Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans and Fusobacterium nucleatum and total bacteria growing in an in vitro oral biofilm static model.Previous research findings support an antimicrobial effect of polyphenols against a variety of pathogens, but there is no evidence of this effect against periodontal pathogens in complex biofilms. The purpose of this study was to evaluate the antimicrobial activity of red wine and oenological extracts, rich in polyphenols, against the periodontal pathogens Streptococcus oralis, Actinomyces naeslundii, Veillonella parvula, F. nucleatum, P. gingivalis and A. actinomycetemcomitans was developed on sterile hydroxyapatite discs. Red wine , and two polyphenols-rich extracts (from wine and grape seeds) were applied to 72 h biofilms by dipping the discs during 1 and 5 min in the wine solutions and during 30 s and 1 min in the oenological extracts. Resulting biofilms were analyzed by confocal laser scanning microscopy and viable bacteria (colony forming units/mL) were measured by quantitative polymerase chain reaction combined with propidium monoazide. A generalized linear model was constructed to determine the effect of the tested products on the viable bacterial counts of A. actinomycetemcomitans, P. gingivalis and F. nucleatum, as well on the total number of viable bacteria.A previously validated biofilm model, including P. gingivalis after 1 min (p = 0.008) and in A. actinomycetemcomitans after 5 min of exposure (p = 0.011) with red wine. No evidence of relevant antibacterial effect was observed with the oenological extracts, with statistically significant reductions of F. nucleatum after 30 s of exposure to both oenological extracts (p = 0.001).The results showed that red wine and dealcoholized red wine caused reduction in viability of total bacteria within the biofilm, with statistically significant reductions in the number of viable A. actinomycetemcomitans, P. gingivalis and F. nucleatum, encourage further investigations on the potential use of these natural products in the prevention and treatment of periodontal diseases.Although moderate, the antimicrobial impact observed in the total bacterial counts and counts of  Porphyromonas gingivalis, Prevotella intermedia, Tannerella forsythia or Treponema denticola, as well as Aggregatibacter actinomycetemcomitans   .− 1 was automatically generated through informatics analysis .Each DNA sample was analysed in duplicate. Quantification cycle (Cq) values, describing the PCR cycle number at which fluorescence rises above the baseline, were determined using the provided software package . Quantification of viable cells by qPCR was based on standard curves. The correlation between Cq values and CFU mL2 > 0.998 and an efficiency range of 1.9–2.0.All assays were developed with a linear quantitative detection range established by the slope range of 3.3–3.5 cycles/log decade, r® BacLight™ Bacterial Viability Kit solution  was used to stained the biofilms at room temperature. The fluorochromes were incubated (ratio 1:1) during 9 ± 1 min to obtain the optimum fluorescence signal at the corresponding wave lengths : > 600 nm. The CLSM software was set to take a z-series of scans (xyz) of 1 μm thickness . Image stacks were analyzed by using the Olympus® software (Olympus). Image analysis and live/dead cell ratio (i.e. the area occupied by living cells divided by the area occupied by dead cells) was performed with Fiji software .Non-invasive confocal imaging of fully hydrated biofilms was carried out using a fixed-stage Ix83 Olympus inverted microscope coupled to an Olympus FV1200 confocal system . LIVE/DEAD− 1 of A. actinomycetemcomitans, P. gingivalis, F. nucleatum and total bacteria by qPCR, and the live/dead cell ratio of the whole biofilm by CLSM. An experiment-level analysis was performed for each parameter of the study (n = 9 for qPCR and n = 3 for CLSM results). Shapiro–Wilk goodness-of-fit tests and distribution of data were used to assess normality. Data were expressed as means ± SD.The selected outcome variables to study the antibacterial effect of wine solutions and oenological extracts were the counts of viable bacteria present on the biofilms, expressed as viable CFU mL− 1 or live/dead cell ratio), was compared by means of a parametric ANOVA test for independent samples, and a general linear model was constructed for each bacterium  and for total bacteria for qPCR results and for total bacteria for live/dead cell ratio of whole biofilm obtained by CLSM, using the method of maximum likelihood and Bonferroni corrections for multiple comparisons. A similar model was constructed in the case of the experiments with oenological extracts, in order to compare the effect of each solution , the time of exposure (30 s or 1 min) and their interaction with the main outcome variable (CFU mL− 1 and live/dead cell ratio of whole biofilms).In the case of the experiments with red wine, the effect of each solution , the time of exposure (1 or 5 min) and their interaction with the main outcome variable  was used for all data analysis.Results were considered statistically significant at A. actinomycetemcomitans, P. gingivalis, F. nucleatum and total bacteria.Table A. actinomycetemcomitans (CFU mL− 1) (p > 0.05) when compared to control biofilms (exposed to PBS). Conversely, after 5 min a significant reduction of viable A. actinomycetemcomitans (CFU mL− 1) occurred with wine (p = 0.053) and dealcoholized red wine (p = 0.011) when compared to control biofilms. No statistically significant differences were observed between the two wine solutions at any time (p > 0.05). The effect of exposure time (between 1 and 5 min) was however, statistically significant for both red wine (p = 0.030), and dealcoholized red wine (p = 0.006).After 1 min of exposure to red wine or dealcoholized red wine, no statistically significant effect was measured on the viable counts of P. gingivalis (CFU mL− 1) (p = 0.008). Measurable reductions also occurred after 5 min of exposure with both red wine and dealcoholized red wine, although no significance differences were observed when compared to biofilms exposed to PBS . No statistically significant differences were observed in the effectiveness comparing the two wine solutions at applied times or when comparing exposure times .After 1 min exposure to red wine solutions, there were statistically significant reductions in the viable counts of F. nucleatum, reductions in viable counts were not statistically significant after both 1 and 5 min of exposure (Table p > 0.05). The effect of exposure time (between 1 and 5 min) was however, statistically significant for both red wine (p = 0.035), and dealcoholized red wine (p = 0.004).For p > 0.05 for all cases).In regards to biofilm total bacteria, reductions in viable counts were measured Table  after 1 p > 0.05 in all cases). No exposure time effect was observed for red wine or dealcoholized red wine, except for P. gingivalis, for which the effect of time of exposure (1 min versus 5 min) was statistically significant (p = 0.027).Due to the possible antibacterial activity of EtOH present in the red wine, its effect over the three pathogens and total bacteria was evaluated. Although the treatment with 12% EtOH, emulating the alcoholic content of the wines, resulted in a decrease in total counts   .After 72 h of incubation, CLSM observation revealed the control HA discs were covered by a mature biofilm, with multicellular aggregates well spread through the surface, showing a structural organization based bacterial communities forming microcolonies, with a live/dead cell ratio of 2.04 ± 0.43 when dipped in PBS for 1 min and 1.10 ± 0.42 for 5 min .After 30 s and 1 min exposure to the wine and grape seed extracts, there was a reduction in the viable counts of P. gingivalis was observed after exposure to the wine and grape seed extracts during 30 s . No statistically significant differences were observed between the effect reached by the two oenological extracts at any time (p > 0.05 in both cases). The effect of time of exposure (30 s versus 1 min) was statistically significant for the wine extract (p = 0.014), but not for the grape seed extract (p = 0.395).Similarly, no significant effect on viable counts of F. nucleatum, 30 s of exposure to both oenological extracts significantly reduced viable counts  .For p = 0.005), but not for the grape seed extract (p = 0.057).Regarding the total counts of bacteria included in the biofilm, 30 s and 1 min of contact with both, the wine and grape seed extracts, caused a slight reduction in the number of viable counts, but differences were not statistically significant. Similarly, no significant differences were observed between the two extracts at any time. The effect of time of exposure (30 s versus 1 min) was statistically significant for the wine extract , or between exposure time  except for F. nucleatum, for which the effect of exposure time (30 s versus 1 min) was statistically significant (p = 0.012).Due to the possible antibacterial activity of DMSO, its effect on the tested bacterial species and total bacteria was evaluated. It was observed that the treatment with 4% DMSO (v/v), concentration used to solubilize the extracts, had no effect on the bacterial cell viability Table . There wp > 0.05 in both cases) .The CLSM analysis showed that, after 72 h of incubation on HA surfaces, the biofilm covered the disc surface as multicellular aggregates, exhibited a live/dead cell ratio of 1.13 ± 0.50 when dipped for 30 s and 1.10 ± 0.16 for 1 min in PBS  Fig. . In the A. actinomycetemcomitans and P. gingivalis when compared to the polyphenol rich oenological extracts. When comparing the oenological extracts, wine extract was more active against P. gingivalis and F. nucleatum, and the grape seed extract against F. nucleatum. In regards to the effects on total biofilm bacteria, wine solutions  showed significant reductions in the live/dead cell ratios, in contrast, the oenological extracts did not evidence a relevant antibacterial effect.In the present study, the effect of red wine and oenological extracts in a validated oral biofilm model has been studied, demonstrating that wine solutions  had a greater antimicrobial effects against S. mutans and other streptococci. Similarly, Cueva and colleagues [S. mutans and S sobrinus with flavan-3-ols precursors, (+)-catechin and (−)-epicatechin (compounds present in the grape seed extract employed) and reported significant inhibition of bacterial growth. Daglia and colleagues studied the antiseptic effect of dealcoholized red wine in comparison with white wines, demonstrating a stronger action of red wines against oral streptococci, what reinforces the possible role of anthocyanins as bacteriostatic agents [P. gingivalis, F. nucleatum and S. mutans growing planktonically when exposed to two wine phenolic compounds (caffeic and p-coumaric acids) and the same red wine and grape seed extracts  used in the present study.Previous in vitro studies evaluating the antimicrobial effect of phenolic compounds from wines and oenological extracts have demonstrated significant effects against selected Gram-positive and Gram-negative pathogenic bacteria , entericlleagues  incubatec agents . Recentlc agents  showed aS. mutans, S. sobrinus or Lactobacillus spp., but not against the periodontal pathogens usually present in the subgingival microenvironment. Furthermore, most have used planktonic pure cultures and therefore, the reported effects could not be easy to be transferred to the oral environment, where bacteria live in highly complex communities, forming biofilms [P. gingivalis to Provinols™ and Vitaflavan® of 500/≥1000 μg mL− 1 for both extracts, and for F. nucleatum of 500/1000 μg mL− 1 also for the referred extracts. However, in the present study, the observed antimicrobial activity can be considered as moderate against P. gingivalis and only statistically significant for F. nucleatum with both extracts, even at a high concentration . These findings reinforce the importance of using biofilms models when testing antimicrobial activity, since bacterial cells in biofilms express different phenotypes, with greater resistance to antimicrobial agents [These studies, however, have focused the study of their antimicrobial effect on species commonly detected in supragingival plaque, such as biofilms . As mentbiofilms , establil agents –50. Somel agents . Sedlackl agents  describeVitis vinifera (Vitaceae) on a biofilm model composed of S. mutans, S. sobrinus, Lactobacillus rhamnosus, P. gingivalis, and F. nucleatum [A. oris, F. nucleatum, or S. oralis. of red wine and dealcoholized red wine.The results from the present study agree with those reported by Furiga and colleagues evaluating the activity of various extracts obtained from ucleatum , 23; anducleatum , describA. actinomycetemcomitans, P. gingivalis and F. nucleatum, when tested in an in vitro multi-species biofilm model. Although the antibacterial effects of red wine and wine-derived extracts was observed, at least 2 to 3-log reduction of bacterial count would be necessary to ascertain the efficacy and/or availability of these tested agents as antibacterial agents. These results encourage further investigations on the potential use of natural agents in the prevention and treatment of periodontal diseases.This investigation has shown that the use of red wine and wine-derived extracts had a moderate antimicrobial impact in the total bacterial counts and counts of


The power conversion efficiency (PCE) of 10.14% and fill factor (FF) of 74.2% are successfully obtained in ≈300 nm OSC, which outperforms BTR:PC71BM (9.05% and 69.6%) and BTR‐OH:PC71BM (8.00% and 65.3%) counterparts, and stands among the top values for thick‐film ASM OSCs. The performance enhancement results from the enhanced absorption, suppressed bimolecular/trap–assisted recombination, improved charge extraction, optimized domain size, and suitable crystallinity. These findings demonstrate that the donor derivative featuring similar chemical structure but different crystallinity provides a promising third component guideline for high‐performance ternary ASM OSCs.Thick‐film all‐small‐molecule (ASM) organic solar cells (OSCs) are preferred for large‐scale fabrication with printing techniques due to the distinct advantages of monodispersion, easy purification, and negligible batch‐to‐batch variation. However, ASM OSCs are typically constrained by the morphology aspect to achieve high efficiency and maintain thick film simultaneously. Specifically, synchronously manipulating crystallinity, domain size, and phase segregation to a suitable level are extremely challenging. Herein, a derivative of benzodithiophene terthiophene rhodanine (BTR) , namely, BTR‐OH, is synthesized with similar chemical structure and absorption but less crystallinity relative to BTR, and is employed as a third component to construct BTR:BTR‐OH:PC A donor derivative is incorporated in benzodithiophene terthiophene rhodanine (BTR)‐based thick‐film all‐small‐molecule (ASM) organic solar cells (OSC), which achieves power conversion efficiency of 10.14% and fill factor of 74.2%, outperforms its binary counterparts, and stands the record value for thick‐film dual‐donor ternary ASM OSCs. The results demonstrate that the donor derivative is a promising third component to boost the performance of ASM OSCs. In such devices, the third component usually plays the role of broadening the absorption spectra, helping charge transfer and/or transport, reducing carrier recombination, and optimizing the active‐layer morphology.71BM binary host system to construct ternary ASM OSC. The BTR‐OH effectively increased the absorption of the ternary blend films in the region from 400 to 600 nm. Upon adding the less crystalline BTR‐OH, the phase segregation in the active layer was slightly weakened while the high hole mobility and high PCE at thick‐film were retained. This shows that BTR‐OH acts as an excellent morphology adjuster to fine tune active layer morphology, leading to enhanced exciton dissociation and charge collection, reduced charge recombination, and suitable domain size of ≈20 nm. The corresponding ternary ASM OSCs achieved a high PCE of 10.14% and a FF of 74.2% at the film thickness of ≈300 nm, outcompeting BTR:PC71BM counterparts (PCE of 9.05% and FF of 69.6%). To the best of our knowledge, the PCE of 10.14% is the highest reported value for thick‐film ternary ASM OSC using two small molecule donors, which provides new insight into ternary ASM OSC design.To our knowledge, the above‐mentioned simple yet effective strategy of employing a structure similar third component has never been demonstrated in solution‐processed all‐small‐molecule OSCs. Herein, by introducing hydroxyl into the rhodamine group of BTR, we intentionally synthesized a small‐molecule donor derivative of BTR, BTR‐OH, with similar chemical structure and absorption profiles, yet weaker crystallinity.2Figure71BM. The difference between BTR and BTR‐OH lies in the hydroxyl substitution in the BTR's rhodamine end group. Figure 71BM. Comparing these two donor films, BTR clearly has stronger crystallinity, evidenced by the vibronic peak at ≈630 nm, at which BTR‐OH has just an absorption shoulder. Among the films with different weight ratio, the BTR: BTR‐OH: PC71BM  blend film exhibits the highest maximum extinction coefficients at 552 nm . BTR‐OH displays the lowest unoccupied molecular orbital (LUMO) level of −3.45 eV, slightly lower/deeper than that of BTR (−3.39 eV). The highest occupied molecular orbital (HOMO) level of BTR‐OH (−5.49 eV) lies between those of BTR (−5.36 eV) and PC71BM (−6.11 eV), which forms an ideal energy cascade favorable for charge separation.Table71BM, BTR‐OH:PC71BM, and BTR:BTR‐OH:PC71BM as the active layer, all measured under simulated AM1.5G irradiation (100 mW cm−2) condition. FigureJ–V) characteristics of the optimized OSCs.Thick‐film OSCs in bulk heterojunction (BHJ) were fabricated with a conventional device architecture of ITO/PEDOT:PSS/active layer/phenyl(2‐naphthyl)diphenylphosphine oxide (DPO)/Ag. The weight ratio of donor and acceptor was fixed at 1:1, while the ratio of BTR:BTR‐OH was varied from 1:0 to 0:1. The best performing device was obtained when the ratio of BTR‐OH was at 0.8:0.2. 71BM‐based device showed a high PCE of 9.05% with a VOC of 0.93 V, a JSC of 13.95 mA cm−2, and a FF of 69.6%. On the other hand, the device with BTR‐OH:PC71BM as the active layer showed a slightly lower PCE of 8.00%, with a VOC of 0.9 V, JSC of 13.56 mA cm−2, and a FF of 65.3%. By replacing 20% BTR with BTR‐OH in the active layer to form a ternary OSC, the FF was dramatically enhanced from 69.6% to 74.2%, and the Jsc was boosted from 13.95 mA cm−2 to 14.62 mA cm−2, leading to the excellent PCE of 10.14% for corresponding ternary device. Both the maximum and average PCEs show ≈12% relative enhancement in the ternary OSC. To the best of our knowledge, the PCE of 10.14% is the record for ternary ASM OSC with dual donors, and among the leading values for all thick‐film ASM OSCs.The BTR:PCJSC of the BTR‐OH‐ and BTR‐based binary, and the ternary devices are 13.15, 13.69, and 14.03 mA cm−2, respectively, consistent with JSC values measured from solar simulator  spectra of the optimized devices were shown in Figure Generally, the FF mainly relies on the balanced hole and electron mobilities as well as efficient charge extraction under biased voltage. Recent researches also showed that FF was largely correlated to the relative rate of both charge recombination and extraction, because both parameters have nonlinearly incremental relationship with the charge carrier mobility.Jph) as a function of the effective voltage (Veff) was plotted to investigate the charge generation and extraction properties  presented a saturation current density (Jsat) of 15.03 mA cm−2, higher than those of the BTR‐based binary (14.00 mA cm−2) and BTR‐OH‐based device (13.83 mA cm−2), which agrees with the ternary blend's better light absorption capability. The exciton dissociation efficiency (ηdiss = JSC/Jsat) and charge collection efficiency (ηcoll = Jmax power/Jsat) were calculated under the short circuit and maximum power output conditions, respectively. The ternary device revealed a ηdiss of 98.27% and a ηcoll of 87.37%, higher than both of BTR‐based (ηdiss of 97.60% and ηcoll of 83.70%) and BTR‐OH‐based (ηdiss of 96.94% and ηcoll of 78.52%) binary devices, indicating more efficient exciton dissociation and charge collection in the ternary device.First, the photocurrent density  were believed to lead to higher FF of 74.2% in the ternary devices.To investigate the charge transport process, both hole and electron carrier mobilities were measured by employing the space‐charge‐limited‐current (SCLC) method. The hole‐only devices were fabricated with the device architectures of ITO/PEDOT:PSS/active layer/spiro‐TPD:CuPc/Au and electron‐only devices with ITO/Al/active layer/LiF/Al. The modified BTR‐OH‐based binary device exhibited much lower hole mobilities, and higher electron mobilities of 1.4 × 10JSC differences observed reveal that incident photon‐to‐current conversion may not be the only critical factor, and carrier recombination is highly possible to represent a non‐negligible loss channel. To verify the hypothesis, we examined the bimolecular recombination by performing light‐intensity dependence measurements, which examined JSC dependence on illumination intensities , where k, T, and q are the Boltzmann constant, temperature in Kelvin, and the elementary charge, respectively. The parameter n  reflects the presence/absence of carrier traps across the active layer or at interfaces at the semiconductor and electrodes. Any deviations from 1 (trap‐free condition) point to the existence of recombination events and, more specifically, to the existence of trap‐assisted recombination. The fitted data shown in Figure VOC versus light intensity data for ternary system obey a slope of n = 1.04, indicating even lower trap‐assisted SRH recombination. Overall, these findings imply that the incorporation of a structural similar small molecule donor is capable of suppressing bimolecular recombination and reducing trap‐assisted recombination centers, in line with the much enhanced device performance.Previous studies have shown how bimolecular recombination losses in OSCs could be estimated by fitting the Figure−1 with crystal coherence length (CCL) of 13.46 nm for in‐plane (IP) direction, and q ≈ 0.33 Å−1 with CCL of 9.12 nm for out‐of‐plane (OOP) direction  direction  was adopted to investigate molecular packing and orientation in thick films Figure3, and Thus, TG modification is proved to be effective to tune the crystallinity of the small molecule donor. Furthermore, BTR‐based binary blend presented exceedingly strong crystallinity, which was attributed to excessive phase separation and large length scale donor–acceptor (D–A) networks. Replacing 20% BTR with lower crystallinity small molecule donor BTR‐OH could dramatically improve the morphology and tune the crystallinity of the blend, and finally lead to the obvious enhancement of the device performance Table .Figure71BM further decreases the aggregation, resulting in the Rq of BTR:PC71BM (1.04 nm), BTR‐OH:PC71BM (1.03 nm), and BTR:BTR‐OH:PC71BM (1.01 nm)  and transmission electron microscopy (TEM) were used to study the phase separation shown in C71BM 1.0 nm, BTR‐TEM images Figure d,e clearJsc values around 0.01 mA cm−2, confirming that charge transfer between the two small molecule donors is negligible.At last, we noticed that the photoluminescence (PL) of BTR could be partially quenched by BTR‐OH in the BTR:BTR‐OH blend films with different BTR‐OH loadings as presented in Figure S3 in the Supporting Information, indicating that there may be charge transfer between the two donor materials. To verify this, a series of devices were fabricated with BTR and BTR‐OH in neat and blends with different weight ratios as active layer . The BTR and BTR‐OH neat film as well as blend films with different weight ratios presented nearly identical 371BM system. Compared with the binary counterparts, the BTR:BTR‐OH:PC71BM ternary device demonstrated the champion PCE of 10.14% and FF of 74.2% with a film thickness of ≈300 nm at a weight ratio of 0.8:0.2:1, which was among the leading values for thick‐film ASM OSCs. BTR‐OH plays a versatile role in ternary OSCs: 1) BTR‐OH enhanced absorption of photoactive layer, which contributed to the JSC; 2) BTR‐OH suppressed bimolecular/trap‐assisted recombination, and therefore improved charge extraction; 3) BTR‐OH decreased the crystallinity of the donor phase, and optimized the phase‐separated D/A network and domain size to a suitable scale, thereby balancing the hole and electron mobilities. Our findings demonstrated that the donor derivative with similar chemical structure and absorption profile but different crystallinity feature can be a promising third component strategy for constructing high‐performance ternary ASM OSC device.In summary, we designed and synthesized a dehydroxyated derivative of BTR, namely BTR‐OH, which has similar absorption profiles with BTR, and utilized BTR‐OH to construct ternary OSCs based on BTR:BTR‐OH:PCThe authors declare no conflict of interest.SupplementaryClick here for additional data file.


Feeding is a complex motivated behavior controlled by a distributed neural network that processes sensory information to generate adaptive behavioral responses. Accordingly, studies using appetitive Pavlovian conditioning confirm that environmental cues that are associated with food availability can induce feeding even in satiated subjects. However, in mice, appetitive conditioning generally requires intensive training and thus can impede molecular studies that often require large numbers of animals. To address this, we developed and validated a simple and rapid context-induced feeding (Ctx-IF) task in which cues associated with food availability can later lead to increased food consumption in sated mice. We show that the associated increase in food consumption is driven by both positive and negative reinforcement and that spaced training is more effective than massed training. Ctx-IF can be completed in ~1 week and provides an opportunity to study the molecular mechanisms and circuitry underlying non-homeostatic eating. We have used this paradigm to map brain regions that are activated during Ctx-IF with cFos immunohistochemistry and found that the insular cortex, and other regions, are activated following exposure to cues denoting the availability of food. Finally, we show that inhibition of the insular cortex using GABA agonists impairs performance of the task. Our findings provide a novel assay in mice for defining the functional neuroanatomy of appetitive conditioning and identify specific brain regions that are activated during the development of learned behaviors that impact food consumption. Associative learning is a fundamental process that enables organisms to make adaptive decisions based on prior outcomes. However, in some cases, associative learning may also lead to maladaptive decisions, as in the case of drug addiction and obesity . ObesityPavlovian conditioning is an extremely well-studied paradigm and has been used to study many behaviors, in particular feeding and fear , 13. IndAs an alternative, contextual training has also been used to link associative cues to a response. In previous studies of fear conditioning, a specific context  can serve as the conditioned stimulus. For example, it has been shown that placing animals in a novel context paired to a specific stimulus  can induce the subsequent conditioned response (CR) of increased freezing in a single trial . The conWhile context-induced feeding (Ctx-IF) has been used to potentiate food intake in sated rats , this paAll experiments performed in these studies were approved by the Rockefeller University Institutional Animal Care and Use Committee and were in accordance with the National Institute of Health guidelines. Male wild-type C57BL6/J mice  were between 10–20 weeks at the start of behavioral testing. Mice were group housed on a 12 h light/dark cycle with ad libitum access to water and standard mouse chow, PicoLab Rodent Diet , except when single housing or fasting is noted below. Mice were handled for 1–2min per day for 4 days prior to the start of behavioral procedures.Mice were trained similarly as described  with theConditioning took place in Coulbourn Instruments  rat-sized operant chambers outfitted with a house light, a blue LED and an automated food well. The house light and blue LED served as the “light” stimuli; the tones consisted of either white noise or a 2.5 kHz tone. During the “simple” conditioning, mice were placed into the chamber and the CS+ tone + light combination  was played for 2-sec every 30–90 sec at random intervals, with a total of 30 cues presented over a 30 min conditioning session. Each presentation co-terminated with delivery of a 20 mg chocolate-flavored Precision Pellet . Mice were observed to determine whether they nose-poked to the food well during each cue presentation and the # of pellets consumed was counted at the end of the session. The “discrimination” sessions were conducted in the same manner, except that 15 CS+  and 15 CS−  presentations were randomly given every 30–90 sec over the course of a 30 min conditioning session. Following the last “discrimination” session mice were returned to ad libitum feeding for 5 days before testing. Testing consisted of mice being placed into the chambers for two 10 min sessions separated by a 5 min intertrial interval, in which the mouse was placed back into his home cage. In the first session mice were either presented with 10 CS + presentations or CS− presentations. In the second session mice received 10 presentations of whichever CS they did not receive in the first session. The order of presentation was counterbalanced. In both cases, there were 2 g of Precision Pellets freely available in the food cup. Pellets were weighed after each session to determine consumption. During testing, the investigator was blind to the experimental conditions.Prior to habituation mice were given 5–10 chocolate flavored 20 mg Precision Pellets, as above, during each day of handling to prevent neophobia. Mice were habituated to two different contexts. Contexts were easily distinguishable based on shape, size, floor texture and were in different rooms . One hour after the second training session, all mice were placed back on ad libitum food access until testing occurred 48 h later.The protocol was conducted as described above with the following changes. Mice were trained as described, but “fasted” mice were further subdivided into two groups. “Positive valence” mice were trained as described above with Precision Pellets freely available during training. “Negative valence” mice were fasted prior to training, but did not receive Precision Pellets in the training context. To control for food intake, they received a paired amount of standard chow in their home cage following training. In order to assess the metabolic status of the mice, mice were weighed during handling and throughout the behavioral protocol.Mice were trained in Ctx-IF using the original protocol described above, but were tested without food in order to assess activation in response to the context itself. A group of Naïve mice were handled but not trained or tested. Thirty minutes after being tested in Ctx+ or Ctx−, both groups of mice were perfused using 4% paraformaldehyde (PFA). Brains remained overnight in 4% PFA before being transferred to phosphate-buffered saline (PBS) and processed for immunohistochemistry. Brains were sectioned at 50 µm in 4 serial sections. Sections were washed 3 × for 15 min in PBST (PBS-0.1% TritonX) and blocked in 3% goat serum in 3% BSA with 0.05% sodium azide. Sections were then incubated overnight with primary cfos antibody , washed 3× with PBST and then incubated for 1 h at RT with goat anti rabbit AlexaFluor 488 secondary antibody  and washed again 3× in PBST. Sections were mounted using Vectashield with DAPI  and imaged using an Inverted LSM 780 laser scanning confocal microscope . To ensure valid comparisons, equivalent sections from the Naïve, Ctx− and Ctx+ groups were imaged with the same settings  and any post-acquisition manipulations were also made equivalently in both groups. Quantifications were made using ImageJ thresholding and particle analysis by an experimenter blind to conditions.Mice were anesthetized with 3.5% isoflurane, placed in a stereotaxic frame , and maintained at 1.5% isoflurane. The craniotomy was performed using a dental drill (Dremel), and stainless steel guide cannulae  were stereotactically implanted to bilaterally target the insular cortex  and fixed with dental cement. Mice were monitored for 72 h to ensure full recovery, were single housed, and 2–3 weeks later were used in experiments. Fifteen minutes before Ctx-IF  testing, mice received bilateral injections of muscimol  and baclofen (Sigma) . Injections were delivered in a volume of 0.3 μL per side and used a 33-gauge needle that extended 0.8 mm beyond the tip of the guide cannula . Guide cannulae were connected via polyethylene tubing to Hamilton syringes. All infusions were delivered at a rate of 0.1 μL/min using an infusion pump . The injection needle was left in place for 1 min after the injection to allow complete dispersion of the solution.To verify proper placement of cannula implants, at the end of the behavioral experiments, mice were perfused with 4% paraformaldehyde in PBS and brains were post-fixed in the same solution. No animals were excluded for improper cannula placement. Forty micrometer coronal sections were cut through the relevant brain region and examined under a light microscope.t test was used for statistical analyses. All graphs represent mean +/− S.E. Statistics were calculated using Graphpad Prism 6. Group sizes were based on power calculations conducted prior to the experiments and no animals were excluded from analysis.Two-way analysis of variance (ANOVA) followed by the Sidak’s multiple comparisons test, One-way ANOVA followed by Neuman Keul’s multiple comparisons test or unpaired Student’s F=333.2, ***p < 0.0001, Session: F = 1.525, p = 0.1565, Interaction: F = 2.199, *p = 0.0326, followed by Sidak’s multiple comparison test: ***sessions 14–21: CS+ vs. CS−; Fig. t test, t(14) = 3.93, **p = 0.0015, Fig. In this study we set out to identify and test the function of brain regions that play a role in associative learning to control increased feeding in sated animals. We first confirmed that discrete paired auditory and visual cues can drive overeating as previously described . In thesF,112=333.However, from start to finish, this training procedure took a total of 4 weeks using operant chambers, which limits the utility of this approach for generating the larger number of animals that would be required for molecular and functional studies of the neural circuit underlying the observed response. We thus tested whether we could shorten the length of time needed to condition animals by training mice to overeat in response to a specific context rather than in response to discrete cues.We began by habituating ad libitum fed mice to two different novel contexts for one 20 min session each. The two contexts differed in size, shape, wall color and floor texture. In addition, the two contexts were located in different rooms so that the gross location of the room could not be generalized during testing Fig. .After the habituation session, one group of mice continued to be fed ad libitum, while another group of mice were fasted for 24 h prior to training. During the 30 min training sessions, mice were placed into one of the contexts (Ctx+) and were able to freely explore and obtain food from a food well. The unpaired context, in which food was not available, was designated as Ctx−. Following each training session mice were returned to their home cages until the next training session. At the conclusion of this two day training period, the mice were fed ad libitum in their home cages for two days after which they were tested by returning them, serially into each of the two different contexts (Ctx+ and Ctx−) for twenty minutes in the presence of food. They were again able to freely explore and obtain food from the food well. We refer to this paradigm as context-induced feeding (Ctx-IF).F = 31.47, ***p = 0.0001, Test 1 vs. Test 2: F = 14.29, ***p = 0.0026, Interaction:: F = 13.21, ***p = 0.0034, followed by Sidak’s multiple comparison test: ***Train 2: Fed vs. Fasted, ***Fasted: Train 1 vs. Train 2; Fig. F(1.12) = 2.255, p = 0.1591, CS: F(1.12) = 2.738, p = 0.1239, Interaction: F = 7.797, *p = 0.0163, followed by Sidak’s multiple comparison test: *Fasted: CS+ vs. CS−, *CS+: Fasted vs. Fed; Fig. A comparison of the food consumption during the training sessions revealed a significant increase in food intake in the mice fasted prior to training compared to the mice that had been fed prior to training  = 0.097, p = 0.925; Fig. t test: t10 = 1.208, p = 0.2549; Fig. Performance of learning and memory tasks can be affected by even subtle differences in specific aspects of the training paradigm. We thus tested whether varying the length of time between training sessions  influenced the robustness of the conditioning, as has been demonstrated for other paradigms . We firsF = 4.5, p = 0.0781, Fed vs. Fasted: F = 21.74, **p = 0.0035, Interaction: F = 4.5, p = 0.0781; Fig. F = 29.99, **p = 0.0015; Fasted vs Fed: F = 10.22, *p = 0.0187; Interaction: F = 11.02, *p = 0.0160, followed by Sidak’s multiple comparison test: **CS+/− fasted vs. fed, **fasted – CS+ vs. CS−; Fig. We then tested the robustness of the conditioning when the two training sessions were conducted 24 h apart, or longer. As before, mice were habituated and fasted overnight, after which the first training session in Ctx+ was conducted. The mice were then fed ad libitum for 24 h after which they were again fasted overnight followed by a second training session in Ctx+. In total, 48 h separated the two training sessions. Mice were then returned to ad libitum food access for two days. The food intake in the sated mice was then compared in Ctx+ vs Ctx− Fig. . During We next asked whether the increased food intake in the conditioned animals’ response was driven primarily by negative valence  or positive valence . We hypothesized that if the conditioned behavior (food intake) was driven primarily by food reward (and not by hunger), mice that were fasted and then trained in the Ctx+ without receiving food there  = 34.86, P < 0.0001, ***Fed vs. Fasted: F = 49.31, P < 0.0001, ***Interaction: F = 34.86, followed by Sidak’s multiple comparison: ***Train 1: fed vs. positive, ***Train 2: fed vs. positive; Fig. F = 11.34, ***p = 0.0002, followed by Neuman Keul’s multiple comparison test: ***Fed vs. Positive, *Positive vs. Negative; *Fed vs. Negative; Fig. F = 4.891, *p = 0.0261, followed by Neuman Keul’s multiple comparison test: *Fed vs. Positive, *Positive vs. Negative; Fig. Body weight did not differ between groups before training or during habituation , while a number of regions showed very little cFos in any condition, including S1 and the caudate putamen  = 21.74, **p = 0.0098, followed by Neuman Keul’s multiple comparison Test: *Saline-Ctx- vs. Saline Ctx+ ; *Saline-Ctx+ vs. Muscimol/Baclofen-Ctx+ ; Fig. We found the insular cortex to be of particular interest because a previous study suggested that the insula projects to multiple efferent sites that play a role in feeding . A detaiFeeding is a complex motivated behavior and in recent years numerous neural circuits that regulate the homeostatic control of ingestion have been identified , 31. HowCurrent paradigms in use as models of overeating are of limited utility for studying associative mechanisms that convey food reward and lead to increased food consumption. For example, diet-induced obese mice (DIO) only have access to a high fat diet (HFD) , and thuAs an alternative, several groups have shown that cue-induced feeding can be used to study increased consumption in sated subjects. Unfortunately, also shown here, the task requires extensive training over a long period of time, thus limiting its utility for using optogenetics or equivalent methods to dissect the neural mechanisms that control this set of responses. This is because optogenetic and chemogenetic methods to modulate neural activity require that one perform stereotaxic surgeries (~1–2 week for recovery) with viral injections  further extending the time required to complete a study. In addition, the limited throughput using operant chambers in a cue-induced feeding protocol also makes it difficult to generate a sufficiently large cohort of animals. We therefore set out to employ a version of this task that offered greater throughput and less extensive training with which to study the mechanisms and circuitry underlying conditioned overeating. We reasoned that since context is associated with defined sensory inputs, it would be possible to assign motivational salience to a specific environment (referred to as Ctx+ ) rather than the use of discrete cues. The utility of context as an associative stimulus for feeding in sated mice was validated even when using a protocol that employed few as 2 training sessions over 2 days. Shorter training periods however were not effective for inducing food intake in fed mice. That spaced training is more effective than massed training is consistent with the vast majority of learned behaviors –39. ThusFood intake can be driven either by the relief of negative valence (ie. the unpleasant experience of hunger) or by positive valence, which refers to the reinforcing or reward value of food , 41. We What then are the neural mechanisms underlying non-homeostatic eating? In this study we used cFos as a marker of neural activity to determine which brain regions are activated following Ctx-IF. We found that a number of cortical and subcortical brain regions including insular cortex, central amygdala, lateral septum and lateral hypothalamus were activated in sated animals placed into the Ctx+. These findings are consistent with a previous study that measured cFos induction during and after the training portion of a Cue-IF  paradigmWhile cFos is a reliable marker for neural activation, it does not provide any insight into the specific neural populations involved in generating the behavior. Recently, PhosphoTRAP, a method that can be used to profile neurons activated by a specific stimulus, has been developed, thus making it possible to identify the specific neural subpopulations that are activated by the conditioned stimulus  . The ideFinally, we also used GABA agonists, muscimol and baclofen, to test whether the insular cortex is necessary for the development of Ctx-IF. We chose to focus on the insular cortex because of the observation that it indirectly projects to multiple efferent sites that are activated during feeding. In addition, the IC receives projections from the basolateral amygdala, which is critical for associative learning and the IC also sends projections to the central amygdala, lateral hypothalamus and nucleus accumbens, brain regions that are also known to impact feeding behavior –50. FurtIn summary, we have developed a context-induced feeding assay that can be rapidly and efficiently employed to condition mice to eat more in response to specific environmental cues. We used this task to study brain circuits that are activated during conditioning and further provide evidence for a functional role of the insular cortex. These findings will provide the basis for future experiments to identify the specific neural populations and establish their function as well as the neural connections through which they exert this effect.Supplementary Figure 1Supplementary Figure 2Supplementary Figure 3Supplementary Figure 4Supplementary Figure 5


High‐throughput DNA sequencing technology provides base‐level and statistically rich information about the genomic content of a sample. In the contexts of cancer research and precision oncology, thousands of genomes from paired tumor and matched normal samples are profiled and processed to determine somatic copy‐number changes and single‐nucleotide variations. Higher‐order informative analyses, in the form of allele‐specific copy‐number assessments or subclonality quantification, require reliable estimates of tumor DNA ploidy and tumor cellularity. CLONETv2 provides a complete set of functions to process matched normal and tumor pairs using patient‐specific genotype data, is independent of low‐level tools  and offers high‐level functions to compute allele‐specific copy number from segmented data and to identify subclonal population in the input sample. CLONETv2 is applicable to whole‐genome, whole‐exome and targeted sequencing data generated either from tissue or from liquid biopsy samples. © 2019 The Authors. Massive sequencing efforts, such as those of The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have generated a comprehensive collection of sequenced genomes of cancer patients, opening a new era for genomics. Advanced analyses of genomic sequencing data require accurate estimation of DNA cellularity  and tumor ploidy to allow appropriate comparative computation. DNA admixture refers to the amount of non‐cancer cells in a tumor sample, whereas ploidy represents the average number of chromosome set in a cell. Human healthy cells are diploid, whereas tumor cells often demonstrate a dramatically variable ploidy number, depending on the tumor type  and sequencing coverage resemble the BAF and logR data of SNP arrays, respectively. The statistically richer data offered by sequencing makes it possible to perform more complex analyses such as allele‐specific copy‐number and clonality estimates.These considerations call for the development of computational tools to quantify tumor purity and ploidy. In the pre‐sequencing era, several tools were developed for high‐density single‐nucleotide polymorphism (SNP) array data  that includes significant improvements over the original CLONET implementation. This is the result of its application to several clinical cohorts, including tissue and plasma samples, and to a variety of sequencing platforms, such as whole‐genome, whole‐exome, and targeted sequencing panels. In Carreira et al. , an R package  supporting the reference or the alternative SNP represents the number of copies and the origin of the alleles present in the tumor sample. Each informative SNP can be characterized by its allelic fraction (AF), which depends on the genomic context. For instance, let us consider the two informative SNPs within a monoallelic deletion of the genomic segment denoted A in Figure 1, only the alternative allele is present and AF = 1, whereas at position pn, the alternative allele is deleted and AF = 0. In contrast, in the wild‐type genomic segment B, the AF values of informative SNPs at positions pn+1 and pm are distributed around 0.5, as both alleles contribute equally to the local coverage. Now, the percentage of neutral reads  at p1 and pn is equal to 0, regardless of which allele is deleted, whereas at wild‐type genomic positions, pn+1 and pm each approximate 1, as no active reads are present. Overall, SNPs within somatically aberrant segments are easier to characterize using the beta values as compared to the AFs, as the former is independent from the deleted allele. In a heterogeneous tumor sample, the distributions of AFs and betas result from the convolution of the distribution observed in basic wild‐type and monoallelic deleted segments. As an example, Figure 2 ratio values adjusted by ploidy logRp, and the DNA admixture G, as:All reads of a human DNA next‐generation sequencing experiment that map within a genomic segment derive from either one of the parental chromosomes of origin. Reads can be split into two sets: a 2 ratio space implied by Equation 2 ratio signal in a tumor sample: peaks in the distribution correspond to different copy‐number states, whereas deviations from the position of the expected peaks (below) depend on ploidy and DNA admixture values. It is difficult to identify the peak that corresponds to wild‐type segments using only log2 ratio signal. When we expand the monodimensional logR space with beta , allelic fraction, and coverage;min_af_het_snps, max_af_het_snps: for each SNP in the pileup_normal table, set minimum and maximum allelic fraction to consider the SNP as informative;min_required_snps: the minimum number of informative SNPs in a genomic segment from seg_tb to retain the segment;min_coverage: the minimum mean coverage of informative SNPs to retain a segment.The function compute_beta_table extends the input table seg_tb. For each segment in seg_tb, the function compute_beta_table returns the following values:beta: estimated value for the input segment;nsnps: number of informative SNPs in the input segment;cov: mean coverage of informative SNPs in the input segment;n_beta: estimated value for the input segment considering the matched normal sample. This value is expected to be 1, except in the case of germline copy‐number variation or sequencing‐related errors.As output, the function compute_beta_table output is not an easy task due to the identifiability problem — i.e., the fact that more than one combination of ploidy and DNA admixture fit the observed data , and therefore, this value combined with input segment lengths gives information about the quality of the pileup data.The interpretation of the function 64‐bit computer running Linux with ≥8 GB RAMThe library has been tested with R version 3.5.2 and the R libraries parallel 3.5.2, ggplot2 3.1.0, sets 1.0‐18, arules 1.6‐3, and ggrepel 0.8.01tumor.pileup and normal.pileup.Prepare tumor and normal pileups as described in Support Protocol 2tumor_segments.txt with columns compatible with the parameter seg_tb described above.Prepare tumor segmented data in the file 3$ RRun R from the command line:4> install.packages(“CLONETv2”)Install CLONETv2 for the first time:5> library(CLONETv2)Load the library:6> seg_tb <‐ read.table,header = T, as.is=T)> pileup_tumor <‐ read.table,header = T, as.is=T)> pileup_normal <‐ read.table,header = T, as.is=T)Load input files:7> bt <‐ compute_beta_tableCompute beta for each input segment with default parameters:8plot_stats parameter:> bt <‐ compute_beta_tableCompute beta activating the Computed beta table of sample “sample1”Number of processed segments: 65Number of segments with valid beta: 49 (75%)Quantiles of input segment lengths:0%: 286025%: 1750418550%: 3800479975%: 59311449100%: 147311449Quantiles of input segment coverage:0%: 47.000025%: 137.789350%: 168.382075%: 186.6769100%: 695.6145Quantiles of number of informative SNPs per input segment:0%: 025%: 1250%: 9975%: 213100%: 404This results in the following output:Support Protocol 1.bam (BAM) files . We suggest starting from the largest possible set of SNPs, as the larger the number of informative SNPs, the more reliable the CLONETv2 estimates. Pileups from BAM files can be obtained using any of several tools. Here we describe how to prepare pileups using ASEQ ;mbq: minimum base quality ;mdc: minimum depth of coverage (ASEQ output only reports positions with coverage ≥ mdc);threads: number of threads available for ASEQ computation.1$ curlhttp://demichelislab.unitn.it/lib/exe/fetch.php?media=aseq‐v1.1.11‐linux64.tar.gz> aseq‐v1.1.11‐linux64.tar.gz$ tar xvf aseq‐v1.1.11‐linux64.tar.gzDownload and uncompress the last version of ASEQ:binaries/linux64/.ASEQ code will be available in the subfolder 2$ curlhttp://demichelislab.unitn.it/lib/exe/fetch.php?media=aseq‐examples.tar.gz> aseq‐examples.tar.gz$ tar xvf aseq‐examples.tar.gzDownload and uncompress ASEQ examples:examples/VCF_samples/.ASEQ examples will available in the subfolder 3$./binaries/linux64/ASEQ mode=PILEUP vcf=examples/VCF_samples/sample1.vcf bam=examples/BAM_samples/sample1.bam mbq=20 mrq=20 mdc=1 threads=1 out=.Run ASEQ on example data 1:sample1.PILEUP.ASEQ, reporting allelic fraction and read coverage from the BAM file sample1.bam, for each position in the VCF file sample1.vcf. The parameters mbq = 20 and mrq = 20 tell ASEQ to ignore, respectively, bases and reads with quality <20. The parameter mdc = 1 instructs ASEQ to ignore positions in the BAM file with no reads. The parameters and the format of the output file .PILEUP.ASEQ are compatible with pileup data required in Basic Protocol ASEQ produces the file Basic Protocol 22 of the ratio between the tumor coverage and the normal sample coverage within the segment. To account for different mean coverage in different sequencing experiments, logR is normalized over the ratio between the mean tumor and the mean normal coverage; this applies both to whole‐genome and whole‐exome data. In the case of higher coverage in the tumor sample, if without normalization the ratio between the mean tumor and the mean normal coverage is X, a wild‐type segment would have logR = log2(X), whereas the expected value is 0 . The normalization would, however, introduce a bias whenever the difference in mean coverage between the tumor and the normal sample was due to the presence of an abnormal number of alleles in the tumor (aneuploid) genome. In this case, the normalization leads to a shift in the logR signal. Figure Segmentation algorithms partition input genomic space into segments with homogenous coverage. Given a pair of matched tumor and normal samples, the logR value of a genomic segment is the logThe proof Equation  is reporcompute_ploidy builds on this definition and is implemented to identify wild‐type genomic segments and to estimate how far the logR values deviate from 0. The key step in the search is to restrict the genomic segments space to those with beta = 1, i.e., those with an equal number of maternal and paternal copies. In Figure compute_ploidy includes the following input parameters:beta_table: a table created using the function described in Basic Protocol max_homo_dels_fraction (default 0.05): homozygous deletions can provide a confounding factor in the determination of sample ploidy; the parameter sets a percentage of genomic segments that will not be used for ploidy computation as putative homozygous deletion, and overestimating this value does not affect ploidy computation;beta_limit_for_neutral_reads (default 0.90): in theory, neutral reads correspond to beta = 1, but experimental noise lowers this value; therefore only segments with beta above the limit are used to compute ploidy;min_coverage (default 20): only genomic segments with average coverage at least min_coverage are used to compute DNA admixture;min_required_snps (default 10): only genomic segments covering at least min_required_snps informative SNPs are considered for DNA admixture computation.The function The function returns the ploidy for the input sample.64‐bit computer running Linux with ≥4 GB RAMThe library has been tested with R version 3.5.2 and R libraries parallel 3.5.2, ggplot2 3.1.0, sets 1.0‐18, arules 1.6‐3, ggrepel 0.8.0.1$ RRun R from the command line:2Compute beta table as described in Basic Protocol 3bt:> pl <‐ compute_ploidy(bt)Compute ploidy from beta table Basic Protocol 32(½)). However, if the purity is 50%, then only half of the total number of cells harbor the hemizygous deletion, and the expected logR is log2(¾), or around –0.415. Similarly, the value of beta of a genomic segment varies depending on the level of DNA admixture. In Basic Protocol compute_dna_admixture searches the  space defined by the function compute_beta_table : only genomic segments with average coverage at least min_coverage are used to compute DNA admixture;min_required_snps (default 10): only genomic segments covering at least min_required_snps informative SNPs are considered for DNA admixture computation;error_tb: the number of informative SNPs and the coverage of the considered segment affect the accuracy of the estimation of beta of a genomic. The table error_tb reports, for each combination of number of informative SNPs and coverage, the expected error around the beta estimate. CLONETv2 embeds a pre‐computed error_tb  and SNV signal. In a 100% pure tumor sample, the expected coverage across a monoallelic  deletion should be about half of coverage of wild‐type segments, and therefore the logR should be equal to –1 Given beta table Support Protocol 2check_ploidy_and_admixture that plots beta‐vs.‐logR space for a given samples. Figure , respectively. For each genomic segment, the plot reports the logR as well as the beta computed by function compute_beta_table. To help the user, the function predicts expected  given the input ploidy and DNA admixture level according to the equations described in CLONET paper check_plot is a ggplot object Print final plot with the command:Basic Protocol 42 ratio is equal to 0; then beta could either be equal to 1  or be equal to 0 . The approach is generalized in Beltran et al.  pairs into allele‐specific copy‐number pairs . The function requires estimates of purity and ploidy and has the following parameters:beta_table: a table created using the bt function described in Basic Protocol ploidy_table: a table created using the pl function described in Basic Protocol admixture_table: a table created using the adm function described in Basic Protocol error_tb: the same error_tb used in the function compute_dna_admixture of Basic Protocol allelic_imbalance_th (default 0.5): function compute_allele_specific_scna_table also returns integer values cnA.int and cnB.int for cnA and cnB, respectively. The value cnA.int is the rounded‐off value of cnA if |cnA.int ‐ cnA| < allelic_imbalance_th; otherwise cnA.int is not defined. cnB.int is defined similarly with respect to cnB.Figure n et al.  by definn et al. B simplifcompute_allele_specific_scna_table extends input beta_table with columns related to allele‐specific copy‐number:log2.corr: logR value adjusted by ploidy and purity: i.e., the logR value the segment would have in a diploid 100% pure tumor sample;cnA,cnB: number of copies of major (cnA) and minor (cnB) allele; the values do not contain information about ploidy and purity — indeed, cnA + cnB equals 2 × 2log2.corr;cnA.int,cnB.int: integer number of copies of major and minor alleles, respectively.The function 64‐bit computer running Linux with ≥4 GB RAMThe library has been tested with R version 3.5.2 and the R libraries parallel 3.5.2, ggplot2 3.1.0, sets 1.0‐18, arules 1.6‐3, and ggrepel 0.8.01$ RRun R from the command line:2bt, ploidy table pl, and DNA admixture table adm, respectively.Follow Basic Protocols 3bt, ploidy table pl, and DNA admixture table adm, compute the allele‐specific SCNA table:> as_tb <‐ compute_allele_specific_scna_tableGiven beta table Basic Protocol 51 and D2, with 100% and 50% clonality, respectively: i.e., all tumor cells harbor D1 deletion, but only 50% harbor D2 deletion. The expected logR is then log2(½) = –1 for D1 and log2(¾) (about –0.415) for D2. Note that the expected logR for D2 is the same that would result given a clonal deletion in a 50% pure sample : the function compute_scna_clonality_table returns minimum and maximum clonality for input genomic segments; clonality_threshold is used to discretize clonality as described by Prandi et al. : input beta values below beta_theshold are marked as potentially aberrant and used for clonality estimates.A somatic aberration is clonal if all the tumor cells harbor the aberration. Suppose a 100% pure tumor sample with monoallelic deletions of genomic segments Dcompute_scna_clonality_table extends input beta_table with clonality‐related columns:clonality: real value representing the estimated percentage of tumor cells with uniform copy number for a given genomic segment;clonality.min,clonality.max: real values representing minimum and maximum estimated clonality given the distribution of beta and logR values;clonality.status: discretized clonality.The function 64‐bit computer running Linux with ≥4 GB RAMThe library has been tested with R version 3.5.2 and the R libraries parallel 3.5.2, ggplot2 3.1.0, sets 1.0‐18, arules 1.6‐3, and ggrepel 0.8.01$ RRun R from the command line:2bt, ploidy table pl, and DNA admixture table adm, respectively.Follow Basic Protocols 3bt, ploidy table pl, and DNA admixture table adm, compute the SCNA clonality table:> clonality_tb <‐ compute_scna_clonality_tableGiven beta table Basic Protocol 6compute_dna_admixture), we first estimate the minimum and maximum clonality and next, as for SCNA, assign a discretize clonality value . Figure y axis) distributions per discretized class (x axis) regardless of the copy number of the genomic segments harboring the SNVs : fraction of SNVs to exclude based on adjusted VAF distribution.Each SNV is characterized by the variant allele fraction (VAF), i.e., the proportion of reads supporting the alternative allele; intuitively, the VAF is representative of the amount of tumor DNA harboring the mutation . Therefore, low VAF values correspond to low clonality. In a 100% pure diploid sample, a clonal monoallelic SNV within a wild‐type genomic segment is expected to show a VAF of 0.5 ,header = T, as.is=T, comment.char = “", check.names = F, na.strings = ”‐")Read an SNV table 4bt, ploidy table pl, and DNA admixture table adm, compute the clonality of SNVs:> snv_clonality_tb <‐ compute_snv_clonalityGiven beta table ). EXCAVATOR2 and CNVkit data in this space are similarly distributed, although the former shows noisier signal, and the ploidy and DNA admixture estimates perfectly match. In contrast, with this specific example, the FACETS estimates are different, as expected given, for instance, a set of segments with logR around –0.75 and beta = 1.We present a complete R package to compute allele‐specific data from next‐generation sequencing experiments with paired tumor and matched normal DNA samples. CLONETv2 works on preprocessed data (not on BAM or fastq files), including segmented genomic profiles and pileups of relevant genomic positions. This makes CLONETv2 more flexible than tools such as ABSOLUTE . In Figure compute_dna_admixture, in turn, estimates a DNA admixture value of 0.42. Subclonal copy‐number segments  would benefit from min_coverage higher than 20 ; in fact, that value corresponds to 0.4% of the expected coverage for a sequencing study of that depth and can hardly be distinguished from the background experimental noise. In contrast, low‐pass whole‐genome sequencing experiments (coverage >4×) require a lower min_coverage by design.CLONETv2 default parameters have been tested in a variety of studies spanning tissue and plasma samples in different tumor settings. However, data analysis from specific experimental conditions or analysis prerequisites would benefit from tweaking CLONETv2 parameters. The parameter error_table, a table reporting the error around beta estimates for different combinations of coverage and number of informative SNPs. CLONETv2 has an error table bundled in, obtained by simulating different inputs to the function compute_beta_table with combinations of values for the coverage and the number of informative SNPs. If, for a given genomic segment, the number of informative SNPs and the mean coverage are not reported in the error_table, CLONETv2 uses the nearest available pair of values, as previously described (Prandi et al., A second critical parameter is CLONETv2 offers a robust framework for the genomic analysis of somatic copy‐number data together with the possibility of manually curating estimates (see Support Protocol Figure A second problematic case is presented in Figure


This resulted in a corrected serum sodium of 202 mmol/L. He was treated with fluid and electrolyte replacement in the intensive care unit, and had returned to essentially normal function by hospital discharge. The aetiology was believed to be due to severe dehydration and a new diagnosis of diabetes mellitus. Extreme hypernatraemia (serum sodium level greater than 190 mmol/L) is rare and associated with a high mortality. The mainstay of treatment is careful fluid and electrolyte management. Most recommendations advise to reduce the serum sodium by 0.5 mmol/L/hour, due to concerns over cerebral oedema; however, there are reports that slower correction is associated with higher mortality. In this case, the initial corrected sodium of 202 mmol/L was steadily corrected to 160 mmol/L over 91 hours, at a rate of 0.46 mmol/L/hour. This demonstrates the safety of the recommended approach.We report a case of a previously well 58-year-old man, who presented with delirium and low GCS, and was found to have extreme hypernatraemia (Na Hypernatraemia can occur either due to excess sodium-containing solute gain  or, more often, nonreplaced water losses, particularly from the gastrointestinal or renal tracts. Severe hypernatraemia is associated with neurological deficits and carries a mortality of 37–60% [+] > 190 mmol/L) at admission. We present a case of extreme hypernatraemia, associated with marked dehydration and hyperglycaemia in an adult with a corrected Se[Na+] of 202 mmol/L at presentation. Following a period of observation and careful fluid and electrolyte management in the Intensive Care Unit (ICU), the serum sodium returned to normal. The patient was discharged back to the community after a period in rehabilitation.Hypernatraemia is defined as a serum sodium (Se[Naf 37–60% . There a+] of 191 mmol/L and Se[Glc]) 31 mmol/L (558.6 mg/dL). He had been found outside where the ambient temperature had been recorded as 34.2°C (93.5°F). Although he had no significant past medical history, he had presented to the emergency department of the same hospital seven days prior with complaints of mild abdominal pain. No formal diagnosis was made, and he had been discharged to his general practitioner for follow-up. Investigations at this stage showed Se[Na+] 141 mmol/L, and Se[Glc] 6 mmol/L. Collateral history confirmed that the patient had been well following his previous discharge, attended work every day, and appeared to have been normal thirteen hours prior to presentation, mowing the lawn. He had been found collapsed inside and unable to get up from the floor. He was partially clothed and making incoherent sounds.A 58-year-old man presented to Sunshine hospital, a major tertiary hospital in the west of Melbourne, Victoria, Australia, with altered mental state, Se 191 mmol/L and Se[Glc] of 31 mmol/L. The corrected sodium was calculated using Hillier's equation (Corrected sodium = measured sodium + 0.024 × (serum glucose −100)), giving a value of Se[Na+] 202 mmol/L  correction rate of 0.5 mmol/L/hour or 10–12 mmol per 24 hours [Management of extreme hypernatraemia is challenging. In theory, overly rapid correction of hypernatraemia can cause intracellular water movement into neuronal cells, causing cerebral edema. This has been well-documented in paediatric cases and animal studies , 5. Curr24 hours .Acute hypernatraemia may be safely corrected more rapidly. Indeed, several cases report no evidence of cerebral edema despite rapid sodium correction , 8. AlshIf hypernatraemia is coexistent with hyperglycaemia, this must be considered, including correcting Serum sodium measurements. This allows for the sodium-lowering effect of fluid rehydration to be differentiated from the increase in sodium that accompanies correction of hyperglycaemia . ParalleWe present a rare case of altered conscious state due to acute extreme hypernatraemia caused by hyperglycaemia and dehydration, with an initial corrected sodium level of 202 mmol/L. The patient was managed with the recommended correction rate of serum sodium concentration of 0.5 mmol/L/. He had almost complete neurological recovery with mild cognitive impairment by the time of hospital discharge. This supports the safety of a sodium correction of 0.5 mmol/L/hour in extreme hypernatraemia with concomitant hyperglycaemia.


Callitropsis nootkatensis (Alaska yellow‐cedar) is experiencing extensive climate change‐induced canopy mortality due to fine‐root death during soil freezing events following warmer winters and the loss of insulating snowpack. Here, we examine the effects of ongoing, climate‐driven canopy mortality on forest community composition and identify potential shifts in stand trajectories due to the loss of a single canopy species. We sampled canopy and regenerating forest communities across the extent of C. nootkatensis decline in southeast Alaska to quantify the effects of climate, community, and stand‐level drivers on C. nootkatensis canopy mortality and regeneration as well as postdecline regenerating community composition. Across the plot network, C. nootkatensis exhibited significantly higher mortality than co‐occurring conifers across all size classes and locations. Regenerating community composition was highly variable but closely related to the severity of C. nootkatensis mortality. Callitropsis nootkatensis canopy mortality was correlated with winter temperatures and precipitation as well as local soil drainage, with regenerating community composition and C. nootkatensis regeneration abundances best explained by available seed source. In areas of high C. nootkatensis mortality, C. nootkatensis regeneration was low and replaced by Tsuga. Our study suggests that climate‐induced forest mortality is driving alternate successional pathways in forests where C. nootkatensis was once a major component. These pathways are likely to lead to long‐term shifts in forest community composition and stand dynamics. Our analysis fills a critical knowledge gap on forest ecosystem response and rearrangement following the climate‐driven decline of a single species, providing new insight into stand dynamics in a changing climate. As tree species across the globe are increasingly stressed by climate change‐induced alteration of suitable habitat, identifying the autecological factors contributing to successful regeneration, or lack thereof, will provide key insight into forest resilience and persistence on the landscape.Climate change is altering the conditions for tree recruitment, growth, and survival, and impacting forest community composition. Across southeast Alaska, USA, and British Columbia, Canada,  Local adaptation to historical climate is already creating mismatches between species’ current distributions and suitable habitat conditions  available sources of seed and/or vegetative reproduction and (b) the establishment environment . Seed supply generally decreases with a loss of mature trees  is experiencing extensive, climate change‐driven mortality over more than 400,000 ha and ten degrees of latitude  (western hemlock) and Picea sitchensis Bong. (Carr) (Sitka spruce) are not considered sensitive to snow loss  of southeast Alaska, USA, and British Columbia, Canada, ge Buma,  and be rge Buma, . Co‐occuC. nootkatensis forests over a five‐degree latitude range in the NPCTR of southeast Alaska to ask the following: (a) “Does climate‐induced mortality occur across all size classes of the affected species, and, specifically, does regeneration response correspond with the same climate conditions driving mortality?”, (b) “If response is differential, what climatic and community factors drive tree mortality versus postdecline composition of the regenerating community?”, and (c) “Is community composition stable or in the process of a decline‐induced shift?”. Our analysis provides new insight into stand dynamics in a changing climate by increasing understanding of forest ecosystem response and rearrangement following the decline of a single species. Obtaining information on life stage response to climate‐induced mortality, postdecline community composition, and stand dynamics in the NPCTR and beyond will be essential to scientifically based forest management and vital to supporting conservation efforts in the face of climate change.To address these knowledge gaps and isolate the conditions differentiating habitat suitable for mature tree survival versus those key to successful regeneration in a climate mortality‐affected system, we sampled declining 22.1Callitropsis nootkatensis is distributed across ~20° of latitude from northern California into Prince William Sound, Alaska , Thuja plicata Donn ex D. Don (western redcedar), Tsuga heterophylla, and Tsuga mertensiana (Bong.) Carriere. Sphagnum spp. are common in areas of poor drainage and low forest productivity, decreasing in abundance with increasing slope and increasing depth to groundwater  were randomly selected from areas stratified by C. nootkatensis status—live forests (n = 20) and three time‐since‐mortality classes  on Chichagof Island and in Glacier Bay National Park  for mature trees  and (b) 6m radius (~113 m2) for saplings (<2.5 cm DBH and >1 m in height), treelets (2.5–9.9 cm DBH), and small trees  installed in the cardinal directions at five and eight meters from plot center.Plots installed by Oakes and colleagues , n = 50 C. nootkatensis' distribution in southeast Alaska, we leveraged 17 additional nested, fixed‐radius plots on Kupreanof and Prince of Wales Islands  were sampled in a nested 100‐m2 plot. Germinants and seedlings (<10 cm height) were tallied by species in four one‐m2 quadrats at the corners of the 400‐m2 plot . Hereafter, the term regeneration refers to the combined germinant and seedling classes, including all recently emerged germinants  to seedlings less than 10cm in height and, presumably, <2 years of age.To extend inference across C. nootkatensis snags classified into time‐since‐mortality classes according to Hennon, Shaw, et al. (Tsuga germinants (heterophylla and mertensiana) but to the species for all other species and size classes. Disturbed areas  were eliminated from plot selection to avoid the confounding influence of disturbance on community composition.Across all plots, species, DBH, tree status (live/dead), live crown condition, and snag class were sampled for all trees. Snags were identified to species, if possible, and , et al. . Unident2.3http://www.climatewna.com, Wang et al., C. nootkatensis cones take two to three years to mature and produce viable seed  while retaining those considered biologically important in Sphagnum coverage. To do so, we sampled bryophyte (n = 67) and Sphagnum  cover on replicate 1‐m2 quadrats (8–10 per plot). Bryophyte coverage was correlated with Sphagnum‐specific coverage, so, on plots with bryophyte coverage only, a log‐linear model was created to estimate Sphagnum coverage . Predicted Sphagnum coverages were used in overall model creation. At the landscape scale, we used contributing area derived from the ASTER elevation data, a metric of upslope area potentially contributing runoff to a location in which higher values indicate wetter, lower‐lying areas. All data processing and subsequent analyses were conducted in R version 3.4.1.  and used to derive elevation, slope, and aspect. To assess the role of local soil drainage, which influences competition and rooting, we selected two metrics operating at different scales. At the local scale, we used 2.4C. nootkatensis‐specific mortality, we used simple linear regressions to assess the correlation between the proportion of dead C. nootkatensis (log‐transformed) as compared to that of the other dominant co‐occurring tree species, and Kruskal–Wallis tests for nonparametric, ranked data to assess variation in proportional mortality among species, locations, and size classes (as defined above). There were no significant concurrent declines in any co‐occurring species (p > 0.05), so we focused subsequent mortality analyses on C. nootkatensis alone. We also used a Kruskal–Wallis analysis to test for significant differences in live tree abundances among species and locations. We then tested for significant differences in canopy and regenerating community composition among locations and as related to C. nootkatensis mortality with multivariate analysis of variance (MANOVA) tests using Bray–Curtis distances in the vegan package. The proportion of dead C. nootkatensis in the canopy  was also compared to regeneration density using a negative log‐transformed linear regression. In Kruskal–Wallis and MANOVA tests, proportional mortality was categorized into the following mortality severity classes: low = 1%–25%, moderate = 25.1%–69.9%, high = 70%–99%, and all = 100%.To control for the possibility of general forest declines, which would affect all species and obscure C. nootkatensis canopy mortality versus regeneration. Two random forests analyses were run—one to model the proportion of dead C. nootkatensis in the canopy and another the abundance of C. nootkatensis regeneration. Both were based on potential drivers related to climate, disturbance exposure, topography, drainage, and competition  ordinations on regeneration abundances based on Bray–Curtis dissimilarity with the 33.1Tsuga (heterophylla and/or mertensiana), P. sitchensis, and C. nootkatensis. Picea sitchensis, although common in the region, was rare on the plot network, with only one tree documented on many of the plots. Pinus contorta occurred in peatlands on Chichagof Island, while T. plicata was found only at higher elevations on Prince of Wales Island (other plots fell outside T. plicata's range). Total basal area ranged from 9 to 87 m2/ha, and higher C. nootkatensis basal areas were generally found on higher productivity, upland forests or in areas north of the decline , and no mortality was documented for T. plicata.Mortality was documented in each of the most common conifers on the plot network  to 75 years  across the extent of decline, while background mortality in healthy stands north of the decline averaged 16% . Mortality of common co‐occurring conifers was significantly lower than that of C. nootkatensis . Canopy mortality was also significantly different among co‐occurring species ; however, within‐species mortality was not significantly different between size classes  = 2.77, df = 2, p > 0.05) or locations  = 6.81, df = 3, p > 0.05).rs Table . Mortalidf = 5, p < 0.001), but significance was driven by variation in live C. nootkatensis among sampling locations . Live, mature C. nootkatensis continued to dominate the canopy in Glacier Bay National Park (mean dead = 8%), an area currently north of the region of decline, but was a minor component  at the southern end of decline on Prince of Wales Island  and as a function of the severity of C. nootkatensis mortality . Regenerating community composition  but closely related to canopy condition . Areas of high severity C. nootkatensis mortality had the lowest abundances of C. nootkatensis regeneration . The top four variables in the model were basal area of dead and live C. nootkatensis, total live stand basal area , and contributing area , C. nootkatensis (CANO), Tsuga‐P. sitchensis (Tsuga‐PISI), or P.contorta (PICO) clusters . The Tsuga‐PISI type was found on steeper slope, higher productivity plots and consisted of Tsuga  and P. sitchensis  in areas of moderate to complete C. nootkatensis mortality. The PICO type was assigned to low slope, lower‐productivity peatlands with high bryophyte coverage . Plots of similar condition generally clustered into the same community types; plots with high to complete C. nootkatensis mortality were regenerating to Tsuga‐PISI or PICO, with C. nootkatensis regeneration dominating in low mortality, lower‐productivity peatlands. Location , severity of C. nootkatensis mortality , and time since mortality  were significant predictors of regenerating community types, indicating that climate and both the timing and the extent of decline are primary determinants of community composition stability.Four regenerating community types were identified in the cluster and indicator species analysis. Community types were largely determined by extent of s Figure . The MixC. nootkatensis decline, C. nootkatensis regeneration was low and replaced by Tsuga spp.  and five‐year average precipitation as snow  and P. sitchensis  live basal area plus slope  drove community clustering . Axis 1 relates to a gradient of canopy mortality and local hydrologic condition (and associated ecosystem productivity), with C. nootkatensis successfully regenerating on low slope peatlands and Tsuga species dominating well‐drained upland forests .In areas of high . Figure , and com4C. nootkatensis, our work suggests that climate‐induced forest mortality is driving alternate successional pathways in forests where C. nootkatensis was once a major component, which is likely to lead to long‐term shifts in community composition and stand dynamics. As suitable habitat conditions shift with ongoing global climate change, both mature trees and regeneration will be increasingly exposed to novel conditions, and identifying the autecological factors contributing to successful regeneration, or lack thereof, is an essential first step toward predicting forest response and resilience to climate change.As the climate changes, successful tree regeneration is threatened by novel climate and disturbance conditions and associated shifts in canopy community composition. Our study highlights how species‐specific sensitivity to climate change can lead to restructuring of the forest community following canopy mortality. We demonstrate that reductions in both mature tree and regeneration abundances after single‐species mortality events occur via the same mechanism—death of mature, seed‐producing trees. In the case of 4.1C. nootkatensis, allowing for a focused examination of the effects of climate, seed source, and local conditions on regenerating community response and subsequent stand dynamics. Mature tree mortality across the study area corresponded with a reduction in C. nootkatensis regeneration  and magnitude . Consistent with studies in other forest types and under different climate‐induced disturbances, our findings suggest that seed source (DeRose & Long, Documenting changes in communities following canopy mortality will be crucial to identifying the ecological, and potentially economic, consequences of these losses. Identifying successional trajectories, in particular, will reduce the large uncertainty around the long‐term impacts of climate change‐driven forest mortality events. Forest response to novel disturbance conditions will be hard to predict, and the lessons learned from these cases can help shift our expectations of postdisturbance stand dynamics, particularly in climate‐impacted systems.4.3C. nootkatensis’ shallow root system and early response to spring warming were beneficial, allowing for early, rapid growth each growing season (Hennon et al., Fraxinus in response to Agrilus planipennis invasion (Klooster et al., T. heterophylla‐dominated forests even if C. nootkatensis maintains some temporary or disjunct presence on the landscape. For species or forest types undergoing similar climate‐induced mortality events, ecologists will need to determine which legacies contribute to ecosystem resilience, or if novel climate or disturbance will remove these legacies that were historically critical.The consequences of rapid climate change on forest resilience remain uncertain and are likely to be highly variable, based on a particular forest's ecological memory (i.e., information legacies of ecological adaptations to disturbance; Johnstone et al., C. nootkatensis’ distribution is experiencing mortality, but, if emissions scenarios continue toward worst‐case trajectories, it is possible that the mortality “donut‐hole” will be relatively short‐lived (Buma, Ecosystem resilience may also vary as a function of regional climate fluctuations over space and time, known as transitional climate mortality Buma, , where med Buma, . Short, 5C. nootkatensis forests, there is no increase in C. nootkatensis regeneration abundances to offset canopy mortality. As a result, this forest type is not resilient to mortality associated with ongoing snow loss, and a type change appears to be underway. This example of climate change‐driven mortality in a single species highlights how species‐specific sensitivity can lead to shifts in community composition and stand dynamics following canopy mortality via the same mechanism—death of mature seed trees.Widespread mortality of tree species due to changing climate is a major concern in forests worldwide, but the potential for resilience is rarely assessed during an ongoing mortality event, a gap we have attempted to fill here. Species’ responses to climate change‐induced mortality will vary widely based on species‐specific traits, sensitivity to climatic extremes, biotic stressors, and abiotic conditions of the establishment environment, thus requiring autecological studies on factors limiting versus promoting success. In C. nootkatensis, common garden studies using seed sources from across the species’ range could allow for identification of genotypes with fine‐root frost tolerance. Planting gardens across decline severity gradients would also allow for targeted research on regeneration response to concurrent canopy mortality and associated establishment conditions. Given the potential for a wave of transitional mortality across the C. nootkatensis range (Buma, Few strategies or solutions exist for forests vulnerable to ecosystem type conversion due to mature tree mortality and associated loss of seed source, and numerous knowledge gaps remain. In the case of ge Buma, , howeverNone declared.A.B., B.B, and S.B. conceived of the study. A.B, B.B., S.B., J.K, and L.E.O. collected the data. B.B. and S.B analyzed the data. S.B. led the writing with contributions from all authors.


Groups were as follows: (A) AMH < 1 ng/mL (n = 32), (B) AMH 1–3.99 ng/mL (n = 109), (C), AMH ≥ 4 ng/mL (n = 60). The primary outcome measure was serum P level at trigger prior to oocyte retrieval. Mean serum P levels among groups A, B, and C were 0.92 ng/mL, 0.96 ng/mL, and 0.84 ng/mL, respectively. One-way ANOVA showed that there was no difference in mean serum P level among groups A, B, and C . Multivariable linear regression with P as the dependent variable showed that total gonadotropin dose and peak estradiol level on day of trigger each had a significant positive relationship with P, and clinical pregnancy had a significant negative relationship. Although AMH is a predictor of certain IVF outcomes, AMH is not a predictor of elevated serum P level at trigger among women who undergo COH for IVF.Serum Antimüllerian hormone (AMH) has been shown to predict various in vitro fertilization (IVF) outcomes. AMH and progesterone (P) are products of granulosa cells of the ovary. Since overall granulosa cell number directly correlates with oocyte number and AMH production, the aim of this study is to evaluate whether or not serum AMH is associated with elevated P during controlled ovarian hyperstimulation (COH) for IVF. For this retrospective study, data were abstracted from charts of first IVF cycles of women ( During the process of controlled ovarian hyperstimulation (COH) for in vitro fertilization (IVF) treatment, an elevated serum progesterone (P) level prior to oocyte retrieval may result from the multi-follicular development which is an inherent step prior to oocyte retrieval for IVF. This phenomenon may occur in up to 38% of COH cycles, despite the use of gonadotropin releasing hormone (GnRH) analogues –3. Such Antimüllerian Hormone (AMH) is a member of the transforming growth factor (TGF)-β superfamily of glycoproteins and a widely-used serum biomarker to assess ovarian reserve in women undergoing COH for IVF. AMH is produced by ovarian granulosa cells which surround the primordial to pre-antral ovarian follicles in which oocytes are located . AMH hasSince both the steroid hormone P and the glycoprotein AMH are products of granulosa cells  and sincn = 201) who had undergone COH at Austin Fertility & Reproductive Medicine/Westlake IVF between May 2014 and May 2017. Inclusion criteria were females between the ages of 18 and 44 years, all causes of infertility, and cases of COH with exogenous gonadotropins. The exclusion criteria were initial intent of IVF for freeze-only either due to preimplantation genetic testing (PGT) or from an elective standpoint, COH with oral ovulation induction agents or a combination of oral ovulation induction agents with exogenous gonadotropins, as well as donor oocyte/IVF and donor embryo cases. Due to the de-identified nature of the data collected, Institutional Review Board exemption was obtained from St. David’s Healthcare Institutional Review Board.A retrospective chart review was performed, with data abstracted from first IVF cycles of women (n = 32), Group (B) was that of women with AMH of 1–3.99 ng/mL (n = 109), and Group (C) was comprised of women with AMH ≥ 4 ng/mL (n = 60). A high AMH threshold of 4 mg/mL was used in this study because, in our patient population, a patient is more likely to have a significantly greater ovarian response. In addition, the authors of the following article indicate that an increased risk of severe ovarian hyperstimulation syndrome, consistent with excessive ovarian response, is generally seen in women with AMH level of 4–5 ng/mL [An AMH level > 1.0 ng/mL, regardless of day of the cycle, was considered as a normal level. Group (A) consisted of women with AMH < 1 ng/mL  zygotes. Data were expressed as means ± SD. Statistical analyses were performed with one-way ANOVA and multivariable linear regression. p = 091; Group A vs C, p = 0.03; Groups B vs C, p = 0.03). Characteristics and IVF outcomes of women in Group A, B, and C are shown in Table p-value = 0.28). In terms of secondary outcome measures, although there were no differences among groups in for number of days of COH , there were statistically significant differences among groups for total gonadotropin dosage during COH , number of follicles > 14 mm in average diameter on the day of trigger , estradiol level on the day of trigger , number of M2 oocytes at retrieval , and number of 2PN zygotes .The incidence of elevated progesterone level on the day of trigger for Groups A, B, and C were 15.6% (5/32), 12.8% (14/109) and 1.7% (1/60), respectively  when adjusting for the other covariates, but overall, group is only marginally significant as a factor .A multivariable linear regression model, with P as the dependent variable and independent variables shown in Table es Table . Based oAMH has been shown to be a strong predictor of ovarian response to exogenous gonadotropins during COH for IVF as well as outcomes such as oocyte yield , blastocFreeze-all cycles are more common in recent years and have been instrumental in improving overall IVF and obstetric outcomes; however, there are data that support fresh embryo transfer in subsets of patients , 33. In In our study, a one-way ANOVA showed no difference in mean serum P levels among women grouped into those with AMH < 1 ng/mL, AMH 1–3.99 ng/mL and AMH ≥ 4 ng/mL. Subsequent multivariable linear regression showed that total gonadotropin dose and peak estradiol level at trigger each had a positive relationship with P level, and clinical pregnancy had a negative relationship with P level. There was weak evidence of group differences based on this model when adjusting for the other covariates, and these differences may become more apparent in future studies with larger sample sizes.Limitations of our study include the retrospective nature with the inherent biases with such study design. Another study limitation was the sample size, particularly in the low AMH group (Group A). Basal AMH levels were not checked amongst the patients, which may be considered a minor limitation of this study and may be a potential area to explore in future studies. Although there may have been concerns about luteal deficiency in PCOS, this has predominantly been in the scenario of ovulation induction instead of the COH protocols used for IVF. A previous study, which involved IVF patients, showed a higher mean progesterone level on the day of trigger among women with PCOS when compared to women without PCOS . StrengtWe conclude that although AMH is a predictor of certain IVF outcomes, AMH is not a predictor of elevated serum P level at trigger among women who undergo COH for IVF. Although larger studies are necessary due to the limitations of the retrospective study design and sample size of subjects, these data suggest that the mechanism of action of elevated P in COH cycles is unlikely to be due to directly proportionate granulosa cell numbers, and from a clinical standpoint, AMH cannot be used to counsel patients of the likelihood of a freeze-all cycle due to elevated P if their initial intent is fresh embryo transfer.