The isotopic analysis highlighted a higher ratio of δ2H in synthetic safranal than in natural safranal; the mean values were 36‰ (+/− 40) and −210‰ (+/− 35), respectively. The δ13C between Iranian, Spanish and other saffron was significantly different and represents median values of −28.62‰, −30.12‰ and −30.70‰, respectively. Moreover, linear and quadratic discriminant analyses (LDA and QDA) were computed using the two isotope ratios of safranal and the saffron metabolites. A first QDA showed that trans-crocetin and the δ13C of safranal, picrocrocin, and crocin C3 concentrations clearly differentiated Iranian saffron from other origins. A second model identified δ13C, trans-crocetin, crocin C2, crocin C3, and picrocrocin as good predictors to discriminate saffron samples from Iran, Spain, or other origins, with a total ability score classification matrix of 100% and a prediction matrix of 82.5%. This combined approach may be a useful tool to authenticate the origin of unknown saffron.Saffron is a very high value-added ingredient used in the food supplement market and contains a high level of safranal. Adding synthetic safranal to saffron, which is significantly cheaper, and falsifying the origin of saffron may represent recurrent fraud. Saffron from different countries was analyzed to determine the stable isotope ratios δ Crocus sativus and is traditionally used in cooking, infusion or in food products. Its earliest representation appeared 4000 years ago in some craft products of the Minoan civilization on Crete Island [Saffron refers to the stigmas of e Island . Iran ise Island . The othe Island . Spain ie Island .Some health and well-being applications have been demonstrated in the literature. Health benefits of saffron have been suggested for the treatment of anxiety disorders ,7,8, preSince saffron is the most expensive spice in the world, the saffron trade is often affected by fraud. Therefore, the authentication of the raw materials or extracts is crucial, especially for the food industry or the dietary supplement market using powder as the main form of trading.Carthamus tinctorius, or Turmeric longa [Crocus sativus and to authenticate and estimate its quality (categories I to III). DNA analysis could be complementary performed to confirm identity [Firstly, traditional fraud consists of fully or partially replacing the saffron by another plant, such as other crocus, ic longa , easily identity , but it Crocus sativus is also a possible fraud. Recently, a metabolomic approach by UHPLC/QTOF-MS demonstrated the potential of phenolics screening, particularly anthocyanins and glycosidic flavonols, as the best markers of this type of adulteration [Secondly, the addition of other parts of teration . The preteration .1H NMR technique identifying adulterated saffron with 20% gardenia or other plant adulterants has been proposed [w/w [Thirdly, there are more sophisticated frauds, including natural compounds of saffron from other plants. Crocins from Gardenia jasminoide, which is cheaper than saffron, could be added to saffron powder. A powerful proposed . The UHPproposed  or the ased [w/w .13C isotopic analysis of safranal by GC-C-IRMS has already been proposed to determine the naturality of safranal, with synthetic safranal having a δ13C slightly different from natural ones [Finally, safranal, the main volatile compound of saffron, guarantees organoleptic quality. Since it has been demonstrated that the ISO method is a source of significant overestimation error , safranaral ones . However2H, δ13C and δ15N determinations undertaken on the bulk saffron [However, the frauds of saffron geographical origins are also of major concern, and the need for methods to authenticate its origin is growing. Stable isotope techniques have demonstrated huge advances in geographical origin authentication. The multi-isotope approach, such as δ saffron , permitt saffron . Geograp saffron .2H on saffron samples to detect the addition of synthetic safranal has never been published. Moreover, the combination of metabolite quantification through the LC separation technique and the safranal-based stable isotope ratio has never been used to differentiate the saffron origin and its naturality.To our knowledge, the analysis of δThus, this study aims to propose an authentication method to determine the naturality of safranal contained in saffron samples using stable isotope ratios of safranal and to discriminate the Iranian origin of saffron from others using a multivariate analysis of saffron metabolites and stable isotope ratios of safranal.2H and δ13C ratios, were very different  for natural safranal and 36‰ (+/− 40) for synthetic safranal. The δ13C ratio for natural safranal ranged between −27‰ and −33‰, the mean ratio was −30‰ (+/− 2), and the δ13C ratio for synthetic safranal ranged from 26‰ to −28‰, with a mean ratio of −27‰ (+/− 1).The dispersion of natural safranal in 33 saffrons and synthetic safranal from 4 available batches, according to δifferent . The δ2H2H ratio  is higher, giving −89, compared to values observed for natural safranal, ranging from −260 to −149 in After quantification of safranal content in Iranian genuine saffron, the spice was voluntarily adulterated with addition of synthetic safranal. Analyses of these fraudulent examples containing 0.25% to 1% synthetic safranal on total safranal showed a negative value of δ2H ratio , indicatForty-one saffron samples from different geographical origins were analyzed: Iran (N = 20), Spain (N = 9), France (N = 6), Greece (N = 3), Morocco (N = 2), and Italy (N = 1). 13C of safranal were significantly different from the different saffron geographical origins, based on the results of the Kruskal–Wallis test with post hoc comparisons . Compared to Iranian saffron, the Spanish saffron had higher levels of picrocrocin and picrocrocin derivatives (p < 0.05) but lower levels of picrocrocin P1 and crocin C2, compared to Iranian or other saffron.Compared to Spanish and other origins, Iranian saffron had higher δThe first three principal components (PC) represented 79% of the total variance from the original data . PC1 proPC1 mainly differentiated the Iranian saffron, rich in crocins from the Spanish saffron. The crocin concentrations are mostly determined by postharvest processing, as opposed to the actual geographic origin of the spice, as already mentioned in the literature . However13C ratio.Two models of discriminant analyses were generated and compared to classify saffron samples based on their saffron metabolites and δ13C ratio of safranal, picrocrocin, and crocin C3. The QDA model based on the above four selected variables exhibited a very good performance in calibration and prediction  for natural safranal and 36‰ (+/− 40) for synthetic safranal. These differences are consistent with another study focused on the authentication of bitter almond oil terpenes [2H mean value of natural benzaldehyde was −125‰ and significantly lower than synthetic ones, which were −40‰ and 777‰ for synthetic benzaldehyde obtained from benzal chloride or toluene synthetic pathway, respectively. In another study focused on vanillin authentication, the isotopic ratio from natural vanillin, which has been determined through solid-phase microextraction and gas chromatography–isotope, gave a negative δ2H between −91‰ and −75‰, while synthetic vanillin gave a positive δ2H between 38‰ and 104‰ [2H values than natural standards from Allium species in another recent study [To our knowledge, the difference between δterpenes . In thisand 104‰ . In addint study . Unlike 13C between Iranian, Spanish, and other saffron show a first level of geographical differentiation with median values of −28.62‰, −30.12‰, and −30.70‰, respectively. No difference in the δ2H ratio was observed according to the saffron origin. In another study, the δ13C ratio between Spanish and Iranian saffrons were not statistically differents, contrary to δ2H ratio for which a higher deuterium content was observed in Iranian saffron [Differences in δ saffron . These aConcerning the chemical composition of the different types of saffron determined by UHPLC-DAD, significant differences were observed between the Iranian and Spanish saffron and saffron from other origins. In order to limit the biases related to the degradation of the crocins and the other compounds of the saffron, the analyses were realized a few months after being harvested.High purity gases carbon dioxide 99.998%  and hydrogen 99.999%  were used as working reference gas. Helium flow at 99.999% was used as carrier gas. One microliter of paraffin 1-decane  was injected to install a coating in the pyrolysis alumina tube prior to the start of the analysis process.13C (‰) −32.15, δ2H (‰) −100.3) and IAEA NBS 22 (oil: δ13C (‰) −29.79, δ2H (‰) −120) supplied by IAEA  permit the calibration of safranal synthesis standards.International standards IAEA CH7 . Unfortunately, Sigma Aldrich was the only supplier of synthesis safranal origin found on commercial market. For this reason, three safranal batches were bought at different times, with high levels of purity (98.4%), and were stored at 4 °C., and β-cyclocitral was purchased from Sigma Aldrich (Ref 16976. Purity ≥ 97%); crocin (trans-crocetin-4-GG) was purchased from Phytolab ; kaempferol-3-O-glucoside was purchased from Extrasynthese (Ref 1243 S batch purity 99%); trans-crocetin was purchased from Toronto Research .Thirty authentic dried saffron spices were collected from different producers and cooperatives in Iran (Khorasan province), Spain (AOP Castilla la Mancha), Greece (AOP Krokos Kosanis), Morocco  France (PGI Quercy province), and Italy (Sardegna), with all of them meeting ISO3632 quality criteria and traceability requirements, including certificate of origin obtained from independent laboratories. The saffron crop years were comprised between 2011 and 2019.w/v). Methanol was chosen for its ability to solubilize safranal.For the naturality study, adulterated samples were prepared by adding different amounts of synthetic safranal into a crushed Iranian saffron, from 0.25% to 1% was added to the total safranal contained in saffron . The addition of 0.5 to 2 µL of pure synthetic safranal was performed in 1 mL of a preparation containing authentic saffron powder and methanol  was analyzed according to the UHPLC method, as previously described . The UHPOne hundred milligrams of crushed saffron stigma were extracted by 19 mL of MeOH 50% in distilled water in a 20 mL brown glass flask, consisting of 5 min sonication at 30 °C followed by 1 h of magnetic stirring at room temperature and 5 min of sonication at 30 °C. After filling the flask, the product was slightly mixed and diluted in MeOH 50% in water before filtration through a 0.45 µm PTFE membrane. In other recent studies, MeOH 50% was the most suitable solvent to extract a wide range of compounds, including crocins and safranal, among different solvents: water, ethanol, methanol, ethanol:water (1:1), and methanol:water (1:1) .LC separation was performed through a core–shell silica reversed-phase, Kinetex C18, Phenomenex 2.6 μm, 150 × 2.1 mm. The mobile phase was 0.01% formic acid in water (A) and acetonitrile (B). The elution and calibration were performed, as mentioned in the study. The results are presented in absolute concentration, the quantification is obtained exactly according to the calculation mode described in our previous work .The calibration curves are obtained with:All compounds are fitted to the coefficients of the linear equation (slope and intercept), obtained with each corresponding standard as follows:w/w) as follows:V is the volume of the sample preparation and w the sample weight of the preparation. Total crocins are expressed as Crocin trans-4-GG. The content of Picrocrocine or derivatives are expressed as β-cyclocitral equivalent. The content of kaempferol derivatives is expressed as kaempferol glucoside equivalent. Safranal is quantified with its proper standard. For picrocrocin, a conversion factor was used to convert the β-cyclocitral equivalent expression into picrocrocin according to their respective molecular weights .All results are express in % of powder . The limit of quantification was, respectively 0.037 mg/L, 0.072 mg/L, 0.1304 mg/L and 0.037 mg/L.Validation criteria have been determined according to ICH Guidance, the limit of detection was 0.012 mg/L, 0.024 mg/L, 0.043 mg/L and 0.012 mg/L, respectively, for crocins (trans-4-GG Eq.), safranal, Kaempferol 3-O-sophoroside.Linearity was determined from 0.10 mg/L to 225.29 mg/L for trans-4-GG giving coefficient of determination (R2) of 0.9988; from 0.5 mg/L to 500 mg/L for the safranal and β-cyclocitral giving R2 of 0.9997 and 0.9998; and from 0.10 mg/L to 509 mg/L for the Kaempferol 3-13C and 2H isotopic ratio of synthesis safranal used as reference standards for the measurements of saffron using GC-C/P-IRMS ) were first determined using Bulk stable isotope analysis on 2 EA-IRMS coupling systems  and .Both 13C value) of synthetic safranal standards was measured using a Flash HT elemental analyzer connected to a Delta V Plus via a Conflo IV interface . Samples in tin capsules  fell in the unit combustion maintained at 950 °C in a stream of 100 mL min−1 helium with a pulse of oxygen. Flash combustion (close to 1800 °C) allowed the complete oxidation of carbon into CO2. The gases conveyed throughout a reduction furnace held at 650 °C were made by copper wires, where excess oxygen was trapped, followed by a water trap containing anhydrous magnesium perchlorate. A Flash HT elemental analyzer was connected to a Thermo Delta V Plus isotope ratio mass spectrometer via an online interface (Conflo IV). This intermediary device permitted the adjustment of automatic sample dilution and the generation of reference gas pulses, allowing isotopic determination.The carbon isotopic analysis . Samples weighed in silver capsules  dropped in the pyrolysis unit were held at 1450 °C. This high-temperature reactor was made of an inner glassy carbon tube, half filled with glassy granular carbon, integrated into an external ceramic tube. Hydrogen and oxygen were converted in H2 and CO and separated on a gas chromatography column prior to being introduced in the IRMS. The interface operated similarly to that previously described, providing steady and regular reference gas pulses.The δ2H or 13C and iR correspond to 2H/1H and 13C/12C ratio, respectively.The isotope ratio of a sample is typically expressed in δ notation according to the following formula, defined as:The delta values were multiplied by 1000 and expressed in units “per mil” (‰).13C and δ2H were determined versus the Vienna Pee Dee Bee Belemnite (VPDB) 13C international standard and standard mean ocean water (SMOW) 2H international standard. δ13C and δ2H values. The uncertainty was assessed at +/− 0.30 (‰) for 13C and +/− 3 (‰) for 2H.Five replicates of synthetic safranal were done to determine with a good precision δ1 g of saffron sample was suspended in 10 mL methanol and placed in an ultrasound bath for 30 min. After 10 min of decantation time, the liquid supernatant was recovered and concentrated to 1 mL by evaporation under nitrogen 20 mL/min flow.13C and δ2H measurements. . This equipment enables the identification of peaks and isotopic ratio measurements in the same runoff injection.GC-C/P-IRMS was performed using a GC Trace 1310 doubly connected to a single quadrupole mass spectrometer ISQ and an isotope ratio mass spectrometer Delta V Plus via a GC Isolink II interface for δ−1, 170 °C at a rate of 5 °C min−1 and 340 °C at a rate of 20 °C min−1 (held for 5 min at this temperature). Components were separated at a flow rate of 1 mL min−1 and divided in two ways using a Sil Flow connector.From 1 to 3 µL of liquid sampling were injected using a Tri Plus RSH autosampler in a TRACE 1310 Gas Chromatograph equipped with an Agilent DB-XLB column  for chromatographic separation. The injection port was held at 250 °C, fitted with a split liner containing glass wool and operated in split mode of (1:10). The oven temperature program started at 50 °C for 2 min, increased to 125 °C at a rate of 10 °C min2H measurement in H2 was carried out at higher temperature (1400 °C) using a thermo-conversion tube made by an empty alumina tube, where a layer of carbon was deposited by injections of 1 decane.The combustion of carbon in carbon dioxide was undertaken using an alumina tube containing nickel oxide and held at 1000 °C in a furnace. Pyrolysis of the sample for 13C and +/− 5(‰) for 2H.The system was checked by passing a synthetic safranal standard sample every two analyses. Each sample was determined in two replicates. The uncertainty was established at +/− 0.50 (‰) for Statistical analysis was conducted using SAS software version 9.4 . Saffron metabolites with more than 20% of values below the detection limit (DL) were removed. For saffron metabolites with less than 20% of values below the DL, any values reported as <DL were substituted with half the value of the DL. This strategy was used to avoid introducing bias through the substitution of a many censored values, while trying to minimize loss of information through the removal of variables .p < 0.05.Normality of the saffron metabolites and the two stable isotope ratios were tested using the Shapiro-Wilk test. As several metabolites were not normally distributed, comparisons according to three geographical origins  were performed with the Kruskal-Wallis test. Dwass, Steel, Critchlow-Fligner multiple comparisons post hoc procedure was done to determine which pairs significantly differed. Differences were considered statistically significant for Prior to multivariate statistical analysis, saffron metabolites and isotope ratios were standardized to z scores to impart normality on the dataset and reduce the appearance of outliers. Principal component analysis (PCA) was performed on the transformed variables, and the percentage of variance for each specific principal component was reported.Classificatory discriminant analysis was used to differentiate the saffron samples from different geographical origins  based on stable isotope ratios and saffron metabolites. The discriminant variables were identified by a backward or stepwise algorithm based on Wilks’ lambda method using PROC STEPDISC. When the within-class covariance matrices were assumed to be unequal, a quadratic discriminant function (QDA) was preferred to a linear discriminant function (LDA). This supervised multivariate classification technique determines combinations of variables to maximize between-group variance while minimizing within-group variance. QDA predictive ability was evaluated by the leave-one-out cross-validation methodThe stable isotope ratios of carbon and hydrogen from safranal may be a useful tool for the authentication of saffron. The δ13C between Iranian, Spanish and other saffron was significantly different and represents median values of −28.62‰, −30.12‰ and −30.70‰, respectively. The δ2H ratio allows the controlling of the absence of the synthetic safranal in significant proportion to identify a possible adulteration of saffron. An addition of 1% synthetic safranal to total safranal could be detected in the adulterated samples. In addition, the UHPLC-DAD analysis of crocins, picrocrocin derivatives, kaempferol, and safranal coupled with the analysis of the stable isotope δ13C ratio of safranal allows us to distinguish Iranian saffron from other origins. Further investigation is required to extend these models on saffron samples from other major producers, such as Spain, Morocco, or Greece.


Background: Fat grafting is a frequently used technique; however, its survival/ regeneration mechanism is not fully understood. The browning of white adipocytes, a process initiated in response to external stimuli, is the conversion of white to beige adipocytes. The physiologic significance of the browning of adipocytes following transplantation is unclear.Methods: C57BL/6 mice received 150 mg grafts of inguinal adipose tissue, and then the transplanted fat was harvested and analyzed at different time points to assess the browning process. To verify the role of browning of adipocytes in fat grafting, the recipient mice were allocated to three groups, which were administered CL316243 or SR59230A to stimulate or suppress browning, respectively, or a control group after transplantation.Results: Browning of the grafts was present in the center of each as early as 7 days post-transplantation. The number of beige cells peaked at day 14 and then decreased gradually until they were almost absent at day 90. The activation of browning resulted in superior angiogenesis, higher expression of the pro-angiogenic molecules vascular endothelial growth factor A (VEGF-A) and fibroblast growth factor 21 (FGF21), fewer macrophages, and ultimately better graft survival , whereas the inhibition of browning led to poor angiogenesis, lower expression of VEGF-A, increased inflammatory macrophages, and poor transplant retention at week 10 .Conclusion: The browning of WAT following transplantation improves the survival of fat grafts by the promotion of angiogenesis and reducing macrophage. Autologous fat grafting is a procedure for soft-tissue reconstruction and repair . AlthougEarly angiogenesis, immune cells, and activated adipose stem cells (ASCs) have been reported to have effects on the survival of transplanted fat tissue . AccumulWu et al. were the first to identify a distinct type of thermogenic adipocyte within subcutaneous white adipose tissue (WAT), which was called a beige adipocyte . The conWhite adipocytes in WAT depots, and especially in subcutaneous depots, are able to be converted to beige adipocytes in response to specific stimuli . Unlike Previous studies have shown that various treatments can be used to activate browning in fat tissue and that these significantly improve fat graft survival . Howevern = 8 mice per time point were sacrificed and the transplanted adipose tissue was collected for further analyses. In a second experiment, to investigate the role of browning after transplantation, 36 mice were randomly allocated to three groups (n = 12 per group): a browning upregulation group, a browning downregulation group, and a control group. The browning upregulation group was administered CL316243  (1 mg/kg body mass) subcutaneously at the site of transplantation for 14 consecutive days following transplantation, the browning downregulation group was administered SR59230A  in the same way, and the control group was administered phosphate-buffered saline (PBS) (5 ml/kg body mass). After 2 or 10 weeks, the mice were killed and the fat grafts were harvested and analyzed.The study was approved by the Nanfang Hospital Animal Ethics Committee and was conducted according to the guidelines of the National Health and Medical Research Council of China. Sixty-eight 8-week-old male C57BL/6 mice were obtained from Southern Medical University . For transplantation, the mice were anesthetized by the intraperitoneal injection of pentobarbital sodium (50 mg/kg). One hundred fifty milligrams of fat tissue was harvested from the inguinal fat pad, cut into small pieces, and inserted through a 5 mm-long skin incision into a small dorsal subcutaneous pocket in 32 mice, which was followed by skin closure with 7-0 nylon sutures. After 7, 14, 30, and 90 days, Samples were fixed in 4% paraformaldehyde for 24 h, embedded in paraffin and cut from the middle. Sections were prepared and stained with hematoxylin and eosin, and examined under a light microscope . Paraffin-embedded tissue sections were incubated with rabbit anti-UCP1 antibody  or rabbit anti-cluster of differentiation (CD)31 , followed by secondary antibody and then an avidin-biotin-horseradish peroxidase detection system . The slides were then examined using an Olympus BX51 microscope. For immunofluorescence staining, tissue sections were incubated with rabbit anti-mouse macrophage antigen 2 (MAC2) , or guinea pig anti-mouse perilipin . For double fluorescence staining, sections were incubated with AlexaFluor 488-conjugated  goat anti-guinea pig immunoglobulin G , and AlexaFluor rhodamine-conjugated chicken anti-rabbit immunoglobulin G . Nuclei were stained with 4′,6-diamidino-2-phenylindole . To quantify the degree of browning, at least five microscopic fields per samples of grafts were randomly chosen from the middle to the edge by two of the authors in a double-blinded fashion. To quantify the proinflammatory cells, and angiogenesis in the transplanted fat tissue, the numbers of UCP-1-positive cells, Mac-2-positive cells, and CD31-positive cells were counted in at least five microscopic fields per sample by two of the authors in double-blinded fashion. The percentage of perilipin positive adipose cells area per field was determined with the use of ImageJ . The diameter of mature adipocytes was determined by analyzing the captured image of mature adipocytes by means of light microscopy with the use of the ImageJ  program as described previously .Adipose tissue was collected, frozen in liquid nitrogen, and stored at −80°C. RNA was extracted from 50 mg tissue samples using RNeasy Lipid Tissue Mini Kits , according to the manufacturer’s instructions. cDNA was synthesized and amplified over 40 cycles using a QuantiTect Reverse Transcription Kit (Qiagen) and a Rotor-Gene 3000 Real-Time PCR Detection System . Expression levels were calculated using the 2−ΔΔCt method. The primer sequences were as follows: Ucp1: forward 5′-CTGATGAAGTCCAGACAGACAG-3′ and reverse 5′-CCAGCATAGAAGCCCAATGA-3′; PRDM16: forward 5′-CAGCACGGTGAAGCCATTC-3′ and reverse 5′-GCGTGCATCCGCTTGTG-3′; PGC1-α: forward 5′-CGACAGCTATGAAGCCTATGAG-3′ and reverse 5′-CTTCTGCCTCTCTCTCTGTTTG-3′; FGF21: forward: 5′-CTGGGGGTCTACCAAGCATA-3′ and reverse 5′-CACCCAGGATTTGAATGACC-3′; and VEGF-A: forward 5′-GGAGATCCTTCGAGGAGCACTT-3′ and reverse 5′-GGCGATTTAGCAGCAGATATAAGAA-3′HIF1α: forward: 5′-CAAGATCTCGGCGAAGCAA-3′ and reverse 5′-GGTGAGCCTCATAACAGAAGCTTT-3′.2) and carbon dioxide expiration (VCO2) were collected and measured by indirect calorimetry in a Promethion Metabolic Screening System  that maintained constant environmental temperature across a 12-h light, 12-h dark cycle. The mice in different groups were individually housed in chambers maintained at 25°C, with free access to food and water. Measurements were made every 15 min for 3 days after the mice had acclimatized to their surroundings for 1 day. Mean values were calculated for all the parameters of interest over the 3 days of monitoring.Oxygen consumption . The data are expressed as mean ± SEM and were compared among the groups using one-way analysis of variance or the Kruskal-Wallis test. Comparisons between two groups were performed using the least significant difference method or the Mann-Whitney U-test. p < 0.05; **p < 0.01; ***p < 0.001) (p < 0.01)    . Ucp1 ge < 0.01) . Mean ad< 0.001) . Based o< 0.001) . Countin< 0.001) .p < 0.05) (p < 0.05)  . And the < 0.05) . Swellin < 0.05) . Hematox < 0.05) , above.  < 0.05) , below.  < 0.05) , below. 2 production, and heat production were not affected by CL316243 and SR59230A  when the browning level was the highest  (p < 0.05)  . However < 0.05) . Further < 0.05) . The exp < 0.05) . Compare < 0.05) .p < 0.05) and week 10  but they were significantly less in the browning upregulation groups than in the control group only at week 2 but not week 10    and Mac2 (red) to identify tissue macrophages showed that grafts from the browning upregulation group had a superior structure, and contained more smaller, perilipin-positive adipocytes and fewer Mac2-positive cells than the control group , above.  week 10 . Quantif 0.0001) .Little is known regarding the mechanism underlying fat graft survival/ regeneration after transplantation. Although the important roles of inflammatory cells and ASCs have been recognized, the role of mature adipocytes themselves has been less well researched . PreviouFirstly, we characterized the kinetics of the browning of WAT grafts. However, this finding raised the question of when the browning program of WAT is turned on and how long this lasts. In the present study, we have shown that the beige phenotype of the fat graft changes with time. Beige adipocytes appeared in fat grafts around the regenerating zone as early as 7 days following surgery, peaked in number on day 14, and then became fewer in number until week 12, when they were almost completely absent. This time course of browning suggests that it is a transient state that develops in response to certain stimuli associated with fat grafting, and that after these stimuli are withdrawn, the beige adipocytes lose this phenotype. Similar results were obtained in our previous study, in which the transplantation of tamoxifen-induced beige adipose tissue was followed by whitening of the graft within 12 weeks . Indeed,in situ.Next, we evaluated whether the browning level would affect fat graft retention by pharmaceutical up-/ down- regulation of browning. Experiments by Cai et al., which demonstrated that transplantation of tamoxifen-induced beige adipose tissue improves fat graft retention, along with more recent work that showed that the browning of WAT induced by extracellular vesicles improves fat graft survival, indicate a potential role of beige adipocytes within fat grafts . Howeverβ3-adrenoceptor activation is thought to be a key stimulus for the formation of beige adipocytes . TherefoBased on the fact that beige adipocytes contain multiple, small lipid droplets, and that fat grafts exchange nutrients by means of passive diffusion when avascular, Cai et al. suggested that tamoxifen-induced browning is associated with a higher surface area-to-volume ratio, and thus more efficient diffusion of nutrients and an improvement in survival . Thus, iIn addition to investigating their differing capacity for energy dissipation as heat, investigators have also focused on the contrasting secretomes of white adipocytes and brown/beige adipocytes . EvidencWhen thinking about the factors secreted by beige adipose tissue that might have beneficial effects in fat grafts, we reasoned that VEGF-A and FGF21 would be good candidates because VEGF-A is a “BATokine,” secreted by BAT, and FGF21 can be released by WAT during thermogenic stimulation . To deteThe results of the present study suggest that beige adipocytes exist in fat grafts and have beneficial effects, particularly in the early stage of engraftment. We speculate that avascularity and hypoxia during the early stage promote the conversion of white to beige adipocytes, which produce more VEGF-A and FGF21, leading to early revascularization . Early rVarious browning agents have been used clinically. In the present study, we have highlighted the clinical potential for browning agents, such as CL316243, to improve fat engraftment. Even though the present findings were made in an animal model and the clinical validation of CL316243 requires further studies, mirabegron, a β3-adrenoceptor agonist, has been approved for the treatment of overactive bladder symptoms . BesidesBecause various signaling pathways, besides β3 adrenoceptor signaling pathways, contribute to browning of WAT under different conditions and the exact mechanism underlying the browning process after fat grafting is unknown, CL316243 and SR59230A applied in the present study may manipulate the degree of browning partly. Since transcriptional regulatory protein PRDM16 appears to control development of beige cells,the use of transgenic mice, such as overexpression or deletion of PRDM16 transgenic mice, would provide better understanding of contribution of browning process to remodeling of fat grafts . TherefoIn the present study, we have shown that the local post-transplantation browning of WAT proceeds along a well-defined time course, and is accompanied by early angiogenesis and superior final graft retention. In a mouse model, treatment with CL316243 for 2 weeks promoted beige adipocyte formation and led to superior fat graft survival. The finding that browning within fat grafts might influence their survival should provoke further research to explore the mechanisms involved in the graft retention and to translate these findings into a clinical means of improving fat graft quality.


Density records are presented as cell cm−3 sediment, cell g−1 sediment/ sulfide/ vent precipitate, and cell m−2. This database should serve as reference to close sampling gaps in the future.Benthic prokaryotes include Bacteria and Archaea and dominate densities of marine benthos. They play major roles in element cycles and heterotrophic, chemoautotrophic, and phototrophic carbon production. To understand how anthropogenic disturbances and climate change might affect these processes, better estimates of prokaryotic biomasses and densities are required. Hence, I developed the ProkaBioDen database, the largest open-access database of benthic prokaryotic biomasses and densities in marine surface sediments. In total, the database comprises 1,089 georeferenced benthic prokaryotic biomass and 1,875 density records extracted from 85 and 112 studies, respectively. I identified all references applying the procedures for systematic reviews and meta analyses and report prokaryotic biomasses as g C cm  They have densities of 108 to 109 cells cm−3 2 in the upper 10 cm of sediment and are therefore the most abundant benthic organisms. On a regional scale, prokaryotic densities in surface sediments decrease with depth . Globally, however, their densities and biomasses do not decline with increasing water depth5. Prokaryotes are major players in the global cycling of elements, such as carbon12, nitrogen15, phosphorus17 and sulfur21, and they dominate sediment community oxygen consumption (SCOC) in deep-sea ecosystems >3,500 m water depth22.Unicellular prokaryotes comprise the domains Bacteria and Archaea6. In comparison, for an intertidal area of the Scheldt estuary (North Sea), a model combined with a pulse-chase tracer experiment estimated that 3% of the prokaryotic carbon production was grazed upon by meiobenthos and 24% of this carbon production was consumed by macrobenthos23. In the deep-sea sediments of the Fram Strait (N Atlantic) and of the Clarion-Clipperton Fracture Zone , however, no direct transfer of labelled carbon from prokaryotes to metazoan meiobenthos or metazoan macrobenthos was detected24.Depending on the environmental conditions, prokaryotes can be involved in the transfer of organic matter to higher trophic levels: In the oxygen minimum zone of the Arabian Sea (Indian Ocean), the transfer of labelled carbon, that was taken up by prokaryotes, to their metazoan meiobenthic and macrobenthic consumers is relatively inefficient28. This corresponds to about 1029 bacteria cells living on our planet (uncertainty: 10-fold)29, of which 3.5 × 1028 ± 0.9 × 1028 occur in deep-sea surface sediments4. Hence, the upper 50 cm of said sediment are estimated to contain 1.29 Pg C4 (1 Pg = 1 petagram = 1015g) bacterial carbon which is up to 99% of the total estimated marine bacterial biomass . Bacteria can alter their environment, such as benthic cyanobacteria that can form so-called “microbially induced sedimentary structures”30. Particularly long, filamentous bacteria, known as “cable bacteria”, are even able to conduct long-distance electron transport over several centimeters35.Bacteria in marine surface sediments, i.e., sediment layers ranging from the sediment surface to – depending on the study – approximately 5 cm to 20 cm depth, contribute between 30 and 70% to total prokaryotic densities36, account for <1 to 40% of prokaryotic densities in surface sediments37. Our planet is estimated to host 1028 marine benthic Archaea cells which is equivalent to 0.3 Pg C Archaea carbon (uncertainty: 13-fold)29.Archaea, whose most abundant phyla in the deep sea are Thaumarchaeota  and Euryarcheota (29% of Archaea density)38 that, in comparison to preceding databases by Wei et al.39 and Rex et al.5, allows direct and free access to the data and transparently reports the selection process. It also covers the whole globe and not only the Atlantic Ocean and the Mediterranean Sea like in Danovaro et al.36 or is limited to specific water depths like in Danovaro et al.4.Due to the prominent role of prokaryotes in the global carbon cycle, detailed knowledge about their biomasses and densities are necessary to understand how these microorganisms will be impacted by climate change and anthropogenic disturbances. Therefore, I prepared the open access “ProkaBioDen database”40. Based on this compilation, I extracted 1,089 georeferenced benthic prokaryotic biomass records and 1,875 georeferenced benthic prokaryotic density records from 85 and 112 chosen studies, respectively. I present benthic prokaryotic biomasses as g C cm−3 wet sediment, g C g−1 dry sediment, g C g−1 wet sediment, g C m−2 and benthic prokaryotic densities as cell cm−3 dry sediment, cell cm−3 wet sediment, cell g−1 dry sediment, cell g−1 dry sulfide, cell g−1 vent precipitate, cell g−1 wet sediment, cell m−2. All data further contain information about substrate type  and the methods applied to determine prokaryotic biomasses and densities and how researchers differentiated between Bacteria and Archaea densities. In this way, scientists can focus specifically on Archaea or Bacteria if they wish. The database is the first systematic open-access compilation of benthic Archaea and Bacteria densities and prokaryotic biomasses and densities and points towards undersampled geographic locations and water depth.The “ProkaBioDen database” lists 1,299 benthic prokaryotic biomass and 1,104 benthic prokaryotic density studies that were identified applying procedures for systematic reviews and meta analyses40. In the so-called “Identification” step, I identified 1,553 peer-reviewed articles in the Web of Science by using the key words “microb* biomass benth*”, “benthic prokaryotic biomass”, “benth* bacteria* biomass marin*”, and “Archaea biomass marin*”. Additionally, I found 138 publications in other sources, such as PANGAEA® Data Publisher (https://www.pangaea.de/) and peer-reviewed publications known to the author. After removing duplicate publications, I screened all titles and abstracts of 1,299 studies ds Table .Table 1S40, I searched the Web of Science using the key words “marin* microb* abundance benth*”, “‘benthic bacteria’ abundance marin*”, “prokaryotic abundance marin*”, “prokaryotic density marin*”, “Archaea density abundance marin*”, “Archaea density marin*”, “Archaea abundance marin* benth*”, “Crenarchaea density abundance marin*”, “Crenarchaea density marin*”, “Crenarchaea abundance marin* benth*”, “Euryarchaea density abundance marin*”, and “Euryarchaea abundance marin* benth*” and found 1,204 peer-reviewed articles  of the sampling stations. In these cases, I approximated the sampling locations using Prokaryotic biomasses were often not directly measured, but determined by extraction of bacterial adenosine triphosphate (ATP), extraction of bacterial phospholipid-derived fatty acid (PLFA), or by measuring prokaryotic densities. Subsequently, the authors of the original publications converted these data to prokaryotic biomasses using conversion factors Table .Table 2R41.For cases where the prokaryotic biomasses and densities were not reported in the text or in tables, but were shown in figures, I extracted the data using ImageJDryad Digital Repository and contains two txt.files, i.e., the List of studies for ProkaBio database and the List of studies for ProkaDen database, and two xlsx.files, i.e., the file ProkaBio database and the file ProkaDen database38. The List of studies files report all studies in alphabetical order  that I identified in the “Identification” step of the systematic review after I eliminated duplicates. Each data entry in the “ProkaBioDen database” includes information about the region and the ocean where the samples were taken, the geographical location , the water depth (in m), and the depth range after Dunne et al.42. The authors of said study classified the ocean into near-shore areas from 0 to 50 m water depth, continental shelves from >50 to 200 m water depth, continental slopes from >200 to 2,000 m water depth, and continental rises and abyssal plains >2,000 m depth. The database includes biomass and density records for individual sediment layers and information about the thickness of said sediment layers and its specific upper and lower boundaries when a layer was sliced horizontally, but also biomass and density records for vertically integrated sediment profiles. Additionally, the database contains information about sediment type, median sediment grain size (µm), sediment density (g cm−3), and porosity, and whether prokaryotic densities were reported for total prokaryotes, Bacteria, or Archaea.The “ProkaBioDen database” is an open access database in the In the database, 40% of the benthic prokaryotic biomass samples originated in the Mediterranean Sea, 34% in the Atlantic, and 11% in the Arctic Ocean Fig. . Most be42. In comparison, only 15% of all benthic prokaryotic biomass samples were taken at the continental rise/in abyssal plains that contribute 89% to the global ocean floor area42. Benthic prokaryotic densities were sampled to 39% in near-shore areas, to 8% at continental shelves, to 31% at continental slopes, and to 22% at the continental rise/in abyssal plains  and two-third of the density (67%) records were surface sediment records. 32% (benthic prokaryotic biomass) to 48% (benthic prokaryotic density) of these surface sediment layers stretched from 0 to 1 cm. The thinnest sediment layers had thicknesses of 0.3 cm and the thickest sediment layers reached to 20 cm below the sediment surface.


Negative subsequent memory effects in functional MRI studies of memory formation have been linked to individual differences in memory performance, yet the effect of age on this association is currently unclear. To provide insight into the brain systems related to memory across the lifespan, we examined functional neuroimaging data acquired during episodic memory formation and behavioral performance from a memory recognition task in a sample of 109 participants, including three developmental age groups  and one additional group of older adults (55–85 year-olds). Young adults showed the highest memory performance and strongest negative subsequent memory effects, while older adults showed reduced negative subsequent memory effects relative to young adults. Across the sample, negative subsequent memory effects were associated with better memory performance, and there was a significant interaction between negative subsequent memory effects and memory performance by age group. Posthoc analyses revealed that this moderation effect was driven by a stronger association between negative subsequent memory effects and memory performance in young adults than children, and that neither children nor older adults showed a significant association. These findings suggest that negative subsequent memory effects may differentially support memory performance across a lifespan trajectory characterized by developmental maturation and support further investigation of this effect in aging. While these region-specific studies have been informative, more systematic analyses considering the whole lifespan are lacking.Episodic memory is a core cognitive process that encompasses memory for personal experiences, which are tied to specific times and places. This ability to draw on previous experience enriches our quality of life and its impairment proves to be distressing. Behavioral evidence demonstrates that the capacity for episodic memory greatly improves in typical development from childhood to early adulthood deactivation to subsequently remembered items than subsequently forgotten items In examining the development and aging of episodic memory, the subsequent memory paradigm is frequently utilized Recently, a few studies have investigated the relevance of the negative SME in the context of memory development. Chai et al. Given the similarity in the lifespan trajectory of negative SME and behavioral measures of memory, it is plausible that understanding the linkage between these two may provide additional information on behavioral measures. Several previous studies have explored the link between negative SME and memory performance in development and aging. For example, Tang et al. We investigated the effect of age group on the association between negative SME and memory performance in a sample of participants ranging in age from middle childhood to late-life. Participants completed a subsequent memory paradigm, with fMRI acquisition during memory encoding followed by a postscan recognition test. We predicted better performance and larger negative SME in young adults compared to children and older adults. Based on prior literature demonstrating an association between negative SME and memory performance Employing identical stimuli, similar experimental design and analyses protocols we thus addressed our study objective and tested the null hypothesis that the association of negative SME with memory performance does not differ between age groups. This null hypothesis would suggest that the relevance of regions demonstrating negative SME is consistent, across age, in explaining individual variability in memory performance. That is, from middle childhood to late adulthood, regions demonstrating a negative SME similarly support memory performance. The alternative hypothesis, that the association between negative SME and memory performance does differ between age groups, would suggest that the relevance of these regions in supporting episodic memory is different across age groups. That is, these regions may support memory performance in some age groups and not others.We recruited 145 healthy participants from the Metro Detroit community, including younger participants ranging in age from 8 to 25 years  and older adult participants ranging in age from 55 to 85 years . Younger and older participants were recruited in separate studies which were developed in parallel, aiming to align study design parameters between the two. The main experimental task that serves as the basis of this study was first developed for testing with the younger participants and then adapted for testing with older participants. Testing of older and younger participants was conducted within an overlapping time period. Several methodological differences exist in the overall testing of the two cohorts and in the specific adaptation of the experimental paradigm. All analyses were carefully planned to overcome any modifications in experimental design and to optimize generalization across cohorts and validity of this unique lifespan study. A subset of participants in the present sample were also included in the samples of previously published work; that is, a subset of the older adults helped compose the sample in J.M. Hayes et al Data for one older adult was excluded for task non-compliance (miss rate > 93%). We handled missing data with listwise deletion, therefore two participants, one child and one adolescent, were excluded as they did not complete IQ testing. We excluded an additional 33 participants for neuroimaging data quality concerns. Such concerns included excessive motion , fMRI signal dropout, and an unbalanced number of trials between conditions (ratio > 4)  = 4.07, p = 0.004, as the older adults had lower IQ scores than the children and young adults. Gender was included as a covariate in all statistical models, and IQ was included as a covariate in models for which memory performance was the dependent variable.To examine the effect of age we grouped participants into four age groups in our analyses. Three age groups were defined from the sample of younger participants: children (age 8–12 years), adolescents (age 13–17 years), and young adults (age 18–25 years). These cut-offs were chosen to roughly align with general stages of puberty, with children in early puberty, adolescents in puberty, and young adults in post-puberty. Due to sample size the older adults were kept as a single age group (age 55–85 years). We characterized age using groups, rather than as a continuous variable, due to the gap between the upper limit of our younger adults (25 years) and the lower limit of our older adults (55 years). Participant demographics across groups are presented in Participants were presented with indoor and outdoor scenes from a stimulus set used in prior studies Participants were presented with scenes while lying in the MRI scanner. Participants were instructed to make an indoor/outdoor judgment for each scene using a 2-button response box placed in their right hand. In addition, participants were instructed to try their best to memorize the scenes for a subsequent recognition memory test. The scanned study phase was followed by a recognition test conducted outside of the scanner, approximately 30 min after the end of the study phase. At recognition test, participants made a two-part response to each scene. First, participants made judgments on whether the scene presented was “old” or “new”, depending on whether they had seen the picture during the encoding phase. Subsequently, participants indicated their confidence in the “old/new” judgment by making a “sure/not sure” confidence decision. All participants were asked to reflect on how sure they were regarding their “old/new” decision although children, adolescents, and young adults were also specifically instructed to use “sure” in response for a picture they decided was previously seen, if they “remember anything specific about it” or “not sure” if it “just looks familiar”. The older adult participants were asked after each image to decide if they were sure or unsure of their “old/new” decision.All participants studied scenes while in the scanner and given the recognition test outside the scanner though some specific details were different between the samples. All differences in procedure were accounted for in analyses to generate equivalence for comparison across the full age range across the two samples (age 8–85 years). Children, adolescents, and young adults studied 120 scenes in three consecutive runs . Each scene was shown for 3 s followed by an intertrial interval of 0.5 s + 0–8 s . The assignment of 40-scene sets for study or recognition was counterbalanced across participants, with each participant allocated 1 of 6 pseudorandom list orders. Eighty additional scenes  were used as foils in a subsequent recognition test. Older adults studied 80 scenes from the same stimulus set used in the sample of younger participants in one run (40 indoor and 40 outdoor). Each scene was shown for 3.4 s followed by an intertrial interval of 1 s + 0–11 s . Sixty additional scenes were randomly selected from the stimuli pool to be used as foils in the subsequent recognition test. Put succinctly, participants in the younger groups studied 120 scenes during encoding and 80 foils were added during recognition, while participants in the older adult group studied 80 scenes during encoding with 60 foils added during recognition. All participants practiced the task prior to entering the scanner and were reminded of the instructions.Our paradigm utilizes intentional encoding rather than incidental encoding; the latter is often used to minimize differences in spontaneous encoding strategies between older and younger adults. We chose to employ intentional encoding because adults often anticipate a recognition task to follow while children may not. By acknowledging the recognition task, children are brought to a level playing field in expecting a later recognition task.Scenes were presented via an Avotec Silent Vision (SV-6011) projection system and were viewed by participants through a mirror mounted on the head coil. The task was programmed and presenting using PsychToolbox  were indicated by participants for each response at recognition. Trials in which studied scenes were subsequently recognized with high confidence were labelled high-confidence hit trials (Hit-HC), whereas trials in which scenes were subsequently recognized with low confidence were labelled low-confidence hit trials (Hit-LC). Trials in which studied scenes were later classified as ‘new’ were labelled as miss trials (Miss) regardless of the confidence rating. Foils that were falsely endorsed as ‘old’ with high confidence were labelled high-confidence false alarms (FA-HC). Recognition accuracy was determined as the proportion of Hit-HC  – the proportion of FA-HC . Low-confidence responses were not included in the calculation of recognition accuracy as such responses are less likely to indicate reliable discriminability between remembered items and foils All scans were conducted at the Wayne State University MR Research Facility at Harper University Hospital , with a 3-T Siemens Magnetom Verio scanner using a 32-channel Head Matrix coil. T1-weighted whole-brain anatomy images were acquired using a magnetization-prepared rapid gradient-echo sequence. Children, adolescents, and young adults: 192 sagittal slices, repetition time (TR) = 2200 ms, echo time (TE) = 4.26 ms, flip angle (FA) = 9°, field of view (FOV) = 256 mm, 192 × 256 voxels, and voxel size = 1 mm × 0.5 mm × 1 mm. Older adults: 176 slices, TR = 1680 ms, TE = 3.51 ms, FA = 9°, FOV = 256 mm, voxel size = 0.7 mm × 0.7 mm × 1.3 mm).Functional images were acquired using a T2*-weighted gradient-echo sequence . For children, adolescents, and young adults, the encoding task was completed in three consecutive functional runs of 118 volumes, encoding 40 scenes in each, or 120 scenes in total. For older adults, the task was completed in one functional run of 276 volumes, encoding 80 scenes in total.The major consideration in developing our analytic approach was to assure that we can combine data collected with slightly different parameters in the two samples. We note that partial data from each of the samples were included in our prior publications https://fsl.fmrib.ox.ac.uk/fsl/; F = 9.02, p < 0.001, 2Pη = 0.205, as children and older adults demonstrated greater displacement than adolescents and young adults  running on MATLAB (R2016b). Images were motion-corrected, normalized to the Montreal Neurological Institute (MNI) template, and smoothed with an 8 mm full-width half-maximum Gaussian kernel. Additionally, we screened functional images using Artifact Detection Tools (http://www.nitrc.org/projects/artifact_detect/) to identify outlier volumes in pre-processed data. Specifically, an outlier volume was identified if (1) the global mean intensity of the volume was >3 SD from the mean volume intensity of the run, or (2) volume-to-volume difference of a composite motion parameter exceeded 1 mm. Two participants for whom > 20% of volumes were identified as outliers were excluded from analysis (1 child and 1 older adult). In the remaining participants, percent of outlier volumes did not differ between age groups, F = 2.50, p = 0.064, 2Pη = 0.067. Following all exclusions, individual-level models were created for 109 participants.Functional imaging data were then analyzed with the SPM12 package  or without (M = 0.24 ± 0.16) encoding data (T(1 0 7) = 1.028, p = 0.306). Children, adolescents, and young adults completed three encoding runs and overall 120 scenes compared to a total of 80 encoded scenes by older adults. We therefore elected to include only the two runs with the lowest average framewise displacement in the individual-level models of the children, adolescents, and young adults. We made this choice, rather than using the first two runs, to reduce the group differences in motion and utilize more participant data. For example, several children had large motion spikes in either the first or second encoding run. If we opted to use only the first two runs, these participants would be excluded. By instead selecting the runs with the lowest motion, we can discard the run with the spike and use the other two runs. This approach brings the average framewise displacement of the children (the highest motion age group) in line with the other age groups, to the extent that average framewise displacement in children is not significantly higher than that in older adults  included 3 task-related regressors for each run: high-confidence hits (Hit-HC), low-confidence hits (Hit-LC), and misses (Miss). Seven motion regressors  were also included per run. Individual-level GLMs also included an “error” regressor for trials in which the participant made an incorrect indoor/outdoor judgment or did not make a button response; therefore only trials that were given a correct encoding task response were included in group-level imaging analysis. Only these trials were included in task contrasts to better ensure that stimuli included in the analyses were appropriately attended to. For 18 participants the button presses did not register during encoding due to technical difficulties . These data were still included in imaging analyses, in line with prior protocols using these samples r adults .Each encoding event was modeled as an impulse function and convolved with a canonical model of the hemodynamic response function. Temporal derivatives were included in the GLM to account for any temporal shifts in response to the stimuli p < 0.05 FWE). Clusters resulting from these analyses were saved as a binary mask, and individual contrast values were extracted using the positive and negative SME masks as regions of interest  = 3.51, p = 0.029, with accuracy of children significantly lower than that of older adults , therefore the encoding task accuracy results presented here reflect data from the remaining 91 participants. Given the very high encoding accuracy in the rest of the sample, we elected to include all data from the 18 participants for whom encoding judgments were not recorded in subsequent behavioral and imaging analyses.As mentioned above (Methods section 2.5.2), indoor/outdoor judgments were not recorded for a subset of participants due to technical issues  = 4.11, p = 0.008; FA-HC: F = 6.98, p < 0.001). Hit-HC rates were lower for children than young and older adults. FA-HC rates were higher for older adults than adolescents and young adults. Low-confidence hit and false alarm rates did not differ by age group (p > 0.1).High confidence hit and false alarm rates significantly differed by age group  = 8.10, p < 0.001, with recognition accuracy in children and older adults significantly lower than that in young adults (Recognition accuracy (proportion of Hit-HC – proportion FA-HC) across all participants was 0.27 ± 0.14. Recognition accuracy differed by age group, g adults .Fig. 1NeF = 2.51, p = 0.064. Put another way, the difference in encoding judgment reaction time between subsequent high-confidence hits and subsequent misses did not significantly differ by age group. Therefore, we did not include encoding judgment reaction time in our main analyses. See Supplementary We examined effects of age group and subsequent memory outcome  in encoding judgment reaction time. Specifically, we tested whether the outcome effect in encoding judgment reaction time differed by age group (i.e. an interaction effect between age group and outcome). There was not a significant interaction effect between age group and outcome: As noted in the Methods, the current implementation of the subsequent memory task was developed for the younger participants and later adapted for older adult participants. There are, therefore, some procedural differences described there. Of particular note, older participants completed one encoding run of 80 stimuli, while younger participants completed three encoding runs each with 40 stimuli, with the two lowest-motion runs used for analysis. Please refer to the Methods for a more complete description of methodological differences in the subsequent memory task for younger and older participants.negative SME—activation to subsequent Miss relatively above activation to subsequent Hit-HC  = 5.47, p = 0.002, 2Pη = 0.14 (p = 0.001); no other pairwise comparison reached statistical significance. See Subsequent memory effects were assessed by contrasting activations for subsequently remembered items (Hit-HC) with activations for subsequently forgotten items (Miss). In this study we focus on P = 0.14 , controlF = 10.87, p < 0.001, adjusted 2R = 0.27. Negative SME was significantly associated with recognition accuracy after controlling for age group, gender, and IQ: β = -0.43, t = -5.14, p < 0.001  and its addition significantly improved the model (ΔR2 = 0.084). Full results of this analysis are provided in Given that negative SME was associated with recognition accuracy across the whole sample, we investigated whether age group moderated this association by adding an age group by negative SME interaction term to the regression model. The interaction term was significant  and young adults , but not in children  or older adults   b-e. Givet = 3.114, p = 0.002). All other pairwise group comparisons were not statistically significant  and unsuccessful (miss) encoding. From this, we identify subsequent memory effect-associated brain regions across all age groups. However, the question remains of whether the magnitude of subsequent memory effect in these regions account for the variance in memory performance across individuals. For this, we conducted a between-subjects analysis: a regression model investigating the association between subsequent memory effect and recognition accuracy. As both are indicators of successful memory performance, one may expect that they should be associated. However, just because a participant has higher or lower magnitude of effects in specific brain regions during hit compared to miss does not necessarily imply they will have higher or lower frequency of hits compared to misses. It is this question that we have investigated across age groups, and our results suggest that the variance in recognition accuracy accounted for by this within-subject measure (negative SME) differs by age group in a between-subjects analysis. Put another way, we have identified a dissociation in which recognition accuracy and negative SME are associated in adolescents and young adults but not in children and older adults.differences among age groups across the lifespan, we are not able to examine change within individuals as they age. Longitudinal data may show different patterns of aging than are found in cross-sectional data. The current results must be evaluated with this in mind, and replication of these effects in longitudinal data are needed to more strongly support the proposed maturation and decline of the association between negative SME and memory performance. Sixth, in this study we focus on memory sensitivity rather than attempting to assess age group differences in metamemory, or an individual’s knowledge of their own memory. Development of metamemory enables utilization of mnemonic strategies We acknowledge our approach has limitations which must be considered when interpreting the findings. First, some parameters for fMRI acquisition and task design in the children, adolescents, and young adults differ from the older adults . There were additional protocol differences as well: wording of instructions was not identical, and while the individual-level general linear models include an equal number of trials from all participants, these trials were collected across two shorter runs for children, adolescents, and young adults and one longer run for older adults. However, given expected age-group patterns of memory performance and overall neural task effects in expected regions, we believe direct comparisons between the developmental and older adult samples are warranted despite minor methodological differences across groups. Second, relatively few of our participants had enough low confidence hit (Hit-LC) trials for the use of that condition in task contrasts to be sufficiently powered. As a result, we were unable to delineate subsequent recollection (Hit-HC > Hit-LC) and subsequent familiarity (Hit-LC > Miss) effects, which are conflated in the Hit-HC > Miss contrast used in this study. Previous work e.g.  has suggIn conclusion, our findings demonstrate that the association between negative SME (ostensibly reflecting a subset of the default mode network) and memory performance is different across developmental age groups, most strongly between children and young adults, suggesting developmental maturation of successful memory facilitation by the default mode network. While the association was not significant in older adults, neither was it significantly weaker than in younger age groups, thus supporting the need for further investigation into this effect in older adults using a larger sample and more finely-grained age-groupings. Such a follow-up investigation would help clarify whether the developmental effect demonstrated here is mirrored by age-related deterioration in late-life. That said, this work supports a role for negative SME in age-related differences in episodic memory across typical development and potentially across the lifespan.Patrick J. Pruitt: Conceptualization, Formal analysis, Writing - original draft, Visualization. Lingfei Tang: Conceptualization, Formal analysis. Jessica M. Hayes: Investigation, Data curation. Noa Ofen: Conceptualization, Writing - review & editing. Jessica S. Damoiseaux: Conceptualization, Writing - review & editing, Supervision.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Error in Author ListIn the published article, there was an error in the author list, and author’s first and last names were erroneously inverted. The corrected author list appears below.François Poumeaud, Anna Fontanier, Jérémie Dion, Quentin Mathevet, Olivier Cointault, Emmanuelle Uro-Coste, Céline Marty, Florence Dalenc, Pierre Girardie6 and Anaïs Rataboul1*The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


These results remained significant after controlling for gender, residence, only-child status, family financial situation, and paternal education. Significant others in an adolescent’s life play multiple essential roles in forming and developing adolescent behavior and in directly influencing them. To guide the prosocial behaviors of middle school students and reduce delinquent behavior, we should build harmonious parent—child, peer, and teacher–student relationships, teach according to their aptitudes, pay attention to particular groups and strengthen psychological health education to develop their self-esteem and self-confidence.Adolescent behavior is closely related to academic and long-term personal development, and adolescents are vulnerable to the influences from people around them. This study aimed to analyze the factors and mechanisms that influence the behavior of adolescents. It examines the impact of family, teachers, and peers on adolescent prosocial behavior and misconduct. Data were obtained from the China Education Panel Survey (CEPS) follow-up data (2014–2015 school year) and 7835 middle school student participants were used for analysis. Structural equation modeling (SEM) was used to explore the influence and mechanisms of family, teachers, and peers on the development of adolescent social behavior. The findings showed that parental relationships, parental discipline, teacher supervision, and positive peer behavior were positively associated with adolescent prosocial behaviors and reduced the incidence of delinquent behaviors, while frequent home–school contact was associated with misconduct (all  The wellness and development of adolescents are increasingly valued, especially in terms of psychological and behavioral development . HoweverAs socialization progresses, adolescents gradually adapt to their social roles, acquire social skills and exhibit different social behaviors. Scholars generally agree that social behavior can be divided into two types: prosocial behaviors and misconduct . WeisberEarly research has found that various parenting methods, such as inductive discipline, empathic didactics, positive behavior modeling, and responsibility allocation, help promote social development. Parents can have a positive and profound influence on their children’s behavioral development for three main reasons: Firstly, by investing in and guiding their children’s education, parents pass on their ideas about the importance of education, allowing their children to internalize these values and put them into practice in daily behavior ,17,18. SAdolescent students spend most of their time in school, apart from home. During this period, teachers take on the responsibility of supervising children in place of parents and significantly impacting young people’s normal development . TeacherPeers are a vital interaction group in adolescent development, and their behavior can significantly influence behavioral developmental tendencies . AdolescThe development of adolescent behavior has received increasing interest in the literature, and a large number of a priori findings have laid the foundation for future theoretical and practical products. However, there is still room left for improvement. Most studies have taken a single perspective on the influence of adolescent delinquency, with the independent and dependent variables in only one dimension. However, the human environment is complex and interactive, with parents, teachers, and peers influencing adolescent values and behaviors differently. There are still controversies and gaps in previous research, with scholars not reaching a consensus on parental and teacher supervision and insufficient research on whether parent–school contact impacts adolescent behavioral development. Whether the mechanisms of influence of significant others on adolescents in different cultural contexts remain reliable remains to be studied.Thus, in this paper, parents, teachers, and peers were all considered exogenous variables, and the endogenous variables included both positive and negative aspects of adolescent behavioral development. Structural equation modeling was used to investigate the underlying mechanisms.This study used data from the China Education Panel Survey (CEPS), a large-scale nationally representative tracking survey designed and implemented by the National Survey Research Centre (NSRC) at Renmin University of China. The survey was conducted at the student, parent, teacher, and school levels to investigate adolescents’ educational performance and behavioral development. CEPS used the 2013–2014 school year as the baseline, and 28 county-level units  were randomly selected as survey sites from across the country using the average level of education of the population and the proportion of the nonresidential, floating population as stratification variables. The survey was implemented on a school basis, with 112 schools and 438 classes randomly selected for the study in the selected county-level units. All students in the sampled grades were enrolled, and a total of approximately 20,000 students were surveyed in the baseline survey. Two waves of the survey were conducted: a baseline survey for the 2013–2014 school year and a follow-up survey for the 2014–2015 school year. This study selected follow-up data from the 2014–2015 school year, with essential information matched to the baseline data. We further excluded participants with missing data on critical variables, resulting in a final analysis sample of 7835 participants. All of the participants were in eighth grade.The endogenous variable in this study was the adolescents’ social behavior, which, in light of other studies in the literature, includes two main aspects: prosocial behavior and misconduct. Therefore, we used question D1 in the student questionnaire to measure pro-sociality, namely, “Whether you could do the following in the past year?” including the three observation variables of “helping the elderly” (D1-1), “obeying orders, voluntarily queuing” (D1-2) and “being sincere and friendly to others” (D1-3), which were used to define the latent variable of “pro-sociality”. Question D2 measured misconduct: “In the past year, have you had any of the following problems?” including “swearing (D2-1), quarreling (D2-2), fighting (D2-3), bullying (D2-4), feeling grumpy (D2-5), being inattentive (D2-6), truancy (D2-7), plagiarizing homework (D2-8), smoking and drinking (D2-9), going to internet cafes and game halls (D2-10)”. These ten observational variables define the latent variable “misconduct”. The questions above were scored on a five-point Likert-type scale from “never = 1” to “always = 5”, with a higher score indicating a higher frequency of occurrence.After considering the research needs and the validation factor analysis results, the endogenous latent variables were adjusted as follows: The latent variable “pro-sociality” was defined using the two observed variables, “mannered” and “friendly”, denoted by D1-2 and D1-3, respectively. D1-1 was excluded because its factor loading was below 0.55. In addition, the latent variable “misconduct” was defined as the two observed variables, “self-behavior” and “language & psychology”. The former was expressed by the sum of questions D2-3, D2-4, D2-7, D2-8, D2-9, and D2-10, forming a variable with values in the range 6-30 . The second was represented by the sum of questions D2-1, D2-2, D2-5, and D2-6, forming a variable with values in the range of 4–20 .The family effect was represented by three latent variables: parental relationship, parental discipline, and parent–school contact. The parental relationship was measured by the two observed variables, “relationship with father” and “relationship with mother”, and was scored on a three-point scale of “not close = 1, average = 2, very close = 3”. Parental discipline was assessed by question A20, “Are parents strict in the following matters?” including “homework (A20-1), school performance (A20-2), friendships (A20-3), dress (A20-4), time spent online (A20-5), and time spent watching television (A20-6)”. These six items were measured on a three-point scale, with “do not care = 1, care but not strictly = 2, care a lot = 3”. Based on previous research, these were combined into “study” (sum of A20-1 and A20-2), “life” (sum of A20-3 and A20-4), and “electronics” (sum of A20-5 and A20-6) as the three observed variables for “parental discipline”. Finally, “parent–school contact” was denoted by the two observed variables “parents initiate contact with teachers” and “teachers initiate contact with parents (parents passively contact teachers)” during the school year, with the values “never = 1, once = 2, two to four times = 3, five or more times = 4”.The “teacher supervision” dimension used the teacher’s classroom management and attention toward the students and corresponded to question B5, “Do you agree with the following statements about the main course?” Three courses, mathematics, Chinese, and English, were included, each measuring three aspects of the teacher’s attention, questioning, and commendations in the class. A total of nine questions were asked, with a four-point scale ranging from “totally disagree = 1” to “totally agree = 4”. Thus, the three observed variables that make up the latent variable “teacher supervision”, namely, “attention”, “question”, and “commendation”, are the sum of the related questions from the mathematics, Chinese and English teachers at each level.The peer effect was in the form of two latent variables, “positive peer effect” and “peer misconduct”; question D11 was used to evaluate this dimension. “Did any of the following happen to your best friend?”, including “excellent grades (D11-1), hard-working (D11-2), desire to go to university (D11-3), truancy (D11-4), indiscipline (D11-5), fighting (D11-6), smoking and drinking (D11-7), frequenting internet cafes and game halls (D11-8), early love (D11-9), dropping out of school (D11-10)”. Assignment was on a three-point scale, from “none of them = 1” to “many of them = 3”. After adjustment, we redefined it as follows: The latent variable “positive peer effect” was represented by three observed variables, which were “excellent grades” (D11-1), “hard working” (D11-2), and “purposeful” (D11-3). In addition, the latent variable “peer misconduct” was expressed in terms of the two observed variables “school performance” and “life behavior”. “School performance” was expressed as the sum of questions D11-4, D11-5 and D11-10, forming a variable with values in the range 3–9 . Similarly, “life behavior” was represented by the sum of questions D11-6, D11-7, D11-8 and D11-9, forming a variable with values in the range 4–12 .We conducted confirmatory factor analysis (CFA) for the instruments used to measure parental level , teacher level (teacher supervision), peer level (peer positive behavior and misconduct), and self-level . The goodness-of-fit index of the parental level was as follows: (1) chi-square/df  =  16.74; (2) RMSEA  =  0.045; (3) GFI  =  0.99; (4) AGFI  =  0.98; (5) TLI  =  0.96; (6) CFI  =  0.98. The goodness-of-fit index of the teacher level was as follows: (1) chi-square/df  =  6.288; (2) RMSEA  =  0.049; (3) GFI  =  1.00; (4) AGFI  =  0.99; (5) TLI  =  0.98; (6) CFI  =  0.99. The goodness-of-fit index of peer level was as follows: (1) chi-square/df  =  23.11; (2) RMSEA  =  0.053; (3) GFI  =  1.00; (4) AGFI  =  0.98; (5) TLI  =  0.99; (6) CFI = 1.00. The goodness-of-fit index of the self level was as follows: (1) chi-square/df  =  12.01; (2) RMSEA  =  0.038; (3) GFI  =  1.00; (4) AGFI  =  0.99; (5) TLI  =  0.99; (6) CFI = 1.00. These outcomes indicate that all the instruments have good validity.Control variables include gender, residence, being an only child, family financial situation, and parental education level. Age was not included as a control variable, considering that all students in the sample were in grade 8, and their ages did not differ significantly. After reassignment, the dichotomous variables were gender , residence  and only child . The family’s financial situation was divided into five levels, from “very poor = 1” to “very rich = 5”. Paternal education is converted from education to the corresponding number of years, specifically, “no schooling = 0, elementary school = 6, middle school = 9, special secondary school/technical school = 11, vocational high school/general high school = 12, junior college = 15, undergraduate college = 16, graduate and above = 19”.This study used Stata version 15.0 for data processing and analysis. First, a descriptive statistical analysis of the variables was performed. Correlation analysis was also carried out for the main variables. Second, the structural equation model (SEM) was used for modeling estimation and fitting.2), root mean square error of approximation (RMSEA), and comparative fit index (CFI).Specifically, we defined exogenous latent variables at the family level, the teacher level, and the peer level. The endogenous latent variable was the development of self-social behavior. Before constructing the structural equation model, a validation factor analysis was conducted on the latent variables to validate the measurement model, excluding observed variables with factor loadings less than 0.55. Then, path diagrams were constructed by including all latent and control variables following the research needs and relevant hypotheses. After estimating the parameters , direct effects on the development of self-social behavior at the family, teacher, and peer levels and the degree of influence between the variables were derived. Finally, the structural equations were corrected (MI ≥ 3.841) and tested for overall goodness-of-fit. The goodness-of-fit was verified using the chi-square test . Among them, 3972 male students (50.71%) and 3962 female students (49.29%) were included. As many as 6439 students  were from the prefecture in terms of residence distribution, with 1396 students (17.78%) from outside the prefecture occupying the sample. Concerning family structure, the number of “one-child family” was 3563 (45.48%), and the number of multiple-children families was 4272 (54.52%). When the family’s financial situation was considered a continuous variable, the mean was 2.95 (SD 0.60), indicating that most households were in the medium range. After converting parental education level to specific years, the mean value is 10.41 (SD 3.29). The basic sample sociodemographic characteristics (covariates) are illustrated in p < 0.01) and were negatively associated with the development of misconduct (p < 0.01). The better the relationship between the child and the parents was, the better the development of prosocial behavior.p < 0.01) and negatively related to misconduct (p < 0.01), suggesting that the stricter parental discipline was with regard to school, life, and electronics use, the better the child’s prosocial development and the less delinquent behavior (p < 0.01). Notably, there was a positive association between parent-initiated teacher contact and children’s prosocial development (p < 0.01), and the association with delinquent behavior did not reach statistical significance. However, for children whose teachers-initiated contact with their parents, there was only a positive association with delinquent behavior (p < 0.01), and the other associations did not reach statistical significance. The family plays an essential role in the behavioral development of adolescents.Similarly, parental discipline showed similar results, with parental discipline being positively related to children’s prosocial development (p < 0.01). Simultaneously, these three areas were negatively associated with adolescent misconduct (p < 0.01). They demonstrated that teachers also play an essential role in the development of adolescent behavior.At the teacher level, the correlation coefficients between the three observed variables, teacher attention, teacher questions, and teacher commendations all exceeded 0.5, suggesting that changes in any one dimension may cause changes in other dimensions. The data also showed that teacher attention, questioning, and commendations all positively affected adolescent prosocial development (p < 0.01) and negatively correlated with the development of misconduct (p < 0.01). Positive peer behavior was positively correlated with adolescents’ development of pro-sociality (p < 0.01) and negatively correlated with the development of misconduct (p < 0.01). Conversely, peer misconduct was negatively correlated with adolescent prosocial development (p < 0.01) and positively correlated with misconduct (p < 0.01). It also indicated that peer groups influence adolescent behavioral development, and it is necessary to pay close attention to group behavior to prevent and reduce destructive behaviors.Even more significant is the impact of peer behavior on the development of adolescent social behavior. The peer effect consisted of two latent variables, positive peer behavior and peer misconduct. The three observed variables represented positive peer behavior with correlation coefficients greater than 0.5, and the two observed variables represented negative peer behavior with correlation coefficients greater than 0.5. At the same time, positive peer behavior was positively correlated with adolescents’ development of pro-sociality , with the RMSEA value being less than 0.05, an acceptable result, and the CFI exceeding the critical value of 0.90, giving an overall good fit to the model.After constructing the structural equation model , we esti2R) of the independent variables on the two dependent variables were 0.483 and 0.397, respectively, which reached a moderate level. The active effects on adolescent prosocial development were parental relationship , parental discipline , teacher supervision , and positive peer behavior . Peer misconduct is detrimental to adolescent prosocial development . The impact of parent-school contact in this area did not reach a significant level. With regard to adolescent misconduct, parental relationship , parental discipline , teacher supervision , and peer-positive behavior  significantly reduced the occurrence of such events. Parent–school contact  and peer misconduct  were positively associated with adolescent misconduct. As a result, good family relationships, parental and teacher supervision in daily life and learning, combined with good peer behavior, can effectively reduce adolescent misconduct and help adolescents develop good behavior and healthy personalities. A peer group’s delinquent behavior exacerbates the tendency for adolescents to behave inappropriately, and too much contact with undesirable peers is detrimental to the development of prosocial behavior. It can even lead to more severe undesirable behavior.Concerning the relationship of the independent variables with the dependent variables , the coep < 0.01) and more likely to exhibit delinquent behavior . Adolescents whose permanent residence was local were less prosocial than nonlocal adolescents , and the difference in delinquent behavior was not significant. Only children also outperformed multiples in terms of behavioral development  and had a lower incidence of delinquent behavior . The better the family’s economic conditions are, the better the children’s social performance in terms of pro-sociality . Finally, the level of paternal education also significantly promoted children’s social behavior (β = 0.083 p < 0.01) and reduced delinquent behavior .This study’s covariates were gender, residence, being an only child, family financial situation, and parental education level. Specifically, boys were less prosocial than girls (β = −0.055, Our study investigated the influences of significant others on adolescent social behaviors by looking at three aspects: parents, teachers, and peers. The results show that parental relationships, family–school contact, parental discipline, teacher supervision, and peer group behavior all have direct and significant impacts on adolescent behavior development, suggesting that significant others play different roles in the development of adolescent behavior.The study results showed that good parent—child relationships significantly improved children’s behavioral development and reduce the incidence of delinquent behavior. This finding is similar to the results of other studies. A good family relationship creates a warm, stable communication channel that allows parents to communicate openly with their children, which subconsciously influences adolescents’ personalities and behavior ,32. SimuWe found as teacher supervision increased, the better the adolescent performed at prosocial behavior as measured by teacher influence, with a corresponding decrease in delinquent behavior. One of the groups with which adolescents are most in contact, teachers, also have a crucial influence on them. Teacher discipline often complements parental supervision, and when adolescents are out of the reach of their parents during school hours, teachers take over this role and correct adolescent behavioral development . This reThe findings further confirmed that there was also a significant role in the influence of peer behavior on adolescent behavioral development. Prosocial behavior in peer groups promoted acceptable behavioral norms in oneself, while poor behavior in peers led to delinquency in oneself. An explanation for this can be found in differential interaction theory. People learn and imitate their peer group’s behavior and develop similar values to the group to fit better with the group . The finDifferences in the behavioral development of adolescents are also reflected in several other areas. Boys are more likely to exhibit delinquent behavior than girls. Scholars generally believe that this is related to biological structure and the state of psychological maturity. Girls mature earlier than boys and tend to deal with problems in a rational way and behave in a more self-disciplined manner. Boys, on the other hand, are more likely to adopt impulsive behavior . In termAlthough this study yielded a large number of significant findings, several limitations remain. Firstly, although the CEPS data used in this study are authoritative, it is not a questionnaire that professionally measures adolescent behavioral delinquency and falls short in terms of professionalism and relevance. Secondly, the latest available data from the CEPS survey are still from the 2014–2015 follow-up period. With the changes in social life and the economy, the continuation of the global epidemic will bring more challenges to the psychological and behavioral development of adolescents, so a new wave of investigation is necessary. Thirdly, the mechanism of action explored in this study is the direct influence of significant others on adolescent behavioral development, but the indirect factors are challenging to identify and exclude. Future research could be based on data surveys specifically targeting adolescent behavioral problems, while more complicated and pathways of action need to be established, and more comprehensive models need to be developed.Using mainly CEPS data, this study aims to analyze the factors influencing adolescent behavior development and the mechanisms at play. The study examines the influence of family, teachers, and peers on adolescents’ prosocial and delinquent behaviors. It is intended to provide a theoretical basis for the development of theory, the formulation of policy, and the improvement of social work. This study’s findings have theoretical and practical implications, and the results enrich the empirical research for this age cohort in China. The results can also positively improve systems and measures to regulate the behavior of young people at different levels. It is necessary to pay attention to the values of significant others in the process of adolescent development to provide a healthy, harmonious, and stable environment for adolescents to grow up in.