The purposes were to investigate the drinking patterns and hydration biomarkers among young adults with different levels of habitual total drinking fluids intake.1 (low drinker), LD2, LD3 and HD (high drinker) groups, stratified according to the quartiles of total drinking fluids, were compared using one-way ANOVA, Kruskal-Wallis H test and chi-square test.A cross-sectional study was conducted among 159 young adults aged 18–23 years in Baoding, China. Total drinking fluids and water from food were assessed by 7-day 24-h fluid intake questionnaire and duplicate portion method, respectively. The osmolality and electrolyte concentrations of the 24 h urine and fasting blood samples were tested. Differences in LD1, LD2 and LD3 groups (p < 0.05). Participants in HD group had higher amounts of water and water from dishes than participants in LD1, LD2 and LD3 groups (p < 0.05). No significant differences were found in the contributions of different fluids to total drinking fluids within the four groups (p > 0.05). The osmolality of urine was 59–143 mOsm/kg higher in LD1 than that in LD2, LD3 and HD group (p < 0.05). The percentage of participants in optimal hydration status increased from 12.8% in LD1 group to 56.4% in HD group (p < 0.05). HD and LD3 groups had 386~793 higher volumes of urine than that of LD1 and LD2 groups (p < 0.05). Differences were found in the concentrations of electrolytes among the four groups (p < 0.05). No significant differences were found in the plasma biomarkers (p > 0.05), with the exception of higher concentration of Mg in LD3 and HD groups than that in LD1 and LD2 groups (p < 0.05).A total of 156 participants  completed the study. HD group had greater amounts of TWI , water from food, higher and lower contributions of total drinking fluids and water from food to TWI, respectively, than LDParticipants with higher total drinking fluids had better drinking pattern and hydration status. Interventions should be undertaken to advise adults to have adequate total drinking fluids, in order to keep in optimal hydration status.ChiCTR-ROC-17010320, which was registered on the Chinese clinical trial registry.The registration number was  Water is of vital importance for the health of humans. Water accounts for almost 50–60% of the total body mass of adults . MoreoveTo keep the body functioning properly, people need to consume adequate volumes of water. Many factors such as individuals’  and environmental conditions  influence the requirements of fluids. Consequently, the dietary reference values of adequate intake of TWI , such as the EFSA (European Food Safety Authority) , the UniDrinking patterns, defined here as the amounts and types of fluids consumed, have been associated with health , 24. WatIn reference to the volumes of water from food, the contributions of water from food to TWI were different among countries, such as in the United States , in EuroThe objectives of this study were, firstly, to investigate the differences in drinking patterns among young adults; secondly, to explore the hydration biomarkers among young adults with different levels of habitual total drinking fluids intake in free-living conditions. This will lead to the provision of a science-based education of fluids intake for young adults.The inclusion criteria were that participants including males and females were healthy and aged 18–23 years. The exclusion criteria were that participants with smoking, habitual alcohol consumptions (> 20 g/day) or habitual high caffeine consumptions (> 250 mg/day) or had chronic diseases or other diseases  were excluded from the study .From a study performed among young male adults in China, the standard deviation of total drinking fluids was 468 mL . Then, tThis was a cross-sectional study, which lasted for 7 consecutive days. On day 1, all participants were asked to take the anthropometric measurements including height, weight and waist circumference. After that, all participants were instructed to complete the self-designed 7-day 24-h fluid intake questionnaire to record the fluids intake from water and other beverages from day 1 to day 7. To ensure the competence and accurateness of the fluids intake, the questionnaires were checked by investigators every day. All the food the participants ate for three consecutive days  during the 7 consecutive days were weighed and recorded. From day 5 to day 7, 24-h urine samples including the first morning urine of three consecutive days (two weekdays and one weekend day) were collected by participants. On day 6, the fasting blood samples of all participants were collected. The temperature and humidity of indoor and outdoor was recorded each day for 7 days. The study procedure was shown in Fig. Total water intake (mL) = Total drinking fluids (mL) + Water from food (mL).A self-designed 7-day 24-h fluid intake questionnaire was used to assess the total drinking fluids, as showed before. Moreover, the type and amount of drinking fluid for each time was measured using a cup to the nearest of 5 mL. T According to the General standard for beverages of China (GB/T 1–789-2015) , all theIn order to assess the amount of water from food, all foods the participants ate before and after were weighed for three consecutive days of the 7 days . The duplicate portion method, with samples of food being collected and sent to the laboratory immediately, was used to measure the water from food. Samples of all foods were measured according to the national standard of GB 5009.3–2016  and the The temperature and humidity of indoors and outdoors were recorded each day for 7 days .Height, weight and waist circumference were measured twice with the participants wearing light clothes and barefoot   and the concentrations of electrolyte.24-h urine samples including the first morning urine were collected by participants using self-designed containers. Every urine sample was collected and tested immediately. Before being measured, all urine samples were stored at + 4°Cin refrigerator. Urine volume was measured to the nearest 0.1 g using a desktop electronic scale . Urine osmolality was assessed with freezing point method by osmotic pressure molar concentration meter . USG, pH, urea and creatinine were tested by automatic urinary sediment analyzer with uric dry-chemistry method . Urine electrolyte concentrations  were measured by automatic biochemical analyzer with the ion-selective electrode potentiometer method . The optimal hydration was defined when urine osmolality ≤500 mOsm/kg, middle hydration was defined as 500 mOsm/kg < urine osmolality ≤800 mOsm/kg, dehydration was defined as urine osmolality > 800 mOsm/kg , 46.Fasting blood samples were collected to measure the osmolality and electrolyte concentrations.Plasma osmolality was assessed with freezing point method by osmotic pressure molar concentration meter . The electrolyte concentrations  were tested by automatic biochemical analyzer with the ion-selective electrode potentiometer method .1 (low drinker 1), LD2 (low drinker 2), LD3 (low drinker 3) and HD (high drinker) groups, according to the quartiles of total drinking fluids of participants . One-way ANOVA, Kruskal-Wallis H test and chi-square test were used to compared the differences among the four groups. Differences between every two groups were compared using SNK (Student-Newman-Keuls) (p < 0.05). Significance levels were set at 0.05 (p < 0.05).The SAS 9.2 software  was used for statistical analysis. The results were reported as mean ± standard deviation (SD) if the data was normal distributed. While, median and quartile ranges (M and Q) was used to describe the data, which was not in normal distribution. Participants were divided into four groups, including LD2, LD3, HD groups with group LD1, respectively (p < 0.05), with no differences between LD2, LD3 and HD groups. There were no significant differences in age and BMI among the four groups (p > 0.05).Eventually, a total of 159 participants were recruited, and 156  completed the study, with 98% complete rate. Table The average indoor and outdoor temperature for the 7 days was 21.8 °C and 20.7 °C, respectively. The average indoor and outdoor humidity was 39.9% and 35.9%, respectively, see Table p < 0.05). Participants in HD group had 515-1085 mL, 125–282 mL and 664–1383 mL more than those in LD1-LD3 groups in total drinking fluids, water from food and TWI (p < 0.05), respectively. Contributions of total drinking fluids to TWI were different among the four groups comparing with each other (p < 0.05), ranging from 38.7% in LD1 group to 59.1% in the HD group. The water from food accounted for 61.3–40.9% of TWI from LD1 group to HD group, which were also significantly different among the four groups (p < 0.05). Participants in HD group had a 20.4% higher and a 20.4% lower contribution of total drinking fluids and water from food to TWI than those in LD1 group, respectively (p < 0.05).TWI and water from food all increased with higher total drinking fluids among the four groups . Significant differences were found in the amounts of water in the four groups (p < 0.05), with the amounts of water increased with higher total drinking fluids. Nevertheless, there were no statistically significantly differences in the amounts of tea, milk and milk products, SSBs, alcoholic and other beverages among the four groups (p > 0.05). Moreover, there were no significant differences in the contributions of tea, milk and milk products, SSBs, alcoholic and other beverages to total drinking fluids among the four groups (p > 0.05). SSBs were the second contributor to total drinking fluids followed water in the LD1, LD2, LD3 groups, but in the HD group, milk and milk products followed water.The main contributor of total drinking fluids was water in the four groups, which accounted for 77.3–85.6% and there were no significant differences among LD1, LD2 and LD3 groups (p < 0.05). There were no statistically significant differences in the amounts of water from staple food, soup and porridge in the four groups (p > 0.05). Regarding the contributions of water from different foods, the contributions of water from snacks were different among the four groups (p < 0.05), as shown in Table Participants in HD group had 125–282 mL more water from food than those in other groups. The main contributor to water from food was dishes followed by staple food in all four groups. Participants in HD group had higher volumes of water from dishes and snacks than participants in LD1 group, to HD group, while the osmolality, USG, and the concentrations of Na, K, Cl, Ca, Mg, phosphate, creatinine, uric acid and urea of urine were significantly different in LD1, LD2, LD3 and HD groups (p < 0.05). The percentages of participants in optimal hydration status was statistical higher in HD and LD3 groups than that in LD1, LD2 groups (p < 0.05).Table p < 0.05). No statistically significant differences were found in plasma osmolality, the concentrations of Na, K, Cl, Ca and phosphate among participants in the four groups (p > 0.05), as shown in Table Statistically significant differences were found in the concentration of Mg among the four groups p < 0.0. No statIn this study, when the total drinking fluids increased significantly, the water from food and TWI increased significantly accordingly among the four groups; the proportions of total drinking fluids in TWI were increased. Whereas, the contributions of water from food to TWI were decreased simultaneously, which were higher than the results among participants both in low and high drinkers in France . The fin1, LD2 and LD3 groups were higher than the standard, while the 24-h urine osmolality of participants in HD group was lower than the standard. Meanwhile, significantly more participants with the 24-h urine osmolality below the standard were in HD group than in other groups. Participants with lower total drinking fluids had less and more concentrated urine than participants with higher total drinking fluids, which indicated that participants with lower total drinking fluids maybe in dehydration, whereas, participants with high total drinking fluids maybe in optimal hydration status. Moreover, in France, the osmolality of urine among those with higher total drinking fluids and those with lower total drinking fluids were 371 mOsm/kg and 767 mOsm/kg, respectively [Researches demonstrated that, TWI was strongly associated with 24-h urine volume, osmolality, USG and solute concentrations , 49. Morectively , 22. Bloectively . Indeed,ectively . Also, iectively , 22, 53.1, LD2 and LD3 groups, which contributed to over 10% of total drinking fluids, while the milk and milk products were the second contributor among participants in HD group. This observation suggested that participants with higher total drinking fluids may have healthier drinking behaviors. However, the consumptions of milk and milk products of participants in the four groups, which ranged from 21 mL to 71 mL, were much lower than the recommendation of China (300 mL) [Regarding the drinking patterns, water was the main contributor of total drinking fluids among the four groups with the proportions higher than French, while the contributions of other beverages were lower than French . The dif(300 mL) . In Chin(300 mL) . Therefo(300 mL) .In this study, water from food was also an important source of TWI, accounting for 40.9–61.3% to TWI among the four groups. Regarding the sources of water from food, dishes  and staple food (including steamed bread and rice) were the first and second contributor among the four groups. Moreover, participants with higher total drinking fluids had more amounts of water from dishes (such as vegetables) and snacks  than those with lower total drinking fluids. But there were no significant differences in the contributions of water from different sources of food.2, LD3, HD groups with LD1 group, respectively, but with no differences between LD2, LD3 and HD groups. However, the indexes, such as the amounts of total drinking fluids and water from food, were different between LD2, LD3 and HD groups. In addition, there were also significant differences of hydration biomarkers, for instance, the volume of urine between LD2, LD3 and HD groups. It meant that, the differences of anthropometric measurements at baseline did not influence the results of drinking patterns and hydration biomarkers among participants from different groups.Research showed that heights were also different among the people of China and other countries due to race differences, which contributes to difference in body surface area and has corresponding effects on water requirements . In thisIn terms of the strengths, it was the first study investigated the drinking patterns and hydration biomarkers among participants with different total drinking fluids. Moreover, 7-day 24 h fluid intake questionnaire was used to assess the total drinking fluids for seven consecutive days (two weekdays and one weekend day), which would reduce the recall bias. The duplicate portion method was used to assess the amounts of water from food, with all the foods were being weighed before and after participants ate for three consecutive days. In addition, 24 h urine was collected every time they urinated using specific container for three consecutive days (two weekdays and one weekend day), which would include incorporate factors fluctuating during weekdays and weekends. Moreover, the samples of urine collected by the participants, were stored at + 4 °C and sent to the laboratory for test immediately. All the tests were performed by professional investigators. Despite strengths mentioned above, the study employed did have limitations. This survey was performed with young adults only so extrapolation of the study findings to different age groups should be done with care. However, despite the limitation, the association between hydration status, drinking pattern and total drinking fluids intake remains meaningful. Future studies will determine the general application of our results to a wider population, including middle-aged adults and the elderly.Participants with higher total drinking fluids had higher water from food and TWI than those with lower total drinking fluids. Participants with higher total drinking fluids had better hydration status and drinking pattern than those with lower total drinking fluids. Interventions should be undertaken to encourage people to drink an adequate amount of total drinking fluids, preferably water, in order to be in optimal hydration.


Hdac4). Lastly, we found that LMK235, an HDAC4/5 inhibitor, reduced binge-like drinking. Thus, we were able to target specific molecular pathways using pharmacology to mimic the behavioral effects of DREADDs. Binge drinking is a dangerous pattern of behavior. We tested whether chronically manipulating nucleus accumbens (NAc) activity (via clozapine-N-oxide (CNO) and Designer Receptors Exclusively Activated by Designer Drugs (DREADD)) could produce lasting effects on ethanol binge-like drinking in mice selectively bred to drink to intoxication. We found chronically increasing NAc activity  decreased binge-like drinking, but did not observe CNO-induced changes in drinking with the inhibitory DREADD, hM4Di. The CNO/hM3Dq-induced reduction in ethanol drinking persisted for at least one week, suggesting adaptive neuroplasticity via transcriptional and epigenetic mechanisms. Therefore, we defined this plasticity at the morphological and transcriptomic levels. We found that chronic binge drinking (6 weeks) altered neuronal morphology in the NAc, an effect that was ameliorated with CNO/hM3Dq. Moreover, we detected significant changes in expression of several plasticity-related genes with binge drinking that were ameliorated with CNO treatment (e.g.,  Binge drinking is defined by the National Institute on Alcohol Abuse and Alcoholism as having 4 to 5 drinks within 2 h and/or achieving a blood alcohol level (BAL) >80 mg% . Limited access ethanol drinking paradigms are used to model binge/intoxication in animal models . In the limited access Drinking in the Dark (DID) paradigm, mice are offered an ethanol solution early into the active period of their circadian cycle and can achieve BALs >80 mg%, suggesting they drink to intoxication  and the treatment (CNO vs. Vehicle) as fixed effects, and p-values were obtained by likelihood ratio tests. For all morphometric parameters, we examined the main effect of both variables, as well as their interaction. We then corrected for multiple comparisons using the Benjamini–Hochberg approach to adjust the nominal p-values to the q-values of the False Discovery Rate (FDR) [p-values and q-values less than 0.05 were considered significant, and data was presented as mean ± standard error of the mean (SEM).As the data was nested, with multiple neurons within a brain (dependent) being compared to multiple neurons from other brains (independent), we used a linear mixed effect analysis . We did te (FDR) . Sholl aMus musculus strain C57BL/6J . Basecall quality was assessed using FastQC [p < 0.05) between fluid and treatment groups [2O(VEH) and EtOH(CNO) vs. H2O(CNO). Target genes identified for comparison were based on literature by Russo et al., 2010, Arikkath et al., 2012 and Uys et al., 2016 [https://github.com/Pozhidayevad/Ozburn_RNA-Seq_Analyses. RNA Seq data is accessible via GEO (accession number GSE).Raw FASTA files generated from sequencing were aligned to the Genome Reference Consortium Mouse Reference 38, based on the g FastQC  and aligg FastQC   vs. CNO, p < 0.05; VEH (baseline) vs. VEH (washout), p < 0.05). Thus, chronically increasing Gq signaling in the NAc produced lasting effects on binge-like drinking behaviors.To identify the effect of manipulating NAc activity (via CNO and hM3Dq- or hM4Di-DREADDs) on binge-like ethanol drinking behavior, HDID-1 mice were subjected to the DID paradigm. The effects of treatment  on ethanol binge-like drinking in mice expressing GFP A, hM4Di p < 0.01; Dunnett post-hoc test revealed significant differences for VEH baseline vs. CNO (p < 0.01) and VEH baseline vs. VEH washout (p < 0.05)). Replication of this finding provides strong evidence for behavioral plasticity. We next sought to identify how this plasticity manifested on a morphological and transcriptomic level.Results from experiment 1 showed that chronic stimulation using hM3Dq excitatory DREADDs produced a robust and lasting reduction in binge-like drinking in a model of genetic risk for binge-like drinking, thus the previous study was replicated using hM3Dq DREADDs exclusively. Here, the results of chronic CNO administration in mice hM3Dq excitatory DREADDs in the NAc were similar to data from experiment 1. 2O), or whether there was an interaction between the two treatments. We analyzed neuronal complexity, determined via the equation  ×  , fluid type (EtOH or Hndrites) A, lengthndrites) B, numberndrites) C, numberndrites) D, total ndrites) E, the sundrites) F, the qundrites) G, and thndrites) H. In mosndrites) . This mandrites) . There w2O exposed groups, an effect which was significant in the nodes ) and control water drinking (H2O (VEH)) animals. Comparisons made between groups involved the pairwise comparison of all groups with the control H2O (VEH) only. In this way, we identified differentially expressed genes (DEGs) from all groups relative to the control (2O (CNO) 612 (out of the 1157) genes were differentially expressed. The overlap for DEGs was highest between the EtOH (VEH) and EtOH (CNO) (n = 513) and lowest between EtOH (VEH) and H2O (CNO) (n= 112). Expression patterns for the unique DEGs identified for EtOH (VEH), EtOH (CNO) and H2O (CNO) are shown in To identify gene expression changes related to binge drinking and CNO treatment, differential expression analysis was performed using four groups from experiment 2. These four groups were defined as untreated binge-drinking (EtOH (VEH)), treated binge-drinking EtOH (CNO), treated water drinking (H control . The lis control . The ove2O (VEH) or EtOH (CNO) vs. H2O (CNO). DEG lists and expression values are listed in p < 0.05, ** p < 0.01. Here, 11 DEGs were identified between the two comparisons: 8 were found to be differentially expressed in EtOH (VEH) vs. H2O (VEH) and 3 in EtOH (CNO) vs. H2O (CNO). No overlap was observed within the DEGs between the two comparisons. Of the DEGs identified, all were associated with increased structural changes, excluding Dlg4 and Mef2d. The 8 DEGs identified within the EtOH (VEH) vs. H2O (VEH) comparison included Nrtk2, Wasf2, Dlg4, Grin2b, Grin2a, Was, Actl6a and Mir132, where Nrtk2, Wasf2, Grin2b, Grin2a, and Was were significantly upregulated and Dlg4, Actl6a and Mir132 were downregulated. The 3 DEGs identified within the EtOH (CNO) vs. H2O (CNO) comparison included Mef2d, Camk2g and Pak1, where Camk2g and Pak1 were significantly upregulated and Mef2d was downregulated. In conjunction with the morphological analyses presented, these results suggest that expression of the mediators of structural plasticity are altered with ethanol drinking and ameliorated with CNO treatment.To identify the transcriptomic mechanisms of morphological changes observed in previous analyses, we identified gene expression changes of key molecular mediators of structural plasticity , a class II HDAC that deacetylates lysine residues of core histones and represses transcription via MEF2C and MEF2D, tended to be most prevalent in those related to the regulation of metabolic cellular processes. Comparison of the total normalized gene expression of Hdac4 between groups is presented in 2O and EtOH groups receiving vehicle treatment  = 4.45, p < 0.05; main effect of fluid—F = 6.23, p < 0.05; post-hoc test revealed EtOH(VEH) vs H2O(VEH), p < 0.05). The results of gene ontology (GO) term enrichment for biological processes in ETOH (VEH) are highlighted in WGCNA was performed to identify the impact of chronic binge-like drinking and the lasting effects of CNO treatment on coordinated regulated gene expression networks. Though not discussed here, the results implicated the direct susceptibility of gene expression in a specific module to binge-like drinking and highlighted it as a point of interest (results in p < 0.01). Previous work has shown that the HDAC4/5 inhibitor, LMK-235, requires several administrations to exert its effects on signaling and morphology. We followed up on this finding by calculating a change score for each animal  = 2.44, p < 0.05).The effects of HDAC4/5 inhibitor, LMK-235, on binge-like alcohol drinking were significant. Notably, reductions in binge drinking became more apparent over time, suggesting transcriptional mechanisms. When comparing ethanol intake across baseline and treatment weeks, a two-way ANOVA revealed a significant main effect of treatment period on ethanol intake , exhibit behavioral impairment after binge-like drinking, and exhibit relapse and withdrawal behavior following binge-like drinking sessions . GeneticWe found that chronic binge-like drinking resulted in increased complexity of medium spiny neurons in the NAc of HDID-1 female mice, an effect that was ameliorated by chronic treatment with CNO. Interestingly and unexpectedly, no effect of CNO was observed in water drinking mice, suggesting that chronically increasing Gq signaling in the NAc does not produce robust and lasting changes in neuronal complexity. We were unable to identify any published reports on the effects of DID or binge-like drinking on neuronal morphology in the NAc (or any other brain region) for comparison. However, there are several published studies evaluating the effects of chronic intermittent ethanol vapor on neuronal morphology in different brain regions. Most relevant to this work are studies by Wang et al. (2015), Uys et al. (2016), and DePoy et al. (2013) evaluating the effects of chronic intermittent ethanol vapor (CIE) on aspects of neuronal morphology in the dorsal and ventral striatum ,51,52. INtrk2 (encodes BDNF tyrosine receptor kinase-B (TRKB)), Wasf2, Was (genes encoding Wiskott-Aldrich Syndrome proteins), Grin2a, and Grin2b (genes encoding NMDA receptors) with chronic binge drinking (as compared to water controls). Within the same comparison, we also observed a significant downregulation of genes Dlg4 , Mir132 (CREB mediated microRNA) and Actl6a (encodes the actin-related protein-4 ARP4). These significant changes in expression patterns were absent within the EtOH (CNO) vs. H2O (CNO) comparison. In the context of untreated binge drinking, these changes in gene expression likely represent the underlying mechanisms for the increased neuronal complexity observed with chronic binge drinking. Further, the absence of these expression patterns with CNO treatment supports that the expression of these genes is ameliorated with CNO. Support for these mechanisms have also been shown by Uys et al., 2016, where their group observed significant changes in NAc core MSN morphology and expression of PSD95 (Dlg4) with chronic intermittent ethanol exposure [Neuroadaptative changes, such as increased complexity, in NAc MSNs induced by ethanol exposure are the products of dysregulation in signaling systems, gene transcription and protein expression at the cellular level . Previouexposure . Additioexposure ,56,57. 2O(CNO) group and represented genes that were related exclusively to chronic CNO-induced activation of hM3Dq. Reductions in ethanol consumption observed during the washout period of the DID paradigm suggest neuroplastic changes as a result of chronic neuronal stimulation in mice expressing hM3Dq. Transcriptomic analyses were carried out to identify differentially expressed genes (DEGs) for each group  and usedHdac4 was identified in selection responsive modules as functionally overrepresented. We identified Hdac4 as a gene of interest in our study and have discussed this below.Progression from acute to chronic alcohol intake is thought to incite changes in gene expression levels that occur to maintain system homeostasis . ChronicHdac4 expression in binge drinking. HDAC4 is a class IIa histone deacetylase that represses transcription when tethered to a promoter (via a multiprotein complex with MEF2C and MEF2D). In pathways such as cellular macromolecule metabolism, positive regulation of cellular processes, negative regulation of biological processes and regulation of macromolecule metabolism, Hdac4 was consistently found to be overrepresented. Additionally, we observed gene representation related to circadian and neuroimmune pathways, as others have previously reported [Hdac4 was present in the majority of significant GO pathways, we hypothesized that Hdac4 played a large role in a number of biological pathways related to chronic binge drinking. We were encouraged to use this approach to test whether pharmacologically targeting HDAC4 could reduce binge-like drinking.We observed 688 genes uniquely differentially expressed in the EtOH (VEH) group, where those exhibiting the most extreme changes in expression were ameliorated in CNO treatment groups. GO analysis of DEGs identified for the EtOH (VEH) group identified the importance of reported ,61. BecaHdac4 was selected as a target based on transcriptomic data from female mice, the compound reduced ethanol drinking in both male and female mice. Others have shown that HDAC4 is regulated by neuronal activity and plasticity, and inhibiting HDAC4 via LMK 235 can alter neuronal morphology [It has been shown that alcohol dependence and withdrawal result in a global reduction of expressed transcripts, accompanied by an increase in expressed transcripts coding for regulators of epigenetic silencing . One sucrphology ,65,66. OHdac4). We tested whether inhibiting HDAC4 would reduce drinking and found that chronic treatment with LMK235, an HDAC4/5 inhibitor, reduced binge-like drinking. Thus, we were able to target specific molecular pathways using pharmacology to mimic the behavioral effects of DREADDs.We found chronically increasing NAc activity (4 weeks via CNO/hM3Dq) decreased binge-like drinking. This effect persisted for 7 days post-CNO treatment, suggesting the development of plasticity. We investigated whether this behavioral plasticity could be observed at the morphological and/or transcriptomic levels. We found that chronic binge drinking increased neuronal complexity in the NAc, an effect not present in mice treated with CNO. We detected significant changes in expression of several morphology and plasticity-related genes with binge drinking that were ameliorated with CNO treatment (e.g.,


FaceSync, which provides an automated method for synchronizing videos. We provide four proof-of-concept studies demonstrating the benefits of this recording system in reliably measuring and analyzing facial expressions in diverse experimental setups, including group interaction experiments.Advances in computer vision and machine learning algorithms have enabled researchers to extract facial expression data from face video recordings with greater ease and speed than standard manual coding methods, which has led to a dramatic increase in the pace of facial expression research. However, there are many limitations in recording facial expressions in laboratory settings.  Conventional video recording setups using webcams, tripod-mounted cameras, or pan-tilt-zoom cameras require making compromises between cost, reliability, and flexibility. As an alternative, we propose the use of a mobile head-mounted camera that can be easily constructed from our open-source instructions and blueprints at a fraction of the cost of conventional setups. The head-mounted camera framework is supported by the open source Python toolbox Facial expressions provide rich information about how a person is feeling, what they are thinking, and how they might act . Facial Extracting facial expression information through FACS coding, however, can be a labor intensive and time-consuming process. Becoming a certified FACS coder not only requires 100 hours of training  but evenAs an alternative to manual FACS coding, automated measurements of facial expressions can substantially reduce the amount of time required to extract facial expression information. One technique, known as facial electromyography (fEMG), measures the electrical impulses associated with facial muscle movements. With fEMG, researchers can continuously measure the activity from muscle groups associated with facial AUs, such as the zygomaticus major and corrugator supercilii muscles, at a high sampling rate. However, fEMG requires a separate electrode for each facial muscle group, which means that only one or two muscle groups are recorded simultaneously in practice . Even so1Automated extraction of facial expression information from face video recordings have emerged as a promising alternative that offers quick, continuous, and simultaneous measurement of multiple facial muscle movements without manual coding. Advances in computer vision and machine-learning techniques  and large-scale data collection have facilitated the development of models that learn to transform pixels from videos into predictions of facial AUs and emotional facial expressions  cameras  pulse triggering capable camcorders are often more expensive.The PTZ camera setup provides researchers centralized control over cameras that can be rotated or zoomed to account for subject movement. PTZ camera setups require a dedicated experiment room with cameras installed and an adjacent console room where experimenters can monitor incoming video feeds and control the camera. Central management of cameras can facilitate integration with other softwares or triggering the cameras to record simultaneously. The installation of cameras to corners of ceilings distant from the participant allows cameras to be less conspicuous. However, this forces participants to stand or sit in particular locations in the room and renders the setup particularly susceptible to head rotations or occlusions of the face from body gestures unless multiple cameras are installed. It is the least flexible option because changing camera locations or installing additional cameras would require additional construction. The PTZ camera setup can therefore be the best option to minimize participants’ attention to the camera but at the cost of increased possibility of artifacts and reduced adaptability to other experimental setups.Overall, webcams, tripod-mounted cameras, and PTZ cameras do not provide an optimal solution for recording facial expressions. They commonly suffer data loss due to out-of-plane head motions and head rotations , small in size, and lightweight. We provide step-by-step assembly instructions for building a head-mount for GoPro cameras in the Supplementary Information (seeUnderlying data) , but manually synchronizing each video is labor intensive and can introduce unsystematic noise in the alignment (https://github.com/cosanlab/facesync).To facilitate the synchronization of videos, we developed FaceSync, an open-source Python toolbox, to automatically synchronize video recordings based on audio. In stimulus-based experiments, it requires a short audio segment to be played at the beginning of the experiment, which is recorded by the camera. Based on this shared audio, the toolbox can align the video to the beginning of the experiment finding the optimal alignment with the original audio segment. In unstructured social interaction experiments, multiple videos can also be aligned to a target video. The toolbox offers a sliding window search to find the offset that maximizes the correlation between two audio signals  and a foIn the following sections, we demonstrate that the head-mounted camera recording system provides a robust way to record facial behavior in laboratory experiments. In Study 1, we show that the head-mounted cameras can reliably record facial behaviors invariant to head rotation. In Study 2–4, we demonstrate the flexibility of using head-mounted cameras in multiple experimental setups including an event-based paradigm (Study 2), naturalistic video watching paradigm (Study 3), and a social interaction paradigm (Study 4). In these three proof of concept experiments, we also compare the performance of the FaceSync software in synchronizing recordings in comparison to the manual alignment method.Study 1: Face detection with head rotation.Methods. To examine the impact of head rotation on face registration, we recorded the face of one male participant (author J.H.C.) using a webcam and our head-mounted camera. In each recording session, the participant rotated the head 90 degrees left, returned to center, 90 degrees to the right, then returned to center. The head-mounted camera used a GoPro Hero4 camera to record 1280 x 720 resolution videos at 30 frames per second (fps). The webcam recording used the integrated camera on a Macbook Pro Retina laptop at 1080 x 720 resolution at approximately 30fps. Facial expressions were extracted using the iMotions Emotient FACET engine  viewed 10 positive images  and 10 negative images  presented in MATLAB usingPsychtoolbox version 3.0.12  was a two-second harmonic tune constructed with sine waves at different frequencies. Four independent raters (including author J.H.C.) incrementally shifted the extracted audio in 1ms precision to the target audio using AudioAlign while listening to the shifted sound to minimize echo artifacts as well as visually checking the two waveforms for misalignment. For automatic alignment, we used the FaceSync sliding window correlation function (find_offset_corr) to detect the offset that maximizes the correlation similarity between the two audios. Alignment results from additional test videos that are not included in the face expression analysis were also tested for alignment and are reported as supplementary tests inResults. An independent-samples t-test was conducted to compare positive facial expressions while viewing positive and negative images. Evidence of positive facial expressions while viewing positive images  was significantly greater than the evidence while viewing negative images  = 15.58, p < 0.001;Audio alignment results are reported inDiscussion. In Study 2, we demonstrate the feasibility of using head-mounted cameras to record facial expressions in response to time-locked stimuli. The participant displayed positive facial expressions (i.e. smiling) to positive images and disgust facial expressions to negative images which was accurately retrieved from the analysis. Facial expressions were successfully linked to the stimuli that elicited the response by accurate alignment of the face recording to stimulus timing. Only a small difference was observed between the offsets determined automatically and manually. This study demonstrates the feasibility of using a head-mounted camera setup for standard computer-based experiments and that facial expressions can be linked with the stimuli that elicited the response with high temporal precision.Study 3 and 4: Watching and discussing naturalistic stimuli togetherTo demonstrate the feasibility of using the head-mounted camera setup to simultaneously record facial expressions from several individuals, we recorded individual facial expressions of a group while they watched a video together in Study 3. Subsequently in Study 4, we recorded facial expressions of the group members while they discussed the contents of the video. We compare the facial expression behavior of each participant to one another with the expectation that participants would show synchronization of facial expressions in both conditions but to a greater extent in the movie watching experiment. Audio alignment offsets determined by manual and automatic alignment are compared.Method. In Study 3, we measured the facial expressions of a group  watching a video, Big Buck Bunny  using the FaceSync AudioAlign graphical interface and automatically using the sliding window correlation alignment function (find_offset_corr). Differences in offset measured by the two methods were submitted to a one sample t-test to assess whether there was any difference between the two methods. After alignment, videos were trimmed using the FaceSync trim function.Facial expressions, including AU activations and emotion predictions, were extracted using the iMotions Emotient FACET engine  weighed 92 grams but now more recent models  weigh only 74 grams. Small reductions in weight can lead to increased comfort as the tugging force is significantly reduced based on the length of the headset. Researchers can take advantage of newer and lighter cameras as they become available as the 3D printable camera mount provided with the blueprints is compatible with other cameras.In summary, we hope that these tools can benefit other researchers and further accelerate facial expression research. We anticipate that this framework will aid in improving our understanding of how emotions are experienced in naturalistic settings and how emotions surface and influence social interactions. In addition, we hope that these tools can aid researchers in developing new models of complex emotions such as regret, guilt, and gratitude. We look forward to a new era of facial expression research that examines facial behavior in both controlled and naturalistic settings to yield a robust and holistic understanding of how facial expressions reflect our thoughts, emotions, and future behavior.This study was approved by the Institutional Review Board at Dartmouth College and written consent from all participants was obtained. Informed written consent was obtained for all individuals whose full faces are shown in the figures or in the videos in data repository.https://github.com/cosanlab/facesyncSource code for FaceSync toolbox available from:https://doi.org/10.5281/zenodo.2638335  - Data folder containing: Raw data necessary to recreate the analyses, such as videos, extracted facial expression information, and sounds used for synchronizing the videos - Supplementary Information folder containing: FaceSync_build_information.pdf - Creative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of the First, the authors provide step-by-step assembly instructions to enable researchers to easily construct their own head-mounted camera setup as an affordable alternative option that requires minimal engineering expertise.Second, the authors introduce FaceSync, an open-source Python toolbox that they developed to automatically synchronize video recordings based on audio cues. A short audio segment can be played at the beginning of the experiment by e.g. a stimuli presenting software and be recorded by the head mounted camera. The toolbox allows the alignment of the video to the sound produced by the experimental software. In unstructured social interaction recordings, multiple videos can be aligned to a target video. The toolbox offers a sliding window search to find the offset that maximises the correlation between two audio signals and a fourier transform based cross-correlation method. To test that the head-mounted camera recording system provides a robust way to record facial expressions, the authors ran several studies with users.  The article describes head-mounted camera systems as the most appropriate state-of-the-art recording devices for facial expression analysis. Two drawbacks of such setups are emphasised by the authors: the cost of commercialised headmounts and synchronisation of recordings with studied stimuli. Nowadays, however, facial expression analysis is not limited to fixed cameras and webcams. The authors describe perfectly the added value of head mounted cameras for (automatic) facial expression analysis but do not describe the current state of the art in terms of comparable hardware and software. Given fast progress, e.g. in immersive technologies, a variety of solutions exist. The authors should consider discussing the expensive commercial options  and the open access options that do exist, e.g. for video alignment and facial expression recording from similar devices. If no user study can be done on similar hardware and software for comparison, please provide sufficient information in the state of the art and discussion sections. Please consider emphasising the added value of the article/framework in the title and abstract. Please make it clearer whether FaceSync is a synchronisation system (based on audio) as described in the manuscript or a framework for recording facial expression, as in the title.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This is a “proof of concept” publication that introduces a novel head-mounted camera apparatus for obtaining spatially accurate videos of facial expressions that is able to compensate for unwanted head movements that often occur during research projects, especially those that involve spontaneous facial expressions. The apparatus uses a small, light-weight and relatively inexpensive Go-Pro video camera  that is mounted on a rectangular.  The authors provide directions for construction of the apparatus with blueprints and provide a means for the facial expression videos to be synchronized with audio recordings for accurate off-line analyses (FaceSync). In addition, the authors present 4 brief “proof of concept” experiments that demonstrate the utility of their apparatus and FaceSync. The most important is Study 1, in which the subject moves his head 90 degrees to the left and then 90 degrees to the right of center. The video was then analyzed off-line using the iMotion Emotient FACET engine that provides “face registration success, landmark positions, AU predictions and emotion predictions.” Face detection was 100% successful for the entire video. In comparison, using a webcam (stationary) video recording for the same sequence, only 75% of the video was successfully analyzed because the iMotion Emotient FACET engine failed to detect the face during head rotation. This publication makes a major contribution to the quantitative analysis of facial expressions that use video recordings. To date, research videos of facial expressions are obtained by using a fixed camera set-up that cannot compensate for spontaneous head movements, i.e. a computer webcam, a tripod-mounted camera or a pan-tilt-zoom camera . Although software exists that can partially compensate for head movement in a given plane, it is mathematically very difficult, if not impossible, to compensate for head movements that occur over multiple planes, especially rotatory head movements, when attempting to quantitatively analyze facial expressions using computer software techniques. Thus, the head-mounted camera apparatus will allow researchers to obtain better and more complete quantitative data regarding facial expressions. My only suggestion to the authors is that they might consider adding an adjustable counter weight to the back of their apparatus to make the frame more comfortable for the subject.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Successful prevention of post-transplantation relapse after donor lymphocyte infusion (DLI) depends on its capability to mediate an effective graft-versus-leukemia (GVL) response while minimizing DLI-related toxicity, including graft-versus-host disease (GVHD).We assessed the effects of decitabine (DEC), a hypomethylating agent, upon allogeneic immune reaction in a murine model of DLI.Significantly greater tumor growth retardation and survival prolongation occurred in mice administered with 1.0 mg/kg DEC for 5 days (DEC-1.0) than in control or DEC-0.1 mice. Upon prompt DEC and DLI co-administration, dendritic cells (DCs) were activated; DEC-1.0/DLI induced severe GVHD, and survival was significantly lower than with DLI alone or DEC-0.1/DLI treatments. IFN-γ and CD28 levels were higher in splenic DCs of DEC-1.0 mice than in those of control mice. Assessment of delayed DLI co-administration with DEC, when IFN-γ levels were normalized to control levels, revealed that DEC-1.0/DLI successfully facilitated tumor management without causing severe GVHD.Our results suggest that DEC primes allogeneic immune reactions of DLI via DC activation, and GVHD and GVL effects are separable through optimal DLI timing based on DEC-induced increase in IFN-γ expression levels. Allogeneic hematopoietic stem cell transplantation (HSCT) is a curative treatment option for selected patients with hematological malignancies. The recent improvement in long-term survival after transplantation may result from a reduction in treatment-related mortality due to organ damage, infection, and severe acute graft-versus-host disease (GVHD) ; howeverDLI is usually used to prevent imminent relapse or treat early-stage relapse, with the aim of inducing allogeneic immune responses against minimally residual leukemic cells; however, its overall success rate is relatively low due to limited GVL effects or unwanted adverse events, including severe GVHD or marrow aplasia , 11. As In the present study, we hypothesized that the administration of DEC activates host DCs and that this DEC priming may enhance the antitumor effects of subsequent DLI. To test this hypothesis, a mouse HSCT model mimicking early-stage relapse, wherein DLI is usually attempted clinically, was selected. Furthermore, to assess the adverse effects of DEC on GVHD, a mouse model wherein DLI typically fails to induce clinical signs of GVHD was used.Female C57BL/6  and B6D2F1  mice were purchased from Orient Bio  and maintained in our facilities under pathogen-free conditions. Animal care and experiments were conducted in accordance with the guidelines of our institutional animal care and use committee. Murine mastocytoma P815 cells (H-2d) were used to verify GVHD and GVL effects based on previous studies  and were7 cells were administered intravenously from B6 mice. The early-stage relapse with low tumor burden was modeled via subcutaneous inoculation of P815 cells at a density of 1 × 106 cells 1 day before DEC administration to recipient mice.The early stage of post-transplantation relapse is characterized by a low tumor burden in mixed donor chimerism. Mixed chimeric status was modeled using a haploidentical (B6→F1) transplantation mouse model, receiving nonmyeloablative conditioning . Briefly7) from B6 donors were resuspended in HBSS  supplemented with 2% FBS , washed twice, and injected into the tail vein of recipient mice at specified time points. Tumor growth was assessed every 3–4 days by measuring the largest perpendicular diameter with a caliper; thus, tumor sizes were recorded in terms of area (mm3). Survival was monitored daily, and the degree of clinical GVHD was assessed every 3–4 days based on weight loss, posture, the occurrence of diarrhea, and skin lesions, such as alopecia and dermatitis [To investigate the effects of DEC on tumor growth, allogeneic immune reaction, and resultant mouse survival, DEC at variable doses was intraperitoneally administered once daily, and its effects were compared with those in the controls. In the transplantation setting, mice were divided into two groups following DEC administration: DLI and non-DLI. For DLI, spleen cells (2 × 10rmatitis . For his5 cells) as stimulators. For cytokine analyses, serial serum samples were collected and analyzed via cytometric Bead Array flex kits (BD Biosciences) containing IFN-γ, TNF-α, IL-2, IL-6, IL-10, and IL-17A.Frequencies of T cells  and NK cells (CD3-NK1.1 + B220 +) were analyzed using the following antibodies : APC-cy7-conjugated anti-CD3 (Clone SK7), PE-conjugated anti-CD4 (Clone RPA-T4), PerCP-conjugated anti-CD8 (Clone RPA-T8), PerCP 5.5-conjugated anti-CD25 (Clone PC61), APC-conjugated-Foxp3 (Clone 259D/C7), PE-conjugated anti-NK1.1 (Clone PK136), and PerCP-conjugated anti-B220 (Clone RA3-6B2). The estimation and analysis of T cells, NK cells, and cytokines were performed using Fortessa  and FlowJo software . T-cell proliferative capacities were assessed via flow cytometric analysis of carboxyfluorescein diacetate succinimidyl ester  in the mixed lymphocyte reaction (MLR) with irradiated (13 Gy) splenic DCs . Data were analyzed in accordance with the RT2 Profiler PCR Array Data Analysis Template, Version 3.3. Relative changes in gene expression based on DEC exposure were calculated using the ΔΔCt method, and a difference in gene expression of ≥ 1.5-fold at P < 0.05 was considered statistically significant. Real-time PCR was performed to quantify interferon-γ (IFN-γ) and CD28 in splenic DCs using the primers listed in Table Bone marrow-derived immature and mature DCs  from F1 mice were generated in vitro, as described previously . BrieflyData are presented as mean ± standard error of the mean. The Wilcoxon signed-rank test was performed to compare tumor sizes, body weights, T cell and NK cell numbers, and cytokine levels. Spearman’s rank test was performed for correlation analysis. Survival curves were plotted using the Kaplan–Meier method, and differences were evaluated using the log-rank test. P values < 0.05 were considered significant throughout.6 cells), and a palpable mass was observed approximately 6 days after inoculation. On day 7, mice were divided into five groups, and daily doses of 0, 0.1, 0.5, 1.0, or 2.0 mg/kg body weight of DEC were administered intraperitoneally for 5 consecutive days, respectively. Tumor size reduction and survival prolongation after DEC treatment were observed at doses of 0.5 mg/kg or higher  , delayedIn conclusion, DEC-induced activation of DCs was shown to facilitate the eradication of remnant tumor cells via GVL effects, and the activation status of DCs could be optimized by the time interval between DEC and DLI to help separate GVL effects from unwanted GVHD.Additional file 1: Figure S1. Optimal decitabine (DEC) dose selection for co-administration with donor lymphocyte infusion. Effects of DEC on tumor growth and survival in a non-transplant setting: a tumor size and b survival. Systemic toxicities of DEC in the haploidentical (B6→F1) transplantation mouse model: c survival and d body weight. The experiment was conducted in 2 times.


YouTube. While the course contents were not turned on their heads, throughout the semester we adopted a video style that has been popularized by the live coding community. This new focus on the live interaction with the underlying database systems, led us (1) to adopt the idea of SQL probe queries that are specifically crafted to reveal database internals and (2) a study of database-supported computation that treats SQL like a true programming language. We are happy to share videos, slides, and code with anyone who is interested.We report on the conversion of two advanced database courses from their classical in-lecture-hall setup into an all-digital remote format that was delivered via  COVID-19 pandemic at bay, during the summer semester of 2020 all lectures at the University of Tübingen were held in an online-only format. This also applied to the two courses “Implementation of Database Systems”  and an extensive archive of video and code material was created that I am happy to share with everyone.To help keep the Advanced SQL  PragmataPro, a font whose 9000+ characters support to lay out code, tables, trees, graphs, and other regular structures , encourages hot fixes, and allows to keep slide text as well as associated code samples together. We will see that the latter was crucial. These slide decks were largely kept unchanged, yet bugs were fixed and adaptations to the then current versions of the database systems (PostgreSQL v12 and MonetDB 5) in use were applied.The backbones of both courses have always been two extensive slide decks, deliberately designed to be heavy on illustrations and light on text. Since around 2016, I author all slide decks via (1) Markdeep , a MarkdYouTube videos, was a dire prospect for both, students and myself. A closer look at the core contents of both courses turned things around:probe (SQL) queries and subsequent study of which system components interacted exactly how to respond to these probes.Advanced SQL treats SQL like a true programming language that can be used to express (very) complex computation close to the data.Both courses naturally lend themselves to a lecture format that uses code extensively  to develop and express the central ideas, formulate examples, and devise experiments. A focus on code and the delivery via YouTube led to a course redesign that revolved around live coding. There is a vibrant YouTube subculture on live coding in many forms, and I entirely banked on its success from the first to the last lecture video. The approach held the promise that it would be far more engaging than the slide-only lectures. The evaluation of both courses by my students–most of which were a experienced live coding audience, it turned out–showed that that promise was kept  psql and mclient of PostgreSQL and MonetDB, respectively. Less often, the unix shell was used to compile and run C code or initiate benchmarking runs. To save time and not bore viewers with the authoring and mistyping of larger code fragments, I used a text editor with a split view: code was rearranged and completed in the split’s top half, marked for evaluation, and (on key press) submitted to the bottom half which hosted the current REPL  viewers could focus on the code alone and (2) a code-only file could be distributed to the students: everyone was able to replay the code experiments on their own machines without the need to copy verbatim code from slides . Call-outs in the videos explicitly named the current code file in use, making it immediate for viewers exactly which piece of the distributed material currently was in focus. PragmataPro font to typeset and edit both, leading to identifiers and symbols on the slides that were instantly recognizable in the live code. PragmataPro is highly legible yet narrow which ensured that code fragments remained readable even if the YouTube streams were watched at lower resolutions. At the same time, this allowed slides as well as editor windows to show reasonably-sized portions of code despite my deliberate use of large fonts throughout.During the video shoot itself, I constantly switched from slides to code and back. It was absolutely instrumental that this switch was instant and seamless. Video recording and streaming software supports such multi-scenario setups; we come back to these at the end of this section. To tie slide contents and code samples tighter together, I used the YouTube (see below), the coding during the shot itself was truly live: I edited and ran code, applying modifications and extensions to it when they crossed my mind during the shoot. Such ad-hoc code changes led to some of the most insightful experiments during the semester but, inevitably, also conjured syntactic or logic errors here and there. I fixed these during the shoots while commenting on my interpretation of error messages. Quite quickly student feedback made it clear that this was seen as insightful rather than a nuisance. In consequence, I relied on pre-recorded coding footage only once during the semester, when an experiment based on CPU caching and branch prediction behavior turned out to be so time-sensitive, that the simultaneously running video recording process would ruin all measurements.While videos were recorded in advance and then uploaded to Advanced SQL courses were laid out to emphasize the live coding parts.As intimidating and daunting the live coding sections felt at first, the more I enjoyed them towards the end of the semester. Knowing that true boo-boos could be removed through post-shoot video editing helped but was seldomly necessary. The frequent switches from slide to code and back led to changes of pace, structured the video’s narrative, and allowed for interactive bits of content that slides could never contain. Sects. Push and pull: weekly assignments. The prevalence of code and the time spent to develop, modify, and discuss fragments of SQL and C during the videos, also set the tone for the weekly assignments that accompanied the entire semester: students outright expected to read and write significant chunks of code during these assignments. With essential support by my assistants, we made sure that the coding style  in the assignments matched those in the lecture videos. After a while we found that many students submitted code that mimicked the lectures’ style, leading to well-structured submissions that were easy to grade by assistants and tutors.PostgreSQL and MonetDB, facilitated new kinds of assignments. Once a lecture had identified an algorithm or data structure as a central database kernel concept, we asked the students to locate its implementation in the systems’ source. Invariably, this turned up variations or simplifications of the original textbook concept, which helped to appreciate the complexity of a true and tested DBMS.For the GitHub. Students pulled the assignment text along with code skeletons and completed the latter to build their solutions in teams of two, using GitHub’s collaboration features . Tutors then annotated the solutions, pushing the graded submissions back to GitHub from which the students pulled and reviewed the tutors’ comments. The tutors’ ability to place annotations right next to or inline with the students’ code led to very specific and actionable feedback, sometimes in the form of actual code snippets that would improve or correct the students’ original solution. GitHub’s push-pull model turned out to be a very fitting two-way communication channel that helped to establish a connection between students and their tutors, making up at least partly for the lack of face-to-face meetings.Just like the lecture slides and associated code, we published the assignments on Discourse-based forum that served as a hub for discussion of the video lectures and the weekly assignments. Any new posting in that forum would trigger a notification such that my research assistants and myself were able to respond immediately, typically within less than 30 minutes. This led to a lively atmosphere of back-and-forth in the forum with several hundreds of postings during the overall semester. While even a bustling forum is hardly a replacement for live interaction, polls among the students showed that, taken together, (1) a forum with quick turnaround, (2) the around-the-clock availability of YouTube, and (3) the ability to pause/rewind lecture videos as needed, formed an acceptable replacement for in-class tutorial sessions.Throughout the entire semester and beyond, we operated a Think like a YouTuber. Staying alert and attentive throughout a 90-minute in-hall lecture is challenging. To expect the same from viewers of a video stream at home–where distraction is abundant–is straight out wishful thinking. From the get go, I thus aimed for brief lecture videos that averaged about 25 to 30 minutes in length . While this payed respect to limited attention spans and helped to blend with the “YouTubeculture,” there also were ample practical benefits:The resulting video recordings were of manageable file size, typically less than Video editing software faced manageable file sizes and remained snappy. In effect, video post-production time shrank.YouTube was a matter of seconds. YouTube’s own encoding process for HD video completed in mere minutes.Video upload to Further, limited video length enforced a bite-sized presentation of the course contents: it helped to entirely focus on the Advanced SQL, 58 videos were shot. YouTube’s play lists turned out to be ideal to organize these collections: viewers let their video players walk the list from front to back to take in the entire contents. In addition, I was disciplined in numbering all videos, gave descriptive titles, and added keywords which facilitated to hop and search through the play lists at will. Weekly assignments then referred to these video numbers (“Relevant videos for this assignment: # to #”). Students used these hints to adjust the rate at which they consumed the play lists.For Not least important, a video length of about half an hour helped me to keep my concentration throughout the shoot. The intricacies of the upcoming slides  and code fragments to be discussed in that time frame, were easily remembered. As a consequence, almost all videos were shot in one take from start to finish, a process that would not last much longer than the 30-minute target. Still, close attention and care was required. After about four to five video shoots, I started to feel exhausted and adjusted my daily pensum accordingly. call-outs onto a sequence of frames , or use similar gestures. The talking head definitely helped to keep myself engaged and student feedback suggests a similar effect on the viewers’ side.Most of the scenes were configured to insert a camera picture of my head and upper waist into the frame’s lower-right corner as shown in Figures e.g., due to weather or time of day. The face and overhead cameras definitely benefited from consistent and homegenous illumination. However, the key piece of the equipment puzzle has been a professional microphone. Installed on an extendible arm, I adjusted the microphone to be next to my mouth, just outside the camera frame. Regarding audio quality, the microphones built into off-the-shelf computers are simply no match: in comparison to the studio mic, you sound as if you were sitting in a tin can, honestly. If you can invest into a single piece of recording equipment only, make it the microphone.Let me not close this section without stressing the importance of good lighthing and audio recording. Two LED light panels with adjustable white/yellow tint allowed me to continue shooting even if exterior light was dim,  Play list: http://tiny.cc/DB2-summer-2020.) is more engaging and satisfying than the confrontation with the overwhelmingly complex architecture of database components, the operation of which is  opaque.This series of SQL Stack Exchange Q&As, SQL references and standards, RDBMS documentation and kernel source code, as well as experience and best practices. A dedicated chapter on the choice of indexes that fit a given set of queries was inspired by Markus Winand’s book SQL Performance Explained :A selection of the algorithmic problems discussed in Given an unknown shape in the two-dimensional plane, perform a scan to trace its border. Use these traces to render the shape. In a partially occupied cinema seating plan, find a group of close seats that can host a group of friends. This problem was lifted straighted out of ACM’s ICPC annual programming contest , a sourcmaximum-scan across the two-dimensional plane to determine object visibility in a hilly landscape. Perform a scans along an input expression to check whether its subexpressions are properly parenthesized. Use Given a log of system activity, try to identify sessions that can be attributed to legitimate users/intruders. Compress the pixel-based representation of an image using run-length encoding. Decompress the encoded image to restore the original. See Fig. Given a sequence of altitude measurements, identify peaks and valleys. This is a variant of a time series scenario. e.g., bibliographic references) to identify and compress consecutive numbers ranges. Reorder a series of integers  in parallel. generate-and-test Sudoku solver. Derived from an example in the documentation of SQLite3 It is foreseeable that the majority of students will never be required to build parsers or simulate cellular automata with SQL. With the above computational tasks successfully implemented, however, students built confidence in both, the potential of SQL and their own ability to express these problems in a declarative, set-oriented fashion. I am positive that most students who completed the course will understand RDBMSs as capable data processors rather than mere keepers of piles of tabular data.Advanced SQL play lists have a cumulative view count of 26 200+ (more than 3 300 hours of watch time). Viewers predominantly are from the Tübingen area, but YouTube analytics data and e‑mail feedback indicate that the videos have a global reach.As I write this in October 2020, the videos in the Advanced SQL and Advanced SQL ranked in the two top spots.With the summer semester of 2020 drawing to a close in late June, the Computer Science department at University of Tübingen contacted its students to conduct the regular teaching evaluation. Survey forms were sent to the Die Qualtität der Videos ist wunderbar, gerade das Unterteilen der Themen in kleinere Videosegmente ist eine sehr gute Idee. Der Mix aus Theorie (Folien) und Praxis (Shell/Queries) ist genau das was ich brauch um ein Thema gut zu durchblicken.Die Veranstaltung wurde optimal digital umgesetzt. Die Videos von Herrn Grust haben eine extrem hohe Qualität.Die Videos haben eine angemessene Länge und mit den großen Folien und dem kleinen Greenscreen-Grust ein sehr vorlesungsnahes Feeling - und es ist auch schön, dass sie auf YouTube im Prinzip für Alle, die es interessiert, verfügbar sind. … Wie immer wird gut erklärt und es gibt viele Beispiele, Folien mit stylischen ASCII-Grafiken (Hut ab!), …Auch das Aufteilen einer Vorlesung in mehrere kleine Videos finde ich sehr gut. Im Vergleich zu anderen Vorlesungen, die ihre Inhalte immer am Stück hochladen ist es in dieser Vorlesung einfacher, den einzelnen Gedankengängen zu folgen und spezielle Themen zu wiederholen. Auch dass die Übungsblätter angeben bis zu welchem Video sie den Stoff abfragen, macht die Leistungsanforderung sehr transparent und man weiß genau welche Teile der Vorlesung man wiederholen sollte, ….Dass die Vorlesung aufgezeichnet wird, statt live stattzufinden finde ich sehr erfreulich, da die Videos zu persönlich gut passenden Zeiten geschaut werden können, auch mitten in der Nacht. Außerdem kann man die Vorlesung pausieren um nachzudenken oder einer anderen Tätigkeit nachzugehen, die Wiedergabegeschwindigkeit bei Bedarf verändern und sich Inhalte wiederholt ansehen. Die Videos speziell zu dieser Veranstaltung haben außerdem eine sehr gute Länge, sodass sie sich gut einteilen lassen und kurze Erholungspausen einfach ermöglichen.[Positiv war] Aufbau und Struktur der Vorlesung, Übersichtlichkeit der Videos in der Playlist ….Prof. Grust erklärt in seinen Videos wirklich sehr gut und man merkt, dass er dieses Thema sehr gerne lehrt! Das motiviert natürlich nochmal ein bisschen mehr. Er ist einer der einzigen Dozenten, die sehr schnell die Umstellung auf digitale Medien (toll) hinbekommen hat,On Demand Videos in Top Qualität und aufwendig erstellt. Gut erklärte Sachverhalte. Anschauliche Beispiele.Ich finde die Veranstaltung sehr gut und setze mich gerne mit dem Stoff auseinander. Mir gefällt die Lehre mittels asynchroner Videos. Gerne immer so!Auch wenn es wieder Präsenzlehre gibt, sollten die Videos für zukünftige Generationen auf youtube erhalten bleiben.Massives Lob auch dass Übungen über Github laufen, falls Fehler/Unklarheiten in Aufgabenstellungen sind, werden Changes gepusht, das ist super! Falls dann etwas unklar wäre, [sind] die Reaktionszeiten und Antworten im Forum sehr gut.


Stress has become an increasingly serious problem in the current society, threatening mankind’s well-beings. With the ubiquitous deployment of video cameras in surroundings, detecting stress based on the contact-free camera sensors becomes a cost-effective and mass-reaching way without interference of artificial traits and factors. In this study, we leverage users’ facial expressions and action motions in the video and present a two-leveled stress detection network (TSDNet). TSDNet firstly learns face- and action-level representations separately, and then fuses the results through a stream weighted integrator with local and global attention for stress identification. To evaluate the performance of TSDNet, we constructed a video dataset containing 2092 labeled video clips, and the experimental results on the built dataset show that: (1) TSDNet outperformed the hand-crafted feature engineering approaches with detection accuracy 85.42% and F1-Score 85.28%, demonstrating the feasibility and effectiveness of using deep learning to analyze one’s face and action motions; and (2) considering both facial expressions and action motions could improve detection accuracy and F1-Score of that considering only face or action method by over 7%. Stress has become more and more widespread and severe in the modern society. Stress that is left unchecked and handled could contribute to many health problems, threatening people’s feelings, thoughts, behaviors, and well-being. Being able to detect stress can help people take active steps to manage the stress before bad consequences are incurred.Traditional stress detection relies on psychological questionnaires  or profeCurrently, the ubiquitous deployment of contact-free video cameras in surroundings, together with the rapid progress of data collection and analysis techniques, offers us another channel to detect one’s stress based on image sequences captured from a monitoring video camera. Compared with previous sensory devices, the later offers the following three benefits. First, it is more convenient, particularly in places like schools, hospitals, and restricted areas like prisons, where no carry-on devices are needed or allowed. Second, it has a very long standby time and can easily reach mass audience at a very low-cost. Third, the continuous frames it captures enable us to grasp and analyze people’s stressful states more naturally without interference of artificial traits and factors.The aim of this study is to leverage contact-free video cameras for stress detection.There are several recent studies reporting findings that facial signs and expressions can provide insights into the identification of stress ,14,15, aDeep learning has been widely and successfully applied in many fields such as computer vision, emotion analysis and so on. Different from the existing work which extracted the features through hand-crafted feature engineering methods, in this work, we conduct stress detection through deep learning of features’ representations. Furthermore, beyond facial regions analysis, we leverage and integrate user’s action cues to enhance the video-based stress detection. The rationale could be glimpsed from To this end, we proposed a Two-leveled Stress Detection Network (TSDNet), which firstly learns face- and action-level representations separately, and then fuses the results through a stream weighted integrator for stress identification.To address the challenge that images manifesting subject’s stressed states usually hide in a long sequence of image frames with subtle distinctions, in addition to fusing actions and facial expressions, we designed a number of attention mechanisms, including face-level multi-scaled pooling attention, action-level frame attention, aiming to capture affective facial expressions and action motions from the video. A stream weighted integrator with local and global attention was also implanted to strengthen the detection performance.We presented a two-leveled stress detection network (TSDNet), which learns to fuse facial expressions and action motions in videos for stress detection.A set of attention mechanisms were particularly designed to capture affective facial expressions and action motions from the video, and integrate the results with local and global attention.A video dataset containing 2092 labeled video clips was constructed. The experimental results on the built dataset showed that: (1) TSDNet outperformed the hand-crafted feature engineering approaches with detection accuracy 85.42% and F1-Score 85.28%, demonstrating the feasibility and effectiveness of using deep learning to analyze one’s face and action motions; (2) considering both facial expressions and action motions could effectively improve detection accuracy and F1-Score of that considering only face or action method by over 7%.Overall, the contributions of the paper can be summarized as follows.The remainder of the paper is organized as follows. In In this section, we review some closely related work on image-based and video-based stress detection.Observing that the signs of stress could be more easily detected by looking at the condition of the face, particularly the lines or wrinkles around the nose, mouth, and eyes, ,23 invesAs symptoms of stress are usually linked with fluctuations in physiological  and physical activities , such faRef.  extendedBased on the findings that mouth activities correlate with signs of psychophysical status,  developeRef.  developeRef. ,21 used Ref.  examinedRef.  decided Inspired by the research results that stress could be successfully detected from thermal imaging due to changes in skin temperature under stress , as wellFurthermore,  further The work reported here differs from the previous work in the following two aspects. Firstly, we took a deep learning strategy to avoid the labor-intensive hand-crafted feature engineering approach. Secondly, besides facial regions analysis, we employed user’s action cues to enhance the detection performance. A stream weighted integration method embedded with local and global attention mechanisms was particularly designed and evaluated.We invited 122 volunteers  to participant in our study. The participants are college students from eight universities located in three different places  in China. A Participant Consent Form was issued and signed by each participant before the study.Preparation for Data Collection. There are many kinds of stressors that may stimulate stress. Playing computer games ,38, answProcedure of Data Collection. We let the participants firstly watch a relaxed video clip followed by a neutral one with 10 s as a break in between. Before playing the third science video clip, we guided the participants to go through the follow-up test questions for 30 s in advance, and completed the online tests after watching the video clip.We developed an application tool to automatically collect and save the videos of the participants when they watched the three types of video clips. Correspondingly, each obtained video lasted for 2 min. The videos collected while the participants watching the relaxed and neutral video clips were labeled “unstressed”, and “stressed” otherwise.Pre-Processing of Collected Video Data. We collected totally 490 videos about the participants. The total duration of the collected videos was 2 h 38 min 52 s. The frame rate of the camera used is 30 fps.We dropped the collected videos which failed to capture the faces due to the misaligned camera or the dim ambient light, or had the short recording time due to the abnormal program exit.To cut down the training time, we partitioned each video into eight 15-s samples. If the last sample was less than 15 s, we appended it with its precedent sample. In this way, we acquired 2092 video samples, including 920 labeled “stressed” and 1172 labeled “unstressed”.We randomly split the subjects into three groups, where 60% of the subjects for training, 20% of the subjects for validation, and the rest 20% of the subjects for testing. Especially, to obtain the more reliable results, we did three divisions and calculated the average results. The numbers of segmented 15-s video samples used for training, validation, and testing are given in We further resized all the input images  to 70 × 70 pixels. To prevent over-fitting, we conducted a random 64 × 64 cropping and normalization to the training images, and a center-around 64 × 64 cropping and normalization to the validation and testing images.The task of our video based stress detection is to sense the affective state (stressed or unstressed) of a user based on his/her video data The proposed model TSDNet firstly learns face- and action-level representations separately, and then fuses the results through a stream weighted integrator with local and global attention for stress identification.The learning of the face-level representation proceeds in three steps.Step 1: Localize the face region in each frame of the video.V.We adopted the technique  to automStep 2: Identify the key face images from the sequence of face images.Considering the subtle differences among the face images in the video, to capture affective expressions hidden in the sequence of similar face images and strengthen the detection performance, we identified two key face images (the most expressive face image and the most expressionless face image) from the sequence of face images. Their distance would be served as the basis for stress detection in the next Step 3.We turned the identification of these two key face images into a binary classification and sorting problem. For each face image, we expected to obtain the probability We built an expression classifier to discriminate expressive and expressionless face images based on Resnet . We traiWe fed each face image Step 3: Learn the face-level representation.(1)Computing the Fine-Grained DifferenceThe face level learning of one’s affective state was based on the difference between the most expressive and the most expressionless face images. Apart from the multi-scaled fine and coarse grained differences exploration, we also investigated possible difference correlations between the two images. Through the thorough and extensive comparison of the most expressive and expressionless face images, we established the face level representation for stressful state detection.C, H, and W represent the channel number, height, and weight, respectively. In the study, Applying two parameter-shared Resnets to face image We computed their fine-grained difference (2)Computing the Coarse-Grained DifferencesTo learn the difference further, we fed To target at high-level differences covering multiple regions of the face, we rolled up from the basic fine-grained difference between As shown in To learning the influential distribution of each coarse-grained metric, an attention block using convolutional layers, batch normalization layers, and ReLU function layers with Softmax function was designed, and obtained the attention distribution feature maps We employed element-wise multiply to the attention distribution feature maps and the original post-pooling feature maps, mapping the attention distribution back to the post-pooling feature maps.R.For ease of computation, we reshaped (3)Learning the Difference CorrelationsConsidering difference correlations exist among different parts of the face , we implanted a self-attention mechanism  to extraS.C is the channels and We applied the scaled dot product operation twice to obtain the matrix representation of the correlation between each pair of channels, and then got the weighted average representation S to one dimension:m = 20 in the study.Finally, we reshaped V, which were (1) an image sequence https://github.com/feichtenhofer/gpu_flow) for optical flow extraction. Two networks were built for concurrently learning action-level representations. As both networks followed the same structure, we detail one of them in the following. The learning of the action-level representation intends to grasp user’s action cues linked to stress. We explored the used of two streams derived from the user’s video Step 1: Learn and assign contribution weights to the still image frames.Like the previous face images in To cut down the size of the feature maps, we executed the  to initialize all the trainable parameters in the model. The learning rates were initialized as 0.01. All the learning rates were divided by 2 every 15 epochs. The batch size was 64. We used 120 epochs to train our stress detection model. The optimization process fine-tuned all the layers with stochastic gradient descent (SGD) through a weight decay of 0.01 with a momentum of 0.9.As the study focused on low-end video camera without thermal spectrums, we compared the performance of our method with the following two categories of video-based stress detection approaches.(1) Action Units (AUs) based: (1) The Dependent Model  extracte(2) Facial Cues (FCs) based:  was a reOur implementation was based on the deep learning framework PyTorch. All the experiments were conducted on two NVIDIA GTX Titan X GPU with 24 GB on-board memory in total.Three sets of experiments were conducted to evaluate the performance of TSDNet in stress detection, as well as its design details, including face-level, action-level, and integration local and global attention mechanisms and different integration strategies.From the TSDNet’s confusion detection matrix shown in Moreover, considering the motions of both face and action in TSDNet could effectively improve the detection accuracy and F1-Score of that considering only face or action method by over 7%.The second experiment investigated the effectiveness of different attention mechanisms , which we designed and incorporated in TSDNet. We conducted three ablation studies which respectively removed the attention mechanisms from TSDNet. From the results presented in In the face-level multi-scaled pooling attention, we took an average pooling with kernel size of (1 × 1), (2 × 2), and (4 × 4). We compared the different pooling combination methods, i.e., (1 × 1) + (2 × 2) pooling, (1 × 1) + (2 × 2) + (4 × 4) pooling, and (1 × 1) + (2 × 2) + (4 × 4) + (8 × 8) pooling. As shown in We compared our designed stream weighted integration with local and global attention method with three other integration approaches, which are early integration, loss-based early integration, and later integration, as illustrated in In this paper, we presented a video-based Two-leveled Stress Detection Network (TSDNet), which integrates face-level detector and action-level detector to understand facial expressions and action motions for stress identification. In particularly, we designed a face-level multi-scale pooling attention mechanism and an action-level frame attention mechanism. The former employed the multi-scaled average pooling with different kernel sizes to grasp stress-related facial features, and the latter focused on key body movement frames related to stressed states. A stream weighted integrator with local and global attention was used to fuse the results from face- and action-level detectors. We built a video dataset containing 2092 labeled video clips, and evaluated the performance of TSDNet on the data set. The experimental results show that TSDNet outperformed the existing hand-crafted feature-engineering strategies, and integrating face-level and action-level detectors could improve detection accuracy and F1-Score by over 7%.In future work, we plan to add the audio stream into the framework to explore the audio–video methods for stress detection.