Humanmilk (HM) lipidomestability during storage is crucial inlipidomic studies to avoid misinterpretations. Facing the lack ofcomprehensive work on the HM lipidome stability, we performed a studyon a potential alteration in the lipid profiles of HM samples storedunder different conditions. An untargeted LC-Q-TOF-MS-based approachwas applied to study the influence of storage conditions as well asthe interaction of the storage temperature and time on HM lipid profiles.The samples were stored for 4–84 days at temperatures in therange from 4 to −80 °C and also were exposed to up tothree freeze–thaw cycles. The results showed that the storageat 4 °C for just 4 days as well as being subjected to three freeze–thawcycles can lead to a change in the content of lipids. The observeddifferences in levels of some lipid species in samples stored at −20°C in comparison to the concentration level of those lipids insamples stored at −80 °C were not statistically significant,and inter-individual variance regardless of sample storage conditionwas maintained. The storage of HM samples at −20 °C forup to 3 weeks and −80 °C for up to 12 weeks ensures samplelipidome stability. This can lead to erroneous biological interpretationsand false conclusions.Lipidomics is definedas the field of “omics” researchfocused on the comprehensive and quantitative analysis of lipids inbiological materials. In a lipidomic study, it is expected that observeddifferences in lipidome reflect real biological changes and do notoriginate from technical variability. This corresponds also to theHM lipidome studies, when the HM lipid composition is monitored toreveal changes in the course of lactation, between individual mothersor as a response to diet.Lipidomic study design should includethe control and minimizationof unwanted variation in sample composition to prevent any errorsin data interpretation. One of the major sources of error in lipidomicstudies is an inappropriate pre-analytical process including samplecollection and handling (storage time and condition and freeze–thawing)that may produce degradation or conversion of lipids. Artificial variationin lipid concentration can lead to incorrect interpretation of thedata obtained from lipidomic experiments. In an LC–MS-basedlipidomic study, the best practice is to analyze the complete sampleset in a single batch. Due to that, typically, biological samplesare collected and kept in a freezer for various periods ranging fromseveral days to even a few months before analysis. Nevertheless, suchpractice is appropriate only if lipids contained in the analyzed samplesare stable and thus no alteration in lipid concentration occurs duringstorage.8 However, it is not available for HM samples. A previousresearch that studied the effects of storage on HM components focusedmainly on HM macronutrient content. The lipid fraction was analyzedas total fat  or the total amount of lipid classes.12 In only one research, besides total fat analysis, the content ofindividual unesterified fatty acids was determined.13The importance of the pre-analytical aspects for thelipidomicstudy output resulted in the establishment of the collection and handlingprotocols for biofluids such as blood and urine.To follow up potential influence of sample storage,the stabilityof lipid profiles in HM samples, which were stored for different periodsand under various temperature conditions, were investigated usinguntargeted lipidomics. HM samples were collected from five healthyfemale volunteers, divided for storage under different conditions,and further analyzed by reversed-phase liquid chromatography coupledwith mass spectrometry (LC-Q-TOF-MS) at intervals. The sets of sampleswere stored under different conditions. Three additional sets of thesamples underwent a series of up to three freeze–thaw cycles.We applied an analytical approach that allowed simultaneous detectionof high-abundance glycerolipids and low-abundance phospholipids inone LC–MS run. The comparative analyses were performed withthe use of chemometrics and statistical tools in terms of lipid compositionalterations among several lipid classes depending on storage conditions.Research ethics approval was obtainedfrom the Human Research Ethics Committee of the Medical Universityof Gdańsk, Poland .Supporting Information.Reagentsand chemicals usedin this study are described in the HM samples were donatedby five healthy female volunteers being at different lactation periods.The HM samples were collected by the full expression of one breastusing an electronic breast pump at approximately 10 a.m. After collectionand gentle shaking, approximately 10 mL of HM was transferred intosterile disposable tubes and delivered to the laboratory .The time between the collection, sample aliquoting, and placing inthe freezer constituted approximately 4 h. Written informed consentwas obtained from each participant. The characteristic of collectedsamples is presented in A quality control (QC) sample was immediatelyprepared by poolingequal volumes of 2 mL from each of the five collected HM samples.Afterward, sets of 750 μL aliquots were prepared for each HMsample and the QC and extraction blank sample  into sterile Eppendorf tubes. Each HM sampleand blank extraction sample was aliquoted into 11 separate tubes,while the QC sample was aliquoted into 17 separate Eppendorf tubes.In consequence, 10 sets of samples consisting of 6 HM samples, QCsample, and blank extraction sample were obtained.The setsof samples were stored under different conditions. Concerningstorage temperatures, the samples were exposed to +4, −20,and −80 °C. Concerning the storage duration, the sampleswere stored at +4 °C for 4 days; at −20 °C for 4,7, and 14 days; and −80 °C for 7, 21, and 84 days. Tostudy the influence of freezing and thawing on the stability of lipidomeof HM samples, three sample sets underwent a series of three freeze–thawcycles. All sample sets were frozen at −80 °C. On thefirst day, each set was left to thaw for 1.5 h at room temperature.Then, after thorough mixing, two sets were returned to the freezerand one was analyzed. On the second day, two sample sets were leftto thaw for 1.5 h and thoroughly mixed in a vortex mixer. Afterward,one set was returned to the freezer and one was analyzed. On the thirdday, the last sample set was left to thaw for 1.5 h, thoroughly vortexed,and analyzed. Consequently, three sample sets have undergone fromone to three freeze–thaw cycles.14 Details are describedin the Supporting Information.HM samples were thawed at room temperaturebefore extraction. Extraction of lipids was based on a previouslypublished dilution-enrichment LLE- and SPE-based strategy with smallmodifications.14 Details are describedin the Supporting Information. A volumeof 10 μL was taken from each lipid extract and pooled to formthe quality assurance (QA) sample, which served to assess the analyticalacquisition quality within a batch.15 HMsamples were injected randomly, and QC samples were injected everyfive injections to control LC-Q-TOF-MS stability and to assess dataquality. Also, PC 18:0/18:0 was added to the final extracts (1 μg/mL)and was used as an internal standard to control the stability of theLC–MS system.HM metabolite profiling was performedwith an LC-Q-TOF-MS system: an Agilent 1290 LC system equipped witha binary pump, an online degasser, an autosampler, and a thermostatedcolumn compartment coupled to a 6540 Q-TOF-MS with a dual electrosprayionization (ESI) source .To monitor global HM lipidome, reversed-phase chromatography has beenimplemented. The chromatographic and mass spectrometry condition waspublished previously.3 Detailsregarding feature extraction, data alignment, and filtration are includedin the Supporting Information. Only molecularfeatures (MFs) that fulfilled the criteria of frequency  andMF’s volume %RSD (<30%) in all analyzed QC samples (3 extractionreplicates) were included in further statistical and chemometric analysis.Finally, peak volumes of 28 TGs, 15 DGs, 4 lysoPCs, 2 lysoPEs, 4 PCs,4 PEs, and 6 SMs were included in the calculation. Statistical tests,%RSD, and fold change calculations were conducted using the peak volumeor percentage relative amount of lipids within the specified lipidclass  by dividing the lipid species peak volume by thesum of the peak volume of all lipid species detected within the specificclass). The statistical analyses  andchemometric analyses including PCA were conducted using the Metaboanalystonline package . The data for the analysis in Metaboanalyst 4.0 were prepared inMicrosoft Excel 2016 software .The peak areas ofthe identified humanmilk lipids from the raw LC–MS data were obtained using theBatch Targeted Feature Extraction algorithm implemented in the AgilentMassHunter Workstation Profinder 10.0 . As an input, self-prepared lipid database containingmolecular formulas, monoisotopic masses, and retention time informationof HM lipids was used. Lipids included in the database were identifiedby comparing the mass accuracy of potential lipids against the customdatabase (Δ5ppm tolerance) and manual interpretation of theobtained MS/MS spectra of HM samples.20In this study, we investigatedthe influence of sample storageconditions on the stability of lipidome in HM samples employing an untargetedlipidomic approach. We focused on the main components of the HM lipidfraction: phospholipids  andTGs. The DGs that originate majorly from the TG lipolysis performedby human milk lipoprotein lipases present in raw milk were also includedin the analysis.21 Theintegral experimental design is detailed in We studied the lipidome stability of samples stored at +4°Cfor 4 days; at −20 °C for 4, 7, and 14 days; and at −80°C for 7, 21, and 84 days. While it is often not possible toanalyze the HM samples on the day of collection and typically samplesare frozen immediately after collection, samples stored at −80°C were used as a baseline reference in this study. Reports regardingmetabolome stability studies of other biological samples had previouslyshown that this temperature enables one to maintain the stabilityof metabolome.We compared the lipid profiles of the studied samplesstored underdifferent conditions and evaluated variation of the lipid contentsby the calculation of (i) the relative standard deviation (%RSD) ofthe volumes of MFs detected under a specific temperature condition(−20 vs −80 °C) and (ii) the fold change of thecontent of lipid species between a group of samples stored under differenttemperatures . Theinter-individual variance regardless of sample storage condition wasinvestigated using PCA visualization and statistical tests.Finding an appropriate method of datanormalization for glycerolipids (both diacylglycerols and triacylglycerols)was complicated due to the great diversity of lipid concentrationlevels detected in samples stored under 4 °C and other temperatures.Comparison of % relative amounts  of a given lipid caused falsificationof lipidomic variances because of the significant decrease and increasein content of a few TG and DG, respectively, with simultaneous lackof or only small changes in the concentration level of other TG andDG in samples stored at 4 °C. Due to that, comparison of lipidprofiles of samples stored in 4 and −80 °C was performedon peak volumes instead of relative amounts. To avoid bias in thedata arising from LC-Q-TOF-MS signal batch intensity differences,only samples with similar peak areas of the internal standard (%RSD< 15%) were included (first 3 weeks of the experiment) in thispart of the data analysis.n = 5) and stored separately at −20 °C and −80°C  during different durations by the calculation of the relativestandard deviation (%RSD) of the MF volumes. The results showed thatall of the detected TGs (n = 28) had acceptable %RSDvalues among all samples stored at −20 and −80 °C among all women(below 20% in the case of samples stored at −80 °C andbelow 30% in the case of samples stored at −20 °C). Dueto that, in subsequent data analysis, samples stored under specifictemperature conditions for different storage times were consideredtogether as one group of samples. The calculated %RSD values are presentedin Table S1.First, we studied the stability ofthe TGs contained in the samples collected from the individual women(n = 28) were not statistically significantlychanged (p < 0.05) with a fold change below 25%(except TG34:1 (p < 0.005), TG40:1 (p < 0.005), and TG44:2 (p < 0.005) that wereindicated as significantly changed among samples donated by W18).The fold changes of TG species peak volumes are visualized as a heatmap in To further study thedifference between the samples stored at −20and −80 °C, we performed a statistical test (Mann–Whitneyunpaired test). The % relative amount of TG species in samples storedat −20 and −80 °C was similar, all TGs includedin the test (n = 5) and storedat 4 °C  and −80 °C  was investigated. Thecalculation of the average fold change of the MF peak volumes indicateda significant decrease in the concentration level of many TGs in samplesstored at 4 °C in comparison to samples stored at −80°C. Importantly, the value of calculated fold change was at adifferent level in various women, as shown in In the next step, the stability of the TGs contained in thesamplescollected from the individual women in samples stored at 4 °C in comparison to the total DG’sMS signal in samples stored at −80 °C. The total MS signalof DGs detected in HM samples stored at 4 °C was higher (foreach woman at a different degree) than the total MS signal for DGsof HM samples stored at −80 °C, as shown in In samples stored at4 °C, the highest decrease in the peakvolume was detected for TGs with a maximum of two unsaturated bonds.According to this observation, the highest increase in concentrationlevel of DGs with at most two unsaturated bonds was expected.3 Thus, even a small reduction of the content of several TGs withthe same DG in the structure may produce a high increase in this particularDG species concentration.Interestingly, the highest increase in peak volume was observedfor DGs with at least two unsaturated bonds. Linking the concentrationlevel of individual TGs with their chemical structure and observedfold change clarified this discrepancy. The evaluation of TG structuressuggests that DGs with at least two unsaturated bonds could be producedfrom the breakdown of TGs more frequently than others DG species.Moreover, HM contains a higher number of abundant TGs with more thantwo unsaturated bonds than those with less than two unsaturated chemicalbonds (which are low-abundant TG species).p < 0.005) and DG36:3 (p < 0.005)present in samples collected from W13A). The calculated %RSD valuesare presented in Table S2.The comparison of DG data obtainedfrom samples stored at −20and −80 °C demonstrated a similar trend to results obtainedas an outcome of TG data analysis. The results showed that all ofthe detected MFs in samples stored at −20 and −80 °Chad acceptable %RSD values (below 20%) and did not differ at a statisticallysignificant level under a storage condition , two lyso-glycerophosphocholines(lysoPEs), four diacylglycerophosphocholines (PCs), four diacylglycerophosphoethanoloamines(PEs), and six sphingomyelins (SMs). Here, we observed that the contentof all examined phospholipids in the studied HM samples was not affectedby any of the applied storage conditions. The relative amount of particularlipids was at the same level regardless of storage condition 4A.Therefore, peak volumes of MFs detected in all samples stored underdifferent temperatures and for various storage times  were included to calculate %RSD values. The comparisonof %RSD value of the percentage relative amount of lipid species withina specific lipid class revealed acceptable %RSD values (below 15%)among all samples stored at 4, −20, and −80 °Camong all women (except lysoPC 18:0 with %RSD below 20% in samplesdonated by W4). A diagram with calculated %RSD values is presentedin p < 0.05).Althougha small number of samples do not allow for the reliable evaluationof the inter-individual differences between women, we used collecteddata to investigate if the observed lipidomic differences betweenwomen are maintained independently of the storage conditions. Here,PCA was used to visualize the variation in the lipid profiles amongthe collected HM samples stored at both −20 and −80°C. The ANOVA unequal variance test was used to determine statisticallysignificant changes  amongsamples stored at −20 °C. The differences in lipid compositionbetween individual mothers were equal under both storage conditions:at −80 and −20 °C . A table with calculated p values is included in the Supporting Information (Table S3).First, weanalyzed the data set containing only TG profiles of HM samples. PCAshowed a similar, clear grouping of milk samples of each participantindicating that inter-individual variance between different motherswas maintained, regardless of storage condition. The results of theunsupervised analysis are presented in the p values were not equal (Table S4). The grouping of samples at PCA score plots slightly differed for−20 and −80 °C storage temperatures . Points representing lipid profilesof HM samples collected by a woman in the 13th month of lactation(W13B) group separately from the samples of other participants inthe PCA score plot were generated based on samples stored at −20°C. Interestingly, in the PCA score plot generated based on samplesstored at −80 °C, those samples group together with samplescollected by a woman in the 8th and 13th month of lactation (W8 andW13A). Nevertheless, a detailed examination of the results revealedthat the intra-individual differences can still be observed in samplesstored in −20 °C and also in −80 °C differences in the concentration level of DGs and TGs contained insamples stored at −20 °C compared to samples stored at−80 °C. Those differences did not affect the results oflipidomic comparative analysis of HM samples collected by women beingat different lactation periods. Comparison of lipid profiles revealedthe same significant differences between individual mothers regardlessof the storage temperature. However, it should be noted that the presentedherein study included only samples that were stored at −20°C for a maximum of 3 weeks and −80 °C for a maximumof 12 weeks. The research mentioned before, reporting that storageat −20 °C does not prevent lipid composition alterations,included storage of HM samples for several months.12 Reports suggesting that HM lipid composition is stable in samplesstored at −20 °C for up to 3 months can be also found.13 Those discrepancies between scientific findings might be a resultof different sample sizes, investigated features , and applied analytical techniques . Further studies on lipidome stability at −20°C are necessary to clarify lipid composition alteration underthis condition. Fat loss might be attributed to its adherence to thecontainer, lipolysis, or lipid peroxidation.22 Thus, all those features should be included and investigated ona representative number of HM samples.We observed that 4 days of storing samples at 4 °C significantlyaltered the concentration level of DGs and TGs  when compared to storageat −80 °C. This is contrary to other researchers who studiedthe loss of TG in HM samples and stated that the TG concentrationremains stable during storage at 4 °C. However, they investigatedthe level of the total concentration of TGs using a chemistry-routineanalyzer with the colorimetric test in samples stored at 4C for only48 h.23 Generally, the activity of the lipase dependson various factors, e.g., the presence of compounds that decreaseHM lipase activity  or milk fat globule(MFG) size distribution and composition.25 Therefore, observed differences in the TG disruption might be aresult of all these factors that have an impact on lipase activity.Additional studies focused on the inter-individual variation of lipaseactivity may be helpful to clarify those differences.The observed alterationswere not at the same levels in studiedwomen. The content of TGs that underwent breakdown was different inthe samples collected from different women. These results indicatethat alteration of lipid composition during storage can be associatedwith lipase activity present in HM. The huge inter-individual variabilityamong HM lipase activity was already mentioned in a previous study.Multiple freeze–thawingof the biological sample can result in the degradation of lipid componentsand falsify the data as a consequence. Nevertheless, the study ofthe impact of such sample treatment on the stability of HM lipidomesamples by the analysis of each lipid species separately was not carriedout so far. We have examined the effect of up to three freeze–thawcycles on HM lipidome stability. Three samples collected from eachwoman underwent freeze–thaw cycles over 1 day, and subsequentlyall 15 samples were analyzed in a single batch. We compared the TG,DG, and phospholipid profiles of HM samples separately to investigatethe impact of freeze–thawing cycles on HM lipid stability.The PCA was implemented to investigate variation in HM lipidomic profiles.Supporting Information in Table S5, whereas the changes in the contribution of lipid species amongclass are shown in Supporting Information in Table S6).First, raw data sets containing information on TG peak volumesdetected in all studied HM samples that underwent freeze–thawcycles were analyzed. PCA was applied to visualize the variance betweenthe analyzed samples. The obtained score plots are presented in 23Freeze–thaw cycles resulted in the alterationof the lipidconcentration level in HM samples. The decrease in TG concentrationlevels indicates the progressive breakdown of those molecules to DGswith subsequent freeze–thaw cycles. The increasing concentrationlevel of DGs with subsequent freeze–thaw cycles confirmed thevalidity of this theory. According to the literature, freeze–thawcycles do not activate or inactivate HM lipases but disrupt the milkfat globule membrane and simplify the access of HM lipases to theTGs contained within the core of the fat globule that could be thereason for their progressive hydrolysis.Supporting Information in Table S7. The stability of the contentlevel of two phospholipids regardless of the number of freeze–thawingcycles is demonstrated in PCA was performed also to investigate the differences andsimilaritiesbetween phospholipid profiles of HM samples that underwent freeze–thawingcycles. The obtained 2D PCA score plot is shown in The results shown here indicate that storingHM samples for upto 12 weeks at −80 °C might be considered to be suitableto ensure HM lipidome stability, while for a shorter storage period(<3 weeks), storage at −20 °C can be considered. Wehave shown that there are no statistically significant differencesbetween the lipid profiles of HM samples stored at −20 °C(for up to 3 weeks) and −80 °C (for up to 12 weeks), andthe inter-individual variance regardless of sample storage conditionwas maintained. However, both storage at 4 °C for up to 4 daysand subsequent freeze–thaw cycles (up to three) affected thesample lipid composition and caused the decrease in TG peak volumes.The short-term storage at 4 °C still seems to provide samplesusable for phospholipid analysis since significant changes in phospholipidprofiles were not observed regardless of sample storage conditionsand also number of freeze–thawing cycles. It can be assumedthat the storage of HM samples under studied storage conditions  and up to three freeze–thawingcycles does not affect the output of the HM phospholipid analysis.Due to the particular components of the sample being stable underdifferent conditions, storage conditions should be selected accordingto lipids of interest. We strongly recommend storing HM samples attemperatures of at least −20 °C for short and −80°C for longer periods and avoiding the number of freeze and thawcycles to reduce potential biological misinterpretation of HM lipidomicsdata.


Membrane stiffness increased during development being consistently lower in ‘Apple’ than in ‘Tommy Atkins’. Membrane fracture force (Fmax) was low and constant in developing ‘Apple’ but increased in ‘Tommy Atkin’. Membrane strain at fracture (εmax) decreased linearly during development but was lower in ‘Apple’ than in ‘Tommy Atkins’. Frequency of membrane failure associated with lenticels increased during development and was consistently higher in ‘Apple’ than in ‘Tommy Atkins’. The lower rate of cuticular deposition, the higher strain releases on excision, isolation and wax extraction and the weaker cuticle account for the high russet susceptibility of ‘Apple’ mango.Russeting compromises appearance and downgrades the market value of many fruitcrops, including of the mango cv. ‘Apple’. The objective was to identify the mechanistic basis of ‘Apple’ mango’s high susceptibility to russeting. We focused on fruit growth, cuticle deposition, stress/strain relaxation analysis and the mechanical properties of the cuticle. The non-susceptible mango cv. ‘Tommy Atkins’ served for comparison. Compared with ‘Tommy Atkins’, fruit of ‘Apple’ had a lower mass, a smaller surface area and a lower growth rate. There were little differences between the epidermal and hypodermal cells of ‘Apple’ and ‘Tommy Atkins’ including cell size, cell orientation and cell number. Lenticel density decreased during development, being lower in ‘Apple’ than in ‘Tommy Atkins’. The mean lenticel area increased during development but was consistently greater in ‘Apple’ than in ‘Tommy Atkins’. The deposition rate of the cuticular membrane was initially rapid but later slowed till it matched the area expansion rate, thereafter mass per unit area was effectively constant. The cuticle of ‘Apple’ is thinner than that of ‘Tommy Atkins’. Cumulative strain increased sigmoidally with fruit growth. Strains released stepwise on excision and isolation ( Russeting of the skin compromises the appearance of many fruitcrop species, including of mango. Although the problem is mostly cosmetic, russeted fruits are usually excluded from high-end and export markets because of their unattractive, blotchy, dull-brown, appearance. Microscopic cracks (microcracks) in the cuticle are the first microscopic signs of russeting. Microcracks impair the barrier function of the cuticle. The consequences are: 1) an increased incidence of fruit rots  an incre and out The development of russeting is triggered by environmental factors including surface moisture and high humidity –6. Largeεtotal) represents the increase in surface area during fruit growth. This is partitioned into various component strains by stepwise removal of the stresses and monitoring the resulting strain releases. The component stresses leading to εtotal include the strain released on excision of an epidermal segment (εexc), the strain released on (enzymatic) isolation of the cuticle (εiso) and that released on extraction of the cuticular wax (εextr). The remaining or residual strain (εresid) may be calculated as the difference between the εtotal minus the sum of εexc, εiso, and εextr. The residual strain εresid is irreversible, and it remains associated with the extracted cuticular membrane. In general, the buildup of elastic strain is considered not to be critical, provided the overall thinning of the cutin matrix is prevented by the continuous deposition of new cutin beneath it, and of wax within it, as its area expands.Stress/strain relaxation analysis of the fruit skin is a tool that provides useful insight into the causal relationships underlying the formation of microcracks . In thisThe objective of this work was to understand the basis for the high susceptibility of ‘Apple’ mangoes to russeting. A better understanding is helpful in developing suitable countermeasures to reduce or prevent russeting in this cultivar. We focus on fruit growth, skin anatomy, cuticle deposition, stress/strain relaxation analysis and the mechanical properties of the cuticle. The russeting non-susceptible mango cultivar ‘Tommy Atkins’ served for comparison.Mangifera indica L.) were grafted on local, unclassified rootstocks. Fruit were harvested from a commercial orchard located in Mwala, Kenya . The orchard was managed conventionally using local integrated crop management programs. Permission to sample fruit was given by the owner of the orchard Mr. and Mrs. Musyoka.‘Apple’ and ‘Tommy Atkins’ mangoes (2 d-1) was calculated from the first derivative.The time course of fruit growth was established first. Fruits were sampled every 1–3 weeks from 41 to 159 days after full bloom (DAFB). Fruits were weighed  and their length, and two orthogonal diameters were measured using calipers . Fruit surface area was calculated assuming spherical shape. A sigmoid regression model was fitted through a plot of surface area vs. time. From this model, the rate of surface area growth . Calibrated digital photographs were taken from two orthogonal aspects . The three orthogonal fruit diameters were quantified using image analysis . Fruit were then peeled, the peels flattened between two glass plates and digital photographs taken . Peel area was quantified using image analysis (ImageJ 1.53P).2 d-1) from a sigmoid curve fitted through a plot of ‘enclosed’ surface area vs. time for each region.To obtain information on the spatial distribution of the skin area increase over the whole fruit surface, a set of fruit was tagged. A square pattern of four dots of a non-phytotoxic white silicon rubber  was printed in five different regions on the fruit surface. The regions were: stem end, cheek, apex, back and nak see . The dotTissue blocks were excised from the fruit skin and the outer flesh, and incubated in Karnovsky fixative. These were stored in a cold room until use . For proThe epidermal segments (ES) were excised from the stem end, cheek, apex, back and nak regions using a biopsy punch (8 mm diameter). The ES were photographed  and the number of lenticels per disc counted (ImageJ 1.53P). Lenticel density was established by dividing the number of lenticels per ES by the area of the ES. The number of replicates was 30.The areas of individual lenticels were quantified using enzymatically isolated cuticles. Lenticels were viewed under bright incident light using a binocular microscope . Calibrated digital photographs were taken . The areas of three to four lenticels per CM were measured using image analysis . The number of replicates was 60 lenticels from 10 fruits.-1; Panzym Super E flüssig, Novozymes A/S, Krogshoejvej, Bagsvaerd, Denmark), cellulase  [3 was added at a final concentration of 30 mM to suppress microbial growth. The solution was refreshed regularly until the cuticular membrane (CM) separated from adhering cellular debris. The CM were cleaned using a camel-hair brush, then rinsed at least 5-times using deionized water.Cuticles were isolated enzymatically. Briefly, ES were excised from the cheek region using a biopsy punch  at regular time intervals. The ES were incubated in an isolation medium containing pectinase (90 ml l3/MeOH) for a minimum of 2 h. The dewaxed cuticular membranes (DCMs) were dried overnight at 40°C (Memmert 100–800) and weighed. The CM, DCM and wax masses per unit area were calculated. The number of replicates was 10–20.For mass determination, CMs were dried at 40°C  and then weighed . Wax was extracted in a Soxhlet apparatus using chloroform: methanol  and their areas quantified by image analysis (ImageJ 1.53P). Following enzymatic isolation, the fully-hydrated CMs were again photographed using a binocular microscope . Before dewaxing, a square pattern of holes was punched into the center of each CM disc and the hole pattern photographed. This was necessary because the DCMs curled up following extraction and it became impossible to flatten them. Two orthogonal diameters were measured on each ES or CM disc. For the DCM discs the areas enclosed by the hole patterns were calculated.2mm-2) strain of the skin was calculated as the strain released upon excision, and upon isolation, and upon wax extraction [The developmental time course of change in apparent  and cumulative  of developing fruit [εtotal) is partitioned into several component strains. The total cumulative strain  at time t = i was estimated as the change in surface area (ΔAi) relative to the initial surface area (A0) at time t = 0 . We therefore defined the surface area at the time of initiation A0 somewhat arbitrarily as about 1 cm2 and calculated the total cumulative strain from These pitfalls are avoided when calculating the absolute cumulative strain .In this equation, εexc+iso) and after dewaxing (εextr) were calculated as the differences in areas of the ES disc before excision (Ai), and of the isolated CM (ACM), and of the extracted DCM (ADCM) . This calculation assumes the amount of strain release is uniform across the entire fruit surface.The cumulative strains released after excision and CM isolation (DCM) Eqs  and 5. Tεresid) is fixed mostly by the cutin matrix and is calculated from The strain that remains after dewaxing  represents the sum of these individual component strains because all were calculated relative to the same base (A0)  :εtotalεextr and εresid represent the plastic strain (εplastic), whereas the εexc+iso is the elastic strain.Of the three component strains, l0 = 10 mm)  equipped with a 10 N force transducer . Frames were cut open and the test initiated at a strain rate of 0.25 mm min-1.Strips (5 mm wide) were excised from the CM isolated between 55 and 159 DAFB using parallel-mounted razor blades. Strips were mounted in a cardboard frame made from masking tape to prevent unintentional strain during handling. Strips were hydrated overnight by incubation in deionized water. Thereafter, strips were mounted in a universal material testing machine (clamping distance S) was calculated as the maximum slope of the force  vs. strain  diagram. Uniaxial strain was calculated as the ratio of the applied strain (Δl) divided by the length of the relaxed sample, i.e., the clamping distance l0 (mm) and multiplied by 100. The maximum force (Fmax) and maximum strain  represent the force and strain recorded at failure. Following testing, the fracture site was inspected on each specimen to identify whether or not the fracture was associated with a lenticel.Stiffness (Data are presented as means and standard errors (SE). Where not visible, the SEs are smaller than the data symbols. Data were analyzed using analysis of variance with R statistical software  and SAS . Means were separated using Turkey’s studentized range test (α = 0.05). Regression analyses were conducted in R (R version 4.0.3) and Sigma Plot . Significances of regression equations at the 5, 1 and 0.1% levels are indicated by *, ** and ***, respectively.2 d-1 at 94 DAFB. On the other hand, ‘Tommy Atkins’ had a maximum growth rate of 3.85 cm2 d-1 at about 103 DAFB (2 = 0.98***) between fruit surface area measured on excised peels and that calculated from fruit dimensions  and 83 DAFB (‘Tommy Atkins’) onwards, the mass of the CM and DCM per unit area remained constant indicating that the rate of deposition kept pace with surface area expansion . The masIn both cultivars, the apparent strains released on excision and isolation (ultivars . Howeverd εextr′ . At matu ± 1.1%) .εresid accounted for most of the total cumulative strain  , to wax extraction (εextr) and their sum (εexc+iso+extr) were significantly lower than the εresid  . The cone εresid .εexc+iso, εextr, εresid and εplastic vs. εtotal revealed biphasic relationships for εexc+iso and εextr vs. εtotal, but linear relationships for εresid and εplastic vs. εtotal  in ‘Apple’ during development, but Fmax in ‘Tommy Atkins’ increased up to about 83 DAFB (Fmax was significantly lower in ‘Apple’ than in ‘Tommy Atkins’ (εmax) decreased linearly with development. The value of εmax was lower in ‘Apple’ than in ‘Tommy Atkins’ (Stiffness (S) of the CMs increased during development but was consistently lower in ‘Apple’ than in ‘Tommy Atkins’ . There w 83 DAFB . The val Atkins’ . The str Atkins’ . The fre Atkins’ .This discussion focusses on (1) the relationship between fruit growth, cuticle deposition, and stress/strain relaxation and (2) the mechanical characteristics of the cuticle and their relationships to russet susceptibility.εexc+iso and εtotal  of the cuticle on the expanding fruit surface resulted in the fixation of elastic strain and, thus, the development of a radial strain gradient within the cuticle [Malus apple, also occur in ‘Apple’ and ‘Tommy Atkins’ mangoes. Here, the ongoing deposition of CM fixes the reversible elastic strains by converting them into irreversible plastic strains in both ‘Apple’ and ‘Tommy Atkins’. Accordingly, when the rate of CM deposition decreased, approaching an asymptote at 59 DAFB  in water potential in the region surrounding the microcracks due to increase transpiration [The trigger(s) that initiates the dedifferentiation in the hypodermis, the differentiation of a phellogen and the cell division in the phellogen to produce the phellem is not known. Potential candidates are a change in the Opiration .Our findings indicate that the susceptibility to russeting of ‘Apple’ compared to ‘Tommy Atkins’ is due to the following sequence of events. Compared to the non-susceptible ‘Tommy Atkins’, ‘Apple’ mango has a lower rate of CM deposition which results in higher elastic strain. The high elastic strain serves to initiate cuticular microcracking, which is exacerbated by surface wetness. In this sequence of events, the different elasticity of the lenticels causes local stress concentrations in ‘Apple’ mango, but not in ‘Tommy Atkins’ mango. Microcracking weakens the cuticle, that also now has impaired barrier properties as indexed by increased transpiration . The impS1 FileWhere regression lines were fitted, parameter estimates of regression equations and coefficients of determination are provided.(XLSX)Click here for additional data file.


Lymphoblastoid cell lines (LCLs) derive from blood infected in vitro by Epstein–Barr virus and were used in several genetic, transcriptomic and epigenomic studies. Although few changes were shown between LCL and blood genotypes (SNPs) validating their use in genetics, more were highlighted for other genomic features and/or in their transcriptome and epigenome. This could render them less appropriate for these studies, notably when blood DNA could still be available. Here we developed a simple, high-throughput and cost-effective real-time PCR approach allowing to distinguish blood from LCL DNA samples based on the presence of EBV relative load and rearranged T-cell receptors γ and β. Our approach was able to achieve 98.5% sensitivity and 100% specificity on DNA of known origin (458 blood and 316 LCL DNA). It was further applied to 1957 DNA samples from the CEPH Aging cohort comprising DNA of uncertain origin, identifying 784 blood and 1016 LCL DNA. A subset of these DNA was further analyzed with an epigenetic clock indicating that DNA extracted from blood should be preferred to LCL for DNA methylation-based age prediction analysis. Our approach could thereby be a powerful tool to ascertain the origin of DNA in old collections prior to (epi)genomic studies. EBV infection is mediated through the interaction of viral gp350 and gp42 glycoproteins with B-lymphocytes CD21/CR2 and HLAII receptor proteins, while the viral genome is maintained and replicated in the cells as episomal DNA or integrated in the nuclear genome in a lesser proportion5. The transformation of B-cells into proliferating and immortalized LCLs is under the control of latency III viral gene expression program comprising more than ten coding (EBNAs and LMPs) and non-coding  genes6.Lymphoblastoid cell lines (LCLs) result from the immortalization of B-lymphocytes from blood samples though stable infection with Epstein–Barr Virus (EBV) of the herpesvirus family in vitro10. LCLs allow access to unlimited amount of DNA and overcome the need of high amount of blood and/or resampling from donors, while allowing their conservation in DNA and cell line biobanks  and distribution to the scientific and biomedical community7.Since the establishment of first LCLs, these cells have proven to be extremely useful in several genetic, functional and pharmacogenomic studies as well as for the development of immunotherapies11 or the HGDP-CEPH (Human genome Diversity Project-Centre d’Etude du Polymorphisme Humain)12 panels, which have extensively been used in several large scale genetic studies, including the construction of human genetic maps11, description and analysis of genetic variations across human populations 15, and genome-wide association studies16. In addition to genetic studies, LCLs have also been used as a surrogate biological material that could be representative of blood in other genomic18, transcriptomic21 and epigenomic23 studies.Historically, DNAs from LCLs have allowed to set up some worldwide-used reference DNA samples such as those from the CEPH reference families26, some copy number variations and chromosomal aberrations28, mtDNA mutations and copy number changes30, frequent DNA methylation variations34 as well as modification of transcriptomes38. As a result LCLs may not completely reflect the tissue of origin and most of these studies have recommended their use with caution in genomic and transcriptomic studies and even more in epigenomic studies39. Thus, the use of blood should be preferred to LCLs for these types of studies, notably when blood DNA or RNA samples could still be available.However, several comparative studies highlighted the presence of modifications in the (epi)genome and transcriptome of LCLs compared to blood due to immortalization and in vitro culture, as well as the absence of representativity of all types of blood cells. These modifications included few mutationsEBV genome whose copy number is very high in LCLs and the detection of rearranged TCRβ and TCRγ that are specific to T-cells in blood. It was developed and optimized using 458 blood samples from healthy donors from the SU.VI.MAX cohort40 and the French blood bank (EFS) as well as 316 LCL reference DNA samples from CEPH families11.In this context, our study aimed to develop a simple and efficient high-throughput real-time PCR approach allowing the rapid identification of the biological material from which the DNA was extracted (blood or LCL). The method is intended to be used on large scale DNA collections as a screening and/or quality control test that could be used to validate, ascertain or identify their tissue of origin i.e. blood or LCL, prior to downstream (epi)genomic studies. The approach is based on the detection of different genetic features specific either to LCLs or blood DNA, including the relative quantification of 42. The collection includes more than 10,000 DNA samples extracted from blood or LCLs, but this information was dated, uncertain or sometimes missing and needed to be verified or determined. Following the identification of their tissue of origin, we performed DNA methylation-based age prediction on a subset of DNA samples from blood and LCLs using an epigenetic clock based on three loci and pyrosequencing44 and compared the age predictions to their chronological ages. The results confirmed that the use of blood DNA should be preferred over LCL DNA for DNA methylation analyses and that the developed tissue of origin test could be a useful tool for the rapid identification, verification or validation of the DNA origin. It could be easily implemented in biobanks and used along with the other quality controls of DNA on several large scale and/or ancient DNA collections prior to (epi)genomic studies.We further applied our tissue-of-origin test on 1957 DNA samples from the CEPH Aging cohort, which was recruited during the years 1990 to 2000 and comprises more than 2000 nonagenarians, centenarians and super-centenarians as well as their offspringThe study was conducted in accordance with current ethical and legal frameworks and approved by an institutional review board . Informed consents were obtained from all participants.11 provided by the CEPH Biobank, 364 blood DNA of healthy individuals from the SU.VI.MAX cohort and 93 blood DNA of healthy donors45 from the French blood bank, EFS . Sex and age at collection of the individuals from the different cohorts were given in Table DNA extracted from LCL and blood was used as reference for the development of real-time PCR assays Table , includi42. The cohort comprises 10,173 DNA extracted from blood or LCL and the information about their tissue of origin was sometimes uncertain or missing. 1957 DNA samples from 1813 individuals were used for the assessment of their blood or LCL origin.The CEPH aging cohort comprises 1561 French nonagenarians, centenarians and super-centenarians born between 1875 and 1910 and recruited during the years 1990 to 2000, including 528 individuals from 228 families, as well as 468 of their offspring belonging to 147 familiesDNA from all collections was quantified using Quant-IT™ dsDNA Broad-Range assay kit on a Synergy HTX (BioTek) for fluorescence measurement and analysis  or Qubit™ dsDNA BR assay Kit on a Qubit 3 Fluorometer (Thermo Fischer Scientific), according to the manufacturer’s instructions. DNA sample concentrations were equalized to 5 ng/µL and dispensed in 96 wells PCR plates using a JANUS Liquid Handler Workstation (Perkin Elmer).EBV, GAPDH, TCR-β and TCR-γ real-time PCR assay primers were given in Supplementary Table 46 for EBV, Sprouse et al.47 for TCRγ and van Dongen et al.48 for TCRβ. All PCR reactions were performed in 384 PCR plates on a LightCycler 480 (Roche) with 10 ng of DNA in 10 µL volume using a Bravo Automation Liquid Handling Platform (Agilent) for plate preparation. The PCR mix included 1 × HotStar Taq DNA polymerase buffer, 1.6 mM of additional MgCl2, 200 µM of each dNTP, 1.5 µM of SYTO™ 9 (Invitrogen), 200 nM of each primer and 0.5 U of HotStar Taq DNA polymerase (Qiagen). PCR conditions included an initial denaturation step performed for 10 min at 95 °C, followed by 50 cycles of denaturation, annealing and elongation (Supplementary Table t) values from GAPDH, EBV and TCRγ PCR assays as well as the melting temperature(s) (Tm) of TCRβ amplicons were obtained using the 2nd derivative max analysis and the melting curve analysis modules of the LightCycler® 480 SW 1.5.1 software (Roche), respectively. A Ct value of 40 for EBV assay and 45 for TCRγ assay was set for all samples with no PCR amplification to allow analyses.49 and diluted to a final concentration of 20 ng/µL for PCR. 20 ng of bisulfite-treated DNA was used as template for each PCR reaction using three bisulfite-specific PCR primer pairs  according to the PCR reaction and cycling conditions described in Ref.43. After PCR, 10 µL of amplified product was purified and prepared for pyrosequencing using the pyrosequencing primers and assays described in Ref.43 and according to the detailed protocol described in Refs.51. DNA methylation analysis was performed using the PyroMark Gold SQA Q96 Kit (Qiagen) on a PyroMark Q96 MD (Qiagen) and the data were analysed with PyroMark CpG software (Qiagen). DNA methylation-based age predictions were performed using DNA methylation values of ELOVL2 (CpG5), KLF14 (CpG2) and TRIM59 (CpG5) with a multiple linear regression model (predicted age = − 20.372 + 0.830 × ELOVL2 (CpG5) + 1.723 × KLF14 (CpG2) + 0.715 × TRIM59 (CpG5))44.One µg of DNA was bisulfite-treated using the EpiTect Bisulfite 96 Kit (Qiagen) according to the manufacturer’s instructions. Bisulfite-converted DNA was quantified using the quantitative real-time PCR QC1 methylight assayGAPDH was used as a control single copy gene in genomic DNA for the normalization of Ct values. CGAPDHt /CGene/Genome of interestt  ratios were calculated for EBV and TCRγ and used to classify DNA samples in three different groups  according to CGAPDHt /CGene/Genome of interestt  ratio using two thresholds chosen empirically. For TCRβ, the highest melting temperature (Tm) was selected to distinguish between blood and LCL DNA using a single threshold. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of the three real-time PCR tissue-of-origin tests used alone or in combination were calculated. For each calculation, samples identified as blood were considered as positive results while those identified as LCL and of uncertain origin were considered as negative.52 and low to zero in blood DNA of individuals with no ongoing EBV infection or EBV-associated diseases55. We also searched for other genetic features that could be specific to blood DNA and absent in LCL DNA and identified rearranged T-cell receptor (TCR) genes and extra-chromosomal signal joint T-cell receptor excision circles (sjTREC) that are specific from T lymphocytes57. As sjTRECs drastically decrease in blood with age until being barely detectable around 80 years old58, we focused on rearranged TCR genes from T lymphocytes whose number is maintained throughout life60. We further restricted our choice to TCRβ and TCRγ and excluded TCRδ, as it is known to be frequently rearranged in B-lymphocytes61, and TCRα due to the high complexity of this gene locus, which presents a large number of V/J segments, and of its rearrangement62. Thus, to develop our tissue-of-origin test we decided to focus on three genetic features i.e. EBV DNA relative load and rearranged TCRβ and TCRγ.Our study aimed to develop a real-time PCR approach allowing to differentiate DNA extracted from blood or LCL. We first searched for genetic features specific to LCL or blood DNA. The first genetic feature relied on the detection of EBV genomes in the DNA, whose copy number is high in LCL DNA EBV PCR assay using DNA samples of known origin. DNA extracted from blood were obtained from EFS healthy donors (n = 93) and from healthy individuals of the SU.VI.MAX cohort (n = 364)  see “, while Dod Table .Figure 1EBV assay, we developed a second tissue-of-origin real-time PCR assay to distinguish blood from LCL DNA samples based on a second genetic feature that is assumed to be absent in LCL DNA and present in blood DNA, i.e. rearranged TCRγ genes. The TCRγ assay used one primer pair corresponding to VII and JI/II segments and amplifying a large proportion of the recombined TCRy gene repertoire47. Indeed, a small number of V and J segments allowed the use of a limited number of consensus primers, leading to amplification of a majority of rearranged TCRγ genes63. Ct values for rearranged TCRγ assay were normalized with CGAPDHt  values to obtain a ratio that also presented a bimodal distribution among blood and LCL DNA samples  obtained with melting curve analysis after PCR amplification: Tm results for blood DNA samples were over 89.5 °C with a low proportion of primer dimers with lower Tm (< 89.5 °C), whereas LCL DNA melting curves presented only Tm values under 89.5 °C corresponding to primer dimers and non-specific amplification products  and 457 DNA samples from NC group’s offspring (NCO group) of the CEPH Aging cohort  and 26 LCL DNA (CEPH families) from control samples of individuals aged from 19 to 53 years genomes might arise during immortalization and in vitro culture and might not reflect their cells of origin39. Thus, the use of genomic DNA extracted from blood should be preferred over LCLs for (epi)genomic studies, and this despite the development of bioinformatics tools that might allow the filtering of LCL-specific alterations before data interpretation28. In some genomic studies such as the 1000 Genomes Project, whole genome sequencing experiments were performed on DNA samples extracted either from blood or LCLs, and some annotations about the tissue-of-origin could be missing or inaccurate, thereby potentially impacting downstream bioinformatic analyses and the interpretation and significance of the data52.The rapid increase in number of genetic and genomic studies in the last thirty years became possible with the development of new high-throughput genotyping and sequencing technologies as well as bioinformatics resources, associated to the reduction of their costs. These studies also required the availability of an ever-growing number of DNA samples that were collected and stored in DNA biobanks or biological resource centers, which also allowed their distribution to the scientific and biomedical community worldwideTCRβ and TCRγ  and epigenotyping microarrays data and found the epigenetic clock and age prediction were altered in these cell lines82. The poorer age prediction performance observed on LCL DNA from CEPH families compared to the CEPH aging cohort might be attributed to the high number of passages for the former, as DNA methylation alterations were described to be stronger in LCLs with high passage numbers35. Taken together, our results and the aforementioned studies indicated that when possible, blood extracted DNA should be preferred to LCL DNA for DNA methylation and age prediction analyses.Finally, to measure the impact of the origin of our DNA samples on epigenetic analyses, we ran an age prediction model using DNA methylation of three CpG sites on about a hundred individuals from control groups and CEPH Aging collection in order to predict their chronological age Fig. . The ageOur study presented for the first time an experimental approach for the identification of the tissue of origin of DNA samples, whether extracted from blood or LCLs. It is intended to be used in large and/or ancient DNA collections to validate, ascertain or identify their origin. We proposed this approach as a quality control test that could be implemented in DNA biobanks and used along with other quality control tests prior to (epi)genomic studies. In our experimental conditions, we evaluated the cost per PCR reaction at 1 euro (≈ 1.2 $) for a total of 4 euros (≈ 4.5 $) per DNA sample for the combined approach, which is cost-effective. We also anticipate the development of additional tissue-of-origin tests that could be applied to DNA from other tissue types or from other nucleic acid types, i.e. RNA, which would further improve the practices for biobanks and contribute to the science of biobanking.Supplementary Information.


Background: Mental disorder of people living with HIV (PLWH) has become a common and increasing worldwide public health concern. We aimed to explore the relationship between anxiety, depression, and sleep disturbance for PLWH from a network perspective.Methods: The network model featured 28 symptoms on the Hospital Anxiety and Depression scale questionnaire and Pittsburgh Sleep Quality Index questionnaire in a sample of 4,091 HIV-infected persons. Node predictability and strength were computed to assess the importance of items. We estimated and compared 20 different networks based on subpopulations such as males and females to analyze similarities and differences in network structure, connections, and symptoms.Results: Several consistent patterns and interesting differences emerged across subgroups. Pertaining to the connections, some symptoms such as S12–S13 (“sleepy”—“without enthusiasm”) shown a strong positive relationship, indicating that feeling sleepy was a good predictor of lacking enthusiasm, and vice versa. While other symptoms, such as A3–D3 (“worried”—“cheerful”), were negatively related in all networks, revealing that nodes A3 and D3 were bridge symptoms between anxiety and depression. Across all subgroups, the most central symptom was A7 “panic” and S2 “awake”, which had the greatest potential to affect an individual's mental state. While S3 “bathroom” and S5 “cough or snore” shown consistent lower node importance, which would be of limited therapeutic use.Conclusions: Mental conditions of PLWH varied considerably among subgroups, inspiring psychiatrists and clinicians that personalized invention to a particular subgroup was essential and might be more effective during treatment than adopting the same therapeutic schedule. According to the National Health Commission of the People's Republic of China, there were approximately 958 thousand people living with HIV (PLWH) as of October 2019 . PreviouHowever, to date, this issue has been largely ignored and insufficiently addressed in global policy guidelines . In ChinIn recent years, the network approach to psychopathology has been proposed as a novel way of analyzing mental disorders . It has 0=Not at all; 1 = Sometimes; 2 = Very often; 3 = Nearly all the time'. The PSQI questionnaire is composed of 18 items measuring the quality and patterns of sleep. Among them, 14 items are scored on a 0–3 scale, wherein three reflects the negative extreme of sleep disorders. Each item in the questionnaire is a symptom from a clinical standpoint and is represented as a node in the network model.Data for the analyses carried out in the present study came from 4,101 HIV-infected persons who participated in a cross-sectional study among 20 HIV treatment clinics across China. All enrolled participants completed the Hospital Anxiety and Depression (HAD) scale questionnaire  and the 3 and >350 cells/mm3), and HIV RNA levels (<50 copies/mL and ≥50 copies/mL) were included in the present study as baseline characteristics for downstream analyses. This study was approved by the Beijing You'an Hospital institutional review board. All participants provided written informed consent to complete a survey and extract their clinical data from medical records.Demographics and behavioral data were also collected through the self-administered survey. Clinical data were extracted from medical records, including current CD4+ cell counts and current HIV RNA levels. Therefore, a total of 11 factors, namely age (18~35 years and >35 years), gender , education , marital status (married and single), employment , transmission route , HIV status disclosed to friends (yes and no), HIV status disclosed to family (yes and no), support from society/family (with and without), current CD4+ cell counts . All P-values were two-sided, and P < 0.05 were considered significant. These analyses were conducted using SPSS 25.0.The rating scores of all items in HAD-A/D were summed and the total score ≥8 on the anxiety and depression items indicated positive signs for anxiety and depression . The par2-tests, we identified the factors showing significant differences in any mental disorders. This univariate statistical inference was intuitive and easy-to-understand without considering pairwise relations of all symptoms in the questionnaire. Thus, we further conducted the network analysis for a comprehensive consideration and thorough comparison in the symptom-level of the HAD and PSQI questionnaires. The software R  was used to carry out the network analysis. Packages and functions used in this study included estimate Network (bootnet package) (qgraph package) (mgm package) (According to the Pearson χpackage)  for netwpackage)  for netwpackage)  for nodeDue to the requirements of network analysis, 14 items with an ordinal scale in the PSQI questionnaire and all 14 items in the HAD questionnaire were used to build networks in this study. We first estimated one network for each subgroup based on each group's total sample of participants. We then calculated a regularized partial correlation network . The GraWe further calculated the relative and the absolute importance measure for each node to seek potential risk factors or treatment inventions for mental disorders of the HIV-infected persons. The centrality of nodes, which is used to identify central nodes, is a relative measurement for node importance and a key in network analysis –29. TherWe also calculated the node predictability for each node in the networks. Node predictability, an absolute measure of the interconnectedness of a node, is the percentage of the variance of a given node explained by its surrounding nodes . It descGenerally, we first built network models for all subgroups split according to stratification factors. Then we compared a pair of networks' complexity, structure, and edge weights. Simultaneously, we compared the strength and predictability of each node between the paired networks and analyzed the role it played in different subgroups.3, and 40.8% were virally suppressed. Over 60% had disclosed their HIV status to their family members, and close to 70% had support from their family members or society. In contrast, just over one-quarter had disclosed their status to a friend . Approximately 40.0% of participants were infected through anal sex, and 57.1% had a current CD4+ T cell count over 350 cells/mma friend .P < 0.001). On the contrary, the depression rate of participants infected with homosexuality was about 6% lower than that of participants whose transmission route was heterosexual (P < 0.001). The HIV-infected persons without spouses also shown a 10% higher sleep disturbance rate than those married (P < 0.001). Student infectors with the anxiety and depression rate of 33.3 and 42.6% far exceeded blue collars and white collars (both P < 0.001). Furthermore, persons feeling supported by society and family had significantly lower rates of anxiety, depression, and sleep disturbance than those without support .As shown in There were significant variations in the proportion of depression among all 11 factors except for current CD4+ cell counts and current HIV RNA levels. Actually, no significant variations in the two clinical factors were found among the three mental disorders. Therefore, we excluded the current CD4+ cell counts and HIV RNA levels, leaving nine stratification factors that showed significant variations in the proportion of any mental disorders for further network analyses.In this section, we built 20 networks for nine stratification factors. Each network featured 28 mental disorder symptoms related to anxiety, depression, and sleep disturbance. Using a network model, we investigated the differences and similarities in mental disorder symptoms between individuals split by each factor. sleepy’ and S13 ‘without enthusiasm’ in each network with an average weight of 0.36 ± 0.026. This result implied that feeling sleepy was a good predictor of lacking enthusiasm, and vice versa. In addition, associations between S1–S2 (‘insomnia’—‘awake’) and S2–S3 (‘awake'—‘bathroom’) were presented across 20 networks with the average weights of 0.31 ± 0.031 and 0.33 ± 0.023, separately. Anxiety symptoms A1 ‘tense’, A2 ‘frightened’, A5 ‘extreme fear’, and A7 ‘panic’ also showed strong positive connections with the average weights of 0.29 ± 0.028 for A1–A2, 0.27 ± 0.037 for A2–A7, and 0.25 ± 0.023 for A5–A7, respectively, implying a consistent pattern across subgroups.The average value of all edges' absolute weights was 0.07 . Going through the 20 networks, several interesting symptom connections were found. For instance, there was a very strong positive connection between nodes S12 ‘worried’ and D3 ‘cheerful’ were negatively related with an average weight of −0.25 ± 0.021. This finding mean that nodes A3 and D3 were bridge symptoms between anxiety and depression. Additionally, sleep symptoms were almost completely independent with anxiety and depression symptoms in networks for males, blue collars, participants with support from society/family, and participants who had disclosed their HIV status to family members.In contrast, nodes A3 ‘medicine’ and S12 ‘sleepy’ for males and participants with homosexual transmission, indicating that the two nodes were independent controlling for other nodes in the networks. However, the two nodes were strongly connected in networks for females and participants with heterosexual transmission with weights of 0.25 and 0.21, separately. Such difference in association was also found for nodes S5 ‘cough or snore’ and D7 ‘enjoy something’ between networks for males  and females (0.20).We then estimated and compared networks for each stratification factor to explore the unique patterns of symptom interactions within each subgroup. Apparently, the network built for females with 86 edges was more complex than that for males with 76 edges . Female relaxed’ and S11 ‘medicine’ were independent after controlling other nodes in all networks except for those built on females  and participants with homosexual transmission (−0.13). Besides, the connection between nodes D2 ‘laugh’ and S11 ‘medicine’ was merely found in the network built on students with the edge weight of −0.14. Nodes A5 ‘extreme fear’ and D6 ‘expect everything’ were solely negatively related in the networks built on blue collars  and participants that disclosed HIV status to friends (−0.15). Nodes S11 ‘medicine’ and S14 ‘self-rate sleep quality’ were independent except for the network built on white collars with the weight of 0.19.Additionally, some connections were merely observed in specific subpopulations. For example, nodes A4 ‘panic’ and S2 ‘awake’ had consistent high strength among all networks. Meanwhile, nodes S3 ‘bathroom’ and S5 ‘cough or snore’ showed the lowest strength. Other symptoms, such as S6 ‘cold’ varied substantially across networks built on patients with different ages, gender, educations, transmission routes, and marital status in terms of strength.The standardized estimates of node strength were presented in panic’ yielded the highest average node predictability of 47% , meaning it shared 47% of variance with surrounding items. It was followed by nodes A2 ‘frightened’ and S2 ‘awake’, with the average predictability of 46% (42–50%) and 41% (38–48%), respectively. It is plausible that an intervention on the surrounding symptoms of panic, frightened, and awake may have a considerable impact on these mental problems. On the contrary, nodes S11 ‘medicine’, S5 ‘cough or snore’, and S3 ‘bathroom’ shown consistent lower node predictability.Some nodes shown consistently higher or lower predictability in all networks, whereas others varied considerably. Node A7 ‘comfortless breath’ in the network for participants without support from society/family was twice as much on those with support (31 vs. 16%). Also, variations were found in networks built on participants with different employments. For instance, node predictability of A1 ‘tense’ varied from 37–48% among networks built on students, blue collars, and white collars, as well as D1 ‘enjoy things’ (19–31%), S1 ‘insomnia’ (35–45%), S2 ‘awake’ (38–48%), S4 ‘comfortless breath’ (18–29%), and S13 ‘lack of enthusiasm’ (28–40%). Besides, some nodes had quite a different node predictability when estimated in different item networks. Node predictability of S4 ‘In this study, we conducted a network analysis to provide an analytic framework, aiming to examine the association among symptoms of anxiety, depression, and sleep disturbance in PLWH. This was, to our knowledge, the first application of network analysis on the HAD and PSQI symptoms for PLWH.medicine’, A3 ‘worried’, and D3 ‘cheerful’ were bridge symptoms that could spread activation from one disorder to another. Such bridge symptoms are essential in the development and maintenance of comorbidities , he was more likely to develop other symptoms than those who developed a peripheral symptom such as S11  to help you sleep?)  . An inte sleep?) . Therefocomfortless breath’ showing lower node predictability for participants with support from society/family than those without support. Thus, an intervention on sleeping disordered breathing problems may be effective for the former subpopulation. These results inspired psychiatrists and clinicians that personalized invention to a particular subgroup was essential and might be more effective during treatment than adopting the same therapeutic schedule.On the other hand, some symptoms differed considerably in their node predictability across subgroups, aligning with previous studies that the psychometric properties of symptoms usually differed across subgroups , 39. Pre(Cough or snore loudly) and D7 (I can enjoy a good book or radio or TV program) ranged from strong for females to absent for other subgroups. Based on the network perspective, two symptoms with a strong connection could influence each other directly  and the National Science and Technology Major Project of China during the 13th Five-year Plan Period (2017ZX10201101).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Here, autistic and non-autistic adults (N = 60) matched on age, gender, non-verbal reasoning ability and alexithymia, completed an emotion recognition task, which employed dynamic point light displays of emotional facial expressions manipulated in terms of speed and spatial exaggeration. Autistic participants exhibited significantly lower accuracy for angry, but not happy or sad, facial motion with unmanipulated speed and spatial exaggeration. Autistic, and not alexithymic, traits were predictive of accuracy for angry facial motion with unmanipulated speed and spatial exaggeration. Alexithymic traits, in contrast, were predictive of the magnitude of The online version contains supplementary material available at 10.1007/s10803-021-05083-9. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, characterized by difficulties in social communication, and restricted and repetitive interests  whilst controlling for the influence of alexithymia. There are, however, some studies that have compared autistic and non-autistic processing of full  dynamic facial expressions without controlling for alexithymia. For example, Sato et al.  at alpha level 0.01 for the emotion × spatial interaction. Moreover, 11 participants are required in the control group in order to have 95% power to detect an effect size of 0.53 (ηP2) for the emotion × kinematic interaction at alpha level 0.01. However, Button et al. (https://osf.io/kpefz).The chosen sample size is based on an a priori power analysis conducted using GLIMMPSE . Participants were matched for age, gender, non-verbal reasoning (NVR), as measured by the Matrix Reasoning Item Bank . The control and remaining nine ASD participants were recruited via social media (Facebook and Twitter) and Prolific—an online recruitment platform. All participants in the ASD group had previously received a clinical diagnosis of ASD from a qualified clinician.The PLF task was an adapted version of that developed by Sowden and colleagues  which waThe autistic traits of all ASD and control participants were assessed via the 50-item Autism Quotient , participants first completed the questionnaires  and then moved on to the PLF task. Each trial in this task began with the presentation of a stimulus, which comprised a silent PLF video of an actor expressing one of 3 emotions, whilst saying a sentence, at one of the 3 spatial and 3 kinematic levels. After watching the video, participants were asked to rate how angry, happy and sad the person was feeling. Participants made their ratings on a visual analogue scale, with one end representing ‘Not at all angry/happy/sad’ and the opposite end representing ‘Very angry/happy/sad’. Individuals were asked to make ratings for all three target emotions  on scales, which were presented on screen in a random order, after each PLF video. Each trial took approximately 25 seconds to complete. Participants completed 3 practice trials  and then 108 randomly ordered experimental trials (12 per condition) across three blocks. Participants were invited to take a break between blocks. The structure of each trial is displayed in Fig. Following a pre-registered design  of their devices was measured to ensure that the quality/fluidity of the stimulus videos was not degraded. All participants’ frame rates were 60 FPS or higher with one exception at 50 FPS. When we ran all analyses with and without the 50 FPS participant, treating them as a potential outlier, the pattern of results was unaffected. Therefore, this participant was included in all analyses.The three emotion rating responses for each trial were transformed into magnitude scores from 0 to 10  to 3 decimal places. Emotion recognition accuracy scores were calculated as the correct emotion rating minus the mean of the two incorrect emotion ratings.group  and the within-subjects factors emotion , stimulus spatial level , and stimulus kinematic level . This analysis has the potential to reveal differences between the groups in their accuracy of emotion recognition from facial motion cues. It is possible, however, that the two groups could have comparable accuracy scores but different patterns of ratings. For example, an accuracy score of 2 for an angry stimulus could relate to an anger magnitude rating of 4 and happy and sad ratings of 2, or an anger rating of 4, happy rating of 0, and a sad rating of 4. To more sensitively pick up on any differences between groups, we also used magnitude as the DV and conducted a 2 × 3 × 3 × 3 × 3 ANOVA with the between subjects factor group  and the within-subjects factors emotion , stimulus spatial level , stimulus kinematic level  and rating .To test our first hypothesis, we submitted these accuracy scores to a 2 × 3 × 3 × 3 Analysis of Variance (ANOVA) with the between-subjects factor gender, to multiple regression analyses. The effect of the spatial manipulation (defined as the difference in accuracy between S3 and S1), the effect of the kinematic manipulation (defined as the difference in accuracy between K3 and K1), mean recognition accuracy, and accuracy for angry videos at the normal level  were used as the DVs for each of these analyses. In addition, in order to explore whether autistic and/or alexithymic traits predicted the magnitude of correct and incorrect ratings, we constructed two linear mixed effects models with subject, age, gender and NVR as random intercepts. In these models, ratings for angry facial motion at the normal level, and ratings across all emotions and levels of the spatial and kinematic manipulation, were the DVs respectively. For all analyses, we used a p = 0.05 significance threshold to determine whether to accept or reject the null hypothesis. The frequentist approach was supplemented with the calculation of Bayes Factors, which quantify the relative evidence for one theory or model over another. For all Bayesian analyses, we followed the classification scheme used in JASP , computed z-scores for the transformed data, and submitted the transformed z-scored data, along with the nominal predictor group  and the within-subjects factors emotion , stimulus spatial level , and stimulus kinematic level . This analysis revealed a significant main effect of emotion , a main effect of spatial level  which was qualified by an emotion x spatial interaction , and an emotion × kinematic interaction . Furthermore, this analysis revealed a significant four-way emotion × spatial × kinematic × group interaction . Note that no kinematic × group interaction was found , suggesting that autistic and control participants exhibit similar patterns of accuracy across the kinematic levels. Below, in order to shed light on the effects of the spatial and kinematic manipulations, we first unpack the emotion × kinematic and emotion × spatial interactions. Subsequently we fully unpack the emotion × spatial × kinematic × group interaction.Our primary hypothesis was that emotion recognition accuracy would be affected by both kinematic and spatial manipulation and that these effects would not interact with group. To test this hypothesis, we conducted a mixed 2 × 3 × 3 × 3 ANOVA with the between-subjects factor  = 88.42, p < 0.001, ηP2 = 0.60, BF10 = 7.53e58]. Post-hoc repeated measures ANOVAs revealed that whilst the effect of the spatial manipulation was present for all three emotions , the direction of the effect varied between high and low arousal emotions: recognition scores for angry and happy facial motion were highest for 150% spatial extent (S3) , followed by 100% spatial extent (S2) , and finally 50% spatial extent (S1) . In contrast, for sad facial motion, recognition scores were highest for S1 [sad mean(SEM) = 3.50(0.22)], lowest for S3 [sad mean(SEM) = 2.78(0.22)] and intermediate for S2 [sad mean(SEM) = 3.15(0.20)]. This pattern matches the results reported by Sowden et al., , we observed an emotion × spatial interaction . Whilst there was a main effect of the kinematic manipulation for all three emotions , the direction of the effect differed between high and low arousal emotions. For angry and happy facial motion, emotion recognition improved with increasing speed . For sad facial motion, emotion recognition improved as speed decreased . This pattern of results also matches the findings from Sowden et al.,  = 4.53, p < 0.01, ηP2 = 0.07, BF10 = 5.92] but not S1  or S3  level. To unpack this emotion x kinematic x group interaction at the S2 level, we conducted separate post-hoc ANOVAs for each kinematic level at the 100% (S2) spatial level. This analysis revealed a significant emotion x group interaction at the K2  but not K1  or K3  level. Bonferroni-corrected post-hoc independent sample t tests revealed that control, relative to ASD, participants had higher accuracy for angry facial motion at the 100% spatial (S2) and speed (K2) level . There were no significant group differences in emotion recognition accuracy for happy  or sad  videos at the S2K2 level. Thus, the groups significantly differed in accuracy for angry PLFs that were not spatially or kinematically manipulated. The mean emotion recognition accuracy scores across each emotion for control and ASD participants at the S2K2 level are shown in Fig. In order to unpack the significant four-way interaction, we conducted post-hoc 2 × 3 × 3  ANOVAs for each spatial level. This analysis revealed a significant emotion x kinematic x group interaction at the S2  but not happy  or sad  PLFs. Therefore, for angry videos at the normal spatial level, the effect of the kinematic manipulation varied as a function of group. Bonferroni-corrected paired sample t tests demonstrated that whilst the control group exhibited increasing accuracy across all kinematic levels , the ASD group only showed improvement from K2 to K3  and not K1 to K2 . Furthermore, the groups did not significantly differ at K1  = 0.18, p > 0.05) or K3  = 3.53 p > 0.05) but at K2, controls out-performed autistic participants  = 7.75, p < 0.01, ηP2 = 0.12). These results suggest that, whilst controls improved in their accuracy for angry facial motion across each level of increasing kinematic manipulation, for autistic participants, only the most extreme (K3) level of the kinematic manipulation resulted in an accuracy boost. The mean accuracy scores for angry videos across the kinematic levels (at the unmanipulated S2 level) for control and ASD participants are shown in Fig. To further unpack the emotion x kinematic x group interaction at the S2 level, we conducted separate post-hoc ANOVAs for each emotion at the S2 level. This analysis identified a significant kinematic × group interaction for angry , spatial level , kinematic level  and rating , as well as emotion × rating , spatial x rating , kinematic × rating , emotion × spatial × rating , emotion × kinematic × rating , kinematic × rating × group  and spatial × kinematic × rating × group  interactions. All these interactions and main effects are unpacked in Supplementary Information F.In order to compare the magnitude of the ratings between groups, we conducted a mixed 2 × 3 × 3 × 3 × 3 ANOVA with the between subjects factor  = 1.90, p = 0.058, ηP2 = 0.03]. Since this interaction potentially offers further insight about the between group difference in anger recognition, we unpack it in full here. Post-hoc 2 × 3 × 3 ANOVAs (group × kinematic × rating) for each of the emotional videos revealed a significant kinematic x rating x group interaction for angry  but not happy  or sad  facial motion. Importantly, post-hoc ANOVAs revealed that for control participants, speeding up angry facial motion  improves accuracy by increasing ratings of anger  and lowering ratings of sadness  across all levels of the kinematic manipulation   = 12.18, p < 0.001, ηP2 = 0.29, BF10 = 551.72], however this effect was driven by an increase from the 100% to 150% level , and not the 50% to 100% level . In addition, we found that there was a main effect of kinematic level for sad ratings that approached significance . Importantly, sad ratings only decreased from 100% to 150% speed  and not from 50% to 100% speed   = 0.87, p = 0.425], neither AQ  nor TAS-20  were significant predictors of the effect of the spatial manipulation. In the second analysis, we used the effect of the kinematic manipulation (defined as the difference in accuracy between K3 and K1) as the DV and AQ and TAS-20 as predictors. Again, this analysis resulted in a non-significant model , neither AQ  nor TAS-20  were significant predictors of the effect of the kinematic manipulation. We then conducted a third multiple regression with mean emotion recognition accuracy  as the DV. Once again, neither AQ  nor TAS-20  were significant predictors of mean recognition accuracy and the overall model did not explain a significant amount of variance in the data . To explore the possibility that only extreme scores on the TAS-20 predict performance, we compared mean accuracy for alexithymic  and non-alexithymic  participants . To test whether autistic or alexithymic traits were predictive of the effect of the spatial and kinematic manipulations, we conducted two multiple regression analyses. For the first analysis, we used the effect of spatial manipulation (defined as the difference in accuracy between S3 and S1) as the dependent variable (DV) and AQ and TAS-20 as predictor variables. This analysis resulted in a non-significant model overall . Bayesian analyses revealed that AQ [BFinclusion = 4.230] was over 16 times more likely to be included in a model to predict accuracy for angry videos at the normal spatial and speed level than alexithymic traits [BFinclusion = 0.263].Finally, building on our previous observation that the ASD and control groups differed in accuracy for angry facial motion at the normal (100%) spatial and speed level, we conducted a multiple regression analysis to identify the extent to which autistic and alexithymic traits were predictive of accuracy for angry videos at this level. This analysis revealed that autistic , but not alexithymic , traits were predictive of accuracy for angry facial motion at the normal spatial and speed level . Importantly, of the three demographic variables, only NVR was a significant predictor of accuracy for angry facial motion at the normal spatial and speed level   = 1.20, p = 0.233] or age ). In the second step, AQ was added , producing a statistically significant R2 change . Finally, when TAS-20 was entered into the model, the analysis revealed it was not a significant predictor of accuracy for angry facial motion at the normal level  and resulted in a non-significant R2 change , explaining only 0.5% more variance than that explained by the demographic variables alone. Despite being highly correlated with alexithymia, autistic traits were again a significant predictor of accuracy for angry facial motion at the normal level  when added to the model in Step 3. Adding autistic traits at this step produced a statistically significant R2 change , explaining an additional 14.3% of the variance in accuracy.These analyses suggest that alexithymia accounts for very little variance in accuracy for angry facial motion at the normal (S2K2) level once autistic traits have been accounted for. However, since our autism and alexithymia measures were correlated , when alexithymia is entered into a multiple regression after autistic traits, it may not be a significant predictor due to multi-collinearity. Consequently, we ran one further hierarchical regression, with the demographic variables entered in Step 1, alexithymia in Step 2 and autistic traits in Step 3. Alexithymia failed to significantly improve the model , wherein AQ predicted incorrect  but not correct [p = 0.381] emotion ratings for angry facial motion at the normal level; those with higher AQ scores gave higher incorrect emotion ratings  for angry facial motion at the normal level. Our analysis also identified that the relationship between TAS-20 and ratings (when averaging across correct and incorrect emotions) for angry facial motion at the normal level approached significance . Note that no TAS × rating type interaction was identified [p = 0.288].both correct and incorrect emotion ratings. Since the analyses reported above were restricted to the normal (S2K2) level for angry facial motion, next, we constructed one further linear mixed effects model (following the procedures outlined above) to investigate whether autistic and/or alexithymic traits are predictive of higher correct and incorrect emotion ratings across all emotions and levels of the spatial and kinematic manipulation. This analysis revealed that TAS-20 score was a significant positive predictor of the magnitude of ratings , with those higher in alexithymia giving higher intensity (correct and incorrect) ratings across all emotions and levels of the spatial and kinematic manipulation. Importantly, the TAS × rating type interaction was not significant [p = 0.125], suggesting that alexithymic traits were predictive of both correct and incorrect emotion ratings. Our analysis also revealed that there was a significant AQ × rating type interaction . In line with our previous analysis, AQ predicted incorrect  but not correct [p = 0.175] emotion ratings, such that those higher in autistic traits gave higher incorrect ratings.The analyses reported above suggest that autistic traits contribute to anger recognition by modulating the magnitude of incorrect ratings, but not correct, ratings. In addition, these analyses revealed an interesting additional finding: alexithymic traits may be positively predictive of both correct and incorrect emotion ratings.Therefore, our results suggest that whilst the level of autistic traits is predictive of accuracy for angry facial motion at the normal level (by positively predicting incorrect emotion ratings but not correct emotion ratings), alexithymic traits are not predictive of emotion recognition accuracy across emotions and manipulations but are positively predictive of and 100–150% increase. In contrast, for autistic participants speeding up angry facial motion only increased anger ratings and decreased sad ratings between the 100% and 150% levels (not from 50–100%). In addition, multiple regression analyses revealed that autistic traits and NVR, but not age, gender or alexithymia, were significant predictors of recognition accuracy for angry facial motion at the normal spatial and speed level (where level of autistic traits was a negative predictor and NVR was a positive predictor). Although alexithymic traits were not associated with accuracy, they were associated with higher ratings for both the correct and incorrect emotions. Importantly, our results demonstrate that when autistic and control individuals are matched in terms of alexithymia there are group differences in recognition accuracy, though these are restricted to angry (not happy or sad) facial motion.The current study tested whether autistic individuals, relative to alexithymia-matched controls, have greater difficulty recognising emotions from facial motion cues. We hypothesized that emotion recognition would vary as a function of kinematic and spatial manipulation and that these effects would not interact with diagnostic group, but rather Bayesian statistics would provide evidence that the groups perform comparably. We also aimed to explore whether the effects of spatial and kinematic manipulation on emotion recognition accuracy would covary with scores on a self-report alexithymia measure. In replication of Sowden et al., , our resOf particular note is our finding that differences between autistic and control individuals are restricted to the recognition of anger from facial motion. This finding is in line with previous research suggesting that angry expressions are better recognized by non-autistic compared to autistic individuals . This lack of a change in angry and sad ratings from 50% to 100% speed resulted in the autistic participants displaying significantly lower emotion recognition accuracy for angry facial motion at 100% speed. Further to this, the lack of a decrease in sad ratings may also explain why autistic traits were associated with higher incorrect emotion ratings for angry facial motion at the normal level (as found in our linear mixed effects model).An important observation is that in the current paradigm both groups performed equally well for slowed angry facial motion, but whilst the controls benefitted from all elevations in speed , the autistic participants only benefitted from the 100% to 150% speed increase. Our analysis of the magnitude of angry, happy and sad ratings for angry PLFs provided further insight into this effect: for non-autistic participants, speeding up angry PLFs from 50% to 100% angrier to ASD participants). This idea builds upon the findings of a previous study that used static photographic stimuli at varying expressive intensities  to estimate identification thresholds  for autistic and control participants  before it is identified as angry. Further research is necessary to investigate whether the group difference in recognising angry expressions at the unmanipulated spatial and speed level is underpinned by a difference in kinematic identification thresholds.These findings raise the possibility that autistic individuals may have a higher ‘kinematic threshold’ for perceiving anger from facial motion  explanation for why the autistic individuals may have particular difficulty recognizing angry expressions relates to movement production. Previous studies have documented differences between autistic and control participants in the production of facial expressions of emotion . To resolve this interpretational issue, further research is required to establish the extent of overlap between dynamic happy, angry and sad expressions.Perhaps of most interest for the field of alexithymia research is our finding that alexithymic traits are predictive of increased magnitude of In the present study, we aimed to produce statistically rigorous and replicable results. The standard alpha level (p < 0.05) has recently been called into question for its utility and appropriateness in psychological research  spatial and speed level. Interestingly, whilst for controls recognition accuracy improved across all levels of the kinematic manipulation for angry videos, autistic participants only benefitted from the 100% to 150% speed increase. Alexithymic traits were associated with elevated correct and elevated incorrect emotion ratings, but not accuracy. Our results draw attention to anger specific differences in emotion recognition between autistic and non-autistic individuals. Future research should aim to elucidate why autistic individuals exhibit differences that are specific to angry expressions.Supplementary file1 (DOCX 1639 kb)Below is the link to the electronic supplementary material.