115 Furthermore, exercise fat oxidation was not affected when an LGI or HGI meal was provided the evening before116 and 117; this suggests that the “second meal effect” does not apply to fat oxidation. In the only study, we are aware of, to investigate GI and substrate oxidation in young people, Zakrzewski et al.65 examined the effect of HGI and LGI mixed-breakfast meals

on fat oxidation in overweight and non-overweight girls. They focused on the 2-h postprandial rest period and a subsequent 30-min walk at 50% V̇O2peak. Although breakfast GI did Talazoparib ic50 not affect postprandial fat oxidation during rest or exercise in either group of girls, it is noteworthy that LGI breakfast consumption resulted in 12% higher exercise fat oxidation (adjusted for fat free mass (FFM)) in both groups, a finding that may have meaningful health-related

implications if experienced regularly over an extended period.102 The similar insulin response between HGI and LGI reported in this study may have underpinned the similarity in fat oxidation.106 Furthermore, fructose has a lower GI than glucose, but results in higher blood lactate concentrations.127 It is possible that higher lactate concentrations compromised fat oxidation following the LGI breakfast through direct inhibition GDC-0973 cell line of adipose tissue FFA release.128 Indeed, resting fat oxidation was lower 17-DMAG (Alvespimycin) HCl after high fructose compared with high glucose meals in obese adults, despite lower glucose and insulin responses to the high fructose

meal.119 It is also possible that the 1.5 g CHO/kg body mass breakfast, 2-h postprandial period, and 30-min exercise duration at 50% V̇O2peak was a sub-optimal combination to induce differences in fat oxidation between HGI and LGI. However, higher exercise fat oxidation following LGI breakfasts has been reported 45 min to 3 h85 and 120 following breakfasts containing 1–2.5 g CHO/kg body mass during exercise lasting 60 or 30 min at 50%–71% V̇O2peak in adults.85 and 114 It is, therefore, difficult to ascertain which factors contribute specifically to the higher fat oxidation following LGI breakfasts in some adult studies. Furthermore, differences in fat metabolism between adolescents and adults129 may have resulted in discrepancies between this study and some of the adult literature. Consequently, these results require confirmation with larger independent samples of young people. It has been suggested that the reduced-fat oxidation following HGI breakfasts is largely due to the higher insulin response, which increases muscle glycogen stores and utilisation, resulting in higher CHO and lower fat oxidation.114 Indeed, Wee et al.

In addition to distinctions in timing, ON RGCs tend to receive more excitation than inhibition and OFF RGCs more inhibition than excitation. Similar patterns of synaptic inputs to ON and OFF RGCs are elicited by light stimulation in mature retinal circuits (Murphy and Rieke, 2006 and Pang et al., 2003) and

differences PD-1/PD-L1 inhibitor 2 in excitation/inhibition ratios of ON and OFF RGCs persist after photoreceptor degeneration (Margolis et al., 2008 and Yee et al., 2012). This suggests that key circuits in the inner retina, particularly those mediating ON-to-OFF crossover inhibition, are established prior to vision, maintained following its loss, and play an important role in patterning both spontaneous Lumacaftor solubility dmso and light-evoked RGC activity. Because inhibition stereotypically precedes excitation to OFF RGCs during stage III waves and light-evoked spike trains of OFF RGCs are shaped by disinhibition (Manookin et al., 2008 and Murphy and Rieke, 2006), we tested the contribution of postinhibitory rebound to the delayed bursting

of OFF RGCs (Figure S1). Unlike in mature OFF RGCs (Margolis and Detwiler, 2007), we found that rebound depolarizations following somatic current injections rarely elicited spikes at P11–P13 and observed no differences in the intrinsic excitability of ON and OFF RGCs (Myhr et al., 2001). With the caveat that somatic current injections may not adequately capture the influence of dendritic 4-Aminobutyrate aminotransferase inhibition (Gidon and Segev, 2012), we therefore conclude that offset excitatory synaptic inputs account for the sequential spiking of ON and OFF RGCs. Asynchronous excitation of RGCs suggested

that ON and OFF CBCs, which provide input to ON and OFF RGCs, respectively, participate differently in stage III waves. Indeed, we found that during the ON phase of each wave ON CBCs depolarize, whereas OFF CBCs hyperpolarize (Figure 2). In conjunction with the timing of RGC EPSCs, these data imply that OFF CBCs release glutamate as their voltage returns to baseline following transient hyperpolarizations. The ability of CBCs to continuously vary neurotransmission as a function of voltage relies on the specialized release machinery of ribbon synapses (Matthews and Fuchs, 2010). The importance of ribbon synapses to stage III waves is underlined by the observation that these waves first appear as synaptic ribbons are being assembled in the IPL (Fisher, 1979), a period that is predated by conventional glutamate release from CBCs (Johnson et al., 2003). The mechanisms by which an OFF CBC’s return to baseline voltage without appreciable overshoot (Figure 2) is translated into a phasic EPSC in an OFF RGC are discussed in the supplement (Supplemental Discussion).

, 2011). At the cellular level, expression of DISC1 is developmentally regulated within the nervous system (Miyoshi et al., 2003) and DISC1 in turn regulates multiple processes of both embryonic and adult neurogenesis (Christian et al., 2010). At the molecular level, a large number of potential DISC1 binding partners have been identified from a yeast two-hybrid screen (Chubb et al., 2008), many of which are also involved in neurodevelopmental processes implicated in the pathophysiology of psychiatric diseases. Regarded Veliparib in vivo as an “edge piece” of psychiatric genetics, DISC1 may thus provide an entry point to understand molecular mechanisms and etiology underlying

complex psychiatric disorders. Using a combinatorial approach to analyze the effect of genetic manipulations on individual neurons in the animal model, biochemical interactions of endogenous proteins in a homogenous cell population, and genetic associations

in clinical cohorts, we demonstrate two parallel pathways for FEZ1 and NDEL1 that independently cooperate with DISC1 to regulate different aspects of NVP-BGJ398 supplier neuronal development and risk for schizophrenia. In the dentate gyrus of the hippocampus, a region implicated in schizophrenia pathophysiology (Harrison, 2004), neurogenesis continues throughout life in all mammals and contributes to specific brain functions (Zhao et al., 2008). Adult hippocampal neurogenesis provides a unique model system for dissecting signaling mechanisms that regulate neurodevelopment and

offers several distinct advantages for molecular analysis, including a prolonged developmental time course for more precise temporal resolution, a single neuronal subtype, MTMR9 and amenability to birth-dating, lineage tracing, and genetic manipulations (Christian et al., 2010). Using this in vivo model system, we have identified novel functions of FEZ1 in regulating dendritic growth and soma size of newborn dentate granule cells in the adult hippocampus (Figure 1). Furthermore, results from concomitant suppression of DISC1 and FEZ1 support a synergistic interaction between these two proteins in regulating dendritic growth in vivo (Figure 3). In parallel, the NDEL1-DISC1 interaction regulates a complementary subset of developmental processes, namely, neuronal positioning and development of primary dendrites (Duan et al., 2007). Interestingly, there is no apparent synergistic interaction between FEZ1 and NDEL1 in regulating neuronal development (Figure 4) and no protein-protein interaction in the absence of DISC1 (Figure 5). These results illustrate two discrete pathways associated with the DISC1 interactome that, in conjunction, account for most of the DISC1-mediated effects in orchestrating development of newborn neurons during adult hippocampal neurogenesis (Table 1).

In this study, we identify Sip1 as a common downstream target gene of Olig1 and Olig2. Overexpression or upregulation of Olig1 and Olig2 can activate Sip1 expression. Sip1 appears to bridge Olig Epacadostat supplier activities to balance the signaling pathways mediated by BMP/Smad and Wnt/β-catenin to control the timing of oligodendrocyte myelination. Our findings point to Sip1 as a master regulator that coordinates opposing signaling pathways to promote myelination and a nexus that connects extracellular signaling pathways to intracellular transcriptional programs for myelination in the CNS. The severe

myelination defect but preservation of OPCs in the CNS of Sip1 mutants suggests that Sip1 is a key regulator for the transition from immature to mature myelinating oligodendrocytes. Sip1 is robustly upregulated during OPC differentiation in vitro and in the postnatal CNS, consistent with the requirement for Sip1 in oligodendrocyte maturation. However, low levels of Sip1 in OPCs may

still regulate early steps of differentiation [e.g., by targeting negative regulatory genes Id2 and Hes1 in OPCs ( Figure 5)], which may in turn lead to more Sip1 accumulation in a positive feedback loop. Interaction of Sip1 with Smad1/Smad4/p300 complexes was found here to block BMP-Smad-activated expression of differentiation inhibitors, leading to derepression of myelin gene expression. In addition to Smad1, we expect similar outcome of Sip1 action on the activity of other closely related Smads (i.e., the other BMP-Smads, Smad5 and Smad8) by blocking the activity of LY294002 cell line almost p-Smads. In addition to interacting physically with p-Smads, Sip1 also antagonizes BMP signaling by activating at the transcriptional level an I-Smad,

Smad7, which in turn downregulates BMP receptor signaling. Recently, Sip1 was also found to inhibit expression of BMP ligands, like the BMP4 gene ( van Grunsven et al., 2007). Given that inhibition of BMP signaling (e.g., by ablating BMPR1a or by adding BMP antagonists) was shown to increase the number of mature oligodendrocytes and promote remyelination ( Sabo et al., 2011 and Samanta et al., 2007), the findings from our present studies and others suggest that Sip1 inhibits the BMP signaling pathway at multiple levels including the BMP ligand, its receptor, and its intracellular effectors to promote oligodendrocyte myelination. The modulation of various differentiation regulators by Sip1 appears to be stage-dependent. For instance, Sip1 binds the Smad7 promoter when OPCs begin to differentiate, while being recruited to Id2 and Hes1 promoters in OPCs and the Id4 promoter in differentiating oligodendrocytes, respectively, suggesting that stage-specific cofactors may direct the binding of Sip1 to different targets to modulate their expression.

, 2007, Merkle et al., 2007 and Young et al., 2007). Each population of olfactory bulb interneurons is produced in a unique temporal pattern and turnover

rate (Lledo et al., 2008). This suggests that the neurogenic processes occurring during development and in the adult are not directly equivalent (De Marchis et al., 2007 and Lemasson et al., 2005). Interestingly, bromodeoxyuridine (BrdU) labeling experiments revealed that the relative ratio of the different subtypes of olfactory bulb interneurons remains relatively constant from birth to adulthood, although they seem to be produced selleck at different rates. For instance, CR+ cells make up the largest proportion of newborn neurons in adult mice (Batista-Brito et al., 2008), while TH+ and CB+ periglomerular interneurons are produced to a lesser extent, and PV+ interneurons are not significantly turned over in the adult (Kohwi et al., 2007 and Li et al., 2011). It is presently unclear what physiological circumstances determine the precise turnover of the different classes of olfactory bulb interneurons in the adult. The mechanisms controlling the migration of embryonic interneurons to the

olfactory bulb resemble in many aspects that of cortical interneurons (Long et al., 2007) and will not be considered here in detail. However, the migration of interneurons to the olfactory bulb changes dramatically Antiinfection Compound Library as the brain matures, because the brain parenchyma becomes progressively less permissive for migration. Adult-born interneurons migrate to the olfactory bulb through the rostral migratory stream (RMS), a highly specialized structure in which chains of migrating neuroblasts are ensheathed by astrocytes (Doetsch and Alvarez-Buylla, 1996, Jankovski and Sotelo, 1996, Lois et al., 1996 and Thomas et al., 1996) (Figure 6). Interneurons migrate, crawling into each other in a process that is

known as chain migration (Wichterle et al., 1997). Many Org 27569 factors have been shown to influence the tangential migration of olfactory neuroblasts through the RMS (reviewed in Belvindrah et al., 2009), but very little is known on the mechanisms that control the final distribution of newborn interneurons in the olfactory bulb. Newborn interneurons seem to distribute uniformly throughout the rostrocaudal extent of the olfactory bulb (Lemasson et al., 2005). In contrast, interneurons target a specific layer within the olfactory bulb, according to their fate, in a process that is likely determined at the time of their specification. In agreement with this notion, overexpression of the transcription factor Pax6 in migrating neuroblasts promotes their differentiation to periglomerular TH+ cells at the expense of other interneuron classes (Hack et al., 2005). These results reinforce the view that the laminar allocation is largely linked to the fate of cells originating from different progenitor cells.

Many questions remain, of course. Betizeau et al. (2013) observed OSVZ progenitors in the occipital lobe of the fetal macaque neocortex. Should we expect progenitors in the frontal, temporal, and ABT-737 cell line parietal lobes to exhibit essentially similar behavior? Because neurogenesis and neuron density (as

observed in the adult neocortex) follow a posterior-anterior gradient, it is important to know whether the findings of Betizeau et al. (2013) apply, in principle, also to other areas of the developing neocortex. Furthermore, it is well established that cortical layers and cortical areas can be distinguished by their gene expression profiles. So, to what extent are gene expression profiles of OSVZ progenitor populations—if they are, in fact, transcriptionally discrete populations—characteristic of their laminar versus areal positions? Ultimately, we want to know what selection pressures these morphologically and behaviorally distinct OSVZ progenitor populations have evolved

in response to. Is the coordination of these proliferative and differentiative behaviors required to simply generate the impressive number of neurons in the primate neocortex? Or have progenitors evolved such a range of behaviors in order to organize this website the diversity of neuronal phenotypes in the neocortex? It will be interesting, and no doubt rewarding, to investigate to what extent the infra- and supragranular OSVZ lineage transition networks are functionally and transcriptionally modular. Future work may examine which genes regulate each network, which genes or regulatory elements are involved in the switching between networks, and whether these are conserved between macaques and humans. The study by Betizeau et al. (2013) advances the field considerably toward understanding how cortical neuron numbers and complexity may be achieved in development and evolution. An advantage of working with nonhuman primate neocortex is the viability of the ex vivo preparation. This approach has revealed a 2-fold increase in the number

of distinct Adenosine progenitor populations identifiable in the OSVZ and, furthermore, clarified the general importance of proliferative divisions in this basal germinal zone in large-brained primates. We are one step closer to comprehending how cortical stem and progenitor cells build the most complex organ in the natural world. “
“Nerves and blood vessels form highly branched, ramified networks extending into nearly every part of our body. The intimate association of some blood vessels and nerves in peripheral tissues reflects the functional interdependence relationship between the two systems: the nervous system requires vascularization to ensure nutrient and oxygen supply, and nerve cells in turn provide precise control of vascular caliber and blood flow.

, 2004, Nijnik et al., 2007 and Rossi et al., 2007). HSCs and primitive hematopoietic progenitors accumulate DNA lesions during aging, marked by γH2AX foci (Figure 2D) (Rossi et al., 2007). DNA damage may accumulate in HSCs because quiescent HSCs have enhanced survival mechanisms compared to differentiated progenitors and rely on error-prone nonhomologous end joining to repair DNA double-strand breaks BMS354825 (Mohrin et al., 2010). The reliance upon nonhomologous end joining to repair DNA double-strand breaks is also observed in epidermal stem cells (Sotiropoulou et al., 2010), but not in all stem cells (Blanpain et al., 2011). Stem cells thus share mechanisms to suppress the

accumulation of DNA damage. Although experimental elimination of DNA repair mechanisms leads to a premature depletion of stem cells, an open question is the extent to which DNA damage in stem cells affects the properties of these cells during physiological aging. Systemic environmental and metabolic changes also contribute to the aging of stem cells (Figure 2D). Aging reduces the selleck compound regenerative capacity of muscle satellite cells through increases in the levels of Wnt and TGF-β and a decrease in the expression of Notch ligands (Brack et al., 2007, Carlson et al., 2008, Conboy et al., 2003, Conboy et al., 2005 and Liu et al., 2007). Declines in mitochondrial

function are also observed during aging (Balaban et al., about 2005 and Chan, 2006) and can be precipitated by premature declines in telomere length as a consequence of telomerase deficiency (Sahin et al., 2011). Given that defects in mitochondrial function can yield phenotypes that resemble premature aging (Balaban et al., 2005 and Chan, 2006), these results suggest that defects in energy metabolism are one mediator of the effects of telomere attrition on aging. Telomere maintenance is also critical for chromosome stability and stem cell maintenance. Stem cells express telomerase to attenuate the decline in telomere length with age or upon tissue regeneration (Morrison et al.,

1996 and Vaziri et al., 1994). Telomerase deficiency leads to reduced stem cell self-renewal, stem cell depletion, and defects in the regeneration of proliferative tissues (Allsopp et al., 2003, Ferrón et al., 2004, Jaskelioff et al., 2011 and Lee et al., 1998). In telomerase-deficient mice, these defects are only observed beginning in the third generation after loss of telomeres or upon serial transplantation of HSCs; however, inbred mice have much longer telomeres and shorter life spans than humans. It therefore remains uncertain whether telomere length is limiting for stem cell function or tissue regeneration in the context of normal human aging. Surprisingly, reactivation of telomerase can elongate telomeres, rescuing epithelial stem cell function (Flores et al.

Northern blots were

processed using the North2South Chemiluminescent Hybridization and Detection kit, according to the manufacturer’s instructions (Pierce, Rockford, IL). For probe production see Supplemental Experimental Procedures. All procedures were carried out on 1-day-old fly heads, prior to retinal degeneration, unless otherwise specified. For western blotting, proteins were separated by electrophoresis and transferred to nitrocellulose membranes as previously described (Colley et al., 1991). For immunocytochemistry, fixation, and sucrose infiltration (or O.C.T. embedding) of fly heads was carried out as previously described (Colley et al., 1991). For each experiment, at least five individual heads were sectioned and between 50 and 100 ommatidia were observed per eye. For antibodies and microscope details see Supplemental PF-02341066 chemical structure Experimental Procedures. Adult heads were fixed find more and processed according to a modification of the methods of Baumann and Walz, as previously described (Colley et al., 1991 and Colley et al., 1995). Ultrathin sections were viewed at 80 kV on a Phillips CM120 electron microscope. For all genotypes described, at least three individual heads were sectioned and 50–100 ommatidia were observed per eye. The DNA constructs were transfected into S2 cells using the Effectene Transfection Reagent (QIAGEN Inc., Valencia, CA). Following a 7 day copper induction, cells were fixed in 2% formaldehyde in PBS for 10 min and blocked

with 1% BSA, 0.1% Triton in PBS for 30 min. For quantification of cell surface labeling, cells were observed with transmitted light. For vector identities,

DNA concentrations and additional antibody and reagent information see Supplemental Experimental Procedures. We thank Drs. W. Baehr, L. ADAMTS5 Levin, K. Moses, A. Polans, L. Puglielli, G. Wistow, C.S. Zuker and the reviewers for valuable discussions and comments on the manuscript. The authors thank A. Gajeski, B. Larsen, A. Muller, E. Pirie, E. Solberg, and M. Sookochoff for their expert technical assistance, as well as B. Krieber and Dr. B. Ganetzky for assistance with fly stocks. Dr. J. O’Tousa provided the pGaSpeR expression vector and Dr. A. Huber provided the trp-pMT/V5 construct. We thank the following people for contributing antibodies to the study: Dr. M. Ramaswami, Dr. C. Montell, Dr. C.S. Zuker, A. Becker, M. Welsh and Dr. P. Robinson. We acknowledge Dr. D. Wassarman and R. Katzenberger for generous assistance with the S2 cell transfections. We thank R. Kalil, L. Rodenkirch, and M. Hendrickson of the W.M. Keck Laboratory for Biological Imaging and B. August and R. Massey of the UW-Med. School Electron Microscope Facility. We are grateful to C. Vang for his assistance with the computer graphics. Finally, Dr. C.S. Zuker generously provided us with the opportunity to screen the EMS-generated alleles from the Zuker Collection. This work was supported by funding from NIH EY008768 (N.J.C.), NIH AG321762 (E.E.R.

We did not observe a significant decrease in phospho-p70S6K, but this may be due to an increase in total p70S6K levels induced by this treatment. This effect of rapamycin was specific for mTORC1, since there was no evidence of altered mTORC2 activity, based on normal levels of phospho-AKT (Ser-473)

and phospho-PKCα, in VTA. Rapamycin treatment of morphine-naive mice had no effect on VTA DA cell surface area, demonstrating that decreasing mTORC1 activity per se is not sufficient to alter the size of these neurons (Figure 5H). Further, when mice were pretreated with rapamycin and then treated chronically with morphine, we still observed the expected morphine-induced decrease in DA soma size. These findings show that preventing the morphine-induced increase in mTORC1 signaling in VTA does not block the morphine-induced decrease in soma size. Since there is no selective small selleck compound molecule inhibitor of mTORC2, we used a conditional neuronal knockout strategy, with recently developed floxed-Rictor mice (Siuta et al., 2010) to directly study the contribution of mTORC2 in morphine action. Knocking out Rictor enables a selective reduction in mTORC2 activity, without any discernable effect on mTORC1. To achieve a local knockout of Rictor from VTA, we

BTK inhibitor concentration injected AAV-Cre into VTA of floxed-Rictor mice or into wild-type littermates as a control (Figure 6A). Knockout was validated by RT-PCR and western blot analysis, where we observed a significant decrease in Rictor mRNA in VTA and decreased phosphorylation of the mTORC2 substrates AKT (Ser-473) and PKCα (Figure S2A). Local Rictor knockout also decreased DA cell surface area by ∼20% (Figure 6B). We next developed an HSV to overexpress Rictor-T1135A. This Rictor mutant increases mTORC2 activity, and lacks the p70S6K Montelukast Sodium phosphorylation site, eliminating

the possibility of mTORC1 negative feedback regulation of mTORC2 (see Discussion). This vector increased Rictor expression and mTORC2 signaling in VTA (Figure S2B), and blocked the morphine-induced decrease in DA neuron soma size (Figure 6B). These results demonstrate that downregulation of mTORC2 signaling in VTA is both necessary and sufficient for mediating the morphine-induced decrease in DA soma size. In addition to the mTOR pathway, another downstream target of AKT that has been observed to affect neuronal size and structure in other systems is GSK3β (van Diepen et al., 2009). Since we observe changes consistent with increased GSK3β activity (decreased phospho-GSK3β, Figure S1A) in VTA after chronic morphine, we studied the possible influence of GSK3β in regulating VTA DA soma size. Overexpression of wild-type GSK3β in VTA, which mimics morphine regulation of the protein, did not alter soma size (Figure S3).

For the experiment in the MRI scanner, two tasks, Control and Other, were employed. Three conditions, one Control and two Others, were used in a separate behavioral experiment (Figure 1C). The settings for the Control and “Other I” task were the same as in the fMRI experiment, but in the

“Other II” task, a risk-averse RL model was used to generate the other’s choices. Several computational models, based on and modified from the Q learning model (Sutton and Barto, 1998), were fit to the subjects’ choice behaviors in both tasks. In the Control task, the RL www.selleckchem.com/products/LBH-589.html model, being risk neutral, constructed Q   values of both stimuli; the value of a stimulus was the product of the stimulus’ reward probability, p(A)p(A) (for stimulus A  ; the following description is made for this case), and the reward magnitude of the stimulus in a given trial, R(A)R(A), equation(1) QA=p(A)R(A).QA=p(A)R(A). To account for possible risk behavior of the subjects, we followed the approach of Behrens et al. (2007) by using a simple nonlinear function (see the Supplemental Selleckchem Ibrutinib Information for more details and for a control analysis of the nonlinear function). The choice probability is given by q(A)=f(QA−QB)q(A)=f(QA−QB), where ff is a sigmoidal function. The reward prediction error was used to update the stimulus’ reward probability (see the Supplemental

Information for a control analysis), equation(2) δ=r−p(A),δ=r−p(A),where r   is the Ribonucleotide reductase reward outcome (1 if stimulus A   is rewarded and 0 otherwise). The reward probability was updated using p(A)←p(A)+ηδp(A)←p(A)+ηδ. In the Other task, the S-RLsRPE+sAPE model computed the subject’s choice probability using q(A)=f(QA−QB)q(A)=f(QA−QB); here, the value of a stimulus is the product of the subject’s fixed reward outcome and their reward probability

based on simulating the other’s decision making, which is equivalent to the simulated-other’s choice probability: qo  (A  ) = f  (QO  (A  ) − QO  (B  )), wherein the other’s value of a stimulus is the product of the other’s reward magnitude of the stimulus and the simulated-other’s reward probability, pO(A)pO(A). When the outcome for the other (rO)(rO) was revealed, the S-RLsRPE+sAPE model updated the simulated-other’s reward probability, using both the sRPE and the sAPE, equation(3) pO(A)←pO(A)+ηsRPEδO(A)+ηsAPEσO(A),pO(A)←pO(A)+ηsRPEδO(A)+ηsAPEσO(A),where the two η’s indicate the respective learning rates. The sRPE was given by equation(4) δo(A)=ro−po(A).δo(A)=ro−po(A). The sAPE was defined in the value level, being comparable to the sRPE. After being generated first in the action level, equation(5) σO′(A)=IA(A)−qO(A)=1−qO(A),the sAPE was obtained by a variational transformation, pulled back to the value level, equation(6) σO(A)=σO′(A)K,(see the Supplemental Information for the algebraic expression of K).