The high prevalence of drinking in young adults is a serious public health concern. Alcohol use among young adults often is associated with a wide variety of risky behaviors and both immediate and long-term negative consequences. The 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) presents a unique opportunity to examine young adult drinking because it has an excellent response rate, oversamples young adults ages 18–24, and includes college-related group housing. According to the NESARC data, in 2001–2002 over three-quarters of young adults ages 21–24 were current drinkers, as were nearly two-thirds of those ages 18–20, despite the fact that the legal drinking age is 21. More than half of young adult men exceeded the recommended daily drinking limit, as did two-fifths of young adult women. Although the prevalence of exceeding the daily limit is higher for those ages 21–24 than for those ages 18–20, it also is substantial for those ages 18–20. Because drinking more than the recommended per-occasion maximum is likely to impair mental and physical performance, the increase over the past decade in the prevalence among young adults of drinking five or more drinks 12 or more times per year may help explain the increased risk of injury and other acute negative consequences commonly observed among college students ages 18–24. Over the life course, drinkers in the United States tend to have the highest level of alcohol consumption in their late teens and early twenties . AccordiThe high prevalence of drinking in young adults is a serious public health concern because alcohol use by this age group often is associated with a wide variety of risky behaviors and various negative consequences. Many of these consequences are immediate and tragic —most notCollege students continue to stand out from other young adults because of their relatively high rates of heavy drinking, even though their average daily alcohol consumption generally is lower than that of their noncollege peers . Until rHealthy People 2010 . NESARC is uniquely suited to examine young adult drinking for three reasons—the excellent response rate, the oversampling of young adults ages 18–24, and the inclusion of college-related group housing. Findings presented here include the prevalence of alcohol use in youth ages 18–24; drinking frequency and quantity; frequency of heavy drinking, intoxication, and driving after drinking; as well as age of drinking onset and choice of drinking locations for different types of alcoholic beverages. In particular, this Bulletin examines the number of young adults who exceed daily and weekly guidelines for low-risk drinking. The current public health recommendations for the nation—ple 2010 —include In addition, this Bulletin provides insight into the drinking behavior of subgroups of young adults based on age, sex, race/ethnicity, and college enrollment status. For example, the definition of young adults conventionally includes all people ages 18–24. Yet people who fall into the younger part of this group  are below the legal drinking age of 21. Underage drinking remains a major public health concern. To better understand the scope of this problem, the data presented here are given for the total young adult population as well as for the subgroups of people ages 18–20 and 21–24. Finally, recent trends in young adult risk drinking are briefly examined by comparing the NESARC results with those from the 1991–1992 National Longitudinal Alcohol Epidemiologic Survey (NLAES).The analyses presented here are based on data from the 2001–2002 NESARC, which was designed and sponsored by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and fielded by the U.S. Bureau of the Census. Data were collected in face-to-face, computer-assisted personal interviews that were conducted in respondents’ homes. The NESARC sample represents the civilian, noninstitutionalized adult population of the United States, including residents of all 50 States and the District of Columbia . It inclA total of 43,093 respondents age 18 and older completed the survey. The data reported here are based on responses from 5,199 young adults ages 18–24, including 3,551 current drinkers .NESARC measures alcohol consumption separately for four specific types of alcoholic beverages , detailing for each beverage the usual and heaviest quantities and frequencies, as well as the usual time and place of drinking. Nevertheless, the variables selected to represent the current drinking status of young adults pertain to any alcohol use regardless of beverage type. These items were taken directly from the survey and had a reference period of the last 12 months. The only exception is the daily volume of alcohol  consumption. This measure was derived by NIAAA using a somewhat complex algorithm that summed beverage-specific volumes across the four beverage types.Current drinking—Had at least one drink of alcohol in the past year.Drinking frequency—Number of days on which any alcoholic beverages were consumed in the past year.2Drinking quantity—Usual number of drinks  consumed on drinking days in the past year.Risk drinking—Drinking in excess of recommended drinking limits .Intoxication—Drinking enough to feel intoxicated or drunk .Tolerance—Reported maximum number of drinks the respondent can hold without feeling intoxicated or drunk.Driving after drinking—Driving a car or other motor vehicle  after consuming three or more drinks on one occasion in the past year.Drinking location for beverage type—Usual location of drinking for each type of alcoholic beverage , including own home, home of friends or relatives, or public places .Age of drinking onset—Age when the respondent first started drinking .For risk drinking, the following subcategories were specified:Exceeding the weekly drinking limit—On average, consuming more than two drinks (1.2 ounces of ethanol) per day for men and more than one drink (0.6 ounces of ethanol) per day for women in the past year.Exceeding the daily drinking limit—Consuming more than four drinks (2.4 ounces of ethanol) in a single day for men and more than three drinks (1.8 ounces of ethanol) in a single day for women one or more times in the past year.Episodic heavy drinking—Consuming 5+ drinks for men and four or more drinks for women in a single day in the past year.Data presented in this article are descriptive statistics. The estimates of the drinking measures for the various subpopulations are crude values and are not adjusted for other variables. Percentages of drinking and risk drinking were calculated for the total young adult population, whereas drinking behaviors—including frequencies of drinking, episodic heavy drinking, intoxication, and driving after drinking 3+ drinks—as well as drinking quantities and tolerance were calculated for current drinkers. Both for the total young adult sample and for the two age subgroups, analyses were carried out by sex, race/ethnicity , and college enrollment status .All estimates were weighted by the sampling weights to represent the entire U.S. young adult population. Standard errors of estimates were produced by the software package SUDAAN, which takes into account the complex sampling design employed by NESARC using the Taylor series linearization method . In figuIn order to put young adult drinking in context, According to these data, in 2001–2002 about 70 percent of young adults in the United States (or about 19 million) consumed alcohol in the year prior to the survey. Alcohol consumption was more common among males, with three-quarters of young adult men (about 10 million) reporting drinking in the past year, compared with two-thirds of young adult women (about 9 million).Risk drinking also was common among young adults. About 46 percent (12.4 million) of young adults engaged at least once in drinking that exceeded the recommended daily limits and 14.5 percent (3.9 million) had an average consumption that exceeded the recommended weekly limits.Drinking was more common among the older subgroup of young adults, with more than three-quarters of young adults ages 21–24 reporting drinking in the past year, compared with slightly less than two-thirds of those ages 18–20. The prevalence of exceeding the daily and weekly limits also was generally higher among young adults ages 21–24  than among those ages 18–20 . This was true for all breakdowns by sex, race/ethnicity (with the exception of AIAN), and college enrollment status, although some of the differences were not statistically significantSubstantial gender differences existed in the drinking behavior of young adults see . More thAmong the racial/ethnic groups, prevalence of drinking was higher for Whites than for other groups in 2001–2002. An estimated 77.1 percent of Whites were classified as current drinkers, compared with 70.7 percent of AIAN, 60.1 percent of Blacks, 60.4 percent of Hispanics, and 59.1 percent of Asians/NHOPI. The prevalence of exceeding the recommended daily and weekly drinking limits also was higher for Whites  and AIAN (53.0 and 27.4 percent) than for Asians/NHOPI (36.5 percent and 10.5 percent), Hispanics (37.3 percent and 8.5 percent), and Blacks (29.0 percent and 8.9 percent).Differences also existed with respect to college enrollment status. College students had a higher prevalence of drinking and risk drinking than did their noncollege peers. Thus, 74.9 percent of fulltime college students and 76.3 percent of part-time college students drank alcohol during the past 12 months, compared with 68.3 percent of their noncollege peers. Full-time college students also had the highest percentage of exceeding the recommended daily and weekly limits , followed by part-time college students (45.9 percent and 14.6 percent), and noncollege peers (43.3 percent and 12.9 percent).On average, young adult drinkers consumed alcohol on about 66 days per year. On the days that they drank, respondents reported engaging in episodic heavy drinking on 39 days, drinking enough to be intoxicated on 18 days, and driving after drinking 3 or more drinks on approximately 5 days. The respondents’ average drinking quantity was 3.7 drinks per drinking day, and the reported tolerance was 4.2 drinks. The values for all these measures were more than 50–90 percent higher for young men than for young women.Comparison of young adults ages 18–20 with those ages 21–24 found some differences between the two age groups. People in the older age group had a higher frequency of drinking and episodic heavy drinking  compared with people in the younger age group . In contrast, young adults ages 18–20 had a higher frequency of intoxication (20.0 days) and driving after drinking three or more drinks (5.1 days), higher drinking quantity (4.1 drinks), and higher tolerance (4.3 drinks) than those ages 21–24.Among the racial/ethnic groups, AIAN young adult drinkers had the youngest average age of drinking onset 15.4 years); the highest estimates for frequency of drinking (97.2 days), episodic heavy drinking (60.2 days), and intoxication (39.8 days); and the highest drinking quantity (5.4 drinks/drinking day). years; tWith regard to college enrollment status, full-time college student drinkers appeared to have the highest estimates for frequencies of drinking (71.6 days), episodic heavy drinking (44.3 days), intoxication (21.1 days), and driving after drinking three or more drinks (6.2 days), whereas part-time college student drinkers had the lowest estimates and their noncollege peers had intermediate estimates for these measures. For drinking quantity and tolerance, noncollege drinkers reported slightly higher averages  than the two college groups. It is important to note that because of some relatively large standard errors, not all differences among the three groups may be statistically significant.The choice of drinking location for beer varied depending on sex, race/ethnicity, and college enrollment status. For example, among young adults ages 21–24, men appeared most likely to choose their own homes (41.2 percent), whereas women were most likely to choose public places (50.7 percent) to consume beer. Blacks and Hispanics were most likely to choose their own homes  to consume beer, whereas Whites chose public places (43 percent). Differences in drinking locations for beer also were observed between college students and their noncollege peers in the 21–24 age group, with college students being more likely to drink in public places.Despite slight differences in the survey designs, comparing data from the 2001–2002 NESARC with that of its predecessor, the 1991–1992 National Longitudinal Alcohol Epidemiologic Survey , provides valuable insight into the trends in young adult drinking over the past decade. One comparable measure of risk drinking in the two surveys—the frequency of drinking 5+ drinks on a single day—was selected to illustrate the changes see .The comparison shows that during the 10 years between the two surveys, some progress was made in reducing episodic heavy drinking among young adults ages 18–24. In 2001–2002, 62.7 percent of young adults in this age group did not engage in episodic heavy drinking, compared with 57.7 percent in 1991–1992 . Among tThis Epidemiological Bulletin provides a broad overview of the unique consumption patterns and hazardous nature of young adult drinking in the United States using data from NESARC, NIAAA’s most comprehensive and up-to-date national survey. According to the NESARC data, over three-quarters of young adults ages 21–24 were current drinkers in 2001–2002. Interestingly, nearly two-thirds of 18- through 20-year-olds were current drinkers, despite the fact that the legal drinking age is 21. More than half of young adult men exceeded the recommended daily drinking limit, as did two-fifths of young adult women. Although the prevalence of exceeding the daily limit is higher for those ages 21–24 than for those ages 18–20, the prevalence for those ages 18–20 is still substantial.A comparison of NESARC and NLAES indicates a trend toward a higher frequency of 5+ drinking for young adults who engaged in 5+ drinking in NESARC. The National Alcohol Survey  found a It should be stressed, however, that the estimates for population subgroups are descriptive in nature and are not adjusted for possible confounding factors. Therefore, caution should be taken when interpreting the results or making causal inferences based on patterns observed in this study.Although comparisons with other national surveys will invariably yield more informed conclusions, interviewer training, respondents’ interpretation of questions, and other differing survey processes may contribute to the discrepancies between NESARC and other national surveys. For example, the prevalence estimate of past-year alcohol use among young adults ages 18–24 from the 2001–2002 NESARC is 70.8 percent . Two other national surveys that allow for prevalence estimates of past-year alcohol use among young adults are NSDUHHealthy People 2010 low-risk drinking guidelines, particularly among young adults.Early initiation of alcohol use has been found to be positively associated with alcohol abuse and dependence as well as other alcohol-related problems  . Therefo


Excessive, binge alcohol drinking is a potent and pernicious obstacle to treating alcohol use disorder (AUD), and heavy-drinking humans are responsible for much of the substantial costs and harms of AUD. Thus, identifying key mechanisms that drive intake in higher-drinking individuals may provide important, translationally useful therapeutic interventions. Orexin-1-receptors (Ox1Rs) promote states of high motivation, and studies with systemic Ox1R inhibition suggest a particular role in individuals with higher intake levels. However, little has been known about circuits where Ox1Rs promote pathological intake, especially excessive alcohol consumption. We previously discovered that binge alcohol drinking requires Ox1Rs in medial nucleus accumbens shell (Shell), using two-bottle-choice Drinking-in-the-Dark (2bc-DID) in adult, male C57BL/6 mice. Here, we show that Shell Ox1Rs promoted intake during intermittent-access alcohol drinking as well as 2bc-DID, and that Shell inhibition with muscimol/baclofen also suppressed 2bc-DID intake. Importantly, with this large data set, we were able to demonstrate that Shell Ox1Rs and overall activity were particularly important for driving alcohol consumption in higher-drinking individuals, with little overall impact in moderate drinkers. Shell inhibition results were compared with control data combined from drug treatments that did not reduce intake, including NMDAR or PKC inhibition in Shell, Ox1R inhibition in accumbens core, and systemic inhibition of dopamine-1 receptors; these were used to understand whether more specific Shell Ox1R contributions in higher drinkers might simply result from intrinsic variability in mouse drinking. Ineffectiveness of Shell inhibition in moderate-drinkers was not due to a floor effect, since systemic baclofen reduced alcohol drinking regardless of basal intake levels, without altering concurrent water intake or saccharin consumption. Finally, alcohol intake in the first exposure predicted consumption levels weeks later, suggesting that intake level may be a stable trait in each individual. Together, our studies indicate that Shell Ox1Rs are critical mediators of binge alcohol intake in higher-drinking individuals, with little net contribution to alcohol drinking in more moderate bingers, and that targeting Ox1Rs may substantially reduce AUD-related harms. Excessive, binge-level alcohol consumption is a major obstacle when treating alcohol use disorder (AUD) . ImportaOrexin signaling has been identified as being of particular importance for driving many motivation- and addiction-related behaviors . Orexin Importantly, some studies using systemic Ox1R inhibition have suggested that Ox1Rs are particularly important for promoting alcohol intake in higher-drinking individuals . HoweverTo help develop a larger data set to robustly understand whether Shell Ox1Rs underlie excessive consumption in higher-drinking individuals, we examined intake of 15% alcohol under a two-bottle-choice variant of Drinking-in-the-Dark paradigm (2bc-DID) that we have previously utilized ,b. We alad libitum, for all subjects.All procedures followed the Guide for Care and Use of Laboratory Animals provided by the National Institutes of Health, and with approval of the Institutional Animal Care and Use Committee of UCSF. Male C57BL/6 mice, 6–8 week of age, were purchased from Jackson Laboratories. Animals were single-housed under a reverse 12:12 light:dark cycle, with lights off at 10:00 a.m. Food and water were available, We used a modified drinking in the dark paradigm as previously described ,b,c. AftAfter ∼2 weeks acclimation, mice drank under an intermittent access schedule where they received overnight (∼24 h) two-bottle choice access to 20% alcohol (v/v) and water starting on Monday, Wednesday, and Friday at ∼3 h after lights off .After ∼2-week of alcohol access under 2bdc-DID or 2bc-IA, surgery was performed to bilaterally implant guide cannulae (Plastics One) aimed at either the Shell , NAc Core  or vmPFC . All given coordinates are relative to Bregma from skull surface. After surgery, mice were allowed to recover for at least 3 days before alcohol drinking was resumed. At the end of experiments, brains were harvested for histology and cannula placement verification.Agents, doses, and relevant references for the doses used are given in All animals were habituated to handling and injection prior to drug treatment sessions. After 2–3 days of simple handling, animals have 2–3 days of handling where cannula plug is removed and returned; finally, animals have one saline injection prior to drug test sessions. Drug deliveries occurred 30 min prior to drinking. Drugs and their vehicles were injected in a counter-balanced manner in different cohorts of mice. For many cohorts, vehicle and drug were each tested twice in each animal in a counter-balanced, randomized fashion, and averaged to give a single vehicle intake value and single drug intake value for each animal. SCH, orexinA ± TCS, vmPFC AP5, and some SB and M/B in 2bc-DID were tested with only a single injection of vehicle and drug. There was at least one drinking day between drug treatments and not more than two injections per week. For i.p. delivery, drugs were injected at a volume of 10 mL/kg. For intracranial injections, drugs were bilaterally injected at a volume of 200 nL at a rate of 200 nL/min, with the exception of OXA, which was injected at a volume of 300 nL. The infusion needles (Plastics One) were left in place for an extra 60 s before retraction. For Shell and Core, needles projected 0.3 mm past end of guide cannulae, and for vmPFC needles projected 0.5 mm.After each drinking session, alcohol (g/kg of body weight) or saccharin intake (mL/kg of body weight), water intake, and the preference ratio  were measured. Data were statistically analyzed using Prism (GraphPad), SPSS (IBM), and R v3.4.4 .t-tests comparing vehicle vs. drug treatment within each animal. Non-normal data, including concurrent water intake, were tested with Wilcoxon match-paired signed rank test (for paired data) or Mann–Whitney test (for unpaired data). To compare the basal intake vs. change in drinking slope relationships across different groups, a one-way ANCOVA was performed with a dependent variable of Change in Drinking, an independent variable of Treatment Group, and a covariate of Basal Drinking. The presence of a statistically significant interaction between Treatment Group and Basal Drinking indicates a dissociable effect of Treatment Group on the slope of a regression line which examines Change in Drinking as a function of Basal Drinking. All bar data are shown as mean ± SEM.Alcohol drinking was analyzed with paired t202 = 0.8891, p = 0.3750).We were particularly interested in understanding the relationship between basal drinking levels and the requirement for Shell Ox1Rs/activity for driving alcohol intake. Thus, we took advantage of the large data sets we had acquired to study a Shell inhibition group, which consisted of data combined from Ox1R or global GABA-mediated inhibition within the Shell. Since there was variability in mouse intake, we also compared Shell inhibition results with data combined from a series of control groups where no change was observed with drug treatment. This gave us a robust comparison to help account for intrinsic variability in mice drinking and allowed us to study whether a greater Shell contribution in higher-drinking individuals represented mathematical effects such as regression to the mean. Basal alcohol intake was normally distributed in both groups, and there was no difference in basal intake level between the Shell inhibition group and control group (∗(intake during drug treatment)/(intake during vehicle)]. This allows us to express the impact of Shell inhibition or other drug treatment on drinking relative to baseline consumption levels, but also partially correct for the high percent increases that can be observed when lower basal intake levels go up. Using this measure, a log value of 2 (log[100]) indicates no intake change with treatment. For subjects where treatment reduced drinking to zero, the log was set to 1. This method thus reflected a useful compromise to examine the percent drop in drinking so that it could be compared with basal drinking levels across individuals. In addition, in order to examine the distribution of treatment effects across individuals (shown in ∗(intake during drug treatment)/(intake during vehicle)] values: 1–1.3, >1.3–1.5, >1.5–1.7, >1.7–1.9, >1.9–2.1, >2.1–2.3, >2.3–2.5, >2.5.We first compared basal drinking levels with the actual change in alcohol intake with drug treatment. In addition, because lower basal drinkers have a smaller maximal change in drinking levels, we next normalized the drug-related drinking change to account for different basal intake levels in each animal. In particular, we calculated the log . Using this analysis, the Shell inhibition group still showed an overall similar distribution as in F1,70 = 6.742, p = 0.0115, slope = -0.1155, R2 = 0.08786). In contrast, control animals showed much less difference between animals with moderate and higher intake , although the slope was still significant, likely for mathematical reasons described above. We note that Shell inhibition did reduce drinking in some moderate-drinking subjects, and thus we performed several analyses to better understand Shell inhibition’s impact in subjects with different basal consumption. First, we performed a median split to divide animals into moderate versus higher drinkers (p = 0.0170 Mann–Whitney). In contrast, there was no difference in treatment effect between moderate and higher drinkers in the control (no-change) group (p = 0.2102 Mann–Whitney). Thus, these results suggest that Shell Ox1Rs and activity played a stronger role in driving alcohol consumption in higher-drinking individuals.Since lower basal drinking limits the actual change in drinking levels, we next normalized the treatment-related intake change (or lack thereof) in relation to the basal consumption levels in each animal. In particular, we determined the percent change in drinking with treatment, and took the logarithm of this, determined as log[100drinkers . Figure drinkers , while tdrinkers . In addip < 0.0001 Mann–Whitney). Mice with moderate basal intake (p = 0.4925 Mann–Whitney). Together, these results confirm that Shell Ox1Rs and activity were essential for driving the excessive alcohol binging in higher drinkers, with limited contribution across moderate drinkers.We also generated histograms to examine the distribution of treatment effect across moderate and higher basal drinkers. In mice with higher basal intake , controll intake  had moret14 = 3.816, p = 0.0019), and, importantly, this was observed in both moderate and higher drinkers . The baclofen reduction in intake was specific to alcohol, since 5 mg/kg baclofen did not reduce concurrent water intake during alcohol sessions (p = 0.3594), and did not reduce saccharin consumption in separate mice . 1 mg/kg baclofen also slightly but significantly reduced alcohol drinking , without an effect on concurrent water intake (p = 0.8987). Thus, baclofen produced a specific reduction in alcohol drinking that was similar across moderate and higher drinkers. In addition, to further rule out simple mathematical effects, we examined concurrent water intake, which was not reduced overall by Shell Ox1R inhibition  although not 2bc-IA . Taken together, these results suggest that the minimal impact of Shell inhibition across moderate drinkers was highly unlikely to be due to a floor effect or other artifact . Also, the median alcohol drinking level in the moderate drinkers was ∼1.55–1.65 mg/kg, and we previously showed that ∼1.6 g/kg/2 h leads to ∼80 mg% BACs  or NMDARs . Second, dopamine receptors are necessary for some alcohol-related behaviors . Third, since Shell Ox1R blockers decrease intake, we tested whether orexinA peptide (100 pmol/side) might increase drinking, but found that Shell orexinA actually decreased intake . In this regard, co-infusion of the Ox2r blocker TCS-OX2-29 . Shell Ox2rs do not regulate alcohol drinking . Together, these findings suggest that Shell PKC and NMDARs, vmPFC NMDARs, and D1Rs , were not critical for driving alcohol consumption. Thus, in order to construct the large no-change group to compare with our aggregated Shell inhibition group  group , we examined whether alcohol drinking on the first day of access  predicted 2 h intake levels averaged within each week of 2bc-DID intake. In fact, the initial-day drinking significantly predicted subsequent 2bc-DID intake levels on week 1 , week 2 , and week 3 . Also, alcohol intake slightly but significantly increased across the first 3 weeks of 2bc-DID consumption . Our results support the possibility that inter-individual variation in drinking levels is a trait, although there is some variability in the data that make it possible that some individuals increase or decrease intake across weeks of drinking. Thus, basal intake for all other studies was determined later in intake, nearer to the actual test sessions.Since Shell Ox1R and activity were critical for driving alcohol drinking in animals with higher basal intake, it would be particularly interesting to determine whether higher basal intake reflected a trait within particular individuals. Again, taking advantage of the large data set that we possess . In our work, we find that low doses of SB (given i.p.) strongly reduce compulsion-like alcohol drinking , relativHeavy-drinking individuals are responsible for much of the substantial costs and harms of AUD, and our results strongly suggest that Shell Ox1Rs are critical drivers of the high alcohol consumption levels in excessive-drinking individuals. Using large, combined data sets, we compared the impact of Shell inhibition on alcohol intake with the consumption pattern seen in an aggregated control group, which allowed us to account for intrinsic variability of mouse drinking. Furthermore, systemic baclofen reduced intake in higher and moderate-drinkers, suggesting that the overall lack of effect of Shell inhibition in moderate drinkers did not reflect a floor effect. Finally, initial drinking strongly predicted alcohol consumption in subsequent weeks, suggesting that basal intake level was a more stable trait in each subject. Thus, our results support the possibility that targeting Ox1Rs in higher-drinking individuals could substantially decrease the overall negative impact of AUD.KL, CK, JY, SW, LN, SG, VP, LA, and MF performed the experiments. KL, DD, and FH performed the analyses. KL and FH prepared the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Glycine max) carrying the Rj2 allele restricts nodulation with specific Bradyrhizobium strains via host immunity, mediated by rhizobial type III secretory protein NopP and the host resistance protein Rj2. Here we found that the single isoleucine residue I490 in Rj2 is required for induction of symbiotic incompatibility. Furthermore, we investigated the geographical distribution of the Rj2-genotype soybean in a large set of germplasm by single nucleotide polymorphism (SNP) genotyping using a SNP marker for I490. By allelic comparison of 79 accessions in the Japanese soybean mini-core collection, we suggest substitution of a single amino acid residue (R490 to I490) in Rj2 induces symbiotic incompatibility with Bradyrhizobium diazoefficiens USDA 122. The importance of I490 was verified by complementation of rj2-soybean by the dominant allele encoding the Rj2 protein containing I490 residue. The Rj2 allele was also found in Glycine soja, the wild progenitor of G. max, and their single amino acid polymorphisms were associated with the Rj2-nodulation phenotype. By SNP genotyping against 1583 soybean accessions, we detected the Rj2-genotype in 5.4% of G. max and 7.7% of G. soja accessions. Distribution of the Rj2-genotype soybean plants was relatively concentrated in the temperate Asian region. These results provide important information about the mechanism of host genotype-specific symbiotic incompatibility mediated by host immunity and suggest that the Rj2 gene has been maintained by environmental conditions during the process of soybean domestication.Cultivated soybean ( Glycine max [L.] Merr.) is an important leguminous crop and source of nutrition for humans and livestock worldwide. Wild soybean (Glycine soja Sieb. and Zucc.) is also an important genetic resource for soybean breeding. It has been suggested that G. max was domesticated from G. soja in East Asia 6000–9000 years ago [Cultivated soybean  in G. max are known to restrict nodulation to specific rhizobial strains [Rj genotypes, rather than the recessive rj genotypes, would be suitable to eliminate infection with those indigenous rhizobia in order to promote nodulation by specific inoculants with high symbiotic nitrogen-fixing ability [Rj2–genotype of G. max cultivars blocks nodule induction by specific B. diazoefficiens and B. japonicum strains, including strain USDA 122 [Rj2-genotype of various soybean cultivars has been identified by a phenotyping analysis with an incompatible strain [Rj2-mediated symbiotic incompatibility in soybeans is required for manipulating the genotype and efficient surveying of the Rj2-genotype in diverse soybean accessions by genetic analysis, such as single nucleotide polymorphism (SNP) genotyping.Several dominant alleles  protein of the Toll-interleukin receptor / nucleotide-binding site / leucine-rich repeat (TIR-NBS-LRR) class. Plant R proteins containing both NBS and LRR domains often directly or indirectly recognize pathogenic effectors and mount a strong immune response that is referred to as effector-triggered immunity (ETI) [B. diazoefficiens USDA 122 restores its nodulation capability in the Rj2 soybean cultivar ‘Hardee’ [Rj2-mediated symbiotic incompatibility [Rj2-soybeans monitor the specific variants of rhizobial NopP via the Rj2 protein, and thereby block infection by incompatible rhizobia through the process of ETI.Yang and colleagues  reportedty (ETI) ,30. The ‘Hardee’ . We recetibility . These fG. max revealed that the Rj2 alleles translate a protein containing residues of glutamic acid (E) and isoleucine (I) at positions 452 and 490, respectively, whereas the products of the recessive allele rj2 share a haplotype containing residues of lysine (K) and arginine (R) at the same positions as E and I, respectively [rj2-soybean complemented with the chimeric Rj2 gene, which translates a protein having E452K and I490R substitutions in the Rj2 allelic product, did not induce NopP-mediated incompatibility with B. diazoefficiens USDA 122 [An allelic comparison of 21 cultivars of ectively  Fig 1A)G. max reUSDA 122 .Rj2-mediated incompatibility with bradyrhizobia, the specific amino-acid residue(s) in the Rj2 protein responsible for this incompatibility have not been identified. Moreover, the evolutionary process behind this symbiotic compatibility in soybean-bradyrhizobium interactions remains unclear, because the Rj2 and rj2 alleles in G. soja have not been identified. In this study, we report that a single isoleucine residue in Rj2 is responsible for Rj2-mediated symbiotic incompatibility. In addition, we identified the Rj2 allele in G. soja accessions and investigated the geographical distribution of the Rj2 allele in 1583 soybean accessions using SNP genotyping.Although previous studies have considered the mechanisms of G. max ‘Williams 82,' the recessive allele rj2 (Glyma16g33780) encodes a 1,051 bp amino-acid protein containing TIR, NBS, and LRR domains  classified in JC1 . Nodule number at 28 days after inoculation (DAI) indicated that all cultivars identified as JC1 failed to nodulate with B. diazoefficiens USDA 122  formed more nodules (approximately 26–80 nodules per plant) and grew well in nitrogen-free medium . Meanwhile, the representative JC2- and JC3-type soybean cultivars inoculated with B. diazoefficiens USDA 122 formed many nodules (approximately 35–55 per plant) and the symbiotic phenotypes were similar to those inoculated with 122ΩrhcJ in terms of the number of formed nodules and growth   and newly identified sequence patterns JC2 and JC3 (E452/R490) with e medium . These rd growth . Therefo(I or R) .rj2-soybean cultivar Lee complemented with Rj2 and its chimeric genes. We prepared cDNAs of Rj2 obtained from cultivar Hardee and its chimeric DNAs that encode the Rj2 protein containing E452K and/or I490R substitutions , which were introduced using a binary vector, pUB-GW-GFP , and the rj2-type was detected in 1248 accessions (94.3%)  . The Rj2ng China .G. max accessions tested were mainly derived from Japan (995 accessions), we analyzed the geographical distribution of the Rj2/rj2 genotypes of G. max landraces in Japan. Rj2-genotype cultivated soybean was not found in the landraces that from Hokkaido (the most northern island) and Okinawa (the most southern island), but approximately 2–14% of G. max landraces originating in other areas (including mainland Japan) showed the Rj2-genotype  shows symbiotic incompatibility with a Rj2-incompatible strain [Rj2 gene in G. soja have not been identified. According to the results of our SNP-genotyping, the Rj2-type SNP was detected in 20 accessions (7.7%) of the 259 G. soja accessions from Japan, Korea, China, and Russia  from Japan were estimated to have the Rj2-genotype  which close to the latitude 35°N, and were not found in accessions originating from northern Japan or Hokkaido island  on the four selected G. soja accessions having the Rj2- or rj2-genotype  failed to form effective nodules after inoculation with B. diazoefficiens USDA 122, whereas those inoculated with 122ΩrhcJ formed significantly more nodules and grew well in a nitrogen-free medium  inoculated with USDA 122 formed effective nodules as efficient as those inoculated with 122ΩrhcJ and USDA 110T . Four accessions of Rj2-genotype G. soja inoculated with 122nopP110 formed significantly more nodules on the roots than did wild-type USDA 122 and the leaves of 122nopP110 were green, indicating nitrogen-fixing activity of the nodules  having high amino-acid sequence homology (94% identity) and containing rj2-type residues E452/R490. However, with a length of 538 aa, this protein was shorter than Rj2 of G. max ‘Hardee’  and without predicted LRR domain. Therefore, we determined the full-length cDNA sequence in the G. soja accession JP90948 (Rj2) by using 5′/3′ rapid amplification of cDNA ends (RACE). The deduced amino-acid sequence shows high homology with that of G. max ‘Hardee’  genes encoding the NBS-LRR protein are abundant in every plant species and the structures are highly diverse [Rj2 for a haplotype containing both E452 and I490 in the TIR-NBS-LRR class of the Rj2 protein, following allelic comparison of 21 soybeans including Hardee (Rj2) and Williams 82 (rj2). Based on allelic comparisons using the soybean mini-core collection [B. diazoefficiens USDA 122  Genebank  in the present study .The Bradyrhizobium strains were grown aerobically at 30°C in HM salt medium [Escherichia coli strains were grown at 37°C in Luria–Bertani medium [Agrobacterium rhizogenes was grown at 28°C in Yeast Extract Peptone (YEP) medium [E. coli, kanamycin (Km) at 50 mg l−1; for A. rhizogenes, chloramphenicol at 30 mg l−1 or Km at 100 mg l−1.Bacterial strains and plasmids used in this study are listed in t medium  supplemei medium . Agrobac) medium . The folThe genomic region of the second exon in the Glyma16g33780 locus of each soybean accession was obtained by PCR using PrimeSTAR Max DNA Polymerase  and oligonucleotide primers Glyma_Rgene_F2 and Glyma_Rgene_R2 . Total DG. max were surface-sterilized in 0.5% sodium hypochlorite for 1 min and then washed 10 times with sterile distilled water. Four seeds were sown in a 300 ml plant box  containing sterile vermiculite, and were watered with a nitrogen-free plant nutrient solution [Glycine soja seeds were scarified and surface-sterilized using concentrated sulfuric acid for 7 min. After washing, seeds were soaked in sterile distilled water and stored at 4°C overnight, and then sown as described above. Bradyrhizobium strains were cultured in liquid medium for 5 or 6 days. The number of cells was adjusted to 107 ml−1 in sterile water by direct counting using a Thoma hemocytometer . Aliquots of 1 ml of the cell suspension were inoculated onto surface-sterilized seeds of G. max or G. soja. Plants were grown in a plant growth cabinet  at 25°C with a photoperiod of 16 h light / 8 h dark. The seedlings were thinned out to one (for G. max) or two (for G. soja) per box 7 days after sowing. The number of nodules and the dry weights of nodule and plant were determined 28 DAI.Seeds of solution . GlycineRj2 and its chimeric genes was performed using hairy root transformation as described by Sugawara et al. [Rj2 cDNA were substituted with A1354 or G1469 by overlap extension PCR, and we used pMS145 (LjUbq1) promoter and the nos terminator, using Gateway LR Clonase II . These constructs were used to transfect A. rhizogenes K599. Six-day-old seedlings of rj2 soybean cultivar Lee were used for complementation testing; A. rhizogenes-mediated hairy root transformation was based on the protocol described by Kereszt et al. [A. rhizogenes K599 carrying Rj2 cDNAs. Infected seedlings were maintained in sterile vermiculite pots in a growth chamber under high humidity until hairy roots developed at the infection site (~2 weeks). These seedlings, with the main roots and non-GFP roots removed under a fluorescence binocular microscope, were cultured for 1 week in sterile vermiculite pots containing half-strength B&D solution [4NO3. The seedlings were inoculated with B. diazoefficiens USDA 122 under nitrogen-free conditions [Complementation of a et al. . We useda et al. , respectd pMS145  as the tt et al. . Brieflynditions , and nodrj2 cDNA sequence position 1470 of soybean cultivar Williams 82) were conducted using the iPLEX Gold kit  following the manufacture’s protocol. The reaction mixture was dispensed onto a silicon matrix preloaded SpectroCHIP  using Nanodispenser  and analyzed by Compact MassARRAY MALDI-TOF . The genotypes were determined using MassARRAY Typer4.0 .SNP genotyping was conducted using the MassARRAY system as described by Kaga et al. . BrieflyRj2 gene in one of the G. soja accessions was determined using the 5′ and 3′ rapid amplification of cDNA ends (RACE) technique with the SMARTer RACE 5′/3′ Kit (Takara Bio Inc.). To investigate the Rj2 cDNA sequences, first-strand cDNA was synthesized from total RNA using a SuperScript III First Strand Synthesis System for RT-PCR  with the gene-specific primer GsRj2_R  and additionally treated with DNase I  according to the manufacturer’s instructions. The full-length cDNA sequences of the  GsRj2_R . The ful GsRj2_R . The PCRP-values < 0.05 were considered statistically significant. Sample size (n) used for experiments is indicated in the figure legends.Statistical significance of formed nodule number between two groups was determined using a two-tailed Student’s t-test performed by Microsoft Excel for pairwise comparisons. For non-normal distribution data from comparisons of multiple test samples, significance of difference among groups was evaluated by Steel–Dwass test using JMP software . S1 FigRj2-gonotype of G. soja accessions inoculated with B. diazoefficiens USDA 122 or 122nopP110 28 days after inoculation. Error bars show SEM (n = 4). Significant differences from 122nopP110 were detected using Student’s t-test: *P < 0.01. (B) Shoots and roots of accessions JP90948 (Rj2) at 28 days after inoculation with B. diazoefficiens strains. Scale bar: 0.5 mm.(A) Number of nodules formed on roots of (PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


The LS video presented the longest time for first response (p < 0.001) and the AV video presented the shortest time for maximum response (p = 0.016). The LS video had the highest mean likeability rating with 1.43 ± 2.31 points; and the CM video had the lowest with 0.57 ± 1.77. Multiple linear regression models were created to predict the likeability of each video using the following physiological indicators; AV: power in beta band at C4 and P4 ; CM: alpha power in Fp2  and LS: alpha power in P4, F8, and Fp2; beta power in C4 and P4 and pupil size, . Despite its limitations  our findings suggest that there is a considerable difference in the psychophysiological correlates of stimuli with different contextual properties and that the use of time-dependent methods to assess videos should be considered as best practices.Liking is one of the most important psychological processes associated with the reward system, being involved in affective processing and pleasure/displeasure encoding. Currently, there is no consensus regarding the combination of physiological indicators which best predict liking, especially when applied to dynamic stimuli such as videos. There is a lack of a standard methodology to assess likeability over time and therefore in assessing narrative and semantic aspects of the stimulus. We developed a time-dependent method to evaluate the physiological correlates of likeability for three different thematic categories, namely: adventure (AV), comedy (CM), and nature landscape (LS). Twenty-eight healthy adults with ages ranging from 18 to 35 years (average: 23.85 years) were enrolled in the study. The participants were asked to provide likeability ratings for videos as they watched them, using a response box. Three 60-s videos were presented, one for each category, in randomized order while the participant’s physiological data  was recorded. The comedy video (CM) presented the smallest minimum accumulated normalized rating (ANR;  The brain reward system is composed of three subsystems involving learning, emotional and motivational processing, according to Berridge and collaborators. Respectively, these components are responsible for reward learning, liking and wanting  and arousal , we noted that only one study .This study involved healthy adults, aged 20–35 years old, with no history of neurological, psychiatric or cardiological diseases. To diminish socioeconomic biases, it was established that all participants were from socioeconomic classes A and B (middle class and above).The following were included as exclusion criteria: individuals with scores higher than 7 on the SRQ-20, a self-evaluation psychiatric screening questionnaire , comedy (CM) and nature landscape (LS), were selected from the Internet, following these selection criteria: (a) creative commons license or any license that allowed editing and reproducing for non-commercial purposes; (b) absence of verbal content; (c) at least 60 s of duration; and (d) 720p quality or higher. The selected videos were “Goliath Roller Coaster 4K POV Walibi Holland” (AV), “Centraal Beheer TVC 70–Acupuncture” (CM) and “Inukshuks under the stars” (LS).Videos were edited so that they were all the same size and durationEye movements and pupillometry were recorded using Tobii X2–60 fixed ET, a binocular video-based eye tracker with 60 Hz sample rate. EEG and ECG recordings were made using g.tec gUSBAmp 3.0 from g.tec medical engineering, an FDA and CE certificated amplifier with 24 bits resolution and 16 channels. Electrophysiological data was collected at a 256 Hz sample rate. Twelve channels were used for EEG recordings and two channels for ECG recordings. EEG was recorded with g. SAHARA active system of dry electrodes and ECG was recorded using AgCl passive gel electrodes. EEG electrode positioning was based on the study by Yılmaz et al.  and follLikeability rating was assessed through an Arduino-based response box, with a rotating potentiometer attached to an encoder. Participants were instructed to rotate the potentiometer clockwise to increase the likeability rating or counterclockwise to decrease it, according to their experiences during the video. This device has small ticks, which provide tactile feedback to the participant while rotating it. The reason for using a potentiometer instead of a more conventional approach, like a slider or some rating button models, is that this piece can be rotated infinitely, which allows participants to rate without being constrained by upper or lower limits. A light sensor connected to the monitor was used to synchronize the stimulus presentation, physiological, and likeability data collection.Experiments were conducted in a quiet, dimly lit room. Demographic data, socioeconomic data, education level, laterality, gaze dominance, hours of sleep, use of psychoactive drugs and history of psychiatric, neurological or cardiac diseases were all collected upon arrival. The SRQ-20 questionnaire was also filled out at this moment. Subsequently, participants were asked to sit in front of the monitor and the sensors were attached. After calibration and signal quality assessment, the baseline was recorded and the experiment was set to begin.An instruction screen was presented telling the participant to rotate the potentiometer to indicate his liking of the videos as he watched them. He was told to the potentiometer clockwise to indicate that he liked the video and counterclockwise to indicate the opposite. In this sense, the more the participants rotated the potentiometer towards a direction, the more intense the corresponding experience (liking or disliking). Participants were also instructed to avoid moving or blinking excessively during the video presentations.After being certain that each participant understood the instructions, the researcher left the room and the experiment started; a wireless doorbell was provided to call the researcher after the experiment was finished or in case of an issue. A 15-s resting interval was added between each video, to allow the participant to recover emotional homeostasis, avoiding carryover effects from one video to the next.Subjective liking was processed in three complementary analyses: accumulated normalized rating (ANR), sample percentage of response and moving rating sum. The purpose of the ANR was to highlight likeability trends over time. It was calculated by accumulating given ratings over time . This procedure was done in the whole interval and z-scores for all samples for each individual were then calculated; this approach is especially sensitive to the narrative aspects of the video, capturing accumulated variability over time.The purpose of the sample percentage of response was to create a manipulation test for the method, by assessing whether the participants presented similar response patterns to the stimuli over time, therefore evaluating its performance to assess group trends and providing validity measures for the method. It was calculated by dividing the data into 50% overlapped epochs of 1,000 ms  and determining the percentage of the sample which presented responses in each epoch, regardless of valence .The purpose of the moving rating sum was to assess granular likeability changes over time, highlighting the moments when the stimulus was most relevant. It was calculated by totaling the number of responses in 50% overlapped epochs of 500 ms, comprising 238 epochs. Each epoch was considered independently and each response that indicated liking (rotating clockwise) was coded as +1 and each response that indicated disliking (rotating counterclockwise) was coded as −1 . By these means, it was possible to determine the moments of maximum and minimum likeability of each video , which were used for subsequent physiological pattern comparison and the regression analysis. The purpose and processing methods for each indicator is summarized in Electrophysiological signals were filtered offline using finite impulse response bandpass filters (EEG 2–40 Hz and ECG 2–100 Hz) and a notch filter (60 Hz). Blinks and other eye movements were removed using independent component analysis and noisy channels were interpolated using spherical interpolation. Finally, the signal was visually inspected and sections with artifacts were removed. EEG data preprocessing was made using EEGLAB 14.1 , theta (4–8 Hz), alpha (8–15 Hz), beta (15–30 Hz) and gamma (30–40 Hz). In order to calculate the mean power in each frequency band, the frequency vector was divided into 0.5 Hz bins and the average values within the frequency intervals were calculated; these procedures were made using Fieldtrip  in the 5 s preceding the moments of maximum and minimum liking ratings were calculated using Kardia, a Matlab-based toolbox for cardiologic data processing and analysis  and maximum ratings (p = 0.049). Post hoc tests showed that CM video minimum rating is significantly lower than AV video minimum rating (p = 0.04) while no significant differences were found in the pairwise comparison of maximum rating. In addition, significant differences were found between the time to first response in each video (p < 0.001) and the time of maximum rating (p = 0.016). Post hoc tests showed that the AV and LS videos’ times to first response are significantly lower than CM video time to first response (p < 0.001 and p = 0.023 respectively); and the time of maximum rating of the LS video is significantly higher than the AV video’s time (p = 0.027). No significant differences were found between mean rating, time for minimum response, number of responses and the difference between the minimum and maximum ratings. Results are presented in further detail in Significant differences were observed in minimum .Significant differences in liking were found among each video for nearly 65% of the video’s total duration, more specifically at the following intervals: 0–8.129 s; 9.023 s to 9.172 s; 51.66 s to 52.77 s; 54.85 s to 54.91 s and 56.04 s to 58.17 s, as presented in AV video presented a mean sample percentage of the response of 30% ± 11% on each second, with a maximum of 61% between 5.5 s and 6.5 s, which is the moment just after the scene in which a rollercoaster goes downhill for the first time. The CM video presented a mean sample percentage of the response of 20% ± 9% in each second, with a maximum of 43% between 15.5 s and 16.5 s, which occurs during a scene in which acupuncture needles are being placed on the character. Regarding the LS video, the mean sample percentage was 34% ± 10% in each second, with a maximum of 57% between 19.5 s and 20.5 s, when a scene appears that features a beautiful landscape of tundra, river, and mountains on the horizon. The results for each video are presented in Post hoc tests showed differences between AV-CM, in 23%, AV-LS in 2% and CM-LS in 21% of the stimuli duration. The mean pair difference was 0.591 ± 0.339 points in AV-CM, 0.587 ± 0.354 points in AV-LS and −0.331 ± 0.390 points in CM-LS. The results for each video are presented in The maximum mean rating for the AV video was 1.36 ± 2.5 points at 5.75 s, corresponding to the same scene with the highest sample percentage of response; in the case of the CM video, the maximum mean rating was 0.57 ± 1.77 points at 52.75 s, which corresponds to the moment when the character has to decide whether to jump onto a safety net while his body is covered with needles or stay in a building on fire. Finally, in the LS video, the maximum mean rating was 1.43 ± 2.31 points at 36.25 s, which corresponds to a scene showing a stunning landscape with colorful flowers, mountains, and a lake. Significant differences were found in 47% of the duration of the stimulus. F = 6.822, p = 0.004, adj. R2 = 0.301, RMSE = 1.95. Two variables added statistically significant results to the prediction p < 0.05: beta power in C4 and P4. Regression coefficients and standard errors can be found in A multiple linear regression was run to predict the likeability of the AV video based on EEG and ECG data. The multiple regression model significantly predicted the likeability rating for adventure, F = 14,079, p = 0.001, adj. R2 = 0.326, RMSE = 1.91. Only one variable added significant results to the prediction p < 0.05: alpha power in Fp2. Regression coefficients and standard errors can be found in A multiple regression was run to predict the likeability of the CM video from EEG, ECG and ET data. The multiple regression model statistically significantly predicted the likeability rating on comedy, F(6.21) = 5.304, p = 0.002, adj. R2 = 0.489, RMSE = 1.91. Six variables added significant results to the prediction p < 0.05: alpha power in P4, F8, and Fp2; beta power in C4 and P4 and pupil size. Regression coefficients and standard errors can be found in A multiple regression was run to predict the likeability of the landscape video from EEG, ECG, and ET data. The multiple regression model significantly predicted the likeability rating for the LS video, The study of the affective reactions induced by complex stimuli such as videos and the dynamics of its psychophysiological correlates over time can be a challenging, yet important, task. Videos are among the most engaging and expensive media products and the use of time-dependent methods to assess affective reaction fluctuation scans provide valuable information which is not observable through conventional methods. This study is among the first to employ this kind of technique, providing a proof-of-concept on how to assess likeability and its psychophysiological correlates in three stimuli with marked contextual differences.The results of the first phase have shown that there are distinct patterns among each video, which reflect the videos’ narrative and semantic natures. The AV video presents results that could be described as following a rational curve trend, with a fast rating increase in the beginning of the presentation followed by a progressive deceleration towards the end of the video. This pattern suggests that the video produces an increase in arousal in the beginning, which is accompanied by a higher frequency of responses compared to the other video modalities, followed by a habituation phase, in which we observe a decrease in the responses. The rating curve of the LS video resembles a line with a constant increase rate; this pattern suggests that it is positive-valenced stimuli, presenting lower arousal than the AV video.The physiological pattern underlying the duration of the CM video resembles a polynomial curve, beginning with few responses until 15 s, then establishing a descending pattern until nearly 50 s, when inversion occurs, presenting a fast rating increase. This three-phase pattern is common in comedy narratives: the presentation of the characters and the context happens in the beginning and then consecutive events create a minor tension, which is liberated by the comedic “gag” itself ; and resolution, in which the incongruent element is linked in a meaningful way to rest of narrative, resolving the incongruity, e.g., the character has to choose between dying or facing a great deal of pain, . We opted for this approach to deal with overfitting issues and allow direct comparisons with the literature, which was particularly important, given that application of this method to evaluate the psychophysiological correlates of liking videos is innovative.Univariate analyses presented significant differences in most of the selected variables. The ones with the greatest difference between the EEG power in maximum and minimum ratings were theta in F1 for the adventure, alpha in P4 for the comedy and theta in P4 for the landscape video. These results are aligned with previous findings relating frontal asymmetry and parietal asymmetry, which are consistent with emotional regulation involved in approach-withdraw processing and valence coding .The adoption of a multiple regression linear model, a classical technique, relied on its clear interpretability of the weights of each predictor over the dependent variable, directly answering the questions of “what is the best combination” and “what is the corresponding importance of each physiological measure to predict liking for stimuli with distinct semantic categories while using a time-dependent technique.” All models presented significant results and were able to explain the difference between the maximum and minimum rating with 30% more accuracy than by chance , it was the one that presented a final model which needed the fewest variables to explain the differences in minimum and maximum liking. Indeed, with only the information on the spectral power in alpha in Fp2 at 688 ms before the rating response, it is possible to predict the likeability score with more than 30% accuracy beyond chance. Interestingly, our results are contrary to the literature , which resulted in loss of information, given that only acceptable data was used in the study. Despite using the same monitor settings throughout the entire experiment, it was not possible to correct the brightness in all scenes across stimuli, which may have influenced the pupillometry.In regard to the likeability assessment, as with any self-reported measure, the results are widely influenced by individual variability and this study was not an exception . In addition, there was no comparison with any standard evaluation methods for likeability assessment, such as SAM, or even a global scaling technique.In terms of the predictive model, it is important to note that not all existent indicators found in the literature were investigated [such as skin conductance level  and context- sensitive setup. The use of a time-dependent measure can provide valuable information, such as narrative nuances, sample response over time and scene relevance, which cannot be assessed through traditional methods. Conversely, it is possible to establish the physiological correlates of likeability in a much more precise manner, by pinpointing the most relevant moments of the video and using their corresponding physiological patterns in order to evaluate not only which combination of indicators has the best predictive power, but also the optimal time interval. Despite the methodological limitations of the current study, these findings have important implications for the field of consumer and affective neuroscience, suggesting that there is a considerable difference in the psychophysiological correlates of stimuli with different contextual properties and that the use of time-dependent methods to assess videos should be considered as best practices.This study was carried out in accordance with the recommendations of the National Health Council of Brazil , ethics committee for research involving human beings from the Institute of Psychology of the University of Sao Paulo. The protocol was approved by the ethics committee for research involving human beings from the Institute of Psychology of the University of Sao Paulo. All subjects gave written informed consent in accordance with the Declaration of Helsinki.HA: study design, data processing and analysis, manuscript writing. MC: study design, manuscript review. JG and PS: manuscript review. EO: data processing and analysis, manuscript review. AD: study design, manuscript writing, manuscript review.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Informal settlements pose a continuing health concern. While spatial methodologies have proven to be valuable tools to support health interventions, several factors limit their widespread use in these challenging environments. One such technology, spatial video, has been used for fine-scale contextualized mapping. In this paper, we address one of the limitations of the technique: the global positioning system (GPS) coordinate error. More specifically, we show how spatial video coordinate streams can be corrected and synced back to the original video to facilitate risk mapping. Past spatial video collections for the Mathare informal settlement of Kenya are used as an illustration as these data had been previously discarded because of excessive GPS error. This paper will describe the bespoke software that makes these corrections possible, and then will go on to investigate patterns in the coordinate error. Informal settlements (IS) pose a considerable global challenge as they are the sites of multiple health, environmental, and social problems. At the same time, they are constantly changing and are data poor. To counter this spatial data deficiency, public health or epidemiological initiatives often rely on global positioning system (GPS) support. One example of a geospatial technology that has been used in challenging environments is spatial video (SV). SV combines imagery with locational information and can be used as a digitizing source for both cross-sectional and longitudinal research ,2,3. OneThis paper will show how problematic GPS coordinate paths can be corrected and then synced to the original video. This is not an improvement of the actual GPS signal, or even an automated search for error, but rather an SV-specific post-processing procedure. To illustrate this, we will use SV data collected over a three-year period for the Mathare IS in Kenya. IS mapping occurs for a variety of reasons including establishing legitimacy , identifMapping IS environments is critical for many reasons, for example addressing the area’s high disease burden ,14. InteResearch in the developed world has considered similar GPS problems, for example, in association with accelerometers . Issues Solutions beyond simple imputation have included recreating activity paths using supporting data, such as photographs, video, or recall . For SV In this paper, we return to archived SV collected in the Mathare IS of Nairobi and show how bespoke software can correct and even create coordinate paths that can be linked back to the original video. We describe the process of how this correction occurs before using the newly created SV to explore the location and extent of the original GPS error.The Mathare IS is located within Nairobi County, Kenya, and along with the Kibera IS, present two of the oldest and most health-challenged settlements in Kenya. Limited access to safe water, unsanitary practices including open defecation and trash dumping, high human density often in close proximity to disease-causing environmental factors (such as standing water), and generally unsafe living conditions all occur in the IS . In MathMapping water points, water fetching routes, human activity and domiciles, localized water flow, and other micro-environmental risks could all be vital in preventing disease in Mathare ,34. AttrSpatial video transects were collected by a local researcher for the Mathare slum between 2013 and 2015 using a hand-carried Contour + 2 camera. This is a simple to use and robust video camera designed for extreme sports use. The camera contains an internal GPS receiver, which means locational information is attached to each second of video. The initial goal of data collection was to map fine-scale health-related environmental risks across different time periods. All SVs were either uploaded through the cloud or delivered by carrier to the GIS Health and Hazard Lab at Kent State University. An initial assessment of these videos found that the quality of the GPS paths varied dramatically due to the physical conditions within Mathare. As a result, it was not possible to use these as a digitizing resource and these data were discarded for approximately two years. With advances in spatially supported programming tools, these SVs were again reassessed in 2017 for the purposes of mapping. The GPS was extracted from each usable video, meaning there was useful content lasting at least 30 seconds. These GPS coordinates were input into the bespoke software that allows for the creation of a new (corrected) path, which could be synced back to the original video. This new SV could then be used as a source to digitize risks in the same software using a Google Maps interface. The GPS correction software, which was built on freely available software services including Google Maps API, YouTube API, and the JavaScript framework Data-Driven Documents (D3), uses a linear spatial interpolation algorithm to create the new GPS path. To add imagery to help the correction, the YouTube Iframe API was used to embed videos to webpages with JavaScript being used to control the YouTube player in the webpage. As has been previously mentioned, the issue of GPS coordinate dropouts is well known. However, Contour Storyteller software (which is the supporting software of the SV camera) masks these omissions by imputing between the coordinates on either end of this dropout. The resulting path in Contour Storyteller does not distinguish between actual coordinates and the imputed path. Our new SV player accounts for these errors by color coding actual GPS points and filling between the coordinate gaps with an interpolated line.The user views the video and matches it with the extracted GPS path. When this GPS path wanders from where it should go (ascertained by comparing the video and overhead imagery), the user then manually corrects the coordinates. This is done by using easily discernible landmarks such as notable buildings, empty spaces, water bodies, vegetation, roads (and turns), bridges, and rivers/drainage canals. Pin markers are manually dropped onto the map when these features are seen on the YouTube video, with the software adding the associated media time stamp from the video as an attribute to the pin. This same approach can be used to backtrack from a GPS coordinate to “fill in” sections of the path where there had been no coordinates. For example, in 1,y1,t1> and <xn,yn,tn>, the linear interpolation algorithm generates the series <x2,y2,t2>, <x3,y3,t3>, …, <xn−2,yn−2,tn−2>, <xn−1,yn−1,tn−1>. Therefore, if we have two track points with a data tuple of the form <x1,y1,t1> and <xk,yk,t20> and there is a temporal difference of 20 seconds, the algorithm calculates the slope for the line between <x1,y1> and <xk,yk> and uses this information to generate 20 points between <x1,y1> and <xk,yk>. The assumption of slope being constant works for straight segments but tends to be erroneous when there are sharp turns. To counter this, or on generally non-regular paths, the user provides additional reference points as needed.More specifically, each pin  is encoded with both a location attribute and an associated time stamp from the video; location <x,y> at video time t seconds will have the information as a triplet of the form <x,y,t>. A linear interpolation algorithm is used to generate spatiotemporal triplets between two track points that are separated in space and time. For two spatiotemporal triplets of the form <xAfter GPS correction, the new path is downloaded as a GPX GPS file. The video can then be synced back to the GPS as a JSON file, in effect creating a new SV where the video progresses simultaneously with a cursor on the map. This JSON file has a small data footprint and can easily be emailed between collaborators. Validation of the new data can occur by matching newly digitized locations to secondary data sources, such as independently corrected GPS locations. Alternatively, this can be achieved where two different SV paths intersect at the same location, such as when routes are collected at different times . During the period August 2013 to October 2015, 26 handheld SVs were collected in the Mathare informal settlement of Kenya. All of these SV coordinate paths were improved using the GPS correction software. In previous research, GPS paths in an IS have been used to gain insights into different activities, such as water fetching. However, the physical distance as calculated by just using uncorrected GPS paths could lead to both over-  and underestimation (due to missing sections of coordinates). To determine the degree of this error, we calculated the differences in distance between each of the corrected and uncorrected paths. The amount of overestimation is considerable, with 85% of the corrected paths being less than 1 km long as compared to 69% of the uncorrected paths. An alternative approach to visualize this error is to map the differences between the corrected and uncorrected paths at every coordinate. As the SV progresses, each frame has a corresponding coordinate and, therefore, we can measure the distance between the frame coordinate of the uncorrected and corrected paths. The error for each path was then visualized using a kernel density estimation (KDE), which is a common technique applied in epidemiological studies to show variations in spatial intensity. In this case the error for every corrected coordinate on all the SV routes was combined and used as input into the KDE. To further consider the geographic pattern of this error, IS neighborhoods continue to pose one of the greatest global health challenges. Yet, an important public health intervention tool having updateable, fine-scale mapping remains largely elusive in these environments. As health resources tend to be limited, having this type of spatial support is essential. It is vital to know where health risks are greatest, how these risks change over time, and where patients reside and how best to reach them. Spatial video and geonarrative collection provide an exciting opportunity to remedy this. The low cost and relative ease of use of these techniques also makes longitudinal collection possible, which is vital for capturing these ever-changing environments. An achievable goal using these technologies would be a comprehensive spatial characterization of health risks; for example, which water points are safest at any time period. Risks could also be identified around the home, along known pathways, and in the activity/play spaces of children. In other words, we can map a complex environment, dynamic in nature, contextualized with insights that not only explain why risks occur but also how local response strategies have developed. In this way, newly settled families could, for example, be warned about and then told how to deal with externalities, such as flooding and the human waste it brings into the home.This type of near real time mobile geospatial support is not wishful thinking. There are already localized mapping initiatives used by residents and activist groups wanting to improve the quality of existence in Kenyan IS. As SV cameras become even smaller and more powerful—the new generation of police body-worn cameras being a good example—this technique could become a standard part of a researcher or health official’s toolkit. A further benefit would be to bring local community involvement into the process through data collection and knowledge creation. In so doing, existing online maps showing key features could be continuously updated with more  detail .As one example, the previously described Map Kibera crowdsourcing group have also produced an online map of Mathare. While the online Mathare map covers a much larger area than that described in this paper, there is still an overlapping section containing eight toilets and six water points. SV paths used in this paper intersected with six of the toilets, though only four were visible on the SV (one for two different time periods). All six of the water points fell on SV paths with four being visible in the video. Of these, one water point was visible for three different time periods and another for two. It is not hard to imagine how SV updates could regularly update this map. They could even provide additional detail through images, mapping of the surrounding environment, and narratives of local concerns. By adding in biological testing, such as water point quality, a dynamic web map could be created that would inform both residents and professionals about their best water choices in near real time. This type of quality control map would be essential for the wellbeing of residents, especially during disease outbreaks. However, an impediment to such SV use in IS environments has traditionally been the poor quality of the GPS coordinates. While traditional GPS use can potentially be improved by using aerial photography to drop a point at the correct location, SV use requires a more complicated correction procedure that links the new coordinate stream back to the original imagery. In this paper we have shown how correction is possible, meaning that past and future SV imagery can now be used to map out health risks and document fine-scale change. In addition, the same software allows for basic mapping, meaning that the combination of GPS correction, risk mapping, and online availability of the videos make this a ubiquitous IS resource.The paper has also shown geographic patterns in the original coordinate error. While these are not surprising , they do reveal two considerations that are important for future work. First, even with more powerful GPS units, these errors, though reduced in intensity, are still likely to occur. Secondly, it should be possible to develop automation procedures to correct GPS coordinate paths. By knowing the coordinates of key features (ascertained from previous SV paths), time differences that span error hotspots could be used to impute the correct SV coordinate path. Currently, this assumes the SV path goes in the same direction and at the same speed but as more pathway overlays are collected, so more reference points are added and direction becomes unimportant. Eventually, a grid of correctly referenced features and the paths connecting them would only require the SV to be snapped into place with far less manual involvement. While this next step is being worked on, the correction approach described in this paper means that IS initiatives should already consider using SV for localized mapping and health promotion. There are several limitations to the implementation of the coordinate correction software. Firstly, the video needs to be uploaded to YouTube, which can be problematic if internet speeds are slow. The authors are currently working with NSF funding to develop new spatial video compression techniques that will improve this problem. Once uploaded, however, Internet speed is less important, with the biggest issue then being able to view high definition YouTube videos. Secondly, the accuracy measure of the newly corrected coordinate path is dependent on three things: the effort of the user, the quality of the overhead imagery, and the type of environment. If the user is dedicated, has access to high-resolution overhead imagery (which can be used (in combination with the video) to identify where the path goes), and the landscape is relatively heterogeneous, then there is no reason why the path cannot be accurately recreated. Thirdly, while this software is free and available from the authors on request, it is not currently available for download on a public website with technical support. Until such a time, the authors welcome any request for access to the software or code.Issues regarding inter-rater reliability, the effort required to turn SV into usable maps, and the ethics and privacy issues regarding spatial video use have been covered in several prior publications ,43,44.In this paper, we have shown that new spatial software can correct or even create GPS paths that can be synced back to the original video. This allows for existing SV collections to be repurposed, and even ordinary video spatialized through the geocoding of key features on the video. As a result, SV can now be used to update existing maps, create better fine-scale contextualized geographic data for research and health intervention, and generally make available the types of health risk mapping that have not been commonly available in these types of environments.


Aeromonas hydrophila (A. hydrophila) is an opportunistic bacterial pathogen widely distributed in the environments, particular aquatic environment. The pathogen can cause a range of infections in both human and animals including fishes. However, the application of antibiotics in treatment of A. hydrophila infections leads to the emergence of resistant strains. Consequently, new approaches need to be developed in fighting this pathogen. Aerolysin, the chief virulence factor produced by pathogenic A. hydrophila strains has been employed as target identifying new drugs. In our present study, we found that morin, a flavonoid without anti-bacterial activity isolated from traditional Chinese medicine, could directly inhibit the hemolytic activity of aerolysin. To determine the binding sites and the action of mechanism of morin against AerA, several assays were performed. Ser36, Pro347, and Arg356 were identified as the main binding sites affecting the conformation of AerA and resulted in block of the heptameric formation. Moreover, morin could protect Vero cells from cell injury mediated by aerolysin. In vivo study showed that morin could provide a protection to channel catfish against A. hydrophila infection. These results demonstrated that morin could be developed as a promising candidate for the treatment of A. hydrophila infections by decreasing the pathogenesis of A. hydrophila. Aeromonas hydrophila (A. hydrophila) is a gram-negative aquatic bacterium widely distributed in aquatic water worldwide leading to a number of diseases in fish , aerolysin (AerA), enterotoxins, and acetylcholinesterase which contribute to the pathogenicity of the bacterium  infected with A. hydrophila.Morin Figure , a FlavoAeromonas hydrophila strain XS-91-4-1 was isolated from diseased Hypophthalmichthys molitrix XS-91-4-1 was cultured in brain heart infusion (BHI) medium at 28°C for experiments described below. Morin (purity > 98%) and enrofloxacin (purity > 98%) were obtained from National Institutes for Food and Drug Control . Stock solutions of both drugs were prepared in dimethyl sulfoxide . For in vivo study, morin was dissolved in sterile PBS. The minimum inhibitory concentrations (MIC) of morin and enrofloxacin were determined by broth micro-dilution method recommended by Aeromonas hydrophila strain XS-91-4-1 was cultured in BHI medium at 28°C to obtain optical density at 600 nm (OD600nm) of 0.3, and then the culture was aliquoted into a 100-ml flask and a series of morin with a final concentration of 0, 4, 8, 16, 32, and 64 μg/mL were added. The mixture was further cultured for 5 h at 28°C. Cell growth was evaluated by recording the OD600nm values at intervals of 30 min using a spectrophotometer.A. hydrophila co-cultured with different concentrations of morin ranging from 1 to 8 μg/mL. Hemolytic assays were performed according to previous reports (6 cells/ml) in hemolytic buffer . For hemolytic activity of purified AerA, 2 μL of concentrated protein were added into 975 μL hemolytic buffer, 25 μL defibrinated sheep red blood cells were added after incubation at 37°C for 15 min. Following centrifugation, the hemolytic activities of the mixtures were determined by measuring the absorption at 543 nm after further incubated at 37°C for 20 min. Morin-free group was served as 100% hemolytic control.Hemolytic activities were performed using purified AerA and supernatants of  reports . For hemA. hydrophila strain XS-91-4-1 was incubated at 28°C in BHI medium with morin at concentrations of 1, 2, 4, and 8 μg/mL to OD600nm = 1.5. Supernatants of the culture were centrifuged and the supernatants were used for sodium dodecyl sulfate (SDS)-polyacrylamide (12%) gel electrophoresis, BHI medium was employed as negative control. Then a semi-dry transfer cell was used to transfer the proteins onto a polyvinylidene fluoride membrane. After blocking the membrane with 3% skim milk for 60 min at room temperature, an anti-aerolysin primary polyclonal antibody was incubated with the membrane at 1:1000 dilution overnight at 4°C, and followed incubating with a HRP-conjugated secondary goat anti-rabbit antiserum (diluted 1:5000) for 1 h. The blots were then treated with ECL western blotting detection regents.Western blot assay was performed as reported elsewhere . In brie1. The structure of morin was drawn by ChemBioDraw Ultra 12.0 and ChemBio3D Ultra 12.0 software. The files for docking were generated by the AutoDockTools 1.5.6 package  pAerA was constructed as we reported previously . PlasmidAK) of morin to WT-AerA and mutants were determined by the fluorescence quenching method using a Cary Eclipse fluorescence spectrophotometer  as described previously  of oligomerization. The mixtures were then loaded onto 8% SDS-PAGE gels for electrophoresis after incubated at 4°C for 1 h.2 in a humidified incubator. Cells with a density of 1.5 × 105 per well were seeded into 96-well cell culture plates. After incubated for 16 h at 37°C with 5% CO2, cells were co-cultured with 100 μL of AerA at a concentration of 1 μg/mL and indicated concentrations of morin at 37°C for 24 h. All assays were performed in triplicate measurements.Vero cells were obtained from the American Tissue Culture Collection (ATCC) and were cultured in DMEM supplemented with 10% fetal bovine serum at 37°C with 5% COThe determinations of cell viability were performed by lactate dehydrogenase (LDH) release using a Cytotoxicity Detection Kit and live/dead assay with a live (green)/dead (red) regent. Dead cells were stained by propidium iodide with a fluorescent-red dye, while live cells were stained by calcein AM with a fluorescent-green dye. Cell images were taken by a confocal laser scanning microscope . LDH activity was determined on a microplate reader .All animal assays were carried out according to the experimental practices and standards developed by the Animal Welfare and Research Ethics Committee at Yangtze River Fisheries Research Institute. All the assays were approved and supervised by the animal care committee (Permit No. 20171105-009C).A. hydrophila by injecting 200 μL XS-91-4-1 suspension intraperitoneally. Fish injected with sterile PBS served as negative control. Infected channel catfish were administered with 25 mg/kg of morin or PBS 6 h postinfection and at 12-h intervals for 3 days. For negative control, fish were administered with PBS at the same intervals. Each group contains 10 channel catfish. The death of each group were monitored every day for 8 days.Channel catfish weighing 200 ± 5 g were separated into three groups and maintained in 100 L glass aquaria tanks at 28°C for 15 days before infection. Channel catfish were infected with t-test with SPSS 14.0 statistical software . Survival rate of channel catfish was analyzed with Kaplan-Meier test, log-rank test was carried out to analyze the significance of different groups. A p-value <0.05 was considered to be statistically significant.The experimental data were compared by independent Student’s A. hydrophila. According to the results of MICs, morin had no evident inhibitory effect on A. hydrophila XS-91-4-1 strain, while the MIC of enrofloxacin was 4 μg/mL. Moreover, the results of growth curves with different concentrations of morin showed that morin could not influence the growth of A. hydrophila XS-91-4-1 strain from the concentration of 4–64 μg/mL . As shown in Supplementary Figure Van der Waals, solvation, and total contribution of the residues to the binding free energy were analyzed using the MMGBSA method to evaluate the binding sites and contribution of residues to the system. The per residue interaction free energies were separated into Van der Waals (ΔvdwE), electrostatic (ΔeleE), solvation (ΔsolE), and total contribution . In the pAerA–morin complex, the residue Asp-360 have a strong electrostatic (ΔeleE) contribution, with the value of <-9.0 kcal/mol . In addition, the total binding free energy for the pAerA–morin complex calculated according to the MMGBSA approach, and the an estimated binding free energy (ΔbindG) of -14.4 kcal/mol was found for morin, which revealed that morin could strongly bind to and interact with the binding site of the pAerA. Moreover, to confirm the main binding sites of morin–AerA system, ΔbindG of morin binding to active site of AerA was calculated  and the number of binding sites (n) between morin and proteins calculated by the fluorescence quenching method were obtained, indicating that findings obtained by the computational methods could be used for further research. Moreover, the oligomerization assay were carried out to analyze the mechanism of morin inhibiting the activity of AerA. As expected, morin could reduce the production of heptamer in a dose-dependent manner  were then treated with either 25 mg/kg morin or sterilized PBS as control. As shown in Figure A. hydrophila alone exhibited swelling and ulcers. Fish administered with PBS resulted in 90% death, while 30% of fish treated with 25 mg/kg morin .According to previous studies, the pathogenicity of fections . Our resn Figure . In concA. hydrophila, a typical pathogenic strain of aquatic environment, has been recognized as a human pathogen responsible for several diseases in human and other terrestrial animal worldwide , Streptococcus pneumoniae (S. pneumoniae), A. hydrophila, Clostridium perfringens (C. perfringens), and Escherichia coli (E. coli)  . PFTs haE. coli) . Studies in vivo .A. hydrophila was contributed by expression and secretion of extracellular virulence factors  and AerA (aerA) gene were double mutated  produced by A. sobria, the mechanism and binding site of morin inhibiting AerA were determined using molecular dynamics and fluorescence quenching methods (in vivo effect against A. hydrophila infection has been evaluated by a channel catfish model. As expected, 25 mg/kg morin could provide a protection of 70% to fish infected with A. hydrophila. Rosmarinic acid has been reported that could inhibit the hemolysis induced by A. hydrophila supernatant by down-regulate the transcription of aerA and ahh1 (in vivo effect was achieved (A. hydrophila infections than small molecules, the protection against different A. hydrophila isolates needs to be further studied (A. hydrophila both in vitro and in vivo, more efforts are still needed before morin can be used in aquaculture farming and human clinic. Moreover, the risk of A. hydrophila infections after antibiotic treatment has not been clarified, thus antibiotic treatment combined with morin is not recommended before any experimental data was carried out. Despite this, the findings provided a new approach in identifying novel drugs against A. hydrophila infections. Collectively, it is reasonable to infer that morin may be a potential agent for the treatment of infections caused by resistant A. hydrophila.There have been several successful attempts identifying small molecules inhibiting the activity or expression of AerA and antibodies. In our previous study, we found that magnolol isolated from traditional Chinese medicine could significantly reduce the mortality of channel catfish infected with  of aerA . In this. aureus . Similarsuilysin . However methods . Moreoveand ahh1 . The inhachieved . Some meachieved . Another studied . AlthougJD and XA conceived the project. JD, XA, and YL designed the experiments. YL, NX, and QY performed the experiments. JD and XA wrote the paper and all authors made the editorial input.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Over entire drinking maneuvers, intervals between broadcast pulses accommodate both the proximate task of registering the water surface while drinking and registering echoes from the farther reaches of the scene. During approach to the water, both low and high frequency bats emit longer, more stable interpulse intervals that matched the time interval covering echo arrival-times out to the frequency-dependent maximum operating range. High frequency bats use shorter interpulse intervals than low frequency bats, consistent with the shorter operating range at higher frequencies. Bats then accelerate their pulse rate to guide the dive down to drinking, with low frequency bats continuing to decrease pulse intervals and high frequency bats maintaining a more steady interval during the drinking buzz. The circumstance that both groups were engaged in the same task made this a natural experiment on the behavior during approach.During nightly foraging activity, echolocating bats drink by flying low over the water surface and dipping the lower jaw while avoiding further bodily contact with the water. This task poses different sensorimotor challenges than flying in the open to forage for insects. Of interest is how bats adjust the timing of their echolocation pulses to accommodate the surrounding scene, from the progressively nearer water surface itself to objects at longer distances. Drinking behavior has been described in only a few of the roughly 1,000 echolocating bat species, and in none of the 110 species in the Indian subcontinent. Here, we describe how bats emitting frequency-modulated (FM) echolocation pulses behaved while drinking from a swimming pool in urban northeast India. At least two different bat species were present, using 1 Research on echolocation is concerned with what bats perceive, how the sounds they broadcast affect their perceptual space, and how differences in the emitted sounds relate to perception in the context of the surrounding scene . Work tyEcholocating bats, like many animals, drink to supplement their water requirements –7. Bats When frequency-modulated (FM) bats forage in the open for flying prey, they systematically change the timing of their echolocation pulses as they approach the target to produce recognizable search, approach, and terminal-buzz stages , 19. PulBarbastella barbastellus, Eptesicus serotinus, Hypsugo bodenheimeri, Hypsugo savii, Myotis mystacinus, Myotis nattereri, Nyctalus leisleri, Pipistrellus kuhlii, Pipistrellus pipistrellus, Pipistrellus pygmaeus, Plecotus auritus in Italy . Mean IPI was 99 ms for LF bats and 77 ms for HF bats. During the drinking buzz stage, HF bats maintained a steady IPI  averaging 12.52 ms; conversely, in LF bats, IPI continued to decrease over time .All drinking bouts began with echolocation pulses at long IPIs during the approach. IPI decreased slightly over time and ended about 700 ms prior to drinking . This apThe internal dynamics of echolocation pulse streams during the entire drinking bout is further illustrated by a contingency plot  of the rAcoustic parameters of pulses in the approach and drinking buzz stages differed with frequency operating range Figs  and 7. BLF and HF bats differed in the number and IPI of pulses that they produced within the drinking buzz sequence . LF batsSummary data for all bats, organized by LF or HF EF group, are provided in Prior studies on drinking behavior of FM bats from other parts of the world –14 indicOne unique observation from our study is the presence of an occasional terminal pulse at the very end of the drinking buzz, just before the splash, that differs from the preceding pulses in the buzz. These were observed in just three separate drinking bouts, and only from low frequency bats. The significance of these terminal pulses cannot be determined due to the small sample size within our study; nevertheless, it is important to report here so its presence or absence can be noted in future drinking studies. These terminal pulses were not noted in prior drinking investigations , so it iPipistrellus [Our recordings identify two bat groups with different operating ranges of FM echolocation pulses, which we divided into those with EF of the first harmonic of less than 40 kHz and those with EF greater than 40 kHz. This 40 kHz dividing frequency is consistent with the 35 kHz dividing frequency used to categorize North American species by acoustic monitoring in field studies , 27. Thestrellus .The data reported here have ramifications for understanding the bat’s accommodation to acoustics at different frequencies. The maximum operating range of echolocation is limited by the strength of broadcasts, the distance to objects that return detectable echoes, and the target’s reflective strength .2 = -89 dB; atmospheric absorption of 1.1 dB/m × 2 × 17 m = 37 dB). The 77-ms mean approach IPI of the HF bats corresponds to a spatial extent of 13 m in range. Echoes returning from the small ideally-reflective target would be attenuated by 129 dB . Assuming that all the bats emitted pulses of 130 dB SPL at a location 10 cm in front of the mouth [Each emitted pulse propagates outward, away from the bat, in a spherical spreading wavefront that declines in sound pressure in proportion to the distance traveled. Along the way, it accumulates an additional decline due to atmospheric absorption. After reflecting from the target, the returning echo undergoes an identical spreading loss if the target is small, such as an insect, plus the same amount of atmospheric absorption. The total decrease in the strength of the sound as an echo is proportional to the reciprocal of the square of the distance plus atmospheric absorption for twice the target’s distance due to the outgoing and returning path lengths combined. Echoes become weaker as distance increases until the strength of the echo falls below the threshold for echo detection. Atmospheric absorption is greater at higher frequencies, so other features of the pulses being equal, lower broadcast frequencies translate into longer echolocation operating ranges, both for detection of individual insect targets and for perception of the surrounding sonar scene as a whole . If objehe mouth .While the dynamics of IPIs do seem to be linked to the acoustics of echolocation operating ranges in the approach stage, they are different during the drinking buzz stage. Here, when the bat is in very close proximity to the water and is closely engaged in control of its flight altitude and posture with respect to the water’s surface, pulses are emitted in rapid bursts. These bursts are more abrupt for the HF bats, which adopt short, stable buzz IPIs quickly, than for the LF bats, which decrease their IPIs more gradually during the buzz . The HF In conclusion, our results demonstrate that FM bats in northeast India operating in different frequency ranges use different acoustic strategies for the complex sensorimotor challenge of drinking while in flight. Both LF and HF echolocators reduce pulse IPI as they approach the water surface, but in the final stages of drinking, two patterns emerge. LF bats continue to reduce pulse IPI over time, without reducing buzz pulse EF, while HF bats maintain a constant IPI during the buzz with a reduction in buzz EF. Additionally, some LF bats emit a different terminal pulse, just prior to water contact, that is separate from the drinking buzzes. Collectively, our results demonstrate that drinking behavior follows a stereotyped behavior of approach and buzz pulses similar to that seen in pursuit of insects in open environment, but acoustic behavior within the buzz and prior to water contact can vary among bats. Further acoustic investigations into bat water detection and approach can help elucidate universal or species-specific adaptations in echolocation behavior during drinking.