing and demonstrates the number and type of postgraduate and research qualifications obtained by specialists in training in Australia. This is the first study to assess the number and type of credentials acquired by registrars in each specialty and to provide insight into differences between specialities. METHODS: Information on specialists in training was obtained through the Medicine in Australia: Balancing Employment and Life (MABEL) survey conducted between 2008 and 2014. The number of any additional qualifications and specific PhD, Master's degree, postgraduate diploma/certificate and research degrees from medical school were assessed for each specialist training scheme in the database. RESULTS: Overall, 995 registrars representing 13 specialties were included. Just under a third (30.4%) completed a research-based degree during their medical degree and almost half (46.7%) of specialist registrars obtained further qualifications after completing medicine. A significantly higher proportion of ophthalmology (78.6%) and paediatric (67.5%) registrars, and a lower percentage of emergency medicine (36.7%) registrars, held postgraduate qualifications. Overall, 2.4% of registrars held a PhD and 10.1% held a Master's degree. A higher percentage of either PhD or Master's was held by ophthalmology (64.3%) and surgical (30.6%) trainees and a lower percentage by anaesthetics (6.3%) and physician trainees (7.9%). Postgraduate diplomas or certificates were most common among paediatric (41.2%) and obstetrics and gynaecology (25.6%) registrars. CONCLUSION: This is the first study to investigate the additional qualifications of specialists in training in Australia. Almost half of specialists in training surveyed (46.7%) have completed some form of additional study, whether it is a PhD, Master's, postgraduate diploma/certificate or research degree from medical school. Trainees of specialist training schemes are more qualified than specialists who trained in the past Aust Fam Physician 32:92-4, 2003.

Healthcare use, costs and quality of life in patients with end-stage kidney disease receiving conservative management: results from a multi-centre observational study (PACKS).

BACKGROUND: Previous research has explored the cost of providing renal replacement therapies in patients with end-stage kidney disease and their quality of life. This is the first study to examine the healthcare costs of patients receiving conservative care without dialysis for end-stage kidney disease. This alternative to dialysis is an option for patients who prefer a supportive and palliative care approach. AIM: Descriptive cost and quality of life analyses alongside a UK-based multi-centre observational study in patients receiving conservative management for end-stage kidney disease. DESIGN: Health service use was recorded up to 12 months after making the decision to receive conservative management. Mean costs were calculated for each 3-month time period. The annual cost was calculated in two ways: by using only patients with complete cost data and by using all available data weighted by the number of patients at each time point. SETTING: In total, 42 patients who opted for conservative management over dialysis were recruited. RESULTS: Mean costs were £1622 (0-3 months), £1008 (3-6 months), £554 (6-9 months) and £2626 (9-12 months). Mean annual cost based on complete data ( n = 8) was £5511, and the weighted mean annual cost was £5620. CONCLUSION: The importance of this study is twofold. First, it provides substantive new information for health and social care planning of conservative management by demonstrating where demand exists for services, in both the United Kingdom and other countries with a comparable health service structure. Second, methodologically, it indicates that it is feasible to collect service use data directly from this patient population.

Observation and Analysis of RAD51 Nucleation Dynamics at Single-Monomer Resolution.

Human RAD51 promotes accurate DNA repair by homologous recombination and is involved in protection and repair of damaged DNA replication forks. The active species of RAD51 and related recombinases in all organisms is a nucleoprotein filament assembled on single-stranded DNA (ssDNA). The formation of a nucleoprotein filament competent for the recombination reaction, or for DNA replication support, is a delicate and strictly regulated process, which occurs through filament nucleation followed by filament extension. The rates of these two phases of filament formation define the capacity of RAD51 to compete with the ssDNA-binding protein RPA, as well as the lengths of the resulting filament segments. Single-molecule approaches can provide a wealth of quantitative information on the kinetics of RAD51 nucleoprotein filament assembly, internal dynamics, and disassembly. In this chapter, we describe how to set up a single-molecule total internal reflection fluorescence microscopy experiment to monitor the initial steps of RAD51 nucleoprotein filament formation in real-time and at single-monomer resolution. This approach is based on the unique, stretched-ssDNA conformation within the recombinase nucleoprotein filament and follows the efficiency of Förster resonance energy transfer (EFRET) between two DNA-conjugated fluorophores. We will discuss the practical aspects of the experimental setup, extraction of the FRET trajectories, and how to analyze and interpret the data to obtain information on RAD51 nucleation kinetics, the mechanism of nucleation, and the oligomeric species involved in filament formation.

Increasing the Time Resolution of Single-Molecule Experiments with Bayesian Inference.

Many time-resolved single-molecule biophysics experiments seek to characterize the kinetics of biomolecular systems exhibiting dynamics that challenge the time resolution of the given technique. Here, we present a general, computational approach to this problem that employs Bayesian inference to learn the underlying dynamics of such systems, even when they are much faster than the time resolution of the experimental technique being used. By accurately and precisely inferring rate constants, our Bayesian inference for the analysis of subtemporal resolution dynamics approach effectively enables the experimenter to super-resolve the poorly resolved dynamics that are present in their data.

Screening method for the detection of residues of amphenicol antibiotics in bovine, ovine and porcine kidney by optical biosensor.

An immunobiosensor assay was developed for the multi-residue screening of the parent amphenicols, thiamphenicol and florfenicol, along with the metabolite florfenicol amine, in bovine, ovine and porcine kidney. A polyclonal antiserum raised in a rabbit after inoculation with a florfenicol amine-protein conjugate was employed in the assay. Sample homogenates were extracted directly into acetonitrile, reconstituted in buffer and diluted prior to biosensor analysis. Validation data obtained from the analysis of fortified samples has shown that the method has a detection capability (CCß) of less than 25µgkg-1 (1/2 MRL) for thiamphenicol in the kidney of all three species, less than 150µgkg-1(1/2 MRL) for florfenicol and florfenicol amine and less than 250µgkg-1 (1/2 MRL) for florfenicol and florfenicol amine in bovine/ovine and porcine kidney respectively. Intra-assay variation (n=10) was calculated at 4.5% and 2.6% at concentrations of 10µgkg-1 and 150µgkg-1respectively for bovine kidney while inter-assay variation (n=3) was determined to be 5.0% and 16.5% respectively at the same concentrations. The cross-reactivity profile and validation data for the detection of these amphenicols is presented together with the results obtained following the analysis of florfenicol incurred samples using the developed method.

Clinician views of patient decisional conflict when deciding between dialysis and conservative management: Qualitative findings from the PAlliative Care in chronic Kidney diSease (PACKS) study.

BACKGROUND: Only a paucity of studies have addressed clinician perspectives on patient decisional conflict, in making complex decisions between dialysis and conservative management (renal supportive and palliative care). AIM: To explore clinician views on decisional conflict in patients with end-stage kidney disease. DESIGN: Interpretive, qualitative study. SETTING AND PARTICIPANTS: As part of the wider National Institute for Health Research, PAlliative Care in chronic Kidney diSease study, semi-structured interviews were conducted with clinicians (nephrologists n = 12; 7 female and clinical nurse specialists n = 15; 15 female) across 10 renal centres in the United Kingdom. Interviews took place between April 2015 and October 2016 and a thematic analysis of the interview data was undertaken. RESULTS: Three major themes with associated subthemes were identified. The first, 'Frequent changing of mind regarding treatment options', revealed how patients frequently altered their treatment decisions, some refusing to make a decision until deterioration occurred. The second theme, 'Obligatory beneficence', included clinicians helping patients to make informed decisions where outcomes were uncertain. In weighing up risks and benefits, and the impact on patients, clinicians sometimes withheld information they thought might cause concern. Finally, 'Intricacy of the decision' uncovered clinicians' views on the momentous and brave decision to be made. They also acknowledged the risks associated with this complex decision in giving prognostic information which might be inaccurate. LIMITATIONS: Relies on interpretative description which uncovers constructed truths and does not include interviews with patients. CONCLUSION: Findings identify decisional conflict in patient decision-making and a tension between the prerequisite for shared decision-making and current clinical practice. Clinicians also face conflict when discussing treatment options due to uncertainty in equipoise between treatments and how much information should be shared. The findings are likely to resonate across countries outside the United Kingdom.

A discrete Markov metapopulation model for persistence and extinction of species.

A simple discrete generation Markov metapopulation model is formulated for studying the persistence and extinction dynamics of a species in a given region which is divided into a large number of sites or patches. Assuming a linear site occupancy probability from one generation to the next we obtain exact expressions for the time evolution of the expected number of occupied sites and the mean-time to extinction (MTE). Under quite general conditions we show that the MTE, to leading order, is proportional to the logarithm of the initial number of occupied sites and in precise agreement with similar expressions for continuous time-dependent stochastic models. Our key contribution is a novel application of generating function techniques and simple asymptotic methods to obtain a second order asymptotic expression for the MTE which is extremely accurate over the entire range of model parameter values.

PAlliative Care in chronic Kidney diSease: the PACKS study--quality of life, decision making, costs and impact on carers in people managed without dialysis.

BACKGROUND: The number of patients with advanced chronic kidney disease opting for conservative management rather than dialysis is unknown but likely to be growing as increasingly frail patients with advanced renal disease present to renal services. Conservative kidney management includes ongoing medical input and support from a multidisciplinary team. There is limited evidence concerning patient and carer experience of this choice. This study will explore quality of life, symptoms, cognition, frailty, performance decision making, costs and impact on carers in people with advanced chronic kidney disease managed without dialysis and is funded by the National Institute of Health Research in the UK. METHODS: In this prospective, multicentre, longitudinal study, patients will be recruited in the UK, by renal research nurses, once they have made the decision not to embark on dialysis. Carers will be asked to 'opt-in' with consent from patients. The approach includes longitudinal quantitative surveys of quality of life, symptoms, decision making and costs for patients and quality of life and costs for carers, with questionnaires administered quarterly over 12 months. Additionally, the decision making process will be explored via qualitative interviews with renal physicians/clinical nurse specialists. DISCUSSION: The study is designed to capture patient and carer profiles when conservative kidney management is implemented, and understand trajectories of care-receiving and care-giving with the aim of optimising palliative care for this population. It will explore the interactions that lead to clinical care decisions and the impact of these decisions on informal carers with the intention of improving clinical outcomes for patients and the experiences of care givers.

Quantitative Connection between Ensemble Thermodynamics and Single-Molecule Kinetics: A Case Study Using Cryogenic Electron Microscopy and Single-Molecule Fluorescence Resonance Energy Transfer Investigations of the Ribosome.

At equilibrium, thermodynamic and kinetic information can be extracted from biomolecular energy landscapes by many techniques. However, while static, ensemble techniques yield thermodynamic data, often only dynamic, single-molecule techniques can yield the kinetic data that describe transition-state energy barriers. Here we present a generalized framework based upon dwell-time distributions that can be used to connect such static, ensemble techniques with dynamic, single-molecule techniques, and thus characterize energy landscapes to greater resolutions. We demonstrate the utility of this framework by applying it to cryogenic electron microscopy (cryo-EM) and single-molecule fluorescence resonance energy transfer (smFRET) studies of the bacterial ribosomal pre-translocation complex. Among other benefits, application of this framework to these data explains why two transient, intermediate conformations of the pre-translocation complex, which are observed in a cryo-EM study, may not be observed in several smFRET studies.