This cohort study investigates whether state cannabis legalization is associated with psychosis-related healthcare utilization. Is state cannabis legalization or commercialization associated with increased rates of psychosis-related health care claims?In this cohort study of claims data from 63 680 589 beneficiaries from 2003 to 2017, there was no statistically significant difference in the rates of psychosis-related diagnoses or prescribed antipsychotics in states with medical or recreational cannabis policies compared with states with no such policy.The findings of this study do not support an association between state policies legalizing cannabis and psychosis-related outcomes; further research into this topic may be informative. Psychosis is a hypothesized consequence of cannabis use. Legalization of cannabis could therefore be associated with an increase in rates of health care utilization for psychosis.To evaluate the association of state medical and recreational cannabis laws and commercialization with rates of psychosis-related health care utilization.Retrospective cohort design using state-level panel fixed effects to model within-state changes in monthly rates of psychosis-related health care claims as a function of state cannabis policy level, adjusting for time-varying state-level characteristics and state, year, and month fixed effects. Commercial and Medicare Advantage claims data for beneficiaries aged 16 years and older in all 50 US states and the District of Columbia, 2003 to 2017 were used. Data were analyzed from April 2021 to October 2022.State cannabis legalization policies were measured for each state and month based on law type  and degree of commercialization (presence or absence of retail outlets).Outcomes were rates of psychosis-related diagnoses and prescribed antipsychotics.This study included 63 680 589 beneficiaries followed for 2 015 189 706 person-months. Women accounted for 51.8% of follow-up time with the majority of person-months recorded for those aged 65 years and older (77.3%) and among White beneficiaries (64.6%). Results from fully-adjusted models showed that, compared with no legalization policy, states with legalization policies experienced no statistically significant increase in rates of psychosis-related diagnoses  or prescribed antipsychotics . In exploratory secondary analyses, rates of psychosis-related diagnoses increased significantly among men, people aged 55 to 64 years, and Asian beneficiaries in states with recreational policies compared with no policy.In this retrospective cohort study of commercial and Medicare Advantage claims data, state medical and recreational cannabis policies were not associated with a statistically significant increase in rates of psychosis-related health outcomes. As states continue to introduce new cannabis policies, continued evaluation of psychosis as a potential consequence of state cannabis legalization may be informative. An association between cannabis use and psychosis has since been demonstrated in numerous longitudinal studies.16 Findings from experimental research, genome-wide association studies, and mendelian randomization studies further support a causal link between cannabis use and schizophrenia.19 Whether cannabis plays a causal role in the onset of psychosis nevertheless remains a point of controversy.20Psychosis has long been investigated as a potential consequence of cannabis use. Among Swedish conscripts followed from 1969 to 1983, Andréasson and colleagues21 As of June 2022, medical cannabis is legal in 38 states, and 19 permit recreational use.22 With legalization, the price of cannabis has decreased substantially.24 Simultaneously, the average THC content of herbal cannabis in the US increased markedly from 4% in 1996 to 17% in 2017.27 Past research on cannabis legalization in the US suggests a range of potential outcomes including decreased arrest rates,28 increased clearance rates for violent crimes,29 increased rates of cannabis use disorder,31 and increased rates of self-harm among men younger than 40 years.32 A limited number of studies have further identified increased rates of psychotic disorders associated with state and regional cannabis legalization in the US and with national policies in Canada and Portugal.36 As states continue to introduce cannabis legislation, a thorough and comprehensive understanding of their potential health outcomes is essential. Yet to our knowledge, no studies have examined trends in psychosis-related outcomes as a function of medical and recreational cannabis laws across all US states.In the US, an estimated 48.2 million people aged 12 years and older used cannabis at least once in 2019.37 we define a measure of state cannabis policy that considers both medical and recreational laws and identifies whether states permitted commercial sales through retail outlets. We hypothesized a priori that rates of psychosis-related diagnoses and prescribed antipsychotics would be increased in states with recreational policies and in those permitting commercial sales. As the health outcomes of state cannabis policies may differ within populations,32 we considered rates of psychosis-related claims by sex, age, and race and ethnicity.We evaluate the association of state cannabis legalization with rates of psychosis-related health care claims among privately insured individuals followed from 2003 to 2017. As the outcomes of cannabis policies may depend on the provisions included,The Optum Clinformatics Data Mart Database is a deidentified commercial and Medicare Advantage claims database composed of more than 63 million unique individuals followed from January 1, 2003, to December 31, 2017. Study data included member enrollment data, diagnostic codes, and pharmacy claims deterministically linked across file types with a unique patient identifier. This study included all beneficiaries aged 16 years and older with at least 1 month of insurance eligibility during the study period.38 We counted the number of unique claims with psychosis-related diagnoses, prescribed antipsychotics, and enrolled individuals for each state-month of follow-up. These values were merged with time-varying categorical measures of state cannabis policy level and state-level demographic, social, and economic characteristics. This study was approved by the institutional review board at Stanford University and is reported per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. The requirement for written informed consent was waived for this study by all participating institutions because data were deidentified. The analysis plan was prepared and preregistered in August 2021.39 Additions to the preregistered plan are summarized in eAppendix 1 in In this retrospective cohort study, we leveraged a panel fixed-effects design—an extension of differences-in-differences—in which the state-month was the unit of analysis to evaluate the association of state cannabis policies with rates of psychosis-related health care claims.40 This analysis therefore focuses on legalization, in which personal use; cultivation of cannabis; or its production, promotion, and sale is permitted.Decriminalization removes criminal penalties for simple possession and use of cannabis. Past research suggests that decriminalization does not exert a sufficiently large effect on cannabis use rates to influence rates of psychotic disorders.32 we created a time-varying categorical variable reflecting the type of cannabis use permitted  and whether retail outlets were open and operational. Cannabis legalization policies without retail outlets allowed only home-grown cannabis or had not yet implemented commercial sales.41 Data for recreational cannabis laws were derived from the Alcohol Policy Information System cannabis law database. Data for medical cannabis laws were derived from public research available through 2017.42 For each state-month, we assigned state cannabis policy levels as follows: no medical or recreational policy; medical only, no retail outlets; medical only, retail outlets; recreational, no retail outlets; or recreational, retail outlets. In all analyses, states with no medical or recreational policy  served as the referent.As in prior research,International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10) and subclassified as: nonaffective psychoses; mood disorders with psychotic features; substance-related psychosis; and other psychosis  were identified using codes from the 43; the annual percentage of non-Hispanic Asian, non-Hispanic Black, non-Hispanic White, and Hispanic residents from the US Census (2002-2009) and American Community Survey (2010-2017); annual percent living in poverty and median income from the Small Area Income and Poverty Estimates Program; and monthly percent unemployed from the Bureau of Labor Statistics Local Area Unemployment Files. We also included the count of all unique claims and prescriptions for each state-month as a measure of overall utilization.Time-varying state-level covariates included alcohol law stringency score38 We used generalized negative binomial regression, as statistical testing of dispersion indicated it was more appropriate than Poisson or quasi-Poisson. Counts of psychosis-related diagnoses and prescribed antipsychotics were the outcomes of interest. The number of eligible beneficiaries for each state-month was specified as the offset to estimate rate ratios (RR).We used panel fixed-effects to model rates of psychosis-related health care claims as a function of state cannabis policy level.ICD transition. Month fixed effects were included to address potential seasonality in psychosis-related outcomes. We additionally adjusted for the previously-described time-varying state-level characteristics lagged by 1 year to ensure temporal order. For all analyses, we calculated 95% CIs with robust SEs to account for repeated observations within states over time. Statistical inferences are presented based on α = 0.05. Data were analyzed using R Statistical Software, version 4.0.0 , and analyses were completed from April 2021 to October 2022.All analyses included state fixed effects to account for potential confounding by time-invariant state characteristics. Calendar year fixed effects were included to address state-invariant secular trends including increased consumption of cannabis-containing products over time and potential discontinuities in psychosis-related claims introduced by the 44To examine potential differences in psychosis-related outcomes across key population subgroups, we conducted analyses within strata defined by sex , age group , categorical race and ethnicity, and within subgroups of diagnoses and prescriptions. Data on sex, age, race and ethnicity were all provided in beneficiary enrollment files, and details on the ascertainment of race data were not available for data retrieved from these files. Secondary analyses were exploratory, and therefore we did not adjust for multiple comparisons consistent with expert recommendations.46; (3) we tested a 3-category exposure variable ; (4) we restricted to state-months with some form of cannabis policy ; (5) we conducted negative control analyses47 including use of the rate of all unique diagnoses and all unique prescriptions as negative control outcomes, hypothetical law changes at randomly assigned dates as a negative control exposure, and naloxone overdose prevention laws as a negative control exposure to assess potential residual confounding by factors that influence drug policy; (6) we implemented an alternative estimator proposed by Callaway and Sant’Anna that relaxes the typical assumption of panel fixed effects estimators that policy effects are constant over time and do not depend on the timing of legalization.48 (eAppendix 2 in We conducted the following robustness checks: (1) we defined the sum of all unique claims and prescriptions for all health conditions as alternative offsets to account for changes in overall health care utilization; (2) we tested a 6-category exposure that separated recreational cannabis policies into those with and without THC dose-related restrictionsThis study included 63 680 589 beneficiaries with 2 015 189 706 total person-months of follow-up. Women accounted for 51.8% of follow-up time with 77.3% of person-months recorded among individuals 65 years or older and 64.6% among White beneficiaries. There were 7 503 907 psychosis-related diagnoses and 20 799 285 filled prescriptions for antipsychotics recorded over the study period. 29 states adopted medical or recreational cannabis legalization policies . CharactCrude rates were highest for state-months with recreational policies allowing retail outlets for both psychosis-related diagnoses  and prescribed antipsychotics   among those aged 55 to 64 years  and among Asian beneficiaries . We observed no statistically significant association with prescribed antipsychotics . AnalysiResults of sensitivity analyses were generally consistent with our main analyses with alternative offsets; 3- and 6-category exposure metrics; and when analysis was restricted to state-months with some form of cannabis policy in place  visits following legalization of recreational cannabis from 2012 to 2014. They found a 9-fold increase in the prevalence of schizophrenia and other psychotic disorders in cannabis-associated ED visits compared with visits unrelated to cannabis. Using cross-sectional data from the 2017 National Inpatient Sample, Moran et al34 found that in the Pacific census division—where most states had introduced recreational cannabis policies by 2017—odds of psychosis-related hospitalization were higher than elsewhere in the US. Callaghan et al36 found that ED presentations for cannabis-induced psychosis in Ontario and Alberta doubled between April 2015 to December 2019 following legalization via the Cannabis Act on October 17, 2018. Finally, Gonçalves-Pinho et al35 reported an increase in the percentage of patients with a psychotic disorder and increases in cannabis use prevalence from 0.87 to 10.6% in Portugal in the 15 years following decriminalization.A limited number of prior studies have generally reported increased rates of psychosis-related health outcomes in association with state and local cannabis policies. In Colorado, Hall et al34 our analysis considers both medical and recreational policies and whether commercial sales were permitted.41 Cannabis commercialization is hypothesized to magnify cannabis use and associated outcomes through reduced prices, widespread marketing, and expanded availability of high-potency cannabis-containing products.50 Prior studies have also demonstrated that associations depend on whether the policy permitted commercial sales.37Our analysis naturally extends the existing literature by leveraging prospectively recorded health care claims for the entire US. Whereas prior studies have focused on the effects of medical or recreational cannabis policies alone,In contrast with these prior studies, we did not observe a statistically significant association of state cannabis policy level with overall rates of psychosis-related diagnoses or prescribed antipsychotics. Importantly, our outcome measures were composed of psychosis-related diagnosis codes associated with health care delivery, and therefore do not capture episodes of psychosis among individuals who do not receive treatment. Because we cannot reliably distinguish new from existing psychotic disorders using administrative data, it is possible that state cannabis legalization has differential effects on incident vs prevalent psychosis that our results do not reflect. As states continue to introduce cannabis policies, the implications of state cannabis legalization for psychotic disorders warrants continued study, particularly in data settings where direct measures of disease onset and severity are available.23 but are an important area for future study given the potential for differences in social class, norms around mental illness, material resources, access to mental health care, and clinician bias to create and perpetuate disparities. In analysis by sex and age, results were accentuated among men and among those aged 55 to 64 years. Past research identifies heavy cannabis use in adolescence as a salient risk factor for onset of psychosis in young adulthood,51 but little research has examined middle-aged and older adults. However, age is a significant predictor of mental health care utilization, and middle-aged adults are generally more likely to receive services than those at the extremes of age.52 More broadly, findings by sex, age, and race and ethnicity underscore the importance of continued examination of heterogeneous effects of cannabis policies and the implications of these differences for health inequities.Finally, our analysis included exploratory subgroup analysis by sex, age, and race and ethnicity. Racial and ethnic disparities have been reported less frequently in the literature on cannabis and psychosis,53 generalizability of our study findings is limited by our focus on insured individuals likely with fluctuating representativeness within states over the study period. We aimed to minimize confounding by controlling for state and time fixed effects and time-varying state-level characteristics, but residual confounding by factors associated with the broader policy environment such as expanded social safety net programs, rates of comorbid substance use, and preferential relocation by individuals predisposed to psychosis is possible. This is evidenced by the nonnull association of naloxone access laws (designated as a negative control exposure) with psychosis-related diagnoses and prescribed antipsychotics. In addition, a potential limitation of the study was the inability to assess how participant race was determined and the source of the categorizations.As psychotic disorders are associated with lower socioeconomic position,The study period spans the period before and after the Affordable Care Act (ACA) was passed into law in 2010. We anticipate that potential confounding by passage of the ACA in our analysis is minimized. First, there is no clear evidence of an immediate effect of the ACA on state cannabis policy level as only 2 states (Delaware and New Jersey) and the District of Columbia introduced new state cannabis policy shortly after passage of the ACA. Second, our analysis includes fixed effects for calendar year. This means that any changes that applied to all states at the same time are controlled by design with these fixed effects. If there is residual confounding due to the ACA, it would be because the influence of the ACA differs in a time-varying and state-specific way55 Additionally, several unexpected secondary findings are not easily explained and warrant further consideration. These include the minimal association with recreational cannabis policies that permit retail outlets but make no THC dose-related restrictions, and the dose-response association between cannabis policy level and overall rates of prescriptions. Future analyses should explore systematic differences in state-level factors including prescribing patterns that may be correlated with cannabis policies.We did not adjust for multiple comparisons in exploratory secondary analyses. Although Bonferroni correction may have desirable properties when the sample size is large and a moderate number of tests are performed, we note the effective sample size in our analysis is much smaller than the number of claims because the unit of analysis is the state-month. Nevertheless, we acknowledge the potential risk of type I error  without correction for multiple comparisons.In this retrospective cohort study of commercial and Medicare Advantage claims data, state medical and recreational cannabis policies were not associated with a statistically significant increase in rates of psychosis-related health outcomes. As US states continue to legalize the use, production, promotion, or sale of cannabis, continued examination of the implications of state cannabis policies for psychotic disorders may be informative, particularly with study designs that yield precise estimates in high-risk population subgroups.


Bicuspid aortic valve is the most common congenital cardiac malformation and the leading cause of aortopathy and aortic stenosis in younger patients. Aortic wall remodelling secondary to altered haemodynamic flow patterns, changes in peak velocity, and wall shear stress may be implicated in the development of aortopathy in the presence of bicuspid aortic valve and dysfunction. Assessment of these parameters as potential predictors of disease severity and progression is thus desirable. The anatomic and functional information acquired from 4D flow MRI can allow simultaneous visualisation and quantification of the pathological geometric and haemodynamic changes of the aorta. We review the current clinical utility of haemodynamic quantities including velocity, wall sheer stress and energy losses, as well as visual descriptors such as vorticity and helicity, and flow direction in assessing the aortic valve and associated aortopathies. The significant heterogeneity in its valvular and aortic phenotypes, clinical presentations, associated disorders and prognosis present multifaceted challenges. Recent international consensus recognises three BAV types: fused BAV, two-sinus BAV and partial-fusion BAV, each with specific phenotypes. Fused BAV is the most common type, defined by fusion of two of the three cusps (Type 1 has fusion of the right and left coronary cusps (RL-BAV), Type 2 has right and non-coronary fusion (RN-BAV) and Type 3 has left and non-coronary fusion (LN-BAV).Bicuspid aortic valve (BAV) is the most common congenital cardiac malformation, affecting 1–2% of the population. Coarctation of the aorta is also commonly associated with BAV.Patients with BAV are likely to develop aortic valve dysfunction, such as aortic stenosis (AS) or regurgitation, at a much younger age than those with tri-leaflet aortic valves (TAV). BAV is also associated with an increased incidence of aortopathies including larger dimensions of the proximal aorta and increased incidence of aortic aneurysms and dissection.The aetiology of bicuspid aortopathy is still debated but two theories prevail. The first is that a congenital disorder of vascular connective tissue increases the aortic wall fragility, which is therefore prone to damage. Alternatively, according to the haemodynamic theory, altered turbulent flow patterns, changes in peak velocity and wall shear stress (WSS) are associated with aortic wall remodelling. In reality, both theories are thought to coexist. However, the heterogeneity in aortic geometry and blood flow parameters, even among AS patients presenting with similar AS severity, suggests a need to broaden the classification system. Time-resolved 3D phase-contrast MRI (3D PC MRI), or 4D flow MRI, enables the non-invasive, in-vivo study of vessel anatomy and complex blood flow patterns. Anatomic and functional information acquired from 4D flow images combine the advantages of cardiac MRI with echocardiography, and can illustrate pathological geometric and haemodynamic alterations of the thoracic aorta, including assessment of the aortic valve function.Transthoracic echocardiography is the current gold-standard for diagnosis and evaluation of AS severity and relies on Doppler assessment of flow across the aortic valve.We review the basic principles of 4D flow MRI and evaluate the current status of the resulting haemodynamic biomarkers as predictors for improving risk stratification and intervention timing in patients with BAV-associated aortopathies. Phase shifts induced in protons (herein referred to as spins) in stationary tissues are nullified by the opposing bipolar gradient. However, spins that have moved along the applied gradient will retain their phase that is directly related to their velocity. The resultant phase difference is isolated to the moving spins and is proportional to their through-plane velocity.PC MRI is achieved with bipolar velocity encoding gradients applied in the slice-selection direction, orthogonal to the imaging plane slice (“through-plane” encoding). In a typical 2D PC MRI protocol, an oblique plane selected perpendicular to the ascending aorta (AAo) at the level of the aortic valve permits transvalvular assessment of peak velocities, pressure gradient and retrograde flow (or regurgitant fraction) similar to Doppler echocardiography for measuring AS severity.For cardiovascular applications, integration of 2D PC into the MRI protocol has already enabled 2D velocity mapping in a region-of-interest in the imaging plane perpendicular to the vessel of interest.Full 3D cine enables retrospective visualisation and quantification in the entire 3D volume in any desired plane without the restrictions of a predefined imaging plane as in 2D PC MRI. For aortic imaging, 4D flow is most effectively acquired with a sagittal oblique volume covering the thoracic aorta, including the left ventricular outflow tract, AAo, aortic arch and descending aorta. Further reductions to acquisition times of the order of 5–10 min are achieved by combining interleaved 4-point velocity encoding with k-space segmentation. This decreases the imaging time by a factor equivalent to the number of segments and permits simultaneous acquisition of multiple cardiac phases or anatomical slices. Navigator gating of the diaphragm motion at the end of each cardiac cycle aids respiration control and minimises breathing artefacts. Using the 4-point technique, four simultaneous velocity-encoded acquisitions in all three x-, y-, z-directions are acquired for each single k-space line. Other k-space acquisition trajectories  can also accelerate acquisition speed but can be challenging to implement and reconstruct in post-processing.In k-space segmentation, multiple lines of k-space are acquired per segment after each trigger.During each heartbeat, the R-R interval of the cardiac cycle is divided into multiple time-resolved cine frames. Cine images are acquired by repeat imaging over multiple cardiac cycles using electrocardiogram-gated synchronisation. Image reconstruction yields a series of magnitude  and phase-difference (velocity-encoded) images that track the temporal evolution of the anatomy and velocity on the imaging plane slice respectively. Radiofrequency-spoiled gradient echo sequences with the minimum achievable echo and repetition times, TE and TR, are typically used to saturate background and stationary tissues.Optimal velocity sensitivity relies on a user-defined velocity encoding parameter, Venc, that corresponds to a phase shift of ± π (±180 degrees). Compromise is required between high Venc to avoid aliasing in the velocity images and low Venc to reduce noise and improve image quality. Changes in Venc also correspond to changing the strengths and duration of the velocity encoding gradient, thus influencing the minimum achievable TE and TR. Typical velocity sensitivities are Venc = 150 cm/s for aortic flow measurements and Venc = 100 cm/s for flow in the pulmonary artery, with higher velocity sensitivities in the presence of AS. Sources of error such as velocity aliasing and background phase contributions from eddy-currents, Maxwell terms, and gradient field distortions, that are not completely eliminated by the initial subtraction operation, require post-processing correction strategies.Compromise is also required between spatial resolution, scan time and signal-to-noise ratio. Spatial resolution should be as high as possible and temporal resolution should be as short as possible for accurate flow quantification and to identify smaller-scaled temporal variations in pulsating blood flow. However, the smaller the voxel and the greater the number of temporal steps per cycle, the longer the scan time and the lower the signal-to-noise ratio. In 4D flow, voxel sizes of 2.5–3.0 mm and a temporal resolution of around 40 ms are typical for pulsatile aortic flow measurements. “Forward flow” refers to the movement of blood in the expected direction of movement while “reverse flow” is equivalent to regurgitant volume in the opposite direction. “Net flow” is the sum of these two figures.Commercial software permits surface rendering of the vascular structures of interest to generate a non-contrast PC MRI angiogram for further analysis and visualisation of 3D blood-flow pattern, shown in  Flow displacement is defined as the distance between the centre of the lumen and the centre of the forward velocity-weighted centre of mass position, and normalised to the lumen diameter. Elevation of these values are an indicator of outflow asymmetry and eccentricity.Aortic flow angle represents the angle between the line perpendicular to the short axis analysis plane of the aorta and the instantaneous mean flow vector at peak systole. The challenge with 4D flow-derived parameters is to find a numerical method that accurately characterises these flow structures and links them to abnormal valve morphology within the available spatial and temporal resolution.4D flow also allows quantification of advanced haemodynamic biomarkers using the 3D velocity values per voxel. These including WSS, vorticity, helicity, turbulent kinetic energy, viscous energy loss, pressure mapping and pulse wave velocity (PWV), summarised in  However, these studies are either feasibility studies, or are typically small in size and highly dependent on the analysis method developed. The main findings of studies utilising 4D flow in aortic valve disease are summarised in While flow phenomena in the aorta can be appreciated visually using the path- and streamlines, evaluation remains limited by the observers’ subjective assessments. Qualitative and semi-quantitative approaches have demonstrated potential correlations between BAV and TAV aortic valve disease and these advanced haemodynamic parameters. The smaller the aortic valve area, the higher the peak transvalvular flow velocity. Peak flow velocity across a normal aortic valve is approximately 1.0 m s−1, which increases to 2.5–2.9 m s−1 and 3.0–4.0 m s−1 in mild and moderate stenosis respectively. If severe disease is present, forward flow may be greater than 4.0 m s−1. 4D flow has demonstrated significantly higher peak systolic velocities in both BAV and TAV patients in the presence of AS, although these may be underestimated compared with transthoracic echocardiography and 2D PC MRI values.AS severity is defined by the aortic valve area and the transvalvular pressure gradient derived from the flow velocity using the continuity equation. A statistically significant decrease in average systolic peak velocity was demonstrated in patients with symptomatic aortic regurgitation  after successful aortic valve repair, suggesting that the improved aortic valve geometry resulted in a reduction in forward-, reverse-, and net-flow. A reduction in the mean aortic diameters at the levels of the anulus, bulbus and mid AAo was also noted, although the additional aortic remodelling from suture annuloplasty or Dacron prostheses in some patients likely influenced this. The study was also limited by male gender bias in the cohort that may have resulted in an overestimation of the measurements given their increased heart and major blood vessels sizes, and the error arising from manual positioning of the 2D analysis planes.Aortic regurgitation has also been measured in a subgroup of BAV AS patients. Physiological helicity is thought to stabilise flow, preserve energy, and reduce flow stagnation offering some protection from atherosclerosis. Pathologically altered helicity and vortical flow formation in the presence of valve disease exert detrimental effects. These include geometrical changes due to abnormal mechanical wall stresses thought to lead to the incidence of aortic dilation in BAV. 4D flow can provide a detailed evaluation of the spatial and temporal distribution of helical and vortical flow in BAV aortopathies, with moderate to high inter- and intraobserver agreement depending on the method used.Helical flow is a feature of both normal and abnormal aortas arising from the curved geometry of the thoracic aorta and left ventricular twisting during systole. A significant post-surgical reduction in pathological, secondary helical flows was recorded in 13 patients and vortex formation was reduced to 9 patients, attributed to an improvement in valve symmetry and competence. Increasing the median time from 9 days between pre- and post-aortic valve replacement scans may have allowed for further potential remodelling resulting in a change in helical flow in more patients.In a study comprising 20 patients with symptomatic aortic regurgitation, 4D flow imaging demonstrated right-handed helical flow in the AAo of all patients and vortical flow patterns in the AAo of 15 patients prior to aortic valve repair. Vortical flow formations were also noted to be more pronounced in BAVs than TAVs, but there was a lack of further exploration into BAV subtypes and its resulting impact on blood flow. In a larger study , flow dynamics and dilation in the aortic arch were investigated for each BAV fusion type. Increased eccentricity and rotational flow were demonstrated in the proximal aortic arches of BAV patients compared with TAV. RN-BAV, rotational flow and flow reversal ratio were also found to be independently associated with arch dilation. The authors hypothesised that the increased rotational flow in BAV explained the dilatation in the proximal arch. However, longitudinal imaging studies are required to confirm causation.Strong negative correlation was demonstrated between the aortic valve area and vortical flow, eccentricity and flow displacement in the AAO of patients with mild to severe AS (16 BAV and 21 TAV) compared to healthy controls. Regional increases in aortic WSS have also been correlated with eccentric outflow jet direction for different BAV fusion types. In one study, jet flow impingement was found mainly to elevate the right anterior WSS of the aorta for RL-BAV types and the right posterior WSS for RN-BAV types. Another study explored the correlation between BAV fusion types and aortopathy phenotypes according to the proposed definitions: Type 0: normal aorta; Type 1: dilated aortic root; Type 2: aortic enlargement involving the tubular portion of the AAo; and Type 3: diffuse involvement of the entire ascending aorta and the transverse aortic arch. The majority of RL-BAV patients (13 out of 15) exhibited Type 2 phenotypes, while RN-BAV patients had a much higher incidence of Type 1 (8 out of 15) and Type 3 (5 out of 15) aortopathies. While flow displacement and high WSS are found to be sensitive predictors of BAV phenotypes with good inter- and intraobserver variability, this study was however limited by a lack of age-matched controls.Association between outflow asymmetry and maximum systolic WSS link the altered haemodynamics to the asymmetric shear stress patterns that may lead to aortic remodelling, shown in Figure 3. WSS may be a better predictive marker. Statistically significant association between the circumferential component of WSS and local aortic growth rate in recent longitudinal studies suggest that WSS can potentially identify patients at risk for aortic dissection. In another study, however, the percentage area of elevated WSS was greater in BAV patients with faster rates of AAo growth while no association was demonstrated by maximum and mean systolic WSS values. This would indicate that WSS heat maps are better predictors of faster aortic growth rate.Given aortic diameter and growth rate are subject to measurement error and are not ideal predictors of acute aortic syndrome These contradictory findings have prompted the suggestion that aneurysm formation may be driven by two different mechanistic pathways. High spatial WSS triggers mural-cell-mediated destructive remodelling, whereas low WSS triggers inflammatory-cell-mediated destructive remodelling when coupled with a high oscillatory shear index, a dimensionless parameter that describes the directional variations in WSS throughout the cardiac cycle. Nonetheless, regional abnormalities of low aortic WSS and high oscillatory shear index were shown not to be concomitant with aortic dilatation regions by 4D flow in patients with BAV, thus opposing the low and oscillatory shear stress pathogenesis theory in BAV-related aortic dilatation.On the other hand, both high and low WSS have been separately associated with intracranial aneurysmal growth and rupture. Volumetric WSS methods have since been proposed but have yet to be used in practice.However, WSS is known to be underestimated due to the limited temporal and spatial resolution of 4D flow and is restricted to measurements at predefined levels. Catheterisation also has the inherent risks associated with invasive procedures.Aortic valve pressure gradients measured by echocardiography or catheterisation are subject to overestimates due to pressure recovery. The maximum pressure gradients at nine pre-defined aortic locations between the left ventricular outflow tract and the descending aorta have been shown to be significantly higher in BAV patients than in TAV controls. Despite good intra- and interobserver variability between pressure drop measurements, variable bias is demonstrated in the scan–rescan assessment depending on measurement location. Pressure drop measurements are difficult to compare with absolute values, such as catheterisation measurement, as they depend on an arbitrarily specified reference point. The discrete spatiotemporal resolution of 4D flow and signal dephasing from turbulence and complex flow also compromise accuracy.Pressure drop mapping may be another feasible proxy for AS severity in describing abnormal blood flow. Kinetic energy can be decomposed in the mean kinetic energy and turbulent kinetic energy components. Viscous dissipation of turbulent kinetic energy into heat is the dominant cause of irreversible pressure loss in the transitionally turbulent flow regime distal to the vena contracta.Energetic losses due to the altered transvalvular flow dynamics and pressure loss are of particular interest in 4D flow as a potential non-invasive alternative to assessing AS severity with increased accuracy. Moreover, the energetic losses provide a measure of the increased left ventricular load required to overcome this loss, serving as a potential prognostic marker for the degree of left ventricular dilatation and failure. Significantly elevated turbulent kinetic energies have been measured in patients with dilated AAo or BAV from a cohort comprising mixed severity AS and valve phenotypes as well as healthy age-matched volunteers. 4D flow can also quantify viscous energy loss caused by regions of abnormal or complex non-turbulent flow, such as vortex formation or helical flow. Early studies have indicated near double viscous energy losses between the left ventricular outflow tract and proximal first branch of the aorta in BAV patients with aortic dilatation or AS compared to healthy volunteers, although further studies are required for full qunatification.Feasibility estimates of turbulent kinetic energy losses that quantify regions of turbulent flow have been demonstrated by 4D flow measurements. Increased aortic wall stiffness can lead to left-ventricular remodelling due to the increased afterload. High correlation between 4D flow-derived aortic PWV with carotid-femoral PWV and left-ventricular mass-to-volume ratio provide some evidence for the validity of the metric to examine aortic stiffness.PWV is a clinical measure of arterial wall stiffness by its inverse relation to distensibility and can be calculated using the Bramwell-Hill equation, assuming changes in the vessel wall thickness are negligible compared to the diameter and that the circulating fluid is incompressible and nonviscous. Patients with RN-BAV demonstrated significantly lower aortic stiffness (higher distensibility and lower PWV) compared to RL-BAV, despite a trend towards higher AA area. Conversely, another study with 30 non-dilated and 106 dilated BAV patients, 18 dilated TAV and 44 patients with Marfan’s Syndrome suggested an association between distensibility and regional PWV. The dilated and non-dilated BAV patients were found to have similar PWV and AAo distensibility, after adjustment for AAo diameter, to healthy controls and dilated TAV patients respectively, while both dilated and non-dilated Marfan’s syndrome patients had significantly increased PWV values. The authors thus inferred that BAV and TAV patients, with and without aortic dilatation, exhibited similar regional aorta mechanical properties. In this study, the PWV in the AAo initially decreased with dilatation until a diameter of 50 mm, beyond which the PWV then increased, suggesting that distensibility and PWV should be considered in context with aorta size. The conflicting results suggest a continuing need for further investigation.Dissociation between aortic dilatation and PWV was demonstrated in a study using 2D PC MRI in 63 BAV and 106 TAV patients with AS, with higher AAo dimensions in BAV, but no difference in PWV.The feasibility of 4D flow acquisition and analysis methods has resulted in a burgeoning of studies on its utility in characterising AS and its associated aortopathy. The consequence is a heterogeneity in current 4D flow data acquisition and reconstruction protocols and the assessment tools for analysing the large volumes of subsequent data generated. The lack of conformity in post-processing and analysis methods hampers cross-site comparison and limits the accuracy and external validity of numerical values. Limited temporal and spatial resolution further restricts the accuracy of derivative calculations of higher-order parameters such as vorticity and shear stress. Better understanding of these limitations and how to overcome them is required to improve 4D flow data quality and standardise its wide-scale clinical applicability. k-t GRAPPA, and k-t PCA already allow for acceleration factors of the order of 5 to 8. Compressed sensing is now being offered as a commercial product that achieves increased acceleration rates by incoherent sampling. Acquisition times are reduced by sampling only a small portion of k-space, done semi-randomly  to eliminate aliasing artefacts. Transform sparsity and non-linear iterative reconstruction remove the noise-like artefacts, superimposed on the image by incoherent sampling, while retaining as much data consistency achievable. This has opened cardiac MRI to a larger patient group such as those with arrhythmia or those who cannot breath-hold. Previously hampered by relatively long offline reconstruction times, ranging 45–60 min, feasibility studies of compressed sensing combined with direct online reconstruction have demonstrated time saving without loss of data quality. The higher resolutions at constant scan times potentially afforded by compressed sensing may improve the accuracy and precision of the higher-order metrics.K-space undersampling techniques that exploit spatiotemporal correlations such as k-t BLAST or k-t SENSE, Automated analysis can potentially reduce analysis time and may enable broader clinical applications. Commercially available software now allows rapid, semi-automated segmentation. However, reliance on user-adjusted thresholding for masking and manually positioned analysis planes has resulted in regional sensitivity of measurements to plane placement and increased partial volume error. Potential segmentation-free methods have been compared favourably to traditional manual segmentation. Advances in machine learning have also markedly improved automated processing. Deep learning methods, such as convolutional neural networks, have the potential for fully automated aortic segmentation. Multisite studies have achieved subsecond segmentation with similar geometrical reproducibility and accuracy of blood flow measurements to manual analysis across a wide range of patient characteristics, vascular territories and congenital heart diseases. Current studies are limited to parameters that may not be particularly sensitive to haemodynamic differences between the TAV and BAV groups. Further studies with larger multisite, multivendor training data sets are required and the outcomes compared against gold-standard measurements to determine the accuracy, reproducibility, and observer variability of regional flow dynamics for clinical applications.Visualisation and computation of haemodynamic metrics have been previously impeded by labour-intensive manual segmentation of the aorta.Previously hampered by inherently long scan time and a lack of widely available standardised cardiac MRI protocols and analysis methods, 4D flow is now emerging as an important research tool and a potentially cost-effective and feasible technique in future clinical practice. Aorta 4D flow acquisition and analysis methods can provide non-invasive quantitative assessment of aortic blood flow patterns and haemodynamic parameters within clinically feasible times. Quantification of aortic flow can play an important complementary role in the characterisation and management of patients with BAV and associated aortopathy. However, current studies are limited with heterogeneous populations (with and without AS/AR) and control groups, small numbers and heterogeneity in post-processing and reported haemodynamic parameters. Further studies are needed to firmly establish its role in the diagnosis and management of patients with BAV and its associated aortopathy.


The emergence of Marburg virus (MARV) in Guinea and Ghana triggered the assembly of the MARV vaccine “MARVAC” consortium representing leaders in the field of vaccine research and development aiming to facilitate a rapid response to this infectious disease threat. Here, we discuss current progress, challenges, and future directions for MARV vaccines. On August 3, 2021, the World Health Organization (WHO) was notified of a confirmed case of Marburg virus (MARV) disease (MVD) in the Guéckédou prefecture in Guinea, with 173 contacts, including 14 high-risk contacts based on exposure . A . A 21]. le units . The vacThe first of 3 vesicular stomatitis virus (VSV)-based vaccines described here is the attenuated VSV-N4CT1-MARV vaccine developed by Auro Vaccines. It is a VSV full-length vector encoding a VSV N gene translocation together with a truncation of the VSV G protein cytoplasmic tail and the MARV-Angola GP as viral antigen. Two doses have been shown to uniformly protect NHPs in preclinical studies in a trivalent  GP, MARV GP) or quadrivalent  vaccine formulation, with the trivalent version also showing full protection as a single dose  23,24].,24.23,24The remaining 2 VSV-based vaccines both originate from the attenuated VSVΔG vector in which the VSV-G is replaced by the MARV GP. This strategy follows the example of the US FDA- and EMA-approved EBOV vaccine Ervebo by Merck. The International AIDS Vaccine Initiative (IAVI) is developing the VSV-MARV Musoke vector, and Public Health Vaccines (PHV) the VSV-MARV Angola vector. Both vectors have shown uniform protection after a single-dose vaccination in the NHP model of infection against both MARV and RAVV  26–28].–28.26–28Supported by the success of a ring vaccination trial for EBOV carried out in Guinea , efficieCompared to preclinical and clinical evaluation of EBOV therapeutics, the development of MARV therapeutics has evolved at a much slower pace. As with EBOV, antiviral efficacy against MARV infection in NHPs has historically served as the benchmark to rate predictive efficacy in humans and justify subsequent clinical trial efforts. Several promising approaches ranging from pan-filoviral small molecule antivirals to MARV-specific monoclonal antibody approaches or even combinations of the two have shown impressive postexposure efficacy in NHPs at late-stage disease . These tWith the MDV cases in Guinea and Ghana, outbreaks of this filovirus disease have now been reported across the entire sub-Saharan Africa region. With case fatality rates of up to 88% and the lack of licensed medical countermeasures, there is an urgent need for action to accelerate the evaluation and approval of MVD vaccines and therapeutics. Following the inception of the WHO-coordinated MARVAC, we have presented here the current status of the most advanced MVD medical countermeasures. We highlight the need for global research coordination, including sharing of assays and continuous efforts on capacity building among private and public sectors. Increased surveillance and diagnostics are also required for early detection of future outbreaks as well as rapid deployment of vaccines and therapeutics.


This protocol is a practical guide for preparing acute coronal slices from the midbrain of young adult mice for electrophysiology experiments. It describes two different sets of solutions with their respective incubation strategies and two alternative procedures for brain extraction: decapitation under terminal isoflurane anaesthesia and intracardial perfusion with artificial cerebrospinal fluid under terminal isoflurane anaesthesia. Slices can be prepared from wild-type mice as well as from mice that have been genetically modified or transfected with viral constructs to label subsets of cells. The preparation can be used to investigate the electrophysiological properties of midbrain neurons in combination with pharmacology, opto- and chemogenetic manipulations, and calcium imaging; which can be followed by morphological reconstruction, immunohistochemistry, or single-cell transcriptomics. The protocol also provides a detailed list of materials and reagents including the design for a low-cost and easy to assemble 3D printed slice recovery chamber, general advice for troubleshooting common issues leading to suboptimal slice quality, and some suggestions to ensure good maintenance of a patch-clamp rig. The acute brain slice remains an essential preparation for studying the nervous system . For decDespite the long tradition of patch-clamp experiments and the many adaptations of slicing protocol described in the literature –40, the https://dx.doi.org/10.17504/protocols.io.8epv51pz5l1b/v6 and is included for printing as The protocol described in this peer-reviewed article is published on protocols.io, The protocol described here has been routinely used to obtain patch-clamp recordings from midbrain neurons in acute slices containing the superior colliculus (SC) and periaqueductal gray (PAG) for investigating their biophysical properties and their role within the neural circuits controlling instinctive behaviours , 22, 25.This method can be combined with the use of transgenic lines or viral strategies to target genetically labelled subsets of neurons in the midbrain area of interest  or any oS1 FileStep-by-step protocol, also available on protocols.io.(PDF)Click here for additional data file.


Retinal artery macroaneurysm (RAM) is an acquired retinal arteriole dilatation with vision-threatening complications. Diagnosis of this condition can be made clinically, supported by multi-modal imaging modalities, commonly optical coherence tomography (OCT) and dye-based angiography studies which show the lesion itself and the complications to the adjacent retina. We report a case of an 83-year-old patient with renal impairment who had the diagnosis and monitoring of RAM done using optical coherence tomography angiography (OCT-A) as an alternative to conventional fluorescein angiography. This case highlighted the use of OCT-A using Cirrus 5000 with AngioPlex  as a useful diagnostic and monitoring tool for RAM with its features that enables objective quantification of the disease activity via vessel and perfusion density pre- and post-laser treatment. Retinal artery macroaneurysm (RAM) is an acquired dilatation of the retinal arterial vasculature, usually involving the first three branches of the arteriolar tree, with the second-order arteriole being the most common . They maDifferent imaging modalities play a significant role in helping to establish the diagnosis of RAM, monitor disease progression, and respond to treatment. Optical coherence tomography (OCT), fundus fluorescence angiography (FFA), and indocyanine green angiography (ICGA) have been widely used in the diagnosis and management of retinal vascular diseases. A more recent imaging advancement involves the use of optical coherence tomography angiography (OCT-A), a non-invasive imaging modality that provides structural and functional (blood flow) information from different layers of the retina and choroid .This case report describes the use of OCT-A imaging, using the commercially available Cirrus 5000 with AngioPlex  in the diagnosis and management of a patient who has multiple comorbidities that prevent the use of the classical dye-based angiography studies.An 83-year-old gentleman with a history of chronic hypertension, dyslipidemia, ischemic heart disease, stage 4 chronic kidney disease (CKD), and a history of a recent stroke, presented with a progressive right eye with blurry vision for the past two months which was more noticeable in the centre. However, the patient denies metamorphopsia. There were no relieving or aggravating factors to this symptom. He also reported no other associated eye symptoms. His presenting visual acuity (VA) was counting fingers for his right eye and 6/24 for his left eye. There was no relative afferent pupillary defect. The patient had bilateral cataracts with nuclear sclerosis 2+ and posterior subcapsular cataract 1+. Dilated fundus examination of the left eye was unremarkable whilst his right eye revealed an oval lesion along the superotemporal arcade with surrounding hard exudates Figure . There wThe patient was promptly treated with argon green laser photocoagulation performed with an Area Centralis lens; 70 shots x 0.15s x 200 um x 220-280 mW were applied to the RAM and the adjacent area of retinal thickening to produce a light grey burn with the aim to seal-off the macroaneurysm and resolve the surrounding exudates. The argon laser treatment showed an effect on the RAM as early as two hours post-laser treatment as shown by the reduction in vessel and perfusion density, derived from the OCT-A (Figure At six weeks post presentation and treatment, right eye vision improved to 6/60, N48. SD-OCT imaging of the right eye showed reduced subfoveal hard exudates Figure . The patOCT and FFA are widely used to support the diagnosis of RAM and to monitor treatment response. Active RAM on OCT can be visualized as a circular or oval lesion in the intraretinal layers with a dark lumen surrounded by a hyperreflective wall . The lumOCT-A provides a good alternative to dye-based angiography. The method is non-contact, non-invasive, quick to perform and avoids the risks of dye use. OCT-A is superior for its capability of capturing 3D flow data that are compressed to produce 2D en-face images or slabs representing flow in specific retinal and deeper choroidal layers. Pathologies in the retinal and choroidal layers can be visualized without the need for separate tests like in dye-based angiography where FFA is used to study retinal pathologies and ICGA for choroidal pathologies. Retinal artery macroaneurysm has distinct and unique features on OCT-A where the depth of RAM, exact location in relation to the main vessel and level of blood flow can be documented , as showTreatment with laser ablation has mixed results with some cases having worsening of vision. Hence the treatment is controversial . In the OCT-A is a useful new tool for the diagnosis and monitoring of retinovascular abnormalities, including retinal artery macroaneurysm, without the risks involved in fluorescein angiography. Its capability to quantify angiographic information by providing flow signal, vessel and perfusion densities allows for more objective monitoring of disease progression and treatment response of RAM. OCT with its ability to visualize the intraretinal, and subretinal cystic spaces and hard exudates in exudative RAM complements the OCT-A to objectively monitor the treatment response. For the diagnosis and management of RAM, OCT-A may be at par if not superior to its dye-based angiography counterparts. We believe OCT-A will be the next powerful tool in the management of RAM and eye care practice in general.


We also present a minority class focussed sampling strategy, which allows effective representation of minority class samples produced by all three data augmentation techniques and contributes to the classification performance. The method was evaluated on two publicly available datasets of immortalised human T-lymphocyte cells and Red Blood Cells. Classification performance evaluated using F1-score shows that our proposed approach outperforms existing methods on the same datasets.Due to progression in cell-cycle or duration of storage, classification of morphological changes in human blood cells is important for correct and effective clinical decisions. Automated classification systems help avoid subjective outcomes and are more efficient. Deep learning and more specifically Convolutional Neural Networks have achieved state-of-the-art performance on various biomedical image classification problems. However, real-world data often suffers from the data imbalance problem, owing to which the trained classifier is biased towards the majority classes and does not perform well on the minority classes. This study presents an imbalanced blood cells classification method that utilises Wasserstein divergence GAN,  Likewise, in non-nucleated blood cells (erythrocytes/Red Blood Cells (RBCs)), morphological variations due to storage need to be identified for the prediction of blood quality for life-saving blood transfusions2.Blood cells undergo various morphological changes as they progress in their life cycle or undergo the impact of environmental factors. Classification of cell-cycle phases in nucleated blood cells (lymphocytes) is vital for diagnostic and prognostic research studies of pathological conditions and impacts clinical decision making4, subcellular localisations5 and cell-cycle phases6. However, the efficacy of CNN based classifiers is logarithmically proportional to the amount of training data7. Since data collection in biomedical studies is restricted by the nature of the biological phenomena, data imbalance is a common issue in CNN based classification7. In data imbalance settings, some classes have far higher numbers of samples compared to other classes in the dataset. Consequently, samples from majority classes are frequently observed and those from minority classes rarely encountered. The underrepresentation of minority classes causes model training to be heavily biased towards the majority classes, which in turn leads to a significant performance drop of the trained model on minority classes. Furthermore, minority classes often constitute more than half the number of total classes in a biomedical image dataset. Therefore, in order to achieve a model that is applicable in real world settings, it is important for the model to be trained on all the classes equitably.Convolutional Neural Networks (CNNs) have exhibited state-of-the-art performances to identify cellular morphologies8, approaches to handle data imbalance can be categorised into parameter-level and data-level. Parameter-level methods alter the learning or decision process by assigning higher weights and often undergo a precarious process of weight determination. Data-level methods include oversampling through various data augmentation techniques to enlarge the minority class image set.In existing studies9. Handcrafted methods are efficient, computationally inexpensive and intuitive to apply. However, these techniques take more time to design, as all features need to be mathematically modelled with subjective criteria by humans, which leads to biased decisions and limited improvement. Handcrafted methods could also easily result in overfitting, particularly when some classes contain very few samples. Therefore, techniques such as mixup10, Synthetic Minority Oversampling Technique (SMOTE)11 and their variants21 have been proposed. mixup as a data augmentation based model regulariser improves classification performance by linear interpolation of samples to generate synthetic data. Based on mixup, Summers et al.12 presented multiple nonlinear alternatives, including VH-Mixup 12 that combines linear mixup with one of their nonlinear methods and performs better than linear mixup on benchmark datasets. CutMix13 cuts and pastes random patches between the training images and has shown strong classification and localisation ability. MixMatch14 is based on a semi-supervised approach that estimates low-entropy labels for unlabelled samples by mixing up labelled and unlabelled images. Balanced-MixUp15 performs mixup on two images selected using instance-based and class-based sampling simultaneously. Remix16 assigns higher weight to the reference image from the minority class and assigns the minority class label to the obtained hybrid image.Data augmentation is usually achieved using two types of approaches. The first is the alteration of original images using various image processing techniques such as geometric transformation, colour space augmentation and random erasing, also known as handcrafted methods22 to generate new images by learning the distribution of training data through adversarial learning. Due to their capability of learning data distributions, GANs have been extensively used for image generation, image-to-image translation and image super-resolution23. Recently, they have been applied to handle the class imbalance problem as they are able to reproduce the distribution of minority classes34. On the other hand, training of GANs is complicated with a few common failure modes such as vanishing gradient, mode collapse and unstable training35. In order to cope with these problems, Wasserstein GAN (WGAN) was proposed36. Advanced versions of WGAN, such as Wasserstein GAN with gradient penalty37 (WGAN-GP) and Wasserstein divergence GAN38 (WGAN-div), offer more stable optimisation processes and realistic synthetic images. Therefore, recent studies have employed them for various applications such as preprocessing of brain MRI images39, image quality improvement of X-ray images40 and segmentation of fundus images41. However, application of WGAN in biomedical studies to handle data imbalance has not been explored much.The second type of data augmentation uses deep learning based Generative Adversarial Networks (GANs)42, a combination of WGAN-div and mixup generates more diverse samples and helps achieve better classification performance. Extending our previous work42, in this study, we propose to include a novel non-linearmixup along with WGAN-div and mixup to further increase the diversity of the training dataset for more robust classification  32, 266 images of immortalised human T-lymphocyte cells (Jurkat cells)  of different cell-cycle phases and RBCs of different morphologies. Both datasets contain single cell images.1 consists of 7 classes, representing different phases of the cell-cycle: Interphase  and Mitosis   of size I at pixel p.Consider that the RGB colour space can be geometrically represented as a 3D cube, with red, green and blue components representing each dimension  and Discriminator (D). The G network generates synthetic/fake data and the D network decides whether the generated samples are real or fake. Both networks are trained alternatively, with the primary goal of maximising the probability of classifying real images as real and generated samples as synthetic/fake. The GAN is well-trained when the generator learns to generate data samples that are as diverse as the original data distribution and can fool the discriminator into accepting them as real.35. The loss function utilised for GAN training is one of the primary reasons for the training related issues in GAN. As an improved GAN, WGAN36 employs a new loss function based on the Wasserstein distance (W-Dis) instead of the Jensen-Shanon Divergence (the standard objective function of GAN) to compute the loss. W-Dis estimates how easy it is to distinguish between synthetic and real images, giving rise to valuable gradients. The objective function of WGAN is:Training of GAN is coupled with a few common failure modes such as vanishing gradient , mode collapse (generator produces limited similar samples) and unstable trainingD within a compact space to maintain the Lipschitz constraint, which often creates convergence issues. Therefore, WGAN-GP was proposed to improve WGAN that uses gradient penalty to enforce the Lipschitz constraint. As an improved WGAN, Wu et al.38 proposed Wasserstein divergence GANs (WGAN-div) which uses Wasserstein-divergence (W-div) to compute the objective function instead of W-Dis. WGAN-div approximates W-div through optimisation, without the 1-Lipschitz constraint and offers more stabilised training. The objective function of WGAN-div is:p and k are hyperparameters that control the gradient, z is random noise and WGAN offers more stabilised training than GAN, however the computation of W-Dis requires a 1-Lipschitz constraint, which is a strict constraint. WGAN explicitly clips the weight of the critic In order to handle data imbalance, this study utilised the WGAN-div model to generate synthetic samples for minority classes. A WGAN-div model was trained on the original training set images of each minority class in both datasets. In the cell-cycle phases dataset, the minority classes are Anaphase, Prophase, Metaphase and Telophase, while in the RBCs dataset, the minority classes are Crenated disc, Crenated discoid, Crenated spheroid, Crenated sphere and Smooth sphere  of the minority class to the batch. Accordingly, the batch size of original images was adjusted to maintain the overall  batch size of m is usually Estimation of sample distribution ratio (proportion of majority to minority samples) during oversampling is a crucial step for effective and efficient training of the classification model. As a standard practice of performing oversampling, the sample distribution ratio is preset, which decides the number of samples to be added to the original set for achieving the desired ratiomixup and nonlinear mixup samples, which were then used for training along with the original images using the minority class focussed sampling approach.The proposed method to handle data imbalance was evaluated on two datasets: cell-cycle phases and RBC morphologies dataset. The original images from the training set were used to train four WGAN-div models in the cell-cycle phases dataset and five WGAN-div models in the RBC morphologies dataset, one for each minority class. WGAN-div generated images along with the original images were further used to construct 44 optimiser, while WGAN used the RMSprop45 optimiser. Batch size was set to 16. Hyperparameter p and k for WGAN-div were set to their default values of 6 and 2 respectively38. The clipping threshold for WGAN-GP was set to 0.01. The learning scheduler and other hyper-parameter initial settings  were the same for all the experiments. We note that the Frechet Inception Distance score46 (FID scores) vs epoch was plotted for GAN models  and other studies conducted on the same dataset, as shown in Table mixup approach applies 3D rotation to each pixel of the synthetic image obtained after applying standard mixup on two reference images. In this way, transformations in the spatial domain and colour space are combined to increase the regularisation capability of mixup. It is observed that combining transformations in two different domains  is more advantageous than to further expand the space in the spatial domain to generate synthetic images. We utilised bilinear interpolation, which applies standard mixup on the two synthetic images obtained from the standard mixup of the two sets of original images  of the proposed approach  WGAN + mixup + nonlinear mixup, which is oversampling with WGAN generated images and corresponding mixup and nonlinear mixup samples, using minority class focussed sampling; (3) GAN + mixup + nonlinear mixup, which is oversampling with GAN generated images and corresponding mixup and nonlinear mixup samples, using minority class focussed sampling; (4) WGAN-div + mixup + nonlinear mixup12, which is oversampling with WGAN-div generated images and corresponding mixup and nonlinear mixup samples using minority class focussed sampling, where nonlinear mixup samples were generated following the method presented by Summers et al.12; (5) WGAN-div + mixup , which is oversampling with WGAN-div generated images and corresponding mixup samples using minority class focussed sampling; (6) WGAN-div + nonlinear mixup, which is oversampling with WGAN-div generated images and corresponding nonlinear mixup samples using minority class focussed sampling; (7) standard sampling, creating mini-batches using standard random sampling strategy which randomly selects the images from the original and synthetic samples with no supplementary batch as proposed in the current study.We conducted ablation studies of each component . The curves for WGAN and WGAN-GP show sudden drop to the converged critic loss value, which indicates high probability of overfitting. It is also observed that the Prophase class required higher value of Anaphase and Telophase being consecutive phases in cell-cycle have visually similar appearance Fig. a. The lomixup and nonlinear mixup achieved similar macro F1-scores for both datasets when used separately, however classification performance is improved if both algorithms are applied together during training. Experiments also show better performance of previously42 proposed minority focussed sampling than the standard sampling approach. The proposed sampler finetunes the target distribution and allows the inclusion of the minimum number of synthetic samples required, without affecting the representation of original samples during training. Therefore, it is observed that despite very few samples in Telophase, it still achieved an F1 score of 1 with n set to 1. The proposed sampler helps avoid overfitting, reduces the computational cost and does not require a preset sample distribution ratio.It is also observed that mixup for data augmentation in image classification problems, and extend our previous work42 to demonstrate its superiority in classifying microscopy images of blood cells from two datasets. The non-linearmixup technique combines transformations in the spatial domain and colour space to further improve the regularisation capability of mixup. Nonlinear mixup samples are used for training along with the mixup and WGAN-div generated images through a minority focussed sampling approach. Our experimental results demonstrate the advantages of using three different data augmentation approaches for oversampling of minority classes to increase the diversity of the training dataset for more robust classification. The proposed classification pipeline outperforms state-of-the art approaches on publicly available cell-cycle phases dataset and red blood cells morphologies dataset.In this paper, we propose a novel nonlinear Supplementary Information.


Understanding how neurons interact across the brain to control animal behaviors is one of the central goals in neuroscience. Recent developments in fluorescent microscopy and genetically-encoded calcium indicators led to the establishment of whole-brain imaging methods in zebrafish, which record neural activity across a brain-wide volume with single-cell resolution. Pioneering studies of whole-brain imaging used custom light-sheet microscopes, and their operation relied on commercially developed and maintained software not available globally. Hence it has been challenging to disseminate and develop the technology in the research community. Here, we present PyZebrascope, an open-source Python platform designed for neural activity imaging in zebrafish using light-sheet microscopy. PyZebrascope has intuitive user interfaces and supports essential features for whole-brain imaging, such as two orthogonal excitation beams and eye damage prevention. Its camera module can handle image data throughput of up to 800 MB/s from camera acquisition to file writing while maintaining stable CPU and memory usage. Its modular architecture allows the inclusion of advanced algorithms for microscope control and image processing. As a proof of concept, we implemented a novel automatic algorithm for maximizing the image resolution in the brain by precisely aligning the excitation beams to the image focal plane. PyZebrascope enables whole-brain neural activity imaging in fish behaving in a virtual reality environment. Thus, PyZebrascope will help disseminate and develop light-sheet microscopy techniques in the neuroscience community and advance our understanding of whole-brain neural dynamics during animal behaviors. Animal behaviors occur through the collective actions of neurons distributed across the brain. Understanding such distributed neural dynamics in their entirety has been one of the central goals in neuroscience . Toward Despite the above advantages of DSLM in achieving both high optical resolution and imaging speed compared to other large-scale volumetric imaging methods, such as structured illumination microscopy , multiphRecently, software interface for light-sheet microscopy in zebrafish, μSPIM , was devToward this goal, we developed PyZebrascope, an open-source Python platform designed for whole-brain imaging in zebrafish using light-sheet microscopy. PyZebrascope supports essential features for whole-brain imaging, such as two orthogonal excitation beams and eye damage prevention. Its user interfaces allow the users to adjust parameters for lasers, filters, beam scanning, camera, sample positions, and eye damage prevention intuitively. Its multi-threaded camera module can handle stable high data throughput from multiple cameras. It is also possible to add advanced algorithms for microscope control and image processing developed in the Python community . As a prhttps://www.spyder-ide.org/). We used a PC for microscope control that has Windows 10 operation system, two processors , 384 GB of DDR4 memory, a GPU (nVidia GeForce GTX1660), and a 15 TB solid-state drive . We followed manufacturers’ manuals for connecting devices, including the camera, data acquisition board and sample stages.PyZebrascope: All the modules of PyZebrascope were written in Python programming language on Spyder IDE (https://github.com/giampaolo/psutil) in Python. We identified the process id of PyZebrascope by using Windows task manager and monitored its CPU and memory usage at an interval of 5 s in a separate Python instance while PyZebrascope records camera images for 30 min. Disk writing speed was also monitored using psutil by tracking the increase of disk usage in the above solid-state drive at the same time interval.We benchmarked the resource usage and disk writing speed of PyZebrascope  by usingF is in the identified maximum frequency component (pixel distance after the polar transformation).We calculated the image resolution measure in Light-sheet microscope: We designed a custom light-sheet microscope that has a virtual reality setup for behavioral recordings, two optical paths for excitation beams, two cameras for fluorescence detection, and additional space for future implementation of the third optical path for excitation beam and neural perturbations . We desijf96 and Tg(tph2:epNTR-TagRFP)jf41, courtesy of Dr. Misha Ahrens) for the imaging experiment. The zebrafish was immobilized and mounted to an imaging chamber as described previously .Data analysis: We processed acquired imaging data on a Linux server in High Performance Computing (HPC) division in the Weizmann Institute of Science. This server has two Xeon processors , 384 GB RAM, 13-TB SSD array, and a GPU computing board . We performed data processing using custom Python scripts that execute the same algorithms as those established in our previous work . All theBriefly, we first registered time-series images from the same Z-planes by using phase correlation algorithms on the above GPU. We then examined residual drifts in the lateral and axial directions and discarded data with excessive drifts (>5 μm) in either direction. We then identified individual neurons that express nuclear-localized GCaMP based on the average image by using an algorithm for detecting circular shapes in images. We then extracted fluorescent time series from the central part of identified neurons (49 pixels). We identified 79,176 neurons across the brain in the experiment described in p-values (p < 0.001) for consistent task-dependent activity modulation across sessions. We then extracted neurons for each brain area by their spatial locations. We classified neurons into three groups in each area by applying a k-means clustering method (n = 3) to the trial average activities of neurons. We then picked a neuron that shows the largest response amplitude from each identified cluster in each brain area and plotted their time series in For the analyses shown in For the analysis shown in For the analysis shown in We developed PyZebrascope for our custom-designed light-sheet microscope  for low-level device interface of μManager (https://nidaqmx-python.readthedocs.io/) for data acquisition interface devices. Its outputs can be modified to drive alternative components of light-sheet microscopes that operate according to voltage inputs, such as electrically tunable lens  allows the users to save all the microscope configuration parameters in an XML file. Savable parameters include the state of all connected devices, the position of the 3-axis stage, the choice of lasers and their output intensity, type of neutral density filters, the region of interest on the camera’s field of view, and parameters for volumetric scanning. This file is also automatically saved for every experiment with time logs. The experimenter can load this setting file later to connect necessary devices and configure all the device parameters automatically. Although the user still needs to fine-tune sample positions and microscope scanning parameters for each experiment, this automatic loading minimizes the users’ efforts in conducting experiments consistently across sessions.The fish for imaging resides Sample Stage Control a water chamber with glass windows . Within The PyZebrascope sample stage unit (stage.py) moves the motorized stage holding the water chamber according to the user’s commands from the software interface. It also allows users to automatically move the stage between stereotypical positions, such as a home position for replacing water chambers and an imaging position for performing experiments. During imaging experiments, the water chamber, the detection objective, and the excitation objectives need to be placed in close physical proximity. Therefore, the stage unit moves the stage in a constrained manner to avoid the risk of accidental collisions between these components.https://doc.qt.io/qt-5/qtdesigner-manual.html). All of the above device parameters are organized in two tabs in the main window  . This feOne of the challenges in neural activity imaging using light-sheet microscopy is its high image data throughput. Modern scientific CMOS cameras for light-sheet microscopy can acquire several megapixels per frame at the speed of over a hundred frames per second, which amounts to a data throughput of several hundred megabytes per second (MB/s). This high load poses significant challenges in ensuring continuous data flow from camera acquisition to file writing while avoiding writing delays and memory overflows.https://www.h5py.org/) yielded higher data throughput than writing in other formats, such Tagged Image File Format (TIFF), at the time of our prototyping. Each HDF5 file stores a set of images for each volumetric scan or a set of 100 images for single-plane imaging. In parallel with these file writing processes, the reading thread intermittently copies image data to another FIFO buffer on system memory , achieved a throughput of over 800 MB/s during the continuous acquisition of full-frame (2304 by 2304 pixels), 16-bit images at the speed of 80 frames per second . PyZebraWe synchronize behavioral control software to the image acquisition of PyZebrascope by diverging the camera trigger cable to an analog input channel for the behavioral software . The wavThe waveform generator module  allows the addition of digital outputs necessary for synchronizing image acquisition to other types of behavioral software. It is possible to add separate digital outputs for the start of the volumetric scan or individual image acquisitions as described above by adding binary arrays to the digital output channels.The Python software ecosystem offers a variety of highly optimized libraries for image processing and machine learning. Such distinct advantages of using Python, as well as the modular architecture of PyZebrascope, enable the implementation of advanced algorithms for microscope control and online image analyses. As a proof of concept to demonstrate this advantage, we developed an autofocusing module (auto_focusing.py) that adjusts the axial position of the excitation beam in the sample to the best focus of the detection objective . This prhttps://cupy.dev/) to minimize computation time for Fourier transform and polar transform.We implemented a novel automatic algorithm to assist such a time-consuming alignment process by using an image resolution measure based on Fourier transformation  Figure . OptimalZ-axis of a volumetric scan. It then estimates the optimal transformation function between the DO movements and scanning voltage for the excitation beams. Transformation functions obtained in a real zebrafish brain showed nonlinearity  for a given position of the detection objective (DO). It calculates the above resolution measure for each acquired plane and normalizes resolution measures to between 0 (poor resolution) and 1 (best resolution) across different planes. The peak position is detected by fitting a Gaussian distribution function to the normalized resolution measures. The center of the estimated distribution was designated as the best focal plane. These sampling and computing processes take less than 2 s in total and accurately detect the best focus for the dorsal  and ventinearity , and theinearity . This es package  to transtph2+ serotonergic neurons . It only requires pre-installation of Anaconda Python package, μManager package (PyZebrascope and its components are written in a generalizable manner, enabling research teams that use different types of devices to adapt the code to their configurations. It is available from Github ( package  with a mThe architecture of PyZebrascope further enables the implementation of advanced microscope control and image processing algorithms. For example, a common issue during whole-brain imaging in zebrafish is the sample’s translational and rotational drift resulting from gravity force, tail motions of unparalyzed fish , or the It will also be possible to implement real-time image processing features to identify neurons of specific activity patterns for subsequent neural perturbation experiments at a single-cell resolution . Real-tiLastly, PyZebrascope will enable further development of voltage imaging techniques in zebrafish. Voltage imaging requires high image resolution, high photon collection efficiency, and high imaging speed at the rate of at least 300 frames per second . Light-s


We describe the successful detection of human, porcine and canine picornaviruses (CanPV) in sewage sludge (at each stage of treatment) from Louisville, Kentucky, USA, using Pan-enterovirus amplicon-based long-read Illumina sequencing. Based on publicly available sequence data in GenBank, this is the first detection of CanPV in the USA and the first detection globally using wastewater-based epidemiology. Our findings also suggest there might be clusters of endemic porcine enterovirus (which have been shown capable of causing systemic infection in porcine) circulation in the USA that have not been sampled for around two decades. Our findings highlight the value of WBE coupled with amplicon based long-read Illumina sequencing for virus surveillance and demonstrates this approach can provide an avenue that supports a “One Health” model to virus surveillance. Finally, we describe a new CanPV assay targeting the capsid protein gene region that can be used globally, especially in resource limited settings for its detection and molecular epidemiology. To the Editor: In situations where most pathogenic, human-infecting virus infections do not result in clinical manifestations, such as with Enteroviruses (EVs)  were collected (three per week), over three weeks a from thAssay 1 amplicons from these five confirmed EV positive samples were subjected to assay 3 and Long-read Illumina sequencing (LRIS). Seventy-three long-read contigs were recovered from the five EV positive samples (Table S2). Though more variants were recovered using LRIS, both SS and LRIS were congruent with respect to the EV types detected in four  of the five samples (Tables S1 and S2). SS showed multiple peaks in the fifth sample  while LRIS delineated the different virus types   does noter year] ). A simiPhylogenetic analysis of the two CanPV contigs  showed that they belong to a group of unclassified canine picornaviruses that (based on publicly available sequence data in GenBank) have not been previously described in the USA. They have however been described in dogs in the United Arab Emirates (UAE), China and Hong Kong for over a decade (2008 to 2019)  and moreOne-Health framework [Our findings show that sludge from different stages  of conventional wastewater treatment can be used for virus surveillance. We recovered porcine (EV-G), canine (CanPV) and human (EV-A and EV-C) picornaviruses demonstrating this approach provides an avenue that facilitates surveillance of both human viruses and animal viruses and a ramework . In addiClick here for additional data file.