Outer tropical cyclone rainbands (TCRs) are a concentrated region of heavy precipitation and hazardous weather within tropical cyclones (TCs). Outer TCRs pose considerable risk to human societies, but their origin remains unresolved. Here, we identify a total of 1029 outer TCRs at their formative stage from 95 TCs and present a large collection of radar observations in order to establish a robust foundation of the natural diversity of rainband origin. The results show the dominance of outer origin for the observed outer TCRs, in distinct contrast to theoretical modeling works of outer TCRs, which propose inner-origin scenarios. Our analysis also suggests that squall-line dynamics are a common, but not the sole, mechanism responsible for outer TCR formation. The nature of preexisting outer precipitation is found to be an important factor to influence the squall-line and non-squall-line outer TCR initiation. Convectively active, hazardous rainbands within tropical cyclones are shown to originate primarily from outside the inner core of the cyclone. This outer-origin dominance is partly attributed to squall-line processes and deserves future attention. In addition to eyewalls, tropical cyclone rainbands (TCRs) are a primary, concentrated region of heavy precipitation and hazardous weather within TCs10. TCRs not only play a vital role in influencing the development and intensity of TCs18 but also represent vast threats to societies because of their high potential to produce catastrophic rainfall and floods23. Therefore, improving our fundamental understanding of how TCRs form and develop is critically important for disaster mitigation and the prediction of the natural hazards and climate extremes in TC-prone regions.Tropical cyclones (TCs) are the most devastating storms in nature since they can result in the significant loss of human life and tremendous property damage due to torrential rainfall and intense winds24. Moist convection of inner TCRs is usually weaker and organized into a more circular geometry due to the pronounced influence of the inner-core vortex and filamentation effect25. The precipitation features of outer TCRs are typically asymmetric but much more convective than those of inner TCRs due to a larger convective instability in the outer region of TCs31. Outer TCRs tend to have stronger and broader impacts on human and natural communities through the generation of severe weather conditions and flooding32.TCRs are conventionally classified into inner and outer rainbands based on their location relative to the TC center. Inner TCRs are located within the TC inner core that has a radial distance of 100–200 km or ~3 times the radius of maximum wind (RMW), whereas outer TCRs are located beyond the inner core33, a general consensus is that their appearance is related to the inner-core wave activities  initiated near the eyewall38. However, the origin of the outer TCRs remains uncertain and the formative mechanisms proposed previously are diverse. Earlier modeling and theoretical investigations have suggested that outer TCRs are probably a manifestation of outward-propagating gravity waves (GWs) that are excited close to the TC center or inside the inner core42. In this view, the moist convection occurring in outer TCRs may be triggered by gravity-wave-induced upward motions43. Despite the potential discrepancy in the propagation speed between theoretical GWs and observed outer TCRs47, what we learned from these previous works is still unable to adequately address whether outer TCRs would be initially triggered by the GWs within the inner-core region and then propagate outward to the outer region of TCs.Although the processes that form the inner TCRs have not been wholly conclusive48. That study demonstrates that convectively active outer TCRs form as weakly convective inner TCRs propagate to the outer region where filamentation and stabilization are reduced. Li et al.48 argue that their simulated inner TCRs would most likely be related to inner-core VRWs instead of GWs. In the context of the inner origin, the inner and outer TCRs are supposed to have no inherent distinction, although their associated precipitation characteristics may undergo some sort of convective transformation in response to the differences in environmental conditions between the inner and outer regions of TCs.Recently, the inner-origin scenario above was further explored by an idealized numerical TCR simulation by Li et al.5 and is typically quasi-stationary relative to the TC center50. Outer TCRs are a broader class of rainbands than just PB that exhibits unique precipitation and kinematic features tied to TC vortex dynamics. Although possible mechanisms responsible for the formation of PB have been explored by numerical modeling51, previous PB observational studies rely dominantly on the analyses of airborne flight-level and radar data collected over a relatively short period of time during the mature or late stage of oceanic TCRs. Hence, these observational works do not provide strong clues to where and how outer TCRs form within TCs.Although many previous theoretical and modeling works have emphasized the importance of the inner-origin processes for outer TCRs, they also seem likely to form locally near the inner-core boundary or in the outer region of TCs, which may be considered outer-origin. Understanding of the outer-origin processes has been limited due to scarce observations during the formative stage of outer TCRs. A particular, well-known type of rainband usually exists near the boundary between the inner-core vortex of TCs and environmental flow. This type of rainband has been referred to as the “principal band (PB)”56. These investigations have revealed the frequent presence of surface cold pool signatures and squall-line-like airflow patterns for outer TCRs, which imply the potential importance of squall-line dynamics on the development of moist convection associated with outer TCRs. However, none of the previous observational studies, except for the TCR case described in Yu et al.30, capture the formative stage of outer TCRs or confirm how relevant the squall-line dynamics would be in the outer TCR initiation process.With advances in high-resolution radar and surface observations and numerical simulations, a growing number of TC studies have improved our understanding of the detailed structural characteristics of outer TCRs30 is the only observational study of TCRs to provide a complete picture of the outer-origin processes. By tracking the history of a long-lived outer TCR associated with Typhoon Jangmi (2008), Yu et al.30 documented that the outer rainband was initiated as it detached from the upwind segment of a stratiform precipitation region located slightly beyond the inner-core boundary. A distinct transformation from generally stratiform precipitation to highly convective precipitation, which was related to the increasing intensity of convectively generated cold pools and their interactions with low-level vertical shear57, was documented as the rainband propagated cyclonically outward.To the best of our knowledge, Yu et al.59. It is thus likely that the outer TCR origin would be also modulated by diurnal and shear effects. However, this likelihood has never been confirmed, and whether the formation of outer TCRs would exhibit diurnal cycles or some sort of dependence on large-scale vertical shear remains unclear.The diurnal forcings and large-scale vertical wind shear have been both recognized to have a strong impact on the development and structure of TC convection. Previous studies have provided observational evidence that these two factors can influence convective intensities and distributions over a very broad area not only within the inner region of TCs but also beyond their inner core62. On the basis of theoretical approaches, this kind of instability can occur in the rotational environment under the presence of suitable vertical shear in the boundary layer, taking the form of spiral-shaped roll vortices. Two of the most remarkable aspects for the instability-generated perturbations are their storm-relative stationary characteristics and a nearly constant crossing angle of about ~15o with respect to the TC center4. The crossing angle is defined as the angle between the rainband’s orientation and the tangent to a circle with radius from the band to the TC center4. Different wavelengths of roll vortices, ranging from subkilometers to several kilometers, have also been observed in the tropical cyclone boundary layer65. However, the potential relevance of the boundary-layer instabilities and roll vortices to the appearance of outer TCRs remains highly uncertain at present due to the lack of observational validation.A final possible contributor to the origin of outer TCRs, as proposed by a few earlier studies, is related to the boundary-layer instability from an Ekman-like flowIt is clear from the literature reviews above that current research is still far from adequate to clarify the relative prevalence of the inner-origin and outer-origin scenarios and of various possible realistic formative scenarios for outer TCRs. To establish a robust foundation of knowledge regarding the natural diversity of the rainband origin, a critical and challenging task is thus to collect detailed observational information from a very early stage of rainband development from a considerable number of outer TCR cases.1. The statistics based on the typhoon database provided by the Taiwan Central Weather Bureau (CWB) (https://rdc28.cwb.gov.tw/TDB/public/warning_typhoon_list/) indicate that, since 1958, there have been approximately four to ten TCs that approach the area around Taiwan every year. In addition, an excellent ground-based Doppler radar observation network has been in operation in Taiwan since the 2000s. Both of the above aspects make it possible to collect a large dataset of outer TCR cases as TCs approach from the northwestern Pacific. The particular focus of this study is to explore the important characteristics of outer TCRs during their initiation stage in terms of their formative location and attendant propagation. The observations consisting of 1029 outer TCR cases associated with 95 TCs can provide important insight into the origin of outer TCRs.The primary objective of this study is to use long-term radar observations collected during 2002–2019 to identify the origin of numerous outer TCRs associated with TCs as they approach the ocean areas near Taiwan. The island of Taiwan is located within the main path of TCs that originate over the northwestern Pacific Ocean, a basin with the highest TC activity on Earth29. Specifically, the precipitation information of the front quadrants for a typical westward-moving TCs over the northwestern Pacific Ocean could be usually monitored by coastal radars in Taiwan for a longer period of time. In contrast, when the rear quadrants of TCs reach the observational coverage of coastal radars, the inner-core circulations of TCs have been closer to the landmass of Taiwan so both TC circulations and rainbands have greater chances to experience orographic modifications and/or make landfall. This sampling preference of coastal radars is expected to capture more outer TCR cases in the front quadrants than in the rear quadrants, consistent with the asymmetric characteristic of formative locations of the observed outer TCRs, as shown in Fig. A plan view of the formative locations, with respect to the typhoon center and the direction of typhoon motion, for all identified TCRs is illustrated in Fig. 30. Note that few of the outer TCRs (32 cases) are initiated inside three times the RMW . It is fairly reasonable to conclude that actual outer TCRs tend to be initiated locally in the outer TC region  and do not preferably originate from the inner-core region.The formative number of TCRs as a function of radial distance, as shown in Fig. RMW Fig. , implyin25, the inner-core flow of TCs tends to be strain-dominated and is characterized by a rapid filamentation zone with a time scale less than that of deep moist convective overturning (~30 min). Relative magnitudes of the filamentation time are useful for providing a dynamic distinction between the inner and outer regions of TCs66. In this study, the filamentation time, as defined in Rozoff et al.25, is calculated using the wind information at the formative location of each rainband at 1.5 km above mean sea level (MSL) from the ERA5 reanalysis data. The reason why the 1.5-km level is chosen for calculation is because this height is immediately above the typical top of TC boundary layer (0.5 ~ 1 km MSL) so the winds from this height are expected to better represent the TC intensity and circulation. Figure A complementary view of dynamic regimes for outer TCR formation can be provided by analyzing the filamentation time for each of the identified TCRs. As demonstrated in Rozoff et al.69. Because the TC size may vary considerably with different thresholds chosen, we use the average radius of 34 kt winds (R34) as a storm radius for TCs in this study. R34 is commonly considered as the area of the TC warnings issued by operational forecasting centers31. The statistical distribution of the TCR formative number, as a function of normalized radial distance by JTWC-recorded R34, is shown in Fig. 31.Since outer TCR formation is observed to be active over extensive outer regions of TCs, as shown in Figs. It is noteworthy that diurnal forcings, as described in the Introduction, are well known to influence convective development of TCs. The formative times of all outer TCRs observed in this study are also analyzed to understand if there are any diurnal signals. It is found that the number of outer TCRs formed at different times in a day is similar and approximately equal to 80 cases for each time interval. This result generally reflects the lack of obvious diurnal variations for the formation of the studied outer TCRs.−1  reported in previous observational studies of TCs43. Small proportions of the observed outer TCRs have slow radial propagations within ±1 m s−1 , and the radially propagating feature is prevalent from our TCR dataset. Assuming the 2–5 times RMW as an approximate area for the outer boundary of the inner-core vortex of TCs, where PBs typically form, only 13 out of 144 outer TCR cases are actually located downshear within this area. Such a small number of TCR cases  suggests that the possible impact of PB-like rainbands on the outer TCR dataset analyzed in this study may be considered negligible. One of the primary factors contributing to the potential preclusion from the class of outer TCRs is related to the fact that PBs tend to occur preferentially inside the TC vortex core or near its outer boundary (~3 times RMW), as learned from the comprehensive examination of radar-observed precipitation field associated with 95 TCs investigated in this study.The large collection of TCR cases in this study provides a good opportunity to explore the generality and diversity of propagation and geometry characteristics for outer TCRs near Taiwan. Statistical results of the radial and tangential propagation velocities and the rainband’s width and crossing angles for all identified TCRs are shown in Fig. −1 Fig. . Outwards−1 Fig. , consist−1, which is somewhat slower than the average ambient tangential flow (14.4 m s−1) within the layer of precipitation associated with the rainbands (0.1 ~ 12 km MSL).Compared with the radial propagation, the tangential propagation of outer TCRs is generally much faster and predominantly cyclonic  Fig. , which i65. The most frequent occurrence of the observed crossing angles is found between 10° and 30°  have cross-band widths ranging from 5 to 25 km Fig. , equival30° Fig. , which i70:Cgw is the GW phase velocity, υ is the intrinsic wave frequency, k is the horizontal wave number, m is the vertical wave number, and N is the Brunt-Väisälä frequency that is computed from the ERA5 reanalysis data below 12 km MSL nearest the location of each identified TCR. The value equal to twice the radar-observed TCR width is again used to approximate the horizontal wavelengths. The vertical wavelength is assumed to be 12 km MSL based on the typical depth of convective motions associated with outer TCRs29. The scatterplot shown in Fig. −1, which is the same order of magnitude as the typical radial propagation speeds of several meters per second for GWs excited within TCs47. These theoretical propagation estimates are distinctly separated from the primary velocity distributions of the observed radial propagation, which are within the range of ±10 m s−1  active within TCs are calculated and compared with the observed propagation velocities of outer TCRs presented above. The classical dispersion relation of internal gravity waves may be written as−1 Figs. a, 4a.Figω) and radial and tangential phase velocities (Cr and Cλ), as explored by Möller and Montgomery (2000)71, may be expressed as:k, n, and m are the radial, tangential, and vertical wave numbers, respectively; R is the reference radius; N is the Brunt-Väisälä frequency; 72:V is the tangential wind; f is the Coriolis parameter; and N is the Brunt-Väisälä frequency. As described earlier, the radial wavelength (k) and the vertical wavelength (n) are again approximated as being twice the radar-observed TCR width and 12 km, respectively. The mean tangential wind in (5) and (6) is calculated by averaging tangential winds within the layer of 0.1–12 km MSL that is corresponding to the vertical wavelength of considered wave motions. The tangential vertical shear in (7) is approximated by the difference in the tangential wind between 0.1 and 12 km MSL. The reason why we choose the 0.1 km MSL as the lowest level in these calculations is that this height is well above the top of surface layer so the uncertainty on winds due to strong and direct impact of surface friction can be appropriately eliminated. A typical tangential wave number (n = 2) that characterizes VRWs is assumed73. Note that the tangential wave number does not have a strong impact on the calculated propagation velocities, and choosing other tangential wave numbers, such as n = 1 or 3, for calculation in (2) gives rise to nearly identical propagation estimates. The radial distance of the observed TCRs is used to represent the reference radius (R) and −1) and predominantly outward propagations, in contrast to a very wide range of the observed velocities with evidence of both outward and inward propagation  and the warm, moist inflow  present at low levels are two of the most fundamental characteristics of squall-line dynamics. The presence of frontward shear is not only the most typical environment characterizing squall-line systems but is also the mandatory criterion needed to facilitate the lifting of convectively generated cold pools74. The low-level front-to-rear flow that feeds warm air to the leading edge of squall lines can be considered a critical source of convective energy for the development of deep convection. If squall-line dynamics plays a substantial role in the initiation of outer TCRs observed in this study, one might expect to see some sort of preference in the distribution of the low-level band-normal vertical shear and band-relative flow consistent with the above outlined squall-line characteristics.According to the classical squall-line theory proposed by Rotunno et al.30. Similar to the consideration for the VRW calculations as described in the previous section, choosing the lowest level at 0.1 km MSL is to preclude the uncertainty on winds due to direct impact of surface friction. Moreover, the band-normal wind is calculated below 1.5 km MSL, corresponding to the mean depth of low-level inflow feeding the leading edge of the outer TCRs documented in this study. A large proportion of the outer TCRs  is observed within the fourth quadrant (IV) of the analysis with positive band-normal vertical shear  and negative band-normal wind components   Fig. . There aVcp) perpendicular to its leading edge can be approximated by the following expression75:H is the depth of the cold air, θv2 and θv1 are the ambient and cold pool virtual potential temperatures, respectively, Vc is the ambient band-normal wind component ahead of the cold pool (negative for wind moving toward the cold air and vice versa), and k and b are empirical constants. The best fit of (8) to atmospheric observations gives constant k and b values equal to 0.9 and 0.6, respectively75. Considering the propagation of cold pools embedded within the moving TCs, we may need to approximate Vc in (8) by taking the TC motion into account so that the positive and negative Vcp calculated thus represent band-normal propagation motions away from and toward the TC center, respectively. Since direct measurements for the depth of the cold air and the cold pool virtual potential temperatures during the TCR initiation are not available, we attempt to determine the velocity range of Vcp by giving a possible range of values for H and the cold pool intensity  and to check if the propagation velocity of the observed outer TCRs fit well into the calculated velocity range of Vcp. Based on previous observational studies of outer TCRs30, the most reasonable ranges of values for H and θv2 -θv1 may be 1–2.5 km and 0.5–3 K, respectively. The values of θv2 and Vc in (8) are obtained from the ERA reanalysis data nearest the location of each outer TCR observed in this study.Some clarification on the issue above may be performed by evaluating the relative consistency of whether the observed outer TCRs propagate at a speed similar to the atmospheric cold pool. In this evaluation, we simply classify the observed outer TCRs, according to their environmental characteristics, into the squall-line TCR group , the preformation time (t0-1) and the formation time (t0). The preformation time is defined as the earliest, unambiguous detection of TCRs by radar observations whereas the formation time refers to a time satisfying the formative criteria, as described in the Methods section. In this analysis, we choose the squall-line TCR cases with faster propagation velocities  and corresponding to the theoretical velocity range of atmospheric cold pools to maximize composite signals for the squall-line outer TCRs. Similarly, those non-squall-line TCR cases that propagate relatively slower  and beyond the theoretical velocity range of atmospheric cold pools are chosen for the non-squall-line composite. As shown in the composite results, the position of the rainband is clearly marked by the highest precipitation frequency centered at  at t0-1 and t0 for both the squall-line TCRs and non-squall-line TCRs  with respect to the location of each outer TCR at three particular time periods, 1 h prior to the preformation time  tend to be initiated locally in the outer TC region. This outer-origin dominance is in distinct contrast to numerous theoretical modeling studies of outer TCRs that propose inner-origin scenarios. Moreover, the formative fractions of the identified outer TCRs within and outside the storm radius of TCs (R34) are dramatically different, equal to 29% and 71%, respectively, indicating that outer TCR formation occurs mostly in regions beyond the operational TC alert area. A dominant majority of the identified TCRs possess radial propagation velocities within the range of ±10 m sAnalyses from this study provide supportive evidence that squall-line dynamics may play an important role in contributing to the formation of outer TCRs. The observed outer TCRs appear to have a statistical preference for both a squall-line environment and cold pool propagation behavior. In particular, the initiation of faster, squall-line-like outer TCRs preferentially take place immediately adjacent to the outer boundary of prominent precipitation in the outer regions of TCs, and this preexisting outer precipitation may act as an initial, critical provider of cold pools to activate the operation of squall-line dynamics Fig. . Examini29 and Atlantic Ocean55. In addition, outer TC circulations observed over different basins seem to have similar characteristics of low-level winds with the presence of some band-normal vertical shear54. It is thus reasonable to expect that what we learn from the study would be generally applicable to outer TCRs developing over other basins. However, a comprehensive dataset of outer TCRs collected from different geographical locations during their formative stage will be still required to clarify whether any possible basin-dependent characteristics of the TCR origin may exist.Despite the different evolving stages of outer TCRs, the cold pool signatures associated with outer TCRs have been reported for TCs developing over different oceanic basins, such as the northwestern Pacific OceanAlthough this study provides important findings and implications, our understanding on the origin of outer TCRs is clearly incomplete. Future efforts and observational programs should pay special attention to the outer-origin processes and the identification of possible cold pool sources by collecting more complete observations of three-dimensional kinematics, thermodynamics and precipitation in the outer environment of TCs during a very early stage of rainband development. Making these datasets available, together with high-resolution numerical simulations, is critical for developing a theoretical model of how squall-line dynamics operates to form outer TCRs and to seek other unidentified processes responsible for the initiation of outer TCRs.29, and the locations of the four radar sites are shown in Fig. 77) are also used to determine various environmental factors associated with the studied TCRs. Compared to other reanalysis data, the ERA5 reanalysis data have higher spatial and temporal resolutions at 0.25° × 0.25° and 1 h, respectively, thus providing a more realistic representation of TCs and their environment80. For convenience of relevant calculations and analyses, the ERA5 reanalysis data valid at different pressure levels are interpolated linearly to obtain vertical grid spacing at 0.1 km. The TC information, including center locations, RMW, and other storm characteristics, analyzed in this study comes from the best track data provided by the Joint Typhoon Warning Center (JTWC).The datasets used to identify TCRs in this study are provided by four S-band (10 cm) ground-based Doppler radars operated by the CWB at Wu-Fen-San (WFS), Chi-Gu (CG), Ken-Ting (KT) and Hua-Lien (HL), which are located in different coastal regions of Taiwan. The basic characteristics of these Doppler radars are described in Yu et al.V in (5), (6) and (7)] that is the ERA5-provided kinematic quantity involved with the calculation of VRWs.Although ERA5 data have been widely used to study relevant scientific issues for TCs, whether they can provide adequate representation of wind information nearby the studied outer TCRs is still somewhat uncertain. To fully address this issue is beyond the scope of this study, but the possible impact of this uncertainty on the calculated propagation velocity of VRWs presented in the Results section may be evaluated by comparing the ERA5 winds with other independent wind measurements nearby the rainband’s location. In making such comparisons, surface observations from eight offshore island stations  near Taiwan –(7). The potential errors in the calculated propagation velocity for VRWs may be considered minor because they are unlikely to alter the statistical distributions of the scatterplots shown in Fig. A good correlation between the ERA5 data and offshore island observations is found, and the correlation coefficient, average error and root mean square error (RMSE) are calculated to be 0.74, 2.9 m sVc  from ERA5 is calculated be 2.9 m s−1, yielding a velocity error of 1.7 m s−1 in the estimate of theoretical cold pool velocity (Vcp) based on (8).Using the similar method of evaluating the uncertainty associated with ERA5-provided tangential winds in the rainband environment, the potential error of The error analyses above support the reasonable representation of the ERA5 reanalysis data for the rainband environment, although the accuracy of the ERA data to represent TC circulations and their ambient wind conditions is possible to vary case by case. This uncertainty may have greater chances to become a potentially serious problem if scientific approach is placed on the understanding of individual TC/TCR events. However, because the present study focuses on the statistical aspects of outer TCR formation using long-term reanalysis data for a very large set of TCR cases, the potential impact of this data uncertainty may be appropriately mitigated.29 but more specific criteria are used herein to define an outer TCR and determine its initiation. First, a precipitation feature with a maximum reflectivity of at least 30 dBZ, a minimum length of 100 km and a large length-to-width ratio (>5) must be satisfied. Second, the precipitation signature described above must persist for 1 h or longer. These two criteria can assure that a prominent and persistent rainband has been identified, and that a relatively transient, non-banded or loosely organized precipitation feature could be effectively avoided. Third, a rainband is classified as an outer TCR as long as its precipitation occurs in or propagates into regions beyond a radial distance of 3 times the RMW for a certain time period during rainband formation and development. This criterion allows us to include both possible formative scenarios: one is that the rainband begins development within the inner TC core and then propagates outward over time into the outer region, and the other is that the rainband is initiated locally in the outer region. Fourth, the formation time for an outer TCR is defined as when the rainband initially satisfies the first criterion above.To seek TCRs, a very large set of radar reflectivity images and data from the lowest plan position indicator (PPI) scans (elevation ~0.4°) for TCs approaching Taiwan is initially prepared. The dataset comes from the four radars and contains observations of 95 TCs for which the CWB issued typhoon warnings during 2002–2019. All of the reflectivity images produced are first screened visually to determine potential candidates of TCRs whose radar echoes can be tracked backward from their mature to formative stages. The primary purpose of this subjective screening is to retain TCR cases that could be captured successfully by radars during their initiation time. Reflectivity data from these tentatively chosen TCR cases are then used to calculate basic rainband information, such as location, length and width, for each of the TCRs. The specific location of rainbands at a given time is determined by averaging the azimuthal angles and radial distances with respect to the TC center over the rainband area with reflectivities ≥30 dBZ. Given the convective nature of outer TCRs, the 30 dBZ threshold not only represents the minimum requirement of precipitation intensity used to identify outer TCRs as described below but also appropriately increases the weighting of stronger and/or convective precipitation on the location determination. The length of rainbands is determined by the distance between two ends of the long axis of the rainband area with reflectivities ≥20 dBZ. The reason why a lower reflectivity threshold of 20 dBZ is used here is because it encompasses a more continuous, larger spatial extent of radar echoes and provides a better representation of the entire rainband entity. Similarly, the width of rainbands is approximated by a mean distance in the band-normal direction along the rainband entity. With these calculated rainband characteristics above, it is possible to identify an outer TCR based on objective, quantitative criteria. The criteria used to identify a TCR are similar to those used by previous TCR studies82 that sometimes occur along the coastal zone of eastern Taiwan in the outer region of TCs due to orographic effects may contaminate the TCR dataset. One of the most common features of these topographically generated rainbands is that they are oriented approximately parallel to the coast without obvious curvature. In addition, these rainbands tend to be locked with coastal mountains and do not propagate rapidly with cyclonic circulations of TCs. These characteristics contrast sharply with evolving and propagation aspects of TCRs. Hence, if rainbands are observed to form locally within the coastal zone (from coastline to ~25–100 km offshore) and meet the above signatures, they are assumed to be topographically generated rainbands and are thus removed during TCR identification.It is noteworthy that Taiwan rainbandsA sample plot of the horizontal reflectivity patterns of an evolving outer TCR associated with Typhoon Saola on 31 July 2012 is shown in Fig. With the comprehensive examination of the available radar measurements collected from 2002 to 2019, a total of 1029 outer TCRs satisfying the above criteria are identified from 95 TC events. These identified TCR cases are all successfully captured and tracked by radar observations over a reasonable time period from their formative to developing/mature stage, which is expected to provide important and unique clues for the possible origin of outer TCRs. A complete list of 1029 identified TCR cases is provided in Supplementary Data −1 in both the radial and tangential directions. Given no perfect observation is available to estimate the TC motions, the differences calculated herein do not actually represent the errors, and they just imply a possible uncertainty for the estimation of TC motions based on independent datasets and methods.The radial and tangential propagation velocities of each rainband are then obtained by calculating the difference in the rainband’s location between the formative and mature period of the identified TCRs divided by the corresponding time interval. The reliability of the propagation estimate above relies mainly on the accuracy of the TC center positions. Note that if such a position error remains unchanged with time, there is no effect on the propagation estimate. Evaluation of the uncertainty of the TC centers provided by JTWC may be practically performed for some particular periods when the center vicinity of the TCs is well captured by radar observations so that the TC centers can be determined by calculating the geometric center of the weak echo regions surrounding the TC eye. Based on a group of outer TCRs (174 rainband cases from 39 TCs) with available radar-determined TC centers, the mean difference in the propagation velocities of the observed outer TCRs calculated from JTWC best track data and radar-determined center information are calculated to be 0.7 m sPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Source Data


Sociopetal design methods can offer interesting means to support therapeutic concepts within ward environments. They can help to forge group identities through offering patients, staff and visitors opportunities to identify with the spaces they inhabit. “Sociopetal space” has been defined as “spaces which help bring people together"; but how does this actually work and what role can these types of spaces play in a hospital ward setting?Some of these elements operate at a detail level and can be rather simple to deploy. Normalising the environment by making “regular” design decisions such as by using real rather then simulated materials ; or through offering a mix of lighting  can create more homely spaces for patients and staff alike. Ultimately, design decisions at the detail scale can create phenomenal elements which can play a large role towards generating a favorable atmospheric experience on the ward.It is also possible to explore how specific moments or places within a psychiatric ward might be designed to support patient agency, even on a closed ward. Sociopetal elements such as well-sited sitting spaces can offer moments of safety or retreat, leading to a greater sense of control. This can help patients feel more open to positive interactions with their colleagues and staff because they can safely observe or choose less committed ways of participation in daily or group activities.Zooming out from these details, we will also look at the layout of a psychiatric ward (ie. accommodations) to help identify where opportunities such as those listed can be found. Simple gestures such as a slight widening of the corridor leading to important shared areas or better access to light or views of nature have been shown to improve outcomes for patients. What other design elements can be placed on or within wards to further this approach? Recent and ongoing projects within our practice will be shared to help workshop participants gather literacy in case they may be involved in future design projects.None Declared


Most of the video content on the Internet today is distributed through online streaming platforms. To ensure user privacy, data transmissions are often encrypted using cryptographic protocols. In previous research, we first experimentally validated the idea that the amount of transmitted data belonging to a particular video stream is not constant over time or that it changes periodically and forms a specific fingerprint. Based on the knowledge of the fingerprint of a specific video stream, this video stream can be subsequently identified. Over several months of intensive work, our team has created a large dataset containing a large number of video streams that were captured by network traffic probes during their playback by end users. The video streams were deliberately chosen to fall thematically into pre-selected categories. We selected two primary platforms for streaming - PeerTube and YouTube The first platform was chosen because of the possibility of modifying any streaming parameters, while the second one was chosen because it is used by many people worldwide. Our dataset can be used to create and train machine learning models or heuristic algorithms, allowing encrypted video stream identification according to their content resp. type category or specifically. Specifications Table•The dataset contains sufficient samples to train machine learning models primarily designed to identify encrypted video streams.•Data analysts or cybersecurity experts can use the dataset.•The dataset can be a benchmark for comparing the quality of different algorithms or models designed to identify encrypted video streams.•The use of the dataset lies in the possibility of speeding up the research, as it took 4 months and required 3 people to create it.•.The dataset is free of malicious content and is freely distributable1The main reason and motivation for creating this dataset was the possibility of training and validating different machine learning models to identify encrypted video streams and also that a thematically identical or similar freely downloadable dataset does not yet exist.The authors of Our dataset is carefully crafted, freely downloadable, and structured into categories. In addition, it contains data from PeerTube 2The basic structure of our dataset is shown in The video streams of the YouTube dataset part is further divided into four sub-parts, namely Art &Entertainment, Food & Drink, Computers & Electronics, and Autos & Vehicles. PeerTube dataset part video streams are divided into only 5 subparts, namely Games, Movies, Cartoon, Sport and Other. The distribution of samples for both dataset parts can be seen in Further, a vector of twenty-five classification features (VCF) has been computed for each video stream. The meaning and explanation of each component of the VCF are presented in all.csv. This file is available in the root directory of the dataset. The format of each item is as follows (An entire list of all video streams, including VCFs for all items in the dataset, is available in the file named  follows .Table 2RComplete measurements of all video streams are available in files in the RAW directory. The files are text files, and the structure of their content is in JSON format. In each JSON file, there is an array of flows. A flow is represented as an object where its principal information is stored, such as protocol name and used ports. The description of the flow object is found in Inside all flow objects, there is an array of the captured packets during that flow. Each packet is represented as a JSON object. The presence of attributes in packets may differ from one flow protocol to the other. The description of the packet object is in 3This section describes how we gathered the data and used the environment. The first part of this section describes the YouTube dataset, followed by the PeerTube dataset.3.1In •Router: A physical router that is dedicated to collecting IPFlows. It hosts a probe that submits collected flows to the Kafka server.•Kafka server: Kafka Virtual Machine: The Debian 11.4 server runs the background daemon, which is used for automatic video playback. And the ipFlowDetector, which collects flows for individual videos. The machine has multiple network interfaces to prevent packet interference between YouTube playback and other network traffic.The YouTube playback daemon in this infrastructure does the primary heavy lifting. It is a service written in the Python programming language, and it accepts a sequence of YouTube videos to play. The videos are played sequentially and from a dedicated interface not used by anything else on the virtual machine to prevent interference. Packets from this interface are routed through a dedicated router that collects the beforementioned IP Flows.•The service resolves from which URL YouTube will start the playback for the given video ID. This is achieved using the yt-dlp •Then, we resolve the hostnames of the mentioned URLs of the video file to retrieve the playback server's IP addresses.•We enforce packet routing for these IP addresses to be routed through the dedicated network interface.•Next, we spawn the ipFlowDetector, which will collect the flows for the dedicated interface.•Service spawns a virtual display environment with Full HD resolution, where we will launch the Selenium •We then play the whole video.•After it is finished, we terminate the ipFlowDetector, which creates a JSON file containing all collected information.In the main program loop, we iterate over provided YouTube video IDs, and for each ID, we do the following in this order:This process is repeated for each video. Furthermore, it is done multiple times for each video.3.2•Before the program is executed, we pass a text file with all URLs of all the videos we want to measure directly to the program as an input argument.•After the whole page containing the video is loaded, the ipFlowDetector is started with the PeerTube server's IP address as a filter. We can do that because P2P sharing is disabled in the configuration file of our PeerTube server. So, all packets come directly from the server itself.•All videos uploaded to our PeerTube server are only in Full HD resolution, so we do not need to specify when Selenium plays the video.For Peertube video-stream measurement, the same environment described in Fig. is used. The whole process is also done by a program written in Python with the use of ipFlowDetector and Selenium library with ChromeDriver. The main program loop works similarly, as mentioned above in Our work does not involve studies with animals and humans.Jan Fesl: Conceptualization, Methodology, Writing – original draft, Supervision. Daniel Sedlák: Data curation, Software, Writing – original draft. Tomáš Macák: Data curation, Software, Writing – original draft. Marie Feslová: Methodology, Resources. Michal Konopa: Visualization.


Here, this cross-species analysis focused on KCNN3 DNA methylation, gene expression, and single nucleotide polymorphisms, including alternative promoters in KCNN3, that could influence surface trafficking and function of KCa2 channels. Bisulfite sequencing analysis of the nucleus accumbens tissue from alcohol-drinking monkeys and alcohol-dependent mice revealed a differentially methylated region in exon 1A of KCNN3 that overlaps with a predicted promoter sequence. The hypermethylation of KCNN3 in the accumbens paralleled an increase in the expression of alternative transcripts that encode apamin-insensitive and dominant-negative KCa2 channel isoforms. A polymorphic repeat in macaque KCNN3 encoded by exon 1 did not correlate with alcohol drinking. At the protein level, KCa2.3 channel expression in the accumbens was significantly reduced in very heavy-drinking monkeys. Together, our cross-species findings on epigenetic dysregulation of KCNN3 represent a complex mechanism that utilizes alternative promoters to potentially impact the firing of accumbens neurons. Thus, these results provide support for hypermethylation of KCNN3 as a possible key molecular mechanism underlying harmful alcohol intake and alcohol use disorder.The underlying genetic and epigenetic mechanisms driving functional adaptations in neuronal excitability and excessive alcohol intake are poorly understood. Small-conductance Ca Alcohol (ethanol) use disorder (AUD) is a devastating brain disease driven by complex interactions between genetic, epigenetic, and environmental factors. Genetic factors increase the propensity for risky drinking , 2, chro2+-activated K+ (KCa2 or SK) channels in cortical-striatal brain circuitry involved in motivational processing have emerged from preclinical studies as a target for treating AUD [Ca2.3 channels encoded by the KCNN3. Previous functional mouse genomics studies, found quantitative trait loci (QTL) containing Kcnn3, particularly NAcC Kcnn3 transcript levels, were negatively correlated with voluntary drinking in genetically diverse BXD strains [Kcnn3 was associated with ethanol preference in selectively bred rat lines [Ca2 channel function and KCa2.3 channel protein expression were increased in the NAcC of excessive ethanol drinking and dependent rodents [Ca2 channels in the NAcC with apamin increased voluntary ethanol drinking in mice [Ca2 channel function reduced home cage drinking and operant self-administration [Ca2 channel function was completely lost in NAcC MSNs from ethanol-dependent mice, but not in rats that had access to 7 weeks of operant self-administration of moderate amounts of ethanol [KCNN3 in general, and NAcC KCa2 channel function in particular, as a potential regulator of excessive ethanol consumption and dependence in rodent models of chronic ethanol exposure.Small-conductance Cating AUD , 14–19. ting AUD  of nucle strains , 18. Furat lines . In addi rodents , 17, 22.stration , 19, 22. ethanol . TogetheKCNN3 encoded by exon 1 [Ca2 channel function in transfected HEK293 cells [Ca2.3 channel regulation of cognitive function and plasticity of intrinsic excitability which is an important mechanism for forming new learned associations [There are two polymorphic CAG repeats in the N-terminus of human y exon 1 . Higher 93 cells , and thi93 cells , 25–28. 93 cells . This ficiations , 29, 30.ciations , 29–31 aciations –34, the Kcnn3 expression and increased intrinsic excitability of cultured cortical neurons [KCNN3 promoter. In humans, the KCNN3 gene encodes four known transcripts by making use of alternative first exons and alternative splicing. Similar to the CAG trinucleotide repeat, alternative KCNN3 transcripts influence the function of KCa2 channels. KCNN3 transcript SK3_1B encodes a truncated channel that functions as a dominant-negative to suppress endogenous KCa2 channel currents [hSK3_ex4 encodes a protein with an additional 15 amino acid insertion within the S5-PHelix loop that renders the channel insensitive to apamin block [KCNN3 transcripts in the NAcC. Here, we report a complex cross-species relationship between NAcC KCNN3 and excessive ethanol drinking that ultimately leads to reduced KCa2.3 channel protein expression in mice and monkeys.DNA methylation (DNAm) is an epigenetic mark that contributes to the modulation of gene expression by modifying the accessibility of transcription factors to chromatin. Alterations in DNAm are reported in heavy-drinking humans, monkeys and rodents , 35, 36, neurons . Thus, wcurrents , whereasin block . In chron = 66, Macaca mulatta) from seven different cohorts  were included in this study  at ~7 weeks of age. Baseline ethanol drinking (22 h day/15% ethanol v/v) was established prior to treatment with 4 repeated weekly cycles of chronic intermittent ethanol (CIE) exposure in vapor inhalation chambers, alternated with weekly home cage drinking sessions, as previously described  and in fAfter the 12-month open access period, a detailed necropsy protocol was used to systematically collect tissues from all macaques . NucleusKCNN3 CAG repeat region as previously described [Blood DNA was used to analyze the number of CAG repeats within the second escribed . The priKCNN3 promoter region (MR-ex1) using NAcC tissue from macaques and mice following published methods [KCNN3 within exon 1A and intron 1 in human, rhesus macaque, and mice. Because of the length of the region, two sets of primers were designed to cover the whole region  gene. This gene was demonstrated to be a reliable control for brain gene expression [The NAcC RNA quantity and quality were evaluated and qPCR was performed in triplicate assays, as described in pression . We alsopression .After extraction, tissue samples containing the NAcC extracted from female control and long-term drinking rhesus macaques were prepared for western blot analysis following our previously published methods in monkey brain tissue , as fullCa2.3 protein expression level per comparison group. All variables analyzed followed a normal distribution. Welch’s one-way ANOVA was used to compare the difference in average methylation between controls and ethanol drinkers with Games-Howell post hoc tests. One-way ANOVA was used to compare mRNA relative expression levels between groups. Prior to applying one-way ANOVA, Levene’s test was used to test homogeneous variance assumption for parametric methods. When heterogeneous variance was detected, we used the nonparametric Kruskal-Wallis test. Bonferroni or Tukey correction for the multiple comparisons was used to correct the overall type I error rate. Two-tailed independent t-test was used to compare the difference in average methylation rate between controls and dependent mice. Based on Levene’s test for homogeneous variance, we used the appropriate p-value (homogeneous or heterogeneous variance). The allele frequency distribution of CAG trinucleotide repeats between controls and drinking monkeys was compared using the Kruskal-Wallis test . Normalized western blot data were analyzed by a two-tailed t-test in Prism. Ethanol drinking data in mice were analyzed by a repeated-measures mixed linear model with a Tukey post hoc test .The sample size for each experiment was estimated based on our prior studies , 5. DataSixty-six, male and female rhesus macaques, enrolled based on no common parents, were used in this study. Previously, daily ethanol drinking was mathematically modeled based and determined 4 categorical levels of intake: low, binge, heavy and very heavy. In the subjects of this study, there were 16 low drinking (LD), 9 binge drinking (BD), 9 heavy drinking (HD), and 15 very heavy drinking (VHD) monkeys. The average daily (22 h) ethanol intake (range: 0.47–5.15 g/kg) across the 12 months of open access for each of the drinking monkeys along with their age at drinking onset and percentage of drinking days over 3 g/kg are shown in Fig. n = 15 mice/group). Consistent with studies using limited access to ethanol [p = 0.0491). Post hoc analysis indicated that the two treatment groups were not different at baseline (p = 0.6685), but differed during weekly test drinking sessions 1 (p = 0.0074), 2 (p = 0.0468), and 3 (p = 0.0034). In the ethanol-dependent mice, drinking in all four test sessions was significantly higher than their intake during baseline (p = <0.0001). On average, CIE-exposed mice consumed 33 g/kg of ethanol more than control mice during the drinking paradigm  = 2.226, p < 0.05).To more closely match the monkey drinking paradigm, the standard two-bottle choice, limited-access mouse model of dependence-induced escalation of drinking was modified to allow mice open access to ethanol (15% v/v) for 22 h/day. Ethanol drinking prior to and following each weekly exposure to CIE is shown in Fig.  ethanol , 45, 46,KCNN3 contains two polymorphic sites composed of a variable number of CAG repeats that have been associated with KCa2 channel activity. It has been reported that higher numbers of CAG repeats reduce KCa2 channel currents using cultured cells  [KCNN3 regulation and transcript levels. In our studies, the first CAG repeat array was not polymorphic; however, the second CAG repeat copy number was highly variable across rhesus monkeys  = 2.354, p = 0.5023; Fig. p = 0.6946; Fig. KCNN3 transcripts  . To deteeys Fig.  and rangKCNN3 gene and promoter across human, rhesus macaque, and mouse, we identified ten regions with potential conserved regulatory function, based on sharing over 95% sequence homology and ≥75% CpG identity. We then analyzed the DNAm patterns in these ten candidate regions between ethanol-naïve, LD, and HD/VHD rhesus macaques. Because of the small sample size, BD were not included in this analysis, while HD and VHD were combined based on their similar drinking behavior. Among the different regions, only a region of 646 bp overlapping with exon 1 and intron 1 of KCNN3  and rhesus macaque (MacaM or Mmul10) genomes are not annotated with as much detail as the human. Thus, in order to investigate the effects of ethanol drinking on the expression of the different KCNN3 transcripts, we designed primers to amplify two exons common to all reported transcripts (SK3_ex7/8), as well as primers to specifically amplify transcript SK3_ex4, SK3_ex1B, and SK3_ex1C in human. Next, each amplicon’s orthologous sequence was identified in mouse and rhesus macaque, and species-specific primers for the different transcripts were designed. It should be noted that transcripts SK3_ex1B and SK3_ex4 encode dominant-negative and apamin-insensitive isoforms of KCa2.3 channels, respectively, and transcript SK3_ex1C was not detected in the NAcC, in agreement with previous studies indicating this transcript is not expressed in the brain [We next evaluated the potential relevance of MR-ex1 hypermethylation in regulating he brain .SK3_ex7/8) showed no differences among the different ethanol-drinking male monkey groups  = 2.721, p = 0.0870, n = 8–9/group; Fig. SK3_ex1B  = 3.547, p = 0.0462, n = 7–10/group) and SK3_ex4  = 9.89, p = 0.0009, n = 8–9/group; Fig. SK3_ex4 was significantly increased in the NAcC of LD male monkeys  = 1.802, p = 0.2134; SK3_ex1B: two-tailed t-test; t(2) = 0.1494, p = 0.8950; SK3_ex4: two-tailed t-test; t(2) = 0.6137, p = 0.6019); thus, samples from these two age groups were pooled for further analysis. When collapsed across age, 12 months of open access to ethanol regardless of drinking phenotype significantly increased KCNN3 transcript SK3_ex4 expression  = 10.88, p = 0.0009; Fig. SK3_ex7/8: one-way ANOVA: F = 1.798, p = 0.1941; SK3_ex1B: one-way ANOVA: F = 2.935; p = 0.0803; Fig. The expression of the exons common to all transcripts  = 0.036, p = 0.972, n = 4–6/group; Fig. SK3_ex1B (two-tailed t-test: t(10) = 2.658, p = 0.024, n = 5–7/group; Fig. SK3_ex4 (two-tailed t-test: t(9) = 2.477, p = 0.037, n = 5–6/group; Fig. KCNN3 and the shifts in transcript expression affected KCa2.3 channel protein expression, we performed western blot analysis in accumbal tissue from the same female rhesus macaques. Characterization of KCa2.3 channel immunoreactivity in ethanol-naïve monkey NAcC samples revealed a linear dynamic range across twofold dilutions between 1.25 and 40 μg of protein  = 2.585, p = 0.0324, n = 5/group; Fig. Ca2.3 channel, such as the dominant-negative isoform 3 with a predicted molecular weight of 47 kDa, were unsuccessful with multiple commercially available antibodies.Because estrogen , analysiCa2 currents and expression in the nucleus accumbens of rats and mice [Kcnn3 transcript expression is altered in ethanol-dependent mice. Similar to the monkey data, ethanol drinking and/or dependence did not affect Kcnn3 expression of SK3_ex7/8  = 3.931, p = 0.0529; Fig. SK3_ex1B expression  = 7.228, p = 0.0114; Fig. SK3_ex4 was significantly increased in drinking mice regardless of their history of CIE exposure  = 32.29, p < 0.0001; Fig. Given that chronic ethanol drinking and dependence reduced Kand mice , 17, 22,KCNN3 and changes in gene expression in heavy-drinking macaques and ethanol-dependent mice. In both monkeys and mice, ethanol drinking and dependence were associated with hypermethylation of conserved CpGs at a predicted regulatory region in exon 1A (MR-ex1) of KCNN3. In parallel with the hypermethylation, excessive drinking increased the expression of a dominant-negative transcript of KCNN3 that is transcribed using an alternative exon downstream of exon 1A. Consistent with chronic ethanol-induced loss of apamin-sensitive currents in accumbens and orbitofrontal neurons [Ca2 channels. We also found a reduction in KCa2.3 channel protein in heavy-drinking female macaques that is congruent with decreased expression reported in rodent models of chronic ethanol exposure. These results suggest that ethanol-induced regulation of KCNN3 transcripts is a conserved mechanism that underlies functional changes in KCa2 channels reported in rodent models.The results from these studies provide cross-species evidence for alcohol-associated alterations in DNAm signals mapping to  neurons , 17, 61,KCNN3 in a region containing CpGs that are highly conserved across species. Across male and female monkeys, more than half of the CpGs in this DMR were hypermethylated in HD/VHD, but not in LD or ethanol-naïve monkeys. However, the differentially methylated CpGs differ between males and females, which could be confounded by the smaller sample size in females. Thus, to be able to explore sex differences in the alcohol-associated role of DNAm in this regulatory region, future studies with increased female sample size need to be conducted. In dependent male mice, the levels of DNAm in this region were lower than what we observed in rhesus macaques, and while most of the CpGs showed similar DNAm levels as compared to controls, three conserved CpGs were hypermethylated, as reported in male rhesus macaques. The results observed in mice could suggest, that due to the high metabolic rate in rodents as compared to primates or the difference in length duration of alcohol exposure across species, dependent mice are behaving more like LD macaques or something in between LD and H/VHD macaques. To better address this question, future studies should include genetically heterogeneous male and female mice (such as the diversity outbred mice resource) to better replicate the primate drinking behaviors. Besides these differences in levels of methylation across species, the MR-ex1 of KCNN3 coincides with a predicted promoter region with binding sites for neural relevant transcription factors. Furthermore, this DMR is upstream of two alternative exons 1 (1B and 1C). Together, these findings suggest that the DMR is strategically located to potentially regulate alternative transcript expression of KCNN3. A previous characterization of the promoter region upstream of the exon 1A transcription start site (TSS) identified consensus sequence binding sites for CREB, AP-1, and AP-2 [KCNN3 expression. AP-1 complexes containing FosB, which accumulates in the NAc after drug intake, modulates promoters of genes relevant to addiction, such as GluA2 and dynorphin [Ca2 channels [KCNN3 expression.In the current study, we identified a DMR that maps to an ion channel gene previously implicated in excessive drinking, ethanol-seeking behaviors, and ethanol-induced plasticity of intrinsic excitability , 22, 62.and AP-2 . Additioand AP-2 , a key mand AP-2 , and ourynorphin . The MR-ynorphin , 67. Othchannels –71. ThusKCNN3 DMCs  collected from alcohol-naïve monkeys revealed that the DNAm rate of several of the differentially methylated CpGs included in this study  with alcohol intake levels following prolonged alcohol drinking and repeated cycles of abstinence and relapse (unpublished data). These results suggest that pre-existing DNAm signatures of KCNN3 and other genes could be a risk factor for future heavy alcohol drinking. In mice, induction of ethanol dependence increased evoked firing in NAcC MSNs [Using a genome-wide approach, we previously reported DMRs associated with the modulation of genes that regulate synaptic plasticity in the NAcC of heavy-drinking monkeys . In a reAcC MSNs  and induAcC MSNs . BecauseAcC MSNs , 74, ourKCNN3 transcripts that encode KCa2.3 channels that reduce surface trafficking and apamin sensitivity. A combination of transcripts and protein expression data suggests that with heavy ethanol consumption, there is a decrease in transcript SK3_ex1A expression while there is an upregulation in transcripts SK3_ex1B and SK3_ex4. While expression of the dominant-negative transcript was only elevated in heavy-drinking monkeys, and expression of the apamin-insensitive transcript was increased by ethanol intake regardless of the drinking phenotype or the age of drinking onset. These results are in agreement with our functional and behavioral data on reduced apamin sensitivity in the nucleus accumbens and orbitofrontal cortex of ethanol-dependent mice and self-administering rats [Ca2-mediated currents in NAcC MSNs in non-dependent C57BL/6J mice. However, the ability of apamin to influence drinking and KCa2 currents was completely lost when mice were exposed to CIE. Moreover, ethanol intake and evoked firing in NAcC MSNs were increased and there was a reduction in KCa2 channel currents and protein levels in the ethanol-dependent mice. Although it is unknown if heavy ethanol drinking in macaques alters the firing properties of NAcC MSNs, the shift in transcripts and the reduction in total KCa2 channel protein suggests that prolonged ethanol intake increases intrinsic excitability similar to results from rodent models of ethanol self-administration [Kcnn3 gene expression and increased intrinsic excitability, a recent study reported a loss of apamin’s ability to increase evoked firing in cultured cortical neurons treated with DNA methyltransferase inhibitors [SK3_ex1B and SK3_ex4 transcript expression through hypermethylation of KCNN3 exon 1A could be an underlying mechanism driving these functional and behavioral adaptations across species and brain regions. Future functional studies overexpressing and downregulating these specific transcripts, and evaluating their impact on KCa2 channel currents are needed.In monkeys and mice, heavy ethanol drinking and dependence were associated with increased expression of ing rats , 17, 61.stration  and depestration . In addihibitors . Thus, tKcnn3 as a candidate signature gene that is associated with binge-like and excessive ethanol drinking [KCNN3 did not segregate with a heavy-drinking phenotype in this population of rhesus macaques. There are a number of possibilities that could explain these negative findings. Although long CAG repeats in KCa2.3 channels reduced apamin-sensitive currents [Previous studies using functional genomics and rodents with divergent drinking phenotypes have identified drinking –19, 21. currents , functiocurrents , 77. ThuKCNN3 by excessive alcohol consumption represent a complex mechanism through the use of alternative promoters that likely impact the intrinsic excitability of NAcC MSNs, and, ultimately, ethanol-seeking behaviors. We propose a model in which MR-ex1 functions as a regulatory region to modulate the expression of the alternative transcripts SK3_ex1B and SK3_ex4. Our findings provide the first evidence that hypermethylation of the MR-ex1 region of KCNN3 by heavy alcohol drinking is a key cross-species mechanism that may be important for the maintenance of excessive drinking and the development of AUD.In summary, these cross-species findings of genetic and epigenetic adaptations in Supplemental methodsSupplemental table 1Supplemental table 2Supplemental table 3Supplemental table 4Supplemental figures


N = 736). Applying elicitation methodology, participants responded to a survey with experimental scenarios aimed at invoking applications of “representation” and five other ways of describing how the brain responds to stimuli. While we find little disciplinary variation in the application of “representation” and other expressions , the results suggest that researchers exhibit uncertainty about what sorts of brain activity involve representations or not; they also prefer non-representational, causal characterizations of the brain’s response to stimuli. Potential consequences of these findings are explored, such as reforming or eliminating the concept of representation from use.The concept of representation is commonly treated as indispensable to research on brains, behavior, and cognition. Nevertheless, systematic evidence about the ways the concept is applied remains scarce. We present the results of an experiment aimed at elucidating what researchers mean by “representation.” Participants were an international group of psychologists, neuroscientists, and philosophers ( Accordingly, neuroscientists commonly aim at identifying and characterizing these representations in order to answer questions such as the following: what do they represent, what are their vehicles, and how are they used e.g., ? This isFollowing suit, philosophers of psychology and neuroscience have proposed various explications of the concept of representation, sometimes inspired by traditional philosophy of mind e.g., , sometimviz., the widespread assumption in the neural and psychological sciences that the concept of representation is understood precisely enough to guide the development of hypotheses, interpretations of experimental data, and explanations. Systematically elucidating what researchers mean by “representation” may draw attention to imprecisions in the concept of representation, which could weaken the strength of conclusions drawn in research that hinges in crucial ways on its meaning. The imprecision of a scientific concept manifests itself in uncertainties concerning what follows from applying it  and what must be the case for this concept to apply .While the widespread appearance of the concept of representation in the neural and psychological sciences is indubitable, systematic evidence about the ways this concept is applied in these sciences remains scarce  or a handful of concepts , few concepts can be defined , particusome uses of the concept of representation in the neural and psychological sciences can certainly be understood this way, such instances are not what we draw attention to. Consider the following two recent examples from neuroscience. First, in an article on finger movement, the concept of representation is used in contexts such as “different spatial representations,” “low-dimensional representation,” “n members can be represented at time t,” “schematic representation of behavioral mode segmentation,” “the cerebral cortex represents,” and “well-represented in neural state space” . Since the number of respondents was too small to disaggregate the sample along participants’ subdisciplines, each group was analyzed as a combination of subdisciplines. For example, the group of neuroscientists included respondents who identified cellular and molecular neuroscience, cognitive neuroscience, and systems neuroscience as their subdisciplines. We tested the following four preregistered hypotheses:Some handpicked examples, such as those mentioned thus far, are insufficient to provide evidence about how neuroscientists or psychologists use “representation.” All the same, a literature review of the uses of “representation” in even one discipline would be a Herculean task. Thus, how can we empirically assess the current state of neuroscientists’ and psychologists’ understanding of the concept of representation? One option is to utilize what linguists call “elicitation studies” . InsteadScientists will not demonstrate statistically significant differences among the concepts selected in the contrasts: representing, carrying information, being about, responding to, processing, and identifying.Scientists will be sensitive to the specificity of content  but will not be sensitive to its functional integration or the nature of the vehicle .On average, scientists will be willing to assign misrepresentations with their average responses falling within the range of either “strongly agree,” “agree,” or “somewhat agree.”In the following three areas: (A) drawing distinctions between concepts; (B) being sensitive to the specificity and functional integration; and (C) being more willing to assign misrepresentation—philosophers will select for more distinctions (A), more sensitivity (B), and more willingness (C), than scientists as a group .representing, carrying information, being about, responding, processing, and identifying the stimuli.The experiment consisted of four studies with the same basic structure. Participants were given a cover story about a neuroscientific study recording brain response to various stimuli, including faces and artifacts . ParticiThese six terms were selected in order to provide participants with ways of describing the brain’s response to stimuli that (1) are used in neuroscience and psychology and (2) are of three different kinds: They describe this response as having an “intentional” component , in causal terms (“responding” and “processing”), or as having an information-theoretic nature (“carrying information”). Our goal was to examine whether ways of describing the brain’s response to stimuli that are similar are treated similarly by scientists and to compare scientists’ willingness to use different kinds of descriptions (see Hypothesis 1).The first term, “representing,” expresses the concept we are investigating. The examples from psychology and neuroscience given above are but a few of the countless instances of researchers using the term in peer-reviewed work. The third term, “about,” or the clause “being about,” describes the brain’s response as intentional. In the current context, being “intentional” means, roughly, standing for something else in the way a sign or a symbol stands for something else. Both describing a response to stimuli as “representing” or as “being about” something assume that this response is correctly understood as intentional e.g., , p. 945.The fourth and fifth terms, “responding” and “processing,” differ from the three terms just discussed in describing the brain’s response to stimuli causally, without necessarily implying that this response has an intentional nature. “Responding” is often used in neuroscience, for instance, in research on visual perception e.g., , p. 765,Finally, we use the second term, “carrying information,” to examine whether it would behave more like intentional terms such as “representing,” more like causal terms like “responding,” or in a sui generis manner, perhaps aligned with Shannon and Weaver’s information theory . InformaWhile these six terms are not exhaustive of all potentially relevant concepts we could have used, for the purposes of our experimental design they capture diverse ways of thinking about the brain’s response to stimuli across the neural and psychological sciences.The goal of Study 1 was to examine whether neuroscientists, psychologists, and philosophers make any assumptions about the scale at which the vehicles of neural representations, that is, the brain substrates that represent stimuli, are to be found. Participants were randomly assigned to one of two conditions. In the neuron condition, they were told that the response of a neuron was measured by means of a microelectrode  when presented with faces; in the population condition, they were told that the response of a neural population ; in the low specificity condition, it responded to faces but also to houses.The goal of Study 3 was to examine whether evidence that the brain’s response to stimuli is used by a broader neural network and thus has a function , in addiFinally, the goal of Study 4 was to examine whether neuroscientists, psychologists, and philosophers are willing to describe the brain’s response as erroneous, for example, whether it misrepresents stimuli. Philosophers concur that for a state to count as a representation, misrepresentation must be possible e.g., , p. 10. These four studies focus on characteristics that brain states would have to possess if they are to count as representations. Representations must occur at some scale in brain organization (Study 1); the occurrence of representations must causally depend, in some way, on what they represent (Study 2); representations must be used by downstream processes (Study 3); and representations can be misapplied (Study 4). To have a precise concept of representation is to have a sense of the scale at which representations occur, of the nature of representations’ causal dependence on what they represent, and on the significance of the use of representations, or at least to have some sense for some of these issues. Additionally, a precise concept of representation ought to distinguish cases for which misapplication matters and those for which it does not (Study 4). In what follows, we report the results from each study.2.2.1.https://osf.io/mskwy/; doi: 10.17605/OSF.IO/SARVU).The study was approved by the Institutional Review Boards at the University of Central Florida (IRB STUDY00002612) and the University of Pittsburgh (IRB STUDY20050065). All research was performed in accordance with relevant guidelines/regulations. Hypotheses and data collection methods including the stopping rule, exclusion criteria, and data analytic strategies were preregistered with the Open Science Framework . We also limited our analysis to neuroscientists, psychologists, and philosophers , setting2.2.https://osf.io/mskwy/; doi:10.17605/OSF.IO/SARVU)].The recruitment materials included a link to a survey on Qualtrics. Participants were first asked a few demographic questions before being asked to complete successively four studies in a random order (described below). They were then asked several philosophical questions related to representation, computation, and their broader commitments related to the foundations of neuroscience and cognitive science [full survey available at the preregistration site .2.3.https://osf.io/mskwy/; doi:10.17605/OSF.IO/SARVU). All analyses were conducted on R . As preregistered, the significance level was set at 0.005  repository  = 167.8, p < 0.001, η2 = 0.2), a suggestive effect of discipline  = 5.0, p = 0.007, η2 = 0.003), and no effect of condition  = 3.5, p = 0.06, η2 ≤ 0.001). Post-hoc analysis revealed that the suggestive effect observed for discipline is due to a suggestive difference between philosophers and psychologists (t(3083) = 3.0, p = 0.007, d = 0.14); no other comparison reaches the 0.05 level. All post-hoc comparisons between the six questions used were significant except for the non-significant comparison between represents and identifies (t(3083) = 0.9, p = 0.9) and for the suggestive comparison between is about and identifies (t(3083) = −3.3, p = 0.01). The main effects of discipline and question were qualified by a suggestive two-way interaction  = 2.4, p = 0.007, η2 = 0.006; The distribution of responses is presented in F = 63.5, p < 0.001, η2 = 0.23), no effect of condition, and no interaction (both ps > 0.7). All post-hoc comparisons were significant except for the non-significant comparisons between represents and is about, represents and identifies, and is about and processes (ps > 0.5) and for the suggestive comparisons between represents and processes and is about and identifies (ps > 0.01). For psychologists, we observed a main effect of question  = 53.9, p < 0.001, η2 = 0.22), no effect of condition, and no interaction (both ps > 0.8). All post-hoc comparisons were significant except for the non-significant comparisons between represents and identifies, is about and processes, carries information and processes , is about and carries information, and is about and identifies .In line with this interaction, an exploratory, not-preregistered mixed-design ANOVA with the question as a within-participant factor (six levels) and condition as a between-participant factor (two levels), was conducted for neuroscientists and psychologists separately. For neuroscientists, we observed a main effect of question . By contrast, they appear uncertain about intentional characterizations. On average, participants chose “neither agree nor disagree” for “representing,” “identifying,” and “being about.” We will come back to this point in the general discussion below. Importantly, neuroscientists’ and psychologists’ overall uncertainty is not the result of a bimodal distribution, which would be indicated by half of the participants willing to strongly agree to use the concept of representation to describe the brain’s response to stimuli and half of them strongly disagreeing. Rather, the distribution is centered around its mean (The same is true of the three other studies.).Second, the results suggest that it made very little difference to neuroscientists and psychologists whether the vehicle of representation was verbally and pictorially represented as a single neuron or as a brain area. This negative result suggests that neuroscientists and psychologists do not have any expectations about the scale at which representations are to be found in the brain. In other words, they may subscribe to mainstream representationalism, but their concept of representation is not specific enough to dictate what kind of brain structure or pattern at what level of aggregation  would be a representation.Third, while philosophers were somewhat less likely to agree with our prompts than psychologists, the variation across disciplines was small. This finding suggests that the concept of representation has not specialized in the disciplines we are considering  = 191.4, p < 0.001, η2 = 0.23), a suggestive effect of discipline  = 4.9, p = 0.007, η2 = 0.002), and an effect of condition  = 131.3, p < 0.001, η2 = 0.03). Post-hoc analysis revealed that the suggestive effect observed for discipline is due to a difference between neuroscientists and philosophers (t(3083) = 2.6, p = 0.03, d = 0.1) and neuroscientists and psychologists (t(3083) = 3.0, p = 0.008, d = 0.14). All post-hoc comparisons between the six questions used were significant except for the non-significant comparisons between represents and identifies (t(3083) = 2.2, p = 0.2), between carries information and processes (t(3083) = −1.0, p = 0.9), and between is about and identifies (t(3083) = −0.7, p = 0.98) and for the suggestive comparison between represents and is about (t(3083) = 3.1, p = 0.03). The main effects of discipline and condition were qualified by a two-way interaction  = 9.0, p < 0.001, η2 = 0.004): Psychologists are more sensitive to the manipulation of specificity than philosophers and neuroscientists.The distribution of responses is presented in t(173.07) = −1.90; p = 0.059, d = 0.1); by contrast, we found a significant effect for psychologists (t(155.12) = −5.7; p < 0.001, d = 0.3).In addition, we explored the impact of specificity on representation alone . For neuTwo main findings emerge from Study 2. First, as we observed in Study 1, the results indicate preferences by neuroscientists and psychologists for thin, causal descriptions of the brain’s response to stimuli (responds to and processes) and information-theoretic descriptions over intentional descriptions and uncertainty about the latter. Second, specificity matters in describing how the brain responds to stimuli (in line with the preregistered second hypothesis). When one aggregates across ways of describing the brain’s response, neuroscientists, psychologists, and philosophers agree more  when the brain’s response is maximally sensitive. Turning to the concept of representation, we only found evidence for the significance of specificity for psychologists. It would, thus, seem that psychologists take specificity to be relevant to whether some brain state can count as a representation. However, even perfect specificity does not appear to lead psychologists to express certainty when it comes to describing the brain’s response to stimuli in representational or, more generally, intentional terms.3.4.F = 150.2, p < 0.001, η2 = 0.19), and suggestive effects of discipline  = 4.7, p = 0.009, η2 = 0.002) and condition  = 6.9, p = 0.009, η2 = 0.002), but no interaction. Post-hoc analysis revealed that the suggestive effect observed for discipline is due to a suggestive difference between neuroscientists and psychologists (t(3083) = 2.9, p = 0.009, d = 0.1); no other comparison was significant at the 0.05 level. All post-hoc comparisons between the six questions used were significant except for the non-significant comparisons between carries information and processes (t(3083) = 2.0, p = 0.4) and between is about and identifies (t(3083) = −1.0, p = 0.9) and for the suggestive comparison between represents and identifies (t(3083) = 3.2, p = 0.02). To explore the role of function in the assignment of representation, we conducted an ANOVA with question as a within-participant factor (six levels) and condition as a between-participant factor (two levels). No significant or suggestive effect was observed.The distribution of responses is presented in Two main findings emerge from Study 3. First, as was found in Studies 1 and 2, the results indicate preferences by neuroscientists and psychologists for thin, causal vocabulary to describe the brain’s response to stimuli and uncertainty about intentional vocabulary. Second, whether or not the brain area’s response to a stimulus is embedded in a larger network, and thus, whether it has a function, influenced how the brain’s response was described, although it did not appear to influence whether it was described in representational terms. When it comes to representation, we found no evidence that having a function matters (in line with the preregistered second hypothesis).3.5.F = 11.5, p < 0.001, η2 = 0.02) and discipline  = 26.6, p < 0.001, η2 = 0.02), but no interaction. Post-hoc analysis revealed that the effect observed for discipline is due to significant differences between all disciplines, with philosophers being less unwilling to view the brain’s response as erroneous (in line with the fourth preregistered hypothesis). All pairwise comparisons between questions were significant at the 0.005 level, except for represents and is about, represents and identifies, carries information and represents, carries information and processes, is about and identifies, and responds and processes, which were not significant at the 0.05 level, and carries information and identifies and processes and identifies, which were only suggestive (0.01 < ps < 0.05). We also found that neuroscientists and psychologists are unwilling to assign misrepresentation .The distribution of responses is presented in The main finding to emerge from Study 4 is that neuroscientists, and to a smaller extent psychologists, did not describe the brain’s response to stimuli as erroneous, that is, as failing to do what the brain is meant to do (contrary to the preregistered third hypothesis). In particular, neuroscientists and psychologists appear unwilling to say that it misrepresents something as something else.4.Neuroscientists, psychologists, and philosophers commonly embrace the idea that the concept of representation is indispensable to investigating and understanding brains, behavior, and cognition. While neuroscientists, psychologists, and philosophers occasionally differ in their responses to the experimental stimuli used in our four studies, those differences appear to be very small. The concept of representation does not appear to have specialized in different directions as scientific concepts sometimes do when they are used in different disciplines . While oprecise concept of representation requires having some sense of what follows from something being a representation (or of what is required for something to count as a representation), including the scale at which it occurs, the way it depends on stimuli, or how it features in downstream processes. Additionally, having a precise concept of representation would facilitate distinguishing representations from other kinds of signs. Despite the centrality of representations for investigating and explaining brains, cognition, and behavior  or at the population level (what we refer to in the current study as an “area”) made little difference to their answers.Third, neuroscientists appear not to require the brain’s response to be used in a broader neural network and, thus, to have a function  to countThese first three points tentatively suggest that psychologists’ and, to an even greater extent, neuroscientists’ concept of representation is imprecise: Psychologists and neuroscientists selected responses indicating uncertainty about what properties a brain pattern must have to count as a representation and what follows from calling a brain pattern a representation. This uncertainty extends to other intentional notions and contrasts with thinner, causal notions.One of the few things philosophers working on representation agree upon is that representation requires misrepresentation e.g., , that isIf, as our results suggest, there is indeed widespread uncertainty in applications of the concept of representation, then such a state of affairs might not be innocuous. They could breed fruitless debates about whether or not some brain part that responds to some stimulus represents it; barring a clearer concept of representation, such debates cannot be resolved. For instance, in the embodied cognition literature, cognitive neuroscientists have provided ample fMRI evidence that at least sometimes e.g., , motor areformed or, as philosophers say, “explicated” .The datasets analyzed for this study can be found in the Open Science Framework  and the University of Pittsburgh (IRB STUDY20050065). No consent was required for this study by the Institutional Review Boards.LF and EM contributed equally to the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.