Background: Abnormal tau accumulation in the brain has a positively correlation with neurodegeneration and memory deterioration, but the mechanism underlying tau-associated synaptic and cognitive impairments remains unclear. Our previous work has found that human full length tau (hTau) accumulation activated signal transducer and activator of transcription-1 (STAT1) to suppress N-methyl-D-aspartate receptors (NMDARs) expression, followed by memory deficits. STAT3 also belongs to STAT protein family and is reported to involve in regulation of synaptic plasticity and cognition. Here, we investigated the role of STAT3 in the cognitive deficits induced by hTau accumulation.Methods:In vitro studies HEK293 cells were used. EMSA, Luciferase reporter assay, and Immunoprecipitation were applied to detect STAT3 activity. In vivo studies, AAV virus were injected into the hippocampal CA3 region of C57 mice. Western blotting, quantitative real-time polymerase chain reaction, and immunofluorescence were applied to examine the level of synaptic proteins. Electrophysiological analysis, behavioral testing and Golgi impregnation were used to determine synaptic plasticity and memory ability recovery after overexpressing STAT3 or non-acetylated STAT1.Results: Our results showed that hTau accumulation acetylated STAT1 to retain STAT3 in the cytoplasm by increasing the binding of STAT1 with STAT3, and thus inactivated STAT3. Overexpressing STAT3 or non-acetylated STAT1 ameliorated hTau-induced synaptic loss and memory deficits by increasing the expression of NMDARs.Conclusions: Taken together, our study indicates that hTau accumulation impaired synaptic plasticity through STAT3 inactivation induced suppression of NMDARs expression, revealing a novel mechanism for hTau-associated synapse and memory deficits. The mammalian signal transducer and activator of transcription (STAT), consisted of seven members, i.e., STAT1, STAT2, STAT3 and STAT4, STAT5a, STAT5b and STAT6, are latent self-signaling transcription factors in cytoplasm, and rapidly turn on gene expression in nuclei used by most cytokine receptors It's well known that STATs play a role in development, cell growth and differentiation, proliferation, immune responses, cell survival and apoptosis Recently, we found that STAT1 negative regulated NMDARs mRNA and protein levels by directly binding the gamma interferon activation site (GAS) elements in the promoter of NMDARs DNA sequence, human wild type full length microtubule associated protein tau (hTau) accumulation activated JAK2 to phosphorylate STAT1 at Tyr701 and promote STAT1 to translocate into the nucleus and negatively regulate NMDARs expression Using AAV-hTau injected into the hippocampus of 2-m-old C57 mice to mimic intraneuronal tau accumulation as seen in the majority of AD cases, we found that overexpression of hTau acetylated STAT1 to increase STAT1 binding with STAT3 in the cytoplasmic fraction, and then, decreased STAT3 translocation into the nucleus and inactivated STAT3, though phosphorylated level of STAT3 at Tyr705 increased. Further study found that overexpressing STAT3 or non-acetylated STAT1 (STAT1KR) ameliorated the hTau-induced synaptic plasticity impairment and cognitive deficits with upregulated NMDARs expression. This study reveals a new mechanism underlying hTau accumulation induced cognitive deficits, and provides acetylated STAT1 or wild type STAT3 as a new therapy target for tauopathies.in vitro data indicated that the intracellular hTau accumulation inhibited STAT3 activity.As STAT3 is involved in the regulation of cognition in vivo and in vitro.AAV-hTau virus were injected into the bilateral hippocampus of 2-m-old C57 male mice to establish tau accumulated animal model, after one month, tau overexpression was proved by Western blotting Figure C and thiIncreased phosphorylated STAT3 at Tyr705 would induce STAT3 translocate from cytoplasm into the nucleus th and 6th compared to those of the control, however, overexpressing STAT3 attenuated the learning deficits induced by hTau accumulation at 2nd, 3rd, 5th and 6th  were injected into the hippocampus of 2-m-old C57 mice with AAV-hTau, the animal's cognition was detected after one month. Western blotting and immunofluorescence showed the high transfected effects of AAV virus  attenuated the P301L-hTau or hTau induced-decreased STAT3 activity, and the activity of STAT3 increased higher in hTau than P301L-hTau overexpressed HEK293 cells Figure A. We alsin vitro and in vivoIt's well recognized that intracellular accumulation of hTau plays a critical role in the cognitive deficits of tauopathies, such as Alzheimer's disease, Parkinson disease and amyotrophic lateral sclerosis, and so on Recently, we have found that tau accumulation upregulated JAK2/STAT1 signaling to induce STAT1 translocation into nucleus and activate STAT1 to bind with the special GAS elements in the promoter of GluN1, GluN2A, or GluN2B, and thus repress the expression of NMDARs, which reveals a novel mechanism underlying hTau-induced synapse impairment and memory deficits Using multiple measures including EMSA, dimerization, phosphorylation (pS727 STAT3 level) and luciferase activity assay, we provide strong evidence showing that STAT3 activity was inhibited by hTau accumulation, which contradicted with the increased phospho-STAT3 (Tyr705) in the present study. It's known that increased phospho-STAT3 (Tyr705) induces STAT3 nuclear translocation and increase DNA binding In our previous papers, we found that STAT1 and STAT3 both regulate NMDAR expression through directly binding the GAS elements (TTC(N2-4) GAA for STAT1, and TTC(N3) GAA for STAT3) in the promoter of GluN1, GluN2A or GluN2B. However, like twins of Gemini of Greek mythology, we firstly found that STAT1 negative but STAT3 positive regulate NMDARs transcription We have found that non-acetylated STAT1 (STAT1KR) overexpression had no effects in the synaptic and memory impairments induced by P301L mutant human tau STAT3 is the main transcriptional enhancer of several autophagy-related genes in the nucleus In conclusion, we find here that intracellular accumulation of hTau acetylated STAT1 to binding with STAT3, and then inhibit STAT3 translocate from cytoplasm to nucleus. Overexpressing STAT1KR or activation of STAT3 efficiently rescues the hTau-induced synaptic dysfunction and memory impairments in mice.The antibodies used in the present study were listed in the Supplementary Male C57 mice were purchased from the animal center of Tongji Medical College, Huazhong University of Science and Technology. All mice were kept at 24 ± 2 ºC on daily 12 h light-dark cycles with ad libitum access to food and water. All animal experiments were performed according to the 'Policies on the Use of Animals and Humans in Neuroscience Research' revised and approved by the Society for Neuroscience in 1995, and the Guidelines for the Care and Use of Laboratory Animals of the Ministry of Science and Technology of the People's Republic of China, and the Institutional Animal Care and Use Committee at Tongji Medical College. The animal study was approved by the Academic Review Board of Tongji Medical College, Huazhong University of Science and Technology.13 vg/mL, that of AAV-STAT3 virus was 1.2×1013 vg/mL, and the titer for AAV-STAT1KR (K410/3R-STAT1) virus was 1.0×1013 vg/mL. All AAV viruses were driven by CMV-promoter. For brain injections, 2 m-old C57 mice were positioned respectively in a stereotaxic instrument, the virus was bilaterally injected into the hippocampal CA3 region  at a rate of 0.10 μL/min. The needle syringe was left in place for 10 min before being withdrawn. The injection did not significantly increase the death rate or change the normal activity of the mice compared with the non-injected controls.Adeno-associated virus coded for human full-length hTau with the N-terminal fused with enhanced green fluorescent protein (eGFP) or the control AAV-eGFP, AAV-STAT3, AAV-K410/3R-STAT1 virus were purchased from OBiO Biologic Technology Co., Ltd. The titer of AAV- hTau was 1.5×10Four weeks after brain infusion of the viral vectors, the spatial learning and memory were assessed by Morris water maze (MWM) test The contextual fear conditioning test was performed as the procedures established in our lab The mice were habituated to the arenas (50 cm × 50 cm container) for 5 min without objects 24 h prior to the test. The day after the mice reentered the arenas from the same starting point, they were granted 5 min to explore two different objects A and B. After each familiarization period the arena and objects were cleaned with 70% ethanol. After one day, object A was replaced with another object C, and the mice were granted 5 min to explore both objects. The behavior was recorded by a video camera positioned above the arena. The recognition index was calculated by TC/ (TA + TC) 3 26 mM, NaH2PO4 1.25 mM, CaCl2 2 mM, MgCl2 2 mM, glucose 10 mM, equilibrated with 95% O2 and 5% CO2. Immediately after slicing, sections were transferred in an interface chamber continuously perfused with aCSF. After 30 min, individual slices were laid down over an 8×8 array of planar microelectrodes, each 50 × 50 mm in size, with an interpolar distance of 450 mm  and kept submerged in aCSF  with a nylon mesh glued to a platinum ring. Voltage signals were acquired using the MED64 System (Alpha MED Sciences). Field excitatory postsynaptic potentials (fEPSPs) were recorded from CA3 in hippocampus by stimulating mossy fibers. Stimulation intensity was adjusted to evoke fEPSP amplitudes that were 30% of maximal size. For induction of long-term potentiation (LTP), theta-burst stimulation (TBS) was used which consisted of 3 pulses at 100 Hz, repeated 3 times with a 200 ms interval. The magnitudes of LTP are expressed as the mean percentage of baseline fEPSP initial slope.Mice brains were cut into horizontal sections of 300 μm thickness by a vibration microtome in cold artificial cerebrospinal fluid (aCSF) containing NaCl 126 mM, KCl 2.5 mM, NaHCOPostmortem human brain paraffin sections were provided by Dr. K Ye of the Emory University School of Medicine, USA. The human brain tissues were approved by Dr. Chao Ma of Peking Union Medical College, and Chinese Academy of Medical Sciences Human Brain Bank, Beijing, China. AD was diagnosed according to the criteria of the Consortium to Establish a Registry for AD and the National Institute on Aging. All subjects signed informed consent. The patients' information was listed in Supplementary The HEK293 cells and C57BL/6J mice brain tissues were lysed in RIPA buffer  on ice for 10 min to collect the total cellular proteins. The nuclear and cytoplasmic proteins were fractionated using the Nuclear Extraction Kit (Signosis) by following the manufacturer's instructions. The protein concentration was determined by using BCA Protein Assay Kit. After electrophoresis, the proteins were transferred onto the nitrocellulose membrane, following blocked using 5% BSA for 1 h at room temperature. The membranes were incubated with primary antibody  were added to the slices for 1 h at 37 °C. Finally, DAPI was applied for nuclear staining for 10 min at room temperature. The images were observed with a laser confocal microscope .The EMSA-STAT3  was performed according to the protocol supplied by the manufacturer. The nuclear samples were incubated with a biotinized oligonucleotide probe containing a STAT3 binding site at 22 °C for 30 min. Then, the samples were separated on a non-denaturing polyacrylamide gel and transferred to Nylon Membrane. The transferred oligonucleotides were immobilized by UV cross-linking at 120,000 J for 1 min. For detection, streptavidin-HRP was added to the membrane, and the blots were developed by ECL. Competition experiment was performed using excess amounts of unlabeled cold probe containing STAT3 binding site.Total RNA was extracted from mouse CA3 hippocampus samples using Trizol reagent . The reverse transcription and quantitative PCR was performed according to manufacturer's instruction . The PCR system contains 0.5 μM forward and reverse primers, 2 μL SYBR Green PCR master mixes and 2 μL cDNA, and the standards for each gene were prepared using appropriate primers by a conventional PCR. RT-PCR was performed in a StepOnePlus Real-Time PCR Detection System . The expression level of the interest gene was normalized by the housekeeping gene β-actin, which was not changed by the treatments. PCR primers employed in the present study were listed in the supplementary The HEK293 cells were transfected with hTau plasmid or its empty vector control in combination with pSTAT3-Luc reporter construct and pRL-TK for 48 h. Then the cells were washed with PBS and lysed in 1 × CCLR for 10 min. The supernatant of the lysate was used to measure luciferase reporter gene expression using the Dual-Luciferase Reporter Assay System . The activity of TF (i.e. firefly luciferase) was normalized to transfection efficiency by using Renilla luciferase activity (pRL-TK).To generate luciferase reporter plasmids of GluN1, GluN2A or GluN2B promoter, PCR fragments from the mouse genomic DNA were inserted into the BglII and NcoI sites of the pGL3 basic luciferase expression vector  The HEK293 cell was homogenized on ice in RIPA buffer  for 10 min at 4 °C and then centrifuged at 12,000 × g for 15 min. 200 μg total proteins were incubated with primary antibodies (Supplementary 4 and 3.5% K2Cr2O7 at room temperature for two days. The sections were then incubated in 1% AgNO3(aq) for one month at room temperature in dark. Then the slide assemblies were dismantled in water and the sections were mounted on gel-coated slides (0.5% porcine gelatin), dehydrated in gradient alcohol, counterstained with hematoxylin. The images were taken using Olympus BX60 (Tokyo).For Golgi stain, the mice were anesthetized and then perfused transcardially with 0.9% NaCl, and the brain tissues were incubated in 0.4% OsOAll data were collected and analyzed in a blinded manner. Data were expressed as mean ± SD or mean ± SEM and analyzed using SPSS 12.0 statistical software . Statistical analysis was performed using Student's t-test, one-way ANOVA or two-way repeated measures ANOVA followed by Bonferroni's post hoc test. The level of significance was set at p < 0.05.Supplementary figures and tables.Click here for additional data file.


By testing people with and without aphantasia, we investigate the relationship between sensory imagery and sensory sensitivity . In Experiment 1 we first show that people with aphantasia report impaired imagery across multiple domains  rather than simply vision. Importantly, we also show that imagery is related to sensory sensitivity: aphantasics reported not only lower imagery, but also lower sensory sensitivity. In Experiment 2, we showed a similar relationship between imagery and sensitivity in the general population. Finally, in Experiment 3 we found behavioural corroboration in a Pattern Glare Task, in which aphantasics experienced less visual discomfort and fewer visual distortions typically associated with sensory sensitivity. Our results suggest for the very first time that sensory imagery and sensory sensitivity are related, and that aphantasics are characterised by both lower imagery, and lower sensitivity. Our results also suggest that aphantasia  may be more accurately defined as a subtype of a broader imagery deficit we name dysikonesia, in which weak or absent imagery occurs across multiple senses.People with  Mental images themselves are “iconic” in that visual images in some way resemble internal pictures, and auditory images in some way resemble internal sounds. Being able to form mental images is an essential part of life for many people but this capability varies from person to person. For some people, visual mental imagery is exceptionally strong and nearly as vivid as real-life perception, while for others it is virtually or completely absent, a condition known as hantasia , 2020. Ovisual imagery , textures (by imagined touch), tastes or smells?’; Bett's Questionnaire Upon Mental Imagery  at particular spatial frequencies that trigger visual sensitivities (pattern glare’), indicative of visual sensitivity (low/absent imagery (aphantasics) might report lower sensory sensitivity than the average person.The role of cortical excitability in imagery also raises a second important question. Visual cortex excitability has also been linked to a second phenomenon, ty e.g., . Sensorytivities . Visual sitivity . Importasitivity . Hyper-esitivity  has beensitivity . Other rsitivity , 2015 resitivity , 2017, asitivity . Taken thyper-sensitivity . But properly speaking, sensory sensitivity encompasses both hyper- and hypo- sensitivities  to have lower levels of hyper-/hypo-sensitivity, when compared to imaging controls.Adaptation is when we stop noticing the smell of someone's perfume, for example, after having been in their company for a while . Normal and low sensitivities to incoming sensory information. Our model makes several predictions. First, people with aphantasia  may report fewer sensory sensitivities within the visual sense, relative to imaging controls (Experiment 1). Second, we predict that people with aphantasia may in fact have poor imagery across multiple domains . If so, we predict, thirdly, that people with aphantasia may also therefore self-report lower sensory sensitivities across multiple domains (Experiment 1). Thirdly, we predict that the sensory sensitivities reported in Experiment 1 by people with aphantasia should be corroborated by behavioural findings in a subsequent study (Experiment 3): aphantasics should experience fewer visual distortions and less visual discomfort than controls in response to irritable visual gratings in a Pattern Glare Task. Finally, if our model holds true in the population at large, then we predict visual imagery and sensory sensitivity to be positively correlated in a general population sample (Experiment 2). In summary, by investigating, for the first time, the relationship between imagery and sensory sensitivity, the present research aims to enhance our understanding of imagery, sensory sensitivity, cortical excitation, and aphantasia.We summarise our hypothesised link between imagery, sensory sensitivity, and cortical excitation in Autism Spectrum Quotient  and 138 controls 1. The majority of our aphantasic participants (n = 158) were recruited from the University of Sussex's Imagery Lab - Aphantasia Cohort while the remaining aphantasic participants (n = 6) were recruited from the student body of the University of Sussex. Control participants were recruited from multiple sources including Amazon's Mechanical Turk , while our non-aphantasic controls scored above 32 . As compensation for taking part, non-students were entered into a prize draw for a shopping voucher, and MTurk participants were given a monetary payment of $4 (for our 25 min test). For all experiments reported in this paper, participants provided informed consent prior to taking part, and ethical approval came from the University of Sussex Sciences and Technology Cross-Schools Research Ethics Board.Participants were separated into their two groups using the ‘gold standard’ questionnaire for aphantasia  Six questionnaires measured imagery (summarised in www.syntoolkit.org).Participants completed eight questionnaires online in a random order. . The VVIQ was our aphantasia diagnostic. In this questionnaire, participants were asked to imagine a series of four scenarios  and to rate the strength of their visual imagery for four aspects of each scene . Imagery was rated on a five-point scale, comprising (1) , (2) (“Vague and dim”), (3) (“Moderately clear and vivid”), (4) (“Clear and reasonably vivid”), and (5) . Responses were summed to give scores ranging from 16–80. As noted above, responses between 16–32 (no imagery or vague/dim) are indicative of aphantasia.Clarity of Auditory Imagery Scale . Here, participants were asked to imagine a series of 16 sounds  and to rate how clearly they could ‘hear’ the sounds on a scale of 1  to 5 . All scale-points here  mirror the wording of the VVIQ . To achieve this, we slightly re-worded the original response-scale  to ensure consistency with other imagery questionnaires in our study . Total scores range from 16–80.Vividness of Olfactory Imagery Questionnaire . Participants were asked to imagine four different odorous situations . Participants rated the vividness of their olfactory imagery for four aspects of each scenario  on a scale from 1  to 5 . Total scores range from 16–80.Adapted Shortened Betts’ Questionnaire Upon Mental Imagery . We used this questionnaire to measure imagery for taste, tactile, and bodily sensations. Participants were asked to imagine tasting 12 items , touching 12 items , and the experience of 12 bodily sensations . Given the methodological issues noted in our Introduction, we used the version of the Betts’ questionnaire updated by Vividness of Movement Imagery Questionnaire 2 . Using the kinaesthetic subscale of this questionnaire, participants were asked to imagine the feeling of performing 12 movements . The direction of the scoring scale was reversed to match our other imagery questionnaires, meaning participants rated vividness from 1  to 5 . Responses were summed, with possible scores ranging from 12–60.Plymouth Sensory Imagery Questionnaire . This imagery questionnaire covers multiple domains, and was used in addition to our separate measures to ensure reliability of our results. Participants were asked to form a mental image of five items for each of the seven domains: visual , auditory , olfactory , gustatory , tactile , bodily sensation , feeling . Participants rated each item on a scale from 0  to 10 . This questionnaire provided imagery scores for each sense domain by averaging the items for each modality separately, with possible scores 0–10.Glasgow Sensory Questionnaire . This 42 item questionnaire measures sensory sensitivity across seven sense domains  with six items per sense. Within each sense, half of the items (n = 3) measure hyper-sensitivity and half measure hypo-sensitivity. Examples for hyper-sensitivity include “Do bright lights ever hurt your eyes/cause a headache?”  and items for hypo- sensitivity include “Do you really like listening to certain sounds ?” (auditory). Each question is rated on a scale of 0 (“Never”), 1 (“Rarely”), 2 (“Sometimes”), 3 (“Often”), and 4 (“Always”). The measure outputs an overall sensitivity score summed across all items (ranging 0 to 168), as well as one score for each of the seven senses . It also produces two scores collapsed over senses for hypo- and hyper-sensitivity respectively (ranging from 0 to 84 each).The Autism Spectrum Quotient . This 50 item questionnaire asks about five types of autism symptomatology: imagination , social skills (e.g. “I find it hard to make new friends”), communication (e.g. “I frequently find that I don't know how to keep a conversation going”), attention switching (e.g. “I prefer to do things the same way over and over again”), and attention-to-detail (e.g. “I tend to notice details that others do not”). Participants rated their agreement on a 4-point scale: “Definitely agree”, “Slightly agree”, “Slightly disagree”, “Definitely disagree”. Responses are scored 1 for slightly/definitely agreeing with autism traits , or 0 otherwise. Scores of ≥32 are taken as the usual suggestive threshold for autism  = 858.17, p < .001, M = 1.29; SD = 1.94; controls: M = 7.62; SD = 1.78) and there was also a main effect of sense domain  = 22.72, p < .001, F = 20.17, p < .001, p < .001, with large hedges’ g effect sizes = 1.77–4.48; see Table S1 in our Supplementary Information, SI, for further details). Here, and throughout our manuscript, we report bias-corrected and accelerated (BCa) bootstrapped confidence intervals  for group comparisons where deviations from normality are present in our data. After vision, tactile imagery was most affected, followed by olfactory, gustatory, bodily, auditory, and feeling imagery , we first conducted a 2 × 7 ANOVA crossing group  with sense . Aphantasics reported significantly weaker imagery than controls overall . Given that our imagery questionnaires were independent measures and had different total possible scores (see methods), we ran a series of bootstrapped independent samples t-tests with Welch correction comparing aphantasics to controls. Again, group differences were significant for every sense . These differences are depicted visually in Next, we sought to replicate our findings using our other sensory imagery questionnaires  and identified participants who scored within the standard aphantasia range , albeit for non-visual imagery. We found that 159 (97%) aphantasics showed aphantasia-like imagery weakness in at least one other  domain, compared to 15 controls (11%), which was highly significant  = 223.89, p < .001; using Yates correction). Moreover, 101 aphantasic individuals (62%) had aphantasia-like imagery (absent or vague/dim) across all other sense domains, compared to 0 controls  = 124.94, p < .001), and 56 aphantasics (34%) reported no imagery at all (not vague/dim) in any domain at all  = 55.61, p < .001). In summary, aphantasics not only had weaknesses in visual imagery, but almost always (97%) had another imagery deficit , and sometimes (62%) had deficits across all imagery modalities.Next, we asked whether the imagery deficits seen in our aphantasics were severe enough to be considered ‘aphantasia-like’ , even outside the visual domain. We therefore looked within each independent sensory imagery questionnaire  = 27.08, p < .001, M = 50.52, SD = 22.88) relative to controls .Next, we asked whether aphantasics reported fewer sensory sensitivities than controls by looking at the GSQ. We conducted a 2 × 2 × 7 ANCOVA crossing group  with sensitivity type  and sense domain . We included AQ scores as a covariate to control for the influence of autism traits. The ANCOVA revealed a significant main effect of group  = 1.81, p = .277, F = 9.16, p = .003, M = 27.68; SD = 13.60), (t(263.81) = 2.84, p = .005, g = 0.33, BCa 95% CI )), and hypo-sensitivities , (t(224.09) = 4.81, p < .001, g = 0.58, BCa 95% CI )), than controls ,  = 7.41, p < .001, p < .01, g = 0.41–0.54). All differences here survive correction for multiple comparisons. There was, however, no difference between aphantasics and controls in auditory sensory sensitivity . A Bayes Factor  = 92.94, p < .001, F = 12.60, p < .001, Our ANCOVA also revealed other effects unrelated to our hypotheses . For example, there was a significant main effect of AQ score,  senses compared to controls. Indeed, 97% of aphantasics  had imagery deficits not only in the visual domain but also in at least one other. Aphantasics also reported fewer sensory sensitivities overall (in both hyper- and hypo-sensitivity), and fewer sensitivities within each of the senses with the exception of audition. In sum, our results show that imagery and sensory sensitivity are related: people with aphantasia experience lower levels of imagery and sensory sensitivity across multiple sense domains.In Experiment 1 we demonstrated that imagery and sensory sensitivity are linked. But we are left with the question of whether the link between imagery and sensory sensitivity is seen specifically in aphantasic people, or whether it applies to the general population also. In Experiment 2 we addressed this question, by examining whether there is an association between visual imagery and sensory sensitivity in a student general population sample.M age = 19.87, SD = 3.60). Participants took part in our study in return for research participation credits, and were sampled without mention of imagery or aphantasia in order to represent a random sampling of the population. Six of these participants had VVIQ scores in the aphantasia range (16–32), but they are included here because their scores represent part of the natural continuum of imagery within a general population sample.Our participants were 83 undergraduate students registered at the University of Sussex  and they completed the study from their own homes.Participants completed the VVIQ , GSQ se, and theF = 21.35, p < .001) and model two  = 13.18, p < .001) significantly predicted sensory sensitivity scores. Model two was a significantly better model than model one, explaining 24.8% of the variance in sensory sensitivity scores (3.9% more than model one),  = 4.17, p = .045). As expected from prior literature  = .27, t = 4.61, p < .001, 95% CI ), showing that sensory sensitivity increased with levels of autism traits. Importantly, model two also showed a significant positive association between VVIQ score and GSQ score,  = .13, t = 2.04, p = .045, 95% CI ), indicating that as visual imagery scores increased, so did overall sensory sensitivity scores. Overall, our analysis shows that even when taking into account the influence of autistic traits (std. b = .45), visual imagery is a significant positive predictor (std. b = .20) of sensory sensitivity (see To examine whether there was an association between imagery and sensory sensitivity we conducted a linear regression (using the enter method) predicting sensory sensitivity (GSQ) from visual imagery (VVIQ) and autism traits (AQ score). We constructed two regression models, the first predicting GSQ score from AQ score, and the second adding VVIQ score as an additional predictor. Our data was normally distributed, and the residuals in our models met the required assumptions for parametric tests . Both model one , but that this relationship is also evident in the general population (Experiment 2). In other words, while in Experiment 1 we found that people with aphantasia (who are low in imagery) tend to experience reduced levels of sensory sensitivity, here we found further support in the general population: as visual imagery level increases, so does overall sensory sensitivity.Pattern Glare Task. In response to gratings designed to elicit pattern glare, we asked whether people with poor imagery would experience less visual sensory sensitivity  than we might otherwise expect. Here, we compared a sample of people with aphantasia (low imagery) to controls with typical visual imagery abilities. We predict that the former will show lower pattern glare effects, indicative of lower sensory sensitivity.In Experiments 1 and 2 we demonstrated that people who report lower visual imagery also report lower sensory sensitivities – both in aphantasia, and in the general population. Experiment 3 aims to validate our findings using behavioural evidence from a M age = 33.66; SD = 8.36) and 56 controls . Participants were matched in age (t(80.39) = −1.52, p = .133). Controls were recruited from the same sources as Experiment 1, with the addition of the undergraduate student body at the University of Sussex, and excluding Amazon's Mechanical Turk. Most of our aphantasic participants were recruited from the University of Sussex's Imagery Lab - Aphantasia Cohort (n = 54), with the remaining participants (n = 2) recruited via the same sources as controls. Participants were verified as aphantasics in the same way as in Experiment 1 . Of our participants, 24 also took part in Experiment 1 , and 5 also took part in Experiment 2 (5 controls). Participants were advised not to take part in the study if they had a history of epilepsy given that uncomfortable visual patterns can trigger photosensitive epilepsy , as well as the VVIQ protocol.All participants completed the pattern glare task online using the testing platform Inquisit . Our version was based on low , and high (approx. 10 cycles per degree). The low spatial frequency grating acted as a baseline stimulus, whilst the medium and high spatial frequency gratings were ‘irritable’ gratings expected to elicit ‘pattern glare’ (n glare’ . GratingParticipants were told they should fixate their gaze on the centre of each pattern presented. Each grating was presented twice (each for 5 s) in a random order . Following each grating , participants were asked two questions: how uncomfortable the image was, and how many visual effects were experienced. Comfort was measured on a sliding scale from 1 (“Extremely uncomfortable”) to 11 (“Extremely comfortable”), and visual effects were reported by checking any that applied . See For each of our measures (see below), we conducted a 2 × 3 ANOVA crossing group  and grating spatial frequency . These were followed by bootstrapped independent samples t-tests with Welch correction to examine differences in response to the gratings designed to elicit visual sensory sensitivity . We did not conduct pairwise comparisons for the low (baseline) grating to avoid proliferation of multiple comparisons, and because our hypotheses did not motivate this  = 46.25, p < .001, M = 5.31, SD = 1.67) and medium gratings overall , relative to the low (baseline) grating . There was no significant main effect of group  = 2.21, p = .140, F = 4.25, p = .019, M = 6.04, SD = 1.84) and controls  = −1.42, p = .159, d = 0.27, BCa 95% CI ; Bayes factor = 0.49, i.e., anecdotal support for null hypothesis), there was a significant difference for the high spatial frequency grating. Here, aphantasics reported significantly less visual discomfort  than controls , (t(109.03) = -2.62, p = .010, d = 0.49, BCa 95% CI ). This demonstrates that although both groups found the high grating uncomfortable , aphantasic individuals reported less discomfort than controls  = 130.70, p < .001, M = 2.86, SD = 1.68) and medium gratings overall , relative to the low (baseline) grating . Our analysis also revealed a significant interaction  = 6.95, p = .002, F = 3.18, p = .077, We next looked at the M = 2.50, SD = 1.44) than controls , (t(104.41) = 2.30, p = .024, d = 0.43, BCa 95% CI ). However, aphantasics also reported significantly fewer visual effects  than controls  in response to the medium grating (t(106.03) = 2.26, p = .026, d = 0.43, BCa 95% CI ); see As before, for the high spatial frequency grating, aphantasics reported significantly fewer visual effects  reported no imagery whatsoever in any sense whatsoever.Our intention was to characterise the sensory imagery deficits and sensory sensitivities experienced by people with aphantasia, and in doing so, examine how imagery and sensory sensitivity may be linked. Until now, aphantasia has been characterised by an absence of ry e.g., , 2020. BSheehan's adapted version of Betts’ Questionnaire Upon Mental Imagery; Our results are in line with research showing that imagery strength is often positively correlated across sensory modalities , and fitwhy aphantasics often experience multi-modal imagery deficits. We have suggested that understanding the neural mechanisms underlying mental imagery may shed light on this. Engaging in imagery is associated with activation in the relevant sensory cortex  and this has been shown for visual . Therefore, although having low imagery in one domain  is often associated with imagery impairments in other senses, this may not always be the case. If our model holds true, having low cortical excitability in one sensory area may increase the likelihood of low excitability in other sensory areas, but we are proposing likelihoods not absolutes, and therefore expect individual differences. Testing these models with neuroscientific methods will be a fruitful avenue for future research. Alternatively, however, it is possible that even these 3% did have other imagery weaknesses, but simply in sense domains we did not test . Therefore, testing these domains will be an important avenue for future research.It is important to recognise that there were rare instances where aphantasics reported Pattern Glare Task . Overall, our findings suggest that imagery and sensory sensitivity are linked, potentially (our model suggests) via cortical excitability. We noted that cortical excitability has been traditionally associated with levels of hyper-sensitivity . However, the auditory channel is also remarkable in being the most sensitive . In support of our model, we found that aphantasic individuals reported fewer sensory sensitivities, both overall  and within every sense domain tested with the exception of audition. This effect remained even when factoring out autistic traits. In Experiment 2 we extended this finding to the general population, showing again a positive correlation between imagery and sensitivity, while again factoring out AQ scores. In Experiment 3, we provided behavioural support: aphantasic individuals demonstrated less sensitivity than controls in a aptation . This leWhat specific neural mechanisms might underpin low excitation across multiple sensory cortices in aphantasia? Our model fits broadly with neural noise theories from the autism literature which suggest that high levels of sensory sensitivity in people with autism are driven – at least in part – by increased levels of neural noise (excitation) within sensory cortices . This thhigh levels of autism traits . A simple explanation may lie in the fact that autism is a cluster of traits in different domains, and some populations  may show higher scores in only certain domains. We know already that aphantasics show weak imagination and social skills, but match controls on other autism traits such as attention-to-detail, attention switching, and communication , or indeed for cases where the particular domain of the imagery deficit has not been specified. Its etymological root (icon2) has the useful meaning of a form which reflects its referent, in the same way that visual imagery has qualities that reflect visual entities in the world, and auditory imagery has qualities that reflect auditory entities in the world, and so on. People with dysikonesia, we suggest, therefore have visual and auditory (and other sensory) knowledge, but without the iconic quality of imagery. This classification makes aphantasia one sub-class of dysikonesia, and 3% of our aphantasic group experienced this subclass only . Overall, our results provide an important step forward in understanding the experience of imagery in aphantasic individuals, and we open up a wider debate about the phenomenology, and indeed the causes and consequences of absent imagery.Finally, our study also provides answers about the very nature of aphantasia. Aphantasia was originally named for its links to the Greek word for imagination (φαντασία) and the related term φάντασμα (phántasma, “phantasm, an appearance, image, apparition, spectre” . It alsodysikonesia. We present both self-report and behavioural methods, and propose a model linking imagery and sensory sensitivity via neural excitability within sensory cortices.In conclusion, our study is the first to characterise the relationship between mental imagery and sensory sensitivity, the first to model this relationship, and the first to name and model a phenotype of poor imagery cross-senses. Our findings raise questions about the best way to define aphantasia, which we now embed within a broader multi-modal imagery deficit we term Click here for additional data file.Supplemental material, sj-docx-1-pec-10.1177_03010066211042186 for What is the Link Between Mental Imagery and Sensory Sensitivity? Insights from Aphantasia by C. J. Dance, J. Ward and J. Simner in Perception


Power optimization is an important part of network-on-chip(NoC) design. This paper proposes an improved algorithm based on genetic algorithm on how to properly map IP  cores to 3D NoC. First, in view of the randomness of the traditional genetic algorithm in individual selection, an improved greedy algorithm is used in the initial population generation stage to make the generated individuals reach the optimal. Secondly, in view of the weak local optimization ability of the traditional genetic algorithm and prone to premature problems, the simulated annealing algorithm is added in the crossover operation stage to make the offspring reach the global optimum. The experimental results show that compared with the traditional genetic algorithm, the algorithm has better convergence and low power consumption performance, which can quickly search for a better solution, in the case of a large number of cores (124 IP cores), the average power consumption can be reduced by 42.2%. As the mainstream design technology of next-generation IC, NoC will be the inevitable trend of system-on-chip development . AlthougAt present, the related research of 3D NoC mainly focuses on the topological structure, quality of service, routing algorithm and communication power consumption of 3D NoC ,6. With Many international experts and scholars have proposed many ways to reduce power consumption for 3D NoC in different aspects and levels, which are mainly reflected in logic signal design, routing algorithm optimization and design, and low-power mapping of network topology . 3D NoC 3D NoC mapping algorithms can be divided into two categories: the traditional mapping optimization algorithm used in the early days and the new intelligent mapping algorithm based on heuristics. Traditional mapping algorithms mainly include exhaustive method, linear programming algorithm, and branching algorithm, which mainly solve the situation where the early structure of the on-chip network is simple and the application is rare . Most ofThe 3D NoC mapping platform is composed of resource nodes, routing, and physical links. This article uses a regular 3D NoC topology, as shown in Network mapping on a chip refers to assigning the logical IP cores with known communication relations and traffic volumes on a given task characteristic graph (TCG) to the corresponding resource nodes of the architecture characteristic graph (ARCG) under specified system constraints, so that various applications can be completed smoothly and efficiently under the constraints ,17. The Definition 1.The task graph TCG is an acyclic directed graph, the vertex Among them, Definition 2.Structural feature graph ARCG is also a directed graph, vertex Definition 3.When the task graph TCG and the structural feature graph ARCG are the same in size, that is, Definition 4.For 3D-Mesh NoC, Therefore, the communication volume Therefore, for a given task graph TCG and feature structure graph ARCG, when it satisfies Definition 3, the objective function of the NoC mapping problem can be defined as:The power consumption of a 3D NoC system is mainly the power consumption generated by data communication between arbitrary resource nodes. Therefore, in order to maximize the reduction of power consumption, this paper adopts the power consumption model proposed in , which iAmong them, Therefore, the formula for calculating the energy consumed by 1-bit data from node Among them, Then the communication power consumption of the entire NoC system is:From Equation (3), we can get:Among them, all are constants except Combining the above formula, it can be seen that the key factor of low-power mapping is Due to the emergence of TSV technology, the line length of 3D NoC in the vertical direction is much smaller than the length in the horizontal direction. Therefore, to transmit the same amount of data, the energy consumption in the vertical direction is much smaller than the energy consumption in the horizontal direction.The genetic algorithm is to construct an adaptive value function according to the solution of the problem to generate a population composed of multiple solutions. Individuals in the population are selected for genetic operation to generate a new population according to the adaptive value. When the evolution reaches the termination condition, individuals with good adaptive value are selected as the solution of the problem. The idea of genetic algorithm is easy to implement, the algorithm is highly efficient, has strong global search ability, and is suitable for solving discrete problems. Therefore, genetic algorithm is very suitable for solving 3D NoC mapping problems .The initial population of the traditional genetic algorithm is generated randomly, the individual distribution of the initial population is not uniform, and the quality of the population is low. At the same time, the traditional genetic algorithm has strong global search ability, but poor local search ability, and easy to fall into the trap of precocity, it takes a long time to get the optimal solution. Therefore, the traditional genetic algorithm needs to be improved to solve the 3D NoC mapping problem ,21,22.In view of the shortcomings of traditional genetic algorithm that generate the initial population with large randomness and low population quality, this paper adds an improved greedy algorithm in the selection operation stage; in view of the shortcomings of traditional genetic algorithm of strong global optimization ability and poor local optimization ability, this work adds a simulated annealing algorithm in the crossover operation stage. The improved genetic algorithm realizes the optimization of the initial population and the global optimization of the offspring.The process of improving genetic algorithm is shown in Implementation steps of improved genetic algorithm:Step l. Initialize the population with an improved greedy algorithm.i is less than or equal to the maximum number of iterations, continue to step 3, otherwise it ends, and the optimal solution is returned.Step 2. When Step 3. Calculate the individual fitness value.Step 4. The male parent is selected according to the fitness value, the male parent with the higher fitness value is selected, and the male parent with the lower fitness value is eliminated.Step 5. The simulated annealing algorithm is used to crossover the parent chromosomes to generate offspring.Step 6. Perform mutation operations on the offspring’s chromosomes to generate new offspring.Step 7. Obtain the optimal individual, update the population, and return to step 2 until the optimal solution is produced.Individual vector representation is the first problem to be solved when designing genetic algorithms, and it is also a key step in it. The way of vector representation will affect the calculation methods of crossover and mutation operators, and determines the efficiency of genetic evolution.X = , n is the number of IP cores of the TCG, and ix is the number of the IP core. The code of individual X is a sequence from 1 to n, and a mapping scheme can be obtained by decoding the code of individual X [Since each processing unit can send data to any other processing unit, the IP core in the TCG can be mapped to any processing unit. In the 3D NoC mapping, the population individuals are coded by integers, assuming that the individual vidual X .A simple task graph PIP is shown in t0 to t7, t0 to t3 represent the first layer, and t4 to t7 represent the second layer, as shown in The IP cores in the task map are represented by numbers 0 to 7, mapping PIP to a 2 × 2 × 2 3D NoC. Each processing unit on the 3D NoC is represented by numbers X =  means to map IP cores  to processing units , the mapping result is shown in Individual After all IP cores are mapped to the 3D NOC, calculate the total amount of communication between all nodes on the 3D NoC. The genetic algorithm calculates its fitness value based on the total amount of communication on the 3D NoC. The larger the total amount of communication, the smaller the fitness value. The genetic algorithm performs genetic operations based on the fitness value.The initial population selection of traditional genetic algorithms is generally random, the quality of the obtained population is usually low while the iteration speed is slow. In response to this situation, this paper proposes an initialization method based on an improved greedy algorithm to generate population Pop, which can greatly increase the iteration speed of the population, effectively reduce the amount of data communication, and reduce power consumption .The initial population optimization process of the improved greedy algorithm is shown in The initial population optimization steps of the improved greedy algorithm:i , and then map this random number i to the first position of individual X, and use an improved greedy algorithm to generate an array of the smallest spanning tree with i as the first node.Step l. Randomly generate a number P = {1, 2, …, n}, where n is the number of IP cores of the TCG, and delete i from the available set P.Step 2. Initialize the available set n in the available set P, put n into all available positions of the individual X, and calculate the fitness value of X after putting them in these positions. Find the maximum fitness value itf from these fitness values, mark the position m of the maximum fitness value itf after n is placed in X, and save the element <n, m, itf> to the set F.Step 3. Traverse the number F, find a group of elements <n, m, itf> with the largest itf value, put n into the m-th position of the individual X, delete n from the available set P, and delete m from the available positions.Step 4. Traverse the set P is empty. When P is empty, it means that a new individual X has been produced, and X is added to the temporary population Temppop.Step 5. Repeat Step 3 and Step 4 until the available set n individuals.Step 6. Repeat Step 5 above to generate X to generate a neighbor individual of X. Use this method to generate 20 neighbor individuals for the individual X generated by the above steps, and put these neighbor individuals into the temporary population Temppop.Step 7. Exchange any two coordinate numbers of individual n times to generate n individuals obtained by the improved greedy algorithm and 20 neighbor individuals of these individuals.Step 8. Repeat all the above steps Step 9. Select multiple individuals with the largest fitness value from Temppop and put them into Pop as the initial population.The simulated annealing algorithm is based on the Monte-Carlo iterative solution strategy to find the optimal solution randomly. It can probabilistically jump out of the local optimal solution during the solution process and obtain the final global optimal solution. The simulated annealing algorithm overcomes the poor local optimization ability of the traditional genetic algorithm and its premature phenomenon. Combining it with the traditional genetic algorithm will better exert the global optimization ability of the traditional genetic algorithm and the local optimization ability of the simulated annealing algorithm .Based on this consideration, this work adds the simulated annealing algorithm to the crossover operation stage of the traditional genetic algorithm to ensure that the generated offspring will not fall into the local optimum, so that the population jumps out of the local optimum and finally reaches the global optimum.The population global optimization process of the simulated annealing algorithm is shown in f, and the threshold of change is f′. When f > f′, the new individual is accepted; otherwise, the new individual is accepted with a certain probability T represents temperature.Suppose the fitness value of the newly generated individual is The population global optimization steps of the simulated annealing algorithm:T0 and the lowest temperature Tfinal.Step 1. Set the starting temperature T = T0, i = 0.Step 2. Initialize temperature T > Tfinal, execute the next step; otherwise, it ends and returns the optimal solution.Step 3. When i ≤ the number of crossover operations, execute the next step; otherwise, it ends and returns to the optimal solution.Step 4. If n pairs of individuals from the population as male parents, and perform the following operations on each male parent:Step 5. Select P1 and P2 to generate offspring S1 and S2, and the fitness values of S1 and S2 are calculated.Step 5.1. The crossover operation is performed on the parents P1 and P2 with S1 and S2; otherwise, keep P1 and P2 with the probability of Step 5.2. If i = i + 1.Step 6. Cool down according to method Step 7. Go to Step 4.T0 and Tfinal represent the initial temperature and the end temperature, respectively.Among them, The simulation experiment is under the Ubuntu 14.04 operating system, using C++ language to write the algorithm implementation program; under the Codeblocks 13.11 environment, using Access Noxim 0.2 as the simulation software to simulate the 3D NoC mapping algorithm .(1)Topology SelectionThe 3D Mesh structure is obtained by directly extending the 2D Mesh to the three-dimensional structure, so its structure is relatively simple. At the same time, it also has certain advantages in terms of layout and routing. As the TSV technology is used in the vertical direction, the overall wiring length is reduced and the transmission efficiency is improved. Therefore, this work chooses to use the 3D Mesh structure for experiments .(2)RoutingIn terms of routing algorithm, this article adopts XYZ dimension order routing algorithm: XYZ routing algorithm is simple to implement and it is the most commonly used routing algorithm in 3D NoC .(1)Algorithm Parameter SettingsSuppose the experimental population size is 200, the number of genetic iterations is 100, the crossover rate is 0.9, and the mutation rate is 0.02.(2)Parameter Setting of Simulation SoftwareThe data packet is injected using Memory-less Poisson Distribution, and the packet injection rate is 0.02; the size of the data packet ranges from 2 flits to 10 flits; the buffer size of each channel of the router is 8 flits. The simulation software counts the total power consumption of 5000 cycles.The hardware environment configuration of the simulation experiment: A PC with an Intel Core i5-3470 CPU, 3.2 GHz main frequency, and 8 GB memory is used.In the experiment, two mapping algorithms, improved genetic algorithm and traditional genetic algorithm, were used to compare and analyze the convergence speed and power consumption of a given task graph and its mapping model.The simulation experiment uses three classic task maps MWD (Multi Window Displayer), VOPD (Video Object Plane Decoder), and DVOPD (Double Video Object Plane Decoder) for simulation experiments. Among them, MWD has 12 nodes, which are mapped to a 2 × 2 × 3 3D NoC. VOPD has 16 nodes, which are mapped to a 2 × 2 × 4 3D NoC. DVOPD has 32 nodes, which are mapped to a 2 × 2 × 4 3DNoC. As shown in Three classic task graphs MWD, VOPD and DVOPD are simulated, which MWD has 12 nodes, VOPD has 16 nodes, and DVOPD has 32 nodes. The number of simulation iterations is 100. The abscissa in the figure represents the number of iterations, and the ordinate represents the fitness value. The fitness value is negatively correlated with the communication volume of NoC. The lower the communication volume, the greater the fitness value.(1)Convergence Rate Analysis for MWDThe convergence speed comparison of MWD iteration 100 times is shown in (2)Convergence Rate Analysis for VOPDThe convergence speed comparison of VOPD iteration 100 times is shown in (3)Convergence Speed Analysis for DVOPDThe comparison of the convergence speed of DVOPD iteration 100 times is shown in Comparative experiments show that as the number of iterations increases, the improved genetic algorithm has a greater advantage in convergence speed. With the increase in the number of IP cores and the amount of communication, this advantage will become more obvious.Because the traditional genetic algorithm has a certain degree of randomness, in the experiment, the traditional genetic algorithm and the improved genetic algorithm are used to solve the three task graphs 10 times respectively, and the average value is taken.(1)Power Consumption Analysis for MWDThe experimental results of the two algorithms of MWD are shown in (2)Power Consumption Analysis for VOPDThe experimental results of the two VOPD algorithms are shown in (3)Power Consumption Analysis for DVOPDThe experimental results of the two DVOPD algorithms are shown in Comparative experiments show that as the number of IP cores increases, the total amount of communication decreases more and more, which means that the system power consumption will decrease more and more. Therefore, the improved genetic algorithm proposed is effective to reduce the power consumption for classic task graphs.The task generator TGFF is used to generate random task graphs with IP cores of 45, 60, 80, 98, and 124 . For tas(1)Analysis of Average Power ConsumptionWhen the number of IP cores is 45, the power consumption of the improved genetic algorithm is reduced by 36.8% compared with the traditional genetic algorithm. When the number of IP cores is 60, the power consumption of the improved genetic algorithm is reduced by 39.0% compared with the traditional genetic algorithm. When the number of IP cores is 98, the power consumption of the improved genetic algorithm is reduced by 39.3% compared with the traditional genetic algorithm. When the number of IP cores is 124, the power consumption of the improved genetic algorithm is reduced by 42.2% compared with the traditional genetic algorithm. The average power consumption comparison of the two algorithms is shown in (2)Analysis of Maximum Power ConsumptionWhen the number of IP cores is 45, the power consumption of the improved genetic algorithm is reduced by 16.3% compared with the traditional genetic algorithm. When the number of IP cores is 60, the power consumption of the improved genetic algorithm is reduced by 37.7% compared with the traditional genetic algorithm. When the number of IP cores is 98, the power consumption of the improved genetic algorithm is reduced by 26.4% compared with the traditional genetic algorithm. When the number of IP cores is 124, the power consumption of the improved genetic algorithm is reduced by 25.6% compared with the traditional genetic algorithm. The maximum power consumption comparison of the two algorithms is shown in (3)Analysis of Minimum Power ConsumptionCompared to the traditional genetic algorithm, when the number of IP cores is 45, the power consumption of the improved genetic algorithm is reduced by 43.6%; when the number of IP cores is 60, the power consumption of the improved genetic algorithm is reduced by 47.8%; when the number of IP cores is 98, the power consumption of the improved genetic algorithm is reduced by 21.0%; and finally when the number of IP cores is 124, the power consumption of the improved genetic algorithm is reduced by 40.5%. The minimum power consumption comparison of the two algorithms is shown in It can be seen from the above experimental data analysis that when the number of IP cores is small, the reduction in power consumption of the improved genetic algorithm compared with the traditional genetic algorithm is not obvious. This is because when the number of tasks is small, the initial population improvement of the improved genetic algorithm and the selection of good genes are not outstanding, and the algorithm may converge earlier. However, with the increase in the number of IP cores, from the overall trend, the reduction in power consumption of the improved genetic algorithm is gradually greater than that of the traditional genetic algorithm.Through the above experiments, the following conclusions can be drawn:In terms of convergence speed, when the task graph is small, the traditional genetic algorithm is easy to fall into local optimization, and the advantages of the improved genetic algorithm over the traditional genetic algorithm are not obvious. As the scale of the task graph increases and the number of iterations increases, the convergence speed of the improved genetic algorithm is faster than that of the traditional genetic algorithm. The maximum fitness value obtained by the improved genetic algorithm in the early stage of the algorithm is larger than that of the traditional genetic algorithm, while the improved genetic algorithm in the later period of the algorithm has a faster convergence speed and a larger fitness value.In terms of the power consumption of the classic task graph, when the task graph is small, the power consumption of the improved genetic algorithm is sometimes higher than that based on the traditional genetic algorithm. This is because when there are too few nodes in the task graph, the initial population obtained by improving the greedy strategy may sometimes fall into a local optimum. After the task scale gradually increases, this situation will be improved, the advantages of the improved genetic algorithm will be reflected, and the system power consumption will be reduced more significantly.In terms of power consumption of random task graphs, when the task graph is small, the advantages of improved genetic algorithms over traditional genetic algorithms are not obvious, and the reduction in power consumption is small. This is because when the number of IP cores is small, it is easy to cause the two algorithms to fall into the local optimum. However, judging from the overall trend, as the number of IP cores increases, the advantages of improved genetic algorithms are gradually revealed, and the reduction in power consumption has increased significantly.In view of the randomness of the traditional genetic algorithm when generating the initial population, adding an improved greedy algorithm in the population initialization stage can optimize the individual of the initial population and improve the quality of the initial population. Then in view of the lack of local optimization ability in the traditional genetic algorithm in the crossover operation stage, adding the simulated annealing algorithm to the crossover operator can realize the global optimization of the offspring population and improve the quality of the offspring population. The improved genetic algorithm proposed not only retains the fast random search characteristics of the traditional genetic algorithm, but also solves the shortcomings of the traditional genetic algorithm in the 3D NoC mapping process. Experimental results show that the improved genetic algorithm makes the layout of IP cores more reasonable, reduces the long-distance communication between IP cores, and significantly reduces the power consumption of the chip.This paper first introduces the 3D NoC power mapping algorithm based on traditional genetic algorithm, analyzes the problems of traditional genetic algorithm, and proposes an improved genetic mapping algorithm based on initial population selection and genetic operator selection. After that, for the 3D NoC model, its coding scheme, topology, routing algorithm and genetic parameter settings are given, and the convergence speed and power consumption of the two algorithms are compared for the classic task graph and the random task graph. The experimental results show that compared with the traditional genetic algorithm, the improved genetic algorithm proposed in this paper has a good effect in solving 3D NoC low-power mapping. With the increase in the number of IP cores, improved genetic algorithms have more significant advantages in reducing power consumption, and can better reduce the power consumption of 3D NoC compared to traditional genetic algorithms.


Strong genetic signals of post-colonial admixture and family-based structure highlight the localized impacts of colonial forces and island ecologies in this region, and more data from other groups are needed to more broadly inform on Dominica’s complex history and present diversity.The Caribbean is a genetically diverse region with heterogeneous admixture compositions influenced by local island ecologies, migrations, colonial conflicts, and demographic histories. The Commonwealth of Dominica is a mountainous island in the Lesser Antilles historically known to harbor communities with unique patterns of migration, mixture, and isolation. This community-based population genetic study adds biological evidence to inform post-colonial narrative histories in a Dominican horticultural village. High density single nucleotide polymorphism data paired with a previously compiled genealogy provide the first genome-wide insights on genetic ancestry and population structure in Dominica. We assessed family-based clustering, inferred global ancestry, and dated recent admixture by implementing the fastSTRUCTURE clustering algorithm, modeling graph-based migration with TreeMix, assessing patterns of linkage disequilibrium decay with ALDER, and visualizing data from Dominica with Human Genome Diversity Panel references. These analyses distinguish family-based genetic structure from variation in African, European, and indigenous Amerindian admixture proportions, and analyses of linkage disequilibrium decay estimate admixture dates 5–6 generations (~160 years) ago. African ancestry accounts for the largest mixture components, followed by European and then indigenous components; however, our global ancestry inferences are consistent with previous mitochondrial, Y chromosome, and ancestry marker data from Dominica that show uniquely higher proportions of indigenous ancestry and lower proportions of African ancestry relative to known admixture in other French- and English-speaking Caribbean islands. Our genetic results support local narratives about the community’s history and founding, which indicate that newly emancipated people settled in the steep, dense vegetation along Dominica’s eastern coast in the mid-19 The Centuries , 6. Mitoenturies , 8. We aArchaeological, historical, and genetic data indicate that populations across the Antilles have had complex histories and interactions over at least 6,000 years of human occupation in the Caribbean . Currentth and early 18th centuries as Spanish, British, and French conquests reached the area [th and 18th centuries, until approximately 14,000 people were legally emancipated in 1834 [Multiple Amerindian groups are known to have joined forces in Dominica against Spanish invaders following Christopher Columbus’s contact in 1493, and it is estimated that the Kalinago population declined by as much as 90% between the late 15 in 1834 . In 1903 in 1834 .th century [Bwa Mawego is a rural horticultural community in Dominica located on the island’s steep eastern coast, south of the indigenous Kalinago Territory. This village is one of the most remote on the island and is thought to have been populated by newly emancipated people who settled in the exceptionally steep windward landscape during the 19 century . Challen century –17. Popu century , yet gen century , 7. Peop century , and rel century , 23. As  century , 25.An analysis of admixture throughout the English-speaking Caribbean that used a targeted set of ancestry informative markers found significantly more indigenous and European ancestry in Dominica relative to all other islands that had more African ancestry, indicating that patterns of genetic admixture in Dominica are unique . Our samfastSTRUCTURE [Kmax = 4 indicates that four clusters maximize the marginal likelihood of observed genetic variation, and the upper bound estimate of K = 9 accounts for additional weaker population structure in Bwa Mawego  in reference to data from 919 people representing 53 populations in the Human Genome Diversity Panel (HGDP) dataset . The lowtructure . Bwa Mawan women .smartpca [We derived the first two principal components for the HGDP reference dataset in smartpca  and thenTreeMix [f3 statistics show the most significant negative branch lengths between Dominica and Yoruba, French, and Karitiana samples  and the local Village Council in Dominica. All participants gave written informed consent prior to any data collection, and parental consent was obtained for participants under the age of 18. Informed by the previously compiled pedigree  and longPLINK v.1.90 [-40 and individuals with call rates <0.90. Filtering removed 1,181 SNPs due to low call rate, one SNP due to Hardy-Weinberg Equilibrium p-value, and one individual due to low call rate. We used reference genotypes from the HGDP [Buccal swabs were stabilized at room temperature using Dri-Capsules  during dK v.1.90  to removthe HGDP  for ancefastSTRUCTURE [PLINK’s ‘—rel-cutoff’ flag, we down-sampled individuals from Dominica to exclude close relatives which produced a subsample of n = 22 individuals for which the relatedness among all pairs of individuals was r<0.025. This subset of Dominican individuals was used for clustering with the HDGP dataset in fastSTRUCTURE to infer ancestry with less confounding due to family-based structure. To visualize potential sex-biased admixture, we also ran fastSTRUCTURE to compare clustering between autosomes and the X chromosomes for 336 females from the down-sampled Dominica and HGDP datasets.We inferred population structure and admixture proportions via K-means clustering using the variational Bayesian algorithm in TRUCTURE . AllowinKmax from fastSTRUCTURE identified four well-defined genetic clusters in Bwa Mawego, and we modeled individual cluster affinities for these four groups as outcomes in four separate models for 91 people who were both genotyped and recorded in the previously compiled pedigree [MCMCglmm package in R v.3.6.3, we modeled individual identities as a random effect with the pedigree-derived kinship matrix representing the covariance among individuals to predict cluster affinity outcomes [fastSTRUCTURE. Each model ran for 1,020,000 iterations with a burn-in of 20,000, and we report posterior modes, 90% credible intervals, and effective sample sizes to summarize these Bayesian posterior distributions Click here for additional data file.


Background and Objectives: Altruism is a form of prosocial behavior with the goal of increasing the fitness of another individual as a recipient while reducing the fitness of the actor. Although there are many studies on its heterogeneity, only a few behavioral genetic studies have been conducted to examine different recipient types: family members favored by kin selection, the dynamic network of friends and acquaintances as direct reciprocity, and strangers as indirect reciprocity. Materials and Methods: This study investigated the genetic and environmental structure of altruism with reference to recipient types measured by the self-report altruism scale distinguished by the recipient (the SRAS-DR) and examine the relationship to personality dimensions measured by the NEO-FFI with a sample of 461 adult Japanese twin pairs. Results: The present study shows that there is a single common factor of altruism: additive genetic effects explain 51% of altruism without a shared environmental contribution. The genetic contribution of this single common factor is explained by the genetic factors of neuroticism (N), extraversion (E), openness to experience (O), and conscientiousness (C), as well as a common genetic factor specific to altruism. Only altruism toward strangers is affected by shared environmental factors. Conclusions: Different types of altruistic personality are constructed by specific combinational profiles of general personality traits such as the Big Five as well as a genetic factor specific to altruism in each specific way. In an evolutionary context, human altruism refers to the motivation to increase the fitness of another person as a recipient while reducing one’s own fitness . VariouAccording to the research findings on economic games, including the dictator game (DG), the ultimatum game (UG), and the public goods game (PGG), although the level of altruistic behaviors differs based on the characteristics of social groups, altruistic behaviors are common in humans . There However, this does not mean that all individuals are uniformly altruistic. Cultural and individual differences have been reported in the DG and the UG , and thA behavioral genetic approach is effective in describing the underlying structure of phenotypic altruism because it can divide phenotypic variability into genetic and environmental structures. Behavioral genetic studies on altruism and prosocial behavior show mixed results. The majority of studies follow the so-called three laws of behavioral genetics: (i) (additive) genetic (A) and (ii) non-shared environmental contributions (E), which are unique to individuals, are substantial; and (iii) shared (common) environmental effects (C), which make family members resemble each other due to environmental influences, are minimal or even nil. The so-called “AE model”, which explains total phenotypic variance only by the additive genetic and non-shared environmental effects, best fits actual data , around 20% by shared family environment (C), and 30% by non-shared or unique environments (E) , whereaDifferent types of altruism could be affected differently by psychological characteristics, such as personality traits, in relation to their functional significance, by which an individual takes some specific survival strategy to adapt to their social environment.For example, kin selection is hypothesized to relate to empathy, whereas non-kin reciprocity is related to forgiveness  and personality affordances  suggestThis study will investigate more sophisticated pictures of the genetic and environmental structure of altruism with reference to recipient types and examine its relationship to personality dimensions in a sample of the Japanese population.The study sample was drawn from the Keio Twin Study (KTS), a more than 20-year longitudinal twin cohort study conducted in Japan, beginning in 1998. The data structure and the method for zygosity diagnosis were reported by Ando et al., 2013  and AndoThe self-report altruism scale, which is distinguished by the recipient, the SRAS-DR   to obtain latent estimates of the additive genetic factors, shared, and non-shared by univariate, multivariate, and Cholesky decomposition, Mplus ver. 7  was useThe first two columns of The results of univariate analysis that estimate relative contributions of the additive-genetic effects (A), shared environment (C), and non-shared environment (E) are summarized in the remaining columns of To examine the genetic and environmental structure of altruism, the independent pathway (IP) model and the common pathway model (CP) were compared. Both CP and IP are statistical models that explain multiple intercorrelated phenotypic variables using more simple and parsimonious latent genetic and environmental factor structures. The IP model assumes latent genetic and environmental factors that are independent of each other and load on each variable with a specific weight. The CP model assumes an underlying latent common factor (or factors) affected by genetics and environmental effects that load on each variable in specific ways; it suggests some common psychological functions that affect multiple related behavioral traits and is a nested model of IP.The likelihood ratio test suggested that the CP model did not significantly harm the fit to the present data compared to the IP model. Additionally, despite a slightly lower CFI value than that of the other two models, the CP model showed a better fit to the data based on AIC, BIC, and RMSEA . TherefoThe finding of the CP model as the best-fit model suggests that there is a single general psychological mechanism underlying altruism toward different types of recipients. This single common factor is affected by genes and non-shared environment but not by shared environments. Specific altruistic factors toward family and friends/acquaintances are also explained only by genetics and non-shared environment. However, altruism toward others is affected by shared environment as well as by genetics and non-shared environments.2 of 241.833 and 224 degrees of freedom (p = 0.197) were quite good . The parameter estimates are presented in Genetic and environmental relationships between personality traits and the three types of altruism were examined using the Cholesky decomposition technique . BecauseWith the exception of A, personality dimensions  explain the overlap of the three types of altruism, which may consist of a single common factor of altruism. More specifically, the genetic factors of altruism toward family were characterized primarily by genetic overlap with C (0.277), as well as with N (−0.144), E (0.197), and O (0.118); there was no genetic overlap with A. Genetic factors of altruism toward friends/acquaintances were characterized by genetic overlap with all the five personality dimensions, with E being the most common (0.371), as well as N (−0.221), A (0.184), O (0.184) and C (0.131). Genetic factors of altruism toward others were characterized by genetic overlap primarily with E (0.340), as well as with N (−0.182), O (0.118), and C (0.179); no genetic overlap was observed with A. The genetic factor of A was only significantly related to altruism toward friends.The effect of a non-shared environmental structure bridging personality and altruism appears weaker than that of additive genetic structures. No significant relationships were found, except for correlations between E and family, E and friends/acquaintances, and A and family. Non-shared environmental interrelationships between the three types of altruism were greater than that between personality and altruism.Additionally, we observed novel genetic and non-environmental contributions to altruistic behavior, which cannot be explained by personality-related genetic and non-environmental factors. Specifically, a genetic factor of family significantly loaded on friends/acquaintances and strangers, as well, showing a general affect across all three different types of altruism.The results of the present study replicate a single-factor structure of multiple facets of altruistic behavior from the perspective of recipient difference. Genetic factors explain nearly 51% of the variance of this single latent factor of altruism, and the rest is explained by a non-shared environment, which is consistent with the literature. The shared environment factor is negligible for this common latent factor of altruism. The genetic and environmental structure of altruism is quite similar to that of prosocial behavior in childhood .Behavioral genetic studies usually indicate that non-shared environmental effects are trait-specific (or opportunity-specific). Although the present study has replicated this pattern, there were significant overlaps via non-shared environmental factors among the three types of altruism.The present study has also replicated E and O, which commonly contribute to all three types of altruism of recipients , as welThe finding that there are relatively weak genetic overlaps of altruism toward different types of recipients with major personality dimensions, such as the Big Five, is theoretically important because it suggests that basic personality traits partly underlie the variation in altruism. The “altruistic personality” has often been debated. The present study also suggests that different types of altruistic personality are constructed by specific combinational profiles of general personality traits such as the Big Five in each specific way. Altruism-specific genetic factors have been discovered, which are independent from those specific to personality traits. The proportions of variances explained by additive genetic effects specific to altruistic behaviors were much larger than those explained by additive genetic effects specific to personality traits. This indicates that altruistic behaviors have different evolutionary roots from basic behavioral tendencies. Animal studies, such as those on birds, have shown that different types of personalities  adopt different styles of defense against predator strategies, and not all personality types can be involved in reciprocity in animals . Both aAnother important finding is that only altruism toward strangers is affected by the shared environment. This suggests that it is possible to alter altruism toward others by informing them about strangers at risk or in helpless situations and teaching skills to help those strangers. On the contrary, altruistic attitudes toward family members and friends/acquaintances are difficult to alter by educational treatment because they are a kind of personality and are not constructed based on knowledge and skills.The finding that the genetic influence of A had a significant genetic relationship only with altruism toward friends/acquaintances and not toward family members and strangers appears strange because A is semantically the core concept of all kinds of altruism, and there is a significant phenotypic correlation between A and the three altruistic variables . The resThe limitations of the present study are acknowledged. First, because of the limited and unbalanced sample size, especially with a small number of male participants, gender differences in genetic and environmental structures could not be examined. Although significant mean differences between females and males were observed in the present sample, we could not conduct sex-limitation models. By using sex-limitation models, we can investigate sex differences in expression of genetic and/or environmental factors . FutureSecond, although the study sample was involved in a longitudinal cohort project, change and stability of genetic and environmental structures were not investigated. This was because altruism has been measured once up to now. By measuring it in the following waves, we will be able to investigate this.Finally, the findings of the present study were exclusively obtained using self-report questionnaire data and not by experimental or real situations. Behavioral data, such as economic games and observational data, will be necessary to confirm these findings.Despite these limitations, the current study indicates several possibilities for future research. The result of a common pathway model suggested the existence of a general factor of altruism (GFA); therefore, it is an interesting research question to investigate the relationships between GFA and other general psychological factors such as general intelligence (g) and a general factor of personality (GFP) . In parIn conclusion, the present study shows that a single-factor structure among different types of altruism, according to the recipients, is a genetic mixture of personality traits such as extraversion, openness to experiences, and conscientiousness. Altruism toward strangers is the only type that is affected by a shared environment of agreeableness.


High‐throughput sequencing of amplicons (HTSA) has been proposed as an effective approach to evaluate taxonomic and genetic diversity at the same time. However, there are still uncertainties as to how the results produced by different bioinformatics treatments impact the conclusions drawn on biodiversity and population genetics indices.Botrylloides spp., sampled in the wild in ten marinas around Brittany (France). We used DNA extracted from preservative ethanol (ebDNA) after various time of storage , and from a bulk of preserved specimens (bulkDNA). DNA was amplified with primers designed for targeting this ascidian genus. Results obtained from HTSA data were compared with Sanger sequencing on individual zooids .We evaluated the ability of six bioinformatics pipelines to recover taxonomic and genetic diversity from HTSA data obtained from controlled assemblages. To that end, 20 assemblages were produced using 354 colonies of S and FST), despite producing false positives and missing rare haplotypes. However, they did not perform equally and dada2 was the only pipeline able to retrieve all expected haplotypes.Species identification and relative abundance determined with HTSA data from either ebDNA or bulkDNA were similar to those obtained with traditional individual barcoding. However, after 12 months of storage, the correlation between HTSA and individual‐based data was lower than after shorter durations. The six bioinformatics pipelines were able to depict accurately the genetic diversity using standard population genetics indices (Hdada2, including a chimera‐removal step. Even if the possibility to use multiplexed primer sets deserves further investigation to expand the taxonomic coverage in future similar studies, we showed that primers targeting a particular genus allowed to reliably analyze this genus within a complex community.This study showed that ebDNA is a nondestructive alternative for both species identification and haplotype recovery, providing storage does not last more than 6 months before DNA extraction. Choosing the bioinformatics pipeline is a matter of compromise, aiming to retrieve all true haplotypes while avoiding false positives. We here recommend to process HTSA data using  Botrylloides. We compared six bioinformatics pipelines to retrieve jointly taxonomic and haplotypic data. All correctly described the species distribution, as compared to standard barcoding. Although they differ in the number of false positives and negatives retrieved, all depicted accurately the genetic diversity using standard population genetics indices.High‐throughput sequencing was applied to DNA extracted from ethanol used to preserve a mixture of field‐collected specimens of the tunicate genus  The first pair was designed by Callahan et al. (B. violaceus (644 bp), and the second pair was newly designed  for B. diegensis (713 bp). Both target the same COI region as Folmer et al.’s primers. Details are provided in Appendix For each piece of colony preserved individually, DNA was extracted from a single zooid using the NucleoSpin® Tissue extraction kit (Macherey‐Nagel) Appendix . Becausen et al.  for B. v2.32.3.1After 3, 6, and 12 months of storage, DNA was extracted from preservative ethanol (ebDNA), with three replicates of 1 ml per jar  or the vsearch (vs + swarm) processing tools. Parameter choice is of critical importance to get optimal results. As for a regular study, where the sample composition is not known a priori, the parameter values were first selected to be the most sensitive and effective in retrieving highly similar haplotypes. For further evaluation of the impact of these parameters on the amount of false positives and false negatives, other sets of values were tested. The overall conclusions remained unchanged with these other settings  was tested by a Friedman test with R‐3.4.4. For each factor, pairwise comparisons were done with a paired Wilcoxon test, with p‐values adjusted for multiple comparisons. For picturing the molecular distance between known and unassigned ASVs/OTUs, haplotype networks were built with the pegas v‐0.10 R package  the average gene diversity per locus HS; Nei,  and (b) Wickham,  and dend33.1Botrylloides violaceus with cytochrome b (not shown). Only the two non‐ indigenous species B. diegensis and B. violaceus were present, B. diegensis being the most abundant  were already reported in Viard et al.  were already reported.Out of the 354 colonies, 353 were successfully amplified. The one that failed was later assigned to d et al. , and two3.2B. diegensis or B. violaceus with more than 97% identity, 45 being assigned with more than 99% identity. The 16 ASVs/OTUs with less than 99% identity accounted for only 2% of the total amount of reads. In agreement with SSIZ, HTSA revealed the presence of B. diegensis in every location, whereas B. violaceus was detected in three marinas only , whereas mothur had the lowest number of unexpected sequences (14) but recovered only six expected haplotypes  were used to compute a haplotype network  were detected by HTSA, with all pipelines and all sample types, in both jars from CON, where only one colony, and thus one haplotype, was associated to this species with SSIZ  than SSIZ. For example, two haplotypes of obitools , but none of the pairwise comparisons were significant , with pairwise comparisons significant in most cases  and of the type of sample  was detected. However, none of the pairwise comparisons between pipelines were significant, even with dada2, which exhibited the highest correlation values , whatever the pipeline or the type of sample  and taxonomic diversity, from high‐throughput sequencing (HTS) of DNA from preservative ethanol (ebDNA). Using mock communities, we evaluated the reliability of HTS as compared to Sanger haplotype sequencing carried out on the same assemblages. All tested pipelines were able to depict accurately both taxonomic and genetic diversity from ebDNA and bulkDNA datasets, showing the reliability of the HTS‐based approach. However, they performed differently regarding the balance between false positives and false negatives. Even if this experiment has been performed on a restricted taxonomic range, we believe that this approach can be successfully applied to a wide variety of organisms. In that sense, we highlight below the important issues to be taken into consideration for further studies.4.1DNA from preservative ethanol has been used in a few metabarcoding studies on terrestrial or freshwater arthropods and fish  were correctly recovered because most spurious ASVs/OTUs accounted for only a small proportion of reads (1.5%–9%).All tested pipelines described successfully the species composition and the overall genetic diversity of each community. However, they produced a high number of unexpected sequences, as reported in similar studies using other pipelines  detected only five haplotypes, whereas seven were revealed with vsearch (clustering‐based). In pipelines that failed to retrieve some haplotypes, the missed ones were always removed at the denoising/clustering steps, except Bd‐H2, which was accurately clustered with vsearch‐ and swarm‐based pipelines but discarded at the index‐jump and replicate filtering steps. In such cases, the threshold chosen for post‐treatment filtering could be loosen but this would be at the expense of specificity with additional false positives. For example with vsearch, the OTU corresponding to Bd‐H2 is only represented by 1–6 reads per sample. Keeping it would require not to apply an index‐jump correction, which would lead to a total of 1,149 OTUs after the data processing steps.All pipelines produced results highly correlated to SSIZ. The correlation between haplotype distributions was highly influenced by sample types but only slightly by pipelines Figure . ContrarBotrylloides niger Herdman, 1886 colonies with a prevalence of 1.9% , except if some errors occurred repeatedly because of particular sequence properties , the others retained between 11 and 14 chimeras. Contrary to Tsuji, Miya, et al. (Sixteen ASVs/OTUs were highly divergent (<99% identity) from the known haplotypes and were easily identified as technical chimeras. The two pipelines including a chimera‐removal step successfully removed most of them , were also the ones producing the highest number of unexpected sequences. Results might be improved by fine‐tuning some of the parameters used  using post‐treatment filters based on index‐jump correction and on PCR replicates filtering.Overall, we showed that, when using community samples, ebDNA is a nondestructive alternative for a joint assessment of taxonomic and genetic diversity, thus expanding its applicability to a wider range of organisms and environments. The results detailed here, however, were obtained on a restricted set of organisms (two species of a given genus), and our conclusions might differ when considering other taxonomic groups. Some bioinformatics pipelines were able to discriminate between very similar haplotypes (with only 1 bp difference), which leads us to believe that the estimation of both inter‐ and intraspecific diversity would be effective for any kind of organism. In case of taxonomic groups in which cryptic species have been reported , the approach would also allow to identify them and describe the distribution of the genetic diversity within and among these lineages. Nonetheless, this approach requires that the targeted accepted species are evolutionarily divergent enough  and polymorphic with the marker used. The choice of the marker and the primer design are thus key steps of the process and must be adapted to the targeted taxa for maximizing the possibility to identify either accepted species or cryptic lineages. So far, similar studies are rare, and we cannot ascertain if our results would remain similar with other case studies. We do feel, however, that the use of this HTS approach to reveal both taxonomic and genetic diversity should remain effective when used in a range of biological settings. This hypothesis needs to be confirmed by further investigations, for which we can give some recommendations based on our results: (a) using primer sets designed to target a genus or a family, if possible multiplexed to overcome limitations in taxonomic and genomic coverage, (b) using None declared.Marjorie Couton: Data curation (lead); formal analysis ; investigation ; methodology ; software (lead); writing–original draft ; writing–review and editing . Aurélien Baud: Formal analysis ; investigation . Claire Daguin‐Thiébaut: Investigation ; methodology ; validation ; writing–review and editing . Erwan Corre: Methodology ; resources ; writing–review and editing . Thierry Comtet: Conceptualization ; investigation ; methodology ; supervision ; writing–original draft ; writing–review and editing . Frédérique Viard: Conceptualization ; formal analysis ; funding acquisition (lead); investigation ; methodology ; supervision ; writing–original draft ; writing–review and editing .Supplementary MaterialClick here for additional data file.