Whereas typical fMRI connectivity analyses cannot disambiguate the direction of connections, dynamic causal modeling (DCM) can model directionality. Using DCM, we provide new evidence that directed connections within the dorsal attention network are differentially modulated for voluntary versus involuntary attention. These results suggest that the intraparietal sulcus exerts a baseline inhibitory effect on the frontal eye fields that is strengthened during exogenous orienting and attenuated during endogenous orienting. Furthermore, the attenuation from endogenous attention occurs even with salient peripheral cues when those cues are known to be counter predictive. Thus, directed connectivity between frontal and parietal regions of the dorsal attention network is highly influenced by the type of attention that is engaged.The moment‐to‐moment focus of our mind's eye results from a complex interplay of voluntary and involuntary influences on attention. Previous neuroimaging studies suggest that the brain networks of voluntary versus involuntary attention can be segregated into a frontal‐versus‐parietal or a dorsal‐versus‐ventral partition—although recent work suggests that the dorsal network may be involved in both bottom‐up and top‐down attention. Research with nonhuman primates has provided evidence that a key distinction between top‐down and bottom‐up attention may be the  A wealth of experimental evidence shows that selective spatial attention leads to faster and more accurate responses to stimuli at an attended location —spatial attention does the same thing, once it is focused on a location. However, a number of recent findings suggest that voluntary and involuntary attention may actually be orienting two different “spotlights.” Briand  and BriaWhereas neuroimaging evidence has shown largely overlapping regions implicated in voluntary and involuntary attention, crucial insights into the mechanisms of these types of attention come from single‐unit recordings from macaque monkeys. Specifically, Buschman and Miller  demonstr2This experiment uses a dataset that has been described in a previous, recent publication .2.2Participants viewed images on a translucent screen through an angled mirror attached to the head coil. Commercial software  was used to present stimuli and record responses and reaction times. Before completing experimental runs, all participants completed a behavioral training session to ensure high performance accuracy. There were three different attention conditions; endogenous, exogenous, and anti‐predictive exogenous. Conditions were blocked such that participants completed only one condition type per run. Each run consisted of 64 trials. Participants completed two runs of each attention condition for a total of six functional runs  per participant. The order of conditions was counterbalanced across participants. An illustration of the trial paradigm is shown in Figure covertly shifted attention to the anticipated target location. Then, the target, a black‐and‐white checkerboard, appeared in one of the two peripheral boxes for 150 ms. The center of the checkerboard square was a solid gray square for 50% of trials and alternated between white and black for the other 50%. Participants were asked to report as quickly and accurately as possible whether the center was gray or not by pressing a key. Intertrial intervals (ITIs) ranged from 4 to 11 s. The target did not appear at all for 25% of the total trials . Of the 48/64 trials per run in which the target did appear, in 40/48, the cue color predicted the location accurately. Although participants were not formally tested for color blindness, all participants were asked if they were colorblind during screening and only subjects reporting no colorblindness were enrolled in the study. In addition, all subjects reported being able to differentiate the red cues versus the green cues during training before the MRI sessions.In the endogenous cueing condition, a color change of the fixation cross signaled participants to engage voluntary attention. After 600–700 ms, the central cross flashed either green (50%) or red (50%) for 100 ms. Participants were instructed that the color of the cross indicated the most likely  location of the upcoming target on that trial. For half of the participants, a green cross indicated that the target was more likely to appear on the left side, and a red cross indicated the target was more likely on the right side; the color‐location predictability was reversed for the other half of participants. All participants were instructed truthfully about the color‐location mapping condition they were in. After the cue, there was an interstimulus interval (ISI) of 600–900 ms during which participants uninformative about the upcoming target location. After the flash, there was an ISI of 66–266 ms before the target appeared for 150 ms. The checkerboard target and task goal were the same as in the endogenous condition. The ITI varied from 4,600 to 11,700 ms to balance our overall run length across conditions. Again, 25% of trials were catch trials. For the 48/64 trials with a target, in 24/48 the cue and target appeared in the same location, and in the other 24/48, the target appeared opposite the cue.In the exogenous condition, involuntary attention was captured using a salient peripheral flash. As in the endogenous condition, trials began with a 600–700 ms preview display of a fixation cross. Next, one of the two black boxes flashed white for 34 ms. Participants were instructed (truthfully) that this cue was predictive of the target location being opposite the cue, and participants were instructed as such. In 83% of target‐present trials, the target appeared opposite the cue, and in 17% of target‐present trials, the target appeared in the same location as the cue.In the anti‐predictive condition, physical stimuli were identical to the exogenous condition; a 34 ms salient white flash followed by a target (75%) or no target . However, for this condition, the cue (flash) was 2.33 resolution). Functional images included 10 transverse slices (4 × 4 × 6 mm3 resolution) collected interleaved inferior to superior. Images were acquired using a T2‐weighted echo‐planar imaging (EPI) sequence , and the first two scans were discarded to allow for magnetic field stabilization. Participants completed two runs of each condition for a total of six runs .Images were collected using a 3 T Siemens Trio Total imaging matrix MRI system at the University of North Carolina Biomedical Research Imaging Center. Partial brain imaging focused on the cortex at the level of the dorsal frontoparietal network, allowing rapid image acquisition. A structural scan was acquired for each participant before the experiment . Preprocessing steps included slice‐time correction, spatial realignment, and spatial normalization, using the mean image constructed at the realignment stage to determine parameters to normalize the EPI datasets into MNI space using the EPI template). Data were then smoothed with an 8 mm (FWHM) isotropic kernel.2.5The fMRI timeseries from all sessions from a given subject were concatenated, and the three attention conditions were modeled as boxcar functions in the usual way . In the GLM, each session included two conditions: catch (target‐absent) trials and target‐present trials. There were an additional eight nuisance regressors in the model: six movement parameters of the rigid body realignment and two physiologic regressors representing cerebrospinal fluid (CSF) and white matter (WM) signal. Coordinates for the two physiologic regressors were identified separately: CSF from the Harvard‐Oxford Atlas   and WM from the Johns Hopkins University White Matter Atlas .We then selected regions of interest (ROIs) for subsequent DCM. ROIs included the bilateral frontal eye fields (FEFs) and intraparietal sulci (IPS), in accordance with literature on the dorsal attention network ; where the underlying (average) connectivity is denoted by an adjacency A matrix. This means that there are as many B matrices as attentional effects. Our models or hypotheses were therefore defined in terms of the B matrices that specify where attentional effects are deployed. Our models included reciprocal connectivity between regions across‐hemisphere and within‐hemisphere. In order to limit the number of models, we did not include every possible connection between areas. In particular, we did not include heterotopic connections across hemispheres  because they are generally considered to be less plausible than a combination of homotopic connections and “U” fibers  to further examine differences between our conditions at the level of individual parameters. BPA computes an average of parameters for a single model over multiple participants. BPA is a Bayesian approach in that it calculates posteriors, not with regard to the probability of models but with the precision of individual parameter estimates (the inverse of the parameter posterior covariance matrix) Penny, . We thenWe used this form of BPA—as opposed to random effects Bayesian Model Comparison or Parametric Empirical Bayes over subjects—because we were not interested in establishing the presence of attentional effects, or whether they were conserved over subjects: our question was whether there were quantitative asymmetries in attentional effects on forward and backward connections, assuming that they exist in all subjects. In this setting, BPA provides a very efficient estimate of (changes in) directed connectivity, by accumulating evidence over subjects.33.1Brain regions that responded significantly to cue stimuli are shown in Figure 3.2In order to focus on the modulation of connectivity—between the frontal and parietal regions—without a potential influence of where the driving input was delivered, we allowed the driving input to enter each of the four regions. To test whether this choice was justified, we performed an initial DCM comparison, in which models varied in terms of which areas received driving input . As can be seen in Figure 3.3n = 188 model set revealed a winning model , so this result was interesting in that it suggested that each of the attentional effects in our “full” model were necessary to explain the data above and beyond the inherent complexity cost of including an extra attentional parameter. As noted above, the winning model had a posterior probability of >99.9% in comparison to all other models; therefore, no other models received closely comparable support.Fixed‐effects BMS performed on our full l Figure , with a Regarding the large disparity in posterior probability between the winning model and all other models, this is partially due to our use of fixed‐effects Bayesian model selection, which is highly efficient because one has very precise estimates from each subject. If the estimates of model parameters are precise, the evidence for marginal likelihood is usually large, relative to the next best model. When plotted in terms of probability, this makes it look as if all the other models have a zero posterior probability. Indeed, DCM studies using fixed‐effects model selection have found posterior probabilities exceeding 99% for a single model, illustrating the utility of this selection scheme in scenarios such as ours where the fixed‐effects assumptions hold  as the endogenous condition on all four frontal–parietal connections. Since the physical stimulus is identical for the exogenous and anti‐predictive conditions, this suggests that the present results are not overly influenced by physical stimuli, but rather reflect the type of attention engaged. Together, these results provide evidence that the modulatory effects of purely exogenous attention were indeed distinct from the modulatory effects produced by endogenous attention.Our specific interest was in how frontal–parietal connections were differentially modified by the three attention conditions. As shown in Figure In regards to hemispheric differences, it is noteworthy that the connection from FEF to IPS in the left hemisphere is modulated in the same direction by all types of attention tested here, whereas the FEF to IPS connection in the right hemisphere is modulated in an antisymmetric fashion for Endo versus Exo attention. This relates to our previous report, using this dataset, showing hemispheric asymmetry and further supports research suggesting that attention mechanisms may not be the same in the two hemispheres.4Much previous research has shown that a dorsal attention network is associated with attentional control. In the current study, we asked whether the directed connectivity between the frontal and parietal portions of this network is affected in different ways by endogenous versus exogenous attention. Using DCM, we assessed directed connectivity between the FEF and IPS, and how attention, across three different conditions, modulated that connectivity. Of 188 models tested, the winning model comprised modulations by each of our three attention conditions—on both directions of frontoparietal connectivity and within both hemispheres. This suggests that each type of attention had a substantial effect on frontal‐to‐parietal and parietal‐to‐frontal connectivity. Analysis of the parameters in the winning model, averaged across subjects, revealed a baseline (average) connectivity with a pattern of an inhibitory influence of parietal areas on frontal areas and a facilitatory effect of frontal areas on parietal areas. Crucially, for our main hypothesis, a distinct pattern of modulatory effects was observed for endogenous and exogenous attention on those frontoparietal connections. With the exception of the modulation of the FEF to IPS in the left hemisphere, exogenous attention modulated connectivity in the opposite direction as endogenous attention. Thus, these results provide new evidence for distinct neural mechanisms underlying exogenous versus endogenous attention.From the perspective of predictive coding, this is very sensible. Most formulations of attention under predictive coding focus on the neuronal excitability of prediction error units that encode the precision of ascending (forward) prediction errors , the anti‐predictive condition allowed us to examine whether the pattern of modulations was dominated by the sensory input alone. The results showed, however, that the voluntary allocation of attention had a stronger impact on connectivity in this condition. Indeed, the direction of modulatory effect on all four frontal–parietal connections for the anti‐predictive condition was the same as for the endogenous condition. Although the anti‐predictive condition would be expected to show an initial triggering of the exogenous system, the present results confirm that predictive information—about the target location—has a robust and lasting effect on the allocation of attention and related neural dynamics.opposed the observed intrinsic connectivity between frontal and parietal regions. This pattern is consistent with top‐down attention being an effortful control over perceptual and cognitive processes. In contrast, exogenous attention alone  modulated connectivity in the manner of boosting intrinsic connectivity. This was consistent across the frontal–parietal connections, except in one case: exogenous attention modulated extrinsic frontal‐to‐parietal connectivity in the left hemisphere in a manner opposing the direction of opposing the intrinsic connectivity at that connection. Furthermore, at this one connection , exogenous and endogenous attention  modulated the connectivity in the same direction. The similarity in this specific modulatory effect across attention types was not predicted; however, it could potentially relate to the asymmetries observed in studies of attentional neglect. It has recently been suggested that endogenous orienting mechanisms remain intact in those with neglect, but the exogenous orienting system is disturbed . Although these data were elicited under a carefully designed paradigm—and the regions were chosen in a principled way—our results should not be generalized to other subgraphs or experimental manipulations of attentional set. As with many DCM studies of computational architectures, the results in this article should be seen as contributing to the process of hypothesis testing and subsequent hypothesis building. In other words, certain questions now arise that call for further studies. For example, to what extent are the hemispheric differences in attention‐modulated connectivity seen here due to left hemispheric dominance? Would attention to different features—crossed with spatial attention—lead to similar results? The analyses in this article suggest that these sorts of questions can now be addressed and answered.modulatory effects within the dorsal frontoparietal network, somewhat contrasts a prior finding of an absence of hemispheric asymmetry within the dorsal network during attentional shifts and target detection  and gamma frequency bands (30–100 Hz) may be especially important for understanding the mechanisms of attention. Changes in alpha activity are usually found ~500 ms after the instructive cue and are thought to be related to the biasing of sensory processing regions following a shift of attention (Thut, Nietzel, Brandt, & Pascual‐Leone, All authors report no conflicts of interest.Table S1 Architecture of all 188 models tested.Click here for additional data file.Table S2 Top 10 models ranked on log evidenceClick here for additional data file.


Image‐guided radiation therapy (IGRT) technologies are routinely used by radiation therapists (RTs) in clinical departments. However, there is limited literature on the acquisition and assessment of IGRT image‐matching competencies in undergraduate educational environments. This commentary paper aims to share the authors’ experiences in the development of teaching IGRT and image‐matching concepts in an undergraduate radiation therapy programme. It outlines how MOSAIQ oncology information systems (OIS) have enabled the university to embed hands‐on IGRT image matching on a range of clinical cases. The hands‐on exposure to case‐based planar and volumetric kilovoltage (kV) image matching has resulted in improved teaching and better preparation of students for clinical IGRT encounters. Students are likely to benefit from critical image assessment and decision‐making as well as the improved engagement in teaching and learning. The aim of this commentary paper is to share the authors’ experiences in the optimisation of teaching image‐guided radiation therapy (IGRT) and image‐matching concepts in an undergraduate radiation therapy programme. It outlines how MOSAIQ oncology information systems (OIS) enabled the university to embed in undergraduate teaching, hands‐on IGRT image matching on a range of clinical cases. The hands‐on exposure to case‐based planar kilovoltage (kV) and cone‐beam computed tomography (CBCT) image matching and resulted in improved teaching and better preparation of students for clinical learning. The role of image‐guided radiation therapy (IGRT) is well‐documented in the literature. It is through effective IGRT practices that random and systematic uncertainties are quantified and corrected to ensure accurate treatment delivery.In undergraduate education, IGRT image‐matching skills and abilities are often acquired and developed in clinical settings utilising authentic clinical technology. Facilitating hands‐on experience with IGRT technologies in a university setting provides RT students with the opportunity to transfer theoretical knowledge gained into clinical practice. This was achieved through the integration of clinical software such as the MOSAIQ Oncology Information Systems (OIS) , which is used in many clinical departments to support IGRT decision‐making, as well as tracking and reporting daily shifts. To our knowledge, the implementation of case‐based teaching with hands‐on IGRT image matching utilising MOSAIQ in a university setting has not been reported in the literature.Following the development of IGRT teaching image matching at undergraduate level at Queensland University of Technology (QUT), we report successful implementation that has resulted in better teaching and learning approaches of IGRT image‐matching concepts. This also improved student engagement and provided students with the ability to practise IGRT image matching in a safe learning environment.The set‐up of MOSAIQ for teaching into the Bachelor of Radiation Therapy programme was well aligned to QUT’s strong emphasis on the development of authentic teaching and assessment. MOSAIQ was funded by the university and installed on thirty (30) computer workstations for teaching. This was a great opportunity for the lecturers to develop case‐based volumetric IGRT teaching modules. Several MOSAIQ‐based learning activities were developed, which now form part of the curriculum. In 2017, a major review of the image‐matching teaching tasks resulted in a decision to embed more hands‐on image‐matching tasks into teaching for 2018. To improve pre‐clinical IGRT teaching, a greater educational database of planar megavoltage (MV) and kilovoltage (kV) and kV‐CBCT images were acquired to enable MOSAIQ‐based teaching to be implemented over several semesters in the four‐year programme.An important task was to undertake a review of the literature to learn from several studies that reported and discussed the rationale for image matching competency training in clinical environments. Several articles reviewed highlighted it is imperative RTs are trained in IGRT, particularly image analysis and decision‐making.MOSAIQ has several image matching and visualisation tools for planar image matching, point, curve, greyscale and manual registration, with the ability to fuse the images and display the chequerboard, graticule and field apertures. Each semester, students are provided with comprehensive module handbooks, created by the university lecturers. These contain a step by step guide for the students to work through the planar and volumetric image‐matching tasks. Further information provided includes instructions for performing the image‐matching task, the image analysis tools to use and the matching techniques. Each student is allocated patients in MOSAIQ for them to analyse the reference digitally reconstructed radiographs (DRRs) and planar kV images, fuse the images, and record the shifts and comments in a spread‐sheet provided. They comment on whether the match is within tolerance, a move is required or whether the images need re‐acquiring. Students perform image matching of single images and orthogonal image pairs using MOSAIQ tools for 2D matching.For three‐dimensional (3D) image matching, each student will access the allocated patients for image matching and refer to their workbooks for specific instructions on automatic and manual matching. These also stipulate whether a soft tissue or bony match is required. Using the MOSAIQ’s image‐matching tools and viewing options such as colour blend, quartered, chequerboard and spyglass, they align the planning computed tomography (CT) and CBCT datasets and make a clinical judgement regarding accuracy of the image registration. Similar to the requirements for 2D matching, students record their results and document their decisions. Results documented include translational and rotational shifts which include pitch, yaw and roll and any anatomical changes. The students also learn how to use the clip box tool in MOSAIQ to apply automatic registration to a selected region of interest using either a maximising mutual information or chamfer algorithm. In this way, teaching provides an understanding of the correct use of the tools and the limitations of relying on automatic matching tools in clinical decision‐making scenarios.The university clinical partners were part of this process and provided the support required to achieve the outcome. Expertise from the clinical departments included RTs who ensured appropriate cases were selected for teaching. Information technology staff from the hospital and university, as well as vendor support, ensured the correct MOSAIQ set‐up and export/import of digital imaging and communications in medicine (DICOM) datasets. The process involved appropriate anonymisation and duplication of parent datasets consisting of reference CT data, plan dose and RT structure sets as well as the planar kV and kV‐CBCTs and generation of a unique instance identifier (UID) attribute for all datasets. The destination DICOM device settings, such as the local application entity (AE), the port and localhost settings, had to be checked for successful export/import. Patient registration and MOSAIQ data import were similar to the clinical settings where, prior to exporting a treatment plan from the planning software to MOSAIQ, the patient’s demographic data must be registered in MOSAIQ ensuring the attributes match the data with a registered patient name in MOSAIQ. Hence, before exporting CBCT data into MOSAIQ, the patients in MOSAIQ had to match the demographic data of the CBCTs. Once the images were imported into MOSAIQ, treatment fields and a site set‐up definition was generated and approved mimicking an authentic clinical workflow.Several case‐based image‐matching modules with hands‐on teaching were developed which incorporated planar (kV and MV) and volumetric (kV‐CBCT) image‐matching tasks. Students are allocated a range of patients in MOSAIQ to perform matching tasks individually, ensuring that all students have the opportunity to participate ‘hands‐on’. We found the best solution was to provide specific details such as the set‐up and immobilisation, diagnosis, care plan and dose prescription for each module. This is consistent with the case‐based approach recommended in the literatureIt was beneficial that students access a range of daily CBCTs for each patient case. For example, a head and neck case was set‐up so students could access 33 days of daily CBCTs. This made it possible for the students to assess geometry and anatomic changes, challenging the students to re‐think the impact of weight loss, tumour shrinkage or other changes that can cause a discrepancy between the CBCT and the pre‐treatment CT.There was a need to provide didactic content specific to IGRT and align teaching to other units within the programme. This also involved an analysis of the contributions Virtual Environment for Radiotherapy Training (VERT) makes in the teaching of image matching concepts. Figure With support from the clinical partners, the university was able to develop more authentic and engaging IGRT image‐matching tasks for the students. To further improve teaching and learning, we are extending the range of case‐based planar and volumetric image‐matching teaching to a variety of tumour sites, incorporating new cases to ensure clinical relevance. Continued emphasis on strengthening teaching and learning strategies and building the students’ confidence is important. However, the main challenge in the implementation process is the time‐consuming task of manually creating pseudo‐patients and the duplication of the kV and CBCT images sets, to allow each student to have their own case and set of images to practise image matching. Despite this, creating these image matching activities was necessary to ensure are well‐prepared for clinical image‐matching tasks. The lecturers acknowledge this pre‐clinical case‐based teaching in university environments does not replace real‐world online clinical scenarios; however, it results in better preparation before clinical placement.The changes in radiation therapy technology and clinical protocols necessitate equipping radiation therapy students with knowledge and skills in IGRT decision‐making to ensure safe and accurate delivery of radiation therapy. Embracing the challenges of handling clinical software in university environments, we were able to embed hands‐on image matching that provided more effective and comprehensive teaching. As the literature also suggests, more emphasis needs to be placed on the development of more robust IGRT teaching, incorporating image analysis, decision‐making and relevant theory that underpins knowledge of image matching and IGRT. This may be achieved through the establishment of increased collaborative partnerships with clinical departments to share ideas, resources and develop tools necessary to enhance the quality of teaching and learning and improve skills critical to success in students’ future clinical roles.This review received no grant from any funding agency, commercial or not‐for‐profit sectors.The authors declare no conflict of interest.


New York City (NYC) has been one of the hotspots of the COVID‐19 pandemic in the United States. By the end of April 2020, close to 165 000 cases and 13 000 deaths were reported in the city with considerable variability across the city's ZIP codes.In this study, we examine: (a) the extent to which the variability in ZIP code‐level case positivity can be explained by aggregate markers of socioeconomic status (SES) and daily change in mobility; and (b) the extent to which daily change in mobility independently predicts case positivity.COVID‐19 case positivity by ZIP code was modeled using multivariable linear regression with generalized estimating equations to account for within‐ZIP clustering. Daily case positivity was obtained from NYC Department of Health and Mental Hygiene and measures of SES were based on data from the American Community Survey. Changes in human mobility were estimated using anonymized aggregated mobile phone location systems.Our analysis indicates that the socioeconomic markers considered together explained 56% of the variability in case positivity through April 1 and their explanatory power decreased to 18% by April 30. Changes in mobility during this time period are not likely to be acting as a mediator of the relationship between ZIP‐level SES and case positivity. During the middle of April, increases in mobility were independently associated with decreased case positivity.Together, these findings present evidence that heterogeneity in COVID‐19 case positivity during NYC’s spring outbreak was largely driven by residents’ SES. First, individuals living in wealthier ZIP codes may have found it easier to circumvent the restrictive initial testing guidelines on eligibility for a COVID‐19 diagnostic test, resulting in a lower proportion receiving the test actually being COVID‐19 positive. Conversely, individuals living in less wealthy ZIP codes may have been less able to receive tests unless clinically sick due to a lower proportion having a primary care physician and therefore reliant on emergency care for clinical consultation, suggesting that individuals living in poorer neighborhoods who eventually receive tests are more likely to be COVID‐19 positive. Second, evidence strongly suggests that the actual prevalence of COVID‐19 is substantially higher among Black individuals and those of lower socioeconomic status (SES).This analysis utilizes routinely reported public data from the NYC Department of Health and Mental Hygiene (NYC DOHMH), coupled with anonymized cell phone data assessing frequency of visits to businesses and US Census data to examine2In this ecologic study, our study population consists of aggregate data collected among residents in 177 ZIP codes of NYC covering all five boroughs. Our primary outcome of interest is the proportion of COVID‐19 tests found positive in each ZIP code. These data were extracted from a versioned public repository updated daily by the NYC DOHMH Proportion of the 18‐ to 64‐year‐old population that is uninsured (B27010),Median household income Proportion of population that self‐identified their race as white (B02001)Proportion of population living in households with more than three inhabitants (B11016)Proportion of population using public transportation to commute to work that includes bus travel (B08301)Proportion of population that is elderly (65+ years of age) (B01001)ZIP code‐level characteristics used as explanatory variables in this study were extracted from the US Census and the American Community Survey 2016  and include:vzd, visits per in ZIP code z during day d). As a measure of baseline or reference mobility (vz^), we calculated median daily visits per during pre‐pandemic period, defined as a 6‐month period from September 2019 to February 2020. Our exposure of interest, referred to henceforth as mobility, is the proportional change from pre‐pandemic mobility for each ZIP code, operationalized as the proportion of baseline mobility experienced in a given day of post‐pandemic response, vzd‐vz^/vz^. To account for the estimated incubation period of COVID‐19,Anonymized location data from cell phone visits to businesses within a ZIP code were obtained via SafeGraph.2.1R2). A multivariable prediction model was then constructed by sequentially adding variables in order from the highest to the lowest R2 from univariate analysis, until the adjusted R2 changed by <5% or all variables were exhausted. Post hoc investigations discovered that regardless of the time period under consideration, three of the four distinct time periods presented  resulted in selection of the same four variables  while all but median household income was included in the April 1 model. For ease of interpretation across time periods presented, all four of these variables are presented across all time points.Our first goal was to build a model estimating the proportion of total variability in percent positivity by ZIP code that could be explained by aggregate levels of SES. We considered ZIP code‐level measures of SES to be time‐invariant across the time period of the study (April 1‐30). As by the end of April a very consistent downward trend in daily cases was evident and testing was widely available, we limited the study period to April. As percent positivity changed daily during the study period, multivariable linear regression with generalized estimating equations to account for within‐ZIP clustering was used. Specifically, we assumed that outcome measurements within each ZIP code were more highly correlated with each other than across ZIP codes and that the strength of this correlation was dependent on the time separation between measures by assuming an autoregressive correlation structure. ZIP codes were assumed to be independent of one another. Standard errors reported are robust estimates. First, ZIP code‐level measures were considered in univariate analyses and ranked according to the percentage of variability explained  the change in the magnitude of the regression parameter estimate for uninsured proportion between the two models; and (b) assessed the magnitude and precision of the regression parameter estimate for change in mobility, adjusting for the SES variables. All models used linear regression with generalized estimating equations to account for within‐ZIP correlation.We report the results of the analyses at four time points during the study period, April 1, 10, 20, and 30, using data available through the given day. For April 1 time point, as daily data are not publicly available before this date, the outcome was calculated from cumulative cases and tests for the month of March.3A total of 277 520 COVID‐19 tests were performed across NYC during the month of April, of which 124 135 (44.7%) tests were confirmed positive, with a median (IQR) ZIP code‐level positivity of 43.6% (38%‐48.1%). Across all ZIP codes, the median (IQR) proportion of 18‐ to 64‐year‐olds without health insurance was 13% (9%‐17%), the household income was found to be 63.1 thousand , a quarter of the population lived in households of four or more (15.1%‐30.7%), about half of the population identified themselves as being white (25%‐68%), 9.7% (6.5‐15) of those who relied on public transportation to commute to work used buses for part of their commute, and 11.8% (9.8%‐14.4%) of the population was found to be elderly. Figure Figure R2 = 41%). R2 for the remaining explanatory variables considered are as follows: proportion of 18‐64 adults who are uninsured (38%), proportion of population self‐identifying as white (34%), median income (32%), change in mobility (19%), proportion using bus for commute (13%), and proportion elderly (3%). Sequentially including variables in our prediction model in order of decreasing R2 resulted in a model with four SES variables that explained 56% of the total variability in COVID‐19 positivity: proportion living in households with four or more individuals, proportion of adults who were uninsured, proportion identifying as white, and median household income. Further addition of variables resulted in marginal changes.Table R2 from 56% (April 1) to a model R2 of 18% (April 30).In analyses conducted at later time points, median income emerged as the variable explaining the most or second‐most variability in COVID‐19 positivity through univariate analyses (data not shown). The other variables explaining substantial variability on April 1 remained. The explanatory power of these variables, assessed together, decreases over time from a model Following on the observation that the proportion of uninsured adults is the most or second‐most important variable in univariate analysis at all four time points, we examined the independent relationship between the proportion uninsured and percent positivity, adjusting for variables thought to act as confounders of the proportion uninsured→COVID‐19 positivity relationship. We additionally examined whether including mobility reduced the magnitude of the relationship between proportion uninsured and percent positivity Table . AdjustiIn analyses of the possibility that changes in mobility may partially explain the observed SES→COVID‐19 positivity relationship, we compared the adjusted regression parameter estimate for proportion uninsured in models with and without measure of change in mobility. After additionally including mobility, the estimate for uninsured proportion was largely unchanged with the exception of the April 10 time point  , shortly after the peak (April 10), and during the reduction in daily cases (April 20 and April 30). In unadjusted models, on April 1, ZIP codes differing in change in mobility from baseline by 10% had mean case positivity proportions differ by 3.1% , with the higher case positivity among ZIP codes reducing mobility less. After adjusting for covariates (Table 4Our findings indicate that the heterogeneous distribution in neighborhood level case positivity during the first 2 months (of the presumably first wave) of the COVID‐19 pandemic in NYC largely followed underlying SES markers. We also found that spatial differences in change in mobility were independently related to case positivity during the middle of April—with a smaller reduction in mobility independently associated with reduced positivity rates—but not at the beginning and the end of the month. A likely explanation for this is increased COVID testing during April. At the beginning of April, routine testing for COVID was not widely available in NYC and likely differed by ZIP code in its availability. At the same time, change in mobility was the most dramatic during the early parts of April. However, as testing became more widely performed, case positivity decreased overall, leading to an overall inverse association between change in mobility and reduction on COVID positivity. Alternatively, this could be due to differences in how quickly ZIP codes adapted to shelter in place guidelines even if, by the end of April, the overall reduction in mobility was similar across ZIP codes. Analysis of change in mobility as a potential mediator of a SES→case positivity relationship yielded mixed results, with a 12% reduction in the independent relationship between proportion uninsured and case positivity on April 10 but minimal differences elsewhere.Across all analyses, there remained a strong relationship between markers of SES and case positivity by ZIP code. These findings align with a host of literature on the disproportionate impact of acute events on lower SES communities Our analysis found that during the peak of the epidemic, and early on in the city's response to the pandemic, differences in COVID‐19 positivity by ZIP code could largely be explained by static markers of neighborhood SES. Over half of the variability in positivity on April 1, for example, was explained by just three measures . Note that daily COVID‐19 cases peaked on April 6 in NYC. As the epidemic progressed, these markers of SES, while still independently associated with positivity, became less predictive. Taken together, this is consistent with two plausible mechanisms: (a) lower case positivity among higher SES ZIP codes may in part be explained by greater ease in being able to receive tests despite not being clinically warranted among individuals in higher SES neighborhoods and (b) higher actual numbers of infections among individuals in lower SES ZIP codes. While our outcome measure of interest (case positivity) cannot definitively separate out these mechanisms, both can be important drivers of the heterogeneous experience of the epidemic across NYC.Similarly, the importance of household density in these models underscores what is known from studies on transmissibility of other respiratory infections, including influenza, that most infections occur among members of a household (or in workplaces) due to prolonged exposure to infected individuals. Dense housing also limits the ability of those known to be infected to self‐isolate; providing infected individuals the option to isolate at a location different from home could prove beneficial in these cases.Our analysis showed that mobility reduced quite rapidly across all ZIP codes, beginning before mandated restrictions from NYS on PAUSE. This strongly suggests that many city residents dramatically curtailed their activities. The rate and magnitude of reduction varied by neighborhood, but higher reductions in mobility were actually independently associated with higher case positivity on April 10 and 20 but not on April 1 or 30. These mixed findings are partially due to the correlation between time and overall city‐wide case positivity; as testing became more prevalent, NYC observed a large decrease in overall case positivity.cumulative case and test counts, and hence, it is assumed that the increments in cumulative counts are new case/tests and not revisions to counts from previous days.Our study has a number of limitations. First, this is an ecologic analysis and thus inference is limited to the ZIP code‐level, not the individual level. ZIP code is not a perfect measure of neighborhood and can mask some important heterogeneity in both exposure and outcome measures in this study. Second, we are unable to adjust for differential changes in population density by ZIP code as individuals with the means to leave NYC during this time period were likely to be disproportionately those of higher SES.Our study also has several important strengths. First, it uses a novel approach to measuring mobility that does not focus on distance travelled or average distance from presumed home location of a cell phone, but instead focuses on physical check‐ins at POIs within an individual's presumed neighborhood. As such, this metric is conceptually different measure of “mobility” than other tracking measures, and one now being used by the CDC.5Dramatic reductions on mobility following ramp‐up of governmental interventions strongly suggests that the NYS on PAUSE worked as intended, although more recent data indicative of increases in mobility suggest that there may be a point at which individuals begin relaxing their adherence to these interventions. Evidence from the first wave at the epicenter of the US COVID‐19 pandemic strongly suggests that COVID‐19 incidence is unequally distributed, largely by SES. Intervention efforts should target communities most in need.JS and Columbia University disclose partial ownership of SK Analytics. JS discloses consulting for BNI.Matthew Raymond Lamb: Conceptualization ; Formal analysis (supporting); Investigation ; Methodology (lead); Supervision ; Writing‐original draft ; Writing‐review & editing . Sasikiran Kandula: Conceptualization ; Data curation (lead); Formal analysis (lead); Funding acquisition (supporting); Investigation ; Methodology (supporting); Project administration ; Resources ; Visualization ; Writing‐original draft ; Writing‐review & editing . Jeffrey Shaman: Conceptualization (supporting); Formal analysis (supporting); Funding acquisition (lead); Investigation (supporting); Methodology (supporting); Supervision (supporting); Writing‐review & editing (supporting).


Organic dirt on touch surfaces can be biological contaminants (microbes) or nutrients for those but is often invisible by the human eye causing challenges for evaluating the need for cleaning. Using hyperspectral scanning algorithm, touch surface cleanliness monitoring by optical imaging was studied in a real‐life hospital environment. As the highlight, a human eye invisible stain from a dirty chair armrest was revealed manually with algorithms including threshold levels for intensity and clustering analysis with two excitation lights (green and red) and one bandpass filter (wavelength λ = 500 nm). The same result was confirmed by automatic k‐means clustering analysis from the entire dirty data of visible light  and filters 420 to 720 nm with 20 nm increments. Overall, the collected touch surface samples (N = 156) indicated the need for cleaning in some locations by the high culturable bacteria and adenosine triphosphate counts despite the lack of visible dirt. Examples of such locations were toilet door lock knobs and busy registration desk armchairs. Thus, the studied optical imaging system utilizing the safe visible light area shows a promising method for touch surface cleanliness evaluation in real‐life environments. Touch surface cleanliness monitoring by optical imaging was studied in a real‐life hospital environment using both manual and automatic hyperspectral scanning algorithms. As the highlight, a human eye invisible stain from a dirty chair armrest was revealed. Thus, the optical imaging system in the safe visible light area shows a promising method for touch surface cleanliness evaluation in real‐life environments. Touch s2.2Enterococci), Mannitol Salt Agar plates (Labema Oy) (coagulase‐positive Staphylococcus and the presence of Staphylococcus aureus that are later in the manuscript referred as S. aureus) and MacConkey II Agar‐plate (BD) (Enterobacteriaceae and other Gram‐negative rods that are later in the manuscript referred to as Gram‐negatives). ATP as relative light units (RLU) was collected with a dry swab sample (Ultrasnap) and were read with the portable luminometer (Hygiena SystemSURE PLUS) following manufacturer's instructions . Total plate count (TPC) value ≥2.5 CFU cm−2 was used as surface hygiene benchmark as commonly used in hospitals −2 as an unacceptable  guideline value based on 100 RLU limit Microbiological sampling and microbiological analyses were described in more detailed elsewhere 2.3Escherichia coli and Bacillus subtilisrel) from 50 to 255 while the other two lights were set to zero. Lower Irel was chosen when the light source wavelength was close to filter wavelength to avoid overexposure.Hyperspectral imaging system is described more detailed in our other study in a laboratory environment with 2.4Microbial and dirt levels information were compared with the images of AutoDet to evaluate the cleanliness of the surface and to evaluate the real need for additional cleaning. Raw figures were cropped to smaller size figures with GIMP picture operating tool and Python (version 2.7.15) scripts for further analyses. Python NumPy and PyLab programs were used in cropping and visualization. The occurrence of stains was confirmed by comparing dirty and cleaned images that were taken in exactly same conditions. Transforming the figures to two‐colored images by a chosen color intensity threshold value and comparing to a cleaned figure enabled to spot also the human eye invisible stains. For the clustering analysis 2.5P = .05) was used to detect differences between material  and the sampling location for ATP and TPC  (significance level PC Table . Post ho33.1P < .05) for culturable bacteria count and ATP levels  than visually clean surfaces . Same trend was observed in ATP . Most of the human eye visible dirt were detected from table surfaces (N = 19) that represented cleanest locations of the study (Table −2−2) and a chair armrest (85 CFU cm−2). Despite the lack of very dirty surfaces, 52% of culturable bacteria and 59% ATP samples exceeded hygiene guideline levels for common and near‐patient areas used in this study.Public hospital, including emergency lounge area and toilet Figure A, was chls Table  in studi3.2−2 (109 RLU/surface) confirmed the surface as dirty. Raw figures in any of the studied RGB wavelengths and filters (420‐720 nm) did not indicate the occurrence of the human eye invisible stain, for example, green light and filter 520 nm , more importance was given to the invisible stains. The optical device (AutoDet) was able to detect the human eye invisible dirt from a normal wooden armrest of a chair that is presented as a case study example Figure A. ATP varel were plotted and the stain and the background separates from each other  and filters 420‐720 nm) with 20 nm increments. The same invisible stain was identified by automatic K‐means algorithm  and one filter, and the same result was confirmed utilizing automatically all three lights (RGB) and different filters between 420 and 720 nm. We were not able to confirm that the human eye invisible dirt was the cause of organic dirt detected on that surface  but it seems highly likely as a stain can appear in small clusters or nonevenly spread areas. For the analyzing of the dirt, the local occurrence of the stain instead of evenly distributing to the surface causes challenges for the algorithm development. With SVM, a correlation was found between ATP and our cleanliness estimator. Based on the optical imaging data acquired here, the dirt could not be identified. However, based on the properties of the stain emitting light at wavelength near 500 nm with lower excitation wavelengths, that is, green light as presented detailed and also with blue light (data not shown), riboflavin In general, the optical inspection methods covering large areas aim at the same hygiene level as current techniques. The studied hospital was challenging environment due to high cleanliness typical for hospitals. If the optical method functions in a highly clean environment, it can be expected to function also in dirtier environments such as public places with less frequent cleaning. Methods like cultivation and ATP are still needed to indicate the cleanliness of a surface. Utilizing visible light is feasible in real‐life environments such as a hospital unlike harmful low wavelength UV lights. Thus, the optical methods utilizing visible wavelengths instead of UV‐light make a promising method for future cleanliness measurements in human occupied real‐life environments.The exact details of the algorithm in Supporting Information are proprietary information due to the commercial aspects of the Autodet project.Appendix S1: Supporting InformationClick here for additional data file.FigureClick here for additional data file.


Lung carcinoid tumours are rare neoplasms with a favourable prognosis. Bronchoplasty can be a conservative treatment for typical carcinoid tumours and can be applied for patients with limited respiratory function. We report the case of a 34‐year‐old woman, with a polypoid tumour located at the distal right main bronchus. Bronchial biopsy showed a typical carcinoid tumour. After resection of the tumour in the right main bronchus, bronchoplasty was performed by end‐to‐end anastomosis of the remaining right main bronchus, right upper lobar bronchus, and the upper bronchus intermedius. Bronchoscopy showed a good quality anastomosis with slightly reduced endoluminal calibre only. The post‐operative period was uneventful and the patient was discharged at the seventh day. One year later, no complications occurred and the patient is still being followed up regularly. In this article, we try to demonstrate the possibility of creating a "neo‐spur" without lobectomy to treat an intra‐bronchial typical carcinoid tumor. Lung carcinoid tumours are neuroendocrine tumours accounting for 0.4–3% of resected lung tumours. They are classified into typical and atypical carcinoid tumours according to the World Health Organization 2004 classification. Typical carcinoid tumours usually have a favourable prognosis after surgical resection alone .Here, we describe a bronchoplasty procedure to resect the tumour without lobectomy or pneumonectomy.A 34‐year‐old woman presented to the Pneumology Department with a one‐year history of haemoptysis.The clinical examination and the chest X‐ray were normal. Computed tomography (CT) scan showed well‐circumscribed endobronchial tumour in the posterior wall of the right bronchus measuring 2 cm Fig. . BronchoThrough a posterolateral right thoracotomy, exploration found a 2‐cm lesion located at the distal right main bronchus just in front of the upper lobe bronchial spur.After resection of the tumour in the right main bronchus, at 2 cm from the main carina, the upper lobar bronchus and intermediate bronchus were anastomosed with the remaining right main bronchus (Fig. The post‐operative period was uneventful and the patient was discharged on the seventh day after a bronchoscopy showed the same results as the previous one.The final stage of the tumour was pT1aN0MX.One year later, no complications occurred: CT scan did not show any change in the airway calibre and no evidence of tumour recurrence. The patient is still being followed up regularly.Lung carcinoid tumour is a well‐differentiated neuroendocrine neoplasm accounting for approximately 2% of pulmonary lung tumours. The majority of them are located in the bronchus with only 10% in the lung parenchyma. Occasionally, they are located in the main carina or trachea [After surgical treatment, typical carcinoid tumours have a favourable prognosis compared to other lung neoplasm. Recurrence or metastatic disease occurs in 2–11% .Surgery offers curative treatment. If surgical resection cannot be performed due to poor cardiopulmonary status, tumours without cartilaginous extension can be removed by endobronchial therapy .Bronchoplastic resection for lung cancer is typically indicated for tumours located in bronchial orifice . The AmeBronchial anastomosis can be made using various techniques. In a recent study, Palade et al.  found noBronchial anastomosis can be complicated by bronchopleural fistula, dehiscence, ischaemia, and stenosis. Thus, we continue to follow‐up our patient who had an uneventful post‐operative period until the date of writing this article.Neo‐spur creating technique can be a conservative treatment for typical carcinoid tumours with good result. It can be applied for patients with limited respiratory function.Appropriate written informed consent was obtained for publication of this case report and accompanying images.


The HPTN 071 (PopART) trial evaluated the impact of an HIV combination prevention package that included “universal testing and treatment” on HIV incidence in 21 communities in Zambia and South Africa during 2013‐2018. The primary study endpoint was based on the results of laboratory‐based HIV testing for> 48,000 participants who were followed for up to three years. This report evaluated the performance of HIV assays and algorithms used to determine HIV status and identify incident HIV infections in HPTN 071, and assessed the impact of errors on HIV incidence estimates.HIV status was determined using a streamlined, algorithmic approach. A single HIV screening test was performed at centralized laboratories in Zambia and South Africa . Additional testing was performed at the HPTN Laboratory Center using antigen/antibody screening tests, a discriminatory test and an HIV RNA test. This testing was performed to investigate cases with discordant test results and confirm incident HIV infections.HIV testing identified 978 seroconverter cases. This included 28 cases where the participant had acute HIV infection at the first HIV‐positive visit. Investigations of cases with discordant test results identified cases where there was a participant or sample error (mixups). Seroreverter cases  were excluded from the primary endpoint analysis. Statistical analysis demonstrated that exclusion of those cases improved the accuracy of HIV incidence estimates.This report demonstrates that the streamlined, algorithmic approach effectively identified HIV infections in this large cluster‐randomized trial. Longitudinal HIV testing  and quality control testing provided useful data on the frequency of errors and provided more accurate data for HIV incidence estimates. To address these challenges, customized approaches were used for sample and data management, HIV testing and determination of HIV status. This report describes the methods that were used to identify incident HIV infections in the trial, and the results obtained. This included identification and characterization of acute and seropositive incident infections and analysis of the performance of HIV screening assays included in the testing algorithms. HIV incidence assessments can be distorted by errors in participant and sample identification (mixups). Determination of HIV status for all PC participants at all visits allowed us to estimate the frequency of those errors and assess the potential impact of those errors on the accuracy of study results.22.1Samples and data were obtained from PC participants at baseline (PC0) and annual follow‐up visits  (2013‐2018). At each visit, participants were offered HIV rapid testing, and a 10‐mL blood sample was collected for laboratory‐based HIV testing. This report includes results from only laboratory‐based testing; performance of point‐of‐care HIV rapid testing are reported elsewhere 2.2Five assays were used to determine HIV status , and laboratory personnel were blinded to study arm throughout the trial. All samples with a reactive in‐country Architect test result were tested at the LC with the BioRad test  or the BioPlex test (PC36); a random subset (~10%) of the samples with a non‐reactive in‐country Architect test result were tested at the LC using the same assay (Architect test) . Additional testing was performed at the LC for those samples Figure . Within‐2.5HIV test results were compared across study visits to identify samples that required additional testing. This included cases that had a NEG visit followed by a visit with a reactive test , cases that had a POS or INC visit followed by a visit with a non‐reactive test  and other cases with discrepant test results. Across‐visit HIV status  was determined using in‐country and LC test results. Cases were provisionally classified as HIV POS , HIV NEG , potential seroconverter, potential seroreverter, or to be determined (across‐visit status unclear due to missing and/or discrepant HIV test results). For seroconverter and seroreverter cases, additional testing was performed at selected study visits to confirm the change in HIV infection status Figure . In conf2.6Within‐visit and across‐visit HIV status were determined at the LC  and at the SDMC . Cases that had concordant LC and SDMC HIV status determinations were not reviewed further. All the remaining cases were reviewed by a Virology Endpoint Adjudication Committee (VEAC) that included two virologists from the LC and three external virologists , where p^1andp^2 represent the observed prevalence of a POS within‐visit status at the first or second of the paired visits respectively:We evaluated the frequency of errors due to participant or sample mixups by comparing within‐visit HIV status for participants who had paired HIV status results from consecutive visits in three time intervals: PC0‐PC12, PC12‐PC24 and PC24‐PC36. Statistical methods used to derive error rates and the probability of true incident cases are shown in Supplemental File d^, of a true incident infection among those who were HIV uninfected at the first visit where both samples came from the same study participant is as follows:After accounting for the probability of errors, the probability, 2.8Written informed consent was obtained from all PC participants 33.1Overall, 48,301 participants were enrolled in the PC. HIV status was evaluated in 47,470 PC participants; 831 (1.7%) participants had no samples available for testing (missing HIV status). Table 3.2Architect test results were obtained in‐country for 119,270 (99.3%) of the 120,164 samples collected. The remaining 894 samples had no aliquot available for in‐country testing or did not have a test result. The in‐country Architect test was non‐reactive for 92,186 (77.3%) of the 119,270 samples tested. The same assay was performed at the LC for 10,731 (11.6%) of the 92,186 samples; this included samples that were randomly selected for QC testing , results of the two Architect tests were discordant . Those samples were tested at the LC with the BioRad or BioPlex test, the Geenius test and the HIV RNA test  of the 119,270 samples had a reactive in‐country Architect test; 26,972 (99.6%) of those samples were tested with a second HIV screening test at the LC . Those cases, which had false reactive results with two different screening tests, were identified during evaluation of possible seroconverter or seroreverter cases.3.4Across‐visit HIV status was provisionally determined for each participant by analysing test results from longitudinal study visits; a pre‐determined plan was used to identify cases that required additional adjudication to determine across‐visit HIV status, to determine the timing of seroconversion events, and to identify participants who had an acute HIV infection visit (see Methods). Overall, 369 (0.76%) of the 48,301 cases were referred for VEAC review. The final across‐visit status changed in 69 28.7%) of those cases and two cases that were not referred for review  of these cases had at least one sample tested from a subsequent study visit. Potential seroconverter events were identified when a visit classified as NEG was followed by a visit where the in‐country test result or the LC QC test result was reactive; additional testing was performed at the LC in these cases  of those cases had results that indicated an error or specimen mixup (seroreverter cases). In 154 of the 213 cases, the participant had a POS status at enrolment; those cases were never considered eligible for the incidence analysis. In the remaining 59 cases, the participant had a NEG status at enrolment, and had a sample from a subsequent visit with confirmed HIV infection . We used data from the seroreversion cases to estimate the impact of these errors on the accuracy of identification of incident infections Table . In this4This report describes the methods used to determine HIV status and identify incident infections in the HPTN 071 (PopART) trial. The size of this trial  presented challenges in sample and data management. A streamlined approach was used to reduce the cost, effort and complexity of HIV testing. Customized data management procedures were used to reduce the frequency of clerical errors. An external adjudication committee reviewed > 300 cases with complex test results. Across‐visit HIV status was determined in all but 16 cases. Limitation of testing for many samples to a single HIV screening test had a minimal impact on study results.In a previous community‐randomized study, the primary HIV incidence endpoint was determined by analysing samples collected in a cross‐sectional survey of > 46,000 individuals Overall, 978 seroconverter cases were identified; 553 of these cases were used for the primary HIV incidence analysis Twenty‐eight participants had acute HIV infection at the first HIV‐positive visit. In four cases, the viral load was <400 copies/mL at the acute visit. Since the 978 seroconversion events were likely distributed evenly over the year preceding the first HIV‐positive visit, we would expect that some seroconverter events would be detected during the acute infection period (e.g. within one week of HIV infection) and that a portion of those cases would be detected only 1‐2 days after infection, at a time when viral load might be low. The sensitivity for detecting acute infections was similar for the three laboratory‐based antigen/antibody HIV screening tests . The 5th‐generation test (BioPlex) identified only 25% of the acute samples as positive for HIV antigen only. ART initiation during acute infection can suppress viraemia and HIV antibody expression; loss of HIV antibodies (seroreversion) has been described in some cases This report included comparison of Architect test results obtained in‐country and at the HPTN LC for >10,000 samples. Two non‐reactive results were obtained in 10,680 (99.5%) of these cases. We evaluated cases with discordant Architect tests, and cases where the in‐country Architect test and LC BioRad/BioPlex tests were discordant. Investigation indicated that some of these cases likely represented laboratory errors . Other cases involving participant or sample mixups were identified by longitudinal testing (seroreverter cases). Longitudinal HIV testing is not performed in most studies once an individual is determined to be HIV infected 5This report demonstrates that the streamlined, algorithmic approach was effective for identifying incident HIV infections in this large community‐randomized trial. The report also demonstrates the utility of QC testing for investigation of discordant test results, the value of performing HIV testing for all participants at all visits, removal of seroreverter cases and the use of statistical methods for investigating the impact of sample mixups on HIV incidence estimates increased the accuracy of HIV incidence estimates.None of the authors has a conflict of interest or potential conflict of interest, with the following exceptions: Susan Eshleman has collaborated on research studies with investigators from Abbott Diagnostics; Abbott Diagnostics provided reagents for previous research studies.All authors have read and approved the final manuscript. Additional author roles are listed below.File 1. Assays used to determine HIV status.File 2. Determination of within‐visit and across‐visit HIV status.File 3. Derivation of estimates for error rate and probability of true incident cases for paired samples from sequential visits.File 4. Analysis of 51 samples that had a non‐reactive in‐country Architect test and a reactive HPTN LC Architect test.File 5. Analysis of 227 samples that had a reactive in‐country Architect test and a non‐reactive HPTN LC BioRad or BioPlex test.File 6. Characteristics of acute HIV infections.File 7. Pattern of HIV test results in seroreverter cases.Click here for additional data file.


The approach an institution takes to tracheostomy during the coronavirus disease 2019 (COVID‐19) pandemic has broad potential to influence morbidity and mortality, as tracheostomy is at the intersection of patient‐centred care, healthcare worker safety and resource allocation [Our institution has been at the epicentre of the UK pandemic, admitting hundreds of patients to regular and ‘surge’ ICUs. There has been a very high requirement for tracheal intubation and mechanical ventilation, and subsequently for tracheostomy. We established mobile emergency rapid intubation teams (MERIT) early in the pandemic response, which follow specific protocols for personal protective equipment (PPE) and minimising aerosol generation and thus operator risk of infection . Mobile ENT surgeons have been involved in ‘surge’ tracheostomy strategies in other centres, for example, instituting a dedicated tracheostomy theatre. However, our approach was distinct and unusual both in combining ENT surgeons with anaesthetists to form a tracheostomy team and, crucially, in this team undertaking percutaneous tracheostomies independently at the bed‐side in the ICU. This was in contrast to normal practice where tracheostomies would be inserted either by ICU operators alone, or alternatively in the operating theatre as a surgical tracheostomy.The ENT/MERIT tracheostomy team has quickly developed expertise in using advanced PPE and minimising aerosolisation in accordance with carefully devised procedures including a step‐by‐step protocol for bed‐side tracheostomy Fig. . This prThe presence of ENT surgeons in the team may have allowed more challenging tracheostomies that would not normally be attempted in ICU (e.g. obese patients) to still be undertaken in the most resource‐efficient manner. While the ideal location for performing a tracheostomy on a COVID‐19‐positive patient is in a negative pressure side room or operating theatre, this may not be available, and transferring critically ill patients to theatre also has major resource and safety implications. Recent preliminary data from a national UK audit (UK COVIDTrach) indicate that percutaneous tracheostomy is at least as safe as surgical tracheostomy in COVID‐19 . AnecdotUntil further data are forthcoming, we recommend consideration of this multidisciplinary team model and protocol for tracheostomy in COVID‐19 in order to deliver safe patient‐centred care and support healthcare worker safety while simultaneously optimising the balance between these priorities and the efficient utilisation of scarce resources.


The cultivation of yeasts from up to 5000‐year‐old beer vessels in Israel allows insights into early domestication of microbes for food production, but also raises questions about long‐term survival of microbes under dormancy or slow growth. A milestone was the introduction of absolute dating methods in the 1950s based on the determination of the radioactive decay from the carbon‐14 isotope in organic material discovered at the excavation site. As an illustration, the Cortaillod culture in a lake region from Western Switzerland was dated by this approach to 3000 to 2600 BC. Thanks to an exceptional preservation of artifacts in a humid soil, this culture has yielded numerous archaeological findings documenting early agricultural practices in prehistoric Switzerland. The discovery of pitchers with many holes in the bottom of the vessel suggested to some archaeologists ‘cheese strainers’ and thus early cheese making activity in Switzerland. Other archaeologists disagreed and proposed filtration of fruit juice as alternative use. By archaeological means, it was difficult to settle the dispute. In the meantime, rock paintings from the Sahara were discovered that documented even earlier dairy activities. While it is clear that these scenes stem from a greener past of the Sahara, rock paintings are difficult to date. Here the chemical analysis of milk fat residues extracted from the potsherds and the investigation of the stable carbon‐13 isotope distribution allowed a dating of this Saharan dairy activity into the fifth millennium BC. The development of the chemical methodology for milk fat analysis pioneered by R.P. Evershed from the Bristol Biogeochemistry Research Center substantially enriched the toolbox of archaeologists. The systematic investigation of pottery vessels derived from archaeological sites covering the Near East to South‐Eastern Europe demonstrated that dairy activity started in the seventh millennium BC in the north‐western Turkey and spread from there to Europe. Vessels with holes in the bottom associated with milk fat residues documented cheese making activity in Poland at the sixth millennium BC  efficiently attacking biological structures. However, with respect to the rRNA gene sequence, the isolates were close relatives of extant B. sphaericus raising again doubts about their antiquity. Current Bacillus spore survival experiments explore more modest time intervals like half of a millennium. They are inspired by experiments with permafrost sediments. In a PNAS paper from 2007, E. Willerslev and colleagues used a long, but conserved 4‐kbp amplification product around rRNA genes as a proxy for intact DNA and a liberal criterion for potential viability. In the 5 to 30 kilo‐year (Kyr) age range, spore‐forming bacteria accumulated hydrolytic damage largely preventing PCR amplification of such fragments. In 400 to 600 Kyr‐old ice cores, no spore formers gave amplification products, demonstrating that dormancy, which confers high resistance against harsh environmental conditions, is not a long term survival strategy. These authors still detected 4‐kb amplification products in non‐spore forming Actinobacteria and linked this long‐term survival activity to DNA repair mechanisms in cells displaying still a very low basic metabolic activity not seen in dormancy. An AEM paper from 2019 by R. Liang suggested viable microorganism in up to 1 million‐year‐old permafrost soil on the basis of lower than expected amino acid racemization. The oldest layers were enriched in spore‐forming Firmicutes. In contrast, a recent mBio report using metagenome, meta‐transcriptome and metabolome analyses in up to 50 000‐year‐old deep marine sediments revealed fascinating mechanisms of long‐term microbial survival in energy‐limited environments (Bird et al., Important questions arise from this report. One is how far back in time can we isolate ‘ancient’ microbes? The Israeli scientists argue that occasional supply of moisture and nutrients might have allowed the ceramic‐associated yeasts to grow and to survive over millennia. They suggested that bacteria, which are tougher than yeasts, and particularly bacterial spores, which are designed by evolution as the hardiest survival form of bacteria, might yield viable isolates from even older materials. Claims to the reactivation of ancient It thus seems plausible to describe a viability limit for ancient organisms by the absence of growth and repair to the chemical stability of the genetic material. Since RNA is chemically more labile than DNA, the oldest sequenced RNA virus genome is a 1000‐year‐old maize virus. Notably, it belonged to a virus group with double‐stranded RNA genome making it chemically more resistant than single‐stranded RNA. DNA is chemically more resistant than RNA, but unrepaired DNA will be fragmented into 100 bp pieces or heavily crosslinked in < 1 million years under optimal storage conditions. This estimate is corroborated by the currently oldest ancient DNA sequencing record held by M. Meyer, S. Pääbo and colleagues with heavily damaged 430 000‐year‐old hominin DNA from Spain.PNAS paper. The oldest ceramic vessels with chemical traces of dairy fermentation like the Polish cheese strainers might be another fascinating target for microbial isolation. Such isolates could provide insights into the domestication of microbes during the early stages of agriculture, food processing and food preservation in prehistoric times.While these data make the isolation of more than 1 million‐year‐old microorganisms unlikely, they do not exclude the yeast isolation results of the Israeli researchers. As everywhere in science, independent confirmation by other laboratories will be essential to separate facts from artifacts. Anticipating that the yeast data from the archaeological samples of Israel are correct, it might be interesting to extend these microbial cultivation experiments to oldest ceramics with chemical traces of wine making in the form of tartaric acid, currently held by pots of the Shulaveri culture in the Republic of Georgia, which is dated to 6000 to 5000 BC, as reported by D. Lordkipanidze and colleagues in a 2017 Homo erectus specimen in Dmanisi/ Georgia dated to 1.8 million years ago. An slightly older Homo erectus specimen was even found in China, demonstrating an out‐of‐Africa movement long before Homo sapiens followed suit. No DNA suitable for sequencing could be recovered from the H. erectus specimen. However, proteins are more stable than DNA; collagen had been be recovered from bones of this age. However, collagen is a protein with a highly repetitive primary structure making it of limited use for establishing ancestral relationships between skeletons too old for DNA sequencing. D. Lordkipanidze, E. Willerslev and colleagues argued that teeth are the most resistant tissue in the human body and also the most abundant archaeological findings. These researchers found in 1.8 million‐year‐old teeth from Dmanisi about 1000 peptide matches to the dental enamel proteome, which showed chemical modifications proving their antiquity (Cappellini et al., H. erectus teeth were too valuable to be used for this proof of principle work, which was done with animal teeth allowing the molecular reconstruction of phylogenetic relationships of Pleistocene rhinoceros. Forty thousand‐year‐old teeth from Neanderthals have also revealed substantial insights for microbiologists (Weyrich et al., Methanobrevibacter oralis genome was sequenced and is currently the oldest microbial draft genome. It diverged from the corresponding species associated with modern humans more than 100 000 years ago. One individual had a dental abscess that was possibly treated with aspirin produced by a poplar extract and an antibiotic produced by a food contaminating Penicillium species. Research papers like this document that the relationship between archaeology and microbiology becomes increasingly an asset for both disciplines.At the end of this short overview on bioarchaeology, one should mention a new technology that might help where DNA sequencing reaches its current technological limits. Georgian archaeologists discovered a Homo erectus was discovered.Islamic/ Christian co‐culture in Dmanisi/Georgia, near the site where a 1.8 million‐year‐old


A 70‐year‐old male was referred to our hospital with lower limb muscle weakness and numbness of the left hand. The patient had previously been diagnosed seven years ago with lung cancer accompanied by central airway obstruction and had received chemoradiotherapy following placement of a metallic stent. Computed tomography (CT) scan revealed an osteolytic lesion which was adjacent to the fractured stent. T2‐weighted magnetic resonance imaging (MRI) demonstrated high signal intensity in the disc space. The patient was diagnosed with spondylodiscitis and spinal epidural abscess related to the airway stent. Despite hemilaminectomy, laminectomy and long‐term antibiotic therapy, the infection was uncontrolled. Moreover, osteolytic destruction and kyphotic deformity progressed. Removal of the airway stent was necessary; however, it was impossible because bronchial resection was required and the risk of mediastinal injury was considered to be high. The patient subsequently received palliative care. Long‐term airway stenting can cause spondylodiscitis and spinal epidural abscess. Indications for the placement of metallic stents for malignant central airway obstruction should be carefully evaluated after considering the difficulty in removal and the long‐term risk of severe complications.Significant findings of the studyLong‐term placement and fracture of the airway stent can cause spondylodiscitis and spinal epidural abscess.What this study addsThe indication of placement of a metallic stent for malignant central airway obstruction should be considered with caution, especially if long‐term survival can be expected. Malignant central airway obstruction (CAO) is a life‐threatening complication of lung cancer and often requires immediate treatment.Here, we report a case involving a man who developed spondylodiscitis and spinal epidural abscess related to airway stent fracture seven years after airway stenting and chemoradiotherapy for lung cancer with CAO.A 70‐year‐old male was referred to our hospital complaining of lower limb muscle weakness and left‐hand numbness. The patient had been diagnosed with squamous cell lung cancer  accompanied by CAO seven years previously, and following the placement of a self‐expandable metallic stent , had received chemoradiotherapy with cisplatin plus S‐1 and 59 Gy of thoracic radiation.On admission, blood examination revealed high levels of inflammatory markers. Computed tomography (CT) revealed an osteolytic lesion at the T1–T2 disc level, which was adjacent to the fractured airway stent. T2‐weighted magnetic resonance imaging (MRI) demonstrated high signal intensity in the disc space, with an epidural abscess compressing the spinal cord (Fig Staphylococcus aureus was isolated from the wound and blood cultures. Antibiotic therapy was initiated, and the patient's symptoms gradually ameliorated with a reduction in inflammatory markers. Removal of the airway stent for infection control was considered; however, bronchoscopy showed the fractured airway stent was covered with granulation tissue (Fig The clinical course of the patient is summarized in Fig ssue Fig . Stent rssue Fig . SurgicaThere were two important clinical issues found in this study. First, long‐term placement and fracture of airway stents can cause spondylodiscitis and spinal epidural abscess. Second, indications for airway metallic stenting should be considered with caution because of difficulty in removal and the long‐term risk of severe complications.This is the first report of spondylodiscitis and spinal epidural abscess related to an airway stent. In a previous study, although 19% patients with airway stents experienced stent‐associated respiratory tract infection,Long‐term placement of metallic stents could cause several complications.In conclusion, long‐term placement of metallic stents can cause spondylodiscitis and spinal epidural abscess. Indications for the placement of metallic stents for malignant CAO should be cautiously evaluated after considering the difficulty in removal and the long‐term risk of infection.The authors have no conflicts of interest.


H and VL, influences the shape of the antigen binding site, that is, the paratope, and is essential to understand antigen specificity. ABangle characterizes the VH‐VL orientation by using five angles and a distance and compares it to other known structures. Molecular dynamics simulations of antibody variable domains (Fvs) reveal fluctuations in the relative domain orientations. The observed dynamics between these domains are confirmed by NMR experiments on a single‐chain variable fragment antibody (scFv) in complex with IL‐1β and an antigen‐binding fragment (Fab). The variability of these relative domain orientations can be interpreted as a structural feature of antibodies, which increases the antibody repertoire significantly and can enlarge the number of possible binding partners substantially. The movements of the VH and VL domains are well sampled with molecular dynamics simulations and are in agreement with the NMR ensemble. Fast Fourier transformation of the ABangle metrics allows to assign timescales of 0.1‐10 GHz to the fastest collective interdomain movements. The results clearly show the necessity of dynamics to understand and characterize the favorable orientations of the VH and VL domains implying a considerable binding interface flexibility and reveal in all antibody fragments  very similar VH‐VL interdomain variations comparable to the distributions observed for known X‐ray structures of antibodies.The relative orientation of the two variable domains, VH and VL. The relative orientation of the VH and VL domains influences the shape of the antigen‐binding site and is a major objective in antibody design. In agreement with NMR experiments and molecular dynamics simulations, we show a considerable binding site flexibility in the low nanosecond timescale. Thus we suggest that this flexibility and its implications for binding and specificity should be considered when designing and optimizing therapeutic antibodies.Antibodies have become key players as therapeutic agents. The binding ability of antibodies is determined by the antigen‐binding fragment (Fab), in particular the variable fragment region (Fv). Antigen‐binding is mediated by the complementarity‐determining regions consisting of six loops, each three of the heavy and light chain variable domain V Besides lengths and sequence of the CDRs, the relative orientation of VH and VL is a third very important factor that determines the shape of the antigen‐binding site.H and VL domains to one another is an additional structural feature of antibodies, which directly increases the repertoire of antibody specificity.H‐VL domain orientation directly change the binding site geometry and have an effect on the specificity of the paratope, the antigen‐binding site, for target antigens.H1‐CL.H‐VL domain orientation is crucial in understanding the antigen‐binding process. Antibody‐antigen binding can be understood in terms of the conformational selection mechanism.Antibodies have become an important tool in therapeutics and clinical diagnostics.H‐VL domain movements. We compare the VH‐VL domain orientation observed in our simulations to the NMR ensemble of a single‐chain variable fragmentThis work uses experimental Nuclear Magnetic Resonance (NMR) Nuclear Overhauser Effect (NOE) data in combination with molecular dynamics simulations to understand the V2The NOEs and suggested structures for the scFv complex and the Fab were provided by the group of Mark Carr at University Leicester.All structures were prepared in MOE 2.1The scFv with and without IL‐1β, Fv, and the Fab structures were simulated for 1 μs using molecular dynamics as implemented in the AMBER 18 simulation package.2.2The NOE distances are intramolecular NOEs and define the distances between amide protons.2.3H and VL) using six measurements . The ABangle script can calculate these measures for an arbitrary Fv region by aligning the consensus structures to the found core set positions and fitting the planes and distance vector from this alignment. This tool available online was combined with an in‐house python script to reduce computational time and to visualize our simulation data over time. The in‐house script makes use of ANARCIABangle3H‐VL domain orientation in these six metrics. These high variations are obvious in comparison to the distributions of angles and the distance of the originally published 352 crystal structures found in the PDB displayed in the background. Comparison of 1‐μs molecular dynamics simulations of the scFv without the presence of the IL‐1β .Figure S3: Complex scFv ABangle measure of 1 μs molecular dynamics simulation.Figure S4: Fab ABangle measures of 1 μs molecular dynamics simulation.Figure S5: FFT Spectrum of the HL angle fluctuations of the Fab simulated NMR ensemble.Figure S6: Comparison of experimental scFv complex NOEs with the calculated NOEs of 1 μs simulations with and without the antigen bound and with the complex NOE restraints. The plot shows the scFv fragment. The gap is caused by the peptide (G4S) linker, which directly connects the light and the heavy chain variable domains.Figure S7: Overlay of the HL angle distributions of the Fv (green) and the scFv (forestgreen) fragments.Figure S8: Overlay of the HC2 angle distributions of the Fv (green) and the NOE Fab simulations (blue) fragmentsTable S1: Average and standard deviations of the six ABangle measures for all six considered antibody fragments.Figure S9: Overlay of the ABangle histogram (blue) with the angle variations observed in the 0.1 to 10 ns timescale (orange).Figure S10: Illustration of the ABangle angle and distance definitions.Click here for additional data file.