Natural gas of organic origin is primarily biogenic or thermogenic; however, the formation of natural gas is occasionally attributed to hydrothermal activity. The Precambrian dolomite reservoir of the Anyue gas field is divided into three stages. Dolomite-quartz veins were precipitated after two earlier stages of dolomite deposition. Fluid inclusions in the dolomite and quartz are divided into pure methane (P-type), methane-bearing (M-type), aqueous (W-type), and solid bitumen-bearing (S-type) inclusions. The W-type inclusions within the quartz and buried dolomite homogenized between 107 °C and 223 °C. Furthermore, the trapping temperatures and pressures of the fluid  are obtained from the intersections of the isochores of the P-type and the coeval W-type inclusions in the quartz. However, the burial history of the reservoir indicates that the maximum burial temperature did not exceed 230 °C. Thus, the generation of the natural gas was not caused solely by the burial of the dolomite reservoir. The results are also supported by the presence of paragenetic pyrobitumen and MVT lead-zinc ore. A coupled system of occasional invasion by hydrothermal fluids and burial of the reservoir may represent a new genetic model for natural gas accumulation in this gas field. The Anyue gas field was formed by the in-situ pyrolysis of an ancient oilfield5. The petroleum within the ancient oilfield was generated from a black shale and was captured by the Sinian dolomite6. However, the natural gas charging age is still controversial6. The fluid inclusion method provides an effective means of determining natural gas charging ages8. Studies of hydrocarbon-bearing fluid inclusions have been carried out by many geologists and employ the bulk compositions, phase envelopes, and isochores of coeval aqueous fluid inclusions14. In this study, a group of methane fluid inclusions and the associated aqueous fluid inclusions are examined using laser Raman spectroscopy. The trapping temperatures and pressures of inclusions are determined using the intersections of isochores. Furthermore, inclusion petrography, the trapping temperatures and pressures of pure methane inclusions and the burial history of well GS6 indicate a new mode of gas generation that couples the effects of burial and hydrothermal fluid systems.The Anyue gas field lies in the center of the Sichuan Basin in southwestern China and is one of the oldest and largest gas fields in China2  . The inclusion temperature measurements were performed using a Linkam THMSG600 heating-freezing stage following standard procedures in the State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing. The homogenization temperatures of both gas inclusions and the coeval aqueous fluid inclusions were measured; the freezing points of aqueous fluid inclusions in authigenic quartz were also measured. The heating rate was 15 °C/min during the initial stages of each heating run and was reduced to 0.3–1 °C/min close to the phase transitions. The salinities of the aqueous fluid inclusions were calculated using ice points of H23. The dolomite resulting from the second stage (II-dolomite) is represented by overgrowths along the pores and cracks among the mudclasts and bioclasts  occupies the pore centers and cracks , methane-bearing (M-type), aqueous (W-type), and solid-bearing (S-type) Fig. .Pure met4 (−82.6 °C). The contents of the P-type fluid inclusions changed from a single phase to two phases, and bubbles formed when the temperature was decreased below −110 °C . It is almost impossible to measure the ice point temperatures for most of the W-type fluid inclusions, due to their small sizes (<10 μm). Only 8 sufficiently large W-type fluid inclusions were selected for ice point temperature measurements  were used as an index of thermal maturity43. Two linear regression equations proposed by Xiao et al.43 were used to calculate Ro from the VLMRo values:The Leshan-Longnvsi paleo-uplift records a complex burial history2dn2 (132 m), Z2dn3 (76 m), Z2dn4 (281 m), Є1q-l (451 m), Є1g (74 m), Є3x (119 m), O (134 m), P1 (300 m), P2 (224 m), T1+2 (1346 m), T3 (514 m), and 1803 m of Jurassic strata43 , Є/Z2dn2 (100 m), and T/P (200 m) boundaries were estimated according to the regional geology6. Zhu et al. (2015) studied the heat flow of the Sichuan basin using apatite fission track data and (U-Th)/He thermochronology47. The results suggest that the following heat flux history for the Sichuan Basin: (1) the heat flux was 55 ± 5 mW/m2 before and during the Late Carboniferous; (2) an abrupt rise in the heat flux to 80 ± 5 mW/m2 occurred in the Permian; (3) the heat flux gradually decreased after the Triassic, and the present day heat flux was calculated to be as low as 50 mW/m2 47. To ensure the reliability of the modeling results, an interactive optimization process was carried out until the vitrinite-like reflectance values were matched . While the II-dolomite was precipitating, the liquid oil may have been captured. The oil inclusions then pyrolyzed at high temperatures and formed the triple-phase S-type inclusions in the II-dolomite. Furthermore, the homogenization temperatures of the fluid inclusions within the quartz are within 10–15 °C of one another, and the salinity values fall within a range of 6 wt% NaCl. Therefore, the distribution of homogenization temperatures and the salinities of different inclusions provide the best evidence of the original temperature conditions7.As mentioned above, inclusions are observed in different host minerals, which show a clear precipitation sequence. Thus, the relationship of the P-type, M-type, and W-type fluid inclusions and the S-type inclusions in the different host minerals indicate the evolution of the fluid. Large amounts of solid bitumen (pyrobitumen) were observed in the hydrothermal mineral veins Fig. , and a m49. The earlier stage trapping temperature (185 °C) is accompanied by lower trapping pressures. We infer that the lowest trapping temperature represents a fluid system that was equilibrated between deep epizonogenic hydrothermal fluids and formation water 52. Therefore, paleo-oil pyrolysis should have occurred before the invasion of external epizonogenic hydrothermal fluids. However, the temperature did not reach the peak pyrolytic temperature of liquid oil52. The introduction of the natural gas was related primarily to the precipitation of the III-dolomite (dolomite-quartz veins) because the three-phase S-type inclusions and the P-type fluid inclusions were both observed in the III-dolomite and the quartz.The trapping temperatures and pressures were obtained from the intersections of the isochores of the P-type and the coeval W-type fluid inclusions in the quartz Fig. . The twoter Fig. . Becauseerature 15 °C is aerature 15 °C is a53. The homogenization temperatures of the fluid inclusions in the II-dolomite range from 107 to 212 °C. However, these fluid inclusions are too small to obtain ice point temperature data. Therefore, it is impossible to exclude the influence of re-equilibration or to determine the original trapping temperatures7. We infer that the precipitation occurred before the introduction of the natural gas, based on the absence of the P-type and the S-type inclusions in the II-dolomite.In addition to the dolomite-quartz vein, the II-dolomite was the earliest host mineral in which large quantities of inclusions were captured. Furthermore, the W-type fluid inclusions are heavily distributed in the boundaries between the “cloudy centers” and the “clear rims”. This feature suggests that the fluid inclusions in the II-dolomite are related to the precipitation of the dolomiteBased on previous studies of P-type inclusions, this study displays two anomalous phenomena. First, the trapping temperatures and pressures of the P-type inclusions display a wide range and can be divided into two groups Fig. . Second,52. If hydrothermal fluid entered the reservoir at a later time, when the liquid oil had already been pyrolyzed, free methane would have been trapped in hydrothermal minerals, and the trapping temperatures and pressures of free methane would have no significance for gas formation. However, mineral paragenesis shows that the quartz precipitated after the II-dolomite. Moreover, the P-type fluid inclusions are seldom found in the II-dolomite but are abundant in the III-dolomite and the quartz (Fig. 52. Therefore, the reservoir did not reach the peak temperature of gas generation before the hydrothermal minerals started to precipitate. The burial history shows that the study area experienced rapid burial from 251 to 238 Ma, and the heat flow have just abruptly increased at the same period (Fig. Experiments on in-reservoir petroleum destruction show that the temperature of the pyrolysis of petroleum commonly exceeds 150 °C, and the peak temperature is always 170 °C to 190 °Ciod Fig. . This su56. The high-temperature fluid significantly heated the reservoir and resulted in gas generation.Thus, when the temperature was below 150 °C, little methane became trapped in the reservoir Fig. . While tBased on the characteristics of diagenetic evolution, mineral paragenesis, and the fluid inclusion, different groups of the W-type fluid inclusions that represent different geological events were recognized and measured. The W-type fluid inclusions in the III-dolomite and the quartz may represent the stages of gas generation. Microthermometry of the P-type fluid inclusions and the coeval W-type fluid inclusions in the quartz were measured. The trapping temperatures of the P-type fluid inclusions show that gas generation was associated with the invasion of hydrothermal fluids, which was induced by regional tectonic movement. Furthermore, mineral paragenesis and the distribution of methane inclusions in the dolomite and the quartz indicate that the hydrothermal fluids invaded before the reservoir reached the peak pyrolysis temperature. Thus, pyrolysis driven by in situ burial generated natural gas, and the invasion of hydrothermal fluids triggered the gas generation process.Dataset 1


Varicellovirus simian varicella virus (SVV) mimics VZV disease in humans. In this study, we determined the applicability of the SVV nonhuman primate model to study Varicellovirus infection of the ENS. We confirmed VZV infection of the gut in latently infected adults and demonstrated that SVV DNA was similarly present in gut of monkeys latently infected with SVV using quantitative real-time PCR. In situ analyses showed that enteric neurons expressed SVV open reading frame (ORF) 63 RNA, but not viral nucleocapsid proteins, suggestive of latent ENS infection. During primary infection, SVV-infected T-cells were detected in gut-draining mesenteric lymph nodes and located in close vicinity to enteric nerves in the gut. Furthermore, flow cytometric analysis of blood from acutely SVV-infected monkeys demonstrated that virus-infected T-cells expressed the gut-homing receptor α4β7 integrin. Collectively, the data demonstrate that SVV infects ENS neurons during primary infection and supports the role of T-cells in virus dissemination to the gut. Because SVV reactivation can be experimentally induced, the SVV nonhuman primate model holds great potential to study the pathogenesis of enteric zoster.The pathogenesis of enteric zoster, a rare debilitating complication of reactivation of latent varicella-zoster virus (VZV) in the enteric nervous system (ENS), is largely unknown. Infection of monkeys with the closely related  Varicellovirus varicella-zoster virus (VZV) [Most adults worldwide are infected with the human neurotropic us (VZV) . Primaryus (VZV) ,2. Reactus (VZV) . VZV mayus (VZV) . The patVaricellovirus, simian varicella virus (SVV), closely mimics the immunological and clinical features of VZV infection in humans, including varicella, lifelong neuronal latency and  HZ [Varicellovirus infection of the ENS.VZV infection of the ENS has been studied in ante- and postmortem human intestine biopsies, and more thoroughly in the guinea pig model of VZV infection ,4,5,6,7.uced) HZ . Herein,Formalin-fixed and paraffin-embedded (FFPE) tissues were obtained from two Chinese rhesus macaques (RM) intratracheally infected with wild-type SVV  at 21 days post-infection (dpi), and African green monkeys (AGM) intratracheally infected with SVV.wt (AGM 269 and 279) or recombinant SVV expressing enhanced green fluorescent protein  at 9 dpi (AGM 269 and 294) and 13 dpi (AGM 273 and 279). The experimental procedures on the animals analyzed were reported previously . RM 2207Archived snap-frozen human intestine specimens, obtained for diagnostic purposes, were kindly provided by the Erasmus MC Tissue Bank  . MateriaDNA was isolated from ten consecutive FFPE intestine sections (10 µm thick) of RMs using the QIAamp DNA FFPE Tissue Kit (Qiagen). For some experiments, DNA was isolated from pooled sections from colon, duodenum, ileum, jejunum and cecum of the same animal, while in other experiments all sections were obtained from the same tissue block .US4), VZV open reading frame 62 (ORF62), SVV ORF21, and the human and nonhuman primate genes hydroxymethylbilane synthase (HMBS) and oncostatin-M (OSM) as described [Quantitative real-time PCR (qPCR) was performed in duplicate on DNA using Taqman 2× PCR Universal Master Mix  with primers and probes specific for herpes simplex virus 1 (HSV-1) unique short gene 4  were subjected to heat-induced antigen retrieval in citrate buffer, blocked and incubated overnight at 4 °C with the following primary antibodies: mouse anti-human neural cell adhesion molecule (NCAM) , mouse anti-CD3 , rabbit anti-SVV nucleocapid proteins (SVV-NP)  or approIn situ hybridization (ISH) was performed using the RNAscope 2.0 kit red  and oligonucleotide probes directed to SVV ORF63, human ubiquitin C , the bacterial dihydrodipicolinate reductase gene  and VZV ORF63 (negative control) were designed and obtained from ACD. Sections were counterstained with hematoxylin and mounted in Ecomount . The highly sensitive RNAScope methodology (Advanced Cell Diagnostics) enables in situ detection of single RNA molecule visualized as a punctuate signal dot in tissue sections with a standard microscope .Peripheral blood mononuclear cells (PBMC) from three SVV.EGFP-infected AGM obtained at 0, 5, 7 and 9 dpi were stained with fluochrome-conjugated antibodies directed to human CD28 (clone CD28.2), CD69 (FN50), CD95 (DX-2), CD3 (SP34-2), CD4 (L200), CD8 (SK1) , and macaque α4β7 integrin (NIH nonhuman primate reagent resource), as described . Cells wStatistical differences were analyzed in GraphPad Prism 5 .To determine VZV infection of the human GI tract ,4,5, 18 P°S intestine sections by SVV-specific IHC and ISH analyses. Like VZV [To study the establishment of SVV latency in the ENS of SVV-infected nonhuman primates, we first determined the presence of SVV DNA in FFPE intestine tissue sections of two latently SVV.wt-infected RM , skin-tropic (CLA+CCR4+) T-cells [+) T-cells were readily detected in blood at 5, 7 and 9 dpi  compared to earlier reports (>85%) [We confirmed that VZV DNA, but not HSV-1 DNA, is present in gut biopsies of human adults without herpesvirus-related diseases at the time of biopsy. Unfortunately, we did not have access to blood samples to analyze potential subclinical VZV reactivation. We report lower frequencies of VZV DNAs >85%) ,4,5, mos% ,4,5, ms (>85%) ,4,5,26. SVV reactivation is associated with lesions in the GI tract in severely immunocompromised nonhuman primates, suggesting that SVV can also establish latency and reactivate from the ENS ,28. HereVaricellovirus latency, including DRG and trigeminal ganglia in the same animal.The neuronal cell types harboring latent virus and mechanisms underlying VZV latency in the ENS are incompletely understood. The ENS is composed of a complex network of diverse neuron subtypes that involve both sympathetic and parasympathetic motor and sensory neurons . The low+ T-cells in blood and their localization in close vicinity to enteric neurons during primary SVV infection supports the role of T-cells in SVV dissemination to the ENS in nonhuman primates. However, it is unclear if the virus preferentially infects α4β7+ T-cells, e.g., located in mLN [The route by which SVV and VZV infect enteric ganglion neurons is unclear. SVV and VZV may infect enteric neurons by the transaxonal route, via projections of DRG neurons that innervate both the epidermis and viscera , and/or d in mLN , or indud in mLN ,22. Wherd in mLN , we obseVaricellovirus infection of the ENS. Compared to the VZV guinea pig model [Varicellovirus, adapted to nonhuman primates during long-term co-specification, and the ability to experimentally induce reactivation resembling HZ in humans [In conclusion, the data demonstrate that the SVV nonhuman primate model is of additive value in future studies aimed to elucidate the pathogenesis of ig model , the SVVn humans . The worn humans ,26, warr


The varicella zoster virus affects the central or peripheral nervous systems upon reactivation, especially when cell-mediated immunity is impaired. Among varicella zoster virus-related neurological syndromes, meningoradiculitis is an ill-defined condition for which clear management guidelines are still lacking. Zoster paresis is usually considered to be a varicella zoster virus-peripheral nervous system complication and treated with oral antiviral therapy. Yet in the literature, the few reported cases of herpes zoster with mild cerebral spinal fluid inflammation were all considered meningoradiculitis and treated using intravenous antiviral drugs, despite absence of systemic signs of meningitis. Nevertheless, these two clinical pictures are very similar.We report the case of an alcohol-dependent elderly Caucasian man presenting with left lower limb zoster paresis and mild cerebral spinal fluid inflammation, with favorable outcome upon IV antiviral treatment. We discuss interpretation of liquor inflammation in the absence of clinical meningitis and implications for the antiviral treatment route.From this case report we suggest that varicella zoster virus-associated meningoradiculitis should necessarily include meningitis symptoms with the peripheral neurological deficits and cerebral spinal fluid inflammation, requiring intravenous antiviral treatment. In the absence of (cell-mediated) immunosuppression, isolated zoster paresis does not necessitate spinal tap or intravenous antiviral therapy. Varicella zoster virus (VZV) is an exclusively human virus primarily causing chickenpox . After aHere we report the case of an alcohol-dependent older man with zoster paresis in the context of a VZV-associated meningoradiculitis. This clinical entity is not precisely defined in the literature and clear management guidelines about oral versus IV antiviral therapy are scant . ClearlyA 74-year-old Caucasian man was admitted to the Emergency Room, addressed by his general practitioner for a 3-day history of progressive lower left limb weakness. He complained of a non-traumatic lumbar pain since 10 days. Shortly after, a skin rash appeared on his lower back and extended to the left lower limb. He did not have fever or other new neurological complaints (in particular there was no urine or bowel retention or incontinence). His medical history showed arterial hypertension ; alcohol dependence (with macrocytosis and lower limb polyneuropathy) and benign prostate hypertrophy managed using tamsulosin. Otherwise, he took Aspirin Cardio® and tramadol.In addition to lower limb peripheral length-dependent abnormalities , the initial neurological assessment revealed significant weakness in hip flexion (M3) and foot dorsiflexion (M2), absence of the left patellar reflex and disturbed position sense on the lower left limb. The Leri sign was positive on the left side. On general examination, clusters of small erythematous vesicular lesions were present on the anterior and internal sides of his left thigh and upper leg. A few similar lesions were also seen on the left part of the lower back. In summary, the clinical picture of the patient showed left L3–L4 sensory-motor deficit associated with radicular zoster. A lumbar computerized tomography (CT)-scan excluded local compression (there was only degenerative lumbar discopathy) and the skin smear was positive for VZV DNA but negative for Herpes simplex virus 1 and 2. The lumbar puncture  and showed mainly motor improvement . A few days after admission he developed postherpetic neuralgia that improved with pregabaline and subsequent amitriptyline and a fentanyl patch. Unfortunately analgesic overtreatment led to acute encephalopathy  that was reversed with dose adjustment. The patient was then transferred to a stationary neurorehabilitation center before returning home.The patient described here had zoster paresis associated with mild inflammation and VZV activity in the CSF; leading to treatment mainly by IV acyclovir. The very few reported cases of VZV meningoradiculitis in the literature can be divided into two groups. The first corresponds to radiculitis without meningitis symptoms  –16. The peripheral nervous system complication [If meningoradiculitis comprises clinical meningitis with peripheral nerve root involvement, then it should be treated, as with any other VZV-CNS complication, using IV antiviral drugs after spinal tap and other necessary investigations , 6, 12. lication . Such palication . The VZVlication , 15 to tlication , 16? Morlication . Along tNone of the frequently reported immunosuppressive states  triggering VZV reactivation  were fouIn conclusion, the patient had VZV cutaneous infection with radiculitis coincident with mild CSF inflammation. Reactivation of VZV was most probably precipitated by age- and alcohol-induced cell-mediated immunosuppression. He was diagnosed with meningoradiculitis and treated with IV acyclovir, despite the absence of clinical meningitis. In available published cases, we did not find a consensual definition or management scheme for meningoradiculitis. The presence of CNS symptoms or abnormal CSF findings did not allow discrimination of severe from benign cases and these patients were generally IV treated. However, well-designed clinical trials are needed to review severity criteria for patients with VZV-associated (meningo)-radiculitis, to study the efficiency of oral versus IV antiviral treatment and establish clear guidelines for management. Mild elevation of inflammatory markers in the CSF is not specific or sensitive enough to establish a diagnosis of meningitis without evocative clinical symptoms. Therefore we suggest that until otherwise demonstrated, the term “meningoradiculitis” should be used for and IV antiviral drugs prescribed to patients with clinical meningitis associated with neurological peripheral deficit in the context of VZV infection. Patients with isolated VZV radiculitis should be exempt from CSF investigations and be given oral antiviral treatment, unless they are subject to any condition or pathology decreasing immunity, in particular CMI, in which case CSF workup and IV antiviral therapy may be necessary.


Data abstraction, a critical systematic review step, is time-consuming and prone to errors. Current standards for approaches to data abstraction rest on a weak evidence base. We developed the Data Abstraction Assistant (DAA), a novel software application designed to facilitate the abstraction process by allowing users to (1) view study article PDFs juxtaposed to electronic data abstraction forms linked to a data abstraction system, (2) highlight (or “pin”) the location of the text in the PDF, and (3) copy relevant text from the PDF into the form. We describe the design of a randomized controlled trial (RCT) that compares the relative effectiveness of (A) DAA-facilitated single abstraction plus verification by a second person, (B) traditional (non-DAA-facilitated) single abstraction plus verification by a second person, and (C) traditional independent dual abstraction plus adjudication to ascertain the accuracy and efficiency of abstraction.This is an online, randomized, three-arm, crossover trial. We will enroll 24 pairs of abstractors , each pair comprising one less and one more experienced abstractor. Pairs will be randomized to abstract data from six articles, two under each of the three approaches. Abstractors will complete pre-tested data abstraction forms using the Systematic Review Data Repository (SRDR), an online data abstraction system. The primary outcomes are (1) proportion of data items abstracted that constitute an error (compared with an answer key) and (2) total time taken to complete abstraction .The DAA trial uses a practical design to test a novel software application as a tool to help improve the accuracy and efficiency of the data abstraction process during systematic reviews. Findings from the DAA trial will provide much-needed evidence to strengthen current recommendations for data abstraction approaches.https://wwwcf.nlm.nih.gov/hsr_project/view_hsrproj_record.cfm?NLMUNIQUE_ID=20152269&SEARCH_FOR=Tianjing%20Li. All items from the World Health Organization Trial Registration Data Set are covered at various locations in this protocol. Protocol version and date: This is version 2.0 of the protocol, dated September 6, 2016. As needed, we will communicate any protocol amendments to the Institutional Review Boards (IRBs) of Johns Hopkins Bloomberg School of Public Health (JHBSPH) and Brown University. We also will make appropriate as-needed modifications to the NICHSR website in a timely fashion.The trial is registered at National Information Center on Health Services Research and Health Care Technology (NICHSR) under Registration # HSRP20152269: The online version of this article (doi:10.1186/s13643-016-0373-7) contains supplementary material, which is available to authorized users. The steAs a predominantly manual process, data abstraction is inefficient, being both labor-intensive and error-prone. Errors during abstraction are common and have been well documented in the literature –6. One sCurrently recommended approaches to reducing errors in data abstraction fall into two categories: (1) abstraction by one person followed by checking of the abstraction by a second person (“single abstraction plus verification”) and (2) independent abstraction by two persons followed by resolution of any discrepancies . The former approach also sometimes concludes with adjudication between the two abstractors. Buscemi and colleagues showed that single abstraction plus verification results in approximately 20% more errors than independent dual abstraction plus adjudication, but the latter approach takes approximately 50% longer .so little is known about how best to optimize accuracy and efficiency” , 1813, 1The DAA trial does not include stopping rules or a Data Safety and Monitoring Board because the trial does not evaluate the safety or effectiveness of an intervention on health outcomes. We do not expect any adverse events as a result of abstracting data during this trial.The two primary outcomes for our trial are proportion of data items abstracted that constitute an error (hereafter referred to as “error rates” for simplicity) and the time taken to complete abstraction . To determine errors for Approaches A and B, we will compare the verified data to data independently abstracted and adjudicated by two investigators with extensive abstraction experience (IJS and TL), which will be considered the “answer key”. In Approach C, both abstractors edit their own abstracted data during adjudication. To determine errors for Approach C, we will use the edited data from the more experienced abstractor. We will do this by using a computer program that automatically compares the selected/entered value of a given data item to the answer key value for that data item. An error is defined as any discrepancy or difference between an entry for a data item and the answer key value for that data item. We are interested in abstraction errors resulting from omission or incorrect abstraction. If participants abstract more data items than are in the answer key, the additional data items will not be considered as errors.®). We will use the automatically recorded timestamps to calculate time whenever possible, but will corroborate these data with the latter two manual strategies to assess the accuracy of our assessment of time.The total time taken to complete abstraction for a given article is defined as the sum of the time taken for initial abstraction(s) plus subsequent verification/adjudication. To measure time, we will use three strategies. First, the study data abstraction system  will automatically record when each abstractor logs in and out of the system, including the time spent on each tab. It is possible that this time overestimates the true time spent on the tab if the abstractor steps away from the computer. Second, as part of the Design Tab of the abstraction form in SRDR, we will ask abstractors to record the self-timed duration (in minutes) that was spent abstracting data for the Design Tab. Third, we will ask each abstractor to record the time spent (in minutes) on each step of data abstraction for each article: initial abstraction, verification, and adjudication, recorded using an online survey tool . We expect each of our primary outcomes (error rate and time) to vary by six factors; we have included these factors as covariates in the statistical model for analyzing each of the primary outcomes. These factors include question type , abstractor pair (1 to 24), abstraction sequence (1 to 6), abstraction approach , article (1 to 48), and review from which articles were obtained (1 to 4). We will define errors for each data item as a binary variable (correct/incorrect) and time as a continuous variable. We will analyze error rates using logistic regression and time using linear regression. Each outcome will be analyzed in terms of the six factors using a mixed effects regression model.phijklmn be the probability of an error for the hth item of the ith question type abstracted by the jth abstractor pair following the kth abstraction sequence under the lth abstraction approach from the mth article obtained from the nth review. Then, the logistic regression model isFor modeling error rates, let qi), abstraction approach (bl), and abstraction sequence (gk) are fixed factors; abstractor pair (hj(k)), review (dn), and article (zm(n)) are random factors. The key term of interest is bl, representing the main effect of the abstraction approach. The interaction terms (bq)li, (bg)lk, and (bd)ln in the model examine whether differences between the abstraction approaches vary with type of questions asked on the abstraction form (since some questions might be easier to answer than others), abstraction sequence , and review (which might indicate a level of difficulty effect), respectively. We will check whether other factors explain differences among the abstractors  or among the articles . Each random effect is assumed to follow a normal distribution centered at zero with its own variance component. Correlations among items taken from the same article, for example, are explained by the common variance components. The model for the continuous time outcome has a similar structure, but utilizes a linear regression modeling the mean time with normally distributed errors. We will examine both error rates and time adjusting for type of question on the abstraction form and will also examine error rates and time for each type of question separately. We will compare automatically recorded and self-reported times, and analyze each separately.where “…” indicates additional interaction terms one may wish to add. Question type  describing sufficient detail about the trial, problems caused by missing data, and the need for commitment to the trial as part of the consent process before enrollment; (2) maintaining frequent contact and sending reminders to abstractors; and (3) compensating abstractors US $250 for their time once they have completed all assignments. We will ask and report reasons for those who discontinue some or all types of participation.In handling missing data items, we will compare the characteristics of the missing and non-missing items to determine the nature of the missing data mechanism. If data appear to be missing at random, inference can be drawn based on the observed data mixed model likelihood, as the predictor factors are all part of the design and therefore will be known. If the missing at random assumption appears invalid, we will conduct sensitivity analyses to assess robustness of findings to different missing not at random scenarios.The purpose of our trial is to determine whether (1) use of DAA (Approach A) improves accuracy  compared with Approach B, and maintains the accuracy of the usual Approach C; and (2) use of DAA improves efficiency  compared with Approaches B and C. The adjudicated abstractions of the experienced study investigators (IJS and TL) will be used as the answer key for comparison.The design in Table In our pilot study, we found that the proportion of items incorrectly abstracted by inexperienced abstractors was 24% . If an eσ(1 − ρ)/N, where σ is the within-unit standard deviation, N is the number of units (articles) receiving each sequence of crossovers , and ρ is the correlation between two measurements on the same unit. The standard error for the difference in a crossover trial when comparing two approaches with 24 units each receiving each approach in one of two sequences, given a standard deviation per unit of 3%, ranges from 0.125%, when ρ = 0, to 0%, when ρ = 1, decreasing linearly. Thus, even if measurements are uncorrelated , our design would be able to detect a very small difference between error rates.Using the error rate as the outcome for calculating sample size, we would expect a standard deviation for a 100-item form to be about 3% (exact if the proportion were 9%). The standard error of the difference is then σ/24, which would be less than 2 min for σ as large as 48 min (48 min is much longer than we would expect for the amount of data to be abstracted and the length of the articles).Likewise, we might assume that Approach A would reduce the total time needed to abstract data relative to Approaches B and C, and we would want to estimate this within a few minutes in order to determine meaningful differences in efficiency. The same calculation shows that even with independent measurements, the standard error of the difference between the average times for two different approaches would be no more than We will not share trial data until the trial is completed and primary analyses are done. There are no personally identifying markers for participants in the data we collect. A file linking participant names with the assigned abstraction sequence will be accessible only to the Project Director and saved on a password-protected server maintained by JHBSPH. The server is backed up daily. We will archive trial documentation and electronic data files at the end of the trial and will retain them for at least 10 years. Because data abstracted in SRDR are associated with the abstractor’s name, we do not plan on allowing open access to the abstracted data in SRDR. However, upon request, we can make the exported de-identified abstracted data available after the trial.All investigators will collaborate to disseminate trial results through manuscripts and presentations at scientific meetings. Authorship and roles in preparation of the manuscripts will be decided ahead of time and agreed by all concerned. PCORI, the funder of this trial, will convene an independent team to peer review the final report of the project and will post the finalized report on the PCORI website.best-available research evidence, clinician expertise, and patient values [Current approaches to data abstraction have resulted in a large resource burden on those conducting systematic reviews; however, efforts to reduce the burden may lead to errors in abstraction. These errors could lead to inaccurate conclusions derived in reviews and, consequently, healthcare decisions that are based on faulty evidence summaries. The very foundation of evidence-based healthcare is challenged when one of its three underlying tenets  is compThe first challenge in designing this trial was to identify four reviews from which we could meet our target of identifying 12 appropriate RCTs each. Many of the reviews we selected initially could not be used because the outcomes were poorly defined in the reviews and, in some instances, meta-analyses in the reviews combined data from RCTs inappropriately. Ultimately, after ruling out various topics, we identified four suitable topics and reviews.The second challenge was deriving the answer keys for all 48 articles. Two experienced abstractors on our investigator team (IJS and TL) abstracted data from all 48 articles. However, to be able to truly evaluate abstraction “errors” by abstractors during the trial, we needed to develop instructions and unambiguous language to clearly articulate every question and every answer option on every abstraction form for use during the trial. Ambiguous articulation of these entities on our forms could have led to information bias in a primary outcome of the DAA trial if the abstraction errors resulted not from omission or incorrect abstraction, but rather an abstractor’s misunderstanding of the abstraction requested.To reduce the abstraction process learning curve, we are restricting the trial to individuals with at least some experience with data abstraction for systematic reviews. Another limitation is that, unlike many real-world reviews, we are not allowing resolution of abstraction discrepancies by a third abstractor.This trial has many strengths. First, it tests a novel software application (DAA) designed to make the data abstraction process more accurate and efficient. In addition, used in conjunction with a data abstraction system such as SRDR, DAA would maintain an annotated version of the data abstracted for easy access when the same or another systematic review team updates the review at a later date. Second, using a rigorous and efficient crossover design with random allocation and allocation concealment, this trial tests the effectiveness of the software application vis-à-vis two currently recommended best practice approaches to abstraction. Third, we are collaborating with patient/consumer co-investigators as the trial moves forward, in designing the trial and identifying the review topics, study articles, and outcomes for use during abstraction. Finally, the generalizability of the trial is likely to be high because of the broad eligibility criteria for abstractors from multiple locations with various types of background and levels of experience with abstraction during reviews.In summary, current standards for data abstraction, a key step during systematic reviews, rest on a weak evidence base. Our trial represents a potentially substantial step forward in the quest to reduce errors in data abstraction. The trial will rigorously evaluate whether a novel software application (DAA) could help the systematic review community efficiently use scarce research resources. Because systematic reviews are a key component of comparative effectiveness research, our results could help improve patient care by reducing errors in the synthesis of research evidence that informs that care.


The authors wish to add the following statement to the Acknowledgments section of their article on page 185:MRC UK (grant number G0401355), a BBSRC UK studentship (Y.C.) and the Wellcome Trust (grant number WT085523MA).This work was funded by the


Almost all genomic studies of breast cancer have focused on well-established tumours because it is technically challenging to study the earliest mutational events occurring in human breast epithelial cells. To address this we created a unique dataset of epithelial samples ductoscopically obtained from ducts leading to breast carcinomas and matched samples from ducts on the opposite side of the nipple. Here, we demonstrate that perturbations in mRNA abundance, with increasing proximity to tumour, cannot be explained by copy number aberrations. Rather, we find a possibility of field cancerization surrounding the primary tumour by constructing a classifier that evaluates where epithelial samples were obtained relative to a tumour . We implement a spectral co-clustering algorithm to define biclusters. Relating to over-represented bicluster pathways, we further validate two genes with tissue microarrays and in vitro experiments. We highlight evidence suggesting that bicluster perturbation occurs early in tumour development. Studying the spatial mutational and gene expression alterations in breast cancer could impact our understanding of breast cancer development. Here, the authors analyse a unique dataset of epithelial samples that highlight potential field cancerisation surrounding the primary tumour. These likely represent some of the earliest genetic alterations in breast carcinogenesis as well as predisposing factors. However, alterations in normal epithelium have been elucidated relative to normal breast tissue from other patients10 or with bulk extracted tissue11 making it impossible to confidently distinguish the earliest changes. Others have suggested that presence of contaminating tumour cells beyond the invasive tumour margin, rather than field cancerization (i.e. acquired molecular perturbations over a geographic region surrounding the tumour), may be the cause of local recurrence14. Here, we develop a map of alterations that occur in breast epithelium located at various distances from cancers in 8 patients, using copy number aberrations (CNA) and mRNA profiling, and microdissected under visual inspection. We evaluated how much of the variance in mRNA abundance, after adjusting for baseline levels, can be explained by copy number variation with increasing proximity to the tumour. We subsequently constructed a multi-class classifier that could identify from where the epithelial sample was obtained relative to the tumour; presenting evidence for field cancerization surrounding the primary tumour. Enrichment analysis of selected biclusters, defined using a spectral co-clustering algorithm, identifies pathways that may be increasingly dysregulated with proximity to tumour. Pathway analysis and enrichment of somatic mutations commonly present in breast cancer suggests these perturbations were not a consequence of paracrine influence from the primary tumour. Taken together, our analyses indicate that tumour initiation may not be driven by CNAs and that expression data points to an altered field surrounding the tumours. Identifying such transcriptomic alterations, preceding tumourigenesis, would allow us to better understand carcinoma development as well as develop new screening and treatment approaches.Numerous aberrations have been reported in histologically normal epithelium adjacent to breast tumours, including loss of heterozygosity (LOH), allelic imbalancesn = 5) and some atypical ductal hyperplasia (ADH)/ductal carcinoma in situ . D1 and O1 were all histologically normal. A sample from the tumour was also obtained  and one from a duct on the other side of the nipple within the same breast . Six samples were defined as outliers using a sum of standard error boxplot (Methods section) and were excluded from subsequent analyses. The epithelial sample closest to tumour (D2) presented with mixed histology: with some normal . With this simple classifier, we were able to obtain an 8-fold cross-validated AUC of 0.74  and trained on all remaining patients) Fig. . Classif15. More importantly, no assumption is made on the underlying structure and it is easy to interpret; an ARI score of 0 indicating random assignment and a maximum score of 1 indicating perfect overlap. The 20 clusters maximised overlap with the true proximity-to-tumour annotations  with samples closer to the tumour than those further away, using spectral co-clustering. These biclusters will allow us to delineate which features (genes) of the transcriptome are informative of epithelial distance to tumour. For computational feasibility, we limited the abundance matrix to the top 30% of most informative genes using the univariate non-parametric mutual information estimate implemented for the multi-label classifier. We agglomerated genes into 20 clusters representing modules of genes with the minimum variance (Ward linkage criterion and Euclidean affinity); 20 clusters maximised the match between the sample clustering and the distance annotation label using the Adjusted Rand Index (ARI). ARI is a chance-adjusted similarity measure between two groupings17, METABRIC18, and data sets from the Broad19, Sanger20, and British Columbia Cancer Research Centre21 . We limited our analyses to genes that were both present on the microarray platform and in at least one cohort. We noted that genes in modules that correlate (positively and) significantly with proximity to tumour were more likely to be mutated—compared to all other bicluster genes—in breast cancer patients across all eight data sets ; Supplementary Table χ2 test; Yates p < 0.05; odds ratio = 1.18; 95% confidence interval = ; Supplementary Table Having noted increasing transcriptomic dysregulation with increasing proximity to tumour, we were interested in establishing whether this mRNA abundance perturbation is a consequence of contaminating tumour cells, paracrine (secretory) influences from the tumour or is evidence of field cancerization. As all specimens were manually microdissected by a pathologist and obtained under direct vision, the likelihood of tumour cell contamination was low. To distinguish between paracrine and field cancerization effects, we noted that if modules that correlate with proximity to tumour are drivers of tumourigenesis, we would expect a larger number of mutations  in module genes from breast cancer patients compared to background genes that do not belong to these significantly correlated modules. If paracrine effects predispose cells to increased mutations, all genes should be affected equally (i.e. no enrichment). To test this hypothesis, we collected mutation data from 8 different breast cancer cohorts, including The Cancer Genome AtlasMSI2; Pearson’s r = 0.63; Bonferroni adjusted p < 0.05) and module 2 . Full list of genes and modules is available in Supplementary Data With this genetic evidence in support of ‘field’ transcriptomic perturbation, we were subsequently interested in discovering the functional nature and clinical relevance of the modules significantly correlated with distance to tumour. No module stood out with respect to absolute magnitude of the strength of correlation with proximity to tumour. Thus, we elected to arbitrarily select two module containing genes for which we had tissue microarray (TMA) data: module 16 (containing r = 0.63), we sought to determine the biological relevance of this mRNA module and assess whether there was evidence of changes in pathway activation relative to tumour proximity. The most statistically significantly over-represented pathway in this module relative to all others is formation of the “beta-catenin:TCF-transactivating complex” (q = 5.67 × 10−20), with 25/79 genes in this pathway that were abundance-profiled as being present in the 289-gene module (expectation is ~1 gene in this pathway). Complete lists of genes and enriched pathways are available in Supplementary Data 23, i.e. are novel components of the pathway. To partially validate that module is a Wnt signalling module, we selected a gene not previously associated with Wnt and studied its role in the pathway: MSI2. MSI2 has an established functional role in neural stem cell maintenance24 and in the regulation of hematopoietic stem cell self-renewal25; MSI2 has also been associated with other signalling pathways including Hedgehog and Notch26.Since module 16 is positively correlated with proximity to tumour /LEF-1-dependent luciferase reporter construct (TOPFLASH) in MCF7  to assess the effect of MSI2 overexpression on Wnt signalling. Notably, we observed a significant 1.5-fold increase in luciferase activity  associated with SPAG5 protein levels.Analogous to our previous analysis, we were interested in whether any signal transduction pathways were over-represented in Module 2, a 371-gene module , we were able to draft a spatial map of the genomic events and transcriptomic alterations that occur along the mammary duct (leading up to and including the tumour).2. Furthermore, we have shown that genomic alterations  do not explain much of the expression perturbation that is observed with proximity to tumour. Through principal component analysis of the gene expression matrix, we observed that the first transcriptomic principal component corresponds directly to proximity to tumour. As we discuss below, we showed that transcriptomic alterations in spatially informative coexpression modules are likely more important than the paracrine influence of the tumour and this perturbation may precede tumourigenesis. Furthermore, the enrichment of somatic mutations in genes that correlate with proximity to tumour suggests a non-paracrine mechanism underlying the dysregulation. If paracrine effects predispose cells to increased mutations, all genes should be affected equally (i.e. no enrichment). Furthermore, if a paracrine effect is at play, it is also unlikely that it will extend so far beyond the tumour as our samples were taken and that it would produce a similar effect regardless of the adjacent tissue type .In particular, using aCGH, we confirmed that the region surrounding the tumour is remarkably unstable with heterogeneous and varied genetic alterations; consistent with previous reports of LOH events and allele imbalances in adjacent-to-tumour epithelium 10. Other studies, that demonstrate widespread differential gene expression between patient-matched breast normal tissue, have confounding variables: such as analysis of bulk tissue of various types instead of more pure microdissected epithelial samples11. Unlike previous studies, we have further demonstrated that gene expression can identify from where the epithelial sample was obtained relative to the tumour . This is the strongest evidence that epithelial samples surrounding the tumour differ from epithelial samples from the contralateral duct within an individual. The discriminative ability of the classifier suggests that these differences  are shared across the cohort. Combined with the inability of copy number alterations to explain these transcriptomic patterns, this suggests that other forms of dysregulation  may be the primary drivers of initiation and progression in early tumour development.To the best of our knowledge, this represents the first direct evidence of field cancerization surrounding the primary tumour. All current evidence of global gene expression dysregulation in adjacent-to-tumour (but not necessarily in the same duct system) epithelium depends on comparisons to external patient data sets 30, deep sequencing of 360 primary breast cancers revealed six promoters  that contained single-site mutational hotspots (three or more mutations at a single site). Three genes  were among the top 30% of genes that are univariately informative of the proximity-to-tumour label. Two genes (TBC1D12 and RMRP) were not on the expression platform; FOXA1 was not informative of the spatial label for the samples and was the only gene (among the 4 present on the platform) with the mutational hotspot not located in or adjacent to the 5′ untranslated region. Two genes (ALDOA in module 5 and LEPROTL1 in module 2) also belonged to modules that significantly and positively correlated with proximity to tumour. Thus, non-coding single-nucleotide mutations may be responsible, in part, for the observed field cancerization.Subsequently, using spectral co-clustering, we identified biclusters (i.e. coexpressors) with perturbations that may be among the first molecular alterations shared by breast cancer patients, preceding common aberrations detected by aCGH. Genes in these biclusters were a priori selected as informative for proximity-to-tumour label (prior to the co-clustering). We demonstrate that the genes in modules with significant correlation to proximity to tumour were more likely to be mutated in breast cancer patients (compared to background genes in non-significant modules), suggesting that this perturbation is a consequence of field cancerization (rather than paracrine influence from the primary tumour). As these genes are enriched for somatic mutations in tumours (i.e. they are more likely to be mutated), this further suggests a functional role for many of these genes in tumour development and progression. Furthermore, in an analysis by Rheinbay et al.MAPK14  is present in module 2 and Gauthier et al.12 have noted increased activated phospho-p38 staining intensity in the adjacent normal epithelium to DCIS compared with normal epithelium from reduction mammoplasty specimens. This is consistent with the positive correlation of module 2 with proximity to tumour. MAPK14 acts to stabilise the immediate-early gene COX2, also implicated in tumourigenesis12. We found associations for two of these coexpression modules with signalling cascades, and correlated two genes with clinical covariates. In particular, we noted the presence of MSI2 in module 16, which was positively correlated with proximity to tumour and enriched for genes relating to the formation of the beta-catenin:TCF-transactivating complex . Notably, MSI2 is located at 17q23.2-23.3 (55.1–55.9 Mb)24; part of an amplicon (17q22-24.2) whose gain is a predictor of invasion in DCIS and of nodal metastasis in invasive duct carcinoma (IDC)31. We were able to directly implicate MSI2 in Wnt activation, with overexpression leading to β-catenin localisation to the nucleus and increased luciferase activity (i.e. pathway activity) using a TCF/LEF-1-dependent luciferase reporter construct. This suggests that Wnt signalling may be one of the earliest changes in breast cancer, as well as tumour development and progression. Studies in neuroblastoma and ovarian cancer also revealed amplification of the MSI2 genomic region, suggesting a role for both gene and genomic interval in oncogenesis and tumour progression34. More generally, this hints at the possibility that Wnt signalling may be implicated in early changes in other tumour types as well. Looking at another module that is both significantly and positively correlated with proximity to tumour (module 2), we note the presence of SPAG5, which positively correlates with tumour grade. Module 2 includes genes whose abnormal function has been directly associated with breast cancer, such as MAPK14, BRCA1, CDH1, HIF1A, CDKN2A and other members/regulators of the CDK family. It is important to note that membership to a module that positively correlates with proximity to tumour may be indicative of either a function in early tumourigenesis (e.g. MAPK14) or a protective role in response to the increased stress and dysregulation of the cellular environment (as genes in these modules are enriched for somatic mutations in tumours with both gain-of-function and loss-of-function consequences).Moreover, other genes in high-correlation modules have been previously implicated in early carcinogenesis. For instance, Here, in summary, we presented the first spatially comprehensive CNA and mRNA characterisation of morphologically normal epithelium from primary breast cancer patients of multiple molecular sub-types. Our results emphasise previous findings that Wnt signalling may be one of the earliest changes in breast cancer and we identify a new gene involved in the Wnt pathway. Our discovery of SPAG5 as an early gene suggests the presence of increasing chromosomal alterations with decreasing distance to tumour; consistent with our CNA observations. This study, however, did not address whether common epigenetic field effects, single-nucleotide non-coding variation, miRNA regulation or shared telomere dysfunction may influence the observed transcriptomic perturbation with increased proximity to tumour. However, there is evidence in the expression data indicating field alterations and that CNAs do not readily explain this field cancerization. Our results have important implications for cancer screening and prevention and could lead the way to new therapeutic approaches and targets.n = 8). During surgery, prior to removal of the breast, a ductoscopy procedure was performed using a 0.7 mm mammary ductoscope . Details of tissue sampling are described in the main text. All tissues obtained were bisected, with one half snap frozen and the other half formalin-fixed and paraffin-embedded (FFPE). Normal lymph node tissue was also obtained for control DNA. Snap frozen tissues were sectioned at 8 μm thickness using a microtome and lightly stained with hematoxylin. Histological identification of tumour and normal ducts was confirmed by a pathologist. Needle-microdissection was performed by a pathologist with a stereoscopic dissecting microscope for both tumour and ducts to ensure minimal stromal contamination of epithelial cells. DNA and RNA were extracted from microdissected tissue using the Qiagen Allprep RNA/DNA Micro Kit .Fifteen patients undergoing mastectomy for biopsy-proven carcinoma were recruited at the Princess Margaret Cancer Center, University Health Network  from 2005 to 2008. Informed consent was obtained after the nature and possible consequences of the study were explained. Inclusion criteria were defined as tumours at least 2 cm in diameter as measured by imaging, and located at least 3 cm from the nipple. Institutional REB approval was obtained prior to patient recruitment. Clinico-pathological data was obtained for all patients for whom all eight samples (four expression and four aCGH) passed quality control (QC) at the microarray centre  as per manufacturer’s standard protocol. Pooled lymph node tissue from patients was used as controls and processed in a similar fashion. Raw microarray image files were extracted using Agilent Feature Extraction Software (v9.5). Data were imported into Agilent Genomic Workbench v7.0.4.0  for QC analysis. QC metrics were generated for all samples; none were excluded from subsequent analyses. aCGH data were subsequently analysed and visualised using rCGH v1.2.2 (bioconductor v3.3); package code and resources were modified to work with hg17 (genome build of data).Whole genome amplification was performed using the single-cell comparative genomic hybridisation protocol as described previously36. Briefly, 100 ng of mRNA were reverse transcribed using Superscript II reverse transcriptase  while incorporating Cy3-dCTP or Cy5-dCTP . Raw microarray image files were extracted using Agilent Feature Extraction Software (v9.5). Microarray data were pre-processed and normalised using agilp v3.4.0 (bioconductor v3.3); only probes that matched to a gene symbol were included. In case of multiple ID mappings, the average was calculated between the matching probes, with no filtering on variance or expression threshold. Six samples  were excluded, after QC, and were defined as outliers using a sum of standard error boxplot.Global cDNA Whole-Human Genome 4 × 44 K Microarray (Agilent) gene expression profiling was performed on the same tissue specimens previously mentioned, as described elsewherehttps://github.com/vqv/ggbiplot/). We implemented the multi-label classifier using scikit-learn 0.18 using OneVsRestClassifier with a C-Support Vector Classification linear-kernel estimator. ROC curves were drawn by considering each element of the label indicator matrix as a binary prediction (microaveraging). Spectral co-clustering was also performed using scikit-learn 0.18, with feature agglomeration, incorporating a ward linkage criterion and a Euclidean affinity. The number of clusters was optimised to increase overlap of sample biclusters with known distance annotations, as quantified using the adjusted Rand score.To quantify the variance explained by CNAs, linear regression was performed with R v3.2.2. After filtering based on intersection of genes present in both the processed mRNA abundance and aCGH data, residuals were obtained for each sample along duct leading to tumour  and a linear regression model was built in the form of residuals—aCGH data. Both residuals and aCGH came from the same epithelial sample (i.e. at the same distance from the tumour). Principal component analysis of the expression matrix was completed using base functions of R v3.2.2 and visualised using ggbiplot  were transfected into MCF7 and MDA-MB-231 cells (source: ATCC) using endofectin transfection reagent  according to the manufacturer’s instructions. Expression was determined by fluorescence microscopy, western immunoblotting and qRT-PCR. Cell lines were authenticated and quality controlled at ATCC.5 cells were plated on the upper side of the inserts, and allowed to migrate or invade to the underside of the insert over 48 h. Inserts were fixed and stained. The total number of cells on the underside of the insert was counted at the microscope. Each experiment was performed in triplicate. Welch’s t test was used to assess significance.For the migration and invasion assays, stable (overexpression) clones were selected with G418 (2.3 mg/ml) for 5–10 passages (for both MCF7 and MDA-MB-231). Stable MSI2-knockdown clones were established using shRNA (Origene) using a lentiviral transfection in 293 T cells, followed by transduction and selection with puromycin (0.6 μg/ml). Knockdown of MSI2 expression in individual clones was confirmed by RT-PCR. For invasion and migration assays, 24-Transwell plates  were coated with Matrigel (BD Biosciences) or without a coating, respectively. 10MCF7 cells were grown on 22 mm round collagen type I coated coverslips (BD Biosciences) until 70% confluent. The cells were then fixed with 4% paraformaldehyde solution for 15 min at room temperature. Fixed cells were permeabilised by washing twice in PBS with 0.01% Tween20 for 5 min. Blocking was carried out with 1% BSA in the above PBS/Tween solution for 30 min at room temperature. Cells were then incubated in a mixture of the two primary antibodies . Following three 3-min washes in ice cold PBS, fluorescently labelled secondary antibodies were applied for one hour. Using FV10-ASW version 2.1c imaging software, each image was captured sequentially with four channels  to eliminate crosstalk between channels. All images were captured at ×60 magnification.n = 3 replicates each) were completed demonstrating that an increase in Wnt signalling corresponded with MSI2 overexpression. However, only two were plotted and analysed  as the FOP negative control was unsuccessful for 1 of the 3 trials.MCF7 cells were transfected with 400 ng of the TOP Luciferase reporter transgene, 40 ng of a constitutive renilla luciferase transgene and 500 pg or 50 ng of flag-MSI2 using Lipofectamine LTX with Plus Reagent (Life Technologies) according to the manufacturer’s instructions. Cell lysates taken after 48 h and luciferase activities were determined using the Dual-Luciferase Reporter Assay System (Promega) according to manufacturer’s instructions. Three successful assays  collected from a single institution (UHN) in 2006. Endogenous peroxidase was blocked with 3% hydrogen peroxide for 10 min. Microwave antigen retrieval was carried out in 10 mM citrate buffer pH6. Endogenous biotin was blocked with Vector’s biotin blocking kit and slides were then incubated with MSI2  at a concentration of 1:300 at 4 °C overnight. After washing in PBS, biotinylated anti-rabbit IgG incubations were carried out. Immunoreactivity was revealed by incubation in DAB substrate  for 3 min. Slides were counterstained in Mayer’s hematoxylin. MSI2 immunohistochemical staining was evaluated using light microscopy. Cases were blindly scored by a pathologist using the Allred Scoring system as described37. Available clinical data was used to compare MSI2 expression with tumour grade, stage and receptor status. Pearson’s correlation  was used to assess association at a 5% significance level.Immunohistochemical analysis of MSI2 expression in breast cancer cells was performed. Briefly, slides sectioned from an in-house (UHN) TMA were dewaxed in xylene and rehydrated through alcohol to water. The TMAs consisted of a consecutive series of FFPE invasive breast carcinomas (Anti-SPAG5 rabbit antibody (HPA022008) was purchased from Sigma-Aldrich . FFPE TMA blocks of 234 UHN invasive breast cancers were stained with the antibody using the manufacturer’s protocol. Scoring of immunohistochemical staining was performed using a semi-quantitative scoring system. TMAs were analysed in a blinded manner by a pathologist using a four-point semi-quantitative scale  for intensity. The intensity of staining was scored as 3+(strong), 2+(intermediate), 1+(weak) and 0 (no staining). Cases that were stained for SPAG5 showed homogeneous staining of all cancer cells within the core; the percentage positivity was 100%. Tissues showed moderate cytoplasmic positivity. As with MSI2, Pearson’s correlation  was used to assess association at a 5% significance level.Gene expression and aCGH data has been deposited by M.A. and S.J.D. in the National Center for Biotechnology Information’s (NCBI’s) Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE72653 (SuperSeries consisting of patient-matched gene expression [GSE72644] and aCGH [GSE72652] data). Correspondence and requests for materials should be addressed to S.J.D.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7


N = 598), high neuroscience exposure (N = 234), and the general public  and analyzed predictors of individual differences in neuromyths performance. In an exploratory factor analysis, we found that a core group of 7 “classic” neuromyths factored together . The general public endorsed the greatest number of neuromyths (M = 68%), with significantly fewer endorsed by educators (M = 56%), and still fewer endorsed by the high neuroscience exposure group (M = 46%). The two most commonly endorsed neuromyths across all groups were related to learning styles and dyslexia. More accurate performance on neuromyths was predicted by age (being younger), education (having a graduate degree), exposure to neuroscience courses, and exposure to peer-reviewed science. These findings suggest that training in education and neuroscience can help reduce but does not eliminate belief in neuromyths. We discuss the possible underlying roots of the most prevalent neuromyths and implications for classroom practice. These empirical results can be useful for developing comprehensive training modules for educators that target general misconceptions about the brain and learning.Neuromyths are misconceptions about brain research and its application to education and learning. Previous research has shown that these myths may be quite pervasive among educators, but less is known about how these rates compare to the general public or to individuals who have more exposure to neuroscience. This study is the first to use a large sample from the United States to compare the prevalence and predictors of neuromyths among educators, the general public, and individuals with high neuroscience exposure. Neuromyth survey responses and demographics were gathered via an online survey hosted at TestMyBrain.org. We compared performance among the three groups of interest: educators ( Educational neuroscience  is an emerging field that draws attention to the potential practical implications of neuroscience research for educational contexts. This new field represents the intersection of education with neuroscience and the cognitive and developmental sciences, among other fields, in order to develop evidence-based recommendations for teaching and learning  drew attention to the issue of neuromyths in 2002, defining a neuromyth as “a misconception generated by a misunderstanding, a misreading, or a misquoting of facts scientifically established (by brain research) to make a case for the use of brain research in education or other contexts”  differences in the training background and professional vocabulary of education and neuroscience  was conducted in the UK by the Wellcome Trust using an online survey of neuromyths  from a broad range of schools across the US through a citizen science website (TestMyBrain.org). This citizen science approach also allowed us to recruit a large sample of individuals from the general public . Our goal was to explore a variety of factors that might predict belief in myths, including demographics, educational attainment, and career and neuroscience-related exposure. We predicted that the most common neuromyths found in previous studies  would also be prominent among US samples. We also predicted that the quality of an individual's neuroscience exposure would be related to belief in neuromyths, such that higher exposure to self-identified media might lead to more neuromyths, while higher exposure to academic neuroscience would lead to fewer neuromyths.Available studies examining the prevalence of neuromyths provide information about neuromyth endorsement from a range of countries and cultures; however, no study to date has compared teachers' beliefs in neuromyths to a group of non-educators. Furthermore, to our knowledge, no study has systematically examined neuromyths in a sample from the United States. Given large variations in teacher preparation across countries, it is worthwhile to explore the prevalence of neuromyths in a US sample. For this reason, the current study recruited a US sample of educators and included a comparison group of individuals from the general public. We also included a second comparison group of individuals with high neuroscience exposure to further contextualize the results from the groups of educators and general public. Notably, most previous studies have used samples of educators in the range of The neuromyths survey and associated demographic questionnaires were hosted on the website TestMyBrain.org from August 2014–April 2015. TestMyBrain.org is a citizen science website where members of the public can participate in research studies to contribute to science and learn more about themselves. In addition to these citizen scientists, we also explicitly advertised this study to individuals with educational or neuroscience backgrounds and encouraged them to visit TestMyBrain.org. Advertisements were distributed through professional and university listservs and social networks  School of Education, AU Behavior, Cognition, and Neuroscience program, AU College of Arts and Sciences Facebook page), as well as professional and personal contacts of the authors. In order to increase participation from educators, we used a snowball sampling technique in which each participant that completed the survey was asked to share the survey link with an educator that he/she knew through various social media . This study was carried out in accordance with the recommendations of the Belmont Report as specified in US federal regulations (45 CFR 46). All participants gave written informed consent. The Institutional Review Board (IRB) at American University approved the research protocol for data collection and the IRB at the University of Denver determined that the research was exempt for de-identified data analysis.The starting sample included surveys from 17,129 respondents worldwide. Only fully completed surveys were logged for further analysis. Participants were excluded if they reported experiencing technical problems with the survey (735 dropped), reported taking the survey more than once , or reported cheating .N = 424 general public, 3 educators, 0 high neuroscience exposure). Therefore, our analyses were limited to those with “some college” experience or beyond. These filtering and data cleaning steps brought our final sample size to N = 3,877 .Further exclusion criteria were age <18 years , missing data on gender or educational background (361 dropped), and non-US participant . Next, we plotted overall survey accuracy by time to complete the survey to filter individuals who might have rushed through the survey without reading the questions (22 dropped). On the demographics questionnaire, participants were asked if they were currently enrolled or completed middle school, high school, some college, college degree, or graduate degree. We excluded individuals with a middle school or high school degree from the analyses because of inadequate representation of these individuals in the sample . We prioritized educator status over neuroscience exposure in this grouping in order to understand the full range of training backgrounds in the educator group. The “general public” group was composed of all individuals who completed the survey but did not self-identify as an educator or having high neuroscience exposure. We use the term “general public” to refer to the citizen scientists who participated, but we acknowledge the selection artifacts inherent to this group and address these limitations in the discussion section. Table One of the goals of this study was to compare the neuromyths performance of three groups: the general public, self-identified educators, and individuals with high neuroscience exposure. We defined “high neuroscience exposure” using a question on the demographics questionnaire which asked, “Have you ever taken a college/university course related to the brain or neuroscience?” Answer options for this item were “none,” “one,” “a few,” or “many.” Those who indicated “many” were categorized in the neuroscience group, unless they also reported being an educator, in which case they remained in the educator group .p < 0.001 in order to account for the individual analyses of the 32 items on the neuromyth questionnaire (0.05/32 = 0.0015) and to adjust in the conservative direction for additional analyses of the composite neuromyth score.The data were analyzed using Stata/IC 14.0 for Windows. The statistical threshold was set to balance competing issues of the multiple comparison burden associated with 32 individual items on the survey and the expected correlation between the items. We opted to set the statistical threshold at An exploratory factor analysis (EFA) was conducted to examine the underlying relationships among the survey items. Because each survey item was dichotomous (correct or incorrect), a polychoric correlation matrix was used. Based on this EFA, a neuromyths factor score was constructed by summing the number of incorrect responses on the 7 neuromyth items that loaded on the first factor. For this factor, a high score indicated poor performance on neuromyths .Group comparisons between the general public, educators, and those with high neuroscience exposure were conducted for the neuromyths factor score and overall survey accuracy using one-way ANCOVAs, covarying for age, gender, and education (dummy coded with college as reference). Performance on individual neuromyth questions  was compared using logistic regressions covarying for age, gender, and education.Tables 7, 8.We were also interested in what factors predicted neuromyth performance in the full sample and in the subsample of educators. Ordinary least squares (OLS) and Poisson regressions were used to test the unique contribution of neuroscience exposure and exposure to science-related media, above and beyond the effects of age, gender, and education. In these regression analyses, categorical indicators  were dummy-coded with reference categories indicated in M = 4.53 (SD = 1.74). The Poisson distribution provided a good fit for the data [Pearson goodness of fit p = 0.99] and we did not find significant evidence of over-dispersion of the distribution  indicating that the standard Poisson model was appropriate for this analysis.We conducted Poisson regressions to analyze the variables predicting neuromyths because ordinary least squares regression (OLS) can give biased standard errors and significance tests for count data  and perhaps more interactions with neuroscience/psychology colleagues because of their college/university setting.r = 0.04–0.21). Factor 1 had high factor loadings (>0.35) for seven “classic” neuromyths  (KR-20 = 0.63) related to learning styles, dyslexia, the Mozart effect, the impact of sugar on attention, the role of the right and left hemispheres in learning, and using 10% of the brain . Further analyses used this neuromyths factor score to examine the prevalence and predictors of these “classic” neuromyths.An exploratory factor analysis based on the polychoric correlation table, which was appropriate for our categorical  data, revealed three factors with eigenvalues >1. We rotated the factors using varimax rotation with Kaiser normalization Kaiser, . An orthin Table . We noteTable 6.The second and third factors were less interpretable and theoretically coherent. Internal consistency for the items loading >0.35 on the two factors was quite low (KR-20 = 0.27–0.35). As a result, these factors were not retained for further analysis, but instead data for individual survey items are reported in M = 68%, educators M = 56%, high neuroscience exposure M = 46%). A similar trend is present for the majority of the individual items that compose the neuromyths factor such that the general public endorsed the myths at the highest rate, followed by educators, followed by individuals with high neuroscience exposure who endorsed the myths at the lowest rate . The second most commonly endorsed neuromyth was also related to learning styles “children have learning styles that are dominated by particular senses ” . Another common misconception was related to dyslexia: “a common sign of dyslexia is seeing letters backwards” . It was interesting to note the very high rates of endorsement of these neuromyths even amongst individuals with high neuroscience exposure, though we note that these items are more closely related to the learning and special education fields than to neuroscience.The most commonly endorsed neuromyths across groups were related to learning styles and dyslexia. The most commonly endorsed neuromyths item was “individuals learn better when they receive information in their preferred learning style ”  in Table Two items (#24 and 29) involving motor coordination and exercise were the most frequently incorrect across groups. These items were endorsed at such high rates by the high neuroscience exposure group that we questioned whether the items were persistent neuromyths or whether the item wording was misunderstood by participants. Because of this ambiguity, we decided not to include these items on the neuromyths factor. After these two items, the most frequently incorrect item across groups was “Boys have bigger brains than girls, on average” (#3).Results from both OLS multiple regression and Poisson regression are presented for the full sample and for the subsample of educators in Tables p < 0.001) were age (being younger) and gender . Education level also predicted neuromyth accuracy, such that those with a graduate degree performed better than those who finished college . Exposure to science and career-related information also predicted neuromyths; specifically, those who reported reading peer-reviewed scientific journals performed better on neuromyths items  than those who did not engage with any career-related media. There were nonsignificant trends for those who read popular science magazines or visited career websites to perform better on neuromyths . Overall, the strongest predictors of lower rates of neuromyth endorsement in the full sample (determined by comparing standardized betas) were having a graduate degree, completing many neuroscience courses, and reading peer-reviewed journals. Although these were the strongest predictors, the effect sizes were modest. For instance, for the strongest predictor (graduate education) , those with a graduate degree vs. a college degree endorsed less than 1 fewer item (out of 7) on the survey.The results of the Poisson and OLS regressions were largely consistent. In cases of divergence, we deferred to the Poisson results which are most appropriate for the data distribution. For the full sample, demographic predictors of better performance on the neuromyths survey (ge Table . There wp < 0.001) of neuromyths were similar to the full sample. Better performance was predicted by taking many neuroscience course and reading peer-reviewed scientific journals. Although these were the strongest predictors, the effect sizes were modest. For instance, for the strongest predictor (neuroscience courses) , those who reported taking many neuroscience courses endorsed 1.3 items fewer on the survey  compared to those who had not taken any neuroscience courses.In the subsample of educators, significant predictors (p = 0.45), early education , and higher education . The latter analysis for higher education was conducted only in those with graduate degrees (N = 471) because only a handful (n = 9) of individuals reported that they were teaching college but did not have a graduate degree. Although our results for higher education may suggest a trend for those in higher education to endorse fewer neuromyths, we note that this predictor does not meet our alpha threshold (p < 0.001) and its effect size is more modest than age, graduate school, and exposure to neuroscience and peer-reviewed science. Taken together, there was little support for educator specializations having a strong effect on neuromyth performance.In the educator subsample, we examined the impact of three specializations, special education, early education, and higher education. Each regression mirrored those in Tables Neuromyths are frequently mentioned as an unfortunate consequence of cross-disciplinary educational neuroscience efforts, but there is relatively little empirical data on the pervasiveness of neuromyths, particularly in large samples from the US. Existing empirical data on neuromyths also exclusively focuses on educators, so an important question is whether educators' beliefs are consistent with the general public. One assumes that training in education and in neuroscience would dispel neuromyths, but it is unclear whether and to what extent this is the case. The goal of the current study was to establish an empirical baseline for neuromyth beliefs across three broad groups: the general public, educators, and individuals with high neuroscience exposure. Our results show that both educators and individuals with high neuroscience exposure perform significantly better than the general public on neuromyths, and individuals with neuroscience exposure further exceed the performance of educators. Thus, we find that training in both education and neuroscience (as measured by self-report of taking many neuroscience courses) is associated with a reduction in belief in neuromyths. Notably, however, both educators and individuals with high neuroscience exposure continue to endorse about half or more of the “classic” neuromyths, despite their training.From the individual differences analyses, we found that the strongest predictors of neuromyths for the full sample were having a graduate degree and completing many neuroscience courses. For the subsample of educators, the strongest predictors were completing many neuroscience courses and reading peer-reviewed scientific journals. These findings suggest that higher levels of education and increased exposure to rigorous science either through coursework or through scientific journals are associated with the ability to identify and reject these misconceptions. As we hypothesized, the quality of the media exposure matters: peer-reviewed scientific journals showed the strongest association to neuromyths accuracy compared to other science-related media sources . We discuss the implications of these results further below with an emphasis on implications for professional development and targeted educational programs addressing neuromyths.Our analyses began with a psychometric investigation of our modified version of the widely-used neuromyths survey developed by Dekker et al. . Resultsthe single teaching approach that will be effective for all children (learning styles) or the single sign of dyslexia (reversing letters), or the single explanation for why a child is acting out (sugar). Such over-simplified explanations do not align with the scientific literature on learning and cognition. For example, in the last 20 years, the field of neuropsychology has experienced a paradigm shift marked by an evolution from single deficit to multiple deficit theories to reflect an improved understanding of complex cognitive and behavioral phenotypes  of the neuromyths, on average, which might still be perceived as high. This result is even more striking in the context of the self-reported disciplinary affiliation of these individuals. Eighty-eight percent of the high neuroscience exposure group reported obtaining their highest degree in science, social science, medicine, health and rehabilitation, or nursing , all fields that we would expect to have a strong neuroscience curriculum. Although we cannot be sure of the level and content of the neuroscience courses that the participants reported, it is striking that the vast majority of these individuals obtained their highest degree (88% college or graduate degrees) in a science- or health-related field, and yet the endorsement of neuromyths remained quite high.One possible explanation for this surprising finding is to consider that the most commonly endorsed items by individuals with high neuroscience exposure were more closely related to learning and education than the brain and its function. This suggests that individuals with a significant number of neuroscience classes are susceptible to these misunderstandings because of the different levels of analysis of neuroscience vs. education. For instance, students might learn the synaptic basis of learning in a neuroscience course, but not educational theories of learning and how learning styles as an educational concept has developed and proliferated . Endorsement of these neuromyths ranged from 76 to 93% in the general public group, 59 to 76% in the educator group, and 50 to 78% in the high neuroscience exposure group. Notably, over half of the surveyed teachers endorsed these items, which have direct implications for educational practice.Regarding learning styles theory, a meta-analysis from Pashler et al.  reviewedis associated with improved learning and memory in the cognitive , 33% (bachelor's degree or more), and 12% (graduate degree) . A more realistic approach to control for variations in coursework would be to design studies using students who are enrolled in the same university neuroscience course.Our online survey is subject to legitimate questions regarding quality control. We employed a number of procedures throughout the data cleaning process to ensure that we captured only legitimate responses, including examining the data for participants who were experiencing technical difficulties, cheating, taking the survey too quickly, and taking the survey more than once. Germine and colleagues have replicated well-known cognitive and perceptual experiments using the website used in the current study (TestMyBrain.org) with similar data cleaning techniques  regardless of exposure to education or neuroscience. This finding is concerning because of the time and resources that many school districts may allocate toward pedagogical techniques related to these neuromyths that have very little empirical support. The findings reported in the current study create an opportunity for cross-disciplinary collaboration among neuroscientists and educators in order to develop a brief, targeted, and robust training module to address these misconceptions.LM developed the study concept and collaborated with LG on design and implementation. LM adapted the surveys for the current project. LG developed the platform for launching the survey and for collecting the web-based data. JC, AA, and LG provided consultation to LM regarding survey design and analysis. KM and LM performed the data analyses and planned the manuscript. KM wrote the first draft of the manuscript. KM and LM collaboratively revised the manuscript. LG, JC, and AA provided critical feedback on the manuscript and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.