The sustained COVID-19 case numbers and the associated hospitalizations have placed a substantial burden on health care ecosystem comprising of hospitals, clinics, doctors and nurses. However, as of today, only a small number of studies have examined detailed hospitalization data from a planning perspective. The current study develops a comprehensive framework for understanding the critical factors associated with county level hospitalization and ICU usage rates across the US employing a host of independent variables. Drawing from the recently released Department of Health and Human Services weekly hospitalization data, we study the overall hospitalization and ICU usage—not only COVID-19 hospitalizations. Developing a framework that examines overall hospitalizations and ICU usage can better reflect the plausible hospital system recovery path to pre-COVID level hospitalization trends. The models are subsequently employed to generate predictions for county level hospitalization and ICU usage rates in the future under several COVID-19 transmission scenarios considering the emergence of new COVID-19 variants and vaccination rates. The exercise allows us to identify vulnerable counties and regions under stress with high hospitalization and ICU rates that can be assisted with remedial measures. Further, the model will allow hospitals to understand evolving displaced non-COVID hospital demand. In fact, nearly half of the 28 million cases were reported in the past three months. The total fatalities in the US associated with COVID-19 have crossed 500,000 while daily deaths have averaged nearly 4000 in recent weeks2. Further, hospitalization rates have experienced a staggering rise with a peak of about 132 thousand COVID patients hospitalized3. Intensive care units (ICUs) are also alarmingly short on space with nearly 33% of the ICUs at a more than 80% occupancy5 and a subset of these ICUs at nearly full occupancy. It is an understatement to suggest that the sustained COVID-19 case numbers and the associated hospitalizations have placed a substantial burden on health care ecosystem comprising of hospitals, clinics, doctors and nurses. The hospital systems have been overwhelmed with COVID-19 cases contributing in the range of 20–50% of the hospitalizations at various facilities. An oft neglected aspect of COVID-19 impacts includes members of the community not contracting COVID-19 but being affected by it. For example, the increasing COVID-19 cases has potentially affected non-COVID hospitalizations i.e. patients are either delaying elective procedures or are being forced to reschedule due to lack of availability in hospitals in some regions. A study conducted in UK6 already anticipated an increase in cancer deaths due to the reduced cancer service triggered by the evolving COVID-19 pandemic. The effect of these delays are likely to extend for months (if not years) after COVID-19 cases are brought under control.The Corona Virus Disease (COVID-19) continues to significantly burden social, economic and health systems across the world. As of February 2021, the number of confirmed cases in the world have surpassed 100 million while US alone accounts for about a quarter of these cases7, there is a need to maintain our guard in monitoring the cases and resulting consequences . Under these circumstances, there is a need to examine hospitalization and ICU bed usage rates for two reasons. First, to ensure there are adequate hospital facilities for COVID-19 patients until we significantly reduce the threat of the virus. Second, an understanding of hospitalization trends over time will allow us to examine the time when hospital systems can revert to pre-COVID demand. The stabilization of the hospital system will only occur after the displaced health needs of non- COVID patients (delayed surgeries and treatments) are addressed. Towards addressing these aforementioned challenges, the current study develops a comprehensive framework for understanding the critical factors associated with county level hospitalization and ICU usage rates across the US. We estimate the overall hospitalization and ICU usage as two components: (1) COVID-19 hospitalization and ICU per capita rates and (2) non-COVID hospitalization and ICU per capita rates. The consideration of two components  will allow us to recognize the distinct impact of various factors on each component. The estimated models are employed to generate predictions for hospitalization and ICU usage rates into the future under a host of COVID-19 transmission scenarios considering the new variants of COVID-19 and vaccination impacts.The emergency use authorization of two vaccines (Pfizer-BioNTech and Moderna) offers a potential path to bringing the virus under control. While the initial vaccination rates in the US were slower than anticipated, in recent days, a vaccination rate above 1 million shots per day has been regularly achieved. Even with this impressive vaccination rate, achieving herd immunity could potentially happen only in the early fall months. Further, as new variants of COVID-19 arise with significantly higher transmissibility9 for details). However, only a limited amount of research has examined detailed hospitalization data19, particularly from a public health planning perspective19. At the planning level, the studies considered the number of people admitted to hospital and ICUs due to COVID-19 as the response variable and employed either time series16 or online interactive models19 for their analysis. In terms of spatial resolution, different spatial resolutions are explored including region14, county19, country16 and city13. With respect to independent variables, the studies found different factors affecting COVID hospitalization and ICU usage rates including demographics , mobility trends19 and COVID-19 transmission rates16. Earlier research efforts on COVID hospitalizations offered several insights. However, given the evolving challenges of COVID-19 the research efforts are still in their infancy.A significant amount of research has been conducted on understanding COVID-19 transmission  weekly hospitalization data for our analysis. Further, the data employed covers hospitalization data during the peak of the pandemic—from August 28th, 2020 to January 22nd, 2021- with 21 weeks of data across each county.The current research contributes to the burgeoning literature on COVID-19 hospitalization with the following objectives. Second, the current study focuses on understanding the influence of COVID-19 on overall hospitalizations—not only COVID-19 hospitalizations. We recognize that the impact of COVID-19 on overall hospitalizations is going to persist even after the pandemic ends. The displaced hospital demand during the pandemic will need to be addressed once COVID-19 cases reduce substantially. Thus, developing a framework that examines overall hospitalizations and ICU bed usage as a result of COVID and non-COVID hospitalizations can better reflect the plausible hospital system recovery path to pre-COVID level hospitalization trends.Third, earlier work on hospitalizations employed simple time series models or focused on descriptive and visualization exercises to understand the association between hospitalization and other factors. In our study, we employ a robust modeling framework to analyze the hospitalization and ICU usage demand at a county level. Specifically, we adopt a mixed linear modelling approach8 to account for the repeated observations at the county level (multiple weeks of data). Further, the model estimation exercise is carried out using a comprehensive list of county level independent variables including (a) COVID-19 transmission related factors; (b) mobility trends; (c) health indicators; (d) demographics; (e) spatial factors and (f) temporal factors. The modeling approach will allow us to identify the factors that contribute to the overall hospitalization and ICU demand in US during the pandemic.Finally, the model developed is employed to generate predictions  for hospital and ICU usage for COVID and non-COVID patients under a host of future scenarios of COVID-19 transmission. The scenarios are generated considering potential influence of vaccination and the uncertainty associated with COVID-19 variants. The exercise provides an understanding of how hospitalizations and ICU bed usage rates might possibly vary over time across the country.20.The dataset provides information on weekly hospitalization and ICU data from July 31st, 2020 through January 22nd, 2021 . For the analysis, we aggregate the weekly level hospital and ICU data at a county level and consider their natural logarithm to generate the final dependent variables.The primary focus of the analysis is to study factors affecting two measures: 1) hospitalization rates (measured as number of beds used) and 2) ICUs used. In our analysis, we examine these measures for COVID and non-COVID patients. Hence, a total of 4 dependent variables are analyzed including: COVID-19 hospitalization and ICU rate per 100 K population; and non-COVID hospitalization and ICU rate per 100 K population. The data for our analysis is drawn from the DHHS database compiled from approximately 5,000 hospitals encompassing 2,462 counties in the country8. Within the COVID-19 related factors, we consider different measures including county level weekly COVID-19 transmission rate, percentage change in the virus transmission rate compared to the preceding 3 week average, and an indicator variable for a possible increase in COVID cases in the last week. We collect the mobility trends from PlaceIQ21 dataset that provides daily exposure matrices through the smartphone movement data for counties with at least 100 devices active in a day. The exposure data is measured based on smartphone device exposure computed as the exposure of a device to distinct other devices at a specific point of interest. The exposure for all devices from a county are aggregated to generate a weekly average county level exposure metric21.In terms of independent variables, the following six broad categories are considered: COVID-19 transmission related factors; mobility trends; health indicators; demographics; spatial factors and temporal factors. An exhaustive list of these variables are presented in Table For our analysis, we selected the 2,018 counties with mobility data available. Out of these 2,018 counties, there was a small sample of counties (~ 250) that do not have any hospitals and thus are excluded from our analysis. The final dataset consist of total 1,765 counties  with 21 weeks of data  for each county. It is important to recognize that these 1,765 counties account for more than 95% of the US population and 97% of the reported COVID-19 cases.A visual representation of salient characteristics of the data are presented in Fig. Table Intercept: The intercept does not have any substantive interpretation after adding other independent variables.COVID-19 related factors: The consideration of COVID-19 transmission rates in the model recognizes the delay of about 5 to 14 days between transmission and hospitalization. Hence, in our analysis we tested COVID-19 transmission variable with lag of 1, 2 and 3 weeks. Of these lag variables, the 2 weeks lag variable offered the best model fit. As expected, increase in COVID-19 transmissions is associated with increased hospitalization rate. Further, we find that the impact of transmission rates is lower (yet positive) in the West and South region relative to the rest of the country. In addition, in our analysis, to represent rising COVID-19 cases in the county, we defined several variables such as (a) an indicator variable for increasing cases defined as number of weekly cases greater than the 3 week moving average (3WMA) and (b) a percentage difference variable representing change in weekly cases relative to the 3WMA. These variable impacts follow expected trends. The indicator variable for increasing cases contributes to increasing hospitalization rates. The difference variable (can be positive or negative) indicates that hospitalizations are sensitive to percentage changes in COVID-19 transmission. The sensitivity is substantially higher for Mid-West region while being slightly lower for the South region.Mobility trends: Consistent with earlier research, our analysis also highlights how increased mobility in the county results in higher hospitalization rates. We recognize that the impact of mobility on hospitalizations has a lagged effect. Hence, we tested weekly mobility with 2 weeks and 3 weeks lag. The two lag variables offer significant and intuitive results highlighting the role of mobility in addition to the impact of COVID-19 cases.Demographics: With respect to demographic characteristics, we find that counties with higher percentage of young population (aged 19 years or less) are likely to have lower hospitalization rates23. The results for ethnicity composition variables also offer expected results. Counties with higher Hispanic and African American populations are likely to have higher hospitalizations, perhaps attributed to their residence in densely populated neighborhoods and pre-existing chronic medical conditions23. Interestingly, in our analysis, hospitalization rates are found to be higher for counties with higher female population. Finally, median county income is also positively associated with hospitalization rates, possibly manifesting the impact of access to hospitals in these counties.Health indicators: Consistent with earlier findings, our analysis also shows a significant positive association between the COVID hospitalization rate and pre-existing health risk factors, especially for population with higher incidence of cardiovascular disease and hepatitis C24.Spatial factors: In our analysis, we tested several spatial factors to account for inherent regional differences in hospital infrastructure, transportation infrastructure, tourism activity, weather patterns and regional culture. The results shows that north east region is likely to experience lower hospitalization rate due to COVID-19 compared to the rest of the country prior to October 30th. However, after October 30th, the north east region experienced slightly higher hospitalization rates. The higher hospitalization rate for this time period is closely aligned with the increasing caseloads across the country. Further, we considered the tourism status of the state in our analysis by identifying the top and bottom 10 desirable states with respect to tourism activity. As expected, we find a positive effect of the top 10 tourist attraction states on the COVID hospitalization rate, perhaps indicative of the higher virus transfer in these regions. However, the effect was marginally reduced from October 30th. Finally, the variable specific to the number of airports per 100 k people reveals a positive impact on the COVID hospitalization rate.Temporal factors: With respect to temporal variables, we consider various functional forms including continuous  and indicator variables , since mid of December and from 25th December, 2020 and later). As expected, we find a positive effect of the second wave variable indicating a higher hospitalization rate across the country from October 30th, 2020. The results also indicate that hospitalization rate for COVID patients accelerated further from 25th December, 2020.Correlation factors: The last row panel of Table 25:Peak and valley (PV): In this scenario, COVID-19 cases are assumed to follow a series of repetitive COVID-19 waves (ups and downs) throughout the spring of 2021 and beyond. The waves could be a result of delays in vaccination production and/or vaccination drives resulting in a longer timeframe for COVID-19 cases to reduce.Unexpected third spike (UP): In the presence of new variants of COVID-19, a potential spike in COVID-19 transmissions that might occur in March or April is considered in this scenario. In this scenario, while the spike results in increased transmission, active vaccination drive can contribute to case reduction starting from the end of April.Slow burn (SB): In this scenario, we assume that the cases will go down slowly in the coming months while not diminishing completely until the end of July.Rapid vaccination (RV): It is possible that multiple vaccine candidates might be approved (such as Johnson and Johnson and others) in the near future rapidly increasing US vaccination rates. In this scenario, COVID-19 transmission rates are likely to reduce and reach a very manageable level early summer.Prior to hospital demand projection into the future, we also evaluate the predictive performance of our proposed model in predicting all four response variable in context, to illustrate the applicability of our model. The figures are available upon request from the authors. The main objective of the current study is to contribute to public health planning by evaluating how hospitalizations trends evolve over time. Specifically, the focus is on drawing insights on future demand while accommodating for the inherent uncertainty in future COVID-19 transmission. In terms of future COVID-19 transmissions, the reader would note that while COVID-19 virus transmissions are receding currently, the emergence of highly transmissible variants might affect the trajectory. Towards this end, we consider four potential COVID-19 evolution scenarios as followsThe first two scenarios represent a pessimistic outlook towards COVID-19 transmission while the scenarios 3 and 4 represent an optimistic outlook. Figure For these four scenarios, we forecasted hospital and ICU capacity using the proposed linear mixed model for all counties considered in the analysis over the next 22 weeks (March 5th to August 6th). The model predictions at the county level can be appropriately aggregated to any spatial resolution to provide insights at the national, regional, state and county level.A national and regional outlook for hospitalization and ICU rates are provided in Fig. To further understand the potential mismatch in demand and supply, we examine what percentage of the counties  might potentially experience a supply and demand mismatch—defined as at least 90% hospital beds and ICU units being used. We also identify the percentage of counties with at least 25% of hospital beds or ICU units allocated to COVID-19 patients for the four scenarios. The results of this exercise, presented in Fig. The results presented so far examine the trends over time. To illustrate our model applicability for county level analysis at a specific time point, we present the results of two scenarios—PV and RV—in July across the states of California and Florida in Fig. In summary, the framework proposed for understanding and quantifying hospitalization rate can allow policy makers to (a) evaluate the impact of COVID-19 virus transmission on hospitalization and ICU rates while controlling for demographics, health indicators and mobility trends so as to identify vulnerable counties that need to prioritized for vaccination; (b) estimate the hospitalization and ICU rates at the county, state level to identify COVID-19 transmission trends under a host of future scenarios (c) Identify vulnerable locations with low projected hospital and ICU capacity (County/State/Region) in advance so that remedial measures can be adopted to provide additional hospital infrastructure and access to personnel and (d) Develop a comprehensive plan for assisting hospital systems across the country to address displaced demand due to COVID-19.To be sure, the paper is not without limitations. The data on the hospitalization rate are continuously updated for few counties to correct for errors or omission. Further, the hospitalization data used in the analysis is an administrative dataset obtained from DHHS and could possibly have over-estimated COVID-19 related hospitalization. A potential avenue for future research would be to reconcile the DHHS data with other hospitalization related datasets and develop updated models with the reconciled dataset. In the scenario analysis, we assume future mobility to be similar across the scenarios while it is quite possible that mobility might also be affected as virus transmission rates change. Therefore, for future research, it might be interesting to explore how the demand on the health care system varies based on mobility behavior. The proposed model is intended to serve as skeletal framework that can be readily updated with newer data on virus transmission and mobility patterns. The plots generated can be readily customized with more up to date information on COVID-19 cases to arrive at estimates of hospitalization and ICU demand. Finally, while we considered counties that account for 97% of the reported COVID cases, it is possible that counties not considered might contribute in small numbers to hospitalizations in the counties considered. Future research might address this limitation by developing spatial models that consider such spillover effects (from neighboring counties) in the analysis. Finally, with the growing availability of vaccination data, it would be useful to update the model using county level vaccination data in modeling hospitalization and ICU demand.8. Therefore we adopted a linear mixed approach  to accommodate for the influence of repeated observations at a county. A brief description of the linear mixed model is provided below:All the response variables considered in the study are continuous in nature and thus, a linear regression framework would be an appropriate choice for analyzing the data. However, in our data, every county is considered 21 times (21 weeks of data) and a simple linear regression method is not appropriate for such repeated measuresz = 1, 2, … , Z = 1,725 be an index to represent county, t = 1, 2, … 21 be index to represent the week for each county. The equation for the linear mixed model can be written as:26. The models are estimated in SPSS using the Restricted Maximum Likelihood Approach (REML). The REML approach estimates the parameters by computing the likelihood function on a transformed dataset. The approach is commonly used for linear mixed models27.Let Supplementary Information.


A receptors (GABAARs) and inhibit almost all mGluR1-expressing UBCs by activating GABAB receptors (GABABRs). PC to UBC synapses allow PC output to regulate the input layer of the cerebellar cortex in diverse ways. Based on optogenetic studies and a small number of paired recordings, GABAAR-mediated feedback is fast and unreliable. GABABR-mediated inhibition is slower and is sufficiently large to strongly influence the input-output transformations of mGluR1-expressing UBCs.Circuitry of the cerebellar cortex is regionally and functionally specialized. Unipolar brush cells (UBCs), and Purkinje cell (PC) synapses made by axon collaterals in the granular layer, are both enriched in areas that control balance and eye movement. Here, we find a link between these specializations in mice: PCs preferentially inhibit metabotropic glutamate receptor type 1 (mGluR1)-expressing UBCs that respond to mossy fiber (MF) inputs with long lasting increases in firing, but PCs do not inhibit mGluR1-lacking UBCs. PCs inhibit about 29% of mGluR1-expressing UBCs by activating GABA Although different lobules of the cerebellar cortex are engaged in diverse motor and non-motor behaviors , a basicOne of the most obvious specializations is that the density of unipolar brush cells (UBCs) exhibits large regional variation . UBCs arA receptors (GABAARs) and has a prominent slow component that suggests the involvement of the extrasynaptic high-affinity GABAARs. These PC to GrC synapses allow the output of the cerebellar cortex to provide slow negative feedback to inhibit the input layer. Like UBCs, these synapses are also most prominent in regions involved in vestibular function.PC feedback via collaterals that are restricted to parasagittal planes is another noteworthy regional specialization . In addiARs, and that UBCs with prominent mGluR1 signaling are preferentially targeted. PCs also provide long-lasting inhibition to most mGluR1-expressing UBCs by activating GABAB receptors (GABABRs). In these ways, PC inhibition of mGluR1-expressing UBCs provides direct feedback from the output layer to input layer to influence the first stage of cerebellar processing.The regional overlap of high densities of UBCs and granular layer PC synapses raises the possibility that PCs might inhibit UBCs and thereby allow PC feedback to refine temporal processing. Here, we examine whether PCs inhibit UBCs. We find that PCs phasically inhibit a subset of UBCs by activating GABAHigh-power confocal imaging of vermal and floccular slices was used to assess the regional density of PC collateral synapses and UBCs, and to detect putative synaptic connections between PCs and UBCs. PC presynaptic boutons were labeled in Pcp2-Cre×synaptophysin-tdTomato mice (n=2). Weaker labeling was evident in PC dendrites and somata red, . InhibitThe densities of UBCs and granular layer PC synapses exhibited considerable regional variability. The highest densities of both UBCs and granular layer PC synapses were found in vestibular regions -3,5-Dihydroxyphenylglycine (DHPG) prior to characterizing light-evoked synaptic responses . DHPG 1, right. mulation .We also directly recorded from connected PC-UBC pairs , n=2. AnBRs and thereby tonically inhibit UBCs. Previous studies established that only mGluR1-positive UBCs contain GABABRs (BRs comprises GB1 and GB2 subunits (Gabbr1 and Gabbr2. UBCs exhibit a gradient of expression of Gabbr2 that is similar to Grm1 (mGluR1), and Gabbr1 has a less pronounced gradient of expression .MF activity elevates firing in the input layer of the cerebellar cortex that in turn alters PC spiking. The PC to UBC feedback described here has the potential to allow PC output to dynamically regulate the firing of mGluR1-expressing UBCs. We have previously shown that a brief MF burst can activate mGluR1 to evoke increases in firing that last for more than 10 s in some UBCs . We brieC firing . Brief aC firing . Thus, aThe PC to UBC synapses described here link two previously described regional specializations of the cerebellar cortex: the high densities of UBCs and the prevalence of PC synapses within the granular layer. PC to UBC synapses provide a new way for PC outputs to dynamically regulate the temporal transformations by UBC populations within the input layer.AR-mediated responses in 29% (30/101) of UBCs. Light-evoked PC GABAAR-mediated inhibition was much more common in cells in which glutamate evoked a large excitatory current but was also observed in three cases where glutamate evoked inhibitory currents , , . This isagonists , that thsubunits , and thasubunits . In contsubunits .Our findings provide new insights into the previous immunohistochemical characterization of the inhibitory synapses onto UBCs that suggested approximately 50% co-release GABA and glycine and the rest only release GABA . PreviouARs and GABABRs, and light-evoked GABAB currents are observed in a larger percentage of mGluR1+ UBCs (>90%) than are light-evoked GABAA currents (29%). This difference in prevalence likely reflects differential sensitivities of GABAARs and GABABRs. Activation of GABAARs on UBCs requires high GABA levels . The major differences between slow PC inhibition of UBCs and GrCs are the state change that can occur in UBCs, and the target dependence of the UBC inhibition, neither of which have been described for PC inhibition of GrCs  × synaptophysin-tdTomato  P40 mice were anesthetized with intraperitoneal injections of ketamine/xylazine/acepromazine mixture at 100, 10, and 3 mg/kg, and transcardially perfused with 4% PFA in phosphate-buffered saline solution. The brain was removed from the skull and post-fixed overnight in 4% PFA. The cerebellar vermis and flocculus were dissected out and embedded in 6% low melting agarose before slicing. Sagittal vermal slices and coronal floccular slices at 50 μm were obtained from two animals using Leica VT1000S vibratome . For immunolabeling of inhibitory synapses and the UBCs, slices were permeabilized  for 30 min and blocked for 1 hr at room temperature  and incubated overnight at 4°C with primary antibodies . The following day, the slices were washed three times for 10 min in PBS and incubated with secondary antibodies overnight at 4°C . Slices were mounted with a #1.5 coverslip using ProLong Diamond Antifade Mountant without DAPI .Z-stack images of entire vermal and floccular slices were obtained on the Olympus FV3000 confocal microscope using the Multi-Area Time-Lapse Software module. A 60× oil immersion objective with 1.42 NA was used and the x, y, z resolutions were 0.212, 0.212, and 0.906 μm, respectively. Each z-step was 1 μm and the entire stack was 20 μm in thickness.2 in width and height) and validated with an 80/20 train-test-split. Our human annotator criteria were that the inhibitory PC synapses should be synaptophysin-TdTomato and VGAT positive puncta with clear morphological features that are present across at least two to three consecutive z-slices. After training the network, we verified that it works consistently across different cerebellar layers and regions using out-of-sample validation. We found that using an ensemble of 10 independently trained networks qualitatively improved the annotation by reducing the amount of variability in synapse identification. The true positive rate of synapse detection was at 95.5% and the false-positive rate was 10.1%. We note however that the false positive rate was likely inflated as the human annotator sometimes missed difficult-to-spot synapses  consisting of (in mM): 125 NaCl, 26 NaHCO3, 1.25 NaH2PO4, 2.5 KCl, 1 MgCl2, 1.5 CaCl2, and 25 glucose  equilibrated with 95% O2 and 5% CO2. The incubation chamber was then removed from the warm water bath and kept at room temperature for recording for up to 6 hr.Adult (P30–P40) C57BL/6 or Pcp2-Cre×ChR2-EYFP  mice were first anesthetized with an intraperitoneal injection of ketamine/xylazine/acepromazine mixture at 100, 10, and 3 mg/kg, and transcardially perfused with an ice-cold choline slicing solution consisting (in mM): 110 choline Cl, 7 MgSO2, 10 HEPES, 4 MgATP, 0.3 NaGTP, 5 EGTA, and 2 QX-314, pH adjusted to 7.2 with KOH) or a K-methanesulfonate internal  was used for whole-cell recordings of GABAAR- or GABABR-mediated current, respectively. A junction potential of −8 mV was corrected for the K-Methansulfonate internal during recording. For cell-attached UBC recordings, ACSF was used as the internal solution. For perforated-patch recording, an internal containing (in mM) was used: 100 K-methanesulfonate, 13 NaCl, 2 MgCl2, 10 EGTA, 1 CaCl2, 10 HEPES, 0.1 Lucifer Yellow, and 0.25 amphotericin B, pH 7.2 with KOH. A junction potential of +7 mV was corrected online. For recordings of the GABABR-mediated current, 10 μM SR95531/gabazine was included in the bath to isolate the metabotropic inhibition. For paired PC-UBC recordings, a whole-cell UBC recording with KCl was obtained first. Then 10–20 PCs in lobule X were screened with cell-attached recordings using a large pipette (1–2 MΩ) containing ACSF.Recordings were performed at 34–36°C in ASCF containing 5 μM NBQX, 2 μM R-CPP, and 1 μM strychnine set to a flow rate of 2 mL/min. Visually guided recording of UBCs was obtained under a 60× objective with differential interference contrast imaging on an Olympus BX51WI microscope. Identity of the UBCs was also verified with fluorescent dye after recording . Borosilicate patch pipette (3–5 MΩ) containing either a KCl internal  was used for blocking GABABR.For identification of UBC subtypes in either whole-cell or perforated recordings, ACSF containing glutamate  or DHPG  was pressure-applied at 5 psi through a borosilicate pipette with a Picospritzer III . For the sequential mGluR1 and GABAperiment , concent current . For was2) light pulses were delivered under the 60× objective and focused down to a 50-μm spot. For the optical modulation of PC spontaneous firing, much lower intensities (10 or 25 μW/mm2) of light were delivered through the 10× objective over the entire lobule X (~1 mm spot) for 2 s. To ensure the quality of the slice, we recorded many PCs before the experiment to check for spontaneous activity and to ensure that these low-intensity stimulations did not result in PCs bursting. Only healthy slices were used for subsequent UBC recordings.Slices from Pcp2-Cre×ChR2-EYFP were kept in the dark before recording. A laser source  was fiber coupled to the excitation path of the microscope. For over bouton stimulation, brief (0.5 ms) high intensity  and controlled by mafPC  in Igor Pro 7 (WaveMetrics). Data were filtered at 4 kHz in MultiClamp and digitized at 50 kHz by InstruTECH ITC-18 (Heka Instrument Inc). Analysis of DHPG evoked spiking in UBC was done via peak detection in MATLAB. sIPSC timing was determined by time of 5% to peak threshold crossing. The timing of the PC spike for determining the latency of synaptic transmission was determined by the first derivative of the action potential. The time constant for the slow GABA In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This paper expands our understanding of the cerebellar microarchitecture, and, in particular, the level of recurrence in this circuit. A combination of electrophysiological, pharmacological and optogenetic approaches demonstrate a new inhibitory feedback connection from Purkinje cells onto a subset of unipolar brush cells. This is an important discovery for the cerebellar field, and should also be of interest to readers studying feedback in neural circuits.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Jennifer L Raymond as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Kenton Swartz as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Philippe Isope (Reviewer #2).Thank you for submitting your article "Purkinje cell outputs selectively inhibit a subset of unipolar brush cells in the input layer of the cerebellar cortex" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:The finding that PCs inhibit mGluR1 positive UBCs is important in its own right and well established by the evidence in Figure 2 and 4.1) The finding of feedback from the PCs to the UBCs opens up exciting new research directions. The reviewers appreciated the authors' efforts to gain insight about the signal processing function of this new feedback connection, however, they agreed that the specific claims about the function of the fast and slow inhibitory feedback were not well supported by the evidence provided, and hence should be clearly identified as highly speculative and the conclusions significantly tempered. Additional evidence to support the claims about the specific signal processing functions of the fast and slow feedback would be highly desirable, but is not essential. See specific suggestions below.2) Some clarifications are needed regarding the histology:– What is the reader supposed to take away from the convolutional neural network analysis of Purkinje cell synapses onto UBCs? It seems that two brains were annotated by a human expert, and then the network was trained on those two brains and could reproduce similar results on held-out data, as shown in Figure 1-Supp 1. What criteria did the human annotator use in marking synapses? Was the neural network approach used for anything beyond what the human annotator did?– Is it absolutely certain that there is no mGluR1 expression in Lugaro cells and Golgi cells?  The high magnification images in Figure 1K do show the characteristic morphology of UBCs, but there is a possibility that low mag. images of distribution in lobules could include these other cell types in spite of the difference in size, in case they express mGluR1. This possibility could change the interpretation of these results, so please clarify.– There seem to be PC synapses on the cell body of UBCs as well as on the brush , although the synapses on the brush are highlighted for mention. Please clarify.Specific, non-essential suggestions for the authors to consider, should they decide to strengthen the evidence about the function of the fast and slow feedback.A+GABAB blockers in the bath.– There is the implicit assumption that regular PC firing hardly affects mGluR1 positive UBC discharge, but it would be interesting to address or at least discuss constitutive PC inhibition. For example, this could be assessed in UBCs that don't express mGluR2, by applying an mGluR2 agonist to inhibit Golgi cell discharge, then GABAB hyperpolarization may be addressed or discussed more.– Tonic UBC hyperpolarization by steady PC excitation in figure 5D-F was not fully quantified. For example, is there different behaviors at different light intensities as in Figure 4? In figure 5D-F, a bigger burst at the beginning of the depolarization step was observed, but the steady state current seems unchanged. This is an important point, as rebound discharge is an important physiological feature of UBCs  which may modify the current knowledge about ON and OFF UBC computation. Noticeably, there is a clear rebound discharge in Figure 4I but not quantified. The interplay between T-type channel and GABAA and GABAB signaling during trains of stimulation? Probability of release at the PC-UBC connection appeared low, but we expect a buildup of GABAA mediated current during trains and a synergistic effect with GABAB signaling on UBC firing rate. However, in the current form, experiments were performed either with GABAA or GABAB antagonist in the bath. Experiments with neither GABAA nor GABAB blockers would likely be informative.– Is there any combined effect of GABAA or GABAB receptors? This question is important as groups of PCs can be synchronized or PCs can fire in burst at high frequencies, suggesting that the low probability of release of GABA may act as a high pass filter for GABAA signaling. While paired recordings  are challenging, loose-cell attached recordings on PCs  or focalized light illumination in ChR2 expressing PCs maybe used instead of cell-attached.– Can you draw any conclusions on the number of PC inputs to a UBC from the paired recordings? How many PCs are required to inhibit UBCs via GABAB inhibition could be relevant for mGluR1 negative UBCs as well. If the authors have these data, it would be an interesting extension.– The study focused on mGluR1 positive cells, but showed that Gabbr1 is also expressed in other UBCs. Therefore, GABA– A discussion on the physiological consequences of a selective inhibition of mGluR1 positive vs mGluR2 UBCs would strengthen the study. Can we extrapolate that "ON" UBCs are inhibited by PCs while "OFF" cells are not?A current recorded in figure 2 would be informative as it relates to glutamate induced current or DHPG induced increase in firing rate.– Quantification of the GABA– It has been thought that UBCs serve to prolong GrC firing after brief mossy fiber inputs. The negative feedback described here would serve the opposite purpose. Thus, it is important to understand the time course of the negative feedback relative to mossy fiber input.B-dependent feedback, the role of direct feedback vs. feedback onto UBCs without a direct connection is important.– The major conclusions of this paper relate to feedback regulation of the input layer. But the data presented here don't directly show a feedback loop, i.e., that the GrCs that feedforward mossy fiber information to the Purkinje cells are the ones that are regulated by the UBCs that these PCs project to. Given the observation of slow GABA– In relation to the previous question, it remains unclear how modulating UBC input would affect Purkinje cells.– The authors previously showed that PC-GrC feedback is also heterogeneously distributed, and will serve to inhibit the input layer. Since PCs directly provide both fast and slow feedback to GrCs in these regions, the PC-UBC connection demonstrated here suggests additional negative feedback via UBCs to GrCs. Therefore, the effectiveness of second level of negative feedback regulation of PCs to the input layer remains unclear.– Until the current finding of PC-UBC feedback, inhibitory inputs to UBCs were thought to be limited to those from Golgi cells. The relative time course of Golgi cell vs. PC feedback inhibition in controlling UBCs is worth consideration.– It would be important to know whether PC feedback to UBCs is in the same parasagittal plane.– As an additional example that relates to the same overall question about how this feedback loop will actually affect neural signals via mossy fibers, the authors also show that the faster inhibitory mGluR2/3-mediated current comes first, and the mGluR1-mediated excitation comes later . When would PC feedback come, in relation to this sequence?– There is known to be a heterogeneous distribution of UBCs, as shown in this study too. Is there also preferential localization of mGluR1-positive UBCs? Figure 1 shows only mGluR1-positive UBCs. Since the presence of mGluR1 and the response to glutamate are related to PC feedback, it is important to know whether the proportion of UBCs that do not get PC feedback even in these UBC-rich regions is different from elsewhere, or not. This would have important implications for circuit processing in vestibular-input-related vs. other regions– A significant minority of DHPG-responsive cells don't have PC feedback . Notwithstanding this caveat, I think this significant minority should also be discussed.– In the PC-UBC paired recordings, were there UBC responses that were not timed to PC spikes?– The entire study is motivated by describing specialization of different regions of the cerebellum, but the physiology was only performed in lobule X of vermis. Although there are fewer UBCs in other parts of the cerebellum, do the same patterns of feedback from the Purkinje cells apply?Summary:A receptors, as well as long-lasting inhibition via GABAB receptors. Since PCs constitute the output stage of the cerebellar cortex, the inhibitory signaling described in this study identifies a new feedback control pathway to the input layer, which constitutes a computational specialization of lobule X.The major strength of this study is the straightforward and convincing demonstration of a previously unknown synaptic connection from Purkinje cells (PCs) to mGluR1-positive unipolar brush cells (UBCs) in lobule X of the cerebellum. UBCs are interneurons of the cerebellar input layer, which preferentially distributed in some lobules of the cerebellum; especially those involved in vestibular function. The authors utilize a combination of electrophysiological recordings from UBCs in the acute rodent cerebellar slice, optogenetic activation of PCs, and pharmacological manipulations to establish that PCs inhibit UBCs in cerebellar lobule X through both fast synaptic transmission through GABAA-mediated PC feedback affects a majority of, but not all, mGluR1-containing UBCs, but GAABB-mediated PC feedback affected all UBCs tested. This finding suggests tonic control of the UBC by PCs even without a direct PC connection.Another strength of this study is the determination of how molecular heterogeneity corresponds to circuit connectivity and variable responses to synaptic input. There is heterogeneity in the molecular profiles of UBCs, and expression of mGluR1 is a key marker for a sub-population of UBCs. The authors demonstrate that mGluR1-expressing UBCs are preferentially targeted by PC feedback. – It is noteworthy that GABAA and via GABAB, especially in the context of the full complement of signaling pathways in the intact cerebellar circuit.The results raise the interesting question of the signal processing and computational function of this newly identified inhibitory feedback pathway, which should inspire future research. The authors offer some ideas along with limited evidence, however, the reviewers felt that additional research would be required to establish the authors' claims about the specific roles of PC inhibition of UBCs via GABAAR-mediated PC-UBC synapses convey noisy and unreliableThe claim that "phasic GABAA mediated current during spike trains and potentially a synergistic effect with GABAB signaling on UBC firing rate.output to input layer feedback" is based on recordings from two pairs of synaptically recorded PCs and UBCs. The reviewers fully appreciate how technically challenging these experiments are, nevertheless it is difficult to conclude much from n=2 observations. Moreover, even if the high (85%) synaptic failure rate were to be replicated in a large number of PC-UBC pairs, it is not clear that this should be interpreted as "noise." Purkinje cells fire at high rates of 50-100 Hz, and one would expect a buildup of GABABR-mediated inhibition allows elevated PC activity to sharpen the input-output transformation of UBCs."Likewise, the pharmacological approach used in Figure 5G-J lacks the temporal resolution to establish the claim that "Slow GABAeLife. Your revised article has been evaluated by Kenton Swartz as the Senior Editor, and a Reviewing Editor.Thank you for resubmitting your work entitled "Purkinje cell outputs selectively inhibit a subset of unipolar brush cells in the input layer of the cerebellar cortex" for further consideration by The two main conclusions from the original review still apply in their entirety:1) The finding that PCs inhibit mGluR1 positive UBCs is important in its own right and well established by the evidence provided.AR-mediated feedback is fast, unreliable, noisy, and may help linearize input-output curves and decrease gain. Slow GABABR-mediated inhibition could allow elevated PC activity to sharpen the input-output transformation of UBCs, and allow dynamic inhibitory feedback of mGluR1-expressing UBCs."2) The specific claims about the function of the fast and slow inhibitory feedback were not well supported, in particular the following two claims, which are made in the last two lines of the abstract "GABADuring the review of the original submission, the reviewers and editor agreed there were two reasonable paths forward to publication: i) provide more evidence for the unsubstantiated claims, or ii) simply back off on the unsubstantiated claims, and focus on the main finding of the new connection from PCs to mGluR1+ UBCs. Inexplicably, the authors did not pursue either of these options, but rather resubmitted the original manuscript almost verbatim, complete with the two unsubstantiated claims at the end of the abstract.The editor apologizes if placement of the key concerns of the reviewers at the end of the decision letter caused the authors to overlook these central concerns, which left the reviewers completely unconvinced of the functional claims:AR-mediated PC-UBC synapses convey noisy and unreliableThe claim that "phasic GABAA mediated current during spike trains and potentially a synergistic effect with GABAB signaling on UBC firing rate.output to input layer feedback" is based on recordings from two pairs of synaptically recorded PCs and UBCs. The reviewers fully appreciate how technically challenging these experiments are, nevertheless it is difficult to conclude much from n=2 observations. Moreover, even if the high (85%) synaptic failure rate were to be replicated in a large number of PC-UBC pairs, it is not clear that this should be interpreted as "noise." Purkinje cells fire at high rates of 50-100 Hz, and one would expect a buildup of GABABR-mediated inhibition allows elevated PC activity to sharpen the input-output transformation of UBCs."Likewise, the pharmacological approach used in Figure 5G-J lacks the temporal resolution to establish the claim that "Slow GABAThe authors provided a lengthy rebuttal of the suggestions the reviewers had offered about how the unsubstantiated claims might be strengthened, however, the second option of removing those claims still seems like a reasonable path forward, if the authors are willing to substantially revise the manuscript. Essential revisions:The finding that PCs inhibit mGluR1 positive UBCs is important in its own right and well established by the evidence in Figure 2 and 4.1) The finding of feedback from the PCs to the UBCs opens up exciting new research directions. The reviewers appreciated the authors' efforts to gain insight about the signal processing function of this new feedback connection, however, they agreed that the specific claims about the function of the fast and slow inhibitory feedback were not well supported by the evidence provided, and hence should be clearly identified as highly speculative and the conclusions significantly tempered. Additional evidence to support the claims about the specific signal processing functions of the fast and slow feedback would be highly desirable, but is not essential. See specific suggestions below.We have addressed the specific issues raised below.2) Some clarifications are needed regarding the histology:– What is the reader supposed to take away from the convolutional neural network analysis of Purkinje cell synapses onto UBCs? It seems that two brains were annotated by a human expert, and then the network was trained on those two brains and could reproduce similar results on held-out data, as shown in Figure 1-Supp 1. What criteria did the human annotator use in marking synapses? Was the neural network approach used for anything beyond what the human annotator did?We have clarified the explanation in both the results and methods section. We emphasize that we did not manually annotate two brains. The expert annotator only worked on 100 images, each a single 2D slice of 50x50 micron. This is less than 0.001% of the parasagittal vermis volume in Figure 1C for reference. Our human annotator criteria were that the inhibitory PC synapses should be synaptophysin-TdTomato and VGAT positive puncta with clear morphological features that are present across at least two to three consecutive z-slices. After training the network, we verified that it works consistently across different cerebellar layers and regions using out of sample validation. The neural network approach was necessary to generate high-quality overviews of synaptic density maps over large regions. (pages 12-13).– Is it absolutely certain that there is no mGluR1 expression in Lugaro cells and Golgi cells?  The high magnification images in Figure 1K do show the characteristic morphology of UBCs, but there is a possibility that low mag. images of distribution in lobules could include these other cell types in spite of the difference in size, in case they express mGluR1. This possibility could change the interpretation of these results, so please clarify.The brush expresses extremely high level of mGluR1 and dominates the signals in both the high and low magnification image of UBCs. mGluR1 labeling is used in many previous papers to identify UBCs . Please note that the entire image was obtained at high resolution, which allows us to inspect the morphology of the labelled structures. The distinctive, intense mGluR1 immunofluorescence pattern we observe is characteristic of UBC brush labelling, and is not consistent with Golgi cell or Lugaro cell labelling. (page 4)– There seem to be PC synapses on the cell body of UBCs as well as on the brush , although the synapses on the brush are highlighted for mention. Please clarify.We agree that synapses are near both the brush and the cell body in these images, and we have modified the legend and text (page 4).Specific, non-essential suggestions for the authors to consider, should they decide to strengthen the evidence about the function of the fast and slow feedback.A+GABAB blockers in the bath.– There is the implicit assumption that regular PC firing hardly affects mGluR1 positive UBC discharge, but it would be interesting to address or at least discuss constitutive PC inhibition. For example, this could be assessed in UBCs that don't express mGluR2, by applying an mGluR2 agonist to inhibit Golgi cell discharge, then GABAConstitutive PC inhibition of UBCs is an interesting possibility, and we have added to the discussion to deal with this issue. UBCs can receive GABAR-mediated inhibition from GoCs and PCs. However, it is easy to sever the extensive PC collaterals that extend across lobule X, and GoCs often die in adult slices. In addition, a large percentage of UBCs express mGluR2/3Rs, so the application of an agonist is difficult to interpret. These technical challenges make it difficult to determine the magnitude and fractional contributions of constitutive PC and GoC inhibition onto UBCs.(page 10)– Tonic UBC hyperpolarization by steady PC excitation in figure 5D-F was not fully quantified. For example, is there different behaviors at different light intensities as in Figure 4? In figure 5D-F, a bigger burst at the beginning of the depolarization step was observed, but the steady state current seems unchanged. This is an important point, as rebound discharge is an important physiological feature of UBCs  which may modify the current knowledge about ON and OFF UBC computation. Noticeably, there is a clear rebound discharge in Figure 4I but not quantified. The interplay between T-type channel and GABAB hyperpolarization may be addressed or discussed more.The reviewer makes an important point. We now point out the rebound discharge in Figure 4 and discuss in the context of T-type calcium channels and the study of Diana et al. (2007). We discuss how pauses in PC firing could potentially generate rebound firing in UBCs. (page 10)A and GABAB signaling during trains of stimulation? Probability of release at the PC-UBC connection appeared low, but we expect a buildup of GABAA mediated current during trains and a synergistic effect with GABAB signaling on UBC firing rate. However, in the current form, experiments were performed either with GABAA or GABAB antagonist in the bath. Experiments with neither GABAA nor GABAB blockers would likely be informative.– Is there any combined effect of GABAA and GABAB receptors in isolation, and that performing experiments in the absence of GABAR antagonists did not provide a sufficient additional advance to justify the considerable time and effort required.We had also considered the possibility of performing experiments in the absence of any GABA receptor antagonists. Ultimately, we felt that it was more informative to explore the contributions of GABAA or GABAB receptors? This question is important as groups of PCs can be synchronized or PCs can fire in burst at high frequencies, suggesting that the low probability of release of GABA may act as a high pass filter for GABAA signaling. While paired recordings  are challenging, loose-cell attached recordings on PCs  or focalized light illumination in ChR2 expressing PCs maybe used instead of cell-attached.– Can you draw any conclusions on the number of PC inputs to a UBC from the paired recordings? How many PCs are required to inhibit UBCs via GABAA responses, GABAB responses do not require direct contacts and can be activated by spillover. In order to estimate input numbers, it is necessary to determine the average size of an individual input and determine the size of the response to all inputs activated simultaneously. We used loose-cell attached recordings to record from PCs, but these experiments were very challenging and the n’s were small . We are less confident about our ability to reliably activate all PC inputs to a UBC. There is the challenge that over bouton stimulation and the use of ChR2 can artificially elevate the initial probability of release. We also think it likely that many PC axons and their associated synapses may be damaged, which would cause us to underestimate the number of inputs.We share the reviewer’s desire to determine the number of PCs that can influence each UBC. However, we do not think that electrophysiological studies will provide a reliable estimate of the number of PCs that inhibit a UBC. This is essentially two questions, because it is likely that direct contacts are required for GABAB response is much more challenging because in addition to the challenges outlines for direct GABAA synapses, it is extremely difficult to determine the magnitude of a single PC to UBC GABAB component. It is also highly likely that responses from different inputs will not sum linearly and that there will be a strong frequency dependence to the magnitude. For these reasons we prefer to be cautious and not draw conclusions that go beyond our data regarding the numbers of inputs.For these reasons, in future studies (within the next 2 or 3 years) we hope to determine the total number of PC inputs onto a single UBC using serial EM reconstruction, which we feel will provide much more reliable estimates. Estimating the number of PCs that can contribute to the GABAB inhibition could be relevant for mGluR1 negative UBCs as well. If the authors have these data, it would be an interesting extension.– The study focused on mGluR1 positive cells, but showed that Gabbr1 is also expressed in other UBCs. Therefore, GABAB receptors are heterodimers consisting of GABAB1 and GABAB2 receptors (Frangaj and Fan 2018). It is therefore of considerable interest that Gabbr2 shows such a pronounced gradient and such low expression levels in mGluR1- cells, but Gabbr1 expression does not exhibit such a gradient. We now comment on this in the text (page 6)It is generally thought that GABA– A discussion on the physiological consequences of a selective inhibition of mGluR1 positive vs mGluR2 UBCs would strengthen the study. Can we extrapolate that "ON" UBCs are inhibited by PCs while "OFF" cells are not?We have added additional discussion in the possible physiological consequences of PCs selectively inhibiting a subset of UBCs. In this discussion we strive to avoid the convenient “ON: and “OFF” terms that we feel are insufficiently precise. We have demonstrated in a recent manuscript  that the population of UBCs have graded responses, and mGluR1 excitation is prominent in some cells, mGluR2/3 inhibition is prominent in others, but for most mGluR1 and mGluR2/3 are both present. We find that PCs preferentially inhibit UBCs with high mGluR1 expression, and predominantly excitatory MF responses.A current recorded in figure 2 would be informative as it relates to glutamate induced current or DHPG induced increase in firing rate.– Quantification of the GABAWe have provided an additional scatter plot  to address this issue.– It has been thought that UBCs serve to prolong GrC firing after brief mossy fiber inputs. The negative feedback described here would serve the opposite purpose. Thus, it is important to understand the time course of the negative feedback relative to mossy fiber input.A component, and will slowly inhibit UBCs if there is a GABAB component. MFs can excite many UBCs for a few hundred milliseconds to tens of seconds, and MFs inhibit many UBCs for hundreds of milliseconds by activating mGluR2/3 receptors. However, it is not clear whether MF evoked population activity in UBCs will increase or decrease PC firing, because UBCs excite granule cells that both directly excite PCs and disynaptically inhibit PCs by activating MLIs. We therefore do not know if the UBCgRCPCUBC loop leads to positive or negative feedback. Hopefully, future in vivo studies will address this issue, but they will be challenging. We feel that it is reasonable and appropriate to restrict this study and the discussion to a basic description of the PC to UBC synapse, and that many of the interesting issues raised by the reviewer are perfect topics for future studies.This is not a simple issue. In general, elevated PC inhibition will rapidly suppress firing in UBCs if there is a GABA– The major conclusions of this paper relate to feedback regulation of the input layer. But the data presented here don't directly show a feedback loop, i.e., that the GrCs that feedforward mossy fiber information to the Purkinje cells are the ones that are regulated by the UBCs that these PCs project to. Given the observation of slow GABAB-dependent feedback, the role of direct feedback vs. feedback onto UBCs without a direct connection is important.We feel that it is appropriate to indicate that PCs are providing feedback from the output layer to UBCs in the input layer. Also note that each UBC excites many granule cells, and each granule cell excites about half of the local PCs. While it seems extremely likely that many GrCs activated by a UBC will directly excite the PCs that inhibit that UBC, we have not explicitly shown that this is the case.– In relation to the previous question, it remains unclear how modulating UBC input would affect Purkinje cells.We echo the sentiment of the reviewer. Indeed, in general the manner in which UBCs influence PC firing is complex and poorly understood, as is the case for extrinsic MFs. UBCs and MFs activate granule cells that in turn excite PCs directly and inhibit PCs through disynaptic inhibition, so the net effect is difficult to predict. We have therefore focused additional discussion on the fact that PC-UBC feedback allows the output of the cerebellar cortex to shape the temporal representation of the input layer.– The authors previously showed that PC-GrC feedback is also heterogeneously distributed, and will serve to inhibit the input layer. Since PCs directly provide both fast and slow feedback to GrCs in these regions, the PC-UBC connection demonstrated here suggests additional negative feedback via UBCs to GrCs. Therefore, the effectiveness of second level of negative feedback regulation of PCs to the input layer remains unclear.On page 9 we point out: The major differences in slow inhibition of UBCs and GrCs are the state change of UBCs and the target dependence of the UBC inhibition, neither of which have been described for PC inhibition of GrCs .– Until the current finding of PC-UBC feedback, inhibitory inputs to UBCs were thought to be limited to those from Golgi cells. The relative time course of Golgi cell vs. PC feedback inhibition in controlling UBCs is worth consideration.This is an interesting issue that is part of the larger question of the distinctive functional roles of PC and GC inhibition of UBCs. However, the relative times courses of GC and PC inhibition would begin with determining the activity patterns of these two cell types during behaviors.– It would be important to know whether PC feedback to UBCs is in the same parasagittal plane.We reconstructed PC collaterals in a previous paper across various cerebellar regions. The PC feedback appears to be confined in the parasagittal plane  as we now point out. This will potentially allow the PC-UBC feedback to operate within functional macrozones. (page 2)– As an additional example that relates to the same overall question about how this feedback loop will actually affect neural signals via mossy fibers, the authors also show that the faster inhibitory mGluR2/3-mediated current comes first, and the mGluR1-mediated excitation comes later . When would PC feedback come, in relation to this sequence?A and GABAB components, etc.) that we feel it would be premature to discuss this issue (regardless of how interesting it is).The reviewer raises an interesting question regarding the relative timing of mGluR2 inhibition and PC inhibition. The timing of mGluR2/3 current is diverse across UBCs, ranging from about one hundred milliseconds to a few seconds. There are so many factors contributing to relative time courses of PC inhibition and mGluR2-mediated inhibition . Notwithstanding this caveat, I think this significant minority should also be discussed.The reviewer rightly points out the very important caveat that “the absence of an optogenetically-induced response is not definitive proof of lack of PC inputs”. We have added a sentence dealing with this issue (page 6)– In the PC-UBC paired recordings, were there UBC responses that were not timed to PC spikes?Yes, there were IPSCs not time locked to PC spikes. This could come from either other PCs or GoCs. We did not use mGluR2 agonist to suppress GoC input in these recordings because many UBCs express mGluR2 receptors (including many mGluR1+ UBCs).– The entire study is motivated by describing specialization of different regions of the cerebellum, but the physiology was only performed in lobule X of vermis. Although there are fewer UBCs in other parts of the cerebellum, do the same patterns of feedback from the Purkinje cells apply?This is an interesting question that we are unable to answer definitively. Images of other regions such as lobule IX, the paraflocculus and the flocculus  suggest that collateral synapses may target UBCs in these regions as well. We are, however, reluctant to draw conclusions based solely on these light level images. It would require a great deal of effort to address the issue of PC inhibition of UBCs in other regions. We would need to repeat our experiments of Figures 2 and 4 for each of the other regions under consideration. We now briefly discuss this issue (page 9).The two main conclusions from the original review still apply in their entirety:1) The finding that PCs inhibit mGluR1 positive UBCs is important in its own right and well established by the evidence provided.AR-mediated feedback is fast, unreliable, noisy, and may help linearize input-output curves and decrease gain. Slow GABABR-mediated inhibition could allow elevated PC activity to sharpen the input-output transformation of UBCs, and allow dynamic inhibitory feedback of mGluR1-expressing UBCs."2) The specific claims about the function of the fast and slow inhibitory feedback were not well supported, in particular the following two claims, which are made in the last two lines of the abstract "GABADuring the review of the original submission, the reviewers and editor agreed there were two reasonable paths forward to publication: i) provide more evidence for the unsubstantiated claims, or ii) simply back off on the unsubstantiated claims, and focus on the main finding of the new connection from PCs to mGluR1+ UBCs. Inexplicably, the authors did not pursue either of these options, but rather resubmitted the original manuscript almost verbatim, complete with the two unsubstantiated claims at the end of the abstract.The editor apologizes if placement of the key concerns of the reviewers at the end of the decision letter caused the authors to overlook these central concerns, which left the reviewers completely unconvinced of the functional claims:AR-mediated PC-UBC synapses convey noisy and unreliableThe claim that "phasic GABAA mediated current during spike trains and potentially a synergistic effect with GABAB signaling on UBC firing rate.output to input layer feedback" is based on recordings from two pairs of synaptically recorded PCs and UBCs. The reviewers fully appreciate how technically challenging these experiments are, nevertheless it is difficult to conclude much from n=2 observations. Moreover, even if the high (85%) synaptic failure rate were to be replicated in a large number of PC-UBC pairs, it is not clear that this should be interpreted as "noise." Purkinje cells fire at high rates of 50-100 Hz, and one would expect a buildup of GABAR-mediated inhibition allows elevated PC activity to sharpen the input-output transformation of UBCs."Likewise, the pharmacological approach used in Figure 5G-J lacks the temporal resolution to establish the claim that "Slow GABABThe authors provided a lengthy rebuttal of the suggestions the reviewers had offered about how the unsubstantiated claims might be strengthened, however, the second option of removing those claims still seems like a reasonable path forward, if the authors are willing to substantially revise the manuscript.A component’s functional role, as per your suggestion.Your additional clarification has been most helpful. We now have a much better idea of the issues and the remedies. We have made a real effort to remove anything that you would consider to be speculative from the abstract, the end of the introduction and the results. We have also eliminated the discussion of the GABAThe one area where I would like to push back a bit concerns Figure 5. This is the figure where we try to provide insight into what this feedback might be doing to PCs. Dr. Raymond has expressed reservations about this figure. Nonetheless, we feel that this figure adds to the paper and furthers our understanding of what PC to UBC feedback might be doing. In our point-by-point response we explain why we feel that these experiments and the associated discussion should be included. Of course, if you still feel that including Figure 5 and the associated discussion is a deal breaker, we will remove it. But we request that you allow us to retain it, because we feel that it makes the paper better.


Dengue virus (DENV) is a member of insect vector-borne viruses, and it causes dengue fever. Southeast Asia is the epi-center of dengue fever in the world. The characterization of the virus is essential to identify the transmission and evolution of DENV.In 2017, there was an outbreak of Dengue virus type 1 (DENV1) in northern Vietnam and the neighboring countries. To identify the genetic character of the outbreak virus in the area, we conducted whole-genome sequencing analysis on the samples positive for the DENV1 along with real-time PCR.In total, 1026 blood samples were collected from patients with suspected dengue fever in Ha Nam and Hai Duong province, nearby areas of the capital of Vietnam. After screening by real-time PCR, 40 of DENV1 positive samples were subjected to whole-genome sequencing, and 28 complete coding sequences were obtained.All 28 sequences were genotype I of DENV1, which is dominant in the southeast and East Asian countries. The phylogenetic analysis of the E region showed that they fell into a single cluster with the reported sequences from Vietnam between 2009 and 2016, in which the isolates from other countries are very rare. Our results suggested that the 2017 outbreak in the area was caused by locally circulating viruses.The online version contains supplementary material available at 10.1186/s41182-021-00386-0. Aedes aegypti and A. albopictus [Dengue virus (DENV) is the causative agent of dengue fever and dengue hemolytic fever or dengue shock syndrome. Four different serotypes DENV1–4) have been identified and are known to be transmitted by two mosquito species, –4 have bbopictus . Accordibopictus . The DENIn this study, we aimed to elucidate the genetic characteristics of the DENV1 strain that caused the 2017 outbreak. To this end, we conducted a whole-genome analysis of DENV1 on serum samples collected from the patients in Ha Nam and Hai Duong, which are located in northern Vietnam.Blood samples were collected from patients suspected to have dengue fever, who visited Ha Nam Province General Hospital and Hai Duong Preventive Medicine Center. Among 1026 tested samples, 252 were positive for DENV, determined by real-time PCR (RT-PCR), including 227 cases of DENV1, 23 cases of DENV2, two cases of DENV3, and two cases of DENV4 [Anti-dengue IgM and IgG detection was performed on the samples available for the whole genome sequences using an ELISA kit  to determine the infection status (primary or secondary). Whether a sample was positive or negative was judged according to the manufacturer’s instructions. Since the DENV1 infection was confirmed by serotype-specific RT-PCR, we defined the infection status as follows: Primary infection was defined as IgG negative and IgM positive or IgG and IgM negative; secondary infection; IgG positive.We succeeded in obtaining 28 complete coding sequences from 40 patients with DENV1 infection with more than 30 coverage. All of the newly identified DENV1 sequences from northern Vietnam fell into the genotype I cluster, which is dominant in southeastern and eastern Asia  together with reference sequences obtained from the Virus Pathogen Database and Analysis Resource  before the year of 2017 . ConsistThe analysis of IgM and IgG indicated that 13 samples were categorized as a primary infection, and 13 samples as secondary infection  (Additional file The DENV1 sequences collected during 2017 outbreak period fell into a single cluster of genotype I with the V312L substitution in the E region. The cluster was consisted with the reported sequence from Vietnam after 2009 with a few exceptions from China and Cambodia. The V312L substitution was not found any other DENV1 sequence record on the database so far, it is suggested that the DENV1 variant causing for the 2017 outbreak was locally circulating variant. In Northern Vietnam, large scale DENV1 outbreak is recorded in 2009 , 12, andDengue incidence has been increasing over time globally. In Vietnam, the disease was reported to spread to South and Central regions but was not frequent in Northern and Highland regions until 1996. However, seasonal endemics were observed in Hanoi, northern region, after 1997, and large-scale outbreaks were also reported every 3–5 years. Such expansion of DENV infection is considered to be related to the environmental conditions and human mobility according to the social development of the affected countries. Since further civilization cannot be stopped, the risk of dengue will increase globally. Identification of the trigger of the outbreak may lead to the early prevention from DENV infection. Various viral and nonviral factors should be analyzed in the affected area . AlthougOur results suggested that the 2017 outbreak in the area was caused by locally circulating viruses. Control of locally circulating viruses is more complicated than preventing new viral invasion; however, the need to develop useful tools to reduce the risk of DENV infection is urgent.Additional file 1: Table S1. Amino acid substitutions detected in 2017 DENV1 viruses. Substitutions are indicated on the location of DENV1 standard strain EU848545.Additional file 2:Table S2. List of sequence data used for phylogenetic analysis. The accession numbers of the 28 newly sequenced DENV1 are also provided in the Table.Additional file 3: Table S3. The results of IgG and IgM detection by ELISA kit.